BMC Medical Genomics

Heart failure (HF) is the most common potential cause of death, causing a huge health and economic burden all over the world. So far, some impressive progress has been made in the study of pathogenesis. However, the underlying molecular mechanisms leading to this disease remain to be fully elucidated. The microarray data sets of GSE76701, GSE21610 and GSE8331 were retrieved from the gene expression comprehensive database (GEO). After merging all microarray data and adjusting batch effects, differentially expressed genes (DEG) were determined. Functional enrichment analysis was performed based on Gene Ontology (GO) resources, Kyoto Encyclopedia of Genes and Genomes (KEGG) resources, gene set enrichment analysis (GSEA), response pathway database and Disease Ontology (DO). Protein protein interaction (PPI) network was constructed using string database. Combined with the above important bioinformatics information, the potential key genes were selected. The comparative toxicological genomics database (CTD) is used to explore the interaction between potential key genes and HF. We identified 38 patients with heart failure and 16 normal controls. There were 315 DEGs among HF samples, including 278 up-regulated genes and 37 down-regulated genes. Pathway enrichment analysis showed that most DEGs were significantly enriched in BMP signal pathway, transmembrane receptor protein serine/threonine kinase signal pathway, extracellular matrix, basement membrane, glycosaminoglycan binding, sulfur compound binding and so on. Similarly, GSEA enrichment analysis showed that DEGs were mainly enriched in extracellular matrix and extracellular matrix related proteins. BBS9, CHRD, BMP4, MYH6, NPPA and CCL5 are central genes in PPI networks and modules. The enrichment pathway of DEGs and GO may reveal the molecular mechanism of HF. Among them, target genes EIF1AY, RPS4Y1, USP9Y, KDM5D, DDX3Y, NPPA, HBB, TSIX, LOC28556 and XIST are expected to become new targets for heart failure. Our findings provide potential biomarkers or therapeutic targets for the further study of heart failure and contribute to the development of advanced prediction, diagnosis and treatment strategies.

N7 methylguanosine (m7G) has a crucial role the development of hepatocellular carcinoma (HCC). This study aimed to investigate the impact of the m7G methylation core genes (METTL1 and WDR4) and associated RNA risk signatures on HCC. we found m7G methylation core genes (METTL1 and WDR4) were upregulated in four HCC cell lines, and downregulation of METTL1 and WDR4 attenuated HCC cell proliferation, migration, and invasion. Moreover, METTL1 and WDR4 are upregulated in HCC tissues, and that there is a significant positive correlation between them. METTL1 and WDR4 were identified as independent prognostic markers for HCC by employing overall survival (OS), disease-specific survival (DSS), Progression Free Interval survival (PFI), and univariate/multivariate Cox analyses. We identified 1479 coding RNAs (mRNAs) and 232 long non-coding RNAs (lncRNAs) associated with METTL1 / WDR4 by using weighted coexpression network analysis (WGCNA) and co-clustering analysis. The least absolute shrinkage and selection operator (lasso) were used to constructing mRNA and lncRNA risk signatures associated with the METTL1 / WDR4. These risk were independent poor prognostic factors in HCC. Furthermore, we found that METTL1 / WDR4 expression and mRNA / lncRNA risk scores were closely associated with TP53 mutations. Clinicopathological features correlation results showed that METTL1 / WDR4 expression and mRNA / lncRNA risk score were associated with the stage and invasion depth (T) of HCC. To predict the overall survival of HCC individuals, we constructed a nomogram with METTL1/WDR4 expression, mRNA/lncRNA risk score, and clinicopathological features. In addition, we combined single-cell sequencing datasets and immune escape-related checkpoints to construct an immune escape-related protein–protein interaction(PPI) network. In conclusion, M7G methylated core genes (METTL1 and WDR4) and associated RNA risk signatures are associated with prognosis and immune escape in HCC.

Parkinson’s disease (PD) is a long-term degenerative disease that is caused by environmental and genetic factors. The networks of genes and their regulators that control the progression and development of PD require further elucidation. We examine common differentially expressed genes (DEGs) from several PD blood and substantia nigra (SN) microarray datasets by meta-analysis. Further we screen the PD-specific genes from common DEGs using GCBI. Next, we used a series of bioinformatics software to analyze the miRNAs, lncRNAs and SNPs associated with the common PD-specific genes, and then identify the mTF-miRNA-gene-gTF network. Our results identified 36 common DEGs in PD blood studies and 17 common DEGs in PD SN studies, and five of the genes were previously known to be associated with PD. Further study of the regulatory miRNAs associated with the common PD-specific genes revealed 14 PD-specific miRNAs in our study. Analysis of the mTF-miRNA-gene-gTF network about PD-specific genes revealed two feed-forward loops: one involving the SPRK2 gene, hsa-miR-19a-3p and SPI1, and the second involving the SPRK2 gene, hsa-miR-17-3p and SPI. The long non-coding RNA (lncRNA)-mediated regulatory network identified lncRNAs associated with PD-specific genes and PD-specific miRNAs. Moreover, single nucleotide polymorphism (SNP) analysis of the PD-specific genes identified two significant SNPs, and SNP analysis of the neurodegenerative disease-specific genes identified seven significant SNPs. Most of these SNPs are present in the 3′-untranslated region of genes and are controlled by several miRNAs. Our study identified a total of 53 common DEGs in PD patients compared with healthy controls in blood and brain datasets and five of these genes were previously linked with PD. Regulatory network analysis identified PD-specific miRNAs, associated long non-coding RNA and feed-forward loops, which contribute to our understanding of the mechanisms underlying PD. The SNPs identified in our study can determine whether a genetic variant is associated with PD. Overall, these findings will help guide our study of the complex molecular mechanism of PD.

Accurately predicting pathogenic human genes has been challenging in recent research. Considering extensive gene–disease data verified by biological experiments, we can apply computational methods to perform accurate predictions with reduced time and expenses. We propose a probability-based collaborative filtering model (PCFM) to predict pathogenic human genes. Several kinds of data sets, containing data of humans and data of other nonhuman species, are integrated in our model. Firstly, on the basis of a typical latent factorization model, we propose model I with an average heterogeneous regularization. Secondly, we develop modified model II with personal heterogeneous regularization to enhance the accuracy of aforementioned models. In this model, vector space similarity or Pearson correlation coefficient metrics and data on related species are also used. We compared the results of PCFM with the results of four state-of-arts approaches. The results show that PCFM performs better than other advanced approaches. PCFM model can be leveraged for predictions of disease genes, especially for new human genes or diseases with no known relationships.

Polycystic ovary syndrome (PCOS) is not only a kind of common endocrine syndrome but also a metabolic disorder, which harms the reproductive system and the whole body metabolism of the PCOS patients worldwide. In this study, we aimed to investigate the differences in serum metabolic profiles of the patients with PCOS compared to the healthy controls. 31 PCOS patients and 31 matched healthy female controls were recruited in this study, the clinical characteristics data were recorded, the laboratory biochemical data were detected. Then, we utilized the metabolomics approach by UPLC-HRMS technology to study the serum metabolic changes between PCOS and controls. The metabolomics analysis showed that there were 68 downregulated and 78 upregulated metabolites in PCOS patients serum compared to those in the controls. These metabolites mainly belong to triacylglycerols, glycerophosphocholines, acylcarnitines, diacylglycerols, peptides, amino acids, glycerophosphoethanolamines and fatty acid. Pathway analysis showed that these metabolites were enriched in pathways including glycerophospholipid metabolism, fatty acid degradation, fatty acid biosynthesis, ether lipid metabolism, etc. Diagnosis value assessed by ROC analysis showed that the changed metabolites, including Leu–Ala/Ile–Ala, 3-(4-Hydroxyphenyl) propionic acid, Ile–Val/Leu–Val, Gly–Val/Val–Gly, aspartic acid, DG(34:2)_DG(16:0/18:2), DG(34:1)_DG(16:0/18:1), Phe–Trp, DG(36:1)_DG(18:0/18:1), Leu–Leu/Leu–Ile, had higher AUC values, indicated a significant role in PCOS. The present study characterized the difference of serum metabolites and related pathway profiles in PCOS patients, this finding hopes to provide potential metabolic markers for the prognosis and diagnosis of this disease.

Mutations in lysyl-tRNA synthetase (KARS1), an enzyme that charges tRNA with the amino acid lysine in both the cytoplasm and mitochondria, have been associated thus far with autosomal recessive Charcot–Marie–Tooth type CMTRIB, hearing loss type DFNB89, and mitochondrial encephalohepatopathy (MEH) featuring neurodevelopmental disorders with microcephaly, white matter changes, and cardiac and hepatic failure in less than 30 patients. We report the clinical, biochemical and molecular findings of a 14-month-old girl with severe MEH compatible clinical features, profound sensorineural hearing loss, leopard spot retinopathy, pancytopenia, and advanced liver disease with portal hypertension leading to death at the age of 30 months. Whole exome sequencing identified two rare variants in KARS1 gene. Our report expands the allelic and clinical features of tRNA synthase disorders. Moreover, with our report we confirm the usefulness of WES as first tier diagnostic method in infants with complex multisystem phenotypes.