zbMATH — the first resource for mathematics

Finding gastric cancer related genes and clinical biomarkers for detection based on gene-gene interaction network. (English) Zbl 1341.92036
Summary: Background/objective: Gastric cancer (GC) is the second leading cause of death resulted from cancer globally. The most common cause of GC is the infection of Helicobacter pylori, approximately 11% of cases are caused by genetic factors. The objective of this study was to develop an effective computational method to meaningfully interpret these GC-related genes and to predict potential prognostic genes for clinical detection.
Methods: We employed the shortest path algorithm and permutation test to probe the genes that have relationship with known GC genes in gene-gene interaction network. We calculated the enrichment scores of gene ontology and pathways of gastric cancer related genes to characterize these genes in terms of molecular features. The optimal features that primly representing the gastric cancer related genes were selected using Random Forest classification and incremental feature selection. Random Forest classification was also used for the prediction of the novel gastric cancer related genes based on the selected features and the identification of novel prognostic genes based on the expression of genes.
Results: Based on the shortest path analysis of 36 known GC genes, 39 genes occurring in shortest path were identified as GC-related genes. In subsequent classification, 4153 gene ontology terms and 157 pathway terms were identified as the optimal features to depict these gastric cancer related genes. Based on them, a total of 886 genes were predicted as related genes. These 886 genes could serve as expression biomarkers for clinical detection and they achieved a 100% accuracy for distinguishing gastric cancer from a case-control dataset, better than any of 886 random selected genes did.
Conclusion: By analyzing the features of known GC-related genes, we employed a systematic method to predict gastric cancer related genes and novel prognostic genes for accurate clinical detection.
92C50 Medical applications (general)
92D10 Genetics and epigenetics
Full Text: DOI
[1] Crew, K. D., Epidemiology of gastric cancer, World J. Gastroenterol., 12, 3, 354-362, (2006)
[2] Hohenberger, P.; Gretschel, S., Gastric cancer, Lancet, 362, 8, 55-76, (2003)
[3] Guggenheim, D. E.; Shah, M. A., Gastric cancer epidemiology and risk factors, J. Surg. Oncol., 107, 3, 230-236, (2013)
[4] Chen, W., Report of incidence and mortality in China cancer registries, 2009, Chin. J. Cancer Res., 25, 1, 10-21, (2013)
[5] Carcas, L. P., Gastric cancer review, J. Carcinog., 13, 14, (2014), Published online 2014 Dec 19
[6] Batsis, C., Precision medicine: neoadjuvant treatment and laparoscopic rectal cancer resection, Gastric Breast Cancer, (2012)
[7] Kim, J. M., Identification of gastric cancer-related genes using a cdna microarray containing novel expressed sequence tags expressed in gastric cancer cells. clinical cancer research an official, J. Am. Assoc. Cancer Res., 11, 2 Pt 1, 473, (2005)
[8] Obayashi, N., Comparison of gene expression between pediatric and adult gastric mucosa with helicobacter pylori infection, Helicobacter, (2015)
[9] Zhi-Hai, L. I., The expression of multidrug resistance related genes and invasion/metastasis ability of gastric cancer cell, Mod. Dig. Interv., (2014)
[10] Yu, G. W.; Liang, H.; College, M., Gastric cancer susceptibility genes, World Chin. J. Digestol., (2014)
[11] Nakagawa, M., Assessment of serum copper state after gastrectomy with roux-en-Y reconstruction for gastric cancer, Dig. Surg., 32, 4, 301-305, (2015)
[12] Zhai, H., Role of the cacybp/SIP protein in gastric cancer, Oncol. Lett., 9, 5, 2031-2035, (2015)
[13] Shi, D., The PSCA polymorphisms derived from genome-wide association study are associated with risk of gastric cancer: a meta-analysis, J. Cancer Res. Clin. Oncol., 138, 8, 1339-1345, (2012)
[14] Hu, N., Genome-wide association study of gastric adenocarcinoma in Asia: a comparison of associations between cardia and non-cardia tumours, Gut, (2015)
[15] McClellan, J.; King, M. C., Genetic heterogeneity in human disease, Cell, 141, 2, 210-217, (2010)
[16] Yamada, Y., Identification of prognostic biomarkers in gastric cancer using endoscopic biopsy samples, Cancer Sci., 99, 11, 2193-2199, (2008)
[17] Bertrand, D., Patient-specific driver gene prediction and risk assessment through integrated network analysis of cancer omics profiles, Nucleic Acids Res., 43, e44, (2015)
[18] Durães, C., Genetic variants in the IL1A gene region contribute to intestinal‐type gastric carcinoma susceptibility in European populations, Int. J. Cancer, 135, 6, 1343-1355, (2014)
[19] Espinosa‐Parrilla, Y., Genetic association of gastric cancer with mirna clusters including the cancer‐related genes MIR29, MIR25, MIR93 and MIR106: results from the EPIC‐EURGAST study, Int. J. Cancer, 135, 9, 2065-2076, (2014)
[20] Companioni, O., Polymorphisms of helicobacter pylori signaling pathway genes and gastric cancer risk in the European prospective investigation into cancer‐eurgast cohort, Int. J. Cancer, 134, 1, 92-101, (2014)
[21] Ghoshal, U., Genetic polymorphism of cytochrome P450 (CYP) 1A1, CYP1A2, and CYP2E1 genes modulate susceptibility to gastric cancer in patients with helicobacter pylori infection, Gastric Cancer, 17, 2, 226-234, (2014)
[22] Companioni, O., Polymorphisms of helicobacter pylori signaling pathway genes and gastric cancer risk in the European prospective investigation into cancer-eurgast cohort, Int. J. Cancer, 134, 1, 92-101, (2014)
[23] Mocellin, S., Genetic variation and gastric cancer risk: a field synopsis and meta-analysis, Gut, (2015)
[24] Wu, H., A novel functional tagsnp Rs7560488 in the DNMT3A1 promoter is associated with susceptibility to gastric cancer by modulating promoter activity, PLoS One, 9, 3, e92911, (2014)
[25] Yan, L. H., New insights into the functions and localization of the homeotic gene CDX2 in gastric cancer, World J Gastroenterol., 20, 14, 3960-3966, (2014)
[26] Lim, B., Integrative genomics analysis reveals the multilevel dysregulation and oncogenic characteristics of TEAD4 in gastric cancer, Carcinogenesis, 35, 5, 1020-1027, (2014)
[27] Yu, P., MIR210 as a potential molecular target to block invasion and metastasis of gastric cancer, Med. Hypotheses, 84, 3, 209-212, (2015)
[28] Xu, Y., Association of the polymorphisms in the fas/fasl promoter regions with cancer susceptibility: a systematic review and meta-analysis of 52 studies, PLoS One, 9, 3, e90090, (2014)
[29] Szklarczyk, D., The STRING database in 2011: functional interaction networks of proteins, globally integrated and scored, Nucleic Acids Res., 39, Database issue, D561-D568, (2011)
[30] Kanehisa, M., KEGG for integration and interpretation of large-scale molecular data sets, Nucleic Acids Res, 40, Database issue, D109-D114, (2012)
[31] Carmona-Saez, P., GENECODIS: a web-based tool for finding significant concurrent annotations in gene lists, Genome Biol, 8, 1, R3, (2007)
[32] Huang, T., Deciphering the effects of gene deletion on yeast longevity using network and machine learning approaches, Biochimie, 94, 4, 1017-1025, (2012)
[33] Yang, J., Analysis of tumor suppressor genes based on gene ontology and the KEGG pathway, PLoS One, 9, 9, (2014)
[34] Harrison, K. M., Risk factor redistribution of the national HIV/AIDS surveillance data: an alternative approach, Public Health Rep., 123, 5, 618-627, (2008)
[35] Peng, H.; Long, F.; Ding, C., Feature selection based on mutual information: criteria of MAX-dependency, MAX-relevance, and MIN-redundancy, IEEE Trans. Pattern Anal. Mach. Intell., 27, 8, 1226-1238, (2005)
[36] Mao, R., Comparative analyses between retained introns and constitutively spliced introns in arabidopsis thaliana using random forest and support vector machine, PLoS One, 9, 8, (2014)
[37] Ge, H.; Zhang, G., RETRACTED: identifying halophilic proteins based on random forests with preprocessing of the pseudo-amino acid composition, J Theor. Biol, 361, 175, (2014) · Zbl 1304.92061
[38] Bouckaert, R. R., WEKA—experiences with a Java open-source project, J. Mach. Learn. Res., 9999, 2533-2541, (2010) · Zbl 1242.68001
[39] Li, H., Characterization of differentially expressed genes involved in pathways associated with gastric cancer, PLoS One, 10, 4, (2015)
[40] Camargo, M. C., Interleukin-1beta and interleukin-1 receptor antagonist gene polymorphisms and gastric cancer: a meta-analysis, Cancer Epidemiol. Biomark. Prev., 15, 9, 1674-1687, (2006)
[41] Van Damme, J., Homogeneous interferon-inducing 22 K factor is related to endogenous pyrogen and interleukin-1, Nature, 314, 6008, 266-268, (1985)
[42] Diakowska, D., Serum and tissue levels of interleukin-12 and interleukin-18 in intestinal type gastric cancer, Gastroenterol. Polska, 3, 103-108, (2011)
[43] Saeki, N., Rs2294008T, a risk allele for gastric and gallbladder cancers, suppresses the PSCA promoter by recruiting the transcription factor YY1, Genes Cells, 20, (2015)
[44] Wang, A. M., Yin Yang 1 is a target of microrna-34 family and contributes to gastric carcinogenesis, Oncotarget, 5, 13, (2014)
[45] Consortium, G. O., Gene ontology annotations and resources, Nucleic Acids Res., 41, (2013)
[46] Kanehisa, M.; Goto, S., KEGG: Kyoto encyclopedia of genes and genomes, Nucleic Acids Res., 28, 1, 29-34, (2000)
[47] Nakaya, A.; KEGG, OC, A large-scale automatic construction of taxonomy-based ortholog clusters, Nucleic Acids Res., 41, (2013)
[48] Zhu, Y., Helicobacter pylori FKBP-type ppiase promotes gastric epithelial cell proliferation and anchorage-independent growth through activation of ERK-mediated mitogenic signaling pathway, FEMS Microbiol. Lett., 362, 7, (2015)
[49] Lee, W. S., Myeloid cell leukemia-1 promotes epithelial-mesenchymal transition of human gastric cancer cells, Oncol. Rep., 34, 2, 1011-1016, (2015)
[50] Liu, H., Predictive value of cystic fibrosis transmembrane conductance regulator (CFTR) in the diagnosis of gastric cancer, Clin. Investig. Med., 37, 4, E226-E232, (2014)
[51] Shen, J., Plasma mrna expression levels of BRCA1 and TS as potential predictive biomarkers for chemotherapy in gastric cancer, J. Transl. Med., 12, 1, 355, (2014)
This reference list is based on information provided by the publisher or from digital mathematics libraries. Its items are heuristically matched to zbMATH identifiers and may contain data conversion errors. It attempts to reflect the references listed in the original paper as accurately as possible without claiming the completeness or perfect precision of the matching.