TBX15 and SDHB expression changes in colorectal cancer serve as potential prognostic biomarkers.

Alterations in the expression of certain genes could be associated with both patient mortality rates and drug resistance. This study aimed to identify genes in colorectal cancer (CRC) that potentially serve as hub genes influencing patient survival rates. RNA-Seq data were downloaded from the cancer genome atlas database, and differential expression analysis was performed between tumors and healthy controls. Through the utilization of univariate and multivariate Cox regression analyses, in combination with the MCODE clustering module, the genes whose expression changes were related to survival rate and the hub genes related to them were identified. The mortality risk model was computed using the hub genes. CRC samples and the RT-qPCR method were utilized to confirm the outcomes. PharmacoGx data were employed to link the expression of potential genes to medication resistance and sensitivity. The results revealed the discovery of seven hub genes, which emerged as independent prognostic markers. These included HOXC6, HOXC13, HOXC8, and TBX15, which were associated with poor prognosis and overexpression, as well as SDHB, COX5A, and UQCRC1, linked to favorable prognosis and downregulation. Applying the risk model developed with the mentioned genes revealed a markedly higher incidence of deceased patients in the high-risk group compared to the low-risk group. RT-qPCR results indicated a decrease in SDHB expression and an elevation in TBX15 levels in cancer samples relative to adjacent healthy tissue. Also, PharmacoGx data indicated that the expression level of SDHB was correlated with drug sensitivity to Crizotinib and Dovitinib. Our findings highlight the potential association between alterations in the expression of genes such as HOXC6, HOXC13, HOXC8, TBX15, SDHB, COX5A, and UQCRC1 and increased mortality rates in CRC patients. As revealed by the PPI network, these genes exhibited the most connections with other genes linked to survival.

Identification of gene signature in RNA-Seq hepatocellular carcinoma data by Pareto-optimal cluster algorithm.

Aim: This study aimed to detect gene signatures in RNA-sequencing (RNA-seq) data using Pareto-optimal cluster size identification. Background: RNA-seq has emerged as an important technology for transcriptome profiling in recent years. Gene expression signatures involving tens of genes have been proven to be predictive of disease type and patient response to treatment. Methods: Data related to the liver cancer RNA-seq dataset, which included 35 paired hepatocellular carcinoma (HCC) and non-tumor tissue samples, was used in this study. The differentially expressed genes (DEGs) were identified after performing pre-filtering and normalization. After that, a multi-objective optimization technique, namely multi-objective optimization for collecting cluster alternatives (MOCCA), was used to discover the Pareto-optimal cluster size for these DEGs. Then, the k-means clustering method was performed on the RNA-seq data. The best cluster, as a signature for the disease, was found by calculating the average Spearman's correlation score of all genes in the module in a pair-wise manner. All analyses were performed in the R 4.1.1 package in virtual space with 100 Gb of RAM memory. Results: Using MOCCA, eight Pareto-optimal clusters were obtained. Ultimately, two clusters with the greatest average Spearman's correlation coefficient scores were chosen as gene signatures. Eleven prognostic genes involved in HCC's abnormal metabolism were identified. In addition, three differentially expressed pathways were identified between tumor and non-tumor tissues. Conclusion: These identified metabolic prognostic genes help us to provide more powerful prognostic information and enhance survival prediction for HCC patients. In addition, Pareto-optimal cluster size identification is suggested for gene signature in other RNA-Seq data.

Mutation in CYP27A1 identified in family with coronary artery disease.

Coronary artery disease (CAD) is a leading cause of death worldwide. Myocardial infarction is the most severe outcome of CAD. Despite extensive efforts, the genetics of CAD is poorly understood. We aimed to identify the genetic cause of CAD in a pedigree with several affected individuals. Exome sequencing led to identification of a mutation in CYP27A1 that causes p.Arg225His in the encoded protein sterol 27-hydroxylase as the likely cause of CAD in the pedigree. The enzyme is multifunctional, and several of its functions including its functions in vitamin D metabolism and reverse cholesterol transport (RCT) are relevant to the CAD phenotype. Measurements of vitamin D levels suggested that the mutation does not affect CAD by affecting this parameter. We suggest that the mutation may cause CAD by affecting RCT. Screening of all coding regions of the CYP27A1 in 100 additional patients led to finding four variations (p.Arg14Gly, p.Arg26Lys, p.Ala27Arg, and p.Val86Met) in seven patients that may contribute to their CAD status. CYP27A1 is the known causative gene of cerebrotendinous xanthomatosis, a disorder which is sometimes accompanied by early onset atherosclerosis. This and the observation of potentially harmful variations in unrelated CAD patients provide additional evidence for the suggested causative role of the p.Arg225His mutation in CAD.