Nomogram for predicting gastric cancer recurrence using biomarker gene expression

Sang Ho Jeong, Rock Bum Kim, Sun Yi Park, Jiho Park, Eun Jung Jung, Young tae Ju, Chi Young Jeong, Miyeong Park, Gyung Hyuck Ko, Dae Hyun Song, Hyun Min Koh, Woo Ho Kim, Han Kwang Yang, Young Joon Lee, Soon Chan Hong

Research output: Contribution to journalArticle

Abstract

Background: Recently, researchers have tried to predict patient prognosis using biomarker expression in cancer patients. The aim of this study was to develop a nomogram predicting the 5-year recurrence-free probability (RFP) of gastric cancer patients using prognostic biomarker gene expression. Methods: We enrolled 360 patients in the training data set to develop the predictive model and nomogram. We analyzed the patients’ general variables and the gene expression levels of 10 prognostic biomarker candidates between the nonrecurrence and recurrence groups. We also performed external validation using 420 patients from the validation data set. Results: The final nomogram was composed of age, sex, and the expression levels of CAPZA, PPase, OCT-1, PRDX4, gamma-enolase, and c-Myc. The five-year RFPs were 89%, 75%, 54% and 32% for the patients in the low-risk, intermediate-risk, high-risk and very-high-risk groups in the development cohort, respectively. In the external validation cohort, the 5-year RFPs were 89%, 75%, 63% and 60%, respectively. The areas under the curve were 0.718 (95% CI, 0.65–0.78) and 0.640 (95% CI, 0.57–0.70) for the training and validation data sets, respectively. The RFP Kaplan-Meier curves were significantly different among the 4 groups in the training and validation data sets (p < 0.0001). Conclusion: This newly developed nomogram using gene expression can predict the 5-year RFP for gastric cancer patients after surgical treatment. We hope that this nomogram will help in the therapeutic decision between endoscopic treatment and gastrectomy.

Original languageEnglish
Pages (from-to)195-201
Number of pages7
JournalEuropean Journal of Surgical Oncology
Volume46
Issue number1
DOIs
StatePublished - Jan 2020

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Nomograms
Stomach Neoplasms
Biomarkers
Gene Expression
Recurrence
Phosphopyruvate Hydratase
Gastrectomy
Area Under Curve
Therapeutics
Research Personnel
Datasets

Keywords

  • Biomarkers
  • Gastric neoplasm
  • Gene expression
  • Nomograms

Cite this

Jeong, S. H., Kim, R. B., Park, S. Y., Park, J., Jung, E. J., Ju, Y. T., ... Hong, S. C. (2020). Nomogram for predicting gastric cancer recurrence using biomarker gene expression. European Journal of Surgical Oncology, 46(1), 195-201. https://doi.org/10.1016/j.ejso.2019.09.143
Jeong, Sang Ho ; Kim, Rock Bum ; Park, Sun Yi ; Park, Jiho ; Jung, Eun Jung ; Ju, Young tae ; Jeong, Chi Young ; Park, Miyeong ; Ko, Gyung Hyuck ; Song, Dae Hyun ; Koh, Hyun Min ; Kim, Woo Ho ; Yang, Han Kwang ; Lee, Young Joon ; Hong, Soon Chan. / Nomogram for predicting gastric cancer recurrence using biomarker gene expression. In: European Journal of Surgical Oncology. 2020 ; Vol. 46, No. 1. pp. 195-201.
@article{39b84baac72849d0a6404e390ebad015,
title = "Nomogram for predicting gastric cancer recurrence using biomarker gene expression",
abstract = "Background: Recently, researchers have tried to predict patient prognosis using biomarker expression in cancer patients. The aim of this study was to develop a nomogram predicting the 5-year recurrence-free probability (RFP) of gastric cancer patients using prognostic biomarker gene expression. Methods: We enrolled 360 patients in the training data set to develop the predictive model and nomogram. We analyzed the patients’ general variables and the gene expression levels of 10 prognostic biomarker candidates between the nonrecurrence and recurrence groups. We also performed external validation using 420 patients from the validation data set. Results: The final nomogram was composed of age, sex, and the expression levels of CAPZA, PPase, OCT-1, PRDX4, gamma-enolase, and c-Myc. The five-year RFPs were 89{\%}, 75{\%}, 54{\%} and 32{\%} for the patients in the low-risk, intermediate-risk, high-risk and very-high-risk groups in the development cohort, respectively. In the external validation cohort, the 5-year RFPs were 89{\%}, 75{\%}, 63{\%} and 60{\%}, respectively. The areas under the curve were 0.718 (95{\%} CI, 0.65–0.78) and 0.640 (95{\%} CI, 0.57–0.70) for the training and validation data sets, respectively. The RFP Kaplan-Meier curves were significantly different among the 4 groups in the training and validation data sets (p < 0.0001). Conclusion: This newly developed nomogram using gene expression can predict the 5-year RFP for gastric cancer patients after surgical treatment. We hope that this nomogram will help in the therapeutic decision between endoscopic treatment and gastrectomy.",
keywords = "Biomarkers, Gastric neoplasm, Gene expression, Nomograms",
author = "Jeong, {Sang Ho} and Kim, {Rock Bum} and Park, {Sun Yi} and Jiho Park and Jung, {Eun Jung} and Ju, {Young tae} and Jeong, {Chi Young} and Miyeong Park and Ko, {Gyung Hyuck} and Song, {Dae Hyun} and Koh, {Hyun Min} and Kim, {Woo Ho} and Yang, {Han Kwang} and Lee, {Young Joon} and Hong, {Soon Chan}",
year = "2020",
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Jeong, SH, Kim, RB, Park, SY, Park, J, Jung, EJ, Ju, YT, Jeong, CY, Park, M, Ko, GH, Song, DH, Koh, HM, Kim, WH, Yang, HK, Lee, YJ & Hong, SC 2020, 'Nomogram for predicting gastric cancer recurrence using biomarker gene expression', European Journal of Surgical Oncology, vol. 46, no. 1, pp. 195-201. https://doi.org/10.1016/j.ejso.2019.09.143

Nomogram for predicting gastric cancer recurrence using biomarker gene expression. / Jeong, Sang Ho; Kim, Rock Bum; Park, Sun Yi; Park, Jiho; Jung, Eun Jung; Ju, Young tae; Jeong, Chi Young; Park, Miyeong; Ko, Gyung Hyuck; Song, Dae Hyun; Koh, Hyun Min; Kim, Woo Ho; Yang, Han Kwang; Lee, Young Joon; Hong, Soon Chan.

In: European Journal of Surgical Oncology, Vol. 46, No. 1, 01.2020, p. 195-201.

Research output: Contribution to journalArticle

TY - JOUR

T1 - Nomogram for predicting gastric cancer recurrence using biomarker gene expression

AU - Jeong, Sang Ho

AU - Kim, Rock Bum

AU - Park, Sun Yi

AU - Park, Jiho

AU - Jung, Eun Jung

AU - Ju, Young tae

AU - Jeong, Chi Young

AU - Park, Miyeong

AU - Ko, Gyung Hyuck

AU - Song, Dae Hyun

AU - Koh, Hyun Min

AU - Kim, Woo Ho

AU - Yang, Han Kwang

AU - Lee, Young Joon

AU - Hong, Soon Chan

PY - 2020/1

Y1 - 2020/1

N2 - Background: Recently, researchers have tried to predict patient prognosis using biomarker expression in cancer patients. The aim of this study was to develop a nomogram predicting the 5-year recurrence-free probability (RFP) of gastric cancer patients using prognostic biomarker gene expression. Methods: We enrolled 360 patients in the training data set to develop the predictive model and nomogram. We analyzed the patients’ general variables and the gene expression levels of 10 prognostic biomarker candidates between the nonrecurrence and recurrence groups. We also performed external validation using 420 patients from the validation data set. Results: The final nomogram was composed of age, sex, and the expression levels of CAPZA, PPase, OCT-1, PRDX4, gamma-enolase, and c-Myc. The five-year RFPs were 89%, 75%, 54% and 32% for the patients in the low-risk, intermediate-risk, high-risk and very-high-risk groups in the development cohort, respectively. In the external validation cohort, the 5-year RFPs were 89%, 75%, 63% and 60%, respectively. The areas under the curve were 0.718 (95% CI, 0.65–0.78) and 0.640 (95% CI, 0.57–0.70) for the training and validation data sets, respectively. The RFP Kaplan-Meier curves were significantly different among the 4 groups in the training and validation data sets (p < 0.0001). Conclusion: This newly developed nomogram using gene expression can predict the 5-year RFP for gastric cancer patients after surgical treatment. We hope that this nomogram will help in the therapeutic decision between endoscopic treatment and gastrectomy.

AB - Background: Recently, researchers have tried to predict patient prognosis using biomarker expression in cancer patients. The aim of this study was to develop a nomogram predicting the 5-year recurrence-free probability (RFP) of gastric cancer patients using prognostic biomarker gene expression. Methods: We enrolled 360 patients in the training data set to develop the predictive model and nomogram. We analyzed the patients’ general variables and the gene expression levels of 10 prognostic biomarker candidates between the nonrecurrence and recurrence groups. We also performed external validation using 420 patients from the validation data set. Results: The final nomogram was composed of age, sex, and the expression levels of CAPZA, PPase, OCT-1, PRDX4, gamma-enolase, and c-Myc. The five-year RFPs were 89%, 75%, 54% and 32% for the patients in the low-risk, intermediate-risk, high-risk and very-high-risk groups in the development cohort, respectively. In the external validation cohort, the 5-year RFPs were 89%, 75%, 63% and 60%, respectively. The areas under the curve were 0.718 (95% CI, 0.65–0.78) and 0.640 (95% CI, 0.57–0.70) for the training and validation data sets, respectively. The RFP Kaplan-Meier curves were significantly different among the 4 groups in the training and validation data sets (p < 0.0001). Conclusion: This newly developed nomogram using gene expression can predict the 5-year RFP for gastric cancer patients after surgical treatment. We hope that this nomogram will help in the therapeutic decision between endoscopic treatment and gastrectomy.

KW - Biomarkers

KW - Gastric neoplasm

KW - Gene expression

KW - Nomograms

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EP - 201

JO - European Journal of Surgical Oncology

JF - European Journal of Surgical Oncology

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