Dataset Information

CEACAM6 is upregulated by Helicobacter pylori CagA and is a biomarker for early gastric cancer.

ABSTRACT: Early detection of gastric cancers saves lives, but remains a diagnostic challenge. In this study, we aimed to identify cell-surface biomarkers of early gastric cancer. We hypothesized that a subset of plasma membrane proteins induced by the Helicobacter pylori oncoprotein CagA will be retained in early gastric cancers through non-oncogene addiction. An inducible system for expression of CagA was used to identify differentially upregulated membrane protein transcripts in vitro. The top hits were then analyzed in gene expression datasets comparing transcriptome of gastric cancer with normal tissue, to focus on markers retained in cancer. Among the transcripts enriched upon CagA induction in vitro, a significant elevation of CEACAM6 was noted in gene expression datasets of gastric cancer. We used quantitative digital immunohistochemistry to measure CEACAM6 protein levels in tissue microarrays of gastric cancer. We demonstrate an increase in CEACAM6 in early gastric cancers, when compared to matched normal tissue, with an AUC of 0.83 for diagnostic validity. Finally, we show that a fluorescently conjugated CEACAM6 antibody binds avidly to freshly resected gastric cancer xenograft samples and can be detected by endoscopy in real time. Together, these results suggest that CEACAM6 upregulation is a cell surface response to H. pylori CagA, and is retained in early gastric cancers. They highlight a novel link between CEACAM6 expression and CagA in gastric cancer, and suggest CEACAM6 to be a promising biomarker to aid with the fluorescent endoscopic diagnosis of early neoplastic lesions in the stomach.

SUBMITTER: Roy RK

PROVIDER: S-EPMC5342417 | biostudies-literature | 2016 Aug

REPOSITORIES: biostudies-literature

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CEACAM6 is upregulated by Helicobacter pylori CagA and is a biomarker for early gastric cancer.

Roy Rony K RK Hoppe Michal M MM Srivastava Supriya S Samanta Animesh A Sharma Neel N Tan Kar Tong KT Yang Henry H Voon Dominic C DC Pang Brendan B Teh Ming M Murata-Kamiya Naoko N Hatakeyama Masanori M Chang Young-Tae YT Yong Wei Peng WP Ito Yoshiaki Y Ho Khek Yu KY Tan Patrick P Soong Richie R Koeffler Phillip H PH Yeoh Khay Guan KG Jeyasekharan Anand D AD

Oncotarget 20160801 34

Early detection of gastric cancers saves lives, but remains a diagnostic challenge. In this study, we aimed to identify cell-surface biomarkers of early gastric cancer. We hypothesized that a subset of plasma membrane proteins induced by the Helicobacter pylori oncoprotein CagA will be retained in early gastric cancers through non-oncogene addiction. An inducible system for expression of CagA was used to identify differentially upregulated membrane protein transcripts in vitro. The top hits were ...[more]

PMID: 27421133

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Project description:BackgroundAs a marker of Helicobacter pylori, Cytotoxin-associated gene A (cagA) has been revealed to be the major virulence factor causing gastroduodenal diseases. However, the molecular mechanisms that underlie the development of different gastroduodenal diseases caused by cagA-positive H. pylori infection remain unknown. Current studies are limited to the evaluation of the correlation between diseases and the number of Glu-Pro-Ile-Tyr-Ala (EPIYA) motifs in the CagA strain. To further understand the relationship between CagA sequence and its virulence to gastric cancer, we proposed a systematic entropy-based approach to identify the cancer-related residues in the intervening regions of CagA and employed a supervised machine learning method for cancer and non-cancer cases classification.MethodologyAn entropy-based calculation was used to detect key residues of CagA intervening sequences as the gastric cancer biomarker. For each residue, both combinatorial entropy and background entropy were calculated, and the entropy difference was used as the criterion for feature residue selection. The feature values were then fed into Support Vector Machines (SVM) with the Radial Basis Function (RBF) kernel, and two parameters were tuned to obtain the optimal F value by using grid search. Two other popular sequence classification methods, the BLAST and HMMER, were also applied to the same data for comparison.ConclusionOur method achieved 76% and 71% classification accuracy for Western and East Asian subtypes, respectively, which performed significantly better than BLAST and HMMER. This research indicates that small variations of amino acids in those important residues might lead to the virulence variance of CagA strains resulting in different gastroduodenal diseases. This study provides not only a useful tool to predict the correlation between the novel CagA strain and diseases, but also a general new framework for detecting biological sequence biomarkers in population studies.

Dataset Information

CEACAM6 is upregulated by Helicobacter pylori CagA and is a biomarker for early gastric cancer.

Publications

CEACAM6 is upregulated by Helicobacter pylori CagA and is a biomarker for early gastric cancer.

Similar Datasets

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