Identification Of Hub Genes Research Articles

Identification and Functional Investigation of Hub Genes Associated with Follicular Lymphoma.

Follicular lymphoma (FL), the most common type of indolent lymphoma, originates from germinal center B cells within the lymphoid follicle. However, the underlying mechanisms of this disease remain unclear. This study aimed to identify the potential hub genes for FL and evaluate their functional roles in clinical applications. Microarray data and clinical characteristics of patients with FL were obtained from the Gene Expression Omnibus database. Differential expression analysis and weighted gene co-expression network analysis (WGCNA) were employed to explore hub genes for FL. Functional enrichment analysis was performed to investigate the potential roles of these hub genes in FL. Mendelian randomization (MR) analysis was performed to verify the causal effect of the top genes on FL risk. In addition, gene set enrichment analysis (GSEA) and immune cell analysis were performed to elucidate the involved mechanisms of the crucial genes in FL. A total of 1363 differentially expressed genes and 157 central genes were identified by differential expression analysis and WGCNA, respectively, resulting in 117 overlapping genes considered as hub genes for FL. Functional enrichment analysis revealed significant correlations between immune-related pathways and FL. MR analysis revealed a significant association only between zeta chain of T-cell receptor-associated protein kinase 70 (ZAP70) and FL risk, with no significance observed for the other top genes. GSEA and immune cell analysis suggested that ZAP70 may be involved in the development and progression of FL through immune-related pathways. By integrating bioinformatics and MR analyses, ZAP70 was successfully identified and validated as a promising FL biomarker. Functional investigations indicated a significant correlation between immune-related pathways and FL. These findings have important implications for the identification of targets for the diagnosis and treatment of FL and provide valuable insights into the molecular mechanisms underlying FL.

Identification of key modules and hub genes involved in regulating the feather follicle development of Wannan chickens using WGCNA

Carcass appearance is important economic trait, which affects customers in making purchase decisions. Both density and diameter of feather follicles are two important indicators of carcass appearance. However, the regulatory network and key genes be involved in feather follicle development remain poorly understood. To identify key genes and modules that involved in feather follicle development in chickens, 16 transcriptome datasets of Wannan chickens skin tissue (3 birds at the E9, E11, and E14, respectively, and 7 birds at the 12W) were used for weighted gene co-expression network analysis (WGCNA) analysis, and 12 skin tissue samples (3 birds for each stage) were selected for DEGs analysis. A total of 5,025, 2,337, and 10,623 DEGs were identified in 3 comparison groups, including the E9 vs. E11, the E11 vs. E14, and the E14 vs. 12W. Additionally, 31 co-expression gene modules were identified by WGCNA and the dark-orange, cyan, and blue module were found to be significantly associated with feather follicle development (p < 0.01). In total, 92,898 and 8,448 hub genes were obtained in the dark-orange, cyan, and blue modules, respectively. We focused on the cyan and blue modules, as 6 and 336 hub genes of these modules were identified to overlap with the DEGs of the three comparison groups, respectively. The 6 overlapped genes such as LAMC2, COL6A3, and COL6A2 etc., were over-represented in 12 categories such as focal adhesion and ECM-receptor interaction signaling pathway. Among the 336 genes that overlapped between the blue module and different DEGs comparison groups several genes including WNT7A and WNT9B were enriched in Wnt and ECM-receptor interaction signaling pathway. These results suggested that the LAMC2, COL6A3, COL6A2, WNT7A, and WNT9B genes may play a crucial role in the regulation of feather follicle development in Wannan chickens. Our results provided a reference for the molecular regulatory network and key genes in the development of feather follicles and contribute to molecular breeding for carcass appearance traits in chickens.

Weighted gene co-expression network analysis reveals hub genes regulating response to salt stress in peanut

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

Identification of hub genes and therapeutic siRNAs to develop novel adjunctive therapy for Duchenne muscular dystrophy

ObjectiveDuchenne muscular dystrophy (DMD) is a devastating X-linked neuromuscular disorder caused by various defects in the dystrophin gene and still no universal therapy. This study aims to identify the hub genes unrelated to excessive immune response but responsible for DMD progression and explore therapeutic siRNAs, thereby providing a novel treatment.MethodsTop ten hub genes for DMD were identified from GSE38417 dataset by using GEO2R and PPI networks based on Cytoscape analysis. The hub genes unrelated to excessive immune response were identified by GeneCards, and their expression was further verified in mdx and C57 mice at 2 and 4 months (M) by (RT-q) PCR and western blotting. Therapeutic siRNAs were deemed as those that could normalize the expression of the validated hub genes in transfected C2C12 cells.Results855 up-regulated and 324 down-regulated DEGs were screened from GSE38417 dataset. Five of the top 10 hub genes were considered as the candidate genes unrelated to excessive immune response, and three of these candidates were consistently and significantly up-regulated in mdx mice at 2 M and 4 M when compared with age-matched C57 mice, including Col1a2, Fbn1 and Fn1. Furthermore, the three validated up-regulated candidate genes can be significantly down-regulated by three rational designed siRNA (p < 0.0001), respectively.ConclusionCOL1A2, FBN1 and FN1 may be novel biomarkers for DMD, and the siRNAs designed in our study were help to develop adjunctive therapy for Duchenne muscular dystrophy.

Differential Gene Analysis of Langerhans Cell Histiocytosis and the Significance of MMP1-Targeted Drug Repositioning.

Langerhans cell histiocytosis (LCH) is a rare condition predominantly affecting young children. Activation of the MAPK pathway has offered key new insights into the pathogenesis of LCH; however, the precise mechanisms underlying its occurrence and development are still far from being completely elucidated. There is still a relapse/reactivation rate in patients with multisystem LCH. Therefore, this study aimed to investigate other potential LCH pathophysiologies and prospective therapeutic targets. The gene expression omnibus (GEO) database was used to retrieve gene expression profiles of LCH (GSE16395). Three distinct types of analyses were performed after identifying the common differentially expressed genes (DEGs) in LCH: hub gene identification, functional annotation, module construction, drug repositioning, and expression analysis via immunohistochemistry (IHC). We identified 417 common DEGs and 50 central hub genes. This functional study highlighted the significance of keratinization, skin development, and inflammation. In addition, we predicted new drug candidates (RS2 drugs targeting matrix metalloprotease1, MMP1) that could be used for LCH treatment. Finally, gene-miRNA and gene-TF networks and immune cell infiltration were analyzed for MMP1-related genes. MMP1 expression levels in LCH tissues were validated by IHC. Our study identified the central communal genes and novel drug candidates. These shared pathways and hub genes offer new perspectives on future mechanisms of action and therapeutic targets.

Systems biology approach: identification of hub genes, signaling pathways, and molecular docking of COL1A1 gene in cervical insufficiency

Systems biology approach: identification of hub genes, signaling pathways, and molecular docking of COL1A1 gene in cervical insufficiency

Identification of potential hub genes and regulatory networks of smoking-related endothelial dysfunction in atherosclerosis using bioinformatics analysis.

Endothelial dysfunction, the earliest stage of atherosclerosis, can be caused by smoking, but its molecular mechanism requires further investigation. This study aimed to use bioinformatics analysis to identify potential mechanisms involved in smoking-related atherosclerotic endothelial dysfunction. The transcriptome data used for this bioinformatics analysis were obtained from the Gene Expression Omnibus (GEO) database. The GSE137578 and GSE141136 datasets were used to identify common differentially expressed genes (co-DEGs) in endothelial cells treated with oxidized low-density lipoprotein (ox-LDL) and tobacco. The co-DEGs were annotated using Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomics (KEGG) databases. Additionally, a protein-protein interaction (PPI) network was constructed to visualize their interactions and screen for hub genes. GSE120521 dataset was used to verify the expression of hub genes in unstable plaques. The miRNA expression profile GSE137580 and online databases (starBase 2.0, TargetScan 8.0 and DGIdb v4.2.0) were used to predict the related non-coding RNAs and drugs. A total of 232 co-DEGs were identified, including 113 up-regulated genes and 119 down-regulated genes. These DEGs were primarily enriched in detrimental autophagy, cell death, transcription factors, and cytokines, and were implicated in ferroptosis, abnormal lipid metabolism, inflammation, and oxidative stress pathways. Ten hub genes were screened from the constructed PPI network, including up-regulated genes such as FOS, HMOX1, SQSTM1, PTGS2, ATF3, DDIT3, and down-regulated genes MCM4, KIF15, UHRF1, and CCL2. Importantly, HMOX1 was further up-regulated in unstable plaques (p= 0.034). Finally, a regulatory network involving lncRNA/circRNA-miRNA-hub genes and drug-hub genes was established. Atherosclerotic endothelial dysfunction is associated with smoking-induced injury. Through bioinformatics analysis, we identified potential mechanisms and provided potential therapeutic targets.

Identification of potential hub genes as biomarkers for breast, ovarian, and endometrial cancers

Breast cancer (BC) and gynecological cancers have emerged as significant threats to women’s health and are known to be among the primary causes of cancer-related fatalities in women. Innovative treatments and early detection may significantly cut mortality rates for these diseases. In this study, potential hub genes were thoroughly evaluated in the contexts of BC, ovarian cancer (OC), and endometrial cancer (EC). Initially, a total of 374 overlapping differentially expressed genes (DEGs) were identified within the microarray datasets. The STRING database and Cytoscape software analyzed protein-protein interaction (PPI) network structure, whereas FunRich found hub genes. The five hub genes that were ultimately discovered are PTEN, SMAD2, FASN, CYCS, and KRAS. As a result, these genes may serve as potential biomarkers for the aforementioned diseases. Importantly, this study offers valuable insights into all three diseases based on recent molecular advancements. However, further investigation is required to precisely measure these biomarkers’ effectiveness.

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

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

Identification of potential hub genes and biological mechanism in rheumatoid arthritis and non-small cell lung cancer via integrated bioinformatics analysis

BackgroundAlthough there is evidence of the association between RA and NSCLC, little is known about their interaction mechanisms. The aim of this study is to identify potential hub genes and biological mechanism in RA and NSCLC via integrated bioinformatics analysis. MethodsThe gene expression datasets of RA and NSCLC were downloaded to discover and validate hub genes. After identifying DEGs, we performed enrichment analysis, PPI network construction and module analysis, selection and validation of hub genes. Moreover, we selected the hub gene PTPRC for expression and prognosis analysis, immune analysis, mutation and methylation analysis in NSCLC. Finally, we performed real-time PCR, colony formation assay, wound healing assay, transwell invasion assay, sphere formation assay and western blotting to validate the role of PTPRC in A549 cells. ResultsWe obtained 320 DEGs for subsequent analysis. Enrichment results showed that the DEGs were mainly involved in Th1, Th2 and Th17 cell differentiation. In addition, four hub genes, BIRC5, PTPRC, PLEK, and FYN, were identified after selection and validation. These hub genes were subsequently shown to be closely associated with immune cells and related pathways. In NSCLC, PTPRC was downregulated, positively correlated with immune infiltration and immune cells. Experiments showed that PTPRC could promote the proliferation, migration and invasion, and the ability to form spheroids of A549 cells. In addition, PTPRC could regulate the increased expression of CD45, β-catenin, c-Myc and LEF1 proteins. ConclusionsThis study explored the hub genes and related mechanisms of RA and NSCLC, demonstrated the central role of the inflammatory response and the adaptive immune system, and identified PTPRC as an immune-related biomarker and potential therapeutic target for RA and NSCLC patients. In addition, PTPRC can significantly promote the proliferation, migration and invasion of A549 cells, and its mechanism may be to promote the EMT process by regulating the Wnt signaling pathway and promote cell stemness, which in turn has a promoting effect on A549 cells.

Integrated multi-omics analysis and machine learning identify hub genes and potential mechanisms of resistance to immunotherapy in gastric cancer.

Patients with gastric cancer respond poorly to immunotherapy. There are still unknowns about the biomarkers associated with immunotherapy sensitivity and their underlying molecular mechanisms. Gene expression data for gastric cancer were gathered from TCGA and GEO databases. DEGs associated with immunotherapy response came from ICBatlas. KEGG and GO analyses investigated pathways. Hub genes identification employed multiple machine algorithms. Associations between hub genes and signaling pathways, disease genes, immune cell infiltration, drug sensitivity, and prognostic predictions were explored via multi-omics analysis. Hub gene expression was validated through HPA and CCLE. Multiple algorithms pinpointed Cancer-Associated Fibroblasts genes (CAFs), with ten machine-learning methods generating CAFs scores for prognosis. Model gene expression was validated at the single-cell level using the TISCH database. We identified 201 upregulated and 935 downregulated DEGs. Three hub genes, namely CDH6, EGFLAM, and RASGRF2, were unveiled. These genes are implicated in diverse disease-related signaling pathways. Additionally, they exhibited significant correlations with disease-associated gene expression, immune cell infiltration, and drug sensitivity. Exploration of the HPA and CCLE databases exposed substantial expression variations across patients and cell lines for these genes. Subsequently, we identified CAFs-associated genes and established a robust prognostic model. The analysis in the TISCH database showed that the genes in this model were highly expressed in CAFs. The results unveil an association between CDH6, EGFLAM, and RASGRF2 and the immunotherapeutic response in gastric cancer. These genes hold potential as predictive biomarkers for gastric cancer immunotherapy resistance and prognostic assessment.

Identification of hub genes in meat quality of grass carp (Ctenopharyngodon idellus) fed with faba bean by muscle tissue transcriptomic analysis

Crisp grass carp is a type of grass carp (Ctenopharyngodon idella) that has been fed faba beans. Flesh quality and expression of muscle quality-related genes in muscle of grass carp were evaluated. Compared with ordinary grass carp, 1901 differentially expressed genes (DEGs) were up-regulated and 1628 DEGs were down-regulated in crisp grass carp. The GO analysis results showed that the DEGs were enriched in ribosomes and proteins. Kyoto Encyclopaedia of genes and genomes (KEGG) pathway analysis results showed that amino acid metabolism was closely related to meat quality. COX3, ACADS, ACAT1, ECHS1, EHHADH, HADHAA, HADH, HMGCL, MCCC1 and MCCC2 were the top 10 hub genes most closely associated with other nodes and showed a strong positive correlation. Hardness was positively correlated with the COX3, ECHS1, ACADS, ACAT1, HADHA, HADH, HMGCL and MCCC1. These results have been demonstrated to play important roles in clarifying the molecular mechanism of muscle firmness increase in crisp grass carp.

Molecular mechanisms and potential therapeutic targets in the pathogenesis of hypertension in visceral adipose tissue induced by a high-fat diet.

Hypertension (HTN) presents a significant global public health challenge with diverse causative factors. The accumulation of visceral adipose tissue (VAT) due to a high-fat diet (HFD) is an independent risk factor for HTN. While various studies have explored pathogenic mechanisms, a comprehensive understanding of impact of VAT on blood pressure necessitates bioinformatics analysis. Datasets GSE214618 and GSE188336 were acquired from the Gene Expression Omnibus and analyzed to identify shared differentially expressed genes between HFD-VAT and HTN-VAT. Gene Ontology enrichment and protein-protein interaction analyses were conducted, leading to the identification of hub genes. We performed molecular validation of hub genes using RT-qPCR, Western-blotting and immunofluorescence staining. Furthermore, immune infiltration analysis using CIBERSORTx was performed. This study indicated that the predominant characteristic of VAT in HTN was related to energy metabolism. The red functional module was enriched in pathways associated with mitochondrial oxidative respiration and ATP metabolism processes. Spp1, Postn, and Gpnmb in VAT were identified as hub genes on the pathogenic mechanism of HTN. Proteins encoded by these hub genes were closely associated with the target organs-specifically, the resistance artery, aorta, and heart tissue. After treatment with empagliflozin, there was a tendency for Spp1, Postn, and Gpnmb to decrease in VAT. Immune infiltration analysis confirmed that inflammation and immune response may not be the main mechanisms by which visceral adiposity contributes to HTN. Our study pinpointed the crucial causative factor of HTN in VAT following HFD. Spp1, Postn, and Gpnmb in VAT acted as hub genes that promote elevated blood pressure and can be targets for HTN treatment. These findings contributed to therapeutic strategies and prognostic markers for HTN.

Identification of hub genes and establishment of a diagnostic model in tuberculosis infection

Tuberculosis (TB) poses significant challenges due to its high transmissibility within populations and intrinsic resistance to treatment, rendering it a formidable respiratory disease with a substantial susceptibility burden. This study was designed to identify new potential therapeutic targets for TB and establish a diagnostic model. mRNA expression data for TB were from GEO database, followed by conducting differential expression analysis. The top 50 genes with differential expression were subjected to GO and KEGG enrichment analyses. To establish a PPI network, the STRING database was utilized, and hub genes were identified utilizing five algorithms (EPC, MCC, MNC, Radiality, and Stress) within the cytoHubba plugin of Cytoscape software. Furthermore, a hub gene co-expression network was constructed using the GeneMANIA database. Consistency clustering was performed on hub genes, and ssGSEA was utilized to analyze the extent of immune infiltration in different subgroups. LASSO analysis was employed to construct a diagnostic model, and ROC curves were used for validation. Through the analysis of GEO data, a total of 159 genes were identified as differentially expressed. Further, GO and KEGG enrichment analyses revealed that these genes were mainly enriched in viral defense, symbiotic defense, and innate immune response-related pathways. Hub genes, including DDX58, IFIT2, IFIH1, RSAD2, IFI44L, OAS2, OAS1, OASL, IFIT1, IFIT3, MX1, STAT1, and ISG15, were identified using cytoHubba analysis of the PPI network. The GeneMANIA analysis unmasked that the co-expression rate of hub genes was 81.55%, and the physical interaction rate was 12.27%. Consistency clustering divided TB patients into two subgroups, and ssGSEA revealed different degrees of immune infiltration in different subgroups. LASSO analysis identified IFIT1, IFIT2, IFIT3, IFIH1, RSAD2, OAS1, OAS2, and STAT1 as eight immune-related key genes, and a diagnostic model was constructed. The ROC curve demonstrated that the model exhibited excellent diagnostic performance. DDX58, IFIT2, IFIH1, RSAD2, IFI44L, OAS2, OAS1, OASL, IFIT1, IFIT3, MX1, STAT1, and ISG15 were hub genes in TB, and the diagnostic model based on eight immune-related key genes exhibited good diagnostic performance.

Identification of hub genes and diagnostic efficacy for triple-negative breast cancer through WGCNA and Mendelian randomization

ObjectiveTriple-negative breast cancer (TNBC) represents a particularly aggressive form of breast cancer with a poor prognosis due to a lack of targeted treatments resulting from limited a understanding of the underlying mechanisms. The aim of this study was the identification of hub genes for TNBC and assess their clinical applicability in predicting the disease.MethodsThis study employed a combination of weighted gene co-expression network analysis (WGCNA) and differentially expressed genes (DEGs) to identify new susceptible modules and central genes in TNBC. The potential functional roles of the central genes were investigated using Kyoto Encyclopedia of Genes and Genomes (KEGG) and Gene Ontology (GO) analyses. Furthermore, a predictive model and ROC curve were developed to assess the diagnostic performance of the identified central genes. The correlation between CCNB1 and immune cells proportion was also investigated. At last, a Mendelian randomization (MR) analysis utilizing Genome-Wide Association Study (GWAS) data was analyzed to establish the causal effect of CCNB1 level on TNBC.ResultsWGCNA was applied to determine gene co-expression maps and identify the most relevant module. Through a screening process, 1585 candidate hub genes were subsequently identified with WGCNA and DEGs. GO and KEGG function enrichment analysis indicated that these core genes were related to various biological processes, such as organelle fission, chromosome segregation, nuclear division, mitotic cell cycle phase transition, the cell cycle, amyotrophic lateral sclerosis, and motor proteins. Using STRING and Cytoscape, the top five genes with high degrees were identified as CDC2, CCNB1, CCNA2, TOP2A, and CCNB2. The nomogram model demonstrated good performance in predicting TNBC risk and was proven effective in diagnosis, as evidenced by the receiver operating characteristic (ROC) curve. Further investigation revealed a causal association between CCNB1 and immune cell infiltrates in TNBC. Survival analysis revealed high expression of the CCNB1 gene leads to poorer prognosis in TNBC patients. Additionally, analysis using inverse variance weighting revealed that CCNB1 was linked to a 2.8% higher risk of TNBC (OR: 1.028, 95% CI 1.002–1.055, p = 0.032).ConclusionWe established a co-expression network using the WGCNA methodology to detect pivotal genes associated with TNBC. This finding holds promise for advancing the creation of pre-symptomatic diagnostic tools and deepening our comprehension of the pathogenic mechanisms involved in TNBC risk genes.

Identification of candidate hub genes for COVID-19-induced idiopathic pulmonary fibrosis by bioinformatic analysis

Background: Idiopathic pulmonary fibrosis (IPF) is an incurable lung disease characterized by progressive scarring leading to alveolar stiffness, and reduced lung capacity, which might be induced by Coronavirus Disease 2019 (COVID-19). This study aims to explore the molecular mechanism of COVID-19-induced IPF and find the immune changes in the process of IPF. Methods: The data from the GEO database were processed by using the R algorithm package to explore the correlation between COVID-19 and IPF. Differential analysis, Venn analysis, Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) analysis, immune infiltration analysis, transcriptional analysis, and protein-protein analysis were performed to find the candidate hub genes, related pathways, and immune cell changes in IPF. Results: 69 differentially expressed genes (DGEs) were identified by differential analysis, including 27 up-regulated genes and 42 down-regulated genes. 15 hub genes were identified, which are CEA Cell Adhesion Molecule 8 (CEACAM8), Cathelicidin Antimicrobial Peptide (CAMP), Ribosomal Protein L9 (RPL9), Lactotransferrin (LTF), CD177 Molecule (CD177), Annexin A3 (ANXA3), CD3 Delta Subunit Of T-Cell Receptor Complex (CD3D), Granulin Precursor (GRN), Granzyme A (GZMA), Granzyme K (GZMK), Interleukin 1 Receptor Type 2 (IL1R2), Lipocalin 2 (LCN2), Matrix Metallopeptidase 9 (MMP9), Neural EGFL Like 2 (NELL2) and Ribosomal Protein L4 (RPL4), among which ANXA3, IL1R2, MMP9, LCN2, NELL2 might be the most significant candidate genes for COVID-19-induced IPF. The transcriptional factors of hub genes were found to be related to Sp1 Transcription Factor (SP1), Signal Transducer And Activator Of Transcription 1 (STAT1), RELA Proto-Oncogene, NF-KB Subunit (RELA), and Nuclear Factor Kappa B Subunit 1 (NFKB1). Immune cell analysis revealed that Plasma cells, Macrophages MO, Dendritic cells activated, Mast cells resting, and Eosinophils were significantly elevated in the IPF compared with healthy subjects. Conclusions: The findings shed light on the risk factors of COVID-19 in the development of IPF, and immune changes in COVID-19 and IPF. The shared DEGs might be prognostic in the treatment of IPF.

Identification of hub genes and potential molecular mechanisms related to drug sensitivity in acute myeloid leukemia based on machine learning.

Background: Acute myeloid leukemia (AML) is the most common form of leukemia among adults and is characterized by uncontrolled proliferation and clonal expansion of hematopoietic cells. There has been a significant improvement in the treatment of younger patients, however, prognosis in the elderly AML patients remains poor. Methods: We used computational methods and machine learning (ML) techniques to identify and explore the differential high-risk genes (DHRGs) in AML. The DHRGs were explored through multiple in silico approaches including genomic and functional analysis, survival analysis, immune infiltration, miRNA co-expression and stemness features analyses to reveal their prognostic importance in AML. Furthermore, using different ML algorithms, prognostic models were constructed and validated using the DHRGs. At the end molecular docking studies were performed to identify potential drug candidates targeting the selected DHRGs. Results: We identified a total of 80 DHRGs by comparing the differentially expressed genes derived between AML patients and normal controls and high-risk AML genes identified by Cox regression. Genetic and epigenetic alteration analyses of the DHRGs revealed a significant association of their copy number variations and methylation status with overall survival (OS) of AML patients. Out of the 137 models constructed using different ML algorithms, the combination of Ridge and plsRcox maintained the highest mean C-index and was used to build the final model. When AML patients were classified into low- and high-risk groups based on DHRGs, the low-risk group had significantly longer OS in the AML training and validation cohorts. Furthermore, immune infiltration, miRNA coexpression, stemness feature and hallmark pathway analyses revealed significant differences in the prognosis of the low- and high-risk AML groups. Drug sensitivity and molecular docking studies revealed top 5 drugs, including carboplatin and austocystin-D that may significantly affect the DHRGs in AML. Conclusion: The findings from the current study identified a set of high-risk genes that may be used as prognostic and therapeutic markers for AML patients. In addition, significant use of the ML algorithms in constructing and validating the prognostic models in AML was demonstrated. Although our study used extensive bioinformatics and machine learning methods to identify the hub genes in AML, their experimental validations using knock-out/-in methods would strengthen our findings.

Bioinformatic Identification of Signaling Pathways and Hub Genes in Vascular Dementia.

Vascular dementia (VaD) is a prevalent neurodegenerative disease characterized by cognitive impairment due to cerebrovascular factors, affecting a significant portion of the aging population and highlighting the critical need to understand specific targets and mechanisms for effective prevention and treatment strategies. We aimed to identify pathways and crucial genes involved in the progression of VaD through bioinformatics analysis and subsequently validate these findings. We conducted differential expression analysis, Weighted Gene Co-expression Network Analysis (WGCNA), Gene Ontology (GO), Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis, and Protein-Protein Interaction (PPI) analysis. We utilized pheochromocytoma 12 (PC12) cells to create an in vitro oxygen-glucose deprivation (OGD) model. We investigated the impact of overexpression and interference of adrenoceptor alpha 1D (ADRA1D) on OGD PC12 cells using TdT-mediated dUTP nick-end labeling (TUNEL), reverse transcription-quantitative polymerase chain reaction (RT-qPCR), western blot (WB), and Fluo-3-pentaacetoxymethyl ester (Fluo-3 AM) analysis. We found 187 differentially expressed genes (DEGs) in the red module that were strongly associated with VaD and were primarily enriched in vasoconstriction, G protein-coupled amine receptor activity, and neuroactive ligand-receptor interaction, mitogen-activated protein kinase (MAPK) signaling pathway, and cell adhesion. Among these pathways, we identified ADRA1D as a gene shared by vasoconstriction, G protein-coupled amine receptor activity, and neuroactive ligand-receptor interaction. The TUNEL assay revealed a significant decrease in PC12 cell apoptosis with ADRA1D overexpression (p < 0.01) and a significant increase in apoptosis upon silencing ADRA1D (p < 0.01). RT-qPCR and WB analysis revealed elevated ADRA1D expression (p < 0.001) and decreased phospholipase C beta (PLCβ) and inositol 1,4,5-trisphosphate receptor (IP3R) expression (p < 0.05) with ADRA1D overexpression. Moreover, the Fluo-3 AM assessment indicated significantly lower intracellular Ca2+ levels with ADRA1D overexpression (p < 0.001). Conversely, interference with ADRA1D yielded opposite results. Our study provides a new perspective on the pathogenic mechanisms of VaD and potential avenues for therapeutic intervention. The results highlight the role of ADRA1D in modulating cellular responses to OGD and VaD, suggesting its potential as a target for VaD treatment.

Identification of the Hub Genes Involved in Chikungunya Viral Infection.

Background Chikungunya virus (CHIKV) infection poses a significant global health threat, necessitating a deeper understanding of its molecular mechanisms for effective management and treatment. This study aimed to understand the molecular and geneticmechanisms of CHIKV infection by analyzing microarray expression data. Methodology National Center for Biotechnology Information (NCBI) GEO2R with an adjusted p-value cut-off of <0.05 and |log2FC ≥ 1.5| was used to identify the differentially expressed genes involved in CHIKV infection using microarray data from the Gene Expression Omnibus (GEO) database, followed by enrichment analysis, protein-protein interaction (PPI) network construction, and, finally, hub gene identification. Results Analysis of the microarray dataset revealed 25 highly significant differentially expressed genes (DEGs), including 21 upregulated and four downregulated genes. PPI network analysis elucidated interactions among these DEGs, with hub genes such as ACTB and CTNNB1 exhibiting central roles. Enrichment analysis identified crucial pathways, including leukocyte transendothelial migration, regulation of actin cytoskeleton, and thyroid hormone signaling, implicating their involvement in CHIKV infection. Furthermore, the study highlights potential therapeutic targets such as ACTB and CTNNB1, which showed significant upregulation in infected cells. Conclusions These findings underscore the complex interplay between viral infection and host cellular processes, shedding light on novel avenues for diagnostic marker discovery and advancing antiviral strategies. In this study, we shed light on the molecular and genetic mechanisms of CHIKV infection and the potential role of ACTB and CTNNB1 genes.

Identification of hub genes involved in gibberellin-regulated elongation of coleoptiles of rice seeds germinating under submerged conditions.

Rapid elongation of coleoptiles from rice seeds to reach the water surface enables plants to survive submergence stress and therefore plays a crucial role in allowing direct seeding in rice cultivation. Gibberellin (GA) positively influences growth in rice, but the molecular mechanisms underlying its regulation of coleoptile elongation under submerged conditions remain unclear. In this study, we performed a weighted gene co-expression network analysis to conduct a preliminarily examination of the mechanisms. Four key modules were identified with high correlations to the GA regulation of submergence tolerance. The genes within these modules were mainly involved in the Golgi apparatus and carbohydrate metabolic pathways, suggesting their involvement in enhancing submergence tolerance. Further analysis of natural variation revealed that the specific hub genes Os03g0337900, Os03g0355600, and Os07g0638400 exhibited strong correlations with subspecies divergence of the coleoptile elongation phenotype. Consistent with this analysis, mutation of Os07g0638400 resulted in a lower germination potential and a stronger inhibition of coleoptile elongation under submerged conditions. The hub genes identified in this study provide new insights into the molecular mechanisms underlying GA-dependent tolerance to submergence stress in rice, and a potential basis for future modification of rice germplasm to allow for direct seeding.