Jonghwan Choi1, Sanghyun Park2, Youngmi Yoon3, Jaegyoon Ahn1. 1. Department of Computer Science and Engineering, Incheon National University, Incheon, The Republic of Korea. 2. Department of Computer Science, Yonsei University, Seoul, The Republic of Korea. 3. Department of Computer Engineering, Gachon University, Seongnam-si, Gyeonggi-do, The Republic of Korea.
Abstract
MOTIVATION: Identification of genes that can be used to predict prognosis in patients with cancer is important in that it can lead to improved therapy, and can also promote our understanding of tumor progression on the molecular level. One of the common but fundamental problems that render identification of prognostic genes and prediction of cancer outcomes difficult is the heterogeneity of patient samples. RESULTS: To reduce the effect of sample heterogeneity, we clustered data samples using K-means algorithm and applied modified PageRank to functional interaction (FI) networks weighted using gene expression values of samples in each cluster. Hub genes among resulting prioritized genes were selected as biomarkers to predict the prognosis of samples. This process outperformed traditional feature selection methods as well as several network-based prognostic gene selection methods when applied to Random Forest. We were able to find many cluster-specific prognostic genes for each dataset. Functional study showed that distinct biological processes were enriched in each cluster, which seems to reflect different aspect of tumor progression or oncogenesis among distinct patient groups. Taken together, these results provide support for the hypothesis that our approach can effectively identify heterogeneous prognostic genes, and these are complementary to each other, improving prediction accuracy. AVAILABILITY AND IMPLEMENTATION: https://github.com/mathcom/CPR. CONTACT: jgahn@inu.ac.kr. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.
MOTIVATION: Identification of genes that can be used to predict prognosis in patients with cancer is important in that it can lead to improved therapy, and can also promote our understanding of tumor progression on the molecular level. One of the common but fundamental problems that render identification of prognostic genes and prediction of cancer outcomes difficult is the heterogeneity of patient samples. RESULTS: To reduce the effect of sample heterogeneity, we clustered data samples using K-means algorithm and applied modified PageRank to functional interaction (FI) networks weighted using gene expression values of samples in each cluster. Hub genes among resulting prioritized genes were selected as biomarkers to predict the prognosis of samples. This process outperformed traditional feature selection methods as well as several network-based prognostic gene selection methods when applied to Random Forest. We were able to find many cluster-specific prognostic genes for each dataset. Functional study showed that distinct biological processes were enriched in each cluster, which seems to reflect different aspect of tumor progression or oncogenesis among distinct patient groups. Taken together, these results provide support for the hypothesis that our approach can effectively identify heterogeneous prognostic genes, and these are complementary to each other, improving prediction accuracy. AVAILABILITY AND IMPLEMENTATION: https://github.com/mathcom/CPR. CONTACT: jgahn@inu.ac.kr. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.
Authors: N Ashokkumar; S Meera; P Anandan; Mantripragada Yaswanth Bhanu Murthy; K S Kalaivani; Tahani Awad Alahmadi; Sulaiman Ali Alharbi; S S Raghavan; S Arockia Jayadhas Journal: Biomed Res Int Date: 2022-08-10 Impact factor: 3.246
Authors: Jong Seung Kim; Jae Seok Jeong; Kyung Bae Lee; So Ri Kim; Yeong Hun Choe; Sam Hyun Kwon; Seong Ho Cho; Yong Chul Lee Journal: Sci Rep Date: 2018-10-30 Impact factor: 4.379
Authors: Melanie A Krook; Russell Bonneville; Hui-Zi Chen; Julie W Reeser; Michele R Wing; Dorrelyn M Martin; Amy M Smith; Thuy Dao; Eric Samorodnitsky; Anoosha Paruchuri; Jharna Miya; Kaitlin R Baker; Lianbo Yu; Cynthia Timmers; Kristin Dittmar; Aharon G Freud; Patricia Allenby; Sameek Roychowdhury Journal: Cold Spring Harb Mol Case Stud Date: 2019-08-01