Identifying Immune Cell Infiltration and Effective Diagnostic Biomarkers in Lung Adenocarcinoma by Comprehensive Bioinformatics Analysis and In Vitro Study.

Abstract

Family with sequence similarity 72B (FAM72B) has been characterized in the regulation of neuronal development. Nevertheless, the prognostic value of FAM72B expression and its function in the immune microenvironment of lung adenocarcinoma (LUAD) currently remains elusive. In this study, by adopting bioinformatics methodology and experimental verification, we found that FAM72B was upregulated in lung cancer tissues and cell lines, and a higher FAM72B level predicted an unfavorable clinical outcome in LUAD patients. The knockdown of FAM72B significantly inhibited cell proliferation, cell migration, and induced cell apoptosis in LUAD. The receiver operating characteristic curve suggested that FAM72B had a high predictive accuracy for the outcomes of LUAD. Kyoto Encyclopedia of Genes and Genomes and Gene Set Enrichment Analyses confirmed that FAM72B-related genes were involved in cell proliferation and immune-response signaling pathway. Moreover, upregulated FAM72B expression was significantly associated with immune cell infiltration in the LUAD tumor microenvironment. Meanwhile, a potential ceRNA network was constructed to identify the lncRNA-AL360270.2/TMPO-AS1/AC125807.2/has-let-7a/7b/7c/7e/7f/FAM72B regulatory axis that regulates FAM72B overexpression in LUAD and is associated with a poor prognosis. We also confirmed that AL360270.2, TMPO-AS1, and AC125807.2 were significantly upregulated in LUAD cell lines than in human bronchial epithelial cells. In conclusion, FAM72B may serve as a novel biomarker in predicting the clinical prognosis and immune status for lung adenocarcinoma.

• Authors: Xinyan Li, Xi Chen, Yixiao Yuan, RuiQing Zhai, William C Cho, Xiulin Jiang
• Journal: Frontiers in oncology (Front Oncol) Vol. 12 Pg. 916947 ( 2022) ISSN: 2234-943X [Print] Switzerland
• PMID: 35707363 (Publication Type: Journal Article)
• Copyright: Copyright © 2022 Li, Chen, Yuan, Zhai, Cho and Jiang.