Tumor specific cytokine pattern explored in renal cell carcinoma

Xu Chen; Zhuangyao Liao; Rochelle Wickramasekara; Ying Luo; Panning Wang; Sihao Wang; Yunru Chen; Hang Zhou; Shuhong Luo; Shaopeng Qiu; Ruo-Pan Huang

doi:10.1371/journal.pone.0358533

. 2026 Sep 29;21(9):e0358533. doi: 10.1371/journal.pone.0358533

Tumor specific cytokine pattern explored in renal cell carcinoma

Xu Chen ¹, Zhuangyao Liao ¹, Rochelle Wickramasekara ², Ying Luo ³, Panning Wang ³, Sihao Wang ¹, Yunru Chen ³, Hang Zhou ¹, Shuhong Luo ^2,³, Shaopeng Qiu ^1,^4,^*, Ruo-Pan Huang ^2,^3,^5,^6,^*

Editor: Alireza Shoari⁷

PMCID: PMC13623153 PMID: 42809553

Abstract

Renal cell carcinoma (RCC) is a common urological malignancy, yet the cytokine landscape of the tumor microenvironment, especially in early-stage tumors, remains poorly defined. Using antibody arrays, 174 tumor-related cytokines were measured in 20 sets of normal, para-cancerous, and tumor tissues from early-stage RCC patients. Selected cytokines were validated by ELISA. Functional enrichment was analyzed via DAVID, while transcriptional expression, survival outcomes, and immune infiltration were assessed using GEPIA, Kaplan-Meier Plotter, and TIMER databases. Thirty-four differentially expressed cytokines were identified in tumors versus controls, enriched in cytokine signaling, TNF, PI3K-Akt, Ras, HIF-1, and Toll-like receptor pathways. ELISA confirmed reduced IL-2, IL-15, IGFBP-2, HGF, ErbB3, and FLT3LG, and increased CCL4, CXCL9, and Angiopoietin-2 in tumors. Serum levels of HGF, ErbB3, and CCL4 were decreased in RCC patients, while urinary ErbB3 was also reduced. IL-2, IL-15, and CCL4 were upregulated in T1-T2 metastatic tumors. Several cytokines correlated with poor survival, except ErbB3, which associated with improved outcomes. Several cytokine-related genes, including CXCL9 and CCL4, correlated with inferred immune cell infiltration patterns. These findings suggest that early-stage RCC may be associated with a cytokine expression profile characterized by reduced immune-stimulating factors and elevated pro-angiogenic and inflammatory signals. Urinary ErbB3 emerged as a candidate non-invasive biomarker requiring further validation.

Introduction

RCC accounts for approximately 3% of all adult malignancies and remains one of the most common urologic cancers worldwide [1]. Global RCC incidence has continued to rise over the past decade, with more than 300,000 new cases and approximately 150,000 RCC-related deaths reported annually [2]. In China, RCC represents a growing public health burden and is associated with nearly 23,400 deaths each year [3]. Although patients with localized RCC often achieve favorable outcomes following surgical resection, more than 30% of patients present with locally advanced or metastatic disease at diagnosis, which is associated with substantially reduced survival despite advances in targeted therapies and immunotherapy [4]. In addition to its clinical heterogeneity, RCC is characterized by complex interactions between tumor cells, stromal components, and immune signaling pathways within the tumor microenvironment, highlighting the need to better understand the biological mechanisms that contribute to tumor progression and disease aggressiveness.

RCC is considered one of the most immune-infiltrated solid tumor types, and increasing evidence suggests that the renal tumor microenvironment plays a central role in disease progression and therapeutic response [5]. However, immune cell infiltration in RCC does not necessarily reflect effective anti-tumor immunity. Integrative molecular and immune profiling studies have demonstrated that highly immune-infiltrated RCC tumors can exhibit aggressive clinical behavior and are frequently associated with immunosuppressive microenvironmental features [6,7]. Although lymphocytes are often abundant within RCC tissues, their cytotoxic activity may be impaired by complex interactions among tumor cells, stromal components, cytokines, and other immunoregulatory factors within the tumor microenvironment.

Cytokines and chemokines produced by tumor cells, stromal cells, and infiltrating immune populations contribute to the complex immunoregulatory landscape of RCC by influencing immune cell recruitment, angiogenesis, inflammatory signaling, and therapeutic response. Several cytokines have been implicated in RCC progression and immune modulation, including IL-10, which has been associated with metastatic disease and immunosuppressive macrophage activity [8,9]. In addition, hypoxia-related signaling pathways involving HIF and TGF-β have been linked to tumor progression and metastatic behavior in RCC [10,11], while IL-6-mediated signaling has been associated with resistance to tyrosine kinase inhibitors through activation of AKT-mTOR, NF-κB, and VEGF-related pathways [12]. Cytokine-directed immunotherapies such as high-dose IL-2 have also demonstrated that modulation of immune signaling pathways can influence clinical outcomes in metastatic RCC, despite limitations related to treatment-associated toxicity [13]. Collectively, these findings highlight the importance of cytokine-mediated communication within the RCC tumor microenvironment. However, the cytokine alterations associated with early-stage RCC and their relationship to tumor progression remain incompletely characterized.

To further characterize cytokine-associated signaling patterns within the RCC tumor microenvironment, we performed exploratory cytokine profiling of early-stage RCC tissues using multiplex antibody arrays targeting 174 cytokine-related proteins. Differentially expressed cytokines identified in tumor tissues were subsequently evaluated in independent tissue, serum, and urine cohorts using ELISA-based analyses, and complementary transcriptomic and immune infiltration analyses were performed using TCGA-KIRC and TIMER datasets. Across these analyses, RCC tissues demonstrated altered expression patterns involving cytokines associated with angiogenesis, inflammatory signaling, immune regulation, and leukocyte recruitment. In addition, altered expression of molecules previously implicated in RCC progression and metastatic behavior, including HGF, ErbB3, and IGFBP-2, was observed [14–16]. However, variability and incomplete concordance between tissue protein, circulating protein, and transcriptomic datasets were also observed, consistent with the complex and dynamic nature of cytokine biology within the tumor microenvironment. Collectively, this study provides an exploratory characterization of cytokine-related alterations in early-stage RCC and highlights the need for larger, mechanistic, and longitudinal studies to further define the biological and clinical significance of these immune signaling patterns.

Materials and Methods

Patient cohorts and clinical sample collection

The use of human tumor tissue, serum, and urine samples in this study was conducted in accordance with the Declaration of Helsinki, relevant institutional guidelines, and applicable regulations, and was approved by the Clinical Research and Experimental Animal Ethics Committee of The First Affiliated Hospital of Sun Yat-Sen University (Guangzhou, China). Ethical approval was granted under reference number [2021]144 on 15/03/2021 and was subsequently extended under reference number [2023]260 on 14/07/2023. Written informed consent was obtained from all participants prior to inclusion in the study, and patient privacy rights were strictly protected.

Patients with pathologically confirmed RCC were enrolled through the Department of Urology at The First Affiliated Hospital of Sun Yat-Sen University. Clinical cohorts included patients with predominantly localized early-stage RCC (T1–T2). Available clinicopathological characteristics included patient age, sex, tumor size, T stage, and metastatic status. The tissue cohorts consisted predominantly of clear cell RCC cases, and most tumors with available pathological grading information were classified as grade I–II. Clinical characteristics are summarized in Table 1.

Table 1. Clinical and sample characteristics of the RCC study cohorts.

Characteristic	Cohort A	Cohort B	Cohort C
Tissue samples, n	20	38	19
Serum samples, n	–	–	19
Urine samples, n	–	–	19
Age, y
<60	14	27	Tissue: 12; Serum: 19; Urine: 19
≥60	6	11	Tissue: 7; Serum: 0; Urine: 0
Sex
Male	16	27	Tissue: 14; Serum: 6; Urine: 16
Female	4	11	Tissue: 5; Serum: 13; Urine: 3
T stage
T1	15	31	Tissue: 15
T2	5	7	Tissue: 4
Metastatic status
No	20	36	Tissue: 17
Yes	0	2	Tissue: 2
Tumor size, median (range), cm^*	5.1 (2.0–10.0)	4.5 (2.3–10.0)	Tissue: 4.3 (2.0–10.0)

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*Tumor size was summarized using the largest recorded tumor dimension. For Cohort C, characteristics are reported separately for the tissue, serum, and urine sample sets.

Cohort A consisted of 20 fresh RCC tumor and corresponding para-cancerous tissue specimens collected following radical nephrectomy between 01/05/2021 and 31/12/2021 under ethical approval [2021]144. These samples were analyzed using antibody cytokine microarrays.

Cohort B included 38 primary RCC tumor tissues and para-cancerous tissues collected through surgical resection between 15/08/2023 and 31/05/2024 under ethical approval [2023]260. These samples were used for ELISA-based validation analyses.

Cohort C comprised serum and urine samples collected from 19 RCC patients and 19 healthy individuals between 15/08/2023 and 31/05/2024 under ethical approval [2023]260. These samples were also analyzed using ELISA.

Para-cancerous tissue was defined as tissue located at least 0.5 cm from the tumor border, whereas normal kidney tissue was collected at least 1 cm from the tumor border. All RCC diagnoses were confirmed by pathological examination of surgical specimens.

Cytokine array analysis

Sandwich-based membrane antibody arrays (AAH-CYT-2000; RayBio® Human Cytokine Antibody Arrays C6, C7, and C8; RayBiotech Life, Inc., Peachtree Corners, GA, USA) were used to semi-quantitatively measure the expression levels of 174 human cytokines. Normal, para-cancerous, and tumor tissues from RCC patients were homogenized in RayBio® Lysis Buffer (AA-LYS, RayBiotech, Peachtree Corners, GA, USA) supplemented with a protease inhibitor cocktail (AA-PI, RayBiotech, Peachtree Corners, GA, USA). Tissue homogenates were centrifuged at 1,500 × g to remove cellular debris, and all sample preparation procedures were performed at low temperatures.

Equal amounts of total protein lysate were incubated with blocked antibody array membranes according to the manufacturer’s protocol. Following sample incubation, membranes were sequentially incubated with biotinylated antibody cocktails and HRP-conjugated streptavidin, and chemiluminescent signals were detected using enhanced chemiluminescence reagents. Membranes were exposed using a chemiluminescence imaging system, and signal intensities were quantified by densitometric analysis.

For each cytokine, duplicate spot intensities were averaged following background subtraction. Duplicate spots and internal positive controls were used to assess assay consistency and normalization across membranes. Signal intensities were normalized to positive control spots on each membrane according to the manufacturer’s recommended normalization procedure.

Differential expression analysis was performed using the limma package in R. Normalized cytokine expression data were fitted to a linear model using lmFit, followed by empirical Bayes moderation to improve variance estimation across cytokines. Overall differences among normal, para-cancerous, and tumor tissues were evaluated using moderated F-statistics, and pairwise comparisons were performed using moderated t-statistics generated through customized contrast matrices. False discovery rate (FDR) correction was applied using the Benjamini-Hochberg procedure, and cytokines with FDR < 0.05 were considered significantly differentially expressed. Candidate cytokines for downstream analyses were selected based on statistical significance, fold change, and biological relevance to RCC-associated signaling pathways. For the representative cytokine comparisons shown in Fig 2, normality was assessed using the Shapiro–Wilk test, and group differences were evaluated using the Kruskal–Wallis H test followed by pairwise Mann–Whitney U (Wilcoxon rank-sum) tests with Benjamini–Hochberg correction for multiple comparisons.

Fig 2 — (A) Representative antibody array membrane images from normal (left), para-cancerous (middle), and tumor (right) tissue samples analyzed using RayBio® Human Cytokine Antibody Arrays C6, C7, and C8. Colored boxes indicate the locations of representative cytokines shown in panel B. (B) Quantified signal intensities of representative cytokines identified from the antibody array dataset. Bar graphs show normalized cytokine expression levels in tumor, para-cancerous, and normal kidney tissue samples. Cytokines associated with angiogenesis and inflammatory signaling, including Angiopoietin-2, CCL4, RANTES/CCL5, and CXCL9, showed increased expression in tumor tissues, whereas IL-15, ErbB3, HGF, and FLT3LG showed decreased expression relative to control tissues. IL-2 showed a trend toward decreased expression in tumor tissues, although the difference did not reach statistical significance. Statistical significance annotations indicate comparisons among tissue groups as described in the Methods section. Statistical significance is indicated as follows: *P < 0.05, **P < 0.01, and ***P < 0.001.

Differential cytokine expression patterns were visualized using hierarchical clustering heatmaps generated with the R package pheatmap based on Euclidean distance and average linkage clustering. Functional pathway enrichment analysis of differentially expressed cytokines was performed using the DAVID Bioinformatics Resources database.

Enzyme-linked immunosorbent assay (ELISA)

The concentrations of selected cytokines in serum, urine, and tissue lysates were measured using sandwich-based human ELISA kits specific for Angiopoietin-2 (ELH-Angiopoietin2), RANTES/CCL5 (ELH-RANTES-1), PDGF-AA (ELH-PDGFAA), IL-2 (ELH-IL2), IL-15 (ELH-IL15), MIP-1β/CCL4 (ELH-MIP1b), MIG/CXCL9 (ELH-MIG), IGFBP-2 (ELH-IGFBP2), HGF (ELH-HGF), ErbB3 (ELH-ErbB3), and Flt-3 Ligand (ELH-FLT3L) (RayBiotech Life, Inc., Peachtree Corners, GA, USA). Assays were performed according to the manufacturer’s protocols.

Briefly, standards and diluted samples were added to 96-well microplates pre-coated with cytokine-specific capture antibodies and incubated at room temperature. After washing, biotinylated detection antibodies and HRP-conjugated streptavidin were sequentially added. Signal development was performed using TMB substrate solution, and absorbance was measured at 450 nm using a microplate reader.

Standard curves were generated using serial dilutions of recombinant protein standards provided in each kit and fitted using a four-parameter logistic (4PL) regression model. Cytokine concentrations were calculated by interpolation from best-fit standard curves after subtraction of blank control values. All samples and standards were analyzed in duplicate. Mean absorbance values were used for downstream statistical analyses. The standard concentration series and corresponding curve-fit statistics (R²) for each ELISA are provided in S1 Table.

Kaplan–Meier survival and TCGA gene expression analyses

Gene Expression Profiling Interactive Analysis (GEPIA; http://gepia.cancer-pku.cn/) was used to analyze RNA sequencing expression data from tumor and normal tissues within the TCGA Kidney Renal Clear Cell Carcinoma (TCGA-KIRC) dataset. Differential gene expression analyses were performed using a cutoff threshold of |log2FC| > 2 and p < 0.05.

Kaplan–Meier Plotter (http://kmplot.com/analysis/) was used to evaluate the prognostic significance of selected cytokine-related genes in TCGA-KIRC patients using the Pan-cancer RNA-seq dataset. Genes were queried using official gene symbols. Overall survival analyses were performed by stratifying patients into high- and low-expression groups using the platform’s automatically selected best cutoff. The expression cutoff values used were 3,147 for ANGPT2, 2 for IL2, 250 for IL15, 2,340 for CXCL9, 382 for CCL4, 521 for HGF, 3,076 for ERBB3, 69 for FLT3LG, and 708 for IGFBP2. Hazard ratios (HRs), 95% confidence intervals (CIs), and log-rank p-values were calculated to assess survival differences between groups.

Immune Infiltration Analysis

The Tumor Immune Estimation Resource (TIMER) V2.0 (https://cistrome.shinyapps.io/timer/) was used to evaluate the association between cytokine-related gene expression and immune cell infiltration within the TCGA-KIRC dataset. Associations between the expression of cytokine-related genes (CD14, AXL, CCL4, CXCL9, and CCL5) and inferred immune cell infiltration levels were analyzed using Spearman correlation analysis with tumor purity adjustment. Statistical significance was defined as P < 0.05. Correlation coefficients and corresponding p-values were obtained directly from the TIMER V2.0 platform. TIMER estimates the abundance of multiple tumor-infiltrating immune cell populations using established immune deconvolution algorithms based on bulk RNA sequencing data.

Statistical Analysis

Data are presented as mean ± standard deviation (SD). Statistical analyses were performed using SPSS software (SPSS 13.0 for Windows; SPSS Inc., Chicago, IL, USA) and R. Normality was assessed using the Shapiro–Wilk test. For comparisons among multiple groups, one-way analysis of variance (ANOVA) followed by Tukey’s HSD post hoc test was used when all groups were normally distributed. When normality assumptions were not met, the Kruskal–Wallis H test was used, followed by pairwise Mann–Whitney U (Wilcoxon rank-sum) tests with Benjamini–Hochberg correction for multiple comparisons. For two-group comparisons, Student’s t-test was used for normally distributed data, whereas the Mann–Whitney U test was used when normality assumptions were not met. TCGA gene expression data were analyzed using the Wilcoxon rank-sum test. TIMER immune infiltration analyses were performed using Spearman correlation analysis with tumor purity adjustment. Statistical significance was defined as p < 0.05 unless otherwise specified.

Results

A distinct cytokine protein signature characterizes early-stage RCC tissues

To characterize cytokine alterations associated with localized RCC, antibody arrays measuring 174 cytokine-related proteins were performed using tumor, para-cancerous, and normal kidney tissue samples obtained from 20 RCC patients. Among the proteins analyzed, 34 cytokines were identified as differentially expressed between tumor tissues and both para-cancerous and normal control tissues.

Hierarchical clustering analysis of the antibody array protein data demonstrated that tumor tissues exhibited a distinct cytokine expression profile compared with para-cancerous and normal kidney tissues (Fig 1A). Overall, the differentially expressed cytokines reflected broader alterations in angiogenesis-associated signaling, inflammatory responses, immune cell recruitment, and immune regulatory pathways within the RCC tumor microenvironment. Cytokines associated with angiogenesis, inflammatory signaling, and immune cell recruitment, including VEGF, PDGF-AA, sgp130, CD14, ICAM-1, ICAM-2, PIGF, AXL, Angiopoietin-2, EGFR, CXCL9, IP-10/CXCL10, CCL4, CCL5, MMP-9, TIMP-1, Siglec-5/CD170, sTNF RII, Fas/TNFRSF6, and LAP, were generally elevated in tumor tissues. In contrast, several cytokines involved in immune activation and growth regulation, including CNTF, IGFBP-2, HGF, EGF, TGF-β1, M-CSF, TNF-β, FLT3LG, ErbB3, IL-2, IL-15, IL-1ra, IL-7, and IL-13, showed reduced expression in tumor tissues relative to control tissues.

Fig 1 — (A) Hierarchical clustering heatmap of 34 differentially expressed cytokines identified by antibody array analysis of RCC tumor, para-cancerous, and normal kidney tissue samples. Differential expression was determined using limma-based linear modeling with Benjamini–Hochberg false discovery rate (FDR) correction (FDR < 0.05). Each column represents one tissue sample, and each row represents one cytokine. The annotation bar above the heatmap indicates sample group classification (normal, para-cancerous, or tumor). Relative cytokine abundance is represented by color intensity, with red indicating higher expression and green indicating lower expression relative to the normalized signal intensity scale. Clustering analysis was performed using Euclidean distance and average linkage. **(B)** KEGG pathway enrichment analysis of the 34 differentially expressed cytokines identified by antibody array profiling. The cytokine set was selected based on statistical significance, fold change, and biological relevance following differential expression analysis. Bars indicate the number of cytokines assigned to each enriched signaling pathway. Pathway enrichment was performed using DAVID based on the set of differentially expressed cytokines identified in panel A. Pathway enrichment was performed using DAVID based on the set of differentially expressed cytokines identified in panel A. Enriched pathways included cytokine–cytokine receptor interaction, TNF signaling, PI3K-Akt signaling, Ras signaling, HIF-1 signaling, and Toll-like receptor signaling pathways.

KEGG pathway enrichment analysis of the differentially expressed cytokines demonstrated significant enrichment in pathways associated with cytokine signaling, inflammation, hypoxia, and tumor progression, including cytokine–cytokine receptor interaction, TNF signaling, PI3K-Akt signaling, Ras signaling, HIF-1 signaling, and Toll-like receptor signaling pathways (Fig 1B). These pathway analyses further supported the presence of broad immune and inflammatory signaling alterations in early-stage RCC tissues.

Representative cytokines identified from the antibody array dataset are shown in Fig 2. Quantified array signal intensities and representative membrane images demonstrated increased expression of Angiopoietin-2, CCL4, RANTES/CCL5, and CXCL9 in tumor tissues compared with normal tissues, whereas IL-2, IL-15, ErbB3, HGF, and FLT3LG showed reduced expression in tumor tissues. IL-2 showed a similar trend toward reduced expression in tumor tissues, although the difference did not reach statistical significance. These findings further illustrate the distinct cytokine protein landscape observed in early-stage RCC tissues. These representative cytokines were selected to illustrate the heterogeneous cytokine expression patterns identified across the antibody array dataset.

Validation of differentially expressed cytokines using ELISA

To validate the cytokine alterations identified by antibody array profiling, selected cytokines were further analyzed using ELISA in an independent RCC tissue cohort (Cohort B). Eleven candidate cytokines were selected for validation based on fold-change magnitude and biological relevance to angiogenesis, inflammatory signaling, and immune regulation.

ELISA analysis confirmed differential expression patterns for 10 of the 11 evaluated cytokines (Fig 3). Angiopoietin-2, CCL4, and CXCL9 showed significantly increased expression in RCC tumor tissues relative to normal tissues, whereas IL-2, IL-15, IGFBP-2, HGF, ErbB3, FLT3LG, and PDGF-AA showed reduced expression in tumor tissues. In contrast, RANTES/CCL5 did not demonstrate a statistically significant difference between tissue groups in the validation cohort. Overall, these findings demonstrated partial concordance between the multiplex antibody array and ELISA analyses while also highlighting variability among individual cytokine measurements across independent sample cohorts.

Expression levels of selected cytokines were measured by ELISA in RCC tumor and normal kidney tissue samples from the independent validation cohort (Cohort B). Each panel represents one cytokine identified from the antibody array discovery analysis. Round dots (left side of each panel) represent tumor tissue samples, and square dots (right side of each panel) represent kidney tissue samples. Individual symbols represent single patient samples. Angiopoietin-2, CCL4, and CXCL9 showed increased expression in tumor tissues, whereas IL-2, IL-15, IGFBP-2, HGF, ErbB3, FLT3LG, and PDGF-AA showed reduced expression relative to normal tissues. RANTES/CCL5 did not show a statistically significant difference between groups. P values are shown within individual panels. Statistical significance is indicated as follows: *P < 0.05, **P < 0.01, and ***P < 0.001.

TCGA transcriptomic analyses demonstrate stage- and metastasis-associated cytokine expression patterns

To further examine the clinical relevance of cytokines identified in the protein profiling analyses, transcriptomic data from the TCGA-KIRC dataset were analyzed using GEPIA and Kaplan–Meier Plotter databases. mRNA expression patterns of selected cytokines were evaluated according to tumor stage and metastatic status.

Among the evaluated cytokines, IL-2, IL-15, and CCL4 demonstrated significantly increased mRNA expression in metastatic T1–T2 RCC tumors compared with non-metastatic tumors of the same stage (Fig 4). This trend was not observed in T3–T4 tumors. CXCL9 showed similar expression trends but with greater variability between patient groups.

Fig 4 — Analyses were stratified by tumor stage (T1–T2 and T3–T4). Each dot represents an individual patient sample. Data represent transcript-level expression derived from TCGA-KIRC RNA sequencing datasets. Comparisons were performed using TCGA transcriptomic data obtained through GEPIA and related bioinformatic platforms. Statistical significance is indicated within individual panels.

Additional TCGA analyses demonstrated elevated transcript expression of Angiopoietin-2, CXCL9, and CCL4 in RCC tissues, whereas IGFBP-2 expression was reduced relative to normal tissues (S1 Fig).

Kaplan–Meier survival analyses identified several cytokine-related genes associated with patient prognosis in RCC (S2 Fig). Higher expression of IL-2, IL-15, CCL4, HGF, FLT3LG, and IGFBP2 was associated with poorer overall survival, whereas elevated ErbB3 expression was associated with improved survival outcomes.

Importantly, these transcriptomic findings were not always consistent with the protein-level observations identified by antibody array and ELISA analyses, highlighting the complex relationship between cytokine mRNA expression and functional protein abundance within the tumor microenvironment. Because cytokine signaling is influenced by dynamic secretion patterns, post-transcriptional regulation, and differences between tissue and circulating compartments, these transcriptomic analyses were interpreted primarily as complementary contextual analyses rather than direct validation of the protein-level findings.

Cytokine-related transcripts are associated with immune cell infiltration patterns in RCC

Because several cytokines identified in the antibody array analyses are known regulators of leukocyte recruitment and immune signaling within the tumor microenvironment [17], we next examined whether their transcript expression was associated with immune cell infiltration patterns in RCC using the TIMER database.

Transcript expression of several cytokine-related genes demonstrated positive correlations with inferred immune cell infiltration within the TCGA-KIRC dataset (Fig 5; S3 Fig). Fig 5 highlights the strongest cytokine–immune cell associations identified in the analysis, whereas the complete correlation dataset is presented in S3 Fig. Among the strongest associations observed, CXCL9, CCL4, and CCL5 expression positively correlated with CD8 + T-cell infiltration, with correlation coefficients of 0.68, 0.59, and 0.69, respectively. Dendritic cell infiltration also showed positive associations with CXCL9, CCL4, CCL5, and CD14 expression. In addition, macrophage infiltration correlated with CD14 and AXL expression, whereas neutrophil infiltration was associated with CD14, AXL, CXCL9, and CCL4 expression. CXCL9 expression also demonstrated a moderate positive correlation with B-cell infiltration.

These findings suggest that cytokine-related transcriptional programs identified in RCC may be associated with broader immune microenvironment remodeling. Because these analyses were derived from transcriptomic datasets and computational immune deconvolution approaches, the observed associations should be interpreted within the context of RNA-level immune infiltration estimates.

Heatmap showing the strongest correlations between cytokine-related gene expression and inferred immune cell infiltration within the TCGA-KIRC dataset using TIMER V2.0. Rows represent immune cell populations and columns represent cytokine-related genes identified from the antibody array analyses. Correlation coefficients derived from Spearman correlation analysis with tumor purity adjustment are displayed within individual heatmap cells and represented by the continuous color gradient. Positive correlations indicate associations between increased transcript expression and higher estimated immune cell infiltration levels. Additional cytokine–immune cell correlation analyses are provided in S3 Fig.

Altered serum and urine cytokine levels in patients with localized RCC

To further explore whether selected cytokine alterations identified in RCC tissues could also be detected in circulating or urinary compartments, serum and urine samples from Cohort C were analyzed by ELISA. In serum, HGF, ErbB3, and MIP-1β/CCL4 levels were significantly decreased in patients with localized ccRCC compared with healthy controls, whereas CXCL9 levels were not significantly different. In urine samples, ErbB3 and MIP-1β/CCL4 levels were significantly reduced in ccRCC patients, while HGF and CXCL9 showed no significant differences (Fig 6). These findings demonstrate that selected cytokine-related alterations identified in RCC tissues may also be detectable in serum or urine; however, the degree and direction of these changes varied across biological compartments.

Fig 6 — Serum (top row) and urine (bottom row) levels of HGF, ErbB3, MIP-1β/CCL4, and MIG/CXCL9 were measured by ELISA in patients with ccRCC and healthy controls. Box plots represent the median, interquartile range, and minimum-to-maximum values. Statistical significance is indicated as follows: *P < 0.05, **P < 0.01, and ***P < 0.001. P values indicate statistical significance between groups.Top of Form.

Discussion

RCC is characterized by a highly immune-infiltrated yet functionally complex tumor microenvironment [18] in which cytokine-mediated signaling plays important roles in angiogenesis, inflammatory regulation, immune cell recruitment, and immune suppression [6,19]. Previous studies have reported altered cytokine patterns in RCC tissues, including reduced expression of T cell-associated cytokines such as IL-2 and IFN-γ alongside elevated inflammatory cytokines including IL-6 and IL-10 [20]. In addition, hypoxia-associated signaling within RCC has been linked to inflammatory cytokine production and activation of pathways involved in tumor progression and metastatic behavior, including STAT3 and PI3K-Akt signaling [11]. Although RCC tumors frequently demonstrate substantial immune cell infiltration, increasing evidence suggests that these infiltrating immune populations often exist within an immunoregulatory microenvironment that may limit effective anti-tumor immune responses [6]. Consistent with these observations, our exploratory cytokine profiling analyses identified broad alterations in cytokine-associated signaling pathways related to angiogenesis, inflammatory signaling, and immune regulation in early-stage RCC tissues.

Our exploratory cytokine profiling analyses identified altered expression patterns involving angiogenesis-associated, inflammatory, and immune regulatory signaling molecules in early-stage RCC tissues. Cytokines and related signaling proteins associated with angiogenesis and vascular remodeling, including Angiopoietin-2, VEGF, PIGF, MMP-9, and TIMP-1, demonstrated increased expression in RCC tumor tissues, whereas several cytokines involved in immune activation and lymphocyte regulation, including IL-2, IL-15, IL-13, IL-7, and M-CSF, showed reduced expression. In addition, elevated expression of inflammatory chemokines and immune-associated molecules such as CD14, AXL, CXCL9, CCL4, and CCL5 was observed, suggesting broader alterations in immune cell recruitment and inflammatory signaling within the RCC tumor microenvironment. Several molecules previously implicated in tumor progression and metastatic signaling, including HGF, IGFBP-2, ErbB3, and FLT3LG, unexpectedly demonstrated lower expression in early-stage RCC tissues relative to adjacent control tissues. Notably, selected findings from the discovery cohort, including alterations in Angiopoietin-2, CCL4, CXCL9, HGF, and ErbB3, were further evaluated in independent tissue, serum, or urine validation analyses. Because the present study primarily evaluated localized T1–T2 RCC tumors, these findings may reflect stage-specific differences in cytokine-associated signaling patterns within the evolving renal tumor microenvironment. Collectively, these observations support the concept that early-stage RCC is associated with complex and heterogeneous alterations in cytokine-related signaling networks rather than uniform activation of a single immune or tumor-promoting pathway [6,18].

Angiogenesis-related signaling molecules represented one of the most prominent cytokine-associated patterns identified in early-stage RCC tissues. Among these, Angiopoietin-2 (Ang2) and VEGF demonstrated increased expression in RCC tumor samples, with ELISA validation further confirming significantly elevated Ang2 levels in tumor tissues relative to normal tissues. Previous studies have identified Ang2 as an important regulator of tumor-associated angiogenesis and vascular remodeling through its interactions with VEGF and endothelial Tie-2 signaling pathway [21,22]. In addition to its vascular effects, Ang2 has also been implicated in inflammatory and immune-associated microenvironment remodeling, including modulation of endothelial activation and leukocyte trafficking [23]. Interestingly, a prior RCC study reported higher Ang2 expression in stage I tumors compared with more advanced stage IV disease [24], which is broadly consistent with the elevated Ang2 expression observed in our early-stage RCC cohort. Collectively, these findings suggest that angiogenesis-associated cytokine signaling alterations may occur early during RCC development and could contribute to vascular and inflammatory remodeling within the tumor microenvironment. However, because the present study was limited to exploratory cytokine profiling analyses, further mechanistic studies are required to clarify the specific functional roles of Ang2 and related angiogenic signaling pathways across different stages of RCC progression.

Chemokine-associated inflammatory signaling represented another prominent feature of the cytokine alterations observed in early-stage RCC tissues. CC and CXC chemokines are important regulators of leukocyte trafficking and immune cell localization within the tumor microenvironment [17]. Previous studies have suggested that chemokine receptor networks involving CXCR3, CXCR6, CCR5, and CCR6 may contribute to immune cell recruitment into RCC tissues [25]. In the present study, elevated expression of CXCL9, CCL4, and CCL5 was identified in RCC tumor tissues by antibody array profiling, while ELISA validation further confirmed significantly increased levels of CCL4 and CXCL9 in tumor tissues relative to normal controls. TCGA transcriptomic analyses also demonstrated elevated expression of CCL4 and CXCL9 in RCC tissues, and higher CCL4 expression was observed in metastatic T1–T2 tumors compared with non-metastatic tumors of the same stage. Previous studies have reported that CXCL9 may participate in immune cell recruitment and inflammatory remodeling within the RCC tumor microenvironment, including interactions involving tumor-associated macrophages and CXCR3-expressing T-cell populations [26]. In addition, CCL4 has been implicated in recruitment of immunoregulatory cell populations, including Foxp3 + regulatory T cells, in several tumor types [25,27]. Consistent with these observations, transcriptomic immune infiltration analyses in the present study demonstrated positive associations between CXCL9 and CCL4 expression and inferred infiltration of CD8 + T cells, dendritic cells, neutrophils, and other immune cell populations within the TCGA-KIRC dataset. However, because these analyses were based on transcriptomic datasets and computational immune deconvolution approaches rather than direct immune phenotyping, the biological significance of these associations should be interpreted cautiously. Collectively, these findings suggest that altered chemokine-associated signaling patterns may contribute to broader immune microenvironment remodeling in RCC.

IL-2 and IL-15 are important regulators of both innate and adaptive immune responses through their roles in activation and maintenance of CD8 + T cells and NK cells [28]. In the present study, reduced IL-2 and IL-15 protein expression was observed in early-stage RCC tumor tissues, which is broadly consistent with previous reports describing decreased levels of these cytokines within localized renal tumors [29]. However, transcriptomic analyses using TCGA-KIRC data demonstrated higher IL-2 and IL-15 mRNA expression in RCC tissues, particularly in metastatic T1–T2 tumors relative to non-metastatic tumors of the same stage, while this trend was less apparent in T3–T4 tumors. In addition, higher IL-2 and IL-15 transcript expression was associated with poorer overall survival in RCC patients. These findings highlight the complex relationship between cytokine protein abundance and transcriptomic expression within the RCC tumor microenvironment. Cytokine expression patterns may vary substantially according to tumor stage, immune cell composition, local inflammatory conditions, secretion dynamics, and post-transcriptional regulatory mechanisms, potentially contributing to discordant findings across tissue protein- and RNA-based analyses [30,31]. Given the dynamic and context-dependent nature of cytokine signaling within the tumor microenvironment, the biological significance of IL-2 and IL-15 alterations in RCC likely depends on multiple interacting immune and microenvironmental factors. Collectively, these findings underscore the importance of cautious interpretation of cytokine-associated transcriptomic and protein-level alterations within the heterogeneous RCC tumor microenvironment.

Several molecules previously implicated in tumor proliferation, metastatic progression, and therapy resistance, including HGF, IGFBP-2, ErbB3, and FLT3LG, demonstrated lower expression in early-stage RCC tissues relative to adjacent control tissues in the present study. HGF-mediated MET signaling has been associated with tumor survival, angiogenesis, and metastatic behavior in multiple malignancies, including RCC [32]. Similarly, IGFBP-2, FLT3 signaling, and ErbB3-associated PI3K/AKT pathway activation have been linked to tumor progression and therapeutic resistance in several cancer types [33–35]. The reduced expression of these molecules in predominantly T1-stage RCC tissues was therefore somewhat unexpected and may reflect differences between early-stage and advanced RCC microenvironmental signaling states. Because many prior studies evaluating these pathways have focused on metastatic or treatment-resistant disease settings, cytokine and growth factor expression patterns observed in localized RCC may not fully mirror those reported in more advanced tumors. In addition, variability between tissue protein abundance, circulating cytokine levels, and transcriptomic expression further highlights the context-dependent and heterogeneous nature of RCC-associated signaling networks. Exploratory serum and urine analyses demonstrated reduced HGF and ErbB3 levels in serum, with ErbB3 also decreased in urine samples from RCC patients. Although these findings suggest that altered ErbB3-associated signaling may be detectable across multiple biological compartments, larger validation studies will be required to determine the potential biological or clinical relevance of these observations.

Several limitations of the present study should be acknowledged. First, the study was designed as an exploratory cytokine profiling analysis of early-stage RCC and was conducted using relatively small patient cohorts, particularly within the validation and body fluid analyses. Given the substantial molecular and immune heterogeneity that characterizes RCC, larger independent cohorts will be necessary to further evaluate the reproducibility and generalizability of the cytokine-associated signaling patterns identified in this study [5,6]. Accordingly, the cytokine patterns identified in this study may not be uniformly represented across all RCC tumors and could reflect the presence of biologically distinct disease subsets that were not specifically evaluated in the current analysis. In addition, although complementary protein-, transcript-, and computational immune infiltration–based analyses were performed, incomplete concordance was observed across tissue protein expression, circulating cytokine measurements, and transcriptomic datasets. Such variability is consistent with the multi-layered regulation of cytokine expression and signaling and may reflect differences in tumor stage, immune cell composition, local inflammatory conditions, secretion dynamics, post-transcriptional regulation, and compartment-specific signaling within the tumor microenvironment [30,31]. Furthermore, immune infiltration analyses were based on transcriptomic deconvolution methods derived from TCGA datasets rather than direct cellular phenotyping, and therefore should be interpreted as indirect estimates of immune microenvironment composition. In addition, the present analyses were based on samples collected at a single clinical time point, limiting assessment of temporal changes in cytokine expression and tumor microenvironment remodeling during RCC progression. Finally, while the present study identified several cytokine-associated alterations linked to angiogenic, inflammatory, and immune regulatory pathways in early-stage RCC, additional mechanistic and longitudinal studies will be required to clarify the functional significance and temporal evolution of these signaling networks during RCC progression. Despite these limitations, the multi-platform approach used in this study provides an exploratory overview of cytokine-associated alterations in localized RCC and may help inform future investigations of RCC tumor microenvironment biology.

In conclusion, the present study identified altered cytokine-associated signaling patterns in early-stage RCC involving angiogenesis-related, inflammatory, and immune regulatory pathways. Although these findings require further validation and mechanistic investigation, this exploratory multi-platform analysis provides additional insight into cytokine-associated tumor microenvironment remodeling in localized RCC.

Declaration of generative AI and AI-assisted technologies in the writing process

During the preparation of this work, the authors used GPT-4o to assist with language and grammar correction. After using this service, the authors reviewed and edited the content as needed and take full responsibility for the content of the publication.

Supporting information

S1 Fig. Differential mRNA expression of cytokines in renal tumors and normal tissues from the TCGA-KIRC dataset.

mRNA expression levels (log2(TPM + 1)) of Angiopoietin-2, IL-2, IL-15, MIG/CXCL9, MIP-1β/CCL4, IGFBP-2, HGF, ErbB3, and Flt-3 Ligand in renal tumors and normal kidney tissues from the TCGA-KIRC dataset. Differential expression analysis was performed using GEPIA with cutoffs of p < 0.05 and |log2FC| > 2.

(TIF)

pone.0358533.s001.tif^{(1.8MB, tif)}

S2 Fig. Kaplan-Meier survival analysis of nine cytokine-related genes in renal cell carcinoma (TCGA-KIRC dataset).

Kaplan-Meier plots of nine cytokine-related genes in the TCGA-KIRC dataset generated using the Kaplan-Meier Plotter Pan-cancer RNA-seq dataset. Patients were stratified into high- and low-expression groups using the platform’s automatically selected cutoff settings. Statistical significance was defined as p < 0.05.

(TIF)

pone.0358533.s002.tif^{(3.1MB, tif)}

S3 Fig. Additional immune infiltration correlation analyses of cytokine-related genes in the TCGA-KIRC dataset.

Correlations between CD14, AXL, CCL4, CXCL9, and CCL5 expression and immune cell infiltration estimates were evaluated using TIMER V2.0 based on TCGA-KIRC transcriptomic data. Spearman correlation analysis with tumor purity adjustment was used. Correlation coefficients and corresponding p-values are shown.

(TIF)

pone.0358533.s003.tif^{(6.5MB, tif)}

S1 Table. ELISA standard curve concentrations and curve-fit statistics.

Standard concentration series, four-parameter logistic (4PL) curve-fitting model, and corresponding R² values for ELISA assays used in the study.

(XLSX)

pone.0358533.s004.xlsx^{(10.5KB, xlsx)}

S1 Data. De-identified underlying data for the cytokine antibody array and ELISA analyses.

Workbook containing the processed normalized cytokine antibody array data and ELISA concentration data underlying the study analyses, with accompanying data descriptions.

(XLSX)

pone.0358533.s005.xlsx^{(424.6KB, xlsx)}

Data Availability

All relevant study-generated data are within the manuscript and its Supporting Information files. The de-identified underlying data supporting the cytokine antibody array and ELISA analyses are provided as S1 Data. All clinical and demographic data available for the study cohorts are reported in Tables 1–3 of the manuscript. Publicly available RNA-sequencing and clinical data from the TCGA Kidney Renal Clear Cell Carcinoma (TCGA-KIRC) project are accessible through the National Cancer Institute Genomic Data Commons (GDC) at https://portal.gdc.cancer.gov/projects/TCGA-KIRC. TCGA-KIRC gene expression data were analyzed using Gene Expression Profiling Interactive Analysis (GEPIA; http://gepia.cancer-pku.cn/). Overall survival analyses were performed using the Kaplan–Meier Plotter Pan-cancer RNA-seq platform (https://kmplot.com/analysis/). Immune infiltration analyses of TCGA-KIRC data were performed using the Tumor Immune Estimation Resource (TIMER) V2.0 (https://compbio.cn/timer2/).

Funding Statement

This study was financially supported by the National Natural Science Foundation of China in the form of grants received by XC (No. 81572522) and XC (No. 81202011). This study was also financially supported by the Guangdong Innovation Platform Project in the form of a grant (2012B090600021) received by RH and SQ. This study was also financially supported by the Guangzhou 2024 Annual Special Project on Agricultural and Social Development Science and Technology in the form of grants (2024B03J1332 and 2024B03J1249) received by SL. This study was also financially supported by the National Key R&D Program of China in the form of grants (2024YFA1307601, 2024YFA1307602, and 2024YFA1307603) received by RH. This study was also financially supported by RayBiotech, Inc. in the form of salaries for authors YL, PW, YC, SL, RW and RH. The specific roles of these authors are articulated in the ‘author contributions’ section. RayBiotech antibody arrays were used in this study. The funders had no additional role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.

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PLoS One. doi: 10.1371/journal.pone.0358533.r001

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Reviewer #1: In the cytokine array analysis methods, the authors report samples were homogenized in RayBio Lysis Buffer containing a protease inhibitor cocktail. It would be helpful to have the part numbers for both of these products, as they have for other materials.

Generally, the methods are not in sufficient detail that the study could be replicated. More details should be added than “following manufacturer’s instructions”. In cases where algorithms or web servers were used, the authors should include settings/variables. For ELISA, the authors should report concentrations used for standard curves, as well as fits of the data to the standard curves. For KM Plotter, Affymetrix ID(s) for each gene should be reported (in addition to settings e.g. how high/low expression cutoff was selected and what the cutoffs were for each gene).

The cohorts should be better defined with standard and RCC context dependent epidemiological and demographic descriptors, such as age, gender, tobacco use, BMI, presence of hypertension, comorbidities, etc. Were all renal cancer patients chemotherapy naïve? If not, do the patients who received therapy or have distinct expression patterns from ones who did not? Or perhaps different therapies segregate into characteristic expression patterns. Overall, tumor vs paratumor vs normal is an oversimplification and the results of this study could be entirely driven by hidden variables.

Figure 2 does not appear to have statistical comparisons. It should, which would also require multiple comparison corrections as there are more than 2 samples being compared per gene.

In general, the figure legends need improved descriptions that allow for essentially full interpretability without requiring digging into the text or external material.

Typically, there is not a strong correlation between gene and protein expression. This is especially true for secreted proteins, particularly cytokines. There are many regulatory mechanisms between RNA expression and functional proteins being released and used. Cytokines also tend to have short half-lives, are active distal to their production site, and are by nature downstream of a more fundamental regulator. These facts combine to explain why there is poor correlation between results in the manuscript (compare patterns for each gene across figures). Often the antibody array (Fig 2), ELISA (Fig 3), and TCGA RNA (Fig S1) disagree with each other. It is difficult to imagine prognostic and mechanistic relevance of these transient signaling molecules taken at a single timepoint with such wide within-group distributions and lack of agreement between different assays. In fact, the 5th discussion paragraph states “these findings highlight the potential inconsistency between cytokine protein levels and mRNA expression when analyzing renal tumor samples”. This is of course true but extends more broadly than renal tumor samples. It’s also why we need to cautiously assess the significance of protein/RNA findings that oppose each other.

In the section entitled “several upregulated cytokines correlate with immune cells in early stage RCC” the type of correlation (e.g. Pearson, Spearman, etc) is not stated, nor is this in the methods section. I also suggest pruning Figure 5 to include only the most significant ones to draw attention to them, rather than losing them in the large mix of mostly insignificant genes; the full set can be a supplemental figure. This will improve interpretability of the figure. It should also be stated more clearly that it is the gene level data from TCGA, which is not necessarily the same as the cytokine (protein) level itself.

Overall, the manuscript should improve transitions between results sections and the results themselves, which are little more than lists.

In the discussion section 2nd paragraph, there are proteins stated that are not cytokines, but more importantly this study did not validate through their assays. The discussion should clearly differentiate and cite previous findings in the literature from specific cytokines validated through the assays in this study. It should also differentiate cytokines from the up or downstream proteins that regulate or are regulated by them.

The third and fourth paragraphs of discussion highlight that the variability in the measurements are nearly as large as the measurements themselves. While there may be statistically significant differences between tumor and normal samples, it is possible there are subsets of tumor samples which drive this, while other subsets rely on a different mechanism. Some sort of clustering or similar approach could be taken to segregate the tumor samples into molecularly defined subtypes. These might be related to treatment vs naïve, or one of the epidemiological/demographic features.

The discussion and conclusion should be more carefully worded given the inherent limitations of using single timepoint sampling on very transient signaling proteins, lack of agreement with the protein and RNA, etc.

Reviewer #2: This manuscript addresses an interesting question regarding cytokine dynamics in early-stage RCC, which is relatively underexplored. The integration of protein-level profiling with public transcriptomic datasets is a strength.

However, the study has several limitations that should be carefully considered. The sample size is relatively small, especially for high-dimensional cytokine profiling, and there is no correction for multiple testing, which raises concerns about false-positive findings. Additionally, the conclusions, particularly regarding biomarker potential such as ErbB3, are somewhat overstated given the limited validation cohort.

The work appears more exploratory than confirmatory, and while it may be suitable for PLOS ONE after revision, it requires substantial improvement in statistical rigor, transparency, and interpretation.

I recommend major revision, with particular attention to statistical analysis and data presentation.

Comments to the Authors

This manuscript presents cytokine profiling in early-stage renal cell carcinoma using antibody arrays, ELISA validation, and bioinformatic analyses. The focus on early tumor microenvironment dynamics is relevant and potentially impactful.

However, several aspects of the study require clarification and improvement, particularly regarding statistical rigor, methodological transparency, and interpretation of results.

Major Comments

1. Sample Size and Statistical Power

The discovery cohort includes 20 paired samples, and validation cohorts are also relatively small. Given that 174 cytokines were profiled, this raises concerns about statistical robustness.

Please provide a justification for sample size or power calculation.

Expand discussion of this limitation.

2. Multiple Testing Correction

You identified 34 differentially expressed cytokines, but no correction for multiple comparisons appears to have been applied.

Please apply false discovery rate (FDR) correction or similar methods.

Reassess which cytokines remain significant after correction.

3. Data Processing and Normalization

The manuscript lacks sufficient detail regarding array normalization methods, handling of batch effects, and quality control procedures. This limits reproducibility.

Please clearly describe data preprocessing steps.

Consider including quality control plots or supplementary figures.

4. Protein vs mRNA Discrepancy

You report inconsistencies between protein (ELISA) and mRNA (TCGA) levels for several cytokines such as IL-2 and IL-15.

This is an important observation but is not sufficiently explored.

Please provide a more detailed discussion addressing potential biological explanations such as post-transcriptional regulation or secretion dynamics.

5. Biomarker Claims (ErbB3)

The claim that urinary ErbB3 may serve as a non-invasive biomarker is interesting but currently not well supported.

The validation cohort is small.

Please tone down the conclusion and present this as a preliminary finding.

6. Lack of Functional Validation

The study is primarily descriptive and does not include mechanistic experiments.

Please acknowledge this limitation more explicitly and avoid causal interpretations.

7. Clinical Variables

There is limited discussion of patient heterogeneity.

Please clarify whether variables such as age, sex, tumor subtype, or grade were considered.

Include additional clinical context if available.

8. Statistical Methods

Use of t-tests for multiple comparisons may not be appropriate.

Please clarify whether assumptions of normality were tested.

Consider non-parametric approaches where appropriate.

Minor Comments

Some grammatical issues and repetitive phrasing are present. A careful language edit would improve readability.

Figures are informative but need improvement. Units should be standardized, for example pg/ml should be used consistently. Figure legends should also be more descriptive.

Terminology should be consistent throughout the manuscript, particularly terms such as para-tumor and para-cancerous tissue.

Major Revision Recommended and can be publishable

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Reviewer #1: No

Reviewer #2: Yes: Mahesh Gokara

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PLoS One. 2026 Sep 29;21(9):e0358533. doi: 10.1371/journal.pone.0358533.r002

Author response to Decision Letter 1

23 Jun 2026

PLOS ONE Revision Response Document

Manuscript ID: PONE-D-26-01255

Title: Tumor Specific Cytokine Pattern Explored in Renal Cell Carcinoma

SECTION 1 — JOURNAL REQUIREMENTS

________________________________________

Journal Requirement 1

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https://journals.plos.org/plosone/s/file?id=ba62/PLOSOne_formatting_sample_title_authors_affiliations.pdf”

Response

We thank the editor for this comment. The manuscript has been reformatted according to the PLOS ONE style requirements and templates, including updates to the title page, section headings, figure citations and captions, references, and overall manuscript formatting. File naming conventions were also updated accordingly.

________________________________________

Journal Requirement 2

“2. Please include your tables as part of your main manuscript and remove the individual files. Please note that supplementary tables (should remain/ be uploaded) as separate "supporting information" files.”

Response

We thank the editor for this comment. All tables were incorporated into the main manuscript file and individual table files were removed. For improved clarity and organization, the original Table 1 was separated into Table 1 (“Clinical characteristics of RCC tissue cohorts (Cohorts A and B).”) and Table 2 (“Clinical characteristics of serum and urine validation cohorts (Cohort C)”). The additional summary table, Table 3 (“Summary of clinical characteristics across RCC patient cohorts”), was also incorporated into the manuscript.

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Journal Requirement 3

“3. Thank you for stating the following financial disclosure:

‘National Natural Science Foundation of China (No. 81572522 and No. 81202011), Guangdong Innovation Platform Project (2012B090600021), Guangzhou 2024 Annual Special Project on Agricultural and Social Development Science and Technology (2024B03J1332, 2024B03J1249). National Key R&D Program of China (2024YFA1307601, 2024YFA1307602, 2024YFA1307603).’

Please state what role the funders took in the study. If the funders had no role, please state: ‘The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.’

If this statement is not correct you must amend it as needed.

Please include this amended Role of Funder statement in your cover letter; we will change the online submission form on your behalf.”

Response

We thank the editor for this comment. The Role of Funder statement has now been clarified and included in the revised cover letter. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.________________________________________

Journal Requirement 4

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Response

We thank the editor for this comment. All funding-related information was removed from the manuscript and will be provided only through the Funding Statement section.

________________________________________

Journal Requirement 5

“5. Thank you for stating the following in the Competing Interests section:

‘Ying Luo, Panning Wang, Shuhong Luo and Ruo-Pan Huang are employees of and have a financial stake in RayBiotech, Inc. The authors declare no additional competing interests.’

We note that one or more of the authors are employed by a commercial company: RayBiotech, Inc

Please also include the following statement within your amended Funding Statement.

‘The funder provided support in the form of salaries for authors [insert relevant initials], but did not have any additional role in the study design, data collection and analysis, decision to publish, or preparation of the manuscript. The specific roles of these authors are articulated in the ‘author contributions’ section.’

If your commercial affiliation did play a role in your study, please state and explain this role within your updated Funding Statement.

Within your Competing Interests Statement, please confirm that this commercial affiliation does not alter your adherence to all PLOS ONE policies on sharing data and materials by including the following statement: ‘This does not alter our adherence to PLOS ONE policies on sharing data and materials.’ (as detailed online in our guide for authors http://journals.plos.org/plosone/s/competing-interests) . If this adherence statement is not accurate and there are restrictions on sharing of data and/or materials, please state these. Please note that we cannot proceed with consideration of your article until this information has been declared.

Please include both an updated Funding Statement and Competing Interests Statement in your cover letter. We will change the online submission form on your behalf.”

Response

We thank the editor for this comment. The funding and competing interest disclosures have now been clarified and included in the revised cover letter and manuscript.

Funding statement:

“National Natural Science Foundation of China (No. 81572522 and No. 81202011), Guangdong Innovation Platform Project (2012B090600021), Guangzhou 2024 Annual Special Project on Agricultural and Social Development Science and Technology (2024B03J1332, 2024B03J1249), and National Key R&D Program of China (2024YFA1307601, 2024YFA1307602, 2024YFA1307603).”

Role of Funder statement:

“The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.”

Competing interests statement:

“Ying Luo, Panning Wang, Shuhong Luo, Rochelle Wickramasekara and Ruo-Pan Huang are employees of and have a financial stake in RayBiotech, Inc. RayBiotech antibody arrays were used in this study. The company provided support in the form of salaries for these authors but did not have any additional role in study design, data collection and analysis, decision to publish, or preparation of the manuscript. This does not alter our adherence to PLOS ONE policies on sharing data and materials. The authors declare no additional competing interests.”

________________________________________

Journal Requirement 6

“6. In the online submission form you indicate that your data is not available for proprietary reasons and have provided a contact point for accessing this data. Please note that your current contact point is a co-author on this manuscript. According to our Data Policy, the contact point must not be an author on the manuscript and must be an institutional contact, ideally not an individual. Please revise your data statement to a non-author institutional point of contact, such as a data access or ethics committee, and send this to us via return email. Please also include contact information for the third party organization, and please include the full citation of where the data can be found.”

Response

We thank the editor for this comment. The Data Availability Statement has been revised to replace the co-author contact with a non-author institutional point of contact in accordance with the PLOS ONE Data Policy. Updated contact information and data access details are provided below.

Updated Data Availability Statement:

“The data supporting the findings of this study are available from the Office of Research Administration, The First Affiliated Hospital of Sun Yat-sen University, upon reasonable request. Contact person: Haifeng Lin, Research Officer. Email: zsyykyc@mail.sysu.edu.cn; Phone: (086)020-87755766. Address: Office of Research Administration, The First Affiliated Hospital of Sun Yat-sen University, No. 58, Zhongshan 2nd Road, Guangzhou, Guangdong Province, China, 510080.”

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Journal Requirement 7

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Response

We thank the editor for this comment. The corresponding author ORCID iD (https://orcid.org/0000-0002-5884-8026) has been validated and updated in Editorial Manager

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Journal Requirement 8

“8. Please upload a new copy of Figures 1 to 6 as the detail is not clear. Please follow the link for more information: https://journals.plos.org/plosone/s/figures”

Response

We thank the editor for this comment. Figures 1–6 were regenerated and uploaded as revised high-resolution TIFF files in accordance with the PLOS ONE figure preparation guidelines to improve image clarity and readability. Supplementary figures were also updated accordingly.

________________________________________

Journal Requirement 9

“9. Please include captions for your Supporting Information files at the end of your manuscript, and update any in-text citations to match accordingly. Please see our Supporting Information guidelines for more information: http://journals.plos.org/plosone/s/supporting-information.”

Response

We thank the editor for this comment. Captions for all Supporting Information files were added to the end of the manuscript under a “Supporting information” section, and in-text citations were updated accordingly to match PLOS ONE formatting guidelines.

________________________________________

Journal Requirement 10

“10. If the reviewer comments include a recommendation to cite specific previously published works, please review and evaluate these publications to determine whether they are relevant and should be cited. There is no requirement to cite these works unless the editor has indicated otherwise.”

Response

We thank the editor for this comment. Reviewer comments and suggested revisions were evaluated and addressed where applicable in the revised manuscript.

Reviewer 1.1

“In the cytokine array analysis methods, the authors report samples were homogenized in RayBio Lysis Buffer containing a protease inhibitor cocktail. It would be helpful to have the part numbers for both of these products, as they have for other materials.”

Response

We thank the reviewer for this comment. Additional methodological details were added to the cytokine array analysis section, including reagent catalog numbers, sample preparation procedures, chemiluminescent detection workflow, densitometric signal quantification, and normalization methods to improve reproducibility and methodological transparency.

________________________________________

Reviewer 1.2

“Generally, the methods are not in sufficient detail that the study could be replicated. More details should be added than “following manufacturer’s instructions”. In cases where algorithms or web servers were used, the authors should include settings/variables. For ELISA, the authors should report concentrations used for standard curves, as well as fits of the data to the standard curves. For KM Plotter, Affymetrix ID(s) for each gene should be reported (in addition to settings e.g. how high/low expression cutoff was selected and what the cutoffs were for each gene).”

Response

We thank the reviewer for this comment. The Methods section was expanded substantially to improve reproducibility and transparency. Additional methodological details were added for the cytokine array, ELISA, TCGA, Kaplan–Meier Plotter, and TIMER analyses, including data preprocessing, normalization procedures, statistical methods, differential expression criteria, and analysis parameters. For the cytokine array analysis, differential expression testing, Benjamini–Hochberg FDR correction, clustering methods, and pathway enrichment procedures are now explicitly described. For TIMER analyses, the database version, TCGA dataset, correlation method, tumor purity adjustment, significance criteria, and analyzed genes are now reported. Kaplan–Meier Plotter query parameters and gene symbol–based analyses have also been clarified in the revised Methods section.

________________________________________

Reviewer 1.3

“The cohorts should be better defined with standard and RCC context dependent epidemiological and demographic descriptors, such as age, gender, tobacco use, BMI, presence of hypertension, comorbidities, etc. Were all renal cancer patients chemotherapy naïve? If not, do the patients who received therapy or have distinct expression patterns from ones who did not? Or perhaps different therapies segregate into characteristic expression patterns. Overall, tumor vs paratumor vs normal is an oversimplification and the results of this study could be entirely driven by hidden variables.”

Response

We thank the reviewer for this important comment. We agree that clinical and demographic variables may contribute to heterogeneity in cytokine expression patterns and should be considered when interpreting the findings.

To address this concern, we revised the Patient Cohorts and Clinical Sample Collection section to provide additional clinicopathological information for the analyzed cohorts. The revised manuscript now clarifies that the tissue cohorts consisted predominantly of localized early-stage RCC (T1–T2) cases and were composed primarily of clear cell RCC tumors. Available clinicopathological characteristics, including age, sex, tumor size, T stage, and metastatic status, are now described in the Methods section, and Table 1 has been updated to include metastatic status.

We acknowledge that additional epidemiological and clinical variables, including smoking history, BMI, hypertension status, comorbidities, and detailed treatment history, were not available for all retrospective samples included in this study. Consequently, stratified analyses based on these variables could not be performed.

To further address this limitation, we expanded the Discussion section to acknowledge the molecular and immune heterogeneity of RCC and the possibility that unmeasured clinical variables may contribute to the cytokine expression patterns observed. We now emphasize that this study should be interpreted as an exploratory cytokine profiling analysis and that larger, clinically well-annotated cohorts will be required to further evaluate the influence of patient- and disease-specific variables on cytokine-associated signaling patterns in RCC.

________________________________________

Reviewer 1.4

“Figure 2 does not appear to have statistical comparisons. It should, which would also require multiple comparison corrections as there are more than 2 samples being compared per gene.”

Response

We thank the reviewer for this comment. Statistical comparisons and significance annotations have been added to Figure 2. In addition, the figure layout was revised to improve clarity, with the representative antibody array images now presented as Panel A and the corresponding quantified signal intensity data presented as Panel B. The figure legend has also been expanded to provide additional detail regarding the displayed cytokines and sample groups. The Methods section was revised to clarify the statistical framework used for cytokine array analysis, including moderated linear modeling, multi-group comparisons, pairwise testing, and Benjamini–Hochberg FDR correction for multiple testing.

________________________________________

Reviewer 1.5

“In general, the figure legends need improved descriptions that allow for essentially full interpretability without requiring digging into the text or external material.”

Response

We thank the reviewer for this comment. All figure legends were revised to improve standalone interpretability and reduce the need to reference the main text. The revised legends now provide additional detail regarding sample groups, experimental methods, statistical annotations, panel organization, and the principal findings illustrated in each figure. Supplemental figure legends were also expanded to improve clarity and reproducibility.________________________________________

Reviewer 1.6

“Typically, there is not a strong correlation between gene and protein expression. This is especially true for secreted proteins, particularly cytokines. There are many regulatory mechanisms between RNA expression and functional proteins being released and used. Cytokines also tend to have short half-lives, are active distal to their production site, and are by nature downstream of a more fundamental regulator. These facts combine to explain why there is poor correlation between results in the manuscript (compare patterns for each gene across figures). Often the antibody array (Fig 2), ELISA (Fig 3), and TCGA RNA (Fig S1) disagree with each other. It is difficult to imagine prognostic and mechanistic relevance of these transient signaling molecules taken at a single timepoint with such wide within-group distributions and lack of agreement between different assays. In fact, the 5th discussion paragraph states “these findings highlight the potential inconsistency between cytokine protein levels and mRNA expression when analyzing renal tumor samples”. This is of course true but extends more broadly than renal tumor samples. It’s also why we need to cautiously assess the significance of protein/RNA findings that oppose each other.”

Response

We thank the reviewer for this important comment. We agree that cytokine protein abundance, circulating cytokine levels, and transcriptomic expression are not expected to demonstrate complete concordance, particularly for secreted cytokines that are subject to complex regulation at multiple biological levels. We also agree that transcriptomic findings should not be interpreted as direct validation of protein-level observations.

To address this concern, we substantially revised the Results and Discussion sections to present the TCGA analyses as complementary transcriptomic contextualization rather than validation of the antibody array or ELISA findings. For example, in the Results section we now state that: “these transcriptomic analyses were interpreted primarily as complementary contextual analyses rather than direct validation of the protein-level findings” (Results, TCGA transcriptomic analyses section).

We also expanded the Discussion to explicitly acknowledge the biological factors that may contribute to discordance between protein- and RNA-based measurements. Specifically, we now state that: “These findings highlight the complex relationship between cytokine protein abundance and transcriptomic expression within the RCC tumor microenvironment” and that “cytokine expression patterns may vary substantially according to tumor stage, immune cell composition, local inflammatory conditions, secretion dynamics, and post-transcriptional regulatory mechanisms, potentially contributing to discordant findings across tissue protein- and RNA-based analyses” (Discussion, IL-2/IL-15 section).

In addition, we expanded the study limitations to acknowledge the incomplete concordance observed across tissue protein expression, circulating cytokine measurements, and transcriptomic datasets. The revised manuscript now states: “Incomplete concordance was observed across tissue protein expression, circulating cytokine measurements, and transcriptomic datasets,” and further notes that such variability “is consistent with the multi-layered regulation of cytokine expression and signaling” (Discussion, Limitations section).

Overall, these revisions provide a more cautious interpretation of the multi-platform findings and better acknowledge the biological complexity and limitations associated with relating cytokine protein measurements, circulating biomarkers, and transcriptomic expression data.

________________________________________

Reviewer 1.7

“In the section entitled “several upregulated cytokines correlate with immune cells in early stage RCC” the type of correlation (e.g. Pearson, Spearman, etc) is not stated, nor is this in the methods section. I also suggest pruning Figure 5 to include only the most significant ones to draw attention to them, rather than losing them in the large mix of mostly insignificant genes; the full set can be a supplemental figure. This will improve interpretability of the figure. It should also be stated more clearly that it is the gene level data from TCGA, which is not necessarily the same as the cytokine (protein) level itself.”

Response

We revised this section and corresponding figure legend to clarify that these analyses were performed using TCGA-KIRC transcriptomic data and TIMER-based immune infiltration estimates rather than direct cytokine protein measurements. The Results text was also revised to improve interpretation of these findings and avoid overstatement.

In addition, Figure 5 was simplified to focus on the strongest cytokine–immune cell associations (LPS/CD14, AXL, CXCL9, CCL4, and CCL5) in the main figure, while additional correlation analyses were moved to S3 Fig to improve clarity and interpretability.

The statistical correlation method and TIMER analysis parameters have now been explicitly described in the Methods section, including the use of Spearman correlation analysis with tumor purity adjustment.

________________________________________

Reviewer 1.8

“Overall, the manuscript should improve transitions between results sections and the results themselves, which are little more than lists.”

Response

We thank the reviewer for this helpful suggestion. To improve readability and narrative flow, the Results section was extensively revised to reduce list-like presentation of findings and strengthen transitions between major analyses. Rather than presenting sequential observations as isolated results, we added contextual statements that explain how each analysis relates to the overall study objective and how subsequent analyses build upon preceding findings.

For example, following the cytokine array analysis, we added text stating that “these findings suggest that early-stage RCC is associated with coordinated alterations in angiogenic, inflammatory, and immune regulatory signaling pathways,” thereby providing a biological framework for the differentially expressed cytokines before introducing downstream validation studies. Similarly, before presenting the TCGA analyses, we added text explaining that these analyses were performed to provide “complementary transcriptomic context for the observed protein-level alterations” rather than introducing the dataset abruptly.

Additional transitions were incorporated throughout the Results section to improve continuity between the antibody array profiling, ELISA validation, TCGA transcriptomic analyses, and TIMER immune infiltration analyses. These examples are representative of broader revisions made throughout the Results section to improve organization, interpretation, and overall readability.

________________________________________

Reviewer 1.9

“In the discussion section 2nd paragraph, there are proteins stated that are not cytokines, but more importantly this study did not validate through their assays. The discussion should clearly differentiate and cite previous findings in the literature from specific cytokines validated through the assays in this study. It should also differentiate cytokines from the up or downstream proteins that regulate or are regulated by them.”

Response

We thank the reviewer for this helpful comment. We agree that the proteins identified in the antibody array represent a diverse group of cytokines, chemokines, growth factors, receptors, adhesion molecules, and other immune regulatory proteins, and that these categories should not be discussed interchangeably. We also agree that findings identified in the discovery array should be clearly distinguished from those that underwent additional validation analyses.

To address this concern, the Discussion was substantially revised to replace broad references to “cytokines” with more precise descriptions such as “cytokine-associated signaling patterns,” “angiogenesis-associated signaling molecules,” “inflammatory chemokines,” and “immune regulatory signaling molecules,” where appropriate. For example, the revised Discussion now states that “our exploratory cytokine profiling analyses identified altered expression patterns involving angiogenesis-associated, inflammatory, and immune regulatory signaling molecules in early-stage RCC tissues” rather than grouping all identified proteins under a single cytokine category.

In addition, the revised Discussion now distinguishes observations derived from the initial antibody array screen from findings that underwent subsequent evaluation in independent tissue, serum, or urine cohorts. Specifically, we added the statement: “Notably, selected findings from the discovery cohort, including alterations in Angiopoietin-2, CCL4, CXCL9, HGF, and ErbB3, were further evaluated in independent tissue, serum, or urine validation analyses.” This clarification avoids presenting all detected proteins with equivalent levels of validation.

We also revised the Discussion to more clearly differentiate cytokines and chemokines from growth factors, receptors, and downstream signaling-associated proteins when discussing their potential biological relevance. These changes were made throughout the Discussion to improve biological accuracy, distinguish validated findings from discovery-stage observations, and avoid overinterpretation of the screening results.

________________________________________

Reviewer 1.10

“The third and fourth paragraphs of discussion highlight that the variability in the measurements are nearly as large as the measurements themselves. While there may be statistically significant differences between tumor and normal samples, it is possible there are subsets of tumor samples which drive this, while other subsets rely on a different mechanism. Some sort of clustering or similar approach could be taken to segregate the tumor samples into molecularly defined subtypes. These might be related to treatment vs naïve, or one of the epidemiological/demographic features.”

Response

We thank the reviewer for this insightful comment. We agree that RCC is a heterogeneous disease and that distinct molecular subgroups may contribute to the variability observed in cytokine expression patterns.

The primary objective of this study was to identify cytokine alterations associated with RCC through exploratory protein profiling and subsequent validation analyses. Due to the relatively limited cohort size and incomplete availability of detailed clinical variables, robust subgroup or clustering analyses could not be performed reliably.

To address this concern, we expanded the Discussion section to acknowledge the biological and clinical heterogeneity of RCC and the possibility that the observed cytokine patterns may reflect multiple underlying disease subtypes. We now note that larger, clinically annotated cohorts will be required to evaluate molecular subgroups and determine whether distinct cytokine signatures are associated with specific clinicopathological characteristics or disease states.

________________________________________

Reviewer 1.11

“The discussion and conclusion should be more carefully worded given the inherent limitations of using single timepoint sampling on very transient signaling proteins, lack of agreement with the protein and RNA, etc.”

Response

We thank the reviewer for this important comment and agree that cytokine measurements represent dynamic biological processes that should be interpreted cautiously. To address this concern, we substantially revised the Discussion and Conclusion sections to adopt a more cautious interpretation of the findings and expanded the limitations section.

Specifically, we now acknowledge the observed discordance across analytical platforms, stating that “incomplete concordance was observed across tissue protein expression, circulating cytokine measurements, and transcriptomic datasets.” We also added a limitation noting that “the present analyses were based on samples collected at a single clinical time point, limiting assessment of temporal changes in cytokine expression and tumor microenvironment remodeling during RCC progression.” Finally, we note that “additional mechanistic and longitudinal studies will be required to clarify the functional significance and temporal evolution of these signaling networks during RCC progression.”

These revisions provide a more balanced interpretation of the findings and more clearly acknowledge the limitations inherent to exploratory cytokine profiling studies.

________________________________________

Reviewer #2

________________________________________

Reviewer 2.1

“The discovery cohort includes 20 paired samples, and validation cohorts are also relatively small. Given that 174 cytokines were profiled, this raises concerns about statistical robustness.

Please provide a justification for sample size or power calculation.

Expand discussion of this limitation.”

Response

We thank the reviewer for this comment. We agree that the relatively small discovery and validation cohorts represent an important limitation of the study. To address this concern, we expanded the limitations section of the Discussion to more explicitly acknowledge the exploratory nature of the study, the relatively small cohort sizes, and the need for validation in larger independent patient cohorts. Specifically, the revised manuscript now states that the study was “designed as an exploratory cytokine profiling analysis” and that “larger independent cohorts will be necessary to further evaluate the reproducibility and generalizability of the cytokine-associated signaling patterns identified in this study.”

________________________________________

Reviewer 2.2

“You identified 34 differentially expressed cytokines, but no correction for multiple comparisons appears to have been applied.

Please apply false discovery rate (FDR) correction or similar methods.

Reassess which cytokines remain significant after correction.”

Response

Differential expression analysis of the cytokine array data was performed using the limma package in R. FDR correction was applied using the Benjamini–Hochberg procedure, and cytokines with FDR < 0.05 were considered significantly differentially expressed. The Methods section has been revised to explicitly describe the statistical procedures used for differential expression analysis and multiple-testing correction. The final set of 34 cytokines was selected from the significantly differentially expressed cytokines based on statistical significance, fold change, and biological relevance to RCC-associated signaling pathways.

________________________________________

Reviewer 2.3

“The manuscript lacks sufficient detail regarding array normalization methods, handling of batch effects, and quality control procedures. This limits reproducibility.

Please clearly describe data preprocessing steps.

Consider including quality control plots or supplementary figures.”

Response

We expanded the cytokine array Methods section to provide additional detail regarding array preprocessing, normalization, and quality control procedures. Specifically, we clarified that duplicate spot intensities were averaged following background subtraction, signal intensities were normalized to internal positive control spots, and duplicate spots and internal controls were used to assess signal consistency across membranes. Additional methodological detail regarding densitometric signal quantification, clustering analysis, and pathway enrichment workflows was also added to improve reproducibility and interpretability of the array analyses.

________________________________________

Reviewer 2.4

“You report inconsistencies between protein (ELISA) and mRNA (TCGA) levels for several cytokines such as IL-2 and IL-15.

This is an important observation but is not sufficiently explored.

Please provide a more detailed discussion addressing potential biological explanations such as post-transcriptional regulation or secretion dynamics.”

Response

We thank the reviewer for this important observation. To address this concern, we substantially expanded the Discussion of discordant protein- and transcript-level findings, particularly for IL-2 and IL-15. The revised Discussion now states that “these findings highlight the complex relationship between cytokine protein abundance and transcriptomic expression within the RCC tumor microenvironment” and further notes that cytokine expression patterns may vary according to “tumor stage, immune cell composition, local inflammatory conditions, secretion dynamics, and post-transcriptional regulatory mechanisms, potentially contributing to discordant findings across tissue protein- and RNA-based analyses.” We also expanded the limitations section to acknowledge the incomplete concordance observed across tissue protein, circulating cytokine, and transcriptomic datasets and to emphasize the need for cautious interpretation of these multi-platform findings.

________________________________________

Reviewer 2.5

“The claim that urinary ErbB3 may serve as a non-invasive biomarker is interesting but currently not well supported.

The validation cohort is small.

Please tone down the conclusion and present this as a preliminary finding.”

Response

We thank the reviewer for this comment and agree that the urinary ErbB3 findings should be interpreted cautiously given the relatively small validation cohort. To address this concern, we substantially toned down the discussion of ErbB3 as a potential biomarker and now present these observations as preliminary findings requiring further validation. Specifically, the revised Discussion states that “larger validation studies will be required to determine the potential biological or clinical relevance of these observations” rather than suggesting clinical utility. We also removed stronger biomarker-oriented language from the summary and conclusion sections to better reflect the exploratory nature of the study.

________________________________________

Reviewer 2.6

“The study is primarily descriptive and does not include mechanistic experiments.

Please acknowledge this limitation more explicitly and avoid causal interpretations.”

Response

We thank the reviewer for this comment and agree that the present study is primarily exploratory and descriptive in nature. To address this concern, we revised the manuscript to more clearly frame the work as an exploratory characterization of cytokine-associated alterations in early-stage RCC and substantially revised the Discussion and Conclusion sections to avoid causal interpretations. In addition, the limitations section now explicitly acknowledges that “additional mechanistic and longitudinal studies will be required to clarify the functional significance and temporal evolution of these signaling networks during RCC progression.” These revisions are intended to ensure that the findings are presented as associative observations rather than direct evidence of causality.

________________________________________

Reviewer 2.7

“There is limited discussion of patient heterogeneity.

Please clarify whether variables such as age, sex, tumor subtype, or grade were considered.

Include additional clinical context if available.”

Response

We thank the reviewer for this comment. Additional clinical context has been incorporated into the revised manuscript. The Patient Cohorts and Clinical Sample Collection section now clarifies that the tissue cohorts consisted predominantly of localized early-stage RCC (T1–T2) cases and were composed primarily of clear cell RCC tumors. Available clinicopathological characteristics, including age, sex, tumor size, T stage, and metastatic status, are described in the Methods section, and Table 1 has been updated to include metastatic status.

We acknowledge that additional clinical variables, including detailed comorbidity information and other epidemiological characteristics, were not available for all retrospective samples included in this study. The Discussion section has been expanded to recognize patient heterogeneity and the potential contribution of unmeasured clinical variables to the observed cytokine expression patterns as a limitation of the study.

________________________________________

Reviewer 2.8

“Use of t-tests for multiple comparisons may not be appropriate.

Please clarify whether assumptions of normality were tested.

Consider non-parametric approaches where appropriate.”

Response

We thank the reviewer for this comment. The Statistical Analysis section has been revised to provide additional detail regarding the statistical methods used throughout the study. Comparisons between two groups were performed using Student's t-test or paired t-test as appropriate, while comparisons involving more than two groups were analyzed using one-way ANOVA followed by post hoc multiple-comparison testing. In addition, the cytokine array analysis was revised to explicitly describe the limma-based statistical framework and Benjamini–Hochberg FDR correction used for differential expression analysis.

________________________________________

Reviewer 2.9

“Some grammatical issues and repetitive phrasing are present. A careful language edit would improve readability.

Figures are informative but need improvement. Units should be standardized, for example pg/ml should be used consistently. Figure legends should also be more descriptive.

Terminology should be consistent throughout the manuscript, particularly terms such as para-tumor and para-cancerous tissue.”

Response

We thank the reviewer for these helpful suggestions. The manuscript has undergone extensive language editing to improve grammar, readability, and overall flow. Figure legends were substantially expanded to provide clearer descriptions of the experimental design, sample groups, analytical approaches, and data interpretation. In addition, terminology was reviewed and standardized throughout the manuscript, including consistent descriptions of tissue sample types. Units and figure labeling were also reviewed for consistency across the revised manuscript and figures.

Attachment

Submitted filename: PONE-D-26-01255_Response_to_Reviewers.docx

pone.0358533.s006.docx^{(33.1KB, docx)}

PLoS One. doi: 10.1371/journal.pone.0358533.r003

Decision Letter 1

Alireza Shoari

19 Jul 2026

Dear Dr. Huang,

Please submit your revised manuscript by Sep 02 2026 11:59PM. If you will need more time than this to complete your revisions, please reply to this message or contact the journal office at plosone@plos.org. When you're ready to submit your revision, log on to https://www.editorialmanager.com/pone/ and select the 'Submissions Needing Revision' folder to locate your manuscript file.

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Academic Editor

PLOS One

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Reviewer #1: The authors appear to have addressed most of the peer review concerns.

I appreciate the significant efforts to include method and cohort details (and state that certain data were not available). The statistical approaches seem reasonable given the study limitations.

Overall, the main points that still remain are the Affymetrix IDs and cutoff values for each gene and the ELISA standard curve concentrations and the curve fit statistics. These matter for reproducibility because KM Plotter can map one gene to multiple different probe sets and the cutoff value changes periodically even when automatic settings are used. The ELISA standard curve concentrations and fits matter for reproducibility, but also to judge how well the sample concentrations could be interpolated from the assay based on both detection range and good fit of the standard curve to the regression model.

The authors included much more detail into the statistical methods, stating which tests and corrections were used. However, it still isn’t clear that normality was checked (e.g. Shapiro-Wilk) and that the data were normally distributed.

If those last things are addressed, the study will be publishable.

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PLoS One. 2026 Sep 29;21(9):e0358533. doi: 10.1371/journal.pone.0358533.r004

Author response to Decision Letter 2

28 Aug 2026

PLOS ONE Revision Response Document

Manuscript ID: PONE-D-26-01255

Title: Tumor Specific Cytokine Pattern Explored in Renal Cell Carcinoma

Reviewer #1

Comment 1

The authors appear to have addressed most of the peer review concerns. I appreciate the significant efforts to include method and cohort details (and state that certain data were not available). The statistical approaches seem reasonable given the study limitations.

Response: We thank the reviewer for the positive assessment and appreciate the constructive feedback provided throughout the review process.

________________________________________

Comment 2a

Overall, the main points that still remain are the Affymetrix IDs and cutoff values for each gene. These matter for reproducibility because KM Plotter can map one gene to multiple different probe sets and the cutoff value changes periodically even when automatic settings are used.

Response: We thank the reviewer for this comment. Upon further review of the Kaplan–Meier Plotter analysis, we recognized that our previous description did not clearly specify the type of gene-expression dataset used. The survival analyses were performed using the Pan-cancer RNA-seq dataset for TCGA-KIRC, rather than an Affymetrix microarray dataset. Affymetrix probe-set IDs, which identify the specific microarray probes used to measure gene expression, are therefore not applicable to these RNA-seq analyses. We have clarified this distinction in the revised Methods and S2 Fig legend.

To further improve reproducibility, we have also added the exact expression cutoff used to stratify patients into high- and low-expression groups for each gene: ANGPT2, 3,147; IL2, 2; IL15, 250; CXCL9, 2,340; CCL4, 382; HGF, 521; ERBB3, 3,076; FLT3LG, 69; and IGFBP2, 708. These cutoffs were selected using the Kaplan–Meier Plotter “auto select best cutoff” setting, as described in the revised Methods.

Comment 2b

The ELISA standard curve concentrations and the curve fit statistics matter for reproducibility, but also to judge how well the sample concentrations could be interpolated from the assay based on both detection range and good fit of the standard curve to the regression model.

Response:

Thank you for this comment. We have now provided the standard concentration series used for each ELISA, together with the curve-fitting model and corresponding R² values, in a new Supporting Information table (S1 Table). Standard curves were fitted using a four-parameter logistic (4PL) regression model, with R² values ranging from 0.9938 to 1.0000. The ELISA Methods section has also been revised to specify the curve-fitting model and direct readers to S1 Table. These additions provide the standard concentration ranges and goodness-of-fit information for the ELISA assays and improve reproducibility of the reported measurements.

________________________________________

Comment 3

Response: We thank the reviewer for this comment. To directly address this concern, we performed Shapiro–Wilk normality testing for the relevant datasets and re-evaluated the statistical comparisons using distribution-appropriate methods. The Statistical Analysis section has been revised to explicitly describe the normality assessment and selection of parametric or non-parametric tests.

Overall, the revised analyses were consistent with the previously reported expression patterns, with the direction of cytokine changes remaining unchanged. Most statistical conclusions were also retained, with only a few significance annotations requiring revision. In Fig. 2, IL-2 remained lower in tumor tissues but no longer reached statistical significance; this has now been explicitly stated in the Results and figure legend. Notably, reduced IL-2 expression remained significant in the independent ELISA validation cohort (Fig. 3). In the ELISA analyses, PDGF-AA in tissue (Fig. 3) and MIP-1β/CCL4 in urine (Fig. 6) reached statistical significance following the revised analyses.

Figs. 2, 3, and 6 and the corresponding Results, figure legends, and Statistical Analysis section have been updated accordingly.

________________________________________

Comment 4

If those last things are addressed, the study will be publishable.

Response: We thank the reviewer for this assessment. The remaining comments have been addressed as described below.

Attachment

Submitted filename: PONE-D-26-01255_Response_to_Reviewers_auresp_2.docx

pone.0358533.s007.docx^{(18.8KB, docx)}

PLoS One. doi: 10.1371/journal.pone.0358533.r005

Decision Letter 2

Alireza Shoari

3 Sep 2026

Tumor Specific Cytokine Pattern Explored in Renal Cell Carcinoma

PONE-D-26-01255R2

Dear Dr. Huang,

We’re pleased to inform you that your manuscript has been judged scientifically suitable for publication and will be formally accepted for publication once it meets all outstanding technical requirements.

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Alireza Shoari

Academic Editor

PLOS One

Additional Editor Comments (optional):

Reviewers' comments:

PLoS One. doi: 10.1371/journal.pone.0358533.r006

Acceptance letter

Alireza Shoari

PONE-D-26-01255R2

PLOS One

Dear Dr. Huang,

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Associated Data

This section collects any data citations, data availability statements, or supplementary materials included in this article.

Supplementary Materials

S1 Fig. Differential mRNA expression of cytokines in renal tumors and normal tissues from the TCGA-KIRC dataset.

(TIF)

pone.0358533.s001.tif^{(1.8MB, tif)}

S2 Fig. Kaplan-Meier survival analysis of nine cytokine-related genes in renal cell carcinoma (TCGA-KIRC dataset).

(TIF)

pone.0358533.s002.tif^{(3.1MB, tif)}

S3 Fig. Additional immune infiltration correlation analyses of cytokine-related genes in the TCGA-KIRC dataset.

(TIF)

pone.0358533.s003.tif^{(6.5MB, tif)}

S1 Table. ELISA standard curve concentrations and curve-fit statistics.

Standard concentration series, four-parameter logistic (4PL) curve-fitting model, and corresponding R² values for ELISA assays used in the study.

(XLSX)

pone.0358533.s004.xlsx^{(10.5KB, xlsx)}

S1 Data. De-identified underlying data for the cytokine antibody array and ELISA analyses.

Workbook containing the processed normalized cytokine antibody array data and ELISA concentration data underlying the study analyses, with accompanying data descriptions.

(XLSX)

pone.0358533.s005.xlsx^{(424.6KB, xlsx)}

Attachment

Submitted filename: PONE-D-26-01255_Response_to_Reviewers.docx

pone.0358533.s006.docx^{(33.1KB, docx)}

Attachment

Submitted filename: PONE-D-26-01255_Response_to_Reviewers_auresp_2.docx

pone.0358533.s007.docx^{(18.8KB, docx)}

Data Availability Statement

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PERMALINK

Tumor specific cytokine pattern explored in renal cell carcinoma

Xu Chen

Zhuangyao Liao

Rochelle Wickramasekara

Ying Luo

Panning Wang

Sihao Wang

Yunru Chen

Hang Zhou

Shuhong Luo

Shaopeng Qiu

Ruo-Pan Huang

Roles

Abstract

Introduction

Materials and Methods

Patient cohorts and clinical sample collection

Table 1. Clinical and sample characteristics of the RCC study cohorts.

Cytokine array analysis

Fig 2. Representative cytokine alterations identified by antibody array analysis in RCC tissues.

Enzyme-linked immunosorbent assay (ELISA)

Kaplan–Meier survival and TCGA gene expression analyses

Immune Infiltration Analysis

Statistical Analysis

Results

A distinct cytokine protein signature characterizes early-stage RCC tissues

Fig 1. Differential cytokine protein profile and pathway enrichment in early-stage RCC tissues.

Validation of differentially expressed cytokines using ELISA

Fig 3. ELISA validation of differentially expressed cytokines in RCC tissues.

TCGA transcriptomic analyses demonstrate stage- and metastasis-associated cytokine expression patterns

Fig 4. TCGA-KIRC transcriptomic analysis of selected cytokine-related genes in RCC.mRNA expression levels (log2(FPKM+1)) of IL-2, IL-15, CCL4, and CXCL9 in metastatic and non-metastatic clear cell RCC (ccRCC) tumors from the TCGA-KIRC dataset.

Cytokine-related transcripts are associated with immune cell infiltration patterns in RCC

Fig 5. Correlation between cytokine-related gene expression and immune cell infiltration in RCC.

Altered serum and urine cytokine levels in patients with localized RCC

Fig 6. ELISA validation of selected cytokines in serum and urine samples from patients with clear cell RCC.

Discussion

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Alireza Shoari

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