<i>In silico</i> analyses for potential key genes associated with gastric cancer

Yan, Pengwei; He, Yingchun; Xie, Kexin; Kong, Shan; Zhao, Weidong

doi:10.7717/peerj.6092

Cited by 80 publications

(59 citation statements)

References 36 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…Furthermore, downregulation of FN1 inhibits proliferation, migration and invasion, and thus reduces progression of colorectal cancer (47). The results of the present study suggest that FN1 may be a potential biomarker and therapeutic target for diagnosis and treatment of GC, consistent with previous studies (13,48,49), and thus further confirming the significance of FN1 in GC.…”

Section: Discussionsupporting

confidence: 91%

Identification of potential key genes in gastric cancer using bioinformatics analysis

Wang

Zhao

et al. 2020

biom rep

View full text Add to dashboard Cite

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.

show abstract

Section: Discussionsupporting

confidence: 91%

Identification of potential key genes in gastric cancer using bioinformatics analysis

Wang

Zhao

et al. 2020

biom rep

View full text Add to dashboard Cite

show abstract

“…In accordance with previous studies (Wang et al, 2013;Yan et al, 2018;Zhao et al, 2019), the genes in gap junction, focal adhesion, and CAMs were significantly associated with gastric cancer prognosis. In addition, PI3K/Akt signaling pathway has been widely reported to regulate the tumorigenesis and progression (Tapia et al, 2014;Matsuoka and Yashiro, 2014) and act as a potential therapeutic target in gastric cancer (Ye et al, 2012;Singh et al, 2015).…”

Section: Discussionsupporting

confidence: 91%

Identification of the Prognosis-Related lncRNAs and Genes in Gastric Cancer

Zhang

Yang

et al. 2020

Front. Genet.

View full text Add to dashboard Cite

Gastric cancer is a common malignant tumor with high occurrence and recurrence and is the leading cause of death worldwide. However, the prognostic value of protein-coding and non-coding RNAs in stage III gastric cancer has not been systematically analyzed. In this study, using TCGA data, we identified 585 long noncoding RNAs (lncRNAs) and 927 protein-coding genes (PCGs) correlated with the overall survival rate of gastric cancer. Functional enrichment analysis revealed that the prognostic genes positively correlated with death rates were enriched in pathways, including gap junction, focal adhesion, cell adhesion molecules (CAMs), and neuroactive ligand-receptor interaction, that are involved in the tumor microenvironment and cell-cell communications, suggesting that their dysregulation may promote the tumor progression. To evaluate the performance of the prognostic genes in risk prediction, we built three multivariable Cox models based on prognostic genes selected from the prognostic PCGs and lncRNAs. The performance of the three models based on features from only PCGs or lncRNAs or from all prognostic genes were systematically compared, which revealed that the features selected from all the prognostic genes showed higher performance than the features selected only from lncRNAs or PCGs. Furthermore, the multivariable Cox regression analysis revealed that the stratification with the highest performance was an independent prognostic factor in stage III gastric cancer. In addition, we explored the underlying mechanism of the prognostic lncRNAs in the Cox model by predicting the lncRNA and protein interaction. Specifically, CTD-2218G20.2 was predicted to interact with PSG4, PSG5, and PSG7, which could also interact with cancer-related proteins, including KISS1, TIMP2, MMP11, IGFBP1, EGFR, and CDKN1C, suggesting that CTD-2218G20.2 might participate in the cancer progression via these cancer-related proteins. In summary, the systematic analysis of the prognostic lncRNAs and PCGs was of great importance to the understanding of the progression of stage III gastric cancer.

show abstract

“…The secretion of PDGFB by gastric carcinoma cells was associated with lymphatic metastasis [ 41 ]. For ITGA5, it was reported to be a potential diagnosis biomarker and therapeutic target for GC [ 42 , 43 ]. The secretion of TGFβ2 by melanoma cells was essential for their metastases to the brain parenchyma [ 44 ].…”

Section: Discussionmentioning

confidence: 99%

Identification of the subtypes of gastric cancer based on DNA methylation and the prediction of prognosis

Chen

et al. 2020

Clin Epigenet

View full text Add to dashboard Cite

Background Gastric cancer (GC) is a digestive system cancer with a high mortality rate globally. Previous experiences and studies have provided clinicians with ample evidence to diagnose and treat patients with reasonable therapeutic options. However, there remains a need for sensitive biomarkers that can provide clues for early diagnosis and prognosis assessment. Results We found 610 independent prognosis-related 5′-cytosine-phosphate-guanine-3′ (CpG) sites (P < 0.05) among 21,121 sites in the training samples. We divided the GC samples into seven clusters based on the selected 610 sites. Cluster 6 had relatively higher methylation levels and high survival rates than the other six clusters. A prognostic risk model was constructed using the significantly altered CpG sites in cluster 6 (P < 0.05). This model could distinguish high-risk GC patients from low-risk groups efficiently with the area under the receiver operating characteristic curve of 0.92. Risk assessment showed that the high-risk patients had poorer prognosis than the low-risk patients. The methylation levels of the selected sites in the established model decreased as the risk scores increased. This model had been validated in testing group and its effectiveness was confirmed. Corresponding genes of the independent prognosis-associated CpGs were identified, they were enriched in several pathways such as pathways in cancer and gastric cancer. Among all of the genes, the transcript level of transforming growth factor β2 (TGFβ2) was changed in different tumor stages, T categories, grades, and patients’ survival states, and up-regulated in patients with GC compared with the normal. It was included in the pathways as pathways in cancer, hepatocellular carcinoma or gastric cancer. The methylation site located on the promoter of TGFβ2 was cg11976166. Conclusions This is the first study to separate GC into different molecular subtypes based on the CpG sites using a large number of samples. We constructed an effective prognosis risk model that can identify high-risk GC patients. The key CpGs sites or their corresponding genes such as TGFβ2 identified in this research can provide new clues that will enable gastroenterologists to make diagnosis or personalized prognosis assessments and better understand this disease.

show abstract

In silico analyses for potential key genes associated with gastric cancer

Cited by 80 publications

References 36 publications

Identification of potential key genes in gastric cancer using bioinformatics analysis

Identification of potential key genes in gastric cancer using bioinformatics analysis

Identification of the Prognosis-Related lncRNAs and Genes in Gastric Cancer

Identification of the subtypes of gastric cancer based on DNA methylation and the prediction of prognosis

Contact Info

Product

Resources

About