Construction of a hypoxic immune microenvironment associated gene-based model for prognosis prediction of lung adenocarcinoma.

OBJECTIVE

Lung adenocarcinoma (LUAD) accounts for the majority of cancer deaths worldwide, with a high incidence rate and mortality. It is highly important to develop biomarker model to accurately predict the prognosis.

MATERIALS AND METHODS

RNA-Seq data and clinical follow-up data of LUAD were downloaded from The Cancer Genome Atlas (TCGA) database. Hypoxia-related gene sets were collected from the Gene Set Enrichment Analysis (GSEA) website. A gene signature model was established using the Limma package in the R software, univariate and multivariate survival analyses, and least absolute shrinkage and selection operator (LASSO) algorithms.

RESULTS

Two hypoxia subtypes (C1 and C2) were classified according to the expressions of 55 prognostic hypoxic-related genes. Differentially expressed genes (DEGs) between two hypoxia subtypes and immune group were analyzed. Then, 390 DEGs related to hypoxic immune microenvironment were filtered. According to hypoxia type and immune type, the samples were classified into hypoxia-high & immune-low group, hypoxia-low & immune-high group. Based on these differentially expressed genes (DEGs), a 5-genes signature model, which showed a stable prediction performance on datasets of different platforms and immunotherapy datasets, was finally developed. Meanwhile, it demonstrated a better performance compared with other existing models. The AUC of the 5-gene signature was high in both the training dataset and 4 independent validation datasets and was confirmed as a clinical feature-independent prognostic model.

CONCLUSIONS

This study developed a hypoxic immune microenvironment associated gene-based model for prognostic prediction of LUAD, providing clinicians with a reliable prognostic assessment tool and facilitating clinical treatment decision-making.