BackgroundThe role of inflammation in the formation of idiopathic pulmonary fibrosis (IPF) has gained a lot of attention recently. However, the involvement of genes related to inflammation and immune exchange environment status in the prognosis of IPF remains to be further clarified. The objective of this research is to establish a new model for the prediction of the overall survival (OS) rate of inflammation-related IPF.MethodsGene Expression Omnibus (GEO) was employed to obtain the three expression microarrays of IPF, including two from alveolar lavage fluid cells and one from peripheral blood mononuclear cells. To construct the risk assessment model of inflammation-linked genes, least absolute shrinkage and selection operator (lasso), univariate cox and multivariate stepwise regression, and random forest method were used. The proportion of immune cell infiltration was evaluated by single sample Gene Set Enrichment Analysis (ssGSEA) algorithm.ResultsThe value of genes linked with inflammation in the prognosis of IPF was analyzed, and a four-genes risk model was constructed, including tpbg, Myc, ffar2, and CCL2. It was highlighted by Kaplan Meier (K-M) survival analysis that patients with high-risk scores had worse overall survival time in all training and validation sets, and univariate and multivariate analysis highlighted that it has the potential to act as an independent risk indicator for poor prognosis. ROC analysis showed that the prediction efficiency of 1-, 3-, and 5-year OS time in the training set reached 0.784, 0.835, and 0.921, respectively. Immune infiltration analysis showed that Myeloid-Derived Suppressor Cells (MDSC), macrophages, regulatory T cells, cd4+ t cells, neutrophils, and dendritic cells were more infiltrated in the high-risk group than in the low-risk group.ConclusionInflammation-related genes can be well used to evaluate the IPF prognosis and impart a new idea for the treatment and follow-up management of IPF patients.

Ras‐association domain family 1A (RASSF1A) is one of the most methylated genes in lung cancer (LC). We investigate whether the high DNA methylation level of RASSF1A can relieve the resistance of RASSF1A to LC by inhibiting RASSF1A's transcription factor binding to RASSF1A. RASSF1A expression in tissues and cells was tested utilizing quantitative real‐time polymerase chain reaction (qRT‐PCR), and Western blot. RASSF1A expression and RASSF1A methylation level in LC cells exposed to 5‐Aza‐dc were assessed by qRT‐PCR and quantitative methylation‐specific PCR. The association between CTCF and RASSF1A was assessed using hTFtarget, ChIP, and luciferase reporter gene analysis. The effects of 5‐Aza‐dc, CTCF, and RASSF1A on cell biological behaviors and epithelial‐mesenchymal transition (EMT)‐related markers were assessed by cell function experiments and Western blot. Moreover, we constructed the xenograft tumor and pulmonary nodule metastasis models, and assessed tumor volume and weight. RASSF1A expression and pulmonary nodule metastasis were tested utilizing qRT‐PCR, Western blot, and H&E staining. RASSF1A was under‐expressed in LC tissues and cells. 5‐Aza‐dc enhanced RASSF1A level and weakened RASSF1A methylation level in LC cells. RASSF1A silencing neutralized 5‐Aza‐dc‐mediated repressing effects on LC cell biological function and EMT. The loss of CTCF binding to RASSF1A in LC cells was associated with DNA methylation. The effect of 5‐Aza‐dc on RASSF1A level, LC cell malignant behaviors, and EMT‐related factors were strengthened by CTCF upregulation. RASSF1A overexpression suppressed LC tumor growth and pulmonary nodule metastasis in vivo. DNA methylation blocked the modulation of RASSF1A expression by CTCF and relieved the resistance of RASSF1A to LC.