USP31 serves as a potential biomarker for predicting prognosis and immune responses for clear cell renal cell carcinoma via single-cell and bulk RNA-sequencing.

BACKGROUND: Currently, there is no research available on the prognosis, potential effect and therapeutic value of USP31 in clear cell renal cell carcinoma (ccRCC). To address this gap, the present study aimed to shed light on its potential roles and possible mechanisms in ccRCC. METHODS: R software was utilized to conduct bioinformatics analyses with the data derived from The Cancer Genome Atlas (i.e. KIRC) and Gene Expression Omnibus datasets. The expression of USP31 in ccRCC was validated by a PCR. The independent prognostic ability of USP31 was evaluated by Cox regression analysis. We conducted gene set enrichment analysis (GSEA) to explore the potential USP31-related pathways. We also discussed the relationships between USP31 and immunity, by predicting its possible upstream transcription factors (TFs) by ChEA3. RESULTS: In ccRCC, USP31 demonstrated a high level of expression and this increased expression was correlated with a poor prognosis (p < 0.05). Through univariate and multivariate Cox regression analysis, USP31 was identified as an independent prognostic factor for ccRCC (p < 0.05). Furthermore, eight USP31-related pathways were identified by GSEA (p < 0.05). Moreover, USP31 was found to be associated with microsatellite instability, tumor microenvironment, a variety of immune cells and immune checkpoints and immune infiltration (p < 0.05). Additionally, Patients with high USP31 expression in ccRCC were shown to have better curative effects after immunotherapy (p < 0.05). Finally, we found that AR, USF1, MXI1 and CLOCK could be the potential upstream TFs of USP31. CONCLUSIONS: USP31 could serve as a potential biomarker for predicting both prognosis and immune responses, revealing its potential mechanisms of TF-USP31 mRNA networks in ccRCC.

KDM4B: A promising oncology therapeutic target.

Epigenetic modifications are significant in tumor pathogenesis, wherein the process of histone demethylation is indispensable for regulating gene transcription, apoptosis, DNA replication, and repair of damaged DNA. The lysine demethylases (KDMs) serve an essential role in the aforementioned processes, with particular emphasis on the KDM4 family, also referred to as JMJD2. Multiple studies have underscored the significance of the KDM4 family in the regulation of various biological processes including, but not limited to, the cell cycle, DNA repair mechanisms, signaling pathways, and the progression of tumor formation. Nevertheless, it is imperative to elucidate the underlying mechanism of KDM4B, which belongs to the KDM4 gene family. This review presents a comprehensive examination of the structure, mechanism, and function of KDM4B, as well as a critical analysis of the current body of research pertaining to its involvement in tumorigenesis and development. Furthermore, this review explores the potential therapeutic strategies that specifically target KDM4B.

Development and verification of a deep learning-based m6A modification model for clinical prognosis prediction of renal cell carcinoma.

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.

A machine learning model based on MRI for the preoperative prediction of bladder cancer invasion depth.

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.

Histone demethylase KDM4B contributes to advanced clear cell renal carcinoma and association with copy number variations and cell cycle progression.

Advanced renal cell carcinoma (RCC) poses a threat to patient survival. Epigenetic remodelling is the pathogenesis of renal cancer. Histone demethylase 4B (KDM4B) is overexpressed in many cancers through various pathways. However, the role of KDM4B in clear cell renal carcinoma has not yet been elucidated. The differential expression of KDM4B was first verified by analysing public databases. The expression of KDM4B in fresh tissues and pathology slides was further analysed by western blotting and immunohistochemical staining. KDM4B overexpression and knockdown cell lines were also established. Cell Counting Kit-8 (CCK-8) assay was used to detect cell growth. Transwell assays were performed to assess cell migration. Xenografts were used to evaluate tumour growth and metastasis in vivo. Finally, KDM4B expression levels associated with copy number variation (CNV) and cell cycle stage were evaluated based on single-cell RNA sequencing data. KDM4B was expressed at higher levels in tumour tissues than in the adjacent normal tissues. High levels of KDM4B are associated with worse pathological features and poorer prognosis. KDM4B also promotes cell proliferation and migration in vitro, as well as tumour growth and metastasis in vivo. Tumour cells with high KDM4B expression exhibited higher CNV levels and a greater proportion of cells in the G1/S transition phase. Our results confirm that KDM4B promotes the progression of clear cell renal carcinoma, is correlated with poor prognosis, and may be related to high levels of CNV and cell cycle progression.

Bile acid profiles in choledocholithiasis and their ability to predict common bile duct stone recurrence after endoscopic retrograde cholangiopancreatography treatment.

BACKGROUND/PURPOSE: This study aimed to explore the alteration of bile acid (BA) profiles in patients with choledocholithiasis (CDC) and construct a prediction model for evaluating the risk of common bile duct stone (CBDS) recurrence after endoscopic treatment. METHODS: A total of 320 patients (218 with CDC and 102 with nonneoplastic polyps) were enrolled. The serum BA profiles were compared between groups. Both diagnostic score of CDC and prognostic risk score of CBDS recurrence based on BAs were established by least absolute shrinkage and selection operator regression. A nomogram model was developed combining the risk score with clinical factors selected by Cox regression analysis. RESULTS: The BA profiles of patients with CDC were different from those of controls, which was mainly exhibited by an increase in conjugated BAs and the ratio of primary to secondary BA and a decrease in the hydrophobic BA ratio. The diagnostic model effectively distinguished patients with CDC from controls with an area under the curve of 0.763. Patients with CDC with a low BA risk score exhibited a high possibility of stone recurrence-free survival. The hazard ratios of history of cholecystectomy, multiple stones (n ≥ 2), bile duct angulation ≥132.7, and low BA risk score were 2.43, 4.18, 0.42, and 0.31, respectively. CONCLUSIONS: The serum BA profiles were altered in patients with CDC and could be used to distinguish patients with CDC from controls. The nomogram model developed for predicting the risk of CBDS recurrence in patients with CDC after endoscopic retrograde cholangiopancreatography treatment had high accuracy and clinical usability.

Machine learning-based prognosis signature for survival prediction of patients with clear cell renal cell carcinoma.

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.

The Dual Role of USP11 in Cancer.

Ubiquitination is one of the most crucial ways of protein degradation and plays an indispensable role in various living activities of cells. The deubiquitinating enzyme (DUB) is the main practitioner of the reversal of ubiquitination. Up till the present moment, nearly 100 DUBs from six families have been confirmed. USP11 is a member of the largest subfamily of cysteine protease DUBs, involving in the regulation of cell cycle, DNA repair, regulating signaling pathways, tumor development, and other important biological behaviors. This review briefly describes the structure and function of USP11 and comprehensively describes its dual role in tumorigenesis and development, as well as its targeted therapy.

Robust Prediction of Prognosis and Immunotherapeutic Response for Clear Cell Renal Cell Carcinoma Through Deep Learning Algorithm.

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.

A novel ferroptosis-related gene signature associated with cell cycle for prognosis prediction in patients with clear cell renal cell carcinoma.

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.

Machine learning-based pathomics signature could act as a novel prognostic marker for patients with clear cell renal cell carcinoma.

BACKGROUND: Traditional histopathology performed by pathologists through naked eyes is insufficient for accurate survival prediction of clear cell renal cell carcinoma (ccRCC). METHODS: A total of 483 whole slide images (WSIs) data from three patient cohorts were retrospectively analyzed. We performed machine learning algorithm to identify optimal digital pathological features and constructed machine learning-based pathomics signature (MLPS) for ccRCC patients. Prognostic performance of the prognostic model was also verified in two independent validation cohorts. RESULTS: MLPS could significantly distinguish ccRCC patients with high survival risk, with hazard ratio of 15.05, 4.49 and 1.65 in three independent cohorts, respectively. Cox regression analysis revealed that the MLPS could act as an independent prognostic factor for ccRCC patients. Integration nomogram based on MLPS, tumour stage system and tumour grade system improved the current survival prediction accuracy for ccRCC patients, with area under curve value of 89.5%, 90.0%, 88.5% and 85.9% for 1-, 3-, 5- and 10-year disease-free survival prediction. DISCUSSION: The machine learning-based pathomics signature could act as a novel prognostic marker for patients with ccRCC. Nevertheless, prospective studies with multicentric patient cohorts are still needed for further verifications.

CDK1/FBXW7 facilitates degradation and ubiquitination of MLST8 to inhibit progression of renal cell carcinoma.

Recent studies have reported that MLST8 is upregulated in many malignant tumors. Nevertheless, the underlying molecular mechanism is still unclear. The aim of this work was to investigate how MLST8 contributes to the development and progression of clear cell renal cell carcinoma (ccRCC). MLST8 is an oncogenic protein in the TCGA database and ccRCC clinical specimens. We also ascertain that MLST8 interacts with FBXW7, which was universally regarded as an E3 ubiquitin ligase. MLST8 can be degraded and ubiquitinated by tumor suppressor FBXW7. FBXW7 recognizes a consensus motif (T/S) PXX (S/T/D/E) of MLST8 and triggers MLST8 degradation via the ubiquitin-proteasome pathway. Strikingly, the activated cyclin dependent kinase 1 (CDK1) kinase engages in the MLST8 phosphorylation required for FBXW7-mediated degradation. In vitro, we further prove that MLST8 is an essential mediator of FBXW7 inactivation-induced tumor growth, migration, and invasion. Furthermore, the MLST8 and FBXW7 proteins are negatively correlated in human renal cancer specimens. Our findings suggest that MLST8 is a putative oncogene that functions via interaction with FBXW7, and inhibition MLST8 could be a potential future target in ccRCC treatment.

USP39 promotes tumorigenesis by stabilizing and deubiquitinating SP1 protein in hepatocellular carcinoma.

Deubiquitinating enzyme (DUB) can hydrolyze ubiquitin molecules from the protein bound with ubiquitin, and reversely regulate protein degradation. The ubiquitin-specific proteases (USP) family are cysteine proteases, which owns the largest members and diverse structure among the currently known DUB. The important roles of ubiquitin-specific peptidase39 (USP39) in cancer have been widely investigated. However, little is known about the putative de-ubiquitination function of USP39 in hepatocellular carcinoma (HCC) and the mechanisms of USP39 regulating tumor growth. Here, we used bioinformatics methods to reveal that USP39 expression is significantly upregulated in several cancer database. High expression of USP39 is correlated with poor prognosis of HCC patients. Then, we identify the specificity protein 1 (SP1), as a novel subtract of the USP39. We observe that USP39 stabilizes SP1 protein and prolongs its half-life by promoting its deubiquitylation pathway. In addition, our results show USP39 promotes cell proliferation by SP1-depenet manner in vivo and vitro. Knocking-down of USP39 promotes the cell apoptosis and arrest of the cell cycle, whereas SP1 forcefully reversed these effects. Taken together, our results suggest that USP39 participates the deubiquitylation of SP1 protein, providing new pathway for understand the upstream signaling for oncogene SP1.

A Novel Nomogram Based on Machine Learning-Pathomics Signature and Neutrophil to Lymphocyte Ratio for Survival Prediction of Bladder Cancer Patients.

Traditional histopathology performed by pathologists through naked eyes is insufficient for accurate survival prediction of bladder cancer (BCa). In addition, how neutrophil to lymphocyte ratio (NLR) could be used for prognosis prediction of BCa patients has not been fully understood. In this study, we collected 508 whole slide images (WSIs) of hematoxylin-eosin strained BCa slices and NLR value from the Shanghai General Hospital and The Cancer Genome Atlas (TCGA), which were further processed for nuclear segmentation. Cross-verified prediction models for predicting clinical prognosis were constructed based on machine learning methods. Six WSIs features were selected for the construction of pathomics-based prognosis model, which could automatically distinguish BCa patients with worse survival outcomes, with hazard ratio value of 2.19 in TCGA cohort (95% confidence interval: 1.63-2.94, p <0.0001) and 3.20 in General cohort (95% confidence interval: 1.75-5.87, p = 0.0014). Patients in TCGA cohort with high NLR exhibited significantly worse clinical survival outcome when compared with patients with low NLR (HR = 2.06, 95% CI: 1.29-3.27, p <0.0001). External validation in General cohort also revealed significantly poor prognosis in BCa patients with high NLR (HR = 3.69, 95% CI: 1.83-7.44 p <0.0001). Univariate and multivariate cox regression analysis proved that both the MLPS and the NLR could act as independent prognostic factor for overall survival of BCa patients. Finally, a novel nomogram based on MLPS and NLR was constructed to improve their clinical practicability, which had excellent agreement with actual observation in 1-, 3- and 5-year overall survival prediction. Decision curve analyses both in the TCGA cohort and General cohort revealed that the novel nomogram acted better than both the tumor grade system in prognosis prediction. Our novel nomogram based on MLPS and NLR could act as an excellent survival predictor and provide a scalable and cost-effective method for clinicians to facilitate individualized therapy. Nevertheless, prospective studies are still needed for further verifications.

Clinical use of machine learning-based pathomics signature for diagnosis and survival prediction of bladder cancer.

Traditional histopathology performed by pathologists by the naked eye is insufficient for accurate and efficient diagnosis of bladder cancer (BCa). We collected 643 H&E-stained BCa images from Shanghai General Hospital and The Cancer Genome Atlas (TCGA). We constructed and cross-verified automatic diagnosis and prognosis models by performing a machine learning algorithm based on pathomics data. Our study indicated that high diagnostic efficiency of the machine learning-based diagnosis model was observed in patients with BCa, with area under the curve (AUC) values of 96.3%, 89.2%, and 94.1% in the training cohort, test cohort, and external validation cohort, respectively. Our diagnosis model also performed well in distinguishing patients with BCa from patients with glandular cystitis, with an AUC value of 93.4% in the General cohort. Significant differences were found in overall survival in TCGA cohort (hazard ratio (HR) = 2.09, 95% confidence interval (CI): 1.56-2.81, P < .0001) and the General cohort (HR = 5.32, 95% CI: 2.95-9.59, P < .0001) comparing patients with BCa of high risk vs low risk stratified by risk score, which was proved to be an independent prognostic factor for BCa. The integration nomogram based on our risk score and clinicopathologic characters displayed higher prediction accuracy than current tumor stage/grade systems, with AUC values of 77.7%, 83.8%, and 81.3% for 1-, 3-, and 5-y overall survival prediction of patients with BCa. However, prospective studies are still needed for further verifications.

Tumor suppressor DCAF15 inhibits epithelial-mesenchymal transition by targeting ZEB1 for proteasomal degradation in hepatocellular carcinoma.

Epithelial-mesenchymal transition (EMT) is an evolutionarily conserved developmental program that has been implicated in tumorigenesis and confers metastatic properties upon cancer cells. ZEB1 is a master transcription factor that activates the EMT process in various cancers. ZEB1 is reportedly degraded through the ubiquitin proteasome pathway, but the underlying molecular mechanism of this process remains largely unknown in hepatocellular carcinoma (HCC). Here, we identified ZEB1 as a substrate of the CRL4-DCAF15 (DDB1 and CUL4 associated factor 15) E3 ubiquitin ligase complex. DCAF15 acts as an adaptor that specifically recognizes the N-terminal zinc finger domain of ZEB1, then triggers its degradation via the ubiquitin-proteasome pathway. DCAF15 knockdown led to upregulation of ZEB1 and activation of EMT, whereas overexpression of DCAF15 suppressed ZEB1 and inhibited EMT. DCAF15 knockdown also promoted HCC cell proliferation and invasion in a ZEB1-dependent manner. In HCC patients, low DCAF15 expression was predictive of an unfavorable prognosis. These findings reveal the distinct molecular mechanism by which DCAF15 suppresses HCC malignancy and provides insight into the relationship between the CUL4-DCAF15 E3 ubiquitin ligase complex and ZEB1 in HCC.

Clinical use of a machine learning histopathological image signature in diagnosis and survival prediction of clear cell renal cell carcinoma.

Due to the complicated histopathological characteristics of renal neoplasms, traditional distinguishing of clear cell renal cell carcinoma (ccRCC) by naked eyes of experienced pathologist remains labor intensive and time consuming. Here, we extracted quantitative features of hematoxylin-eosin-stained images using CellProfiler and performed machine learning method to develop and verify a novel computational recognition of digital pathology for diagnosis and prognosis of ccRCC patients in the training, test and external validation cohort. The diagnostic model based on digital pathology could accurately distinguish ccRCC from normal renal tissues, with area under the curve (AUC) of 96.0%, 94.5% and 87.6% in the training, test and external validation cohorts, respectively. It could also accurately distinguish ccRCC from other pathological types of renal cancer, with AUC values of 97.0% and 81.4% in the Cancer Genome Atlas (TCGA) cohort and General cohort. We next developed and verified a computational recognition prognosis model with risk score. There was a significant difference in disease-free survival comparing patients with high vs low risk score in training cohort (hazard ratio = 2.72, P < .0001) and validation cohort (hazard ratio = 9.50, P = .0091). The integrated nomogram based on our computational recognition risk score and clinicopathologic factors demonstrated excellent survival prediction for ccRCC patients, with increased accuracy by 6.6% in patients from Shanghai General Hospital and by 2.5% in patients from TCGA cohort when compared to current tumor stages/grade systems. These results indicate the potential clinical use of our machine learning histopathological image signature in diagnosis and survival prediction of ccRCC.

Ubiquitin ligase KLHL2 promotes the degradation and ubiquitination of ARHGEF7 protein to suppress renal cell carcinoma progression.

Recent studies have revealed that ARHGEF7 is upregulated in many malignant tumors, but the underlying molecular mechanisms to this response remain to be fully elucidated. In this study, we confirm that ARHGEF7 physically interacts with KLHL2, which was previously identified to be an E3 ubiquitin ligase. KLHL2 is capable of promoting ARHGEF7 degradation via the ubiquitin-proteasome pathway. We identify that the Kelch domain of KLHL2 is necessary for binding with ARHGEF7 and downstream activities. In addition, we find that ARHGEF7 is overexpressed in clear cell renal cell carcinoma (ccRCC) specimens, and that the level of expression negatively correlates with that of KLHL2. Moreover, we utilize knockdown loss-of-function assays to demonstrate that ARHGEF7 in 786-O and A498 cell lines can act as a regulator of cell proliferation, migration and invasion, and that these effects can be reversed by KLHL2 inactivation. Taken together, our data suggest that ARHGEF7 is a putative oncogene that functions via an interaction with KLHL2, and control of ARHGEF7 can be a potential future target to inhibit tumor progression.

Biomarkers of the Response to Immune Checkpoint Inhibitors in Metastatic Urothelial Carcinoma.

The mechanisms underlying the resistance to immune checkpoint inhibitors (ICIs) therapy in metastatic urothelial carcinoma (mUC) patients are not clear. It is of great significance to discern mUC patients who could benefit from ICI therapy in clinical practice. In this study, we performed machine learning method and selected 10 prognostic genes for constructing the immunotherapy response nomogram for mUC patients. The calibration plot suggested that the nomogram had an optimal agreement with actual observations when predicting the 1- and 1.5-year survival probabilities. The prognostic nomogram had a favorable discrimination of overall survival of mUC patients, with area under the curve values of 0.815, 0.752, and 0.805 for ICI response (ICIR) prediction in the training cohort, testing cohort, and combined cohort, respectively. A further decision curve analysis showed that the prognostic nomogram was superior to either mutation burden or neoantigen burden for overall survival prediction when the threshold probability was >0.35. The immune infiltrate analysis indicated that the low ICIR-Score values in mUC patients were significantly related to CD8+ T cell infiltration and immune checkpoint-associated signatures. We also identified differentially mutated genes, which could act as driver genes and regulate the response to ICI therapy. In conclusion, we developed and validated an immunotherapy-responsive nomogram for mUC patients, which could be conveniently used for the estimate of ICI response and the prediction of overall survival probability for mUC patients.

A Novel m6A Gene Signature Associated With Regulatory Immune Function for Prognosis Prediction in Clear-Cell Renal Cell Carcinoma.

The important role of N6-methyladenosine (m6A) RNA methylation regulator in carcinogenesis and progression of clear-cell renal cell carcinoma (ccRCC) is poorly understood by now. In this study, we performed comprehensive analyses of m6A RNA methylation regulators in 975 ccRCC samples and 332 adjacent normal tissues and identified ccRCC-related m6A regulators. Moreover, the m6A diagnostic score based on ccRCC-related m6A regulators could accurately distinguish ccRCC from normal tissue in the Meta-cohort, which was further validated in the independent GSE-cohort and The Cancer Genome Atlas-cohort, with an area under the curve of 0.924, 0.867, and 0.795, respectively. Effective survival prediction of ccRCC by m6A risk score was also identified in the Cancer Genome Atlas training cohort and verified in the testing cohort and the independent GSE22541 cohort, with hazard ratio values of 3.474, 1.679, and 2.101 in the survival prognosis, respectively. The m6A risk score was identified as a risk factor of overall survival in ccRCC patients by the univariate Cox regression analysis, which was further verified in both the training cohort and the independent validation cohort. The integrated nomogram combining m6A risk score and predictable clinicopathologic factors could accurately predict the survival status of the ccRCC patients, with an area under the curve values of 85.2, 82.4, and 78.3% for the overall survival prediction in 1-, 3- and 5-year, respectively. Weighted gene co-expression network analysis with functional enrichment analysis indicated that m6A RNA methylation might affect clinical prognosis through regulating immune functions in patients with ccRCC.