Identification of the Immune Microenvironment, Mutation Burden, Immunotherapy, and Drug Sensitivity Related to Lung Adenocarcinoma Tumor Stem Cells via WGCNA.

Abstract

Objective: To analyze LUAD cases in The Cancer Genome Atlas (TCGA), the mRNA expression-based stemness index (mRNAsi) was used. Models of cancer immunity and LUAD prognosis were developed on the basis of correlations between immune and stem cell genes.

Methods: We investigated the differential expression of mRNA dryness index (mRNAsi) in LUAD, survival prognosis, and correlation with clinical parameters. Iden-tify key mRNAsi-related modules and genes by weighted gene co-expression network analysis (WGCNA). Gene set enrichment analysis (GSEA/GSVA) was used to identify stem cell markers and immune-related differentially expressed genes (SC IRGs), and 10 key genes were enriched. Subgroup enrichment, gene mutations, genetic correlated-ness, gene expression, immunity, tumor mutational burden (TMB), and drug sensitivity were further performed in the comprehensive analysis of pivot genes and subgroups.

Results: Compared with normal cells, LUAD cells presented significantly greater mRNAsi values through differential expression analysis. The mRNAsi was highly cor-related with clinical parameters (age, sex, and T stage). On the basis of WGCNA, blue-green and brown modules were identified as the most significant modules (including positive and negative correlations) associated with mRNAsi expression. The functions and pathways of the two mRNAsi-related modules were enriched mainly in tumor oc-currence, development, and metastasis. Cox regression analysis was used to identify 30 SCIRGs associated with prognosis by combining the stem cell indices of the DEGs and the immune-related DEGs. A LASSO regression model was constructed after 10 DEGs related to the prognosis of patients with LUAD were detected. There were significant differences between the high-risk and low-risk groups in terms of GSEA/GSVA, im-mune cell correlation, clinical correlation, etc., following model validation (P<0.05).

Discussion: There are a total of 10 genes in our study model, including four key pre-dictors: DGRIK2, PTTG1, LGR4, and PDGFB. The other 6 genes need to be further delineated and verified. To date, our research has some limitations and has not been validated in cell or animal experiments. These findings provide a relevant theoretical basis for subsequent experimental research on lung adenocarcinoma stem cells. Further research into these cancer stem cell genes will increase the likelihood that they play a role in cancer. There is an opportunity to use it as a therapeutic target for targeted ther-apy for lung adenocarcinoma in the future.

Conclusion: mRNAsi is associated with immunity, which was previously overlooked in the gene analysis of LUAD stem cells. These key genes have a strong overall corre-lation, which can be achieved by inhibiting the stemness characteristics of cancer cells, which may lay the foundation for future research on LUAD.