Identification Of Hub Genes Research Articles

The Molecular Landscape of Lung Metastasis in Primary Head and Neck Squamous Cell Carcinomas.

Background Lung metastasis in head and neck cancer (HNC) patients is a critical concern, often indicating an advanced disease stage and a poor prognosis. This study explores the molecular complexities of such metastases, identifying specific genes and pathways that may serve as valuable targets for diagnosis and treatment. The findings underscore the potential for significantly improved patient outcomes through targeted therapeutic strategies. Methodology In this research, we systematically collected raw gene expression data from head and neck squamous cell carcinoma (HNSCC) and lung squamous cell carcinoma (LSCC). By comparing tumorous and normal gene expression profiles from paired patient samples, we identified differentially expressed genes (DEGs). Network analysis helped visualize protein interactions and pinpoint crucial hub genes. Through validation and comparison across several datasets, we identified common DEGs. Additionally, we employed Kaplan-Meier analysis and log-rank testing to examine the relationship between gene expression patterns and patient survival. Result The study identified 145 overlapping DEGs in both HNSCC and LSCC, which are crucial for cancer progression and linked to lung metastasis, offering vital targets for personalized therapy by identifying key genes affecting disease development and patient survival. Pathway analyses linked these to lung metastasis, while protein-protein interaction network construction and hub gene identification highlighted genes crucial for development and patient survival, offering targets for personalized therapy. Conclusion Identifying key genes and pathways in lung metastasis from HNC, this study highlights potential targets for enhanced diagnosis and therapy. It underscores the crucial role of molecular insights in driving forward personalized treatment approaches and improving patient outcomes.

Investigation of Common Pathways and Putative Biomarker Candidates of Colorectal Cancer and Insomnia by Using Integrative In-Silico Approaches.

Colorectal cancer (CRC) is one of the leading causes of cancer-related mortalities across the globe. Accumulating evidence shows that individuals having sleep disorders such as insomnia are at high risk of developing CRC, yet the association of sleep disorders with CRC risk is still unclear. Here, we investigated the potential molecular connections between CRC and insomnia using integrative in silico approaches. This study aims to explore the potential molecular connections between CRC and insomnia utilizing integrative in-silico methodologies. Gene expression microarray datasets for CRC and insomnia samples were retrieved from the NCBI-GEO database and analyzed using R. Functional enrichment analysis of common differentially expressed genes (DEGs) was performed by the g: Profiler tool. Cytoscape software was used to construct a protein-protein interaction network and hub gene identification. Expression profiles of hub genes in TCGA datasets were also determined, and predicted miRNAs targeting hub genes were analyzed by miRNA target prediction tools. Our results revealed a total of 113 shared DEGs between the CRC and insomnia datasets. Six genes (HSP8A, GAPDH, HSP90AA1, EEF1G, RPS6, and RPLP0), which were also differently expressed in TCGA datasets, were prioritized as hub genes and were found to be enriched in pathways related to protein synthesis. hsa-miR-324-3p, hsa-miR-769-3p, and hsa-miR-16-5p were identified as promising miRNA biomarkers for two diseases. Our in-silico analysis provides promising evidence of the molecular link between CRC and insomnia and highlights multiple potential molecular biomarkers and pathways. Validation of the results by wet lab work can be utilized for novel translational and precision medicine applications to alleviate the public health burden of CRC.

Machine learning-based identification of novel hub genes associated with oxidative stress in lupus nephritis: implications for diagnosis and therapeutic targets

BackgroundLupus nephritis (LN) is a complication of SLE characterised by immune dysfunction and oxidative stress (OS). Limited options exist for LN. We aimed to identify LN-related OS, highlighting the need...

Identification of hub genes and key pathways in spinal cord injury via bioinformatics analysis.

This study aimed to explore the hub genes and related key pathways in Spinal Cord Injury (SCI) based on the bioinformatics analysis. Two microarray datasets (GSE45006, GSE45550) were obtained from the GEO database and were merged and batch-corrected. The differentially expressed genes (DEGs) in SCI were explored with the Limma, and the weighted gene co-expression network analysis (WGCNA) was conducted to explore the module genes. Functional enrichment analysis and Gene set variation analysis (GSVA) were used to investigate the biological functions and key pathways of the key genes related to SCI. Then the protein-protein interaction (PPI) network was generated using the STING online tool, and the hub genes in SCI were identified. Receiver operating characteristic (ROC) curves were applied to assess the diagnostic value of the selected hub genes. We identified 554 DEGs in SCI, and 1236 key genes in SCI were selected via WGCNA. Totally 111 key genes related to SCI were discovered. Furthermore, the functional enrichment analysis showed that these key mRNAs were primarily enriched in the extracellular matrix (ECM)-related pathways and processes associated with wound healing and cell growth. The PPI network further filtered six hub genes (Cd44, Timp1, Loxl1, Col6a1, Col3a1, Col5a1) ranked by the degree, and the diagnostic value of the six hub genes was confirmed by the ROC curves. Six hub genes including Cd44, Timp1, Loxl1, Col6a1, Col3a1, and Col5a1 were identified in SCI, with differential expression and excellent diagnostic value, which might provide insight into the targeted therapy of SCI.

Bioinformatic validation and machine learning-based exploration of purine metabolism-related gene signatures in the context of immunotherapeutic strategies for nonspecific orbital inflammation.

Nonspecific orbital inflammation (NSOI) represents a perplexing and persistent proliferative inflammatory disorder of idiopathic nature, characterized by a heterogeneous lymphoid infiltration within the orbital region. This condition, marked by the aberrant metabolic activities of its cellular constituents, starkly contrasts with the metabolic equilibrium found in healthy cells. Among the myriad pathways integral to cellular metabolism, purine metabolism emerges as a critical player, providing the building blocks for nucleic acid synthesis, such as DNA and RNA. Despite its significance, the contribution of Purine Metabolism Genes (PMGs) to the pathophysiological landscape of NSOI remains a mystery, highlighting a critical gap in our understanding of the disease's molecular underpinnings. To bridge this knowledge gap, our study embarked on an exploratory journey to identify and validate PMGs implicated in NSOI, employing a comprehensive bioinformatics strategy. By intersecting differential gene expression analyses with a curated list of 92 known PMGs, we aimed to pinpoint those with potential roles in NSOI. Advanced methodologies, including Gene Set Enrichment Analysis (GSEA) and Gene Set Variation Analysis (GSVA), facilitated a deep dive into the biological functions and pathways associated with these PMGs. Further refinement through Lasso regression and Support Vector Machine-Recursive Feature Elimination (SVM-RFE) enabled the identification of key hub genes and the evaluation of their diagnostic prowess for NSOI. Additionally, the relationship between these hub PMGs and relevant clinical parameters was thoroughly investigated. To corroborate our findings, we analyzed expression data from datasets GSE58331 and GSE105149, focusing on the seven PMGs identified as potentially crucial to NSOI pathology. Our investigation unveiled seven PMGs (ENTPD1, POLR2K, NPR2, PDE6D, PDE6H, PDE4B, and ALLC) as intimately connected to NSOI. Functional analyses shed light on their involvement in processes such as peroxisome targeting sequence binding, seminiferous tubule development, and ciliary transition zone organization. Importantly, the diagnostic capabilities of these PMGs demonstrated promising efficacy in distinguishing NSOI from non-affected states. Through rigorous bioinformatics analyses, this study unveils seven PMGs as novel biomarker candidates for NSOI, elucidating their potential roles in the disease's pathogenesis. These discoveries not only enhance our understanding of NSOI at the molecular level but also pave the way for innovative approaches to monitor and study its progression, offering a beacon of hope for individuals afflicted by this enigmatic condition.

Investigation of autophagy‑related genes and immune infiltration in calcific aortic valve disease: A bioinformatics analysis and experimental validation.

The present study aimed to elucidate the role of autophagy-related genes (ARGs) in calcific aortic valve disease (CAVD) and their potential interactions with immune infiltration via experimental verification and bioinformatics analysis. A total of three microarray datasets (GSE12644, GSE51472 and GSE77287) were obtained from the Gene Expression Omnibus database, and gene set enrichment analysis was performed to identify the relationship between autophagy and CAVD. After differentially expressed genes and differentially expressed ARGs (DEARGs) were identified using CAVD samples and normal aortic valve samples, a functional analysis was performed, including Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes pathway enrichment analyses, protein-protein interaction network construction, hub gene identification and validation, immune infiltration and drug prediction. The results of the present study indicated a significant relationship between autophagy and CAVD. A total of 46 DEARGs were identified. GO and pathway enrichment analyses revealed the complex roles of DEARGs in regulating CAVD, including multiple gene functions and pathways. A total of 10 hub genes were identified, with three (SPP1, CXCL12 and CXCR4) consistently upregulated in CAVD samples compared with normal aortic valve samples in multiple datasets and experimental validation. Immune infiltration analyses demonstrated significant differences in immune cell proportions between CAVD samples and normal aortic valve samples, thus showing the crucial role of immune infiltration in CAVD development. Furthermore, therapeutic drugs were predicted that could target the identified hub genes, including bisphenol A, resveratrol, progesterone and estradiol. In summary, the present study illuminated the crucial role of autophagy in CAVD development and identified key ARGs as potential therapeutic targets. In addition, the observed immune cell infiltration and predicted autophagy-related drugs suggest promising avenues for future research and novel CAVD treatments.

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

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

Identification of Novel Tumor Pyroptosis-Related Antigens and Pyroptosis Subtypes for Developing mRNA Vaccines in Pancreatic Adenocarcinoma.

As one of the important components of immunotherapies, mRNA vaccines have displayed promising clinical outcomes in solid tumors. Nonetheless, their efficacy remains unclear in pancreatic adenocarcinoma (PAAD). Given the interaction of pyroptosis with anticancer immunity, our study aims to identify pyroptosis-related antigens for mRNA vaccine development and discern eligible candidates for vaccination. Utilizing gene expression data from TCGA and ICGC, we integrated RNA-seq data and compared genetic alterations through cBioPortal. Differential gene expressions were integrated using GEPIA. Relationships between immune cell abundance and tumor antigens were analyzed and visualized via TIMER. WGCNA facilitated the clustering of pyroptosis-related genes, identification of hub genes, and pathway enrichment analyses. Pyroptosis landscape was depicted through graph learning-based dimensional reduction. Four overexpressed and mutant pyroptosis-related genes associated with poor prognosis were identified as potential antigens for mRNA vaccines in PAAD, including ANO6, PAK2, CHMP2B, and RAB5A. These genes displayed positive associations with antigen-presenting cells. PAAD patients were stratified into three pyroptosis subtypes. Notably, the PS3 subtype, characterized by a lower mutation count and TMB, exhibited "cold" immunological traits and superior survival compared to other subtypes. The pyroptosis landscape exhibited considerable heterogeneity among individuals. Furthermore, the turquoise module emerged as an independent prognostic indicator and patients with high expressions of hub genes might not be suitable candidates for mRNA vaccination. In PAAD, ANO6, PAK2, CHMP2B, and RAB5A are prospective pyroptosis-related antigens for mRNA vaccine development, which holds potential benefits for patients classified as PS3 and those with diminished hub gene expressions, providing insights into personalized mRNA vaccine strategies.

Identification of Potential Hub Genes Related to Acute Pancreatitis and Chronic Pancreatitis via Integrated Bioinformatics Analysis and In Vitro Analysis.

Acute pancreatitis (AP) and chronic pancreatitis (CP) are considered to be two separate pancreatic diseases in most studies, but some clinical retrospective analyses in recent years have found some degree of correlation between the two in actual treatment, however, the exact association is not clear. In this study, bioinformatics analysis was utilized to examine microarray sequencing data in mice, with the aim of elucidating the critical signaling pathways and genes involved in the progression from AP to CP. Differential gene expression analyses on murine transcriptomes were conducted using the R programming language and the R/Bioconductor package. Additionally, gene network analysis was performed using the STRING database to predict correlations among genes in the context of pancreatic diseases. Functional enrichment and gene ontology pathways common to both diseases were identified using Metascape. The hub genes were screened in the cytoscape algorithm, and the mRNA levels of the hub genes were verified in mice pancreatic tissues of AP and CP. Then the drugs corresponding to the hub genes were obtained in the drug-gene relationship. A set of hub genes, including Jun, Cd44, Epcam, Spp1, Anxa2, Hsp90aa1, and Cd9, were identified through analysis, demonstrating their pivotal roles in the progression from AP to CP. Notably, these genes were found to be enriched in the Helper T-cell factor (Th17) signaling pathway. Up-regulation of these genes in both AP and CP mouse models was validated through quantitative real-time polymerase chain reaction (qRT-PCR) results. The significance of the Th17 signaling pathway in the transition from AP to CP was underscored by our findings. Specifically, the essential genes driving this progression were identified as Jun, Cd44, Epcam, Spp1, Anxa2, Hsp90aa1, and Cd9. Crucial insights into the molecular mechanisms underlying pancreatitis progression were provided by this research, offering promising avenues for the development of targeted therapeutic interventions.

Identification and validation of mutual hub genes in idiopathic pulmonary fibrosis and rheumatoid arthritis-associated usual interstitial pneumonia

ObjectivesThe study aims at exploring common hub genes and pathways in idiopathic pulmonary fibrosis (IPF) and rheumatoid arthritis-associated usual interstitial pneumonia (RA-UIP) through integrated bioinformatics analyses. MethodsThe GSE199152 dataset containing lung tissue samples from IPF and RA-UIP patients was acquired from the Gene Expression Omnibus (GEO) database. The identification of overlapping differentially expressed genes (DEGs) in IPF and RA-UIP was carried out through R language. Protein–protein interaction (PPI) network analysis and module analysis were applied to filter mutual hub genes in the two diseases. Enrichment analyses were also conducted to analyze the possible biological functions and pathways of the overlapped DEGs and hub genes. The diagnostic value of key genes was assessed with R language, and the expressions of these genes in pulmonary cells of IPF and rheumatoid arthritis-associated interstitial lung disease (RA-ILD) patients were analyzed with single cell RNA-sequencing (scRNA-seq) datasets. The expression levels of hub genes were validated in blood samples from patients, specimens of human lung fibroblasts, lung tissue samples from mice, as well as external GEO datasets. ResultsFour common hub genes (THBS2, TIMP1, POSTN, and CD19) were screened. Enrichment analyses showed that the abnormal expressions of DEGs and hub genes may be connected with the onset of IPF and RA-UIP by regulating the progression of fibrosis. ScRNA-seq analyses illustrated that for both IPF and RA-ILD patients, THBS2, TIMP1, and POSTN were mainly expressed in lung fibroblasts, while CD19 was uniquely high-expressed in B cells. The qRT-PCR and immunohistochemistry (IHC) results verified that the expression levels of hub genes were mostly in accordance with the findings obtained from the bioinformatics analyses. ConclusionThough IPF and RA-UIP are distinct diseases, they may to some extent have mutual pathogenesis in the development of fibrosis. THBS2, TIMP1, POSTN, and CD19 may be the potential biomarkers of IPF and RA-UIP, and intervention on related pathways of these genes could offer new strategies for the precision treatment of IPF and RA-UIP.

Retracted: Identification of Hub Genes for Early Diagnosis and Predicting Prognosis in Colon Adenocarcinoma.

[This retracts the article DOI: 10.1155/2022/1893351.].

Identification and validation of hub genes expressed in ulcerative colitis with metabolic dysfunction-associated steatotic liver disease.

Ulcerative colitis (UC) and metabolic dysfunction-associated steatotic liver disease (MASLD) are closely intertwined; however, the precise molecular mechanisms governing their coexistence remain unclear. We obtained UC (GSE75214) and MASLD (GSE151158) datasets from the Gene Expression Omnibus (GEO) database. Differentially expressed genes (DEGs) were acquired by the 'edgeR' and 'limma' packages of R. We then performed functional enrichment analysis of common DEGs. Hub genes were selected using the cytoHubba plugin and validated using GSE87466 for UC and GSE33814 for MASLD. Immunohistochemistry was employed to validate the hub genes' expression in clinical samples. Immune infiltration and gene set enrichment analyses of the hub genes were performed. Finally, we estimated the Spearman's correlation coefficients for the clinical correlation of the core genes. Within a cohort of 26 differentially regulated genes in both UC and MASLD, pathways involving cytokine-mediated signaling, cell chemotaxis, and leukocyte migration were enriched. After further validation, CXCR4, THY1, CCL20, and CD2 were identified as the hub genes. Analysis of immune infiltration patterns highlighted an association between elevated pivotal gene expression and M1 macrophage activation. Immunohistochemical staining revealed widespread expression of pivotal genes in UC- and MASLD-affected tissues. Furthermore, significant correlations were observed between the increased expression of hub genes and biochemical markers, such as albumin and prothrombin time. This bioinformatics analysis highlights CXCR4, THY1, CCL20, and CD2 as crucial genes involved in the co-occurrence of UC and MASLD, providing insights into the underlying mechanisms of these two conditions.

Proteomic analysis of mitochondria associated membranes in renal ischemic reperfusion injury

BackgroundThe mitochondria and endoplasmic reticulum (ER) communicate via contact sites known as mitochondria associated membranes (MAMs). Many important cellular functions such as bioenergetics, mitophagy, apoptosis, and calcium signaling are regulated by MAMs, which are thought to be closely related to ischemic reperfusion injury (IRI). However, there exists a gap in systematic proteomic research addressing the relationship between these cellular processes.MethodsA 4D label free mass spectrometry-based proteomic analysis of mitochondria associated membranes (MAMs) from the human renal proximal tubular epithelial cell line (HK-2 cells) was conducted under both normal (N) and hypoxia/reperfusion (HR) conditions. Subsequent differential proteins analysis aimed to characterize disease-relevant signaling molecules. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) analysis was applied to total proteins and differentially expressed proteins, encompassing Biological Process (BP), Cell Component (CC), Molecular Function (MF), and KEGG pathways. Further, Protein–Protein Interaction Network (PPI) exploration was carried out, leading to the identification of hub genes from differentially expressed proteins. Notably, Mitofusion 2 (MFN2) and BCL2/Adenovirus E1B 19-kDa interacting protein 3(BNIP3) were identified and subsequently validated both in vitro and in vivo. Finally, the impact of MFN2 on MAMs during hypoxia/reoxygenation was explored through regulation of gene expression. Subsequently, a comparative proteomics analysis was conducted between OE-MFN2 and normal HK-2 cells, providing further insights into the underlying mechanisms.ResultsA total of 4489 proteins were identified, with 3531 successfully quantified. GO/KEGG analysis revealed that MAM proteins were primarily associated with mitochondrial function and energy metabolism. Differential analysis between the two groups showed that 688 proteins in HR HK-2 cells exhibited significant changes in expression level with P-value < 0.05 and HR/N > 1.5 or HR/N < 0.66 set as the threshold criteria. Enrichment analysis of differentially expressed proteins unveiled biological processes such as mRNA splicing, apoptosis regulation, and cell division, while molecular functions were predominantly associated with energy metabolic activity. These proteins play key roles in the cellular responses during HR, offering insights into the IRI mechanisms and potential therapeutic targets. The validation of hub genes MFN2 and BNIP3 both in vitro and vivo was consistent with the proteomic findings. MFN2 demonstrated a protective role in maintaining the integrity of mitochondria associated membranes (MAMs) and mitigating mitochondrial damage following hypoxia/reoxygenation injury, this protective effect may be associated with the activation of the PI3K/AKT pathway.ConclusionsThe proteins located in mitochondria associated membranes (MAMs) are implicated in crucial roles during renal ischemic reperfusion injury (IRI), with MFN2 playing a pivotal regulatory role in this context.

Identification of hub genes in calcific aortic valve disease

Calcific aortic valve disease (CAVD) is a heart valve disorder characterized primarily by calcification of the aortic valve, resulting in stiffness and dysfunction of the valve. CAVD is prevalent among aging populations and is linked to factors such as hypertension, dyslipidemia, tobacco use, and genetic predisposition, and can result in becoming a growing economic and health burden. Once aortic valve calcification occurs, it will inevitably progress to aortic stenosis. At present, there are no medications available that have demonstrated effectiveness in managing or delaying the progression of the disease. In this study, we mined four publicly available microarray datasets (GSE12644 GSE51472, GSE77287, GSE233819) associated with CAVD from the GEO database with the aim of identifying hub genes associated with the occurrence of CAVD and searching for possible biological targets for the early prevention and diagnosis of CAVD. This study provides preliminary evidence for therapeutic and preventive targets for CAVD and may provide a solid foundation for subsequent biological studies.

HBPGEO: AN APPLICATION SYSTEM FOR ANALYZING AND VISUALIZING GENE EXPRESSION DATA OF HYPERTENSION

The analysis of gene expression data using existing methods and tools has become a fundamental approach for researchers to explore the mechanisms of hypertension. However, many researchers lack programming skills or statistical knowledge. Although gene expression data is widely available, they originate from various experiments with different standards and quality levels, causing inconvenience to researchers. While tools and databases with interactive interfaces exist for data analysis, they may have limited applicability. This study aimed to construct High Blood Pressure Gene Expression Omnibus (HBPGEO), a program based on the R Shiny platform. It integrated the discovery of differentially expressed genes, hub gene identification, gene function analysis, and more. It allowed users to select suitable methods and set different parameters during analysis, visualize results interactively, and download the analyzed results. The study successfully utilized HBPGEO to identify hypertension-related biomarkers.

Identification of novel hub genes for Alzheimer's disease associated with the hippocampus using WGCNA and differential gene analysis.

Alzheimer's disease (AD) is a common, refractory, progressive neurodegenerative disorder in which cognitive and memory deficits are highly correlated with abnormalities in hippocampal brain regions. There is still a lack of hippocampus-related markers for AD diagnosis and prevention. Differently expressed genes were identified in the gene expression profile GSE293789 in the hippocampal brain region. Enrichment analyses GO, KEGG, and GSEA were used to identify biological pathways involved in the DEGs and AD-related group. WGCNA was used to identify the gene modules that are highly associated with AD in the samples. The intersecting genes of the genes in DEGs and modules were extracted and the top ten ranked hub genes were identified. Finally GES48350 was used as a validation cohort to predict the diagnostic efficacy of hub genes. From GSE293789, 225 DEGs were identified, which were mainly associated with calcium response, glutamatergic synapses, and calcium-dependent phospholipid-binding response. WGCNA analysis yielded dark green and bright yellow modular genes as the most relevant to AD. From these two modules, 176 genes were extracted, which were taken to be intersected with DEGs, yielding 51 intersecting genes. Then 10 hub genes were identified in them: HSPA1B, HSPB1, HSPA1A, DNAJB1, HSPB8, ANXA2, ANXA1, SOX9, YAP1, and AHNAK. Validation of these genes was found to have excellent diagnostic performance. Ten AD-related hub genes in the hippocampus were identified, contributing to further understanding of AD development in the hippocampus and development of targets for therapeutic prevention.

Revealing Novel Genes Related to Parkinson's Disease Pathogenesis and Establishing an associated Model

Parkinson's disease (PD) represents a multifaceted neurological disorder whose genetic underpinnings warrant comprehensive investigation. This study focuses on identifying genes integral to PD pathogenesis and evaluating their diagnostic potential. Initially, we screened for differentially expressed genes (DEGs) between PD and control brain tissues within a dataset comprising larger number of specimens. Subsequently, these DEGs were subjected to weighted gene co-expression network analysis (WGCNA) to discern relevant gene modules. Notably, the yellow module exhibited a significant correlation with PD pathogenesis. Hence, we conducted a detailed examination of the yellow module genes using a cytoscope-based approach to construct a protein–protein interaction (PPI) network, which facilitated the identification of central hub genes implicated in PD pathogenesis. Employing two machine learning techniques, including XGBoost and LASSO algorithms, along with logistic regression analysis, we refined our search to three pertinent hub genes: FOXO3, HIST2H2BE, and HDAC1, all of which demonstrated a substantial association with PD pathogenesis. To corroborate our findings, we analyzed two PD blood datasets and clinical plasma samples, confirming the elevated expression levels of these genes in PD patients. The association of the genes with PD, as reflected by the area under the curve (AUC) values for FOXO3, HIST2H2BE, and HDAC1, were moderate for each gene. Collectively, this research substantiates the heightened expression of FOXO3, HIST2H2BE, and HDAC1 in both PD brain and blood samples, underscoring their pivotal contribution to the pathogenesis of PD.

Identification of hub genes within the CCL18 signaling pathway in hepatocellular carcinoma through bioinformatics analysis.

Hepatocellular carcinoma (HCC) is an aggressive malignancy, and CCL18, a marker of M2 macrophage activation, is often associated with tumor immune suppression. However, the role of CCL18 and its signaling pathway in HCC is still limited. Our study focuses on investigating the prognostic impact of CCL18 and its signaling pathway in HCC patients and biological functions in vitro. HCC-related RNA-seq data were obtained from TCGA, ICGC, and GEO. The 6 hub genes with the highest correlation to prognosis were identified using univariate Cox and LASSO regression analysis. Multivariate Cox regression analysis was performed to assess their independent prognostic potential and a nomogram was constructed. In vitro experiments, including CCK8, EdU, RT-qPCR, western blot, and transwell assays, were conducted to investigate the biological effects of exogenous CCL18 and 6 hub genes. A core network of highly expressed proteins in the high-risk group of tumors was constructed. Immune cell infiltration was evaluated using the ESTIMATE and CIBERSORT packages. Finally, potential treatments were explored using the OncoPredict package and CAMP database. We identified 6 survival-related genes (BMI1, CCR3, CDC25C, CFL1, LDHA, RAC1) within the CCL18 signaling pathway in HCC patients. A nomogram was constructed using the TCGA_LIHC cohort to predict patient survival probability. Exogenous CCL18, as well as overexpression of BMI1, CCR3, CDC25C, CFL1, LDHA, and RAC1, can promote proliferation, migration, invasion, stemness, and increased expression of PD-L1 protein in LM3 and MHCC-97H cell lines. In the high-risk group of patients from the TCGA_LIHC cohort, immune suppression was observed, with a strong correlation to 21 immune-related genes and suppressive immune cells. Exogenous CCL18 promotes LM3 and MHCC-97H cells proliferation, migration, invasion, stemness, and immune evasion. The high expression of BMI1, CCR3, CDC25C, CFL1, LDHA, and RAC1 can serve as a biomarkers for immune evasion in HCC.

Integrated bioinformatics analysis identifies autophagy-associated genes as candidate biomarkers and reveals the immune infiltration landscape in psoriasis

Autophagy is implicated in the pathogenesis of psoriasis. We aimed to identify autophagy-related biomarkers in psoriasis via an integrated bioinformatics approach. We downloaded the gene expression profiles of GSE30999 dataset, and the “limma” package was applied to identify differentially expressed genes (DEGs). Then, differentially expressed autophagy-related genes (DEARGs) were identified via integrating autophagy-related genes with DEGs. CytoHubba plugin was used for the identification of hub genes and verified by the GSE41662 dataset. Subsequently, a series of bioinformatics analyses were employed, including protein–protein interaction network, functional enrichment, spearman correlation, receiver operating characteristic, and immune infiltration analyses. One hundred and one DEARGs were identified, and seven DEARGs were identified as hub genes and verified using the GSE41662 dataset. These validated genes had good diagnostic value in distinguishing psoriasis lesions. Immune infiltration analysis indicated that ATG5, SQSTM1, EGFR, MAPK8, MAPK3, MYC, and PIK3C3 were correlated with infiltration of immune cells. Seven DEARGs, namely ATG5, SQSTM1, EGFR, MAPK8, MAPK3, MYC, and PIK3C3, may be involved in the pathogenesis of psoriasis, which expanded the understanding of the development of psoriasis and provided important clinical significance for treatment of this disease.

Potential bioactive compound and hub gene identification of endometrial carcinoma using systems biology

Endometrial carcinoma is a frequent cancer of the female genital tract. Endometrial carcinoma accounts for 97% of all uterine malignancies and 3 % of sarcomas that develop from the endometrium’s glands. Endometrial cancer is frequently found in its early stages since most women quickly report postmenopausal vaginal hemorrhage. The need for more advanced medications to improve survival in such situations is still unfulfilled. As a result, there is growing interest in employing an herbal treatment to treat endometriosis, which seems to be an effective strategy. We have discovered a few unintended targets (ligands) in our investigation that are active components of common therapeutic herbs. The differentially expressed genes (DEG - target protein) for endometrial cancer were found using the NCBI and CIViC databases. In our investigation, the protein used for docking and simulation was PDB ID: 3THW. Using the Cytoscape server, the gene-encoding protein network has been identified. It was discovered that the Protein 3THW's binding energy to the bioactive substance (Asarone) was −7.15 Kcal/mol. It was discovered that the crucial interacting amino acid residues were ILE648, PHE650, ILE651, VAL802, TYR815, VAL817. The properties of the pharmaceutical target are further investigated by employing a molecular simulation study for 100 ns with NAMD software. Low RMSD and SASA (Solvent accessible surface area), high RMSF, High hydrogen bonds, between Asarone and MSH2 demonstrated their potency as endometrial cancer inhibitor compounds. Based on these analyses we infer that the bioactive substances originating from medicinal plants may be an effective treatment for endometrial cancer. Communicated by Ramaswamy H. Sarma