scholarly journals Combining multi-dimensional data to identify key genes and pathways in gastric cancer

PeerJ ◽  
2017 ◽  
Vol 5 ◽  
pp. e3385 ◽  
Author(s):  
Wu Ren ◽  
Wei Li ◽  
Daguang Wang ◽  
Shuofeng Hu ◽  
Jian Suo ◽  
...  
Keyword(s):  
Gastric Cancer ◽  
Candidate Genes ◽  
Copy Number ◽  
Interaction Networks ◽  
Differentially Expressed ◽  
Cancer Data ◽  
Normal Tissues ◽  
P53 Signaling ◽  
Key Genes ◽  
Pathway Analyses

Gastric cancer is an aggressive cancer that is often diagnosed late. Early detection and treatment require a better understanding of the molecular pathology of the disease. The present study combined data on gene expression and regulatory levels (microRNA, methylation, copy number) with the aim of identifying key genes and pathways for gastric cancer. Data used in this study was retrieved from The Cancer Genomic Atlas. Differential analyses between gastric cancer and normal tissues were carried out using Limma. Copy number alterations were identified for tumor samples. Bimodal filtering of differentially expressed genes (DEGs) based on regulatory changes was performed to identify candidate genes. Protein–protein interaction networks for candidate genes were generated by Cytoscape software. Gene ontology and pathway analyses were performed, and disease-associated network was constructed using the Agilent literature search plugin on Cytoscape. In total, we identified 3602 DEGs, 251 differentially expressed microRNAs, 604 differential methylation-sites, and 52 copy number altered regions. Three groups of candidate genes controlled by different regulatory mechanisms were screened out. Interaction networks for candidate genes were constructed consisting of 415, 228, and 233 genes, respectively, all of which were enriched in cell cycle, P53 signaling, DNA replication, viral carcinogenesis, HTLV-1 infection, and progesterone mediated oocyte maturation pathways. Nine hub genes (SRC, KAT2B, NR3C1, CDK6, MCM2, PRKDC, BLM, CCNE1, PARK2) were identified that were presumed to be key regulators of the networks; seven of these were shown to be implicated in gastric cancer through disease-associated network construction. The genes and pathways identified in our study may play pivotal roles in gastric carcinogenesis and have clinical significance.

scholarly journals Progressive and Prognostic Performance of an Extracellular Matrix-Receptor Interaction Signature in Gastric Cancer

2020 ◽  
Vol 2020 ◽  
pp. 1-23
Author(s):  
Xiangchou Yang ◽  
Liping Chen ◽  
Yuting Mao ◽  
Zijing Hu ◽  
Muqing He
Keyword(s):  
Gastric Cancer ◽  
Extracellular Matrix ◽  
Risk Score ◽  
The Cancer Genome Atlas ◽  
Differentially Expressed ◽  
Receptor Interaction ◽  
Clinicopathologic Features ◽  
Normal Tissues ◽  
Prognostic Application ◽  
Score Model

The role of an extracellular matrix- (ECM-) receptor interaction signature has not been fully clarified in gastric cancer. This study performed comprehensive analyses on the differentially expressed ECM-related genes, clinicopathologic features, and prognostic application in gastric cancer. The differentially expressed genes between tumorous and matched normal tissues in The Cancer Genome Atlas (TCGA) and validation cohorts were identified by a paired t -test. Consensus clusters were built to find the correlation between clinicopathologic features and subclusters. Then, the least absolute shrinkage and selection operator (lasso) method was used to construct a risk score model. Correlation analyses were made to reveal the relation between risk score-stratified subgroups and clinicopathologic features or significant signatures. In TCGA (26 pairs) and validation cohort (134 pairs), 25 ECM-related genes were significantly highly expressed and 11 genes were downexpressed in gastric cancer. ECM-based subclusters were slightly related to clinicopathologic features. We constructed a risk score model = 0.081 ∗ log 2   CD 36 + 0.043 ∗ log 2   COL 5 A 2 + 0.001 ∗ log 2   ITGB 5 + 0.039 ∗ log 2   SDC 2 + 0.135 ∗ log 2   SV 2 B + 0.012 ∗ log 2   THBS 1 + 0.068 ∗ log 2   VTN + 0.023 ∗ log 2   VWF . The risk score model could well predict the outcome of patients with gastric cancer in both training ( n = 351 , HR: 1.807, 95% CI: 1.292-2.528, P = 0.00046 ) and validation ( n = 300 , HR: 1.866, 95% CI: 1.347-2.584, P = 0.00014 ) cohorts. Besides, risk score-based subgroups were associated with angiogenesis, cell adhesion molecules, complement and coagulation cascades, TGF-beta signaling, and mismatch repair-relevant signatures ( P < 0.0001 ). By univariate (1.845, 95% CI: 1.382-2.462, P < 0.001 ) and multivariate (1.756, 95% CI: 1.284-2.402, P < 0.001 ) analyses, we regarded the risk score as an independent risk factor in gastric cancer. Our findings revealed that ECM compositions became accomplices in the tumorigenesis, progression, and poor survival of gastric cancer.

scholarly journals Bioinformatics analysis for the identification of differentially expressed genes and related signaling pathways in H. pylori-CagA transfected gastric cancer cells

PeerJ ◽  
2021 ◽  
Vol 9 ◽  
pp. e11203
Author(s):  
Dingyu Chen ◽  
Chao Li ◽  
Yan Zhao ◽  
Jianjiang Zhou ◽  
Qinrong Wang ◽  
...  
Keyword(s):  
Gastric Cancer ◽  
Helicobacter Pylori ◽  
Differentially Expressed Genes ◽  
Pathway Analysis ◽  
Kegg Pathway ◽  
Differentially Expressed ◽  
Ppi Network ◽  
Ags Cells ◽  
Kegg Pathway Analysis ◽  
Key Genes

Aim Helicobacter pylori cytotoxin-associated protein A (CagA) is an important virulence factor known to induce gastric cancer development. However, the cause and the underlying molecular events of CagA induction remain unclear. Here, we applied integrated bioinformatics to identify the key genes involved in the process of CagA-induced gastric epithelial cell inflammation and can ceration to comprehend the potential molecular mechanisms involved. Materials and Methods AGS cells were transected with pcDNA3.1 and pcDNA3.1::CagA for 24 h. The transfected cells were subjected to transcriptome sequencing to obtain the expressed genes. Differentially expressed genes (DEG) with adjusted P value < 0.05, — logFC —> 2 were screened, and the R package was applied for gene ontology (GO) enrichment and the Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis. The differential gene protein–protein interaction (PPI) network was constructed using the STRING Cytoscape application, which conducted visual analysis to create the key function networks and identify the key genes. Next, the Kaplan–Meier plotter survival analysis tool was employed to analyze the survival of the key genes derived from the PPI network. Further analysis of the key gene expressions in gastric cancer and normal tissues were performed based on The Cancer Genome Atlas (TCGA) database and RT-qPCR verification. Results After transfection of AGS cells, the cell morphology changes in a hummingbird shape and causes the level of CagA phosphorylation to increase. Transcriptomics identified 6882 DEG, of which 4052 were upregulated and 2830 were downregulated, among which q-value < 0.05, FC > 2, and FC under the condition of ≤2. Accordingly, 1062 DEG were screened, of which 594 were upregulated and 468 were downregulated. The DEG participated in a total of 151 biological processes, 56 cell components, and 40 molecular functions. The KEGG pathway analysis revealed that the DEG were involved in 21 pathways. The PPI network analysis revealed three highly interconnected clusters. In addition, 30 DEG with the highest degree were analyzed in the TCGA database. As a result, 12 DEG were found to be highly expressed in gastric cancer, while seven DEG were related to the poor prognosis of gastric cancer. RT-qPCR verification results showed that Helicobacter pylori CagA caused up-regulation of BPTF, caspase3, CDH1, CTNNB1, and POLR2A expression. Conclusion The current comprehensive analysis provides new insights for exploring the effect of CagA in human gastric cancer, which could help us understand the molecular mechanism underlying the occurrence and development of gastric cancer caused by Helicobacter pylori.

scholarly journals ADAR expression and copy number variation in patients with advanced gastric cancer

2020 ◽  
Author(s):  
Javad Behroozi ◽  
Shirin Shahbazi ◽  
Mohammad Reza Bakhtiarizadeh ◽  
Habibollah Mahmoodzadeh
Keyword(s):  
Gastric Cancer ◽  
Copy Number Variation ◽  
Copy Number ◽  
Stage Iv ◽  
World Health ◽  
Stage Iii ◽  
Normal Tissues ◽  
Keywords Gastric Cancer ◽  
Number Variation ◽  
Adar Gene

Abstract Background Gastric cancer (GC) is a world health problem and it is the third leading cause of cancer deaths worldwide. The current practice for prognosis assessment in GC is based on radiological and pathological criteria and they may not result in an accurate prognosis. The aim of this study is to evaluate expression and copy number variation of the ADAR gene in advanced GC and clarify its correlation with survival and histopathological characteristics. Methods Forty two patients with stage III and IV GC were included in this study. ADAR gene expression and copy number variation were measured by real-time PCR and Quantitative multiplex fluorescent-PCR, respectively. Survival analysis performed based on the Kaplan–Meier method and Mantel–Cox test. Results ADAR mRNA was significantly overexpressed in the tumor tissues when compared to the adjacent normal tissues (p <0.01). Also, ADAR expression level in stage IV was higher than stage III. 40% of patients showed amplification in ADAR gene and there was a positive correlation between ADAR copy number and expression. Increased ADAR expression was clearly correlated with poorer survival outcomes and Mantel–Cox test showed statistically significant differences between low and high expression groups (p <0.0001). ADAR overexpression and amplification were significantly associated with metastasis, size and stage of tumor. Conclusions Together, our data indicate that amplification leads to over expression of ADAR and it could be used as a prognostic biomarker for disease progression, especially for the metastatic process in GC. Keywords Gastric cancer, ADAR gene, Overexpression, Amplification, Prognosis

scholarly journals Identification of Potential Biomarkers in Glioblastoma through Bioinformatic Analysis and Evaluating Their Prognostic Value

2019 ◽  
Vol 2019 ◽  
pp. 1-13 ◽  
Author(s):  
Yangmei Zhou ◽  
Li Yang ◽  
Xiaoxi Zhang ◽  
Rui Chen ◽  
Xiuqiong Chen ◽  
...  
Keyword(s):  
Bioinformatic Analysis ◽  
Differentially Expressed ◽  
Protein Protein Interaction ◽  
Normal Tissues ◽  
P53 Signaling ◽  
Promising Target ◽  
The Central Nervous System ◽  
Underlying Mechanisms ◽  
P53 Signaling Pathway ◽  
Potential Biomarkers

Glioblastoma is a common malignant tumor in the central nervous system with an extremely poor outcome; understanding the mechanisms of glioblastoma at the molecular level is essential for clinical treatment. In the present study, we used bioinformatics analysis to identify potential biomarkers associated with prognosis in glioblastoma and elucidate the underlying mechanisms. The result revealed that 552 common genes were differentially expressed between glioblastoma and normal tissues based on TCGA, GSE4290, and GSE 50161 datasets. Gene Oncology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment and protein-protein interaction (PPI) network were carried out to gain insight into the actions of differentially expressed genes (DEGs). As a result, 20 genes (CALB1, CDC20, CDCA8, CDK1, CEP55, DLGAP5, KIF20A, KIF4A, NDC80, PBK, RRM2, SYN1, SYP, SYT1, TPX2, TTK, VEGFA, BDNF, GNG3, and TOP2A) were found as hub genes via CytoHubba in Cytoscape and functioned mainly by participating in cell cycle and p53 signaling pathway; among them, RRM2 and CEP55 were considered to have relationship with the prognosis of glioblastoma, especially RRM2. High expression of RRM2 was consistent with shorter overall survival time. In conclusion, our study displayed the bioinformatic analysis methods in screening potential oncogenes in glioblastoma and underlying mechanisms. What is more is that we successfully identified RRM2 as a novel biomarker linked with prognosis, which might be expected to be a promising target for the therapy of glioblastoma.

scholarly journals Identification and Verification of the Main Differentially Expressed Proteins in Gastric Cancer via iTRAQ Combined with Liquid Chromatography-Mass Spectrometry

2019 ◽  
Vol 2019 ◽  
pp. 1-14
Author(s):  
Zhihua Gao ◽  
Jiabao Wang ◽  
Yuru Bai ◽  
Jun Bao ◽  
Erqing Dai
Keyword(s):  
Gastric Cancer ◽  
Western Blotting ◽  
Normal Mucosa ◽  
Gastric Cancer Tissue ◽  
Differentially Expressed ◽  
Cancer Tissue ◽  
Exploratory Research ◽  
Differentially Expressed Proteins ◽  
Normal Tissues ◽  
Itraq Analysis

Background. To find the potential intersections between the differentially expressed proteins and abnormally expressed genes in gastric cancer (GC) patients. Methods. Gastric cancer tissue and adjacent normal mucosa tissue were used for iTRAQ analysis. Gene ontology (GO), Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis, and protein-protein interaction (PPI) analysis were used to evaluate gene function. Western blotting and immunohistochemistry (IHC) were applied to verify the protein expression. Results. A total of 2770 proteins were identified, of which 147 proteins were upregulated and 159 proteins were downregulated. GO analysis revealed that the differentially expressed genes were mainly enriched for the terms “cellular process,” “binding,” and “cell.” The results of the KEGG analysis showed that the most abundantly enriched proteins were involved in the “focal adhesion” pathway. The results of the PPI analysis showed that VCAM1 was located at the center of the PPI network. Western blotting and IHC analysis demonstrated that VCAM1, FLNA, VASP, CAV1, PICK1, and COL4A2 were differentially expressed in GC and adjacent normal tissues, which was consistent with the results of the iTRAQ analysis. Conclusion. In conclusion, 6 highly differentially expressed proteins were identified as novel differentially expressed proteins in human GC. This exploratory research may provide useful information for the treatment of gastric cancer in the clinic.

scholarly journals MiRNA biomarkers and potential critical genes associated with the prognosis of gastric cancer

2021 ◽  
Author(s):  
Zhou Chen ◽  
Hao Xu ◽  
Zhongtian Bai ◽  
Shi Dong ◽  
Jian Zhang ◽  
...  
Keyword(s):  
Gastric Cancer ◽  
Target Genes ◽  
Cox Model ◽  
Differential Expression Analysis ◽  
Differentially Expressed ◽  
Rna Seq ◽  
Diagnosis And Prognosis ◽  
Kaplan Meier ◽  
Differentially Expressed Mirnas ◽  
Pathway Analyses

Abstract Background Dysregulated expression of miRNAs in gastric cancer (GC) is associated with tumor progression. MiRNA markers are important for the prognosis and therapeutic targeting of GC patients. Methods To detect differentially expressed miRNAs in GC from the TCGA database and predict their target genes. We downloaded RNA sequencing (RNA-seq), miRNA-seq and clinical data of GC from TCGA. Differential expression analysis of RNA-seq and miRNA-seq data was performed by R 3.6.1. MiRNAs associated with prognosis were evaluated with the Cox model, and differentially expressed miRNAs were assessed by Kaplan–Meier curve analysis. Risk factors were identified in the Cox model. Target genes of differentially expressed miRNAs were searched in three databases. GO enrichment and KEGG pathway analyses were used to evaluate the biological functions of these target genes.Results Five miRNAs (hsa-miR-135b-3p, hsa-miR-143-5p, hsa-miR-196b-3p, hsa-miR-942-3p, hsa-miR-9-3p) were related to survival. Eight target genes (AKAP12, AR, DZIP1, PCDHA11, PCDHA12, PI15, SH3BGRL and TMEM108) were closely correlated with patient overall survival (OS). Conclusion Differentially expressed miRNAs and their target genes have an important influence on the diagnosis and prognosis of GC and may be used as tumor biomarkers in further studies and as potential therapeutic targets.

scholarly journals Dissecting the Single-Cell Transcriptome Network in Superficial/Deep Tumor Tissues of Diffuse Gastric Cancer

2021 ◽  
Author(s):  
Yili Ren ◽  
Beibei Zhang ◽  
Chenkai Xu ◽  
Lei Zhang
Keyword(s):  
Gastric Cancer ◽  
Survival Analysis ◽  
Single Cell ◽  
Differentially Expressed Genes ◽  
Expression Profiles ◽  
Differentially Expressed ◽  
Cell Subpopulation ◽  
Diffuse Gastric Cancer ◽  
Normal Tissues ◽  
Tumor Tissues

Abstract Background and purpose: Gastric cancer is a type of highly heterogeneous malignant tumor and the prognosis of gastric cancer is hard to be improved due to limited knowledge on the molecular mechanism of heterogeneity. Single-cell sequencing technology is recently widely used for the investigation of both inter-tumoral heterogeneity and intra-tumoral heterogeneity. The present study aims to explore the potential oncogene by analyzing the single-cell data in the GSE167297 dataset.Methods: The GSE167297 dataset was downloaded from the GEO database, followed by quality control to remove data with lower quality. The division on cell subtypes was determined by the characteristic marker expressed in each cell subpopulation. Wilcoxon rank-sum test was used to screen out differentially expressed genes. Survival analysis was performed to evaluate the prognostic value of G-protein subunit g 11 (GNG11) gene which was significantly overexpressed in deep tumor tissues of diffuse gastric cancer.Results: In both normal tissues and tumor tissues, subtypes of immune cells and stromal cells were identified, with a higher proportion of infiltrated macrophages observed in deep tumor tissues. EPCAM was found significantly highly expressed in a cell subpopulation from gastric tumor tissues. 515 differentially expressed genes (| log2FC | > 2 and FDR < 1e-5) were screened out between normal tissues and tumor tissues. 86 differentially expressed genes (| log2FC | > 1 and FDR < 0.01) were screened out between superficial and deep tumor tissues, in which GNG11 was most highly expressed in deep tumor tissues (mean expression value: 0.1247, FC value: 52.2109). Disease-specific survival analysis on GNG11 results showed that the HR [95%CI] in the constructed univariate Cox proportional risk model was 4.419 [1.399-13.96] and the P-value in the log-rank test was 0.0056.Conclusion: Differentially expression profiles were provided both extratumorally and intratumorally, indicating a higher infiltration of macrophages in deep tumor tissues. Additionally,GNG11 was screened out to be a significant risk factor in STAD patients.

scholarly journals Identification of Potential Biomarkers and Therapy Targets involved in Gastric Cancer Using Bioinformatics Analysis

2021 ◽  
Author(s):  
YangYang Teng ◽  
Na Shan ◽  
GuangRong Lu ◽  
LeYi Ni ◽  
ZeJun Gao ◽  
...  
Keyword(s):  
Gastric Cancer ◽  
Bioinformatics Analysis ◽  
Risk Model ◽  
Malignant Tumors ◽  
Differentially Expressed ◽  
Hub Genes ◽  
Normal Tissues ◽  
Therapy Targets ◽  
Selection Operator ◽  
Cancer Tissues

Abstract Gastric cancer remains one of the five major malignant tumors in the world, posing a great threat to public life and health. As gene sequencing technology develops, it is urgent to find out specific molecular markers for cancer therapy. In this study, datasets of GSE13911, GSE30727, GSE63089 and GSE118916 were investigated by bioinformatics analysis, and differentially expressed genes (DEGs) between GC tissues and normal tissues were screened for potential cancer therapeutic targets. Furthermore, the GSE63089 dataset was analyzed by Weighted Gene Co-expression Network Analysis (WGCNA), and the highly related genes were clustered. Then, the hub genes were searched using co-expression network and Molecular Complex Detection (MCODE) plug-in from Cytoscape software. Finally, ASPM, COL11A1 and CDC20 were obtained by intersection of hub genes and DEGs. The expressions of ASPM, COL11A1 and CDC20 gene in gastric cancer tissues and normal tissues from TCGA database were detected. For these genes, the least absolute shrink and selection operator (LASSO) Cox expression analysis was used to establish the prognostic risk model. COL11A1 and CDC20 genes were identified as candidate prognostic risk markers for GC. Analysis using qRT-PCR has shown that COL11A1 and CDC20 were significant differentially expressed between gastric cancer tissues and normal gastric tissues (P < 0.01). In conclusion, our study identifies specific DEGs involved in ECM process and metabolism by cytochrome P450 process, and these DEGs may be potential targets for GC therapy. The model constructed by COL11A1 and CDC20 genes can predict the prognosis risk of GC patients. Taken together, these findings provide reference for further analyses of key alterations during GC progression.

scholarly journals Identification of genes universally differentially expressed in gastric cancer

2019 ◽  
Author(s):  
Haidan Yan ◽  
Yidan Shi ◽  
Jiahui Zhang ◽  
Haifeng Chen ◽  
Qingzhou Guan ◽  
...  
Keyword(s):  
Gastric Cancer ◽  
Mapk Signaling ◽  
Differentially Expressed ◽  
Cancer Tissue ◽  
Special Importance ◽  
Cancer Genes ◽  
Down Regulation ◽  
Individual Level ◽  
Normal Tissues ◽  
Direct Neighbor

Abstract Background Due to the remarkable heterogeneity of gastric cancer (GC), differentially expressed genes (DEGs) identified at the population-level by case-control comparison cannot afford dysregulation frequency of each DEG in GC. Methods Firstly, the individual-level DEGs were identified for 1,090 GC tissues without paired normal tissues by the RankComp method. Secondly, we directly compared the gene expression in a cancer tissue with its paired normal tissue to identify individual-level DEGs for 448 paired cancer-normal gastric tissues. Results We found 25 DEGs dysregulated not only in more than 90% of 1,090 GC tissues, but also in more than 90% of 448 GC tissues with paired normal tissues. The 25 genes were defined as universal DEGs for GC which were further validated in our additionally measured 24 paired cancer-normal gastric tissues. Among the universal DEGs, four up-regulation genes ( BGN , E2F3 , PLAU and SPP1 ) and one down-regulation gene ( UBL3 ) were found to be cancer genes documented in the COSMIC or F-Census database. By analyzing protein-protein interaction network, we found 12 universal up-regulation genes and their 284 direct neighbor genes were significantly enriched with cancer genes and key biological pathways related to cancer, such as MAPK signaling pathway, Cell cycle and Focal adhesion. The 13 universal down-regulation genes and 16 direct neighbor genes were also significantly enriched with cancer genes and gastric acid secretion related pathway. Conclusion These universal DEGs may be of special importance for GC diagnosis and treatment targets, and can help to study the molecular mechanism of GC.

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