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
0
조회
0
다운로드
0
인용
0
참고문헌
키워드
초록
암을 유발하는 유전자는 모든 암 환자에게 공통적인 것은 아니며, 이러한 환자 특이적 암 유발 유전자의 탐색은 개인 맟춤형 암 치료 및항암제 개발에 있어서 매우 중요하다. 환자 특이적 암 유발 유전자를 찾기 위한 생물 정보학 연구들이 있어왔지만, 아직 정확도 면에서는 발전의 여지가 있다. 본 논문에서는 환자 특이적 암 유발 유전자를 탐색하기 위하여 NPD (Network based Patient-specific Driver geneidentification)라는 방법을 제안한다. NPD는 환자 특이적 유전자 네트워크를 구축하고, 여기에 수정된 PageRank 알고리즘을 적용하여 유전자에 점수를 부여한 후, 유전적 변이 데이터를 사용한 승률 계산 방법을 통하여 암 유발 유전자를 찾는 세 단계로 이루어진다. TCGA 데이터베이스의 여섯 개의 암 데이터에 NPD를 적용한 결과, NPD가 기존의 환자 특이적 암 유발 유전자 탐색 방법들보다 전체적으로 높은 F1 점수를 보여줌을 확인할 수 있었다.
Abstract
Cancer patients can have different kinds of cancer driver genes, and identification of these patient-specific cancer driver genes isan important step in the development of personalized cancer treatment and drug development. Several bioinformatic methods havebeen proposed for this purpose, but there is room for improvement in terms of accuracy. In this paper, we propose NPD (Networkbased 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 identifyingcancer driver genes through a score comparison method. We applied NPD on six cancer types of TCGA data, and found that NPDshowed generally higher F1 score compared to existing patient-specific cancer driver gene identification methods.
참고문헌 (23)
- M. S. Lawrence, "Mutational heterogeneity in cancer and the search for new cancer-associated genes", Nature, 499(7457), pp.214~218 (2013)
- C. Arnedo-Pac, "OncodriveCLUSTL : a sequencebased clustering method to identify cancer drivers", Bioinformatics, 35(22), pp.4788~4790 (2019)
- P. Luo, "deepDriver : Predicting cancer driver genes based on somatic mutations using deep convolutional neural networks", Frontiers in Genetics, 10, pp.13~ (2019)
- J. Nulsen, "Pancancer detection of driver genes at the single-patient resolution", Genome Medicine, 13(1), pp.1~14 (2021)
- H. Yang, "Cancer driver gene discovery through an integrative genomics approach in a non-parametric Bayesian framework", Bioinformatics, 33(4), pp.483~490 (2017)
- D. Bertrand, "Patient-specific driver gene prediction and risk assessment through integrated network analysis of cancer omics profiles", Nucleic Acids Research, 43(7), pp.e44~e44 (2015)
- V. V. Pham, "DriverGroup : A novel method for identifying driver gene groups", Bioinformatics, 36(Supplement_2), pp.i583~i591 (2020)
- D. Pe'er, "Principles and strategies for developing network models in cancer", Cell, 144(6), pp.864~873 (2011)
- M. R. Stratton, "Journeys into the genome of cancer cells", EMBO Molecular Medicine, 5(2), pp.169~172 (2013)
- L. Ding, "Analysis of next-generation genomic data in cancer : Accomplishments and challenges", Human Molecular Genetics, 19(R2), pp.R188~R196 (2010)
- J. Reimand, "Systematic analysis of somatic mutations in phosphorylation signaling predicts novel cancer drivers", Molecular Systems Biology, 9(1), pp.637~ (2013)
- J. P. Hou, "DawnRank : Discovering personalized driver genes in cancer", Genome Medicine, 6(7), pp.1~16 (2014)
- W. F. Guo, "A novel network control model for identifying personalized driver genes in cancer", PLoS Computational Biology, 15(11), pp.e1007520~ (2019)
- W. F. Guo, "Discovering personalized driver mutation profiles of single samples in cancer by network control strategy", Bioinformatics, 34(11), pp.1893~1903 (2018)
- G. Dinstag, "PRODIGY : Personalized prioritization of driver genes", Bioinformatics, 36(6), pp.1831~1839 (2020)
- L. Page, "The Page-Rank citation ranking: Bringing order to the web" (1999), Stanford InfoLab
- K. Tomczak, "The Cancer Genome Atlas(TCGA) : An immeasurable source of knowledge", Contemporary Oncology, 19(1A), pp.A68~ (2015)
- D. Croft, "Reactome : A database of reactions, pathways and biological processes", Nucleic Acids Research, 39(suppl_1), pp.D691~D697 (2010)
- Z. P. Liu, "RegNetwork : An integrated database of transcriptional and post-transcriptional regulatory networks in human and mouse", Database, 2015 (2015)
- H. Han, "TRRUST v2 : An expanded reference database of human and mouse transcriptional regulatory interactions", Nucleic Acids Research, 46(D1), pp.D380~D386 (2018)
- G. Gundem, "IntOGen : Integration and data mining of multidimensional oncogenomic data", Nature Methods, 7(2), pp.92~93 (2010)
- Z. Sondka, "The COSMIC Cancer Gene Census : Describing genetic dysfunction across all human cancers", Nature Reviews Cancer, 18(11), pp.696~705 (2018)
- D. Repana, "The Network of Cancer Genes(NCG) : A comprehensive catalogue of known and candidate cancer genes from cancer sequencing screens", Genome Biology, 20(1), pp.1~12 (2019)