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This study explored the pathogenesis of human immunodeficiency virus (HIV) and monkeypox co-infection, identifying candidate hub genes and potential drugs using bioinformatics and machine learning. Datasets for HIV (GSE 37250) and monkeypox (GSE 24125) were obtained from the GEO database. Common differentially expressed genes (DEGs) in co-infection were identified by intersecting DEGs from monkeypox datasets with genes from key HIV modules screened using Weighted Gene Co-Expression Network Analysis (WGCNA). After gene ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analysis and construction of protein-protein interaction (PPI) network, candidate hub genes were further screened based on machine learning algorithms. Transcriptional factors (TFs) and miRNA-candidate hub gene networks were constructed to understand regulatory mechanisms and protein-drug interactions to identify potential therapeutic drugs. Seven candidate hub genes-MX2, ADAR, POLR2H, RPL5, IFI16, IFIT2, and RPS5-were identified. TFs and miRNAs associated with these hub genes, playing a key role in regulating viral infection and inflammation due to the activation of antiviral innate immunity, were also identified through network analysis. Potential therapeutic drugs were screened based on these hub genes: AZT, a nucleotide reverse transcriptase inhibitor, suppressed viral replication in HIV and monkeypox co-infection, while mefloquine inhibited inflammation due to the activation of antiviral innate immunity. In conclusion, the study identified candidate hub genes, their transcriptional regulation, signaling pathways, and small-molecule drugs in HIV and monkeypox co-infection, contributing to understanding the pathogenesis of HIV and monkeypox co-infection and informing precise therapeutic strategies., (© 2024. The Author(s).)

Ovarian cancer is one of the types of gynecological cancers that is considered to be particularly dangerous. Ovarian cancer treatment has come a long way in recent years, but the disease is still quite likely to spread to other parts of the body. In this line of research, our goal is to pinpoint the shifts in gene expression profiles that are responsible for the avoidance of ovarian cancer. The dataset GSE54388 which was deposited in the Gene Expression Omnibus (GEO) database was processed in order to find differentially expressed genes (DEGs) that were present between human ovarian surface epithelium samples and tumor epithelial component samples. The weighted gene correlation network analysis, also known as WGCNA, was performed on the modules that were associated with the ovarian cancer group. The Gene Ontology (GO), the Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis, and the Gene Set Enrichment Analysis (GSEA) were used to compile a summary of the DEGs that were found in the Venn analysis of the Royalbule module. This analysis found 186 genes that overlapped in the royal blue module. Using the cytohubba plug-in that is included in the Cytoscape software, the Protein-protein Interaction (PPI) network was created and then searched to identify hub genes. Based on these findings, it seems that 10 genes have a role as hub genes in the prevention of ovarian cancer., Competing Interests: Declaration of competing interest The authors declare no competing interests., (Copyright © 2024 Elsevier B.V. All rights reserved.)

The aim of this study was to identify key genes and investigate the immunological mechanisms of atopic dermatitis (AD) at the molecular level via bioinformatics analysis. Gene expression profiles (GSE32924, GSE107361, GSE121212, and GSE230200) were obtained for screening common differentially expressed genes (co-DEGs) from the gene expression omnibus database. Functional enrichment analysis, protein-protein interaction network and module construction, and identification of common hub genes were performed. Hub genes were validated using receiver operating characteristic curve analysis based on GSE130588 and GSE16161. NetworkAnalyst was used to detect microRNAs (miRNAs) and transcription factors (TFs) associated with the hub genes. The immune cell infiltration was analyzed using the CIBERSORT algorithm to further analyze the correlation between hub genes and immune cells. A total of 146 co-DEGs were obtained, showing significant enrichment in cytokine-cytokine receptor interaction and JAK-STAT signaling pathway. Seven hub genes were identified by Cytoscape and validated with external datasets. Subsequent prediction of miRNAs and TFs targeting these hub genes revealed their regulatory roles. Analysis of immune cell infiltration and correlation revealed a significant positive correlation between CCL22 expression and the number of dendritic cells activated. The identified hub genes represent potential diagnostic and therapeutic targets in the immunological pathogenesis of AD., (© 2024. The Author(s).)

Alopecia areata (AA) is an immune-mediated chronic alopecia disease, but its specific pathogenesis is unclear. Gene expression data for AA patients (AAs) and healthy controls (HCs) were retrieved from the GEO database, and the differentially expressed genes (DEGs) between AAs and HCs were identified. Then, GO, KEGG and GSEA analysis were performed. A PPI network for the DEGs was then constructed to screen for hub genes, which were validated by three additional datasets. Subsequently, the potential miRNAs interacting with the hub genes were obtained through TarBase and miRNet. The differentially expressed lncRNAs (DElncRs) were obtained for subcellular localisation analysis, and the DElncRs located in the cytoplasm were further screened to identify miRNAs that interact with them. The shared miRNAs interacting with the hub genes and lncRNAs were used to construct a network of mRNA-miRNA-lncRNA interactions. Lastly, ROC analysis was performed to evaluate the potential diagnostic value of the hub genes and DElncRs identified. A total of 173 DEGs were obtained, mainly enriched in cytokines, chemokines, hair follicle development and hair cycle related signalling pathways. Through PPI screening and validation based on 3 additional datasets, 24 hub genes were finally yielded. Of them, five hub genes were upregulated and the potential miRNAs that interact with these five hub genes were identified. Additionally, 26 DElncRs were obtained, including 9 upregulated lncRNAs located in the cytoplasm that were predicted to interact with the miRNAs. Finally, an mRNA-miRNA-lncRNA regulatory network was constructed using five hub genes, four lncRNAs and their shared five miRNAs. The regulatory relationship between CD8A, mir-185-5p and FOXD2-AS1 might be crucial in AA pathogenesis, with CD8A and FOXD2-AS1 exhibiting diagnostic potential. CD8A and FOXD2-AS1 may serve as potential therapeutic targets in AA., (© 2024 John Wiley & Sons A/S. Published by John Wiley & Sons Ltd.)

Objective: Patients with rheumatoid arthritis (RA) have an increased risk of developing pulp and periapical disease (PAP), but the causal relationship and shared genetic factors between these conditions have not been explored. This study aimed to investigate the bidirectional causal relationship between RA and PAP and to analyze shared genes and pathogenic pathways., Methods: We utilized GWAS data from the IEU Open GWAS Project and employed five Mendelian randomization methods (MR Egger, weighted median, inverse variance weighted, simple mode, and weighted mode) to investigate the bidirectional causal relationship between RA and PAP. Transcriptome data for RA and irreversible pulpitis (IRP) were obtained from the GEO database. Hub genes were identified through differential analysis, CytoHubba, machine learning (ML), and other methods. The immune infiltration of both diseases was analyzed using the ssGSEA method. Finally, we constructed a regulatory network for miRNAs, transcription factors, chemicals, diseases, and RNA-binding proteins based on the identified hub genes., Results: RA was significantly associated with an increased risk of PAP (OR = 1.1284, 95% CI 1.0674-1.1929, p < 0.001). However, there was insufficient evidence to support the hypothesis that PAP increased the risk of RA. Integrating datasets and differential analysis identified 84 shared genes primarily involved in immune and inflammatory pathways, including the IL-17 signaling pathway, Th17 cell differentiation, and TNF signaling pathway. Using CytoHubba and three ML methods, we identified three hub genes (HLA-DRA, ITGAX, and PTPRC) that are significantly correlated and valuable for diagnosing RA and IRP. We then constructed a comprehensive regulatory network using the miRDB, miRWalk, ChipBase, hTFtarget, CTD, MalaCards, DisGeNET, and ENCORI databases., Conclusion: RA may increase the risk of PAP. The three key genes, HLA-DRA, ITGAX, and PTPRC, have significant diagnostic value for both RA and IRP., Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2024 Wu, Wang and Qiu.)

Background: We aimed to identify hub genes in chronic obstructive pulmonary disease (COPD) plasma through the exploration of a putative miRNA-mRNA regulatory network., Methods: Three datasets (GSE24709, GSE102915, GSE136390) were utilized to discern differentially expressed miRNAs (DEMs) between COPD and normal plasma. miRNET was employed to predict the potential targets of DEMs. Subsequent GO and KEGG analyses were conducted using DAVID. For the construction of the protein-protein interaction (PPI) network and screening of hub genes, STRING and Cytoscape were employed. The expression validation was assessed through GSE56768., Results: The results revealed 395 genes targeted by up-regulated DEMs and 234 genes targeted by down-regulated DEMs. The target genes exhibited significant enrichment in the PI3K-Akt signaling pathway and the p53 signaling pathway. Through the validation of hub genes' expression, we proposed two potential miRNA-mRNA interactions: miR-126-5p/miR-495-3p/miR-193b-3p - YWHAZ and miR-937-5p/miR-183-5p/miR-34c-5p/miR-98-5p/miR-525-3p/miR-215-5p - ACTB., Conclusions: In conclusion, our study posits potential miRNA-mRNA interactions in COPD by analyzing datasets from public databases, contributing valuable insights into the understanding of COPD pathogenesis and potential therapeutic avenues.

Alzheimer's Disease (AD) remains a significant challenge due to its complex etiology and socio-economic burden. In this study, we investigated the roles of macrophage polarization-related hub genes in AD pathology, focusing on their impact on immune infiltration and gene regulation in distinct brain regions. Using Gene Expression Omnibus (GEO) datasets GSE110226 (choroid plexus) and GSE1297 (hippocampal CA1), we identified key genes-EDN1, HHLA2, KL, TREM2, and WWTR1-associated with AD mechanisms and immune responses. Based on these findings, we developed a diagnostic model demonstrating favorable calibration and clinical applicability. Furthermore, we explored molecular interactions within mRNA-transcription factor and mRNA-miRNA regulatory networks, providing deeper insights into AD progression and identifying potential therapeutic targets. The novel identification of WWTR1 and HHLA2 as biomarkers expands the diagnostic toolkit for AD, offering new perspectives on the disease's underlying immune dynamics. However, external dataset validation and further in vitro and in vivo studies are required to confirm these results and their clinical relevance., (© 2024. The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature.)

Objective Gastric cancer (GC) is a high-risk tumor disease worldwide. The goal of the current study was to explore new diagnostic and prognostic indicators for gastric cancer. Methods Database GSE19826 and GSE103236 were gained from the Gene Expression Omnibus (GEO) to screen for differentially expressed genes (DEGs), which were then grouped together as co-DEGs. GO and KEGG pathway analysis were used to investigate the function of these genes. The protein-protein interaction (PPI) network of DEGs was constructed by STRING. Results GSE19826 selected 493 DEGs in GC and gastric normal tissues, including 139 up-regulated genes and 354 down-regulated genes. A total of 478 DEGs were selected by GSE103236, including 276 up-regulated genes and 202 downregulated genes. 32 co-DEGs were overlapped from two databasesand involved in digestion, regulation of response to wounding, wound healing, potassium ion imports across plasma membrane, regulation of wound healing, anatomical structure homeostasis, and tissue homeostasis. KEGG analysis revealed that co-DEGs were mainly involved in ECM-receptor interaction, tight junction, protein digestion and absorption, gastric acid secretion and cell adhesion molecules. Twelve hub genes were screened by Cytoscape, including cholecystokinin B receptor (CCKBR), Collagen type I alpha 1 (COL1A1), COL1A2, COL2A1, COL6A3, COL11A1, matrix metallopeptidase 1 (MMP1), MMP3, MMP7, MMP10, tissue inhibitor of matrix metalloprotease 1 (TIMP1) and secreted phosphoprotein 1 (SPP1). Conclusions Twelve key genes affecting the progression of gastric cancer were obtained by bioinformatics, which may be potential biomarkers for the diagnosis and prognosis of GC.

Rheumatoid arthritis (RA) is the predominant manifestation of inflammatory arthritis, distinguished by an increasing burden of morbidity and mortality. The intricate interplay of genes and signalling pathways involved in synovial inflammation in patients with RA remains inadequately comprehended. This study aimed to ascertain the role of necroptosis in RA, as along with their associations with immune cell infiltration. Differential expression analysis and weighted gene co-expression network analysis (WGCNA) were employed to identify central genes for RA. In this study, identified total of 28 differentially expressed genes (DEGs) were identified in RA. Utilising WGCNA, two co-expression modules were generated, with one module demonstrating the strongest correlation with RA. Through the integration of differential gene expression analysis, a total of 5 intersecting genes were discovered. These 5 hub genes, namely fused in sarcoma (FUS), transformer 2 beta homolog (TRA2B), eukaryotic translation elongation factor 2 (EEF2), cleavage and polyadenylation specific factor 6 (CPSF6) and signal transducer and activator of transcription 3 (STAT3) were found to possess significant diagnostic value as determined by receiver operating characteristic (ROC) curve analysis. The close association between the concentrations of various immune cells is anticipated to contribute to the diagnosis and treatment of RA. Furthermore, the infiltration of immune cells mentioned earlier is likely to exert a substantial influence on the initiation of this disease.

Purpose: The objective of this study was to discern ferroptosis-related genes (FRGs) linked to non-obstructive azoospermia and investigate the associated molecular mechanisms., Method: A dataset related to azoospermia was retrieved from the Gene Expression Omnibus database, and FRGs were sourced from GeneCards. Ferroptosis-related differentially expressed genes (FRDEGs) were discerned. Subsequently, these genes underwent analyses encompassing Gene Ontology and Kyoto Encyclopedia of Genes and Genomes, as well as protein-protein interaction (PPI) networks and assessments of functional similarity. Following the identification of hub genes, an exploration of immune infiltration, single-cell expression, diagnostic utility, and interactions involving hub genes, RNA-binding proteins (RBPs), transcription factors (TFs), microRNAs (miRNAs), and drugs was conducted., Results: A total of 35 differentially expressed FRGs were discerned. These genes demonstrated enrichment in functions and pathways associated with ferroptosis. From the PPI network, eight hub genes were selected. Functional similarity analysis highlighted the potential pivotal roles of HMOX1 and GPX4 in azoospermia. Analysis of immune cell infiltration indicated a significant decrease in activated dendritic cells in the azoospermia group, with notable correlations between hub genes, particularly SAT1 and HMGCR, and immune cell infiltration. Unique expression patterns of hub genes across various cell types in the human testis were observed, with GPX4 prominently enriched in spermatid/sperm. Eight hub genes exhibited robust diagnostic value (AUC > 0.75). Lastly, a comprehensive hub gene-miRNA-TF-RBP-drug network was constructed., Conclusion: In summary, our investigation unveiled eight FRDEGs associated with azoospermia, which hold potential as biomarkers for the diagnosis and treatment of azoospermia., (© 2024. The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature.)

Background: Hepatocellular carcinoma (HCC) represents a primary liver tumor characterized by a bleak prognosis and elevated mortality rates, yet its precise molecular mechanisms have not been fully elucidated. This study uses advanced bioinformatics techniques to discern differentially expressed genes (DEGs) implicated in the pathogenesis of HCC. The primary objective is to discover novel biomarkers and potential therapeutic targets that can contribute to the advancement of HCC research., Methods: The bioinformatics analysis in this study primarily utilized the Gene Expression Omnibus (GEO) database as data source. Initially, the Transcriptome analysis console (TAC) screened for DEGs. Subsequently, we constructed a protein-protein interaction (PPI) network of the proteins associated to the identified DEGs with the STRING database. We obtained our hub genes using Cytoscape and confirmed the results through the GEPIA database. Furthermore, we assessed the prognostic significance of the identified hub genes using the GEPIA database. To explore the regulatory interactions, a miRNA-gene interaction network was also constructed, incorporating information from the miRDB database. For predicting the impact of gene overexpression on drug effects, we utilized CANCER DP., Results: A comprehensive analysis of HCC gene expression profiles revealed a total of 4716 DEGs, consisting of 2430 upregulated genes and 2313 downregulated genes in HCC sample compared to healthy control group. These DEGs exhibited significant enrichment in key pathways such as the PI3K-Akt signaling pathway, nuclear receptors meta-pathway, and various metabolism-related pathways. Further exploration of the PPI network unveiled the P53 signaling pathway and pyrimidine metabolism as the most prominent pathways. We identified 10 hub genes (ASPM, RRM2, CCNB1, KIF14, MKI67, SHCBP1, CENPF, ANLN, HMMR, and EZH2) that exhibited significant upregulation in HCC samples compared to healthy control group. Survival analysis indicated that elevated expression levels of these genes were strongly associated with changes in overall survival in HCC patients. Lastly, we identified specific miRNAs that were found to influence the expression of these genes, providing valuable insights into potential regulatory mechanisms underlying HCC progression., Conclusion: The findings of this study have successfully identified pivotal genes and pathways implicated in the pathogenesis of HCC. These novel discoveries have the potential to significantly enhance our understanding of HCC at the molecular level, opening new ways for the development of targeted therapies and improved prognosis evaluation., (© 2024 The Author(s). Cancer Reports published by Wiley Periodicals LLC.)

Glaucoma is a leading cause of permanent blindness, affecting 80 million people worldwide. Recent studies have emphasized the importance of neuroinflammation in the early stages of glaucoma, involving immune and glial cells. To investigate this further, we used the GSE27276 dataset from the GEO (Gene Expression Omnibus) database and neuroinflammation genes from the GeneCards database to identify differentially expressed neuroinflammation-related genes associated with primary open-angle glaucoma (POAG). Subsequently, these genes were submitted to Gene Ontology and the Kyoto Encyclopedia of Genes and Genomes for pathway enrichment analyses. Hub genes were picked out through protein-protein interaction networks and further validated using the external datasets (GSE13534 and GSE9944) and real-time PCR analysis. The gene-miRNA regulatory network, receiver operating characteristic (ROC) curve, genome-wide association study (GWAS), and regional expression analysis were performed to further validate the involvement of hub genes in glaucoma. A total of 179 differentially expressed genes were identified, comprising 60 upregulated and 119 downregulated genes. Among them, 18 differentially expressed neuroinflammation-related genes were found to overlap between the differentially expressed genes and neuroinflammation-related genes, with six genes (SERPINA3, LCN2, MMP3, S100A9, IL1RN, and HP) identified as potential hub genes. These genes were related to the IL-17 signaling pathway and tyrosine metabolism. The gene-miRNA regulatory network showed that these hub genes were regulated by 118 miRNAs. Notably, GWAS data analysis successfully identified significant single nucleotide polymorphisms (SNPs) corresponding to these six hub genes. ROC curve analysis indicated that our genes showed significant accuracy in POAG. The expression of these genes was further confirmed in microglia, Müller cells, astrocytes, and retinal ganglion cells in the Spectacle database. Moreover, three hub genes, SERPINA3, IL1R1, and LCN2, were validated as potential diagnostic biomarkers for high-risk glaucoma patients, showing increased expression in the OGD/R-induced glaucoma model. This study suggests that the identified hub genes may influence the development of POAG by regulation of neuroinflammation, and it may offer novel insights into the management of POAG.

Background: Cervical cancer has extremely high morbidity and mortality, and its pathogenesis is still in the exploratory stage. This study aimed to screen and identify differentially expressed genes (DEGs) related to cervical cancer through bioinformatics analysis., Methods: GSE63514 and GSE67522 were selected from the GEO database to screen DEGs. Then GO and KEGG analysis were performed on DEGs. PPI network of DEGs was constructed through STRING website, and the hub genes were found through 12 algorithms of Cytoscape software. Meanwhile, GSE30656 was selected from the GEO database to screen DEMs. Target genes of DEMs were screened through TagetScan, miRTarBase and miRDB. Next, the hub genes screened from DEGs were merged with the target genes screened from DEMs. Finally, ROC curve and nomogram analysis were performed to assess the predictive capabilities of the hub genes. The expression of these hub genes were verified through TCGA, GEPIA, qRT-PCR, and immunohistochemistry., Results: Six hub genes, TOP2A, AURKA, CCNA2, IVL, KRT1, and IGFBP5, were mined through the protein-protein interaction network. The expression of these hub genes were verified through TCGA, GEPIA, qRT-PCR, and immunohistochemistry, and it was found that TOP2A, AURKA as well as CCNA2 were overexpressed and IGFBP5 was low expression in cervical cancer., Conclusions: This study showed that TOP2A, AURKA, CCNA2 and IGFBP5 screened through bioinformatics analysis were significantly differentially expressed in cervical cancer samples compared with normal samples, which might be biomarkers of cervical cancer., (© 2024. The Author(s).)

Psoriasis increases the risk of developing various cancers, including colon cancer. The pathogenesis of the co-occurrence of psoriasis and cancer is not yet clear. This study is aimed at analyzing the pathogenesis of psoriasis combined with cancer by bioinformatic analysis. Skin tissue data from psoriasis (GSE117239) and intestinal tissue data from colon cancer (GSE44076) were downloaded from the GEO database. One thousand two hundred ninety-six common differentially expressed genes and 688 common shared genes for psoriasis and colon cancer were determined, respectively, using the limma R package and weighted gene coexpression network analysis (WGCNA) methods. The results of the GO and KEGG enrichment analyses were mainly related to the biological processes of the cell cycle. Thirteen hub genes were selected, including AURKA, DLGAP5, NCAPG, CCNB1, NDC80, BUB1B, TTK, CCNB2, AURKB, TOP2A, ASPM, BUB1, and KIF20A. These hub genes have high diagnostic value, and most of them are positively correlated with activated CD4 T cells. Three hub transcription factors (TFs) were also predicted: E2F1, E2F3, and BRCA1. These hub genes and hub TFs are highly expressed in various cancers. Furthermore, 251 drugs were predicted, and some of them overlap with existing therapeutic drugs for psoriasis or colon cancer. This study revealed some genetic mechanisms of psoriasis and cancer by bioinformatic analysis. These hub genes, hub TFs, and predicted drugs may provide new perspectives for further research on the mechanism and treatment., Competing Interests: The authors declare no conflicts of interest., (Copyright © 2024 Yao Yu et al.)

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., (© 2023. The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature.)

Background: Sleep disorders (SD) are known to have a profound impact on human health and quality of life although their exact pathogenic mechanisms remain poorly understood., Methods: The study first accessed SD datasets from the GEO and identified DEGs. These DEGs were then subjected to gene set enrichment analysis. Several advanced techniques, including the RF, SVM-RFE, PPI networks, and LASSO methodologies, were utilized to identify hub genes closely associated with SD. Additionally, the ssGSEA approach was employed to analyze immune cell infiltration and functional gene set scores in SD. DEGs were also scrutinized in relation to miRNA, and the DGIdb database was used to explore potential pharmacological treatments for SD. Furthermore, in an SD murine model, the expression levels of these hub genes were confirmed through RT-qPCR and Western Blot analyses., Results: The findings of the study indicate that DEGs are significantly enriched in functions and pathways related to immune cell activity, stress response, and neural system regulation. The analysis of immunoinfiltration demonstrated a marked elevation in the levels of Activated CD4+ T cells and CD8+ T cells in the SD cohort, accompanied by a notable rise in Central memory CD4 T cells, Central memory CD8 T cells, and Natural killer T cells. Using machine learning algorithms, the study also identified hub genes closely associated with SD, including IPO9, RAP2A, DDX17, MBNL2, PIK3AP1, and ZNF385A. Based on these genes, an SD diagnostic model was constructed and its efficacy validated across multiple datasets. In the SD murine model, the mRNA and protein expressions of these 6 hub genes were found to be consistent with the results of the bioinformatics analysis., Conclusion: In conclusion, this study identified 6 genes closely linked to SD, which may play pivotal roles in neural system development, the immune microenvironment, and inflammatory responses. Additionally, the key gene-based SD diagnostic model constructed in this study, validated on multiple datasets showed a high degree of reliability and accuracy, predicting its wide potential for clinical applications. However, limited by the range of data sources and sample size, this may affect the generalizability of the results., Competing Interests: The authors declare that the editorial was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2024 Lin, Liu and Hu.)

Background: Whether there are invasive components in pure ground glass nodules(pGGNs) in the lungs is still a huge challenge to forecast. The objective of our study is to investigate and identify the potential biomarker genes for pure ground glass nodule(pGGN) based on the method of bioinformatics analysis., Methods: To investigate differentially expressed genes (DEGs), firstly the data obtained from the gene expression omnibus (GEO) database was used.Next Weighted gene co-expression network analysis (WGCNA) investigate the co-expression network of DEGs. The black key module was chosen as the key one in correlation with pGGN. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways analyses were done. Then STRING was uesd to create a protein-protein interaction (PPI) network, and the chosen module genes were analyzed by Cytoscape software.In addition the polymerase chain reaction (PCR) was used to evaluate the value of these hub genes in pGGN patients' tumor tissues compared to controls., Results: A total of 4475 DEGs were screened out from GSE193725, then 225 DEGs were identified in black key module, which were found to be enriched for various functions and pathways, such as extracellular exosome, vesicle, ribosome and so on. Among these DEGs, 6 overlapped hub genes with high degrees of stress method were selected. These hub genes include RPL4, RPL8, RPLP0, RPS16, RPS2 and CCT3.At last relative expression levels of CCT3 and RPL8 mRNA were both regulated in pGGN patients' tumor tissues compared to controls., Conclusions: To summarize, the determined DEGs, pathways, modules, and overlapped hub genes can throw light on the potential molecular mechanisms of pGGN., (© 2024. The Author(s).)

The present study explores the potentials of bioinformatics analysis to identify hub genes linked to intervertebral disc degeneration (IDD) and explored the potential molecular mechanism of transcription factor-microRNA regulatory network. Furthermore, the hub genes were identified through quantitative reverse transcriptase PCR (qRT-PCR). GEO database expression profile datasets for candidate genes (GSE124272) were downloaded. Genes that were differentially expressed (DEGs) were detected utilizing limma technique in the R programming language. Search Tool for the Retrieval of Interacting Genes/Proteins and NetworkAnalyst software identified hub genes. The Kyoto Encyclopedia of Genes and Genomes pathway enrichment analysis as well as Gene Ontology annotation of the DEGs were performed using Metascape. Using Bioinformatics data from the TRRUST, StarBase, and TransmiR databases, a TF-miRNA-hub genes network was constructed. qRT-PCR was utilized to confirm the result. As compared to healthy persons, 521 DEGs, comprising 203 down-regulated and 318 up-regulated genes, as well as 7 core genes, were found in people with IDD. Analysis revealed that all seven essential genes were under-expressed. qRT-PCR further confirmed the low expression of these seven important genes. Based on the TRRUST database, 16 TFs that could target five junction genes were then predicted. According to the StarBase database, four miRNAs were linked to crucial genes, while the TransmiR database predicted regulatory connections between four miRNAs and five TFs. The expression of the TP53-(hsa-miR-183-5p)-CCNB1 TF-miRNA-mRNA interaction network was discovered to be correlated with IDD. Throughout this investigation, a network of TF-miRNA-mRNA connections was built for investigation of the probable molecular mechanisms responsible for IDD. The identification of hub genes associated with IDD may reveal promising IDD treatment strategies., (© 2023. The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature.)

Peanut (Arachis hypogaea L.) is an important oilseed crop worldwide. However, soil salinization becomes one of the main limiting factors of peanut production. Therefore, developing salt-tolerant varieties and understanding the molecular mechanisms of salt tolerance is important to protect peanut yield in saline areas. In this study, we selected four peanut varieties with contrasting response to salt challenges with T1 and T2 being tolerance and S1 and S2 being susceptible. High-throughput RNA sequencing resulted in more than 314.63 Gb of clean data from 48 samples. We identified 12,057 new genes, 7,971of which have functional annotations. KEGG pathway enrichment analysis of uniquely expressed genes in salt-tolerant peanut revealed that upregulated genes in the root are involved in the MAPK signaling pathway, fatty acid degradation, glycolysis/gluconeogenesis, and upregulated genes in the shoot were involved in plant hormone signal transduction and the MAPK signaling pathway. Na + content, K + content, K + / Na + , and dry mass were measured in root and shoot tissues, and two gene co-expression networks were constructed based on weighted gene co-expression network analysis (WGCNA) in root and shoot. In this study, four key modules that are highly related to peanut salt tolerance in root and shoot were identified, plant hormone signal transduction, phenylpropanoid biosynthesis, starch and sucrose metabolism, flavonoid biosynthesis, carbon metabolism were identified as the key biological processes and metabolic pathways for improving peanut salt tolerance. The hub genes include genes encoding ion transport (such as HAK8, CNGCs, NHX, NCL1) protein, aquaporin protein, CIPK11 (CBL-interacting serine/threonine-protein kinase 11), LEA5 (late embryogenesis abundant protein), POD3 (peroxidase 3), transcription factor, and MAPKKK3. There were some new salt-tolerant genes identified in peanut, including cytochrome P450, vinorine synthase, sugar transport protein 13, NPF 4.5, IAA14, zinc finger CCCH domain-containing protein 62, beta-amylase, fatty acyl-CoA reductase 3, MLO-like protein 6, G-type lectin S-receptor-like serine/threonine-protein kinase, and kinesin-like protein KIN-7B. The identification of key modules, biological pathways, and hub genes in this study enhances our understanding of the molecular mechanisms underlying salt tolerance in peanuts. This knowledge lays a theoretical foundation for improving and innovating salt-tolerant peanut germplasm., (© 2024. The Author(s).)

Background: Parkinson's disease (PD) is a progressive neurodegenerative disease with increasing prevalence. Effective diagnostic markers and therapeutic methods are still lacking. Exploring key molecular markers and mechanisms for PD can help with early diagnosis and treatment improvement., Methods: Three datasets GSE174052, GSE77668, and GSE168496 were obtained from the GEO database to search differentially expressed circRNA (DECs), miRNAs (DEMis), and mRNAs (DEMs). GO and KEGG enrichment analyses, and protein-protein interaction (PPI) network construction were implemented to explore possible actions of DEMs. Hub genes were selected to establish circRNA-related competing endogenous RNA (ceRNA) networks., Results: There were 1005 downregulated DECs, 21 upregulated and 21 downregulated DEMis, and 266 upregulated and 234 downregulated DEMs identified. The DEMs were significantly enriched in various PD-associated functions and pathways such as extracellular matrix organization, dopamine synthesis, PI3K-Akt, and calcium signaling pathways. Twenty-one hub genes were screened out, and a PD-related ceRNA regulatory network was constructed containing 31 circRNAs, one miRNA (miR-371a-3p), and one hub gene (KCNJ6)., Conclusion: We identified PD-related molecular markers and ceRNA regulatory networks, providing new directions for PD diagnosis and treatment., (© 2024. The Author(s).)

Background: Systemic lupus erythematosus (SLE) is a multi-organ chronic autoimmune disease. Inflammatory bowel disease (IBD) is a common chronic inflammatory disease of the gastrointestinal tract. Previous studies have shown that SLE and IBD share common pathogenic pathways and genetic susceptibility, but the specific pathogenic mechanisms remain unclear., Methods: The datasets of SLE and IBD were downloaded from the Gene Expression Omnibus (GEO). Differentially expressed genes (DEGs) were identified using the Limma package. Weighted gene coexpression network analysis (WGCNA) was used to determine co-expression modules related to SLE and IBD. Pathway enrichment was performed using Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) analysis for co-driver genes. Using the Least AbsoluteShrinkage and Selection Operator (Lasso) regressionand Support Vector Machine-Recursive Feature Elimination (SVM-RFE), common diagnostic markers for both diseases were further evaluated. Then, we utilizedthe CIBERSORT method to assess the abundance of immune cell infiltration. Finally,we used the single-cell analysis to obtain the location of common diagnostic markers., Results: 71 common driver genes were identified in the SLE and IBD cohorts based on the DEGs and module genes. KEGG and GO enrichment results showed that these genes were closely associated with positive regulation of programmed cell death and inflammatory responses. By using LASSO regression and SVM, five hub genes (KLRF1, GZMK, KLRB1, CD40LG, and IL-7R) were ultimately determined as common diagnostic markers for SLE and IBD. ROC curve analysis also showed good diagnostic performance. The outcomes of immune cell infiltration demonstrated that SLE and IBD shared almost identical immune infiltration patterns. Furthermore, the majority of the hub genes were commonly expressed in NK cells by single-cell analysis., Conclusion: This study demonstrates that SLE and IBD share common diagnostic markers and pathogenic pathways. In addition, SLE and IBD show similar immune cellinfiltration microenvironments which provides newperspectives for future treatment., Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2024 Sun, Zhang and Shen.)

Soybean mosaic virus (SMV) is one of the main pathogens that can negatively affect soybean production and quality. To study the gene regulatory network of soybeans in response to SMV SC15, the resistant line X149 and susceptible line X97 were subjected to transcriptome analysis at 0, 2, 8, 12, 24, and 48 h post-inoculation (hpi). Differential expression analysis revealed that 10,190 differentially expressed genes (DEGs) responded to SC15 infection. Weighted gene co-expression network analysis (WGCNA) was performed to identify highly related resistance gene modules; in total, eight modules, including 2256 DEGs, were identified. Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis of 2256 DEGs revealed that the genes significantly clustered into resistance-related pathways, such as the plant-pathogen interaction pathway, mitogen-activated protein kinases (MAPK) signaling pathway, and plant hormone signal transduction pathway. Among these pathways, we found that the flg22, Ca 2+ , hydrogen peroxide (H 2 O 2 ), and abscisic acid (ABA) regulatory pathways were fully covered by 36 DEGs. Among the 36 DEGs, the gene Glyma.01G225100 (protein phosphatase 2C, PP2C) in the ABA regulatory pathway, the gene Glyma.16G031900 (WRKY transcription factor 22, WRKY22) in Ca 2+ and H 2 O 2 regulatory pathways, and the gene Glyma.04G175300 (calcium-dependent protein kinase, CDPK) in Ca 2+ regulatory pathways were highly connected hub genes. These results indicate that the resistance of X149 to SC15 may depend on the positive regulation of flg22, Ca 2+ , H 2 O 2 , and ABA regulatory pathways. Our study further showed that superoxide dismutase (SOD) activity, H 2 O 2 content, and catalase (CAT) and peroxidase (POD) activities were significantly up-regulated in the resistant line X149 compared with those in 0 hpi. This finding indicates that the H 2 O 2 regulatory pathway might be dependent on flg22- and Ca 2+ -pathway-induced ROS generation. In addition, two hub genes, Glyma.07G190100 ( encoding F-box protein) and Glyma.12G185400 (encoding calmodulin-like proteins, CMLs), were also identified and they could positively regulate X149 resistance. This study provides pathways for further investigation of SMV resistance mechanisms in soybean.

Background: Rheumatoid arthritis (RA) is a systemic immune-related disease characterized by synovial inflammation and destruction of joint cartilage. The pathogenesis of RA remains unclear, and diagnostic markers with high sensitivity and specificity are needed urgently. This study aims to identify potential biomarkers in the synovium for diagnosing RA and to investigate their association with immune infiltration., Methods: We downloaded four datasets containing 51 RA and 36 healthy synovium samples from the Gene Expression Omnibus database. Differentially expressed genes were identified using R. Then, various enrichment analyses were conducted. Subsequently, weighted gene co-expression network analysis (WGCNA), random forest (RF), support vector machine-recursive feature elimination (SVM-RFE), and least absolute shrinkage and selection operator (LASSO) were used to identify the hub genes for RA diagnosis. Receiver operating characteristic curves and nomogram models were used to validate the specificity and sensitivity of hub genes. Additionally, we analyzed the infiltration levels of 28 immune cells in the expression profile and their relationship with the hub genes using single-sample gene set enrichment analysis., Results: Three hub genes, namely, ribonucleotide reductase regulatory subunit M2 ( RRM2 ), DLG-associated protein 5 ( DLGAP5 ), and kinesin family member 11 ( KIF11 ), were identified through WGCNA, LASSO, SVM-RFE, and RF algorithms. These hub genes correlated strongly with T cells, natural killer cells, and macrophage cells as indicated by immune cell infiltration analysis., Conclusion: RRM2 , DLGAP5 , and KIF11 could serve as potential diagnostic indicators and treatment targets for RA. The infiltration of immune cells offers additional insights into the underlying mechanisms involved in the progression of RA., Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2024 Wu, Liu, Liu, Wu and Xie.)

Wheat (Triticum aestivum) is one of the major crops worldwide and a primary source of calories for human food. Biotic stresses such as fungi, bacteria, and diseases limit wheat production. Although plant breeding and genetic engineering for biotic stress resistance have been suggested as promising solutions to handle losses caused by biotic stress factors, a comprehensive understanding of molecular mechanisms and identifying key genes is a critical step to obtaining success. Here, a network-based meta-analysis approach based on two main statistical methods was used to identify key genes and molecular mechanisms of the wheat response to biotic stress. A total of 163 samples (21,792 genes) from 10 datasets were analyzed. Fisher Z test based on the p-value and REM method based on effect size resulted in 533 differentially expressed genes (p < 0.001 and FDR < 0.001). WGCNA analysis using a dynamic tree-cutting algorithm was used to construct a co-expression network and three significant modules were detected. The modules were significantly enriched by 16 BP terms and 4 KEGG pathways (Benjamini-Hochberg FDR < 0.001). A total of nine hub genes (a top 1.5% of genes with the highest degree) were identified from the constructed network. The identification of DE genes, gene-gene co-expressing network, and hub genes may contribute to uncovering the molecular mechanisms of the wheat response to biotic stress., (© 2022. The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature.)

Background: Pulpitis is a common disease mainly caused by bacteria. Conventional approaches of diagnosing the state of dental pulp are mainly based on clinical symptoms, thereby harbor deficiencies. The accurate and rapid diagnosis of pulpitis is important for choosing the suitable therapy. The study aimed to identify pulpits related key genes by integrating micro-array data analysis and systems biology network-based methods such as weighted gene co-expression network analysis (WGCNA)., Methods: The micro-array data of 13 inflamed pulp and 11 normal pulp were acquired from Gene Expression Omnibus (GEO). WGCNA was utilized to establish a genetic network and categorize genes into diverse modules. Hub genes in the most associated module to pulpitis were screened out using high module group members (MM) methods. Pulpitis model in rat was constructed and iRoot BP plus was applied to cap pulp. Reverse transcription-quantitative polymerase chain reaction (RT-qPCR) was used for validation of hub genes., Results: WGCNA was established and genes were categorized into 22 modules. The darkgrey module had the highest correlation with pulpitis among them. A total of 5 hub genes (HMOX1, LOX, ACTG1, STAT3, GNB5) were identified. RT-qPCR proved the differences in expression levels of HMOX1, LOX, ACTG1, STAT3, GNB5 in inflamed dental pulp. Pulp capping reversed the expression level of HMOX1, LOX, ACTG1., Conclusion: The study was the first to produce a holistic view of pulpitis, screen out and validate hub genes involved in pulpitis using WGCNA method. Pulp capping using iRoot BP plus could reverse partial hub genes., (© 2023. The Author(s).)

Background: DNA methylation plays a vital role as an epigenetic change that contributes to chronic periodontitis., Objective: This study aimed to integrate two methylation datasets (GSE173081 and GSE59962) and two gene expression datasets (GSE10334 and GES16134) to identify abnormally methylated differentially expressed genes related to chronic periodontitis., Methods: Differentially methylated genes were obtained. Functional enrichment analysis of DMGs was performed. The protein-protein interaction (PPI) network was constructed using STRING and Cytoscape software. Finally, the hub genes were selected from the PPI network by using CytoHubba., Results: In total, 122 hypomethylated and highly expressed genes were enriched in the biological mechanisms that are involved in the differentiation of extracellular matrix organization, extracellular structure organization, and cell chemotaxis. The three selected hub genes of the PPI network were IL1B, KDR, and MMP9. A total of 122 hypermethylated and lowly expressed genes were identified, and biological processes, such as cornification, epidermis development, skin development, and keratinocyte differentiation were enriched. CDSN DSG1, and KRT2 were identified as the top 3 hub genes of the PPI network., Conclusion: Based on the comprehensive bioinformatics analysis, six hub genes (IL1B, KDR, MMP9, CDSN DSG1, and KRT2) were associated with chronic periodontitis. Our findings provide novel insights into the mechanisms underlying epigenetic changes in chronic periodontitis.

Background: Pulmonary arterial hypertension (PAH) is characterized by a progressive increase in pulmonary vascular resistance and pulmonary arterial pressure, with complex etiology, difficult treatment and poor prognosis. The objective of this study was to investigate the potential biomarkers for PAH based on bioinformatics analysis., Methods: The GSE117261 datasets were downloaded from the Gene Expression Omnibus database. Differentially expressed genes (DEGs) were identified by screening PAH patients and controls. Then the DEGs were analyzed using a Weighted Gene Co-expression Network Analysis (WGCNA) and the key modules were determined, and to further explore their potential biological functions via Gene Ontology analysis (GO), Kyoto Encyclopedia of Genes and Genomes Pathway analysis (KEGG), and Gene Set Enrichment Analysis (GSEA). Moreover, Protein-protein interaction (PPI) networks were constructed to identify hub gene candidates in the key modules. Finally, real-time quantitative polymerase chain reaction was supplied to detect the expressions of hub genes in human pulmonary arterial smooth cells treated with cobalt chloride (COCl 2 ) which was used to mimic hypoxia., Results: There were 2299 DEGs identified. WGCNA indicated that yellow module was the key one correlated with PAH. GO and KEGG analysis demonstrated that genes in the yellow module were mainly enriched in 'Pathways in cancer'. GSEA revealed that 'HALLMARK&#95;MYC&#95;TARGETS&#95;V1' was remarkably enriched in PAH. Based on the PPI network, vascular endothelial growth factor A, proto-oncogene receptor tyrosine kinase (KIT), PNN interacting serine and arginine rich protein (PNISR) and heterogeneous nuclear ribonucleoprotein H1 (HNRNPH1) were identified as the hub genes. Additionally, the PCR indicated that the elevated expressions of PNISR and HNRNPH1 were in line with the bioinformatics analysis. ROC analysis determined that PNISR and HNRNPH1 may be potential biomarkers to provide better diagnosis of PAH., Conclusion: PNISR and HNRNPH1 were potential biomarkers to diagnosis PAH. In summary, the identified DEGs, modules, pathways, and hub genes provide clues and shed light on the potential molecular mechanisms of PAH., (© 2022. The Author(s).)

Background: Abdominal aortic aneurysm (AAA) is potentially life threatening and characterized by immune-inflammatory cell infiltration and extracellular matrix degradation. Currently, pharmacotherapy mainly aims to control risk factors without reversion of the dilated aorta. This study analyzed the immune-inflammatory response and identified the immune-related hub genes of AAA., Method: Gene Expression Omnibus datasets (GSE57691, GSE47472 and GSE7084) were downloaded. After identification of GSE57691 differentially expressed genes (DEGs), weighted gene co-expression network analysis of the DEGs was performed. Through enrichment analysis of each module and screening in Immunology Database and Analysis Portal, immune-related hub genes were identified via protein-protein interaction (PPI) network construction and lasso regression. CIBERSORT was utilized to analyze AAA immune infiltration. The correlations between the immune-related hub genes and infiltrating immune cells were investigated. Receiver operating characteristic (ROC) curve analysis was performed to determine immune-related hub gene cutoff values, which were validated in GSE47472 and GSE7084., Result: In GSE57691, 1,018 DEGs were identified. Five modules were identified in the co-expression network. The blue and green modules were found to be related to immune-inflammatory responses, and 61 immune-related genes were identified. PPI and lasso regression analyses identified FOS, IL-6 and IL2RB as AAA immune-related hub genes. CIBERSORT analysis indicated significantly increased infiltration of naive B cells, memory activated CD4 T cells, follicular helper T cells, monocytes and M1 macrophages and significantly decreased infiltration of M2 macrophages in AAA compared with normal samples. IL2RB was more strongly associated with immune infiltration in AAA than were FOS and IL6. The IL2RB area under the ROC curve (AUC) value was > 0.9 in both the training and validation set, demonstrating its strong, stable diagnostic value in AAA., Conclusion: AAA and normal samples had different immune infiltration statuses. IL2RB was identified as an immune-related hub gene and a potential hub gene with significant diagnostic value in AAA., (Copyright © 2022 Elsevier B.V. All rights reserved.)

Horseshoe crabs are marine chelicerates that have existed on Earth for about 450 million years, and they are often used as an experimental model for studying marine invertebrate embryology. In this study, we performed transcriptome gene expression profiling of four continuous embryonic stages (Stages 18-21) and first instar larvae of Tachypleus tridentatus. A mean of 50,742,995 high-quality clean reads was obtained from each library. We then conducted weighted gene co-expression network analysis (WGCNA) for 13,698 genes with fragments per kilobase of exon per million mapped fragments values >5. We identified 17 modules, six of which likely play critical roles in development. Kyoto Encyclopedia of Genes and Genomes pathway enrichment analysis of differentially expressed genes was performed on the biologically significant modules. We found that several pathways, such as hedgehog signaling pathway, VEGF signaling pathway, dorso-ventral axis formation, may be involved in the embryonic development process of T. tridentatus. We also identified hub genes that were highly connected in the six critical modules. This is the first study to apply WGCNA to horseshoe crabs to identify hub genes that may play critical roles in development, and our results provide new insight into the mechanisms underlying early development in horseshoe crabs., (Copyright © 2022 Elsevier Inc. All rights reserved.)

Nitrogen metabolism is essential for most cellular activities. Therefore, a deep understanding of its regulatory mechanisms is necessary for the efficient utilization of nitrogen sources for Saccharomyces cerevisiae. In this study, a gene co-expression network was constructed for S. cerevisiae S288C with different nitrogen sources. From this, a key gene co-expression module related to nitrogen source preference utilization was obtained, and 10 hub genes centrally located in the co-expression network were identified. Functional studies verified that the endocytosis-related genes CAP1 and END3 significantly increased the utilization of multiple non-preferred amino acids and reduced the accumulation of the harmful nitrogen metabolite precursor urea by regulating amino acid transporters and TOR pathway. The mitochondria-related gene ATP12, MRPL22, MRP1 and NAM9 significantly increased the utilization of multiple non-preferred amino acids and reduced accumulation of the urea by coordinately regulating nitrogen catabolism repression, Ssy1p-Ptr3p-Ssy5p signaling sensor system, amino acid transporters, TOR pathway and urea metabolism-related pathways. Furthermore, these data revealed the potential positive effects of endocytosis and mitochondrial ribosomes protein translation on nitrogen source preference. This study provides new analytical perspectives for complex regulatory networks involving nitrogen metabolism in S. cerevisiae., (Copyright © 2022. Published by Elsevier B.V.)

Background: Synovial Sarcoma (SS) refers to a malignant Soft Tissue Sarcoma (STS) which often comes about to children and adults and has a poor prognosis in elderly patients. Patients with local lesions can be treated with extensive surgical resection combined with adjuvant or radiotherapy, whereas about half of the cases have recurrent diseases and metastatic lesions, and five-year survival ratio is assessed within the range of 27% - 55% only., Methods: We downloaded a set of expression profile data (GSE40021) related to SS metastasis based on the Gene Expression Omnibus (GEO) database, and selected distinctly represented genes (DEGs) related to tumor metastasis. WGCNA was used to emphasize the DEGs related to tumor metastasis, and obtain co-expression modules. Then, the module most related to SS metastasis was screened out. The genes of enriched in this module were analyzed by Gene Ontology (GO) functional and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway improvement analysis. Cytoscape software was used for constructing protein-protein interaction (PPI) networks, and screening hub genes were made in virtue of Oncomine analysis., Result: We selected 514 DEGs, consisting of 210 up-regulated genes and 304 down-regulated genes. Through WGCAN, we got seven co-expression modules and the module most related to SS metastasis was turquoise module, which contained 66 genes. Finally, we screened out five hub genes (HJURP, NCAPG, TPX2, CENPA, NDC80) through CytoHubba and Oncomine analysis., Conclusion: In this study, we screened out five hub genes to help clinical diagnosis and serve as the latent purpose of SS treatment., (Copyright© Bentham Science Publishers; For any queries, please email at epub@benthamscience.net.)

Background: The pathogenic mechanisms of venous thromboembolism (VT) remain to be defined. This study aimed to identify differentially expressed genes (DEGs) that could serve as potential therapeutic targets for VT., Methods: Two human datasets (GSE19151 and GSE48000) were analyzed by the robust rank aggregation method. Gene ontology and Kyoto encyclopedia of genes and genomes pathway enrichment analyses were conducted for the DEGs. To explore potential correlations between gene sets and clinical features and to identify hub genes, we utilized weighted gene coexpression network analysis (WGCNA) to build gene coexpression networks incorporating the DEGs. Then, the levels of the hub genes were analyzed in the GSE datasets. Based on the expression of the hub genes, the possible pathways were explored by gene set enrichment analysis and gene set variation analysis. Finally, the diagnostic value of the hub genes was assessed by receiver operating characteristic (ROC) analysis in the GEO database., Results: In this study, we identified 54 upregulated and 10 downregulated genes that overlapped between normal and VT samples. After performing WGCNA, the magenta module was the module with the strongest negative correlation with the clinical characteristics. From the key module, FECH, GYPA, RPIA and XK were chosen for further validation. We found that these genes were upregulated in VT samples, and high expression levels were related to recurrent VT. Additionally, the four hub genes might be highly correlated with ribosomal and metabolic pathways. The ROC curves suggested a diagnostic value of the four genes for VT., Conclusions: These results indicated that FECH, GYPA, RPIA and XK could be used as promising biomarkers for the prognosis and prediction of VT., (© 2021. The Author(s).)

Objective: Synovial inflammation influences the progression of osteoarthritis (OA). Herein, we aimed to identify potential biomarkers and analyze transcriptional regulatory-immune mechanism of synovitis in OA using weighted gene coexpression network analysis (WGCNA)., Design: A data set of OA synovium samples (GSE55235) was analyzed based on WGCNA. The most significant module with OA was identified and function annotation of the module was performed, following which the hub genes of the module were identified using Pearson correlation and a protein-protein interaction network was constructed. A transcriptional regulatory network of hub genes was constructed using the TRRUST database. The immune cell infiltration of OA samples was evaluated using the single-sample Gene Set Enrichment Analysis (ssGSEA) method. The hub genes coexpressed in multiple tissues were then screened out using data sets of synovium, cartilage, chondrocyte, subchondral bone, and synovial fluid samples. Finally, transcriptional factors and coexpressed hub genes were validated via experiments., Results: The turquoise module of GSE55235 was identified via WGCNA. Functional annotation analysis showed that "mineral absorption" and "FoxO signaling pathway" were mostly enriched in the module. JUN, EGR1, FOSB, and KLF4 acted as central nodes in protein-protein interaction network and transcription factors to connect several target genes. "Activated B cell," "activated CD4T cell," "eosinophil," "neutrophil," and "type 17 T helper cell" showed high immune infiltration, while FOSB, KLF6, and MYBL2 showed significant negative correlation with type 17 T helper cell., Conclusions: Our results suggest that the expression level of apolipoprotein D (APOD) was correlated with OA. Furthermore, transcriptional regulatory-immune network was constructed, which may contribute to OA therapy.

Objective: Osteoarthritis (OA) is a chronic arthropathy that frequently occurs in the middle-aged and elderly population. The aim of this study was to investigate the molecular mechanism of OA based on autophagy theory., Design: We downloaded the gene expression profile from the Gene Expression Omnibus repository. Differentially expressed genes (DEGs) related to the keyword "autophagy" were identified using the scanGEO online analysis tool. DEGs representing the same expression trend were screened using the MATCH function. Clinical synovial specimens were collected for identification, pathological diagnosis, hematoxylin and eosin staining, and real-time polymerase chain reaction analysis. Differential expression of mRNAs in the synovial membrane tissues and chondrocyte monolayer samples from OA patients was used to identify potential OA biomarkers. Protein-protein interactions were established by the STRING website and visualized with Cytoscape. Functional and pathway enrichment analyses were performed using the Metascape database., Results: GABARAPL1, GABARAPL2, and ATG13 were obtained as co-expressed autogenes in the 3 data sets. They were all downregulated among OA synovial tissues compared with non-OA synovial tissues ( P < 0.01). A protein-protein interaction network was constructed based on these 3 genes and included 63 genes. A functional analysis revealed that these genes were associated with autophagy-related functions. The top hub genes in the protein-protein interaction network were presented. Furthermore, 3 key modules were extracted to be core control modules., Conclusions: These results offer an important molecular understanding of the key transcriptional regulatory genes and modules based on the network of potential autophagy mechanisms in human OA.

Co-expression network may contribute to better understanding molecular interaction patterns underlying cellular processes. To explore microRNAs (miRNAs) expression patterns correlated with AF, we performed weighted gene co-expression network analysis (WGCNA) based on the dataset GSE28954. Thereafter, we predicted target genes using experimentally verified databases (ENOCRI, miRTarBase, and Tarbase), and overlapped genes with differentially expressed genes (DEGs) from GSE79768 were identified as key genes. Integrated analysis of association between hub miRNAs and key genes was conducted to screen hub genes. In general, we identified 3 differentially expressed miRNAs (DEMs) and 320 DEGs, predominantly enriched in inflammation-related functional items. Two significant modules (red and blue) and hub miRNAs (hsa-miR-146b-5p and hsa-miR-378a-5p), which highly correlated with AF-related phenotype, were detected by WGCNA. By overlapping the DEGs and predicted target genes, 38 genes were screened out. Finally, 9 genes (i.e. ATP13A3, BMP2, CXCL1, GABPA, LIF, MAP3K8, NPY1R, S100A12, SLC16A2) located at the core region in the miRNA-gene interaction network were identified as hub genes. In conclusion, our study identified 2 hub miRNAs and 9 hub genes, which may improve the understanding of molecular mechanisms and help to reveal potential therapeutic targets against AF., (© 2021. The Author(s).)

The third leading cause of cancer-related deaths in the world, colorectal cancer (CRC) is a global health issue that should be addressed in both diagnostics and therapeutics to improve patient survival rate. Today, microarray data analysis is increasingly being used as a novel and effective method for classification of malignancies and making prognostic assessments. Built upon the concept of microarray data analysis and aimed at the identification of CRC-associated genes, our study has adopted an integrative analysis for the gene expression patterns of four microarray datasets in gene expression omnibus (GEO) and microRNAs (miRNAs) expression profiles. We downloaded four gene expression profiles, i.e., GSE37182, GSE25070, GSE10950, and GSE113513, miRNAs gene expression profiles and differentially expressed genes (DEGs). We used R software, the DAVID database, protein-protein interaction (PPI) networks, the Cytoscape program and receiver operating characteristic (ROC) curve for data analysis. Out of the four gene expression profiles, a total of 43 common DEGs were identified, including 10 hub genes, SLC26A3, CLCA1, GUCA2A, MS4A12, CLCA4, GUCA2B, KRT20, AQP8, MAOA, and ADH1A, and four differentially expressed miRNAs, miR-552, miR-423-5p, miR-502-3p, and miR-490-5p. The highly enriched modes of the signaling pathways among these DEGs were speculated to be involved in various processes including nitrogen metabolism, mineral absorption, pancreatic secretions, and tyrosine metabolism in Kyoto encyclopedia of genes and genomes (KEGG) database. According to our bioinformatics analysis, the DEGs identified in the present study could be considered as significant hallmarks in the molecular mechanisms of CRC development. Our findings may assist scientists with developing novel strategies not only for prediction of CRC, but also for screening and early diagnosis, and treatment of CRC patients., (© 2021. Springer Science+Business Media, LLC, part of Springer Nature.)

Background: The skin is the largest organ of the body and has multiple functions. Wounds remain a significant healthcare problem due to the large number of traumatic and pathophysiological conditions patients suffer., Methods: Gene expression profiles of 37 biopsies collected from patients undergoing split-thickness skin grafts at five different time points were downloaded from two datasets (GSE28914 and GSE50425) in the Gene Expression Omnibus (GEO) database. Principal component analysis (PCA) was applied to classify samples into different phases. Subsequently, differentially expressed genes (DEGs) analysis, Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway functional enrichment analyses were performed, and protein-protein interaction (PPI) networks created for each phase. Furthermore, based on the results of the PPI, hub genes in each phase were identified by molecular complex detection combined with the ClueGO algorithm., Results: Using principal component analysis, the collected samples were divided into four phases, namely intact phase, acute wound phase, inflammatory and proliferation phase, and remodeling phase. Intact samples were used as control group. In the acute wound phase, a total of 1 upregulated and 100 downregulated DEGs were identified. Tyrosinase (TYR), tyrosinase Related Protein 1 (TYRP1) and dopachrome tautomerase (DCT) were considered as hub genes and enriched in tyrosine metabolism which dominate the process of melanogenesis. In the inflammatory and proliferation phase, a total of 85 upregulated and 164 downregulated DEGs were identified. CHEK1, CCNB1 and CDK1 were considered as hub genes and enriched in cell cycle and P53 signaling pathway. In the remodeling phase, a total of 121 upregulated and 49 downregulated DEGs were identified. COL4A1, COL4A2, and COL6A1 were considered as hub genes and enriched in protein digestion and absorption, and ECM-receptor interaction., Conclusion: This comprehensive bioinformatic re-analysis of GEO data provides new insights into the molecular pathogenesis of wound healing and the potential identification of therapeutic targets for the treatment of wounds.

Background: Despite significant progress in surgical treatment of hypoplastic left heart syndrome (HLHS), its mortality and morbidity are still high. Little is known about the molecular abnormalities of the syndrome. In this study, we aimed to probe into hub genes and key pathways in the progression of the syndrome., Methods: Differentially expressed genes (DEGs) were identified in left ventricle (LV) or right ventricle (RV) tissues between HLHS and controls using the GSE77798 dataset. Then, weighted gene co-expression network analysis (WGCNA) was performed and key modules were constructed for HLHS. Based on the genes in the key modules, protein-protein interaction networks were conducted, and hub genes and key pathways were screened. Finally, the GSE23959 dataset was used to validate hub genes between HLHS and controls., Results: We identified 88 and 41 DEGs in LV and RV tissues between HLHS and controls, respectively. DEGs in LV tissues of HLHS were distinctly involved in heart development, apoptotic signaling pathway and ECM receptor interaction. DEGs in RV tissues of HLHS were mainly enriched in BMP signaling pathway, regulation of cell development and regulation of blood pressure. A total of 16 co-expression network were constructed. Among them, black module (r = 0.79 and p value = 2e-04) and pink module (r = 0.84 and p value = 4e-05) had the most significant correlation with HLHS, indicating that the two modules could be the most relevant for HLHS progression. We identified five hub genes in the black module (including Fbn1, Itga8, Itga11, Itgb5 and Thbs2), and five hub genes (including Cblb, Ccl2, Edn1, Itgb3 and Map2k1) in the pink module for HLHS. Their abnormal expression was verified in the GSE23959 dataset., Conclusions: Our findings revealed hub genes and key pathways for HLHS through WGCNA, which could play key roles in the molecular mechanism of HLHS.

Aims: We aim to provide new insights into the mechanisms of hepatocellular carcinoma (HCC) and identify key genes as biomarkers for the prognosis of HCC. Materials & methods: Differentially expressed genes between HCC tissues and normal tissues were identified via the Gene Expression Omnibus tool. The top ten hub genes screened by the degree of the protein nodes in the protein-protein interaction network also showed significant associations with overall survival in HCC patients. Results: A prognostic model containing a five-gene signature was constructed to predict the prognosis of HCC via multivariate Cox regression analysis. Conclusion: This study identified a novel five-gene signature ( CDK1 , CCNB1 , CCNB2 , BUB1  and KIF11 ) as a significant independent prognostic factor.

Although COVID-19 largely causes respiratory complications, it can also lead to various extrapulmonary manifestations resulting in higher mortality and these comorbidities are posing a challenge to the health care system. Reports indicate that 30-60% of patients with COVID-19 suffer from neurological symptoms. To understand the molecular basis of the neurologic comorbidity in COVID-19 patients, we have investigated the genetic association between COVID-19 and various brain disorders through a systems biology-based network approach and observed a remarkable resemblance. Our results showed 123 brain-related disorders associated with COVID-19 and form a high-density disease-disease network. The brain-disease-gene network revealed five highly clustered modules demonstrating a greater complexity of COVID-19 infection. Moreover, we have identified 35 hub proteins of the network which were largely involved in the protein catabolic process, cell cycle, RNA metabolic process, and nuclear transport. Perturbing these hub proteins by drug repurposing will improve the clinical conditions in comorbidity. In the near future, we assumed that in COVID-19 patients, many other neurological manifestations will likely surface. Thus, understanding the infection mechanisms of SARS-CoV-2 and associated comorbidity is a high priority to contain its short- and long-term effects on human health. Our network-based analysis strengthens the understanding of the molecular basis of the neurological manifestations observed in COVID-19 and also suggests drug for repurposing.

Parkinson's disease (PD) is a progressive neurodegenerative disease affecting the ventral midbrain dopaminergic neurons, resulting in motor defects mainly tremor, rigidity, and bradykinesia along with a wide array of non-motor symptoms. The current study is focused on determining the potential druggable targets of PD by consolidating gene expression profiling and network methodology. Initially, the differentially expressed genes were established from which the central network was constructed by assimilating the interacting partners. Investigating the topological parameters of the network, the genes SYT1, CXCR4, CDC42, KIT, RET, DRD2, NTN1, PRKACB, KDR, NR4A2, SLC18A2, CCK, TH, KCNJ6, and TAC1 were identified as the hub genes and can be explored as potential candidate genes for PD therapeutics. Gene ontology and cluster analysis of the hub genes has provided further insights about the pathophysiology of the disease. Among the hub genes, PRKACB is observed in relatively all the enriched pathways which are modulated by G protein-coupled receptors through protein kinases. Further, we noticed SYT1 as a novel biomarker for PD. Moreover, the regulatory network was constructed with the hub genes as seed nodes with associated transcription factors (TFs) and microRNA (miRNAs). In this analysis, we identified MYC as the major TF and the miRNAs miR-21, miR-155, miR-7, and miR26A1 have a significant role in modulating the hub genes. Briefly, these significant hub genes and their enriched pathways, TFs, and miRNAs have aided in the better understanding of molecular mechanisms underlying PD and its potential core target genes.

Research Question: Is abnormal gene module expression in the eutopic endometrium related to the occurrence of endometriosis?, Design: Nine datasets of normal and eutopic endometrium were searched and collected through the National Center for Biotechnology Information Gene Expression Omnibus, which included genome-wide expression studies of 71 normal cases and 142 endometriosis cases. Surrogate variable analysis was used for dataset integration. The network module and hub genes were selected by weighted gene co-expression network analysis. Machine learning was used to establish a diagnostic model of endometriosis., Results: A gene module that was most relevant to endometriosis was selected through weighted gene co-expression network analysis. After further analysis of this module, four hub genes that represent the function of this module were selected: SCAF11, KRAS, MDM2 and KIF3A. Kyoto Encyclopedia of Genes and Genomes enrichment analysis of the four hub genes revealed that all of them were most highly correlated with genes enriched in the ubiquitin-mediated proteolysis pathway. Moreover, in the correlation analysis between hub genes and Jab1, SCAF11 was found to be closely related to Jab1. Furthermore, hub genes were effective indicators for clinical diagnosis. The deep machine learning diagnostic model based on hub genes was highly sensitive., Conclusions: The gene module identified is highly correlated with endometriosis. The four hub genes in this module degrade p27 kip1 through the ubiquitin-mediated proteolysis pathway to regulate the endometrium cell cycle and affect the development of endometriosis. The hub genes and the deep learning model based on them are valuable for clinical diagnosis., (Copyright © 2020 Reproductive Healthcare Ltd. Published by Elsevier Ltd. All rights reserved.)

The pathogenic mechanisms of Helicobacter pylori infection remain to be defined, and potential interventional microbiota are just beginning to be identified. In this study, gene-set enrichment analysis (GSEA) was used to integrate three H. pylori infection microarray data sets from the gene expression omnibus database and identified ten hallmark gene sets and 35 Kyoto encyclopedia of genes and genomes (KEGG) pathways that differed between healthy and Helicobacter pylori-infected individuals. Weighted gene co-expression network analysis (WGCNA) performed on two of the data sets identified three key gene coexpression modules. These modules contained 54 enriched KEGG pathways, 25 of which overlapped with the GSEA analysis, suggesting potentially important roles in H. pylori-infection. We selected 116 hub genes from the three key modules for in vitro validation at the transcriptional level using H. pylori Sydney Strain 1 and verified the upregulation of 80. WGCNA of the microbiomes based on 20 mucosal samples and a sequence read archive data set revealed four microbiota modules correlated with H. pylori infection. The negatively correlated modules contained 11 microbiome families. These findings provide new insight into the pathogenesis of H. pylori infection and systematically identify 25 key pathways, 80 upregulated hub genes, and 11 families of candidate interventional microbiota for further research., (© 2020 Wiley Periodicals LLC.)

Psoriasis is a common chronic inflammatory skin disease affecting >125 million individuals worldwide. The therapeutic course for the disease is generally designed upon the severity of the disease. In the present study, the gene expression profile GSE78097, was retrieved from the National Centre of Biotechnology (NCBI)‑Gene Expression Omnibus (GEO) database to explore the differentially expressed genes (DEGs) in mild and severe psoriasis using the Affy package in R software. The Kyoto Encyclopaedia of Genes and Genomes (KEGG) pathways of the DEGs were analysed using clusterProfiler, Bioconductor, version 3.8. In addition, the STRING database was used to develop DEG‑encoded proteins and a protein‑protein interaction network (PPI). Cytoscape software, version 3.7.1 was utilized to construct a protein interaction association network and analyse the interaction of the candidate DEGs encoding proteins in psoriasis. The top 2 hub genes in Cytohubba plugin parameters were validated using immunohistochemical analysis in psoriasis tissues. A total of 382 and 3,001 dysregulated mild and severe psoriasis DEGs were reported, respectively. The dysregulated mild psoriasis genes were enriched in pathways involving cytokine‑cytokine receptor interaction and rheumatoid arthritis, whereas cytokine‑cytokine receptor interaction, cell cycle and cell adhesion molecules were the most enriched pathways in severe psoriasis group. PL1N1, TLR4, ADIPOQ, CXCL8, PDK4, CXCL1, CXCL5, LPL, AGT, LEP were hub genes in mild psoriasis, whereas BUB1, CCNB1, CCNA2, CDK1, CDH1, VEGFA, PLK1, CDC42, CCND1 and CXCL8 were reported hub genes in severe psoriasis. Among these, CDC42, for the first time (to the best of our knowledge), has been reported in the psoriasis transcriptome, with its involvement in the adaptive immune pathway. Furthermore, the immunoexpression of CDK1 and CDH1 proteins in psoriasis skin lesions were demonstrated using immunohistochemical analysis. On the whole, the findings of the present integrated bioinformatics and immunohistochemical study, may enhance our understanding of the molecular events occurring in psoriasis, and these candidate genes and pathways together may prove to be therapeutic targets for psoriasis vulgaris.

Heterotopic ossification (HO) is the formation of abnormal mature lamellar bone in extra-skeletal sites, including soft tissues and joints, which result in high rates of disability. The understanding of the mechanism of HO is insufficient. The aim of this study was to explore biomarkers and pathological processes in HO+ samples. The gene expression profile GSE94683 was downloaded from the Gene Expression Omnibus database. Sixteen samples from nine HO- and seven HO+ subjects were analyzed. After data preprocessing, 3,529 genes were obtained for weighted gene co-expression network analysis. Highly correlated genes were divided into 13 modules. Finally, the cyan and purple modules were selected for further study. Gene ontology functional annotation and Kyoto Encyclopedia of Genes and Genomes pathway enrichment indicated that the cyan module was enriched in a variety of components, including protein binding, membrane, nucleoplasm, cytosol, poly(A) RNA binding, biosynthesis of antibiotics, carbon metabolism, endocytosis, citrate cycle, and metabolic pathways. In addition, the purple module was enriched in cytosol, mitochondrion, protein binding, structural constituent of ribosome, rRNA processing, oxidative phosphorylation, ribosome, and non-alcoholic fatty liver disease. Finally, 10 hub genes in the cyan module [actin related protein 3 ( ACTR3 ), ADP ribosylation factor 4 ( ARF4 ), progesterone receptor membrane component 1 ( PGRMC1 ), ribosomal protein S23 ( RPS23 ), mannose-6-phosphate receptor ( M6PR ), WD repeat domain 12 ( WDR12 ), synaptosome associated protein 23 ( SNAP23 ), actin related protein 2 ( ACTR2 ), siah E3 ubiquitin protein ligase 1 ( SIAH1 ), and glomulin ( GLMN )] and 2 hub genes in the purple module [proteasome 20S subunit alpha 3 ( PSMA3 ) and ribosomal protein S27 like ( RPS27L )] were identified. Hub genes were validated through quantitative real-time polymerase chain reaction. In summary, 12 hub genes were identified in two modules that were associated with HO. These hub genes could provide new biomarkers, therapeutic ideas, and targets in HO., Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2020 Wang, Tian, Wang, Liu, Ke, Shang, Yang and Wang.)

Insights into the potential candidate hub genes may facilitate the generation of safe and effective immunity against seasonal influenza as well as the development of personalized influenza vaccines for the elderly at high risk of influenza virus infection. This study aimed to identify the potential hub genes related to the immune induction process of the 2018/19 seasonal quadrivalent inactivated influenza vaccines (QIVs) in the elderly ≥60 years by using weighted gene co-expression network analysis (WGCNA). From 63 whole blood samples from16 elderly individuals, a total of 13,345 genes were obtained and divided into eight co-expression modules, with two modules being significantly correlated with vaccine-induced immune responses. After functional enrichment analysis, genes under GO terms of vaccine-associated immunity were used to construct the sub-network for identification and functional validation of hub genes. MCEMP1 and SPARC were confirmed as the hub genes with an obvious effect on QIVs-induced immunity. The MCEMP1 expression was shown to be negatively correlated with the QIVs-associated reactogenicity within 7 days after vaccination, which could be suppressed by the CXCL 8/IL-8 and exacerbated by the Granzyme-B cytotoxic mediator. Meanwhile, the SPARC expression was found to increase the immune responses to the QIVs and contribute to the persistence of protective humoral antibody titers. These two genes can be used to predict QIVs-induced adverse reaction, the intensity of immune responses, and the persistence of humoral antibody against influenza. This work has shed light on further research on the development of personalized QIVs with appropriate immune responses and long-lasting immunity against the forthcoming seasonal influenza., Competing Interests: JY is studying for his doctorate at Wuhan Institute of Biological Products Co., Ltd. JYZ, WZ, KD, and XL are employees in the company Wuhan Institute of Biological Products Co., Ltd. TH is studying for a master’s degree at Wuhan Institute of Biological Products Co., Ltd. XY is an employee of the company China Biotechnology Co., Ltd. The remaining authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2020 Yang, Zhang, Fan, Zhao, Han, Duan, Li, Zeng, Deng, Zhang and Yang.)

We explored potential biomarkers and molecular mechanisms regarding multiple benefits after bariatric surgery. Differentially expressed genes (DEGs) for subcutaneous adipose tissue (AT) after bariatric surgery were identified by analyzing two expression profiles from the GEO. Subsequently, enrichment analysis, GSEA, PPI network, and gene-microRNAs and gene-TFs networks were interrogated to identify hub genes and associated pathways. Co-expressed DEGs included one that was up-regulated and 22 that were down-regulated genes. The enrichment analyses indicated that down-regulated DEGs were significantly involved in inflammatory responses. GSEA provided comprehensive evidence that most genes enriched in pro-inflammation pathways, while gene-sets after surgery enriched in metabolism. We identified nine hub genes in the PPI network, most of which were validated as highly expressed and hypomethylated in obesity by Attie Lab Diabetes and DiseaseMeth databases, respectively. DGIdb was also applied to predict potential therapeutic agents that might reverse abnormally high hub gene expression. Bariatric surgery induces a significant shift from an obese pro-inflammatory state to an anti-inflammatory state, with improvement in adipocyte metabolic function - representing key mechanisms whereby AT function improves after bariatric surgery. Our study deepens a mechanistic understanding of the benefits of bariatric surgery and provides potential biomarkers or treatment targets for further research.

Background: Intracranial aneurysm (IA) is a disease resulted from weak brain control, characterized by local expansion or dilation of brain artery. This study aimed to construct a gene co-expression network by Weighted Gene Correlation Network Analysis (WGCNA) to explore the potential key pathways and genes for the development of IA. Method: Six IA-related gene expression data sets were downloaded from the Gene Expression Omnibus (GEO) database for identifying differentially expressed genes (DEGs). WGCNA was used to identify modules associated with IA. Functional enrichment analysis was used to explore the potential biological functions. ROC analysis was used to find markers for predicting IA. Results: Purple, greenyellow and yellow modules were significantly associated with unruptured intracranial aneurysms, while blue and turquoise modules were significantly associated with ruptured intracranial aneurysms. Functional modules significantly related to IA were enriched in Ribosome, Glutathione metabolism, cAMP signalling pathway, Lysosome, Glycosaminoglycan degradation and other pathways. CD163, FCEREG, FPR1, ITGAM, NLRC4, PDG, and TYROBP were up-regulated ruptured intracranial aneurysms and serum, these genes were potential circulating markers for predicting IA rupture. Conclusions: Potential IA-related key pathways, genes and circulating markers were identified for predicting IA rupture by WGCNA analysis.

Because the pathogenesis of major depressive disorder (MDD) is still unclear and the accurate diagnosis remains unavailable, we aimed to analyze its molecular mechanisms and develop a gene classifier to improve diagnostic accuracy. We extracted differentially expressed genes from two datasets, GSE45642 (from brain tissue samples) and GSE98793 (from blood samples), and found three key modules to have a significant correlation with MDD traits by weighted gene coexpression network analysis. Hub genes were identified from the key modules according to the connectivity degree in the network and subjected to least absolute shrinkage and selection operator regression analysis. A total of eighty-five hub genes were selected to construct the gene classifier, which had considerable ability to recognize MDD patients in the training set and test set. In addition, the relationship between the key MDD modules and brain tissues indicated that the anterior cingulate should be a notable region in the study of MDD pathogenesis. The results of Gene Ontology (GO) and pathway enrichment analyses reiterate the relationship between depression and immunity. Therefore we identified MDD hub genes in the InnateDB database, and found 14 genes involved in both MDD and the inflammatory response., (Copyright © 2020 Elsevier B.V. All rights reserved.)

An effective feature extraction method is key to improving the accuracy of a prediction model. From the Gene Expression Omnibus (GEO) database, which includes 13,487 genes, we obtained microarray gene expression data for 238 samples from colorectal cancer (CRC) samples and normal samples. Twelve gene modules were obtained by weighted gene co-expression network analysis (WGCNA) on 173 samples. By calculating the Pearson correlation coefficient (PCC) between the characteristic genes of each module and colorectal cancer, we obtained a key module that was highly correlated with CRC. We screened hub genes from the key module by considering module membership, gene significance, and intramodular connectivity. We selected 10 hub genes as a type of feature for the classifier. We used the variational autoencoder (VAE) for 1159 genes with significantly different expressions and mapped the data into a 10-dimensional representation, as another type of feature for the cancer classifier. The two types of features were applied to the support vector machines (SVM) classifier for CRC. The accuracy was 0.9692 with an AUC of 0.9981. The result shows a high accuracy of the two-step feature extraction method, which includes obtaining hub genes by WGCNA and a 10-dimensional representation by variational autoencoder (VAE).