Cancer-testis antigens are considered clinically attractive targets for cancer treatment, but their functions and mechanisms are not well elucidated. Here, based on comprehensive bioinformatics analyses, we identify PRAME, a nuclear cancer-testis antigen, as a potential regulator of metastasis in clear cell renal cell carcinoma (ccRCC). Subsequent RNA-Seq and functional studies illustrate that Netrin-4 (NTN4) is a major downstream effector of PRAME, involved in its oncogenic functions. Mechanism analyses reveal that PRAME interacts with the transcription factor CCAAT/enhancer-binding protein beta (C/EBPβ) and the histone methyltransferase enhancer of zeste homolog 2 (EZH2) simultaneously, thereby forming a ternary complex. Subsequently, this complex co-occupies the NTN4 promoter locus, leading to increased trimethylation of histone H3 lysine 27 and epigenetic repression of NTN4 expression, resulting in AKT activation and promotion of ccRCC development. Interestingly, C/EBPβ is characterized to stimulate PRAME expression by binding to the PRAME promoter. Additionally, a cell-permeable peptide has been designed to disrupt the ternary complex and inhibit ccRCC progression in tumor cells and patient-derived xenografts. Thus, our findings not only provide new insights into the prominent role of PRAME in mediating C/EBPβ and EZH2 regulation of NTN4 and tumor metastasis, but also highlight a promising strategy for ccRCC therapy by targeting the C/EBPβ-PRAME-EZH2 complex.

Lactylation is a novel lysine acylation modification, and its function and exact mechanisms remain unclear. The responsiveness to combined targeted and immunotherapy continues to be a major challenge in cancer treatment. Here, we discovered that lysine lactylation (Kla) is specifically downregulated in targeted therapy-resistant renal cancer cells and tissues. The lactylation status of YAP at lysine 90 (K90) regulates its nuclear translocation, which is critical for the expression of epithelial-mesenchymal transition (EMT)-related genes and the PD-L1 gene. Furthermore, we identified HDAC1 as the “eraser” of YAP-K90 lactylation. By transfecting YAP-K90T (a lactylation mimic), we enhanced antitumor immune activity. In xenograft models established in huCD34+HSC NCG mice, we consistently found that HDAC1 inhibition enhances the response to combination therapy by increasing YAP lactylation and promoting CD8⁺ T cell tumor infiltration. These findings reveal lactylation as a key mechanism in targeted drug response and identify inhibiting YAP delactylation as a promising strategy for renal cell carcinoma treatment.

The progression and therapeutic response of clear cell renal cell carcinoma (ccRCC) are critically shaped by the complex interactions between tumor cell heterogeneity and the tumor immune microenvironment (TIME). However, a comprehensive classification of the ccRCC ecosystem and its clinical relevance is lacking. To address this, we utilized comprehensive bioinformatics approaches to analyze ten public single-cell RNA sequencing datasets from 194 samples across 118 ccRCC patients. Across 1,172,154 cells, we identified four TIME subtypes (immune activation, innate immunity, immunosuppressive myeloid [ISM], and immune exclusion) and six functional states of tumor cells (metabolic, angiogenic, stress-responsive, antigen-presenting, cell cycling, and epithelial-mesenchymal transition [EMT]). The interplay between these components defined four immune ecosystems, among which the ISM subtype, coupled with the EMT tumor state was associated with the poorest prognosis. Using machine learning-based prognostic modeling, we highlighted FKBP10 as a critical prognostic gene. Mechanistically, we demonstrated that FKBP10 not only promoted EMT but also activated the MEK/ERK/ELF3 signaling axis, leading to an increased secretion of CXCL8 by tumor cells. Tumor-derived CXCL8, in turn, drove macrophage M2 polarization and myeloid-derived suppressor cell (MDSC) recruitment, thereby reinforcing an immunosuppressive TIME. Furthermore, targeting FKBP10 synergized with anti-PD-1 therapy in suppressing tumor growth in vivo. Our work provides a comprehensive molecular atlas of the ccRCC ecosystem, establishes FKBP10 as a key regulator of immune suppression, and highlights its potential as a therapeutic target for personalized immunotherapy.

ABSTRACT Background TFE3‐rearranged renal cell carcinoma (TFE3‐rRCC) is a rare, aggressive subtype that predominantly affects adolescents and young adults. Its marked morphologic heterogeneity can delay recognition and downstream confirmatory testing. Methods We assembled a two‐center retrospective cohort of patients < 30 years with renal cell carcinoma (n = 228; 59 TFE3‐rRCC), using fluorescence in situ hybridization (FISH) as the reference standard. Model development was performed in a development cohort (n = 129), followed by independent external validation (n = 99). We developed TFE3‐DualNet, an ensemble of weakly supervised CLAM models trained on routine hematoxylin and eosin (H&E) whole‐slide images (WSIs) using patch embeddings extracted from two pathology foundation models (UNI and CHIEF). We compared performance with three immunohistochemistry (IHC) scoring methods and a feature‐fusion CLAM baseline using concatenated H&E‐derived UNI and CHIEF features, and assessed interpretability by attention mapping. Results In the external validation cohort, TFE3‐DualNet achieved an area under the receiver operating characteristic curve (AUROC) of 0.932, with accuracy 0.879, sensitivity 0.893, and specificity 0.873. The model outperformed IHC scoring methods (AUROC 0.793–0.819; all p < 0.05) and exceeded the feature‐fusion baseline (AUROC 0.906). Attention hotspots localized to diagnostically relevant tumor regions and showed concordance with TFE3 IHC patterns. Conclusions TFE3‐DualNet showed encouraging performance as an interpretable H&E WSI‐based screening model for TFE3‐rRCC in young patients, supporting its potential use to prioritize confirmatory testing and pathologist review in routine diagnostic workflows.

Background:Despite the widespread use of immune checkpoint inhibitors (ICIs) in advanced clear cell renal cell carcinoma (ccRCC), therapeutic resistance persists. The prognostic and immunomodulatory role of ammonia metabolism remains unclear. Methods:We leveraged public RNA-seq data and machine learning to identify ammonia metabolism pathways through enrichment analysis of programmed cell death-related genes. Employing multi-omics data from ccRCC patients, we developed an ammonia metabolism risk score (AMRS) via machine learning, which was validated externally and in immunotherapy cohorts. Additionally, scRNA-seq, WGCNA, TMB analysis, and in vitro assays were performed to characterize the model's functional basis. Results:From 147 prognostic ammonia metabolism-related genes in TCGA, a 4-gene random forest model was constructed using LASSO and multivariate Cox regression. This model demonstrated robust predictive accuracy in external validation (3/5/7-year AUCs: 0.710/0.721/0.771). High-risk patients showed significantly elevated mortality in external cohorts (HR = 4.23, 95% CI 1.57-11.42, p = 0.002) and multiple ICI cohorts (HR = 1.30-1.69, p < 0.05). Functional validation via CSAD-targeted siRNA knockdown suppressed migration and invasion by >44% (p < 0.05) across four ccRCC cell lines. Conclusions:Our integrated approach overcomes modeling constraints from limited samples and high-dimensional data and establishes a novel ammonia metabolism-related prognostic signature for ccRCC. CSAD emerges as a promising biomarker warranting further investigation.

Resistance to cisplatin-based chemotherapy remains a major barrier to effective systemic treatment of bladder cancer, underscoring the need for predictive biomarkers and therapeutic targets. Here, we identify YAP1-K90la as a functional post-translational modification that causally drives cisplatin resistance. Across multi-center clinical cohorts, elevated YAP1-K90la levels were associated with poor therapeutic outcomes and outperformed total YAP1 expression in predicting cisplatin responsiveness. Mechanistically, YAP1-K90la enhances YAP1 nuclear localization and transcriptional activity to induce a FOSL1-dependent program that suppresses ferroptosis and promotes cell survival under cisplatin stress. Notably, AARS1 and SIRT1 function as the "writer" and "eraser" of YAP1-K90la, respectively, with SMURF2-mediated ubiquitination of SIRT1 stabilizing YAP1-K90la and driving resistance. Targeting YAP1-K90la using a cell-penetrating peptide restored ferroptotic vulnerability and sensitized bladder cancer cells to cisplatin. Collectively, these findings reveal the YAP1-K90la/FOSL1 pathway that drives cisplatin resistance and position YAP1-K90la as a clinically actionable biomarker and therapeutic target in bladder cancer.

Background Therapeutic resistance limits durable survival in advanced/metastatic renal cell carcinoma (RCC) treated with first-line tyrosine kinase inhibitor (TKI) plus immune checkpoint inhibitor (ICI). We sought to define key resistance drivers and actionable targets.Methods Integrated RNA sequencing of cabozantinib-resistant RCC cells, lipid metabolomics, and PD-L1 correlation analyses identified ENPP2 as a candidate driver. Its role in TKI resistance and survival signaling was validated by apoptosis, CCK-8, and colony formation assays in vitro and by nude-mouse xenograft models in vivo. ELISA, flow cytometry and tumor cell-T-cell co-culture assays were used to dissect ENPP2-dependent CD8+ T-cell dysfunction. The therapeutic benefit of pharmacologic ATX inhibition combined with standard TKI-ICI regimens was tested in RCC patient-derived xenograft models.Results The ATX-LPA axis conferred TKI resistance via constitutive AKT/mTOR activation and promoted immune evasion by upregulating PD-L1 through TBK1/IRF3 signaling, thereby impairing intratumoral CD8+ T-cell function. ENPP2 enhanced PD-L1 transcription by facilitating IRF3 nuclear translocation and its direct recruitment to the CD274 promoter. ATX inhibition improved the antitumor efficacy of TKI-ICI therapy in preclinical models.Conclusions Targeting the ATX-LPA axis represents a promising strategy to overcome resistance to current TKI-ICI combinations.

Renal cell carcinoma (RCC) remains a formidable clinical challenge, characterized by a high propensity for metastasis and the frequent emergence of intrinsic or acquired resistance to targeted therapies. However, the molecular mechanisms underlying sunitinib resistance and tumor progression in RCC are not fully understood. This study aims to identify Twinfilin actin-binding protein (TWF2) as a key mediator of tumor aggressiveness and therapeutic resistance. TWF2 expression is markedly upregulated in RCC cells, particularly in sunitinib-resistant subtypes, and significantly associated with poor prognosis and therapeutic nonresponsiveness. Functional analyses demonstrate that TWF2 promotes RCC cell invasion, migration, metastasis, and sunitinib resistance by inhibiting the Hippo signaling. Mechanistically, TWF2 interacts with Yes-associated protein (YAP) via the binding residues: TWF2 M99 and YAP M225. By competitively displacing large tumor suppressor kinase 1, TWF2 prevents YAP ubiquitination and degradation, leading to its stabilization and subsequent nuclear translocation. Mutation of the M99 residue abolishes the tumor-promoting activity of TWF2. Furthermore, salvianolic acid E is identified as a small-molecule inhibitor of the TWF2-YAP interaction, and synergistically enhances sunitinib efficacy in RCC cell lines and patient-derived xenograft models. These findings highlight TWF2 as a promising therapeutic target for overcoming drug resistance in RCC.

The heterogeneity of cancer-associated fibroblasts (CAFs) could affect the response to immune checkpoint inhibitor (ICI) therapy. However, limited studies have investigated the role of inflammatory CAFs (iCAFs) in ICI therapy using pan-cancer single-cell RNA sequencing (scRNA-seq) and spatial transcriptomics sequencing (ST-seq) analysis. We performed pan-cancer scRNA-seq and ST-seq analyses to identify the subtype of GSN+ iCAFs, exploring its spatial distribution characteristics in the context of ICI therapy. The pan-cancer scRNA-seq and bulk RNA-seq data are incorporated to develop the Caf.Sig model, which predicts ICI response based on CAF gene signatures and machine learning approaches. Comprehensive scRNA-seq analysis, along with in vivo and in vitro experiments, investigates the mechanisms by which GSN+ iCAFs influence ICI efficacy. The Caf.Sig model demonstrates well performances in predicting ICI therapy response in pan-cancer patients. A higher proportion of GSN+ iCAFs is observed in ICI non-responders compared to responders in the pan-cancer landscape and clear cell renal cell carcinoma (ccRCC). Using real-world immunotherapy data, the Caf.Sig model accurately predicts ICI response in pan-cancer, potentially linked to interactions between GSN+ iCAFs and CD8+ Tex cells. ST-seq analysis confirms that interactions and cellular distances between GSN+ iCAFs and CD8+ exhausted T (Tex) cells impact ICI efficacy. In a co-culture system of primary CAFs, primary tumour cells and CD8+ T cells, downregulation of GSN on CAFs drives CD8+ T cells towards a dysfunctional state in ccRCC. In a subcutaneously tumour-grafted mouse model, combining GSN overexpression with ICI treatment achieves optimal efficacy in ccRCC. Our study provides the Caf.Sig model as an outperforming approach for patient selection of ICI therapy, and advances our understanding of CAF biology and suggests potential therapeutic strategies for upregulating GSN in CAFs in cancer immunotherapy.

IntroductionImmune checkpoint inhibitors (ICIs) have revolutionized the treatment landscape for advanced cancers, yet their efficacy remains heterogeneous among patients. Tumor mutation burden (TMB) has been extensively explored as a potential biomarker for predicting ICI response. However, its application is limited by several factors, including inconsistent predictive power across different tumor types and the lack of a clear relationship with overall survival (OS). This study aimed to explore the complex interplay between TMB and the tumor microenvironment (TME) and to identify novel predictive biomarkers that can enhance the precision of ICI therapy across multiple cancer types.MethodsWe systematically collected and analyzed genomic and clinical data from patients receiving anti-PD-1/PD-L1 immunotherapy across multiple cohorts. Our dataset included information from The Cancer Genome Atlas (TCGA) pan-cancer database and various ICI clinical trials. We first screened immunosuppression-related genes (ISRGs) that might interfere with TMB's predictive role by analyzing the survival data and gene expression profiles of patients. Using LASSO regression and multivariable Cox proportional hazards analysis, we constructed a risk model based on these ISRGs. The model's predictive ability was rigorously validated in multiple independent cohorts. Additionally, we employed algorithms such as CIBERSORT and ESTIMATE to assess the correlation between the risk score and TME components. To further explore the therapeutic implications of our findings, we focused on RPLP0, a ribosomal protein that emerged as a robust biomarker in our model. We investigated its expression in tumor tissues and evaluated the impact of its knockdown on immunotherapeutic efficacy using in vitro and in vivo experiments.ResultsOur comprehensive analysis revealed that the predictive power of TMB varies significantly across different cancer types and is highly dependent on its interaction with the TME. In tumors with a favorable immune microenvironment, characterized by high CD8+ T cell infiltration and M1 macrophage presence, TMB maintained its predictive ability. However, in immunosuppressive microenvironments, TMB alone failed to accurately predict patient outcomes. We identified 304 ISRGs and developed a 10-gene risk signature that demonstrated reliable prognostic predictive ability in both ICI cohorts and TCGA pan-cancer. The risk score derived from this model was significantly associated with stromal components and an immunosuppressive TME, characterized by elevated levels of M0 macrophages and activated mast cells. Notably, RPLP0 was identified as the most robust predictive marker during model building. We demonstrated its abnormal overexpression in tumor tissues and further showed that intratumoral RPLP0 knockdown in a subcutaneous bladder cancer model could enhance the efficacy of immunotherapy. The combination of RPLP0 knockdown and anti-PD-1 treatment resulted in significantly suppressed tumor growth and prolonged survival in mice, accompanied by elevated levels of IFN-γ and TNF-α in serum samples, indicating enhanced anti-tumor immunity.ConclusionThis study establishes a reliable risk model that complements TMB in guiding treatment decisions for ICI therapy. By incorporating the interaction between TMB and the TME, our model provides a more accurate prediction of patient prognosis and treatment response across multiple cancer types. The risk score's association with immunosuppressive TME components underscores the importance of considering the tumor's microenvironment in treatment planning. Furthermore, our findings highlight RPLP0 as a promising therapeutic target for combination immunotherapy. The robust predictive ability of our model across various cohorts and its potential to improve therapeutic outcomes offer new insights and directions for enhancing the efficacy of ICI therapy. Future research should focus on further validating this model in larger and more diverse cohorts, refining the gene set selection process, and exploring the specific mechanisms through which the identified biomarkers influence the TME and treatment response.

The tumour microenvironment (TME) drives bladder cancer (BLCA) progression. Targeting the TME has emerged as a promising strategy for BLCA treatment in recent years. Furthermore, checkpoint blockade therapies are only beneficial for a minority of patients with BLCA, and drug resistance is a barrier to achieving significant clinical effects of anti-programmed cell death protein-1 (PD-1)/programmed death protein ligand-1 (PD-L1) therapy. In this study, higher low-density lipoprotein receptor-related protein 1 (LRP1) levels were related to a poorer prognosis for patients with various cancers, including those with higher grades and later stages of BLCA. Enrichment analysis demonstrated that LRP1 plays a role in the epithelial-mesenchymal transition (EMT), NOTCH signalling pathway, and ubiquitination. LRP1 knockdown in BLCA cells delayed BLCA progression both in vivo and in vitro. Furthermore, LRP1 knockdown suppressed EMT, reduced DLL4-NOTCH2 signalling activity, and downregulated M2-like macrophage polarisation. Patients with BLCA and higher LRP1 levels responded weakly to anti-PD-1 therapy in the IMvigor210 cohort. Moreover, LRP1 knockdown enhanced the therapeutic effects of anti-PD-1 in mice. Taken together, our findings suggest that LRP1 is a potential target for improving the efficacy of anti-PD-1/PD-L1 therapy by preventing EMT and M2-like macrophage polarisation by blocking the DLL4-NOTCH2 axis.

IntroductionBladder cancer (BC) is a significant carcinoma of the urinary system that has a high incidence of morbidity and death owing to the challenges in accurately identifying people with early-stage BC and the lack of effective treatment options for those with advanced BC. Thus, there is a need to define new markers of prognosis and prediction.MethodsIn this study, we have performed a comprehensive proteomics experiment by label-free quantitative proteomics to compare the proteome changes in the serum of normal people and bladder cancer patients—the successful quantification of 2064 Quantifiable proteins in total. A quantitative analysis was conducted to determine the extent of changes in protein species' relative intensity and reproducibility. There were 43 upregulated proteins and 36 downregulated proteins discovered in non-muscle invasive bladder cancer and normal individuals. Sixty-four of these proteins were elevated, and 51 were downregulated in muscle-invasive and non-muscle-invasive bladder cancer, respectively. Functional roles of differentially expressed proteins were annotated using Gene Ontology (GO) and Clusters of Orthologous Groups of Proteins (COG). To analyze the functions and pathways enriched by differentially expressed proteins, GO enrichment analysis, protein domain analysis, and KEGG pathway analysis were performed. The proteome differences were examined and visualized using radar plots, heat maps, bubble plots, and Venn diagrams.ResultsAs a result of combining the Venn diagram with protein-protein interactions (PPIs), Chloride intracellular channel 1 (CLIC1) was identified as the primary protein. Using the Gene Set Cancer Analysis (GSCA) website, the influence of CLIC1 on immune infiltration was analyzed. A negative correlation between CD8 naive and CLIC1 levels was found. For validation, immunohistochemical (IHC), qPCR, and western blotting (WB) were performed.Further, we found that CLIC1 was associated with a poor prognosis of bladder cancer in survival analysis.DiscussionOur research screened CLIC1 as a tumor-promoting protein in bladder cancer for the first time using serum mass spectrometry. And CLIC1 associated with tumor stage, and immune infiltrate. The prognostic biomarker and therapeutic target CLIC1 may be new for bladder cancer patients.

BackgroundClear cell renal cell carcinoma (ccRCC) is a highly invasive and metastatic subtype of kidney malignancy and is correlated with metabolic reprogramming for adaptation to the tumor microenvironment comprising infiltrated immune cells and immunomodulatory molecules. The role of immune cells in the tumor microenvironment (TME) and their association with abnormal fatty acids metabolism in ccRCC remains poorly understood.MethodRNA-seq and clinical data of KIRC from The Cancer Genome Atlas (TCGA) and E-MTAB-1980 from the ArrayExpress dataset. The Nivolumab group and Everolimus group of the CheckMate 025 study, the Atezolizumab arm of IMmotion150 and the Atezolizumab plus Bevacizumab group of IMmotion151 cohort were obtained for subsequent analysis. After differential expression genes identification, the signature was constructed through univariate Cox proportional hazard regression and simultaneously the least absolute shrinkage and selection operator (Lasso) analysis and the predictive performance of our signature was assessed by using receiver operating characteristic (ROC), Kaplan-Meier (KM) survival analysis, nomogram, drug sensitivity analysis, immunotherapeutic effect analysis and enrichment analysis. Immunohistochemistry (IHC), qPCR and western blot were performed to measure related mRNA or protein expression. Biological features were evaluated by wound healing, cell migration and invasion assays and colony formation test and analyzed using coculture assay and flow cytometry.ResultsTwenty fatty acids metabolism-related mRNA signatures were constructed in TCGA and possessed a strong predictive performance demonstrated through time-dependent ROC and KM survival analysis. Notably, the high-risk group exhibited an impaired response to anti-PD-1/PD-L1 (Programmed death-1 receptor/Programmed death-1 receptor-ligand) therapy compared to the low-risk group. The overall levels of the immune score were higher in the high-risk group. Additionally, drug sensitivity analysis observed that the model could effectively predict efficacy and sensitivity to chemotherapy. Enrichment analysis revealed that the IL6-JAK-STAT3 signaling pathway was a major pathway. IL4I1 could promote ccRCC cells' malignant features through JAK1/STAT3 signaling pathway and M2-like macrophage polarization.ConclusionThe study elucidates that targeting fatty acids metabolism can affect the therapeutic effect of PD-1/PD-L1 in TME and related signal pathways. The model can effectively predict the response to several treatment options, underscoring its potential clinical utility.

Background: Triggering receptors expressed by myeloid cells-1 (TREM1) is a receptor belonging to the immunoglobulin super -family and plays an important role in pro-inflammation in acute and chronic inflammatory disorders. However, the understanding of the immunomodulatory roles of TREM1 in the tumor microenvironment remains incomplete.Methods: The expression patterns of TREM1 mRNA in tumors and adjacent normal tissues were compared by analyzing data obtained from the Genotype-Tissue Expression and The Cancer Genome Atlas datasets. Survival analysis was performed to determine the prognostic value of TREM1. Functional enrichment analysis was applied to decipher the discrepancy in biological processes between high-and low-TREM1 groups across various cancers. The correlation between TREM1 and immune cell infiltration determined by using multiple algorithms was evaluated with the Pearson method. Four independent immunotherapy cohorts were adopted to validate the role of TREM1 as a biomarker.Results: TREM1 was elevated in most cancers as verified with clinical samples. Overexpression of TREM1 was linked with undesirable prognosis in patients. Further analysis revealed that TREM1 was positively correlated with immune response, pro -tumor pathways, and myeloid cell infiltration, while being negatively correlated with CD8+ T cell (including infiltration level and biological processes). Concordantly, tumors with high TREM1 levels were more resistant to immunotherapy. Through connective map analysis, therapeutically potential compounds like tozasertib and TPCA-1 were identified, which can be used synergistically with immunotherapy to improve the poor prognosis of patients with high TREM1 levels.Conclusion: Through a systematic and comprehensive pan-cancer analysis, we demonstrated that overexpression of TREM1 in tumors correlated closely with unfavorable outcome, infiltration of immune-suppressive cells, and immune regulation, which high-lights its potential use as a tumor prognostic biomarker and a novel target for immunotherapy.

Background: As a new form of regulated cell death, cuproptosis differs profoundly from apoptosis, ferroptosis, pyroptosis, and necroptosis. The correlation between cuproptosis and long non-coding RNAs (lncRNAs) has been increasingly studied recently. In this study, a novel cuproptosis-related lncRNA prognostic signature was developed to investigate biochemical recurrence (BCR) and tumor immune landscape in prostate cancer (PCa). Methods and Materials: The transcriptome data and clinicopathologic information of PCa patients were downloaded from The Cancer Genome Atlas (TCGA). Pearson’s correlation analysis was applied to identify lncRNAs associated with cuproptosis. Based on Cox regression analysis and the least absolute shrinkage and selection operator (LASSO) regression analysis, we developed a cuproptosis-related lncRNA prognostic model (risk score) to predict the BCR of PCa patients. Additionally, we also constructed a nomogram with the risk score and clinicopathologic features. The biological function, tumor mutation burden (TMB), immune cell infiltration, expression levels of immune checkpoint genes, and anti-cancer drug sensitivity were investigated. Results: We constructed and validated the cuproptosis-related lncRNA signature prognostic model (risk score) by six crlncRNAs. All patients were divided into the low- and high-risk groups based on the median risk score. The Kaplan–Meier (KM) survival analysis revealed that the high-risk group had shorter BCR-free survival (BCRFS). The risk score has been proven to be an independent prognostic factor of BCR in PCa patients. In addition, a nomogram of risk scores and clinicopathologic features was established and demonstrated an excellent predictive capability of BCR. The ROC curves further validated that this nomogram had higher accuracy of predicting the BCR compared to other clinicopathologic features. We also found that the high-risk group had higher TMB levels and more infiltrated immune cells. Furthermore, patients with high TMB in the high-risk group were inclined to have the shortest BCRFS. Finally, patients in the high-risk group were more susceptible to docetaxel, gefitinib, methotrexate, paclitaxel, and vinblastine. Conclusion: The novel crlncRNA signature prognostic model shows a greatly prognostic prediction value of BCR for PCa patients, extends our thought on the association of cuproptosis and PCa, and provides novel insights into individual-based treatment strategies for PCa.

Pyroptosis and necroptosis are two recently identified forms of immunogenic cell death in the tumor microenvironment (TME), indicating a crucial involvement in tumor metastasis. However, the characteristics of necroptosis and pyroptosis that define tumor microenvironment and prognosis in ccRCC patients remain unknown. We systematically investigated the transcriptional variation and expression patterns of Necroptosis and Pyroptosis related genes (NPRGs). After screening the necroptosis-pyroptosis clusters, the potential functional annotation for clusters was explored by GSVA enrichment analysis. The Necroptosis-Pyroptosis Genes (NPG) scores were used for the prognosis model construction and validation. Then, the correlations of NPG score with clinical features, cancer stem cell (CSC) index, tumor mutation burden (TMB), TME, and Immune Checkpoint Genes (ICGs) were also individually explored to evaluate the prognosis predictive values in ccRCC. Microarray screenings identified 27 upregulated and 1 downregulated NPRGs. Ten overall survival associated NPRGs were filtered to construct the NPG prognostic model indicating a better prognostic signature for ccRCC patients with lower NPG scores (P< 0.001), which was verified using the external cohort. Univariate and multivariate analyses along with Kaplan-Meier survival analysis demonstrated that NPG score prognostic model could be applied as an independent prognostic factor, and AUC values of nomogram from 1- to 5- year overall survival with good agreement in calibration plots suggested that the proposed prognostic signature possessed good predictive capabilities in ccRCC. A high-/sNPG score is proven to be connected with tumor growth and immune-related biological processes, according to enriched GO, KEGG, and GSEA analyses. Comparing patients with a high-NPG score to those with a low-NPG score revealed significant differences in clinical characteristics, growth and recurrence of malignancies (CSC index), TME cell infiltration, and immunotherapeutic response (P< 0.005), potentially making the NPG score multifunctional in the clinical therapeutic setting. Furthermore, AIM2, CASP4, GSDMB, NOD2, and RBCK1 were also found to be highly expressed in ccRCC cell lines and tumor tissues, and GASP4 and GSDMB promote ccRCC cells’ proliferation, migration, and invasion. This study firstly suggests that targeting the NPG score feature for TME characterization may lend novel insights into its clinical applications in the prognostic prediction of ccRCC.

Clear cell renal cell carcinoma (ccRCC) is the most common histological and devastating subtype of renal cell carcinoma. Necroptosis is a form of programmed cell death that causes prominent inflammatory responses. miRNAs play a significant role in cancer progression through necroptosis. However, the prognostic value of necroptosis-related miRNAs remains ambiguous. In this study, 39 necroptosis-related miRNAs (NRMs) were extracted and 17 differentially expressed NRMs between normal and tumor samples were identified using data form The Cancer Genome Atlas (TCGA). After applying univariate Cox proportional hazard regression analysis and LASSO Cox regression model, six necroptosis-related miRNA signatures were identified in the training cohort and their expression levels were verified by qRT-PCR. Using the expression levels of these miRNAs, all patients were divided into the high- and low-risk groups. Patients in the high-risk group showed poor overall survival (P<0.0001). Time-dependent ROC curves confirmed the good performance of our signature. The results were verified in the testing cohort and the entire TCGA cohort. Univariate and multivariate Cox regression models demonstrated that the risk score was an independent prognostic factor. Additionally, a predictive nomogram with good performance was constructed to enhance the implementation of the constructed signature in a clinical setting. We then employed miRBD, miRTarBase, and TargetScan to predict the target genes of six necroptosis-related miRNAs. Gene ontology and Kyoto Encyclopedia of Genes and Genomes analyses indicated that 392 potential target genes were enriched in cell proliferation-related biological processes. Six miRNAs and 59 differentially expressed target genes were used to construct an miRNA–mRNA interaction network, and 11 hub genes were selected for survival and tumor infiltration analysis. Drug sensitivity analysis revealed potential drugs that may contribute to cancer management. Hence, necroptosis-related genes play an important role in cancer biology. We developed, for the first time, a necroptosis-related miRNA signature to predict ccRCC prognosis.