Identification of lysosomal genes associated with prognosis in lung adenocarcinoma

Houqiang Li; Xinyu Sha; Wenmiao Wang; Zhanghao Huang; Peng Zhang; Lei Liu; Silin Wang; Youlang Zhou; Shuai He; Jiahai Shi

doi:10.21037/tlcr-23-14

Identification of lysosomal genes associated with prognosis in lung adenocarcinoma

Transl Lung Cancer Res. 2023 Jul 31;12(7):1477-1495. doi: 10.21037/tlcr-23-14. Epub 2023 Jul 18.

Authors

Houqiang Li^#^{1

2}, Xinyu Sha^#^{1

2}, Wenmiao Wang^{1

2}, Zhanghao Huang^{1

3}, Peng Zhang^{1

2}, Lei Liu^{1

2}, Silin Wang^{1

2}, Youlang Zhou⁴, Shuai He¹, Jiahai Shi^{1

2

5}

Affiliations

¹ Department of Thoracic Surgery, Nantong Key Laboratory of Translational Medicine in Cardiothoracic Diseases, Research Institution of Translational Medicine in Cardiothoracic Diseases in Affiliated Hospital of Nantong University, Nantong, China.
² Graduate School, Dalian Medical University, Dalian, China.
³ Medical College of Nantong University, Nantong, China.
⁴ Research Center of Clinical Medicine, Affiliated Hospital of Nantong University, Nantong, China.
⁵ School of Public Health, Nantong University, Nantong, China.

^# Contributed equally.

Abstract

Background: Lung adenocarcinoma (LUAD) is the most common subtype of lung cancer, representing 40% of all cases of this tumor. Despite immense improvements in understanding the molecular basis, diagnosis, and treatment of LUAD, its recurrence rate is still high.

Methods: RNA-seq data from The Cancer Genome Atlas (TCGA) LUAD cohort were download from Genomic Data Commons Portal. The GSE13213 dataset from Gene Expression Omnibus (GEO) was used for external validation. Differential prognostic lysosome-related genes (LRGs) were identified by overlapping survival-related genes obtained via univariate Cox regression analysis with differentially expressed genes (DEGs). The prognostic model was built using Kaplan-Meier curves and least absolute shrinkage and selection operator (LASSO) analyses. In addition, univariate and multivariate Cox analyses were employed to identify independent prognostic factors. The responses of patients to immune checkpoint inhibitors (ICIs) were further predicted. The pRRophetic package and rank-sum test were used to compute the half maximal inhibitory concentrations (IC₅₀) of 56 chemotherapeutic drugs and their differential effects in the low- and high-risk groups. Moreover, quantitative real-time polymerase chain reaction, Western blot, and human protein atlas (HPA) database were used to verify the expression of the four prognostic biomarkers in LUAD.

Results: Of the nine candidate differential prognostic LRGs, GATA2, TFAP2A, LMBRD1, and KRT8 were selected as prognostic biomarkers. The prediction of the risk model was validated to be reliable. Cox independent prognostic analysis revealed that risk score and stage were independent prognostic factors in LUAD. Furthermore, the nomogram and calibration curves of the independent prognostic factors performed well. Differential analysis of ICIs revealed CD276, ICOS, PDCD1LG2, CD27, TNFRSF18, TNFSF9, ENTPD1, and NT5E to be expressed differently in the low- and high-risk groups. The IC₅₀ values of 12 chemotherapeutic drugs, including epothilone.B, JNK.inhibitor.VIII, and AKT.inhibitor.VIII, significantly differed between the two risk groups. KRT8 and TFAP2A were highly expressed, while GATA2 and LMBRD1 were poorly expressed in LUAD cell lines. In addition, KRT8 and TFAP2A were highly expressed, while GATA2 and LMBRD1 were poorly expressed in tumor tissues.

Conclusions: Four key prognostic biomarkers-GATA2, TFAP2A, LMBRD1, and KRT8-were used to construct a significant prognostic model for LUAD patients.

Keywords: Lung adenocarcinoma (LUAD); chemotherapeutic drugs; immune infiltration; lysosome-related genes (LRGs); prognosis.