Co-expression network analysis of diverse wheat landraces reveals markers of early thermotolerance and a candidate master regulator of thermotolerance genes.

Triticum aestivum L. (bread wheat) is a crop relied upon by billions of people around the world, as a major source of both income and calories. Rising global temperatures, however, pose a genuine threat to the livelihood of these people, as wheat growth and yields are extremely vulnerable to damage by heat stress. Here we present the YoGI wheat landrace panel, comprising 342 accessions that show remarkable phenotypic and genetic diversity thanks to their adaptation to different climates. We quantified the abundance of 110 790 transcripts from the panel and used these data to conduct weighted co-expression network analysis and to identify hub genes in modules associated with abiotic stress tolerance. We found that the expression of three hub genes, all heat-shock proteins (HSPs), were significantly correlated with early thermotolerance in a validation panel of landraces. These hub genes belong to the same module, with one (TraesCS4D01G207500.1) being a candidate master-regulator potentially controlling the expression of the other two hub genes, as well as a suite of other HSPs and heat-stress transcription factors (HSFs). In this work, therefore, we identify three validated hub genes, the expression of which can serve as markers of thermotolerance during early development, and suggest that TraesCS4D01G207500.1 is a potential master regulator of HSP and HSF expression - presenting the YoGI landrace panel as an invaluable tool for breeders wishing to determine and introduce novel alleles into modern varieties, for the production of climate-resilient crops.

RNA sequencing identifies key genes involved in intramuscular fat deposition in chickens at different developmental stages.

BACKGROUND: Intramuscular fat (IMF) is an important factor in meat quality, and triglyceride (TG) and Phospholipids (PLIP), as the main components of IMF, are of great significance to the improvement of meat quality. RESULTS: In this study, we used 30 RNA sequences generated from the transcriptome of chicken breast muscle tissues at different developmental stages to construct a gene expression matrix to map RNA sequence reads to the chicken genome and identify the transcript of origin. We used weighted gene co-expression network analysis (WGCNA) and identified 27 co-expression modules, 10 of which were related to TG and PLIP. We identified 150 highly-connected hub genes related to TG and PLIP, respectively, which were found to be mainly enriched in the adipocytokine signaling pathway, MAPK signaling pathway, mTOR signaling pathway, FoxO signaling pathway, and TGF-beta signaling pathway. Additionally, using the BioMart database, we identified 134 and 145 candidate genes related to fat development in the TG-related module and PLIP-related module, respectively. Among them, RPS6KB1, BRCA1, CDK1, RPS3, PPARGC1A, ACSL1, NDUFAB1, NDUFA9, ATP5B and PRKAG2 were identified as candidate genes related to fat development and highly-connected hub genes in the module, suggesting that these ten genes may be important candidate genes affecting IMF deposition. CONCLUSIONS: RPS6KB1, BRCA1, CDK1, RPS3, PPARGC1A, ACSL1, NDUFAB1, NDUFA9, ATP5B and PRKAG2 may be important candidate genes affecting IMF deposition. The purpose of this study was to identify the co-expressed gene modules related to chicken IMF deposition using WGCNA and determine key genes related to IMF deposition, so as to lay a foundation for further research on the molecular regulation mechanism underlying chicken fat deposition.

Knockdown of CENPM activates cGAS-STING pathway to inhibit ovarian cancer by promoting pyroptosis.

OBJECTIVE: We aimed to screen novel gene signatures for ovarian cancer (OC) and explore the role of biomarkers in OC via regulating pyroptosis using bioinformatics analysis. METHODS: Differentially expressed genes (DEGs) of OC were screened from GSE12470 and GSE16709 datasets. Hub genes were determined from protein-protein interaction networks after bioinformatics analysis. The role of Centromeric protein M (CENPM) in OC was assessed by subcutaneous tumor experiment using hematoxylin-eosin and immunohistochemical staining. Tumor metastasis was evaluated by detecting epithelial-mesenchymal transition-related proteins. The proliferation, migration, and invasion were determined using cell counting kit and transwell assay. Enzyme-linked immunosorbent assay was applied to measure inflammatory factors. The mRNA and protein expression were detected using real-time quantitative PCR and western blot. RESULTS: We determined 9 hub genes (KIFC1, PCLAF, CDCA5, KNTC1, MCM3, OIP5, CENPM, KIF15, and ASF1B) with high prediction value for OC. In SKOV3 and A2780 cells, the expression levels of hub genes were significantly up-regulated, compared with normal ovarian cells. CENPM was selected as a key gene. Knockdown of CENPM suppressed proliferation, migration, and invasion of OC cells. Subcutaneous tumor experiment revealed that CENPM knockdown significantly suppressed tumor growth and metastasis. Additionally, pyroptosis was promoted in OC cells and xenograft tumors after CENPM knockdown. Furthermore, CENPM knockdown activated cGAS-STING pathway and the pathway inhibitor reversed the inhibitory effect of CENPM knockdown on viability, migration, and invasion of OC cells. CONCLUSION: CENPM was a novel biomarker of OC, and knockdown of CENPM inhibited OC progression by promoting pyroptosis and activating cGAS-STING pathway.

Comprehensive exploration of hub genes involved in oxidative stress in rhegmatogenous retinal detachment based on bioinformatics analysis.

Rhegmatogenous retinal detachment (RRD) is a type of ophthalmologic emergency, if left untreated, the blindness rate approaches 100 %. The RRD patient postoperative recovery of visual function is unsatisfactory, most notably due to photoreceptor death. We conducted to identify the key genes for oxidative stress (OS) in RRD through bioinformatics analysis and clinical validation, thus providing new ideas for the recovery of visual function in RRD patients after surgery. A gene database for RRD was obtained from the Gene Expression Omnibus (GEO) database (GSE28133). Then we screened differentially expressed OS genes (DEOSGs) from the database and assessed the critical pathways in RRD with Gene ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway. Protein-protein interaction (PPI) networks and hub genes among the common DEOSGs were identified. In addition, we collected general information and vitreous fluid from 42 patients with RRD and 22 controls [11 each of epiretinal membrane (EM) and macular hole (MH)], examined the expression levels of proteins encoded by hub genes in vitreous fluid by enzyme-linked immunosorbent assay (ELISA) to further assess the relationship between the ELISA data and the clinical characteristics of patients with RRD. Ten hub genes (CCL2, ICAM1, STAT3, CD4, ITGAM, PTPRC, CCL5, IL18, TLR2, VCAM1) were finally screened out from the dataset. The ELISA results showed that, compared with the control group, patients with RRD: TLR2 and ICAM-1 were significantly elevated, and CCL2 had a tendency to be elevated, but no statistically significant; RRD patients and MH patients compared with EM patients: STAT3 and VCAM-1 were significantly elevated. We found affected eyes of RRD patients compared with healthy eyes: temporal and nasal retinal nerve fiber layer (RNFL) were significantly thickened. By correlation analysis, we found that: STAT3 was negatively correlated with ocular perfusion pressure (OPP); temporal RNFL was not only significantly positively correlated with CCL2, but also negatively correlated with Scotopic b-wave amplitude. These findings help us to further explore the mechanism of RRD development and provide new ideas for finding postoperative visual function recovery.

RNA sequencing of the thalamus and rostral ventral medulla in rats with chronic orofacial pain.

Diagnosing and treating chronic orofacial pain is challenging due to its complex structure and limited understanding of its causes and mechanisms. In this study, we used RNA sequencing to identify differentially expressed genes (DEGs) in the rostral ventral medulla (RVM) and thalamus of rats with persistent orofacial pain, aiming to explore its development. DEGs were functionally analyzed using gene ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis. Results showed a significant association between immune response and pain in this model. Key DEG mRNA expression trends were further validated using real-time quantitative polymerase chain reaction (RT-PCR), confirming their crucial roles in chronic orofacial pain. After injecting complete Freund's adjuvant (CFA) into the bilateral temporomandibular joint cavity for 14 days, we observed 293 upregulated genes and 14 downregulated genes in the RVM, and 1086 upregulated genes and 37 downregulated genes in the thalamus. Furthermore, we identified 27 common DEGs with altered expression (upregulation) in both the thalamus and RVM, including Cd74, C3, Cxcl13, C1qb, Itgal, Fcgr2b, C5ar1, and Tlr2, which are pain-associated genes. Protein-protein interaction (PPI) analysis using Cytoscape revealed the involvement of Toll-like receptors, complement system, differentiation clusters, and antigen presentation-related proteins in the interaction between the thalamus and RVM. The results of this study show that the immune system seems to have a more significant influence on chronic orofacial pain. There may be direct or indirect influence between the thalamus and RVM, which may participate in the regulation of chronic orofacial pain.

Predictive biomarkers for response to TGF- ß inhibition in resensitizing chemo(radiated) esophageal adenocarcinoma.

Epithelial-mesenchymal transition (EMT) has been identified as a driver of therapy resistance, particularly in esophageal adenocarcinoma (EAC), where transforming growth factor beta (TGF-ß) can induce this process. Inhibitors of TGF-ß may counteract the occurrence of mesenchymal, resistant tumor cell populations following chemo(radio)therapy and improve treatment outcomes in EAC. Here, we aimed to identify predictive biomarkers for the response to TGF-ß targeting. In vitro approximations of neoadjuvant treatment were applied to publicly available primary EAC cell lines. TGF-ß inhibitors fresolimumab and A83-01 were employed to inhibit EMT, and mesenchymal markers were quantified via flow cytometry to assess efficacy. Our results demonstrated a robust induction of mesenchymal cell states following chemoradiation, with TGF-ß inhibition leading to variable reductions in mesenchymal markers. The cell lines were clustered into responders and non-responders. Genomic expression profiles were obtained through RNA-seq analysis. Differentially expressed gene (DEG) analysis identified 10 positively- and 23 negatively-associated hub genes, which were bioinformatically identified. Furthermore, the correlation of DEGs with response to TGF-ß inhibition was examined using public pharmacogenomic databases, revealing 9 positively associated and 11 negatively associated DEGs. Among these, ERBB2, EFNB1, and TNS4 were the most promising candidates. Our findings reveal a distinct gene expression pattern associated with the response to TGF-ß inhibition in chemo(radiated) EAC. The identified DEGs and predictive markers may assist patient selection in clinical studies investigating TGF-ß targeting.

Genetic correlations, shared risk genes and immunity landscapes between COVID-19 and venous thromboembolism: evidence from GWAS and bulk transcriptome data.

BACKGROUND: Patients with coronavirus disease 2019 (COVID-19) were vulnerable to venous thromboembolism (VTE), which further increases the risk of unfavorable outcomes. However, neither genetic correlations nor shared genes underlying COVID-19 and VTE are well understood. OBJECTIVE: This study aimed to characterize genetic correlations and common pathogenic mechanisms between COVID-19 and VTE. METHODS: We used linkage disequilibrium score (LDSC) regression and Mendelian Randomization (MR) analysis to investigate the genetic associations and causal effects between COVID-19 and VTE, respectively. Then, the COVID-19 and VTE-related datasets were obtained from the Gene Expression Omnibus (GEO) database and analyzed by bioinformatics and systems biology approaches with R software, including weighted gene co-expression network analysis (WGCNA), enrichment analysis, and single-cell transcriptome sequencing analysis. The miRNA-genes and transcription factor (TF)-genes interaction networks were conducted by NetworkAnalyst. We performed the secondary analysis of the ATAC-seq and Chip-seq datasets to address the epigenetic-regulating relationship of the shared genes. RESULTS: This study demonstrated positive correlations between VTE and COVID-19 by LDSC and bidirectional MR analysis. A total of 26 potential shared genes were discovered from the COVID-19 dataset (GSE196822) and the VTE dataset (GSE19151), with 19 genes showing positive associations and 7 genes exhibiting negative associations with these diseases. After incorporating two additional datasets, GSE164805 (COVID-19) and GSE48000 (VTE), two hub genes TP53I3 and SLPI were identified and showed up-regulation and diagnostic capabilities in both illnesses. Furthermore, this study illustrated the landscapes of immune processes in COVID-19 and VTE, revealing the downregulation in effector memory CD8+ T cells and activated B cells. The single-cell sequencing analysis suggested that the hub genes were predominantly expressed in the monocytes of COVID-19 patients at high levels. Additionally, we identified common regulators of hub genes, including five miRNAs (miR-1-3p, miR-203a-3p, miR-210-3p, miR-603, and miR-124-3p) and one transcription factor (RELA). CONCLUSIONS: Collectively, our results highlighted the significant correlations between COVID-19 and VTE and pinpointed TP53I3 and SLPI as hub genes that potentially link the severity of both conditions. The hub genes and their common regulators might present an opportunity for the simultaneous treatment of these two diseases.

Identifying Potential Diagnostic and Therapeutic Targets for Infantile Hemangioma Using WGCNA and Machine Learning Algorithms.

Infantile hemangioma (IH) is the most common benign vascular tumor during infancy and childhood and is characterized by abnormal vascular development. It is the most common vascular tumor and its related mechanisms and treatments remain a problem. IH-related biomarkers have been identified using transcriptome analysis and can be used to predict clinical outcomes. This study aimed to identify the key target genes for IH treatment and explore their possible roles in the IH pathophysiology. Gene records were acquired from the Gene Expression Omnibus database. Utilizing integrated weighted gene co-expression network examination, gene clusters were determined. Single-sample gene set enrichment analysis was performed to gauge immune infiltration. Essential genes were identified via Random Forest and Least Absolute Selection and Shrinkage Operator analyses. Ultimately, a set of five pivotal genes associated with the ailment was identified (NETO2, IDO1, KDR, MEG3, and TMSB15A). A nomogram for predicting IH diagnosis was constructed based on hub genes. The calibration curve showed valid agreement between the prediction and conclusion that the key genes in the model were clinically significant. Neuropilin and Tolloid-like 2 (NETO2) are closely associated with tumor development. The role value of NETO2 expression levels increased in hemangioma-derived endothelial cells (HemECs). After silencing NETO2, the growth and migration of cancer cells were significantly restrained. This study revealed the critical role of NETO2 in IH development, suggesting that targeting NETO2 may be effective in improving the therapeutic outcome of IH.

Genes and Pathways Underpinning Klinefelter Syndrome at Bulk and Single-Cell Levels.

Klinefelter syndrome (KS) is the most frequent genetic anomaly in infertile men. Given its unclear mechanism, we aim to investigate critical genes and pathways in the pathogenesis of KS based on three bulk and one single-cell transcriptome data sets from Gene Expression Omnibus. We merged two data sets (GSE42331 and GSE47584) with human KS whole blood samples. When comparing the control and KS samples, five hub genes, including defensin alpha 4 (DEFA4), bactericidal permeability increasing protein (BPI), myeloperoxidase (MPO), intelectin 1 (ITLN1), and Xg Glycoprotein (XG), were identified. Besides, infiltrated degree of certain immune cells such as CD56bright NK cell were positively associated with the expression of ITLN1 and XG. Kyoto Encyclopedia of Genes and Genomes analysis identified upregulated phosphatidylinositol 3-kinase (PI3K)/AKT pathway in KS. Gene set enrichment analysis followed by gene set variation analysis confirmed the upregulation of G2M checkpoint and heme metabolism in KS. Thereafter, the GSE200680 data set was used for external validation of the expression variation of hub genes from healthy to KS testicular samples, and each hub gene yielded excellent discriminatory capability for KS without exception. At the single-cell level, the GSE136353 data set was utilized to evaluate intercellular communication between different cell types in KS patient, and strong correlations were detected between macrophages/ dendritic cells/ NK cells and the other cell types. Collectively, we provided hub genes, pathways, immune cell infiltration degree, and cell-cell communication in KS, warranting novel insights into the pathogenesis of this disease.

Identification of Hub Genes in Liver Hepatocellular Carcinoma Based on Weighted Gene Co-expression Network Analysis.

Liver hepatocellular carcinoma (LIHC) is a malignant cancer with high incidence and poor prognosis. To investigate the correlation between hub genes and progression of LIHC and to provided potential prognostic markers and therapy targets for LIHC. Our study mainly used The Cancer Genome Atlas (TCGA) LIHC database and the gene expression profiles of GSE54236 from the Gene Expression Omnibus (GEO) to explore the differential co-expression genes between LIHC and normal tissues. The differential co-expression genes were extracted by Weighted Gene Co-expression Network Analysis (WGCNA) and differential gene expression analysis methods. The Genetic Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) were carried out to annotate the function of differential genes. Then the hub genes were validated using protein-protein interaction (PPI) network. And the expression level and prognostic analysis were performed. The probable associations between the expression of hub genes and both tumor purity and infiltration of immune cells were explored by TIMER. A total of 68 differential co-expression genes were extracted. These genes were mainly enriched in complement activation (biological process), collagen trimer (cellular component), carbohydrate binding and receptor ligand activity (molecular function) and cytokine - cytokine receptor interaction. Then we demonstrated that the 10 hub genes (CFP, CLEC1B, CLEC4G, CLEC4M, FCN2, FCN3, PAMR1 and TIMD4) were weakly expressed in LIHC tissues, the qRT-PCR results of clinical samples showed that six genes were significantly downregulated in LIHC patients compared with adjacent tissues. Worse overall survival (OS) and disease-free survival (DFS) in LIHC patients were associated with the lower expression of CFP, CLEC1B, FCN3 and TIMD4. Ten hub genes had positive association with tumor purity. CFP, CLEC1B, FCN3 and TIMD4 could serve as novel potential molecular targets for prognosis prediction in LIHC.

Transcriptomic Hallmarks of Hypoxic-Ischemic Brain Injury: Insights from an in Vitro Model.

BACKGROUND: Hypoxic-ischemic injury of neurons is a pathological process observed in several neurological conditions, including ischemic stroke and neonatal hypoxic-ischemic brain injury (HIBI). An optimal treatment strategy for these conditions remains elusive. The present study delved deeper into the molecular alterations occurring during the injury process in order to identify potential therapeutic targets. METHODS: Oxygen-glucose deprivation/reperfusion (OGD/R) serves as an established in vitro model for the simulation of HIBI. This study utilized RNA sequencing to analyze rat primary hippocampal neurons that were subjected to either 0.5 or 2 h of OGD, followed by 0, 9, or 18 h of reperfusion. Differential expression analysis was conducted to identify genes dysregulated during OGD/R. Time-series analysis was used to identify genes exhibiting similar expression patterns over time. Additionally, functional enrichment analysis was conducted to explore their biological functions, and protein-protein interaction (PPI) network analyses were performed to identify hub genes. Quantitative real-time polymerase chain reaction (qRT-PCR) was used for validation of hub-gene expression. RESULTS: The study included a total of 24 samples. Analysis revealed distinct transcriptomic alterations after OGD/R processes, with significant dysregulation of genes such as Txnip, Btg2, Egr1 and Egr2. In the OGD process, 76 genes, in two identified clusters, showed a consistent increase in expression; functional analysis showed involvement of inflammatory responses and signaling pathways like tumor necrosis factor (TNF), nuclear factor kappa-light-chain-enhancer of activated B cells (NF-κB), and interleukin 17 (IL-17). PPI network analysis suggested that Ccl2, Jun, Cxcl1, Ptprc, and Atf3 were potential hub genes. In the reperfusion process, 274 genes, in three clusters, showed initial upregulation followed by downregulation; functional analysis suggested association with apoptotic processes and neuronal death regulation. PPI network analysis identified Esr1, Igf-1, Edn1, Hmox1, Serpine1, and Spp1 as key hub genes. qRT-PCR validated these trends. CONCLUSIONS: The present study provides a comprehensive transcriptomic profile of an in vitro OGD/R process. Key hub genes and pathways were identified, offering potential targets for neuroprotection after hypoxic ischemia.

Immune-Related Molecules CD3G and FERMT3: Novel Biomarkers Associated with Sepsis.

Sepsis ranks among the most common health problems worldwide, characterized by organ dysfunction resulting from infection. Excessive inflammatory responses, cytokine storms, and immune-induced microthrombosis are pivotal factors influencing the progression of sepsis. Our objective was to identify novel immune-related hub genes for sepsis through bioinformatic analysis, subsequently validating their specificity and potential as diagnostic and prognostic biomarkers in an animal experiment involving a sepsis mice model. Gene expression profiles of healthy controls and patients with sepsis were obtained from the Gene Expression Omnibus (GEO) and analysis of differentially expressed genes (DEGs) was conducted. Subsequently, weighted gene co-expression network analysis (WGCNA) was used to analyze genes within crucial modules. The functional annotated DEGs which related to the immune signal pathways were used for constructing protein-protein interaction (PPI) analysis. Following this, two hub genes, FERMT3 and CD3G, were identified through correlation analyses associated with sequential organ failure assessment (SOFA) scores. These two hub genes were associated with cell adhesion, migration, thrombosis, and T-cell activation. Furthermore, immune infiltration analysis was conducted to investigate the inflammation microenvironment influenced by the hub genes. The efficacy and specificity of the two hub genes were validated through a mice sepsis model study. Concurrently, we observed a significant negative correlation between the expression of CD3G and IL-1ß and GRO/KC. These findings suggest that these two genes probably play important roles in the pathogenesis and progression of sepsis, presenting the potential to serve as more stable biomarkers for sepsis diagnosis and prognosis, deserving further study.

Discovering common pathogenetic processes between periodontitis and Alzheimer's disease by bioinformatics and system biology approach.

BACKGROUND: There is increasing evidence that inflammation plays a key role in the pathophysiology of periodontitis (PT) and Alzheimer's disease (AD), but the roles of inflammation in linking PT and AD are not clear. Our aim is to analyze the potential molecular mechanisms between these two diseases using bioinformatics and systems biology approaches. METHODS: To elucidate the link between PT and AD, we selected shared genes (SGs) with gene-disease-association scores of ≥ 0.1 from the Disease Gene Network (DisGeNET) database, followed by extracting the hub genes. Based on these genes, we constructed gene ontology (GO) enrichment analysis, pathway enrichment analysis, protein-protein interaction (PPI) networks, transcription factors (TFs)-gene networks, microRNAs (miRNAs)-gene regulatory networks, and gene-disease association analyses. Finally, the Drug Signatures database (DSigDB) was utilized to predict candidate molecular drugs related to hub genes. RESULTS: A total of 21 common SGs between PT and AD were obtained. Cell cytokine activity, inflammatory response, and extracellular membrane were the most important enriched items in GO analysis. Interleukin-10 Signaling, LTF Danger Signal Response Pathway, and RAGE Pathway were identified as important shared pathways. IL6, IL10, IL1B, TNF, IFNG, CXCL8, CCL2, MMP9, TLR4 were identified as hub genes. Both shared pathways and hub genes are closely related to endoplasmic reticulum (ER) stress and mitochondrial dysfunction. Importantly, glutathione, simvastatin, and dexamethasone were identified as important candidate drugs for the treatment of PT and AD. CONCLUSIONS: There is a close link between PT and AD pathogenesis, which may involve in the inflammation, ER and mitochondrial function.

Prediction of interactomic hub genes in PBMC cells in type 2 diabetes mellitus, dyslipidemia, and periodontitis.

BACKGROUND AND OBJECTIVE: In recent years, the complex interplay between systemic health and oral well-being has emerged as a focal point for researchers and healthcare practitioners. Among the several important connections, the convergence of Type 2 Diabetes Mellitus (T2DM), dyslipidemia, chronic periodontitis, and peripheral blood mononuclear cells (PBMCs) is a remarkable example. These components collectively contribute to a network of interactions that extends beyond their domains, underscoring the intricate nature of human health. In the current study, bioinformatics analysis was utilized to predict the interactomic hub genes involved in type 2 diabetes mellitus (T2DM), dyslipidemia, and periodontitis and their relationships to peripheral blood mononuclear cells (PBMC) by machine learning algorithms. MATERIALS AND METHODS: Gene Expression Omnibus datasets were utilized to identify the genes linked to type 2 diabetes mellitus(T2DM), dyslipidemia, and Periodontitis (GSE156993).Gene Ontology (G.O.) Enrichr, Genemania, and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways were used for analysis for identification and functionalities of hub genes. The expression of hub D.E.G.s was confirmed, and an orange machine learning tool was used to predict the hub genes. RESULT: The decision tree, AdaBoost, and Random Forest had an A.U.C. of 0.982, 1.000, and 0.991 in the R.O.C. curve. The AdaBoost model showed an accuracy of (1.000). The findings imply that the AdaBoost model showed a good predictive value and may support the clinical evaluation and assist in accurately detecting periodontitis associated with T2DM and dyslipidemia. Moreover, the genes with p-value < 0.05 and A.U.C.>0.90, which showed excellent predictive value, were thus considered hub genes. CONCLUSION: The hub genes and the D.E.G.s identified in the present study contribute immensely to the fundamentals of the molecular mechanisms occurring in the PBMC associated with the progression of periodontitis in the presence of T2DM and dyslipidemia. They may be considered potential biomarkers and offer novel therapeutic strategies for chronic inflammatory diseases.

Identification of potential core genes in pseudoexfoliation using bioinformatics analysis and retrospective analysis of 84 patients with this disease.

BACKGROUND: Pseudoexfoliation (XFS) is a common cause of glaucoma in nowadays. Because of XFS causing irreversible blindness secondary to glaucoma (XFG), this study aims to identify the current prevalence of XFS among Xinjiang Province of China, and identify the hub genes involved in XFS. METHODS: A retrospective chart review was conducted from 2007 to 2019 for patients aged 50 and older. All patients with XFS or XFG diagnosed by slit lamp exam were identified through chart review. RESULTS: Of the 84 patient charts available for review, 50% of the patients identified as male, with a mean age of 67 years. The top ten genes evaluated by connectivity degree in the PPI network were identified. The results showed that Tyrobp was the most outstanding gene, followed by Ptprc, Fcgr3, Itgb2, Emr1, Cd68, Syk, Fcerlg, Hck, and Lyz2. All of these hub genes were downregulated in XFS. CONCLUSION: Our findings show a considerably biomarkers of XFS for diagnosis and treatment.

Intratumoral immune heterogeneity of prostate cancer characterized by typing and hub genes.

Discordant abundances of different immune cell subtypes is regarded to be an essential feature of tumour tissue. Direct studies in Prostate cancer (PC) of intratumoral immune heterogeneity characterized by immune cell subtype, are still lacking. Using the single sample gene set enrichment analysis (ssGSEA) algorithm, the abundance of 28 immune cells infiltration (ICI) were determined for PC. A NMF was performed to determine tumour-sample clustering based on the abundance of ICI and PFS information. Hub genes of clusters were identified via weighted gene co-expression network analysis (WGCNA). The multivariate dimensionality reduction analysis of hub genes expression matrix was carried out via principal component analysis (PCA) to obtain immune score (IS). We analysed the correlation between clustering, IS and clinical phenotype. We divided the 495 patients into clusterA (n = 193) and clusterB (n = 302) on the basis of ICI and PFS via NMF. The progression-free survival (PFS) were better for clusterA than for clusterB (p < 0.001). Each immune cell subtypes was more abundant in clusterA than in clusterB (p < 0.001). The expression levels of CTAL-4 and PD-L1 were lower in clusterB than in clusterA (p < 0.001 and p = 0.006). We obtained 103 hub genes via WGCNA. In the training and validation cohorts, the prognosis of high IS group was worse than that of the low IS group (p < 0.05). IS had good predictive effect on 5-year PFS. The expression of immune checkpoint genes was higher in the low IS group than in the high IS group (p < 0.01). Patients with low IS and receiving hormone therapy had better prognosis than other groups. The combination of IS and clinical characteristics including lymph node metastasis and gleason score can better differentiate patient outcomes than using it alone. IS was a practical algorithm to predict the prognosis of patients. Advanced PC patients with low IS may be more sensitive to hormone therapy. CXCL10, CXCL5, MMP1, CXCL12, CXCL11, CXCL2, STAT1, IL-6 and TLR2 were hub genes, which may drive the homing of immune cells in tumours and promote immune cell differentiation.

Weighted gene co-expression network analysis identified hub genes critical to fatty acid composition in Gushi chicken breast muscle.

BACKGROUND: The composition and content of fatty acids in the breast muscle are important factors influencing meat quality. In this study, we investigated the fatty acid composition and content in the breast muscle of Gushi chickens at different developmental stages (14 weeks, 22 weeks, and 30 weeks). Additionally, we utilized transcriptomic data from the same tissue and employed WGCNA and module identification methods to identify key genes associated with the fatty acid composition in Gushi chicken breast muscle and elucidate their regulatory networks. RESULTS: Among them, six modules (blue, brown, green, light yellow, purple, and red modules) showed significant correlations with fatty acid content and metabolic characteristics. Enrichment analysis revealed that these modules were involved in multiple signaling pathways related to fatty acid metabolism, including fatty acid metabolism, PPAR signaling pathway, and fatty acid biosynthesis. Through analysis of key genes, we identified 136 genes significantly associated with fatty acid phenotypic traits. Protein-protein interaction network analysis revealed that nine of these genes were closely related to fatty acid metabolism. Additionally, through correlation analysis of transcriptome data, we identified 51 key ceRNA regulatory networks, including six central genes, 7 miRNAs, and 28 lncRNAs. CONCLUSION: This study successfully identified key genes closely associated with the fatty acid composition in Gushi chicken breast muscle, as well as their post-transcriptional regulatory networks. These findings provide new insights into the molecular regulatory mechanisms underlying the flavor characteristics of chicken meat and the composition of fatty acids in the breast muscle.

Identification of hub genes based on integrated analysis of single-cell and microarray transcriptome in patients with pulmonary arterial hypertension.

BACKGROUND: Pulmonary arterial hypertension (PAH) is a devastating chronic cardiopulmonary disease without an effective therapeutic approach. The underlying molecular mechanism of PAH remains largely unexplored at single-cell resolution. METHODS: Single-cell RNA sequencing (scRNA-seq) data from the Gene Expression Omnibus (GEO) database (GSE210248) was included and analyzed comprehensively. Additionally, microarray transcriptome data including 15 lung tissue from PAH patients and 11 normal samples (GSE113439) was also obtained. Seurat R package was applied to process scRNA-seq data. Uniform manifold approximation and projection (UMAP) was utilized for dimensionality reduction and cluster identification, and the SingleR package was performed for cell annotation. FindAllMarkers analysis and ClusterProfiler package were applied to identify differentially expressed genes (DEGs) for each cluster in GSE210248 and GSE113439, respectively. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genome (KEGG) were used for functional enrichment analysis of DEGs. Microenvironment Cell Populations counter (MCP counter) was applied to evaluate the immune cell infiltration. STRING was used to construct a protein-protein interaction (PPI) network of DEGs, followed by hub genes selection through Cytoscape software and Veen Diagram. RESULTS: Nineteen thousand five hundred seventy-six cells from 3 donors and 21,896 cells from 3 PAH patients remained for subsequent analysis after filtration. A total of 42 cell clusters were identified through UMAP and annotated by the SingleR package. 10 cell clusters with the top 10 cell amounts were selected for consequent analysis. Compared with the control group, the proportion of adipocytes and fibroblasts was significantly reduced, while CD8+ T cells and macrophages were notably increased in the PAH group. MCP counter revealed decreased distribution of CD8+ T cells, cytotoxic lymphocytes, and NK cells, as well as increased infiltration of monocytic lineage in PAH lung samples. Among 997 DEGs in GSE113439, module 1 with 68 critical genes was screened out through the MCODE plug-in in Cytoscape software. The top 20 DEGs in each cluster of GSE210248 were filtered out by the Cytohubba plug-in using the MCC method. Eventually, WDR43 and GNL2 were found significantly increased in PAH and identified as the hub genes after overlapping these DEGs from GSE210248 and GSE113439. CONCLUSION: WDR43 and GNL2 might provide novel insight into revealing the new molecular mechanisms and potential therapeutic targets for PAH.

Potential network markers and signaling pathways for B cells of COVID-19 based on single-cell condition-specific networks.

To explore the potential network markers and related signaling pathways of human B cells infected by COVID-19, we performed standardized integration and analysis of single-cell sequencing data to construct conditional cell-specific networks (CCSN) for each cell. Then the peripheral blood cells were clustered and annotated based on the conditional network degree matrix (CNDM) and gene expression matrix (GEM), respectively, and B cells were selected for further analysis. Besides, based on the CNDM of B cells, the hub genes and 'dark' genes (a gene has a significant difference between case and control samples not in a gene expression level but in a conditional network degree level) closely related to COVID-19 were revealed. Interestingly, some of the 'dark' genes and differential degree genes (DDGs) encoded key proteins in the JAK-STAT pathway, which had antiviral effects. The protein p21 encoded by the 'dark' gene CDKN1A was a key regulator for the COVID-19 infection-related signaling pathway. Elevated levels of proteins encoded by some DDGs were directly related to disease severity of patients with COVID-19. In short, the proteins encoded by 'dark' genes complement some missing links in COVID-19 and these signaling pathways played an important role in the growth and activation of B cells.

Transcriptional landscape of cholangiocarcinoma revealed by weighted gene coexpression network analysis.

Cholangiocarcinoma (CCA) is a type of cancer with limited treatment options and a poor prognosis. Although some important genes and pathways associated with CCA have been identified, the relationship between coexpression and phenotype in CCA at the systems level remains unclear. In this study, the relationships underlying the molecular and clinical characteristics of CCA were investigated by employing weighted gene coexpression network analysis (WGCNA). The gene expression profiles and clinical features of 36 patients with CCA were analyzed to identify differentially expressed genes (DEGs). Subsequently, the coexpression of DEGs was determined by using the WGCNA method to investigate the correlations between pairs of genes. Network modules that were significantly correlated with clinical traits were identified. In total, 1478 mRNAs were found to be aberrantly expressed in CCA. Seven coexpression modules that significantly correlated with clinical characteristics were identified and assigned representative colors. Among the 7 modules, the green and blue modules were significantly related to tumor differentiation. Seventy-eight hub genes that were correlated with tumor differentiation were found in the green and blue modules. Survival analysis showed that 17 hub genes were prognostic biomarkers for CCA patients. In addition, we found five new targets (ISM1, SULT1B1, KIFC1, AURKB and CCNB1) that have not been studied in the context of CCA and verified their differential expression in CCA through experiments. Our results not only promote our understanding of the relationship between the transcriptome and clinical data in CCA but will also guide the development of targeted molecular therapy for CCA.