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

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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.", "claims": [{"id": "claim-method", "statement": "We then validated this model using Cox regression analysis, Kaplan–Meier survival analysis, and principal-component analysis (PCA).", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:38394500", "locator": "sections[0] \"Abstract\" paragraph 1", "quote": "We then validated this model using Cox regression analysis, Kaplan–Meier survival analysis, and principal-component analysis (PCA)."}], "state": "complete"}, {"id": "claim-data-access", "statement": "All data generated or analyzed during this study are included in this published article [and its supplementary information files].", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:38394500", "locator": "sections[28] \"fn-group\" paragraph 1", "quote": "All data generated or analyzed during this study are included in this published article [and its supplementary information files]."}], "state": "complete"}, {"id": "claim-target-result", "statement": "The 5-year area under the ROC curves (AUC) revealed that the nomogram (AUC = 0.876) exhibited superior predictive value compared to individual indicators, such as age (AUC = 0.585), ISUP grade (AUC = 0.673), pathological stage (AUC = 0.718), and prognostic risk score model (AUC = 0.828; Figure 6E).", "kind": "qualitative", "target": null, "evidence": [{"asset": "source:pmid:38394500", "locator": "sections[17] \"3.5. 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