Candidate Hub Research Articles

Identification of shared gene signatures and molecular mechanisms between chronic kidney disease and ulcerative colitis.

There is a complex interaction between chronic kidney disease (CKD) and ulcerative colitis (UC), but the pathophysiological mechanisms underlying the coexistence of CKD and UC are unclear. This study aimed to investigate the key molecules and pathways that may mediate the co-occurrence of CKD and UC through quantitative bioinformatics analysis based on a public RNA-sequencing database. The discovery datasets of CKD (GSE66494) and UC (GSE4183), as well as validation datasets of CKD (GSE115857) and UC (GSE10616), were downloaded from the Gene Expression Omnibus (GEO) database. After identifying differentially expressed genes (DEGs) with GEO2R online tool, the Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analyses for the DEGs were performed. Next, protein-protein interaction network was constructed with Search Tool for the Retrieval of Interacting Genes (STRING) and visualized by Cytoscape. Gene modules were identified by the plug-in MCODE and hub genes were screened using the plug-in CytoHubba. Then, correlation between immune cell infiltration and hub genes was analyzed, and the receiver operating characteristic curves were used to assess the predictive value of hub genes. Finally, immunostaining of human specimens was used to validate the relevant findings. A total of 462 common DEGs were identified and selected for further analyses. GO and KEGG enrichment analyses indicated that these DEGs were primarily enriched in immune- and inflammation-related pathways. Among them, the PI3K-Akt signaling pathway ranked top in both discovery and validation cohorts, and the key signal molecule phosphorylated Akt (p-Akt) was shown to be significantly overexpressed in human CKD kidneys and UC colons, and further elevated in CKD-UC comorbidity specimens. Moreover, nine candidate hub genes, including CXCL8, CCL2, CD44, ICAM1, IL1A, CXCR2, PTPRC, ITGAX, and CSF3, were identified, of which ICAM1 was validated as a common hub gene. Besides, immune infiltration analysis revealed that neutrophils, macrophages, and CD4+ T memory cells significantly accumulated in both diseases, and ICAM1 was remarkably associated with neutrophil infiltration. Furthermore, intercellular adhesion molecule1 (ICAM1)-mediated neutrophil infiltration was validated to be upregulated in kidney and colon biopsies of CKD and UC patients, and further increased in patients diagnosed with both CKD and UC. Finally, ICAM1 had shown critical value as a diagnostic marker for the co-occurrence of CKD and UC. Our study elucidated that immune response, PI3K-Akt signaling pathway, and ICAM1-mediated neutrophil infiltration might be the common pathogenesis of CKD and UC, and identified ICAM1 as a key potential biomarker and therapeutic target for the comorbidity of these two diseases.

Novel diagnostic biomarkers related to immune infiltration in Parkinson's disease by bioinformatics analysis.

Parkinson's disease (PD) is Pengfei Zhang Liwen Zhao Pengfei Zhang Liwen Zhao a common neurological disorder involving a complex relationship with immune infiltration. Therefore, we aimed to explore PD immune infiltration patterns and identify novel immune-related diagnostic biomarkers. Three substantia nigra expression microarray datasets were integrated with elimination of batch effects. Differentially expressed genes (DEGs) were screened using the "limma" package, and functional enrichment was analyzed. Weighted gene co-expression network analysis (WGCNA) was performed to explore the key module most significantly associated with PD; the intersection of DEGs and the key module in WGCNA were considered common genes (CGs). The CG protein-protein interaction (PPI) network was constructed to identify candidate hub genes by cytoscape. Candidate hub genes were verified by another two datasets. Receiver operating characteristic curve analysis was used to evaluate the hub gene diagnostic ability, with further gene set enrichment analysis (GSEA). The immune infiltration level was evaluated by ssGSEA and CIBERSORT methods. Spearman correlation analysis was used to evaluate the hub genes association with immune cells. Finally, a nomogram model and microRNA-TF-mRNA network were constructed based on immune-related biomarkers. A total of 263 CGs were identified by the intersection of 319 DEGs and 1539 genes in the key turquoise module. Eleven candidate hub genes were screened by the R package "UpSet." We verified the candidate hub genes based on two validation sets and identified six (SYT1, NEFM, NEFL, SNAP25, GAP43, and GRIA1) that distinguish the PD group from healthy controls. Both CIBERSORT and ssGSEA revealed a significantly increased proportion of neutrophils in the PD group. Correlation between immune cells and hub genes showed SYT1, NEFM, GAP43, and GRIA1 to be significantly related to immune cells. Moreover, the microRNA-TFs-mRNA network revealed that the microRNA-92a family targets all four immune-related genes in PD pathogenesis. Finally, a nomogram exhibited a reliable capability of predicting PD based on the four immune-related genes (AUC = 0.905). By affecting immune infiltration, SYT1, NEFM, GAP43, and GRIA1, which are regulated by the microRNA-92a family, were identified as diagnostic biomarkers of PD. The correlation of these four genes with neutrophils and the microRNA-92a family in PD needs further investigation.

Different Types of Gene Expression Profiles and Pathway Enrichment Analysis of Oral Dysplastic Keratinocyte Cells Caused by Heavy Smoking

Aim/Background: The aim of the study is to carry out survival analysis on the candidate hub genes to check out their effects on Keratinisation of mucous cells are related to the genes which are responsible for OSCC. Materials and Methods: Gene expression profiling array for two microarray data sets of keratinized and normal oral tissue has been taken from GEO then DEGs and lnc RNAs are identified in oral cancer using LIMMA R package 15 in bio-conductor followed by KEGG analysis. STRING tool used to construct the PPIN for the sub PPIN, MCODE algorithm used for our work. The prognostic correlation co efficient values of the genes responsible for the keratinisation process in cancers have been studied using TCGA project in The Encyclopedia of RNA Interactomes (ENCORI). Functional annotation and co-expression results done with these CD44, CD58, CD40, VIM, KRT19, MMP1, ANGPTL4, FABP4, ENTPD1, and FCER2 key protein coding genes. Results: Hyper-methylated promoters play a crucial role in the inactivation suppressor genes during carcinogenesis and dysregulation of the DEGs are intently related with progression of cancer as functions as reporter genes. The enrichment result of DEGs can reveal the role of some URGs shows various activated levels in keratinocyte cells of heavy smokers and keratinocytes of people who had never smoked. ENTPD1 is connected to the DEGs which are related to TNF receptor, lymphocyte receptor. Ag3 associated with lymphocytes respectively, also to KRT19 which together acts with KRT8 causes mutations and alterations of genes. Conclusion: From this study a hallmark gene ENTPD1 has been identified for oral squamous cell carcinoma along with HNSCC with the seed gene CD44 interconnected with CD40, CD58.

Identification of Key Modules and Candidate Genes for Powdery Mildew Resistance of Wheat-Agropyron cristatum Translocation Line WAT-2020-17-6 by WGCNA

As one of the serious diseases of wheat, powdery mildew (Blumeria graminis f. sp. tritici) is a long-term threat to wheat production. Therefore, it is of great significance to explore new powdery mildew-resistant genes for breeding. The wild relative species of wheat provide gene resources for resistance to powdery mildew breeding. Agropyron cristatum (2n = 4x = 28, genomes PPPP) is an important wild relative of wheat, carrying excellent genes for high yield, disease resistance, and stress resistance, which can be used for wheat improvement. To understand the molecular mechanism of powdery mildew resistance in the wheat-A. cristatum translocation line WAT2020-17-6, transcriptome sequencing was performed, and the resistance genes were analyzed by weighted gene co-expression network analysis (WGCNA). In the results, 42,845 differentially expressed genes were identified and divided into 18 modules, of which six modules were highly correlated with powdery mildew resistance. Gene ontology (GO) enrichment analysis showed that the six interested modules related to powdery mildew resistance were significantly enriched in N-methyltransferase activity, autophagy, mRNA splicing via spliceosome, chloroplast envelope, and AMP binding. The candidate hub genes of the interested modules were further identified, and their regulatory relationships were analyzed based on co-expression data. The temporal expression pattern of the 12 hub genes was verified within 96 h after powdery mildew inoculation by RT-PCR assay. In this study, we preliminarily explained the resistance mechanism of the wheat-A. cristatum translocation lines and obtained the hub candidate genes, which laid a foundation in the exploration of resistance genes in A. cristatum for powdery mildew-resistant breeding in wheat.

Identification and validation of immune cells and hub genes alterations in recurrent implantation failure: A GEO data mining study.

Introduction: Recurrent implantation failure (RIF) is a distressing problem in assisted reproductive technology (ART). Immunity plays a vital role in recurrent implantation failure (RIF) occurrence and development, but its underlying mechanism still needs to be fully elucidated. Through bioinformatics analysis, this study aims to identify the RIF-associated immune cell types and immune-related genes. Methods: The differentially expressed genes (DEGs) were screened based on RIF-associated Gene Expression Omnibus (GEO) datasets. Then, the enrichment analysis and protein-protein interaction (PPI) analysis were conducted with the DEGs. The RIF-associated immune cell types were clarified by combining single sample gene set enrichment analysis (ssGSEA) and CIBERSORT. Differentially expressed immune cell types-related modules were identified by weighted gene co-expression network analysis (WGCNA) and local maximal quasi-clique merger (lmQCM) analysis. The overlapping genes between DEGs and genes contained by modules mentioned above were delineated as candidate hub genes and validated in another two external datasets. Finally, the microRNAs (miRNAs) and long non-coding RNAs (lncRNAs) that interacted with hub genes were predicted, and the competing endogenous RNA (ceRNA) regulatory network was structured. Results: In the present study, we collected 324 DEGs between RIF and the control group, which functions were mainly enriched in immune-related signaling pathways. Regarding differential cell types, the RIF group had a higher proportion of activated memory CD4 T cells and a lower proportion of γδ T cells in the endometrial tissue. Finally, three immune-related hub genes (ALOX5AP, SLC7A7, and PTGS2) were identified and verified to effectively discriminate RIF from control individuals with a specificity rate of 90.8% and a sensitivity rate of 90.8%. In addition, we constructed a key ceRNA network that is expected to mediate molecular mechanisms in RIF. Conclusion: Our study identified the intricate correlation between immune cell types and RIF and provided new immune-related hub genes that offer promising diagnostic and therapeutic targets for RIF.

Identification of Oxidative Stress-Related Biomarkers in Diabetic Kidney Disease.

Diabetic kidney disease (DKD) is a leading cause of end-stage renal disease throughout the world. In kidney disease, oxidative stress has been linked to both antioxidant depletions and increased reactive oxygen species (ROS) production. Thus, the objective of this study was to identify biomarkers related to oxidative stress in DKD. The gene expression profile of the DKD was extracted from the Gene Expression Omnibus (GEO) database. The identification of the differentially expressed genes (DEGs) was performed using the "limma" R package, and weighted gene coexpression network analysis (WGCNA) was used to find the gene modules that were most related to DKD. Gene ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis was performed using "Org.Hs.eg.db" R package. The protein-protein interaction (PPI) network was constructed using the STRING database. The hub genes were identified by the Molecular Complex Detection (MCODE) plug-in of Cytoscape software. The diagnostic capacity of hub genes was verified using the receiver operating characteristic (ROC) curve. Correlations between diagnostic genes were analyzed using the "corrplot" package. In addition, the miRNA gene transcription factor (TF) network was used to explain the regulatory mechanism of hub genes in DKD. DEGs analysis and WGCNA-identified 160 key genes were identified in DKD patients. Among them, nine oxidative stress-related genes were identified as candidate hub genes for DKD. Using the PPI network, five hub genes, NR4A2, DUSP1, FOS, JUN, and PTGS2, were subsequently identified. All the hub genes were downregulated in DKD and had a high diagnostic value of DKD. The regulatory mechanism of hub genes was analyzed from the miRNA gene-TF network. Our study identified NR4A2, DUSP1, FOS, JUN, and PTGS2 as hub genes of DKD. These genes may serve as potential therapeutic targets for DKD patients.

Interaction between glycolysis‒cholesterol synthesis axis and tumor microenvironment reveal that gamma-glutamyl hydrolase suppresses glycolysis in colon cancer.

Metabolic reprogramming is a feature of cancer. However, colon cancer subtypes based on the glycolysis‒cholesterol synthesis axis have not been identified, and little is known about connections between metabolic features and the tumor microenvironment. Data for 430 colon cancer cases were extracted from The Cancer Genome Atlas, including transcriptome data, clinical information, and survival outcomes. Glycolysis and cholesterol synthesis-related gene sets were obtained from the Molecular Signatures Database for a gene set variation analysis. The relationship between the genomic landscape and immune landscape were investigated among four metabolic subtypes. Hub genes were determined. The clinical significance of candidate hub gene was evaluated in 264 clinical samples and potential functions were validated in vitro and in vivo. Colon cancer cases were clustered into four metabolic subtypes: quiescent, glycolytic, cholesterogenic, and mixed. The metabolic subtypes differed with respect to the immune score, stromal score, and estimate score using the ESTIMATE algorithm, cancer-immunity cycle, immunomodulator signatures, and signatures of immunotherapy responses. Patients in the cholesterogenic group had better survival outcomes than those for other subtypes, especially glycolytic. The glycolytic subtype was related to unfavorable clinical characteristics, including high mutation rates in TTN, APC, and TP53, high mutation burden, vascular invasion, right colon cancer, and low-frequency microsatellite instability. GGH, CACNG4, MME, SLC30A2, CKMT2, SYN3, and SLC22A31 were identified as differentially expressed both in glycolytic-cholesterogenic subgroups as well as between colon cancers and healthy samples, and were involved in glycolysis‒cholesterol synthesis. GGH was upregulated in colon cancer; its high expression was correlated with CD4+ T cell infiltration and longer overall survival and it was identified as a favorable independent prognostic factor. The overexpression of GGH in colon cancer-derived cell lines (SW48 and SW480) inhibited PKM, GLUT1, and LDHA expression and decreased the extracellular lactate content and intracellular ATP level. The opposite effects were obtained by GGH silencing. The phenotype associated with GGH was also validated in a xenograft nude mouse model. Our results provide insight into the connection between metabolism and the tumor microenvironment in colon cancer and provides preliminary evidence for the role of GGH, providing a basis for subsequent studies.

Comparative Transcriptome Analysis Unveils the Molecular Mechanism Underlying Sepal Colour Changes under Acidic pH Substratum in Hydrangea macrophylla

The hydrangea (Hydrangea macrophylla (Thunb). Ser.), an ornamental plant, has good marketing potential and is known for its capacity to change the colour of its inflorescence depending on the pH of the cultivation media. The molecular mechanisms causing these changes are still uncertain. In the present study, transcriptome and targeted metabolic profiling were used to identify molecular changes in the RNAome of hydrangea plants cultured at two different pH levels. De novo assembly yielded 186,477 unigenes. Transcriptomic datasets provided a comprehensive and systemic overview of the dynamic networks of the gene expression underlying flower colour formation in hydrangeas. Weighted analyses of gene co-expression network identified candidate genes and hub genes from the modules linked closely to the hyper accumulation of Al3+ during different stages of flower development. F3'5'H, ANS, FLS, CHS, UA3GT, CHI, DFR, and F3H were enhanced significantly in the modules. In addition, MYB, bHLH, PAL6, PAL9, and WD40 were identified as hub genes. Thus, a hypothesis elucidating the colour change in the flowers of Al3+-treated plants was established. This study identified many potential key regulators of flower pigmentation, providing novel insights into the molecular networks in hydrangea flowers.

Identification of crucial genes for predicting the risk of atherosclerosis with system lupus erythematosus based on comprehensive bioinformatics analysis and machine learning

BackgroundSystemic lupus erythematosus (SLE) has become a major public health problem over the years, and atherosclerosis (AS) is one of the main complications of SLE associated with serious cardiovascular consequences in this patient population. The present study aimed to identify potential biomarkers for SLE patients with AS. MethodsFive microarray datasets (GSE50772, GSE81622, GSE100927, GSE28829, GSE37356) were downloaded from the NCBI Gene Expression Omnibus database. The Limma package was used to identify differentially expressed genes (DEGs) in AS. Weighted gene coexpression network analysis (WGCNA) was used to identify significant module genes associated with SLE. Functional enrichment analysis, protein-protein interaction (PPI) network construction, and machine learning algorithms (least absolute shrinkage and selection operator (Lasso, Support Vector Machine-Recursive Feature Elimination (SVM-RFE), and random forest) were applied to identify hub genes. Subsequently, we generated a nomogram and receiver operating characteristic curve (ROC) for predicting the risk of AS in SLE patients. Finally, immune cell infiltrations were analyzed, and Consensus Cluster Analysis was conducted based on Single Sample Gene Set Enrichment Analysis (ssGSEA) scores. ResultsFive hub genes (SPI1, MMP9, C1QA, CX3CR1, and MNDA) were identified and used to establish a nomogram that yielded a high predictive performance (area under the curve 0.900–0.981). Dysregulated immune cell infiltrations were found in AS, with positive correlations with the five hub genes. Consensus clustering showed that the optimal number of subtypes was 3. Compared to subtypes A and B, subtype C presented higher expression of the five hub genes, immune cell infiltration levels and immune checkpoint expression. ConclusionOur study systematically identified five candidate hub genes (SPI1, MMP9, C1QA, CX3CR1, MNDA) and established a nomogram that could predict the risk of AS with SLE using various bioinformatic analyses and machine learning algorithms. Our findings provide the foothold for future studies on potential crucial genes for AS in SLE patients. Additionally, the dysregulated immune cell proportions and immune checkpoint expressions in AS with SLE were identified.

Screening prognostic genes related to leucovorin, fluorouracil, and irinotecan treatment sensitivity by performing co-expression network analysis for colon cancer.

Background: Colon cancer is one of the most common malignant tumors in the world. FOLFIRI (leucovorin, fluorouracil, and irinotecan) is a common combination in chemotherapy regimens. However, insensitivity to FOLFIRI is an important factor in the effectiveness of the treatment for advanced colon cancer. Our study aimed to explore precise molecular targets associated with chemotherapy responses in colon cancer. Methods: Gene expression profiles of 21 patients with advanced colorectal cancer who received chemotherapy based on FOLFIRI were obtained from the Gene Expression Omnibus (GEO) database. The gene co-expression network was constructed by the weighted gene co-expression network analysis (WGCNA) and functional gene modules were screened out. Clinical phenotypic correlation analysis was used to identify key gene modules. Gene Ontology and pathway enrichment analysis were used to screen enriched genes in key modules. Protein-protein interaction (PPI) analysis was used to screen out key node genes. Based on the Gene Expression Profiling Interactive Analysis (GEPIA) database, the correlation between the expression levels of these genes and the overall survival (OS) and disease-free survival (DFS) of colon cancer patients was investigated, and the hub genes were screened out. Immunohistochemistry of candidate hub genes was identified using the Human Protein Atlas database. Finally, clinical information and RNA sequencing data of colon cancer were obtained from The Cancer Genome Atlas project database (TCGA), the GEPIA, and the Human Atlas databases for validation. Results: The WGCNA revealed that three hub genes were closely related to chemotherapy insensitivity of colon cancer: AEBP1, BGN, and TAGLN. The protein expression levels of AEBP1, BGN, and TAGLN in tumor tissues were higher than those in normal tissues. In addition, the gene expression levels of AEBP1, BGN, and TAGLN were negatively correlated with OS and DFS in colon cancer patients. Therefore, AEBP1, BGN, and TAGLN have been identified as potential biomarkers related to the response to FOLFIRI treatment of colon cancer. Conclusion: We found that AEBP1, BGN, and TAGLN, as potential predictive biomarkers, may play an important role in the response to FOLFIRI treatment of colon cancer and as a precise molecular target associated with chemotherapy response in colon cancer.

Identification of Six Diagnostic Biomarkers for Chronic Lymphocytic Leukemia Based on Machine Learning Algorithms.

Chronic lymphocytic leukemia (CLL) is the most common type of leukemia in adults. Thus, novel reliable biomarkers need to be further explored to increase diagnostic, therapeutic, and prognostic effectiveness. Six datasets containing CLL and control samples were downloaded from the Gene Expression Omnibus database. Differential gene expression analysis, weighted gene coexpression network analysis (WGCNA), and the least absolute shrinkage and selection operator (LASSO) regression were applied to identify potential diagnostic biomarkers for CLL using R software. The diagnostic performance of the hub genes was then measured by the receiver operating characteristic (ROC) curve analysis. Functional analysis was implemented to uncover the underlying mechanisms. Additionally, correlation analysis was performed to assess the relationship between the hub genes and immunity characteristics. A total number of 47 differentially expressed genes (DEGs) and 25 candidate hub genes were extracted through differential gene expression analysis and WGCNA, respectively. Based on the 14 overlapped genes between the DEGs and the candidate hub genes, LASSO regression analysis was used, which identified a final number of six hub genes as potential biomarkers for CLL: ABCA6, CCDC88A, PMEPA1, EBF1, FILIP1L, and TEAD2. The ROC curves of the six genes showed reliable predictive ability in the training and validation cohorts, with all area under the curve (AUC) values over 0.80. Functional analysis revealed an abnormal immune status in the CLL patients. A significant correlation was found between the hub genes and the immune-related pathways, indicating a possible tight connection between the hub genes and tumor immunity in CLL. This study was based on machine learning algorithms, and we identified six genes that could be possible CLL markers, which may be involved in CLL pathogenesis and progression through immune-related signal pathways.

Exploration of the effect of Celastrol on protein targets in nasopharyngeal carcinoma: Network pharmacology, molecular docking and experimental evaluations.

Background: Celastrol, an important extract of Tripterygium wilfordii, shows strong antitumor activity in a variety of tumors including nasopharyngeal carcinoma (NPC). However, little is known about its targets in NPC. We aimed to screen the key gene targets of Celastrol in the treatment of NPC by means of in silico analyses (including network pharmacology and molecular docking) and experimental evaluations. Methods: The main target genes of Celastrol and the genes related to NPC were obtained by retrieving the relevant biological databases, and the common targets were screened. Protein-protein interaction analysis was used to screen the hub genes. Then, a "compound-target-disease" network model was created and molecular docking was used to predict the binding of Celastrol to the candidate hub proteins. Afterward, the expression changes of the candidate genes under the administration of Celastrol were verified in vitro and in vivo. Results: Sixty genes common to Celastrol and NPC were screened out, which may be related to numerous biological processes such as cell proliferation, apoptosis, and tube development, and enriched in various pathways such as PI3K- Akt, EGFR tyrosine kinase inhibitor resistance, and Apoptosis. The tight binding ability of the candidate hub proteins (TNF, VEGFA, and IL6) to Celastrol was predicted by molecular docking [Docking energy: TNF, -6.08; VEGFA,-6.76; IL6,-6.91(kcal/mol)]. In vitro experiments showed that the expression of TNF and VEGFA decreased while the expression of IL6 increased in NPC cells (CNE2 and HONE1) treated with Celastrol. In vivo experiments suggested that Celastrol significantly reduced the weight and volume of the transplanted tumors in tumor-bearing mice in vivo. The expression of TNF, VEGFA, and IL6 in the transplanted tumor cells could be regulated by using Celastrol, and the expression trends were consistent with the in vitro model. Conclusion: Several gene targets have been filtered out as the core targets of Celastrol in the treatment of NPC, which might be involved in a variety of signaling pathways. Hence, Celastrol may exert its anti-NPC activity through multiple targets and multiple pathways, which will provide new clues for further research. Future experiments are warranted to validate the findings.

Identification of hub proteins in cerebrospinal fluid as potential biomarkers of Alzheimer's disease by integrated bioinformatics.

Alzheimer's disease (AD) is a heterogeneous neurodegenerative disease with complex pathophysiology. Therefore, the identification of novel effective fluid biomarkers is essential for Alzheimer's disease diagnosis and drug development. This study aimed to identify potential candidate hub proteins in cerebrospinal fluid for precise Alzheimer's disease diagnosis using bioinformatics methods. A total of 29 co-significant differentially expressed proteins were identified by differential protein expression analysis in four different cohorts. Functional enrichment analysis revealed that most of these proteins were enriched in pathways related to glycometabolism. Using the Least Absolute Shrinkage and Selection Operator (LASSO) and random forest feature selection methods, six hub proteins [14-3-3 protein zeta/delta (YWHAZ), SPARC-related modular calcium-binding protein 1 (SMOC1), aldolase A (ALDOA), pyruvate kinase isoenzyme type M2 (PKM), chitinase-3-like protein 1 (CHI3L1), and secreted phosphoprotein 1 (SPP1)] were identified. These six hub proteins were upregulated in the cerebrospinal fluid of patients with Alzheimer's disease compared with cognitively unimpaired control individuals. Meanwhile, SMOC1, ALDOA, and PKM were specifically upregulated in the cerebrospinal fluid of patients with Alzheimer's disease but not in other neurodegenerative diseases. Build AD diagnostic models showed that a single hub protein or six hub proteins combination had an excellent ability to discriminate Alzheimer's disease. In conclusion, our study suggests that these identified hub proteins, which are related to glycometabolism, may be potential biomarkers for further basic and clinical research in Alzheimer's disease.

Identification of immune infiltration and cuproptosis-related subgroups in Crohn's disease.

Crohn's disease (CD) is a type of heterogeneous, dysfunctional immune-mediated intestinal chronic and recurrent inflammation caused by a variety of etiologies. Cuproptosis is a newly discovered form of programmed cell death that seems to contribute to the advancement of a variety of illnesses. Consequently, the major purpose of our research was to examine the role of cuproptosis-related genes in CD. We obtained two CD datasets from the gene expression omnibus (GEO) database, and immune cell infiltration was created to investigate immune cell dysregulation in CD. Based on differentially expressed genes (DEGs) and the cuproptosis gene set, differentially expressed genes of cuproptosis (CuDEGs) were found. Then, candidate hub cuproptosis-associated genes were found using machine learning methods. Subsequently, using 437 CD samples, we explored two distinct subclusters based on hub cuproptosis-related genes. Gene Ontology (GO), Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment, Gene set variation analysis (GSVA) and immune infiltration analysis studies were also used to assess the distinct roles of the subclusters. Overall, 25 CuDEGs were identified, including ABCB6, BACE1, FDX1, GLS, LIAS, MT1M, PDHA1, etc. And most CuDEGs were expressed at lower levels in CD samples and were negatively related to immune cell infiltration. Through the machine learning algorithms, a seven gene cuproptosis-signature was identified and two cuproptosis-related subclusters were defined. Cluster-specific differentially expressed genes were found only in one cluster, and functional analysis revealed that they were involved in several immune response processes. And the results of GSVA showed positive significant enrichment in immune-related pathways in cluster A, while positive significant enrichment in metabolic pathways in cluster B. In addition, an immune infiltration study indicated substantial variation in immunity across different groups. Immunological scores were higher and immune infiltration was more prevalent in Cluster A. According to the current research, the cuproptosis phenomenon occurs in CD and is correlated with immune cell infiltration and metabolic activity. This information indicates that cuproptosis may promote CD progression by inducing immunological response and metabolic dysfunction. This research has opened new avenues for investigating the causes of CD and developing potential therapeutic targets for the disease.

WGCNA Analysis Revealed the Hub Genes Related to Soil Cadmium Stress in Maize Kernel (Zea mays L.).

Soil contamination by heavy metals has become a prevalent topic due to their widespread release from industry, agriculture, and other human activities. Great progress has been made in elucidating the uptake and translocation of cadmium (Cd) accumulation in rice. However, there is still little known about corresponding progress in maize. In the current study, we performed a comparative RNA-Seq-based approach to identify differentially expressed genes (DEGs) of maize immature kernel related to Cd stress. In total, 55, 92, 22, and 542 DEGs responsive to high cadmium concentration soil were identified between XNY22-CHS-8 vs. XNY22-YA-8, XNY22-CHS-24 vs. XNY22-YA-24, XNY27-CHS-8 vs. XNY27-YA-8, and XNY27-CHS-24 vs. XNY27-YA-24, respectively. The weighted gene co-expression network analysis (WGCNA) categorized the 9599 Cd stress-responsive hub genes into 37 different gene network modules. Combining the hub genes and DEGs, we obtained 71 candidate genes. Gene Ontology (GO) enrichment analysis of genes in the greenyellow module in XNY27-YA-24 and connectivity genes of these 71 candidate hub genes showed that the responses to metal ion, inorganic substance, abiotic stimulus, hydrogen peroxide, oxidative stress, stimulus, and other processes were enrichment. Moreover, five candidate genes that were responsive to Cd stress in maize kernel were detected. These results provided the putative key genes and pathways to response to Cd stress in maize kernel, and a useful dataset for unraveling the underlying mechanism of Cd accumulation in maize kernel.

Identification and exploration of novel M2 macrophage-related biomarkers in the development of acute myocardial infarction.

Acute myocardial infarction (AMI), one of the most severe and fatal cardiovascular diseases, is a major cause of morbidity and mortality worldwide. Macrophages play a critical role in ventricular remodeling after AMI. The regulatory mechanisms of the AMI progression remain unclear. This study aimed to identify hub regulators of macrophage-related modules and provide translational experiments with potential therapeutic targets. The GSE59867 dataset was downloaded from the Gene Expression Omnibus (GEO) database for bioinformatics analysis. The expression patterns of 22 types of immune cells were determined using CIBERSORT. GEO2R was used to identify differentially expressed genes (DEGs) through the limma package. Then, DEGs were clustered into different modules, and relationships between modules and macrophage types were analyzed using weighted gene correlation network analysis (WGCNA). Further functional enrichment analysis was performed using significantly associated modules. The module most significantly associated with M2 macrophages (Mϕ2) was chosen for subsequent analysis. Co-expressed DEGs of AMI were identified in the GSE123342 and GSE97320 datasets and module candidate hub genes. Additionally, hub gene identification was performed in GSE62646 dataset and clinical samples. A total of 8,760 DEGs were identified and clustered into ten modules using WGCNA analysis. The blue and turquoise modules were significantly related to Mϕ2, and 482 hub genes were discerned from two hub modules that conformed to module membership values > 0.8 and gene significance values > 0.25. Subsequent analysis using a Venn diagram assessed 631 DEGs in GSE123342, 1457 DEGs in GSE97320, and module candidate hub genes for their relationship with Mϕ2 in the progression of AMI. Finally, four hub genes (CSF2RB, colony stimulating factor 2 receptor subunit beta; SIGLEC9, sialic acid-binding immunoglobulin-like lectin 9; LRRC25, leucine-rich repeat containing 25; and CSF3R, colony-stimulating factor-3 receptor) were validated to be differentially expressed and to have high diagnostic value in both GSE62646 and clinical samples. Using comprehensive bioinformatics analysis, we identified four novel genes that may play crucial roles in the pathophysiological mechanism of AMI. This study provides novel insights into the impact of macrophages on the progression of AMI and directions for Mϕ2-targeted molecular therapies for AMI.

Identifying Prognostic Biomarkers Related to m6A Modification and Immune Infiltration in Renal Cell Carcinoma.

Renal cell carcinoma (RCC) is the largest category of kidney tumors and usually does not have a good prognosis. N6-methyladenosine(m6A) and immune infiltration have received increased attention because of their great influence on the clinical outcome and prognosis of cancer patients. We identified hub genes through multi-dimensional screening, including DEGs, PPI analysis, LASSO regression, and random forest. Meanwhile, GO/KEGG enrichment, cMAP analysis, prognostic analysis, m6A prediction, and immune infiltration analysis were performed to understand the potential mechanism and screen therapeutic drugs. We screened 275 downregulated and 185 upregulated genes using three GEO datasets and the TCGA dataset. In total, 82 candidate hub genes were selected using STRING and Cytoscape. Enrichment analysis illustrated that the top 3 biological process terms and top 1 KEGG term were related to immunity. cMAP analysis showed some antagonistic molecules can be candidate drugs for the treatment of RCC. Then, six hub genes (ERBB2, CASR, P2RY8, CAT, PLAUR, and TIMP1) with strong predictive values for prognosis and clinicopathological features were selected. Meanwhile, P2RY8, ERBB2, CAT, and TIMP1 may obtain m6A modification by binding METTL3 or METTL14. On the other hand, differential expression of CAT, ERBB2, P2RY8, PLAUR, and TIMP1 affects the infiltration of the majority of immune cells. We identified six hub genes through multi-dimensional screening. They all possess strong predictive value for prognosis and clinicopathological features. Meanwhile, hub genes may regulate the progression of RCC via an m6A- and immunity-dependent mechanism.

Identification of Small Airway Epithelium-Related Hub Genes in Chronic Obstructive Pulmonary Disease.

Pulmonary small airway epithelia are the primary site of cellular and histological alterations in chronic obstructive pulmonary disease (COPD), while the potential therapeutic hub genes of pulmonary epithelia are rarely identified to elucidate profound alterations in the progression of the disease. Microarray dataset of GSE11906 containing small airway epithelia from 34 healthy non-smokers and 33 COPD patients was applied to screen differentially expressed genes (DEGs). Weighted gene correlation network analysis (WGCNA) was further used to identify the hub genes related to clinical features. Moreover, single-cell RNA sequencing data from GSE173896 and GSE167295 dataset were applied to explore the expression and distribution of the hub genes. The expression levels of hub genes in epithelial cells stimulated by cigarette smoke extract (CSE) were detected by RT-qPCR. Ninety-eight DEGs correlated with clinical features of COPD were identified via limma and WGCNA. Eight hub genes (including AKR1C3, ALDH3A1, AKR1C1, CYP1A1, GPX2, CBR3, AKR1B1 and GSR) that might exert an antioxidant role in COPD process were identified. Single-cell transcriptomic analysis indicated that the expressions of AKRAC3, ALDH3A1, GPX2, CBR3 and AKR1B1 were significantly increased in the COPD group when compared with the normal group. Moreover, we found that the expression of ALDH3A1 was the most abundantly expressed in ciliated cells. RT-qPCR results indicated that the majority of candidate novel genes were significantly elevated when the epithelial cells were exposed to CSE. Through integrating limma, WGCNA, and protein-protein interaction (PPI) analysis, a total of eight candidate hub genes of pulmonary airway epithelia were identified in COPD. Moreover, single-cell transcriptomic analysis indicated that ALDH3A1 was enriched in ciliated cells, which may provide a new insight into the pathogenesis and treatment of COPD.

Identification of hub genes, signaling pathways and immune infiltration of recurrent spontaneous abortion based on bioinformatics analysis with clinical verification

ObjectiveThe present study was aimed to identify hub genes associated with recurrent spontaneous abortion (RSA) via both bioinformatics analysis and clinical verification, also to evaluate the related pathways and immune infiltration situation of RSA, for exploring its underlying mechanism. Materials and methodsWe screened candidate hub genes associated with RSA via bioinformatic analysis in the microarray datasets GSE22490 downloaded from the Gene Expression Omnibus (GEO) database. Reverse transcription-quantitative polymerase chain reaction (RT-qPCR) and Western blotting were used to validate these hub genes. Several kinds of enrichment analysis were carried out to find out the pathways related to RSA. Additionally, CIBERSORT was used for evaluation of local immune Infiltration status of RSA. ResultsThere were 536 differentially expressed genes (DEGs) including 301 upregulated and 235 downregulated genes in RSA group compared with healthy control group. Four hub genes (STAT3, TLR2, TLR4 and CD86) were finally screened out according to the protein–protein interaction (PPI) network analysis, RT-qPCR and Western blotting. Enrichment analysis showed that Toll-like receptor signaling pathway, neutrophil chemotaxis, chemokine signaling pathway and Fc gamma receptor-mediated phagocytosis were strongly associated with RSA. And in immune infiltration analysis, RSA tissue was found containing a higher proportion of monocytes and eosinophils. ConclusionThis study screened out four hub genes and several important pathways changed in the trophoblastic tissue of RSA patients. We also found that monocytes and eosinophils may be involved in RSA. These findings provide theoretical basis for further studies on the mechanisms of RSA.

Optimizing Intermodal Transport and Hub Location Problem for ECOWAS in the Context of Improving Intra-African Trade

Trade within Africa is at an all-time low, with a lack of an optimal intermodal transportation network and a high cost of business serving as a deterrent to trade. This research studies Intra-African Trade within ECOWAS using a hierarchical spatial aggregation process to identify 27 nodes. Distance-based weighted centrality measures employed TOPSIS to model a ranked node centrality of Economic Community of West African States (ECOWAS) intermodal transport of railway, road, and waterway networks. The ten highest-ranking nodes identified from the mixed-integer linear program adopted as candidate hubs, thus selecting Ibeshe ferry terminal in Lagos and Tema Harbour in Accra for waterway; Thies, Ibadan, Conakry, Monrovia and Thies for railway; and Thies and Ibadan for road. We conclude with sensitivity analysis and a discussion of management implications for ECOWAS. This paper demonstrated that a limited number of transshipment hubs would encourage interregional trade and cut point-to-point transportation costs and lead to economic growth and development.