Protein-protein Interaction Network Of Differentially Expressed Genes Research Articles

Identification of potential characteristic genes in chronic skin infections through RNA sequencing and immunohistochemical analysis.

The objective of the present study was to perform RNA sequencing and immunohistochemical analysis on skin specimens obtained from healthy individuals and individuals afflicted with prolonged skin infections. Bioinformatics methodologies were used to scrutinize the RNA sequencing data with the intention of pinpointing distinctive gene signatures associated with chronic skin infections. Skin tissue samples were collected from 11 individuals (4 subjects healthy and 7 patients with chronic skin infections) at the Affiliated Hospital of Southwest Medical University (Luzhou, China). The iDEP tool identified differentially expressed genes (DEGs) with log2 (fold change) ≥2 and q-value ≤0.01. Functional enrichment analysis using Gene Ontology and KEGG databases via the oebiotech online tool was then performed to determine the biological functions and pathways related to these DEGs. A protein-protein interaction network of DEGs identified HIF1A as a potential key gene. Subsequent immunohistochemistry analyses were performed on the samples to assess any variations in HIF1A expression. A total of 900 DEGs, 365 upregulated and 535 downregulated, were observed between the normal and chronic infection groups. The identified DEGs were found to serve a role in various biological processes, including 'hypoxia adaptation', 'angiogenesis', 'cell adhesion' and 'regulation of positive cell migration'. Additionally, these genes were revealed to be involved in the 'TGF-β', 'PI3K-Akt' and 'IL-17' signaling pathways. HIF1A and nine other genes were identified as central nodes in the PPI network. HIF1A expression was higher in chronically infected skin samples than in healthy samples, indicating its potential as a novel research target.

Expression Levels and Regulation of Selenoprotein Genes in Patients With Coronavirus Disease 2019

Objective To investigate the expression levels of selenoprotein genes in the patients with coronavirus disease 2019 (COVID-19) and the possible regulatory mechanisms.Methods The dataset GSE177477 was obtained from the Gene Expression Omnibus,consisting of a symptomatic group (n=11),an asymptomatic group (n=18),and a healthy control group (n=18).The dataset was preprocessed to screen the differentially expressed genes (DEG) related to COVID-19,and gene ontology functional annotation and Kyoto encyclopedia of genes and genomes enrichment analysis were performed for the DEGs.The protein-protein interaction network of DEGs was established,and multivariate Logistic regression was employed to analyze the effects of selenoprotein genes on the presence/absence of symptoms in the patients with COVID-19.Results Compared with the healthy control,the symptomatic COVID-19 patients presented up-regulated expression of GPX1,GPX4,GPX6,DIO2,TXNRD1,SELENOF,SELENOK,SELENOS,SELENOT,and SELENOW and down-regulated expression of TXNRD2 and SELENON (all P<0.05).The asymptomatic patients showcased up-regulated expression of GPX2,SELENOI,SELENOO,SELENOS,SELENOT,and SELENOW and down-regulated expression of SELP (all P<0.05).The results of multivariate Logistic regression analysis showed that the abnormally high expression of GPX1 (OR=0.067,95%CI=0.005-0.904,P=0.042) and SELENON (OR=56.663,95%CI=3.114-856.999,P=0.006) was the risk factor for symptomatic COVID-19,and the abnormally high expression of SELP was a risk factor for asymptomatic COVID-19 (OR=15.000,95%CI=2.537-88.701,P=0.003).Conclusions Selenoprotein genes with differential expression are involved in the regulation of COVID-19 development.The findings provide a new reference for the prevention and treatment of COVID-19.

Identification of key genes and signalling pathways in clear cell renal cell carcinoma: An integrated bioinformatics approach.

Clear cell Renal Cell Carcinoma (ccRCC) is one of the most prevalent types of kidney cancer. Unravelling the genes responsible for driving cellular changes and the transformation of cells in ccRCC pathogenesis is a complex process. In this study, twelve microarray ccRCC datasets were chosen from the gene expression omnibus (GEO) database and subjected to integrated analysis. Through GEO2R analysis, 179 common differentially expressed genes (DEGs) were identified among the datasets. The common DEGs were subjected to functional enrichment analysis using ToppFun followed by construction of protein-protein interaction network (PPIN) using Cytoscape. Clusters within the DEGs PPIN were identified using the Molecular Complex Detection (MCODE) Cytoscape plugin. To identify the hub genes, the centrality parameters degree, betweenness, and closeness scores were calculated for each DEGs in the PPIN. Additionally, Gene Expression Profiling Interactive Analysis (GEPIA) was utilized to validate the relative expression levels of hub genes in the normal and ccRCC tissues. The common DEGs were highly enriched in Hypoxia-inducible factor (HIF) signalling and metabolic reprogramming pathways. VEGFA, CAV1, LOX, CCND1, PLG, EGF, SLC2A1, and ENO2 were identified as hub genes. Among 8 hub genes, only the expression levels of VEGFA, LOX, CCND1, and EGF showed a unique expression pattern exclusively in ccRCC on compared to other type of cancers.

RNA-seq analysis-based study on the effects of gestational diabetes mellitus on macrosomia.

Both the mother and the infant are negatively impacted by macrosomia. Macrosomia is three times as common in hyperglycemic mothers as in normal mothers. This study sought to determine why hyperglycemic mothers experienced higher macrosomia. Methods: Hematoxylin and Eosin staining was used to detect the placental structure of normal mother(NN), mothers who gave birth to macrosomia(NM), and mothers who gave birth to macrosomia and had hyperglycemia (DM). The gene expressions of different groups were detected by RNA-seq. The differentially expressed genes (DEGs) were screened with DESeq2 R software and verified by qRT-PCR. The STRING database was used to build protein-protein interaction networks of DEGs. The Cytoscape was used to screen the Hub genes of the different group. The NN group's placental weight differed significantly from that of the other groups. The structure of NN group's placenta is different from that of the other group, too. 614 and 3207 DEGs of NM and DM, respectively, were examined in comparison to the NN group. Additionally, 394 DEGs of DM were examined in comparison to NM. qRT-PCR verified the results of RNA-seq. Nucleolar stress appears to be an important factor in macrosomia, according on the results of KEGG and GO analyses. The results revealed 74 overlapped DEGs that acted as links between hyperglycemia and macrosomia, and 10 of these, known as Hub genes, were key players in this process. Additionally, this analysis believes that due of their close connections, non-overlapping Hubs shouldn't be discounted. In diabetic mother, ten Hub genes (RPL36, RPS29, RPL8 and so on) are key factors in the increased macrosomia in hyperglycemia. Hyperglycemia and macrosomia are linked by 74 overlapping DEGs. Additionally, this approach contends that non-overlapping Hubs shouldn't be ignored because of their tight relationships.

Decreased expression of RPL15 and RPL18 exacerbated the calcification of valve interstitial cells during aortic valve calcification.

Calcific aortic valve disease (CAVD)is the most common valvular heart disease, with an increasing prevalence due to an aging population. The pathobiology of CAVD is a multifaceted and actively regulated process, but the detailed mechanisms have not been elucidated. The present study aims to identify the differentially expressed genes (DEGs)in calcified aortic valve tissues, and to analyze the correlation between DEGs and clinical features in CAVD patients. The DEGs were screened by microarray in normal and CAVD groups (n = 2 for each group), and confirmed by quantitative real-time polymerase chain reaction in normal (n = 12) and calcified aortic valve tissues (n = 34). A total of 1048 DEGs were identified in calcified aortic valve tissues, including 227 upregulated mRNAs and 821 downregulated mRNAs. Based on multiple bioinformatic analyses, three 60S ribosomal subunit components (RPL15,RPL18, and RPL18A), and two 40S ribosomal subunit components (RPS15and RPS21) were identified as the top 5 hub genes in the protein-protein interaction network of DEGs. The expression of RPL15 and RPL18 was also found significantly decreased in calcified aortic valve tissues (both p < .01), and negatively correlated with the osteogenic differentiation marker OPN in CAVD patients (both p < .01). Moreover, inhibition of RPL15 or RPL18 exacerbated the calcification of valve interstitial cells under osteogenic induction conditions. The present study proved that decreased expression of RPL15 and RPL18 was closely associated with aortic valve calcification, which provided valuable clues to find therapeutic targets for CAVD.

Screening of immunotherapy-related genes in bladder cancer based on GEO datasets.

As one of the most prevalent genitourinary cancers, bladder cancer (BLCA) is associated with high morbidity and mortality. Currently, limited indicators are available for early detection and diagnosis of bladder cancer, and there is a lack of specific biomarkers for evaluating the prognosis of BLCA patients. This study aims to identify critical genes that affect bladder cancer immunity to improve the diagnosis and prognosis of bladder cancer and to identify new biomarkers and targets for immunotherapy. Two GEO datasets were used to screen differentially expressed genes (DEGs). The STRING database was used to construct a protein-protein interaction network of DEGs, and plug-in APP CytoHubba in Cytoscape was used to identify critical genes in the network. GO and KEGG analyses explored the functions and pathways of differential gene enrichment. We used GEPIA to validate the expression of differential genes, their impact on patient survival, and their relationship to clinicopathological parameters. Additionally, hub genes were verified using qRT-PCR and Western blotting. Immune infiltration analysis and multiple immunohistochemistry reveal the impact of Hub genes on the tumor microenvironment. We screened out 259 differential genes, and identified 10 key hub genes by the degree algorithm. Four genes (ACTA2, FLNA, TAGLN, and TPM1) were associated with overall or disease-free survival in BLCA patients and were significantly associated with clinical parameters. We experimentally confirmed that the mRNA and protein levels of these four genes were significantly decreased in bladder cancer cells. Immunoassays revealed that these four genes affect immune cell infiltration in the tumor microenvironment; they increased the polarization of M2 macrophages. These four genes affect the tumor microenvironment of bladder cancer, provide a new direction for tumor immunotherapy, and have significant potential in the diagnosis and prognosis of bladder cancer.

The hub genes and their potential regulatory mechanisms in chronic spontaneous urticaria revealed by integrated transcriptional expression analysis.

Chronic spontaneous urticaria (CSU) is a recurrent disease characterized by wheals and or angioedema, and its pathogenesis is still unclear. The microarray datasets of skin tissue from CSU patients and healthy controls were integrated and analysed in Gene Expression Omnibus (GEO). Differentially expressed genes (DEGs) were identified using the NetworkAnalyst tool. Then, the Gene Ontology (GO) and Kyoto Encyclopaedia of Genes and Genomes (KEGG) pathway enrichment analyses were performed. Subsequently, a protein-protein interaction (PPI) network of DEGs was constructed by STRING and the related hub genes were identified through the MOCDE tool. The potential miRNAs targeting hub genes were predicted based on the intersection of three online databases, namely TargetScanHuman, TargetBase and miRNet. Differentially expressed lncRNAs (DElncRNAs) was performed using the GEO2R tool. The potential miRNAs targeting DElncRNAs were predicted through miRNet. Finally, the shared miRNAs targeting both hub genes and DElncRNAs were used to construct an mRNA/miRNA/lncRNA regulatory network. A total of 296 DEGs were obtained, which were mainly enriched in inflammatory and immune responses. Further, 14 hub genes were identified by the PPI network of DEGs. Clinical correlation analysis showed that the mRNA expressions of S100A7, S100A8, S100A9, S100A12, IL6 and SOCS3 in CSU were positively correlated with the 7-day urticaria activity score (UAS7), and their potential diagnostic value was supported by the receiver operating characteristic curve (ROC) analysis. Five up-regulated lncRNAs in the cytoplasm were obtained by DElncRNAs analysis. The ROC analysis showed that PVT1, SNHG3 and ZBTB20 - AS1 was of potential diagnostic value for CSU. Eight shared miRNAs targeting both hub genes and DElncRNAs were identified and used to construct a competing endogenous RNA (ceRNA) network. It was found that the IL-6/miR - 149 - 5p/ZBTB20 - AS1 axis might play an important role in the activation of mast cells in CSU. IL-6 and its related regulatory molecules may be used as potential diagnostic markers and therapeutic targets for CSU.

Identification of potential biomarkers for colorectal cancer by clinical database analysis and Kaplan-Meier curves analysis.

This study aimed to explore critical genes as potential biomarkers for the diagnosis and prognosis of colorectal cancer (CRC) for clinical utility. To identify and screen candidate genes involved in CRC carcinogenesis and disease progression, we downloaded microarray datasets GSE89076, GSE73360, and GSE32323 from the GEO database identified differentially expressed genes (DEGs), and performed a functional enrichment analysis. A protein-protein interaction network was constructed, and correlated module analysis was performed using STRING and Cytoscape. The Kaplan-Meier survival curve shows the survival of the hub genes. The expression of cyclin-dependent kinase (CDK1), cyclin B1 (CCNB1), and PCNA in tissues and changes in tumor grade were analyzed. A total of 329 DEGs were identified, including 264 upregulated and 65 downregulated genes. The functions and pathways of DEGs include the mitotic cell cycle, poly(A) RNA binding replication, ATP binding, DNA replication, ribosome biogenesis in eukaryotes, and RNA transport. Forty-seven Hub genes were identified, and biological process analysis showed that these genes were mainly enriched in cell cycle and DNA replication. Patients with mutations in CDK1, PCNA, and CCNB1 had poorer survival rates. CDK1, PCNA, and CCNB1 were significantly overexpressed in the tumor tissues. The expression of CDK1 and CCNB1 gradually decreased with increasing tumor grade. CDK1, CCNB1, and PCNA can be used as potential markers for the diagnosis and prognosis of CRC. These genes are overexpressed in colon cancer tissues and are associated with low survival rates in CRC patients.

CCNB2 as a potential biomarker of bladder cancer via the high throughput technology.

Bladder cancer and oral squamous cell carcinoma (OSCC) seriously affect people's health. However, the relationship between bladder cancer and OSCC remains unclear. Got GSE138206, GSE146483, GSE184616, and bladder cancer datasets GSE65635, GSE100926 from Gene Expression Omnibus database. Weighted gene co-expression network analysis was used to identify the significant module. Functional enrichment analysis was performed via the Gene Ontology analysis and Kyoto Encyclopedia of Genes and Genomes. Furthermore, the Gene Set Enrichment Analysis was also used to complete the enrichment analysis. Comparative Toxicogenomics Database found most relevant diseases to core genes. TargetScan is used to forecast analysis of microRNA and target genes. In Gene Ontology analysis, differentially expressed genes were mostly concentrated in cell differentiation, extrallular region, structural molecule activity, and actin binding. In Kyoto Encyclopedia of Genes and Genomes analysis, the differentially expressed genes were mainly enriched in PI3K-Akt signaling pathway, pathway in cancer, and extracellular matrix-receptor interaction. Seven hub genes (cyclin B2 [CCNB2], TK1, CDC20, PCNA, CKS1B, CDCA5, MCM4) were obtained. Hub genes (CCNB2, CDC20) are highly expressed in OSCC and bladder cancer samples. CCNB2 was one common oncogene of bladder cancer and OSCC.

Identification of significant genes associated with prognosis of gastric cancer by bioinformatics analysis

BackgroundGastric cancer (GC) ranks second in mortality among all malignant diseases worldwide. However, the cause and molecular mechanism underlying gastric cancer are not clear. Here, we used integrated bioinformatics to identify possible key genes and reveal the pathogenesis and prognosis of gastric cancer.MethodsThe gene expression profiles of GSE118916, GSE79973, and GSE29272 were available from the Gene Expression Omnibus (GEO) database. Differentially expressed genes (DEGs) between GC and normal gastric tissues were screened by R software and Venn diagram software. GO and KEGG pathway enrichment of DEGs was performed using the DAVID database. A protein-protein interaction (PPI) network was established by STRING and visualized using Cytoscape software. Then the influence of hub genes on expression and survival was assessed using TCGA database.ResultsA total of 83 DEGs were found in the three datasets, including 41 up-regulated genes and 42 down-regulated genes. These DEGs were mainly enriched in extracellular matrix organization and cell adhesion. The enriched pathways obtained in the KEGG pathway analysis were extracellular matrix (ECM)-receptor interaction and focal adhesion. A PPI network of DEGs was analyzed using the Molecular Complex Detection (MCODE) app of Cytoscape. Four genes were considered hub genes, including COL5A1, FBN1, SPARC, and LUM. Among them, LUM was found to have a significantly worse prognosis based on TCGA database.ConclusionsWe screened DEGs associated with GC by integrated bioinformatics analysis and found one potential biomarker that may be involved in the progress of GC. This hub gene may serve as a guide for further molecular biological experiments.

The Regulatory Network of Gastric Cancer Pathogenesis and Its Potential Therapeutic Active Ingredients of Traditional Chinese Medicine Based on Bioinformatics, Molecular Docking, and Molecular Dynamics Simulation.

This study aims to investigate the functional gene network in gastric carcinogenesis by using bioinformatics; besides, the diagnostic utility of key genes and potential active ingredients of traditional Chinese medicine (TCM) for treatment in gastric cancer have been explored. The Cancer Genome Atlas and Gene Expression Omnibus databases have been applied to analyze the differentially expressed genes (DEGs) between gastric cancer and normal gastric tissues. Then, the DEGs underwent Gene Ontology and Kyoto Encyclopedia of Genes and Genomes enrichment analyses using the Metascape database. The STRING database and the Cytoscape software were utilized for the protein-protein interaction network of DEGs and hub genes screening. Furthermore, survival and expression analyses of hub genes were conducted using Gene Expression Profiling Interactive Analysis and Human Protein Atlas databases. By using the Comparative Toxicogenomics Database, the hub genes interconnected with active ingredients of TCM were analyzed to provide potential information for the treatment of gastric cancer. After the molecular docking of the active ingredients of TCM to specific hub gene receptor proteins, the molecular dynamics simulation GROMACS was applied to validate the conformation of the strongest binding ability in the molecular docking. A total of 291 significant DEGs were found, from which 12 hub genes were screened out. Among these hub genes, the expressions of five hub genes including COL1A1, COL5A2, MMP12, SERPINE1, and VCAN were significantly correlated with the overall survival. Furthermore, four potential therapeutic active ingredients of TCM were acquired, including quercetin, resveratrol, emodin, and schizandrin B. In addition, the molecular docking results exhibited that the active ingredients of TCM formed stable binding with the hub gene targets. SERPINE1 (3UT3)-Emodin and COL1A1 (7DV6)-Quercetin were subjected to molecular dynamics simulations as conformations of continuing research significance, and both were found to be stably bound as a result of the interaction of van der Waals potentials, electrostatic, and hydrogen bonding. Our findings may provide novel insights and references for the screening of biomarkers, the prognostic evaluation, and the identification of potential active ingredients of TCM for gastric cancer treatment.

MiR-148a-3p attenuates apoptosis and inflammation by targeting CNTN4 in atherosclerosis

Atherosclerosis (AS) seriously affects human health. The role of microRNAs (miRNAs) in the pathogenesis and progression of AS has become a focus of research. Our goal was to identify the biological effect of differentially expressed miRNAs (DE-miRNAs) in AS. To analyze differentially expressed genes (DEGs), including differentially expressed mRNAs (DE-mRNAs) and DE-miRNAs, in AS by using the Gene Expression Omnibus (GEO) database and limma package. DEGs protein-protein interaction (PPI) network and functional enrichment analysis were constructed by using the search tool for the retrieval of interacting genes/proteins (STRING) database, Cytoscape software and Cytoscape plugin "ClueGO2.5.6". We established a coexpression network of dysregulated miRNAs and mRNAs to predict the function of miRNAs by using miRWalk database and Pearson correlation coefficient (PCC) analysis. Cellular experiments were used to validate the results of bioinformatics. First, 69 common DEGs were obtained from datasets GSE43292 and GSE97210 using the limma package in R. Next, a DEG PPI network was constructed. Functional enrichment analysis of DEGs showed that 11 functional pathways were significantly enriched, such as positive regulation of monocyte chemotaxis. Seven common DE-miRNAs were obtained from the GSE99685 dataset and DE-mRNAs predicted miRNAs through the miRWalk database. The miRNA-mRNA network constructed using Cytoscape software suggested that miR-148a-3p targeted contactin 4 (CNTN4). Quantitative real-time polymerase chain reaction (qRT-PCR) assay results indicated that miR-148a-3p was downregulated and CNTN4 was upregulated in the THP-1 + phorbol 12-myristate 13-acetate (PMA) + oxidized low-density lipoprotein (oxLDL) group compared with the THP-1 + PMA group. qRT-PCR, flow cytometry, and enzyme-linked immunosorbent assay (ELISA) found that upregulated miR-148a-3p significantly inhibited the expression of CNTN4, cell apoptosis, and interleukin-6 (IL-6) and tumor necrosis factor-alpha (TNF-α) concentrations in oxLDL-induced THP-1 macrophages. In addition, a dual-luciferase reporter assay demonstrated that CNTN4 was a target gene of miR-148a-3p. Overall, these findings suggested that miR-148a-3p inhibited oxLDL-induced cell apoptosis and inflammation via targeting CNTN4 in THP-1 macrophages.

Network exploration of gene signatures underlying low birth weight induced metabolic alterations.

This study explored underlying gene signatures of low birth weight (LBW) by analyzing differentially expressed genes (DEGs) between LBW and normal birth weight (NBW) subjects. Subjects with different birth weight was collected from GEO database. P < .05 and | logFC | ≥ 1.0 were used for screening DEGs. David (2021 Update) was used to perform GO annotation and KEGG signaling pathway enrichment analysis. The protein-protein interaction network of DEGs was constructed using the STRING database, in which hub genes were mined through Cytoscape software. A total of 326 DEGs were identified, including 287 up-regulated genes and 39 down-regulated genes. The GO biological processes enriched by DEGs mainly involved epidermal growth, keratinization and intermediate fibrous tissue. The DEGs were significantly enriched in intracellular insoluble membranes, desmosomes and extracellular space. Their molecular functions mainly focused on structural molecular activity, structural components of epidermis and structural components of cytoskeleton. PI3K/AKT signaling pathway and tight junction were highlighted as critical pathways enriched by DEGs. Ten hub genes which included KRT14, EGF, DSP, DSG1, KRT16, KRT6A, EPCAM, SPRR1B, PKP1, and PPL were identified from the constructed protein-protein interaction network. A total of 326 DEGs and 10 hub genes were identified as candidates for metabolic disorders in LBW individuals. Our results indicated PI3K/AKT signaling pathway as an intrauterine adaptive mechanism for LBW individuals. We observed activated PI3K/AKT pathway in LBW individuals, which would promote growth and development at the early stage of life, but adversely introduce extra metabolic stress and thereby potentially induce metabolic disorders in adulthood.

Astragalus mongholicus Bunge and Curcuma aromatica Salisb. inhibits liver metastasis of colon cancer by regulating EMT via the CXCL8/CXCR2 axis and PI3K/AKT/mTOR signaling pathway

BackgroundOne of the most challenging aspects of colon cancer (CC) prognosis and treatment is liver-tropic metastasis. Astragalus mongholicus Bunge—Curcuma aromatica Salisb. (AC) is a typical medication combination for the therapy of many malignancies. Our previous studies found that AC intervention inhibits liver metastasis of colon cancer (LMCC). Nevertheless, the comprehensive anti-metastasis mechanisms of AC have not been uncovered.MethodsIn bioinformatics analysis, RNA-seq data of CC and LMCC patients were collected from TCGA and GEO databases, and differentially expressed genes (DEGs) were identified. The biological processes and signaling pathways involved in DEGs were enriched by GO and KEGG. The protein–protein interaction (PPI) network of DEGs was established and visualized using the Cytocape software, followed by screening Hub genes in the PPI network using Degree value as the criterion. Subsequently, the expression and survival relevance of Hub gene in COAD patients were verified. In the experimental study, the effects of AC on the inhibition of colon cancer growth and liver metastasis were comprehensively evaluated by cellular and animal models. Finally, based on the results of bioinformatics analysis, the possible mechanisms of AC inhibition of colon cancer EMT and liver metastasis were explored by in vivo and in vitro pharmacological experiments.ResultsIn this study, we obtained 2386 DEGs relevant to LMCC from the COAD (colon adenocarcinoma) and GSE38174 datasets. Results of GO gene function and KEGG signaling pathway enrichment analysis suggested that cellular EMT (Epithelial-mesenchymal transition) biological processes, Cytokine-cytokine receptor interaction and PI3K/Akt signaling pathways might be closely related to LMCC mechanism. We then screened for CXCL8, the core hub gene with the highest centrality within the PPI network of DEGs, and discovered that CXCL8 expression was negatively correlated with the prognosis of COAD patients. In vitro and in vivo experimental evidence presented that AC significantly inhibited colon cancer cell proliferation, migration and invasion ability, and suppressed tumor growth and liver metastasis in colon cancer orthotopic transplantation mice models. Concomitantly, AC significantly reduced CXCL8 expression levels in cell supernatants and serum. Moreover, AC reduced the expression and transcription of genes related to the PI3K/AKT pathway while suppressing the EMT process in colon cancer cells and model mice.ConclusionsIn summary, our research predicted the potential targets and pathways of LMCC, and experimentally demonstrated that AC might inhibit the growth and liver metastasis in colon cancer by regulating EMT via the CXCL8/CXCR2 axis and PI3K/AKT/mTOR signaling pathway, which may facilitate the discovery of mechanisms and new therapeutic strategies for LMCC.

Integrated Bioinformatics Analysis for Identifying the Significant Genes as Poor Prognostic Markers in Gastric Adenocarcinoma.

Gastric adenocarcinoma (GAC) is the most common histological type of gastric cancer and imposes a considerable health burden globally. The purpose of this study was to identify significant genes and key pathways participated in the initiation and progression of GAC. Four datasets (GSE13911, GSE19826, GSE54129, and GSE79973) including 171 GAC and 77 normal tissues from Gene Expression Omnibus (GEO) database were collected and analyzed. Through integrated bioinformatics analysis, we obtained 69 commonly differentially expressed genes (DEGs) among the four datasets, including 20 upregulated and 49 downregulated genes. The prime module in protein-protein interaction network of DEGs, including ADAMTS2, COL10A1, COL1A1, COL1A2, COL8A1, BGN, and SPP1, was enriched in protein digestion and absorption, ECM-receptor interaction, focal adhesion, PI3K-Akt signaling pathway, and amoebiasis. Furthermore, expression and survival analysis found that all seven hub genes were highly expressed in GAC tissues and 6 of them (except for SPP1) were able to predict poor prognosis of GAC. Finally, we verified the 6 high-expressed hub genes in GAC tissues via immunohistochemistry, Western blot, and RNA quantification analysis. Altogether, we identified six significantly upregulated DEGs as poor prognostic markers in GAC based on integrated bioinformatical methods, which could be potential molecular markers and therapeutic targets for GAC patients.

Bioinformatics Study Identified EGF as a Crucial Gene in Papillary Renal Cell Cancer.

Background Due to a lack of knowledge of the disease process, papillary renal cell carcinoma (PRCC) has a dismal outlook. This research was aimed at uncovering the possible biomarkers and the underlying principles in PRCC using a bioinformatics method. Methods We searched the Gene Expression Omnibus (GEO) datasets to obtain the GSE11151 and GSE15641 gene expression profiles of PRCC. We used the R package limma to identify the differentially expressed genes (DEGs). The online tool DAVID and ClusterProfiler package in R software were used to analyze Gene Ontology and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway dominance, respectively. The STRING database was utilized to construct the PPI network of DEGs. Using the Cytoscape technology, a protein-protein interaction (PPI) network that associated with DEGs was created, and the hub genes were identified using the Cytoscape plug-in CytoHubba. The hub genes were subjected to a Kaplan-Meier analysis to identify their correlations with survival rates. Results From the selected datasets, a total of 240 common DEGs were identified in the PRCC, including 50 upregulated genes and 190 downregulated regulated genes. Renal growth, external exosome, binding of heparin, and metabolic processes were all substantially associated with DEGs. The CytoHubba plug-in-based analysis identified the 10 hub genes (ALB, KNG1, C3, CXCL12, EGF, TIMP1, VCAN, PLG, LAMC1, and CASR) from the original PPI network. The higher expression group of EGF was associated with poor outcome in patients with PRCC. Conclusions We revealed important genes and proposed biological pathways that may be implicated in the formation of PRCC. EGF might be a predictive biomarker for PRCC and therefore should be investigated as a novel treatment strategy.

Differentially Expressed Genes in Nasopharyngeal Carcinoma Tissues and Their Correlation with Recurrence and Metastasis of Nasopharyngeal Carcinoma.

In this study, bioinformatics tools were used to identify key genes to study the molecular mechanism of nasopharyngeal carcinoma (NPC) development and to explore the correlation of these key genes with the recurrence and metastasis of NPC. The GSE61218 microarray dataset obtained from the Gene Expression Omnibus Database (GEO) was used. The limma R package was used to screen differentially expressed genes (DEGs) between NPC and normal nasopharyngeal (NP) tissues. KEGG functional enrichment was performed on these selected DEGs. Protein-protein interaction (PPI) networks were constructed using Cytoscape software to identify key node proteins. The NPC-metastasis microarray dataset GSE103611 was obtained from GEO to analyze the expression of DEGs in NPC metastasis. A total of 239 DEGs were identified. DEGs were mainly enriched in oocyte maturation-related pathways, cytokine-related pathways, cell cycle-related pathways, cancer-related pathways, and homologous recombination-related pathways. In addition, the top 10 nodes with the higher degree in the DEG PPI network were as follows: CDK1, CCNB2, BUB1, CCNA2, AURKB, BUB1B, MAD2L1, NDC80, BIRC5, and CENPF. The results indicated that DEGs may be involved in the pathogenesis of NPC by regulating cell cycle and mitosis, which can be used as molecular biomarkers for the diagnosis of NPC. In addition, we identified 87 DEGs with FC > 2 and P < 0.01 from the metastasis spectrum of NPC. The intersection gene between DEGs of NPC and normal NP tissue samples and those of the metastatic spectrum of NPC was identified to be VRK2. The expression of VRK2 in NPC samples was significantly higher than that in normal NP tissue, and similarly, VRK2 expression was significantly upregulated in metastatic samples compared with nonmetastatic samples (P < 0.05). Therefore, VRK2 may be a biomarker for predicting the metastasis of NPC patients after treatment.

Identification and Integrated Analysis of the miRNA-mRNA Regulatory Network in Lens from an H2O2-Induced Zebrafish Cataract Model

Purpose This study aimed to explore the regulatory mechanisms of age-related cataract (ARC) formation. Methods Cataracts in zebrafish were induced by injecting hydrogen peroxide into the fish anterior chamber. The mRNA and miRNA expression profiles of the lens from H2O2-injected and PBS-injected zebrafishes were detected by RNA sequencing. The LIMMA package was applied to identify differentially expressed genes (DEGs). Gene Ontology categories were enriched by the R “cluster Profiler” package and Kyoto Encyclopedia of Genes and Genomes pathway enrichment was performed based on hypergeometric distribution using the R “phyper” function. The protein-protein interaction network of DEGs was built via the STRING. Target genes of differentially expressed miRNAs (DEmiRs) were predicted by miRanda. Furthermore, DEGs were selected as DEmiR targets and a DEmiR-DEG regulatory network was constructed via Cytoscape. Results In total, 3689 DEGs (such as opn1mw4, LOC103908930, si:dkeyp-1h4.8, crispld1b, cyp1a, and gdpd3a) including 2478 upregulated and 1211 downregulated genes were identified. 177 DEmiRs (such as dre-miR-96-3p, dre-miR-182-5p, dre-miR-9-7-3p, and dre-miR-124-4-5p) including 108 upregulated and 69 downregulated miRNAs were detected. The DEGs are involved in cell death, DNA repair, and cell development-related pathways. A protein-protein interaction network including 79 node genes was constructed to explore the interactions of DEGs. Furthermore, a DEmiR-DEG regulatory network focusing on the DNA repair process was constructed, including 21 hub DEGs and 15 hub DEmiRs. Conclusions We identified several DEGs and constructed a miRNA-mRNA regulatory network related to the DNA repair process in a zebrafish cataract model. These genes participate in the oxidative stress response of lens epithelium cells and finally contribute to the formation of zebrafish cataracts. The hub DEGs and hub DEmiRs could be potential therapeutic targets for ARC.

Identification of Crucial Hub Genes and Differential T Cell Infiltration in Idiopathic Pulmonary Arterial Hypertension Using Bioinformatics Strategies.

Background: Idiopathic pulmonary arterial hypertension (IPAH) is a life-threatening disease. Growing evidence indicated that IPAH is a chronic immune disease. This study explored the molecular mechanisms and T cell infiltration of IPAH using integrated bioinformatics methods. Methods: Gene expression profiles of dataset GSE113439 were downloaded from the Gene Expression Omnibus and analyzed using R. Protein-protein interaction (PPI) network and gene set enrichment analysis (GSEA) were established by NetworkAnalyst. Gene Ontology enrichment analysis was performed using ClueGO. Transcription factors of differentially expressed genes (DEGs) were estimated using iRegulon. Transcription factors and selected hub genes were verified by real-time polymerase chain reaction (qPCR) in the lung tissues of rats with pulmonary artery hypertension. The least absolute shrinkage and selection operator regression model and the area under the receiver operating characteristic curve (AUC) were applied jointly to identify the crucial hub genes. Moreover, immune infiltration in IPAH was calculated using ImmuCellAI, and the correlation between key hub genes and immune cells was analyzed using R. Results: A total of 512 DEGs were screened, and ten hub genes and three transcription factors were filtered by the DEG PPI network. The DEGs were mainly enriched in mitotic nuclear division, chromosome organization, and nucleocytoplasmic transport. The ten hub genes and three transcription factors were confirmed by qPCR. Moreover, MAPK6 was identified as the most potent biomarker with an AUC of 100%, and ImmuCellAI immune infiltration analysis showed that a higher proportion of CD4-naive T cells and central memory T cells (Tcm) was apparent in the IPAH group, whereas the proportions of cytotoxic T cells (Tc), exhausted T cells (Tex), type 17 T helper cells, effector memory T cells, natural killer T cells (NKT), natural killer cells, gamma-delta T cells, and CD8 T cells were lower. Finally, MAPK6 was positively correlated with Tex and Tcm, and negatively correlated with Tc and NKT. Conclusion: MAPK6 was identified as a crucial hub gene to discriminate IPAH from the normal group. Dysregulated immune reactions were identified in the lung tissue of patients with IPAH.

Identification of potential biomarkers in hepatocellular carcinoma: A network-based approach

BackgroundHepatocellular carcinoma (HCC) is one of the leading causes of death worldwide. Identification of potential therapeutic and diagnostic biomarkers can be helpful to screen cancer progress. This study was implemented to discover potential biomarkers for HCC within a network-based approach integrated with microarray data. MethodsThrough downloading a gene expression profile GSE62232 differentially expressed genes (DEGs) were identified. Gene ontology (GO) and the Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analysis for DEGs were performed utilizing Enrichr server. Following reconstruction of protein-protein interaction network of DEGs with STRING, network visualization, analyses, and clustering into structural modules were carried out using Cytoscape. Considering degree centrality, 15 hub genes were selected as early biomarker candidates for final validation. To validate hub genes, the GEPIA server was used to perform overall survival (OS) and disease-free survival (DFS). ResultsIn our approach, 1996 DEGs were identified including 995 up-regulated genes and 1001 down-regulated genes. KEGG pathway enrichment analysis shows that DEGs are associated with chemical carcinogenesis and cell cycle. GO term enrichment analysis indicated the relation of DEGs with epoxygenase P450 pathway, arachidonic acid monooxygenase activity, and secretory granule lumen. Following analysis of the protein-protein interaction network of DEGs, the top three structural modules and 15 early hub genes were selected. Validation of hub genes was performed using GEPIA. Consequently, CDK1, CCNB1, CCNA2, CDC20, AURKA, MAD2L1, TOP2A, KIF11, BUB1B, TYMS, EZH2, and BUB1 were considered as our final proposed biomarkers. Conclusionusing an integrated network-based approach with microarray data our results revealed 12 final candidates with the potential to be considered as biomarkers in hepatocellular carcinoma.