Identification of Gefitinib Resistance-Related lncRNA-miRNA-mRNA Regulatory Networks and Corresponding Prognostic Signature in Patients with Lung Adenocarcinoma

Gefitinib metabolism-related lncRNAs for the prediction of prognosis, tumor microenvironment and drug sensitivity in lung adenocarcinoma

The complete compound of gefitinib is effective in the treatment of lung adenocarcinoma. However, the effect on lung adenocarcinoma (LUAD) during its catabolism has not yet been elucidated. We carried out this study to examine the predictive value of gefitinib metabolism-related long noncoding RNAs (GMLncs) in LUAD patients. To filter GMLncs and create a prognostic model, we employed Pearson correlation, Lasso, univariate Cox, and multivariate Cox analysis. We combined risk scores and clinical features to create nomograms for better application in clinical settings. According to the constructed prognostic model, we performed GO/KEGG and GSEA enrichment analysis, tumor immune microenvironment analysis, immune evasion and immunotherapy analysis, somatic cell mutation analysis, drug sensitivity analysis, IMvigor210 immunotherapy validation, stem cell index analysis and real-time quantitative PCR (RT-qPCR) analysis. We built a predictive model with 9 GMLncs, which showed good predictive performance in validation and training sets. The calibration curve demonstrated excellent agreement between the expected and observed survival rates, for which the predictive performance was better than that of the nomogram without a risk score. The metabolism of gefitinib is related to the cytochrome P450 pathway and lipid metabolism pathway, and may be one of the causes of gefitinib resistance, according to analyses from the Gene Set Enrichment Analysis (GSEA), Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG). Immunological evasion and immunotherapy analysis revealed that the likelihood of immune evasion increased with risk score. Tumor microenvironment analysis found most immune cells at higher concentrations in the low-risk group. Drug sensitivity analysis found 23 sensitive drugs. Twenty-one of these drugs exhibited heightened sensitivity in the high-risk group. RT-qPCR analysis validated the characteristics of 9 GMlncs. The predictive model and nomogram that we constructed have good application value in evaluating the prognosis of patients and guiding clinical treatment.

LINC00540 promotes sorafenib resistance and functions as a ceRNA for miR-4677-3p to regulate AKR1C2 in hepatocellular carcinoma

Sorafenib resistance is one of the main causes of poor prognosis in patients with advanced hepatocellular carcinoma (HCC). Long noncoding RNAs (lncRNAs) function as suppressors or oncogenic factors during tumor progression and drug resistance. Here, to identify therapeutic targets for HCC, the biological mechanisms of abnormally expressed lncRNAs were examined in sorafenib-resistant HCC cells. Specifically, we established sorafenib-resistant HCC cell lines (Huh7-S and SMMC7721-S), which displayed an epithelial-mesenchymal transition (EMT) phenotype. Transcriptome sequencing (RNA-Seq) was performed to established differential lncRNA expression profiles for sorafenib-resistant cells. Through this analysis, we identified LINC00540 as significantly up-regulated in sorafenib-resistant cells and a candidate lncRNA for further mechanistic investigation. Functionally, LINC00540 knockdown promoted sorafenib sensitivity and suppressed migration, invasion, EMT and the activation of PI3K/AKT signaling pathway in sorafenib-resistant HCC cells, whereas overexpression of LINC00540 resulted in the opposite effects in parental cells. LINC00540 functions as a competing endogenous RNA (ceRNA) by competitively binding to miR-4677-3p , thereby promoting AKR1C2 expression. This is the first study that demonstrates a role for LINC00540 in enhancing sorafenib resistance, migration and invasion of HCC cells through the LINC00540/miR-4677-3p/AKR1C2 axis, suggesting that LINC00540 may represent a potential therapeutic target and prognosis biomarker for HCC.