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Background: Although pentatricopeptide repeat domain 1 (PTCD1) has been found to modulate mitochondrial metabolic and oxidative phosphorylation, its contribution in the growth of clear cell renal cell carcinoma (ccRCC) remains unknown. Methods: The Cancer Genome Atlas (TCGA) dataset was utilized to examine the transcriptional alterations, patient characteristics, clinical outcomes, as well as pathway activation of PTCD1. The Weighted Gene Co-expression Network Analysis (WGCNA) was performed to investigate potential genes that associated with PTCD1. The researchers estimated the relationship between PTCD1, tumor immunology, and epithelial mesenchymal transition (EMT). Researchers studied how PTCD1 affects the functional behavior of tumor cells in vitro. Results: PTCD1 expression was greater in ccRCC samples than in normal samples, and expression increased gradually as the stage increased. In TCGA cohorts, higher PTCD1 expression was substantially associated with a poorer clinical stage, histological grading, T stage, N stage, M stage, and survival outcomes. The results of multivariate analysis showed that PTCD1 was an independent variable affecting the survival outcomes of ccRCC patients (p < 0.001). PTCD1 regulated ccRCC progression via various cancer mechanisms including PI3K-Akt signaling, focal adhesion, PD-L1 expression, and PD-1 checkpoints in cancer. WGCNA discovered a significant relationship between PTCD1 and IARS2, LRPPRC, MT-ND2, MT-CO1, MT-CO2, MT-CYB, MT-ATP6, and MT-ND4. Furthermore, PTCD1 expression levels was closely associated with immune infiltrating, immunological checkpoint, EMT, immunotherapy responsiveness, and anti-tumor medication sensitivities. Upregulation of PTCD1 in ccRCC cells resulted in considerably increased cellular invasion and migration. Mechanistically, the upregulation of PTCD1 increased the phosphorylation of AKT at Ser473 and GSK-3β at Ser9, as well as enhanced activation of Wnt/β-catenin pathway. Conclusion: Elevated expression of PTCD1 was associated with malignant biological behaviors and poor outcomes of ccRCC patients, and PTCD1 may accelerate tumor cells proliferation and invasion via the Akt/GSK3β/β-catenin pathway. Our findings indicated that PTCD1 had the potential to become a new target for predicting prognosis and targeted therapy. [ABSTRACT FROM AUTHOR]

Abstract The risk of suicide in patients with major depressive disorder (MDD) poses a major concern, with studies suggesting that genetics may be a contributing factor. Although there are many transcriptomic studies on postmortem brain tissue related to suicidal behavior, the blood transcriptional mechanisms of suicidal ideation (SI) remain unknown. This study utilized a weighted gene coexpression network analysis (WGCNA) approach to investigate the associations between gene coexpression modules and SI in individuals with MDD using peripheral blood RNA-seq data from 75 MDD patients with SI (MDD_SI), 82 MDD patients without SI (MDD_nSI), and 149 healthy controls (HC). An ANCOVA was conducted to assess differences in gene coexpression modules among groups, with age and sex included as covariates. The gene ontology (GO) and Kyoto encyclopedia of genes and genomes (KEGG) databases were used to annotate module functions. Results indicated that the magenta module (associated with RNA splicing processes) differentiated MDD_SI from MDD_nSI (p = 0.021), while the green module (related to immune and inflammatory responses) distinguished MDD_SI from HC (p = 0.004). Additionally, three modules showed differences between MDD_nSI and HC: magenta (p = 0.009), brown (related to innate immunity and mitochondrial metabolism; p = 0.001), and turquoise (associated with energy metabolism and neurodegeneration; p = 0.005). Our findings highlight that gene expression regulation, immune response, and inflammation may be linked to SI in patients with MDD, while pathways associated with innate immunity, energy metabolism, mitochondrial function, and neurodegeneration appear to be more broadly related to MDD.

Abstract Background Although pentatricopeptide repeat domain 1 (PTCD1) has been found to modulate mitochondrial metabolic and oxidative phosphorylation, its contribution in the growth of clear cell renal cell carcinoma (ccRCC) remains unknown. Methods The Cancer Genome Atlas (TCGA) dataset was utilized to examine the transcriptional alterations, patient characteristics, clinical outcomes, as well as pathway activation of PTCD1. The Weighted Gene Co-expression Network Analysis (WGCNA) was performed to investigate potential genes that associated with PTCD1. The researchers estimated the relationship between PTCD1, tumor immunology, and epithelial mesenchymal transition (EMT). Researchers studied how PTCD1 affects the functional behavior of tumor cells in vitro. Results PTCD1 expression was greater in ccRCC samples than in normal samples, and expression increased gradually as the stage increased. In TCGA cohorts, higher PTCD1 expression was substantially associated with a poorer clinical stage, histological grading, T stage, N stage, M stage, and survival outcomes. The results of multivariate analysis showed that PTCD1 was an independent variable affecting the survival outcomes of ccRCC patients (p

Breast cancer remains a prevalent global health challenge, with tumor-removal surgeries being among the most common treatments but often leading to aesthetic defects. Adipose-derived stem cell (ADSC)-enriched fat grafting in breast reconstruction offers promising therapeutic benefits. However, concerns about its oncological safety persist, particularly regarding the potential risks of promoting cancer recurrence. This study investigated the effects of ADSCs on breast cancer progression by coculturing ADSCs with the MCF-7 breast cancer cell line for a short cell cultivation period of 3 days. We performed an RNA-seq analysis to identify significant transcriptomic changes in cocultured MCF-7 cells and carried out functional enrichment analyses to uncover key biological pathways influenced by ADSCs. Our findings revealed that transcriptomic alterations in MCF-7 cells are linked to aggressive cancer traits, including the upregulation of epithelial–mesenchymal transition (EMT) and the HIF-1 signaling pathway, which indicate a shift toward aerobic glycolysis. Some of the observed gene expression changes also correlated with relapse risk and mortality. These findings underscore the need for further research to explore the implications of these genes and pathways in driving aggressive cancer phenotypes and assess the safety of ADSCs in clinical settings.

Engineering acid-tolerant microbial strains is a cost-effective approach to overcoming acid stress during industrial fermentation. We previously constructed an acid-tolerant strain (Escherichia coli SC3124) with enhanced growth robustness and productivity under mildly acidic conditions by fine-tuning the expression of synthetic acid-tolerance module genes consisting of a proton-consuming acid resistance system (gadE), a periplasmic chaperone (hdeB), and ROS scavengers (sodB, katE). However, the precise acid-tolerance mechanism of E. coli SC3124 remained unclear. In this study, the growth of E. coli SC3124 under mild acid stress (pH 6.0) was determined. The final OD600 of E. coli SC3124 at pH 6.0 was 131% and 124% of that of the parent E. coli MG1655 at pH 6.8 and pH 6.0, respectively. Transcriptome analysis revealed the significant upregulation of the genes involved in oxidative phosphorylation, the tricarboxylic acid (TCA) cycle, and lysine-dependent acid-resistance system in E. coli SC3124 at pH 6.0. Subsequently, a weighted gene coexpression network analysis was performed to systematically determine the metabolic perturbations of E. coli SC3124 with mild acid treatment, and we extracted the gene modules highly associated with different acid traits. The results showed two biologically significant coexpression modules, and 263 hub genes were identified. Specifically, the genes involved in ATP-binding cassette (ABC) transporters, oxidative phosphorylation, the TCA cycle, amino acid metabolism, and purine metabolism were highly positively associated with mild acid stress responses. We propose that the overexpression of synthetic acid-tolerance genes leads to metabolic changes that confer mild acid stress resistance in E. coli. Integrated omics platforms provide valuable information for understanding the regulatory mechanisms of mild acid tolerance in E. coli and highlight the important roles of oxidative phosphorylation and ABC transporters in mild acid stress regulation. These findings offer novel insights to better the design of acid-tolerant chasses to synthesize value-added chemicals in a green and sustainable manner. [ABSTRACT FROM AUTHOR]

Purpose: Age-related macular degeneration (AMD) is a chronic and progressive macular degenerative disease that culminates in a gradual deterioration of central vision. Despite its prevalence, the key biomarkers for AMD have not yet been fully elucidated. In this study, we aimed to efficiently identify biomarkers crucial for diagnosing AMD. Methods: Three datasets pertaining to retinal pigment epithelium(RPE)/choroid tissues associated withAMDwere selected from theGEOdatabase. The GSE50195 datasetwas utilized to conduct weighted gene co-expression network analysis (WGCNA) for identifying module genes linked to AMD. KEGG and GO enrichment analyses were subsequently conducted on these module genes. GSE29801 and GSE135092 datasets were subjected to differential expression analysis to pinpoint the DEGs intersecting with the module genes. Subsequently, wet AMD (wAMD) and dry AMD (dAMD) mouse models were developed, from which RPE/choroid tissues were harvested to validate the hub genes via RT-qPCR and Western blot. Results: Using theWGCNA, we selected the "antiquewhite4" module (r=0.91 andp = 7e-07), which contains a total of 325 genes. Through the intersection ofmodule genes with DEGs, nine hub genes were identified. Pathways involved in complement and coagulation cascades, ECM-receptor interactions, unsaturated fatty acid biosynthesis, and fatty acid elongation play important roles in AMD. Notably, CDH18 demonstrated notable variance across all three datasets. Post validation using RT-qPCR experiments revealed a significant downregulation of CDH18 in both dAMDand wAMD. EGLN3was expressed at low levels in wAMD. In dAMD, EYA2, LTB, and PODXL were significantly downregulated, whereas APOC1 was notably upregulated. Western blot confirmed that CDH18 was lowly expressed in dAMD and wAMD mouse models. Conclusion: CDH18 was identified as the key gene involved in the pathogenesis of AMD. An imbalance of the complement and coagulation cascades is a potential mechanism of AMD. This study provides a novel idea for diagnosing and treating AMD in the future. [ABSTRACT FROM AUTHOR]

BackgroundCardioembolic Stroke (CS) and Atrial Fibrillation (AF) are prevalent diseases that significantly impact the quality of life and impose considerable financial burdens on society. Despite increasing evidence of a significant association between the two diseases, their complex interactions remain inadequately understood. We conducted bioinformatics analysis and employed machine learning techniques to investigate potential shared biomarkers between CS and AF.MethodsWe retrieved the CS and AF datasets from the Gene Expression Omnibus (GEO) database and applied Weighted Gene Co-Expression Network Analysis (WGCNA) to develop co-expression networks aimed at identifying pivotal modules. Next, we performed Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis on the shared genes within the modules related to CS and AF. The STRING database was used to build a protein-protein interaction (PPI) network, facilitating the discovery of hub genes within the network. Finally, several common used machine learning approaches were applied to construct the clinical predictive model of CS and AF. ROC curve analysis to evaluate the diagnostic value of the identified biomarkers for AF and CS.ResultsFunctional enrichment analysis indicated that pathways intrinsic to the immune response may be significantly involved in CS and AF. PPI network analysis identified a potential association of 4 key genes with both CS and AF, specifically PIK3R1, ITGAM, FOS, and TLR4.ConclusionIn our study, we utilized WGCNA, PPI network analysis, and machine learning to identify four hub genes significantly associated with CS and AF. Functional annotation outcomes revealed that inherent pathways related to the immune response connected to the recognized genes might could pave the way for further research on the etiological mechanisms and therapeutic targets for CS and AF.

Patients with thymoma (THYM)-associated myasthenia gravis (MG) typically have a poor prognosis and recurring illness. This study aimed to discover important biomarkers associated with immune cell infiltration and THYM-associated MG (THYM-MG) development. Gene expression microarray data were downloaded from The Cancer Genome Atlas website (TCGA) and Gene Expression Omnibus (GEO). A total of 102 differentially expressed genes were investigated. According to the immune infiltration data, the distribution of Tfh cells, B cells, and CD4 T cells differed significantly between the THYM-MG and THYM-NMG groups. WGCNA derived 25 coexpression modules; one hub module (the blue module) strongly correlated with Tfh cells. Combining differential genes revealed 21 intersecting genes. LASSO analysis subsequently revealed 16 hub genes as potential THYM-MG biomarkers. ROC curve analysis of the predictive model revealed moderate diagnostic value. The association between the 16 hub genes and infiltrating immune cells was further evaluated in TIMER2.0 and the validation dataset. Draggability analysis identified the therapeutic target genes PTGS2 and ALB, along with significant drugs including Firocoxib, Alclofenac, Pyridostigmine, and Stavudine. This was validated through MD simulation, PCA, and MM-GBSA analyses. The interaction between numerous activated B cells and follicular helper T cells is closely associated with THYM-MG pathogenesis from a bioinformatics perspective. Hub genes (including SP6, SCUBE3, B3GNT7, and MAGEL2) may be downregulated in immune cells in THYM-MG and associated with progression.

Abstract Background Gastric cancer (GC) is a heterogeneous malignancy with variable clinical outcomes. The immune system has been implicated in GC development and progression, highlighting the importance of immune-related gene expression patterns and their prognostic significance. Objective This study aimed to identify differentially expressed immune-related genes (DEIRGs) and establish a prognostic index for GC patients using comprehensive bioinformatic analyses. Methods We integrated RNA sequencing data from multiple databases and identified DEIRGs by overlapping differentially expressed genes with immune-related genes. Functional enrichment analysis was performed to uncover the biological processes and signaling pathways associated with DEIRGs. We conducted a Weighted Gene Co-expression Network Analysis (WGCNA) to identify key gene modules related to with GC. Cox regression analysis was conducted to determine independent prognostic DEIRGs for overall survival prediction. Based on these findings, we developed an immune-related gene prognostic index (IRGPI) based on these findings. The prognostic value of the IRGPI was validated using survival analysis and an independent validation cohort. Functional enrichment analysis, gene mutation analysis, and immune cell profiling were performed to gain insights into the biological functions and immune characteristics associated with the IRGPI-based subgroups. Results We identified 493 DEIRGs significantly enriched in immune-related biological processes and signaling pathways associated with GC. WGCNA analysis revealed a significant module (turquoise module) associated with GC, revealing potential therapeutic targets. Cox regression analysis identified RNASE2, CGB5, CTLA4, and DUSP1 as independent prognostic DEIRGs. The IRGPI, incorporating the expression levels of these genes, demonstrated significant prognostic value in predicting overall survival. The IRGPI-based subgroups exhibited distinct biological functions, genetic alterations, and immune cell compositions. Conclusion Our study identified DEIRGs and established a prognostic index (IRGPI) for GC patients. The IRGPI exhibited promising prognostic potential and provided insights into GC tumor biology and immune characteristics. These findings have implications for guiding therapeutic strategies.

In order to investigate physiological and molecular mechanisms underlying the impacts of organic selenium and nanoselenium on the drought stress of pak choi (Brassica chinensis var. pekinensis. cv. 'Suzhouqing'), we sprayed 20 mL of 0, 5, 10, 20 mg L−1 concentrations of organic selenium (DCK, DO5, DO10, DO20) and nanoselenium (DN5, DN10, DN20). We measured photosynthetic pigment, antioxidant and nutritional quality, performed transcriptome sequencing of leaves using RNA-seq technology, obtained 297,176,114 clean sequences, and identified differential expressed genes. Organic selenium and nanoselenium increased the photosynthetic capacity by upregulating light-trapping pigment proteins, regulated reactive oxygen species homeostasis by activating the antioxidant system through the upregulation of glutathione S-transferase, and maintained water homeostasis through glutathione oxidase activity, thus enhancing weight. They contributed to the elevated total selenium content by increasing expression levels of ABC transporter, adenylate kinase, and cysteine desulphurase. Additionally, hub genes related to these metabolic pathways were identified using a weighted gene coexpression network analysis. The consistent results with the upregulated genes identified in the Kyoto Encyclopedia of Genes and Genomes pathway confirmed the accuracy and reliability of the transcriptome sequencing data. This study provides scientific reference to further in-depth research on the molecular effect of selenium on crops under drought stress conditions. [ABSTRACT FROM AUTHOR]

Objective: The purpose of this study was to identify potential biomarkers in the tubulointerstitium of focal segmental glomerulosclerosis (FSGS) and comprehensively analyze its mRNA-miRNA-lncRNA/circRNA network. Methods: The expression data (GSE108112 and GSE200818) were downloaded from the Gene Expression Omnibus database (https://www.ncbi.nlm.nih.gov/geo/). Identification and enrichment analysis of differentially expressed genes (DEGs) were performed. the PPI networks of the DEGs were constructed and classified using the Cytoscape molecular complex detection (MCODE) plugin. Weighted gene coexpression network analysis (WGCNA) was used to identify critical gene modules. Least absolute shrinkage and selection operator regression analysis were used to screen for key biomarkers of the tubulointerstitium in FSGS, and the receiver operating characteristic curve was used to determine their diagnostic accuracy. The screening results were verified by quantitative real-time-PCR (qRT-PCR) and Western blot. The transcription factors (TFs) affecting the hub genes were identified by Cytoscape iRegulon. The mRNA-miRNA-lncRNA/circRNA network for identifying potential biomarkers was based on the starBase database. Results: A total of 535 DEGs were identified. MCODE obtained eight modules. The green module of WGCNA had the greatest association with the tubulointerstitium in FSGS. PPARG coactivator 1 alpha (PPARGC1A) was screened as a potential tubulointerstitial biomarker for FSGS and verified by qRT-PCR and Western blot. The TFs FOXO4 and FOXO1 had a regulatory effect on PPARGC1A. The ceRNA network yielded 17 miRNAs, 32 lncRNAs, and 50 circRNAs. Conclusions: PPARGC1A may be a potential biomarker in the tubulointerstitium of FSGS. The ceRNA network contributes to the comprehensive elucidation of the mechanisms of tubulointerstitial lesions in FSGS. [ABSTRACT FROM AUTHOR]

The aim of our study was to identify key biomarkers of glomeruli in focal glomerulosclerosis (FSGS) and analyze their relationship with the infiltration of immune cells. The expression profiles (GSE108109 and GSE200828) were obtained from the GEO database. The differentially expressed genes (DEGs) were filtered and analyzed by gene set enrichment analysis (GSEA). MCODE module was constructed. Weighted gene coexpression network analysis (WGCNA) was performed to obtain the core gene modules. Least absolute shrinkage and selection operator (LASSO) regression was applied to identify key genes. ROC curves were employed to explore their diagnostic accuracy. Transcription factor prediction of the key biomarkers was performed using the Cytoscape plugin IRegulon. The analysis of the infiltration of 28 immune cells and their correlation with the key biomarkers were performed. A total of 1474 DEGs were identified. Their functions were mostly related to immune-related diseases and signaling pathways. MCODE identified five modules. The turquoise module of WGCNA had significant relevance to the glomerulus in FSGS. TGFB1 and NOTCH1 were identified as potential key glomerular biomarkers in FSGS. Eighteen transcription factors were obtained from the two hub genes. Immune infiltration showed significant correlations with T cells. The results of immune cell infiltration and their relationship with key biomarkers implied that NOTCH1 and TGFB1 were enhanced in immune-related pathways. TGFB1 and NOTCH1 may be strongly correlated with the pathogenesis of the glomerulus in FSGS and are new candidate key biomarkers. T-cell infiltration plays an essential role in the FSGS lesion process. [ABSTRACT FROM AUTHOR]

Background: Gastric cancer (GC) is a heterogeneous malignancy with variable clinical outcomes. The immune system has been implicated in GC development and progression, highlighting the importance of immune-related gene expression patterns and their prognostic significance. Objective: This study aimed to identify differentially expressed immune-related genes (DEIRGs) and establish a prognostic index for GC patients using comprehensive bioinformatic analyses. Methods: We integrated RNA sequencing data from multiple databases and identified DEIRGs by overlapping differentially expressed genes with immune-related genes. Functional enrichment analysis was performed to uncover the biological processes and signaling pathways associated with DEIRGs. We conducted a Weighted Gene Co-expression Network Analysis (WGCNA) to identify key gene modules related to with GC. Cox regression analysis was conducted to determine independent prognostic DEIRGs for overall survival prediction. Based on these findings, we developed an immune-related gene prognostic index (IRGPI) based on these findings. The prognostic value of the IRGPI was validated using survival analysis and an independent validation cohort. Functional enrichment analysis, gene mutation analysis, and immune cell profiling were performed to gain insights into the biological functions and immune characteristics associated with the IRGPI-based subgroups. Results: We identified 493 DEIRGs significantly enriched in immune-related biological processes and signaling pathways associated with GC. WGCNA analysis revealed a significant module (turquoise module) associated with GC, revealing potential therapeutic targets. Cox regression analysis identified RNASE2, CGB5, CTLA4, and DUSP1 as independent prognostic DEIRGs. The IRGPI, incorporating the expression levels of these genes, demonstrated significant prognostic value in predicting overall survival. The IRGPI-based subgroups exhibited distinct biological functions, genetic alterations, and immune cell compositions. Conclusion: Our study identified DEIRGs and established a prognostic index (IRGPI) for GC patients. The IRGPI exhibited promising prognostic potential and provided insights into GC tumor biology and immune characteristics. These findings have implications for guiding therapeutic strategies. [ABSTRACT FROM AUTHOR]

Engineering acid-tolerant microbial strains is a cost-effective approach to overcoming acid stress during industrial fermentation. We previously constructed an acid-tolerant strain (Escherichia coli SC3124) with enhanced growth robustness and productivity under mildly acidic conditions by fine-tuning the expression of synthetic acid-tolerance module genes consisting of a proton-consuming acid resistance system (gadE), a periplasmic chaperone (hdeB), and ROS scavengers (sodB, katE). However, the precise acid-tolerance mechanism of E. coli SC3124 remained unclear. In this study, the growth of E. coli SC3124 under mild acid stress (pH 6.0) was determined. The final OD600 of E. coli SC3124 at pH 6.0 was 131% and 124% of that of the parent E. coli MG1655 at pH 6.8 and pH 6.0, respectively. Transcriptome analysis revealed the significant upregulation of the genes involved in oxidative phosphorylation, the tricarboxylic acid (TCA) cycle, and lysine-dependent acid-resistance system in E. coli SC3124 at pH 6.0. Subsequently, a weighted gene coexpression network analysis was performed to systematically determine the metabolic perturbations of E. coli SC3124 with mild acid treatment, and we extracted the gene modules highly associated with different acid traits. The results showed two biologically significant coexpression modules, and 263 hub genes were identified. Specifically, the genes involved in ATP-binding cassette (ABC) transporters, oxidative phosphorylation, the TCA cycle, amino acid metabolism, and purine metabolism were highly positively associated with mild acid stress responses. We propose that the overexpression of synthetic acid-tolerance genes leads to metabolic changes that confer mild acid stress resistance in E. coli. Integrated omics platforms provide valuable information for understanding the regulatory mechanisms of mild acid tolerance in E. coli and highlight the important roles of oxidative phosphorylation and ABC transporters in mild acid stress regulation. These findings offer novel insights to better the design of acid-tolerant chasses to synthesize value-added chemicals in a green and sustainable manner.

Objective The aim of our study was to identify key biomarkers of glomeruli in focal glomerulosclerosis (FSGS) and analyze their relationship with the infiltration of immune cells.Methods The expression profiles (GSE108109 and GSE200828) were obtained from the GEO database. The differentially expressed genes (DEGs) were filtered and analyzed by gene set enrichment analysis (GSEA). MCODE module was constructed. Weighted gene coexpression network analysis (WGCNA) was performed to obtain the core gene modules. Least absolute shrinkage and selection operator (LASSO) regression was applied to identify key genes. ROC curves were employed to explore their diagnostic accuracy. Transcription factor prediction of the key biomarkers was performed using the Cytoscape plugin IRegulon. The analysis of the infiltration of 28 immune cells and their correlation with the key biomarkers were performed.Results A total of 1474 DEGs were identified. Their functions were mostly related to immune-related diseases and signaling pathways. MCODE identified five modules. The turquoise module of WGCNA had significant relevance to the glomerulus in FSGS. TGFB1 and NOTCH1 were identified as potential key glomerular biomarkers in FSGS. Eighteen transcription factors were obtained from the two hub genes. Immune infiltration showed significant correlations with T cells. The results of immune cell infiltration and their relationship with key biomarkers implied that NOTCH1 and TGFB1 were enhanced in immune-related pathways.Conclusion TGFB1 and NOTCH1 may be strongly correlated with the pathogenesis of the glomerulus in FSGS and are new candidate key biomarkers. T-cell infiltration plays an essential role in the FSGS lesion process.

Introduction: Flavonoids, as secondary metabolites in plants, play important roles in many biological processes and responses to environmental factors. Methods: Apricot fruits are rich in flavonoid compounds, and in this study, we performed a combined metabolomic and transcriptomic analysis of orange flesh (JN) and white flesh (ZS) apricot fruits. Results and discussion: A total of 222 differentially accumulated flavonoids (DAFs) and 15855 differentially expressed genes (DEGs) involved in flavonoid biosynthesis were identified. The biosynthesis of flavonoids in apricot fruit may be regulated by 17 enzyme-encoding genes, namely PAL (2), 4CL (9), C4H (1), HCT (15), C3'H (4), CHS (2), CHI (3), F3H (1), F3'H (CYP75B1) (2), F3'5'H (4), DFR (4), LAR (1), FLS (3), ANS (9), ANR (2), UGT79B1 (6) and CYP81E (2). A structural genetranscription factor (TF) correlation analysis yielded 3 TFs (2 bHLH, 1 MYB) highly correlated with 2 structural genes. In addition, we obtained 26 candidate genes involved in the biosynthesis of 8 differentially accumulated flavonoids metabolites in ZS by weighted gene coexpression network analysis. The candidate genes and transcription factors identified in this study will provide a highly valuable molecular basis for the in-depth study of flavonoid biosynthesis in apricot fruits. [ABSTRACT FROM AUTHOR]

Purpose While the prognosis of multiple myeloma (MM) has significantly improved over the last decade because of new treatment options, it remains incurable. Aetiological explanations and biological targets based on genomics may provide additional help for rational disease intervention. Materials and Methods Three microarray datasets associated with MM were downloaded from the Gene Expression Omnibus (GEO) database. GSE125364 and GSE39754 were used as the training set, and GSE13591 was used as the verification set. The differentially expressed genes (DEGs) were obtained from the training set, and Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analyses were performed to annotate their functions. The hub genes were derived from the combined results of a protein–protein interaction (PPI) network and weighted gene coexpression network analysis (WGCNA). The receiver operating characteristic (ROC) curves of hub genes were plotted to evaluate their clinical diagnostic value. Biological processes and signaling pathways associated with hub genes were explained by gene set enrichment analysis (GSEA). Results A total of 1759 DEGs were identified. GO and KEGG pathway analyses suggested that the DEGs were related to the process of protein metabolism. RPN1, SEC61A1, SPCS1, SRPR, SRPRB, SSR1 and TRAM1 were proven to have clinical diagnostic value for MM. The GSEA results suggested that the hub genes were widely involved in the N-glycan biosynthesis pathway. Conclusion The hub genes identified in this study can partially explain the potential molecular mechanisms of MM and serve as candidate biomarkers for disease diagnosis.

The clinically heterogeneous presentation of schizophrenia is compounded by the heterogeneity of risk factors and neurobiological correlates of the disorder. Genome-wide association studies in schizophrenia have uncovered a remarkably high number of genetic variants, but the biological pathways they impact upon remain largely unidentified. Among the diverse methodological approaches employed to provide a more granular understanding of genetic risk for schizophrenia, the use of biological labels, such as gene ontologies, regulome approaches, and gene coexpression have all provided novel perspectives into how genetic risk translates into the neurobiology of schizophrenia. Here, we review the salient aspects of parsing polygenic risk for schizophrenia into biological pathways. We argue that parsed scores, compared to standard polygenic risk scores, may afford a more biologically plausible and accurate physiological modeling of the different dimensions involved in translating genetic risk into brain mechanisms, including multiple brain regions, cell types, and maturation stages. We discuss caveats, opportunities, and pitfalls inherent in the parsed risk approach. [ABSTRACT FROM AUTHOR]

The inosine monophosphate (IMP) content in chicken meat is closely related to muscle quality and is an important factor affecting meat flavor. However, the molecular regulatory mechanisms underlying the IMP-specific deposition in muscle remain unclear. This study performed transcriptome analysis of muscle tissues from different parts, feeding methods, sexes, and breeds of 180-day-old Jingyuan chickens, combined with differential expression and weighted gene coexpression network analysis (WGCNA), to identify the functional genes that regulate IMP deposition. Out of the four comparison groups, 1,775, 409, 102, and 60 differentially expressed genes (DEGs) were identified, of which PDHA2, ACSS2, PGAM1, GAPDH, PGM1, GPI, and TPI1 may be involved in the anabolic process of muscle IMP in the form of energy metabolism or amino acid metabolism. WGCNA identified 11 biofunctional modules associated with IMP deposition. The brown, midnight blue, red, and yellow modules were strongly correlated with IMP and cooking loss (p < 0.05). Functional enrichment analysis showed that glycolysis/gluconeogenesis, arginine and proline metabolism, and pyruvate metabolism, regulated by PYCR1, SMOX, and ACSS2, were necessary for muscle IMP-specific deposition. In addition, combined analyses of DEGs and four WGCNA modules identified TGIF1 and THBS1 as potential candidate genes affecting IMP deposition in muscle. This study explored the functional genes that regulate muscle development and IMP synthesis from multiple perspectives, providing an important theoretical basis for improving the meat quality and molecular breeding of Jingyuan chickens. [ABSTRACT FROM AUTHOR]

Abstract Background In intensive care units (ICU), mechanical ventilation (MV) is commonly applied to save patients’ lives. However, ventilator-induced diaphragm dysfunction (VIDD) can complicate treatment by hindering weaning in critically ill patients and worsening outcomes. The goal of this study was to identify potential genes involved in the endogenous protective mechanism against VIDD. Methods Twelve adult male rabbits were assigned to either an MV group or a control group under the same anesthetic conditions. Immunostaining and quantitative morphometry were used to assess diaphragm atrophy, while RNA-seq was used to investigate molecular differences between the groups. Additionally, core module and hub genes were analyzed using WGCNA, and co-differentially expressed hub genes were subsequently discovered by overlapping the differentially expressed genes (DEGs) with the hub genes from WGCNA. The identified genes were validated by western blotting (WB) and quantitative real-time polymerase chain reaction (qRT–PCR). Results After a VIDD model was successfully built, 1276 DEGs were found between the MV and control groups. The turquoise and yellow modules were identified as the core modules, and Trim63, Fbxo32, Uchl1, Tmprss13, and Cst3 were identified as the five co-differentially expressed hub genes. After the two atrophy-related genes (Trim63 and Fbxo32) were excluded, the levels of the remaining three genes/proteins (Uchl1/UCHL1, Tmprss13/TMPRSS13, and Cst3/CST3) were found to be significantly elevated in the MV group (P < 0.05), suggesting the existence of a potential antiproteasomal, antiapoptotic, and antiautophagic mechanism against diaphragm dysfunction. Conclusion The current research helps to reveal a potentially important endogenous protective mechanism that could serve as a novel therapeutic target against VIDD.

Abstract Background The immune system plays a vital role in the pathological process of ischaemic stroke. However, the exact immune-related mechanism remains unclear. The current research aimed to identify immune-related key genes associated with ischaemic stroke. Methods CIBERSORT was utilized to reveal the immune cell infiltration pattern in ischaemic stroke patients. Meanwhile, a weighted gene coexpression network analysis (WGCNA) was utilized to identify meaningful modules significantly correlated with ischaemic stroke. The characteristic genes correlated with ischaemic stroke were identified by the following two machine learning methods: the support vector machine-recursive feature elimination (SVM-RFE) algorithm and least absolute shrinkage and selection operator (LASSO) logistic regression. Results The CIBERSORT results suggested that there was a decreased infiltration of naive CD4 T cells, CD8 T cells, resting mast cells and eosinophils and an increased infiltration of neutrophils, M0 macrophages and activated memory CD4 T cells in ischaemic stroke patients. Then, three significant modules (pink, brown and cyan) were identified to be significantly associated with ischaemic stroke. The gene enrichment analysis indicated that 519 genes in the above three modules were mainly involved in several inflammatory or immune-related signalling pathways and biological processes. Eight hub genes (ADM, ANXA3, CARD6, CPQ, SLC22A4, UBE2S, VIM and ZFP36) were revealed to be significantly correlated with ischaemic stroke by the LASSO logistic regression and SVM-RFE algorithm. The external validation combined with a RT‒qPCR analysis revealed that the expression levels of ADM, ANXA3, SLC22A4 and VIM were significantly increased in ischaemic stroke patients and that these key genes were positively associated with neutrophils and M0 macrophages and negatively correlated with CD8 T cells. The mean AUC value of ADM, ANXA3, SLC22A4 and VIM was 0.80, 0.87, 0.91 and 0.88 in the training set, 0.85, 0.77, 0.86 and 0.72 in the testing set and 0.87, 0.83, 0.88 and 0.91 in the validation samples, respectively. Conclusions These results suggest that the ADM, ANXA3, SLC22A4 and VIM genes are reliable serum markers for the diagnosis of ischaemic stroke and that immune cell infiltration plays a crucial role in the occurrence and development of ischaemic stroke.

Background: Kawasaki disease (KD) is an acute vasculitis, that is, the leading cause of acquired heart disease in children, with approximately 10%–20% of patients with KD suffering intravenous immunoglobulin (IVIG) resistance. Although the underlying mechanism of this phenomenon remains unclear, recent studies have revealed that immune cell infiltration may associate with its occurrence.Methods: In this study, we downloaded the expression profiles from the GSE48498 and GSE16797 datasets in the Gene Expression Omnibus database, analyzed differentially expressed genes (DEGs), and intersected the DEGs with the immune-related genes downloaded from the ImmPort database to obtain differentially expressed immune-related genes (DEIGs). Then CIBERSORT algorithm was used to calculate the immune cell compositions, followed by the WGCNA analysis to identify the module genes associated with immune cell infiltration. Next, we took the intersection of the selected module genes and DEIGs, then performed GO and KEGG enrichment analysis. Moreover, ROC curve validation, Spearman analysis with immune cells, TF, and miRNA regulation network, and potential drug prediction were implemented for the finally obtained hub genes.Results: The CIBERSORT algorithm showed that neutrophil expression was significantly higher in IVIG-resistant patients compared to IVIG-responsive patients. Next, we got differentially expressed neutrophil-related genes by intersecting DEIGs with neutrophil-related module genes obtained by WGCNA, for further analysis. Enrichment analysis revealed that these genes were associated with immune pathways, such as cytokine-cytokine receptor interaction and neutrophil extracellular trap formation. Then we combined the PPI network in the STRING database with the MCODE plugin in Cytoscape and identified 6 hub genes (TLR8, AQP9, CXCR1, FPR2, HCK, and IL1R2), which had good diagnostic performance in IVIG resistance according to ROC analysis. Furthermore, Spearman’s correlation analysis confirmed that these genes were closely related to neutrophils. Finally, TFs, miRNAs, and potential drugs targeting the hub genes were predicted, and TF-, miRNA-, and drug-gene networks were constructed.Conclusion: This study found that the 6 hub genes (TLR8, AQP9, CXCR1, FPR2, HCK, and IL1R2) were significantly associated with neutrophil cell infiltration, which played an important role in IVIG resistance. In a word, this work rendered potential diagnostic biomarkers and prospective therapeutic targets for IVIG-resistant patients.

Nonalcoholic fatty liver disease (NAFLD) is considered the most prevalent chronic liver disease, but the understanding of the mechanism of NAFLD is still limited. The aim of our study was to explore hub lncRNAs and mRNAs and pathological processes in high-fat diet (HFD)-induced and lycopene-intervened liver steatosis. We analyzed the gene profiles in the GSE146627 dataset from the Gene Expression Omnibus (GEO) database to identify differentially expressed lncRNAs and mRNAs, and we constructed coexpression networks based on weighted gene coexpression network analysis (WGCNA). The Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) databases were utilized for functional enrichment analysis. We found that the turquoise, blue, brown, yellow, green, and black modules were significantly correlated with NAFLD. Functional enrichment analysis revealed that some hub lncRNAs (Smarca2, Tacc1, Flywch1, and Mef2c) might be involved in the regulation of the inflammatory and metabolic pathways (such as TNF signaling, metabolic, mTOR signaling, MAPK signaling, and p53 signaling pathways) in NAFLD. The establishment of an NAFLD mouse model confirmed that lycopene supply attenuated hepatic steatosis in HFD-induced NAFLD. Our analysis revealed that the inflammatory and metabolic pathways may be crucially involved in the pathogenesis of NAFLD, and hub lncRNAs provide novel biomarkers, therapeutic ideas, and targets for NAFLD. Moreover, lycopene has the potential to be a phytochemical for the prevention of HFD-induced liver steatosis. [ABSTRACT FROM AUTHOR]

In order to investigate physiological and molecular mechanisms underlying the impacts of organic selenium and nanoselenium on the drought stress of pak choi (Brassica chinensis var. pekinensis. cv. ‘Suzhouqing’), we sprayed 20 mL of 0, 5, 10, 20 mg L−1 concentrations of organic selenium (DCK, DO5, DO10, DO20) and nanoselenium (DN5, DN10, DN20). We measured photosynthetic pigment, antioxidant and nutritional quality, performed transcriptome sequencing of leaves using RNA-seq technology, obtained 297,176,114 clean sequences, and identified differential expressed genes. Organic selenium and nanoselenium increased the photosynthetic capacity by upregulating light-trapping pigment proteins, regulated reactive oxygen species homeostasis by activating the antioxidant system through the upregulation of glutathione S-transferase, and maintained water homeostasis through glutathione oxidase activity, thus enhancing weight. They contributed to the elevated total selenium content by increasing expression levels of ABC transporter, adenylate kinase, and cysteine desulphurase. Additionally, hub genes related to these metabolic pathways were identified using a weighted gene coexpression network analysis. The consistent results with the upregulated genes identified in the Kyoto Encyclopedia of Genes and Genomes pathway confirmed the accuracy and reliability of the transcriptome sequencing data. This study provides scientific reference to further in-depth research on the molecular effect of selenium on crops under drought stress conditions.

Abstract Aims Heart failure (HF) is a chronic heart disease with a high incidence and mortality. Due to the regulatory complexity of gene coexpression networks, the underlying hub genes regulation in HF remain incompletely appreciated. We aimed to explore potential key modules and genes for HF using weighted gene coexpression network analysis (WGCNA). Methods and results The expression profiles by high throughput sequencing of heart tissues samples from HF and non‐HF samples were obtained from the Gene Expression Omnibus database. Differentially expressed genes (DEGs) between HF and non‐HF samples were firstly identified. Then, a coexpression network was constructed to identify key modules and potential hub genes. The biological functions of potential hub genes were analysed by Gene Ontology and Kyoto Encyclopedia of Genes and Genomes pathway enrichment analyses. Finally, a protein–protein interaction (PPI) network was constructed using the STRING online tool. A total of 135 DEGs (133 up‐regulated and 2 down‐regulated DEGs) between HF and non‐HF samples were identified in the GSE135055 and GSE123976 datasets. Moreover, a total of 38 modules were screened based on WGCNA in the GSE135055 dataset, and six potential hub genes (UCK2, ASB1, CCNI, CUX1, IRX6, and STX16) were screened from the key module by setting the gene significance over 0.2 and the module membership over 0.8. Furthermore, 78 potential hub genes were obtained by taking the intersection of the 135 DEGs and all genes in the key module, and enrichment analysis revealed that they were mainly involved in the MAPK and PI3K‐AKT signalling pathways. Finally, in a PPI network constructed with the 78 potential hub genes, CUX1 and ASB1 were identified as hub genes in HF because they were also identified as potential hub genes in the WGCNA. Conclusions To the best of our knowledge, our study is the first to employ WGCNA to identify the key module and hub genes for HF. Our study identified a module and two genes that might play important roles in HF, which may provide potential biomarkers for the diagnosis of HF and improve our knowledge of the molecular mechanisms underlying HF.

Objective To identify M1 macrophage-related genes in rejection after kidney transplantation and construct a risk prediction model for renal allograft survival. Methods GSE36059 and GSE21374 datasets after kidney transplantation were downloaded from Gene Expression Omnibus (GEO) database. GSE36059 dataset included the samples from the recipients with rejection and stable allografts. Using this dataset, weighted gene co-expression network analysis (WGCNA) and differential analysis were conducted to screen the M1 macrophage-related differentially expressed gene (M1-DEG). Then, GSE21374 dataset (including the follow-up data of graft loss) was divided into the training set and validation set according to a ratio of 7∶3. In the training set, a multivariate Cox’s model was constructed using the variables screened by least absolute shrinkage and selection operator (LASSO), and the ability of this model to predict allograft survival was evaluated. CIBERSORT was employed to analyze the differences of infiltrated immune cells between the high-risk group and low-risk group, and the distribution of human leukocyte antigen (HLA)-related genes was analyzed between two groups. Gene set enrichment analysis (GSEA) was used to further clarify the biological process and pathway enrichment in the high-risk group. Finally, the database was employed to predict the microRNA (miRNA) interacting with the prognostic genes. Results In the GSE36059 dataset, 14 M1-DEG were screened. In the GSE21374 dataset, Toll-like receptor 8 (TLR8), Fc gamma receptor 1B (FCGR1B), BCL2 related protein A1 (BCL2A1), cathepsin S (CTSS), guanylate binding protein 2(GBP2) and caspase recruitment domain family member 16 (CARD16) were screened by LASSO-Cox regression analysis, and a multivariate Cox’s model was constructed based on these 6 M1-DEG. The area under curve (AUC) of receiver operating characteristic of this model for predicting the 1- and 3-year graft survival was 0.918 and 0.877 in the training set, and 0.765 and 0.736 in the validation set, respectively. Immune cell infiltration analysis showed that the infiltration of rest and activated CD4+ memory T cells, γδT cells and M1 macrophages were increased in the high-risk group (all P<0.05). The expression level of HLA I gene was up-regulated in the high-risk group. GSEA analysis suggested that immune response and graft rejection were enriched in the high-risk group. CTSS interacted with 8 miRNA, BCL2A1 and GBP2 interacted with 3 miRNA, and FCGR1B interacted with 1 miRNA. Conclusions The prognostic risk model based on 6 M1-DEG has high performance in predicting graft survival, which may provide evidence for early interventions for high-risk recipients. [ABSTRACT FROM AUTHOR]

Simple Summary: Global warming has always been a topic of concern for people. The polar regions are an ideal research area to study the impact of global warming because they are less affected by human activities. Seasonal meltwater lakes are a typical landscape in which parts of the soil are covered with water in the summer and exposed to air in the winter. In this study, three types of soils, including BPK (always above water), INT (sometimes above water, sometimes underwater), and SED (always underwater), were selected from the Kitezh Lake area in Antarctica to investigate the relationship between soil geochemical properties and bacterial and archaeal diversity and community structure. The key geochemical factors were found through RDA (CCA), and the network diagrams were drawn by WGCNA to find the hub OTUs. The results showed that pH, phosphate, nitrite, moisture content, ammonium, nitrate, and total carbon content all played important roles in bacterial and archaeal diversity and structure at different sites. This work provides an idea for future microbial diversity analysis, which is finding hub OTUs through WGCNA. Under the aggravation of global warming, this study also makes a contribution to predicting changes in soil microbial communities in lake regions. It is believed that polar regions are influenced by global warming more significantly, and because polar regions are less affected by human activities, they have certain reference values for future predictions. This study aimed to investigate the effects of climate warming on soil microbial communities in lake areas, taking Kitezh Lake, Antarctica as the research area. Below-peak soil, intertidal soil, and sediment were taken at the sampling sites, and we hypothesized that the diversity and composition of the bacterial and archaeal communities were different among the three sampling sites. Through 16S rDNA sequencing and analysis, bacteria and archaea with high abundance were obtained. Based on canonical correspondence analysis and redundancy analysis, pH and phosphate had a great influence on the bacterial community whereas pH and nitrite had a great influence on the archaeal community. Weighted gene coexpression network analysis was used to find the hub bacteria and archaea related to geochemical factors. The results showed that in addition to pH, phosphate, and nitrite, moisture content, ammonium, nitrate, and total carbon content also play important roles in microbial diversity and structure at different sites by changing the abundance of some key microbiota. [ABSTRACT FROM AUTHOR]

While the prognosis of multiple myeloma (MM) has significantly improved over the last decade because of new treatment options, it remains incurable. Aetiological explanations and biological targets based on genomics may provide additional help for rational disease intervention. Three microarray datasets associated with MM were downloaded from the Gene Expression Omnibus (GEO) database. GSE125364 and GSE39754 were used as the training set, and GSE13591 was used as the verification set. The differentially expressed genes (DEGs) were obtained from the training set, and Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analyses were performed to annotate their functions. The hub genes were derived from the combined results of a protein–protein interaction (PPI) network and weighted gene coexpression network analysis (WGCNA). The receiver operating characteristic (ROC) curves of hub genes were plotted to evaluate their clinical diagnostic value. Biological processes and signaling pathways associated with hub genes were explained by gene set enrichment analysis (GSEA). A total of 1759 DEGs were identified. GO and KEGG pathway analyses suggested that the DEGs were related to the process of protein metabolism. RPN1, SEC61A1, SPCS1, SRPR, SRPRB, SSR1 and TRAM1 were proven to have clinical diagnostic value for MM. The GSEA results suggested that the hub genes were widely involved in the N-glycan biosynthesis pathway. The hub genes identified in this study can partially explain the potential molecular mechanisms of MM and serve as candidate biomarkers for disease diagnosis. [ABSTRACT FROM AUTHOR]

The risk of suicide in patients with major depressive disorder (MDD) poses a major concern, with studies suggesting that genetics may be a contributing factor. Although there are many transcriptomic studies on postmortem brain tissue related to suicidal behavior, the blood transcriptional mechanisms of suicidal ideation (SI) remain unknown. This study utilized a weighted gene coexpression network analysis (WGCNA) approach to investigate the associations between gene coexpression modules and SI in individuals with MDD using peripheral blood RNA-seq data from 75 MDD patients with SI (MDD&#95;SI), 82 MDD patients without SI (MDD&#95;nSI), and 149 healthy controls (HC). An ANCOVA was conducted to assess differences in gene coexpression modules among groups, with age and sex included as covariates. The gene ontology (GO) and Kyoto encyclopedia of genes and genomes (KEGG) databases were used to annotate module functions. Results indicated that the magenta module (associated with RNA splicing processes) differentiated MDD&#95;SI from MDD&#95;nSI (p = 0.021), while the green module (related to immune and inflammatory responses) distinguished MDD&#95;SI from HC (p = 0.004). Additionally, three modules showed differences between MDD&#95;nSI and HC: magenta (p = 0.009), brown (related to innate immunity and mitochondrial metabolism; p = 0.001), and turquoise (associated with energy metabolism and neurodegeneration; p = 0.005). Our findings highlight that gene expression regulation, immune response, and inflammation may be linked to SI in patients with MDD, while pathways associated with innate immunity, energy metabolism, mitochondrial function, and neurodegeneration appear to be more broadly related to MDD., Competing Interests: Declarations. Competing interests: The authors declare no competing interests. Ethics approval and consent to participate: The current study was performed in accordance with the Declaration of Helsinki. All participants provided written informed consent as approved by the Ethics Committee of West China Hospital of Sichuan University (No. [2016] 170)., (© 2025. The Author(s).)

Exposure to extended periods of darkness is a common source of abiotic stress that significantly affects plant growth and development. To understand how Nicotiana benthamiana responds to dark stress, the proteomes and metabolomes of leaves treated with darkness were studied. In total, 5763 proteins and 165 primary metabolites were identified following dark treatment. Additionally, the expression of autophagy‐related gene (ATG) proteins was transiently upregulated. Weighted gene coexpression network analysis (WGCNA) was utilized to find the protein modules associated with the response to dark stress. A total of four coexpression modules were obtained. The results indicated that heat‐shock protein (HSP70), SnRK1‐interacting protein 1, 2A phosphatase‐associated protein of 46 kDa (Tap46), and glutamate dehydrogenase (GDH) might play crucial roles in N. benthamiana’s response to dark stress. Furthermore, a protein–protein interaction (PPI) network was constructed and top‐degreed proteins were predicted to identify potential key factors in the response to dark stress. These proteins include isopropylmalate isomerase (IPMI), eukaryotic elongation factor 5A (ELF5A), and ribosomal protein 5A (RPS5A). Finally, metabolic analysis suggested that some amino acids and sugars were involved in the dark‐responsive pathways. Thus, these results provide a new avenue for understanding the defensive mechanism against dark stress at the protein and metabolic levels in N. benthamiana.

Abstract Background The diameter of the abdominal aortic aneurysm (AAA) is the most commonly used parameter for the prediction of occurrence of AAA rupture. However, the most vulnerable region of the aortic wall may be different from the most dilated region of AAA under pressure. The present study is the first to use weighted gene coexpression network analysis (WGCNA) to detect the coexpressed genes that result in regional weakening of the aortic wall. Methods The GSE165470 raw microarray dataset was used in the present study. Differentially expressed genes (DEGs) were filtered using the “limma” R package. DEGs were assessed by Gene Ontology biological process (GO-BP) and Kyoto Encyclopedia of Genes and Genomes (KEGG) analyses. WGCNA was used to construct the coexpression networks in the samples with regional weakening of the AAA wall and in the control group to detect the gene modules. The hub genes were defined in the significant functional modules, and a hub differentially expressed gene (hDEG) coexpression network was constructed with the highest confidence based on protein–protein interactions (PPIs). Molecular compound detection (MCODE) was used to identify crucial genes in the hDEG coexpression network. Crucial genes in the hDEG coexpression network were validated using the GSE7084 and GSE57691 microarray gene expression datasets. Result A total of 350 DEGs were identified, including 62 upregulated and 288 downregulated DEGs. The pathways were involved in immune responses, vascular smooth muscle contraction and cell–matrix adhesion of DEGs in the samples with regional weakening in AAA. Antiquewhite3 was the most significant module and was used to identify downregulated hDEGs based on the result of the most significant modules negatively related to the trait of weakened aneurysm walls. Seven crucial genes were identified and validated: ACTG2, CALD1, LMOD1, MYH11, MYL9, MYLK, and TPM2. These crucial genes were associated with the mechanisms of AAA progression. Conclusion We identified crucial genes that may play a significant role in weakening of the AAA wall and may be potential targets for medical therapies and diagnostic biomarkers. Further studies are required to more comprehensively elucidate the functions of crucial genes in the pathogenesis of regional weakening in AAA.

AimsThe findings in epidemiological studies suggest that endometrioid endometrial cancer (EEC) is associated with obesity. However, evidence from gene expression data for the relationship between the two is still lacking. The purpose of this study was to explore the merits of establishing an obesity-related genes (ORGs) signature in the treatment and the prognostic assessment of EEC.MethodsMicroarray data from GSE112307 were utilized to identify ORGs by using weighted gene co-expression network analysis. Based on the sequencing data from TCGA, we established the prognostic ORGs signature, confirmed its value as an independent risk factor, and constructed a nomogram. We further investigated the association between grouping based on ORGs signature and clinicopathological characteristics, immune infiltration, tumor mutation burden and drug sensitivity.ResultsA total of 10 ORGs were identified as key genes for the construction of the signature. According to the ORGs score computed from the signature, EEC patients were divided into high and low-scoring groups. Overall survival (OS) was shorter in EEC patients in the high-scoring group compared with the low-scoring group (P

The etiologies and pathogenesis of dilated cardiomyopathy (DCM) with heart failure (HF) remain to be defined. Thus, exploring specific diagnosis biomarkers and mechanisms is urgently needed to improve this situation. In this study, three gene expression profiling datasets (GSE29819, GSE21610, GSE17800) and one single-cell RNA sequencing dataset (GSE95140) were obtained from the Gene Expression Omnibus (GEO) database. GSE29819 and GSE21610 were combined into the training group, while GSE17800 was the test group. We used the weighted gene co-expression network analysis (WGCNA) and identified fifteen driver genes highly associated with DCM with HF in the module. We performed the least absolute shrinkage and selection operator (LASSO) on the driver genes and then constructed five machine learning classifiers (random forest, gradient boosting machine, neural network, eXtreme gradient boosting, and support vector machine). Random forest was the best-performing classifier established on five Lasso-selected genes, which was utilized to select out NPPA, OMD, and PRELP for diagnosing DCM with HF. Moreover, we observed the up-regulation mRNA levels and robust diagnostic accuracies of NPPA, OMD, and PRELP in the training group and test group. Single-cell RNA-seq analysis further demonstrated their stable up-regulation expression patterns in various cardiomyocytes of DCM patients. Besides, through gene set enrichment analysis (GSEA), we found TGF-β signaling pathway, correlated with NPPA, OMD, and PRELP, was the underlying mechanism of DCM with HF. Overall, our study revealed NPPA, OMD, and PRELP serving as diagnostic biomarkers for DCM with HF, deepening the understanding of its pathogenesis.

Background This study aimed to explore predictive biomarkers of coronary collateralization in patients with chronic total occlusion. Methods and Results By using a microarray expression profiling program downloaded from the Gene Expression Omnibus database, weighted gene coexpression network analysis was constructed to analyze the relationship between potential modules and coronary collateralization and screen out the hub genes. Then, the hub gene was identified and validated in an independent cohort of patients (including 299 patients with good arteriogenic responders and 223 patients with poor arteriogenic responders). Weighted gene coexpression network analysis showed that SERPING1 in the light‐cyan module was the only gene that was highly correlated with both the gene module and the clinical traits. Serum levels of serpinG1 were significantly higher in patients with bad arteriogenic responders than in patients with good arteriogenic responders (472.53±197.16 versus 314.80±208.92 μg/mL; P

Background: Breast cancer (BC) is a malignant tumor with the highest morbidity among women, disrupting millions of their lives worldwide each year. However, the molecular mechanisms underlying remain unclear. Materials and Methods: The RNA-Sequencing and clinical data of BC patients from The Cancer Genome Atlas (TCGA) database were analyzed by weighted gene coexpression network analysis (WGCNA). Additionally, coexpressed modules were used to detect their correlation with the clinical traits of BC. Next, nodes of the most significant coexpression modules were used for Gene Ontology (GO), Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway, mRNA-lncRNA coexpression network and survival analyses. Results: In total, 2056 differentially expressed mRNAs (DEmRNAs) and 297 differentially expressed lncRNAs (DElncRNAs) were identified and subjected to WGCNA analysis, and 12 coexpression modules were generated. The top five significant modules (turquoise, green, red, brown, and blue modules) were related to one or more clinical traits of BC. In particular, the turquoise and green modules were chosen for further analysis. Next, by lncRNA-mRNA coexpression analysis of the turquoise and green modules, 12 DEmRNAs and 2 DElncRNAs were identified as hub nodes. The lncRNA-associated mRNAs of the networks were commonly related to several cancer-related pathways. Moreover, these networks also revealed central roles for RP11-389C8.2 and TGFBR2 in the turquoise module and MYLK, KIT, and RP11-394O4.5 in the green module. Furthermore, 16 DEmRNAs and 3 DElncRNAs in these two modules were significantly correlated with the overall survival of BC patients. Conclusions: The authors' study identified some prognostic biomarkers that might play important roles in the development and treatment of BC. In particular, lncRNAs AC016995.3, RP1-193H18.2, and RP11-166D19.1 were novel biomarkers for BC. [ABSTRACT FROM AUTHOR]

Background: In intensive care units (ICU), mechanical ventilation (MV) is commonly applied to save patients' lives. However, ventilator-induced diaphragm dysfunction (VIDD) can complicate treatment by hindering weaning in critically ill patients and worsening outcomes. The goal of this study was to identify potential genes involved in the endogenous protective mechanism against VIDD. Methods: Twelve adult male rabbits were assigned to either an MV group or a control group under the same anesthetic conditions. Immunostaining and quantitative morphometry were used to assess diaphragm atrophy, while RNA-seq was used to investigate molecular differences between the groups. Additionally, core module and hub genes were analyzed using WGCNA, and co-differentially expressed hub genes were subsequently discovered by overlapping the differentially expressed genes (DEGs) with the hub genes from WGCNA. The identified genes were validated by western blotting (WB) and quantitative real-time polymerase chain reaction (qRT–PCR). Results: After a VIDD model was successfully built, 1276 DEGs were found between the MV and control groups. The turquoise and yellow modules were identified as the core modules, and Trim63, Fbxo32, Uchl1, Tmprss13, and Cst3 were identified as the five co-differentially expressed hub genes. After the two atrophy-related genes (Trim63 and Fbxo32) were excluded, the levels of the remaining three genes/proteins (Uchl1/UCHL1, Tmprss13/TMPRSS13, and Cst3/CST3) were found to be significantly elevated in the MV group (P < 0.05), suggesting the existence of a potential antiproteasomal, antiapoptotic, and antiautophagic mechanism against diaphragm dysfunction. Conclusion: The current research helps to reveal a potentially important endogenous protective mechanism that could serve as a novel therapeutic target against VIDD. [ABSTRACT FROM AUTHOR]

Gossypium barbadense possesses a superior fiber quality because of its fiber length and strength. An in-depth analysis of the underlying genetic mechanism could aid in filling the gap in research regarding fiber strength and could provide helpful information for Gossypium barbadense breeding. Three quantitative trait loci related to fiber strength were identified from a Gossypium barbadense recombinant inbred line (PimaS-7 × 5917) for further analysis. RNA sequencing was performed in the fiber tissues of PimaS-7 × 5917 0–35 days postanthesis. Four specific modules closely related to the secondary wall-thickening stage were obtained using the weighted gene coexpression network analysis. In total, 55 genes were identified as differentially expressed from 4 specific modules. Gene Ontology and the Kyoto Encyclopedia of Genes and Genomes were used for enrichment analysis, and Gbar_D11G032910, Gbar_D08G020540, Gbar_D08G013370, Gbar_D11G033670, and Gbar_D11G029020 were found to regulate fiber strength by playing a role in the composition of structural constituents of cytoskeleton and microtubules during fiber development. Quantitative real-time PCR results confirmed the accuracy of the transcriptome data. This study provides a quick strategy for exploring candidate genes and provides new insights for improving fiber strength in cotton. [ABSTRACT FROM AUTHOR]

Background: CD41+ memory T cells (CD4+ MTCs), as an important part of the microenvironment affecting tumorigenesis and progression, have rarely been systematically analyzed. Our purpose was to comprehensively analyze the effect of CD4+ MTC infiltration on the prognosis of colon adenocarcinoma (COAD). Methods: Based on RNA-Seq data, weighted gene co-expression network analysis (WGCNA) was used to screen the CD4+ MTC infiltration genes most associated with colon cancer and then identify hub genes and construct a prognostic model using the least absolute shrinkage and selection operator algorithm (LASSO). Finally, survival analysis, immune efficacy analysis, and drug sensitivity analysis were performed to evaluate the role of the prognostic model in COAD. Results: We identified 929 differentially expressed genes (DEGs) associated with CD4+ MTCs and constructed a prognosis model based on five hub genes (F2RL2, TGFB2, DTNA, S1PR5, and MPP2) to predict overall survival (OS) in COAD. Kaplan--Meier analysis showed poor prognosis in the high-risk group, and the analysis of the hub gene showed that overexpression of TGFB2, DTNA, S1PR5, or MPP2 was associated with poor prognosis. Clinical prediction nomograms combining CD4+ MTC-related DEGs and clinical features were constructed to accurately predict OS and had high clinical application value. Immune efficacy and drug sensitivity analysis provide new insights for individualized treatment. Conclusion: We constructed a prognostic risk model to predict OS in COAD and analyzed the effects of risk score on immunotherapy efficacy or drug sensitivity. These studies have important clinical significance for individualized targeted therapy and prognosis. [ABSTRACT FROM AUTHOR]

Craniopharyngiomas (CPs) are histologically benign tumors located in the sellar-suprasellar region. Although the transcriptome development in recent years have deepened our knowledge to the tumorigenesis process of adamantinomatous craniopharyngioma (ACP), the peritumoral immune infiltration of tumor is still not well understood. In this study, weighted gene coexpression network analysis (WGCNA) was applied to identify different gene modules based on clinical characteristics and gene expression, and then, the protein-protein interaction (PPI) network with the Cytohubba plug-in were performed to screen pivotal genes. In addition, immune cell infiltration (ICI) analysis was used to evaluate the immune microenvironment of ACP patients. In total, 8,568 differential expression genes were identified based on our datasets and two microarray profiles from the public database. The functional enrichment analysis revealed that upregulated genes were mainly enriched in immune-related pathways while downregulated genes were shown in the hormone and transduction of signaling pathways. The WGCNA investigated the most relevant modules, and 1,858 hub genes was detected, from which the PPI network identified 14 pivotal genes, and the Hypoxia-inducible factor 1-alpha (HIF-1α) pathway including four critical genes may be involved in the development of ACP. Moreover, naïve CD4+ and CD8+ T cells were decreased while specific subtypes of T cells were significantly increased in ACP patients according to ICI analysis. Validation by immunofluorescence staining revealed a higher expression of HIF-1α in ACP (ACP vs. control) and adult-subtype (adult vs. children), suggesting a possible state of immune system activation. Notably, children with low HIF-1α scores were related to the hypothalamus involvement and hydrocephalus symptoms. In this study, we successfully identified HIF-1α as a key role in the tumorigenesis and development of ACP through comprehensive integrated analyses and systematically investigated the potential relationship with immune cells in ACP. The results may provide valuable resources for understanding the underlying mechanisms of ACP and strengthen HIF-1α as a potential immunotherapeutic target in clinical application. [ABSTRACT FROM AUTHOR]

Systemic lupus erythematosus (SLE) is a chronic autoimmune disease characterized by excessive activation of T and B lymphocytes and breakdown of immune tolerance to autoantigens. Despite several mechanisms including the genetic alterations and inflammatory responses have been reported, the overall signature genes in CD4+ T cells and how they affect the pathological process of SLE remain to be elucidated. This study aimed to identify the crucial genes, potential biological processes and pathways underlying SLE pathogenesis by integrated bioinformatics. The gene expression profiles of isolated peripheral CD4+ T cells from SLE patients with different disease activity and healthy controls (GSE97263) were analyzed, and 14 co-expressionmodules were identified usingweighted gene co-expression network analysis (WGCNA). Some of these modules showed significantly positive or negative correlations with SLE disease activity, and primarily enriched in the regulation of type I interferon and immune responses. Next, combining time course sequencing (TCseq) with differentially expressed gene (DEG) analysis, crucial genes in lupus CD4+ T cells were revealed, including some interferon signature genes (ISGs). Among these genes, we identified 4 upregulated genes (PLSCR1, IFI35, BATF2 and CLDN5) and 2 downregulated genes (GDF7 and DERL3) as newfound key genes. The elevated genes showed close relationship with the SLE disease activity. In general, our study identified 6 novel biomarkers in CD4+ T cells that might contribute to the diagnosis and treatment of SLE. [ABSTRACT FROM AUTHOR]

Background: The immune system plays a vital role in the pathological process of ischaemic stroke. However, the exact immune-related mechanism remains unclear. The current research aimed to identify immune-related key genes associated with ischaemic stroke.Methods: CIBERSORT was utilized to reveal the immune cell infiltration pattern in ischaemic stroke patients. Meanwhile, a weighted gene coexpression network analysis (WGCNA) was utilized to identify meaningful modules significantly correlated with ischaemic stroke. The characteristic genes correlated with ischaemic stroke were identified by the following two machine learning methods: the support vector machine-recursive feature elimination (SVM-RFE) algorithm and least absolute shrinkage and selection operator (LASSO) logistic regression.Results: The CIBERSORT results suggested that there was a decreased infiltration of naive CD4 T cells, CD8 T cells, resting mast cells and eosinophils and an increased infiltration of neutrophils, M0 macrophages and activated memory CD4 T cells in ischaemic stroke patients. Then, three significant modules (pink, brown and cyan) were identified to be significantly associated with ischaemic stroke. The gene enrichment analysis indicated that 519 genes in the above three modules were mainly involved in several inflammatory or immune-related signalling pathways and biological processes. Eight hub genes (ADM, ANXA3, CARD6, CPQ, SLC22A4, UBE2S, VIM and ZFP36) were revealed to be significantly correlated with ischaemic stroke by the LASSO logistic regression and SVM-RFE algorithm. The external validation combined with a RT‒qPCR analysis revealed that the expression levels of ADM, ANXA3, SLC22A4 and VIM were significantly increased in ischaemic stroke patients and that these key genes were positively associated with neutrophils and M0 macrophages and negatively correlated with CD8 T cells. The mean AUC value of ADM, ANXA3, SLC22A4 and VIM was 0.80, 0.87, 0.91 and 0.88 in the training set, 0.85, 0.77, 0.86 and 0.72 in the testing set and 0.87, 0.83, 0.88 and 0.91 in the validation samples, respectively.Conclusions: These results suggest that the ADM, ANXA3, SLC22A4 and VIM genes are reliable serum markers for the diagnosis of ischaemic stroke and that immune cell infiltration plays a crucial role in the occurrence and development of ischaemic stroke. [ABSTRACT FROM AUTHOR]

Abstract Background This study aimed to screen the feature modules and characteristic genes related to ulcerative colitis (UC) and construct a support vector machine (SVM) classifier to distinguish UC patients. Methods Four datasets that contained UC and control samples were obtained from the Gene Expression Omnibus database. Differentially expressed genes (DEGs) with consistency were screened via the MetaDE method. The weighted gene coexpression network (WGCNA) was used to distinguish significant modules based on the four datasets. The protein–protein interaction network was established based on intersection genes. Enrichment analysis of Gene Ontology (GO) biological processes (BPs) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment were established based on DAVID. An SVM combined with recursive feature elimination was also applied to construct a disease classifier for the disease diagnosis of UC patients. The efficacy of the SVM classifier was evaluated through receiver operating characteristic curves. Results Twelve highly preserved modules were obtained using the WGCNA, and 2009 DEGs with significant consistency were selected using the MetaDE method. Sixteen significantly related GO BPs and 12 KEGG pathways were obtained, such as cytokine-cytokine receptor interaction, cell adhesion molecules, and leukocyte transendothelial migration. Subsequently, 41 genes were used to construct an SVM classifier, such as CXCL1, CCR2, IL1B, and IL1A. The area under the curve (AUC) was 0.999 in the training dataset, whereas the AUC was 0.886, 0.790, and 0.819 in the validation set (GSE65114, GSE37283, and GSE36807, respectively). Conclusions An SVM classifier based on feature genes might correctly identify healthy people or UC patients.

Xiaocong Jiang,1,&ast; Ting Song,2,&ast; Xiuhua Pan,1 Xinyu Zhang,1 Yuhong Lan,1 Li Bai1 1Department of Radiotherapy Oncology, Huizhou Central People’s Hospital, Huizhou, 516001, Guangdong, People’s Republic of China; 2Department of Hepatology, The Sixth People’s Hospital of Qingdao, Qingdao, 266033, Shandong, People’s Republic of China&ast;These authors contributed equally to this workCorrespondence: Li Bai Email bl_0067@126.comObjective: The occurrence and development of hepatocellular carcinoma (HCC) remain unclear. This study aimed to investigate potential diagnostic or prognostic markers for early HCC by applying bioinformatic analysis.Methods: The gene expression profiles of early HCC and normal tissues from a TCGA dataset were used to identify differentially expressed genes (DEGs) and then analysed by weighted gene coexpression network analysis. The integrated genes were selected to construct the protein–protein interaction (PPI) network and determine the hub genes. The prognostic impact of the hub genes was then analysed.Results: A total of 508 integrated genes were selected from the 615 DEGs and 8956 genes in the turquoise module. A PPI network was constructed, and the top 20 hub genes, including apolipoprotein A-IV (APOA4), fibrinogen gamma chain (FGG), vitamin K-dependent protein Z (PROZ), secreted phosphoprotein 24 (SPP2) and fetuin-B (FETUB), were identified. Only PROZ was significantly associated with the prognosis of early HCC.Conclusion: In this study, we demonstrated that the expression of PROZ was decreased in early HCC compared with normal liver controls, and low PROZ expression might result in poor overall survival of early HCC.Keywords: hepatocellular carcinoma, weighted gene coexpression network analysis, bioinformatics analysis, vitamin K-dependent protein Z

Background: CD4+ memory T cells (CD4+ MTCs), as an important part of the microenvironment affecting tumorigenesis and progression, have rarely been systematically analyzed. Our purpose was to comprehensively analyze the effect of CD4+ MTC infiltration on the prognosis of colon adenocarcinoma (COAD).Methods: Based on RNA-Seq data, weighted gene co-expression network analysis (WGCNA) was used to screen the CD4+ MTC infiltration genes most associated with colon cancer and then identify hub genes and construct a prognostic model using the least absolute shrinkage and selection operator algorithm (LASSO). Finally, survival analysis, immune efficacy analysis, and drug sensitivity analysis were performed to evaluate the role of the prognostic model in COAD.Results: We identified 929 differentially expressed genes (DEGs) associated with CD4+ MTCs and constructed a prognosis model based on five hub genes (F2RL2, TGFB2, DTNA, S1PR5, and MPP2) to predict overall survival (OS) in COAD. Kaplan–Meier analysis showed poor prognosis in the high-risk group, and the analysis of the hub gene showed that overexpression of TGFB2, DTNA, S1PR5, or MPP2 was associated with poor prognosis. Clinical prediction nomograms combining CD4+ MTC-related DEGs and clinical features were constructed to accurately predict OS and had high clinical application value. Immune efficacy and drug sensitivity analysis provide new insights for individualized treatment.Conclusion: We constructed a prognostic risk model to predict OS in COAD and analyzed the effects of risk score on immunotherapy efficacy or drug sensitivity. These studies have important clinical significance for individualized targeted therapy and prognosis.

Zetao Ma,1,2,&ast; Xizhi Wang,1,&ast; Qingbo Lv,1 Yingchao Gong,1 Minghong Xia,1 Lenan Zhuang,1 Xue Lu,1 Ying Yang,1 Wenbin Zhang,1 Guosheng Fu,1 Yang Ye,1 Dongwu Lai1 1Key Laboratory of Cardiovascular Intervention and Regenerative Medicine of Zhejiang Province, Department of Cardiology, Sir Run Run Shaw Hospital, Zhejiang University School of Medicine, Hangzhou, Zhejiang Province, 310016, People’s Republic of China; 2Department of Cardiology, Zhongshan People’s Hospital, Zhongshan, Guangdong Province, 528403, People’s Republic of China&ast;These authors contributed equally to this workCorrespondence: Dongwu Lai; Yang YeKey Laboratory of Cardiovascular Intervention and Regenerative Medicine of Zhejiang Province, Department of Cardiology, Sir Run Run Shaw Hospital, Zhejiang University School of Medicine, 3 East Qingchun Road, Hangzhou, Zhejiang Province, 310016, People’s Republic of ChinaTel +86-571-86006241Fax +86-571-86006246Email laidw@zju.edu.cn; yeyang1222@zju.edu.cnBackground: Considered as one of the major reasons of sudden cardiac death, hypertrophic cardiomyopathy (HCM) is a common inherited cardiovascular disease. However, effective treatment for HCM is still lacking. Identification of hub gene may be a powerful tool for discovering potential therapeutic targets and candidate biomarkers.Methods: We analysed three gene expression datasets for HCM from the Gene Expression Omnibus. Two of them were merged by “sva” package. The merged dataset was used for analysis while the other dataset was used for validation. Following this, a weighted gene coexpression network analysis (WGCNA) was performed, and the key module most related to HCM was identified. Based on the intramodular connectivity, we identified the potential hub genes. Then, a receiver operating characteristic curve analysis was performed to verify the diagnostic values of hub genes. Finally, we validated changes of hub genes, for genetic transcription and protein expression levels, in datasets of HCM patients and myocardium of transverse aortic constriction (TAC) mice.Results: In the merged dataset, a total of 455 differentially expressed genes (DEGs) were identified from normal and hypertrophic myocardium. In WGCNA, the blue module was identified as the key module and the genes in this module showed a high positive correlation with HCM. Functional enrichment analysis of DEGs and key module revealed that the extracellular matrix, fibrosis, and neurohormone pathways played important roles in HCM. FRZB, COL14A1, CRISPLD1, LUM, and sFRP4 were identified as hub genes in the key module. These genes showed a good predictive value for HCM and were significantly up-regulated in HCM patients and TAC mice. We also found protein expression of LUM and sFRP4 increased in myocardium of TAC mice.Conclusion: This study revealed that five hub genes are involved in the occurrence and development of HCM, and they are potentially to be used as therapeutic targets and biomarkers for HCM.Keywords: hypertrophic cardiomyopathy, HCM, weighted gene coexpression network analysis, WGCNA, hub gene, biomarkers, bioinformatics analysis

Abstract Purpose Exploring the molecular mechanisms of lung adenocarcinoma (LUAD) is beneficial for developing new therapeutic strategies and predicting prognosis. This study was performed to select core genes related to LUAD and to analyze their prognostic value. Methods Microarray datasets from the GEO (GSE75037) and TCGA-LUAD datasets were analyzed to identify differentially coexpressed genes in LUAD using weighted gene coexpression network analysis (WGCNA) and differential gene expression analysis. Functional enrichment analysis was conducted, and a protein–protein interaction (PPI) network was established. Subsequently, hub genes were identified using the CytoHubba plug-in. Overall survival (OS) analyses of hub genes were performed. The Clinical Proteomic Tumor Analysis Consortium (CPTAC) and the Human Protein Atlas (THPA) databases were used to validate our findings. Gene set enrichment analysis (GSEA) of survival-related hub genes were conducted. Immunohistochemistry (IHC) was carried out to validate our findings. Results We identified 486 differentially coexpressed genes. Functional enrichment analysis suggested these genes were primarily enriched in the regulation of epithelial cell proliferation, collagen-containing extracellular matrix, transforming growth factor beta binding, and signaling pathways regulating the pluripotency of stem cells. Ten hub genes were detected using the maximal clique centrality (MCC) algorithm, and four genes were closely associated with OS. The CPTAC and THPA databases revealed that CHRDL1 and SPARCL1 were downregulated at the mRNA and protein expression levels in LUAD, whereas SPP1 was upregulated. GSEA demonstrated that DNA-dependent DNA replication and catalytic activity acting on RNA were correlated with CHRDL1 and SPARCL1 expression, respectively. The IHC results suggested that CHRDL1 and SPARCL1 were significantly downregulated in LUAD. Conclusions Our study revealed that survival-related hub genes closely correlated with the initiation and progression of LUAD. Furthermore, CHRDL1 and SPARCL1 are potential therapeutic and prognostic indicators of LUAD.

• We investigated the important role of neutrophils and NK cells in delayed cerebral ischemia and suggested for the first time a possible protective role of NK cells. • Plasma biomarkers are now an important part of clinical trial inclusion criteria. We used weighted correlation analysis network (WGCNA), machine learning algorithms and single cell transcriptomics, diagnostic tools to identify plasma biomarkers and experimental validation using blood samples from delayed cerebral ischemia patients. • We propose that valproic acid may be an important drug for the treatment of delayed cerebral ischemia, and our findings may provide new perspectives for future targeted therapies. Delayed cerebral ischemia (DCI) is a common and serious complication of subarachnoid hemorrhage (SAH). Its pathogenesis is not fully understood. Here, we developed a predictive model based on peripheral blood biomarkers and validated the model using several bioinformatic multi-analysis methods. Six datasets were obtained from the GEO database. Characteristic genes were screened using weighted correlation network analysis (WGCNA) and differentially expressed genes. Three machine learning algorithms, elastic networks-LASSO, support vector machines (SVM-RFE) and random forests (RF), were also used to construct diagnostic prediction models for key genes. To further evaluate the performance and predictive value of the diagnostic models, nomogram model were constructed, and the clinical value of the models was assessed using Decision Curve Analysis (DCA), Area Under the Check Curve (AUC), Clinical Impact Curve (CIC), and validated in the mouse single-cell RNA-seq dataset. Mendelian randomization(MR) analysis explored the causal relationship between SAH and stroke, and the intermediate influencing factors. We validated this by retrospectively analyzing the qPCR levels of the most relevant genes in SAH and SAH-DCI patients. This experiment demonstrated a statistically significant difference between SAH and SAH-DCI and normal group controls. Finally, potential small molecule compounds interacting with the selected features were screened from the Comparative Toxicogenomics Database (CTD). The fGSEA results showed that activation of Toll-like receptor signaling and leukocyte transendothelial cell migration pathways were positively correlated with the DCI phenotype, whereas cytokine signaling pathways and natural killer cell-mediated cytotoxicity were negatively correlated. Consensus feature selection of DEG genes using WGCNA and three machine learning algorithms resulted in the identification of six genes (SPOCK2, TRRAP, CIB1, BCL11B, PDZD8 and LAT), which were used to predict DCI diagnosis with high accuracy. Three external datasets and the mouse single-cell dataset showed high accuracy of the diagnostic model, in addition to high performance and predictive value of the diagnostic model in DCA and CIC. MR analysis looked at stroke after SAH independent of SAH, but associated with multiple intermediate factors including Hypertensive diseases, Total triglycerides levels in medium HDL and Platelet count. qPCR confirmed that significant differences in DCI signature genes were observed between the SAH and SAH-DCI groups. Finally, valproic acid became a potential therapeutic agent for DCI based on the results of target prediction and molecular docking of the characterized genes. This diagnostic model can identify SAH patients at high risk for DCI and may provide potential mechanisms and therapeutic targets for DCI. Valproic acid may be an important future drug for the treatment of DCI. [ABSTRACT FROM AUTHOR]

Purpose: Abdominal aortic aneurysm (AAA) is a severe cardiovascular disease that often results in high mortality due to sudden rupture. This paper aims to explore potential molecular mechanisms and effective targeted therapies to prevent and delay AAA rupture. Methods: We downloaded two microarray datasets (GSE98278 and GSE17901) from the Gene Expression Omnibus (GEO) database. Differential analysis and single-sample gene set enrichment analysis (ssGSEA) of hypoxia scores were performed on 48 AAA patients in GSE98278. We identified hypoxia- and ruptured AAA-related gene modules using weighted gene coexpression network analysis (WGCNA). Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) analyses were performed using the R package clusterProfiler. For candidate genes, validation was conducted on the mouse dataset GSE17901. Finally, we predicted drug candidates associated with the hub genes using the HERB Chinese medicine database. Results: Eighty-two differentially expressed genes were screened in the ruptured and stable groups; 103 differentially expressed genes were identified between the high- and low-hypoxia groups; and WGCNA identified 58 differentially expressed genes. Finally, nine candidate genes were screened, including two hub genes (MEDAG and SERPINE1). We identified pathways such as cytokine-cytokine receptor interaction and T-helper 1-type immune response involved in AAA hypoxia and rupture. We predicted 93 traditional Chinese medicines (TCMs) associated with MEDAG and SERPINE1. Conclusion: We identified the hypoxic molecules MEDAG and SERPINE1 associated with AAA rupture. Our study provides an additional direction for the association between hypoxia and AAA rupture. [ABSTRACT FROM AUTHOR]

Abstract Background Intrahepatic cholangiocarcinoma (iCCA) is the second most common malignant hepatic tumor and has a high postoperative recurrence rate and a poor prognosis. The key roles of most tumor recurrence-associated molecules in iCCA remain unclear. This study aimed to explore hub genes related to the postsurgical recurrence of iCCA. Method Differentially expressed genes (DEGs) between iCCA samples and normal liver samples were screened from The Cancer Genome Atlas (TCGA) database and used to construct a weighted gene coexpression network. Module-trait correlations were calculated to identify the key module related to recurrence in iCCA patients. Genes in the key module were subjected to functional enrichment analysis, and candidate hub genes were filtered through coexpression and protein–protein interaction (PPI) network analysis. Validation studies were conducted to detect the “real” hub gene. Furthermore, the biological functions and the underlying mechanism of the real hub gene in iCCA tumorigenesis and progression were determined via in vitro experiments. Results A total of 1019 DEGs were filtered and used to construct four coexpression modules. The red module, which showed the highest correlations with the recurrence status, family history, and day to death of patients, was identified as the key module. Gene Ontology (GO) enrichment and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analyses demonstrated that genes in the red module were enriched in genes and pathways related to tumorigenesis and tumor progression. We performed validation studies and identified estrogen receptor 1 (ESR1), which significantly impacted the prognosis of iCCA patients, as the real hub gene related to the recurrence of iCCA. The in vitro experiments demonstrated that ESR1 overexpression significantly suppressed cell proliferation, migration, and invasion, whereas ESR1 knockdown elicited opposite effects. Further investigation into the mechanism demonstrated that ESR1 acts as a tumor suppressor by inhibiting the JAK/STAT3 signaling pathway. Conclusions ESR1 was identified as the real hub gene related to the recurrence of iCCA that plays a critical tumor suppressor role in iCCA progression. ESR1 significantly impacts the prognosis of iCCA patients and markedly suppresses cholangiocarcinoma cell proliferation, migration and invasion by inhibiting JAK/STAT3 signaling pathway.