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

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. objective: The index of stem cells is useful for assessing the level of dedifferentiation in cancerous tissue. Studies have identified a specific connection between the stemness index of tumors and the expression of immune checkpoints, as well as the infiltration of immune system cells [8]. New biomarkers can be identified with the assistance of these indicators. Currently, there is relatively little research on the relationship between stem cell index and tumor immune infiltration, as well as their combination in LUAD. Therefore, in our study, based on the combination of stem cell index and immune related genes, bioinformatics analysis was used to screen for genes related to stem cell index and immunity, and a model was constructed. Examine the relationship between the immune environment and immune regulatory points. 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. conclusion: In our study, mRNAsi was combined with immunity, something previously overlooked in gene analysis of LUAD stem cells. Their expression increases or decreases with the pathological staging of LUAD, but most genes are significantly upregulated in lung adenocarcinoma. LUAD survival rates can be accurately predicted using the SC-IRG model and prognostic column chart. These key genes have been found to have a strong overall correlation, which can be achieved by inhibiting the stemness characteristics of cancer cells, which may lay the foundation for future research on LUAD. In addition, it can be used a therapeutic target for treating LUAD.