Dataset Information

A transcriptomic study for identifying cardia- and non-cardia-specific gastric cancer prognostic factors using genetic algorithm-based methods.

ABSTRACT: Gastric cancer (GC) is a heterogeneous tumour with numerous differences of epidemiologic and clinicopathologic features between cardia cancer and non-cardia cancer. However, few studies were performed to construct site-specific GC prognostic models. In this study, we identified site-specific GC transcriptomic prognostic biomarkers using genetic algorithm (GA)-based support vector machine (GA-SVM) and GA-based Cox regression method (GA-Cox) in the Cancer Genome Atlas (TCGA) database. The area under time-dependent receive operating characteristic (ROC) curve (AUC) regarding 5-year survival and concordance index (C-index) was used to evaluate the predictive ability of Cox regression models. Finally, we identified 10 and 13 prognostic biomarkers for cardia cancer and non-cardia cancer, respectively. Compared to traditional models, the addition of these site-specific biomarkers could notably improve the model preference (cardia: AUCtraditional vs AUCcombined = 0.720 vs 0.899, P = 8.75E-08; non-cardia: AUCtraditional vs AUCcombined = 0.798 vs 0.994, P = 7.11E-16). The combined nomograms exhibited superior performance in cardia and non-cardia GC survival prediction (C-indexcardia = 0.816; C-indexnoncardia = 0.812). We also constructed a user-friendly GC site-specific molecular system (GC-SMS, https://njmu-zhanglab.shinyapps.io/gc_sms/), which is freely available for users. In conclusion, we developed site-specific GC prognostic models for predicting cardia cancer and non-cardia cancer survival, providing more support for the individualized therapy of GC patients.

SUBMITTER: Xin J

PROVIDER: S-EPMC7417703 | biostudies-literature | 2020 Jul

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

A transcriptomic study for identifying cardia- and non-cardia-specific gastric cancer prognostic factors using genetic algorithm-based methods.

Xin Junyi J Wu Yanling Y Wang Xiaowei X Li Shuwei S Chu Haiyan H Wang Meilin M Du Mulong M Zhang Zhengdong Z

Journal of cellular and molecular medicine 20200710 16

Gastric cancer (GC) is a heterogeneous tumour with numerous differences of epidemiologic and clinicopathologic features between cardia cancer and non-cardia cancer. However, few studies were performed to construct site-specific GC prognostic models. In this study, we identified site-specific GC transcriptomic prognostic biomarkers using genetic algorithm (GA)-based support vector machine (GA-SVM) and GA-based Cox regression method (GA-Cox) in the Cancer Genome Atlas (TCGA) database. The area und ...[more]

PMID: 32649057

Similar Datasets

Project description:Recent decades have seen an alarming increase in the incidence of cardia gastric adenocarcinoma (CGA) while noncardia gastric adenocarcinoma (NCGA) has decreased. In 2012, 260 000 CGA cases (age-standardised rate (ASR); 3.3/100 000) and 691 000 NCGA cases (ASR; 8.8/100 000) were reported worldwide. Compared with women, men had greater rates for both the subsites, especially for CGA. Recently, four molecular subtypes of GC have been proposed by the Cancer Genome Atlas (TCGA) and the Asian Cancer Research Group (ACRG); however, these classifications do not take into account predisposing germline variants and their possible interaction with somatic alterations in carcinogenesis. The etiology of adenocarcinoma of the cardia and the gastroesophageal junction (GEJ) is not known. It is thought that CGA is distinct from adenocarcinomas located in the esophagus or distal stomach, both epidemiologically and biologically. Moreover, CGA is often identified in the advanced stage having a poor prognosis. Therefore, understanding the risk and the role of predisposing factors in etiology of CGA can inform clinical practice and counseling for risk reduction. In this paper, we showed that GC family history, lifestyle, demographics, gastroesophageal reflux disease, Helicobacter pylori infection, and multiple genetic and epigenetic risk factors as well as several predisposing conditions may underlie susceptibility to CGA. However, several genome-wide association studies (GWASs) should be conducted to identify novel high-penetrance genes and pathways as well as causal germline variants predisposing to CGA. They must include different ethnic groups, especially from high-incidence countries for CGA, because some risk loci are ancestry-specific. In parallel, statistical methods can be developed to identify cancer predisposition genes (CPGs) from tumor sequencing data. It is also necessary to find novel long noncoding RNAs related to the risk of CGA. Taken altogether, new cancer risk prediction models, including all genetic and nongenetic factors influencing risk, should be developed to facilitate risk assessment, disease prevention, and early diagnosis and intervention of CGA in the future.

Project description:BackgroundThe TNM staging criteria for gastric carcinoma have seen numerous revisions, the most recent of which are reflected in the seventh edition AJCC TNM cancer staging manual.MethodsA retrospective evaluation of the sixth and seventh TNM classification of gastric cancer on a prospective database, regarding patients operated on for primary gastric cancer, was conducted. The end point of the study was prognosis evaluation in terms of overall survival.Patients operated on for primary gastric cancer between September 2003 and March 2012 at our Department of Emergency and General Surgery, were consecutively retrieved in this study; a total of 114 patients were considered. Cardia gastric cancers, gastric lymphomas and gastrointestinal stromal tumors (GIST) were excluded. Median and mean follow-up periods were 22.5 and 27.7 months (range 15 days to 5 years). Both TNM6 and TNM7 were used to evaluate our patients. Overall survival and survival rates at different stages were analyzed using the Kaplan-Meier method and differences were determined using a log-rank test. Cox's proportional hazard model was used to identify significant factors related to prognosis in a multivariate analysis.ResultsOverall survival between the sixth and seventh TNM classification was not significantly different. Both the Kaplan-Meier analysis and the multivariate analysis showed that the major negative prognostic factor was lymphovascular invasion (P<0.001 in the univariate analysis and P=0.035 to 0.048 in the multivariate analysis). Stage distribution and stage-related survival changed from the sixth to the seventh edition, especially in T3 stage where median survival for the sixth edition was 720 days versus 1,200 days for the seventh edition. Moreover, differences were shown in the survival rate of N1 versus N2 stages within the seventh TNM.ConclusionsEven though further studies are needed in order to increase the number of patients studied, the seventh edition seems to provide a more accurate prognosis, especially regarding N1 and N2 tumors, showing that the most important prognostic factor is lymphovascular invasion.

Project description:In the present study, gene expression profiles were analyzed to identify the molecular mechanisms underlying gastric cardia adenocarcinoma (GCA) and gastric non-cardia adenocarcinoma (GNCA). A gene expression dataset (accession number GSE29272) was downloaded from Gene Expression Omnibus, and consisted of 62 GCA samples and 62 normal controls, as well as 72 GNCA samples and 72 normal controls. The two groups of differentially-expressed genes (DEGs) were compared to obtain common and unique DEGs. A differential analysis was performed using the Linear Models for Microarray Data package in R. Functional enrichment analysis was conducted for the DEGs using the Database for Annotation, Visualization and Integrated Discovery. Protein-protein interaction (PPI) networks were constructed for the DEGs with information from the Search Tool for the Retrieval of Interacting Genes. Subnetworks were extracted from the whole network with Cytoscape. Compared with the control, 284 and 268 genes were differentially-expressed in GCA and GNCA, respectively, of which 194 DEGs were common between GCA and GNCA. Common DEGs [e.g., claudin (CLDN)7, CLDN4 and CLDN3] were associated with cell adhesion and digestion. GCA-unique DEGs [e.g., MAD1 mitotic arrest deficient like 1, cyclin (CCN)B1, CCNB2 and CCNE1] were associated with the cell cycle and the regulation of cell proliferation, while GNCA-unique DEGs (e.g., GATA binding protein 6 and hyaluronoglucosaminidase 1) were implicated in cell death. A PPI network with 141 nodes and 446 edges were obtained, from which two subnetworks were extracted. Genes [e.g., fibronectin 1, collagen type I ?2 chain (COL1A2) and COL1A1] from the two subnetworks were implicated in extracellular matrix organization. These common DEGs could advance our understanding of the etiology of gastric cancer, while the unique DEGs in GCA and GNCA could better define the properties of specific cancers and provide potential biomarkers for diagnosis, prognosis or therapy.

Dataset Information

A transcriptomic study for identifying cardia- and non-cardia-specific gastric cancer prognostic factors using genetic algorithm-based methods.

Publications

A transcriptomic study for identifying cardia- and non-cardia-specific gastric cancer prognostic factors using genetic algorithm-based methods.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets