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Deciphering metastatic route-specific signals and their microenvironment interactions in peritoneal metastasis of gastric cancer

ABSTRACT: Deciphering metastatic route-specific signals and their microenvironment interactions in peritoneal metastasis of gastric cancer

PROVIDER: PRJNA996979 | ENA |

REPOSITORIES: ENA

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			Action	DRS
	SRR25411909_1.fastq.gz	Fastqsanger.gz
	SRR25411909_2.fastq.gz	Fastqsanger.gz
	SRR25411910_1.fastq.gz	Fastqsanger.gz
	SRR25411910_2.fastq.gz	Fastqsanger.gz
	SRR25411911_1.fastq.gz	Fastqsanger.gz

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Deciphering metastatic route-specific signals and their microenvironment interactions in peritoneal metastasis of gastric cancer

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Project description:Purpose: Our study aimed to disclose the specific gene expression profile representing peritoneal relapses inherent in primary gastric cancers and to identify patients at high risk of peritoneal relapse in a prospective study on the basis of the molecular prediction. Experimental Design: RNA samples from 141 primary gastric cancer tissues after curative surgery were profiled using oligonucleotide microarrays covering 30,000 human probes. Firstly we constructed molecular prediction system and validated the robustness and prognostic validity of the analysis by 500 times multiple random sampling in 56 retrospective set consisting of 38 relapse free and 18 peritoneal relapse patients. Secondly we applied this prediction to 85 prospective set to assess the predictive accuracy and prognostic validity. Results: In retrospective phase, 500 times multiple random sampling analysis yielded 68% predictive accuracy in average and 22 gene expression profile associated with peritoneal relapse was identified. This prediction could identify significantly poor prognostic patients. In prospective phase, the molecular prediction yielded 76.9% overall accuracy. Kaplan–Meier analysis with peritoneal relapse free survival showed a significant difference between ‘good signature group’ and ‘poor signature group’ (Log-rank p=0.0017). Multivariate analysis by Cox regression hazards model revealed that the molecular prediction was the only independent peritoneal relapse prognostic factor. Conclusions: Gene expression profile inherent in primary gastric cancer tissues can be useful to predict peritoneal relapse prospectively after curative surgery and individualize postoperative management to improve the prognosis of advanced gastric cancers.

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