Exploration of a hypoxia-immune-related microenvironment gene signature and prediction model for hepatitis C-induced early-stage fibrosis.

BACKGROUND: Liver fibrosis contributes to significant morbidity and mortality in Western nations, primarily attributed to chronic hepatitis C virus (HCV) infection. Hypoxia and immune status have been reported to be significantly correlated with the progression of liver fibrosis. The current research aimed to investigate the gene signature related to the hypoxia-immune-related microenvironment and identify potential targets for liver fibrosis. METHOD: Sequencing data obtained from GEO were employed to assess the hypoxia and immune status of the discovery set utilizing UMAP and ESTIMATE methods. The prognostic genes were screened utilizing the LASSO model. The infiltration level of 22 types of immune cells was quantified utilizing CIBERSORT, and a prognosis-predictive model was established based on the selected genes. The model was also verified using qRT-PCR with surgical resection samples and liver failure samples RNA-sequencing data. RESULTS: Elevated hypoxia and immune status were linked to an unfavorable prognosis in HCV-induced early-stage liver fibrosis. Increased plasma and resting NK cell infiltration were identified as a risk factor for liver fibrosis progression. Additionally, CYP1A2, CBS, GSTZ1, FOXA1, WDR72 and UHMK1 were determined as hypoxia-immune-related protective genes. The combined model effectively predicted patient prognosis. Furthermore, the preliminary validation of clinical samples supported most of the conclusions drawn from this study. CONCLUSION: The prognosis-predictive model developed using six hypoxia-immune-related genes effectively predicts the prognosis and progression of liver fibrosis. The current study opens new avenues for the future prediction and treatment of liver fibrosis.

The Combined Model of CX3CR1-Related Immune Infiltration Genes to Evaluate the Prognosis of Idiopathic Pulmonary Fibrosis.

Background: High expression of chemokine (C-X3-C motif) receptor 1 (CX3CR1) was shown to contribute to the progression of many fibrotic diseases. However, there is still no study for the role of CX3CR1 in idiopathic pulmonary fibrosis (IPF). Therefore, we aimed to identify CX3CR1-related immune infiltration genes (IIGs) in IPF and establish a combined risk model to evaluate the prognosis of IPF. Methods: A discovery cohort of IPF patients (GSE70867) was downloaded from the Gene Expression Omnibus dataset. We identified the composition of 22 kinds of immune cells infiltration by CIBERSORT. The Cox regression model with the LASSO method was used for identifying prognostic genes and developing CX3CR1-related IIGs. Kaplan-Meier was applied to plot the survival curve of prognosis model. Peripheral blood mononuclear cell (PBMC) and bronchoalveolar lavage fluid (BALF) were collected to be tested by quantitative reverse transcriptase-PCR (qRT-PCR) from 15 clinical samples, including 8 healthy controls (HC), 4 patients with usual interstitial pneumonia (UIP) and 3 patients with pulmonary fibrosis (FIB). Results: We found that high expression of CX3CR1 in BALF contributed to the poor prognosis in IPF patients. ALR4C, RAB37, GPR56, MARCKS, PXN and RASSF2 were identified as CX3CR1-related IIGs, which were highly expressed in PBMC of UIP/FIB patients than that of HC. Moreover, the expression of PXN was higher in FIB patients' PBMC than that of UIP ones. In the cohort of IPF patients, high infiltration of activated NK cells in BALF caused poor survival compared to low infiltration group. The infiltration of activated NK was regulated by CX3CR1-related IIGs. The combined risk model predicted that high expression of CX3CR1-related IIGs and high infiltrated activated NK cells caused poor prognosis in IPF patients. Conclusion: We identified a group of CX3CR1-related IIGs in IPF patients. This combined risk model provided new insights in the prognosis and therapy of IPF.

Investigation of a Hypoxia-Immune-Related Microenvironment Gene Signature and Prediction Model for Idiopathic Pulmonary Fibrosis.

Background: There is growing evidence found that the role of hypoxia and immune status in idiopathic pulmonary fibrosis (IPF). However, there are few studies about the role of hypoxia and immune status in the lung milieu in the prognosis of IPF. This study aimed to develop a hypoxia-immune-related prediction model for the prognosis of IPF. Methods: Hypoxia and immune status were estimated with microarray data of a discovery cohort from the GEO database using UMAP and ESTIMATE algorithms respectively. The Cox regression model with the LASSO method was used for identifying prognostic genes and developing hypoxia-immune-related genes. Cibersort was used to evaluate the difference of 22 kinds of immune cell infiltration. Three independent validation cohorts from GEO database were used for external validation. Peripheral blood mononuclear cell (PBMC) and bronchoalveolar lavage fluid (BALF) were collected to be tested by Quantitative reverse transcriptase-PCR (qRT-PCR) and flow cytometry from 22 clinical samples, including 13 healthy controls, six patients with non-fibrotic pneumonia and three patients with pulmonary fibrosis. Results: Hypoxia and immune status were significantly associated with the prognosis of IPF patients. High hypoxia and high immune status were identified as risk factors for overall survival. CD8+ T cell, activated CD4+ memory T cell, NK cell, activated mast cell, M1 and M0 macrophages were identified as key immune cells in hypoxia-immune-related microenvironment. A prediction model for IPF prognosis was established based on the hypoxia-immune-related one protective and nine risk DEGs. In the independent validation cohorts, the prognostic prediction model performed the significant applicability in peripheral whole blood, peripheral blood mononuclear cell, and lung tissue of IPF patients. The preliminary clinical specimen validation suggested the reliability of most conclusions. Conclusions: The hypoxia-immune-based prediction model for the prognosis of IPF provides a new idea for prognosis and treatment.

The networks of m6A-SARS-CoV-2 related genes and immune infiltration patterns in idiopathic pulmonary fibrosis.

Idiopathic pulmonary fibrosis (IPF) is a chronic progressive lung disease with a poor prognosis. The current coronavirus disease 2019 (COVID-19) shares some similarities with IPF. SARS-CoV-2 related genes have been reported to be broadly regulated by N6-methyladenosine (m6A) RNA modification. Here, we identified the association between m6A methylation regulators, COVID-19 infection pathways, and immune responses in IPF. The characteristic gene expression networks and immune infiltration patterns of m6A-SARS-CoV-2 related genes in different tissues of IPF were revealed. We subsequently evaluated the influence of these related gene expression patterns and immune infiltration patterns on the prognosis/lung function of IPF patients. The IPF cohort was obtained from the Gene Expression Omnibus dataset. Pearson correlation analysis was performed to identify the correlations among genes or cells. The CIBERSORT algorithm was used to assess the infiltration of 22 types of immune cells. The least absolute shrinkage and selection operator (LASSO) and proportional hazards model (Cox model) were used to develop the prognosis prediction model. Our research is pivotal for further understanding of the cellular and genetic links between IPF and SARS-CoV-2 infection in the context of the COVID-19 pandemic, which may contribute to providing new ideas for prognosis assessment and treatment of both diseases.