J Cancer 2022; 13(8):2631-2643. doi:10.7150/jca.70725 This issue

Research Paper

Development and Validation of a Combined Hypoxia and Immune Prognostic Classifier for Lung Adenocarcinoma

Hua Huang1#, Guangsheng Zhu1#, Ruifeng Shi1#, Yongwen Li2#, Zihe Zhang1, Songlin Xu1, Chen Chen2, Peijun Cao1, Zhenhua Pan2, Hongbing Zhang1, Minghui Liu1, Hongyu Liu2,3✉, Jun Chen1,2,4✉

1. Department of Lung Cancer Surgery, Tianjin Medical University General Hospital, Tianjin 300052, People's Republic of China.
2. Tianjin Key Laboratory of Lung Cancer Metastasis and Tumor Microenvironment, Tianjin Lung Cancer Institute, Tianjin Medical University General Hospital, Tianjin 300052, People's Republic of China.
3. Quantitative Biomedical Research Center, Department of Population and Data Sciences, University of Texas Southwestern Medical Center, Dallas, TX, USA, 75390.
4. Department of Thoracic Surgery, First Affiliated Hospital, School of Medicine, Shihezi University, Shihezi, Xinjiang 832008, People's Republic of China.
#These authors contributed equally to this work.

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Citation:
Huang H, Zhu G, Shi R, Li Y, Zhang Z, Xu S, Chen C, Cao P, Pan Z, Zhang H, Liu M, Liu H, Chen J. Development and Validation of a Combined Hypoxia and Immune Prognostic Classifier for Lung Adenocarcinoma. J Cancer 2022; 13(8):2631-2643. doi:10.7150/jca.70725. Available from https://www.jcancer.org/v13p2631.htm

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Abstract

Graphic abstract

Lung cancer is the leading cause of cancer-related deaths worldwide. Hypoxia is a crucial microenvironmental factor in lung adenocarcinoma (LUAD). However, the prognostic value based on hypoxia and immune in LUAD remains to be further clarified. The hypoxia-related genes (HRGs) and immune-related genes (IRGs) were downloaded from the public database. The RNA-seq expression and matched complete clinical data for LUAD were retrieved from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) database. The least absolute shrinkage and selection operator (LASSO) Cox regression analysis was applied to model construction. Hypoxia expression profiles, immune cell infiltration, functional enrichment analysis, Tumor Immune Dysfunction and Exclusion (TIDE) score and the somatic mutation status were analyzed and compared based on the model. Moreover, immunofluorescence (IF) staining in human LUAD cases to explore the expression of hypoxia marker and immune checkpoint. A prognostic model of 9 genes was established, which can divide patients into two subgroups. There were obvious differences in hypoxia and immune characteristics in the two groups, the group with high-risk score value showed significantly high expression of hypoxia genes and programmed death ligand-1 (PD-L1), and maybe more sensitive to immunotherapy. Patients in the high-risk group had shorter overall survival (OS). This model has a good predictive value for the prognosis of LUAD. We constructed a new HRGs and IRGs model for prognostic prediction of LUAD. This model may benefit future immunotherapy for LUAD.

Keywords: lung adenocarcinoma, hypoxia, immune, prognosis, immunotherapy