A novel basement membrane-related gene signature for prognosis of lung adenocarcinomas. (March 2023)

• Record Type: Journal Article
• Authors: Zhang, Zhenxing; Zhu, Haoran; Wang, Xiaojun; Lin, Shanan; Ruan, Chenjin; Wang, Qiang
• Is Part Of: Computers in biology and medicine. Volume 154(2023)
• Publication Date: 2023-03
• Subjects: Lung adenocarcinoma -- Basement membrane -- Tumor microenvironment -- Immune therapy -- NELL2 -- T cell; LUAD lung adenocarcinomas -- BM basement membrane -- DEGs differentially expressed genes -- ROC Receiver Operating Characteristic Curve -- TME tumor microenvironment -- ECM extracellular matrix -- TMB Tumor Mutational Burden -- IARC International Agency for Research on Cancer -- TCGA The Cancer Genome Atlas -- GEO Gene Expression Omnibus data base -- NELL2 NEL-like 2 -- PCA Principal Component Analysis -- AUC Area Under Curve -- CAFs cancer-associatedfibroblasts -- ICIs Immune checkpoint inhibitors; Medicine -- Data processing -- Periodicals; Biology -- Data processing -- Periodicals; 610.285
• DOI: 10.1016/j.compbiomed.2023.106597
• Languages: English
• ISSNs: 0010-4825

Abstract

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. Highlights: A precise prognostic model named BMscore was successfully constructed. The prognostic efficacy of the BMscore model was verified in LUAD patients. The BMscore model revealed a potential prognostic value for the immunotherapy response. NELL2 could be a potential BM-related immune-system regulator.