Hub Modules Research Articles

Renal tubular gen e biomarkers identification based on immune infiltrates in focal segmental glomerulosclerosis

Objective The present study identified novel renal tubular biomarkers that may influence the diagnosis and treatment of focal segmental glomerulosclerosis (FSGS) based on immune infiltration. Methods Three FSGS microarray datasets, GSE108112, GSE133288 and GSE121211, were downloaded from the Gene Expression Omnibus (GEO) database. The R statistical software limma package and the combat function of the sva package were applied for preprocessing and to remove the batch effects. Differentially expressed genes (DEGs) between 120 FSGS and 15 control samples were identified with the limma package. Disease Ontology (DO) pathway enrichment analysis was conducted with statistical R software to search for related diseases. Gene set enrichment analysis (GSEA) was used to interpret the gene expression data and it revealed many common biological pathways. A protein-protein interaction (PPI) network was built using the Search Tool for the Retrieval of Interacting Genes (STRING) database, and hub genes were identified by the Cytoscape (version 3.7.2) plug-in CytoHubba. The plug-in Molecular Complex Detection (MCODE) was used to screen hub modules of the PPI network in Cytoscape, while functional analysis of the hub genes and hub nodes involved in the submodule was performed by ClusterProfiler. The least absolute shrinkage and selection operator (LASSO) regression and support vector machine recursive feature elimination (SVM-RFE) analysis were used to screen characteristic genes and build a logistic regression model. Receiver operating characteristic (ROC) curve analyses were used to investigate the logistic regression model and it was then validated by an external dataset GSE125779, which contained 8 FSGS samples and 8 healthy subjects. Cell-type identification by estimating relative subsets of RNA transcripts (CIBERSORT) was used to calculate the immune infiltration of FSGS samples. Results We acquired 179 DEGs, 79 genes with downregulated expression (44.1%) and 100 genes with upregulated expression (55.9%), in the FSGS samples. The DEGs were significantly associated with arteriosclerosis, kidney disease and arteriosclerotic cardiovascular disease. GSEA revealed that these gene sets were significantly enriched in allograft rejection signaling pathways and activation of immune response in biological processes. Fifteen genes were demonstrated to be hub genes by PPI, and three submodules were screened by MCODE linked with FSGS. Analysis by machine learning methodologies identified nuclear receptor subfamily 4 group A member 1 (NR4A1) and dual specificity phosphatase 1 (DUSP1) as sensitive tubular renal biomarkers in the diagnosis of FSGS, and they were selected as hub genes, as well as hub nodes which were enriched in the MAPK signaling pathway. Immune cell infiltration analysis revealed that the genetic biomarkers were both correlated with activated mast cells, which may amplify FSGS biological processes. Conclusion DUSP1 and NR4A1 were identified as sensitive potential biomarkers in the diagnosis of FSGS. Activated mast cells have a decisive effect on the occurrence and development of FSGS through tubular lesions and tubulointerstitial inflammation, and they are expected to become therapeutic targets in FSGS.

Celastrol inhibits LL37-induced rosacea by inhibiting Ca2+/CaMKII-mTOR-NF-κB activation

Rosacea is a common chronic facial inflammatory disease that affects millions of people worldwide. Due to the unclear etiology of rosacea, effective treatments are limited. Celastrol, a plant-derived triterpene, has been reported to alleviate inflammation in various diseases. However, whether celastrol exerts protective effects in rosacea remains to be elucidated. In this study, weighted gene co-expression network analyses (WGCNA) were performed. Hub modules closely related to rosacea clinical characteristics were identified and found to be involved in inflammation- and angiogenesis-related signaling pathways. Then, the pharmacological targets of celastrol were predicted using the TargetNet and Swiss Target Prediction databases. A GO analysis indicated that the biological process regulated by celastrol highly overlapped with the pathogenic biological processes in rosacea. Next, we showed that celastrol ameliorated erythema, skin thickness and inflammatory cell infiltration in the dermis of LL37-treated mice. Celastrol suppressed the expression of rosacea-related inflammatory cytokines and inhibited the Th17 immune response and cutaneous angiogenesis in LL37-induced rosacea-like mice. We further demonstrated that celastrol attenuated LL37-induced inflammation by inhibiting intracellular-free calcium ([Ca2+]i)-mediated mTOR signaling in keratinocytes. Chelating intracellular Ca2+ with BAPTA/AM potentiated celastrol-induced repression of LL37-induced p-S6 elevation. The mTOR agonist MHY1485 dramatically reinforced LL37-induced rosacea-like characteristics, while celastrol attenuated these outcomes. Moreover, celastrol inhibited LL37-activated NF-κB in a mTOR signaling-dependent manner. In conclusion, our findings underscore that celastrol may be a rosacea protective agent by inhibiting the LL37-activated Ca2+/CaMKII-mTOR-NF-κB pathway associated with skin inflammation disorders.

MiRNA-Gene Interaction Network Construction Strategy to Discern Promising Traditional Chinese Medicine against Osteoporosis.

Osteoporosis is a widespread bone disease that affects million cases annually. The underlying mechanisms behind the progress of osteoporosis remain enigmatic, which limits detections of biomarkers and therapeutic targets. Hence, this study was aimed at exploring hub molecules to better understand the mechanism of osteoporosis development and discover the traditional Chinese medicine potential drugs for osteoporosis. miRNA and gene expression profiles were downloaded from Gene Expression Omnibus (GEO). Weighted correlation network analysis (WGCNA) was used to identify the key modules for osteoporosis. DIANA Tools was applied to perform pathway enrichment. A miRNA-gene interaction network was constructed, and hub miRNAs and genes were distinguished using Cytoscape software. Receiver operating characteristic (ROC) curves of hub miRNAs and genes were plotted, and correlations with hub genes and osteoporosis-associated factors were evaluated. Potential drugs for osteoporosis in Traditional Chinese Medicine Systems Pharmacology Database and Analysis Platform (TCMSP) were screened, and molecular docking models between these drugs and target genes were showed by AutoDock tools. Two hub modules, 1 miRNA module and 1 gene module, were identified to be the most strongly correlated with osteoporosis by using WGCNA. Then, 3 KEGG pathways including focal adhesion, PI3K-Akt signaling pathway, and gap junction were shared pathways enriched with the miRNAs and genes screened out by WGCNA and differential expression analyses. Finally, after constructing a miRNA-gene interaction network, 6 hub miRNAs (hsa-miR-18b-3p, hsa-miR-361-3p, hsa-miR-484, hsa-miR-519e-5p, hsa-miR-940, and hsa-miR-1275) and 6 hub genes (THBS1, IFNAR2, ARHGAP5, TUBB2B, FLNC, and NTF3) were detected. ROC curves showed good performances of miRNAs and genes for osteoporosis. Correlations with hub genes and osteoporosis-associated factors suggested implicational roles of them for osteoporosis. Based on these hub genes, 3 natural compounds (kainic acid, uridine, and quercetin), which were the active ingredients of 192 herbs, were screened out, and a target-compound-herb network was extracted using TCMSP. Molecular docking models of kainic acid-NTF3, uridine-IFNAR2, and quercetin-THBS1 were exhibited with AutoDock tools. Our study sheds light on the pathogenesis of osteoporosis and provides promising therapeutic targets and traditional Chinese medicine drugs for osteoporosis.

Single-cell transcriptomics identifies Col1a1 and Col1a2 as hub genes in obesity-induced cardiac fibrosis

Obesity is a risk factor for cardiovascular disease, leading to ventricular dysfunction and cardiac fibrosis, in which non-cardiomyocytes (nonCMs) play an important role. Early detection and treatment of heart illness may help to limit its progression. We screened for key markers of obesity-induced cardiac fibrosis using single-cell transcriptomics techniques. To begin, an obese mouse model was constructed using a high-fat diet. From a pathogenic perspective, pathological alterations in the obesity-induced heart were found. Differentially expressed genes (DEGs) were identified and functional enrichment analysis was performed. Then, to look for hub genes, key modules of DEGs were built. Finally, the cellular location of the hub genes was investigated. In mice, a high-fat diet raised body weight, messed up myocardial shape, and increased cardiac collagen content. NonCMs transcriptome data revealed 15 different cell types, including fibroblasts, immunological cells, and endothelial cells. There were a total of 33 DEGs found, with 22 up-regulated genes and 11 down-regulated genes. DEGs have a high connection with collagen and extracellular matrix (ECM), according to functional enrichment analysis. Col1a1 and Col1a2 scored well in module analysis and hub gene screening, and were chosen as hub genes. Col1a1 and Col1a2 were shown to be mostly expressed by fibroblasts after localization study. As a result, we believe Col1a1 and Col1a2 may be important markers of obesity-induced cardiac fibrosis, in which fibroblasts play a critical role.

The Underlying Molecular Basis and Mechanisms of Venous Thrombosis in Patients with Osteomyelitis: A Data-Driven Analysis.

Objective Osteomyelitis (OM) is one of the most risky and challenging diseases. Emerging evidence indicates OM is a risk factor for increasing incidence of venous thromboembolism (VTE) development. However, the mechanisms have not been intensively investigated. Methods The OM-related dataset GSE30119 and VTE-related datasets GSE19151 and GSE48000 were downloaded from the Gene Expression Omnibus (GEO) database and analyzed to identify the differentially expressed genes (DEGs) (OMGs1 and VTEGs1, respectively). Functional enrichment analyses of Gene Ontology (GO) terms were performed. VTEGs2 and OMGs2 sharing the common GO biological process (GO-BP) ontology between OMGs1 and VTEGs1 were detected. The TRRUST database was used to identify the upstream transcription factors (TFs) that regulate VTEGs2 and OMGs2. The protein-protein interaction (PPI) network between VTEGs2 and OMGs2 was constructed using the Search Tool for the Retrieval of Interacting Genes (STRING) database and then visualized in Cytoscape. Topological properties of the PPI network were calculated by NetworkAnalyzer. The Molecular Complex Detection (MCODE) plugin was utilized to perform module analysis and choose the hub modules of the PPI network. Results A total of 587 OMGs1 and 382 VTEGs1 were identified from the related dataset, respectively. GO-BP terms of OMGs1 and shared DGEs1 were mainly enriched in the neutrophil-related immune response process, and the shared GO-BP terms of OMGs1 and VTEGs1 seemed to be focused on cell activation, immune, defense, and inflammatory response to stress or biotic stimulus. 230 VTEGs2, 333 OMGs2, and 13 shared DEGs2 were detected. 3 TF-target gene pairs (SP1-LSP1, SPI1-FCGR1A, and STAT1-FCGR1A) were identified. The PPI network contained 1611 interactions among 467 nodes. The top 10 hub proteins were TP53, IL4, MPO, ELANE, FOS, CD86, HP, SOCS3, ICAM1, and SNRPG. Several core nodes (such as MPO, ELANE, and CAMP) were essential components of the neutrophil extracellular traps (NETs) network. Conclusion This is the first data-mining study to explore shared signatures between OM and VTE by the integrated bioinformatic approach, which can help uncover potential biomarkers and therapeutic targets of OM-related VTE.

Identification of biomarkers, immune infiltration landscape, and treatment targets of ischemia–reperfusion acute kidney injury at an early stage by bioinformatics methods

BackgroundMechanisms underlying ischemia/reperfusion injury-acute kidney injury (IRI-AKI) are not fully elucidated. We conducted an integrative analysis of IRI-AKI by bioinformatics methods.MethodsWe screened gene expression profiles of the IRI-AKI at early phase from the Gene Expression Omnibus (GEO) database. Differentially expressed genes (DEGs) were identified and enrichment pathways were conducted based on gene ontology (GO), Kyoto Encyclopedia of Genes and Genomes (KEGG) database, and Gene set enrichment analysis (GSEA). Immune cell infiltration analysis was performed to reveal the change of the microenvironment cell types. We constructed protein–protein interaction (PPI), and Cytoscape with plug-ins to find hub genes and modules. We performed robust rank aggregation (RRA) to combine DEGs and analyzed the target genes for miRNA/transcription factor (TF) and drug-gene interaction networks.ResultsA total of 239 and 384 DEGs were identified in GSE87024 and GSE34351 separately, with the 73 common DEGs. Enrichment analysis revealed that the significant pathways involve mitogen-activated protein kinase (MAPK) signaling, interleukin-17, and tumor necrosis factor (TNF) signaling pathway, etc. RRA analysis detected a total of 27 common DEGs. Immune cell infiltration analysis showed the plasma cells reduced and T cells increased in IRI-AKI. We identified JUN, ATF3, FOS, EGR1, HMOX1, DDIT3, JUNB, NFKBIZ, PPP1R15A, CXCL1, ATF4, and HSPA1B as hub genes. The target genes interacted with 23 miRNAs and 116 drugs or molecular compounds such as curcumin, staurosporine, and deferoxamine.ConclusionOur study first focused on the early IRI-AKI adopting RRA analysis to combine DEGs in different datasets. We identified significant biomarkers and crucial pathways involved in IRI-AKI and first construct the immune landscape and detected the potential therapeutic targets of the IRI-AKI by drug-gene network.

Identification of hub genes, modules and biological pathways associated with lung adenocarcinoma: A system biology approach

Lung adenocarcinoma (LUAD) is the most common non-small cell lung cancer with an alarming trend within non-smokers at younger ages. This study is designed to identify mechanisms of LUAD compared to normal lung tissues using systems biology approaches. Four GSE datasets (GSE75037, GSE63459, GSE32863 and GSE10072) were selected from the Gene Expression Omnibus database. Data processing and meta-analysis were performed using the R statistical programming language. The common and unique DEGs between stages of LUAD and normal lung tissues were initiated by the Venny tool. Common genes were then analyzed in a STRING database to obtain protein-protein interaction. STRING output was analyzed by MCODE and CytoHubba applications of Cytoscape to identify modules of co-expression and hub genes, respectively. The correlation between hub genes and survival of LUAD patients was explored using UALCAN. The hub genes from the meta-analysis were confirmed using an independent, comprehensive LUAD RNA-sequencing dataset collected from The Cancer Genome Atlas (TCGA). The shared upregulated and downregulated DEGs among LUAD stages were 22 and 140 genes, respectively, when compared to normal lung tissues. Unique genes for each stage were also identified. The hub genes were PECAM1, TEK, CDH5, VWF and ANGPT1. An inverse correlation was found between the expression levels of ANGPT1 gene and overall survival of LUAD patients (p-value = 0.02). Most of the top cluster genes were enriched for Gα(s) signalling events, GPCR ligand binding, class B/2 (Secretin family receptors), platelet activation, signalling and aggregation in the main three co-expression clusters. Most of the shared genes were enriched in the biological pathway of hemostasis. TCGA analysis showed that hub genes derived from common genes were found statistically significant throughout all stages. In conclusion, unique upregulated and downregulated DEGs at each stage were identified with FERMT1 and SIX1 as specific early-stage diagnostic biomarkers for stage IB and IIB. 5 hub genes were observed, including PECAM1, TEK, CDH5, vWF and ANGPT1 which might be crucial for the onset and progression of LUAD. Interestingly, ANGPT1 can be considered as a potential and valuable prognostic marker in LUAD patients.

Human Plasma Transcriptome Implicates Dysregulated S100A12 Expression: A Strong, Early-Stage Prognostic Factor in ST-Segment Elevated Myocardial Infarction: Bioinformatics Analysis and Experimental Verification.

The ability of blood transcriptome analysis to identify dysregulated pathways and outcome-related genes following myocardial infarction remains unknown. Two gene expression datasets (GSE60993 and GSE61144) were downloaded from Gene Expression Omnibus (GEO) Datasets to identify altered plasma transcriptomes in patients with ST-segment elevated myocardial infarction (STEMI) undergoing primary percutaneous coronary intervention. GEO2R, Gene Ontology/Kyoto Encyclopedia of Genes and Genomes annotations, protein–protein interaction analysis, etc., were adopted to determine functional roles and regulatory networks of differentially expressed genes (DEGs). Dysregulated expressomes were verified at transcriptional and translational levels by analyzing the GSE49925 dataset and our own samples, respectively. A total of 91 DEGs were identified in the discovery phase, consisting of 15 downregulated genes and 76 upregulated genes. Two hub modules consisting of 12 hub genes were identified. In the verification phase, six of the 12 hub genes exhibited the same variation patterns at the transcriptional level in the GSE49925 dataset. Among them, S100A12 was shown to have the best discriminative performance for predicting in-hospital mortality and to be the only independent predictor of death during follow-up. Validation of 223 samples from our center showed that S100A12 protein level in plasma was significantly lower among patients who survived to discharge, but it was not an independent predictor of survival to discharge or recurrent major adverse cardiovascular events after discharge. In conclusion, the dysregulated expression of plasma S100A12 at the transcriptional level is a robust early prognostic factor in patients with STEMI, while the discrimination power of the protein level in plasma needs to be further verified by large-scale, prospective, international, multicenter studies.

Bioinformatics prediction of potential mechanisms and biomarkers underlying dilated cardiomyopathy.

BACKGROUNDHeart failure is a health burden responsible for high morbidity and mortality worldwide, and dilated cardiomyopathy (DCM) is one of the most common causes of heart failure. DCM is a disease of the heart muscle and is characterized by enlargement and dilation of at least one ventricle alongside impaired contractility with left ventricular ejection fraction < 40%. It is also associated with abnormalities in cytoskeletal proteins, mitochondrial ATP transporter, microvasculature, and fibrosis. However, the pathogenesis and potential biomarkers of DCM remain to be investigated.AIMTo investigate the candidate genes and pathways involved in DCM patients.METHODSTwo expression datasets (GSE3585 and GSE5406) were downloaded from the Gene Expression Omnibus database. The differentially expressed genes (DEGs) between the DCM patients and healthy individuals were identified using the R package “linear models for microarray data.” The pathways with common DEGs were analyzed via Gene Ontology (GO), Kyoto Encyclopedia of Genes and Genomes (KEGG), and gene set enrichment analyses. Moreover, a protein-protein interaction network (PPI) was constructed to identify the hub genes and modules. The MicroRNA Database was applied to predict the microRNAs (miRNAs) targeting the hub genes. Additionally, immune cell infiltration in DCM was analyzed using CIBERSORT.RESULTSIn total, 97 DEGs (47 upregulated and 50 downregulated) were identified. GO analysis showed that the DEGs were mainly enriched in “response to growth factor,” “extracellular matrix,” and “extracellular matrix structural constituent.” KEGG pathway analysis indicated that the DEGs were mainly enriched in “protein digestion and absorption” and “interleukin 17 (IL-17) signaling pathway.” The PPI network suggested that collagen type III alpha 1 chain (COL3A1) and COL1A2 contribute to the pathogenesis of DCM. Additionally, visualization of the interactions between miRNAs and the hub genes revealed that hsa-miR-5682 and hsa-miR-4500 interacted with both COL3A1 and COL1A2, and thus these miRNAs might play roles in DCM. Immune cell infiltration analysis revealed that DCM patients had more infiltrated plasma cells and fewer infiltrated B memory cells, T follicular helper cells, and resting dendritic cells.CONCLUSIONCOL1A2 and COL3A1 and their targeting miRNAs, hsa-miR-5682 and hsa-miR-4500, may play critical roles in the pathogenesis of DCM, which are closely related to the IL-17 signaling pathway and acute inflammatory response. These results may provide useful clues for the diagnosis and treatment of DCM.

Identification of a Ferroptosis-Related Prognostic Signature in Sepsis via Bioinformatics Analyses and Experiment Validation.

Ferroptosis is a new type of programmed cell death that is different from apoptosis, cell necrosis, and autophagy, which might be involved in development of sepsis. However, the potential role of ferroptosis-related genes (FRGs) in sepsis remained unclear. We identified 41 ferroptosis-related differential expression genes by weighted correlation network and differential expression analysis. The hub module of 41 ferroptosis-related differential expression genes in the protein-protein interaction network was identified. Next, we estimated diagnostic values of genes in hub modules. TLR4, WIPI1, and GABARAPL2 with high diagnostic value were selected for construction of risk prognostic model. The high risk-scored patients had significantly higher mortality than the patients with low risk scores in discovery dataset. Furthermore, the risk scores of nonsurvivor were higher than those of survivor in validation dataset. It suggested that risk score was significantly correlated to prognosis in sepsis. Then, we constructed a nomogram for improving the clinical applicability of risk signature. Moreover, the risk score was significantly associated with immune infiltration in sepsis. Our comprehensive analysis of FRGs in sepsis demonstrated the potential roles in diagnosis, prognosis, and immune infiltration. This work may benefit in understanding FRGs in sepsis and pave a new path for diagnosis and assessment of prognosis.

A Novel Hypoxia-Related Gene Signature with Strong Predicting Ability in Non-Small-Cell Lung Cancer Identified by Comprehensive Profiling.

Background Non-small-cell lung cancer (NSCLC) is the most common malignant tumor among males and females worldwide. Hypoxia is a typical feature of the tumor microenvironment, and it affects cancer development. Circular RNAs (circRNAs) have been reported to sponge miRNAs to regulate target gene expression and play an essential role in tumorigenesis and progression. This study is aimed at identifying whether circRNAs could be used as the diagnostic biomarkers for NSCLC. Methods The heterogeneity of samples in this study was assessed by principal component analysis (PCA). Furthermore, the Gene Expression Omnibus (GEO) database was normalized by the affy R package. We further screened the differentially expressed genes (DEGs) and differentially expressed circular RNAs (DEcircRNAs) using the DEseq2 R package. Moreover, we analyzed the Gene Ontology (GO) annotation and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment of DEGs using the cluster profile R package. Besides, the Gene Set Enrichment Analysis (GSEA) was used to identify the biological function of DEGs. The interaction between DEGs and the competing endogenous RNAs (ceRNA) network was detected using STRING and visualized using Cytoscape. Starbase predicted the miRNAs of target hub genes, and miRanda predicted the target miRNAs of circRNAs. The RNA-seq profiler and clinical information were downloaded from The Cancer Genome Atlas (TCGA) database. Then, the variables were assessed by the univariate and multivariate Cox proportional hazard regression models. Significant variables in the univariate Cox proportional hazard regression model were included in the multivariate Cox proportional hazard regression model to analyze the association between the variables of clinical features. Furthermore, the overall survival of variables was determined by the Kaplan-Meier survival curve, and the time-dependent receiver operating characteristic (ROC) curve analysis was used to calculate and validate the risk score in NSCLC patients. Moreover, predictive nomograms were constructed and used to predict the prognostic features between the high-risk and low-risk score groups. Results We screened a total of 2039 DEGs, including 1293 upregulated DEGs and 746 downregulated DEGs in hypoxia-treated A549 cells. A549 cells treated with hypoxia had a total of 70 DEcircRNAs, including 21 upregulated and 49 downregulated DEcircRNAs, compared to A549 cells treated with normoxia. The upregulated genes were significantly enriched in 284 GO terms and 42 KEGG pathways, while the downregulated genes were significantly enriched in 184 GO terms and 25 KEGG pathways. Moreover, the function analysis by GSEA showed enrichment in the enzyme-linked receptor protein signaling pathway, hypoxia-inducible factor- (HIF-) 1 signaling pathway, and G protein-coupled receptor (GPCR) downstream signaling. Furthermore, six hub modules and 10 hub genes, CDC45, EXO1, PLK1, RFC4, CCNB1, CDC6, MCM10, DLGAP5, AURKA, and POLE2, were identified. The ceRNA network was constructed, and it consisted of 4 circRNAs, 14 miRNAs, and 38 mRNAs. The ROC curve was constructed and calculated. The area under the curve (AUC) value was 0.62, and the optimal threshold was 0.28. Based on the optimal threshold, the patients were divided into the high-risk score and low-risk score groups. The survival rate in the high-risk score group was lower than that in the low-risk score group. The expression of SERPINE1, STC2, and LPCAT1; clinical stage; and age of the patient were significantly correlated with the high-risk score. Moreover, nomograms were established based on the risk factors in multivariate analysis, and the median survival time, 3-year survival probability, and 5-year survival were possibly predicted according to nomograms. Conclusion The ceRNA network associated with NSCLC was identified, and the hub genes, circRNAs, might act as the potential biomarkers for NSCLC.

Evaluation of Biomarkers and Immune Microenvironment of Osteoarthritis: Evidence From Omics Data and Machine Learning.

Objectives: This study aimed to identify novel biomarkers for osteoarthritis (OA) and explore potential pathological immune cell infiltration. Methods: We identified differentially expressed genes (DEGs) between OA and normal synovial tissues using the limma package in R, and performed enrichment analyses to understand the functions and enriched pathways of DEGs. Weighted gene co-expression network analysis (WGCNA) and distinct machine-learning algorithms were then used to identify hub modules and candidate biomarkers. We assessed the diagnostic value of the candidate biomarkers using receiver operating characteristic (ROC) analysis. We then used the CIBERSORT algorithm to analyze immune cell infiltration patterns, and the Wilcoxon test to screen out hub immune cells that might affect OA occurrence. Finally, the expression levels of hub biomarkers were confirmed by quantitative reverse transcription-polymerase chain reaction (qRT-PCR). Results: We identified 102 up-regulated genes and 110 down-regulated genes. The functional enrichment analysis results showed that DEGs are enriched mainly in immune response pathways. Combining the results of the algorithms and ROC analysis, we identified GUCA1A and NELL1 as potential diagnostic biomarkers for OA, and validated their diagnosibility using an external dataset. Construction of a TF-mRNA-miRNA network enabled prediction of potential candidate compounds targeting hub biomarkers. Immune cell infiltration analyses revealed the expression of hub biomarkers to be correlated with CD8 T cells, memory B cells, M0/M2 macrophages, resting mast cells and resting dendritic cells. qRT-PCR results showed both GUCA1A and NELL1 were significantly increased in OA samples (p < 0.01). All validations are consistent with the microarray hybridization, indicating that GUCA1A and NELL1 may be involved in the pathogenesis of OA. Conclusion: The findings suggest that GUCA1A and NELL1, closely related to OA occurrence and progression, represent new OA candidate markers, and that immune cell infiltration plays a significant role in the progression of OA.

Identification of the Key Genes and Potential Therapeutic Compounds for Abdominal Aortic Aneurysm Based on a Weighted Correlation Network Analysis.

Background: There is still an unmet need for therapeutic drugs for patients with an abdominal aortic aneurysm (AAA), especially for candidates unsuitable for surgical or interventional repair. Therefore, the purpose of this in silico study is to identify significant genes and regulatory mechanisms in AAA patients to predicate the potential therapeutic compounds for significant genes. Methods: The GSE57691 dataset was obtained from Gene Expression Omnibus (GEO) and used to identify the differentially expressed genes (DEGs) and weighted correlation network analysis (WGCNA). The biological function of DEGs was determined using gene ontology (GO) and the Kyoto Encyclopedia of Genes and Genomes (KEGG). AAA-related genes were obtained from the Comparative Toxicogenomics Database (CTD) using the keywords: aortic aneurysm and abdominal. The hub genes in AAA were obtained by overlapping DEGs, WGCNA-based hub genes, and CTD-based genes. The diagnostic values of hub genes were determined using ROC curve analysis. Hereby, a TF-miRNA-hub gene network was constructed based on the miRnet database. Using these data, potential therapeutic compounds for the therapy of AAA were predicted based on the Drug Gene Interaction Database (DGIdb). Results: A total of 218 DEGs (17 upregulated and 201 downregulated) and their biological function were explored; 4093 AAA-related genes were derived by text mining. Three hub modules and 144 hub genes were identified by WGCNA. asparagine synthetase (ASNS), axin-related protein 2 (AXIN2), melanoma cell adhesion molecule (MCAM), and the testis-specific Y-encoded-like protein 1 (TSPYL1) were obtained as intersecting hub genes and the diagnostic values were confirmed with ROC curves. As potential compounds targeting the hub genes, asparaginase was identified as the target compound for ASNS. Prednisolone and abiraterone were identified as compounds targeting TSPYL1. For MCAM and TSPYL1, no potential therapeutic compound could be predicted. Conclusion: Using WGCNA analysis and text mining, pre-existing gene expression data were used to provide novel insight into potential AAA-related protein targets. For two of these targets, compounds could be predicted.

A ceRNA regulatory network in systemic lupus erythematosus and its molecular interplay with cancer.

BackgroundSystemic lupus erythematosus (SLE) is an autoimmune disease defined by the production of autoantibodies and involves multiple organs and systems. Although there are reports on SLE, data on its pathogenesis is limited.MethodsUsing R language software, we constructed a competing endogenous RNA (ceRNA) network. We then utilized the Search Tool for Recurring Instances of Neighbouring Genes (STRING) and cytoHubba databases to generate a protein-protein interaction (PPI) network, which led to the identification of hub genes. The top two hub genes with the highest Maximal Clique Centrality (MCC) score in the PPI network were further validated via quantitative real-time polymerase chain reaction (qRT-PCR) using in-house clinical samples. Also, weighted gene co-expression network analysis (WGCNA) with genes from the Gene Expression Omnibus Series (GSE)121239 dataset identified hub modules that were associated with clinical indicators. In addition, the genes contained in key modules as obtained by WGCNA were enriched and analyzed using the Database for Annotation, Visualization and Integrated Discovery (DAVID) online tool. The top hub gene, X-linked apoptosis inhibitory protein-associated factor (XAF1), was then identified by intersection of the PPI and WGCNA outcomes, and a pan-cancer analysis of this hub gene was subsequently performed.ResultsWe comprehensively profiled the expression of Circular RNAs (circRNAs), MicroRNAs (miRNAs), and messenger RNAs (mRNAs) in SLE. We identified a hub gene, XAF1, based on evidence from the ceRNA network, WGCNA key module genes, and PPI network analyses. Moreover, qRT-PCR analysis demonstrated that the expression of XAF1 was significantly upregulated in SLE. Through the pan-cancer analysis, we demonstrated the common molecular roles of XAF1 in the pathogenesis of SLE and tumors, especially cutaneous melanoma.ConclusionsXAF1 is a key molecular biomarker in SLE. The pan-cancer analysis in this study provided shared genomic characteristics in SLE and cancers, especially for skin cutaneous melanoma (SKCM).

Identification of Novel Prognostic Biomarkers Relevant to Immune Infiltration in Lung Adenocarcinoma.

Background: Programmed death ligand-1 (PD-L1) is a biomarker for assessing the immune microenvironment, prognosis, and response to immune checkpoint inhibitors in the clinical treatment of lung adenocarcinoma (LUAD), but it does not work for all patients. This study aims to discover alternative biomarkers. Methods: Public data were obtained from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO). Weighted gene co-expression network analysis (WGCNA) and gene ontology (GO) were used to determine the gene modules relevant to tumor immunity. Protein–protein interaction (PPI) network and GO semantic similarity analyses were applied to identify the module hub genes with functional similarities to PD-L1, and we assessed their correlations with immune infiltration, patient prognosis, and immunotherapy response. Immunohistochemistry (IHC) and hematoxylin and eosin (H&E) staining were used to validate the outcome at the protein level. Results: We identified an immune response–related module, and two hub genes (PSTPIP1 and PILRA) were selected as potential biomarkers with functional similarities to PD-L1. High expression levels of PSTPIP1 and PILRA were associated with longer overall survival and rich immune infiltration in LUAD patients, and both were significantly high in patients who responded to anti–PD-L1 treatment. Compared to PD-L1–negative LUAD tissues, the protein levels of PSTPIP1 and PILRA were relatively increased in the PD-L1–positive tissues, and the expression of PSTPIP1 and PILRA positively correlated with the tumor-infiltrating lymphocytes. Conclusion: We identified PSTPIP1 and PILRA as prognostic biomarkers relevant to immune infiltration in LUAD, and both are associated with the response to anti–PD-L1 treatment.

Weighted gene co-expression network analysis reveals prognostic and diagnostic significance of PAQR4 in patients with early and late hepatocellular carcinoma.

This study aimed to reveal novel markers for prognostic and diagnostic prediction of hepatocellular carcinoma (HCC). We applied The Cancer Genome Atlas (TCGA) data to screen differentially expressed genes (DEGs). We identified hub modules and genes using weighted gene co-expression network analysis (WGCNA). After verification with the GSE36376 dataset, hub genes were further identified. The expression of progestin and adipoQ receptor 4 (PAQR4) was confirmed in HCC by quantitative reverse transcription polymerase chain reaction (qRT-PCR). The diagnostic and prognosis value of PAQR4 was assessed. The expression of PAQR4 was verified using the GSE76427 dataset. A total of 803 DEGs were obtained between HCC and normal tissue. Through WGCNA, 7 hub modules were screened, among which the blue module was selected to identify the hub genes associated with the HCC. After overlapping all the DEGs with 837 genes of the blue module, we obtained 466 DEGs that were defined as hub genes. Among the hub genes, 239 were related to staging. After verifying with the GSE36376 dataset, PAQR4 was identified as the real hub gene of HCC. The results of qRT-PCR revealed that PAQR4 was upregulated between HCC and normal tissue. Furthermore, PAQR4 was related to the diagnosis and prognosis of patients with HCC. Moreover, the GSE76427 verification results of PAQR4 were consistent with our integration and qRT-PCR results. Ultimately, high expression of PAQR4 was significantly related to cell cycle, DNA replication, and the p53 signaling pathway. The PAQR4 gene may be associated with the prognosis and diagnosis of HCC.

Identification of Prognostic Markers of N6-Methylandenosine-Related Noncoding RNAs in Non-Small-Cell Lung Cancer.

Background Non-small-cell lung cancer (NSCLC) is a major type of lung carcinoma that threatens the health and life of humans worldwide. We aimed to establish an n6-methyladenosine (m6A)-relevant ncRNA model to effectively evaluate the outcome of patients. Methods m6A-Related ncRNAs (lncRNA/miRNA) were acquired from the UCSC Xena database. Pearson's correlation analysis among 21 m6A regulatory factors and ncRNAs were implemented to explore m6A-relevant ncRNAs. Weighted gene co-expression network analysis (WGCNA) identified hub modules of gene associated with prognosis of NSCLC patients. Univariate Cox regression analysis identified 80 m6A-related ncRNAs. Least absolute shrinkage and selector operation (LASSO) filtered out redundant factors and established a risk score model (m6A-NSCLC) in the TCGA training data set. Validation of prognostic ability was performed using testing data sets from the TCGA database. We also conducted a correlation analysis among the risk score and different clinical traits. Both univariate and multivariate Cox analyses were combined to verify prognostic factors which have independent value, and a nomogram on the basis of m6A-NSCLC risk scores and clinical traits was constructed to assess the prognosis of patients. In addition, we screened differentially expressed genes (DEGs) based on different risk scores and performed enrichment analysis. Finally, 21 m6A regulators were detected to be differentially expressed between two risk groups. Results An m6A-NSCLC risk model with 18 ncRNAs was constructed. By comparison with low-risk patients, high-risk score patients had poor prognosis. The distribution of risk score in the tumor size and extent (T), number of near lymph nodes (N), clinical stage, sex, and tumor types was significantly different. The risk score could act as an independent prognostic factor with the nomogram assessing overall survival in NSCLC. DEGs inherent to cell movement and immune regulation were involved in NSCLC development. Furthermore, 18 of 21 m6A regulators were differentially expressed, implying their correlation to survival prognosis. Conclusion The m6A-NSCLC could be effectively utilized for evaluation of prognosis of patients.

Identification of Potential Biomarkers and Small Molecule Drugs for Cutaneous Melanoma Using Integrated Bioinformatic Analysis.

Background: Cutaneous melanoma (CM) is a type of skin cancer with a high fatality rate, and its pathogenesis has not yet been fully elucidated. Methods: We obtained the gene expression datasets of CM through the Gene Expression Omnibus (GEO) database. Subsequently, robust rank aggregation (RRA) method was used to identify differentially expressed genes (DEGs) between CM cases and normal skin controls. Gene functional annotation was performed to explore the potential function of the DEGs. We built the protein–protein interaction (PPI) network by the Interactive Gene database retrieval tool (STRING) and selected hub modules by Molecular Complexity Detection (MCODE). We furthered and validated our results using the TCGA-GTEX dataset. Finally, potential small molecule drugs were predicted by CMap database and verified by molecular docking method. Results: A total of 135 DEGs were obtained by RRA synthesis analysis. GMPR, EMP3, SLC45A2, PDZD2, NPY1R, DLG5 and ADH1B were screened as potential targets for CM. Furazolidone was screened as a potential small molecule drug for the treatment of CM, and its mechanism may be related to the inhibition of CM cell proliferation by acting on GMPR. Conclusion: We identified seven prognostic therapeutic targets associated with CM and furazolidone could be used as a potential drug for CM treatment, providing new prognostic markers, potential therapeutic targets and small molecule drugs for the treatment and prevention of CM.

Co-occurrence pattern and community assembly of broomcorn millet rhizosphere microbiomes in a typical agricultural ecosystem

Rhizosphere microbes are critical for plant health and ecological functioning in terrestrial ecosystems; however, our understanding of microbial co-occurrences and assembly processes in the broomcorn millet rhizosphere remains limited, especially in distinct regions of northern China. In this study, we examined the network topologies and relative contributions of assembly processes in the broomcorn millet fields of northern China. The environmental factors, diversity and taxonomic and functional compositions differed significantly among the northeast (NE), north (Nor), and northwest (NW) regions. Mantel tests showed that the bacterial and fungal community diversities in the Nor and NW regions were mainly associated with soil nutrients (i.e., available phosphorous, NO3, NH4, and organic matter). Additionally, the topological properties of the bacterial and fungal networks varied with regional differences, revealing that these networks in the Nor and NW regions were more complex, and that bacterial and fungal species within the networks co-occurred more frequently in the region. Moreover, we confirmed that the average relative abundance of operational taxonomic units (OTUs) in the bacterial and fungal networks in the NE and Nor regions occupied a higher proportion in the module hubs, whereas more OTUs in the connectors were enriched in these networks in the Nor region. Modules comprised OTUs belonging to Proteobacteria, Actinobacteria, Chloroflexi, Acidobacteria, and Ascomycota among the three regions. Generally, stochastic processes are relatively more important in constraining assembly across the three regions; however, dispersal limitation governed critical turnover in the bacterial community assembly in the NE and NW regions rather than in the Nor region. These results contribute to the understanding of the processes mediating microbial community assembly and the distribution pattern of the microbial community across the three regions.

Microbial Community Structure and Ecological Networks during Simulation of Diatom Sinking.

Microbial-mediated utilization of particulate organic matter (POM) during its downward transport from the surface to the deep ocean constitutes a critical component of the global ocean carbon cycle. However, it remains unclear as to how high hydrostatic pressure (HHP) and low temperature (LT) with the sinking particles affects community structure and network interactions of the particle-attached microorganisms (PAM) and those free-living microorganisms (FLM) in the surrounding water. In this study, we investigated microbial succession and network interactions in experiments simulating POM sinking in the ocean. Diatom-derived 13C- and 12C-labeled POM were used to incubate surface water microbial communities from the East China Sea (ECS) under pressure (temperature) of 0.1 (25 °C), 20 (4 °C), and 40 (4 °C) MPa (megapascal). Our results show that the diversity and species richness of the PAM and FLM communities decreased significantly with HHP and LT. Microbial community analysis indicated an increase in the relative abundance of Bacteroidetes at high pressure (40 MPa), mostly at the expense of Gammaproteobacteria, Alphaproteobacteria, and Gracilibacteria at atmospheric pressure. Hydrostatic pressure and temperature affected lifestyle preferences between particle-attached (PA) and free-living (FL) microbes. Ecological network analysis showed that HHP and LT enhanced microbial network interactions and resulted in higher vulnerability to networks of the PAM communities and more resilience of those of the FLM communities. Most interestingly, the PAM communities occupied most of the module hubs of the networks, whereas the FLM communities mainly served as connectors of the modules, suggesting their different ecological roles of the two groups of microbes. These results provided novel insights into how HHP and LT affected microbial community dynamics, ecological networks during POM sinking, and the implications for carbon cycling in the ocean.