Computational construction of TME‐related lncRNAs signature for predicting prognosis and immunotherapy response in clear cell renal cell carcinoma. Issue 8 (8th July 2022) - Zhou, Libin; Fang, Hualong; Guo, Fei; Yin, Min; Long, Huimin; Weng, Guobin
Abstract: Background: The tumor microenvironment (TME) is closely related to clear cell renal cell carcinoma (ccRCC) prognosis, and immunotherapy response. In current study, comprehensive bio‐informative analysis was adopted to construct a TME‐related lncRNA signature for immune checkpoint inhibitors (ICIs) and targeted drug responses in ccRCC patients. Methods: The TME mRNAs were screened following the immune and stromal scores with the data from GSE15641, GSE29609, GSE36895, GSE46699, GSE53757, and The Cancer Genome Atlas (TCGA)‐kidney renal clear cell carcinoma (KIRC). And the TME‐related lncRNAs were recognized using correlation analysis. The TME‐related lncRNAs prognostic model was constructed using the training dataset. Kaplan–Meier analysis, principal‐component analysis, and time‐dependent receiver operating characteristic were used to evaluate the risk model. The immune cell infiltration in TME was evaluated using the single‐sample gene set enrichment analysis (ssGSEA), ESTIMATE, and microenvironment cell populations counter algorithm. The immunophenoscore (IPS) was used to assess the response to immunotherapy with the constructed model. Results: In the current study, 364 TME‐related lncRNAs were selected based on the integrated bioinformatical analysis. Six TME‐related lncRNAs (LINC00460, LINC01094, AC008870.2, AC068792.1, and AC007637.1) were identified as the prognostic signature in the training dataset and subsequently verified in the testing and entire datasets. Patients in the high‐risk group exhibited poor overall survival and disease‐free survival than those in the low‐risk group. The 1‐, 3‐, and 5‐year areas under the curves of the prognostic signature in the entire dataset were 0.704, 0.683, and 0.750, respectively. The risk score independently predicted ccRCC survival based on univariate and multivariate Cox regression. GSEA analysis suggested that the high‐risk group was concentrated on immune‐related pathways. The high‐risk group were characterized by high immune cell infiltration, high TMB and somatic mutation counters, high IPS‐PD‐1 + CTLA4 scores, and immune checkpoints expression upregulation, reflecting the higher ICIs response. The half inhibitory concentrations of sunitinib, temsirolimus, and rapamycin were low in the high‐risk group. Conclusion: The TME‐related lncRNAs signature constructed could reliably predict the prognosis and immunotherapy response and targeted ccRCC patients' therapy. Abstract : Identification of TME‐related lncRNAs in KIRC. Volcano plot (A) and heatmap (B) of DRGs between high and low immune scores. Volcano plot (C) and heatmap (D) of DRGs between high and low stromal scores. (E) The scale‐free fit index (left) and the mean connectivity (right). (F) Heatmap of the relationships between the state of immune and model eigengenes. (G) Venn diagram of TME‐related mRNAs. (H) The results of gene ontology analysis. (I) The top 15 most significant KEGG pathways.