Background: Clear cell renal cell carcinoma (ccRCC) is the most common and aggressive type of primary kidney cancer worldwide. Transmembrane protein 45A (TMEM45A) has been reported to be closely associated with the progression of several cancers. However, the role of TMEM45A in ccRCC remains unclear. Our study intended to explore the potential role of TMEM45A in ccRCC. Methods: Data on the expression of TMEM45A were obtained from multiple databases, including UCSC, GEPIA2, Oncomine and TIMER. Real-world samples of ccRCC and paired normal renal tissues were used to confirm the information obtained from the databases. In addition, the prognostic value of TMEM45A was evaluated. Loss-of-function assays were performed using TMEM45A-targeting lentivirus to evaluate the biological role of TMEM45A in renal cancer cells. Gene set enrichment analysis (GSEA) was performed to investigate the potential molecular mechanisms. Results: TMEM45A was significantly overexpressed in patients with ccRCC and correlated with poor overall survival and disease-free survival. In addition, the expression of TMEM45A was closely associated with various clinicopathological parameters such as histological grade and TNM stage. Knockdown of TMEM45A inhibited the proliferation and migration and promoted the apoptosis of ccRCC cells in vitro. The results of the GSEA suggested that TMEM45A was potentially involved in the promotion of epithelial–mesenchymal transition (EMT) and inflammatory response in ccRCC. Conclusion: TMEM45A was overexpressed and associated with poor survival and acted as a tumour promoter in ccRCC; therefore, might be a potential prognostic marker and therapeutic target.

Enhancer RNAs are a subclass of long non-coding RNAs transcribed from enhancer regions that play an important role in the transcriptional regulation of genes. However, their role in kidney renal clear cell carcinoma (KIRC) is largely unknown. Herein, we identified the key enhancer RNAs in KIRC via an integrated data analysis method. Gene expression profiles and clinical data of KIRC and 32 other cancer types were acquired using the University of California Santa Cruz Xena platform. Reported enhancer RNAs and genes regulated by them were selected as putative enhancer RNA-target pairs. Kaplan-Meier survival and correlation analyses were performed to identify the key enhancer RNAs. Finally, EMX2OS was identified as the enhancer RNA most associated with survival, with EMX2 as its target. EMX2OS downregulation was significantly associated with higher histological grade, advanced stage, and poorer prognosis. The results were validated in pan-cancer data from The Cancer Genome Atlas and RT-qPCR analysis of 12 pairs of KIRC and normal real-world samples. Functional enrichment analysis indicated that several metabolism-associated signaling pathways were enriched. This study demonstrated that EMX2OS is a key metabolism-associated enhancer RNA in KIRC with a favorable impact on survival and may be a novel therapeutic target in KIRC.

Background: Renal cell carcinoma (RCC) is the most prevalent type of malignant kidney tumor in adults, with clear cell renal cell carcinoma (ccRCC) comprising about 75% of all cases. The SETD2 gene, which is involved in the modification of histone proteins, is often found to have alterations in ccRCC. Yet, our understanding of how these SETD2 mutations affect ccRCC characteristics and behavior within the tumor microenvironment is still not fully understood.Methods: We conducted a detailed analysis of single-cell RNA sequencing (scRNA-seq) data from ccRCC. First, the data was preprocessed using the Python package, “scanpy.” High variability genes were pinpointed through Pearson’s correlation coefficient. Dimensionality reduction and clustering identification were performed using Principal Component Analysis (PCA) and the Leiden algorithm. Malignant cell identification was conducted with the “InferCNV” R package, while cell trajectories and intercellular communication were depicted using the Python packages “VIA” and “cellphoneDB.” We then employed the R package “Deseq2” to determine differentially expressed genes (DEGs) between groups. Using high-dimensional weighted gene correlation network analysis (hdWGCNA), co-expression modules were identified. We intersected these modules with DEGs to establish prognostic models through univariate Cox and the least absolute shrinkage and selection operator (LASSO) method.Results: We identified 69 and 53 distinctive cell clusters, respectively. These were classified further into 12 unique cell types. This analysis highlighted the presence of an abnormal tumor sub-cluster (MT + group), identified by high mitochondrial-encoded protein gene expression and an indication of unfavorable prognosis. Investigation of cellular interactions spotlighted significant interactions between the MT + group and endothelial cells, macrophaes. In addition, we developed a prognostic model based on six characteristic genes. Notably, risk scores derived from these genes correlated significantly with various clinical features. Finally, a nomogram model was established to facilitate more accurate outcome prediction, incorporating four independent risk factors.Conclusion: Our findings provide insight into the crucial transcriptomic characteristics of ccRCC associated with SETD2 mutation. We discovered that this mutation-induced subcluster could stimulate M2 polarization in macrophages, suggesting a heightened propensity for metastasis. Moreover, our prognostic model demonstrated effectiveness in forecasting overall survival for ccRCC patients, thus presenting a valuable clinical tool.

Background: Clear cell renal cell carcinoma (ccRCC) is the most common subtype of renal cancer, marked by high molecular heterogeneity and limited responsiveness to targeted or immune therapies. Ribosome biogenesis (Ribosis), a central regulator of cell growth and metabolism, has emerged as a driver of tumor aggressiveness. However, its role in ccRCC pathogenesis and prognosis remains poorly defined.Methods: We integrated bulk RNA sequencing, single-cell RNA sequencing, and spatial transcriptomics sequencing data to dissect the biological functions and clinical relevance of Ribosis-related genes in ccRCC. Through pseudotime trajectory analysis and metabolic flux inference, we examined malignant progression and metabolic reprogramming. A prognostic model based on a Ribosis-related signature (RBRS) was built using 118 machine learning algorithm combinations and validated in internal and external cohorts. A web-based calculator was also developed. We further analyzed immune infiltration, genomic alterations, tumor microenvironment features, and drug sensitivity. Expression of five core Ribosis-related genes (RPL38, RPS2, RPS14, RPS19, RPS28) was validated by qRT-PCR.Results: We identified a Ribosis-high malignant subpopulation with enhanced stemness, poor prognosis, and elevated oxidative phosphorylation. These cells showed increased metabolic activity, especially in the pyruvate–lactate axis, potentially facilitating immune evasion. The RBRS model outperformed 32 published signatures (C-index = 0.68). High-risk patients exhibited an “immune-activated yet immunosuppressed” microenvironment, with increased CD8⁺ T-cell infiltration and elevated regulatory T cells, myeloid-derived suppressor cells, and immune checkpoint expression (e.g., PDCD1, CTLA-4). Despite active antigen presentation and immune cell recruitment, terminal tumor-killing capacity was impaired. High-risk tumors also showed higher mutation burden, frequent copy number loss of tumor suppressor genes, and resistance to common targeted therapies. The five RBRS genes were significantly upregulated in tumor tissues, consistent with bulk RNA-seq data.Conclusion: We reveal Ribosis as a key driver of ccRCC progression. The RBRS model demonstrates robust prognostic value and translational utility, linking Ribosis to metabolism, immune dysfunction, and therapy resistance, offering new insights for risk stratification and precision treatment in ccRCC.

Kidney renal clear cell carcinoma (KIRC) remains a significant challenge worldwide because of its poor prognosis and high mortality rate, and accurate prognostic gene signatures are urgently required for individual therapy. This study aimed to construct and validate a seven-gene signature for predicting overall survival (OS) in patients with KIRC. The mRNA expression profile and clinical data of patients with KIRC were obtained from The Cancer Genome Atlas (TCGA) and International Cancer Genome Consortium (ICGC). Prognosis-associated genes were identified, and a prognostic gene signature was constructed. Then, the prognostic efficiency of the gene signature was assessed. The results obtained using data from the TCGA were validated using those from the ICGC and other online databases. Gene set enrichment analyses (GSEA) were performed to explore potential molecular mechanisms. A seven-gene signature (PODXL, SLC16A12, ZIC2, ATP2B3, KRT75, C20orf141, and CHGA) was constructed, and it was found to be effective in classifying KIRC patients into high- and low-risk groups, with significantly different survival based on the TCGA and ICGC validation data set. Cox regression analysis revealed that the seven-gene signature had an independent prognostic value. Then, we established a nomogram, including the seven-gene signature, which had a significant clinical net benefit. Interestingly, the seven-gene signature had a good performance in distinguishing KIRC from normal tissues. GSEA revealed that several oncological signatures and GO terms were enriched. This study developed a novel seven-gene signature and nomogram for predicting the OS of patients with KIRC, which may be helpful for clinicians in establishing individualized treatments.