A novel basement membrane-related gene signature for prognosis of lung adenocarcinomas

Zhenxing Zhang; Haoran Zhu; Xiaojun Wang; Shanan Lin; Chenjin Ruan; Qiang Wang

doi:10.1016/j.compbiomed.2023.106597

A novel basement membrane-related gene signature for prognosis of lung adenocarcinomas

Comput Biol Med. 2023 Mar:154:106597. doi: 10.1016/j.compbiomed.2023.106597. Epub 2023 Jan 24.

Authors

Zhenxing Zhang¹, Haoran Zhu², Xiaojun Wang³, Shanan Lin³, Chenjin Ruan³, Qiang Wang⁴

Affiliations

¹ Department of Thoracic and Maxillofacial Surgery (B7X), Taizhou Central Hospital (Taizhou University Hospital), Taizhou, Zhejiang Province, China.
² Xi'an Jiaotong University Health Science Center, Xi'an, Shaanxi Province, China.
³ Department of Thoracic Surgery, Taizhou Central Hospital (Taizhou University Hospital), Taizhou, Zhejiang Province, China.
⁴ Department of Thoracic Surgery, Taizhou Central Hospital (Taizhou University Hospital), Taizhou, Zhejiang Province, China. Electronic address: wangq9414@163.com.

PMID: 36708655
DOI: 10.1016/j.compbiomed.2023.106597

Abstract

Background: Lung adenocarcinoma (LUAD) remains a global health concern with its poor prognosis and high mortality. Whether tumor cells invade through the basement membrane (BM) is the key factor to determine the prognosis of LUAD. This study aimed to identify the BM-related gene signatures to improve the overall prognosis of LUAD.

Materials & methods: A series of bioinformatics analyses were conducted based on TCGA and GEO datasets. Unsupervised consistent cluster analysis was performed, and 500 LUAD patients were assigned to two different groups according to expressions of 222 BM-related genes. The differentially expressed genes (DEGs) between the two clusters were identified, and Lasso regression, ROC curve, univariate and multivariate Cox regression analyses and enrichment analysis were conducted. Besides, ssGSEA, CIBERSORT and ESTIMATE algorithmwere were employed to understand the relationship between the tumor microenvironment (TME) and risk scores. Moreover, single cell clustering and trajectory analyses were performed to further understand the significance of BM-related genes. Finally, qRT-PCR was used to verify the prognosis model.

Results: A total of 31 prognostic BM-related genes were determined for LUAD, and a novel 17-mRNA prognostic model named BMsocre was successfully established to predict the overall survival of LUAD patients. The high BMscore group indicated worse prognosis. Seventeen DEGs were enriched mainly in metabolism, ECM-receptor interaction and immune response. In addition, the high-risk group showed higher TMB and lower immune score. The low-risk group had a better immunotherapeutic response where immune escape was less likely. The BMscore model was verified in our patient cohort. Furthermore, NELL2 was mainly expressed in clusters of T cells, and was identified to play a critical role in T-cell differentiation.

Conclusions: A novel BMscore model was successfully established and might be effective for providing guidance to LUAD therapy.

Keywords: Basement membrane; Immune therapy; Lung adenocarcinoma; NELL2; T cell; Tumor microenvironment.

Publication types

Research Support, Non-U.S. Gov't

MeSH terms

Adenocarcinoma of Lung* / genetics
Basement Membrane
Cell Differentiation
Cluster Analysis
Humans
Lung Neoplasms* / genetics
Tumor Microenvironment / genetics