Search

Abstract Hepatocellular carcinoma (HCC) is one of the most dangerous malignant tumors. The incidence rates of obesity related NAFLD and NASH are increasing year by year, and they are the main risk factors for HCC at present. Finding the mechanism of malignant transformation of NAFLD and NASH is helpful for early prevention and diagnosis. In this study, we performed differential analysis using NAFLD data, NASH data, and HCC data to identify crossover differential genes. Then, using the clinical data of TCGA, a prognostic risk prediction model of three genes (TEAD4, SOCS2, CIT) was constructed, and survival analysis and receiver operating characteristic curves were drawn. The prognostic model was validated using ICGC, GSE116174 and GSE54236 datasets. In addition, we assessed immune status and function in high- and low-risk populations using a prognostic model. Moreover, we assessed the expression of CIT in clinical samples and HCC cell lines and validated its role in HCC development. Our study elucidates the important role of the tumor immune microenvironment in the development of NAFLD/NASH to HCC, deepens the understanding of the pathogenesis of NAFLD/NASH development to HCC, and is helpful for clinical management and decision-making.

Abstract N 6-methyladenosine (m6A) is a form of posttranscriptional modification that plays important roles in cancer including oral squamous cell carcinoma (OSCC). Most studies to date have focused on a limited number of regulators and oncogenic pathways, thus failing to provide comprehensive insight into the dynamic effects of m6A modification. In addition, the role of m6A modification in shaping immune cell infiltration in OSCC has yet to be clarified. This study was designed to assess m6A modification dynamics in OSCC and to understand how such modifications influence clinical immunotherapeutic treatment outcomes. m6A modification patterns linked with 23 m6A regulators were analyzed in 437 OSCC patients from TCGA and GEO cohorts. These patterns were then quantified through m6A score based on algorithms derived from a principal component analysis (PCA) approach. The m6A modification patterns of OSCC samples were grouped into two clusters based on the m6A regulators expression, and immune cell infiltration was linked with the 5-year survival outcomes of patients in these clusters. 1575 genes associated with OSCC patient prognosis were identified and used to re-cluster these samples into two groups. Patients in clusters exhibiting higher levels of m6A regulator expression exhibited poorer overall survival (OS), whereas patients with high m6A scores survived for longer (p