Integrated bioinformatics analysis reveals novel key biomarkers and potential candidate small molecule drugs in gastric cancer

Wu, Qiong; Zhang, Bo; Wang, Ziheng; Hu, Xinyi; Sun, Yidan; Xu, Ran; Chen, Xinming; Wang, Qiuhong; Ju, Fei; Ren, Shengxiang; Zhang, Chenlin; Qi, Fuwei; Ma, Qianqian; Xue, Qun

doi:10.1016/j.prp.2019.02.012

Cited by 19 publications

(17 citation statements)

References 34 publications

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“…2a, 2b). ECM receptor interaction was enriched significantly in CDH6 low expression groups, which was consistent with several previous bioinformatic studies of patients with GC [25][26][27]. This result might be related to the decrease of intercellular adhesion and instability of cellular interactions.…”

Section: Discussionsupporting

confidence: 90%

Overexpression of a novel oncogene Cadherin 6 correlates with tumor progression and poor prognosis in gastric cancer

Zhao

et al. 2021

Preprint

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BackgroundGastric cancer (GC) is one of the most common and fatal cancers worldwide and effective biomarkers aids in GC management and prognosis. Hence, we explored the role and function of cadherin 6 (CDH6) in diagnosis and prognosis of gastric cancer. MethodsThe expression level of CDH6 in GC tissue and normal gastric tissue were analyzed using multiple public databases. Gene set enrichment analysis (GSEA) was performed using The Cancer Genome Atlas dataset (TCGA). The diagnostic efficiency of CDH6 expression in GC patients was determined through receiver operating characteristic (ROC) curve analysis. The associations between clinical variables and expression of CDH6 were evaluated statistically and the prognostic factors for overall survival were analyzed by univariate and multivariate Cox regression. Forty-four GC tissues, corresponding adjacent normal tissues (n=20), and detailed clinical information were collected from Tianjin Medical University General Hospital, CDH6 expression level was detected for further validation. ResultsCDH6 was upregulated in GC samples compared with normal gastric tissue, and GSEA identified the citrate cycle tricarboxylic (TCA) cycle, extracellular matrix (ECM) receptor interaction, glyoxylate and dicarboxylate metabolism oxidative phosphorylation, and pentose phosphate pathway as differentially enriched in GCs. According to the area under the ROC curve (AUC) (AUC=0.829 in TCGA and 0.966 in GSE54129), CDH6 had high diagnostic efficiency. Patients with high expression of CDH6 was associated with higher T classification and worse prognoses than those with low CDH6 expression in GC. Univariate and multivariate Cox regression analysis showed that CDH6 was an independent risk factor for overall survival (univariate: HR = 1.305, P = 0.002, multivariate: HR = 1.481, P < 0.001). ConclusionCDH6 was upregulated in GC and high CDH6 expression indicated higher T classification and worse prognoses. CDH6 could be a potentially independent molecular biomarker for diagnosis and prognosis of GC.

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Section: Discussionsupporting

confidence: 90%

Overexpression of a novel oncogene Cadherin 6 correlates with tumor progression and poor prognosis in gastric cancer

Zhao

et al. 2021

Preprint

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“…Previous research has highlighted a number of genes associated with GC. [24][25][26] The specific molecular mechanisms governing this disease, however, are complex, and as such further data mining efforts are needed to identify relevant candidate biomarkers for GC diagnosis.…”

Section: Discussionmentioning

confidence: 99%

<p>ICAM1 Regulates the Development of Gastric Cancer and May Be a Potential Biomarker for the Early Diagnosis and Prognosis of Gastric Cancer</p>

Chen

et al. 2020

CMAR

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Background: Gastric cancer (GC) is among the most common forms of cancer affecting the digestive system. This study sought to identify hub genes regulating early GC (EGC) in order to explore their potential for early diagnosis and prognosis of patients. Methods: We utilized a publically available dataset from the Gene Expression Omnibus database (GSE55696). Differences between EGC and LGIN with respect to gene expression were compared using the limma software. Identified differentially expressed genes (DEGs) were subjected to gene ontology (GO) and pathway enrichment analyses with the DAVID application, and the STRING website and Cytoscape software were used to construct a protein-protein interaction (PPI) network incorporating these DEGs. This network was in turn used to identify hub genes among selected DEGs, which were analyzed with the Kaplan-Meier Plotter database. In addition, Western blotting, qRT-PCR, immunohistochemistry, and UALCAN were all employed to validate the relationship between the expression of these genes and GC patient prognosis. Results: A total of 482 DEGs were identified, with GO analyses indicating an increase in the expression of genes linked with the development of cancer. Pathway analyses also indicated that these genes play a role in certain cancer-related pathways. The PPI network highlighted four potential hub genes, of which only ICAM1 was linked to a poor GC patient prognosis. This link between ICAM1 and GC patient outcomes was confirmed via UALCAN, Western blotting, immunohistochemistry, and qRT-PCR. Conclusion: ICAM1 may therefore modulate tumor progression in GC, thus potentially representing a valuable prognostic and diagnostic biomarker of EGC.

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“…Recently, gastric gene expression profiles have been investigated in many studies, and thousands of differentially expressed genes (DEGs) have been screened, which might be related to the GC progression [8][9][10][11][12]. Due to specimens that were collected from different backgrounds and analyzed using different technological detection platforms, it is discrepant in the identification of significantly expressed mRNAs among each independent experiment.…”

Section: Ivyspringmentioning

confidence: 99%

Integrated bioinformatics analysis for differentially expressed genes and signaling pathways identification in gastric cancer

Yang¹,

Gong²

2021

Int. J. Med. Sci.

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Background: Gastric cancer (GC) has a high mortality rate in cancer-related deaths worldwide. Currently, the pathogenesis of gastric cancer progression remains unclear. Here, we identified several vital candidate genes related to gastric cancer development and revealed the potential pathogenic mechanisms using integrated bioinformatics analysis. Methods: Two microarray datasets from Gene Expression Omnibus (GEO) database integrated. Limma package was used to analyze differentially expressed genes (DEGs) between GC and matched normal specimens. DAVID was utilized to conduct Gene ontology (GO) and KEGG enrichment analysis. The relative expression of OLFM4, IGF2BP3, CLDN1 and MMP1were analyzed based on TCGA database provided by UALCAN. Western blot and quantitative real time PCR assay were performed to determine the protein and mRNA levels of OLFM4, IGF2BP3, CLDN1 and MMP1 in GC tissues and cell lines, respectively. Results: We downloaded the expression profiles of GSE103236 and GSE118897 from the Gene Expression Omnibus (GEO) database. Two integrated microarray datasets were used to obtain differentially expressed genes (DEGs), and bioinformatics methods were used for in-depth analysis. After gene ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichments analysis, we identified 61 DEGs in common, of which the expression of 34 genes were elevated and 27 genes were decreased. GO analysis displayed that the biological functions of DEGs mainly focused on negative regulation of growth, fatty acid binding, cellular response to zinc ion and calcium-independent cell-cell adhesion. KEGG pathway analysis demonstrated that these DEGs mainly related to the Wnt and tumor signaling pathway. Interestingly, we found 4 genes were most significantly upregulated in the DEGs, which were OLFM4, IGF2BP3, CLDN1 and MMP1. Then, we confirmed the upregulation of these genes in STAD based on sample types. In the final, western blot and qRT-PCR assay were performed to determine the protein and mRNA levels of OLFM4, IGF2BP3, CLDN1 and MMP1 in GC tissues and cell lines. Conclusion: In our study, using integrated bioinformatics to screen DEGs in gastric cancer could benefit us for understanding the pathogenic mechanism underlying gastric cancer progression. Meanwhile, we also identified four significantly upregulated genes in DEGs from both two datasets, which might be used as the biomarkers for early diagnosis and prevention of gastric cancer.

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Integrated bioinformatics analysis reveals novel key biomarkers and potential candidate small molecule drugs in gastric cancer

Cited by 19 publications

References 34 publications

Overexpression of a novel oncogene Cadherin 6 correlates with tumor progression and poor prognosis in gastric cancer

Overexpression of a novel oncogene Cadherin 6 correlates with tumor progression and poor prognosis in gastric cancer

<p>ICAM1 Regulates the Development of Gastric Cancer and May Be a Potential Biomarker for the Early Diagnosis and Prognosis of Gastric Cancer</p>

Integrated bioinformatics analysis for differentially expressed genes and signaling pathways identification in gastric cancer

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