Abstract
Purpose We aimed to identify new prognostic biomarkers of lung adenocarcinoma (LUAD) based on cancer stem cell theory.Materials and Methods: RNA-seq and microarray data were obtained with clinical information downloaded from The Cancer Genome Atlas (TCGA) and the Gene Expression Omnibus (GEO) databases. Weighted gene coexpression network analysis (WGCNA) was applied to identify significant module and hub genes. The hub genes were validated via microarray data from GEO, and a prognostic signature with prognostic hub genes was constructed.Results LUAD patients enrolled from TCGA had a higher mRNA expression-based stemness index (mRNAsi) in tumor tissue than in adjacent normal tissue. Some clinical features and prognoses were found to be highly correlated with mRNAsi. WGCNA found that the green module and blue module were the most significant modules related to mRNAsi; 50 key genes were identified in the green module and were enriched mostly in the cell cycle, chromosome segregation, chromosomal region and microtubule binding. Six hub genes were revealed through the protein-protein interaction (PPI) network and Molecular Complex Detection (MCODE) plugin of Cytoscape software. Based on external verification with the GEO database, these six genes are not only expressed at different levels in LUAD and normal tissues but also associated with different clinical features. In addition, the construction of a prognostic signature with three hub genes showed high predictive value.Conclusion mRNAsi-related biomarkers may suggest a new potential treatment strategy for LUAD.
Highlights
Lung cancer remains the leading malignancy in terms of morbidity and mortality according to the latest large-scale epidemiological survey of 20 regions on five continents, and lung cancer incidence (31.5%) and mortality (27.1%) are the highest in men from both developed and developing countries (Bray et al, 2018)
lung adenocarcinoma (LUAD) patients enrolled from The Cancer Genome Atlas (TCGA) had a higher mRNA expression-based stemness index in tumor tissue than in adjacent normal tissue
Weighted gene coexpression network analysis (WGCNA) found that the green module and blue module were the most significant modules related to mRNA expression-based stemness index (mRNAsi); 50 key genes were identified in the green module and were enriched mostly in the cell cycle, chromosome segregation, chromosomal region and microtubule binding
Summary
Lung cancer remains the leading malignancy in terms of morbidity and mortality according to the latest large-scale epidemiological survey of 20 regions on five continents, and lung cancer incidence (31.5%) and mortality (27.1%) are the highest in men from both developed and developing countries (Bray et al, 2018). An increasing number of studies have shown that tumor stem cells are valuable in research and play an important role in tumor differentiation, metastasis and drug resistance (FriedmannMorvinski and Verma, 2014; Leon et al, 2016; Shibue and Weinberg, 2017) Based on these theories, Malta et al (2018) proposed a new concept – the stemness index – to measure tumor development and evaluate the reliability of stem cell indices for analyzing tumors using TCGA data. The mRNAsi is calculated based on expression data and ranges from 0 to 1, with values closer to 1 indicating low differentiation and strong stem cell characteristics This previous study confirmed that the stem cell index is related to clinical and molecular cancer typing, biological processes, cancer metastasis, intratumoral heterogeneity, and the immune microenvironment, providing new ideas and strategies for cancer research (Malta et al, 2018)
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