The dysfunctional immune response in renal cell carcinoma correlates with changes in the metabolic landscape of ccRCC during disease progression

Circulating microRNA-155-3p levels predicts response to first line immunotherapy in patients with metastatic renal cell carcinoma

Predictive biomarkers of response to immune checkpoint-based therapies (ICI) remain a critically unmet need in the management of advanced renal cell carcinoma (RCC). The complex interplay of the tumour microenvironment (TME) and the circulating immune response has proven to be challenging to decipher. MicroRNAs have gained increasing attention for their role in post-transcriptional gene expression regulation, particularly because they can have immunomodulatory properties. We evaluated the presence of immune-specific extracellular vesicle (EV) microRNAs in the plasma of patients with metastatic RCC (mRCC) prior to initiation of ICI. We found significantly lower levels of microRNA155-3p (miR155) in responders to ICI, when compared to non-responders. This microRNA has unique immunomodulatory properties, thus providing potential biological rationale for our findings. Our results support further work in exploring microRNAs as potential biomarkers of response to immunotherapy.

Fatty acid metabolism-related lncRNAs are potential biomarkers for survival prediction in clear cell renal cell carcinoma

Metabolic reprogramming of energy is a newly recognized characteristic of cancer. In our current investigation, we examined the possible predictive importance of long noncoding RNAs (lncRNAs) associated to fatty acid metabolism in clear cell renal cell carcinoma (ccRCC). We conducted an analysis of the gene expression data obtained from patients diagnosed with ccRCC using the Cancer Genome Atlas (TCGA) database and the ArrayExpress database. We performed a screening to identify lncRNAs that are differentially expressed in fatty acid metabolism. Based on these findings, we developed a prognostic risk score model using these fatty acid metabolism-related lncRNAs. We then validated this model using Cox regression analysis, Kaplan-Meier survival analysis, and principal-component analysis (PCA). Furthermore, the prognostic risk score model was successfully validated using both the TCGA cohort and the E-MTAB-1980 cohort. We utilized gene set variation analysis (GSVA) and gene set enrichment analysis (GSEA) to determine the correlation between fatty acid metabolism and the PPAR signaling pathway in patients with ccRCC at various clinical stages and prognoses. We have discovered compelling evidence of the interaction between immune cells in the tumor microenvironment and tumor cells, which leads to immune evasion and resistance to drugs. This was achieved by the utilization of advanced techniques such as the CIBERSORT method, ESTIMATE R package, ssGSEA algorithm, and TIMER database exploration. Ultimately, we have established a network of competing endogenous RNA (ceRNA) that is related to fatty acid metabolism. The findings of our study suggest that medicines focused on fatty acid metabolism could be clinically significant for individuals with ccRCC. The utilization of this risk model, which is centered around the lncRNAs associated with fatty acid metabolism, could potentially provide valuable prognostic information and hold immunotherapeutic implications for patients with ccRCC.