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Mok, Lydia,Park, Taesung Korea Genome Organization 2019 Genomics & informatics Vol.17 No.4
To identify pathways associated with survival phenotypes using gene expression data, we recently proposed the hierarchical structural component model for pathway analysis of gene expression data (HisCoM-PAGE) method. The HisCoM-PAGE software can consider hierarchical structural relationships between genes and pathways and analyze multiple pathways simultaneously. It can be applied to various types of gene expression data, such as microarray data or RNA sequencing data. We expect that the HisCoM-PAGE software will make our method more easily accessible to researchers who want to perform pathway analysis for survival times.
Ovarian Cancer Prognostic Prediction Model Using RNA Sequencing Data
Jeong, Seokho,Mok, Lydia,Kim, Se Ik,Ahn, TaeJin,Song, Yong-Sang,Park, Taesung Korea Genome Organization 2018 Genomics & informatics Vol.16 No.4
Ovarian cancer is one of the leading causes of cancer-related deaths in gynecological malignancies. Over 70% of ovarian cancer cases are high-grade serous ovarian cancers and have high death rates due to their resistance to chemotherapy. Despite advances in surgical and pharmaceutical therapies, overall survival rates are not good, and making an accurate prediction of the prognosis is not easy because of the highly heterogeneous nature of ovarian cancer. To improve the patient's prognosis through proper treatment, we present a prognostic prediction model by integrating high-dimensional RNA sequencing data with their clinical data through the following steps: gene filtration, pre-screening, gene marker selection, integrated study of selected gene markers and prediction model building. These steps of the prognostic prediction model can be applied to other types of cancer besides ovarian cancer.
고차원 전사체 자료를 이용한 췌장암 환자의예후 예측 모형
정석호(Seokho Jeong),목라디아(Lydia Mok),박태성(Taesung Park) 한국데이터정보과학회 2018 한국데이터정보과학회지 Vol.29 No.6
췌장암은 사망위험이 높은 대표적인 질환이며, 임상변수만으로 예후에 대한 예측이 어려워 유전적인 특성을 고려한 연구가 필요하다. 이를 위해 임상연구와 함께 유전적 연구를 바탕으로 한 예측 모형을 개발하려는 시도들이 진행되고 있다. 그런데, 최근에 관심 받고 있는 차세대 유전체 분석인 RNA 시퀀싱 발현 자료의 경우, 변수의 개수가 수만 개에 이르는 고차원 자료로써 변수의 수가 표본 수보다 훨씬 더 큰 문제가 있다. 본 연구에서는 이러한 고차원 RNA 시퀀싱 자료를 임상자료와 함께 통합하여 예후 예측을 위한 통계모형을 개발하기 위하여 (1) 유전자 필터링, (2) 후보 유전자 마커 선택, (3) 벌점화 Cox 모형을 이용한 최종 마커 선택의 단계별로 모형을 개발하였다. 본 연구에서 소개할 모형 개발 방법은 RNA 시퀀싱 자료에 기반한 타 암종에 대한 예후 예측 모형의 개발을 위한 가이드라인으로 널리 활용될 것으로 기대한다. Pancreatic cancer is a well known disease with a high risk of death. Accurate prediction of prognosis using only clinical information has not been easy. Therefore, an effort to develop a better prediction model by using genetic information along with clinical information is needed. RNA sequencing data consist of tens of thousands of gene expression variables. As a result, the number of variables is much larger than sample size. In this study, we developed the prognosis prediction model by integrating the high dimensional RNA sequencing data with clinical data through the following three steps: (1) gene filtering, (2) selecting candidate genetic markers, (3) final marker selection using penalized Cox model. The prognosis prediction model development procedure introduced in this study is expected to be widely used for the development of prognosis prediction models for other types of cancer as well.
( Jae Seung Kang ),( Lydia Mok ),( Jin Seok Heo ),( In Woong Han ),( Sang Hyun Shin ),( Yoo-seok Yoon ),( Ho-seong Han ),( Dae Wook Hwang ),( Jae Hoon Lee ),( Woo Jung Lee ),( Sang Jae Park ),( Joon S 대한간학회 2021 Gut and Liver Vol.15 No.6
Background/Aims: Several prediction models for evaluating the prognosis of nonmetastatic resected pancreatic ductal adenocarcinoma (PDAC) have been developed, and their performances were reported to be superior to that of the 8th edition of the American Joint Committee on Cancer (AJCC) staging system. We developed a prediction model to evaluate the prognosis of resected PDAC and externally validated it with data from a nationwide Korean database. Methods: Data from the Surveillance, Epidemiology and End Results (SEER) database were utilized for model development, and data from the Korea Tumor Registry System-Biliary Pancreas (KOTUS-BP) database were used for external validation. Potential candidate variables for model development were age, sex, histologic differentiation, tumor location, adjuvant chemotherapy, and the AJCC 8th staging system T and N stages. For external validation, the concordance index (C-index) and time-dependent area under the receiver operating characteristic curve (AUC) were evaluated. Results: Between 2004 and 2016, data from 9,624 patients were utilized for model development, and data from 3,282 patients were used for external validation. In the multivariate Cox proportional hazard model, age, sex, tumor location, T and N stages, histologic differentiation, and adjuvant chemotherapy were independent prognostic factors for resected PDAC. After an exhaustive search and 10-fold cross validation, the best model was finally developed, which included all prognostic variables. The C-index, 1-year, 2-year, 3-year, and 5-year time-dependent AUCs were 0.628, 0.650, 0.665, 0.675, and 0.686, respectively. Conclusions: The survival prediction model for resected PDAC could provide quantitative survival probabilities with reliable performance. External validation studies with other nationwide databases are needed to evaluate the performance of this model. (Gut Liver 2021;15:912-921)