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There is an urgent clinical demand to explore novel diagnostic and prognostic biomarkers for renal cell carcinoma (RCC). We proposed deep learning-based artificial intelligence strategies. The study included 1752 whole slide images from multiple centres. Based on the pixel-level of RCC segmentation, the diagnosis diagnostic model achieved an area under the receiver operating characteristic curve (AUC) of 0.977 (95% CI 0.969-0.984) in the external validation cohort. In addition, our diagnostic model exhibited excellent performance in the differential diagnosis of RCC from renal oncocytoma, which achieved an AUC of 0.951 (0.922-0.972). The graderisk for the recognition of high-grade tumour achieved AUCs of 0.840 (0.805-0.871) in the Cancer Genome Atlas (TCGA) cohort, 0.857 (0.813-0.894) in the Shanghai General Hospital (General) cohort, and 0.894 (0.842-0.933) in the Clinical Proteomic Tumor Analysis Consortium (CPTAC) cohort, for the recognition of high-grade tumour. The OSrisk for predicting 5-year survival status achieved an AUC of 0.784 (0.746-0.819) in the TCGA cohort, which was further verified in the independent general cohort and the CPTAC cohort, with AUCs of 0.774 (0.723-0.820) and 0.702 (0.632-0.765), respectively. Moreover, the competing-risk nomogram (CRN) showed its potential to be a prognostic indicator, with a hazard ratio (HR) of 5.664 (3.893-8.239, p<0.0001), outperforming other traditional clinical prognostic indicators. Kaplan-Meier survival analysis further illustrated that our CRN could significantly distinguish patients with high survival risk. Deep learning-based artificial intelligence could be a useful tool for clinicians to diagnose and predict the prognosis of RCC patients, thus improving the process of individualised treatment.

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The secretagogin (SCGN) was originally identified as a secreted calcium-binding protein present in the cytoplasm. Recent studies have found that SCGN has a close relationship with cancer. However, its role in the occurrence, progression, and prognosis of clear cell renal cell carcinoma (ccRCC) remains unclear. In this study, we utilized a mutual authentication method based on public databases and clinical samples to determine the role of SCGN in the progression and prognosis of ccRCC. Firstly, we comprehensively analyzed the expression characteristics of SCGN in ccRCC in several public databases. Subsequently, we systematically evaluated SCGN expression on 252 microarrays of ccRCC tissues from different grades. It was found that SCGN was absent in all the normal kidney tissues and significantly overexpressed in ccRCC tumor tissues. In addition, the expression level of SCGN gradually decreased with an increase in tumor grade, and the percentage of SCGN staining positivity over 50% was 86.7% (13/15) and 73.4% (58/79) in Grade1 and Grade2, respectively, while it was only 8.3% (12/144) in Grade3, and the expression of SCGN was completely absent in Grade4 (0/14) and distant metastasis group (0/4). Additionally, the expression of SCGN was strongly correlated with the patient's prognosis, with the higher the expression levels of SCGN being associated with longer overall survival and disease-free survival of patients. In conclusion, our results suggest that reduced expression of SCGN in cancer cells is correlated with the progression and prognosis of ccRCC.

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BACKGROUND: Metastasis renal cell carcinoma (RCC) patients have extremely high mortality rate. A predictive model for RCC micrometastasis based on pathomics could be beneficial for clinicians to make treatment decisions. METHODS: A total of 895 formalin-fixed and paraffin-embedded whole slide images (WSIs) derived from three cohorts, including Shanghai General Hospital (SGH), Clinical Proteomic Tumor Analysis Consortium (CPTAC) and Cancer Genome Atlas (TCGA) cohorts, and another 588 frozen section WSIs from TCGA dataset were involved in the study. The deep learning-based strategy for predicting lymphatic metastasis was developed based on WSIs through clustering-constrained-attention multiple-instance learning method and verified among the three cohorts. The performance of the model was further verified in frozen-pathological sections. In addition, the model was also tested the prognosis prediction of patients with RCC in multi-source patient cohorts. RESULTS: The AUC of the lymphatic metastasis prediction performance was 0.836, 0.865 and 0.812 in TCGA, SGH and CPTAC cohorts, respectively. The performance on frozen section WSIs was with the AUC of 0.801. Patients with high deep learning-based prediction of lymph node metastasis values showed worse prognosis. CONCLUSIONS: In this study, we developed and verified a deep learning-based strategy for predicting lymphatic metastasis from primary RCC WSIs, which could be applied in frozen-pathological sections and act as a prognostic factor for RCC to distinguished patients with worse survival outcomes.

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BACKGROUND: Although separate analysis of individual factor can somewhat improve the prognostic performance, integration of multimodal information into a single signature is necessary to stratify patients with clear cell renal cell carcinoma (ccRCC) for adjuvant therapy after surgery. METHODS: A total of 414 patients with whole slide images, computed tomography images, and clinical data from three patient cohorts were retrospectively analyzed. The authors performed deep learning and machine learning algorithm to construct three single-modality prediction models for disease-free survival of ccRCC based on whole slide images, cell segmentation, and computed tomography images, respectively. A multimodel prediction signature (MMPS) for disease-free survival were further developed by combining three single-modality prediction models and tumor stage/grade system. Prognostic performance of the prognostic model was also verified in two independent validation cohorts. RESULTS: Single-modality prediction models performed well in predicting the disease-free survival status of ccRCC. The MMPS achieved higher area under the curve value of 0.742, 0.917, and 0.900 in three independent patient cohorts, respectively. MMPS could distinguish patients with worse disease-free survival, with HR of 12.90 (95% CI: 2.443-68.120, P <0.0001), 11.10 (95% CI: 5.467-22.520, P <0.0001), and 8.27 (95% CI: 1.482-46.130, P <0.0001) in three different patient cohorts. In addition, MMPS outperformed single-modality prediction models and current clinical prognostic factors, which could also provide complements to current risk stratification for adjuvant therapy of ccRCC. CONCLUSION: Our novel multimodel prediction analysis for disease-free survival exhibited significant improvements in prognostic prediction for patients with ccRCC. After further validation in multiple centers and regions, the multimodal system could be a potential practical tool for clinicians in the treatment for ccRCC patients.

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A variety of cancer cells exhibit dysregulated lipid metabolism, characterized by excessive intracellular lipid accumulation, and clear cell renal cell carcinoma (ccRCC) is the most typical disease with these characteristics. As the most common malignancy of all renal cell carcinomas (RCCs), ccRCC is typically characterized by a large accumulation of lipids and glycogen in the cytoplasm and a nucleus that is squeezed by the accumulated lipid droplets and localized to the marginal areas within the cytoplasm. This lipid accumulation has been found to be critically involved in the maintenance of malignant features observed in various cancers. Firstly, it maintains the persistent proliferative and metastasis properties of cancer cells. Secondly, it acts as a buffer against lipid peroxidation, preventing lipid peroxidation-induced ferroptosis. Moreover, lipids can diminish the sensitivity of cancer cells to radiotherapy. As ccRCC is a type of cancer with high lipid synthesis, targeting lipid synthesis-related genes in cancer cells may be a promising therapeutic modality for single treatment or in combination with radiotherapy, chemotherapy, and immunotherapy. This may revolutionize the choice of treatment modality for ccRCC patients. In this review, we concentrate on the current status and progress of research on lipid biosynthesis in ccRCC and the potential applications of targeting lipid synthesis to treat ccRCC. At last, we propose perspective and future research directions for targeting inhibition of lipid biosynthesis in combination with conventional therapeutic approaches for the treatment of ccRCC, which will help to evolve the therapeutic model.

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Due to the complicated histopathological characteristics of clear-cell renal-cell carcinoma (ccRCC), non-invasive prognosis before operative treatment is crucial in selecting the appropriate treatment. A total of 126 345 computerized tomography (CT) images from four independent patient cohorts were included for analysis in this study. We propose a V Bottleneck multi-resolution and focus-organ network (VB-MrFo-Net) using a cascade framework for deep learning analysis. The VB-MrFo-Net achieved better performance than VB-Net in tumor segmentation, with a Dice score of 0.87. The nuclear-grade prediction model performed best in the logistic regression classifier, with area under curve values from 0.782 to 0.746. Survival analysis revealed that our prediction model could significantly distinguish patients with high survival risk, with a hazard ratio (HR) of 2.49 [95% confidence interval (CI): 1.13-5.45, P = 0.023] in the General cohort. Excellent performance had also been verified in the Cancer Genome Atlas cohort, the Clinical Proteomic Tumor Analysis Consortium cohort, and the Kidney Tumor Segmentation Challenge cohort, with HRs of 2.77 (95%CI: 1.58-4.84, P = 0.0019), 3.83 (95%CI: 1.22-11.96, P = 0.029), and 2.80 (95%CI: 1.05-7.47, P = 0.025), respectively. In conclusion, we propose a novel VB-MrFo-Net for the renal tumor segmentation and automatic diagnosis of ccRCC. The risk stratification model could accurately distinguish patients with high tumor grade and high survival risk based on non-invasive CT images before surgical treatments, which could provide practical advice for deciding treatment options.

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BACKGROUND: The deep learning-based m6A modification model for clinical prognosis prediction of patients with renal cell carcinoma (RCC) had not been reported for now. In addition, the important roles of methyltransferase-like 14 (METTL14) in RCC have never been fully explored. METHODS: A high-level neural network based on deep learning algorithm was applied to construct the m6A-prognosis model. Western blotting, quantitative real-time PCR, immunohistochemistry and RNA immunoprecipitation were used for biological experimental verifications. RESULTS: The deep learning-based model performs well in predicting the survival status in 5-year follow-up, which also could significantly distinguish the patients with high overall survival risk in two independent patient cohort and a pan-cancer patient cohort. METTL14 deficiency could promote the migration and proliferation of renal cancer cells. In addition, our study also illustrated that METTL14 might participate in the regulation of circRNA in RCC. CONCLUSIONS: In summary, we developed and verified a deep learning-based m6A-prognosis model for patients with RCC. We proved that METTL14 deficiency could promote the migration and proliferation of renal cancer cells, which might throw light on the cancer prevention by targeting the METTL14 pathway.

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OBJECTIVES: To construct and validate a prediction model based on full-sequence MRI for preoperatively evaluating the invasion depth of bladder cancer. METHODS: A total of 445 patients with bladder cancer were divided into a seven-to-three training set and test set for each group. The radiomic features of lesions were extracted automatically from the preoperative MRI images. Two feature selection methods were performed and compared, the key of which are the Least Absolute Shrinkage and Selection Operator (LASSO) and the Max Relevance Min Redundancy (mRMR). The classifier of the prediction model was selected from six advanced machine-learning techniques. The receiver operating characteristic (ROC) curves and the area under the curve (AUC) were applied to assess the efficiency of the models. RESULTS: The models with the best performance for pathological invasion prediction and muscular invasion prediction consisted of LASSO as the feature selection method and random forest as the classifier. In the training set, the AUC of the pathological invasion model and muscular invasion model were 0.808 and 0.828. Furthermore, with the mRMR as the feature selection method, the external invasion model based on random forest achieved excellent discrimination (AUC, 0.857). CONCLUSIONS: The full-sequence models demonstrated excellent accuracy for preoperatively predicting the bladder cancer invasion status. CLINICAL RELEVANCE STATEMENT: This study introduces a full-sequence MRI model for preoperative prediction of the depth of bladder cancer infiltration, which could help clinicians to recognise pathological features associated with tumour infiltration prior to invasive procedures. KEY POINTS: • Full-sequence MRI prediction model performed better than Vesicle Imaging-Reporting and Data System (VI-RADS) for preoperatively evaluating the invasion status of bladder cancer. • Machine learning methods can extract information from T1-weighted image (T1WI) sequences and benefit bladder cancer invasion prediction.

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PURPOSE: Serine metabolism is frequently dysregulated in many types of cancers and the tumor suppressor p53 is recently emerging as a key regulator of serine metabolism. However, the detailed mechanism remains unknown. Here, we investigate the role and underlying mechanisms of how p53 regulates the serine synthesis pathway (SSP) in bladder cancer (BLCA). METHODS: Two BLCA cell lines RT-4 (WT p53) and RT-112 (p53 R248Q) were manipulated by applying CRISPR/Cas9 to examine metabolic differences under WT and mutant p53 status. Liquid chromatography-tandem mass spectrometry (LC-MS/MS) and non-targeted metabolomics analysis were adopted to identify metabolomes changes between WT and p53 mutant BLCA cells. Bioinformatics analysis using the cancer genome atlas and Gene Expression Omnibus datasets and immunohistochemistry (IHC) staining was used to investigate PHGDH expression. Loss-of-function of PHGDH and subcutaneous xenograft model was adopted to investigate the function of PHGDH in mice BLCA. Chromatin immunoprecipitation (Ch-IP) assay was performed to analyze the relationships between YY1, p53, SIRT1 and PHGDH expression. RESULTS: SSP is one of the most prominent dysregulated metabolic pathways by comparing the metabolomes changes between wild-type (WT) p53 and mutant p53 of BLCA cells. TP53 gene mutation shows a positive correlation with PHGDH expression in TCGA-BLCA database. PHGDH depletion disturbs the reactive oxygen species homeostasis and attenuates the xenograft growth in the mouse model. Further, we demonstrate WT p53 inhibits PHGDH expression by recruiting SIRT1 to the PHGDH promoter. Interestingly, the DNA binding motifs of YY1 and p53 in the PHGDH promoter are partially overlapped which causes competition between the two transcription factors. This competitive regulation of PHGDH is functionally linked to the xenograft growth in mice. CONCLUSION: YY1 drives PHGDH expression in the context of mutant p53 and promotes bladder tumorigenesis, which preliminarily explains the relationship between high-frequency mutations of p53 and dysfunctional serine metabolism in bladder cancer.

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Clear cell renal cell carcinoma (ccRCC) is the most common subtype of renal cell carcinoma, which is characterized by transparent cytoplasm. However, some ccRCC also show eosinophilic cytoplasm, and the molecular difference between eosinophilic and clear subtypes is unclear. In this study, we uncovered that under an optical microscope ccRCC with eosinophilic features has a poor prognosis. Eosinophilic ccRCC tends to have a higher histologic grade. Eosinophilic ccRCC has 16 genes significantly up-regulated compared with ccRCC, of which seven genes have multi-cohort validation prognostic value. Immune infiltration analysis suggested a low number of M1 macrophages and NK tissue-resident cells in eosinophilic ccRCC. Enrichment analysis suggests that ccRCC with eosinophilic features may be closely associated with the transport and metabolism of many substances. The findings of this study have important implications for the study of the malignant transformation of ccRCC.

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BACKGROUND: Identifying lymph node metastasis (LNM) relies mainly on indirect radiology. Current studies omitted the quantified associations with traits beyond cancer types, failing to provide generalisation performance across various tumour types. METHODS: 4400 whole slide images across 11 cancer types were collected for training, cross-verification, and external validation of the pan-cancer lymph node metastasis (PC-LNM) model. We proposed an attention-based weakly supervised neural network based on self-supervised cancer-invariant features for the prediction task. RESULTS: PC-LNM achieved a test area under the curve (AUC) of 0.732 (95% confidence interval: 0.717-0.746, P < 0.0001) in fivefold cross-validation of multiple cancer types, which also demonstrated good generalisation in the external validation cohort with AUC of 0.699 (95% confidence interval: 0.658-0.737, P < 0.0001). The interpretability results derived from PC-LNM revealed that the regions with the highest attention scores identified by the model generally correspond to tumours with poorly differentiated morphologies. PC-LNM achieved superior performance over previously reported methods and could also act as an independent prognostic factor for patients across multiple tumour types. DISCUSSION: We presented an automated pan-cancer model for predicting the LNM status from primary tumour histology, which could act as a novel prognostic marker across multiple cancer types.

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Exploring useful prognostic markers and developing a robust prognostic model for patients with prostate cancer are crucial for clinical practice. We applied a deep learning algorithm to construct a prognostic model and proposed the deep learning-based ferroptosis score (DLFscore) for the prediction of prognosis and potential chemotherapy sensitivity in prostate cancer. Based on this prognostic model, there was a statistically significant difference in the disease-free survival probability between patients with high and low DLFscore in the The Cancer Genome Atlas (TCGA) cohort (P < 0.0001). In the validation cohort GSE116918, we also observed a consistent conclusion with the training set (P = 0.02). Additionally, functional enrichment analysis showed that DNA repair, RNA splicing signaling, organelle assembly, and regulation of centrosome cycle pathways might regulate prostate cancer through ferroptosis. Meanwhile, the prognostic model we constructed also had application value in predicting drug sensitivity. We predicted some potential drugs for the treatment of prostate cancer through AutoDock, which could potentially be used for prostate cancer treatment.

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Cuproptosis is a newly discovered type of cell death. The role and potential mechanism of Cuproptosis-related genes (CRGs) in the prognosis of cancer patients are not fully understood. In this study, we included two cohorts of clear cell renal cell carcinoma patients, TCGA and E-MTAB-1980. The TCGA cohort is used as a training set to construct a CRG signature using the LASSO-cox regression analysis, and E-MTAB-1980 is used as a cohort for verification. A total of eight genes (FDX1, LIAS, LIPT1, DLAT, PDHA1, MTF1, GLS, CDKN2A) were screened to construct a prognostic model in the TCGA cohort. There is a significant difference in OS (p < 0.0001) between the high and low cuproptosis score group, and a similar difference is also observed in the OS (p = 0.0054) of the E-MTAB-1980 cohort. The area under the ROC curves (AUC) were 0.87, 0.82, and 0.78 at 1, 3, and 5 years in the TCGA cohort, respectively. Finally, gene set enrichment analysis revealed that CRGs were associated with cell cycle and mitotic signaling pathways.

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Immunotherapy for cancer has become a revolutionary treatment, with the progress of immunological research on cancer. Cancer patients have also become more diversified in drug selection. Individualized medical care of patients is more important in the era of precision medicine. For advanced clear cell renal cell carcinoma (ccRCC) patients, immunotherapy and targeted therapy are the two most important treatments. The development of biomarkers for predicting the efficacy of immunotherapy or targeted therapy is indispensable for individualized medicine. There is no clear biomarker that can accurately predict the efficacy of immunotherapy for advanced ccRCC patients. Our study found that HIF1A could be used as a biomarker for predicting the anti-PD-1 therapy efficacy of patients with advanced ccRCC, and its prediction accuracy was even stronger than that of PD-1/PD-L1. HIF1A is expected to help patients with advanced ccRCC choose therapeutic drugs.

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The sole clinicopathological characteristic is not enough for the prediction of survival of patients with clear cell renal cell carcinoma (ccRCC). However, the survival prediction model constructed by machine learning technology for patients with ccRCC using clinicopathological features is rarely reported yet. In this study, a total of 5878 patients diagnosed as ccRCC from four independent patient cohorts were recruited. The least absolute shrinkage and selection operator analysis was implemented to identify optimal clinicopathological characteristics and calculate each coefficient to construct the prognosis model. In addition, weighted gene co-expression network and gene enrichment analysis associated with risk score were also carried out. Three clinicopathologic features were selected for the construction of the prognosis risk score model as the prognostic factors of ccRCC, including tumor size, tumor grade, and tumor stage. In the CPTAC (Clinical Proteomic Tumor Analysis Consortium) cohort, the General cohort, the SEER (Surveillance, Epidemiology, and End Results) cohort, and the Huashan cohort, patients with high-risk score had worse clinical outcomes than patients with low-risk score (hazard ratio 5.15, 4.64, 3.96, and 5.15, respectively). Further functional enrichment analysis demonstrated that our machine learning-based risk score was significantly connected with some cell proliferation-related pathways, consisting of DNA repair, cell division, and cell cycle. In summary, we developed and validated a machine learning-based prognosis prediction model, which might contribute to clinical decision-making for patients with ccRCC.

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MicroRNAs (miRNAs) are involved in the regulation of gene expression via incomplete base pairing to sequence motifs at the three prime untranslated regions (3'-UTRs) of mRNAs and play critical roles in the etiology of cancers. Single nucleotide polymorphisms (SNPs) in the 3'-UTR miRNA-binding regions may influence the miRNA affinity. However, this biological mechanism in prostate cancer (PCa) remains unclear. Here, a three-stage genome-wide association study of 3'-UTR SNPs (n=33 117) is performed in 5515 Chinese men. Three genome-wide significant variants are discovered at 8p21.2 (rs1567669, rs4872176, and rs4872177), which are all located in a linkage disequilibrium region of the NKX3-1 gene. Phenome-wide association analysis using the FinnGen data reveals a specific association of rs1567669 with PCa over 2,264 disease endpoints. Expression quantitative trait locus analyses based on both Chinese PCa cohort and the GTEx database show that risk alleles of these SNPs are significantly associated with low expression of NKX3-1. Based on the MirSNP database, dual-luciferase reporter assays show that risk alleles of these SNPs downregulate the expression of NKX3-1 via increased miRNA binding. These results indicate that the SNPs at the 3'-UTR of NKX3-1 significantly downregulate NKX3-1 expression by influencing the affinity of miRNA and increase the PCa risk.

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BACKGROUND: Bladder cancer (BCa) shows its potential immunogenity in current immune-checkpoint inhibitor related immunotherapies. However, its therapeutic effects are improvable and could be affected by tumor immune microenvironment. Hence it is interesting to find some more prognostic indicators for BCa patients concerning immunotherapies. METHODS: In the present study, we retrospect 129 muscle-invasive BCa (MIBC) patients with radical cystectomy in Shanghai General Hospital during 2007 to 2018. Based on the results of proteomics sequencing from 9 pairs of MIBC tissue from Shanghai General Hospital, we focused on 13 immune-related differential expression proteins and their related genes. An immune-related prognostic signature (IRPS) was constructed according to Cancer Genome Atlas (TCGA) dataset. The IRPS was verified in ArrayExpress (E-MTAB-4321) cohort and Shanghai General Hospital (General) cohort, separately. A total of 1010 BCa patients were involved in the study, including 405 BCa patients in TCGA cohort, 476 BCa patients in E-MTAB-4321 cohort and 129 MIBC patients in General cohort. RESULT: It can be indicated that high IRPS score was related to poor 5-year overall survival and disease-free survival. The IRPS score was also evaluated its immune infiltration. We found that the IRPS score was adversely associated with GZMB, IFN-γ, PD-1, PD-L1. Additionally, higher IRPS score was significantly associated with more M2 macrophage and resting mast cell infiltration. CONCLUSION: The study revealed a novel BCa prognostic signature based on IRPS score, which may be useful for BCa immunotherapies.

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Lactic acid was previously considered a waste product of glycolysis, and has now become a key metabolite for cancer development, maintenance and metastasis. So far, numerous studies have confirmed that tumor lactic acid levels are associated with increased metastasis, tumor recurrence and poor prognosis. However, the prognostic value of lactic acid metabolism and transporter related genes in patients with clear cell renal cell carcinoma has not been explored. We selected lactic acid metabolism and transporter related twenty-one genes for LASSO cox regression analysis in the E-MTAB-1980 cohort, and finally screened three genes (PNKD, SLC16A8, SLC5A8) to construct a clinical prognostic model for patients with clear cell renal cell carcinoma. Based on the prognostic model we constructed, the over survival (hazard ratio = 4.117, 95% CI: 1.810−9.362, p < 0.0001) of patients in the high-risk group and the low-risk group in the training set E-MTAB-1980 cohort had significant differences, and similar results (hazard ratio = 1.909, 95% CI: 1.414−2.579 p < 0.0001) were also observed in the validation set TGCA cohort. Using the CIBERSORT algorithm to analyze the differences in immune cell infiltration in different risk groups, we found that dendritic cells, M1 macrophages, and CD4+ memory cells in the high-risk group were significantly lower than those in the low-risk group, while Treg cells were higher than in the low-risk group. Finally, through gene enrichment analysis, we found that the signal pathway that is strongly related to the prognostic model is the cell cycle.

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It is of great urgency to explore useful prognostic markers and develop a robust prognostic model for patients with clear-cell renal cell carcinoma (ccRCC). Three independent patient cohorts were included in this study. We applied a high-level neural network based on TensorFlow to construct the robust model by using the deep learning algorithm. The deep learning-based model (FB-risk) could perform well in predicting the survival status in the 5-year follow-up, which could also significantly distinguish the patients with high overall survival risk in three independent patient cohorts of ccRCC and a pan-cancer cohort. High FB-risk was found to be partially associated with negative regulation of the immune system. In addition, the novel phenotyping of ccRCC based on the F-box gene family could robustly stratify patients with different survival risks. The different mutation landscapes and immune characteristics were also found among different clusters. Furthermore, the novel phenotyping of ccRCC based on the F-box gene family could perform well in the robust stratification of survival and immune response in ccRCC, which might have potential for application in clinical practices.

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BACKGROUND: It is of great urgency to explore useful prognostic markers for patients with clear cell renal cell carcinoma (ccRCC). Prognostic models based on ferroptosis-related gene (FRG) in ccRCC is poorly reported for now. METHODS: Comprehensive analysis of 22 FRGs were performed in 629 ccRCC samples from two independent patient cohorts. We carried out least absolute shrinkage and selection operator analysis to screen out prognosis-related FRGs and constructed prognosis model for patients with ccRCC. Weighted gene co-expression network analysis was also carried out for potential functional enrichment analysis. RESULTS: Based on the TCGA cohort, a total of 11 prognosis-associated FRGs were selected for the construction of the prognosis model. Significantly differential overall survival (hazard ratio = 3.61, 95% CI: 2.68-4.87, p < 0.0001) was observed between patients with high and low FRG score in the TCGA cohort, which was further verified in the CPTAC cohort with hazard ratio value of 5.13 (95% CI: 1.65-15.90, p = 0.019). Subgroup survival analysis revealed that our FRG score could significantly distinguish patients with high survival risk among different tumor stages and different tumor grades. Functional enrichment analysis illustrated that the process of cell cycle, including cell cycle-mitotic pathway, cytokinesis pathway and nuclear division pathway, might be involved in the regulation of ccRCC through ferroptosis. CONCLUSIONS: We developed and verified a FRG signature for the prognosis prediction of patients with ccRCC, which could act as a risk factor and help to update the tumor staging system when integrated with clinicopathological characteristics. Cell cycle-related pathways might be involved in the regulation of ccRCC through ferroptosis.

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