Search Tool For The Retrieval Of Interacting Genes Research Articles

Identification of LOX as a candidate prognostic biomarker in Glioblastoma multiforme

BackgroundGlioblastoma multiforme (GBM) is the most malignant type of glioma. GBM tumors grow rapidly, have a high degree of malignancy, and are characterized by a fast disease progression. Unfortunately, there is a lack of effective treatments. An effective strategy for the treatment of GBM would be to identify key biomarkers correlating with the occurrence and progression of GBM and developing these biomarkers into therapeutic targets. Method and ResultsIn this study, using integrated bioinformatics analysis, we identified differentially expressed genes (DEGs), including 130 genes that were upregulated in GBM compared to normal brain tissue, and 128 genes that were downregulated in GBM. Based on Gene Ontology enrichment analysis and Kyoto Encyclopedia of Genes and Genomes pathway analysis, these genes were associated with regulation of tumor cell adhesion, differentiation, morphology in GBM and were mainly enriched in Complement and coagulation cascades pathway. The Search Tool for the Retrieval of Interacting Genes (STRING) database was used to construct a Protein-Protein Interaction network. Ten hub genes were identified, including FN1, CD44, MYC, CDK1, SERPINE1, COL3A1, COL1A2, LOX, POSTN and EZH2, all of which were significantly upregulated in GBM, these results were confirmed by oncomine database exploration. Alteration analysis of hub genes found that patients with alteration in at least one of the hub genes showed shorter median survival times (p = 0.013) and shorter median disease-free survival times (p = 2.488E-3) than patients without alterations in any of the hub genes. Multiple tests for survival analysis showed that among individual hub genes only expression of LOX was correlated with patient survival (P < 0.05).GDS4467 data set was used to analyze the expression of LOX in gliomas with different degrees of malignancy, and it was found that the expression level of LOX was positively correlated with the malignant degree of gliomas.By analyzing GDS 4535 data set showed that the expression level of LOX was positively correlated with the differentiation degree of GBM cells ConclusionThis research suggests that FN1, CD44, MYC, CDK1, SERPINE1, COL3A1, COL1A2, LOX, POSTN and EZH2 are key genes in GBM. However, only LOX is correlated with patient survival and promotes glioblastoma cell differentiation and tumor recurrence. LOX may be a candidate prognostic biomarker and potential therapeutic target for GBM.

Screening and identification of potential biomarkers for pancreatic cancer: An integrated bioinformatics analysis

Pancreatic cancer is one of the highly invasive and the seventh most common cause of death among cancers worldwide. To identify essential genes and the involved mechanisms in pancreatic cancer, we used bioinformatics analysis to identify potential biomarkers for pancreatic cancer management. Gene expression profiles of pancreatic cancer patients and normal tissues were screened and downloaded from The Cancer Genome Atlas (TCGA) bioinformatics database. The Differentially expressed genes (DEGs) were identified among gene expression signatures of normal and pancreatic cancer, using R software. Then, enrichment analysis of the DEGs, including Gene Ontology (GO) analysis and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis, was performed by an interactive and collaborative HTML5 gene list enrichment analysis tool (enrichr) and ToppGene. The protein-protein interaction (PPI) network was also constructed using the Search Tool for the Retrieval of Interacting Genes (STRING) database and ToppGenet web based tool followed by identifying hub genes of the top 100 DEGs in pancreatic cancer using Cytoscape software. Over 2000 DEGs with variable log2 fold (LFC) were identified among 34,706 genes. Principal component analysis showed that the top 20 DEGs, including H1–4, H1–5, H4C3, H4C2, RN7SL2, RN7SL3, RN7SL4P, RN7SKP80, SCARNA12, SCARNA10, SCARNA5, SCARNA7, SCARNA6, SCARNA21, SCARNA9, SCARNA13, SNORA73B, SNORA53, SNORA54 might distinguish pancreatic cancer from normal tissue. GO analysis showed that the top DEGs have more enriched in the negative regulation of gene silencing, negative regulation of chromatin organization, negative regulation of chromatin silencing, nucleosome positioning, regulation of chromatin silencing, and nucleosomal DNA binding. KEGG analysis identified an association between pancreatic cancer and systemic lupus erythematosus, alcoholism, neutrophil extracellular trap formation, and viral carcinogenesis. In PPI network analysis, we found that the different types of histone-encoding genes are involved as hub genes in the carcinogenesis of pancreatic cancer. In conclusion, our bioinformatics analysis identified genes that were significantly related to the prognosis of pancreatic cancer patients. These genes and pathways could serve as new potential prognostic markers and be used to develop treatments for pancreatic cancer patients.

Novel potential urinary biomarkers for effective diagnosis and prognostic evaluation of high-grade bladder cancer.

High-grade bladder cancer (HGBC) has a higher malignant potential, recurrence and progression rate compared to low-grade phenotype. Its early symptoms are often vague, making non-invasive diagnosis using urinary biomarkers a promising approach. The gene expression data from urine samples of patients with HGBC was extracted from the GSE68020 dataset. The clinical information and gene expression data in tumor tissues of HGBC patients were obtained from The Cancer Genome Atlas (TCGA) database. Multivariate Cox analysis was used to predict the optimal risk model. The protein-protein interaction (PPI) analysis was performed via the Search Tool for the Retrieval of Interacting Genes (STRING) database and visualized using Cytoscape. Overall survival (OS) was evaluated in the Gene Expression Profiling Interactive Analysis (GEPIA) online platform. Competing endogenous RNA (ceRNA) network was also visualized using Cytoscape. The expression levels of specific genes were assessed through quantitative real-time reverse transcription-polymerase chain reaction (qRT-PCR). Moreover, co-expressed genes and potential biological functions related to specific genes were explored based on the Cancer Cell Line Encyclopedia (CCLE) database. A total of 560 differentially expressed genes (DEGs) were identified when comparing the urine sediment samples from HGBC patients with the benign ones. Using these urinary DEGs and the clinical information of HGBC patients, we developed an optimal risk model consisting of eight genes to predict the patient outcome. By integrating the node degree values in the PPI network with the expression changes in both urine and tissue samples, eighteen hub genes were selected out. Among them, DKC1 and SNRPG had the most prominent comprehensive values, and EFTUD2, LOR and EBNA1BP2 were relevant to a worse OS in bladder cancer patients. The ceRNA network of hub genes indicated that DKC1 may be directly regulated by miR-150 in HGBC. The upregulation of both SNRPG and DKC1 were detected in HGBC cells, which were also observed in various tumor tissues and malignant cell lines, displaying high correlations with other hub genes. Our study may provide theoretical basis for the development of effective non-invasive detection and treatment strategies, and further research is necessary to explore the clinical applications of these findings.

Identification of Key Biomarkers in Hepatocellular Carcinoma Induced by Non-alcoholic steatohepatitis or Metabolic Syndrome via Integrated Bioinformatics Analysis.

The burden of hepatocellular carcinoma (HCC) is steadily growing because obesity, type 2 diabetes, and nonalcoholic fatty liver disease (NAFLD) are replacing viral- and alcohol-related liver disease as major pathogenic promoters. The current study attempted to identify the key genes and pathways in the non-alcoholic steatohepatitis (NASH) induced development of HCC using integrated bioinformatics analyses. Two gene expression profiling datasets, GSE102079 and GSE164760 were downloaded. Differentially expressed genes (DEGs) from HCC and healthy control samples were screened. Functional enrichment analyses based on Gene Ontology (GO) resource, Kyoto Encyclopedia of Genes and Genomes (KEGG) resource. Then protein-protein interaction (PPI) of these DEGs was visualized by Cytoscape with Search Tool for the Retrieval of Interacting Genes (STRING). Expression and survival analysis of hub genes, methylation and genetic mutation analysis were explored withGEPIA2, UALCAN, GSCA, and TIMER2.0 databases. We identified 158 overlapping genes from the 2 datasets. Up-regulated genes were mainly related to the proliferation, adhesion and metastasis of tumors, while down-regulated genes were mainly related to oxidative stress and energy metabolism. CDKN2A, SPP1, CYP2C9 and CYP4A11 were associated with prognostic performance and were considered the potential crucial genes, which SPP1, CYP2C9 and CYP4A11 were identified as the DNA methylation-driven genes. In different mutation statuses of HCC, gene expression of CDKN2A, SPP1, CYP2C9 and CYP4A11 showed significant differences. CDKN2A and SPP1 were identified as risk genes, while CYP2C9 and CYP4A11 were identified as protective genes, which may affect the transformation of NASH into HCC.

Characteristics of immune cell infiltration in inflamed mucosa of ulcerative colitis patients, hub gene candidates and key pathways in intestinal macrophage expression

Background: The ulcerative colitis (UC) associated immune cell network was revealed by analyzing the immune cell composition of inflamed and uninflamed mucosal biopsy samples from the same patients with UC. For differentially expressed macrophages, the differentially expressed genes (DEGs), functional enrichment analyses, and protein–protein interaction (PPI) network was analyzed to understand further the role of macrophages in the occurrence and development of disease. Methods: GSE 179285 dataset and GSE123141 dataset were downloaded from the GEO database. We used the CIBERSORT algorithm to map immune cell infiltration between paired inflamed and uninflamed intestinal mucosal biopsies. GO and KEGG functional enrichment analyses were used to analyze DEGs in intestinal macrophage populations. The PPI network was constructed by the Search Tool for the Retrieval of Interacting Genes (STRING) database. Immunohistochemistry and qPCR were used to verify hub genes in the mouse model of UC. Results: The inflamed tissues had a higher infiltration of M1 macrophages and M0 macrophages. M2 macrophages, naive B cells, gamma delta T cells (γδT cells), and resting mast cells were more abundant in uninflamed tissues. Functional enrichment analyses showed that the 141 DEGs are involved in multiple processes in the inflammatory response. The three genes (LCN2, CXCL1, SAA1) had the highest degrees of connectivity and may be hub genes. LCN2 was significantly overexpressed in colitis tissues compared with normal control tissues in the mouse model of UC. Conclusion: Changes in macrophages were notable in the inflamed mucosa of UC patients. 141 DEGs were entered into GO and KEGG functional enrichment analyses; three hub gene candidates were selected. Highlights: There were significant differences in the distribution of macrophages in the inflamed and uninflamed mucosa of UC patients. Functional enrichment analyses of DEGs in UC-associated intestinal macrophages were displayed. Three genes (LCN2, CXCL1, SAA1) were identified as hub gene candidates.

High-Throughput Sequencing Data Reveal an Antiangiogenic Role of HNF4A-Mediated CACNA1A/VEGFA Axis in Proliferative Diabetic Retinopathy.

Proliferative diabetic retinopathy (PDR) is characterized by retinal new vessel formation, pointing to the importance of the antiangiogenic treatment in PDR. Hepatocyte nuclear factor 4A (HNF4A) has been highlighted to inhibit vascular endothelial growth factor (VEGF)-stimulated in vitro angiogenesis. Therefore, this study aims to elucidate the potential antiangiogenic mechanisms of HNF4A in PDR. PDR-related high-throughput sequencing datasets (GSE94019, GSE102485, and GSE191210) were obtained from the Gene Expression Omnibus (GEO) database, followed by the screening of differentially expressed genes (DEGs). The protein-protein interaction (PPI) network of the candidate DEGs was constructed based on gene set enrichment analysis (GSEA) data and Search Tool for the Retrieval of Interacting Genes (STRING) data. In addition, the key genes and pathways related to angiogenesis were screened by functional enrichment analysis. Furthermore, human retinal microvascular cells were used for further in vitro validation. Four key genes (CACNA1A, CACNA1E, PDE1B, and CHRM3) related to PDR were identified in the grey module. CACNA1A affected angiogenesis in PDR by regulating vascular endothelial growth factor A (VEGFA) expression. Furthermore, HNF4A participated in angiogenesis in PDR by activating CACNA1A. In vitro experiments further identified that inhibition of HNF4A reduced CACNA1A expression and increased VEGFA expression, thus promoting angiogenesis in PDR. In conclusion, the obtained findings suggest that antiangiogenic HNF4A activates the CACNA1A/VEGFA axis in PDR. Our work provides new insights into the angiogenic mechanism of PDR and offers potential targets for translational applications.

Decreased zinc-fingers and homeoboxes family expression was associated with unfavorable outcomes and immune infiltration in lung adenocarcinoma.

The zinc-fingers and homeoboxes (ZHX) family is a group of nuclear homodimeric transcriptional repressors that play an essential role in developing and progressing diverse malignancies. However, the association of ZHX family expression with prognosis and immune infiltration in lung adenocarcinoma (LUAD) is still unclear. The current study aimed to investigate the relationship between ZHX family expression and clinical outcomes and immune infiltration in LUAD patients. ZHXs family expression was determined by using the Oncomine database and Cancer Cell Line Encyclopedia (CCLE). The impact of ZHXs family expression on prognosis was analyzed by using the Kaplan-Meier-plotter online database. The Search Tool for the Retrieval of Interacting Genes (STRING) database was utilized to construct the interaction network based on the selected differentially expressed genes associated with ZHXs. The Database for Annotation, Visualization, and Integrated Discovery (DAVID) was used to perform the enrichment of the Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways. The functional state of the ZHXs family in diverse types of malignancies was determined by CancerSEA. The Tumor Immune Estimation Resource (TIMER) database was used to evaluate the association of the ZHXs family with immune cell infiltrates. ZHXs family expression was validated by the Gene Expression Omnibus (GEO) database and real-time polymerase chain reaction (RT-PCR) in 10 paired tumors and normal tissues. ZHX1-3 expression level significantly decreased in LUAD compared with normal tissues. Attenuated ZHXs expression was significantly associated with unfavorable overall survival in LUAD patients. ZHX family members were positively associated with immune infiltration of monocytes, tumor-associated macrophages (TAMs), M1 and M2 macrophages in LUAD. ZHX family expression was also significantly related to a variety of immune marker sets in LUAD. GEO analysis and RT-PCR validated the significant decrease of ZHXs expression level in LUAD. The current study revealed that ZHX family expression was significantly correlated with unfavorable outcomes and immune infiltration in LUAD. The findings herein provide a promising basis for further study into the potential biological function of the ZHX family in LUAD and lay a foundation for developing therapeutic targets for LUAD patients.

SnRNA-Seq analysis reveals ten hub genes associated with alveolar epithelial cell injury during pulmonary acute respiratory distress syndrome

BackgroundAlveolar epithelial cell injury is a key factor in the occurrence and development of pulmonary acute respiratory distress syndrome (ARDSp). Yet the gene expression profile of alveolar epithelial cells of patients with ARDSp remains unclear. MethodsWe analyzed single nuclear RNA sequencing (snRNA-Seq) data from autopsy lung tissues of both ARDSp patients and healthy donors. Sequence data for type 2 alveolar epithelizal cells (AT2) were extracted by the Seurat package. Differentially expressed genes (DEGs) in AT2 were identified by the criteria |log2FC| ≥ 0.25 and P < 0.05 with DESeq2. A protein interaction network was constructed using Search Tool for the Retrieval of Interacting Genes (STRING) and Cytoscape software to identify hub genes. We then constructed an ARDSp rat model through induction by lipopolysaccharide (LPS) airway instillation. Left lung RNA was extracted and sequenced via Illumina Hiseq platforms. Analysis of the rat RNA sequencing data was then used to verify hub genes. Gene ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) analyses were performed on the identified hub genes. ResultsIn AT2, a total of 289 genes were identified as differentially expressed between those from ARDSp patients and healthy donors, and these included 190 upregulated and 99 downregulated genes. Ten hub genes were further identified (RPS27A, ACTG1, CAV1, HSP90AA1, HSPA5, CCND1, ITGA3, B2M, NEDD4L, and SEMA5A). There was a similar expression trend of HSPA5 between rat RNA and snRNA sequencing data. DiscussionARDSp altered the gene expression profile of AT2. The identified hub genes were enriched in biological processes mainly involved in cell growth and transformation. Relatedly, ferroptosis and autophagy are possibly involved in AT2 injury during ARDSp. These novel insights into ARDSp may aid the discovery of potential targets for the diagnosis and treatment of ARDSp.

Identification of hub genes affecting gestational diabetes mellitus based on GEO database

ABSTRACT This research aimed to obtain gestational diabetes mellitus (GDM) related hub genes, providing new targets for clinical diagnosis and treatment of GDM. The microarray data of GSE9984 and GSE103552 were obtained from the Gene Expression Omnibus (GEO). The dataset GSE9984 contained placental gene expression profiles of 8 GDM patients and four healthy specimens. The dataset GSE103552 contained 20 specimens from GDM patients and 17 normal specimens. The differentially expressed genes (DEGs) were identified by GEO2R online analysis. DAVID database was applied to conduct functional enrichment analysis of the DEGs. The Search Tool for the Retrieval of Interacting Genes (STRING) database was adopted to acquire protein–protein interaction (PPI) networks. A total of 195 up-regulated and 371 down-regulated DEGs were selected in the GSE9984, and total of 191 up-regulated and 229 down-regulated DEGs were selected in the GSE103552. In the two datasets, 24 common differential genes were obtained and named co-DEGs. The Gene Ontology (GO) annotation analysis indicated the DEGs participated in multi-multicellular organism process, endocrine hormone secretion, long-chain fatty acid biosynthetic process, cell division, unsaturated fatty acid biosynthetic process, cell adhesion and cell recognition. Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis suggested that GSE9984 and GSE103552 were related to vitamin digestion and absorption, tryptophan metabolism, steroid hormone biosynthesis, Ras signaling pathway, protein digestion and absorption, PPAR signaling pathway, PI3K-Akt signaling pathway, p53 signaling pathway. PPI was constructed in string database, and six hub genes were selected, including CCNB1, APOA2, AHSG and IGFBP1. Four critical genes were identified to be considered as therapeutic potential biomarkers of GDM, including CCNB1, APOA2, AHSG and IGFBP1.

Prognostic Values of Core Genes in Pilocytic Astrocytom

Pilocytic astrocytoma (PA) is the most common primary brain tumor in children and adolescents. Treatment strategy largely depends on its key genes and molecular mutations. This study aimed to identify potential biomarkers of PA closely related to its prognosis. The gene expression profiles (series numbers GSE50161, GSE66354, and GSE86574) of PA and normal brain tissues were downloaded from the Gene Expression Omnibus database. The Gene Expression Omnibus2R was used to identify differentially expressed genes. The overlapping differentially expressed genes were subjected to Gene Ontology and Kyoto Encyclopedia of Genes and Genomes pathway enrichment analyses using the Database for Annotation, Visualization, and Integrated Discovery (DAVID) database. A protein-protein interaction network was constructed using Search Tool for the Retrieval of Interacting Genes (STRING) and Cytoscape. The Gene Expression Profiling Interactive Analysis 2 (GEPIA2) tool analyzed the impact of hub genes on PA prognosis based on the Kaplan-Meier curves. Compared with normal brain tissues (n= 36), a total of 37 upregulated and 144 downregulated genes were identified in PA (n= 40). In the protein-protein interaction network construction and GEPIA2 survival analysis, 2 of the top 10 hub genes were significantly associated with decreased overall survival of PA patients, namely Gamma-aminobutyric acid A receptor alpha 2 (hazard ratio = 2.8, P < 0.01) and regulating synaptic membrane exocytosis protein 1) (hazard ratio= 3.2, P < 0.01). This bioinformatics analysis reveals that low expression of Gamma-aminobutyric acid A receptor alpha 2 and regulating synaptic membrane exocytosis protein 1 is associated with a favorable prognosis for PA patients. These 2 hub genes could be novel biomarkers for prognosis assessment, furthermore a key element for treatment decisions in the future.

Bioinformatics analysis illustrates the functions of miR-377-5p in cervical cancer

ABSTRACT Cervical cancer (CC) is a frequent disease in women whose development is related with miRNA disorder. MiR-377-5p plays a negative role in the development of some tumors, while few studies have revealed its role in CC. In this study, the functions of miR-377-5p in CC were investigated by bioinformatics. Briefly, the expression and survival curve of miR-377-5p in CC was analyzed with the Cancer Genome Atlas (TCGA) database, and the abundance of miR-377-5p in clinical samples and CC cell lines were measured by qRT-PCR. Moreover, the MicroRNA Data Integration Portal (miRDIP) database was used to predict targets of miR-377-5p, and the Database for Annotation Visualization and Integrated Discovery (David) was used for enrichment analysis of the functions of the miR-377-5p. The Search Tool for the Retrieval of Interacting Genes (STRING) database was used to screen the hub targets of miR-377-5p. Moreover, the Gene Expression Profiling Interactive Analysis (GEPIA) database was used to analyze the abundance of the genes in CC. Results showed that decreased miR-377-5p was found in the CC tissues and cell lines, and low miR-377-5p was connected with poor prognosis of patients. Besides, the targets of miR-377-5p were enriched in the PI3K/AKT, MAPK and RAS signaling pathways. Moreover, CDC42, FLT1, TPM3 and CAV1 were screened as hub nodes in the targets of miR-377-5p, and increased CDC42, FLT1, TPM3 and CAV1 also indicated the poor survival rates of the patients in the long term. In conclusion, this study suggests that miR-377-5p downregulation is a biomarker event for CC progression.

Serelaxin Alleviates Fibrosis in Thyroid-Associated Ophthalmopathy via the Notch Pathway.

Fibrosis is the late stage of thyroid-associated ophthalmopathy (TAO), resulting in serious complications. Effective therapeutic drugs are still lacking. We aimed to explore the mechanism of TAO fibrosis and to find a targeted drug. High-throughput RNA sequencing was performed on orbital connective tissues from twelve patients with TAO and six healthy controls. Protein-protein interaction (PPI) network was constructed using the Search Tool for the Retrieval of Interacting Genes (STRING) database and we identified the hub gene by Cytoscape software. Additionally, the RNA sequencing results were validated by quantitative real-time polymerase chain reaction (qRT-PCR). Bioinformatic prediction identified the functions of differentially expressed genes (DEGs). Further orbital connective tissue and serum samples of the TAO and control groups were collected for subsequent experiments. Histologic staining, Western blotting (WB), qRT-PCR, enzyme-linked immunosorbent assays (ELISAs), gene overexpression through lentiviral infection or silencing gene by short interfering RNA (siRNA) were performed. We found that the relaxin signaling pathway is an important regulatory pathway in TAO fibrosis pathogenesis. Serelaxin exerts antifibrotic and anti-inflammatory effects in TAO. Furthermore, the downstream Notch pathway was activated by serelaxin and was essential to the antifibrotic effect of serelaxin in TAO. The antifibrotic effect of serelaxin is dependent on RXFP1.

Potential mechanisms of osthole against bladder cancer cells based on network pharmacology, molecular docking, and experimental validation

BackgroundOsthole was traditionally used in treatment for various diseases. However, few studies had demonstrated that osthole could suppress bladder cancer cells and its mechanism was unclear. Therefore, we performed a research to explore the potential mechanism for osthole against bladder cancer.MethodsInternet web servers SwissTargetPrediction, PharmMapper, SuperPRED, and TargetNet were used to predict the Osthole targets. GeneCards and the OMIM database were used to indicate bladder cancer targets. The intersection of two target gene fragments was used to obtain the key target genes. Protein–protein interaction (PPI) analysis was performed using the Search Tool for the Retrieval of Interacting Genes (STRING) database. Furthermore, we used gene ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analyses to explore the molecular function of target genes. AutoDock software was then used to perform molecular docking of target genes,osthole and co-crystal ligand. Finally, an in vitro experiment was conducted to validate bladder cancer inhibition by osthole.ResultsOur analysis identified 369 intersection genes for osthole, the top ten target genes included MAPK1, AKT1, SRC, HRAS, HASP90AA1, PIK3R1, PTPN11, MAPK14, CREBBP, and RXRA. The GO and KEGG pathway enrichment results revealed that the PI3K-AKT pathway was closely correlated with osthole against bladder cancer. The osthole had cytotoxic effect on bladder cancer cells according to the cytotoxic assay. Additionally, osthole blocked the bladder cancer epithelial-mesenchymal transition and promoted bladder cancer cell apoptosis by inhibiting the PI3K-AKT and Janus kinase/signal transducer and activator of transcription (JAK/STAT3) pathways.ConclusionsWe found that osthole had cytotoxic effect on bladder cancer cells and inhibited invasion, migration, and epithelial-mesenchymal transition by inhibiting PI3K-AKT and JAK/STAT3 pathways in in vitro experiment. Above all, osthole might have potential significance in treatment of bladder cancer.SubjectsBioinformatics, Computational Biology, Molecular Biology.

In silico analysis of differentially expressed-aberrantly methylated genes in breast cancer for prognostic and therapeutic targets.

Breast cancer (BC) is the leading cause of death among women across the globe. Abnormal gene expression plays a crucial role in tumour progression, carcinogenesis and metastasis of BC. The alteration of gene expression may be through aberrant gene methylation. In the present study, differentially expressed genes which may be regulated by DNA methylation and their pathways associated with BC have been identified. Expression microarray datasets GSE10780, GSE10797, GSE21422, GSE42568, GSE61304, GSE61724 and one DNA methylation profile dataset GSE20713 were downloaded from Gene Expression Omnibus database (GEO). Differentially expressed-aberrantly methylated genes were identified using online Venn diagram tool. Based on fold change expression of differentially expressed-aberrantly methylated genes were chosen through heat map. Protein-protein interaction (PPI) network of the hub genes was constructed by Search Tool for the Retrieval of Interacting Genes (STRING). Gene expression and DNA methylation level of the hub genes were validated through UALCAN. Overall survival analysis of the hub genes was analysed through Kaplan-Meier plotter database for BC. A total of 72 upregulated-hypomethylated genes and 92 downregulated-hypermethylated genes were obtained from GSE10780, GSE10797, GSE21422, GSE42568, GSE61304, GSE61724, and GSE20713 datasets by GEO2R and Venn diagram tool. PPI network of the upregulated-hypomethylated hub genes (MRGBP, MANF, ARF3, HIST1H3D, GSK3B, HJURP, GPSM2, MATN3, KDELR2, CEP55, GSPT1, COL11A1, and COL1A1) and downregulated-hypermethylated hub genes were constructed (APOD, DMD, RBPMS, NR3C2, HOXA9, AMKY2, KCTD9, and EDN1). All the differentially expressed hub genes expression was validated in UALCAN database. 4 in 13 upregulated-hypomethylated and 5 in 8 downregulated-hypermethylated hub genes to be significantly hypomethylated or hypermethylated in BC were confirmed using UALCAN database (p < 0.05). MANF, HIST1H3D, HJURP, GSK3B, GPSM2, MATN3, KDELR2, CEP55, COL1A1, APOD, RBPMS, NR3C2, HOXA9, ANKMY2, and EDN1 were significantly (p < 0.05) associated with poor overall survival (OS). The identified aberrantly methylated-differentially expressed genes and their related pathways and function in BC can serve as novel diagnostic and prognostic biomarkers and therapeutic targets.Please confirm if the author names are presented accurately and in the correct sequence (given name, middle name/initial, family name). Author 4 Given name: [Jeewan Ram] Last name [Vishnoi]. Also, kindly confirm the details in the metadata are correct.It is correct.

Comparison of gene expression between human and mouse iPSC-derived cardiomyocytes for stem cell therapies of cardiovascular defects via bioinformatic analysis

BackgroundPreclinical studies have demonstrated the potential use of induced pluripotent stem cells (iPSCs) to treat cardiovascular disease (CVD). In vivo preclinical studies conducted on animal models (murine, porcine, guinea pig, etc.) have employed either syngeneic or human-derived iPSCs. However, no study has been carried out to investigate and report the key genetic differences between the human and animal-derived iPSCs. Our study analysed the gene expression profile and molecular pathway patterns underlying the differentiation of both human and mouse iPSCs to iPSC-cardiomyocytes (iPSC-CMs), and the differences between them via bioinformatic analysis.MethodData sets were downloaded from the Gene Expression Omnibus (GEO) database and included both human and mouse models, and the data for undifferentiated iPSCs and iPSC-CMs were isolated from each. Differentially expressed genes (DEGs) were screened and then analysed. The website g:Profiler was used to obtain the Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment. Protein-protein interaction (PPI) networks of the DEGs were constructed using the Search Tool for the Retrieval of Interacting Genes (STRING) database and Cytoscape software. The subclusters were then extracted from the PPI network for further analysis.ResultsiPSC-derived cardiomyocytes expressed many genes related to vascular, endothelial, and smooth muscle repair in the human iPSC-CMs, and prevention of calcification in the mouse iPSC-CMs with clear differences in gene expression, which will affect how iPSCs act in research. Especially in the human iPSC-CMs, and also prevention of calcification processes in the mouse data. The identified differences in gene expression of iPSCs derived from the two species suggests that in vivo studies using mouse iPSC-CMs may not reflect those in humans.ConclusionThe study provides new insights into the key genes related to the iPSCs, including genes related to angiogenesis, calcification, and striated muscle, endothelium, and bone formation. Moreover, the clear differences between both mouse and human-derived iPSCs have been identified, which could be used as new evidence and guidance for developing novel targeted therapy strategies to improve the therapeutic effects of iPSC treatment in cardiovascular defects.

A bioinformatics system for exploring potential targets and therapies in rheumatoid arthritis

Rheumatoid arthritis (RA), one of the most common chronic inflammatory diseases, is an autoimmune disease that causes cartilage, bone damage, and disability. RA has become a concern, affecting more than 1.5% of the global population. Immunomodulatory or anti-inflammatory targets are the lead candidates in existing clinical trials to develop RA drugs. These targets were reported to be ineffective in reducing symptoms, slowing disease progression, and causing adverse side effects. Therefore, this study focuses on addressing the continuing need for safer, more selective, and more effective treatment options for RA by identifying novel targets and therapeutic approaches. The study involves six important steps: retrieving a dataset from the Gene Expression Omnibus (GEO) database, file conversion, gene differentiation (up-regulated and downregulated), Entrez gene ID retrieval for Search Tool for the Retrieval of Interacting Genes (STRING), protein–protein interaction (PPI) network delineation via STRING, drugs identification via Drug Bank, and relationship establishment between networks and drugs via The Drug–Gene​ Interaction Database (DGIdb). Nine protein hubs, three up-regulated and six down-regulated were identified as interacting with 8 and 18 drugs, respectively. Seven protein sites have never been directly associated with RA and could serve as potential therapeutic targets. Eleven drugs were shown to interact with these seven selected protein hubs as potential therapeutics for RA. These new targets and therapies will help researchers improve the RA treatment pipeline by adding these into the pipeline and bringing successful treatment options for RA patients.

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.

Effect of Shan Zha (Hawthorn or Crataegus) on gastrointestinal cancer: A network pharmacology and molecular docking study

BackgroundShan Zha (Hawthorn or Crataegus) is a traditional Chinese medicine (TCM) most commonly used for the treatment of hyperlipidemia. Gastrointestinal cancer is closely correlated with blood lipid levels. This study illustrates the potential anticancer effects of Shan Zha on gastrointestinal tumors based on network pharmacology and molecular docking. MethodsHawthorn's bioactive ingredients and drug targets were obtained from the Traditional Chinese Medicine Systems Pharmacology Database and Analysis Platform (TCMSP), Integrative Pharmacology-based Research Platform of Traditional Chinese Medicine version 2.0 (TCMIP v2.0), and Herbal Ingredients' Targets Platform (HIT 2.0) databases. Validated disease targets of gastrointestinal cancer were obtained from the Therapeutic Targets Database (TTD) and HIT 2.0 databases. Protein–protein interaction analysis of intersecting genes was performed using the Search Tool for the Retrieval of Interacting Genes (STRING) database. The functions of these genes were further analyzed by performing gene ontology and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analyses. Molecular docking verification was performed using Molecular Operating Environment (MOE) software. ResultsFour main bioactive components were identified in Shan Zha. A total of 271 potential drug targets were identified, and 393 gastrointestinal-tumor targets were obtained. Through protein interaction analysis of intersecting targets, the main components of Shan Zha were found to interact more closely with proteins such as tumor protein p53 (TP53), AKT serine/threonine kinase 1 (AKT1), JUN proto-oncogene (JUN), interleukin 6 (IL6), epidermal growth factor receptor (EGFR), and vascular endothelial growth factor A (VEGFA). KEGG pathway enrichment analysis showed a total of 127 pathways, mainly involving pathways in multiple types of cancer, the Phosphatidylinositol 3-kinase-Akt (PI3K-Akt) signaling pathway, and EGFR tyrosine kinase inhibitor resistance. Combined with The Cancer Genome Atlas (TCGA) differential analysis, key targets, including TP53, cyclin D1 (CCND1), EGFR, and VEGFA, were screened. Molecular docking results showed that quercetin and kaempferol had the good binding potential for TP53, CCND1, EGFR, and VEGFA. ConclusionThese findings suggest that Shan Zha exerts its effects on gastrointestinal cancers through a multitarget, multi-component, and a multi-pathway mechanism.

Prognostic and immunological role of alpha-L-fucosidase 2 (FUCA2) in hepatocellular carcinoma.

This study investigated the prognostic and immunological significance of alpha-L-fucosidase 2 (FUCA2) in hepatocellular cancer (HCC). The expression of FUCA2 and its clinical and prognostic values were explored across several databases, namely the University of Alabama Cancer Database, The Cancer Genome Atlas, Gene Expression Profiling Interactive Analysis, and the Human Protein Atlas. The prognostic relevance of FUCA2 was investigated using Kaplan-Meier curves, nomograms, and Cox analysis. The "limma" package in R was used to identify differentially expressed genes between high and low FUCA2 expression. A protein interaction network was established using the Search Tool for the Retrieval of Interacting Genes (STRING), whereas hub genes and clustering modules were identified using Cytoscape. "clusterProfiler", an R package, was used to examine the potential function of FUCA2. Using gene set enrichment analysis, signaling pathways associated with FUCA2 expression were identified. Cell-type Identification by Estimating Relative Subsets of RNA Transcripts (CIBERSORT), Tumor Immune Estimation Resource (TIMER) 2.0, and Tumor and Immune System Interaction Database (TISIDB) were used to examine immune infiltration and FUCA2 in HCC. Many datasets indicated that FUCA2 expression is higher in HCC, and that this is related to age and overall survival (OS). With the cutoff value of 50% as the dividing threshold, the patients were divided into a high-FUCA2 expression group (n=167) and a low-FUCA2 expression group (n=168). High levels of FUCA2 expression coincided with decreased OS. FUCA2 expression in HCC was associated with immune infiltrates. The functional mechanisms of FUCA2 depend on signal release, extracellular matrix collagen, and neuroactive ligands and receptors. In HCC, increased FUCA2 expression is associated with a poor prognosis and immune infiltration. FUCA2 may serve as an immunological and predictive biomarker for HCC.

Prognostic Role of Long-Chain Acyl-Coenzyme A Synthetase Family Genes in Patients with Clear Cell Renal Cell Carcinoma: A Comprehensive Bioinformatics Analysis Confirmed with External Validation Cohorts

We aimed to determine the prognostic role of long-chain acyl-CoA synthetases (ACSLs) as a disease marker for kidney clear cell carcinoma (KIRC). The Cancer Genome Atlas (TCGA) data were accessed via open access LinkedOmics database for KIRC. Provisional datasets were used for analysis as previously described and gene expression quantification data were downloaded. The corresponding clinical information of patients also were obtained from the database. Five ACSL family members, ACSL1, ACSL3, ACSL4, ACSL5, and ACSL6, were investigated in the TCGA-KIRC cohort. Xena browser, cBioPortal and UALCAN, and Cancer Cell Line Encyclopedia (CCLE) databases were also used to confirm the results. External validation was performed using patient cohorts from the Gene Expression Omnibus (GEO-NCBI) database. Finally, the protein-protein interaction (PPI) was constructed based on the Search Tool for the Retrieval of Interacting Genes (STRING) database and visualized using Cytoscape software. Pathological T3-T4 stage tumors had significantly lower ACSL1 mRNA expression (P=.009). Patients with pathologically confirmed metastasis exhibited significantly lower expression, as well (P=.02). ACSL1 mRNA expression was associated with overall survival (OS) and negatively correlated with OS time. Univariate and multivariate analyses showed that lower ACSL1 mRNA expression level was associated with mortality. Moreover, ACSL1 mRNA expression was exhibited significant difference in some VHL gene region mutations and PBRM1_p.R1010 mutation, and negatively correlated with HIF1-alpha mRNA expression (P < .001). Confirmatory analyses and external validation also revealed similar findings. Lowered ACSL1 mRNA expression is associated with worse tumor histopathology and poor overall survival in KIRC. It may be used for prognostic marker for KIRC.