Assessing the Prognostic Capability of Immune-Related Gene Scoring Systems in Lung Adenocarcinoma

Wenhao Liu; Ruihong Dong; Shuai Gao; Xiaodi Shan; Mian Li; Zhaoyan Yu; Liang Sun

doi:10.1155/2022/2151396

Assessing the Prognostic Capability of Immune-Related Gene Scoring Systems in Lung Adenocarcinoma

J Oncol. 2022 Jul 31:2022:2151396. doi: 10.1155/2022/2151396. eCollection 2022.

Authors

Wenhao Liu¹, Ruihong Dong², Shuai Gao³, Xiaodi Shan¹, Mian Li⁴, Zhaoyan Yu⁵, Liang Sun¹

Affiliations

¹ College of Artificial Intelligence and Big Data For Medical Sciences, Shandong First Medical University & Shandong Academy of Medical Sciences, Jinan, Shandong, China.
² Beijing Mentougou Hospital of Traditional Chinese Medicine, Beijing, China.
³ Department of Rehabilitation, Beijing Rehabilitation Hospital of Capital Medical University, Beijing, China.
⁴ First Affiliated Hospital of Shandong First Medical University, Biomedical Sciences College & Shandong Medicinal Biotechnology Centre, Shandong First Medical University & Shandong Academy of Medical Sciences, Jinan, Shandong 250000, China.
⁵ Department of Otorhinolaryngology, Shandong Public Health Clinical Center, Jinan, Shandong, China.

Abstract

Background: Lung adenocarcinoma (LUAD) is the commonest of the subtypes of lung cancer histologically. For this study, we intended to analyze the expression profiling of the immune-related genes (IRGs) from an independently available public database and developed a potent signature predictive of patients' prognosis.

Methods: Gene expression profiles and the clinical data of lung adenocarcinoma were gathered from the Gene Expression Omnibus database (GEO) and The Cancer Genome Atlas (TCGA), and the obtained data were split into a training set (n = 226), test set (n = 83), and validation set (n = 400). IRGs were then gathered from the ImmPort database. A prognostic model was constructed by analyzing the training set. Then the GO and KEGG analysis was performed, and a gene correlation prognostic nomogram was constructed. Finally, external validation, such as immune infiltration and immunohistochemistry, was performed.

Results: The 110 genes were significant by univariate Cox regression analysis and randomized survival forest algorithm for the training set and showed a good distinction between the low-risk-score and high-risk-score groups in the training set (P < 0.0001) by screening for four prognosis-related genes (HMOX1, ARRB1, ADM, PDIA3) and validated by the test set GSE30219 (P=0.0025) and TCGA dataset (P=0.00059). Multivariate Cox showed that the four gene signatures were an individual risk factor for LUAD. In addition, the genes in the signatures were externally verified using an online database. In particular, PDIA3 and HMOX1 are essential genes in the prognostic nomogram and play an important role in the model of immune-related genes.

Conclusion: Four immune-related genetic signatures are reliable prognostic indicators for patients with LUAD, providing a relevant theoretical basis and therapeutic rationale for immunotherapy.