The mRNA expression-based stemness index (mRNAsi index) (Malta et al., 2018) was used to assess the degree of oncogenic dedifferentiation, which was calculated using an innovative OCLR machine-learning algorithm (Sokolov et al., 2016). Higher mRNAsi scores were associated with malignant biological processes in CSCs and greater tumor dedifferentiation, according to the histopathological grades. In our previous study (Ruiz-Cordero and Devine, 2020), we calculated a set of LUAD stemness genes based on the mRNAsi index. Using WGCNA, stemness genes were clustered into modules. Key genes were screened from the blue module based on the gene significance for mRNA stemness indices (GS.mRNAsi), gene significance for the epigenetically regulated-mRNAsi (GS EREG-mRNAsi) and module membership (MM) scores. In this study, we used genes with the GS mRNAsi/GS EREG-mRNAsi score > 0.5 and P < 0.05 as stem genes for further analysis.
