Identification of KRBA2 as a probable prognostic biomarker correlated with immune infiltration in kidney clear cell carcinoma

Abstract

Background

Kidney renal clear cell carcinoma (KIRC) is a major subtype of renal cell carcinoma. Because of its rapid progression and resistance to targeted therapies, KIRC poses a significant threat to human health. KRBA2, a member of the KRBA family, is recognized as a transcription factor. Nevertheless, limited research has focused on the effect of KRBA2 in KIRC.

Materials and methods

The Cancer Genome Atlas database was utilized to analyze the expression of KRBA2 in KIRC, and quantitative real-time PCR was used to validate KRBA2 mRNA expression in clinical KIRC samples and KIRC cell lines. The correlation between KRBA2 expression and clinicopathological features was determined via the Wilcoxon rank sum test. Next, we assessed the prognostic value of KRBA2 in patients with KIRC using Kaplan-Meier survival analysis. The association between KRBA2 expression and immune infiltration in KIRC was investigated using the Tumor Immune Estimation Resource.

Results

Our research demonstrated that KRBA2 expression was downregulated in KIRC and was correlated with multiple clinicopathological characteristics. Low KRBA2 expression was associated with poorer overall survival, progression-free interval, and disease-specific survival. Enrichment analysis suggested that KRBA2 was related to immune processes and the cell cycle, and Tumor Immune Estimation Resource analysis indicated that KRBA2 expression correlated with immune infiltration levels and immune characteristics of multiple immune cells.

Conclusions

These findings suggest that KRBA2 may serve as a potential prognostic biomarker associated with KIRC immunity and could be a promising target for KIRC diagnosis and treatment.

1. Introduction

Renal cell carcinoma (RCC) accounts for nearly 90% of all renal tumors, with an increasing incidence rate of 2%–4% per year.^[1,2] Clear cell RCC (ccRCC), also known as kidney clear cell carcinoma (KIRC), is the major subtype of RCC, comprising approximately 75% of RCC cases.^[3] Over 30% of patients with RCC are estimated to have metastatic disease at the time of initial diagnosis.^[4] High-grade RCC is associated with a high incidence of nodal, pulmonary, adrenal, and hepatic metastases.^[5] Patients with metastatic RCC have a median survival of approximately 13 months, and fewer than 10% achieve a 5-year survival rate.^[6]

Regarding RCC treatments, approximately one-third of patients with stage T2 or higher disease experience relapse after surgical removal.^[7] The likelihood of recurrence depends on various clinical and histopathologic characteristics, which have been incorporated into approximately 20 different scoring systems, leading to significant variability. Unfortunately, chemotherapy has not demonstrated satisfactory efficacy in treating advanced RCC.^[8,9] Specifically, chemotherapy has not yielded significant therapeutic benefits in patients with advanced RCC.^[10,11] Sunitinib, a multitarget tyrosine kinase inhibitor, suppresses angiogenesis by inhibiting the vascular endothelial growth factor (VEGF) pathway.^[12,13] It has been reported to effectively prolong overall survival (OS), with initial responses observed in approximately 70% of patients. However, the emergence of sunitinib resistance remains a significant challenge in RCC treatment. In addition, immunotherapy has shown relatively promising outcomes and has been recommended as a first-line therapy for metastatic RCC.^[14,15] Nevertheless, significant challenges persist in the diagnosis and treatment of RCC.

At the molecular level, Von Hippel–Lindau gene inactivation is considered the most frequent oncogenic driver event in ccRCC.^[16] Von Hippel–Lindau encodes a substrate of the E3 ligase complex, which facilitates the ubiquitination and subsequent degradation of hypoxia-inducible factor 1α and hypoxia-inducible factor 2α. The accumulation of hypoxia-inducible factor proteins promotes the expression of pro-angiogenic genes, leading to the development of highly vascularized tumors.^[17,18]

Significant advances in next-generation sequencing and its application in large cohorts of tumor patients have contributed to the identification of unconventionally mutated genes in ccRCC.^[19,20] The KRAB domain has been reported to recruit transcriptional repressor complexes, thereby playing a role in cell proliferation, differentiation, apoptosis, nucleolar maintenance, deacetylase activity, and tumor transformation.^[21] KRBA2, located on human chromosome 17, contains the KRAB-A domain. Existing studies suggest that KRBA1, which is upregulated in hepatocellular carcinoma, may serve as a prognostic indicator associated with hepatocellular carcinoma immunity, whereas KRBA2 is recognized as a transcription factor.^[22] However, no research has yet determined whether KRBA2 modulates tumor progression, particularly in KIRC. Therefore, we conducted this study to explore the association between KRBA2 and KIRC progression.

In this study, we assessed KRBA2 expression in KIRC using clinical samples and public databases. We also investigated the correlation between KRBA2 expression and clinicopathological features. Furthermore, we explored the effect of KRBA2 expression on KIRC prognosis using Kaplan-Meier (KM) survival analysis. In addition, we examined the relationship between KRBA2 expression and iron metabolism–related genes, copper metabolism–related genes, and immune infiltration in KIRC. Our findings suggest that KRBA2 is a novel KIRC biomarker that may contribute to prognosis.

2. Materials and methods

2.1. Data source and processing

The Cancer Genome Atlas (TCGA) database (https://cancergenome.nih.gov) was utilized to obtain the gene expression profiles of KIRC and the corresponding clinical information. In total, this study included 539 tumor samples and 72 normal samples. The file type was HTSeq-TPM.

2.2. Patients and tumor specimens

Human KIRC tissues and matched adjacent tissues were obtained from 20 patients who underwent nephrectomy at Shandong Province Hospital between March 2010 and March 2022. Informed consent was obtained from all patients. The ethics committee of Shandong Province Hospital approved the study, and the ethics approval number was NO2022-333.

2.3. Tumor Immune Estimation Resource database analysis

Tumor Immune Estimation Resource (TIMER; https://cistrome.shinyapps.io/timer/) is an integrated web portal that enables comprehensive analysis of immune cell infiltration levels in various cancers. We used the “Diff Exp” module to evaluate KRBA2 expression across multiple tumor types. In addition, the relationship between KRBA2 expression and immune cell infiltration in KIRC was analyzed using TIMER. The “Gene” and “SCNA” modules were utilized to investigate the association between tumor-infiltrating immune cells and KRBA2 mRNA expression as well as copy number variations (CNVs) of KRBA2 in KIRC. The “Correlation” module was used to assess the relationship between KRBA2 expression and gene marker sets of immune cells.

2.4. University of Alabama at Birmingham Cancer Data Analysis Portal database analysis

University of Alabama at Birmingham Cancer Data Analysis Portal (UALCAN; http://ualcan.path.uab.edu/) is an online platform that provides data analysis tools for TCGA datasets. We used UALCAN to investigate KRBA2 expression, its correlation with clinicopathological features, and its prognostic significance in patients with KIRC within the TCGA dataset.

2.5. Gene Expression Profiling Interactive Analysis

Gene Expression Profiling Interactive Analysis (GEPIA; http://gepia.cancer-pku.cn/index.html) is an interactive web tool designed for gene expression analysis based on TCGA and Genotype-Tissue Expression datasets. We used GEPIA to visualize OS and disease-free survival curves for KRBA2 expression.

2.6. The Human Protein Atlas

Human Protein Atlas (https://www.proteinatlas.org/) is a comprehensive database providing proteomic and transcriptomic data for human tissues. We obtained immunohistochemical protein expression data for normal renal tissues and renal cancer tissues from this online platform.

2.7. KM Plotter Database Analysis

The KM Plotter (http://kmplot.com) is an online database containing gene expression data and prognostic information for 530 clinical patients with KIRC. It was used to assess the prognostic value of KRBA2 in KIRC. Patient samples were divided into high- and low-KRBA2 expression groups based on median expression levels to evaluate progression-free survival (PFS), OS, and postprogression survival.

2.8. Analysis of KRB2-interacting genes and proteins

Gene Multiple Association Network Integration Algorithm (GeneMANIA; http://www.genemania.org) is a web tool designed to explore gene interaction networks. We used GeneMANIA to construct the KRBA2 interaction network. In the visualization results, each node represents a protein, and the node size indicates the interaction strength. In addition, the STRING online database (https://string-db.org/) was used to illustrate a protein-protein interaction network for KRBA2.

2.9. Gene Ontology, Kyoto Encyclopedia of Genes and Genomes pathway enrichment, and Gene Set Enrichment Analysis

To investigate the biological functions of KRBA2 in KIRC, we performed Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analyses. Gene Ontology analysis identified biological processes, molecular functions, and cellular components associated with KRBA2. In addition, Gene Set Enrichment Analysis was used to explore the potential mechanisms of KRBA2 function.

2.10. Gene Set Cancer Analysis

Gene Set Cancer Analysis (GSCALite; http://bioinfo.life.hust.edu.cn/web/GSCALite/) is a multifunctional genomics analysis platform. We used GSCALite to examine pathway activity for KRBA2, ZNRF3, ZNF625, LDHAL6A, and EML5.

2.11. Tumor-Immune System Interaction Database analysis

Tumor-Immune System Interaction Database (TISIDB; http://cis.hku.hk/TISIDB/) is an integrated database designed to explore interactions between the immune system and cancers. We used TISIDB to analyze both KRBA2 expression across different immune subtypes in KIRC and the relationship between KRBA2 expression and multiple chemokine expression levels.

2.12. Cell culture

The human KIRC cell lines ACHN, 786-O, A498, 769-P, and Caki-1 and the human renal proximal convoluted tubule cell line HK-2 were purchased from the Shanghai Institute of Cell Biology in China. Cells were cultured in RPMI-1640 medium (BI, Kibbutz Beit Haemek Israel) supplemented with 10% fetal bovine serum (BI) and maintained at 37 °C in a humidified incubator with 5% CO₂.

2.13. Real-time PCR analysis

Total RNA was extracted using the QIAGEN RNeasy kit, and real-time polymerase chain reaction (RT-PCR) was performed using the Evo M-MLV One Step RT-PCR Kit (Accurate Biotechnology, Hunan, China).

2.14. Statistical analysis

All statistical analyses in this study were performed using R software (version 4.2.1, Foundation for Statistical Computing). The results from KM plots and GEPIA were presented with hazard ratio and p values or Cox p value from log-rank tests. Spearman's correlation coefficient was used to evaluate the association between gene expression levels, and statistical significance was determined accordingly. The pheatmap package in R software was used to generate a heatmap depicting the relationship between KRBA2 and iron metabolism–related genes and copper metabolism–related genes using Spearman's correlation coefficient. p values <0.05 were considered statistically significant.

3. Results

3.1. KRBA2 expression was decreased in patients with KIRC

The TIMER online database was first used to analyze KRBA2 mRNA expression across various human cancers. Lower KRBA2 expression was observed in bladder cancer, breast cancer, colon adenocarcinoma, head and neck squamous cell carcinoma, kidney chromophobe, KIRC, kidney renal papillary cell carcinoma, lung squamous cell carcinoma, prostate adenocarcinoma, rectum adenocarcinoma, thyroid cancer, and uterine corpus endometrial carcinoma, compared with their respective normal tissues (Fig. 1A). Furthermore, KRBA2 mRNA expression was also significantly lower in KIRC tumor tissues compared with normal tissues in the UALCAN database (Fig. 1B). In addition, TCGA data were used to analyze KRBA2 mRNA expression in KIRC tumor samples and adjacent normal tissues. The results confirmed that KRBA2 expression was significantly reduced in KIRC samples (Fig. 1C). Moreover, a notable decrease in KRBA2 expression was observed in 72 paired tumor tissues compared with adjacent normal tissues (Fig. 1D). These findings demonstrate that KRBA2 expression is downregulated in renal cancer, suggesting that KRBA2 may play a role in renal cancer progression.

Figure 1.

Expression of KRBA2 in KIRC. (A) KRBA2 expression across various cancers analyzed using the TIMER database. (B) KRBA2 expression in KIRC examined using the UALCAN database. (C) Analysis of KRBA2 expression in KIRC tumor tissues and adjacent renal tissues from the TCGA database. (D) TCGA database and statistical analyses of KRBA2 expression in 72 pairs of KIRC tumor tissues and adjacent normal tissues. *p < 0.05, **p < 0.01, ***p < 0.001. KIRC = kidney renal clear cell carcinoma; TCGA = The Cancer Genome Atlas; TIMER = Tumor Immune Estimation Resource.

We further confirmed KRBA2 mRNA expression levels by RT-PCR, showing that KRBA2 expression was significantly decreased in 769-P and Caki-1 cell lines compared with HK-2 cells, whereas KRBA2 expression was significantly increased in ACHN, 786-O, and A498 cell lines (Fig. 2A). In addition, KRBA2 mRNA expression was significantly lower in renal cancer tissues compared with normal tissues (Fig. 2B). Furthermore, KRBA2 protein expression was detected by immunohistochemistry staining, and Human Protein Atlas database revealed that KRBA2 protein levels were significantly reduced in renal cancer tissues compared with normal renal tissues (Fig. 2C).

Figure 2.

KRBA2 mRNA and protein expression in patients with KIRC. (A) KRBA2 mRNA expression in multiple KIRC cell lines. (B) KRBA2 mRNA expression in 20 pairs of tumor tissues and normal renal tissues. (C) KRBA2 protein expression levels in KIRC tissues based on HPA database analysis. *p < 0.05, **p < 0.01, ***p < 0.001. HPA = Human Protein Atlas; KIRC = kidney renal clear cell carcinoma.

3.2. KRBA2 expression and clinical parameters of patients with KIRC

The Wilcoxon rank sum test was used to analyze clinical data from 539 patients with KIRC samples obtained from the TCGA database, and the clinical characteristics of these patients are summarized in Table 1. KRBA2 was found to be downregulated across all clinicopathological subgroups, with particularly significant differences observed in T stage and histologic grade (Fig. 3A). In addition, logistic regression analysis revealed that low KRBA2 expression was significantly associated with gender, age, T stage, pathological stage, and histologic grade (Table 2). Moreover, UALCAN database analysis further confirmed that KRBA2 expression was correlated with clinicopathological characteristics (Fig. 3B). Overall, these findings suggest that KRBA2 expression is significantly associated with the clinicopathological features of patients with KIRC.

Table 1.

Clinical characteristics of patients with KIRC in TCGA database.

Characteristic	Low expression of KRBA1 (n = 269)	High expression of KRBA1 (n = 270)	p
Age, yr			0.043
≤60	122	147
>60	147	123
Race			0.157
Asian	2	6
Black or African American	24	33
White	241	226
Gender			0.025
Female	80	106
Male	189	164
T stage			<0.001
T1	107	171
T2	43	28
T3	114	65
T4	5	6
N stage			0.142
N0	112	129
N1	11	5
M stage			0.320
M0	212	216
M1	44	34
Pathologic stage			<0.001
Stage I	106	166
Stage II	33	26
Stage III	80	43
Stage IV	48	34
Primary therapy outcome			0.054
PD	8	3
SD	1	5
PR	0	2
CR	51	77
Histologic grade			<0.001
G1	2	12
G2	103	132
G3	112	95
G4	49	26
Serum calcium			0.979
Elevated	5	5
Low	107	96
Normal	79	74
Hemoglobin			0.429
Elevated	3	2
Low	148	115
Normal	96	95
Laterality			0.385
Left	120	132
Right	148	138

CR = complete response; KIRC = kidney renal clear cell carcinoma; PD = progressive disease; PR = partial response; SD = stable disease; TCGA = The Cancer Genome Atlas.

Boldface format is the p values with significant difference.

Figure 3.

KRBA2 expression and prognosis in patients with KIRC. (A) Box plots depicting KRBA2 expression across various KIRC patient subgroups based on clinical characteristics from the TCGA database. Subgroup analyses include T stage, histologic grade, gender, race, N stage, and age. (B) Box plots depicting KRBA2 expression across different clinical subgroups in the UALCAN database. Subgroup analyses include cancer stage, tumor grade, gender, race, nodal metastasis status, and age. *p < 0.05, **p < 0.01, ***p < 0.001. KIRC = kidney renal clear cell carcinoma; TCGA = The Cancer Genome Atlas; UALCAN = University of Alabama at Birmingham Cancer Data Analysis Portal.

Table 2.

Cox regression analysis of the correlation between KRBA2 expression and clinicopathological characteristics.

Characteristics	Total (n)	Odds ratio (95% CI)	p
Sex (male vs. female)	539	0.655 (0.457–0.936)	0.02
Age (>60 vs. ≤60 yr)	539	0.694 (0.494–0.974)	0.035
Race (White vs. Black or African American)	524	0.682 (0.387–1.185)	0.177
T stage
T2 vs. T1	349	0.407 (0.237–0.691)	<0.001
T3 vs. T1	457	0.357 (0.241–0.525)	<0.001
T4 vs. T1	289	0.751 (0.221–2.662)	0.643
T3 vs. T2	250	0.876 (0.499–1.550)	0.645
T4 vs. T2	82	1.843 (0.509–6.952)	0.349
T4 vs. T3	190	2.105 (0.611–7.559)	0.234
N stage (N1 vs. N0)	257	0.395 (0.121–1.120)	0.094
M stage (M1 vs. M0)	506	0.758 (0.464–1.230)	0.265
Pathologic stage
Stage II vs. Stage I	331	0.503 (0.283–0.886)	0.018
Stage III vs. Stage I	395	0.343 (0.219–0.532)	<0.001
Stage IV vs. Stage I	354	0.452 (0.272–0.745)	0.002
Stage III vs. Stage II	182	0.682 (0.362–1.289)	0.237
Stage IV vs. Stage II	141	0.899 (0.457–1.772)	0.758
Stage IV vs. Stage III	205	0.759 (0.427–1.350)	0.347
Histologic grade
G2 vs. G1	249	0.214 (0.033–0.806)	0.046
G3 vs. G1	457	0.357 (0.241–0.525)	0.012
G4 vs. G1	89	0.088 (0.013–0.355)	0.002
G3 vs. G2	442	0.662 (0.454–0.963)	0.031
G4 vs. G2	310	0.414 (0.238–0.706)	0.001
G4 vs. G3	282	0.626 (0.358–1.075)	0.094
Laterality (right vs. left)	538	0.752 (0.535–1.055)	0.100

CI = confidence interval.

Boldface format is the p values with significant difference.

3.3. Decreased KRBA2 expression is associated with poor prognosis in patients with KIRC

Given that KRBA2 expression levels are closely associated with KIRC progression and metastasis, we examined the prognostic value of KRBA2 using multiple databases. The GEPIA database was first utilized to evaluate the association between KRBA2 expression and patient prognosis. The results indicated that patients with low KRBA2 expression had significantly shorter OS and disease-free survival compared with those with high KRBA2 expression (Fig. 4A). Similarly, analysis using the KM plotter database revealed that KIRC patients with low KRBA2 expression exhibited poorer OS. However, no significant difference was observed in relapse-free survival (Fig. 4B). Furthermore, results from the UALCAN database analysis indicated that low KRBA2 expression was significantly associated with shorter survival times in patients with KIRC, particularly in subgroup analyses stratified by tumor grade (Fig. 4C). In addition, KM survival analysis was performed on 539 patients with KIRC from the TCGA database, and survivorship curves were established. The results demonstrated that low KRBA2 expression was significantly correlated with shorter OS, progression-free interval (PFI), and disease-specific survival (DSS; Fig. 4D). Overall, these findings suggest that KRBA2 mRNA expression is strongly associated with KIRC prognosis, with lower expression levels correlating with poorer patient outcomes.

Figure 4.

Survival curve analysis of KRBA2 prognostic value in patients with KIRC. (A) Kaplan-Meier survival curves for OS and DFS from the GEPIA database. (B) Kaplan-Meier survival curves for OS and RFS from the Kaplan-Meier plotter database. (C) Kaplan-Meier survival curves for OS and subgroup analysis stratified by tumor grade from the UALCAN database. (D) Kaplan-Meier survival curves for OS, PFI, and DSS from the TCGA database. DFS = disease-free survival; DSS = disease-specific survival; GEPIA = Gene Expression Profiling Interactive Analysis; KIRC = kidney renal clear cell carcinoma; OS = overall survival; PFI = progression-free interval; RFS = relapse-free survival; TCGA = The Cancer Genome Atlas; UALCAN = University of Alabama at Birmingham Cancer Data Analysis Portal.

3.4. Validation of the prognostic value of KRBA2 across various clinicopathological characteristics

To further evaluate the prognostic significance of KRBA2 expression in patients with KIRC, we performed KM survival analysis to examine the relationship between KRBA2 mRNA expression and clinical features using data from the TCGA database. The results indicated that low KRBA2 expression was significantly associated with poor OS in female patients, Caucasian patients, T1 stage tumors, and pathological stage T1 tumors (Fig. 5A). Interestingly, a statistically significant association was observed between low KRBA2 expression and shorter OS in patients with left renal carcinoma. In addition, lower KRBA2 expression was associated with shorter PFI in both male and female patients, as well as in Caucasian patients. Regarding age and tumor location, low KRBA2 expression was significantly associated with poor PFS in patients of all age groups and in those with bilateral tumors. Moreover, low KRBA2 expression was linked to poor PFS only in M0 stage and pathological patients with stage III KIRC (Fig. 5A). Furthermore, low KRBA2 expression was significantly correlated with poor DSS in patients 60 years or younger, Caucasian patients, patients with M0 stage, and patients with pathological stage III (Fig. 5A). To assess the diagnostic potential of KRBA2, we constructed a receiver operating characteristic curve, which demonstrated high diagnostic accuracy with an area under the curve value of 0.836 (95% confidence interval, 0.795–0.877), indicating that KRBA2 is a potential prognostic biomarker for KIRC (Fig. 5B). In addition, multivariate Cox regression analysis was conducted to examine the association between KRBA2 expression and prognostic outcomes in patients with KIRC. The results revealed that KRBA2 expression independently predicted PFI and DSS but was not a significant independent predictor for OS (Figs. 5C–E). In conclusion, these findings suggest that KRBA2 is a potential prognostic biomarker in KIRC, with low KRBA2 expression being associated with worse clinical outcomes.

Figure 5.

KRBA2 as an independent prognostic factor in KIRC. (A) Forest plots illustrating the relationship between KRBA2 expression and clinicopathological parameters in patients with KIRC for OS, PFI, and DSS. (B) ROC curve demonstrating the diagnostic value of KRBA2 in KIRC. (C) Forest plots depicting the results of univariate and multivariate analyses for OS. (D) Forest plots depicting the results of univariate and multivariate analyses for PFI. (E) Forest plots depicting the results of univariate and multivariate analyses for DSS. CI = confidence interval; DSS = disease-specific survival; FPR = false positive rate; HR = hazard ratio; KIRC = kidney renal clear cell carcinoma; OS = overall survival; PFI = progression-free interval; ROC = receiver operating characteristic; TPR = true positive rate.

3.5. Identification of KRBA2-interacting genes and proteins

GeneMANIA was used to construct the gene-gene interaction network for KRBA2 and its associated neighboring genes. The 20 most frequently altered genes that were strongly associated with KRBA2 were identified, including BRWD3, CRBN, and CYLD (Fig. 6A). In addition, the STRING database was utilized to generate the protein-protein interaction network of KRBA2. The network contained 11 nodes and 20 edges, including ZNF665, SKG494, and DENND4B, as illustrated in Figure 6B. Furthermore, the relationships between KRBA2 and genes associated with ferroptosis and cuproptosis were analyzed using the TCGA database. KRBA2 expression was significantly and positively correlated with several ferroptosis-related genes, including TRFC, VDAC2, ACSL1, ACSL4, ACSL6, LPCAT3, SLC40A1, GCLC, GCLM, PCBP1, and IREB2. Similarly, KRBA2 was positively correlated with cuproptosis-related genes, including ATP7A, ATP7B, DBT, DLAT, DLST, GLS, LIAS, LIPT1, and MTF1 (Figs. 6C, D).

Figure 6.

KRBA2-interacting genes and pathways. (A) The gene-gene interaction network for KRBA2, generated using GeneMANIA. (B) The PPI network for KRBA2, constructed using STRING. (C–D) Heatmaps depicting the relationship between KRBA2 expression and iron metabolism–related genes and copper metabolism–related genes in KIRC, respectively. *p < 0.05, **p < 0.01, ***p < 0.001. GeneMANIA = Gene Multiple Association Network Integration Algorithm; KIRC = kidney renal clear cell carcinoma; PPI = protein-protein interaction.

3.6. Potential pathways regulated by KRBA2 in KIRC

Given that low KRBA2 expression is significantly associated with KIRC prognosis, we sought to investigate the regulatory pathways involving KRBA2. Data from the TCGA database were analyzed to identify genes positively or negatively coexpressed with KRBA2. Using Spearman correlation analysis, a total of 3195 genes were found to be positively correlated with KRBA2, whereas 986 genes were negatively correlated (Fig. 7A). The top 50 positively and negatively correlated genes are displayed in Figures 7B and C. In addition, KRBA2 exhibited a strong positive correlation with 4 key genes, namely, ZNRF3, ZNF625, LDHAL6A, and EML5. These genes were identified as hub genes, suggesting that they may play crucial roles in combination with KRBA2 in KIRC progression (Fig. 7D).

Figure 7.

Potential KRBA2-regulated pathways in KIRC. (A) Volcano plot depicting KRBA2 coexpressed genes in KIRC. (B–C) Heatmaps illustrating top 50 genes positively and negatively correlated with KRBA2 in KIRC. (D) Scatter plots showing the correlation between KRBA2 and hub genes in KIRC. (E) GO enrichment analysis (biological processes, molecular functions, and cellular components) and KEGG pathway analysis of KRBA2 in KIRC. ***p < 0.001. GO = Gene Ontology; KEGG = Kyoto Encyclopedia of Genes and Genomes; KIRC = kidney renal clear cell carcinoma.

Next, a total of 300 genes positively associated with KRBA2 were selected for GO and KEGG enrichment analyses to investigate KRBA2-related pathways and biological functions. The significant GO terms, including biological processes, molecular functions, and cellular components, as well as KEGG enrichment analysis results, are displayed in Figure 7E.

To further investigate the molecular mechanisms influenced by KRBA2 in KIRC, Gene Set Enrichment Analysis was performed. The results identified multiple immune-related gene sets enriched in KIRC, including immunodeficiency, B-cell receptor signaling pathway, natural killer cell–mediated cytotoxicity, and chemokine signaling pathways (Figs. 8A–D). In addition, other tumorigenesis-associated pathways were enriched, including the P53 signaling pathway, janus kinase/signal transducer and transcriptional activator signaling pathway, VEGF signaling pathway, and cell cycle regulation (Figs. 8E–H). These findings strongly suggest that KRBA2 plays a crucial role in modulating immune responses in KIRC.

Figure 8.

GSEA analysis of KRBA2-associated pathways in TCGA dataset. (A) Immunodeficiency pathway. (B) B-cell receptor signaling pathway. (C) Natural killer cell–mediated cytotoxicity. (D) Chemokine signaling pathway. (E) P53 signaling pathway. (F) JAK/STAT signaling pathway. (G) VEGF signaling pathway. (H) Cell cycle regulation. GSEA = Gene Set Enrichment Analysis; JAK/STAT = janus kinase/signal transducer and transcriptional activator; TCGA = The Cancer Genome Atlas; VEGF = vascular endothelial growth factor.

3.7. Interrelationship and cancer pathway analysis of KRBA2 and 4 Hub Genes

We selected KRBA2 and the 4 most strongly correlated genes—ZNRF3, ZNF625, LDHAL6A, and EML5—to analyze their interrelationships and their potential roles in cancer-related pathways. Using GeneMANIA, we constructed a network to visualize potential interactions between these 5 genes and other tumor-related molecules. The analysis identified 20 genes that may be functionally linked to these hub genes, as shown in Figure 9A. In addition, the GSCALite database was used to investigate the potential involvement of these genes in key oncogenic pathways. The results suggested that these 5 genes may modulate tumor progression by activating the hormone AR, hormone ER, TSC/mTOR, RTK, RAS/MAPK, and PI3K/AKT pathways, while inhibiting apoptosis and cell cycle regulation (Fig. 9B).

Figure 9.

Interrelationship and cancer pathway analysis of KRBA2 and immune cell infiltration in KIRC. (A) Interaction network of KRBA2 and its associated genes (GeneMANIA). (B) Role of KRBA2 in cancer-related pathways. (C) Association between KRBA2 expression and tumor-infiltrating immune cells in KIRC. (D) Correlation between KRBA2 copy number variation and immune cell infiltration levels in KIRC. (E) Relationship between KRBA2 expression and immune subtypes in KIRC. *p < 0.05, **p < 0.01, ***p < 0.001. GeneMANIA = Gene Multiple Association Network Integration Algorithm; KIRC = kidney renal clear cell carcinoma.

3.8. Correlation between KRBA2 expression and tumor-infiltrating immune cells in KIRC

Given the established relationship between cancer progression and immune system interactions, we explored the association between KRBA2 expression and immune cell infiltration using the TIMER database. Our analysis revealed that KRBA2 expression was positively correlated with the infiltration levels of B cells, CD8⁺ T cells, CD4⁺ T cells, macrophages, neutrophils, and dendritic cells in KIRC (Fig. 9C). Furthermore, we investigated the association between KRBA2 CNV and immune cell infiltration levels. The results demonstrated that KRBA2 CNV was significantly correlated with B cells, CD8⁺ T cells, CD4⁺ T cells, neutrophils, and dendritic cells (Fig. 9D). To further explore the relationships between KRBA2 and different immune cell subtypes, we examined the correlation between KRBA2 expression and gene markers of immune cells using the TISIDB database (Fig. 9E). Our findings indicated that KRBA2 expression was highest in the C5 (immunologically quiet) subgroup, whereas it was significantly lower in the C1 and C6 subgroups. In addition, KRBA2 expression was negatively correlated with multiple chemokines, including CCL2, CCL3, CCL4, CCL5, CCL8, CCL11, CCL13, CCL14, CCL15, CCL17, CCL18, CCL19, CCL20, CCL21, and CCL22 (Fig. 10). These results suggest that KRBA2 expression may be involved in regulating KIRC progression through alterations in chemokine levels, potentially influencing immune cell recruitment and tumor microenvironment (TME) dynamics.

Figure 10.

Association between KRBA2 expression and chemokines. Correlation between KRBA2 expression and multiple chemokines, including CCL2, CCL3, CCL4, CCL5, CCL8, CCL11, CCL13, CCL14, CCL15, CCL17, CCL18, CCL19, CCL20, CCL21, and CCL22.

3.9. Correlation between KRBA2 expression and various immune markers

To further explore the immune regulatory role of KRBA2 in KIRC, we analyzed the correlation between KRBA2 expression and various immune cell markers using the TIMER database. This analysis aimed to enhance our understanding of KRBA2's interaction with the immune response.

The immune cell markers listed in Table 3 were used to characterize different immune cell subtypes, including B cells, T cells, CD8⁺ T cells, monocytes, tumor-associated macrophages, M1 macrophages, M2 macrophages, neutrophils, natural killer cells, and dendritic cells. In clinical cancer biopsies, tumor purity is a critical factor that influences the assessment of immune infiltration. After adjusting for tumor purity, KRBA2 expression was significantly correlated with most immune markers across various immune cell types in KIRC (Table 3).

Table 3.

Correlation analysis between KRBA2 and immune cell gene markers in TIMER.

		KIRC
		None		Purity
Description	Gene markers	Cor	p	Cor	p
B cell	CD19	−0.10719139	*	−0.276023934	***
	CD79A	−0.218357698	***	−0.322948025	***
T cell (general)	CD3D	−0.190641913	***	−0.35241037	***
	CD3E	−0.148558108	**	−0.346374353	***
	CD2	−0.120219024	**	−0.358556779	***
CD8+ T cell	CD8A	−0.119724351	*	−0.313969127	***
	CD8B	−0.13284565	**	−0.305544386	***
Monocyte	CD86	0.02391216	0.608066618	−0.316830279	***
	CSF1R	0.146034852	**	−0.303181403	***
TAM	CCL2	−0.033439629	0.473367302	−0.139218287	**
	CD68	0.000463094	0.992077578	−0.136844041	**
	IL10	0.057248994	0.219373969	−0.29755659	***
M1	IRF5	0.225128203	***	−0.052054031	0.264171761
	PTGS2	0.061968003	0.183573502	−0.160884955	***
	NOS2	0.316217101	***	−0.158691888	***
M2	CD163	0.19208127	***	−0.223318367	***
	VSIG4	0.04325222	0.353621899	−0.282802645	***
	MS4A4A	0.100812158	*	−0.278277548	***
Neutrophils	CEACAM8	0.189946441	***	0.062860147	0.17740069
	ITGAM	0.181839539	***	−0.247556759	***
	CCR7	−0.030694834	0.51031499	−0.303097299	***
Natural killer cell	KIR2DL1	0.023176299	0.619278676	−0.092879941	*
	KIR2DL3	0.031087393	0.505060161	−0.051105756	0.272983706
	KIR2DL4	−0.112036924	*	−0.147715983	**
	KIR3DL1	0.057964543	0.213657404	−0.038165284	0.413124477
	KIR3DL2	−0.040523302	0.384840142	−0.072173799	0.121349199
	KIR3DL3	0.014141941	0.761767006	−0.055513828	0.233688693
	KIR2DS4	0.056392348	0.22636069	−0.077254534	0.097215724
Dendritic cell	HLA-DPB1	0.008994341	0.847053328	−0.299226666	***
	HLADQB1	0.058769365	0.207356552	−0.211614398	***
	HLA-DRA	0.059249189	0.203583932	−0.31065338	***
	HLA-DPA1	0.069042856	0.138361498	−0.321254581	***
	CD1C	0.219272642	***	−0.238559928	***
	NRP1	0.357599714	***	−0.130216686	**
	ITGAX	0.198897896	***	−0.115600257	*

KIRC = kidney renal clear cell carcinoma; TIMER = Tumor Immune Estimation Resource.

*p< 0.05. **p< 0.01. ***p< 0.001.

In addition, we investigated the relationship between KRBA2 expression and different functional T-cell subtypes, including Th1, Th1-like, Th2, Tregs, resting Tregs, effector Tregs, effector T cells, naive T cells, effector memory T cells, resident memory T cells, memory T cells, and exhausted T cells. Our findings indicate that KRBA2 expression was significantly associated with 34 out of 38 T-cell markers in KIRC, even after adjusting for tumor purity, as analyzed using the TIMER database (Table 4).

Table 4.

Correlation analysis between KRBA2 and different types of T cell gene markers in TIMER.

		KIRC
		None		Purity
Description	Gene markers	Cor	p	Cor	p
Th1 T cell	TBX21	0.112734681	*	−0.210921179	***
	STAT4	0.107933318	*	−0.268720921	***
	STAT1	0.179313605	***	−0.274215279	***
	TNF	0.145967818	***	−0.176012779	***
	IFNG	−0.121983434	***	−0.320725367	***
Th1-like T cell	HAVCR2	0.128764752	***	−0.137984485	***
	IFNG	−0.169870838	***	−0.340811169	***
	CXCR3	0.26073156	***	0.005451676	0.906968808
	BHLHE40	0.097709288	*	−0.32088774	***
	CD4	0.529126713	***	0.063274567	***
Th2 T cell	STAT6	0.216157165	***	−0.325177185	***
	STAT5A	−0.130910285	***	−0.302287131	***
Treg	FOXP3	0.080864624	*	−0.31846569	***
	CCR8	0.037219525	0.424805084	−0.180963867	***
	TGFB1	0.143247892	**	−0.242180119	***
Resting Treg T cell	FOXP3	0.002370474	0.959474173	−0.273408526	***
	IL2RA	0.276549456	***	−0.171689903	***
Effector Treg T cell	FOXP3	0.1832245	***	−0.054246617	0.244551627
	CCR8	0.034330465	0.461512829	−0.296693834	***
	TNFRSF9	−0.030694834	0.351337169	−0.303097299	***
Effector T cell	CX3CR1	0.035943718	0.275066943	−0.389449973	***
	FGFBP2	0.139042748	**	−0.329301345	***
	FCGR3A	−0.130005515	**	−0.328798105	***
Naive T cell	CCR7	−0.195013529	***	−0.322096222	***
	SELL	0.039949351	0.391613676	−0.32130781	***
Effector memory T cell	DUSP4	−0.09385863	*	−0.327048999	***
	GZMK	0.143141899	**	−0.056553247	0.225036487
	GZMA	0.261870516	***	−0.265680935	***
Resident memory T cell	CD69	−0.206477346	***	−0.282267711	***
	CXCR6	−0.231808465	***	−0.277209631	***
	MYADM	0.208522048	***	−0.070229144	0.131736213
General memory T cell	CCR7	0.112734681	*	−0.210921179	***
	SELL	0.107933318	*	−0.268720921	***
	IL7R	0.179313605	***	−0.274215279	***
Exhausted T cell	HAVCR2	0.145967818	***	−0.176012779	***
	LAG3	−0.121983434	***	−0.320725367	***
	CXCL13	0.128764752	***	−0.137984485	***
	LAYN	−0.169870838	***	−0.340811169	***

KIRC = kidney renal clear cell carcinoma; TIMER = Tumor Immune Estimation Resource.

*p< 0.05. **p< 0.01. ***p< 0.001.

3.10. Prognostic analysis of KRBA2 expression based on immune cell infiltration in patients with KIRC

Given that KRBA2 expression is significantly associated with immune cell infiltration and favorable prognosis in KIRC, we investigated whether KRBA2 influences KIRC prognosis by modulating immune cell infiltration. Using the KM Plotter database, we analyzed KRBA2 expression in immune cell subgroups related to OS in patients with KIRC (Figs. 11A–K). Our findings revealed that regardless of whether B cells, CD4⁺ memory T cells, eosinophils, and macrophages were enriched or not, low KRBA2 expression was associated with poor prognosis in patients with KIRC. In addition, low KRBA2 expression in enriched basophils and regulatory T cells was correlated with worse prognosis, but no significant relationship was observed in their depleted subgroups. Conversely, high KRBA2 expression in depleted CD8⁺ memory T cells, mesenchymal stem cells, and natural killer T cells was associated with better prognosis, but no significant association was found in their enriched subgroups. Interestingly, high KRBA2 expression in enriched Type 1 and Type 2 T-helper cells was linked to poor prognosis, whereas the opposite trend was observed in their depleted subgroups (Fig. 11L). Taken together, these findings suggest that KRBA2 is associated with immune cell infiltration in KIRC and that modulating immune infiltration through KRBA2 expression may influence KIRC progression and prognosis.

Figure 11.

Kaplan-Meier survival analysis of KRBA2 in KIRC according to enriched and depleted immune cell subgroups. (A) Basophils. (B) B cells. (C) CD4⁺ memory T cells. (D) CD8⁺ memory T cells. (E) Eosinophils. (F) Macrophages. (G) Mesenchymal stem cells. (H) Natural killer T cells. (I) Regulatory T cells. (J) Type 1 T-helper cells. (K) Type 2 T-helper cells. (L) Forest plot illustrating the prognostic significance of KRBA2 expression across different immune cell subgroups in patients with KIRC. KIRC = kidney renal clear cell carcinoma.

4. Discussion

Kidney renal clear cell carcinoma, particularly metastatic KIRC, has a high incidence and mortality rate, imposing a significant burden on society.^[23] Despite the introduction of VEGF antibodies, tyrosine kinase inhibitors, immune checkpoint inhibitors, and mammalian target of rapamycin inhibitors, which have improved survival outcomes, KIRC remains difficult to cure.^[24,25] Early detection and timely intervention can significantly improve survival rates. In recent years, numerous studies have explored the potential diagnostic and prognostic value of biomarkers.^[26,27] Molecular biomarkers serve as indicators of individual prognosis, predictors of cancer recurrence and progression, and guides for personalized therapy.^[28,29] However, the effectiveness and reliability of biomarker measurement remain significant challenges in clinical practice, limiting their widespread use. Therefore, identifying novel and reliable biomarkers is crucial for KIRC diagnosis and treatment. In this study, we integrated bioinformatics analysis with in vitro experiments to investigate KRBA2 expression, its prognostic significance, and its correlation with immune infiltration in KIRC.

Early identification of molecular markers that predict tumor onset and progression could provide a foundation for improved diagnosis and treatment strategies. Our findings revealed that KRBA2 expression was significantly downregulated in KIRC, as demonstrated by both online databases and clinical tissue samples. Furthermore, KRBA2 expression was closely associated with clinicopathological characteristics and patient prognosis in KIRC. Low KRBA2 expression was significantly correlated with advanced T stage, higher histologic grade, and poor prognosis in patients with KIRC, indicating its potential as an independent prognostic marker.

The development of KIRC is not driven by a single molecular event but rather regulated by a complex network of gene interactions. Alterations in any component of these regulatory pathways can have a profound impact on tumor progression. Therefore, in addition to investigating the role of KRBA2 in KIRC, it is essential to explore the molecular changes induced by KRBA2. To elucidate the biological functions and underlying mechanisms by which KRBA2 influences KIRC progression, we examined the enrichment of its coexpressed genes. Our findings indicate that KRBA2 is involved in immune-related responses and the regulation of cell cycle progression. Numerous genes have been identified as key regulators of KIRC progression through cell cycle modulation. For instance, inhibition of PRMT1 has been shown to suppress cell cycle progression and KIRC development via the LCN2/p-AKT pathway.^[30] In addition, PRR11 is significantly upregulated in KIRC and interacts with E2F1, leading to E2F1 instability and promoting tumor progression.^[31] Moreover, NFYA, a potential therapeutic target for KIRC, upregulates CDK4 transcription, thereby facilitating cell cycle progression.^[32] However, the precise signaling pathway by which KRBA2 regulates the KIRC cell cycle remains unclear and requires further investigation through molecular experiments. Recent studies have highlighted the critical role of iron and copper metabolism in tumor progression.^[33,34] Our analysis revealed a correlation between KRBA2 expression and genes involved in iron and copper metabolism, providing new insights into the potential mechanisms through which KRBA2 contributes to KIRC progression.

The TME is the biological environment in which cancer cells survive and interact with surrounding components, including blood vessels, fibroblasts, immune cells, bone marrow–derived inflammatory cells, lymphocytes, signaling molecules, and the extracellular matrix.^[35] Cancer cells are typically embedded within a dense extracellular matrix composed of collagen and proteoglycans, shaping their microenvironment through the secretion of various cytokines, chemokines, and other factors. The dynamic interactions between cancer cells and the TME play a critical role in tumor initiation, progression, metastasis, and response to therapy.^[36] The occurrence and progression of cancer coincide with changes in the surrounding stromal tissue. Immune cells, as a major component of the tumor stroma, play a pivotal role in this process. The immune response stimulated by KRBA2 suggests that KIRC is an immune-related tumor.^[37] One of the hallmark features of KIRC initiation and progression is immune evasion.^[38] This is facilitated by intracellular accumulation of immunosuppressive cell populations, the activation of multiple suppressive receptor–ligand pathways, and altered antigen expression, all of which contribute to KIRC persistence and accelerated progression.^[39,40] Moreover, immunosuppression within the TME is closely associated with poor prognosis in patients with KIRC.^[41] Our study revealed that KRBA2 expression was positively correlated with the infiltration of dendritic cells, macrophages, neutrophils, CD8⁺ T cells, CD4⁺ T cells, and B cells. In addition, KRBA2 expression was negatively correlated with chemokines, including CCL2, CCL3, CCL4, CCL5, CCL8, CCL11, CCL13, CCL14, CCL15, CCL17, CCL18, CCL19, CCL20, CCL21, and CCL22, further indicating a potential link between KRBA2 and immune cell regulation in KIRC. Several immune checkpoints, including programmed cell death protein-1 and its ligand, as well as cytotoxic T lymphocyte–associated protein-4, are key modulators of cancer immune evasion and are closely associated with KIRC development and progression.^[15,42] Our findings identified a significant relationship between KRBA2 and multiple T-cell subtypes. Notably, gene markers for multiple T-cell subsets were negatively correlated with KRBA2 expression. However, high KRBA2 expression in enriched CD4⁺ memory T cells and regulatory T cells was associated with favorable prognosis, suggesting that KRBA2 may influence KIRC development and patient prognosis by modulating T-cell infiltration.

At present, anticancer immunotherapy is playing an increasingly significant role in tumor treatment. However, several challenges and controversies remain. For instance, some immunotherapeutic approaches rely on empirical strategies, lack specificity, and exhibit limited efficacy. In addition, certain treatments are associated with severe adverse reactions, some of which may be life-threatening. Therefore, it is crucial to design personalized immunotherapy protocols tailored to both tumor characteristics and the individual immune status of each patient to achieve optimal therapeutic outcomes. Immunotherapy represents a promising and innovative approach for advanced cancers, marking a major breakthrough in oncology treatment. Future research should focus on targeting specific immunosuppressive pathways in antitumor responses rather than merely enhancing broad, nonspecific systemic immune activation. Based on this strategy, optimal therapeutic targets should be selected according to their distinct mechanisms, while minimizing toxicity and adverse effects. Enhancing treatment specificity could help address the challenges of postoperative recurrence and metastasis.

Although our study significantly improved the understanding of KRBA2's role in KIRC, certain limitations should be acknowledged. First, most analyses in this study were conducted at the mRNA level, and additional investigations at the protein level would provide stronger validation of our findings. Second, although we primarily focused on KRBA2 expression and its clinical significance in KIRC, the biological function of KRBA2 and the molecular mechanisms underlying KIRC progression remain unexplored. Third, the potential regulatory effects of KRBA2 on cytokines and immune cells in KIRC require further investigation to fully elucidate its role in tumor immunology.

5. Conclusions

In conclusion, KRBA2 is downregulated in KIRC and is associated with distinct clinicopathological features and poor prognosis. In addition, KRBA2 influences immune cell infiltration levels in KIRC, further highlighting its potential role in tumor immunity. These findings suggest that KRBA2 may serve as a diagnostic biomarker and an immune-related therapeutic target, providing new diagnostic and treatment opportunities. Furthermore, this study offers a foundation for future research on cancer immunotherapy.

Acknowledgments

None.

Statement of ethics

All experiments were conducted in accordance with the ethical principles outlined in the Declaration of Helsinki. Informed consent was obtained from all patients. The ethics committee of Shandong Province Hospital approved the study, and the ethics approval number was NO2022-333.

Conflicts of interest statement

No conflict of interest has been declared by the author.

Funding source

This study was supported by the National Natural Science Foundation of China (No. 82072816).

Author contributions

QW, KS, YS: Study concept and design, data acquisition, data analysis, and interpretation, statistical analysis;

QW, KS: Manuscript drafting;

QX: Final approval of the manuscript, funding acquisition, and study supervision;

All authors contributed to the study and approved the submitted version.

Data availability

All data used in this study were obtained from the TCGA database. Raw data, analytical code, and related data sheets are available in the supplementary materials. Further inquiries can be directed to the corresponding author.