BackgroundIdiopathic pulmonary fibrosis (IPF) is a chronic and progressive condition with an unfavorable prognosis. A recent study has demonstrated that IPF patients exhibit characteristic alterations in the fatty acid metabolism in their lungs, suggesting an association with IPF pathogenesis. Therefore, in this study, we have explored whether the gene signature associated with fatty acid metabolism could be used as a reliable biological marker for predicting the survival of IPF patients.MethodsData on the fatty acid metabolism-related genes (FAMRGs) were extracted from databases like Kyoto Encyclopedia of Genes and Genomes (KEGG), Hallmark, and Reactome pathway. The GSE70866 dataset with information on IPF patients was retrieved from the Gene Expression Omnibus (GEO). Next, the consensus clustering method was used to identify novel molecular subgroups. Gene Set Enrichment Analysis (GSEA) was performed to understand the mechanisms involved. The Cell-type Identification by Estimating Relative Subsets of RNA Transcripts (CIBERSORT) algorithm was used to evaluate the level of immune cell infiltration in the identified subgroups based on gene expression signatures of immune cells. Finally, the Least Absolute Shrinkage and Selection Operator (LASSO) regression and multivariate Cox regression analysis were performed to develop a prognostic risk model.ResultsThe gene expression signature associated with fatty acid metabolism was used to create two subgroups with significantly different prognoses. GSEA reveals that immune-related pathways were significantly altered between the two subgroups, and the two subgroups had different metabolic characteristics. High infiltration of immune cells, mainly activated NK cells, monocytes, and activated mast cells, was observed in the subgroup with a poor prognosis. A risk model based on FAMRGs had an excellent ability to predict the prognosis of IPF. The nomogram constructed using the clinical features and the risk model could accurately predict the prognosis of IPF patients.ConclusionThe fatty acid metabolism-related gene expression signature could be used as a potential biological marker for predicting clinical outcomes and the level of infiltration of immune cells. This could eventually enhance the accuracy of the treatment of IPF patients.

The malignancy with the greatest global mortality rate is lung cancer. Lung adenocarcinoma (LUAD) is the most common subtype. The evidence demonstrated that voltage-gated potassium channel subunit beta-2 (KCNAB2) significantly participated in the initiation of colorectal cancer and its progression. However, the biological function of KCNAB2 in LUAD and its effect on the tumor immune microenvironment are still unknown. In this study, we found that the expression of KCNAB2 in tissues of patients with LUAD was markedly downregulated, and its downregulation was linked to accelerated cancer growth and poor clinical outcomes. In addition, low KCNAB2 expression was correlated with a deficiency in immune infiltration. The mechanism behind this issue might be that KCNAB2 influenced the immunological process such that the directed migration of immune cells was affected. Furthermore, overexpression of KCNAB2 in cell lines promoted the expression of CCL2, CCL3, CCL4, CCL18, CXCL9, CXCL10, and CXCL12, which are necessary for the recruitment of immune cells. In conclusion, KCNAB2 may play a key function in immune infiltration and can be exploited as a predictive biomarker for evaluating prognosis and a possible immunotherapeutic target.

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.