Involvement of Essential Signaling Cascades and Analysis of Gene Networks in Diabesity

Identification of Key lncRNAs, circRNAs, and mRNAs in Osteoarthritis via Bioinformatics Analysis

Osteoarthritis (OA) is a common degenerative joint disorder that adversely affects the quality of life of patients. Identification of novel diagnostic biomarkers is pivotal for the early detection and prevention of OA. Dataset GSE185059 was selected from Gene Expression Omnibus database to obtain differentially expressed lncRNAs (DE-lncRNAs), mRNAs (DE-mRNAs), and circRNAs (DE-circRNAs) between OA and normal samples. The Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) analyses as well as protein-protein interaction (PPI) network construction of DE-mRNAs were conducted. Hub genes were identified from PPI networks and validated by RT-qPCR. starBase database was utilized for predicting miRNAs binding with hub genes, selected DE-lncRNAs and DE-circRNAs, respectively. The competing endogenous RNA (ceRNA) networks were constructed. A total of 818 DE-mRNAs, 191 DE-lncRNAs, and 2053 DE-circRNAs were identified. The DE-mRNAs were significantly enriched in several inflammation-related GO terms and KEGG pathways such as positive regulation of cell-cell adhesion, TNF-alpha signaling pathway and NF-kappa B signaling pathway. Thirteen hub genes were identified, which were CFTR, GART, SMAD2, NCK1, TJP1, UBE2D1, EFTUD2, PRKACB, IL10, SNRPG, CHD4, RPS24, and SRSF6. OA-related DE-lncRNA/circRNA-miRNA-hub gene networks were constructed. We identified 13 hub genes and constructed the ceRNA networks related to OA, providing a theoretical basis for further research.

Cumulative effects of exercise training and consumption of propolis on managing diabetic dyslipidemia in adult women: a single-blind, randomized, controlled trial with pre-post-intervention assessments

Dyslipidemia is an imbalance of various lipids, and propolis, as a natural resinous viscos mixture made by Apis mellifera L. could improve in this condition. In this single-blind, randomized trial, 60 women with type 2 diabetes and dyslipidemia were divided into four groups: (1) the patients who did not apply the combined training and 500 mg propolis capsules supplement (Control group); (2) subjects performed combined training, including aerobic and resistance training (EXR); (3) subjects received the 500 mg propolis supplement capsules (SUPP); (4) Subjects performed combined training along with receiving the 500 mg propolis supplement capsules (EXR + SUPP). We evaluated the concentration of CTRP12, SFRP5, interleukin-6 (IL6), superoxide dismutase (SOD), malondialdehyde (MDA), adiponectin, and total antioxidant capacity (TAC) before and after the intervention. MDA, TAC, IL6, CTRP12, SFRP5 IL6, adiponectin, and lipid profile levels ameliorated in the EXR + SUPP group. We found that 8 weeks of treatment by combined exercise training and propolis supplement decreased inflammation activity and increased antioxidant defense in women with diabetic dyslipidemia.Trial registration This study was registered in the Iranian Registry of Clinical Trials; IRCT code: IRCT20211229053561N1.

Building effective intervention models utilizing big data to prevent the obesity epidemic

INTRODUCTION

The exposome consists of factors an individual is exposed to across the life course. The exposome is dynamic, meaning the factors are constantly changing, affecting each other and individuals in different ways. Our exposome dataset includes social determinants of health as well as policy, climate, environment, and economic factors that could impact obesity development. The objective was to translate spatial exposure to these factors with the presence of obesity into actionable population-based constructs that could be further explored.

METHODS

Our dataset was constructed from a combination of public-use datasets and the Center of Disease Control's Compressed Mortality File. Spatial Statistics using Queens First Order Analysis was performed to identify hot- and cold-spots of obesity prevalence; followed by Graph Analysis, Relational Analysis, and Exploratory Factor Analysis to model the multifactorial spatial connections.

RESULTS

Areas of high and low presence of obesity had different factors associated with obesity. Factors associated with obesity in areas of high obesity propensity were: poverty / unemployment; workload, comorbid conditions (diabetes, CVD) and physical activity. Conversely, factors associated in areas where obesity was rare were: smoking, lower education, poorer mental health, lower elevations, and heat.

DISCUSSION

The spatial methods described within the paper are scalable to large numbers of variables without issues of multiple comparisons lowering resolution. These types of spatial structural methods provide insights into novel variable associations or factor interactions that can then be studied further at the population or policy levels.

Analysis of signaling cascades from myeloma cells treated with pristimerin

Multiple myeloma (MM) is the 2nd most frequently diagnosed blood cancer after non-Hodgkin's lymphoma. The present study aimed to identify the differentially expressed genes (DEGs) between the control and pristimerin-treated MM cell lines. We examined the GSE14011 microarray dataset and screened DEGs with GEO2R statistical tool using the inbuilt limma package. We used a bioinformatics pipeline to identify the differential networks, signaling cascades, and the survival of the hub genes. We implemented two different enrichment analysis including ClueGO and Metacore™, to get accurate annotation for most significant DEGs. We screened the most significant 408 DEGs from the dataset based on p-values and logFC values. Using protein network analysis, we found the genes UBC, HSP90AB1, HSPH1, HSPA1B, HSPA1L, HSPA6, HSPD1, DNAJB1, HSPE1, DNAJC10, BAG3, and DNAJC7 had higher node degree distribution. In contrast, the functional annotation provided that the DEGs were predominantly enriched in B-cell receptor signaling, unfolded protein response, positive regulation of phagocytosis, HSP70, and HSP40-dependent folding, and ubiquitin-proteasomal proteolysis. Using network algorithms, and comparing enrichment analysis, we found the hub genes enriched were INHBE, UBC, HSPA1A, HSP90AB1, IKBKB, and BAG3. These DEGs were further validated with overall survival and gene expression analysis between the tumor and control groups. Finally, pristimerin effects were validated independently in a cell line model consisting of IM9 and U266 MM cells. Pristimerin induced in vitro cytotoxicity in MM cells in a dose-dependent manner. Pristimerin inhibited NF-κB, induced accumulation of ubiquitinated proteins and inhibited HSP60 in the validation of bioinformatics findings, while pristimerin-induced caspase-3 and PARP cleavage confirmed cell death. Taken together, we found that the identified DEGs were strongly associated with the apoptosis induced in MM cell lines due to pristimerin treatment, and combinatorial therapy derived from pristimerin could act as novel anti-myeloma multifunctional agents.

Identification and validation of key biomarkers for the early diagnosis of diabetic kidney disease

Background: Diabetic kidney disease (DKD) is the leading cause of end-stage renal disease. This study explored the core genes and pathways associated with DKD to identify potential diagnostic and therapeutic targets.

Methods: We downloaded microarray datasets GSE96804 and GSE104948 from the Gene Expression Omnibus (GEO) database. The dataset includes a total of 53 DKD samples and 41 normal samples. Differentially expressed genes (DEGs) were identified using the R package “limma”. The Metascape database was subjected to Gene Ontology (GO) function and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analyses to identify the pathway and functional annotations of DEGs. A WGCAN network was constructed, the hub genes in the turquoise module were screened, and the core genes were selected using LASSO regression to construct a diagnostic model that was then validated in an independent dataset. The core genes were verified by in vitro and in vivo experiments.

Results: A total of 430 DEGs were identified in the GSE96804 dataset, including 285 upregulated and 145 downregulated DEGs. WGCNA screened out 128 modeled candidate gene sets. A total of eight genes characteristic of DKD were identified by LASSO regression to build a prediction model. The results showed accuracies of 99.15% in the training set (GSE96804) and 94.44% and 100%, respectively, in the test (GSE104948-GPL22945 and GSE104948-GPL24120). Three core genes (OAS1, SECTM1, and SNW1) with high connectivity were selected among the modeled genes. In vitro and in vivo experiments confirmed the upregulation of these genes.

Conclusion: Bioinformatics analysis combined with experimental validation identified three novel DKD-specific genes. These findings may advance our understanding of the molecular basis of DKD and provide potential therapeutic targets for its clinical management.

Integrative ontology and pathway-based approach identifies distinct molecular signatures in transcriptomes of esophageal squamous cell carcinoma

Esophageal squamous cell carcinoma (ESCC) remains a serious concern globally due to many factors that including late diagnosis, lack of an ideal biomarker for diagnosis and prognosis, and high rate of mortality. In this study, we aimed to identify the essential dysregulated genes and molecular signatures associated with the progression and development of ESCC. The dataset with 15 ESCCs and the 15 adjacent normal tissue samples from the surrounding histopathologically tumor-free mucosa was selected. We applied bioinformatics pipelines including various topological parameters from MCODE, CytoNCA, and cytoHubba to prioritize the most significantly associated DEGs with ESCC. We performed functional enrichment annotation for the identified DEGs using DAVID and MetaCore™ GeneGo platforms. Furthermore, we validated the essential core genes in TCGA and GTEx datasets between the normal mucosa and ESCC for their expression levels. These DEGs were primarily enriched in positive regulation of transferase activity, negative regulation of organelle organization, cell cycle mitosis/S-phase transition, spindle organization/assembly, development, and regulation of angiogenesis. Subsequently, the DEGs were associated with the pathways such as oocyte meiosis, cell cycle, and DNA replication. Our study identified the eight-core genes (AURKA, AURKB, MCM2, CDC20, TPX2, PLK1, FOXM1, and MCM7) that are highly expressed among the ESCC, and TCGA dataset. The multigene comparison and principal component analysis resulted in elevated signals for the AURKA, MCM2, CDC20, TPX2, PLK1, and FOXM1. Overall, our study reported GO profiles and molecular signatures that might help researchers to grasp the pathological mechanisms underlying ESCC development and eventually provide novel therapeutic and diagnostic strategies.

Whole-exome sequencing analysis of NSCLC reveals the pathogenic missense variants from cancer-associated genes

BACKGROUND

Non-small-cell lung cancer (NSCLC) is the most common type of lung cancer. NSCLC accounts for 84% of all lung cancer cases. In recent years, advances in pathway understanding, methods for discovering novel genetic biomarkers, and new drugs designed to inhibit the signaling cascades have enabled clinicians to personalize therapy for NSCLC.

OBJECTIVES

The primary aim of this study is to identify the genes associated with NSCLC that harbor pathogenic variants that could be causative for NSCLC. The second aim is to investigate their roles in different pathways that lead to NSCLC.

METHODS

We examined exome-sequencing datasets from 54 NSCLC patients to characterize the variants associated with NSCLC.

RESULTS

Our findings revealed that 17 variants in 14 genes were considered highly pathogenic, including CDKN2A, ERBB2, FOXP1, IDH1, JAK3, KMT2D, K-Ras, MSH3, MSH6, POLE, RNF43, TCF7L2, TP53, and TSC1. Gene set enrichment analysis revealed the involvement of transmembrane receptor protein tyrosine kinase activity, protein binding, ATP binding, phosphatidylinositol-4,5-bisphosphate 3-kinase, and Ras guanyl-nucleotide exchange factor activity. Pathway analysis of these genes yielded different cancer-related pathways, including colorectal, prostate, endometrial, pancreatic, PI3K-Akt signaling pathways, and signaling pathways regulating pluripotency of stem cells. Module 1 from protein-protein interactions (PPIs) identified genes that harbor pathogenic SNPs. Three of the most deleterious SNPs are ERBB2 (rs1196929947), K-Ras (rs121913529), and POLE (rs751425952). Interestingly, one patient has a pathogenic K-Ras variant (rs121913529) co-occurred with the missense variant (rs752054698) inTSC1 gene.

CONCLUSION

This study maps highly pathogenic variants associated with NSCLC and investigates their contributions to the pathogenesis of NSCLC. This study sheds light on the potential applications of precision medicine in patients with NSCLC.

Identification of differential microRNAs and messenger RNAs resulting from ASXL transcriptional regulator 3 knockdown during during heart development

Congenital heart disease (CHD) is the most common birth defect. Although ASXL transcriptional regulator 3 (ASXL3) has been reported to cause hereditary CHD, ASXL3-mediated mechanisms in heart development remain unclear. In this study, we used dimethyl sulfoxide (DMSO) to induce differentiation in P19 cells, observed cell morphology using light microscopy after ASXL3 knockdown, and determined the levels of associated myocardial cell markers using reverse transcription-quantitative polymerase chain reaction and western blotting. Subsequently, we used microRNA sequencing, messenger RNA (mRNA) sequencing, and bioinformatics to initially identify the possible mechanisms through which ASXL3-related microRNAs and mRNAs affect heart development. The results indicated that DMSO induced P19 cell differentiation, which could be inhibited by ASXL3 knockdown. We screened 1214 and 1652 differentially expressed microRNAs and mRNAs, respectively, through ASXL3 knockdown and sequencing; these differentially expressed miRNAs were largely enriched in PI3K-Akt, mitogen-activated protein kinase, and Rap1 signaling pathways. Additionally, 11 miRNAs associated with heart development were selected through a literature review. Our analysis indicated the involvement of mmu-miR-323-3p in P19 cell differentiation through the PI3K-Akt pathway. In conclusion, ASXL3 may be involved in the regulation of heart development. This comprehensive study of differentially expressed microRNAs and mRNAs through ASXL3 knockdown in P19 cells provides new insights that may aid the prevention and treatment of CHD.

Screening differentially expressed genes between endometriosis and ovarian cancer to find new biomarkers for endometriosis

AIM

Endometriosis is one of the most common reproductive system diseases, but the mechanisms of disease progression are still unclear. Due to its high recurrence rate, searching for potential therapeutic biomarkers involved in the pathogenesis of endometriosis is an urgent issue.

METHODS

Due to the similarities between endometriosis and ovarian cancer, four endometriosis datasets and one ovarian cancer dataset were downloaded from Gene Expression Omnibus (GEO) database. Differentially expressed genes (DEGs) were identified, followed by gene ontology (GO), Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway and protein-protein interaction (PPI) analyses. Then, we validated gene expression and performed survival analysis with ovarian serous cystadenocarcinoma (OV) datasets in TCGA/GTEx database, and searched for potential drugs in the Drug-Gene Interaction Database. Finally, we explored the miRNAs of key genes to find biomarkers associated with the recurrence of endometriosis.

RESULTS

In total, 104 DEGs were identified in the endometriosis datasets, and the main enriched GO functions included cell adhesion, extracellular exosome and actin binding. Fifty DEGs were identified between endometriosis and ovarian cancer datasets including 11 consistently regulated genes, and nine DEGs with significant expression in TCGA/GTEx. Only IGHM had both significant expression and an association with survival, three module DEGs and two significantly expressed DEGs had drug associations, and 10 DEGs had druggability.

CONCLUSIONS

ITGA7, ITGBL1 and SORBS1 may help us understand the invasive nature of endometriosis, and IGHM might be related to recurrence; moreover, these genes all may be potential therapeutic targets.KEY MESSAGEThis manuscript used a bioinformatics approach to find target genes for the treatment of endometriosis.This manuscript used a new approach to find target genes by drawing on common characteristics between ovarian cancer and endometriosis.We screened relevant therapeutic agents for target genes in the drug database, and performed histological validation of target genes with both expression and survival analysis difference in cancer databases.

Identification of key molecular markers of acute coronary syndrome using peripheral blood transcriptome sequencing analysis and mRNA-lncRNA co-expression network construction

Acute coronary syndrome (ACS) is a term used to describe major cardiovascular diseases, and treatment of in-stent restenosis in patients with ACS remains a major clinical challenge. Further investigation into molecular markers of ACS may aid early diagnosis, and the treatment of ACS and post-treatment recurrence. In the present study, total RNA was extracted from the peripheral blood samples of 3 patients with ACS, 3 patients with percutaneous coronary intervention (PCI)_non-restenosis, 3 patients with PCI_restenosis and 3 healthy controls. Subsequently, RNA library construction and high-throughput sequencing were performed. DESeq2 package in R was used to screen genes that were differentially expressed between the different samples. Moreover, the intersection of the differentially expressed mRNAs (DEmRNAs) and differentially expressed long noncoding RNAs (DElncRNAs) obtained. GeneCodis4.0 was used to perform function enrichment for DEmRNAs, and lncRNA-mRNA co-expression network was constructed. The GSE60993 dataset was utilized for diagnostic analysis, and the aforementioned investigations were verified using in vitro studies. Results of the present study revealed a large number of DEmRNAs and DElncRNAs in the different groups. We selected genes in the top 10 of differential expression and also involved in the co-expression of lncRNA-mRNA for diagnostic analysis in the GSE60993 dataset. The area under curve (AUC) of PDZK1IP1 (0.747), PROK2 (0.769) and LAMP3 (0.725) were all >0.7. These results indicated that the identified mRNAs and lncRNAs may act as potential clinical biomarkers, and more specifically, PDZK1IP1, PROK2 and LAMP3 may act as potential biomarkers for the diagnosis of ACS.

Comparison of critical biomarkers in 2 erectile dysfunction models based on GEO and NOS-cGMP-PDE5 pathway

BACKGROUND

Erectile dysfunction is a disease commonly caused by diabetes mellitus (DMED) and cavernous nerve injury (CNIED). Bioinformatics analyses including differentially expressed genes (DEGs), enriched functions and pathways (EFPs), and protein-protein interaction (PPI) networks were carried out in DMED and CNIED rats in this study. The critical biomarkers that may intervene in nitric oxide synthase (NOS, predominantly nNOS, ancillary eNOS, and iNOS)-cyclic guanosine monophosphate (cGMP)-phosphodiesterase 5 enzyme (PDE5) pathway, an important mechanism in erectile dysfunction treatment, were then explored for potential clinical applications.

METHODS

GSE2457 and GSE31247 were downloaded. Their DEGs with a |logFC (fold change)| > 0 were screened out. Database for Annotation, Visualization and Integrated Discovery (DAVID) online database was used to analyze the EFPs in Gene Ontology enrichment and Kyoto Encyclopedia of Genes and Genomes networks based on down-regulated and up-regulated DEGs respectively. PPI analysis of 2 datasets was performed in Search Tool for the Retrieval of Interacting Genes/Proteins (STRING) and Cytoscape. Interactions with an average score greater than 0.9 were chosen as the cutoff for statistical significance.

RESULTS

From a total of 1710 DEGs in GSE2457, 772 were down-regulated and 938 were up-regulated, in contrast to the 836 DEGs in GSE31247, from which 508 were down-regulated and 328 were up-regulated. The 25 common EFPs such as aging and response to hormone were identified in both models. PPI results showed that the first 10 hub genes in DMED were all different from those in CNIED.

CONCLUSIONS

The intervention of iNOS with the hub gene complement component 3 in DMED and the aging process in both DMED and CNIED deserves attention.

Biomarkers of Blood from Patients with Atherosclerosis Based on Bioinformatics Analysis

Atherosclerosis is a multifaceted disease characterized by the formation and accumulation of plaques that attach to arteries and cause cardiovascular disease and vascular embolism. A range of diagnostic techniques, including selective coronary angiography, stress tests, computerized tomography, and nuclear scans, assess cardiovascular disease risk and treatment targets. However, there is currently no simple blood biochemical index or biological target for the diagnosis of atherosclerosis. Therefore, it is of interest to find a biochemical blood marker for atherosclerosis. Three datasets from the Gene Expression Omnibus (GEO) database were analyzed to obtain differentially expressed genes (DEG) and the results were integrated using the Robustrankaggreg algorithm. The genes considered more critical by the Robustrankaggreg algorithm were put into their own data set and the data set system with cell classification information for verification. Twenty-one possible genes were screened out. Interestingly, we found a good correlation between RPS4Y1, EIF1AY, and XIST. In addition, we know the general expression of these genes in different cell types and whole blood cells. In this study, we identified BTNL8 and BLNK as having good clinical significance. These results will contribute to the analysis of the underlying genes involved in the progression of atherosclerosis and provide insights for the discovery of new diagnostic and evaluation methods.

Identification of Vital Hub Genes and Potential Molecular Pathways of Dermatomyositis by Bioinformatics Analysis

Dermatomyositis is an autoimmune disease characterized by severe symmetrical muscle dysfunction and pain. This study was aimed at discovering vital hub genes and potential molecular pathways of DM through bioinformatics analysis, which contributes to identifying potential diagnostic or therapeutic biomarkers and targets. In this study, a total of 915 DEGs in DM samples including 167 upregulated genes and 748 downregulated genes were screened out by the limma package based on the GSE142807 dataset from the Gene Expression Omnibus (GEO) database. Furthermore, the results of Gene Ontology (GO) and the Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis indicated that these downregulated genes were highly associated with the immune-related biological processes and pathways. Therefore, 41 genes closely related to DM were extracted for further study based on the subcluster analysis through the Molecular Complex Detection (MCODE) software plugin in Cytoscape. Ultimately, 10 hub genes (including ISG15, DDX58, IFIT3, CXCL10, and STAT1) were identified as the potential candidate biomarkers and targets. Besides, we found that the identified hub genes directly or indirectly communicated with each other via molecular signaling pathways on the protein and transcription level. In general, under the guidance of bioinformatics analysis, 10 vital hub genes and molecular mechanisms in DM were identified and the expression of proinflammatory factors and interferon family proteins and genes showed high association with DM, which might help provide a theoretical foundation for the development of point-to-point targeted therapy in the future treatment of DM.

Biodata Mining of Differentially Expressed Genes between Acute Myocardial Infarction and Unstable Angina Based on Integrated Bioinformatics

Acute coronary syndrome (ACS) is a complex syndrome of clinical symptoms. In order to accurately diagnose the type of disease in ACS patients, this study is aimed at exploring the differentially expressed genes (DEGs) and biological pathways between acute myocardial infarction (AMI) and unstable angina (UA). The GSE29111 and GSE60993 datasets containing microarray data from AMI and UA patients were downloaded from the Gene Expression Omnibus (GEO) database. DEG analysis of these 2 datasets is performed using the “limma” package in R software. DEGs were also analyzed using protein-protein interaction (PPI), Molecular Complex Detection (MCODE) algorithm, Gene Ontology (GO), and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analysis. Correlation analysis and “cytoHubba” were used to analyze the hub genes. A total of 286 DEGs were obtained from GSE29111 and GSE60993, including 132 upregulated genes and 154 downregulated genes. Subsequent comprehensive analysis identified 20 key genes that may be related to the occurrence and development of AMI and UA and were involved in the inflammatory response, interaction of neuroactive ligand-receptor, calcium signaling pathway, inflammatory mediator regulation of TRP channels, viral protein interaction with cytokine and cytokine receptor, human cytomegalovirus infection, and cytokine-cytokine receptor interaction pathway. The integrated bioinformatical analysis could improve our understanding of DEGs between AMI and UA. The results of this study might provide a new perspective and reference for the early diagnosis and treatment of ACS.

Genetic association of anthropometric traits with type 2 diabetes in ethnically endogamous Sindhi families

Background

Ethnically endogamous populations can shed light on the genetics of type 2 diabetes. Such studies are lacking in India. We conducted this study to determine the genetic and environmental contributions of anthropometric traits to type 2 diabetes risk in the Sindhi families in central India.

Methods

We conducted a family study in Indian Sindhi families with at least one case of type 2 diabetes. Variance components methods were used to quantify the genetic association of 18 anthropometric traits with eight type 2 diabetes related traits. Univariate and bivariate polygenic models were used to determine the heritability, genetic and environmental correlation of anthropometric traits with type 2 diabetes related traits.

Results

We included 1,152 individuals from 112 phenotyped families. The ascertainment-bias corrected prevalence of type 2 diabetes was 35%. Waist circumference, hip circumference and the biceps, triceps, subscapular and medial calf skinfold thicknesses were polygenically and significantly associated with type 2 diabetes. The range of heritability of the anthropometric traits and type 2 diabetes related traits was 0.27–0.73 and 0.00–0.39, respectively. Heritability of type 2 diabetes as a discrete trait was 0.35. Heritability curves demonstrated a substantial local influence of type 2 diabetes related traits. Bivariate trait analyses showed that biceps and abdominal skinfold thickness and all waist-containing indexes were strongly genetically correlated with type 2 diabetes.

Conclusions

In this first study of Sindhi families, we found evidence for genetic and environmental concordance of anthropometric traits with type 2 diabetes. Future studies need to probe into the genetics of type 2 diabetes in this population.

Meta-analysis identifying epithelial-derived transcriptomes predicts poor clinical outcome and immune infiltrations in ovarian cancer

BACKGROUND

Previous studies revealed that the epithelial component is associated with the modulation of the ovarian tumor microenvironment (TME). However, the identification of key transcriptional signatures of laser capture microdissected human ovarian cancer epithelia remains lacking.

METHODS

We identified the differentially expressed transcriptional signatures of human ovarian cancer epithelia by meta-analysis of GSE14407, GSE2765, GSE38666, GSE40595, and GSE54388. Then we investigated the enrichment of KEGG pathways that are associated with epithelia-derived transcriptomes. Finally, we investigated the correlation of key epithelia-hub genes with the survival prognosis and immune infiltrations. Finally, we investigated the genetic alterations of key prognostic hub genes and their diagnostic efficacy in ovarian cancer epithelia.

RESULTS

We identified 1339 differentially expressed genes (DEGs) in ovarian cancer epithelia including 541upregulated and 798 downregulated genes. We identified 21 (such as E2F4, FOXM1, TFDP1, E2F1, and SIN3A) and 11 (such as JUN, DDX4, FOSL1, NOC2L, and HMGA1) master transcriptional regulators (MTRs) that are interacted with upregulated and the downregulated genes in ovarian tumor epithelium, respectively. The STRING-based analysis identified hub genes (such as CDK1, CCNB1, AURKA, CDC20, and CCNA2) in ovarian cancer epithelia. The significant clusters of identified hub genes are associated with the enrichment of KEGG pathways including cell cycle, DNA replication, cytokine-cytokine receptor interaction, pathways in cancer, and focal adhesion. The upregulation of SCNN1A and CDCA3 and the downregulation of SOX6 are correlated with a shorter survival prognosis in ovarian cancer (OV). The expression level of SOX6 is negatively correlated with immune score and positively correlated with tumor purity in OV. Moreover, SOX6 is negatively correlated with the infiltration of TILs, CD8+ T cells, CD4+ Regulatory T cells, cytolytic activity, T cell activation, pDC, neutrophils, and macrophages in OV. Also, SOX6 is negatively correlated with various immune markers including CD8A, PRF1, GZMA, GZMB, NKG7, CCL3, and CCL4, indicating the immune regulatory efficiency of SOX6 in the TME of OV. Furthermore, SCNN1A, CDCA3, and SOX6 genes are genetically altered in OV and the expression levels of SCNN1A and SOX6 genes showed diagnostic efficacy in ovarian cancer epithelia.

CONCLUSIONS

The identified ovarian cancer epithelial-derived key transcriptional signatures are significantly correlated with survival prognosis and immune infiltrations, and may provide new insight into the diagnosis and treatment of epithelial ovarian cancer.

Network Pharmacology Combined with Bioinformatics to Investigate the Mechanisms and Molecular Targets of Astragalus Radix-Panax notoginseng Herb Pair on Treating Diabetic Nephropathy

Background

Astragalus Radix (AR)-Panax notoginseng (PN), a classical herb pair, has shown significant effects in treating diabetic nephropathy (DN). However, the intrinsic mechanism of AR-PN treating DN is still unclear. This study aims to illustrate the mechanism and molecular targets of AR-PN treating DN based on network pharmacology combined with bioinformatics.

Materials and Methods

The Traditional Chinese Medicine Systems Pharmacology database was used to screen bioactive ingredients of AR-PN. Subsequently, putative targets of bioactive ingredients were predicted utilizing the DrugBank database and converted into genes on UniProtKB database. DN-related targets were retrieved via analyzing published microarray data (GSE30528) from the Gene Expression Omnibus database. Protein-protein interaction networks of AR-PN putative targets and DN-related targets were established to identify candidate targets using Cytoscape 3.8.0. GO and KEGG enrichment analyses of candidate targets were reflected using a plugin ClueGO of Cytoscape. Molecular docking was performed using AutoDock Vina software, and the results were visualized by Pymol software. The diagnostic capacity of hub genes was verified by receiver operating characteristic (ROC) curves.

Results

Twenty-two bioactive ingredients and 189 putative targets of AR-PN were obtained. Eight hundred and fifty differently expressed genes related to DN were screened. The PPI network showed that 115 candidate targets of AR-PN against DN were identified. GO and KEGG analyses revealed that candidate targets of AR-PN against DN were mainly involved in the apoptosis, oxidative stress, cell cycle, and inflammation response, regulating the PI3K-Akt signaling pathway, cell cycle, and MAPK signaling pathway. Moreover, MAPK1, AKT1, GSK3B, CDKN1A, TP53, RELA, MYC, GRB2, JUN, and EGFR were considered as the core potential therapeutic targets. Molecular docking demonstrated that these core targets had a great binding affinity with quercetin, kaempferol, isorhamnetin, and formononetin components. ROC curve analysis showed that AKT1, TP53, RELA, JUN, CDKN1A, and EGFR are effective in discriminating DN from controls.

Conclusions

AR-PN against DN may exert its renoprotective effects via various bioactive chemicals and the related pharmacological pathways, involving multiple molecular targets, which may be a promising herb pair treating DN. Nevertheless, these results should be further validated by experimental evidence.

Comparison of potential inhibitors and targeting fat mass and obesity-associated protein causing diabesity through docking and molecular dynamics strategies

Genome-wide association studies (GWAS) have identified an association between polymorphisms in the FTO gene and obesity. The FTO: rs9939609, an intronic variant, is considered a risk allele for developing diabesity in homozygous and heterozygous forms. This study aimed to investigate the molecular structure of the available inhibitors specific to the FTO mutations along with the rs9939609 variant. We identified the best-suited inhibitor molecules for each mutant type containing the rs9939609 risk allele. Missense mutations unique to obesity and containing the risk allele of rs9939609 were retrieved from dbSNP for this study. Further stability testing for the mutations were carried out using DynaMut and iStable tools. Three mutations (G187A, M223V, and I492V) were highly destabilizing the FTO structure. These three mutants and native FTO were docked with each of the nine-inhibitor molecules collected from literature studies with the help of PyRx and AutoDock. Further structural behavior of the mutants and native FTO were identified with molecular dynamics simulations and MM-PBSA analyses, along with the 19complex inhibitor compound. We found the compound 19complex exhibited better binding interactions and is the top candidate inhibitor for the M223V and I492V mutants. This study provided insights into the structural changes caused due to mutations in FTO, and the binding mechanism of the inhibitor molecules. It could aid in developing antiobesity drugs for treating patients with mutations and risk alleles predisposing to obesity.

Heterozygous Nme7 Mutation Affects Glucose Tolerance in Male Rats

Complex metabolic conditions such as type 2 diabetes and obesity result from the interaction of numerous genetic and environmental factors. While the family of Nme proteins has been connected so far mostly to development, proliferation, or ciliary functions, several lines of evidence from human and experimental studies point to the potential involvement of one of its members, NME7 (non-metastatic cells 7, nucleoside diphosphate kinase 7) in carbohydrate and lipid metabolism. As a complete lack of Nme7 is semilethal in rats, we compared morphometric, metabolic, and transcriptomic profiles of standard diet-fed heterozygous Nme7+/− on male rats vs. their wild-type Nme7+/+ controls. Nme7+/− animals showed increased body weight, adiposity, higher insulin levels together with decreased glucose tolerance. Moreover, they displayed pancreatic islet fibrosis and kidney tubular damage. Despite no signs of overt liver steatosis or dyslipidemia, we found significant changes in the hepatic transcriptome of Nme7+/− male rats with a concerted increase of expression of lipogenic enzymes including Scd1, Fads1, Dhcr7 and a decrease of Cyp7b1 and Nme7. Network analyses suggested possible links between Nme7 and the activation of Srebf1 and Srebf2 upstream regulators. These results further support the implication of NME7 in the pathogenesis of glucose intolerance and adiposity.

Identification of Multiple Hub Genes and Pathways in Hepatocellular Carcinoma: A Bioinformatics Analysis

Hepatocellular carcinoma (HCC) is a common malignant tumor of the digestive system, and its early asymptomatic characteristic increases the difficulty of diagnosis and treatment. This study is aimed at obtaining some novel biomarkers with diagnostic and prognostic meaning and may find out potential therapeutic targets for HCC. We screen differentially expressed genes (DEGs) from the HCC gene expression profile GSE14520 using GEO2R. Gene Ontology (GO) analysis and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analysis were conducted by using the clusterProfiler software while a protein-protein interaction (PPI) network was performed based on the STRING database. Then, prognosis analysis of hub genes was conducted using The Cancer Genome Atlas (TCGA) database. Quantitative real-time polymerase chain reaction (qRT-PCR) was utilized to further verify the expression of hub genes and explore the correlation between gene expression and clinicopathological parameters. A total of 1053 DEGs were captured, containing 497 upregulated genes and 556 downregulated genes. GO and KEGG analysis indicated that the downregulated DEGs were mainly enriched in the fatty acid catabolic process while upregulated DEGs were primarily enriched in the cell cycle. Simultaneously, ten hub genes (CYP3A4, UGT1A6, AOX1, UGT1A4, UGT2B15, CDK1, CCNB1, MAD2L1, CCNB2, and CDC20) were identified by the PPI network. Five prognosis-related hub genes (CYP3A4, CDK1, CCNB1, MAD2L1, and CDC20) were uncovered by the survival analysis based on TCGA database. The ten hub genes were further validated by qRT-PCR using samples obtained from our hospital. The prognosis-related hub genes such as CYP3A4, CDK1, CCNB1, MAD2L1, and CDC20 could be considered potential diagnosis biomarkers and prognosis targets for HCC. We also use Oncomine for further verification, and we found CCNB1, CCNB2, CDK1, and CYP3A4 which were highly expressed in HCC. Meanwhile, CCNB1, CCNB2, and CDK1 are highly expressed in almost all cancer types, which may play an important role in cancer. Still, further functional study should be conducted to explore the underlying mechanism and biological effect in the near future.

Identification of the correlations between interleukin-27 (IL-27) and immune-inflammatory imbalance in preterm birth

Preterm birth (PTB) is an immune-inflammatory disease that needs to be resolved. This study aimed to identify the role of interleukin-27 (IL-27), an immunomodulatory factor, in PTB and its associated mechanisms. Here, we analyzed the high-throughput of samples data from the maternal-fetal interface to the peripheral circulation obtained from public databases and reported that the elevated IL-27 was involved with the onset of PTB. Further bioinformatics analyses (e.g. GeneMANIA and GSEA) revealed that IL-27 overexpression in the peripheral circulation as well as maternal-fetal interface is related to the activation of the immune-inflammatory process represented by IFN-γ signaling, etc. In addition, IL-27 and immune infiltration correlation analysis demonstrated that IL-27 mediates this immune-inflammatory imbalance, plausibly mainly through monocyte-macrophage and neutrophils. This finding was further validated by analyzing additional datasets. Overall, this is the first study to elaborate on the role of IL-27-mediated immuno-inflammation in PTB from the perspective of bioinformatics, which may provide a novel strategy for the prevention and treatment of PTB.

A Comprehensive Analysis of the Downregulation of miRNA-1827 and Its Prognostic Significance by Targeting SPTBN2 and BCL2L1 in Ovarian Cancer

Background: Previous studies demonstrated that miRNA-1827 could repress various cancers on proliferation, angiogenesis, and metastasis. However, little attention has been paid to its role in ovarian cancer as a novel biomarker or intervention target, especially its clinical significance and underlying regulatory network.

Methods: A meta-analysis of six microarrays was adopted here to determine the expression trend of miRNA-1827, and was further validated by gene expression profile data and cellular experiments. We explored the functional annotations through enrichment analysis for the differentially expressed genes targeted by miRNA-1827. Subsequently, we identified two hub genes, SPTBN2 and BCL2L1, based on interaction analysis using two online archive tools, miRWALK (it consolidates the resources of 12 miRNA-focused servers) and Gene Expression Profiling Interactive Analysis (GEPIA). Finally, we validated their characteristics and clinical significance in ovarian cancer.

Results: The comprehensive meta-analysis revealed that miRNA-1827 was markedly downregulated in clinical and cellular specimens. Transfection of the miRNA-1827 mimic could significantly inhibit cellular proliferation. Concerning its target genes, they were involved in diverse biological processes related to tumorigenesis, such as cell proliferation, migration, and the apoptosis signaling pathway. Moreover, interaction analysis proved that two hub genes, SPTBN2 and BCL2L1, were highly associated with poor prognosis in ovarian cancer.

Conclusion: These integrated bioinformatic analyses indicated that miRNA-1827 was dramatically downregulated in ovarian cancer as a tumor suppressor. The upregulation of its downstream modulators, SPTBN2 and BCL2L1, was associated with an unfavorable prognosis. Thus, the present study has identified miRNA-1827 as a potential intervention target for ovarian cancer based on our bioinformatic analysis processes.

Potential Roles of Endoplasmic Reticulum Stress and Cellular Proteins Implicated in Diabesity

The role of the endoplasmic reticulum (ER) has evolved from protein synthesis, processing, and other secretory pathways to forming a foundation for lipid biosynthesis and other metabolic functions. Maintaining ER homeostasis is essential for normal cellular function and survival. An imbalance in the ER implied stressful conditions such as metabolic distress, which activates a protective process called unfolded protein response (UPR). This response is activated through some canonical branches of ER stress, i.e., the protein kinase RNA-like endoplasmic reticulum kinase (PERK), inositol-requiring enzyme 1α (IRE1α), and activating transcription factor 6 (ATF6). Therefore, chronic hyperglycemia, hyperinsulinemia, increased proinflammatory cytokines, and free fatty acids (FFAs) found in diabesity (a pathophysiological link between obesity and diabetes) could lead to ER stress. However, limited data exist regarding ER stress and its association with diabesity, particularly the implicated proteins and molecular mechanisms. Thus, this review highlights the role of ER stress in relation to some proteins involved in diabesity pathogenesis and provides insight into possible pathways that could serve as novel targets for therapeutic intervention.

Screening differentially expressed proteins of coronary heart disease with congenital cold syndrome based on tandem mass tag (TMT) technology

In our previous studies, we discovered the congenital cold syndrome (CCS), which is characterized by 'qi deficiency and qi stagnation, mixed cold and heat.' And there is a type of syndrome with special incidence characteristic. However, the diagnosis of CCS still lacks an objective basis. In this study, we performed Tandem Mass Tag (TMT) based on quantitative proteomics technology to screen the significantly differentially expressed proteins (DEPs) in serum of patients with coronary heart disease (CHD) patients with CCS, patients with heart and kidney yang deficiency, and healthy people. A total of 22 DEPs (nine upregulated and 13 downregulated) were identified between patients with CCS and healthy subjects. Next, we performed GO and KEGG pathway enrichment analysis, we found the primary functions of DEPs of CCS were binding, catalytic activity, and molecular function regulator. These DEPs were mainly involved in important biological processes, such as cellular process, response to stimulus, localization, metabolic process, and biological regulation. The KEGG analysis revealed that the DEPs showed significant changes in fructose and mannose metabolism, Pentose phosphate pathway, and Arrhythmogenic right ventricular cardiomyopathy. After parallel reaction monitoring (PRM) verification, four upregulated target proteins (ALDOA, PCYOX1, Crisp3 and IGLV4-69) and three downregulated proteins (ALDOC, ADAMTSL-2 and C3) were accurately identified. These proteins were mainly related to immune response and glucose metabolism. These DEPs could be the marker proteins of coronary heart disease with CCS. This findings help to reveal the pathogenesis of CHD with CCS and provide potential therapeutic targets.

Genes That Predict Poor Prognosis in Breast Cancer via Bioinformatical Analysis

Background

Breast cancer is one of the most commonly diagnosed cancers all over the world, and it is now the leading cause of cancer death among females. The aim of this study was to find DEGs (differentially expressed genes) which can predict poor prognosis in breast cancer and be effective targets for breast cancer patients via bioinformatical analysis.

Methods

GSE86374, GSE5364, and GSE70947 were chosen from the GEO database. DEGs between breast cancer tissues and normal breast tissues were picked out by GEO2R and Venn diagram software. Then, DAVID (Database for Annotation, Visualization, and Integrated Discovery) was used to analyze these DEGs in gene ontology (GO) including molecular function (MF), cellular component (CC), and biological process (BP) and Kyoto Encyclopedia of Gene and Genome (KEGG) pathway. Next, STRING (Search Tool for the Retrieval of Interacting Genes) was used to investigate potential protein-protein interaction (PPI) relationships among DEGs and these DEGs were analyzed by Molecular Complex Detection (MCODE) in Cytoscape. After that, UALCAN, GEPIA (gene expression profiling interactive analysis), and KM (Kaplan-Meier plotter) were used for the prognostic information and core genes were qualified.

Results

There were 96 upregulated genes and 98 downregulated genes in this study. 55 upregulated genes were selected as hub genes in the PPI network. For validation in UALCAN, GEPIA, and KM, 5 core genes (KIF4A, RACGAP1, CKS2, SHCBP1, and HMMR) were found to highly expressed in breast cancer tissues with poor prognosis. They differentially expressed between different subclasses of breast cancer.

Conclusion

These five genes (KIF4A, RACGAP1, CKS2, SHCBP1, and HMMR) could be potential targets for therapy in breast cancer and prediction of prognosis on the basis of bioinformatical analysis.

An integrative analysis to distinguish between emphysema (EML) and alpha-1 antitrypsin deficiency-related emphysema (ADL)-A systems biology approach

Lung Emphysema is an abnormal enlargement of the air sacs followed by the destruction of alveolar walls without any prominent fibrosis. This study primarily identifies the differentially expressed genes (DEGs), interactions between them, and their significant involvement in the activated signaling cascades. The dataset with ID GSE1122 (five normal lung tissue samples, five of usual emphysema, and five of alpha-1 antitrypsin deficiency-related emphysema) from the gene expression omnibus (GEO) was analyzed using the GEO2R tool. The physical association between the DEGs were mapped using the STRING tool and was visualized in the Cytoscape software. The enriched functional processes were identified with the ClueGO plugin's help from Cytoscape. Further integrative functional annotation was performed by implying the GeneGo Metacore™ to distinguish the enriched pathway maps, process networks, and GO processes. The results from this analysis revealed the critical signaling cascades that have been either activated or inhibited due to identified DEGs. We found the activated pathways such as immune response IL-1 signaling pathway, positive regulation of smooth muscle migration, BMP signaling pathway, positive regulation of leukocyte migration, NIK/NF-kappB signaling, and cytochrome-c oxidase activity. Finally, we mapped four crucial genes (CCL5, ALK, TAC1, CD74, and HLA-DOA) by comparing the functional annotations that could be significantly influential in emphysema molecular pathogenesis. Our study provides insights into the pathogenesis of emphysema and helps in developing potential drug targets against emphysema.

A systemic approach to explore the mechanisms of drug resistance and altered signaling cascades in extensively drug-resistant tuberculosis

BACKGROUND AND AIM

The persistence of extensively drug-resistant (XDR) strains of Mycobacterium tuberculosis (MTB) continue to pose a significant challenge to the treatment and control of tuberculosis infections worldwide. XDR-MTB strains exhibit resistance against first-line anti-TB drugs, fluoroquinolones, and second-line injectable drugs. The mechanisms of drug resistance of MTB remains poorly understood. Our study aims at identifying the differentially expressed genes (DEGs), associated gene networks, and signaling cascades involved in rendering this pathogen resistant to multiple drugs, namely, isoniazid, rifampicin, and capreomycin.

METHODS

We used the microarray dataset GSE53843. The GEO2R tool was used to prioritize the most significant DEGs (top 250) of each drug exposure sample between XDR strains and non-resistant strains. The validation of the 250 DEGs was performed using volcano plots. Protein-protein interaction networks of the DEGs were created using STRING and Cytoscape tools, which helped decipher the relationship between these genes. The significant DEGs were functionally annotated using DAVID and ClueGO. The concomitant biological processes (BP) and molecular functions (MF) were represented as dot plots.

RESULTS AND CONCLUSION

We identified relevant molecular pathways and biological processes, such as cell wall biogenesis, lipid metabolic process, ion transport, phosphopantetheine binding, and triglyceride lipase activity. These processes indicated the involvement of multiple interconnected mechanisms in drug resistance. Our study highlighted the impact of cell wall permeability, with the dysregulation of the mur family of proteins, as essential factors in the inference of resistance. Additionally, upregulation of genes responsible for ion transport such as ctpF, arsC, and nark3, emphasizes the importance of transport channels and efflux pumps in potentially driving out stress-inducing compounds. This study investigated the upregulation of the Lip family of proteins, which play a crucial role in triglyceride lipase activity. Thereby illuminating the potential role of drug-induced dormancy and subsequent resistance in the mycobacterial strains. Multiple mechanisms such as carboxylic acid metabolic process, NAD biosynthetic process, triglyceride lipase activity, phosphopantetheine binding, organic acid biosynthetic process, and growth of symbiont in host cell were observed to partake in resistance of XDR-MTB. This study ultimately provides a platform for important mapping targets for potential therapeutics against XDR-MTB.

Investigation of the Mechanism of Complement System in Diabetic Nephropathy via Bioinformatics Analysis

OBJECTIVES

Diabetic nephropathy (DN) is a major cause of end-stage renal disease (ESRD) throughout the world, and the identification of novel biomarkers via bioinformatics analysis could provide research foundation for future experimental verification and large-group cohort in DN models and patients.

METHODS

GSE30528, GSE47183, and GSE104948 were downloaded from Gene Expression Omnibus (GEO) database to find differentially expressed genes (DEGs). The difference of gene expression between normal renal tissues and DN renal tissues was firstly screened by GEO2R. Then, the protein-protein interactions (PPIs) of DEGs were performed by STRING database, the result was integrated and visualized via applying Cytoscape software, and the hub genes in this PPI network were selected by MCODE and topological analysis. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analyses were carried out to determine the molecular mechanisms of DEGs involved in the progression of DN. Finally, the Nephroseq v5 online platform was used to explore the correlation between hub genes and clinical features of DN.

RESULTS

There were 64 DEGs, and 32 hub genes were identified, enriched pathways of hub genes involved in several functions and expression pathways, such as complement binding, extracellular matrix structural constituent, complement cascade related pathways, and ECM proteoglycans. The correlation analysis and subgroup analysis of 7 complement cascade-related hub genes and the clinical characteristics of DN showed that C1QA, C1QB, C3, CFB, ITGB2, VSIG4, and CLU may participate in the development of DN.

CONCLUSIONS

We confirmed that the complement cascade-related hub genes may be the novel biomarkers for DN early diagnosis and targeted treatment.