Cited 0 times in Scipus Cited Count

Transcriptomic profiling identifies a risk stratification signature for predicting peritoneal recurrence and micrometastasis in gastric cancer

DC Field Value Language
dc.contributor.authorLee, IS-
dc.contributor.authorLee, H-
dc.contributor.authorHur, H-
dc.contributor.authorKanda, M-
dc.contributor.authorYook, JH-
dc.contributor.authorKim, BS-
dc.contributor.authorWoo, Y-
dc.contributor.authorKodera, Y-
dc.contributor.authorKim, K-
dc.contributor.authorGoel, A-
dc.date.accessioned2023-01-05T03:03:27Z-
dc.date.available2023-01-05T03:03:27Z-
dc.date.issued2021-
dc.identifier.issn1078-0432-
dc.identifier.urihttp://repository.ajou.ac.kr/handle/201003/23697-
dc.description.abstractPurpose: Gastric cancer peritoneal carcinomatosis is fatal. Delay in detection of peritoneal metastases contributes to high mortality, highlighting the need to develop biomarkers that can help identify patients at high risk for peritoneal recurrence or metastasis. Experimental Design: Weperformed a systematic discovery and validation for the identification of peritoneal recurrence prediction and peritoneal metastasis detection biomarkers by analyzing expression profiling datasets from 249 patients with gastric cancer, followed by analysis of 426 patients from three cohorts for clinical validation. Results: Genome-wide expression profiling identified a 12-gene panel for robust prediction of peritoneal recurrence in patients with gastric cancer (AUC = 0.95), which was successfully validated in a second dataset (AUC = 0.86). Examination of 216 specimens from a training cohort allowed us to establish a six gene-based risk-prediction model [AUC = 0.72; 95% confidence interval (CI): 0.66-0.78], which was subsequently validated in an independent cohort of 111 patients with gastric cancer (AUC = 0.76; 95% CI: 0.67-0.83). In both cohorts, combining tumor morphology and depth of invasion further improved the predictive accuracy of the prediction model (AUC = 0.84). Thereafter, we evaluated the performance of the identical six-gene panel for its ability to detect peritoneal metastasis by analyzing 210 gastric cancer specimens (prior 111 patients plus additional 99 cases), which discriminated patients with and without peritoneal metastasis (AUC = 0.72). Finally, our biomarker panel was also remarkably effective for identifying peritoneal micrometastasis (AUC = 0.72), and its diagnostic accuracy was significantly enhanced when depth of invasion was included in the model (AUC = 0.85). Conclusions: Our novel transcriptomic signature for risk stratification and identification of high-risk patients with peritoneal carcinomatosis might serve as an important clinical decision making in patients with gastric cancer.-
dc.language.isoen-
dc.subject.MESHBiomarkers, Tumor-
dc.subject.MESHClinical Decision-Making-
dc.subject.MESHDatasets as Topic-
dc.subject.MESHDisease-Free Survival-
dc.subject.MESHFollow-Up Studies-
dc.subject.MESHGastrectomy-
dc.subject.MESHGene Expression Profiling-
dc.subject.MESHGene Expression Regulation, Neoplastic-
dc.subject.MESHHumans-
dc.subject.MESHNeoplasm Micrometastasis-
dc.subject.MESHPeritoneal Neoplasms-
dc.subject.MESHPrognosis-
dc.subject.MESHRisk Assessment-
dc.subject.MESHStomach-
dc.subject.MESHStomach Neoplasms-
dc.titleTranscriptomic profiling identifies a risk stratification signature for predicting peritoneal recurrence and micrometastasis in gastric cancer-
dc.typeArticle-
dc.identifier.pmid33558424-
dc.identifier.urlhttps://www.ncbi.nlm.nih.gov/pmc/articles/PMC8103893/-
dc.contributor.affiliatedAuthorHur, H-
dc.type.localJournal Papers-
dc.identifier.doi10.1158/1078-0432.CCR-20-3835-
dc.citation.titleClinical cancer research-
dc.citation.volume27-
dc.citation.number8-
dc.citation.date2021-
dc.citation.startPage2292-
dc.citation.endPage2300-
dc.identifier.bibliographicCitationClinical cancer research, 27(8). : 2292-2300, 2021-
dc.relation.journalidJ010780432-
Appears in Collections:
Journal Papers > School of Medicine / Graduate School of Medicine > Surgery
Files in This Item:
33558424.pdfDownload

qrcode

해당 아이템을 이메일로 공유하기 원하시면 인증을 거치시기 바랍니다.

Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.

Browse