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The tumor immune microenvironment in clear cell Renal Cell Carcinoma (ccRCC) still remains poorly understood. Previous methods to study the tumor immune microenvironment have a limitation when accounting for the functionally distinct cell types. In this study, we investigated the differently infiltrated immune cells and their clinical significance in ccRCC for the purpose of shedding some important light on the complex immune microenvironment in ccRCC. The devolution algorithm (CIBERSORT) was applied to infer the proportion of 22 immune infiltrating cells based on gene expression profiles of ccRCC bulk tissue, which were downloaded from TCGA and GEO databases. As a result, we observed considerable differences in immune cells percentage between ccRCC tumor tissue and paired normal tissue; meanwhile, we uncovered their internal correlations and associations with Fuhrman grade. Moreover, dendritic cells resting, dendritic cells activated, mast cells resting, mast cells activated and eosinophils were associated with favorable prognosis, whereas B cells memory, T cells follicular helper and T cells regulatory (Tregs) were correlated with poorer outcome.

Clear cell renal cell carcinoma (ccRCC) has long been considered as a metabolic disease characterized by metabolic reprogramming due to the abnormal accumulation of lipid droplets in the cytoplasm. However, the prognostic value of metabolism-related genes in ccRCC remains unclear. In our study, we investigated the associations between metabolism-related gene profile and prognosis of ccRCC patients in the Cancer Genome Atlas (TCGA) database. Importantly, we first constructed a metabolism-related prognostic model based on ten genes (ALDH6A1, FBP1, HAO2, TYMP, PSAT1, IL4I1, P4HA3, HK3, CPT1B, and CYP26A1) using Lasso cox regression analysis. The Kaplan-Meier analysis revealed that our model efficiently predicts prognosis in TCGA_KIRC Cohort and the clinical proteomic tumor analysis consortium (CPTAC_ccRCC) Cohort. Using time-dependent ROC analysis, we showed the model has optimal performance in predicting long-term survival. Besides, the multivariate Cox regression analysis demonstrated our model is an independent prognostic factor. The risk score calculated for each patient was significantly associated with various clinicopathological parameters. Notably, the gene set enrichment analysis indicated that fatty acid metabolism was enriched considerably in low-risk patients. In contrast, the high-risk patients were more associated with nonmetabolic pathways. In summary, our study provides novel insight into metabolism-related genes' roles in ccRCC. Clear cell renal cell carcinoma (ccRCC), the most common renal cell carcinoma (RCC) subtype, exhibits global health issues due to its growing incidence, extreme heterogeneity among patients and high mortality. It is estimated that 90% of the ccRCC patients died of tumor-specific recurrence and metastasis 1. Regarding this, considerable research efforts have focused on developing a model to predict the prognosis of ccRCC patients; however, these prognosis tools still require improvements to attain a high degree of accuracy 2-4. Therefore, novel and robust prognostic models are urgently needed in clinical practice. Metabolism is fundamental in maintaining all the biological processes necessary for life 5. Tumors are typified by metabolic abnormalities as proliferating cells rewire their mechanism to sustain growth 6. Notably, the rapidly proliferating cells in malignancies take up abundant glucose and glutamine to generate the proteins, lipids, and nucleic acids to support cell growth 7. RCC is regarded as a metabolic disease with diabetes, obesity, and atherosclerosis considered as the risk factors 8,9. CcRCC is a unique RCC subtypes based on the abnormally accumulated lipid droplets in the cytoplasm with research evidence implicating the lipid accumulation in disease progression 10,11. Nevertheless, the underlying mechanism and the prognostic role of these metabolic genes remain largely unknown. In the present study, we screened the differentially expressed metabolism-related genes and evaluated their clinical value based on the cancer genome atlas (TCGA) kidney renal clear ...

Background Although the mortality rates of clear cell renal cell carcinoma (ccRCC) have decreased in recent years, the clinical outcome remains highly dependent on the individual patient. Therefore, identifying novel biomarkers for ccRCC patients is crucial. Material/Methods In this study, we obtained RNA sequencing data and clinical information from the TCGA database. Subsequently, we performed integrated bioinformatic analysis that includes differently expressed genes analysis, gene ontology and KEGG pathway analysis, protein-protein interaction analysis, and survival analysis. Moreover, univariate and multivariate Cox proportional hazards regression models were constructed. Results As a result, we identified a total of 263 dysregulated genes that may participate in the metastasis of ccRCC, and established a predictive signature relying on the expression of OTX1, MATN4, PI3, ERVV-2, and NFE4, which could serve as significant progressive and prognostic biomarkers for ccRCC. Conclusions We identified differentially expressed genes that may be involved in the metastasis of ccRCC. Moreover, a predictive signature based on the expression of OTX1, MATN4, PI3, ERVV-2, and NFE4 could be an independent prognostic factor for ccRCC.