Construction of an immune-related gene prognostic model with experimental validation and analysis of immune cell infiltration in lung adenocarcinoma

Jialei Yang; Chao Tang; Chengxia Li; Xuesen Li; Wenli Yang

doi:10.3892/ol.2024.14430

Construction of an immune-related gene prognostic model with experimental validation and analysis of immune cell infiltration in lung adenocarcinoma

Oncol Lett. 2024 May 1;28(1):297. doi: 10.3892/ol.2024.14430. eCollection 2024 Jul.

Authors

Jialei Yang^{1

2}, Chao Tang¹, Chengxia Li¹, Xuesen Li¹, Wenli Yang^{1

3}

Affiliations

¹ Institute for Cancer Medicine, School of Basic Medicine Sciences, Southwest Medical University, Luzhou, Sichuan 646000, P.R. China.
² Department of Medical Laboratory Medicine, Dehong Prefecture People's Hospital of Yunnan Province, Mangshi, Yunnan 678400, P.R. China.
³ Department of Biochemistry and Molecular Biology, School of Basic Medicine Sciences, Southwest Medical University, Luzhou, Sichuan 646000, P.R. China.

Abstract

There is a correlation between tumors and immunity with the degree of immune cell infiltration in tumors being closely related to tumor growth and progression. Therefore, the present study identified immune-related prognostic genes and evaluated the immune infiltration level in lung adenocarcinoma (LUAD). This study performed Kyoto Encyclopedia of Genes and Genomes, Gene Ontology, and Gene Set Enrichment Analysis (GSEA) enrichment analyses on differential immune-associated genes. A risk model was created and validated using six immune-related prognostic genes. Reverse transcription-quantitative PCR was used to assess the prognostic gene expression in non-small cell lung cancer cells. Immune cell infiltration in LUAD was analyzed using the CIBERSORT method. Single sample GSEA was used to compare Tumor Immune Dysfunction and Exclusion (TIDE) scores between high and low-risk groups and to assess the activation of thirteen immune-related pathways. Multifactor Cox proportional hazards model analysis identified six prognostic risk genes (S100A16, FURIN, FGF2, LGR4, TNFRSF11A and VIPR1) to construct a risk model. The survival and receiver operating characteristic curves indicated that patients with higher risk scores had lower overall survival rates. The expression levels of prognostic genes S100A16, FURIN, LGR4, TNFRSF11A and VIPR1 were significantly increased in LUAD. B cells naive, plasma cells, T cells CD4 memory activated, T cells follicular helper, T cells regulatory, NK cells activated, macrophages M1, macrophages M2, and Dendritic cells resting cells showed elevated expression in LUAD. The prognostic genes were differentially associated with individual immune cells. Immune-related function scores, such as those for antigen presenting cell (APC) co-stimulation, APC co-inhibition, check-point, Cytolytic-activity, chemokine receptor, parainflammation, major histocompatibility complex-class-I, type-I-IFN-reponse and T-cell-co-inhibition, were higher in the high-risk group compared with the low-risk group. Furthermore, the TIDE score of the high-risk group was significantly lower than the low-risk group. This immune-related gene prognostic model has the potential to predict the prognosis of LUAD patients, supporting the development of a personalized clinical diagnosis and treatment plan.

Keywords: bioinformatics; immune cell infiltration; immune-related genes; lung adenocarcinoma; prognosis.

Grants and funding

The present study was supported by the Sichuan Science and Technology Program (grant no. 2022YFS0623), the Sichuan Science and Technology Program Joint Innovation Grant (grant no. 2022YFS0623-B3), Southwest Medical University Grant (grant no. 2021ZKZD005) and Sichuan Science and Technology Grant (grant no. 2020YFH0121).