Protein-protein Interaction Network Construction Research Articles

Promising Novel Biomarkers and Candidate Drugs or Herbs in Osteoarthritis: Evidence from Bioinformatics Analysis of High‐throughput Data

Background: The most common joint illness is osteoarthritis (OA). The goal of this study was to find changes in gene signatures between normal knee joints and OA tissue samples and look for prospective gene targets for OA. Methods: The gene expression profiles of GSE12021, GSE51588, and GSE55457 were downloaded from Gene Expression Omnibus (GEO). A total of 64 samples (40 OA and 24 standard control samples) were used. The limma program was used to find differentially expressed genes (DEGs) in OA versus NC. Functional annotation and protein-protein interaction (PPI) network construction of OA-specific DEGs were performed. Finally, the candidate drugs and herbs as potential drugs to treat OA were predicted in the DGIdb and TCMIO databases. Results: A total of 19 upregulated and 27 downregulated DEGs between OA and NC samples were identified. DEGs, such as PTN, COMP, NELL1, and MN1, have shown a significant correlation with OA and are expected to become new biomarkers. Cellular senescence, positive regulation of ossification, and Vascular endothelial growth factor (VEGF) were significantly enriched for OA‐specific DEGs. In cell composition analysis, DEGs were also found to be highly enriched in the cytosol. We identified a total of 68 types of drugs or molecular compounds that are promising to reverse OA-related DEGs. Honeycomb and cinnamon oil have the possibility of treating OA. Conclusion: Our findings suggest new biomarkers that can be used to diagnose OA. Furthermore, we tried to find drugs and traditional Chinese medicine that may improve the progress of OA. This research may improve the identification and treatment of these uncontrollable chronic diseases.

Interferon Regulatory Factor Family Genes: At the Crossroads between Immunity and Head and Neck Squamous Carcinoma.

Objective This study is aimed at investigating the regulating mechanisms of the interferon regulatory factor (IRF) family genes in head and neck squamous cell carcinoma. Methods Based on the HNSC data in the ‘The Cancer Genome Atlas (TCGA)' database, the expression pattern of IRF family genes was investigated. The association of IRFs family genes and survival outcomes were analyzed by Kaplan–Meier plotter web portal. The relation of IRF genes and tumor stages was evaluated by using stage plots and based on GEPIA portal. 50 genes interacting with IRFs were identified using the NetworkAnalyst's protein-protein interaction (PPI) network construction tool. The top 200 correlated genes with similar expression patterns in HNSC were obtained by the similar gene detection module of GEPIA. Furthermore, functional enrichment analysis was performed to determine the biological functions enriched by the interacting and correlated genes. The potential implication of IRFs in tumor immunity was investigated in terms of tumor-infiltrating immune cells, a pair of immune checkpoint genes (CD274 and PDCD1), and ESTIMATE-Stromal-Immune score. Results The unpaired sample analysis shows that all of the IRF family genes were highly expressed in HNSC tumor samples compared to control samples. The survival analysis results showed that the overexpression of IRF1, IRF4, IRF5, IRF6, IRF8, and IRF9 was associated with better overall survival in HNSC, while the other IRFs genes (IRF2, IRF3. and IRF7) did not show prognostic values for overall survival outcome of HNSC. Four genes (STAT1, STAT2, FOXP3, and SPI1) were overlapping among 50 interacted genes in the PPI network and top 200 correlated genes identified by GEPIA. The 50 interacting genes in the PPI network and top 200 correlated genes were integrated into 246 genes. These 246 genes were found to be overrepresented in multiple KEGG pathways, e.g., Th17 cell differentiation, T cell receptor signaling pathway, cytokine-cytokine receptor interaction, natural killer (NK) cell-mediated cytotoxicity, FOXO signaling, PI3K-Akt signaling, and ErbB signaling. Most correlations between IRF gene members and TIICs were positive. The strongest positive correlation was identified between IRF8 and T cells (r = 0.849, p < 0.001). The majority of correlation between IRF family genes and ESTIMATE-Stromal-Immune score was found to be positive. The highest positive correlation was found to be between IRF8 and Immune score (r = 0.874, p = 1.09E − 158). Most correlations between IRFs and two immunoinhibitor genes (CD274 and PDCD1) were positive. IRF1 and PDCD1 were found to show the highest positive correlation (r = 0.764, p < 2.2e − 16). Conclusions The current analysis showed IRFs were differentially expressed in HNSC, indicated significant prognostic values, were involved in tumor immunity-related signaling pathways, and significantly regulated tumor-infiltrating immune cells. IRF family genes could be potential therapeutic biomarkers in targeting tumor immunity of head and neck cancer.

Target molecular-based neuroactivity screening of Panax ginseng through network pharmacology integrated molecular docking

Panax ginseng is a medicinal herb that has therapeutic effects against neuronal damage. The main goal of the present study is to investigate the therapeutic potential of P. ginseng in treating neuronal damage through in silico analysis. The literature review was conducted to create a ingredients-target database for P. ginseng. The database was essential in identifying the targets related to neuronal damage, which was followed by the construction of protein-protein interaction network by the STITCH database. The enrichment analysis was performed using Cytoscape (ClueGO) software. The molecular docking analysis was ran using AutoDock and Discovery Studio. The literature search revealed the presence of 66 compounds in P. ginseng, which subsequently resulted in the retrieval of 102 potential targets of these compounds. They were screened for their association with neuronal damage. Three proteins, namely, Glutathione S-transferase P1, HSP90, and Mitogen-activated protein kinases (MAPK8), were found to be in prime interaction with the relative compounds. Subsequently, 91 Gene ontology terms were found, of which four genes were associated with neuronal damage. Molecular docking analysis also revealed good binding affinities of various compounds of P. ginseng to the selected target, MAPK8. In conclusion, the biological processes linked to the therapeutic action of P. ginseng involve the positive regulation of JUN kinase activity, chaperone-mediated protein folding, and MyD88-dependent toll-like receptor signaling pathway.

Identification of Four Novel Prognostic Biomarkers and Construction of Two Nomograms in Adrenocortical Carcinoma: A Multi-Omics Data Study via Bioinformatics and Machine Learning Methods.

Background: Adrenocortical carcinoma (ACC) is an orphan tumor which has poor prognoses. Therefore, it is of urgent need for us to find candidate prognostic biomarkers and provide clinicians with an accurate method for survival prediction of ACC via bioinformatics and machine learning methods. Methods: Eight different methods including differentially expressed gene (DEG) analysis, weighted correlation network analysis (WGCNA), protein-protein interaction (PPI) network construction, survival analysis, expression level comparison, receiver operating characteristic (ROC) analysis, and decision curve analysis (DCA) were used to identify potential prognostic biomarkers for ACC via seven independent datasets. Linear discriminant analysis (LDA), K-nearest neighbor (KNN), support vector machine (SVM), and time-dependent ROC were performed to further identify meaningful prognostic biomarkers (MPBs). Cox regression analyses were performed to screen factors for nomogram construction. Results: We identified nine hub genes correlated to prognosis of patients with ACC. Furthermore, four MPBs (ASPM, BIRC5, CCNB2, and CDK1) with high accuracy of survival prediction were screened out, which were enriched in the cell cycle. We also found that mutations and copy number variants of these MPBs were associated with overall survival (OS) of ACC patients. Moreover, MPB expressions were associated with immune infiltration level. Two nomograms [OS-nomogram and disease-free survival (DFS)-nomogram] were established, which could provide clinicians with an accurate, quick, and visualized method for survival prediction. Conclusion: Four novel MPBs were identified and two nomograms were constructed, which might constitute a breakthrough in treatment and prognosis prediction of patients with ACC.

Bioinformatics analysis reveals molecular connections between non-alcoholic fatty liver disease (NAFLD) and COVID-19.

The ongoing coronavirus disease 2019 (COVID-19) pandemic caused by SARS-CoV-2 has devastatingly impacted people’s lives. Non-alcoholic fatty liver disease (NAFLD) is fatal comorbidity of COVID-19 seen with potential risk factors to develop severe symptoms. This research focuses on determining and elucidating the molecular factors and connections that might contribute to the severity of SARS-CoV-2 infection in NAFLD patients. Here, we comprehensively inspected the genes involved in NAFLD and SARS-CoV-2 entry factors (SCEFs) found by searching through the DisGeNet database and literature review, respectively. Further, we identified the SCEFs-related proteins through protein-protein interaction (PPI) network construction, MCODE, and Cytohubba. Next, the shared genes involved in NAFLD and SARS-CoV-2 entry, and hub gene were determined, followed by the GO and KEGG pathways analysis. X2K database was used to construct the upstream regulatory network of hub genes, as well as to identify the top ten candidates of transcription factors (TFs) and protein kinases (PKs). PPI analysis identified connections between 4 top SCEFs, including ACE, ADAM17, DPP4, and TMPRSS2 and NAFLD-related genes such as ACE, DPP4, IL-10, TNF, and AKT1. GO and KEGG analysis revealed the top ten biological processes and pathways, including cytokine-mediated signaling, PI3K-Akt, AMPK, and mTOR signaling pathways. The upstream regulatory network revealed that AKT1 and MAPK14 as important PKs and HIF1A and SP1 as important TFs associated with AKT1, IL-10, and TNF. The molecular connections identified between COVID-19 and NAFLD may shed light on discovering the causes of the severity of SARS-CoV-2 infected NAFLD patients.

Relationship between tumor mutational burden, gene mutation status, and clinical characteristics in 340 cases of lung adenocarcinoma.

Tumor mutational burden (TMB) is an emerging predictive marker of response to immune checkpoint inhibitor therapies. We evaluated the correlation between clinical indicators and high-throughput sequencing results and TMB in lung adenocarcinoma patients, with the aim of finding simpler and more economical factors as surrogate markers for TMB. The medical records, next-generation sequencing data, and immunohistochemistry results of 340 lung adenocarcinoma patients who were admitted to the First Affiliated Hospital of Zhengzhou University between 2019 and 2020 were collected. The mutated genes were screened for, and the obtained mutated genes were subjected to functional enrichment analysis using R software. A protein-protein interaction (PPI) network was also constructed, and significant modules in the network were identified. Gene Ontology (GO) analyses were performed for the core genes. Univariate and multivariate correlation analyses were performed to judge the correlation between gene mutations and TMB. Genes with a junction mutation rate >1 were selected to construct PPI network and 13 high-connection core genes were screened. The results of GO enrichment analysis showed that the biological processes related to mutant core genes mainly included mitotic cell cycle and cell aging. Subsequently, ATM (p=0.006) and PIK3CA (p=0.008) mutation positivity were identified by univariate and multivariate correlation analysis, while TP53 (p=0.003) and EGFR (p=0.008) mutation negativity were significantly associated with elevated TMB. The results of this study demonstrate that ATM- and PIK3CA-positive and EGFR-negative mutation status are strongly associated with high levels of TMB and have the potential to be predictive biomarkers of response to immune checkpoint inhibitors in lung adenocarcinoma patients.

Screening of periodontitis-related genes and immune cells based on an integrated bioinformatics analysis

BackgroundPeriodontitis is a serious health issue; however, there are few reports on periodontitis and immune related. The study sought to analyze differences in gene expression between periodontitis patients before and after treatment using bioinformatics techniques.MethodsThe GSE6751 data set was screened for differentially expressed genes (DEGs) and immune-related genes using the Gene Expression Omnibus (GEO) database, a gene expression data analysis tool. Enrichment, protein-protein interaction (PPI) network construction, and immune cell correlation analyses were also performed on these genes.ResultsA total of 327 DEGs in periodontitis were identified, including 149 upregulated genes, 178 downregulated genes, and 27 immune-related genes. The immune-related DEGs were mainly involved in granulocyte chemotaxis, granulocyte migration, leukocyte chemotaxis, cytokine receptor binding, Toll-like receptor (TLR) binding, the phosphatidylinositol-3-kinase and protein kinase b signaling pathway, viral protein interactions with cytokines and cytokine receptors, the Ras-proximate-1 signaling pathway, the Ras signaling pathway, natural killer cell mediated cytotoxicity, and immune response related pathways. IFNG and TLR2 were the key core genes in the constructed PPI network.ConclusionsThe pathogenesis of periodontitis is closely related to changes in the expression of genes, such as PF4, ANGPT1, TNFRSF1B, IFNG and TLR2, among which IFNG and TLR2 are potential therapeutic targets for periodontitis.

Identification and Validation of Autophagy-Related Genes in Diabetic Retinopathy.

BackgroundDiabetic retinopathy (DR) is one of the most common microvascular complications of diabetes, which is associated with damage of blood-retinal barrier and ischemia of retinal vasculature. It devastates visual acuity due to leakage of retinal vessels and aberrant pathological angiogenesis in diabetic patients. The etiology of DR is complex, accumulated studies have shown that autophagy plays an important role in the pathogenesis of DR, but its specific mechanism needs to be further studied.MethodsThis study chose the online Gene Expression Omnibus (GEO) microarray expression profiling dataset GSE146615 to carry on the research. Autophagy-related genes that were potentially differentially expressed in DR were screened by R software. Then, the differentially expressed autophagy-related genes were analyzed by correlation analysis, tissue-specific gene expression, gene-ontology (GO) enrichment analysis, Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis and protein-protein interaction (PPI) network analysis. Finally, retinal pigment epithelial cell line (ARPE-19) incubated with high glucose (HG) was used to mimic the DR model, and the mRNA level of key genes was verified by quantitative real-time polymerase chain reaction (qRT-PCR) in vitro.ResultsA total of 23 differentially expressed autophagy-related genes (9 up-regulated genes and 14 down-regulated genes) were identified by differential expression analysis. The analysis of tissue-specific gene expression showed that these differentially expressed autophagy-related genes were enriched in the retina. GO and KEGG enrichment analysis showed that differentially expressed autophagy-related genes were significantly enriched in autophagy-related pathways such as regulation of autophagy and macroautophagy. Then 10 hub genes were identified by PPI network analysis and construction of key modules. Finally, qRT-PCR confirmed that the expression of MAPK3 in the DR model was consistent with the results of bioinformatics analysis of mRNA chip.ConclusionThrough bioinformatics analysis, we identified 23 potential DR autophagy-related genes, among which the down-regulated expression of MAPK3 may affect the occurrence and development of DR by regulating autophagy. It provides a novel insight into the pathogenesis of DR.

Distant Metastasis Pattern and Prognostic Prediction Model of Colorectal Cancer Patients Based on Big Data Mining.

AimsThis study aimed to investigate the distant metastasis pattern from newly diagnosed colorectal cancer (CRC) and also construct and validate a prognostic nomogram to predict both overall survival (OS) and cancer-specific survival (CSS) of CRC patients with distant metastases.MethodsPrimary CRC patients who were initially diagnosed from 2010 to 2016 in the SEER database were included in the analysis. The independent risk factors affecting the OS, CSS, all-cause mortality, and CRC-specific mortality of the patients were screened by the Cox regression and Fine–Gray competitive risk model. The nomogram models were constructed to predict the OS and CSS of the patients. The reliability and accuracy of the prediction model were evaluated by consistency index (C-index) and calibration curve. The gene chip GSE41258 was downloaded from the GEO database, and differentially expressed genes (DEGs) were screened by the GEO2R online tool (p < 0.05, |logFC|>1.5). The Kyoto Encyclopedia of Genes and Genomes (KEGG) Pathway and Gene Ontology (GO) annotation and String website were used for enrichment analysis and protein–protein interaction (PPI) analysis of DEGs, respectively, and Cytoscape software was used to construct PPI network and screen function modules and hub genes.ResultsA total of 57,835 CRC patients, including 47,823 without distant metastases and 10,012 (17.31%) with metastases, were identified. Older age, unmarried status, poorly differentiated or undifferentiated grade, right colon site, larger tumor size, N2 stage, more metastatic sites, and elevated carcinoembryonic antigen (CEA) might lead to poorer prognosis (all p < 0.01). The independent risk factors of OS and CSS were included to construct a prognosis prediction model for predicting OS and CSS in CRC patients with distant metastasis. C-index and calibration curve of the training group and validation group showed that the models had acceptable predictive performance and high calibration degree. Furthermore, by comparing CRC tissues with and without liver metastasis, 158 DEGs and top 10 hub genes were screened. Hub genes were mainly concentrated in liver function and coagulation function.ConclusionThe big data in the public database were counted and transformed into a prognostic evaluation tool that could be applied to the clinic, which has certain clinical significance for the formulation of the treatment plan and prognostic evaluation of CRC patients with distant metastasis.

Gene expression study in monocytes: evidence of inflammatory dysregulation in early-onset obsessive-compulsive disorder

Obsessive-compulsive disorder (OCD) has a complex etiology that seems to include immune dysfunction and alterations in circulating monocytes. To investigate the immune basis and the functional dysregulation of monocytes in this disease, we analyzed gene expression in the peripheral monocytes of pediatric patients with OCD (N = 102) compared to controls (N = 47). We examined gene expression in primary cultures of peripheral monocytes from participants, under basal conditions and under exposure to lipopolysaccharide (LPS) to stimulate immune response. Whole-genome expression was assessed in 8 patients and 8 controls. Differentially expressed genes were identified followed by protein-protein interaction network construction and functional annotation analysis to identify the genes and biological processes that are altered in the monocytes of OCD patients. We also explored the expression levels of selected genes in monocytes from the other participants using qPCR. Several changes in gene expression were observed in the monocytes of OCD patients, with several immune processes involved under basal conditions (antigen processing and presentation, regulation of immune system and leukocyte cell adhesion) and after LPS stimulation (immune and inflammatory response, cytokine production and leukocyte activation). Despite the qPCR analysis provided no significant differences between patients and controls, high correlations were observed between the expression levels of some of the genes and inflammatory markers (i.e., T helper 17 and regulatory T cell levels, total monocyte and proinflammatory monocyte subset levels, and the cytokine production by resting and stimulated monocytes) of the study participants. Our findings provide more evidence of the involvement of monocyte dysregulation in early-onset OCD, indicating a proinflammatory predisposition and an enhanced immune response to environmental triggers.

STAT4 and COL1A2 are potential diagnostic biomarkers and therapeutic targets for heart failure comorbided with depression

BackgroundHeart failure (HF) and depression are common disorders that markedly compromise quality of life and impose a great financial burden on the society. Although increasing evidence has supported the closely linkage between the two disorders, the comorbidity mechanisms remain to be fully illuminated. We performed a bioinformatics network analysis to understand potential diagnostic biomarkers and therapeutic targets for HF comorbided with depression. MethodsWe downloaded the datasets of HF and depression from the Gene Expression Omnibus (GEO) database and constructed co-expression networks by Weighted Gene Co-Expression Network Analysis (WGCNA) to identify key modules. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis were performed on the common genes existing in the HF and depression related modules. Then, we employed the STRING database to construct the protein-protein interaction (PPI) network and detected the hub genes in the network. Finally, we validated the expression difference of hub genes from additional datasets of HF and depression. ResultsFunctional enrichment analysis indicated that platelet activation, chemokine signaling and focal adhesion were probably involved in HF comorbided with depression. PPI network construction indicated that HF comorbided with depression is likely related to 5 hub genes, including STAT4, CD83, CX3CR1, COL1A2, and SH2D1B. In validated datasets, STAT4 and COL1A2 were especially involved in the comorbidity of HF and depression. ConclusionOur work indicated a total of 5 hub genes including STAT4, CD83, CX3CR1, COL1A2, and SH2D1B, in which STAT4 and COL1A2 especially underlie the comorbidity mechanisms of HF and depression. These shared pathways might provide new targets for further mechanistic studies of the pathogenesis and treatment of HF and depression.

Identification of Novel Key Molecular Signatures in the Pathogenesis of Experimental Diabetic Kidney Disease.

Diabetic kidney disease (DKD) is a long-term major microvascular complication of uncontrolled hyperglycemia and one of the leading causes of end-stage renal disease (ESDR). The pathogenesis of DKD has not been fully elucidated, and effective therapy to completely halt DKD progression to ESDR is lacking. This study aimed to identify critical molecular signatures and develop novel therapeutic targets for DKD. This study enrolled 10 datasets consisting of 93 renal samples from the National Center of Biotechnology Information (NCBI) Gene Expression Omnibus (GEO). Networkanalyst, Enrichr, STRING, and Cytoscape were used to conduct the differentially expressed genes (DEGs) analysis, pathway enrichment analysis, protein-protein interaction (PPI) network construction, and hub gene screening. The shared DEGs of type 1 diabetic kidney disease (T1DKD) and type 2 diabetic kidney disease (T2DKD) datasets were performed to identify the shared vital pathways and hub genes. Strepotozocin-induced Type 1 diabetes mellitus (T1DM) rat model was prepared, followed by hematoxylin & eosin (HE) staining, and Oil Red O staining to observe the lipid-related morphological changes. The quantitative reverse transcription-polymerase chain reaction (qRT-PCR) was conducted to validate the key DEGs of interest from a meta-analysis in the T1DKD rat. Using meta-analysis, 305 shared DEGs were obtained. Among the top 5 shared DEGs, Tmem43, Mpv17l, and Slco1a1, have not been reported relevant to DKD. Ketone body metabolism ranked in the top 1 in the KEGG enrichment analysis. Coasy, Idi1, Fads2, Acsl3, Oxct1, and Bdh1, as the top 10 down-regulated hub genes, were first identified to be involved in DKD. The qRT-PCR verification results of the novel hub genes were mostly consistent with the meta-analysis. The positive Oil Red O staining showed that the steatosis appeared in tubuloepithelial cells at 6 w after DM onset. Taken together, abnormal ketone body metabolism may be the key factor in the progression of DKD. Targeting metabolic abnormalities of ketone bodies may represent a novel therapeutic strategy for DKD. These identified novel molecular signatures in DKD merit further clinical investigation.

Transcriptional Profiling and Deriving a Seven-Gene Signature That Discriminates Active and Latent Tuberculosis: An Integrative Bioinformatics Approach.

Tuberculosis (TB) is an infectious disease caused by Mycobacterium tuberculosis (M.tb.). Our integrative analysis aims to identify the transcriptional profiling and gene expression signature that distinguish individuals with active TB (ATB) disease, and those with latent tuberculosis infection (LTBI). In the present study, we reanalyzed a microarray dataset (GSE37250) from GEO database and explored the data for differential gene expression analysis between those with ATB and LTBI derived from Malawi and South African cohorts. We used BRB array tool to distinguish DEGs (differentially expressed genes) between ATB and LTBI. Pathway enrichment analysis of DEGs was performed using DAVID bioinformatics tool. The protein–protein interaction (PPI) network of most upregulated genes was constructed using STRING analysis. We have identified 375 upregulated genes and 152 downregulated genes differentially expressed between ATB and LTBI samples commonly shared among Malawi and South African cohorts. The constructed PPI network was significantly enriched with 76 nodes connected to 151 edges. The enriched GO term/pathways were mainly related to expression of IFN stimulated genes, interleukin-1 production, and NOD-like receptor signaling pathway. Downregulated genes were significantly enriched in the Wnt signaling, B cell development, and B cell receptor signaling pathways. The short-listed DEGs were validated in a microarray data from an independent cohort (GSE19491). ROC curve analysis was done to assess the diagnostic accuracy of the gene signature in discrimination of active and latent tuberculosis. Thus, we have derived a seven-gene signature, which included five upregulated genes FCGR1B, ANKRD22, CARD17, IFITM3, TNFAIP6 and two downregulated genes FCGBP and KLF12, as a biomarker for discrimination of active and latent tuberculosis. The identified genes have a sensitivity of 80–100% and specificity of 80–95%. Area under the curve (AUC) value of the genes ranged from 0.84 to 1. This seven-gene signature has a high diagnostic accuracy in discrimination of active and latent tuberculosis.

Identification of 9 Gene Signatures by WGCNA to Predict Prognosis for Colon Adenocarcinoma.

Background A risk assessment model for prognostic prediction of colon adenocarcinoma (COAD) was established based on weighted gene co-expression network analysis (WGCNA). Methods From the Cancer Genome Atlas (TCGA) database, RNA-seq data and clinical data of COAD patients were retrieved. After screening of differentially expressed genes (DEGs), WGCNA was performed to identify gene modules and screen those associated with COAD progression. Then, via protein-protein interaction (PPI) network construction of module genes, hub genes were obtained, which were then subjected to the least absolute shrinkage and selection operator (LASSO) and Cox regression to build a hub gene-based prognostic scoring model. The receiver operating characteristic curve (ROC curve) was plotted for the optimal cutoff (OCO) of the risk score, based on which, patients were assigned to high or low-risk groups. Areas under the ROC curve (AUCs) were calculated, and model performance was visualized using Kaplan–Meier (KM) survival curves and verified in the external dataset GSE29621. Finally, the model's independent prognostic value was evaluated by univariate and multivariate Cox regression analyses, and a nomogram was built. Results Totally 2840 DEGs were screened from COAD dataset of TCGA, including 1401 upregulated ones and 1439 downregulated ones, which were divided into 10 modules by WGCNA. The eigenvalue of the black module was found to have a high correlation with COAD progression. PPI interaction networks were constructed for genes in the black module, and 34 hub genes were obtained by using the MCODE plug-in. A LASSO-Cox regression approach was utilized to analyze the hub genes, and a prognostic risk score model based on the signatures of 9 genes (CHEK1, DEPDC1B, FANCI, MCM10, NCAPG, PARPBP, PLK4, RAD51AP1, and RFC4) was constructed. KM analysis identified shorter overall lower survival in the high-risk group. The model was verified to have favorable predictive ability through training set and validation set. The nomogram, composed of tumor node metastasis (TNM) staging and risk score, was of good predictability. Conclusions The COAD prognostic risk model constructed upon the signatures of 9 genes (CHEK1, DEPDC1B, FANCI, MCM10, NCAPG, PARPBP, PLK4, RAD51AP1, and RFC4) can effectively predict the survival status of COAD patients.

Identification and Analysis of Hub Genes in Diabetic Cardiomyopathy: Potential Role of Cytochrome P450 1A1 in Mitochondrial Metabolism and STZ-Induced Myocardial Dysfunction.

Diabetic cardiomyopathy (DCM) is a primary cause of death in diabetic patients; however, its molecular mechanism is not yet clear, and there is no uniform standard for diagnosis. The aim of this study is to discover the pathogenesis and potential therapeutic targets of DCM through screening and analysis of differentially expressed genes (DEGs) in heart ventricles of DCM, and to testify the role of key hub genes in DCM-induced myocardial dysfunction. Datasets GSE4745 and GSE6880 were downloaded from the GEO database. The difference analysis, visual analysis, cluster analysis and enrichment analysis were performed by using R language, python scripts and bioinformatics software followed by the construction of protein-protein interaction (PPI) network to obtain hub genes. The DCM models were established by streptozocin (STZ) injection to the male mice. The cardiac function and the expressions of hub genes were examined by using echocardiography and real-time quantitative poly-merase chain reaction (RT-qPCR), followed by multiple statistical analyses. Bioinformatic results indicate that mitochondrial dysfunction, disturbed lipid metabolism and decreased collagen synthesis are the main causes of the DCM development. In particular, the hub gene Cyp1a1 that encodes Cytochrome P450 1A1 (CYP4501A1) enzyme has the highest connectivity in the interaction network, and is associated with mitochondrial homeostasis and energy metabolism. It plays a critical role in the oxidation of endogenous or exogenous substrates. Our RT-qPCR results confirmed that ventricular Cyp1a1 mRNA level was nearly 12-fold upregulated in DCM model compared to normal control, which was correlated with abnormal cardiac function in diabetic individuals. CYP4501A1 protein expression in mitochondria was also increased in diabetic hearts. However, we found no significant changes in collagen expressions in cardiac ventricles of mice with DCM. This study provided compact data support for understanding the pathogenesis of DCM. CYP4501A1 might be considered as a potential candidate targeting for DCM therapy. Follow-up animal and clinical verifications need to be further explored.

Identification of potential biomarkers of vascular calcification using bioinformatics analysis and validation in vivo.

BackgroundVascular calcification (VC) is the most widespread pathological change in diseases of the vascular system. However, we know poorly about the molecular mechanisms and effective therapeutic approaches of VC.MethodsThe VC dataset, GSE146638, was downloaded from the Gene Expression Omnibus (GEO) database. Using the edgeR package to screen Differentially expressed genes (DEGs). Gene set enrichment analysis (GSEA) and gene set variation analysis (GSVA) were used to find pathways affecting VC. Gene ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) were performed on the DEGs. Meanwhile, using the String database and Cytoscape software to construct protein-protein interaction (PPI) networks and identify hub genes with the highest module scores. Correlation analysis was performed for hub genes. Receiver operating characteristic (ROC) curves, expression level analysis, GSEA, and subcellular localization were performed for each hub gene. Expression of hub genes in normal and calcified vascular tissues was verified by quantitative reverse transcription PCR (RT-qPCR) and immunohistochemistry (IHC) experiments. The hub gene-related miRNA-mRNA and TF-mRNA networks were constructed and functionally enriched for analysis. Finally, the DGIdb database was utilized to search for alternative drugs targeting VC hub genes.ResultsBy comparing the genes with normal vessels, there were 64 DEGs in mildly calcified vessels and 650 DEGs in severely calcified vessels. Spp1, Sost, Col1a1, Fn1, and Ibsp were central in the progression of the entire VC by the MCODE plug-in. These hub genes are primarily enriched in ossification, extracellular matrix, and ECM-receptor interactions. Expression level results showed that Spp1, Sost, Ibsp, and Fn1 were significantly highly expressed in VC, and Col1a1 was incredibly low. RT-qPCR and IHC validation results were consistent with bioinformatic analysis. We found multiple pathways of hub genes acting in VC and identified 16 targeting drugs.ConclusionsThis study perfected the molecular regulatory mechanism of VC. Our results indicated that Spp1, Sost, Col1a1, Fn1, and Ibsp could be potential novel biomarkers for VC and promising therapeutic targets.

Analysis of the role and mechanism of EGCG in septic cardiomyopathy based on network pharmacology.

BackgroundSeptic cardiomyopathy (SC) is a common complication of sepsis that leads to an increase in mortality. The pathogenesis of septic cardiomyopathy is unclear, and there is currently no effective treatment. EGCG (epigallocatechin gallate) is a polyphenol that has anti-inflammatory, antiapoptotic, and antioxidative stress effects. However, the role of EGCG in septic cardiomyopathy is unknown.MethodsNetwork pharmacology was used to predict the potential targets and molecular mechanisms of EGCG in the treatment of septic cardiomyopathy, including the construction and analysis of protein-protein interaction (PPI) network, gene ontology (GO), and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis and molecular docking. The mouse model of septic cardiomyopathy was established after intraperitoneal injection of LPS (lipopolysaccharide). The myocardial protective effect of EGCG on septic mice is observed by cardiac ultrasound and HE staining. RT-PCR is used to verify the expression level of the EGCG target in the septic cardiomyopathy mouse model.ResultsA total of 128 anti-SC potential targets of EGCGareselected for analysis. The GO enrichment analysis and KEGG pathway analysis results indicated that the anti-SC targets of EGCG mainly participate in inflammatory and apoptosis processes. Molecular docking results suggest that EGCG has a high affinity for the crystal structure of six targets (IL-6 (interleukin-6), TNF (tumor necrosis factor), Caspase3, MAPK3 (Mitogen-activated protein kinase 3), AKT1, and VEGFA (vascular endothelial growth factor)), and the experimental verification result showed levated expression of these 6 hub targets in the LPS group, but there is an obvious decrease in expression in the LPS + EGCG group. The functional and morphological changes found by echocardiography and HE staining show that EGCG can effectively improve the cardiac function that is reduced by LPS.ConclusionOur results reveal that EGCG may be a potentially effective drug to improve septic cardiomyopathy. The potential mechanism by which EGCG improves myocardial injury in septic cardiomyopathy is through anti-inflammatory and anti-apoptotic effects. The anti-inflammatory and anti-apoptotic effects of EGCG occur not only through direct binding to six target proteins (IL-6,TNF-α, Caspase3, MAPK3, AKT1, and VEGFA) but also by reducing their expression.

Estrogen-mediated differential protein regulation and signal transduction in rheumatoid arthritis.

Exploration of the dual and opposing facets of estrogen necessitates a clear understanding to diminish the controversy of estrogen regulation in averting the systemic, autoimmune, joint degrading disorder, and rheumatoid arthritis (RA). Experimental evidences consider estrogen as a pivotal enzyme to modulate the disease progression via managing several cellular mechanisms targeting inflammatory markers such as TNF, ILs, nuclear factor kappa B, and other regulatory proteins like matrix metalloproteinases impeding joint erosion and cartilage degradation. Estrogen modulates cellular signaling associated with inflammation, oxidative stress, related cardiovascular risk, and miRNA regulation during RA progression. Studies determining estrogen regulation in RA complicate the resemblance of the outcome as they represent both hyper and hypo level of estrogen is linked to the disease. Although some reports deliver estrogen as malign, there is now increasing evidence of rendering protection dose dependently. Variation in estrogen level causes differential expression of certain proteins and their related signaling which is directly or indirectly linked to RA pathogenesis. This review summarizes the variations in protein expression levels by focusing on the in vitro, in vivo,and clinical studies of estrogen deficiency and treatment. Construction of protein-protein interaction network, GO, and KEGG pathway enrichment analysis of the differentially expressed proteins assist in hypothesizing a potential molecular mechanism of estrogen in RA via in silico studies. Targeting these differential proteins can emerge a new path for developing advanced therapeutic strategies.

Identifying Potential Mitochondrial Proteome Signatures Associated with the Pathogenesis of Pulmonary Arterial Hypertension in the Rat Model.

Pulmonary arterial hypertension (PAH) is a severe and progressive disease that affects the heart and lungs and a global health concern that impacts individuals and society. Studies have reported that some proteins related to mitochondrial metabolic functions could play an essential role in the pathogenesis of PAH, and their specific expression and biological function are still unclear. We successfully constructed a monocrotaline- (MCT-) induced PAH rat model in the present research. Then, the label-free quantification proteomic technique was used to determine mitochondrial proteins between the PAH group (n = 6) and the normal group (n = 6). Besides, we identified 1346 mitochondrial differentially expressed proteins (DEPs) between these two groups. Gene Ontology (GO) and the Kyoto Encyclopedia of Genes and Genomes (KEGG) were used to analyze the mainly mitochondrial DEPs' biological functions and the signal pathways. Based on the protein-protein interaction (PPI) network construction and functional enrichment, we screened 19 upregulated mitochondrial genes (Psmd1, Psmc4, Psmd13, Psmc2, etc.) and 123 downregulated mitochondrial genes (Uqcrfs1, Uqcrc1, Atp5c1, Atp5a1, Uqcrc2, etc.) in rats with PAH. Furthermore, in an independent cohort dataset and experiments with rat lung tissue using qPCR, validation results consistently showed that 6 upregulated mitochondrial genes (Psmd2, Psmc4, Psmc3, Psmc5, Psmd13, and Psmc2) and 3 downregulated mitochondrial genes (Lipe, Cat, and Prkce) were significantly differentially expressed in the lung tissue of PAH rats. Using the RNAInter database, we predict potential miRNA target hub mitochondrial genes at the transcriptome level. We also identified bortezomib and carfilzomib as the potential drugs for treatment in PAH. Finally, this study provides us with a new perspective on critical biomarkers and treatment strategies in PAH.

Integrative RNA-seq and ATAC-seq analyses of phosphodiesterase 6 mutation-induced retinitis pigmentosa.

Inhibition of poly-ADP-ribose polymerase 1 (PARP1) could relieve phosphodiesterase 6 mutation-induced retinitis pigmentosa (RP). However, the mechanism related to PARP1 overexpression in the RP has not been clarified. We attempted to explore the potential mechanism related to PARP1 regulating RP. ATAC-seq and RNA-seq were performed for retina tissues of C3H and rd1 mice. The differentially expressed genes (DEGs) were identified, followed by the construction of PARP1-DEG co-expression and protein-protein interaction (PPI) networks. Gene ontology-biological process and pathway enrichment of DEGs were performed by clusterProfiler software. The overlapped genes that might play regulatory roles in PARP1 expression were mined by integrated analysis of RNA-seq and ATAC-seq data. A total of 1061 DEGs were identified between C3H and rd1 group. Co-expression network was constructed with 313 PARP1-gene co-expression pairs. The down-regulated DEGs were closely related to visual perception and light stimulus-related biological process, while the up-regulated DEGs were significantly enriched in phototransduction and PPAR signaling pathway. PPI network was constructed with 202 nodes and 375 edges, which was clustered into 3 modules. Module 1 genes were closely related to detection of light stimulus, visual perception related biological process and phototransduction pathway (involved with Gnat1/Guca1b/Gnat2/Sag/Pde6g). By integrated analysis of the RNA-seq and ATAC-seq, the overlapped up-regulated genes were Asxl3 and Nyap2, while the down-regulated genes were Tmem136 and Susd3. Gnat1 may play a key role in RP development by interacting with PARP1. Susd3 may play a regulatory role in PARP1 expression and affect RP formation.