Integrated Bioinformatics Analysis Reveals the Aberrantly Methylated Differentially Expressed Genes in Dilated Cardiomyopathy.

Dilated cardiomyopathy (DCM) causes heart failure and sudden death. Epigenetics is crucial in cardiomyopathy susceptibility and progression; however, the relationship between epigenetics, particularly DNA methylation, and DCM remains unknown. Therefore, this study identified aberrantly methylated differentially expressed genes (DEGs) associated with DCM using bioinformatics analysis and characterized their clinical utility in DCM. DNA methylation expression profiles and transcriptome data from public datasets of human DCM and healthy control cardiac tissues were obtained from the Gene Expression Omnibus public datasets. Then an epigenome-wide association study was performed. DEGs were identified in both DCM and healthy control cardiac tissues. In total, 3,353 cytosine-guanine dinucleotide sites annotated to 2,818 mRNAs were identified, and 479 DCM-related genes were identified. Subsequently, core genes were screened using logistic, least absolute shrinkage and selection operator, random forest, and support vector machine analyses. The overlapping of these genes resulted in DEGs with abnormal methylation patterns. Cross-tabulation analysis identified 8 DEGs with abnormal methylation. Real-time quantitative polymerase chain reaction confirmed the expression of aberrantly methylated DEGs in mice. In DCM murine cardiac tissues, the expressions of SLC16A9, SNCA, PDE5A, FNDC1, and HTRA1 were higher compared to normal murine cardiac tissues. Moreover, logistic regression model associated with aberrantly methylated DEGs was developed to evaluate the diagnostic value, and the area under the receiver operating characteristic curve was 0.949, indicating that the diagnostic model could reliably distinguish DCM from non-DCM samples. In summary, our study identified 5 DEGs through integrated bioinformatic analysis and in vivo experiments, which could serve as potential targets for further comprehensive investigation.

Identification and clinicopathological analysis of potential p73-regulated biomarkers in colorectal cancer via integrative bioinformatics.

This study aims to decipher crucial biomarkers regulated by p73 for the early detection of colorectal cancer (CRC) by employing a combination of integrative bioinformatics and expression profiling techniques. The transcriptome profile of HCT116 cell line p53 - / - p73 + / + and p53 - / - p73 knockdown was performed to identify differentially expressed genes (DEGs). This was corroborated with three CRC tissue expression datasets available in Gene Expression Omnibus. Further analysis involved KEGG and Gene ontology to elucidate the functional roles of DEGs. The protein-protein interaction (PPI) network was constructed using Cytoscape to identify hub genes. Kaplan-Meier (KM) plots along with GEPIA and UALCAN database analysis provided the insights into the prognostic and diagnostic significance of these hub genes. Machine/deep learning algorithms were employed to perform TNM-stage classification. Transcriptome profiling revealed 1289 upregulated and 1897 downregulated genes. When intersected with employed CRC datasets, 284 DEGs were obtained. Comprehensive analysis using gene ontology and KEGG revealed enrichment of the DEGs in metabolic process, fatty acid biosynthesis, etc. The PPI network constructed using these 284 genes assisted in identifying 20 hub genes. Kaplan-Meier, GEPIA, and UALCAN analyses uncovered the clinicopathological relevance of these hub genes. Conclusively, the deep learning model achieved TNM-stage classification accuracy of 0.78 and 0.75 using 284 DEGs and 20 hub genes, respectively. The study represents a pioneer endeavor amalgamating transcriptomics, publicly available tissue datasets, and machine learning to unveil key CRC-associated genes. These genes are found relevant regarding the patients' prognosis and diagnosis. The unveiled biomarkers exhibit robustness in TNM-stage prediction, thereby laying the foundation for future clinical applications and therapeutic interventions in CRC management.

Gene expression analysis reveals diabetes-related gene signatures.

BACKGROUND: Diabetes is a spectrum of metabolic diseases affecting millions of people worldwide. The loss of pancreatic ß-cell mass by either autoimmune destruction or apoptosis, in type 1-diabetes (T1D) and type 2-diabetes (T2D), respectively, represents a pathophysiological process leading to insulin deficiency. Therefore, therapeutic strategies focusing on restoring ß-cell mass and ß-cell insulin secretory capacity may impact disease management. This study took advantage of powerful integrative bioinformatic tools to scrutinize publicly available diabetes-associated gene expression data to unveil novel potential molecular targets associated with ß-cell dysfunction. METHODS: A comprehensive literature search for human studies on gene expression alterations in the pancreas associated with T1D and T2D was performed. A total of 6 studies were selected for data extraction and for bioinformatic analysis. Pathway enrichment analyses of differentially expressed genes (DEGs) were conducted, together with protein-protein interaction networks and the identification of potential transcription factors (TFs). For noncoding differentially expressed RNAs, microRNAs (miRNAs) and long noncoding RNAs (lncRNAs), which exert regulatory activities associated with diabetes, identifying target genes and pathways regulated by these RNAs is fundamental for establishing a robust regulatory network. RESULTS: Comparisons of DEGs among the 6 studies showed 59 genes in common among 4 or more studies. Besides alterations in mRNA, it was possible to identify differentially expressed miRNA and lncRNA. Among the top transcription factors (TFs), HIPK2, KLF5, STAT1 and STAT3 emerged as potential regulators of the altered gene expression. Integrated analysis of protein-coding genes, miRNAs, and lncRNAs pointed out several pathways involved in metabolism, cell signaling, the immune system, cell adhesion, and interactions. Interestingly, the GABAergic synapse pathway emerged as the only common pathway to all datasets. CONCLUSIONS: This study demonstrated the power of bioinformatics tools in scrutinizing publicly available gene expression data, thereby revealing potential therapeutic targets like the GABAergic synapse pathway, which holds promise in modulating α-cells transdifferentiation into ß-cells.

Efficacy, safety, and mechanism of Huangkui Capsules in treating chronic kidney disease: Meta-analysis and integrative bioinformatics / 中国中药杂志

Meta-analysis and integrative bioinformatics were employed to comprehensively study the efficacy, safety, and mechanism of Huangkui Capsules in treating chronic kidney disease(CKD). CNKI, Wanfang, VIP, SinoMed, Cochrane Library, PubMed, EMbase, and Web of Science were searched for randomized controlled trial(RCT) of Huangkui Capsules for CKD from inception to January 3, 2023. The outcome indicators included urine protein, serum creatinine(Scr), and blood urea nitrogen(BUN) levels, and Cochrane Handbook 5.1 and RevMan 5.3 were employed to perform the Meta-analysis of the included RCT. The active ingredients of Huangkui Capsules were retrieved from CNKI, and the targets of CKD from GeneCards, OMIM, and TTD. Cytoscape 3.8.0 was used to build a "component-disease" network and a protein-protein interaction(PPI) network for the screening of core components and targets. Next, a differential analysis of the core targets of Huangkui Capsules for treating CKD was conducted with the clinical samples from GEO to identify the differentially expressed core targets, and correlation analysis and immune cell infiltration analysis were then performed for these targets. A total of 13 RCTs were included for the Meta-analysis, involving 2 372 patients(1 185 in the observation group and 1 187 in the control group). Meta-analysis showed that the Huangkui Capsules group and the losartan potassium group had no significant differences in reducing the urinary protein levels after 12(MD=19.60, 95%CI[-58.66, 97.86], P=0.62) and 24 weeks(MD=-66.00, 95%CI[-264.10, 132.11], P=0.51) of treatment. Huangkui Capsules in combination with conventional treatment was superior to conventional treatment alone(MD=-0.55, 95%CI[-0.86,-0.23], P=0.000 6). Huangkui Capsules combined with conventional treatment was superior to conventional treatment alone in recovering Scr(MD=-9.21, 95%CI[-15.85,-2.58], P=0.006) and BUN(MD=-1.02, 95%CI[-1.83,-0.21], P=0.01). Five patients showed clear adverse reactions, with abdominal or gastrointestinal discomfort. Huangkui Capsules had 43 active ingredients and 393 targets, and the core ingredients were myricetin, quercetin, gossypin, elaidic acid, dihydromyricetin, isochlorogenic acid B, and caffeic acid. CKD and Huangkui Capsules shared 247 common targets, including 25 core targets. The GEO differential analysis predicted 18 differentially expressed core targets, which were mainly positively correlated with immune cell expression and involved in immune inflammation, oxidative stress, pyroptosis, lipid metabolism, sex hormone metabolism, and cell repair. Conclusively, Huangkui Capsules combined with conventional treatment significantly reduced urine protein, Scr, and BUN. Huangkui Capsules alone and losartan potassium had no significant difference in reducing urine protein. This efficacy of Huangkui Capsules may be associated with the multi-component, multi-target, and multi-pathway responses to immune inflammation and oxidative stress. The included RCT had small sample sizes and general quality. More clinical trial protocols with large sample sizes and rigorous design and in line with international norms are needed to improve the evidence quality, and the results of bioinformatics analysis remain to be confirmed by further studies.