Bioinformatics analyses reveal the role of RIPK1 in clear cell renal cell carcinoma

Abstract

Background

Receptor interacting protein kinase 1 (RIPK1) is crucial in the regulation of apoptosis; however, its significance in clear cell renal cell carcinoma (ccRCC) is not well understood.

Methods

Utilizing data collected from public databases, RIPK1 expression level was compared between ccRCC and control groups. The diagnostic capability of RIPK1 was confirmed by drawing receiver operating characteristic (ROC) curve. A nomogram was created based on the RIPK1 and primary clinical features of ccRCC patients. Finally, quantitative reverse transcription polymerase chain reaction (qRT-PCR) was employed to confirm the expression of RIPK1, and cell experiments including colony formation, cell counting kit8 (CCK-8), cell scratch, and Transwell assays were conducted to explore the role of RIPK1.

Results

The expression of RIPK1 was higher in ccRCC samples compared to normal samples (Fold-change = 0.24), which was confirmed in in vitro verification experiments. And the area under the ROC curve (AUC) values in the TCGA-ccRCC, GSE53757, and GSE66272 datasets were all greater than 0.7, indicating that RIPK1 had acceptable diagnostic ability for ccRCC patients. Then, clinical parameters such as stage, grade, and age may serve as independent prognostic indicators for ccRCC, and a nomogram was developed based on these clinical attributes and RIPK1. The AUC values of the nomogram at 1, 3, and 5 years were 0.904, 0.841, and 0.779, respectively, proving that it can effectively distinguish patients with different prognostic outcomes. According to the results of cell experiments, the RIPK1 overexpression (OE) group was associated with increased cell viability, proliferation, invasion, and migration capabilities compared to the control group. In contrast, these capabilities were markedly decreased in cells subsequent to RIPK1 knockdown (KD).

Conclusion

The elevated expression of RIPK1 may be involved in enhancing the viability of ccRCC cells and promoting their proliferation and migration capabilities, therefore establishing a basis for clarifying the function of RIPK1 in the advancement of ccRCC.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12885-026-15728-6.

What is already known / What this study adds

What is already known

Regulatory cell death (RCD) has been widely recognized as a key biological process in controlling the occurrence, progression and treatment response of clear cell renal cell carcinoma (ccRCC). An increasing amount of evidence from transcriptomic analysis and functional studies indicates that RIPK1 is a key mediator of necrotizing necrosis and PCD characteristics, and preliminary clues suggest a potential link to tumor cell viability and patient clinical outcomes. However, the exact role of RIPK1 in ccRCC, its clinical relevance, and the potential regulatory mechanisms have not been fully elucidated.

What this study adds

Compared with previous pan-marker or multi-gene studies, this study adopted a concentrated single-gene approach to systematically characterize the biological function and clinical significance of RIPK1 in ccRCC. Through comprehensive bioinformatics analysis of large-scale clinical cohorts and in vitro functional analysis of ccRCC cell lines, we have provided clear evidence for the role of RIPK1 cell proliferation and invasion. In addition, we conducted an exploratory study on the regulatory network centered on RIPK1, thereby expanding our understanding of the mechanism of its dysregulation in ccRCC. In conclusion, this study has established RIPK1 as a promising prognostic biomarker and potential therapeutic target for ccRCC, laying a solid foundation for future translational research.

Introduction

Renal cell carcinoma (RCC) ranks among the most prevalent cancers globally, with around 300,000 new cases and exceeding 100,000 fatalities per year. The incidence rates in Europe and North America are significantly higher than those in Asia and Africa [1–3]. RCC encompasses more than 10 molecular and pathological subtypes, of which clear cell RCC (ccRCC) is the most prevalent [4]. ccRCC, the predominant pathological subtype, constitutes approximately 80% of all RCC cases [5]. The major risk factors for this disease include obesity, physical inactivity, smoking, and alcohol consumption [6, 7]. Currently, the primary treatment for RCC is surgery, supplemented by chemotherapy, radiotherapy, molecular targeted therapies, and immune checkpoint inhibitors; however, RCC exhibits low sensitivity to chemotherapy and radiotherapy. The 5-year survival rate for early-stage RCC can reach up to 90%, but it drops sharply to 10% in advanced stages, with a high recurrence rate and poor prognosis [8, 9]. Therefore, the identification of effective prognostic genes for RCC and ccRCC will offer a novel avenue for their treatment and improvement of patient prognosis.

Programmed cell death is crucial for organismal development and homeostasis, and necroptosis, as one of its types, is implicated in various diseases and tumor progression [10, 11]. Necroptosis is closely linked to the onset and development of numerous injurious, inflammatory, infectious diseases, as well as tumors. Related investigations have established that necrotic apoptosis significantly contributes to tumor development [12]. The three key proteins in the necroptosis signaling pathway are receptor-interacting protein 1 (RIPK1), receptor-interacting protein 3 (RIPK3), and mixed lineage kinase domain-like protein (MLKL) [13–15]. RIPK1, being a fundamental protein, is crucial in the transduction of signals for cell life and death, and is overexpressed in numerous cancers. Studies have shown that RIPK1 restricts caspase-8-dependent, TNFα receptors-induced apoptosis, while animals with RIPK1 knockout exhibit early postnatal lethality mediated by caspase-8 and RIPK3 [16]. Furthermore, Tang X et al. observed that in RCC cells, shRNA silencing RIPK1 led to the division of caspase-8 and PARP1, indicating apoptotic death [17]. While the significance of RIPK1 in the modulation of cell death is well acknowledged, its mechanism of action in ccRCC remains unclear.

In this study, we integrated RNA sequencing data from the The Cancer Genome Atlas (TCGA), Gene Expression Omnibus (GEO), and ArrayExpress databases to systematically investigate the expression patterns of RIPK1 in ccRCC patients and pan-cancer data. The diagnostic ability of RIPK1 in ccRCC patients was assessed, and a diagnostic nomogram was developed based on clinical characteristics. Furthermore, immune-related analyses, predictions of regulatory networks, and functional enrichment analysis were performed, providing a foundation for future mechanistic studies. This study provides significant insights and serves as a crucial reference for the clinical diagnosis, prevention, and treatment of ccRCC.

Materials and methods

Data collection

For the TCGA-ccRCC cohort, transcriptome data and related clinical details were sourced from the TCGA database (https://xenabrowser.net/), and a total of 607 samples were gathered, comprising 72 samples of normal kidney tissue or surrounding tissue and 535 ccRCC samples. Since TCGA database has a large amount of high-throughput cancer data, including pan-cancer data, we obtained RIPK1 expression data in pan-cancer of the TCGA database. The GSE53757 and GSE66272 datasets were retrieved from the GEO database (http://www.ncbi.nlm.nih.gov/geo/). The GSE53757 dataset includes 72 ccRCC and 72 normal samples. The GSE66272 dataset contains 54 samples with 27 ccRCC samples and 27 normal kidney tissues. To validate the prognostic ability of RIPK1, the E-MTAB-1980 dataset was obtained from the ArrayExpress database (https://www.ebi.ac.uk/biostudies/arrayexpress), comprising 101 ccRCC patients’ survival data and expression profiling data.

Analysis of RIPK1 expression in pan-cancer

The expression of RIPK1 in pan-cancer were analyzed based on the TCGA database. Since various cancers did not have corresponding control group data, we conducted a comprehensive analysis of RIPK1 gene expression across 23 different types of cancers. Then, Using the TCGA-ccRCC, GSE53757, and GSE66272 datasets, the expression levels of RIPK1 were investigated in TCGA-ccRCC, GSE53757 and GSE66272 datasets. We employed the HPA database (http://www.proteinatlas.org/) to examine the expression of RIPK1 in both ccRCC and normal tissues. The “pROC” software (version 1.0.3) was employed to construct the receiver operating characteristic (ROC) curve in the TCGA-ccRCC, GSE53757, and GSE66272 datasets, respectively [18].

Differential expression and functional enrichment analyses

Utilizing the TCGA-ccRCC dataset, we determined the optimal cutoff value using the surv_cutpoint function from the “survminer” package (with minprop = 0.3) and classified ccRCC patients into high and low expression cohorts based on RIPK1 expression levels. Kaplan-Meier curve were employed to assess the suivival differences of two expression groups in the TCGA-ccRCC and E-MTAB-1980 datasets. Differentially expressed genes (DEGs) from the two differential expression groups of RIPK1 were screened using “DESeq2” package (|log₂FC| > 0.5, adj.p < 0.05) based on the raw counts in TCGA-ccRCC dataset [19]. And the “ggplot2” package (version 3.4.1) was employed to create a volcanic map for the visualization of DEGs [20]. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analyses were carried out via the “clusterProfiler” package (version 4.2.2) (p < 0.05) [21].

Construction and verification of the nomogram

Major clinical characteristics (age, gender, stage, grade, and TMN stages) in the TCGA-ccRCC dataset were gathered to examine the link between RIPK1 and clinical features. Univariate and multivariate Cox analyses were performed on clinical data to identify independent prognostic factors. Nomogram was mapped using the “rms” (version 6.5-1) package [22]. Finally, calibration, ROC, and decision curve analysis (DCA) curves were painted to verify the predictive accuracy, discriminative ability, and clinical utility of the nomogram, respectively.

Immune infiltration and immune checkpoint molecules analysis

The immune cell infiltration profiles between two expression groups of RIPK1 for ccRCC were assessed by ssGSEA with gene sets from published literature [23], and accumulation maps were generated for visual analysis. The disparities in invading immune cells among the several groups were analyzed using the Wilcoxon test (adj.p < 0.05). Furthermore, an analysis was conducted on the link between RIPK1 and differentiated immune cells. The immport database (https://www.immport.org/shared/home) was utilized to retrieve 39 immune checkpoint molecules (Supplementary Table 1), after which their expression levels in two differential expression groups of RIPK1 were analyzed, and the results were displayed using box plot diagrams (adj.p < 0.05).

Gene mutation analysis and competing endogenous RNA (ceRNA) regulatory network

The tumor mutational burden (TMB) score for ccRCC patient was determined utilizing the following formula: TMB score = (total mutation / total covering base) × 10⁶. Utilizing the “maftools” program (v 2.18.0), waterfall plots were generated for top 20 genes demonstrating the highest mutation frequencies among two distinct expression groups of RIPK1 [24]. The starBase database (https://rnasysu.com/encori/index.php) was employed to forecast the microRNAs (miRNAs, clipExpNum > 1) and long non-coding RNAs (lncRNAs, clipExpNum > 10) associated with RIPK1, then ceRNA regulatory network was constructed using Cytoscape (v 3.8.2) [25].

Cell culture and transfection

Human RCC cells (769-P, 786-O, and Caki-1) were obtained from Immocell Biotechnology Co.,Ltd, and tubular epithelial cells (HK-2) were obtained from Wuhan Shang’en Biotechnology Co., Ltd. These cells were maintained in RPMI medium, RPMI-1640 medium, McCoy’s 5 A medium, and DMEM medium, respectively, each supplemented with 10% fetal bovine serum (FBS) and 1% penicillin/streptomycin. To establish stable cell lines with RIPK1 knockdown or overexpression, recombinant lentiviruses were generated using 293T cells and packaged with either the RIPK1 gene or specific short hairpin RNAs (shRNAs) targeting RIPK1 (shRIPK1-1, shRIPK1-2, shRIPK1-3). A viral vector with a non-specific sequence served as a negative control. Target cells were infected in serum-rich medium supplemented with the virus suspension and 5 µg/ml polybrene (Merck, Catalog Number: TR-1003). Following infection, cells were selected in medium containing the appropriate antibiotics until all control group cells were eliminated, yielding the desired stable cell lines in the experimental groups.

Quantitative reverse transcription polymerase chain reaction (qRT-PCR)

Following the aspiration of the supernatant from the cell culture medium, the cells were trypsinized, centrifuged to obtain the pellet, and total RNA was subsequently extracted. The RNA concentration was quantified using the Nano-500 microspectrophotometer. Reverse transcription into cDNA was conducted in RNase-free PCR tubes by incorporating 5× ABScript III RT Mix (4 µL), 20× gDNA Remover Mix (1 µL), total RNA (500 ng), and nuclease-free water. GAPDH served as an internal reference. The qRT-PCR instrument was used to automatically record the cycle threshold (Ct value) of each sample. This value characterizes the number of amplification cycles when the fluorescence signal reaches the set threshold. Only Ct values within the range of 15 to 35 were considered valid. Beyond this range, the template concentration needed to be adjusted and the experiment reconducted. The ΔΔCt method was used for data normalization processing. First, the ΔCt value of a single sample was calculated. Then, the ΔΔCt value was obtained through the difference in ΔCt between the experimental group and the control group. Finally, the relative expression level of the target gene was calculated based on the 2^−ΔΔCt method, with primer sequences detailed in Table 1.

Table 1.

The primer sequences of RIPK1 in RT-qPCR

Name	Sequence 5’-3’
H-GAPDH	F:5‘-GGAGTCCACTGGCGTCTTCA − 3’
	R:5‘-GTCATGAGTCCTTCCACGATACC − 3’
RIPK1	F:5’-GGGAAGGTGTCTCTGTGTTTC-3’
	R:5’-CCTCGTTGTGCTCAATGCAG-3’

Western blot analysis

We collected 769P control cells and cells treated under different conditions, and cells were lysed using radioimmunoprecipitation (RIPA) lysis buffer supplemented with 100X PSMF protease inhibitor on ice to provide a total protein solution. A non-denatured protein solution was obtained, and the protein concentration was quantified using the Bicinchoninic Acid Protein (BCA) protein assay kit. Following protein denaturation, we isolated the proteins via Sodium Dodecyl Sulfate-Polyacrylamide Gel Electrophoresis (SDS-PAGE) electrophoresis, subsequently transferring them onto a polyvinylidene fluoride (PVDF) membrane. The transferred membrane was positioned in an incubation chamber containing Tris-Buffered Saline with Tween (TBST) and subjected to blocking with 5% non-fat milk at ambient temperature for 30 min. The primary antibody was administered and incubated on a shaker at 4℃ overnight, then followed by incubation with the secondary antibody at room temperature for 30 min. The ImageJ software was used to conduct gray-scale value analysis on chemiluminescence images, and to determine the Integrated Density (ID) of the target protein band and the internal reference protein band in each sample respectively. On this basis, the relative expression level of the target protein in a single sample was calculated by the ratio of the gray value of the internal reference protein band to that of the target protein band. Taking the average relative expression level of the control group as 1, the relative expression multiple of the protein in the experimental group was further calculated to complete the normalization processing of the data.

Colony formation assay

769P cells were subjected to trypsinization, enumerated, and subsequently inoculated onto 6-well plates. Following 14 days of uninterrupted culture (or until the majority of single-cell clones surpassed 50 cells), the media was replenished every 2–3 days. Following incubation, cells were fixed using 4% paraformaldehyde (700 µL/well, 30 min), washed with phosphate buffered saline (PBS), and subsequently stained with a 0.1% crystal violet solution for 20 min. Plates were air-dried, photographed, and analyzed using ImageJ software (v1.53) with colony thresholds set at > 50 cells/cluster. The threshold of 50 cells was established to exclude stress-induced proliferation events or artificial aggregation of cells that have undergone only 1–2 divisions, thereby ensuring the selection of single-cell clones capable of sustained proliferation through ≥ 6 consecutive divisions.

Cell counting kit8 (CCK-8) proliferation assay

769P cells were inoculated into 96-well plates, with peripheral wells filled with PBS to mitigate edge effects, and permitted to adhere. Subsequent to the treatments (0–72 h, 100 µL final volume per well), 10 µL of CCK-8 reagent was introduced to each well. Following a 30-minute incubation, absorbance was assessed at 450 nm utilizing a microplate reader (BioTek Synergy H1), with blank correction performed using wells containing PBS. Dose-response curves were analyzed with GraphPad Prism 9.0.

Cell scratch assay

Cells were dissociated into a single-cell solution utilizing trypsin and subsequently inoculated into a 6-well plate. The plating principle aims for complete confluence following overnight incubation, utilizing a final volume of 2 mL of culture media per well. Cells were incubated at 37℃ in a 5% CO₂ for 24 h. A 200 µL pipette tip was employed to induce scratches on the well’s bottom, either parallel or perpendicular to the horizontal lines on the plate lid or a ruler. The pipette tip must be maintained in a vertical position without inclination. The cells were subsequently rinsed thrice with PBS to eliminate the detached cells, followed by the addition of serum-free media. The cells were observed under a 4X microscope, ensuring that the scratch was centered and vertical, with consistent background. Samples to be tested were added, and a concentration gradient as set. Samples were taken at 0, 24 h, and 48 h, and images were captured. After opening the images in ImageJ software, 6 to 8 random horizontal lines were drawn, and the average distance between cells was calculated as follows: 24 h wound healing rate = (scratch area at 0 h-scratch area at 24 h)/scratch area at 0 h*100%; 48 h wound healing rate= (scratch area at 0 h-scratch area at 48 h)/scratch area at 0 h*100%.

Transwell assay

Sixty microliters of Matrigel were combined with serum-free culture media and vertically introduced into the Transwell chamber. The mixture was incubate at 37℃ for 3 h, and then the unbound matrigel was aspirated. 500 µL of complete culture media with 10% FBS was introduced into the lower chamber of a 24-well plate, and the Transwell chamber was positioned into the 24-well plate using tweezers. The serum-deprived cells were digested, enumerated with a cell counter, and resuspended in serum-free media for seeding in the upper chamber. The cells underwent incubation for a duration of 24 to 48 h. Subsequently, the Transwell chamber was detached, the cells were fixed using 4% paraformaldehyde, and staining was conducted using 600 µL of 1% crystal violet for a duration of 20 min. The cells were examined microscopically for qualitative assessment. Images were taken from three fields of view, and ImageJ was used to count the cells and calculate the average for quantitative analysis.

Statistical analysis

Analysis was conducted using R version 4.2.2 software, and the Wilcoxon test was applied to evaluate the differences between the high and low expression groups of RIPK1 in ccRCC samples. Specifically, cell experiments underwent three independent biological replicates and three technical replicates, and the data were expressed as mean ± standard deviation. The differences between the two groups were analyzed using the T test, and the differences among more than two groups were analyzed using one-way analysis of variance (ANOVA). The normality of variance was verified using the Shapiro-Wilk test. All statistical analyses were performed using GraphPad Prism software. A p value < 0.05 was considered statistically significant.

Results

Analysis of RIPK1 expression in pan-cancer

To investigate the expression level of RIPK1 in pan-cancer, we analyzed the expression of RIPK1 in 23 types of cancers, of which RIPK1 was significantly differentially expressed in 13 types of cancers, the expression level of breast cancer (BRCA), head and neck squamous carcinoma (HNSC) were relatively high (Fig. 1A). Then, we analyzed the expression of RIPK1 for ccRCC based on TCGA-ccRCC, GSE53757 and GSE66272 datasets, the results indicated significant differences in expression of RIPK1 across all three datasets, and the expression level in ccRCC tissues were elevated compared to normal tissues (Fig. 1B). Finally, we utilized the HPA database to further examine the disparity of protein expression levels for RIPK1 between ccRCC tissue and normal tissue. Figure 1C demonstrated that ccRCC tissues exhibited a greater staining intensity for RIPK1 compared to normal tissues, suggesting an elevated expression level of RIPK1 protein in ccRCC tissues (Fig. 1C). In summary, the elevated expression of RIPK1 in ccRCC tissues could be a significant factor in the development of ccRCC. To further verify diagnostic capability of RIPK1, ROC curve analysis was performed in the TCGA-ccRCC, GSE53757, and GSE66272 datasets, and the results demonstrated that the area under the curve (AUC) values in the three datasets was 0.75, 0.71, and 0.70 (Fig. 1D), indicating the diagnostic effect of RIPK1 for ccRCC.

Fig. 1.

Analysis of expression in various cancers including clear cell renal cell carcinoma (ccRCC). A Analysis of gene expression levels of Receptor interacting protein kinase 1 (RIPK1) in 23 types of cancer. ns indicated p > 0.05, * indicated p < 0.05, ** indicated p < 0.01, *** indicated p < 0.001, and **** indicated p < 0.0001. B Analysis of expression of RIPK1 in ccRCC, the TCGA-ccRCC, GSE53757, and GSE66272 datasets were shown from left to right. ** indicated p < 0.01 and **** indicated p < 0.0001. C Human protein Atlas (HPA) analysis of RIPK1 protein, on the left was ccRCC tissue and on the right was normal tissue. D Receiver operating characteristic (ROC) curve analysis of RIPK1. From top to bottom, the datasets were TCGA-ccRCC, GSE53757, and GSE66272. The vertical coordinate was true positive, the horizontal coordinate was false positive, and the area below the curve in the figure was called area under the curve (AUC) value

DEGs in different expression groups of RIPK1 affect apoptotic pathways

After dividing the ccRCC samples into high and low expression groups based on the expression of RIPK1, the Kaplan-Meier curves revealed significant survival differences between two groups of samples in the TCGA-ccRCC and E-MTAB-1980 datasets, and the survival possibility was greater in the low expression group (Fig. 2A). To clarify the common function of DEGs in two differential expression groups of RIPK1, initially, we discovered 321 DEGs, comprising 292 up-regulated and 29 down-regulated genes relative to the control group (Fig. 2B). Based on the correlation analysis, 257 of the 321 DEGs were statistically significant with RIPK1 expression (adj. p < 0.05, Supplementary Table 2). Then, we carried out GO and KEGG functional analyses for 321 DEGs. According to GO enrichment analysis, the pathways related to apoptosis were enriched, containing anion transmembrane transport, regulation of cellular component size, inorganic anion transmembrane transport, negative regulation of axon extension, and negative regulation of axonogenesis (Fig. 2C, Supplementary Table 3). KEGG was enriched into extracellular matrix (ECM)-receptor interaction (Fig. 2D).

Fig. 2.

Functional enrichment analysis of differentially expressed genes (DEGs) in high and low expression groups of RIPK1. A Kaplan-Meier curve of high and low expression groups. The orange represents the low-expression group and blue represents the high-expression group. B Volcano map of DEGs between high and low expression groups of RIPK1 (adj.p < 0.05). Blue dots represented genes that were differentially up-regulated, orange dots represented genes that were differentially down-regulated, and grey dots represented genes that were not statistically significant. C Gene Ontology (GO) enrichment analysis of DEGs. D Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analysis of DEGs. The grey dots were co-enriched genes, the yellow dots were the designations of the pathways, and the different coloured lines represented different pathways. An adj.p value < 0.05 was considered statistically significant

Construction of nomogram based on different clinical features of CcRCC

To investigate the link between RIPK1 and the major clinical features of ccRCC patients, we grouped the developmental stages of the clinical features, and the study findings indicated that there were notable disparities in expression levels of RIPK1 between the G2 and G4 stages, as well as between the G3 and G4 stages (Fig. 3A). And it has been demonstrated that the grade had a significant correlation with death from RCC patients [1]. In contrast, there were no significant differences in RIPK1 across developmental stages of age, gender, stage, T, M and N. To create a more accurate model, univariate Cox analysis was conducted, the findings from the analysis indicated a significant connection between survival of ccRCC patients and age, stage, grade, T, M, and N (Fig. 3B). Multivariate Cox analysis was conducted on the 6 clinical features selected by univariate Cox, and the findings indicated that stage, grade, and age were independent prognostic factors and and all of them were risk factors for ccRCC (Fig. 3C). Therefore, using the independent prognostic factors and RIPK1 in combination with the survival data from ccRCC patients, we developed a nomogram to forecast the 1, 3, and 5 years survival probabilities of ccRCC patients (Fig. 3D). The nomogram was applied following this standardized procedure: the specific values of RIPK1 expression, stage, grade, and age were located on the respective axes, and the corresponding scores for each variable were aggregated to yield a cumulative total score. By projecting this total score onto the linear predictor axis, the 1-year, 3-year, and 5-year survival probabilities for the individual patient were then determined. Furthermore, the calibration curve showed that after 1,000 resampling corrections, the overall survival (OS) predicted by the nomogram was in good agreement with the actual observed values, ensuring the reliability of the prediction results (Fig. 3E). Time-dependent ROC curve indicated the AUC values of 1, 3, and 5-year OS were 0.904, 0.841, and 0.779, respectively, which can effectively distinguish patients with different prognostic outcomes (Fig. 3F). DCA indicated that within different risk threshold intervals, this nomogram can bring positive net benefits to clinical decision-making, highlighting its potential clinical application value (Fig. 3G).

Fig. 3.

Construction of prognostic nomogram for ccRCC. A Expression of RIPK1 in different developmental stages of grade. ns indicated p > 0.05, * indicated p < 0.05, and ** indicated p < 0.01. B Univariate Cox analysis of clinical features and RIPK1. C Multivariate Cox analysis of clinical features. D Construction of a nomogram based on the RIPK1 and independent predictors. E Calibration curve analysis of nomogram. The horizontal coordinate was the predicted event incidence, the vertical coordinate was the actual event incidence, and the shaded part of the upper line is the confidence interval. F Time-dependent ROC curve of the nomogram. G Decision curve analysis (DCA) curves of the nomogram

Analysis of immune infiltration in two differential expression groups of RIPK1

To explore the difference of immune cell infiltration in high and low expression groups of RIPK1, the number of immune cells was counted and the significant difference between two differential expression groups of RIPK1 was analyzed. The results revealed that there were 16 immune infiltrating cells that were all significantly different between groups, and content of these cells were higher in the low expression group, including central memory CD4⁺ T cell, central memory CD8⁺ T cell, etc. (Fig. 4A-B). Then, the correlation between RIPK1 and differential immune cells was further analyzed, and the results revealed that RIPK1 had a slightly higher correlation with natural killer (NK) cells, and the correlation coefficient was 0.35 (p < 0.05) (Fig. 4C). Taking into account the influence of tumor purity, we performed correction and subsequently conducted differential immune cell analysis. The results revealed consistent differences in the abundance of 8 immune cell types, including NK cells, further underscoring their significant association with ccRCC (Fig. 4D). An investigation of the expression levels of 39 immunological checkpoint molecules was conducted in the high and low expression groups of RIPK1, respectively, and the findings revealed that 27 immune checkpoint molecules were significantly different, including CD274, CD8A, B2M, CD40LG, IL12A, FGL1, TNFSF4, IFNG, CD28, TNFRSF9, CD86, CD80, CD40, CTLA4, PDCD1, LDHA, LDHB, ICOSLG, HLA-G, PTPRC, YTHDF1, TNFSF18, JAK2, JAK1, PDCD1LG2, HAVCR2, and ICOS (Fig. 4E). After tumor purity correction, there were still significant differences in the expression levels of 13 immune checkpoints such as CD40, ICOSLG, IFNG (Fig. 4F). In conclusion, the differential immune cells as well as differential immune checkpoint molecules in two differential expression groups of RIPK1 can provide references for immunotherapy of ccRCC.

Fig. 4.

Analysis of immune infiltration in the high and low expression groups of RIPK1. A-B Analysis of proportion accumulation map and box plot of immune infiltrating cells. ns indicated adj.p > 0.05, * indicated adj.p < 0.05, ** indicated adj.p < 0.01, *** indicated adj.p < 0.001, and **** indicated adj.p < 0.0001. C Heat map analysis of RIPK1 correlation with differential immune infiltrating cells. Red indicates a positive correlation, and the darker the color, the stronger the correlation. D Box plot of immune cell content after tumor purity correlation. ns indicated adj.p > 0.05, * indicated adj.p < 0.05, ** indicated adj.p < 0.01, and **** indicated adj.p < 0.0001. E Expression of immune checkpoint molecules between two expression groups of RIPK1. * indicated adj.p < 0.05, ** indicated adj.p < 0.01, and *** indicated adj.p < 0.001. F Box plot of immune checkpoint molecules expression after tumor purity correlation. * indicated adj.p < 0.05, ** indicated adj.p < 0.01, and **** indicated adj.p < 0.0001. G Waterfall map of the top 20 most frequently mutated genes in ccRCC patients. The top was the group with high expression of RIPK1, and the bottom was the group with low expression of RIPK1. Each row was a gene, each column was a sample, different colors indicated different mutation types. An adj.p value < 0.05 was considered statistically significant in this figure

To further explore the gene mutation difference between different expression groups of RIPK1, we conducted gene mutation analysis. The results indicated that 5 genes had higher mutation frequencies in both groups, including VHL, PBRM1, TTN, SETD2, and BAP1 (Fig. 4G). However, it is important to note that this association is only predictive, and the potential role of these gene mutations in ccRCC still requires further exploration and verification.

Mining of regulatory relationships targeting RIPK1

To further analyze the miRNAs and lncRNAs that could regulate the expression of RIPK1, we predicted 18 miRNAs and 32 lncRNAs. Among them, hsa-miR-1323 could regulate the expression of RIPK1, and hsa-miR-1323 could interact with multiple lncRNAs. including MALAT1, OIP5-AS1, MIR17HG, ZFAS1, NUTM2A-AS1, GAS5, AC016876.2, and NEAT1. Moreover, NEAT1 can interact with multiple miRNAs (hsa-miR-345-5p, hsa-miR-2114-3p, hsa-miR-214-3p, and hsa-miR-2467-3p) (Fig. 5A). After further exploring the relationship between RIPK1 and various diseases, we predicted 45 diseases associated with the RIPK1, and they were related to apoptosis, inflammation, and immunodeficiency diseases (Fig. 5B).

Fig. 5.

Regulatory network analysis based on RIPK1 and drug sensitivity analysis. A Competing endogenous RNA (ceRNA) regulatory network analysis. RIPK1 was shown in red, microRNAs (miRNAs) were indicated in yellow, and long non-coding RNAs (lncRNAs) were shown in blue, and black lines indicate the presence of interactions. B Gene-disease network analysis. The red quadrilateral represents genes, the purple ellipse represents diseases, and the black line indicates the existence of a corresponding relationship

Investigation into the role of RIPK1 in CcRCC cells

To further validate the expression level of RIPK1 under in vitro conditions, we performed qRT-PCR and western blot on human RCC cell lines (769-P, 786-O, and Caki-1) and tubular epithelial cells (HK-2). The results of qRT-PCR showed that RIPK1 expression in ccRCC was significantly higher than in the control samples, which was consistent with predictions from public databases (Fig. 6A). Given that the difference was most pronounced in the 769-P cell line, it was selected for further functional studies on RIPK1. Firstly, we established 769-P cell lines with RIPK1 knockdown (KD) and overexpression (OE), and transfected empty plasmids into cells as controls. The qRT-PCR results in Fig. 6B-C demonstrated that the knockdown and overexpression cell lines were effectively established and were suitable for additional functional studies. Correspondingly, western blot analysis corroborated these findings.

Fig. 6.

Validation of the expression of RIPK1 and construction of ccRCC cells. A Differences of RIPK1 in cells by quantitative reverse transcription polymerase chain reaction (qRT-PCR). ***p < 0.001. B,C The expression levels of RIPK1 after overexpression or knockdown were detected by qRT-PCR (B) and western blot (C). ***p < 0.001

To investigate the effects of RIPK1 expression levels on 769-P cell function, we assessed cell viability (CCK8 assay), proliferation (colony formation assay), and migration ability (cell scratch assay and Transwell assays). The CCK8 experiment demonstrated a significant reduction in cell viability in the RIPK1 KD group compared to the control, while a significant increase was observed in the OE RIPK1 group (Fig. 7A). The proliferation capacity of 769-P cells transfected with RIPK1 KD was significantly diminished, but the OE RIPK1 group exhibited the contrary effect (Fig. 7B). Regarding migration and invasion, we noted that the migration and invasion capabilities of 769-P cells transfected wit RIPK1 KD were dramatically diminished compared to the control group, whereas the OE RIPK1 group had substantially increased migration and invasion capabilities (Fig. 7C-D). Overall, the overexpression of RIPK1 may affect migration and invasion abilities by regulating apoptosis and inflammation-related pathways.

Fig. 7.

Overexpression of RIPK1 influencing 769-P cell activity, proliferation and migration. A The cell viability of each group was detected by CCK8 assay. *p < 0.05, ***p < 0.001. B The colony forming ability of 769-P cells in each group. ***p < 0.001. C, D The migration and invasion abilities of RIPK1 in each group by Cell scratch (C) and Transwell (D) assays. *p < 0.05, **p < 0.01, ***p < 0.001

Discussion

With the extensive mining of the GEO database, numerous studies on biomarker screening for ccRCC have been reported. RIPK1, a serine/threonine kinase, is the first identified member of the RIP family. It was initially found to bind to apoptosis-inducing death receptors through the death domain [26]. Numerous studies have demonstrated the pivotal function of RIPK1 in modulating cell survival and death signaling [27, 28]. Despite RIPK1’s pivotal role in cell death, current studies indicate its overexpression in numerous cancers, correlating with carcinogenesis, invasion, metastasis, and poor prognosis [28–31].

In this study, bioinformatics methods were employed to reveal that RIPK1 expression is significantly elevated in patients with ccRCC, which was consistent with the results observed in cell lines. RIPK1 demonstrated an AUC value greater than 0.7 in multiple independent datasets. This performance was similar to that of the model constructed by Yang X et al. [32], which integrated clinical features and CTLA4 expression, fully demonstrating the independent predictive value of RIPK1 as a biomarker for the risk of ccRCC. Furthermore, by constructing RIPK1 OE and KD models, and validating them through wound healing, Transwell invasion, and CCK8 proliferation assays, we found that OE RIPK1 correlates with enhanced migration, invasion, and survival abilities of ccRCC cells. The expression level of RIPK1 was strongly positively linked with tumor malignancy. Specifically, OE RIPK1 was associated with an enhancement in both proliferation and migration abilities in ccRCC cells. This could be due to a decrease in cell apoptosis or excessive cellular stress responses, which create an intracellular imbalance and activate positive regulatory mechanisms, ultimately promoting tumor cell proliferation and migration. As noted by Al-Lamki RS and colleagues, altered expression of RIPK1/3 in RCC cells enables them to modulate the process of necroptosis in response to TNFR1 signaling [30]. Differential expression analysis revealed that 321 genes were significantly modified in the high RIPK1 expression cohort. Among them, upregulated genes were predominantly enriched in pathways related to “anion transmembrane transport” and “ECM-receptor interaction”, suggesting that expression level of RIPK1 may be associated with ECM remodeling processes in tumor cells, which in turn correlates with tumor invasive potential. This has been further supported by the relevant research, which indicates that RIPK1 regulates reactive oxygen species (ROS) and cell viability during ECM detachment, thereby influencing the survival of cancer cells [33]. Downregulated genes, on the other hand, were associated with the “mitochondrial apoptotic signaling pathway,” implying that it may sustain tumor cell survival by inhibiting the classical apoptotic pathway. This result aligns with the study by Al-Lamki et al. [30], which indicates that high RIPK1 expression drives RCC progression through a dual mechanism of necroptosis and apoptosis inhibition.

In the onset and progression of ccRCC, dynamic changes in the immune microenvironment play a pivotal role. Our investigation demonstrated that the expression level of RIPK1 was correlated with the infiltration of immune cells, particularly showing a substantial association with the fraction of NK cell infiltration. Compared to the group with high RIPK1 expression, the infiltration of NK cells is markedly higher in the group with low RIPK1 expression. This finding suggests that the expression of RIPK1 was closely related to the alteration of the immune microenvironment of ccRCC (especially the infiltration level of NK cells), and it may be involved in the regulation of ccRCC progression by regulating this process. As an essential element of the innate immune system, NK cells directly kill tumor cells through mechanisms such as releasing perforin, granzymes, and secreting interferon-γ (IFN-γ), while also activating adaptive immune responses [31]. Prior research has shown a good link between NK cell infiltration levels and the prognosis of ccRCC patients [34]. In our study, the reduced NK cell activity observed in the group with high RIPK1 expression might be related to tumor cells evading immune surveillance via RIPK1-mediated necroptosis. Al-Lamki [30] reported that renal cancer cells with high RIPK1/3 expression were more prone to TNF receptor 1-induced necroptosis, indirectly inhibiting NK cell function. Furthermore, existing studies have confirmed that the kinase activity of RIPK1 may up-regulate immune checkpoint molecules such as PD-L1 by activating the NF-кB signaling pathway [35], and this mechanism may further reduce the killing efficiency of NK cells, which is consistent with the correlation observed in this study. In conclusion, the current researches support that the expression of RIPK1 is related to the infiltration and functional changes of NK cells. It is speculated that targeted inhibition of RIPK1 may provide a potential direction for enhancing the immunotherapeutic effect of ccRCC by restoring the infiltration and function of NK cells, which is worthy of further verification.

Furthermore, our immune checkpoint research demonstrated notable disparities in the expression levels of diverse immune checkpoint molecules between the RIPK1 low-expression and high-expression cohorts, including B2M, LDHA, and LDHB. These molecules play pivotal roles in immune regulation and tumor metabolism. B2M (β2-microglobulin), as a crucial component of MHC-I molecules, exhibits enhanced expression that may potentiate the antigen-presenting capability of tumor cells, facilitating the recognition and elimination of tumor cells by CD8⁺ T cells [36]. However, mutations or loss of B2M expression constitute an important mechanism of resistance to immune checkpoint inhibitors, such as anti-PD-1 therapy [37]. Therefore, the high expression of B2M observed in this study may indicate increased sensitivity to immunotherapy in patients with low RIPK1 expression. Notably, LDHA and LDHB, key enzymes in glycolysis, show elevated expression levels indicative of significant metabolic reprogramming in ccRCC cells. LDHA facilitates the transformation of pyruvate into lactate, resulting in the buildup of lactate inside the tumor microenvironment. This consequently impedes T-cell proliferation and facilitates the entry of regulatory T cells by acidifying the microenvironment [38]. Recent studies have demonstrated that LDHA inhibitors can reverse lactate-mediated immunosuppression and synergize with anti-CTLA-4 antibodies to enhance antitumor immune responses [39].

In exploring the mechanisms by which RIPK1 regulation influences the onset and progression of ccRCC, we constructed a ceRNA network, revealing that RIPK1 may be regulated by multiple miRNAs, with hsa-miR-1323 playing a particularly crucial role. Notably, hsa-miR-1323 not only directly regulates the expression of RIPK1 but also engages in complex interactions with various long non-coding RNAs (lncRNAs), such as MALAT1, MIR17HG, and ZFAS1. MALAT1 promotes the malignant progression of ccRCC through multiple mechanisms, including regulating gene expression, influencing the cell cycle, and enhancing tumor angiogenesis [40]. MIR17HG also plays a significant role in various tumors by regulating the maturation and stability of miRNAs, thereby influencing the growth and invasive capabilities of tumor cells. In ccRCC, the high expression of MIR17HG may promote malignant progression through similar mechanisms [41]. ZFAS1, intimately associated with cell apoptosis and cycle regulation, may influence the initiation and progression of malignancies in ccRCC by aberrantly impacting these biological processes [42]. Through the ceRNA network, we predicted that these lncRNAs indirectly influence the expression of RIPK1 by interacting with hsa-miR-1323, thereby playing crucial roles in the onset and development of ccRCC. However, this regulatory relationship requires further validation through both in vitro and in vivo experiments.

The core innovation of this study is shifting the focus of RIPK1 research from pan-cancer to ccRCC. By combining bioinformatics analysis with cellular validation, we systematically assess RIPK1’s diagnostic and prognostic value in ccRCC and preliminarily characterize its biological functions, thereby identifying it as a promising biomarker and establishing a theoretical basis for subsequent mechanistic exploration. Nonetheless, this study has certain drawbacks. To enhance the universality of the research results, this study conducted independent analyses on multiple datasets. Although the datasets were not merged to avoid batch effects, there may still be potential confounding factors such as batch effects and treatment heterogeneity within the datasets. Owing to the absence of matched clinical variables with the TCGA-ccRCC cohort in the E-MTAB-1980 dataset, it could not be used to validate the predictive performance of the constructed nomogram. Due to time and resource constraints, the functional validation of this study was only completed in the 769-P cell line, which might affect the universality of the results. In the future, further validation is needed in more ccRCC cell lines to evaluate its generalization. The molecular mechanism of RIPK1 has not been deeply analyzed (such as without the use of specific inhibitors or rescue experiments), and there is a lack of detection of key indicators of the apoptotic/necrotic pathway, making it impossible to directly establish a causal relationship between phenotypes and pathways. Meanwhile, the research mainly relies on bioinformatics analysis and cell experiments, lacking in vivo experiments and clinical trials for verification. Future research can further verify the role of RIPK1 in ccRCC and its potential for targeted therapy by establishing animal models or conducting clinical trials. Finally, the miRNA-mRNA-lncRNA interaction was only screened through the database and was not experimentally verified. In the future, we will utilize the luciferase reporter gene assay and the RIPK1 expression rescue assay to identify the molecules that have direct interactions with RIPK1.

Conclusion

RIPK1 is significantly overexpressed in ccRCC and serves as a potential diagnostic and prognostic biomarker. Its overexpression in 769P cells correlates with enhanced cell proliferation, migration, and invasion, and also correlates with immune infiltration and checkpoint molecules. These findings provide a foundation for future studies to explore the potential clinical utility of RIPK1 in prognosis prediction and targeted therapy.

Authors’ contributions

Conception and design: Daocheng Fang, Hui Wen, Jie FanData Manuscript analysis and interpretation: Yuanyuan Hu, Shuangquan SunManuscript writing: Daocheng FangFinal approval of manuscript: All authors.

Funding

This work was supported by the Key Discipline Construction Project of Songjiang District, Shanghai (Number 2025czys011) and the Science and Technology Innovation Project of Songjiang District, Shanghai (Number 2025SJKJGG043, 2024SJKJGG107).

Data availability

The datasets analyzed in this study were sourced from the TCGA database (https://xenabrowser.net/), GEO database (GSE53757 and GSE66272 datasets, http://www.ncbi.nlm.nih.gov/geo/), and ArrayExpress database (E-MTAB-1980 dataset, https://www.ebi.ac.uk/biostudies/arrayexpress).

Declarations

Ethics approval and consent to participate

Not applicable.

Competing interests

The authors declare no competing interests.