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image of 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. Identify 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 correlatedness, 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 correlated 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 occurrence, 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, immune 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 predictors: 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 therapy 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 correlation, which can be achieved by inhibiting the stemness characteristics of cancer cells, which may lay the foundation for future research on LUAD.

© 2025 Bentham Science Publishers
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2025-09-26
2025-12-24

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/content/journals/ccdt/10.2174/0115680096337566250906092330
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Identification of the Immune Microenvironment, Mutation Burden, Immunotherapy, and Drug Sensitivity Related to Lung Adenocarcinoma Tumor Stem Cells via WGCNA
Bentham Science Publishers ; https://doi.org/10.2174/0115680096337566250906092330
/content/journals/ccdt/10.2174/0115680096337566250906092330
/content/journals/ccdt/10.2174/0115680096337566250906092330
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  • Article Type:     Research Article
Keywords: stem cell index ; cancer stem cell ; immune ; nomogram ; multiomics analysis ; Lung adenocarcinoma

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