Novel Prognosis Model for Clear-cell Renal Cell Carcinoma based on Apoptosis-related Genes

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Few studies have investigated the clinical prognostic significance of multiple apoptosis-related genes (ARGs), particularly in the context of clear-cell renal cell carcinoma (ccRCC). Methods. We explored ARGs in ccRCC prognosis using The Cancer Genome Atlas (TCGA) repository. Transcriptomic expression profiles and corresponding medical information for patients with ccRCC were obtained. Human ARGs were identified through gene-set enrichment analysis. Differentially expressed (DE)-ARGs and prognosis-related ARGs were identified and, subsequently, used for prognosis modelling. The prognostic predictive performance of this model was confirmed using Kaplan-Meier (KM) and receiver operating characteristic (ROC) curves. Relevant clinical prognostic variables were added to construct a predictive nomogram for ccRCC prognosis at the clinical level. Immune-cell penetration evaluation was conducted for genes included within this novel prognosis-linked model. Validation of TOP2A function in ccRCC cells by cellular experiments. Results. Overall, 49 DE-ARGs were identified. Univariate Cox regression evaluation identified 17 genes associated with ccRCC prognosis, and multivariate Cox regression analyses identified eight ARGs ( BID , CD44 , ERBB2 , HMOX1 , PLCB2 , TGFBR3 , TIMP1 , and TOP2A ), which were employed to construct the variable risk scoring. The effectiveness of risk scoring as an independent prognostic variable was verified through KM curve and ROC analyses. Risk scoring and various other relevant clinical prognostic variables were employed to construct a prognostic model. The model was statistically significant regarding its association with immune-cell penetration, immune-related function, and immune checkpoints. TOP2A promotes ccRCC cells growth and metastasis . Conclusions. Based on eight ARGs and other relevant clinical prognostic variables, we established a novel model for predicting ccRCC prognosis, which can be used to inform the individualized treatment of ccRCC patients. Health sciences/Biomarkers Biological sciences/Cancer Biological sciences/Computational biology and bioinformatics Biological sciences/Immunology Health sciences/Oncology Clear-cell Renal Cell Carcinoma Apoptosis Prognosis Model TOP2A Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 Figure 8 Figure 9 Figure 10 Introduction Renal cell carcinoma affects more than 400,000 people worldwide each year and is generally diagnosed at approximately 60 years of age, with the number of male patients being approximately twice that of female patients (Bray et al., 2018 ). There are multiple subtypes of renal cancer, with approximately 70% of patients diagnosed with clear-cell renal cell carcinoma (ccRCC). Although ccRCC can be identified at an early stage with successful therapies using surgery/ablation methodologies, nearly 33% of patients present with or develop metastatic disease (Jonasch et al., 2005). TNM staging is currently being used to predict prognostic odds in renal cancer. However, advancements in technology have led to a deeper and more comprehensive understanding of cancer mechanisms, including the close association of renal cancer with specific genes. Moreover, bioinformatics-based investigations are becoming increasingly valuable for identifying the functions of multiple differential genes and assessing the complexity of ccRCC development and progression. Although several tumour markers for renal cancer have been identified (Cheng et al., 2019 ; Morrissey et al., 2010 ; Morrissey et al., 2015 ), their use is limited owing to their low specificity or cumbersome clinical application. Thus, renal cancer diagnosis continues to rely primarily on imaging and clinical manifestations. Consequently, identification of novel prognosis-predicting biomarkers, together with the construction of advanced prognostic modelling layouts, is crucial for patients with ccRCC. Apoptosis, first described in 1972, is a highly controlled form of programmed cell death that occurs in animals (Kerr et al., 1972 ). Unlike cell necrosis, it is a normal physiological process that often occurs in multicellular organisms (Kerr, 2002 ) and plays a key role in intrinsic tumour suppressive mechanisms (Lowe et al., 2004 ; Menendez et al., 2013 ). Although several studies have demonstrated the association between apoptosis-related genes (ARGs) and various cancers (Bakhshi et al., 1985 ; Hou et al., 2019 ; Liu et al., 2018 ; Tsujimoto et al., 1984 ; Zhou et al., 2018 ), few studies have investigated the importance of ARG expression in the clinical prognosis of renal cancer, particularly in ccRCC. This study aimed to explore the significance of ARGs in the prognosis of ccRCC using The Cancer Genome Atlas (TCGA) database. Compared with existing models in the literature, our model was developed using a new method and was based on a unique set of ARGs. Materials & Methods Acquisition of data and patient information The mRNA data, corresponding to clinicopathological characteristics (age, sex, T stage, N stage, M stage, and TNM stage), and survival information from 539 ccRCC cases/72 healthy controls were downloaded from TCGA (https://portal.gdc.cancer.gov/). Human ARGs were obtained through gene-set enrichment analysis (GSEA) (https://www.gsea-msigdb.org/gsea/index.jsp). A total of 161 ARGs were acquired from the gene set “HALLMARK_APOPTOSIS” in the Molecular Signatures Database v7.1 in GSEA (Subramanian et al., 2005) (Supplementary Table 1). Acquisition of differentially expressed apoptosis-related genes The differentially expressed ARGs (DE-ARGs) in ccRCC were identified using limma in R (ver. 4.1.1), using: |log 2 FC|>1 and FDR<0.05, and Wilcoxon signed-rank test. The R packages pheatmap and ggpubr were employed for generating volcano plots/heatmaps to observe the expression of the differential genes. Construction of the prognosis predictive modelling outcome founded upon differentially expressed apoptosis-related genes Univariate Cox regression analyses were employed to identify prognosis-related ARGs, which were consequently utilised for GSEA analyses to evaluate their prognostic significance in ccRCC. The DE-ARGs for prognosis within tumour/healthy tissue were presented using box-and-whisker plots. Gene Ontology (GO) enrichment analyses for differential genes, Kyoto Encyclopaedia of Genes and Genomes (KEGG) pathway analyses, and result visualisation were performed using the R packages clusterProfiler, enrichplot, and ggplot2, to explore the underlying biomolecular mechanisms of DE-ARG activity. Values of P < 0.05 were set as statistically significant for any variations. A protein-protein interaction (PPI) nexus of ARGs was developed using STRING (http://www.string-db.org/) to analyse the interactions among the selected ARGs (Szklarczyk et al., 2019); the results were visualised using Cytoscape software. Subsequent multivariate Cox regression analyses led to the identification of eight prognostic ARGs, whose expression levels and regression coefficients were used to construct a prognostic model. Expression difference analyses and prognostic performance analyses of the model genes were performed using R packages ggpubr and survival. Validation of the prognostic model Risk scoring (RiskScore) was generated using the multivariate Cox regression coefficients as follows: RiskScore = gene expression 1 × Coef1 + gene expression 2 × Coef2 +... + gene expression n × Coefn (Coef: regression coefficient for genes within multivariate Cox regression analyses, n: total number of prognostic ARGs). Clinical cases were separated into two groups, high-risk and low-risk, using the median risk scoring outcome as the threshold value. The efficacy of risk scores was assessed by plotting risk score distributions for both risk groups, survival distribution, and the expression heatmap of the identified ARGs using the software package survival. Kaplan-Meier (KM) survival curves were plotted using the package survminer to compare the overall survival (OS) between both risk groups. Survival receiver operating characteristic (ROC) curves were plotted using timeROC to determine the area under the curve (AUC) at 1-, 3-, and 5-year timepoints, to evaluate the effectiveness of this model in prognostic predictions. Mutation information of the eight ARGs used to construct the model was analysed using cBioPortal (http://cbioportal.org). The hazard ratios of risk scoring/separate medical prognosis-related variables, determined via univariate/multivariate Cox regression investigations, were employed for assessing whether risk scoring acted as a separate prognosis-related variable for ccRCC. The KEGG enrichment pathways of ARGs were analysed using the GSEA data of the two risk groups. Lastly, a nomogram was generated using the package rms to predict future ccRCC patient survival. Association analysis between medical variables/immune infiltration analysis The association of the eight model genes and risk scores with relevant clinical variables was analysed using the package beeswarm. Immune-cell infiltration data of ccRCC tissues were procured through TIMER (https://cistrome.shinyapps.io/timer), and a corresponding heatmap was designed for observing DE within immune cells to differentiate between risk groups. The association of immune cells and immune function with the risk groups was analysed using the packages GSVA and GSEABase. In addition, the association of immune checkpoints with the two risk groups was analysed to examine the implications of immunotherapy in the model. Cell line and culture Clear-cell Renal Cell Carcinoma cell line ACHN were From Shanghai Cell Bank of China Academy of Sciences cultured in RPMI-1640 medium containing 10% fetal bovine serum. The cells were digested and passaged with 0.25% trypsin when the cell fusion was around 80%, with fluid changes or passages every 2 days. Reverse transcription-quantitative polymerase chain reaction (RT-qPCR) Total RNA was isolated from tissues and cells using Trizol reagent and reverse transcribed into complementary deoxyribonucleic acid (cDNA). After pre-denaturation at 95°C for 1 min, denaturation at 95°C for 20 s and annealing at 60°C for 20 s, a total of 40 cycles, the 2ΔΔCt method was used to calculate the relative expression of the target genes. Primers were designed using Primer 5. 0 . Cell counting kit-8 assay Cells were inoculated in 96-well plates and cultured for 24h, 48h and 72h, respectively. 10μL CCK8 solution was added to each well, protected from light and cultured for 1h at 37℃. The absorbance value of the cells at 450nm (OD450nm value) was detected by enzyme marker and the experiment was repeated 3 times. Transwell assay In the cell invasion assay, preparing Matrigel gel on the upper chamber surface, the cells were adjusted to 2×10 cells/ml with serum-free medium and 100 μL was aspirated and added to the upper chamber. In the lower chamber, 1640 medium containing 20% fetal bovine serum was added. 24h of incubation at 37°C, the cells and Matrigel gel were gently removed from the upper chamber, fixed in 4% paraformaldehyde for 30min and then stained with crystalline violet, photographed and analysed under a microscope. For the cell migration assay, no Matrigel gel was added to the upper chamber of the Transwell and the rest of the steps were the same as for the cell invasion assay, which was repeated three times. Flow cytometry Collect the transfected cells, take 50,000-100,000 resuspended cells, centrifuge at 1000g for 5min and discard the supernatant. 195μL Annexin V-FITC conjugate was added to resuspend the cells. Add 5μLAnnexin V-FITC, 10μL of propidium iodide staining solution, mix well and incubate for 10-20min at room temperature, flow-on assay, and repeat the experiment 3 times. Statistical methods The Wilcoxon test was employed to analyse DE genes in paracancerous/tumour tissues. KM survival curves were plotted, and log-rank sum analysis was performed to assess the OS of patients in both risk groups. ROCs were used for assessing 1-, 3-, and 5-year-survival predictive ability for this prognostic modelling outcome. Univariate/multivariate Cox regression investigations were performed to assess correlations between each clinical variable/risk score and prognosis. All such investigations were conducted using R (4.1.1), and values of P < 0.05 were accepted as statistically significant. Results Screening and functional enrichment analysis of prognostic apoptosis-related genes The mRNA data from 539 ccRCC and 72 healthy control cases from 530 patients, and the clinical information, were procured from TCGA, and 161 ARGs were obtained from GSEA. Transcriptomic expression data of 147 ARGs were extracted, and a total of 49 DE-ARGs, consisting of 35 upregulated and 14 downregulated genes, were identified in ccRCC and normal kidney tissues using the following criteria: FDR 1. Corresponding volcano/heatmap/box plots are shown in Fig. 1 A–C, respectively. The results from 49 ARGs are shown in Supplementary Table 2. The 49 DE-ARGs were then subjected to GSEA and differential expression analyses. Results showed that the ARGs were significantly differentially expressed in ccRCC tissue samples versus normal tissue samples according to its folding change ranking (Fig. 2 A). Univariate Cox investigation was conducted on 49 DE genes, and 17 ARGs were highly linked to prognostic outcomes at a statistically significant level ( P < 0.05). Results are presented in a forest plot (Fig. 2 B). These prognostic ARGs were also subjected to GO functional enrichment and KEGG pathway investigations. GO functional enrichment showed that these genes were associated with platinum-based chemoresistance, onco-proteoglycans, lipids, atherosclerosis, p53 signalling pathway, Epstein-Barr virus infection, and apoptosis (Fig. 3 A and 3 B). KEGG pathway analysis further demonstrated that the ARGs were predominantly enriched within oncogenic pathways, such as platinum drug resistance and apoptosis (Fig. 3 C). To further explore the interactions of these DE-ARGs, we conducted a PPI analysis. The PPI network constructed using the STRING database is shown in Fig. 3 D. The minimum required interaction score for the PPI analysis was set at 0.4 (the medium confidence). In the figure, red represents upregulated genes and blue represents downregulated genes. Figure 3 D shows the interaction between DE-ARGs. Multivariate Cox regression assessment was performed on the 17 prognostic ARGs, leading to the identification of eight ARGs significantly associated with ccRCC prognosis, namely BID , CD44 , ERBB2 , HMOX1 , PLCB2 , TGFBR3 , TIMP1 , and TOP2A (Table 1 ). According to the median gene expression, 530 patients were divided into two groups, with 265 in each group. The KM curves for all eight ARGs (both expression levels) were plotted, and showed that high expression of BID , CD44 , PLCB2 , TIMP1 , and TOP2A and low expression of ERBB2 , HMOX1 , and TGFBR3 were detrimental for ccRCC prognosis (Fig. 4 ). Table 1 Multivariate Cox regression results of prognosis related ARGs in ccRCC. Gene ID Coefficient HR HR.95L HR.95H P value BID 0.131861127 1.140949861 1.074385214 1.211638589 1.71E-05 CD44 0.00622547 1.006244888 1.001615075 1.010896102 0.008149452 ERBB2 -0.028290355 0.97210607 0.945327914 0.999642768 0.047140667 HMOX1 -0.003261351 0.996743961 0.994623173 0.998869271 0.002690725 PLCB2 0.050146347 1.051424958 1.000060652 1.105427395 0.049723219 TGFBR3 -0.064466518 0.937567505 0.879315142 0.999678937 0.048864091 TIMP1 0.000333401 1.000333457 0.999984302 1.000682733 0.061230225 TOP2A 0.036009802 1.036666008 1.009719247 1.064331908 0.007367679 ARGs, apoptosis-related genes; ccRCC, clear-cell renal cell carcinoma Using methods seen in previous studies (Luo et al., 2020 ), risk scoring was constructed depending on expression/regression coefficients for all eight ARGs as follows: RiskScore = (0.131861 × BID expression) + (0.006225 × CD44 expression) - (0.028290 × ERBB2 expression) - (0.003261 × HMOX1 expression) + (0.050146 × PLCB2 expression) - (0.064467 × TGFBR3 expression) + (0.000333 × TIMP1 expression) + (0.03601 × TOP2A expression). The ccRCC cases were separated into either high- or low-risk groups depending on median risk scoring, organised in ascending order of their risk score (Fig. 5 C). The high-risk group had an increased mortality proportion and reduced OS, indicating a reduced prognosis for high-risk patients (Fig. 5 B). BID , CD44 , PLCB2 , TIMP1 , and TOP2A expression was markedly upregulated within the high-risk cohort, indicating an association of poor prognosis with high expression of these genes, whereas the expression of ERBB2 , HMOX1 , and TGFBR3 was downregulated within the high-risk cohort, suggesting an association of poor prognosis with low expression of these genes (Fig. 5 A). These results were in line with survival assessment outcomes (Fig. 4 ). KM survival curves demonstrated that the high-risk cohort displayed severely reduced OS ( P < 0.0001, Fig. 5 D). ROC curves (Fig. 5 E) showed that AUC reached 0.734 after 1-year, 0.69 after 3-years, and 0.722 after 5-years, suggesting that risk scoring had effective predictive qualities for survival in patients with ccRCC. The above results indicated that such a risk model has relatively elevated specificity for predicting ccRCC prognosis. Univariate/multivariate Cox regression investigations were performed to compare risk scoring hazard ratios against additional medical variables (Fig. 6 A and 6 B); the results highlighted that risk scoring represented a separate prognosis-related variable (Fig. 6 A and 6 B). Twelve putative signalling pathways linked to prognostic ARGs were separately explored in both groups using GSEA. The ARGs were predominantly enriched in the adipocytokine signalling pathway, cytokine-cytokine receptor interaction, haemopoietic cell lineage, homologous recombination, inositol phosphate metabolic processes, and intestine-related immunity networking concerning IgA synthesis pathways for the high-risk cohort and in primary immunodeficiency, propanoate metabolism, systemic lupus erythematosus, tight junction, valine, leucine, and isoleucine breakdown, and vasopressin-regulated water reabsorption pathways for the low-risk cohort (Fig. 6 C). Construction of the nomogram to predict patient overall survival To apply risk scores to the prediction of ccRCC prognosis, the risk score was combined with relevant medical clinical parameters for constructing a predictive nomogram for patient OS at 3- and 5-year time points. Depending on risk scoring/medical parameters, the corresponding values of patients were identified and inferred to determine such timepoint OS values (Fig. 7 ). The predictive accuracy of the nomogram was determined by the concordance index (C-index). This estimates the probability that the predicted results are consistent with the actually observed results. The C-index for such a nomogram was 0.788, indicating a good standard across model-based and observed survival outcomes. Correlation analysis of clinical variables and immune infiltration analysis TCGA data were further used to investigate whether the risk scores of the model, as well as each model gene, could be used to predict prognostic outcomes for patients with different clinical manifestations. Dataset outcomes showed risk scoring to be correlated with grade, stage, T, and M; BID was correlated with grade, stage, T, and M; CD44 was correlated with sex, grade, stage, and T; ERBB2 was correlated with grade, stage, and T; PLCB2 was correlated with grade; TGFBR3 was correlated with sex, grade, stage, and M; TIMP1 was correlated with grade, stage, and T; and TOP2A was correlated with sex, grade, stage, T, and M (Fig. 8 ). The treatment of RCC has evolved tremendously over the past decades. Localized disease is often curative with surgical resection of the malignancy. However, in cases where the primary tumour has metastasized, immunotherapy is becoming a more prevalent means to combat metastatic renal cell carcinoma (mRCC). Cytokine and checkpoint inhibitor immunotherapy have been demonstrated to stimulate the immune response through various mechanisms. We explored the correlation between eight ARGs and immune infiltration. The heatmap presenting the correlation across both study cohorts for this prognostic modelling effort and immune cells was plotted using the TIMER database (Fig. 9 A). The single-sample gene-set enrichment analysis (ssGSEA) algorithm was employed for quantifying relative presence of tumour-penetrating immune-system cells and immune functions in ccRCC patients, and the results showed significant differences in antibody-drug conjugates (aDCs), B_/ CD8+_T_cells, macrophages, mast-/NK-cells, plasmacytoid dendritic cells (pDCs), T_helper_cells, T follicular helper cells (Tfh), T-helper 1 (Th1), T-helper 2 (Th2), tumour infiltrating lymphocytes (TILs), T regulatory cells (Tregs), antigen-presenting cell (APC)_co_inhibition, APC_co_stimulation, chimeric costimulatory receptor (CCR), check-point, cytolytic_activity, human leukocyte antigen (HLA), inflammation-promoting, major histocompatibility complex (MHC)_class_l, parainflammation, T_cell_co-inhibition, T_cell_co-stimulation, Type_I_Interferon (IFN)_Response, and Type_II_IFN_Response, across both study cohorts ( P < 0.05, Fig. 9 B, C). Furthermore, significant differences between the two groups were found for almost all immune checkpoints (Fig. 9 D). The above results suggest that the underlying mechanism of the model may be associated with immune activity in the tumour immune microenvironment and that the model could predict sensitivity to immunotherapy. TOP2A promotes ccRCC progression In order to further explore the role of these apoptosis-related genes in regulating ccRCC cell function, we selected TOP2A as the subject of our study and performed l-cell function experiments.Initially, the si-TOP2A and the corresponding negative control were transfected into ACHN cells and RT-qPCR confirmed that TOP2A was successfully knocked down (Fig. 10 A). CCK8 proliferation assay showed that compared with the si-NC group, the ACHN cells in the si-TOP2A group showed significantly reduced proliferation ability (Fig. 10 B); flow cytometry analysis showed increased apoptosis in ACHN cells in the si-TOP2A group (Fig. 10 C); in addition, Transwell migration and invasion assays showed that both the migration and invasion ability of the cells was reduced (Fig. 10 D).Cell experiments suggest that TOP2A may play its corresponding role in ccRCC cell carcinoma. Discussion In the last two decades, RCC incidence has increased significantly with a relatively high associated mortality rate (Ferlay et al., 2013 ; Ferlay et al., 2015 ). Therefore, identifying effective prognostic biomarkers and constructing useful prognostic tools for predicting OS within ccRCC cases, are essential. Bioinformatics analysis has emerged as an important tool for identifying therapy-related targets concerning diagnostic, prognostic, and therapeutic parameters for various tumours. Apoptosis plays a pivotal part in tumour growth and regeneration. Although several studies have developed prognostic models based on selected ARGs, the prognostic model developed in this study was based on a new set of ARGs, which were investigated in detail. Specifically, a total of 49/147 ARGs were identified as differentially expressed in ccRCC tissues compared to adjacent normal tissues. Univariate Cox regression analyses further identified 17 genes associated with ccRCC prognosis, and subsequent multivariate Cox regression analyses identified eight ARGs, namely BID , CD44 , ERBB2 , HMOX1 , PLCB2 , TGFBR3 , TIMP1 , and TOP2A . These genes were applied for the construction of prognosis-related models. A high predictive ability was confirmed for the ARG-based prognosis model via KM curve and ROC analyses, as well as comparisons with relevant clinical prognostic variables. The developed ARG-based model was significantly associated with immune-cell penetration, immunity-related roles, and immune checkpoints. The eight identified ARGs are known to influence the progression of various tumours. For example, BID , which is present on chromosome 22q11.21 and encodes a protein associated with apoptosis, is upregulated in thyroid cancer, and is linked to its prognostic outcome (Lin et al., 2018 ); however, its function in ccRCC tumorigenesis and progression is unclear. We found that high expression of BID was associated with poor outcome for ccRCC. CD44 is a frequently utilised biomarker for cancer stem cells and has prognostic importance for multiple solid tumours, such as colon, lung, and breast cancers (Brown et al., 2011 ; Leung et al., 2010 ; Su et al., 2011 ). In addition, CD44 can promote the migration and proliferation of gastric cancer cells (Chai et al., 2014 ; Hu et al., 2014 ). Such revelations were in line with the prognostic conclusions of our investigation. In ccRCC, the RAS pathway promotes the expression of HMOX1 to protect cancer cells from the killing effects of chemotherapeutic agents (Balan et al., 2015 ; Szklarczyk et al., 2019). Moreover, HMOX1 plays an immunoregulatory role in certain inflammatory disease conditions (Chora et al., 2007 ; Ke et al., 2012 ; Tzima et al., 2009 ), and its low expression is favourable for prognosis. In addition to apoptosis, PLCB2 is associated with methylation in ccRCC, with low PLCB2 expression associated with poor prognosis (Chen et al., 2017 ). TGFBR3 is a transforming growth factor β (TGF-β) receptor that has a role in inhibiting ccRCC development and metastasis. Indeed, downregulated TGFBR3 expression leads to enhanced tumour formation and metastasis in ccRCC cells and, thus, serves as a predictor of poor prognosis (Nishida et al., 2018 ). TOP2A encodes a DNA topoisomerase, an ATP-dependent synthase/hydrolase that has pivotal roles within multiple physiological activities, including DNA replication, chromatin condensation, and chromosomal separation. Previous research showed that upregulated TOP2A promotes breast cancer advancement (Hall et al., 2017 ; Shigematsu et al., 2018 ). ERBB2 encodes a member of the epidermal growth factor receptor (HER) family, and its upregulation is associated with poor prognosis within breast cancer (Ravdin and Chamness, 1995 ), which contrasts with the observations of our present study. Lastly, high expression of TIMP1 is reportedly associated with poor prognosis in renal tumours (Dias et al., 2020 ), which is consistent with the present findings. This study identified eight differential genes that were intimately linked to the survival of patients with ccRCC and, thus, have the potential to become therapeutic targets for precision medicine. A prognosis-related model was developed depending on differential ARGs, and its clinical prognostic predictive ability for ccRCC was evaluated. However, this study had several limitations. First, it only focused on large-scale gene sequencing data from TCGA platform, with some related clinical information, while failing to fully validate other information, such as deoxyribonucleic acid methylation. Second, many samples were excluded from this study owing to incomplete clinical information, and thus it is necessary to include additional samples or data from other platforms to further verify the present results. Finally, the eight differential ARGs, found to be highly related to the survival of patients with ccRCC, were analysed only through data mining and, hence, further verification based on clinical trials is required. Conclusions Based on eight ARGs which were identified from publicly available data and other relevant clinical prognostic variables, we established a novel prognosis model to predict 3-, and 5-year OS for ccRCC cases. The model provides a novel survival-predicting utility in ccRCC cases and reveals the link between ARGs and ccRCC. In addition, the findings of this study will help address the current knowledge gap in the field of ccRCC and provide new insights for further research. Declarations Conflicts of interest The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest. Author contributions ZZ provided research ideas.FF, CC, and LD collected, sorted, and mapped the data. MZ and ZZ summarised and analysed the data. DC provided theoretical guidance and supervision of the project. Data availability statement The datasets generated and/or analysed in this study can be found in The Cancer Genome Atlas (TCGA) database repository [https://portal.gdc.cancer.gov]. Human ARGs were obtained through gene-set enrichment analysis (GSEA) [https://www.gsea-msigdb.org/gsea/index.jsp]. Funding This work was supported by Yancheng Science and Technology Plan Project (Grant Number:YCBK2023081) and Special Research Development Fund Project for Clinical Teaching Bases of Jiangsu Vocational College of Medicine. Acknowledgements We thank Editage for English language editing. References Bakhshi, A., Jensen, J. P., Goldman, P., Wright, J. J., McBride, O. W., Epstein, A. L., et al. (1985). 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U.S.A. 102, 15545-15550. https://doi.org/10.1073/pnas.0506580102 Tsujimoto, Y., Finger, L. R., Yunis, J., Nowell, P. C., and Croce, C. M. (1984). Cloning of the chromosome breakpoint of neoplastic B cells with the t(14;18) chromosome translocation. Science 226, 1097-1099. https://doi.org/10.1126/science.6093263 Tzima, S., Victoratos, P., Kranidioti, K., Alexiou, M., and Kollias, G. (2009). Myeloid heme oxygenase-1 regulates innate immunity and autoimmunity by modulating IFN-beta production. J. Exp. Med. 206, 1167-1179. https://doi.org/10.1084/jem.20081582 Zhou, L., Du, Y., Kong, L., Zhang, X., and Chen, Q. (2018). Identification of molecular target genes and key pathways in hepatocellular carcinoma by bioinformatics analysis. OncoTargets Ther. 11, 1861-1869. https://doi.org/10.2147/OTT.S156737 Additional Declarations No competing interests reported. Supplementary Files SupplementaryTable1.docx Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-7057172","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":483708090,"identity":"8786a6e1-6d85-4f61-8c4e-7b69c505b0c6","order_by":0,"name":"Zhenhai Zou","email":"","orcid":"","institution":"Affiliated Dafeng People's Hospital of Nantong University Medical School","correspondingAuthor":false,"prefix":"","firstName":"Zhenhai","middleName":"","lastName":"Zou","suffix":""},{"id":483708091,"identity":"89135342-d096-4c3a-b438-3cd00795a7d2","order_by":1,"name":"Ming Zhou","email":"","orcid":"","institution":"Affiliated Dafeng People's Hospital of Nantong University Medical School","correspondingAuthor":false,"prefix":"","firstName":"Ming","middleName":"","lastName":"Zhou","suffix":""},{"id":483708092,"identity":"417a0a9a-142c-41c4-93f0-8c001e5b4c54","order_by":2,"name":"Li Dong","email":"","orcid":"","institution":"Affiliated Dafeng People's Hospital of Nantong University Medical School","correspondingAuthor":false,"prefix":"","firstName":"Li","middleName":"","lastName":"Dong","suffix":""},{"id":483708093,"identity":"b910d5f4-d876-4878-9c14-9fb4a49f4905","order_by":3,"name":"Fei Feng","email":"","orcid":"","institution":"Affiliated Dafeng People's Hospital of Nantong University Medical School","correspondingAuthor":false,"prefix":"","firstName":"Fei","middleName":"","lastName":"Feng","suffix":""},{"id":483708094,"identity":"c1ebc16c-8034-4a73-bd98-888179c65706","order_by":4,"name":"Chuntao Chen","email":"","orcid":"","institution":"Affiliated Dafeng People's Hospital of Nantong University Medical School","correspondingAuthor":false,"prefix":"","firstName":"Chuntao","middleName":"","lastName":"Chen","suffix":""},{"id":483708095,"identity":"e49e4cb1-e37f-4dc4-b79e-20eab6555e50","order_by":5,"name":"Daojun Cao","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA2klEQVRIiWNgGAWjYFACxgaGDxUS9fPZmw8c+PCDSC2MM87YMG7sOZZ4cGYPkfYwc7akMTbc8DE+zMFGhHKD481tjxkbDjMzzuD5cJiBh0GeX+wAAS1nDrYbF+44zMYu3bvhcIEFg+HM2Qn4tZjdSGyTnnnmMA/jnLMbDs/gYUgwuE1Iy/2HbdK8bYclGG7kPDjMw0aMlhuMIC1pBkAtDMRpsT+T2CYJDOQEw55jBsBAliDsF8n2488kgFGZIM/e/PjDhx828vzSBLSgAwnSlI+CUTAKRsEowA4AfDVN5WE4c1AAAAAASUVORK5CYII=","orcid":"","institution":"Affiliated Dafeng People's Hospital of Nantong University Medical School","correspondingAuthor":true,"prefix":"","firstName":"Daojun","middleName":"","lastName":"Cao","suffix":""}],"badges":[],"createdAt":"2025-07-06 10:08:16","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-7057172/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-7057172/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":86663093,"identity":"5e182a62-861f-45bd-90f1-18d5eff0e54f","added_by":"auto","created_at":"2025-07-14 10:47:34","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":313383,"visible":true,"origin":"","legend":"\u003cp\u003e(\u003cstrong\u003eA\u003c/strong\u003e) Volcano plot of differentially expressed apoptosis-related genes (ARGs). Yellow represents high expression, blue represents low expression, black represents no difference between ccRCC and normal tissues (\u003cstrong\u003eB\u003c/strong\u003e) The heatmap of 49identified ARGs (\u003cstrong\u003eC\u003c/strong\u003e) The boxplot of 49 identified ARGs. Yellow represents ccRCC tissues, while blue represents normal tissues.\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-7057172/v1/141ecb7c0223d4231fb71750.png"},{"id":86663100,"identity":"d873a4c7-0518-44dd-a138-28cc96deab41","added_by":"auto","created_at":"2025-07-14 10:47:34","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":126578,"visible":true,"origin":"","legend":"\u003cp\u003e(\u003cstrong\u003eA\u003c/strong\u003e) GSEA enrichment plot of KEGG pathways. (\u003cstrong\u003eB\u003c/strong\u003e) Forest plot of univariate Cox regression analysis.\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-7057172/v1/aa4a2e67b096bc8abf75ef45.png"},{"id":86663097,"identity":"6a2cdb49-367e-4847-9713-3fdb558676cb","added_by":"auto","created_at":"2025-07-14 10:47:34","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":260899,"visible":true,"origin":"","legend":"\u003cp\u003e(\u003cstrong\u003eA\u003c/strong\u003e–\u003cstrong\u003eC\u003c/strong\u003e) GO and KEGG enrichment analyses.\u003c/p\u003e","description":"","filename":"3.png","url":"https://assets-eu.researchsquare.com/files/rs-7057172/v1/5523d0287f18f6b7ba4ab1b7.png"},{"id":86665528,"identity":"42657caa-3e7c-4807-9d48-985a7b09a9bf","added_by":"auto","created_at":"2025-07-14 11:03:34","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":179997,"visible":true,"origin":"","legend":"\u003cp\u003eK-M curveof the relationship between overall survival in clear-cell renal cell carcinoma (ccRCC) patients and expression levels of eight screened apoptosis-related genes.\u003c/p\u003e","description":"","filename":"4.png","url":"https://assets-eu.researchsquare.com/files/rs-7057172/v1/ddaa00462938d572c3096fb1.png"},{"id":86663103,"identity":"bc192935-9293-4ce8-84ef-0627621460e0","added_by":"auto","created_at":"2025-07-14 10:47:34","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":223892,"visible":true,"origin":"","legend":"\u003cp\u003e(\u003cstrong\u003eA\u003c/strong\u003e) Expression heatmap of the eight identified apoptosis-related genes (ARGs) in the high- and low-risk groups. (\u003cstrong\u003eB\u003c/strong\u003e) Risk score distribution in ccRCC patients. (\u003cstrong\u003eC\u003c/strong\u003e) Scatter plot of survival time. (D) K-M survival plots. (\u003cstrong\u003eE\u003c/strong\u003e) ROC\u003c/p\u003e","description":"","filename":"5.png","url":"https://assets-eu.researchsquare.com/files/rs-7057172/v1/5529a0058c3c6216728a3eeb.png"},{"id":86663102,"identity":"4d18d396-96f2-4141-91ef-ac7d696ffac5","added_by":"auto","created_at":"2025-07-14 10:47:34","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":314231,"visible":true,"origin":"","legend":"\u003cp\u003eHazard ratios of various clinical variables as determined by univariate (\u003cstrong\u003eA\u003c/strong\u003e) and multivariate (\u003cstrong\u003eB\u003c/strong\u003e) Cox regression analyses. (\u003cstrong\u003eC\u003c/strong\u003e) GSEA enrichment plot of KEGG pathways for high- and low-risk groups.\u003c/p\u003e","description":"","filename":"6.png","url":"https://assets-eu.researchsquare.com/files/rs-7057172/v1/3708260d6ca7ba1760e4d93f.png"},{"id":86663104,"identity":"5540c15f-a82f-4b20-9c9b-ff7ca54b01ac","added_by":"auto","created_at":"2025-07-14 10:47:34","extension":"png","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":120126,"visible":true,"origin":"","legend":"\u003cp\u003eNomogram for the final prognostic prediction model.\u003c/p\u003e","description":"","filename":"7.png","url":"https://assets-eu.researchsquare.com/files/rs-7057172/v1/0bded6a32e7c14367c11f82b.png"},{"id":86663114,"identity":"6b6c1a3a-f4d1-464d-96c2-6eaa6045f9ed","added_by":"auto","created_at":"2025-07-14 10:47:35","extension":"png","order_by":8,"title":"Figure 8","display":"","copyAsset":false,"role":"figure","size":390511,"visible":true,"origin":"","legend":"\u003cp\u003eAssociation of risk score and eight apoptosis-related genes with relevant clinical prognostic variables.\u003c/p\u003e","description":"","filename":"8.png","url":"https://assets-eu.researchsquare.com/files/rs-7057172/v1/3b62254cb0b8c9536b2b2cf7.png"},{"id":86665529,"identity":"b6cab3a2-aa8f-42db-87b6-3cfaa1fc68ff","added_by":"auto","created_at":"2025-07-14 11:03:35","extension":"png","order_by":9,"title":"Figure 9","display":"","copyAsset":false,"role":"figure","size":525783,"visible":true,"origin":"","legend":"\u003cp\u003e(\u003cstrong\u003eA\u003c/strong\u003e) Heatmap of the correlation of high- and low-risk groups with immune characteristics. (\u003cstrong\u003eB\u003c/strong\u003e and \u003cstrong\u003eC\u003c/strong\u003e) Relative expression of immune cells and immune function in high- and low-risk groups. (\u003cstrong\u003eD\u003c/strong\u003e) Association of immune checkpoints with high- and low-risk groups.\u003c/p\u003e","description":"","filename":"9.png","url":"https://assets-eu.researchsquare.com/files/rs-7057172/v1/64fcc105c7e31c3aa3287577.png"},{"id":86663118,"identity":"9ffc9fc8-8cfb-43f0-beb3-eb3b12f9176e","added_by":"auto","created_at":"2025-07-14 10:47:35","extension":"png","order_by":10,"title":"Figure 10","display":"","copyAsset":false,"role":"figure","size":212986,"visible":true,"origin":"","legend":"\u003cp\u003e(\u003cstrong\u003eA\u003c/strong\u003e) TOP2A expression in ACHN cell s determined by RT-qPCR after knock-down. (\u003cstrong\u003eB\u003c/strong\u003e) cell proliferation ability in ACHN cells by CCK8 assays.(\u003cstrong\u003eC\u003c/strong\u003e) cell apoptosis in ACHN cells was detected by flow cytometric analysis(Annexin Fitc/PI).(\u003cstrong\u003eD\u003c/strong\u003e) number of migratory and invasion ACHN cells was detected by Transwell assays.\u003c/p\u003e","description":"","filename":"10.png","url":"https://assets-eu.researchsquare.com/files/rs-7057172/v1/924ff931a022fdcc56351134.png"},{"id":86791711,"identity":"7c504b5b-cd63-4c48-a2a5-2e6dfe3197b3","added_by":"auto","created_at":"2025-07-15 15:02:11","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":3203538,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7057172/v1/8648640c-11c6-4613-a8b7-03971b07b29d.pdf"},{"id":86664718,"identity":"5ba6f956-bd45-4eac-9e66-455f0ebbbea6","added_by":"auto","created_at":"2025-07-14 10:55:34","extension":"docx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":685670,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementaryTable1.docx","url":"https://assets-eu.researchsquare.com/files/rs-7057172/v1/e4c0f83bd4ebdc9899aad7c9.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Novel Prognosis Model for Clear-cell Renal Cell Carcinoma based on Apoptosis-related Genes","fulltext":[{"header":"Introduction","content":"\u003cp\u003eRenal cell carcinoma affects more than 400,000 people worldwide each year and is generally diagnosed at approximately 60 years of age, with the number of male patients being approximately twice that of female patients (Bray et al., \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). There are multiple subtypes of renal cancer, with approximately 70% of patients diagnosed with clear-cell renal cell carcinoma (ccRCC). Although ccRCC can be identified at an early stage with successful therapies using surgery/ablation methodologies, nearly 33% of patients present with or develop metastatic disease (Jonasch et al., 2005). TNM staging is currently being used to predict prognostic odds in renal cancer. However, advancements in technology have led to a deeper and more comprehensive understanding of cancer mechanisms, including the close association of renal cancer with specific genes. Moreover, bioinformatics-based investigations are becoming increasingly valuable for identifying the functions of multiple differential genes and assessing the complexity of ccRCC development and progression. Although several tumour markers for renal cancer have been identified (Cheng et al., \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Morrissey et al., \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e2010\u003c/span\u003e; Morrissey et al., \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e2015\u003c/span\u003e), their use is limited owing to their low specificity or cumbersome clinical application. Thus, renal cancer diagnosis continues to rely primarily on imaging and clinical manifestations. Consequently, identification of novel prognosis-predicting biomarkers, together with the construction of advanced prognostic modelling layouts, is crucial for patients with ccRCC.\u003c/p\u003e\u003cp\u003eApoptosis, first described in 1972, is a highly controlled form of programmed cell death that occurs in animals (Kerr et al., \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e1972\u003c/span\u003e). Unlike cell necrosis, it is a normal physiological process that often occurs in multicellular organisms (Kerr, \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2002\u003c/span\u003e) and plays a key role in intrinsic tumour suppressive mechanisms (Lowe et al., \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2004\u003c/span\u003e; Menendez et al., \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e2013\u003c/span\u003e). Although several studies have demonstrated the association between apoptosis-related genes (ARGs) and various cancers (Bakhshi et al., \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1985\u003c/span\u003e; Hou et al., \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Liu et al., \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2018\u003c/span\u003e; Tsujimoto et al., \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e1984\u003c/span\u003e; Zhou et al., \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e2018\u003c/span\u003e), few studies have investigated the importance of ARG expression in the clinical prognosis of renal cancer, particularly in ccRCC. This study aimed to explore the significance of ARGs in the prognosis of ccRCC using The Cancer Genome Atlas (TCGA) database. Compared with existing models in the literature, our model was developed using a new method and was based on a unique set of ARGs.\u003c/p\u003e"},{"header":"Materials \u0026 Methods","content":"\u003cp\u003e\u003cstrong\u003eAcquisition of data and patient information\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe mRNA data, corresponding to clinicopathological characteristics (age, sex, T stage, N stage, M stage, and TNM stage), and survival information from 539 ccRCC cases/72 healthy controls were downloaded from TCGA (https://portal.gdc.cancer.gov/). Human ARGs were obtained through gene-set enrichment analysis (GSEA) (https://www.gsea-msigdb.org/gsea/index.jsp). A total of 161 ARGs were acquired from the gene set \u0026ldquo;HALLMARK_APOPTOSIS\u0026rdquo; in the Molecular Signatures Database v7.1 in GSEA (Subramanian et al., 2005) (Supplementary Table 1).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcquisition of differentially expressed apoptosis-related genes\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe differentially expressed ARGs (DE-ARGs) in ccRCC were identified using limma in R (ver. 4.1.1), using: |log 2 FC|\u0026gt;1 and FDR\u0026lt;0.05, and Wilcoxon signed-rank test. The R packages pheatmap and ggpubr were employed for generating volcano plots/heatmaps to observe the expression of the differential genes.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConstruction of the prognosis predictive modelling outcome founded upon differentially expressed apoptosis-related genes\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eUnivariate Cox regression analyses were employed to identify prognosis-related ARGs, which were consequently utilised for GSEA analyses to evaluate their prognostic significance in ccRCC. The DE-ARGs for prognosis within tumour/healthy tissue were presented using box-and-whisker plots. Gene Ontology (GO) enrichment analyses for differential genes, Kyoto Encyclopaedia of Genes and Genomes (KEGG) pathway analyses, and result visualisation were performed using the R packages clusterProfiler, enrichplot, and ggplot2, to explore the underlying biomolecular mechanisms of DE-ARG activity. Values of \u003cem\u003eP\u003c/em\u003e \u0026lt; 0.05 were set as statistically significant for any variations. \u0026nbsp;A protein-protein interaction (PPI) nexus of ARGs was developed using STRING (http://www.string-db.org/) to analyse the interactions among the selected ARGs (Szklarczyk et al., 2019); the results were visualised using Cytoscape software. Subsequent multivariate Cox regression analyses led to the identification of eight prognostic ARGs, whose expression levels and regression coefficients were used to construct a prognostic model. Expression difference analyses and prognostic performance analyses of the model genes were performed using R packages ggpubr and survival.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eValidation of the prognostic model\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eRisk scoring (RiskScore) was generated using the multivariate Cox regression coefficients as follows: RiskScore = gene expression 1 \u0026times; Coef1 + gene expression 2 \u0026times; Coef2 +... + gene expression n \u0026times; Coefn (Coef: regression coefficient for genes within multivariate Cox regression analyses, n: total number of prognostic ARGs). Clinical cases were separated into two groups, high-risk and low-risk, using the median risk scoring outcome as the threshold value. The efficacy of risk scores was assessed by plotting risk score distributions for both risk groups, survival distribution, and the expression heatmap of the identified ARGs using the software package survival. Kaplan-Meier (KM) survival curves were plotted using the package survminer to compare the overall survival (OS) between both risk groups. Survival receiver operating characteristic (ROC) curves were plotted using timeROC to determine the area under the curve (AUC) at 1-, 3-, and 5-year timepoints, to evaluate the effectiveness of this model in prognostic predictions. Mutation information of the eight ARGs used to construct the model was analysed using cBioPortal (http://cbioportal.org). The hazard ratios of risk scoring/separate medical prognosis-related variables, determined via univariate/multivariate Cox regression investigations, were employed for assessing whether risk scoring acted as a separate prognosis-related variable for ccRCC. The KEGG enrichment pathways of ARGs were analysed using the GSEA data of the two risk groups. Lastly, a nomogram was generated using the package rms to predict future ccRCC patient survival.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAssociation analysis between medical variables/immune infiltration analysis\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe association of the eight model genes and risk scores with relevant clinical variables was analysed using the package beeswarm. Immune-cell infiltration data of ccRCC tissues were procured through TIMER (https://cistrome.shinyapps.io/timer), and a corresponding heatmap was designed for observing DE within immune cells to differentiate between risk groups. The association of immune cells and immune function with the risk groups was analysed using the packages GSVA and GSEABase. In addition, the association of immune checkpoints with the two risk groups was analysed to examine the implications of immunotherapy in the model.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCell line and culture\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eClear-cell Renal Cell Carcinoma cell line ACHN were From Shanghai Cell Bank of China Academy of Sciences cultured in RPMI-1640 medium containing 10% fetal bovine serum. The cells were digested and passaged with 0.25% trypsin when the cell fusion was around 80%, with fluid changes or passages every 2 days.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eReverse transcription-quantitative polymerase chain reaction (RT-qPCR)\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eTotal RNA was isolated from tissues and cells using Trizol reagent and reverse transcribed into complementary deoxyribonucleic acid (cDNA). After pre-denaturation at 95\u0026deg;C for 1 min, denaturation at 95\u0026deg;C for 20 s and annealing at 60\u0026deg;C for 20 s, a total of 40 cycles, the 2\u0026Delta;\u0026Delta;Ct method was used to calculate the relative expression of the target genes. Primers were designed using Primer 5. 0 .\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCell counting kit-8 assay\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eCells were inoculated in 96-well plates and cultured for 24h, 48h and 72h, respectively. 10\u0026mu;L CCK8 solution was added to each well, protected from light and cultured for 1h at 37℃. The absorbance value of the cells at 450nm (OD450nm value) was detected by enzyme marker and the experiment was repeated 3 times.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTranswell assay\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eIn the cell invasion assay, \u0026nbsp;preparing Matrigel gel on the upper chamber surface, the cells were adjusted to 2\u0026times;10 cells/ml with serum-free medium and 100\u0026nbsp;\u0026mu;L was aspirated and added to the upper chamber. In the lower chamber, 1640 medium containing 20% fetal bovine serum was added. 24h of incubation at 37\u0026deg;C, the cells and Matrigel gel were gently removed from the upper chamber, fixed in 4% paraformaldehyde for 30min and then stained with crystalline violet, photographed and analysed under a microscope. For the cell migration assay, no Matrigel gel was added to the upper chamber of the Transwell and the rest of the steps were the same as for the cell invasion assay, which was repeated three times.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFlow cytometry\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eCollect the transfected cells, take 50,000-100,000 resuspended cells, centrifuge at 1000g for 5min and discard the supernatant. 195\u0026mu;L Annexin V-FITC conjugate was added to resuspend the cells. Add 5\u0026mu;LAnnexin V-FITC, 10\u0026mu;L of propidium iodide staining solution, mix well and incubate for 10-20min at room temperature, flow-on assay, and repeat the experiment 3 times.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eStatistical methods\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe Wilcoxon test was employed to analyse DE genes in paracancerous/tumour tissues. KM survival curves were plotted, and log-rank sum analysis was performed to assess the OS of patients in both risk groups. ROCs were used for assessing 1-, 3-, and 5-year-survival predictive ability for this prognostic modelling outcome. Univariate/multivariate Cox regression investigations were performed to assess correlations between each clinical variable/risk score and prognosis. All such investigations were conducted using R (4.1.1), and values of \u003cem\u003eP\u003c/em\u003e \u0026lt; 0.05 were accepted as statistically significant.\u003c/p\u003e"},{"header":"Results","content":"\u003cp\u003e\u003cb\u003eScreening and functional enrichment analysis of prognostic apoptosis-related genes\u003c/b\u003e\u003c/p\u003e\u003cp\u003eThe mRNA data from 539 ccRCC and 72 healthy control cases from 530 patients, and the clinical information, were procured from TCGA, and 161 ARGs were obtained from GSEA. Transcriptomic expression data of 147 ARGs were extracted, and a total of 49 DE-ARGs, consisting of 35 upregulated and 14 downregulated genes, were identified in ccRCC and normal kidney tissues using the following criteria: FDR\u0026thinsp;\u0026lt;\u0026thinsp;0.05 and |log 2 FC| \u0026gt;1. Corresponding volcano/heatmap/box plots are shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eA\u0026ndash;C, respectively. The results from 49 ARGs are shown in Supplementary Table\u0026nbsp;2. The 49 DE-ARGs were then subjected to GSEA and differential expression analyses. Results showed that the ARGs were significantly differentially expressed in ccRCC tissue samples versus normal tissue samples according to its folding change ranking (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eA). Univariate Cox investigation was conducted on 49 DE genes, and 17 ARGs were highly linked to prognostic outcomes at a statistically significant level (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05). Results are presented in a forest plot (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eB).\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eThese prognostic ARGs were also subjected to GO functional enrichment and KEGG pathway investigations. GO functional enrichment showed that these genes were associated with platinum-based chemoresistance, onco-proteoglycans, lipids, atherosclerosis, p53 signalling pathway, Epstein-Barr virus infection, and apoptosis (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eA and \u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eB). KEGG pathway analysis further demonstrated that the ARGs were predominantly enriched within oncogenic pathways, such as platinum drug resistance and apoptosis (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eC). To further explore the interactions of these DE-ARGs, we conducted a PPI analysis. The PPI network constructed using the STRING database is shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eD. The minimum required interaction score for the PPI analysis was set at 0.4 (the medium confidence). In the figure, red represents upregulated genes and blue represents downregulated genes. Figure\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eD shows the interaction between DE-ARGs.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eMultivariate Cox regression assessment was performed on the 17 prognostic ARGs, leading to the identification of eight ARGs significantly associated with ccRCC prognosis, namely \u003cem\u003eBID\u003c/em\u003e, \u003cem\u003eCD44\u003c/em\u003e, \u003cem\u003eERBB2\u003c/em\u003e, \u003cem\u003eHMOX1\u003c/em\u003e, \u003cem\u003ePLCB2\u003c/em\u003e, \u003cem\u003eTGFBR3\u003c/em\u003e, \u003cem\u003eTIMP1\u003c/em\u003e, and \u003cem\u003eTOP2A\u003c/em\u003e (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). According to the median gene expression, 530 patients were divided into two groups, with 265 in each group. The KM curves for all eight ARGs (both expression levels) were plotted, and showed that high expression of \u003cem\u003eBID\u003c/em\u003e, \u003cem\u003eCD44\u003c/em\u003e, \u003cem\u003ePLCB2\u003c/em\u003e, \u003cem\u003eTIMP1\u003c/em\u003e, and \u003cem\u003eTOP2A\u003c/em\u003e and low expression of \u003cem\u003eERBB2\u003c/em\u003e, \u003cem\u003eHMOX1\u003c/em\u003e, and \u003cem\u003eTGFBR3\u003c/em\u003e were detrimental for ccRCC prognosis (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e).\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eMultivariate Cox regression results of prognosis related ARGs in ccRCC.\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"6\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eGene ID\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eCoefficient\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eHR\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eHR.95L\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003eHR.95H\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e\u003cp\u003e\u003cem\u003eP\u003c/em\u003e value\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eBID\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.131861127\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e1.140949861\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e1.074385214\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e1.211638589\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e1.71E-05\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eCD44\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.00622547\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e1.006244888\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e1.001615075\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e1.010896102\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.008149452\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eERBB2\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e-0.028290355\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.97210607\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.945327914\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.999642768\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.047140667\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eHMOX1\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e-0.003261351\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.996743961\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.994623173\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.998869271\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.002690725\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003ePLCB2\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.050146347\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e1.051424958\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e1.000060652\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e1.105427395\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.049723219\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eTGFBR3\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e-0.064466518\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.937567505\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.879315142\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.999678937\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.048864091\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eTIMP1\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.000333401\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e1.000333457\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.999984302\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e1.000682733\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.061230225\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eTOP2A\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.036009802\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e1.036666008\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e1.009719247\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e1.064331908\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.007367679\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003ctfoot\u003e\u003ctr\u003e\u003ctd colspan=\"6\"\u003eARGs, apoptosis-related genes; ccRCC, clear-cell renal cell carcinoma\u003c/td\u003e\u003c/tr\u003e\u003c/tfoot\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eUsing methods seen in previous studies (Luo et al., \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2020\u003c/span\u003e), risk scoring was constructed depending on expression/regression coefficients for all eight ARGs as follows: RiskScore = (0.131861 \u0026times; \u003cem\u003eBID\u003c/em\u003e expression) + (0.006225 \u0026times; \u003cem\u003eCD44\u003c/em\u003e expression) - (0.028290 \u0026times; \u003cem\u003eERBB2\u003c/em\u003e expression) - (0.003261 \u0026times; \u003cem\u003eHMOX1\u003c/em\u003e expression) + (0.050146 \u0026times; \u003cem\u003ePLCB2\u003c/em\u003e expression) - (0.064467 \u0026times; \u003cem\u003eTGFBR3\u003c/em\u003e expression) + (0.000333 \u0026times; \u003cem\u003eTIMP1\u003c/em\u003e expression) + (0.03601 \u0026times; \u003cem\u003eTOP2A\u003c/em\u003e expression). The ccRCC cases were separated into either high- or low-risk groups depending on median risk scoring, organised in ascending order of their risk score (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eC). The high-risk group had an increased mortality proportion and reduced OS, indicating a reduced prognosis for high-risk patients (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eB). \u003cem\u003eBID\u003c/em\u003e, \u003cem\u003eCD44\u003c/em\u003e, \u003cem\u003ePLCB2\u003c/em\u003e, \u003cem\u003eTIMP1\u003c/em\u003e, and \u003cem\u003eTOP2A\u003c/em\u003e expression was markedly upregulated within the high-risk cohort, indicating an association of poor prognosis with high expression of these genes, whereas the expression of \u003cem\u003eERBB2\u003c/em\u003e, \u003cem\u003eHMOX1\u003c/em\u003e, and \u003cem\u003eTGFBR3\u003c/em\u003e was downregulated within the high-risk cohort, suggesting an association of poor prognosis with low expression of these genes (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eA). These results were in line with survival assessment outcomes (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e). KM survival curves demonstrated that the high-risk cohort displayed severely reduced OS (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.0001, Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eD). ROC curves (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eE) showed that AUC reached 0.734 after 1-year, 0.69 after 3-years, and 0.722 after 5-years, suggesting that risk scoring had effective predictive qualities for survival in patients with ccRCC. The above results indicated that such a risk model has relatively elevated specificity for predicting ccRCC prognosis.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eUnivariate/multivariate Cox regression investigations were performed to compare risk scoring hazard ratios against additional medical variables (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eA and \u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eB); the results highlighted that risk scoring represented a separate prognosis-related variable (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eA and \u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eB). Twelve putative signalling pathways linked to prognostic ARGs were separately explored in both groups using GSEA. The ARGs were predominantly enriched in the adipocytokine signalling pathway, cytokine-cytokine receptor interaction, haemopoietic cell lineage, homologous recombination, inositol phosphate metabolic processes, and intestine-related immunity networking concerning IgA synthesis pathways for the high-risk cohort and in primary immunodeficiency, propanoate metabolism, systemic lupus erythematosus, tight junction, valine, leucine, and isoleucine breakdown, and vasopressin-regulated water reabsorption pathways for the low-risk cohort (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eC).\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003e\u003cb\u003eConstruction of the nomogram to predict patient overall survival\u003c/b\u003e\u003c/p\u003e\u003cp\u003eTo apply risk scores to the prediction of ccRCC prognosis, the risk score was combined with relevant medical clinical parameters for constructing a predictive nomogram for patient OS at 3- and 5-year time points. Depending on risk scoring/medical parameters, the corresponding values of patients were identified and inferred to determine such timepoint OS values (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003e). The predictive accuracy of the nomogram was determined by the concordance index (C-index). This estimates the probability that the predicted results are consistent with the actually observed results. The C-index for such a nomogram was 0.788, indicating a good standard across model-based and observed survival outcomes.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003e\u003cb\u003eCorrelation analysis of clinical variables and immune infiltration analysis\u003c/b\u003e\u003c/p\u003e\u003cp\u003eTCGA data were further used to investigate whether the risk scores of the model, as well as each model gene, could be used to predict prognostic outcomes for patients with different clinical manifestations. Dataset outcomes showed risk scoring to be correlated with grade, stage, T, and M; \u003cem\u003eBID\u003c/em\u003e was correlated with grade, stage, T, and M; \u003cem\u003eCD44\u003c/em\u003e was correlated with sex, grade, stage, and T; \u003cem\u003eERBB2\u003c/em\u003e was correlated with grade, stage, and T; \u003cem\u003ePLCB2\u003c/em\u003e was correlated with grade; \u003cem\u003eTGFBR3\u003c/em\u003e was correlated with sex, grade, stage, and M; \u003cem\u003eTIMP1\u003c/em\u003e was correlated with grade, stage, and T; and \u003cem\u003eTOP2A\u003c/em\u003e was correlated with sex, grade, stage, T, and M (Fig.\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e8\u003c/span\u003e).\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eThe treatment of RCC has evolved tremendously over the past decades. Localized disease is often curative with surgical resection of the malignancy. However, in cases where the primary tumour has metastasized, immunotherapy is becoming a more prevalent means to combat metastatic renal cell carcinoma (mRCC). Cytokine and checkpoint inhibitor immunotherapy have been demonstrated to stimulate the immune response through various mechanisms. We explored the correlation between eight ARGs and immune infiltration. The heatmap presenting the correlation across both study cohorts for this prognostic modelling effort and immune cells was plotted using the TIMER database (Fig.\u0026nbsp;\u003cspan refid=\"Fig9\" class=\"InternalRef\"\u003e9\u003c/span\u003eA). The single-sample gene-set enrichment analysis (ssGSEA) algorithm was employed for quantifying relative presence of tumour-penetrating immune-system cells and immune functions in ccRCC patients, and the results showed significant differences in antibody-drug conjugates (aDCs), B_/ CD8+_T_cells, macrophages, mast-/NK-cells, plasmacytoid dendritic cells (pDCs), T_helper_cells, T follicular helper cells (Tfh), T-helper 1 (Th1), T-helper 2 (Th2), tumour infiltrating lymphocytes (TILs), T regulatory cells (Tregs), antigen-presenting cell (APC)_co_inhibition, APC_co_stimulation, chimeric costimulatory receptor (CCR), check-point, cytolytic_activity, human leukocyte antigen (HLA), inflammation-promoting, major histocompatibility complex (MHC)_class_l, parainflammation, T_cell_co-inhibition, T_cell_co-stimulation, Type_I_Interferon (IFN)_Response, and Type_II_IFN_Response, across both study cohorts (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05, Fig.\u0026nbsp;\u003cspan refid=\"Fig9\" class=\"InternalRef\"\u003e9\u003c/span\u003eB, C). Furthermore, significant differences between the two groups were found for almost all immune checkpoints (Fig.\u0026nbsp;\u003cspan refid=\"Fig9\" class=\"InternalRef\"\u003e9\u003c/span\u003eD). The above results suggest that the underlying mechanism of the model may be associated with immune activity in the tumour immune microenvironment and that the model could predict sensitivity to immunotherapy.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003e\u003cb\u003eTOP2A promotes ccRCC progression\u003c/b\u003e\u003c/p\u003e\u003cp\u003eIn order to further explore the role of these apoptosis-related genes in regulating ccRCC cell function, we selected TOP2A as the subject of our study and performed l-cell function experiments.Initially, the si-TOP2A and the corresponding negative control were transfected into ACHN cells and RT-qPCR confirmed that TOP2A was successfully knocked down (Fig.\u0026nbsp;\u003cspan refid=\"Fig10\" class=\"InternalRef\"\u003e10\u003c/span\u003eA). CCK8 proliferation assay showed that compared with the si-NC group, the ACHN cells in the si-TOP2A group showed significantly reduced proliferation ability (Fig.\u0026nbsp;\u003cspan refid=\"Fig10\" class=\"InternalRef\"\u003e10\u003c/span\u003eB); flow cytometry analysis showed increased apoptosis in ACHN cells in the si-TOP2A group (Fig.\u0026nbsp;\u003cspan refid=\"Fig10\" class=\"InternalRef\"\u003e10\u003c/span\u003eC); in addition, Transwell migration and invasion assays showed that both the migration and invasion ability of the cells was reduced (Fig.\u0026nbsp;\u003cspan refid=\"Fig10\" class=\"InternalRef\"\u003e10\u003c/span\u003eD).Cell experiments suggest that TOP2A may play its corresponding role in ccRCC cell carcinoma.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eIn the last two decades, RCC incidence has increased significantly with a relatively high associated mortality rate (Ferlay et al., \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2013\u003c/span\u003e; Ferlay et al., \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2015\u003c/span\u003e). Therefore, identifying effective prognostic biomarkers and constructing useful prognostic tools for predicting OS within ccRCC cases, are essential. Bioinformatics analysis has emerged as an important tool for identifying therapy-related targets concerning diagnostic, prognostic, and therapeutic parameters for various tumours. Apoptosis plays a pivotal part in tumour growth and regeneration. Although several studies have developed prognostic models based on selected ARGs, the prognostic model developed in this study was based on a new set of ARGs, which were investigated in detail.\u003c/p\u003e\u003cp\u003eSpecifically, a total of 49/147 ARGs were identified as differentially expressed in ccRCC tissues compared to adjacent normal tissues. Univariate Cox regression analyses further identified 17 genes associated with ccRCC prognosis, and subsequent multivariate Cox regression analyses identified eight ARGs, namely \u003cem\u003eBID\u003c/em\u003e, \u003cem\u003eCD44\u003c/em\u003e, \u003cem\u003eERBB2\u003c/em\u003e, \u003cem\u003eHMOX1\u003c/em\u003e, \u003cem\u003ePLCB2\u003c/em\u003e, \u003cem\u003eTGFBR3\u003c/em\u003e, \u003cem\u003eTIMP1\u003c/em\u003e, and \u003cem\u003eTOP2A\u003c/em\u003e. These genes were applied for the construction of prognosis-related models. A high predictive ability was confirmed for the ARG-based prognosis model via KM curve and ROC analyses, as well as comparisons with relevant clinical prognostic variables. The developed ARG-based model was significantly associated with immune-cell penetration, immunity-related roles, and immune checkpoints.\u003c/p\u003e\u003cp\u003eThe eight identified ARGs are known to influence the progression of various tumours. For example, \u003cem\u003eBID\u003c/em\u003e, which is present on chromosome 22q11.21 and encodes a protein associated with apoptosis, is upregulated in thyroid cancer, and is linked to its prognostic outcome (Lin et al., \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2018\u003c/span\u003e); however, its function in ccRCC tumorigenesis and progression is unclear. We found that high expression of \u003cem\u003eBID\u003c/em\u003e was associated with poor outcome for ccRCC. CD44 is a frequently utilised biomarker for cancer stem cells and has prognostic importance for multiple solid tumours, such as colon, lung, and breast cancers (Brown et al., \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2011\u003c/span\u003e; Leung et al., \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2010\u003c/span\u003e; Su et al., \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e2011\u003c/span\u003e). In addition, CD44 can promote the migration and proliferation of gastric cancer cells (Chai et al., \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2014\u003c/span\u003e; Hu et al., \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2014\u003c/span\u003e). Such revelations were in line with the prognostic conclusions of our investigation. In ccRCC, the RAS pathway promotes the expression of HMOX1 to protect cancer cells from the killing effects of chemotherapeutic agents (Balan et al., \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2015\u003c/span\u003e; Szklarczyk et al., 2019). Moreover, HMOX1 plays an immunoregulatory role in certain inflammatory disease conditions (Chora et al., \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2007\u003c/span\u003e; Ke et al., \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2012\u003c/span\u003e; Tzima et al., \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e2009\u003c/span\u003e), and its low expression is favourable for prognosis. In addition to apoptosis, PLCB2 is associated with methylation in ccRCC, with low \u003cem\u003ePLCB2\u003c/em\u003e expression associated with poor prognosis (Chen et al., \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). TGFBR3 is a transforming growth factor β (TGF-β) receptor that has a role in inhibiting ccRCC development and metastasis. Indeed, downregulated TGFBR3 expression leads to enhanced tumour formation and metastasis in ccRCC cells and, thus, serves as a predictor of poor prognosis (Nishida et al., \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). \u003cem\u003eTOP2A\u003c/em\u003e encodes a DNA topoisomerase, an ATP-dependent synthase/hydrolase that has pivotal roles within multiple physiological activities, including DNA replication, chromatin condensation, and chromosomal separation. Previous research showed that upregulated \u003cem\u003eTOP2A\u003c/em\u003e promotes breast cancer advancement (Hall et al., \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2017\u003c/span\u003e; Shigematsu et al., \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). \u003cem\u003eERBB2\u003c/em\u003e encodes a member of the epidermal growth factor receptor (HER) family, and its upregulation is associated with poor prognosis within breast cancer (Ravdin and Chamness, \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e1995\u003c/span\u003e), which contrasts with the observations of our present study. Lastly, high expression of \u003cem\u003eTIMP1\u003c/em\u003e is reportedly associated with poor prognosis in renal tumours (Dias et al., \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2020\u003c/span\u003e), which is consistent with the present findings.\u003c/p\u003e\u003cp\u003eThis study identified eight differential genes that were intimately linked to the survival of patients with ccRCC and, thus, have the potential to become therapeutic targets for precision medicine. A prognosis-related model was developed depending on differential ARGs, and its clinical prognostic predictive ability for ccRCC was evaluated. However, this study had several limitations. First, it only focused on large-scale gene sequencing data from TCGA platform, with some related clinical information, while failing to fully validate other information, such as deoxyribonucleic acid methylation. Second, many samples were excluded from this study owing to incomplete clinical information, and thus it is necessary to include additional samples or data from other platforms to further verify the present results. Finally, the eight differential ARGs, found to be highly related to the survival of patients with ccRCC, were analysed only through data mining and, hence, further verification based on clinical trials is required.\u003c/p\u003e"},{"header":"Conclusions","content":"\u003cp\u003eBased on eight ARGs which were identified from publicly available data and other relevant clinical prognostic variables, we established a novel prognosis model to predict 3-, and 5-year OS for ccRCC cases. The model provides a novel survival-predicting utility in ccRCC cases and reveals the link between ARGs and ccRCC. In addition, the findings of this study will help address the current knowledge gap in the field of ccRCC and provide new insights for further research.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eConflicts of interest\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;ZZ provided research ideas.FF, CC, and LD collected, sorted, and mapped the data. MZ and ZZ summarised and analysed the data. DC provided theoretical guidance and supervision of the project.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData availability statement\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe datasets generated and/or analysed in this study can be found in The Cancer Genome Atlas (TCGA) database repository [https://portal.gdc.cancer.gov]. Human ARGs were obtained through gene-set enrichment analysis (GSEA) [https://www.gsea-msigdb.org/gsea/index.jsp].\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis work was supported by Yancheng Science and Technology Plan Project (Grant Number:YCBK2023081) and Special Research Development Fund Project for Clinical Teaching Bases of Jiangsu Vocational College of Medicine.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgements\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe thank Editage for English language editing.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eBakhshi, A., Jensen, J. P., Goldman, P., Wright, J. J., McBride, O. W., Epstein, A. L., et al. (1985). Cloning the chromosomal breakpoint of t(14;18) human lymphomas: clustering around JH on chromosome 14 and near a transcriptional unit on 18. 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OncoTargets Ther. 11, 1861-1869. https://doi.org/10.2147/OTT.S156737\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Clear-cell Renal Cell Carcinoma, Apoptosis, Prognosis Model, TOP2A","lastPublishedDoi":"10.21203/rs.3.rs-7057172/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7057172/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cstrong\u003eBackground. \u003c/strong\u003eFew studies have investigated the clinical prognostic significance of multiple apoptosis-related genes (ARGs), particularly in the context of clear-cell renal cell carcinoma (ccRCC).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMethods. \u003c/strong\u003eWe explored ARGs in ccRCC prognosis using The Cancer Genome Atlas (TCGA) repository. Transcriptomic expression profiles and corresponding medical information for patients with ccRCC were obtained. Human ARGs were identified through gene-set enrichment analysis. Differentially expressed (DE)-ARGs and prognosis-related ARGs were identified and, subsequently, used for prognosis modelling. The prognostic predictive performance of this model was confirmed using Kaplan-Meier (KM) and receiver operating characteristic (ROC) curves. Relevant clinical prognostic variables were added to construct a predictive nomogram for ccRCC prognosis at the clinical level. Immune-cell penetration evaluation was conducted for genes included within this novel prognosis-linked model. Validation of TOP2A function in ccRCC cells by cellular experiments.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eResults. \u003c/strong\u003eOverall, 49 DE-ARGs were identified. Univariate Cox regression evaluation identified 17 genes associated with ccRCC prognosis, and multivariate Cox regression analyses identified eight ARGs (\u003cem\u003eBID\u003c/em\u003e,\u003cem\u003e CD44\u003c/em\u003e,\u003cem\u003e ERBB2\u003c/em\u003e,\u003cem\u003e HMOX1\u003c/em\u003e,\u003cem\u003e PLCB2\u003c/em\u003e,\u003cem\u003e TGFBR3\u003c/em\u003e,\u003cem\u003eTIMP1\u003c/em\u003e, and \u003cem\u003eTOP2A\u003c/em\u003e), which were employed to construct the variable risk scoring. The effectiveness of risk scoring as an independent prognostic variable was verified through KM curve and ROC analyses. Risk scoring and various other relevant clinical prognostic variables were employed to construct a prognostic model. The model was statistically significant regarding its association with immune-cell penetration, immune-related function, and immune checkpoints. TOP2A promotes ccRCC cells growth and metastasis .\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConclusions. \u003c/strong\u003eBased on eight ARGs and other relevant clinical prognostic variables, we established a novel model for predicting ccRCC prognosis, which can be used to inform the individualized treatment of ccRCC patients.\u003c/p\u003e","manuscriptTitle":"Novel Prognosis Model for Clear-cell Renal Cell Carcinoma based on Apoptosis-related Genes","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-07-14 10:47:29","doi":"10.21203/rs.3.rs-7057172/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"0c706d77-1cc4-4f68-bacc-b287ab5f384a","owner":[],"postedDate":"July 14th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[{"id":51361078,"name":"Health sciences/Biomarkers"},{"id":51361079,"name":"Biological sciences/Cancer"},{"id":51361080,"name":"Biological sciences/Computational biology and bioinformatics"},{"id":51361081,"name":"Biological sciences/Immunology"},{"id":51361082,"name":"Health sciences/Oncology"}],"tags":[],"updatedAt":"2025-07-15T14:53:59+00:00","versionOfRecord":[],"versionCreatedAt":"2025-07-14 10:47:29","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-7057172","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7057172","identity":"rs-7057172","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}