Identification of hypoxia-related gene signatures based on multi-omics analysis in lung adenocarcinoma.
Qineng LuoXing LiZixing MengHao RongYanguo LiGuofang ZhaoHuangkai ZhuLvjun CenQi LiaoPublished in: Journal of cellular and molecular medicine (2023)
Lung adenocarcinoma (LUAD) is the most common type of lung cancer and one of the malignancies with the highest incidence rate and mortality worldwide. Hypoxia is a typical feature of tumour microenvironment (TME), which affects the progression of LUAD from multiple molecular levels. However, the underlying molecular mechanisms behind LUAD hypoxia are not fully understood. In this study, we estimated the level of hypoxia by calculating a score based on 15 hypoxia genes. The hypoxia scores were relatively high in LUAD patients with poor prognosis and were bound up with tumour node metastasis (TNM) stage, tumour size, lymph node, age and gender. By comparison of high hypoxia score group and low hypoxia score group, 1820 differentially expressed genes were identified, among which up-regulated genes were mainly about cell division and proliferation while down-regulated genes were primarily involved in cilium-related biological processes. Besides, LUAD patients with high hypoxia scores had higher frequencies of gene mutations, among which TP53, TTN and MUC16 had the highest mutation rates. As for DNA methylation, 1015 differentially methylated probes-related genes were found and may play potential roles in tumour-related neurobiological processes and cell signal transduction. Finally, a prognostic model with 25 multi-omics features was constructed and showed good predictive performance. The area under curve (AUC) values of 1-, 3- and 5-year survival reached 0.863, 0.826 and 0.846, respectively. Above all, our findings are helpful in understanding the impact and molecular mechanisms of hypoxia in LUAD.
Keyphrases
- endothelial cells
- genome wide
- lymph node
- poor prognosis
- dna methylation
- single cell
- stem cells
- squamous cell carcinoma
- small molecule
- mental health
- genome wide identification
- machine learning
- bioinformatics analysis
- risk factors
- coronary artery disease
- copy number
- signaling pathway
- wastewater treatment
- neoadjuvant chemotherapy
- genome wide analysis
- free survival