Integrative bioinformatics approaches to establish potential prognostic immune-related genes signature and drugs in the non-small cell lung cancer microenvironment

Jiao Zhou; Shan Shi; Yeqing Qiu; Zhongwen Jin; Wenyan Yu; Rongzhi Xie; Hongyu Zhang

doi:10.3389/fphar.2023.1153565

Integrative bioinformatics approaches to establish potential prognostic immune-related genes signature and drugs in the non-small cell lung cancer microenvironment

Front Pharmacol. 2023 Apr 3:14:1153565. doi: 10.3389/fphar.2023.1153565. eCollection 2023.

Authors

Jiao Zhou^{1

2}, Shan Shi^{1

2}, Yeqing Qiu^{1

2}, Zhongwen Jin^{1

2}, Wenyan Yu^{1

2}, Rongzhi Xie^{1

3}, Hongyu Zhang^{1

3}

Affiliations

¹ The Fifth Affiliated Hospital of Sun Yat-sen University, Zhuhai, China.
² Zhongshan School of Medicine, Sun Yat-sen University, Guangzhou, China.
³ Cancer Center, The Fifth Affiliated Hospital of Sun Yat-sen University, Zhuhai, China.

Abstract

Introduction: Research has revealed that the tumor microenvironment (TME) is associated with the progression of malignancy. The combination of meaningful prognostic biomarkers related to the TME is expected to be a reliable direction for improving the diagnosis and treatment of non-small cell lung cancer (NSCLC). Method and Result: Therefore, to better understand the connection between the TME and survival outcomes of NSCLC, we used the "DESeq2" R package to mine the differentially expressed genes (DEGs) of two groups of NSCLC samples according to the optimal cutoff value of the immune score through the ESTIMATE algorithm. A total of 978 up-DEGs and 828 down-DEGs were eventually identified. A fifteen-gene prognostic signature was established via LASSO and Cox regression analysis and further divided the patients into two risk sets. The survival outcome of high-risk patients was significantly worse than that of low-risk patients in both the TCGA and two external validation sets (p-value < 0.05). The gene signature showed high predictive accuracy in TCGA (1-year area under the time-dependent ROC curve (AUC) = 0.722, 2-year AUC = 0.708, 3-year AUC = 0.686). The nomogram comprised of the risk score and related clinicopathological information was constructed, and calibration plots and ROC curves were applied, KEGG and GSEA analyses showed that the epithelial-mesenchymal transition (EMT) pathway, E2F target pathway and immune-associated pathway were mainly involved in the high-risk group. Further somatic mutation and immune analyses were conducted to compare the differences between the two groups. Drug sensitivity provides a potential treatment basis for clinical treatment. Finally, EREG and ADH1C were selected as the key prognostic genes of the two overlapping results from PPI and multiple Cox analyses. They were verified by comparing the mRNA expression in cell lines and protein expression in the HPA database, and clinical validation further confirmed the effectiveness of key genes. Conclusion: In conclusion, we obtained an immune-related fifteen-gene prognostic signature and potential mechanism and sensitive drugs underling the prognosis model, which may provide accurate prognosis prediction and available strategies for NSCLC.

Keywords: drug sensitivity; estimate; non-small cell lung cancer; prognostic gene signature; tumor microenvironment.