The lack of knowledge about the effect of inspiratory hyperoxia on the lung-specific tumour microenvironment and progression of lung cancer has attracted considerable attention. This study proposes that inspiratory hyperoxia has special significance for the malignant phenotype of lung cancer cells. The effects of different oxygenation parameters on the proliferation, apoptosis, invasion and migration of lung cancer cells were systematically evaluatedin vitroandin vivo. Our results reveal that inspiratory hyperoxia treatment (60% oxygen, 6 h·day−1) not only has no tumour progression-promoting effects, but also suppresses lung cancer metastasis and promotes long-term survival. In addition, we combined transcriptome, proteome and metabolome analysis and found that hyperoxia treatment induced significant intracellular metabolic changes in lung cancer cells. Overall, we established that MYC/SLC1A5-induced metabolic reprogramming and glutamine addiction is a new mechanism that drives lung cancer metastasis, which can be significantly suppressed by inspiratory hyperoxia treatment. These findings are relevant to the debate on the perils, promises and antitumour effect of inspiratory hyperoxia, especially for patients with lung cancer.

Background: Idiopathic pulmonary fibrosis, often abbreviated as IPF, is a condition that is both chronic and progressive, and has an unfavorable prognosis. Recent research has demonstrated that individuals with IPF exhibit characteristic alterations in the fatty acid metabolism in their lungs, suggesting an association with the disease pathogenesis. Here, we explored whether the gene signature associated with fatty acid metabolism might be used as a reliable biological marker for predicting the IPF patients' survival.Methods: Data of fatty acid metabolism-related genes (FAMRGs) were extracted from the Kyoto Encyclopedia of Genes and Genomes (KEGG), Hallmark, and Reactome databases. The GSE70866 data set was retrieved for information on individuals diagnosed with IPF. Next, a consensus clustering approach was used to discover novel molecular subgroups. Then, Gene Set Enrichment Analysis (GSEA) was conducted to evaluate the mechanisms involved. CIBERSORT was utilized to ascertain the infiltration levels of immune cells in the identified subgroups premised on specific gene expression signatures of immune cells. Finally, the Least Absolute Shrinkage and Selection Operator (LASSO) algorithm and multivariate Cox regression analysis were utilized in the development of a prognostic risk model.Results: The patterns of gene expression that are linked to fatty acid metabolism were used to construct two subgroups that had remarkably different prognoses. GSEA indicated that immune-related pathways, were significantly altered between the two subgroups, and different subgroups had different metabolic characteristics. Poor prognosis was associated with high immune cell infiltration, particularly activated NK cells, monocytes, and activated mast cells. A risk model that was developed premised on FAMRGs showed excellent significance for use in predicting IPF. The prognosis for individuals diagnosed with IPF might be correctly predicted using a nomogram that incorporated clinical features and the risk model.Conclusion: The proposed fatty acid metabolism-related gene signature is a potential biological marker for predicting the clinical outcomes and infiltration status of immune cells, and may ultimately increase the accuracy of treating patients with IPF.

BackgroundIdiopathic pulmonary fibrosis, often abbreviated as IPF, is a condition that is both chronic and progressive, and has an unfavorable prognosis. Recent research has demonstrated that individuals with IPF exhibit characteristic alterations in the fatty acid metabolism in their lungs, suggesting an association with the disease pathogenesis. Here, we explored whether the gene signature associated with fatty acid metabolism might be used as a reliable biological marker for predicting the IPF patients' survival.MethodsData of fatty acid metabolism-related genes (FAMRGs) were extracted from the Kyoto Encyclopedia of Genes and Genomes (KEGG), Hallmark, and Reactome databases. The GSE70866 data set was retrieved for information on individuals diagnosed with IPF. Next, a consensus clustering approach was used to discover novel molecular subgroups. Then, Gene Set Enrichment Analysis (GSEA) was conducted to evaluate the mechanisms involved. CIBERSORT was utilized to ascertain the infiltration levels of immune cells in the identified subgroups premised on specific gene expression signatures of immune cells. Finally, the Least Absolute Shrinkage and Selection Operator (LASSO) algorithm and multivariate Cox regression analysis were utilized in the development of a prognostic risk model.ResultsThe patterns of gene expression that are linked to fatty acid metabolism were used to construct two subgroups that had remarkably different prognoses. GSEA indicated that immune-related pathways, were significantly altered between the two subgroups, and different subgroups had different metabolic characteristics. Poor prognosis was associated with high immune cell infiltration, particularly activated NK cells, monocytes, and activated mast cells. A risk model that was developed premised on FAMRGs showed excellent significance for use in predicting IPF. The prognosis for individuals diagnosed with IPF might be correctly predicted using a nomogram that incorporated clinical features and the risk model.ConclusionThe proposed fatty acid metabolism-related gene signature is a potential biological marker for predicting the clinical outcomes and infiltration status of immune cells, and may ultimately increase the accuracy of treating patients with IPF.