Expression Signature of the AT-Rich Interactive Domain Gene Family Identified in Digestive Cancer

Identification of Single-Nucleotide Polymorphisms in Differentially Expressed Genes Favoring Soybean Meal Tolerance in Higher-Growth Zebrafish (Danio rerio)

Genetic variability within the same fish species could confer soybean meal (SBM) tolerance in some individuals, thus favoring growth. This study investigates the single-nucleotide polymorphisms (SNPs) in differentially expressed genes (DEGs) favoring SBM tolerance in higher-growth zebrafish (Danio rerio). In a previous work, nineteen families of zebrafish were fed a fish meal diet (100FM control diet) or SBM-based diets supplemented with saponin (50SBM + 2SPN-experimental diet), from juvenile to adult stages. Individuals were selected from families with a genotype-by-environment interaction higher (170 ± 18 mg) or lower (76 ± 10 mg) weight gain on 50SBM + 2SPN in relation to 100FM. Intestinal transcriptomic analysis using RNA-seq revealed six hundred and sixty-five differentially expressed genes in higher-growth fish fed 50SBM + 2SPN diet. In this work, using these results, 47 SNPs in DEGs were selected. These SNPs were genotyped by Sequenom in 340 zebrafish that were fed with a 50SBM + 2SPN diet or with 100FM diet. Marker-trait analysis revealed 4 SNPs associated with growth in 3 immunity-related genes (aif1l, arid3c, and cst14b.2) in response to the 50SBM + 2SPN diet (p-value < 0.05). Two SNPs belonging to aif1l y arid3c produce a positive (+19 mg) and negative (-26 mg) effect on fish growth, respectively. These SNPs can be used as markers to improve the early selection of tolerant fish to SBM diet or other plant-based diets. These genes can be used as biomarkers to identify SNPs in commercial fish, thus contributing to the aquaculture sustainability.

Machine learning and multi-omics data reveal driver gene-based molecular subtypes in hepatocellular carcinoma for precision treatment

The heterogeneity of Hepatocellular Carcinoma (HCC) poses a barrier to effective treatment. Stratifying highly heterogeneous HCC into molecular subtypes with similar features is crucial for personalized anti-tumor therapies. Although driver genes play pivotal roles in cancer progression, their potential in HCC subtyping has been largely overlooked. This study aims to utilize driver genes to construct HCC subtype models and unravel their molecular mechanisms. Utilizing a novel computational framework, we expanded the initially identified 96 driver genes to 1192 based on mutational aspects and an additional 233 considering driver dysregulation. These genes were subsequently employed as stratification markers for further analyses. A novel multi-omics subtype classification algorithm was developed, leveraging mutation and expression data of the identified stratification genes. This algorithm successfully categorized HCC into two distinct subtypes, CLASS A and CLASS B, demonstrating significant differences in survival outcomes. Integrating multi-omics and single-cell data unveiled substantial distinctions between these subtypes regarding transcriptomics, mutations, copy number variations, and epigenomics. Moreover, our prognostic model exhibited excellent predictive performance in training and external validation cohorts. Finally, a 10-gene classification model for these subtypes identified TTK as a promising therapeutic target with robust classification capabilities. This comprehensive study provides a novel perspective on HCC stratification, offering crucial insights for a deeper understanding of its pathogenesis and the development of promising treatment strategies.

The Prognostic Value of AT-Rich Interaction Domain (ARID) Family Members in Patients with Hepatocellular Carcinoma

Objective

Hepatocellular carcinoma (HCC) is one of the most lethal malignancies with a poor prognosis. The AT-rich interaction domain (ARID) family plays an essential regulatory role in the pathogenesis and progression of cancers. This study aims to evaluate the prognostic value and clinical significance of human ARID family genes in HCC.

Methods

ONCOMINE and The Cancer Genome Atlas (TCGA) databases were employed to retrieve ARIDs expression profile and clinicopathological information of HCC. Kaplan–Meier plotter and MethSurv were applied to the survival analysis of patients with HCC. CBioPortal was used to analyze genetic mutations of ARIDs. Gene Expression Profiling Interactive Analysis (GEPIA) and Metascape were used to perform hub gene identification and functional enrichment.

Results

Expression levels of 11 ARIDs were upregulated in HCC, and 2 ARIDs were downregulated. Also, 4 ARIDs and 5 ARIDs were correlated with pathologic stages and histologic grades, respectively. Furthermore, higher expression of ARID1A, ARID1B, ARID2, ARID3A, ARID3B, ARID5B, KDM5A, KDM5B, KDM5C, and JARID2 was remarkably correlated with worse overall survival of patients with HCC, and the high ARID3C/KDM5D expression was related to longer overall survival. Multivariate Cox analysis indicated that ARID3A, KDM5C, and KDM5D were independent risk factors for HCC prognosis. Moreover, ARIDs mutations and 127 CpGs methylation in all ARIDs were observed to be significantly associated with the prognosis of HCC patients. Besides, our data showed that ARIDs could regulate tumor-related pathways and distinct immune cells in the HCC microenvironment.

Conclusions

ARIDs present the potential prognostic value for HCC. Our findings suggest that ARID3A, KDM5C, and KDM5D may be the prognostic biomarkers for patients with HCC.

Comprehensive Landscape of ARID Family Members and Their Association with Prognosis and Tumor Microenvironment in Hepatocellular Carcinoma

As one of the most lethal forms of cancers, hepatocellular carcinoma (HCC) claims many lives around the world, and it is especially common in China. The ARID family plays key roles in the pathogenesis and development of human cancers. The potential of several functional genes used as novel biomarkers has attracted more and more attention. However, the prognostic values of the ARID family in HCC patients are rarely known by people. In this study, we performed comprehensive analysis using TCGA datasets, finding that the expressions of ARID4B, ARID2, ARID3B, JARID2, ARID1A, ARID1B, and ARID3A were increased in HCC specimens compared to nontumor specimens, while the expressions of ARID4A and ARID3C were decreased in HCC specimens. According to the Pearson correlation data, the methylation levels of the majority of ARID members were negatively correlated. Upregulation of ARID3A, ARID5B, and ARID1A was related to a poor HCC outcome according to the data of multivariate assays. Then, we built a LASSO Cox regression model based on ARID3A, ARID5B, and ARID1A in HCC. Overall survival rates were considerably lower for those with high risk scores compared to those with low risk scores. Finally, we studied the associations between risk scores and several types of infiltrating immune cells. The data revealed that the risk score was positively related to the infiltration of CD8+ T cells, B cell, T cell CD8+, neutrophil, macrophage, and myeloid dendritic cell. This study conducted a thorough analysis of the ARID members, resulting in new insights for further examination of the ARID family members as prospective targets in the treatment of HCC.