Identification of microRNAs involved in pathways which characterize the expression subtypes of NSCLC

Abstract

Dysregulation of microRNAs is a common mechanism in the development of lung cancer, but the relationship between microRNAs and expression subtypes in non‐small‐cell lung cancer (NSCLC) is poorly explored. Here, we analyzed microRNA expression from 241 NSCLC samples and correlated this with the expression subtypes of adenocarcinomas (AD) and squamous cell carcinomas (SCC) to identify microRNAs specific for each subtype. Gene set variation analysis and the hallmark gene set were utilized to calculate gene set scores specific for each sample, and these were further correlated with the expression of the subtype‐specific microRNAs. In ADs, we identified nine aberrantly regulated microRNAs in the terminal respiratory unit (TRU), three in the proximal inflammatory (PI), and nine in the proximal proliferative subtype (PP). In SCCs, 1, 5, 5, and 9 microRNAs were significantly dysregulated in the basal, primitive, classical, and secretory subtypes, respectively. The subtype‐specific microRNAs were highly correlated to specific gene sets, and a distinct pattern of biological processes with high immune activity for the AD PI and SCC secretory subtypes, and upregulation of cell cycle‐related processes in AD PP, SCC primitive, and SCC classical subtypes were found. Several in silico predicted targets within the gene sets were identified for the subtype‐specific microRNAs, underpinning the findings. The results were significantly validated in the LUAD (n = 492) and LUSC (n = 380) TCGA dataset (False discovery rates‐corrected P‐value < 0.05). Our study provides novel insight into how expression subtypes determined with discrete biological processes may be regulated by subtype‐specific microRNAs. These results may have importance for the development of combinatory therapeutic strategies for lung cancer patients.

HPV‐related methylation‐based reclassification and risk stratification of cervical cancer

“…To explore differences in potential biological processes in CC patients from different clusters, GSEA was performed using the hallmark gene sets (h.all.v7.0.symbols), which were obtained from the Molecular Signatures Database (https://www.gsea-msigdb.org/gsea/ msigdb/index.jsp). The hallmark gene sets display coordinate expression and represent well-defined biological processes, providing a clearer biological space for GSEA (Halvorsen et al, 2019;Liberzon et al, 2015). The 'fgsea' R package was used, and 10 000 permutations were performed for each parameter analyzed to calculate the enrichment scores based on the threshold of adjusted P-value < 0.05 (Sergushichev, 2016).…”

Prognostic impact of an integrative analysis of [18F] FDG PET parameters and infiltrating immune cell scores in lung adenocarcinoma

“…It has been reported that lung adenocarcinoma subtypes have different metabolism profiles [33,34]. Our group previously demonstrated that the PI group has the highest tumor metabolism index, which is a deeplearning-based index used to predict SUVmax from transcriptomic data among the three subtypes [12].…”

MiRNA expression profiling in adenocarcinoma and squamous cell lung carcinoma reveals both common and specific deregulated microRNAs

“…Furthermore, since miR-140-3p is highly expressed by B cells, low levels of miR-140-3p may indicate the absence of B cell infiltration. [ 25 ] JAK-STAT inhibition has been recently shown to impair K-RAS-driven lung adenocarcinoma progression and is a promising therapy for K-RAS-driven lung LUAC. [ 26 ] Finally, miR-140-3p overexpression was found to attenuate cisplatin resistance and reduce stem cell-like properties in LUAD cells by suppressing Wnt/β-catenin signaling.…”