Co-expression Modules Research Articles

Metabolic subtypes and immune landscapes in esophageal squamous cell carcinoma: prognostic implications and potential for personalized therapies

BackgroundThis study aimed to identify metabolic subtypes in ESCA, explore their relationship with immune landscapes, and establish a metabolic index for accurate prognosis assessment.MethodsClinical, SNP, and RNA-seq data were collected from 80 ESCA patients from the TCGA database and RNA-seq data from the GSE19417 dataset. Metabolic genes associated with overall survival (OS) and progression-free survival (PFS) were selected, and k-means clustering was performed. Immune-related pathways, immune infiltration, and response to immunotherapy were predicted using bioinformatic algorithms. Weighted gene co-expression network analysis (WGCNA) was conducted to identify metabolic genes associated with co-expression modules. Lastly, cell culture and functional analysis were performed using patient tissue samples and ESCA cell lines to verify the identified genes and their roles.ResultsMolecular subtypes were identified based on the expression profiles of metabolic genes, and univariate survival analysis revealed 163 metabolic genes associated with ESCA prognosis. Consensus clustering analysis classified ESCA samples into three distinct subtypes, with MC1 showing the poorest prognosis and MC3 having the best prognosis. The subtypes also exhibited significant differences in immune cell infiltration, with MC3 showing the highest scores. Additionally, the MC3 subtype demonstrated the poorest response to immunotherapy, while the MC1 subtype was the most sensitive. WGCNA analysis identified gene modules associated with the metabolic index, with SLC5A1, NT5DC4, and MTHFD2 emerging as prognostic markers. Gene and protein expression analysis validated the upregulation of MTHFD2 in ESCA. MTHFD2 promotes the progression of ESCA and may be a potential therapeutic target for ESCA.ConclusionThe established metabolic index and identified metabolic genes offer potential for prognostic assessment and personalized therapeutic interventions for ESCA, underscoring the importance of targeting metabolism-immune interactions in ESCA. MTHFD2 promotes the progression of ESCA and may be a potential therapeutic target for ESCA.

Identify novel gene signatures in atrial fibrillation by comprehensive bioinformatics analysis

Background: Atrial fibrillation (AF), one of the most prevalent heart rhythm disorders, may lead to thromboembolism, heart failure, and sudden death. However, the mechanism of AF has not yet been fully explained. Objective: This study aims to identify novel gene signatures and to investigate the potential therapeutic targets of AF with an integrated bioinformatic approach. Methods: The gene expression and methylation datasets of AF were obtained through the Gene Expression Omnibus (GEO) database. Subsequently, a set of differentially expressed genes and differential methylation sites were identified. Gene functional annotation analysis was conducted to explore the potential function of differentially-methylated/expressed genes. Then, we constructed a PPI network and TF–miRNA–mRNA network. Finally, weighted gene co-expression network analysis (WGCNA) was presented to study critical modules of AF. Results: Seven hypomethylated-high expression genes and nine hypermethylated-low expression genes were acquired from AF patients. Functional enrichment results indicated that the differentially-methylated/expressed genes were mainly concentrated in decidualization, maternal placenta development, regulation of nitric-oxide synthase activity, and osteoclast differentiation. Based on the results of the PPI, we defined 4 key genes namely FHL2, STC2, ALPK3, and RAP1GAP2 as the core genes, playing essential roles in the TF-miRNA-mRNA network. In the end, we constructed two co-expression modules that highly correlated with AF-related phenotype. Conclusion: In our study, we found critical genes for AF that might help understand the molecular changes in AF.

Identification of important modules and biomarkers in tuberculosis based on WGCNA.

Tuberculosis (TB) is a significant public health concern, particularly in China. Long noncoding RNAs (lncRNAs) can provide abundant pathological information regarding etiology and could include candidate biomarkers for diagnosis of TB. However, data regarding lncRNA expression profiles and specific lncRNAs associated with TB are limited. We performed ceRNA-microarray analysis to determine the expression profile of lncRNAs in peripheral blood mononuclear cells (PBMCs). Weighted gene co-expression network analysis (WGCNA) was then conducted to identify the critical module and genes associated with TB. Other bioinformatics analyses, including Kyoto Encyclopedia of Genes and Genomes (KEGG), Gene Ontology (GO), and co-expression networks, were conducted to explore the function of the critical module. Finally, real-time quantitative polymerase chain reaction (qPCR) was used to validate the candidate biomarkers, and receiver operating characteristic analysis was used to assess the diagnostic performance of the candidate biomarkers. Based on 8 TB patients and 9 healthy controls (HCs), a total of 1,372 differentially expressed lncRNAs were identified, including 738 upregulated lncRNAs and 634 downregulated lncRNAs. Among all lncRNAs and mRNAs in the microarray, the top 25% lncRNAs (3729) and top 25% mRNAs (2824), which exhibited higher median expression values, were incorporated into the WGCNA. The analysis generated 16 co-expression modules, among which the blue module was highly correlated with TB. GO and KEGG analyses showed that the blue module was significantly enriched in infection and immunity. Subsequently, considering module membership values (>0.85), gene significance values (>0.90) and fold-change value (>2 or < 0.5) as selection criteria, the top 10 upregulated lncRNAs and top 10 downregulated lncRNAs in the blue module were considered as potential biomarkers. The candidates were then validated in an independent validation sample set (31 TB patients and 32 HCs). The expression levels of 8 candidates differed significantly between TB patients and HCs. The lncRNAs ABHD17B (area under the curve [AUC] = 1.000) and ENST00000607464.1 (AUC = 1.000) were the best lncRNAs in distinguishing TB patients from HCs. This study characterized the lncRNA profiles of TB patients and identified a significant module associated with TB as well as novel potential biomarkers for TB diagnosis.

Identification of diagnostic biomarkers correlate with immune infiltration in extra-pulmonary tuberculosis by integrating bioinformatics and machine learning.

The diagnosis of tuberculosis depends on detecting Mycobacterium tuberculosis (Mtb). Unfortunately, recognizing patients with extrapulmonary tuberculosis (EPTB) remains challenging due to the insidious clinical presentation and poor performance of diagnostic tests. To identify biomarkers for EPTB, the GSE83456 dataset was screened for differentially expressed genes (DEGs), followed by a gene enrichment analysis. One hundred and ten DEGs were obtained, mainly enriched in inflammation and immune -related pathways. Weighted gene co-expression network analysis (WGCNA) was used to identify 10 co-expression modules. The turquoise module, correlating the most highly with EPTB, contained 96 DEGs. Further screening with the least absolute shrinkage and selection operator (LASSO) and support vector machine recursive feature elimination (SVM-RFE) narrowed down the 96 DEGs to five central genes. All five key genes were validated in the GSE144127 dataset. CARD17 and GBP5 had high diagnostic capacity, with AUC values were 0.763 (95% CI: 0.717-0.805) and 0.833 (95% CI: 0.793-0.869) respectively. Using single sample gene enrichment analysis (ssGSEA), we evaluated the infiltration of 28 immune cells in EPTB and explored their relationships with key genes. The results showed 17 immune cell subtypes with significant infiltrations in EPTB. CARD17, GBP5, HOOK1, LOC730167, and HIST1H4C were significantly associated with 16, 14, 12, 6, and 4 immune cell subtypes, respectively. The RT-qPCR results confirmed that the expression levels of GBP5 and CARD17 were higher in EPTB compared to control. In conclusion, CARD17 and GBP5 have high diagnostic efficiency for EPTB and are closely related to immune cell infiltration.

Integrative multi-omics analyses reveal vesicle transport as a potential target for thoracic aortic aneurysm

BackgroundThoracic aortic aneurysm (TAA) refers to dilation and enlargement of the thoracic aorta caused by various reasons. Most patients have no apparent symptoms in the early stage and are subject to a poor prognosis once the aneurysm ruptures. It is crucial to identify individuals who are predisposed to TAA and to discover effective therapeutic targets for early intervention. MethodsWe conducted a label-free quantitative proteomic analysis among aorta tissue samples from TAA patients to screen differentially expressed proteins (DEPs) and key co-expression modules. Two datasets from Gene Expression Omnibus (GEO) database were included for integrative analysis, and the identified genes were subjected to immunohistochemistry (IHC) validation. Detailed vesicle transport related enrichment analysis was conducted and two FDA-approved drugs, chlorpromazine (CPZ) and chloroquine (CQ), were selected for in vivo inhibition of vesicle transport in mice TAA model. The diameter of thoracic aorta, mortality and histological differences after interventions were evaluated. ResultsWe found significant enrichments in functions involved with vesicle transport, extracellular matrix organizing, and infection diseases in TAA. Endocytosis was the most essential vesicle transport process in TAA formation. Interventions with CPZ and CQ significantly reduced the aneurysm diameter and elastin degradation in vivo and enhanced the survival rates of TAA mice. ConclusionsWe systematically screened the aberrantly regulated bioprocesses in TAA based on integrative multi-omics analyses, identified and demonstrated the importance of vesicle transport in the TAA formation. Our study provided pilot evidence that vesicular transport was a potential and promising target for the treatment of TAA.

Molecular Pathogenic Mechanisms of IgA Nephropathy Secondary to COVID-19 mRNA Vaccination

Introduction: Accumulating evidence has disclosed that IgA nephropathy (IgAN) could present shortly after the second dose of COVID-19 mRNA vaccine. However, the undying mechanism remains unclear and we aimed to investigate the potential molecular mechanisms. Methods: We downloaded gene expression datasets of COVID-19 mRNA vaccination (GSE201535) and IgAN (GSE104948). Weighted Gene Co-Expression Network Analysis (WGCNA) was performed to identify co-expression modules related to the second dose of COVID-19 mRNA vaccination and IgAN. Differentially expressed genes (DEGs) were screened, and a transcription factor (TF)-miRNA regulatory network and protein-drug interaction were constructed for the shared genes. Results: WGCNA identified one module associated with the second dose of COVID-19 mRNA vaccine and four modules associated with IgAN. Gene ontology (GO) analyses revealed enrichment of cell cycle-related processes for the COVID-19 mRNA vaccine hub genes and immune effector processes for the IgAN hub genes. We identified 74 DEGs for the second dose of COVID-19 mRNA vaccine and 574 DEGs for IgAN. Intersection analysis with COVID-19 vaccine-related genes led to the identification of two shared genes, TOP2A and CEP55. The TF-miRNA network analysis showed that hsa-miR-144 and ATF1 might regulate the shared hub genes. Conclusions: This study provides insights into the common pathogenesis of COVID-19 mRNA vaccination and IgAN. The identified pivotal genes may offer new directions for further mechanistic studies of IgAN secondary to COVID-19 mRNA vaccination.

In-depth analysis of immune cell landscapes reveals differences between lung adenocarcinoma and lung squamous cell carcinoma

BackgroundLung cancer is the leading cause of cancer deaths globally, with lung adenocarcinoma (LUAD) and squamous cell carcinoma (LUSC) being major subtypes. Immunotherapy has emerged as a promising approach for the treatment of lung cancer, but understanding the underlying mechanisms of immune dysregulation is crucial for the development of effective therapies. This study aimed to investigate the distinctive cellular features of LUAD and LUSC and identify potential biomarkers associated with the pathogenesis and clinical outcomes of each subtype.MethodsWe used digital cytometry techniques to analyze the RNA-Seq data of 1128 lung cancer patients from The Cancer Genome Atlas (TCGA) database. The abundance of cell subtypes and ecotypes in LUAD and LUSC patients was quantified. Univariate survival analysis was used to investigate their associations with patient overall survival (OS). Differential gene expression analysis and gene co-expression network construction were carried out to explore the gene expression patterns of LUSC patients with distinct survival outcomes. Scratch wound-healing assay, colony formation assay, and transwell assay were used to validate the candidate drugs for LUSC treatment.ResultsWe found differential expression of cell subtypes between LUAD and LUSC, with certain cell subtypes being prognostic for survival in both subtypes. We also identified differential gene expression and gene co-expression modules associated with macrophages.3/PCs.2 ratio in LUSC patients with distinct survival outcomes. Furthermore, ecotype ratios were found to be prognostic in both subtypes and machine learning models showed that certain cell subtypes, such as epithelial.cells.1, epithelial.cells.5, and endothelial.cells.2 are important for predicting LUSC. Ginkgolide B and triamterene can inhibit the proliferation, invasion, and migration of LUSC cell lines.ConclusionWe provide insight into the distinctive cellular features of LUAD and LUSC, and identify potential biomarkers associated with the pathogenesis and clinical outcomes of each subtype. Ginkgolide B and triamterene could be promising drugs for LUSC treatment.

Exploration of Positive and Negative Schizophrenia Symptom Heterogeneity and Establishment of Symptom-Related miRNA-mRNA Regulatory Network: Based on Transcriptome Sequencing Data.

Schizophrenia (SCZ) symptoms can be classified as positive and negative ones, each of which has distinct traits and possibly differences in gene expression and regulation. The co-expression networks linked to PANSS (Positive and Negative Syndrome Scale) scores were identified by weighted gene co-expression network analysis (WGCNA) using the expression profiles of miRNA and mRNA in the peripheral blood of first-episode SCZ patients. The heterogeneity between positive and negative symptoms was demonstrated using gene functional enrichment, gene-medication interaction, and immune cell composition analysis. Then, target gene prediction and correlation analysis of miRNA and mRNA constructed a symptom-related miRNA-mRNA regulatory network, screened regulatory pairs, and predicted binding sites. A total of six mRNA co-expression modules, two miRNA co-expression modules, and ten hub genes were screened to be significantly associated with positive symptoms; five mRNA co-expression modules and eight hub genes were correlated with negative symptoms. Positive symptom-related modules were significantly enriched in axon guidance, actin skeleton regulation, and sphingolipid signaling pathway, while negative symptom-related modules were significantly enriched in adaptive immune response, leukocyte migration, dopaminergic synapses, etc. The development of positive symptoms may have been influenced by potential regulatory pairings such as miR-98-5p-EIF3J, miR-98-5p-SOCS4, let-7b-5p-CLUH, miR-454-3p-GTF2H1, and let-7b-5p-SNX17. Additionally, immune cells were substantially connected with several hub genes for symptoms. Positive and negative symptoms in SCZ individuals were heterogeneous to some extent. miRNAs such as let-7b-5p and miR-98-5p might contribute to the incidence of positive symptoms by targeting mRNAs, while the immune system's role in developing negative symptoms may be more nuanced.

P081 The integration of transcriptomic and microbiomic data links several intestinal bacterial families with key biological functions in the development of postoperative recurrence in Crohn's disease

Abstract Background Crohn's disease (CD) is characterized by an imbalance between the microbiota and the host. The severity of CD leads to intestinal resection. However, more than half of the patients not receiving prophylaxis experience post-operative recurrence (POR) within one year. The main objective of this study has been to improve the knowledge of the pathophysiological bases of POR through the integration of transcriptome and microbiome data. Methods Samples of inflamed and microscopically non-inflamed ileal mucosa were collected from patients with CD undergoing intestinal resection. The severity of POR was classified according to the Rutgeerts endoscopic index: without recurrence (i0+i1, n=12) and with recurrence (i2+i3+i4, n=8). Samples of healthy ileal tissue were also obtained from patients with colorectal cancer (n=10). Gene expression was evaluated by microarrays and organized into co-expression modules. By sequencing the V3-V4 region of the 16S rRNA gene, intestinal bacteria were identified and classified at the genus and family taxonomic level. The differential expression and correlations between the two sources of omics information were studied. Next, the analysis of pathway enrichment and clustering was carried out according to the severity of the POR and the intestinal area affected or not (graphical abstract A). Results The results of the integrative analysis in the uninflamed area of patients without recurrence showed that Actinomycetales negatively correlated with COX6A1 and MTRF1L, related to mitochondrial respiration (GO:0098803; q=0.004) and protein synthesis (R-HSA-72766; q=0.034) (B). These genes and bacteria also played a key role in modules 3 and 25 related to the respiratory chain. In contrast, the inflamed area of recurrent patients showed a positive correlation of the Xanthomonadaceae family in the regulation of neutrophil granules (GO:0035579; q=0.011), with the key participation of the ITGAM transcript (C). This transcript was also related to integrins (R-HSA-216083; q=0.007) in the transcriptomic analyses. In addition, Porphyromonadaceae correlated with LSR and TFF1, involved in epithelium maintenance (GO:0010669; q=0.012) and A group in the Lachnospiradaceae family correlated with CDC20 and CDT1, linked to the cell cycle (q=0.019). Conclusion Bacterial families, involved in butyrate production, such as Porphyromonadaceae and Lachnospiradaceae, could influence functions that remember to epithelial regeneration in recurrent patients, while Actinomycetales are associated with a normalization in energy production in uninflamed areas of resections of patients without POR. These findings reveal a potential interaction between the microbiota and the pathophysiology of POR in patients with CD.

Identification and Verification of Hub Mitochondrial Dysfunction Genes in Osteoarthritis Based on Bioinformatics Analysis.

Age-related mitochondrial dysfunction and associated oxidative stress may contribute to the development of osteoarthritis. The aim of this study was to identify hub genes associated with mitochondrial dysfunction in osteoarthritis (OA) patients, helping predict the risk of OA, and revealing the mechanism of OA progression. OA expression data and mitochondrial dysfunction genes were downloaded from GEO (GSE55235, GSE82107, and GSE114007) and GeneCard databases. The differentially expressed mitochondrial dysfunction genes (DEMDFGs) between OA and control samples were screened. Gene ontology (GO) and Kyoto encyclopedia of genes and genomes pathways were analyzed for DEMDFGs. The hub genes were determined by WGCNA and LASSO regression analysis. ROC curves manifested the diagnostic efficacy of each hub gene. A nomogram model was constructed and validated to predict OA risk. The expression of hub genes in OA and normal chondrocytes was verified by external datasets, qRT-PCR and western blotting. A total of 31 DEMDFGs were identified, with 15 genes upregulated and 16 genes downregulated. GO functional enrichment analysis revealed that DEMDFGs were enriched in biological processes related to energy metabolism and cellular respiration. By employing weighted gene coexpression network analysis, we identified four distinct coexpression modules, among which the blue module exhibited the strongest correlation with OA. The intersection between DEMDFGs and this module yielded eight candidate genes. After LASSO analysis of the data, four hub genes (ACADL, CYBA, SLC19A2, and UCP2) were identified as potential biomarkers for OA. The expression levels of these four genes were externally validated in the GSE114007 dataset. And the biologically differential expression of these four genes has been verified in OA and normal chondrocytes. Moreover, the four hub genes had good sensitivity and specificity by ROC curve analysis, and the risk model constructed with these four genes showed promising performance. In conclusion, our study may provide novel mitochondrial dysfunction hub genes with potential clinical applications for understanding the pathology, diagnosis, and treatment of OA.

Dysconnectivity of the brain functional network and abnormally expressed peripheral transcriptional profiles in patients with anxious depression

BackgroundMajor depressive disorder (MDD) is a heterogeneous mental disorder, and accompanying anxiety symptoms, known as anxious depression (AD), are the most common subtype. However, the pathophysiology of AD may be distinct in depressed patients without anxiety (NAD) and remains unknown. This study aimed to investigate the relationship between functional connectivity and peripheral transcriptional profiles in patients with AD and NAD. MethodsFunctional imaging data were collected to identify differences in functional networks among patients with AD (n = 66), patients with NAD (n = 115), and healthy controls (HC, n = 200). The peripheral transcriptional data were clustered as co-expression modules, and their associations with AD, AND, and HC were analyzed. The gene ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) functional enrichment analyses of the genes in the significant module were performed. Correlation analysis was performed to identify functional network-associated gene co-expression modules. ResultsA network was identified which consisted of 23 nodes and 28 edges that were significantly different among three sample groups. The regions of the network were located in temporal and occipital lobe. Two gene co-expression modules were shown to be associated with NAD, and one of which was correlated with the disrupted network in the AD group. The biological function of this module was enriched in immune regulation pathways. ConclusionThe results suggested that immune-related mechanisms were associated with functional networks in AD.

RNA-Seq-Based WGCNA and Association Analysis Reveal the Key Regulatory Module and Genes Responding to Salt Stress in Wheat Roots.

Soil salinization is the main abiotic stressor faced by crops. An improved understanding of the transcriptional response to salt stress in roots, the organ directly exposed to a high salinity environment, can inform breeding strategies to enhance tolerance and increase crop yield. Here, RNA-sequencing was performed on the roots of salt-tolerant wheat breeding line CH7034 at 0, 1, 6, 24, and 48 h after NaCl treatment. Based on transcriptome data, a weighted gene co-expression network analysis (WGCNA) was constructed, and five gene co-expression modules were obtained, of which the blue module was correlated with the time course of salt stress at 1 and 48 h. Two GO terms containing 249 differentially expressed genes (DEGs) related to osmotic stress response and salt-stress response were enriched in the blue module. These DEGs were subsequently used for association analysis with a set of wheat germplasm resources, and the results showed that four genes, namely a Walls Are Thin 1-related gene (TaWAT), an aquaporin gene (TaAQP), a glutathione S-transfer gene (TaGST), and a zinc finger gene (TaZFP), were associated with the root salt-tolerance phenotype. Using the four candidate genes as hub genes, a co-expression network was constructed with another 20 DEGs with edge weights greater than 0.6. The network showed that TaWAT and TaAQP were mainly co-expressed with fifteen interacting DEGs 1 h after salt treatment, while TaGST and TaZFP were mainly co-expressed with five interacting DEGs 48 h after salt treatment. This study provides key modules and candidate genes for understanding the salt-stress response mechanism in wheat roots.

Identification of diagnostic signatures for ischemic stroke by machine learning algorithm

ObjectiveIschemic stroke (IS) is one of the major diseases threatening human health and survival and a leading cause of acquired mortality and disability in adults. The aim of this study was to screen diagnostic features of IS and to explore the characteristics of immune cell infiltration in IS pathogenesis. MethodsThe microarray data of IS (GSE16561, GSE58294, GSE37587, and GSE124026) in the GEO database were merged after removing the batch effect. Then integrated bioinformatic analysis and machine-learning strategies were adopted to analyze the functional correlation and select diagnostic signatures. The WGCNA was used to identify the co-expression modules related to IS. The CIBERSORT algorithm was performed to assess the inflammatory state of IS and to investigate the correlation between diagnostic signatures and infiltrating immune cells. ResultsFunctional analysis of dysregulated genes showed that immune response-regulating signaling pathway and pattern recognition receptor activity were enriched in the pathophysiology of IS. The turquoise module was identified as the significant module with IS. By using Lasso and SVM-RFE learning methods, we finally obtained four diagnostic genes, including LAMP2, CR1, CLEC4E, and F5. The corresponding results of AUC of ROC prediction model in training and validation cohort were 0.954 and 0.862, respectively. The immune cell infiltration analysis suggested that plasma cells, resting and activated NK cells, activated dendritic cells, memory B cells, CD8+ T cells, naïve CD4+ T cells, and resting mast cells may be involved in the development of IS. Additionally, these diagnostic signatures might be correlated with multiple immune cells in varying degrees. ConclusionWe identified four biologically relevant genes (LAMP2, CR1, CLEC4E, and F5) with diagnostic effects for IS, our results further provide novel insights regarding molecular mechanisms associated with various immune cells that related to IS for future investigations.

Abnormal functional connectivity within the prefrontal cortex is associated with multiple plasma lipid species in major depressive disorder

BackgroundAbnormalities in functional connectivity (FC) in major depressive disorder (MDD) have been widely reported. Analysis of the relationship between FC and plasma lipid profiles would be meaningful in the exploration of pathophysiological mechanisms and helpful for the identification of biomarkers for MDD. MethodsPatients with MDD (n = 49) and healthy controls (HC, n = 87) were recruited. Resting-state functional magnetic resonance imaging (rs-fMRI) data were collected for FC construction. The plasma lipid profiles were acquired using ultra-performance liquid chromatography (UPLC) and mass spectrometry (MS) analysis and clustered as co-expression modules. The differential FC and lipid modules between HCs and patients with MDD were identified, and then the association between FC and lipid co-expression modules was analyzed using correlation analysis. The modules associated molecular function was explored using metabolite set enrichment analysis (MSEA). ResultsMDD-associated FC and lipid co-expression modules were identified. One module was associated with FC values between the right orbital part of the middle frontal gyrus and the opercular part of the left inferior frontal gyrus, which was enriched in lipid sets of diacylglycerols and fatty alcohols; another module was associated with FC values between the right middle frontal gyrus and the right anterior cingulate and paracingulate gyri, which was enriched in lipid sets of glycerophosphocholines and glycerophosphoethanolamines. ConclusionOur results indicated that abnormal FC in the prefrontal cortex is associated with multiple plasma lipid species, which may provide novel clues for exploring the pathophysiology of MDD.

G6PD is a prognostic biomarker correlated with immune infiltrates in lung adenocarcinoma and pulmonary arterial hypertension.

Lung adenocarcinoma (LUAD) with Pulmonary arterial hypertension (PAH) shows a poor prognosis. Detecting related genes is imperative for prognosis prediction. The gene expression profiles of LUAD and PAH were acquired from The Cancer Genome Atlas (TCGA) and the Gene Expression Omnibus (GEO) database, respectively. The co-expression modules associated with LUAD and PAH were evaluated using the Weighted Gene Co-Expression Network Analysis (WGCNA). The relationship between key gene expression with immune-cell infiltration and the tumor immune microenvironment (TIME) was evaluated. We confirmed the mRNA and protein levels in vivo and vitro. G6PD knockdown was used to conduct the colony formation assay, transwell invasion assay, and scratch wound assay of A549 cells. EDU staining and CCK8 assay were performed on G6PD knockdown HPASMCs. We identified therapeutic drug molecules and performed molecular docking between the key gene and small drug molecules. Three major modules and 52 overlapped genes were recognized in LUAD and PAH. We identified the key gene G6PD, which was significantly upregulated in LUAD and PAH. In addition, we discovered a significant difference in infiltration for most immune cells between high- and low-G6PD expression groups. The mRNA and protein expressions of G6PD were significantly upregulated in LUAD and PAH. G6PD knockdown decreased proliferation, cloning, and migration of A549 cells and cell proliferation in HPASMCs. We screened five potential drug molecules against G6PD and targeted glutaraldehyde by molecular docking. This study reveals that G6PD is an immune-related biomarker and a possible therapeutic target for LUAD and PAH patients.

PLCG2 and IFNAR1: The Potential Biomarkers Mediated by Immune Infiltration and Osteoclast Differentiation of Ankylosing Spondylitis in the Peripheral Blood.

Ankylosing spondylitis (AS) is a chronic inflammatory rheumatic disease characterized by chronic spinal inflammation, arthritis, gut inflammation, and enthesitis. We aimed to identify the key biomarkers related to immune infiltration and osteoclast differentiation in the pathological process of AS by bioinformatic methods. GSE25101 from the Gene Expression Omnibus was used to obtain AS-associated microarray datasets. We performed bioinformatics analysis using R software to validate different expression levels. The purpose of the GO and KEGG enrichment analyses of DEGs was to exclude key genes. Using weighted correlation network analysis (WGCNA), we examined all expression profile data and identified differentially expressed genes. The objective was to investigate the interaction between genetic and clinical features and to identify the essential relationships underlying coexpression modules. The CIBERSORT method was used to make a comparison of the immune infiltration in whole blood between the AS group and the control group. The WGCNA R program from Bioconductor was used to identify hub genes. RNA extraction reverse transcription and quantitative polymerase chain reaction were conducted in the peripheral blood collected from six AS patients and six health volunteers matched by age and sex. 125 DEGs were identified, consisting of 36 upregulated and 89 downregulated genes that are involved in the cell cycle and replication processes. In the WGCNA, modules of MCODE with different algorithms were used to find 33 key genes that were related to each other in a strong way. Immune infiltration analysis found that naive CD4+ T cells and monocytes may be involved in the process of AS. PLCG2 and IFNAR1 genes were obtained by screening genes meeting the conditions of immune cell infiltration and osteoclast differentiation in AS patients among IGF2R, GRN, SH2D1A, LILRB3, IFNAR1, PLCG2, and TNFRSF1B. The results demonstrated that the levels of PLCG2 mRNA expression in AS were considerably higher than those in healthy individuals (P=0.003). IFNAR1 mRNA expression levels were considerably lower in AS than in healthy individuals (P < 0.0001). Dysregulation of PLCG2 and IFNAR1 are key factors in disease occurrence and development of AS through regulating immune infiltration and osteoclast differentiation. Explaining the differences in immune infiltration and osteoclast differentiation between AS and normal samples will contribute to understanding the development of spondyloarthritis.

Analysis of the differentially expressed genes in the combs and testes of Qingyuan partridge roosters at different developmental stages

BackgroundThe sexual maturity of chickens is an important economic trait, and the breeding of precocious and delayed puberty roosters is an important selection strategy for broilers. The comb serves as an important secondary sexual characteristic of roosters and determines their sexual precocity. Moreover, comb development is closely associated with gonad development in roosters. However, the underlying molecular mechanism regulating the sexual maturity of roosters has not yet been fully explored.ResultsIn order to identify the genes related to precocious puberty in Qingyuan partridge roosters, and based on the synchrony of testis and combs development, combined with histological observation and RNA-seq method, the developmental status and gene expression profile of combs and testis were obtained. The results showed that during the early growth and development period (77 days of age), the development of combs and testis was significant in the high comb (H) group versus the low comb (L) group (p < 0.05); however, the morphological characteristic of the comb and testicular tissues converged during the late growth and development period (112 days of age) in the H and L groups. Based on these results, RNA-sequencing analysis was performed on the comb and testis tissues of the 77 and 112 days old Qingyuan Partridge roosters with different comb height traits. GO and KEGG analysis enrichment analysis showed that the differentially expressed genes were primarily enriched in MAPK signaling, VEGF signaling, and retinol metabolism pathways. Moreover, weighted correlation network analysis and module co-expression network analysis identified WNT6, AMH, IHH, STT3A, PEX16, KPNA7, CATHL2, ROR2, PAMR1, WISP2, IL17REL, NDRG4, CYP26B1, and CRHBP as the key genes associated with the regulation of precocity and delayed puberty in Qingyuan Partridge roosters.ConclusionsIn summary, we identified the key regulatory genes of sexual precocity in roosters, which provide a theoretical basis for understanding the developmental differences between precocious and delayed puberty in roosters.

Identification of new subtypes and potential genetic signatures in triple-negative breast cancer using weighted gene co-expression network analysis.

Triple-negative breast cancer (TNBC) is a heterogeneous disease with aggressive behavior and poor prognosis. Here, we used gene expression profiling to define new subtypes of TNBC, which may improve prevention and treatment through personalized medicine. Gene expression profiles from the public datasets GSE76250, GSE61724, GSE61723, and GES76275 were subjected to co-expression analysis to identify differentially expressed genes (DEGs) between TNBC and non-TNBC tissues. Consistency clustering was used to define TNBC subtypes, whose correlation with gene modules was analyzed. Enrichment analysis was used to identify module genes' biological functions and pathways. Single-sample gene set enrichment analysis was used to assess immune cell infiltration in the different TNBC subtypes, and the ChAMP package was used to examine methylation sites in TNBC. A total of 4,958 DEGs in TNBC were identified, which showed the same expression differences across all datasets as in the dataset GSE76250 and clustered into 9 co-expression modules. TNBC samples clustered into two subtypes based on nine hub genes from the modules. Class I showed the most significant correlation with module 1, whose genes were related mainly to interleukin-1 response, while class II showed the most significant correlation with module 6, whose genes were related mainly to the transforming growth factor-β pathway. Class I was significantly enriched in cell cycle and DNA replication, and tumors of this subtype showed lower immune cell infiltration than class II tumors. Tumor infiltration by Th2 cells correlated positively with the expression of MCM10 and negatively with the expression of PREX2. A greater methylation of CIDEC, DLC1, EDNRB, EGR2 and SRPK1 correlated with better prognosis. Class I TNBC, for which a useful biomarker is MCM10, may be associated with a worse prognosis than class II TNBC, for which PREX2 may serve as a biomarker.

Identification and Validation of Prognostic Markers for Endometriosis-Associated Ovarian Cancer.

Background: Growing evidence suggests that endometriosis (EMs) is a risk factor for endometriosis-associated ovarian cancer (EAOC). The aim was to identify and validate gene signatures associated with EMs that may serve as potential biomarkers for evaluating the prognosis of patients with EAOC. Methods: The data of EMs and control samples was obtained from GEO database. The weighted gene co-expression network analysis (WGCNA) identified modular genes significantly associated with EMs. The KEGG pathway and GO functional enrichment analyses were also performed. Univariate Cox regression analysis was conducted to screen marker genes associated with the prognosis of EAOC patients. Finally, RT-qPCR and immunohistochemical verified the expression of ADAMTS19 and TUBB in normal ovarian and EAOC tissues, and the biological functions of ADAMTS19 and TUBB were preliminarily explored by CCK8 and Transwell assays. Results: The WGCNA identified 2 co-expression modules, which in total included 615 genes, and 7642 differentially expressed genes (DEGs) were detected thorough analysis of the EAOC dataset. After taking the intersection of 615 modular genes and 7642 DEGs, 214 shared genes were obtained, and univariate COX regression analysis pointed 10 genes associated with the prognosis of EAOC. Moreover, it was demonstrated by RT-qPCR and immunohistochemical staining experiments that ADAMTS19 expression was elevated, while TUBB expression was reduced in EAOC compared with normal ovarian cells and tissues. Finally, cell experiments revealed that ADAMTS19 promoted the proliferation and invasion in EAOC cells, while overexpression of TUBB inhibited these processes. Conclusions: The present study identified and validated new EMs-associated gene markers, which could serve as potential biomarkers for assessing the prognostic risk of EAOC patients. In addition, some of these genes may have significance as novel therapeutic targets and could be used to guide clinical applications.

Revealing the characteristics of SETD2-mutated clear cell renal cell carcinoma through tumor heterogeneity analysis.

Renal cell carcinoma (RCC) is the most prevalent type of malignant kidney tumor in adults, with clear cell renal cell carcinoma (ccRCC) comprising about 75% of all cases. The SETD2 gene, which is involved in the modification of histone proteins, is often found to have alterations in ccRCC. Yet, our understanding of how these SETD2 mutations affect ccRCC characteristics and behavior within the tumor microenvironment is still not fully understood. We conducted a detailed analysis of single-cell RNA sequencing (scRNA-seq) data from ccRCC. First, the data was preprocessed using the Python package, "scanpy." High variability genes were pinpointed through Pearson's correlation coefficient. Dimensionality reduction and clustering identification were performed using Principal Component Analysis (PCA) and the Leiden algorithm. Malignant cell identification was conducted with the "InferCNV" R package, while cell trajectories and intercellular communication were depicted using the Python packages "VIA" and "cellphoneDB." We then employed the R package "Deseq2" to determine differentially expressed genes (DEGs) between groups. Using high-dimensional weighted gene correlation network analysis (hdWGCNA), co-expression modules were identified. We intersected these modules with DEGs to establish prognostic models through univariate Cox and the least absolute shrinkage and selection operator (LASSO) method. We identified 69 and 53 distinctive cell clusters, respectively. These were classified further into 12 unique cell types. This analysis highlighted the presence of an abnormal tumor sub-cluster (MT + group), identified by high mitochondrial-encoded protein gene expression and an indication of unfavorable prognosis. Investigation of cellular interactions spotlighted significant interactions between the MT + group and endothelial cells, macrophaes. In addition, we developed a prognostic model based on six characteristic genes. Notably, risk scores derived from these genes correlated significantly with various clinical features. Finally, a nomogram model was established to facilitate more accurate outcome prediction, incorporating four independent risk factors. Our findings provide insight into the crucial transcriptomic characteristics of ccRCC associated with SETD2 mutation. We discovered that this mutation-induced subcluster could stimulate M2 polarization in macrophages, suggesting a heightened propensity for metastasis. Moreover, our prognostic model demonstrated effectiveness in forecasting overall survival for ccRCC patients, thus presenting a valuable clinical tool.