Identification of the Core MicroRNAs and Potential Molecular Mechanismsin Sarcoidosis Using Bioinformatics Analysis.
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ABSTRACT:
SUBMITTER: Cao Y
PROVIDER: S-EPMC8155597 | biostudies-literature |
REPOSITORIES: biostudies-literature
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Project description:Background Gastric cancer is the third leading cause of cancer-related mortality in China. Most patients with gastric cancer have no obvious early symptoms; thus, many of them are in the middle and late stages of gastric cancer at first diagnosis and miss the best treatment opportunity. Molecular targeted therapy is particularly important in changing this status quo. Methods Three microarray datasets (GSE29272, GSE33651, and GSE54129) were selected from the Gene Expression Omnibus (GEO) database. Differentially expressed genes (DEGs) were screened using GEO2R. The Database for Annotation, Visualization and Integrated Discovery (DAVID) was used to analyze the functional features of these DEGs and the Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment. The protein-protein interaction (PPI) of these DEGs was visualized by Cytoscape software. The expressions of hub genes were evaluated based on Gene Expression Profiling Interactive Analysis (GEPIA). Moreover, we used the online Kaplan-Meier plotter survival analysis tool to evaluate the prognostic values of hub genes. The Target Scan database was used to predict microRNAs that could regulate the target gene, collagen type IV alpha 1 chain (COL4A1). The OncomiR database was used to analyze the expression levels of three microRNAs, as well as the relationships with tumor stage, grade, and prognosis. Results We identified 78 DEGs, including 53 upregulated genes and 25 downregulated genes. The DEGs were mainly enriched in extracellular matrix organization, extracellular structure organization, and response to wounding. Moreover, three KEGG pathways were markedly enriched, including focal adhesion, complement and coagulation cascades, and extracellular matrix (ECM)-receptor interaction. Among these 78 genes, we selected 10 hub genes. The overexpression levels of these hub genes were closely related to poor prognosis and the development of gastric cancer (except for COL3A1, LOX, and CXCL8). Moreover, we found that microRNA-29a-3p, miR-29b-3p, and miR-29c-3p were the potential microRNAs that could regulate the target gene, COL4A1. Conclusions Our results showed that FN1, COL1A1, TIMP1, COL1A2, SPARC, COL4A1, and SERPINE1 could contribute to the development of novel molecular targets and biomarker-driven treatments for gastric cancer.
| S-EPMC8576244 | biostudies-literature
Project description:PurposeThis study aimed to explore new core genes related to the occurrence of Parkinson's disease (PD) and core genes that can lead to the progression of PD.MethodsThe expression profile data of GSE42966, which contained six substantia nigra tissues isolated from normal individuals and nine substantia nigra tissues isolated from patients with PD, were obtained from Gene Expression Omnibus. Differentially expressed genes (DEGs) were identified, followed by functional enrichment analysis and protein-protein interaction (PPI) network construction. We then identified 10 hub genes and analyzed their expression in different Braak stages.ResultsA total of 773 DEGs were identified that were significantly enriched in metabolic pathways. Ten hub genes were identified through the PPI network, namely, GNG3, MAPK1, FPR1, ATP5B, GNG2, PRKACA, HRAS, HSPA8, PSAP, and GABBR2. The expression of HRAS was different in patients with PD with Braak stages 3 and 4.ConclusionThese 10 hub genes and the metabolic pathways they are enriched in may be involved in the pathogenesis of PD. HRAS may have potential value in predicting the progression of PD.
| S-EPMC8464436 | biostudies-literature
Project description:BackgroundEsophageal squamous cell carcinoma (ESCC) is a common human malignancy worldwide. The tumorigenesis mechanism in ESCC is unclear.Materials and methodsTo explore potential therapeutic targets for ESCC, we analyzed 3 microarray datasets (GSE20347, GSE38129, and GSE67269) derived from the gene expression omnibus (GEO) database. Then, the GEO2R tool was used to screen out differently expressed genes (DEGs) between ESCC and normal tissue. Gene ontology function and kyoto encyclopedia of genes and genomes pathway enrichment analysis were performed using the database for annotation, visualization and integrated discovery to identify the pathways and functional annotation of DEGs. Protein-protein interaction of these DEGs was analyzed based on the search tool for the retrieval of interacting genes database and visualized by Cytoscape software. In addition, we used encyclopedia of RNA interactomes (ENCORI), gene expression profiling interactive analysis (GEPIA), and the human protein atlas to confirm the expression of hub genes in ESCC. Finally, GEPIA was used to evaluate the prognostic value of hub genes expression in ESCC patients and we estimated the associations between hub genes expression and immune cell populations (B Cell, CD8+ T Cell, CD4+ T Cell, Macrophage, Neutrophil, and Dendritic Cell) in esophageal carcinoma (ESCA) using tumor immune estimation resource (TIMER).ResultsIn this study, 707 DEGs (including 385 upregulated genes and 322 downregulated genes) and 6 hub genes (cyclin B1 [CCNB1], cyclin dependent kinase 1 [CDK1], aurora kinase A [AURKA], ubiquitin conjugating enzyme E2C [UBE2C], cyclin A2 [CCNA2], and cell division cycle 20 [CDC20]) were identified. All of the 6 hub genes were highly expressed in ESCC tissues. Among of them, only CCNB1 and CDC20 were associated with stage of ESCC and all of them were not associated with survival time of patients.ConclusionDEGs and hub genes were confirmed in our study, providing a thorough, scientific and comprehensive research goals for the pathogenesis of ESCC.
| S-EPMC8270608 | biostudies-literature
Project description:Background:Triple-negative breast cancer (TNBC) is a subtype of breast cancer with poor clinical outcome and limited treatment options. Lacking molecular targets, chemotherapy is the main adjuvant treatment for TNBC patients. Materials and methods:To explore potential therapeutic targets for TNBC, we analyzed three microarray datasets (GSE38959, GSE45827, and GSE65194) derived from the Gene Expression Omnibus (GEO) database. The GEO2R tool was used to screen out differentially expressed genes (DEGs) between TNBC and normal tissue. Gene Ontology function and Kyoto Encyclopedia of Genes and Genomes pathway enrichment analysis were performed using the Database for Annotation, Visualization and Integrated Discovery to identify the pathways and functional annotation of DEGs. Protein-protein interaction of these DEGs was analyzed based on the Search Tool for the Retrieval of Interacting Genes database and visualized by Cytoscape software. In addition, we used the online Kaplan-Meier plotter survival analysis tool to evaluate the prognostic value of hub genes expression in breast cancer patients. Results:A total of 278 upregulated DEGs and 173 downregulated DEGs were identified. Among them, ten hub genes with a high degree of connectivity were picked out. Overexpression of these hub genes was associated with unfavorable prognosis of breast cancer, especially, CCNB1 overexpression was observed and indicated poor outcome of TNBC. Conclusion:Our study suggests that CCNB1 was overexpressed in TNBC compared with normal breast tissue, and overexpression of CCNB1 was an unfavorable prognostic factor of TNBC patients. Further study is needed to explore the value of CCNB1 in the treatment of TNBC.
| S-EPMC6054764 | biostudies-literature
Project description:Most cases of mRCC without an early finding are not candidates for curative therapies, which may be one of the reasons for the poor patient prognosis. Therefore, candidate markers to diagnose the disease and treatment with high efficiency are urgently demanded. Three datasets of mRNA microarray have been assessed to discover DEGs between tissues from metastatic RCC and RCC. 111 DEGs in total were identified according to the expression profile result of genes in the database of GEO. Enrichment analyses for GO and KEGG have been conducted to reveal the interacting activities in the DEGs. A network of PPI has been established to reveal the interconnection among the DEGs, and we selected 10 hub genes. Subsequently, the disease-free survival rate and total survival rate analysis for the hub genes have been carried out with the method of Kaplan-Meier curve. RCC patients with CDH11, COL3A1, COL5A1, COL5A2, COL6A3 and COL11A1 alteration showed worse overall survival. Nonetheless, RCC patients with CDH11, COL3A1, COL5A1, COL5A2 and COL11A1 alteration showed worse disease-free survival. In the Jones Renal dataset, mRNA levels of 10 hub genes were associated with metastasis, and the gene expression level in patients with mRCC was higher than that in patients without metastasis. COL5A1, COL6A3 and COL11A1 expression levels were remarkably related to RCC patient survival rate using UALCAN. COL5A1, COL6A3 and COL11A1 were positively correlated with each other in RCC. These genes have been recognized as genes with clinical relevance, revealing that they might have important roles in carcinogenesis or development of mRCC.
| S-EPMC6895538 | biostudies-literature
Project description:Sepsis is defined as the systemic inflammatory response to infection and is one of the leading causes of mortality in critically ill patients. The goal of the present study is to elucidate the molecular mechanism of sepsis. Transcription profile data (GSE12624) were downloaded that had a total of 70 samples (36 sepsis samples and 34 non-sepsis samples) from the Gene Expression Omnibus database. Protein-protein interaction network analysis was conducted in order to comprehensively understand the interactions of genes in all samples. Hierarchical clustering and analysis of covariance (ANCOVA) global test were performed to identify the differentially expressed clusters in the networks, followed by function and pathway enrichment analyses. Finally, a support vector machine (SVM) was performed to classify the clusters, and 10-fold cross-validation method was performed to evaluate the classification results. A total of 7,672 genes were obtained after preprocessing of the mRNA expression profile data. The PPI network of genes under sepsis and non-sepsis status collected 1,996/2,147 genes and 2,645/2,783 interactions. Moreover, following the ANCOVA global test (P<0.05), 24 differentially expressed clusters with 12 clusters in septic and 12 clusters in non-septic samples were identified. Finally, 207 biomarker genes, including CDC42, CSF3R, GCA, HMGB2, RHOG, SERPINB1, TYROBP SERPINA1, FCER1 G and S100P in the top six clusters, were collected using the SVM method. The SERPINA1, FCER1 G and S100P genes are thought to be potential biomarkers. Furthermore, Gene oncology terms, including the intracellular signaling cascade, regulation of programmed cell death, regulation of cell death, regulation of apoptosis and leukocyte activation may participate in sepsis.
| S-EPMC5443301 | biostudies-literature
Project description:Testicular seminoma is one of the most common tumours in the field of urology, and its aetiology is still unclear. The aim of the present study was to identify the factors responsible for the development of testicular cancer and to investigate whether mutations in these genes were primarily congenital or acquired. To identify the key genes and miRNAs linked to testicular seminoma, as well as their potential molecular mechanisms, the GSE15220, GSE1818 and GSE59520 microarray datasets were analysed. A total of 5,195 and 1,163 differentially expressed genes (DEGs) were identified after analysing the GSE15220 and GSE1818 datasets, respectively. Among them, 287 genes were common between the two datasets. Of these, 110 were upregulated and 177 were downregulated. Five differentially expressed microRNAs (miRs; DEMs) that were downregulated in seminoma were identified after analysing the GSE59520 dataset. Following protein‑protein interaction network and Gene Ontology analysis, the five nodes with the highest degrees were screened as hub genes. Among them, the high expression of hub genes, such as protein tyrosine phosphatase receptor type C (PTPRC), was associated with worse overall survival. We also predicted the potential target genes of the DEMs. DNA topoisomerase II α (TOP2A), marker of proliferation Ki‑67 (MKI67), PTPRC and ubiquitin conjugating enzyme E2 C were associated with the PI3K/AKT and Wnt/β‑catenin signalling pathways. In addition, hsa‑miR‑650 and hsa‑miR‑665 were associated with the PI3K/AKT and Wnt/β‑catenin signalling pathways. Additionally, TOP2A and MKI67 were strongly associated with the target genes hsa‑miR‑650 and hsa‑miR‑665, respectively. We proposed that the hub genes reported in the present study may have a certain impact on cellular proliferation and migration in testicular seminoma. The roles of these hub genes in seminoma may provide novel insight to improve the diagnosis and treatment of patients with seminoma.
| S-EPMC6797975 | biostudies-literature
Project description:Gastric cancer (GC) is a common malignant neoplasm of gastrointestinal tract. We chose gene expression profile of GSE54129 from GEO database aiming to find key genes during the occurrence and development of GC. 132 samples, including 111 cancer and 21 normal gastric mucosa epitheliums, were included in this analysis. Differentially expressed genes (DEGs) between GC patients and health people were picked out using GEO2R tool, then we performed gene ontology (GO) analysis and Kyoto Encyclopedia of Gene and Genome (KEGG) pathway analysis using The Database for Annotation, Visualization and Integrated Discovery (DAVID). Moreover, Cytoscape with Search Tool for the Retrieval of Interacting Genes (STRING) and Molecular Complex Detection (MCODE) plug-in was utilized to visualize protein-protein interaction (PPI) of these DEGs. There were 971 DEGs, including 468 up-regulated genes enriched in focal adhesion, ECM-receptor interaction and PI3K-Akt signaling pathway, while 503 down-regulated genes enriched in metabolism of xenbiotics and drug by cytochrome P450, chemical carcinogenesis, retinol metabolism and gastric acid secretion. Three important modules were detected from PPI network using MCODE software. Besides, Fifteen hub genes with high degree of connectivity were selected, including BGN, MMP2, COL1A1, and FN1. Moreover, the Kaplan-Meier analysis for overall survival and correlation analysis were applied among those genes. In conclusion, this bioinformatics analysis demonstrated that DEGs and hub genes, such as BGN, might promote the development of gastric cancer, especially in tumor metastasis. In addition, it could be used as a new biomarker for diagnosis and to guide the combination medicine of gastric cancer.
| S-EPMC5642553 | biostudies-literature
Project description:BACKGROUND:Lymph node metastasis is a significant problem in breast cancer, and its underlying molecular mechanism is still unclear. The purpose of this study is to research the molecular mechanism and to explore the key RNAs and pathways that mediate lymph node metastasis in breast cancer. METHODS:GSE100453 and GSE38167 were downloaded from the Gene Expression Omnibus (GEO) database and 569 breast cancer statistics were also downloaded from the TCGA database. Differentially expressed miRNAs were calculated by using R software and GEO2R. Gene ontology and Enriched pathway analysis of target mRNAs were analyzed by using the Database for Database of Annotation Visualization and Integrated Discovery (DAVID) and R software. The protein-protein interaction (PPI) network was performed according to Metascape, String, and Cytoscape software. RESULTS:In total, 6 differentially expressed miRNAs were selected, and 499 mRNAs were identified after filtering. The research of the Kyoto Encyclopedia of Genes and Genomes (KEGG) demonstrated that mRNAs enriched in certain tumor pathways. Also, certain hub mRNAs were highlighted after constructed and analyzed the PPI network. A total of 3 out of 6 miRNAs had a significant relationship with the overall survival (P?<?.05) and showed a good ability of risk prediction model of over survival. CONCLUSIONS:By utilizing bioinformatics analyses, differently expressed miRNAs were identified and constructed a complete gene network. Several potential mechanisms and therapeutic and prognostic targets of lymph node metastasis were also demonstrated in breast cancer.
| S-EPMC7523764 | biostudies-literature
Project description:Colorectal carcinoma (CRC) is one of the most prevalent malignant tumors worldwide. Meanwhile, the majority of CRC related deaths results from liver metastasis. Gene expression profile of CRC patients with liver Metastasis was identified using 4 datasets. The data was analyzed using GEO2R tool. GO and KEGG pathway analysis were performed. PPI network of the DEGs between 1 and 2 gene sets was also constructed. The set 1 is named between primary CRC tissues and metastatic CRC tissues. The set 2 is named between primary CRC tissues and normal tissues. Finally, the prognostic value of hub genes was also analyzed. 35 DEGs (set 1) and 142 DEGs (set 2) were identified between CRC liver metastatic cancer patients. The PPI network was constructed using the top 10 set 1 hub genes which included AHSG, SERPINC1, FGA, F2, CP, ITIH2, APOA2, HPX, PLG, HRG and set 2 hub genes which included TIMP1, CXCL1, COL1A2, MMP1, AURKA, UBE2C, CXCL12, TOP2A, ALDH1A1 and PRKACB. Therefore, ITIH2 might represent the potential core gene for colon cancer liver metastasis. COL1A2 behaves as a key gene in colorectal carcinoma.
| S-EPMC8671463 | biostudies-literature