Background/Aims: Chronic kidney disease (CKD) is a worldwide public health problem. Regardless of the underlying primary disease, CKD tends to progress to end-stage kidney disease, resulting in unsatisfactory and costly treatment. Its common pathogenesis, however, remains unclear. The aim of this study was to provide an unbiased catalog of common gene-expression changes of CKD and reveal the underlying molecular mechanism using an integrative bioinformatics approach. Methods: We systematically collected over 250 Affymetrix microarray datasets from the glomerular and tubulointerstitial compartments of healthy renal tissues and those with various types of established CKD (diabetic kidney disease, hypertensive nephropathy, and glomerular nephropathy). Then, using stringent bioinformatics analysis, shared differentially expressed genes (DEGs) of CKD were obtained. These shared DEGs were further analyzed by the gene ontology (GO) and pathway enrichment analysis. Finally, the protein-protein interaction networks(PINs) were constructed to further refine our results. Results: Our analysis identified 176 and 50 shared DEGs in diseased glomeruli and tubules, respectively, including many transcripts that have not been previously reported to be involved in kidney disease. Enrichment analysis also showed that the glomerular and tubulointerstitial compartments underwent a wide range of unique pathological changes during chronic injury. As revealed by the GO enrichment analysis, shared DEGs in glomeruli were significantly enriched in exosomes. By constructing PINs, we identified several hub genes (e.g. OAS1, JUN, and FOS) and clusters that might play key roles in regulating the development of CKD. Conclusion: Our study not only further reveals the unifying molecular mechanism of CKD pathogenesis but also provides a valuable resource of potential biomarkers and therapeutic targets.

Patients with cardiovascular disease show a panel of differentially regulated serum biomarkers indicative of modulation of several pathways from disease onset to progression. Few of these biomarkers have been proposed for multimarker risk prediction methods. However, the underlying mechanism of the expression changes and modulation of the pathways is not yet addressed in entirety. Our present work focuses on understanding the regulatory mechanisms at transcriptional level by identifying the core and specific transcription factors that regulate the coronary artery disease associated pathways. Using the principles of systems biology we integrated the genomics and proteomics data with computational tools. We selected biomarkers from 7 different pathways based on their association with the disease and assayed 24 biomarkers along with gene expression studies and built network modules which are highly regulated by 5 core regulators PPARG, EGR1, ETV1, KLF7 and ESRRA. These network modules in turn comprise of biomarkers from different pathways showing that the core regulatory transcription factors may work together in differential regulation of several pathways potentially leading to the disease. This kind of analysis can enhance the elucidation of mechanisms in the disease and give better strategies of developing multimarker module based risk predictions.

Type 2 Diabetes Mellitus (T2DM) and pancreatic cancer (PC) are complex diseases with shared molecular mechanisms that are not fully understood. This study used bioinformatics analysis to uncover shared transcriptional patterns and central genes between T2DM and PC, identifying 388 dysregulated genes. Functional annotation and pathway analysis highlighted involvement in immune responses and pathways such as Type II diabetes mellitus and the Rap1 signalling. Ten hub genes, including FN1, FCGR3A, CXCL10, CD86, CCL5, CRP, ITGB2, FCER1G, FCGR2B, and CD48, were identified as potential biomarkers. Machine learning classifiers, particularly Random Forest demonstrated the highest accuracy in classifying samples based on the expression of these hub genes. FN1, a key gene involved in cell adhesion was further investigated using molecular docking and molecular dynamics simulations analysis. This study provides insights into the common molecular mechanisms underlying T2DM and PC, with FN1 as a potential therapeutic target. These findings could potentially be used in the future to develop personalized treatments aimed at preventing the occurrence of both T2DM and PC.

Cardiac fibrosis is a common pathological feature in cardiac remodeling. This study aimed to explore the role of KDM5A in cardiac fibrosis via bioinformatics analysis. Cardiac fibroblasts (CFs) were harvested and cultured from 10 dilated cardiomyopathy (DCM) patients who underwent heart transplantation. Western blotting was applied to verify that KDM5A is regulated by angiotensin II (Ang II) via the PI3k/AKT signaling pathway. The differentially expressed genes (DEGs) were analyzed by transcriptomics. ChIP-seq and ChIP-qPCR were used to identify the genes bound by KDM5A. In integrative analysis, weighted gene coexpression network analysis (WGCNA) was performed to identify highly relevant gene modules. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analyses were performed for the key genes in modules. The STRING database, Cytoscape, and MCODE were applied to construct the protein–protein interaction (PPI) network and screen hub genes. To verify the expression of DEGs regulated by KDM5A, Western blotting and immunofluorescence were performed in myocardial tissue samples. Immunofluorescence verified the vimentin positivity of CFs. Ang II upregulated the expression of KDM5A in CFs via the PI3K/AKT signaling pathway. GO analysis of DEGs indicated that regulation of vasoconstriction, extracellular region, and calcium ion binding were enriched when KDM5A interfered with CPI or Ang II. KEGG analysis of the DEGs revealed the involvement of ECM-receptor interaction, focal adhesion, PI3K-Akt signaling pathway, cell adhesion, and arrhythmogenic right ventricular cardiomyopathy pathways. Three hub genes (IGF1, MYH11, and TGFB3) were identified via four different algorithms. Subsequent verification in patient samples demonstrated that the hub genes, which were regulated by KDM5A, were downregulated in DCM samples. KDM5A is a key regulator in the progression of cardiac fibrosis. In this successful integrative analysis, IGF1, MYH11, and TGFB3 were determined to be coordinately expressed to participate in cardiac fibrosis.

Background: The COVID-19 pandemic poses an imminent threat to humanity, especially for those who have comorbidities. Evidence of COVID-19 and COPD comorbidities is accumulating. However, data revealing the molecular mechanism of COVID-19 and COPD comorbid diseases is limited.Methods: We got COVID-19/COPD -related genes from different databases by restricted screening conditions (top500), respectively, and then supplemented with COVID-19/COPD-associated genes (FDR<0.05, |LogFC|≥1) from clinical sample data sets. By taking the intersection, 42 co-morbid host factors for COVID-19 and COPD were finally obtained. On the basis of shared host factors, we conducted a series of bioinformatics analysis, including protein-protein interaction analysis, gene ontology and pathway enrichment analysis, transcription factor-gene interaction network analysis, gene-microRNA co-regulatory network analysis, tissue-specific enrichment analysis and candidate drug prediction.Results: We revealed the comorbidity mechanism of COVID-19 and COPD from the perspective of host factor interaction, obtained the top ten gene and 3 modules with different biological functions. Furthermore, we have obtained the signaling pathways and concluded that dexamethasone, estradiol, progesterone, and nitric oxide shows effective interventions.Conclusion: This study revealed host factor interaction networks for COVID-19 and COPD, which could confirm the potential drugs for treating the comorbidity, ultimately, enhancing the management of the respiratory disease.

Protein-Protein, Genetic, and Chemical Interactions for Nguyen DD (2018):Integrative Bioinformatics and Functional Analyses of GEO, ENCODE, and TCGA Reveal FADD as a Direct Target of the Tumor Suppressor BRCA1. curated by BioGRID (https://thebiogrid.org); ABSTRACT: BRCA1 is a multifunctional tumor suppressor involved in several essential cellular processes. Although many of these functions are driven by or related to its transcriptional/epigenetic regulator activity, there has been no genome-wide study to reveal the transcriptional/epigenetic targets of BRCA1. Therefore, we conducted a comprehensive analysis of genomics/transcriptomics data to identify novel BRCA1 target genes. We first analyzed ENCODE data with BRCA1 chromatin immunoprecipitation (ChIP)-sequencing results and identified a set of genes with a promoter occupied by BRCA1. We collected 3085 loci with a BRCA1 ChIP signal from four cell lines and calculated the distance between the loci and the nearest gene transcription start site (TSS). Overall, 66.5% of the BRCA1-bound loci fell into a 2-kb region around the TSS, suggesting a role in transcriptional regulation. We selected 45 candidate genes based on gene expression correlation data, obtained from two GEO (Gene Expression Omnibus) datasets and TCGA data of human breast cancer, compared to BRCA1 expression levels. Among them, we further tested three genes (MEIS2, CKS1B and FADD) and verified FADD as a novel direct target of BRCA1 by ChIP, RT-PCR, and a luciferase reporter assay. Collectively, our data demonstrate genome-wide transcriptional regulation by BRCA1 and suggest target genes as biomarker candidates for BRCA1-associated breast cancer.

Antipsychotics (APs) are associated with weight gain and other metabolic abnormalities such as hyperglycemia, dyslipidemia and metabolic syndrome. This translational study aimed to uncover the underlying molecular mechanisms and identify the key genes involved in AP-induced metabolic effects. An integrative gene expression analysis was performed in four different mouse tissues (striatum, liver, pancreas and adipose) after risperidone or olanzapine treatment. The analytical approach combined the identification of the gene co-expression modules related to AP treatment, gene set enrichment analysis and protein-protein interaction network construction. We found several co-expression modules of genes involved in glucose and lipid homeostasis, hormone regulation and other processes related to metabolic impairment. Among these genes, EP300, which encodes an acetyltransferase involved in transcriptional regulation, was identified as the most important hub gene overlapping the networks of both APs. Then, we explored the genetically predicted EP300 expression levels in a cohort of 226 patients with first-episode psychosis who were being treated with APs to further assess the association of this gene with metabolic alterations. The EP300 expression levels were significantly associated with increases in body weight, body mass index, total cholesterol levels, low-density lipoprotein cholesterol levels and triglyceride concentrations after 6 months of AP treatment. Taken together, our analysis identified EP300 as a key gene in AP-induced metabolic abnormalities, indicating that the dysregulation of EP300 function could be important in the development of these side effects. However, more studies are needed to disentangle the role of this gene in the mechanism of action of APs.

Host response biomarkers offer a promising alternative diagnostic solution for identifying acute respiratory infection (ARI) cases involving influenza infection. However, most of the published panels involve multiple genes, which is problematic in clinical settings because polymerase chain reaction (PCR)-based technology is the most widely used genomic technology in these settings, and it can only be used to measure a small number of targets. This study aimed to identify a single-gene biomarker with a high diagnostic accuracy by using integrated bioinformatics analysis with XGBoost. The gene expression profiles in dataset GSE68310 were used to construct a co-expression network using weighted correlation network analysis (WGCNA). Fourteen hub genes related to influenza infection (blue module) that were common to both the co-expression network and the protein–protein interaction network were identified. Thereafter, a single hub gene was selected using XGBoost, with feature selection conducted using recursive feature elimination with cross-validation (RFECV). The identified biomarker was oligoadenylate synthetases-like (OASL). The robustness of this biomarker was further examined using three external datasets. OASL expression profiling triggered by various infections was different enough to discriminate between influenza and non-influenza ARI infections. Thus, this study presented a workflow to identify a single-gene classifier across multiple datasets. Moreover, OASL was revealed as a biomarker that could identify influenza patients from among those with flu-like ARI. OASL has great potential for improving influenza diagnosis accuracy in ARI patients in the clinical setting.

Additional file 22. List 1 and List 2 genes contain DEGs obtained from first stage analysis and gene targets for top 10 DEMIs

Supplementary Material 25.

Supplementary material for the manuscript 'Identification of COVID-19 and dengue host factor interaction networks based on integrative bioinformatics analyses', including potential host factors between COVID-19 and dengue, and bioinformatics analysis result of the manuscript.

Additional file 5: Table S4. The miR-494 target prediction, analyzed using miRecords.

Additional file 24. Common genes between list 1 and 2

Adrenocortical carcinoma (ACC) is a rare malignancy with poor prognosis. Thus, we aimed to establish a potential gene model for prognosis prediction of patients with ACC. First, weighted gene co-expression network (WGCNA) was constructed to screen two key modules (blue: P = 5e-05, R^2 = 0.65; red: P = 4e-06, R^2 = −0.71). Second, 93 survival-associated genes were identified. Third, 11 potential prognosis models were constructed, and two models were further selected. Survival analysis, receiver operating characteristic curve (ROC), Cox regression analysis, and calibrate curve were performed to identify the best model with great prognostic value. Model 2 was further identified as the best model [training set: P < 0.0001; the area under curve (AUC) value was higher than in any other models showed]. We further explored the prognostic values of genes in the best model by analyzing their mutations and copy number variations (CNVs) and found that MKI67 altered the most (12%). CNVs of the 14 genes could significantly affect the relative mRNA expression levels and were associated with survival of ACC patients. Three independent analyses indicated that all the 14 genes were significantly associated with the prognosis of patients with ACC. Six hub genes were further analyzed by constructing a PPI network and validated by AUC and concordance index (C-index) calculation. In summary, we constructed and validated a prognostic multi-gene model and found six prognostic biomarkers, which may be useful for predicting the prognosis of ACC patients.

Table S11 Functional enrichment analysis of protein-protein interaction clusters enriched in DEGs. Functional annotation of the protein clusters 22 (50 genes), 32 (31 genes), and 63 (15 genes) enriched in DEGs was performed with DAVID. The table reports the top 15 ranked (by Benjamini-adjusted p-value) pathways for each cluster. (XLSX 11Â kb)

Additional file 2: Table S2. The top 100 most significantly differentially methylated genes (DMGs) of GSE29801. To identify DMGs, we used the methylation profile from GSE102952 (containing peripheral blood samples of 9 AMD patients and 9 normal controls). After data preprocessing and quality assessment using R software, we identified 4117 hypermethylated genes and 511 hypomethylated genes. The top 100 most significantly DMGs of GSE102952 are shown in Table S2.

Long non-coding RNA (lncRNA) plays a crucial role in modulating genome instability, immune characteristics, and cancer progression, within which genome instability was identified as a critical regulator in tumorigenesis and tumor progression. However, the existing accounts fail to detail the regulatory role of genome instability in lung adenocarcinoma (LUAD). We explored the clinical value of genome instability-related lncRNA in LUAD with multi-omics bioinformatics analysis. We extracted the key genome instability-related and LUAD-related gene modules using weighted gene co-expression network analysis (WGCNA) and established a competing endogenous RNA (ceRNA) network using four lncRNAs (LINC01224, LINC00346, TRPM2-AS, and CASC9) and seven target mRNAs (CCNF, PKMYT1, GCH1, TK1, PSAT1, ADAM33, and DDX11). We found that LINC01224 is primarily located in the cytoplasm and that LINC00346 and TRPM2-AS are primarily located in the nucleus (CASC9 unknown). We found that all 11 genes were positively related to tumor mutational burden and involve drug resistance, cancer stemness, and tumor microenvironment infiltration. Additionally, an eight-lncRNA genome instability-related lncRNA signature was established and validated, predicting the overall survival and immunotherapy outcomes in LUAD. To conclude, we discovered that sponging microRNA, genome instability-related lncRNA functions as ceRNA, modulating genomic integrity. This research provides clinical references for LUAD immunotherapy and prognosis and interprets a potential genome instability-related ceRNA regulatory network in which LINC01224-miR-485-5p/miR-29c-3p-CCNF-RRM2 and LINC01224-miR485-5p-PKMYT1-CDK1 axes were the most promising pathways. However, the potential mechanisms underlying our findings still need biological validation through in vitro and in vivo experiments.

BackgroundClear Cell Renal Cell Carcinoma (ccRCC) is a malignant tumor with high mortality and recurrence rates and the molecular mechanism of ccRCC genesis remains unclear. In this study, we identified several key genes associated with the prognosis of ccRCC by using integrated bioinformatics.MethodsTwo ccRCC expression profiles were downloaded from Gene Expression Omnibus and one dataset was gained from The Cancer Genome Atlas The Robust Rank Aggregation method was used to analyze the three datasets to gain integrated differentially expressed genes The Gene Ontology and KEGG analysis were performed to explore the potential functions of DEGs. The Search Tool for the Retreival of Interacting Genes/Proteins (STRING) and Cytoscape software were used to construct protein-protein interaction network and module analyses to screen the hub genes. Spearman’s correlation analysis was conducted to evaluate the interrelationships among the hub genes. The prognostic value was evaluated through K-M survival analysis, Cox regression analysis, and receiver operating characteristic curve analysis to determine their potential as prognostic biomarkers in ccRCC. The expression of hub genes between ccRCC and adjacent normal tissues was analyzed by RT-qPCR, Western blotting, and immunohistochemical (IHC).Result125 DEGs were identified using the limma package and RRA method, including 62 up-expressed genes and 63 down-expressed genes. GO and KEGG analysis showed some associated pathways. Spearman’s correlation analysis revealed that the hub genes are not only interrelated but also closely associated with immune cell infiltration. Gene expression analysis of the hub genes based on the TCGA-KIRC cohort, along with K-M survival analysis, Cox regression, and ROC curve analysis, consistently demonstrated that CCL5, LOX, and C3 are significantly upregulated in ccRCC and are associated with poor clinical outcomes. In contrast, PLG showed opposite result. These results were further validated at the mRNA and protein levels.ConclusionOur findings indicate that CCL5, LOX, C3, and PLG are significantly associated with the progression and prognosis of ccRCC, highlighting their potential as prognostic biomarkers. These results provide a foundation for future research aimed at uncovering the underlying mechanisms and identifying potential therapeutic targets for ccRCC.

Additional file 1: Table S1. The top 100 most significantly differentially expressed genes (DEGs) of GSE29801. To identify DEGs, we used the expression profile from GSE29801 (containing RPE-choroid and retina tissue samples from 41 patients with AMD and 42 normal samples). After data preprocessing and quality assessment using R software, we identified 827 high-expression genes and 592 low-expression genes. The top 100 most significantly DEGs of GSE29801 are shown in Table S1.

BackgroundCoronary heart disease (CHD) is the most common progressive disease that is difficult to diagnose and predict in the young asymptomatic period. Our study explored a mechanistic understanding of the genetic effects of premature CHD (PCHD) and provided potential biomarkers and treatment targets for further research through high throughput sequencing and integrated bioinformatics analysis.MethodsHigh throughput sequencing was performed among recruited patients with PCHD and young healthy individuals, and CHD-related microarray datasets were obtained from the Gene Expression Omnibus (GEO) database. Differentially expressed genes (DEGs) were identified by using R software. Enrichment analysis and CIBERSORT were performed to explore the enriched pathways of DEGs and the characteristics of infiltrating immune cells. Hub genes identified by protein–protein interaction (PPI) networks were used to construct the competitive endogenous RNA (ceRNA) networks. Potential drugs were predicted by using the Drug Gene Interaction Database (DGIdb).ResultsA total of 35 DEGs were identified from the sequencing dataset and GEO database by the Venn Diagram. Enrichment analysis indicated that DEGs are mostly enriched in excessive immune activation pathways and signal transduction. CIBERSORT exhibited that resting memory CD4 T cells and neutrophils were more abundant, and M2 macrophages, CD8 T cells, and naïve CD4 T cells were relatively scarce in patients with PCHD. After the identification of 10 hub gens, three ceRNA networks of CD83, CXCL8, and NR4A2 were constructed by data retrieval and validation. In addition, CXCL8 might interact most with multiple chemical compounds mainly consisting of anti-inflammatory drugs.ConclusionsThe immune dysfunction mainly contributes to the pathogenesis of PCHD, and three ceRNA networks of CD83, CXCL8, and NR4A2 may be potential candidate biomarkers for early diagnosis and treatment targets of PCHD.