m6A-Related Angiogenic Genes to Construct Prognostic Signature, Reveal Immune and Oxidative Stress Landscape, and Screen Drugs in Hepatocellular Carcinoma

Multifaceted bioinformatic analysis of m6A-related ferroptosis and its link with gene signatures and tumour-infiltrating immune cells in gliomas

Whether N6-Methyladenosine (m6A)- and ferroptosis-related genes act on immune responses to regulate glioma progression remains unanswered. Data of glioma and corresponding normal brain tissues were fetched from the TCGA database and GTEx. Differentially expressed genes (DEGs) were identified for GO and KEGG enrichment analyses. The FerrDb database was based to yield ferroptosis-related DEGs. Hub genes were then screened out using the cytoHubba database and validated in clinical samples. Immune cells infiltrating into the glioma tissues were analysed using the CIBERSORT R script. The association of gene signature underlying the m6A-related ferroptosis with tumour-infiltrating immune cells and immune checkpoints in low-grade gliomas was analysed. Of 6298 DEGs enriched in mRNA modifications, 144 were ferroptosis-related; NFE2L2 and METTL16 showed the strongest positive correlation. METTL16 knockdown inhibited the migrative and invasive abilities of glioma cells and induced ferroptosis in vitro. NFE2L2 was enriched in the anti-m6A antibody. Moreover, METTL16 knockdown reduced the mRNA stability and level of NFE2L2 (both p < 0.05). Proportions of CD8+ T lymphocytes, activated mast cells and M2 macrophages differed between low-grade gliomas and normal tissues. METTL16 expression was negatively correlated with CD8+ T lymphocytes, while that of NFE2L2 was positively correlated with M2 macrophages and immune checkpoints in low-grade gliomas. Gene signatures involved in the m6A-related ferroptosis in gliomas were identified via bioinformatic analyses. NFE2L2 interacted with METTL16 to regulate the immune response in low-grade gliomas, and both molecules may be novel therapeutic targets for gliomas.

Clinical value of -glutamyl transpeptidase to platelet ratio and triglyceride measurement in the diagnosis of nonalcoholic fatty liver disease: A cross-sectional study

Objective

In clinical practice, there are few effective biomarkers for identifying non-alcoholic fatty liver disease (NAFLD). The aim of this study is to investigate the diagnostic value of γ-glutamyl transpeptidase to platelet ratio (GPR) combined with triglyceride (TG) in NAFLD.

Methods

A total of 14,415 individuals participated in the annual physical examination. Multivariate logistic regression analysis was conducted to investigate the exposure factors associated with NAFLD. Spearman's analysis was performed to assess the correlation among the exposure factors of NAFLD. Furthermore, the diagnostic efficacy of the combination of GPR and TG in NAFLD was analyzed using the receiver operating characteristic curve (ROC).

Results

The results of the multivariate logistic regression analysis showed that BMI (OR = 1.619), Systolic Blood Pressure (SBP) (OR = 1.014), Diastolic Blood Pressure (DBP) (OR = 1.028), GPR (OR = 12.809), and TG (OR = 2.936) were all risk factors for NAFLD, while HDL-C (OR = 0.215) was a protective factor. Spearman correlation analysis revealed significant positive correlations between GPR and SBP, DBP, BMI, TG (p < 0.01), but a negative correlation between GPR and HDL-C (p < 0.01). TG was only positively correlated with GPR (p < 0.001). ROC curve analysis demonstrated that the area under the curve (AUC) of GPR combined with TG for diagnosis of NAFLD was 0.855 (95 % CI: 0.819-0.891), sensitivity was 83.45 % and specificity was 73.56 %.

Conclusion

This study indicated that high levels of GPR and TG were risk factors for NAFLD and demonstrated good clinical value in diagnosing NAFLD.

Inhibition of SIRT1 relieves hepatocarcinogenesis via alleviating autophagy and inflammation

Imbalanced Sirtuin 1 (SIRT1) levels may lead to liver diseases through abnormal regulation of autophagy, but the roles of SIRT1-regulated autophagy in hepatocellular carcinoma are still controversial. In this study, we found that SIRT1 mRNA and protein levels were upregulated in hepatocellular carcinoma, and high SIRT1 expression hinted an advanced stage and a poor prognosis. The differentially expressed proteins were significantly elevated in autophagy, cellular response to stress, and immune signaling pathways. In a thioacetamide-induced hepatocellular carcinoma mouse model, we found that SIRT1 expression was highly increased with increased autophagy and excessive macrophage inflammatory response. Next, we established a Hepa 1-6 cells and macrophage co-culture system in vitro to model the alteration of tumor microenvironment, and found that the medium from CCl4-treated or SIRT1-overexpressing Hepa 1-6 cells triggered the polarization of macrophage M1, and the culture medium derived from M1 macrophage promoted Hepa 1-6 cells growth and intracellular oxidative stress. The progression of liver fibrosis in the CCl4-induced liver fibrosis mouse model showed that inhibition of SIRT1 alleviated inflammatory response and ameliorated liver fibrosis. These findings suggest that SIRT1-regulated autophagy and inflammation are oncogenic in hepatocarcinogenesis.

The role of m6A in angiogenesis and vascular diseases

Angiogenesis, whether physiological or pathological, plays a pivotal role in various physiological and disease conditions. This intricate process relies on a complex and meticulously orchestrated signal transduction network that connects endothelial cells, their associated parietal cells (VSMCs and pericytes), and various other cell types, including immune cells. Given the significance of m6A and its connection to angiogenesis and vascular disease, researchers must adopt a comprehensive and ongoing approach to their investigations. This study aims to ascertain whether a common key mechanism of m6A exists in angiogenesis and vascular diseases and to elucidate the potential application of m6A in treating vascular diseases.

New and Old Key Players in Liver Cancer

Liver cancer represents a major health problem worldwide with growing incidence and high mortality, hepatocellular carcinoma (HCC) being the most frequent. Hepatocytes are likely the cellular origin of most HCCs through the accumulation of genetic alterations, although hepatic progenitor cells (HPCs) might also be candidates in specific cases, as discussed here. HCC usually develops in a context of chronic inflammation, fibrosis, and cirrhosis, although the role of fibrosis is controversial. The interplay between hepatocytes, immune cells and hepatic stellate cells is a key issue. This review summarizes critical aspects of the liver tumor microenvironment paying special attention to platelets as new key players, which exert both pro- and anti-tumor effects, determined by specific contexts and a tight regulation of platelet signaling. Additionally, the relevance of specific signaling pathways, mainly HGF/MET, EGFR and TGF-β is discussed. HGF and TGF-β are produced by different liver cells and platelets and regulate not only tumor cell fate but also HPCs, inflammation and fibrosis, these being key players in these processes. The role of C3G/RAPGEF1, required for the proper function of HGF/MET signaling in HCC and HPCs, is highlighted, due to its ability to promote HCC growth and, regulate HPC fate and platelet-mediated actions on liver cancer.

Prognostic-Related Biomarkers in Pancreatic Ductal Adenocarcinoma Correlating with Immune Infiltrates Based on Proteomics

BACKGROUND Pancreatic ductal adenocarcinoma (PDAC) accounts for 85% of pancreatic carcinoma cases. Patients with PDAC have a poor prognosis. The lack of reliable prognostic biomarkers makes treatment challenging for patients with PDAC. Using a bioinformatics database, we sought to identify prognostic biomarkers for PDAC. MATERIAL AND METHODS Using proteomic analysis of the Clinical Proteomics Tumor Analysis Consortium (CPTAC) database, we were able to identify core differential proteins between early and advanced pancreatic ductal adenocarcinoma tissue, and then we used survival analysis, Cox regression analysis, and area under the ROC curves to screen for more significant differential proteins. Additionally, the Kaplan-Meier plotter database was utilized to determine the relationship between prognosis and immune infiltration in PDAC. RESULTS We identified 378 differential proteins in early (n=78) and advanced stages (n=47) of PDAC (P<0.05). PLG, COPS5, FYN, ITGB3, IRF3, and SPTA1 served as independent prognostic factors of patients with PDAC. Patients with higher COPS5 expression had shorter overall survival (OS) and recurrence-free survival, and those with higher PLG, ITGB3, and SPTA1, and lower FYN and IRF3 expression had shorter OS. More importantly, COPS5, IRF3 were negatively associated with macrophages and NK cells, but PLG, FYN, ITGB3, and SPTA1 were positively related to the expression of CD8+ T cells and B cells. COPS5 affected the prognosis of PDAC patients by acting on B cells, CD8+ T cells, macrophages, and NK cells immune infiltration, while PLG, FYN, ITGB3, IRF3, and SPTA1 affected PDAC patient prognosis through some immune cells. CONCLUSIONS PLG, COPS5, FYN, IRF3, ITGB3 and SPTA1 could be potential immunotherapeutic targets and valuable prognostic biomarkers of PDAC.

Construction and Validation of a Novel Prognosis Model in Colon Cancer Based on Cuproptosis-Related Long Non-Coding RNAs

Colon cancer (CC) is one of the most common (6%) malignancies and leading cause of cancer-associated death (more than 0.5 million) worldwide, which demands reliable prognostic biomarkers. Cuproptosis is a novel modality of regulated cell death triggered by the accumulation of intracellular copper. LncRNAs have been reported as prognostic signatures in different types of tumors. However, the correlation between cuproptosis-related lncRNAs (CRLs) and CC remains unclear. Data of CC patients were downloaded from public databases. The prognosis-associated CRLs were identified by co-expression analysis and univariate Cox. Least absolute shrinkage and selection operator were utilized to construct the CRLs-based prognostic signature in silico for CC patients. CRLs level was validated in human CC cell lines and patient tissues. ROC curve and Kaplan-Meier curve results revealed that high CRLs-risk score was associated with poor prognosis in CC patients. Moreover, the nomogram revealed that this model possessed a steady prognostic prediction capability with C-index as 0.68. More importantly, CC patients with high CRLs-risk score were more sensitive to eight targeted therapy drugs. The prognostic prediction power of the CRLs-risk score was further confirmed by cell lines, tissues and two independent CC cohorts. This study constructed a novel ten-CRLs-based prognosis model for CC patients. The CRLs-risk score is expected to serve as a promising prognostic biomarker and predict targeted therapy response in CC patients.

Genes related to N6-methyladenosine in the diagnosis and prognosis of idiopathic pulmonary fibrosis

Introduction: Idiopathic pulmonary fibrosis (IPF) is a chronic progressive pulmonary fibrotic disease with unknown etiology and poor outcomes. It severely affects the quality of life. In this study, we comprehensively analyzed the expression of N6-methyladenosine (m6A) RNA methylation regulators using gene expression data from various tissue sources in IPF patients and healthy volunteers. Methods: The gene expression matrix and clinical characteristics of IPF patients were retrieved from the Gene Expression Omnibus database. A random forest model was used to construct diagnosis signature m6A regulators. Regression analysis and correlation analysis were used to identify prognosis m6A regulators. Consensus cluster analysis was used to construct different m6A prognosis risk groups, then functional enrichment, immune infiltration and drug sensitivity analysis were performed. Result: Five candidate m6A genes from lung tissue were used to predict the incidence, and the incidence was validated using datasets from bronchoalveolar lavage fluid (BALF) and peripheral blood mononuclear cells. Subsequently, the BALF dataset containing outcomes data was used for the prognosis analysis of m6A regulators. METTL14, G3BP2, and ZC3H13 were independent protective factors. Using correlation analysis with lung function in the lung tissue-derived dataset, METTL14 was a protective factor in IPF. Based on METTL14 and G3BP2, a consensus cluster analysis was applied to distinguish the prognostic m6A regulation patterns. The low-risk group's prognosis was significantly better than the high-risk group. Biological processes regulated by various risk groups included fibrogenesis and cell adhesion. Analysis of immune cell infiltration showed upregulation of neutrophils in the m6A high-risk group. Subsequently, five m6A high-risk group sensitive drugs and one m6A low-risk group sensitive drug were identified. Discussion: These findings suggest that m6A regulators are involved in the diagnosis and prognosis of IPF, and m6A patterns are a method to identify IPF outcomes.