mRNAsi Index: Machine Learning in Mining Lung Adenocarcinoma Stem Cell Biomarkers

Abstract

Cancer stem cells (CSCs), characterized by self-renewal and unlimited proliferation, lead to therapeutic resistance in lung cancer. In this study, we aimed to investigate the expressions of stem cell-related genes in lung adenocarcinoma (LUAD). The stemness index based on mRNA expression (mRNAsi) was utilized to analyze LUAD cases in the Cancer Genome Atlas (TCGA). First, mRNAsi was analyzed with differential expressions, survival analysis, clinical stages, and gender in LUADs. Then, the weighted gene co-expression network analysis was performed to discover modules of stemness and key genes. The interplay among the key genes was explored at the transcription and protein levels. The enrichment analysis was performed to annotate the function and pathways of the key genes. The expression levels of key genes were validated in a pan-cancer scale. The pathological stage associated gene expression level and survival probability were also validated. The Gene Expression Omnibus (GEO) database was additionally used for validation. The mRNAsi was significantly upregulated in cancer cases. In general, the mRNAsi score increases according to clinical stages and differs in gender significantly. Lower mRNAsi groups had a better overall survival in major LUADs, within five years. The distinguished modules and key genes were selected according to the correlations to the mRNAsi. Thirteen key genes (CCNB1, BUB1, BUB1B, CDC20, PLK1, TTK, CDC45, ESPL1, CCNA2, MCM6, ORC1, MCM2, and CHEK1) were enriched from the cell cycle Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway, relating to cell proliferation Gene Ontology (GO) terms, as well. Eight of the thirteen genes have been reported to be associated with the CSC characteristics. However, all of them have been previously ignored in LUADs. Their expression increased according to the pathological stages of LUAD, and these genes were clearly upregulated in pan-cancers. In the GEO database, only the tumor necrosis factor receptor associated factor-interacting protein (TRAIP) from the blue module was matched with the stemness microarray data. These key genes were found to have strong correlations as a whole, and could be used as therapeutic targets in the treatment of LUAD, by inhibiting the stemness features.

Prognostic Prediction Using a Stemness Index-Related Signature in a Cohort of Gastric Cancer

“…Firstly, we analyzed the difference in mRNAsi scores in GC tissues versus non-tumor tissues. GC tissues exhibited a higher stemness in comparison to non-tumor tissues, and this was in accordance with other recently published reports in bladder cancer stem cell ( Pan et al, 2019 ), lung adenocarcinoma stem cell ( Zhang et al, 2020 ), and breast cancer stem cell ( Pei et al, 2020 ).…”

mRNAsi Index-based Integrative Analysis for the Identification of New Prognostic Models for Ovarian Cancer

“…However, no signi cant difference in prognosis was observed between the high-and the low-mRNAsi group, which is different from observations in other kinds of cancer [31]. This may due to differences between tumor types or limitations deriving from the current sample size, and requires further investigation.…”

Stemness Related Genes Revealed by Network Analysis Associated With Tumor Immune Microenvironment and the Clinical Outcome in Lung Adenocarcinoma

“…Some previous studies have been published in this field, reporting genes related to stemness, which are associated with cancer recurrence, metastasis, resistance to treatment, and poor prognosis (Pan et al, 2019;Qin et al, 2020;Zhang et al, 2020;Zhao et al, 2020). For instance, a recent study identified 13 key genes enriched from KEGG pathway (Zhao et al, 2020).…”