Immune-related long non-coding RNAs can serve as prognostic biomarkers for clear cell renal cell carcinoma

Abstract

Background:The immune microenvironment is a critical regulator of clear cell renal cell carcinoma (ccRCC) progression. However, the underlying mechanisms the regulatory role of immune-related long non-coding RNAs (irlncRNAs) in the ccRCC tumor microenvironment (TME) are still obscure. Herein, we investigated prognostics role of irlncRNAs for ccRCC. Methods:The raw data of patients with ccRCC were downloaded from The Cancer Genome Atlas (TCGA) database, and immune-related genes were obtained from the ImmPort database. First, we investigated the correlation between the immune-related genes and irlncRNAs. Then, we identified the differentially expressed irlncRNA pairs (ILRPs) between normal and cancer tissue samples, and prognostic model was constructed with the differentially expressed ILRPs. We further explored whether the signature risk scores of ILRPs had a considerable impact on immune cell infiltration. Finally, we performed a drug sensitivity analysis based on risk score.Results: There were 13 upregulated and 40 downregulated irlncRNAs between the ccRCC and normal tissue samples. We further selected the irlncRNAs that significantly affect the prognosis of patients with ccRCC via univariate Cox, lasso regression, and multivariate regression analyses. Twelve ILRPs were used to construct a prognostic signature. The model showed the ILRPs model could be used to assess the prognosis of ccRCC patients. Study of the influence of risk score and clinical characteristics on the prognosis of patients with ccRCC showed risk score to be an independent factor affecting the outcome of ccRCC.We further performed the difference analysis of immune cell abundance between ccRCC and normal tissue samples. The results showed that patients with higher abundance of M0 macrophages, plasma cells, follicular helper T cells, and regulatory T cells (Tregs) had a poor outcome. Finally, we performed a drug sensitivity analysis based on risk score. The results showed that high-risk score patients are sensitive to orafenib, sunitinib, temsirolimus, cisplatin, and gemcitabine. Conclusions:Our study has developed a novel and reasonable ILPRs model for prognostic prediction, which does not require transcriptional levels to be detected.

Construction of a Novel Immune-Related lncRNA Pair Signature with Prognostic Significance for Kidney Clear Cell Renal Cell Carcinoma

“…So immune checkpoint inhibitors which block PD1/PDL1 or CTLA4 have been proven effective and considered as standard treatment [ 11 , 36 ]. In our study, we have found that the risk score is positively related to Tregs, which is consistent with previous studies [ 37 ]. And we also found that CTLA4 expression, CD247 expression, LAG3 expression, PDCD1 expression, and TIGIT expression were positively related to the risk score, which means that this model has a potential predictive significance for the efficacy of immune checkpoint inhibitors.…”

Construction and validation of a novel ten miRNA-pair based signature for the prognosis of clear cell renal cell carcinoma

“…In DEG cluster analysis, we found that the high abundance of macrophages M0 in class B shortened the overall survival of ccRCC patients, and the opposite was true in class A. Previous studies have demonstrated that immunosuppressive cells, such as M0 macrophages, are higher in high-risk ccRCC patients, while the proportion of active immune cells, including naive B cells, resting CD4 memory T cells, resting natural killer (NK) cells were lower, indicating the presence of an immunosuppressive microenvironment [ 86 , 87 ].…”

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

“…[40] The ceRNA hypothesis suggests that lncRNAs can influence the production of messenger RNAs (mRNAs) relevant to fatty acid (FA) metabolism by acting as sponges for microRNAs (miRNAs) in ccRCC. While several studies have established ceRNA networks in ccRCC, [29][30][31][32] the specific regulatory role of FA metabolism-related ceRNA is still not well understood. This study conducted a detailed analysis of the genomic information from 539 ccRCC samples to evaluate the pattern of FA metabolism and develop a predictive risk score model for FA.…”

“…[27,28] Several researchers have investigated the expression profiling of lncRNA in ccRCC and have discovered that they can function as biomarkers for the diagnosis and prognosis of ccRCC. [29][30][31][32] Recent investigations have shown that lncRNAs play a role in controlling the expression of important genes, as well as networks of genes involved in processes related to cholesterol and FA production, reverse cholesterol transport, and lipid storage. [33][34][35] LncRNAs also seem to selectively affect many transcription factors that have crucial functions in controlling lipid metabolism, including liver X receptors, [36] sterol regulatory element binding proteins, [37,38] and peroxisome proliferator-activated receptor α (PPARα).…”