개인별 유전자 네트워크 구축 및 페이지랭크를 이용한 환자 특이적 암 유발 유전자 탐색 방법 = Cancer Patient Specific Driver Gene Identification by Personalized Gene Network and PageRank

다국어 초록 (Multilingual Abstract)

Cancer patients can have different kinds of cancer driver genes, and identification of these patient-specific cancer driver genes is an important step in the development of personalized cancer treatment and drug development. Several bioinformatic methods have been proposed for this purpose, but there is room for improvement in terms of accuracy. In this paper, we propose NPD (Network based Patient-specific Driver gene identification) for identifying patient-specific cancer driver genes. NPD consists of three steps, constructing a patient-specific gene network, applying the modified PageRank algorithm to assign scores to genes, and identifying cancer driver genes through a score comparison method. We applied NPD on six cancer types of TCGA data, and found that NPD showed generally higher F1 score compared to existing patient-specific cancer driver gene identification methods.

참고문헌 (Reference)

1 P. Luo, "deepDriver : Predicting cancer driver genes based on somatic mutations using deep convolutional neural networks" 10 : 13-, 2019

2 L. Page, "The Page-Rank citation ranking: Bringing order to the web" Stanford InfoLab 1999

3 D. Repana, "The Network of Cancer Genes(NCG) : A comprehensive catalogue of known and candidate cancer genes from cancer sequencing screens" 20 (20): 1-12, 2019

4 K. Tomczak, "The Cancer Genome Atlas(TCGA) : An immeasurable source of knowledge" 19 (19): A68-, 2015

5 Z. Sondka, "The COSMIC Cancer Gene Census : Describing genetic dysfunction across all human cancers" 18 (18): 696-705, 2018

6 H. Han, "TRRUST v2 : An expanded reference database of human and mouse transcriptional regulatory interactions" 46 (46): D380-D386, 2018

7 J. Reimand, "Systematic analysis of somatic mutations in phosphorylation signaling predicts novel cancer drivers" 9 (9): 637-, 2013

8 Z. P. Liu, "RegNetwork : An integrated database of transcriptional and post-transcriptional regulatory networks in human and mouse" 2015 : 2015

9 D. Croft, "Reactome : A database of reactions, pathways and biological processes" 39 (39): D691-D697, 2010

10 D. Pe'er, "Principles and strategies for developing network models in cancer" 144 (144): 864-873, 2011

11 D. Bertrand, "Patient-specific driver gene prediction and risk assessment through integrated network analysis of cancer omics profiles" 43 (43): e44-e44, 2015

12 J. Nulsen, "Pancancer detection of driver genes at the single-patient resolution" 13 (13): 1-14, 2021

13 G. Dinstag, "PRODIGY : Personalized prioritization of driver genes" 36 (36): 1831-1839, 2020

14 C. Arnedo-Pac, "OncodriveCLUSTL : a sequencebased clustering method to identify cancer drivers" 35 (35): 4788-4790, 2019

15 M. S. Lawrence, "Mutational heterogeneity in cancer and the search for new cancer-associated genes" 499 (499): 214-218, 2013

16 M. R. Stratton, "Journeys into the genome of cancer cells" 5 (5): 169-172, 2013

17 G. Gundem, "IntOGen : Integration and data mining of multidimensional oncogenomic data" 7 (7): 92-93, 2010

18 V. V. Pham, "DriverGroup : A novel method for identifying driver gene groups" 36 (36): i583-i591, 2020

19 W. F. Guo, "Discovering personalized driver mutation profiles of single samples in cancer by network control strategy" 34 (34): 1893-1903, 2018

20 J. P. Hou, "DawnRank : Discovering personalized driver genes in cancer" 6 (6): 1-16, 2014

21 H. Yang, "Cancer driver gene discovery through an integrative genomics approach in a non-parametric Bayesian framework" 33 (33): 483-490, 2017

22 L. Ding, "Analysis of next-generation genomic data in cancer : Accomplishments and challenges" 19 (19): R188-R196, 2010

23 W. F. Guo, "A novel network control model for identifying personalized driver genes in cancer" 15 (15): e1007520-, 2019