Identification and Validation of Key Genes of Differential Correlations in Gastric Cancer.
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ABSTRACT:
SUBMITTER: Chen T
PROVIDER: S-EPMC8794754 | biostudies-literature |
REPOSITORIES: biostudies-literature
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Project description:Gastric cancer (GC) is one of the most common malignancies of the digestive system with few genetic markers for its early detection and prevention. In this study, differentially expressed genes (DEGs) were analyzed using GEO2R from GSE54129 and GSE13911 of the Gene Expression Omnibus (GEO). Then, gene enrichment analysis, protein-protein interaction (PPI) network construction, and topological analysis were performed on the DEGs by the Gene Ontology (GO), Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway, STRING, and Cytoscape. Finally, we performed survival analysis of key genes through the Kaplan-Meier plotter. A total of 1034 DEGs were identified in GC. GO and KEGG results showed that DEGs mainly enriched in plasma membrane, cell adhesion, and PI3K-Akt signaling pathway. Subsequently, the PPI network with 44 nodes and 333 edges was constructed, and 18 candidate genes in the network were focused on by centrality analysis and module analysis. Furthermore, data showed that high expressions of fibronectin 1(FN1), the tissue inhibitor of metalloproteinases 1 (TIMP1), secreted phosphoprotein 1 (SPP1), apolipoprotein E (APOE), and versican (VCAN) were related to poor overall survivals in GC patients. In summary, this study suggests that FN1, TIMP1, SPP1, APOE, and VCAN may act as the key genes in GC.
| S-EPMC7525308 | biostudies-literature
Project description:BackgroundGastric cancer (GC) is one of the most common cancers all over the world, causing high mortality. Gastric cancer screening is one of the effective strategies used to reduce mortality. We expect that good biomarkers can be discovered to diagnose and treat gastric cancer as early as possible.MethodsWe download four gene expression profiling datasets of gastric cancer (GSE118916, GSE54129, GSE103236, GSE112369), which were obtained from the Gene Expression Omnibus (GEO) database. The differentially expressed genes (DEGs) between gastric cancer and adjacent normal tissues were detected to explore biomarkers that may play an important role in gastric cancer. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) analyses of overlap genes were conducted by the Metascape online database; the protein-protein interaction (PPI) network was constructed by the STRING online database, and we screened the hub genes of the PPI network using the Cytoscape software. The survival curve analysis was conducted by km-plotter and the stage plots of hub genes were created by the GEPIA online database. PCR, WB, and immunohistochemistry were used to verify the expression of hub genes. A neural network model was established to quantify the predictors of gastric cancer.ResultsThe relative expression level of cadherin-3 (CDH3), lymphoid enhancer-binding factor 1 (LEF1), and matrix metallopeptidase 7 (MMP7) were significantly higher in gastric samples, compared with the normal groups (p<0.05). Receiver operator characteristic (ROC) curves were constructed to determine the effect of the three genes' expression on gastric cancer, and the AUC was used to determine the degree of confidence: CDH3 (AUC = 0.800, P<0.05, 95% CI =0.857-0.895), LEF1 (AUC=0.620, P<0.05, 95%CI=0.632-0.714), and MMP7 (AUC=0.914, P<0.05, 95%CI=0.714-0.947). The high-risk warning indicator of gastric cancer contained 8<CDH3<15 and 10<expression of LEF1<16.ConclusionsCDH3, LEF1, and MMP7 can be used as candidate biomarkers to construct a neural network model from hub genes, which may be helpful for the early diagnosis of gastric cancer.
| S-EPMC8416116 | biostudies-literature
Project description:BACKGROUND Globally, gastric cancer (GC) is the third most common source of cancer-associated mortality. The aim of this study was to identify key genes and circular RNAs (circRNAs) in GC diagnosis, prognosis, and therapy and to further explore the potential molecular mechanisms of GC. MATERIAL AND METHODS Differentially expressed genes (DEGs) and circRNAs (DE circRNAs) between GC tissues and adjacent non-tumor tissues were identified from 3 mRNA and 3 circRNA expression profiles. Functional analyses were performed, and protein-protein interaction (PPI) networks were constructed. The signi?cant modules and key genes in the PPI networks were identified. Kaplan-Meier analysis was performed to evaluate the prognostic value of these key genes. Potential miRNA-binding sites of the DE circRNAs and target genes of these miRNAs were predicted and used to construct DE circRNA-miRNA-mRNA networks. RESULTS A total of 196 upregulated and 311 downregulated genes were identified in GC. The results of functional analysis showed that these DEGs were significantly enriched in a variety of functions and pathways, including extracellular matrix-related pathways. Ten hub genes (COL1A1, COL3A1, COL1A2, COL5A2, FN1, THBS1, COL5A1, SPARC, COL18A1, and COL11A1) were identified via PPI network analysis. Kaplan-Meier analysis revealed that 7 of these were associated with a poor overall survival in GC patients. Furthermore, we identified 2 DE circRNAs, hsa_circ_0000332 and hsa_circ_0021087. To reveal the potential molecular mechanisms of circRNAs in GC, DE circRNA-microRNA-mRNA networks were constructed. CONCLUSIONS Key candidate genes and circRNAs were identified, and novel PPI and circRNA-microRNA-mRNA networks in GC were constructed. These may provide useful information for the exploration of potential biomarkers and targets for the diagnosis, prognosis, and therapy of GC.
| S-EPMC6463957 | biostudies-literature
Project description:Gastric cancer (GC) is one of the most common types of cancer worldwide. Patients must be identified at an early stage of tumor progression for treatment to be effective. The aim of the present study was to identify potential biomarkers with diagnostic value in patients with GC. To examine potential therapeutic targets for GC, four Gene Expression Omnibus (GEO) datasets were downloaded and screened for differentially expressed genes (DEGs). Gene Ontology and Kyoto Encyclopedia of Genes and Genomes (KEGG) analyses were subsequently performed to study the function and pathway enrichment of the identified DEGs. A protein-protein interaction (PPI) network was constructed. The CytoHubba plugin of Cytoscape was used to calculate the degree of connectivity of proteins in the PPI network, and the two genes with the highest degree of connectivity were selected for further analysis. Additionally, the two DEGs with the largest and smallest log Fold Change values were selected. These six key genes were further examined using Oncomine and the Kaplan-Meier plotter platform. A total of 99 upregulated and 172 downregulated genes common to all four GEO datasets were screened. The DEGs were primarily enriched in the Biological Process terms: 'extracellular matrix organization', 'collagen catabolic process' and 'cell adhesion'. These three KEGG pathways were significantly enriched in the categories: 'ECM-receptor interaction', 'protein digestion and absorption', and 'focal adhesion'. Based on Oncomine, expression of ATP4A and ATP4B were downregulated in GC, whereas expression of the other genes were all upregulated. The Kaplan-Meier plotter platform confirmed that upregulated expression of the identified key genes was significantly associated with worse overall survival of patients with GC. The results of the present study suggest that FN1, COL1A1, INHBA and CST1 may be potential biomarkers and therapeutic targets for GC. Additional studies are required to explore the potential value of ATP4A and ATP4B in the treatment of GC.
| S-EPMC7054703 | biostudies-literature
Project description:Gastric cancer (GC) is associated with high incidence and mortality rates worldwide. Differentially expressed gene (DEG) analysis and weighted gene coexpression network analysis (WGCNA) are important bioinformatic methods for screening core genes. In our study, DEG analysis and WGCNA were combined to screen the hub genes, and pathway enrichment analyses were performed on the DEGs. SBNO2 was identified as the hub gene based on the intersection between the DEGs and the purple module in WGCNA. The expression and prognostic value of SBNO2 were verified in UALCAN, GEPIA2, Human Cancer Metastasis Database, Kaplan-Meier plotter, and TIMER. We identified 1974 DEGs, and 28 modules were uncovered via WGCNA. The purple module was identified as the hub module in WGCNA. SBNO2 was identified as the hub gene, which was upregulated in tumour tissues. Moreover, patients with GC and higher SBNO2 expression had worse prognoses. In addition, SBNO2 was suggested to play an important role in immune cell infiltration. In summary, based on DEGs and key modules related to GC, we identified SBNO2 as a hub gene, thereby offering novel insights into the development and treatment of GC.
| S-EPMC7685902 | biostudies-literature
Project description:Gastric cancer (GC) is rampant around the world. Most of the GC cases are detected in advanced stages with poor prognosis. The identification of marker genes for early diagnosis is of great significance. Studying the tumor environment is helpful to acknowledge the process of tumorigenesis, development, and metastasis. Twenty-two kinds of immune cells were calculated by CIBERSORT from Gene Expression Omnibus (GEO) database. Subsequently, higher infiltration of macrophages M0 was discovered in GC compared with normal tissues. WGCNA was utilized to construct the network and then identify key modules and genes related to macrophages in TCGA. Finally, 18 hub genes were verified. In the PPI bar chart, the top 3 genes were chosen as hub genes involved in most pathways. On the TIMER and THPA websites, it is verified that the expression levels of CYBB, CD86, and C3AR1 genes in tumor tissues were higher than those in normal tissues. These genes may work as biomarkers or targets for accurate diagnosis and treatment of GC in the future. Our findings may be a new strategy for the treatment of GC.
| S-EPMC8688826 | biostudies-literature
Project description:The biological behavior and clinical outcome of gastric cancers are variable considerably according to anatomic location, such as proximal gastric cancer (PGC) and distal gastric cancer (DGC). Therefore, identification of the biomarkers used for recognizing the biological behavior and predicting clinical outcome has been a major goal in gastric cancer researches.
2013-03-27 | E-MTAB-1307 | biostudies-arrayexpress
Project description:BACKGROUND:Gastric cancer is one of the common cancers seen in south India. Unfortunately more than 90% are advanced by the time they report to a tertiary centre in the country. There is an urgent need to characterize these cancers and try to identify potential biomarkers and novel therapeutic targets. MATERIALS AND METHODS:We used 24 gastric cancers, 20 Paired normal (PN) and 5 apparently normal gastric tissues obtained from patients with non-gastric cancers (Apparently normal - AN) for the microarray study followed by validation of the significant genes (n = 63) by relative quantitation using Taqman Low Density Array Real Time PCR. We then used a custom made Quantibody protein array to validate the expression of 15 proteins in gastric tissues (4 AN, 9 PN and 9 gastric cancers). The same array format was used to study the plasma levels of these proteins in 58 patients with gastric cancers and 18 from patients with normal/non-malignant gastric conditions. RESULTS:Seventeen genes (ASPN, CCL15/MIP-1?, MMP3, SPON2, PRSS2, CCL3, TMEPAI/PMEPAI, SIX3, MFNG, SOSTDC1, SGNE1, SST, IGHA1, AKR1B10, FCGBP, ATP4B, NCAPH2) were shown to be differentially expressed between the tumours and the paired normal, for the first time. EpCAM (p = 0.0001), IL8 (p = 0.0003), CCL4/MIP-1? (p = 0.0026), CCL20/MIP-3? (p = 0.039) and TIMP1 (p = 0.0017) tissue protein levels were significantly different (Mann Whitney U test) between tumours versus AN & PN. In addition, median plasma levels of IL8, CXCL9/MIG, CCL3/MIP-1?, CCL20/MIP-3?, PDGFR-B and TIMP1 proteins were significantly different between the non-malignant group and the gastric cancer group. The post-surgical levels of EpCAM, IGFBP3, IL8, CXCL10/IP10, CXCL9/MIG, CCL3/MIP-1?, CCL20/MIP-3?, SPP1/OPN and PDGFR-B showed a uniform drop in all the samples studied. CONCLUSIONS:Our study has identified several genes differentially expressed in gastric cancers, some for the first time. Some of these have been confirmed at the protein level, as well. Some of these proteins will need to be evaluated further for their potential as diagnostic biomarkers in gastric cancers and some could be useful as follow-up markers in gastric cancer.
| S-EPMC3004887 | biostudies-literature
Project description:Exosomal miRNAs (EmiRs) can be used for prediction of gastric cancer (GC) development. Supposedly, both plasma and urinary microRNAs can also be potential biomarkers for screening, but the diagnostic values of EmiRs in blood and urine are not fully studied. We here collected both types of samples from GC patients and healthy individuals and conducted miRNA sequencing to identify key members of EmiRs in GC. The exosomes samples derived from blood and urine were collected from 3 healthy individuals and 7 GC patients. Differentially expressed miRNAs (DEmiRNAs) were acquired, ontology enrichment analysis and Protein-protein Interaction (PPI) enrichment analysis were performed. There were 8 DEmiRNAs in the serum and 3 DEmiRNAs in the urine. For GC patients, there were three up-regulated DEmiRNAs (hsa-miR-130b-3p, hsa-miR-151a-3p and hsa-miR-15b-3p) in the serum exosomes, and one up-regulated DEmiRNA (hsa-miR-1246) in the urinary exosomes. Using miRNA target prediction databases, we found 418 common targets of hsa-miR-15b-3p, 35 common targets of hsa-miR-151a-3p, 117 common targets of hsa-miR-130b-3p, and 357 common targets of hsa-miR-1246. Some commonly enriched ontology terms were found, including GO BP terms like cell surface receptor signaling pathway involved in cell-cell signaling, positive regulation of catabolic process, morphogenesis of an epithelium, and GO CC terms perinuclear region of cytoplasm. The PPI network show some key nodes, including TAOK1, CMTM6, SCN3A, WASF3, IGF1, CNOT7, GABRG1, PRKD1. Together, this study provided an integrative analysis of expression profile of key circulating exosomal microRNAs. Four key exosomal miRNAs (hsa-miR-130b-3p, hsa-miR-151a-3p and hsa-miR-15b-3p) and the interaction network or enrichments based on their targets (TAOK1, CMTM6, SCN3A, WASF3, IGF1, CNOT7, GABRG1, PRKD1) may provide a reference of the molecular mechanisms in the GC development.
| S-EPMC8323470 | biostudies-literature
Project description:The aim of this study was to construct and validate a DNA repair-related gene signature for evaluating the overall survival (OS) of patients with gastric cancer (GC). Differentially expressed DNA repair genes between GC and normal gastric tissue samples obtained from the TCGA database were identified. Univariate Cox analysis was used to screen survival-related genes and multivariate Cox analysis was applied to construct a DNA repair-related gene signature. An integrated bioinformatics approach was performed to evaluate its diagnostic and prognostic value. The prognostic model and the expression levels of signature genes were validated using an independent external validation cohort. Two genes (CHAF1A, RMI1) were identified to establish the prognostic signature and patients ware stratified into high- and low-risk groups. Patients in high-risk group presented significant shorter survival time than patients in the low-risk group in both cohorts, which were verified by the ROC curves. Multivariate analysis showed that the prognostic signature was an independent predictor for patients with GC after adjustment for other known clinical parameters. A nomogram incorporating the signature and known clinical factors yielded better performance and net benefits in calibration plot and decision curve analyses. Further, the logistic regression classifier based on the two genes presented an excellent diagnostic power in differentiating early HCC and normal tissues with AUCs higher than 0.9. Moreover, Gene Set Enrichment Analysis revealed that diverse cancer-related pathways significantly clustered in the high-risk and low-risk groups. Immune cell infiltration analysis revealed that CHAF1A and RMI1 were correlated with several types of immune cell subtypes. A prognostic signature using CHAF1A and RMI1 was developed that effectively predicted different OS rates among patients with GC. This risk model provides new clinical evidence for the diagnostic accuracy and survival prediction of GC.
| S-EPMC8010105 | biostudies-literature