基于降噪风险基因网络的生存风险基因筛选

doi:10.3969/j.issn.1006-5253.2021.02.007

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Abstract Objective To evaluate the performance of the survival risk gene screening algorithm CoxLASSO-ISIS-N based on the noise-reduction risk gene network, and compare it with five other algorithms to analyze the pros and cons of the algorithm, with a view to providing a new idea of the survival risk gene screening of highdimensional gene expression data. Methods Aiming at the survival risk gene screening problem of highdimensional gene expression data with noise, simulated data and real data were used to analyze six algorithms (CoxLASSO, CoxLASSO-N, CoxLASSO-SIS, CoxLASSO-SIS-N, CoxLASSO-ISIS and CoxLASSO-ISIS-N) to compare and analyze the pros and cons of the algorithms. Results The algorithm CoxLASSO-ISIS-N has the best and most stable performance in the overall estimation effect of the model(LR and CS), the ratio of interpreted information (R²) and consistency (CI). Conclusion CoxLASSO-ISIS-N, a survival risk gene screening algorithm based on noise reduction risk gene network, can reduce noise on highdimensional gene expression data with noise, so as to more accurately screen survival risk genes, better reflect the relationship between the occurrence of death or other outcomes and highdimensional gene expression data and provide a basis for clinical diagnosis and prognosis management.

Key words： gene network gene expression data gene screening noise

Received: 09 September 2020

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	Lu Zhen
	Liu Yan
	Li Jingwei

Cite this article:

Lu Zhen,Liu Yan,Li Jingwei. Survival risk gene screening based on noise reduction risk gene network[J]. journal1, 2021, 28(2): 126-130.

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http://manu58.magtech.com.cn/Jwk_zgyytj/EN/10.3969/j.issn.1006-5253.2021.02.007 OR http://manu58.magtech.com.cn/Jwk_zgyytj/EN/Y2021/V28/I2/126

［1］MINHYEOK L, WON H S, JUNHEE S. Prediction of survival risks with adjusted gene expression through risk-gene networks［J］.Bioinformatics (Oxford, England),2019,35(23):4898-4906.
［2］LOU X Y, CHEN G B, Yan L, et al. A Generalized Combinatorial Approach for Detecting Gene-by-Gene and Gene-by-Environment Interactions with Application to Nicotine Dependence ［J］.The American Journal of Human Genetics,2007,80(6):1125-1137.
［3］CHRISTOS D, KUMAR H S, LUCA H, et al. Network-based integration of multi-omics data for prioritizing cancer genes［J］.Bioinformatics (Oxford, England),2018,34(14):2441-2448.
［4］ASHAR A, HOLGER F. Towards clinically more relevant dissection of patient heterogeneity via survival-based Bayesian clustering［J］. Bioinformatics (Oxford, England),2017,33(22):3558-3566.
［5］PIETRO C, ANGELA S, ROBERTO T. Robust clustering of noisy high-dimensional gene expression data for patients subtyping［J］.Bioinformatics (Oxford, England),2018,34(23):4064-4072.
［6］ZHANG W, OTA T, SHRIDHAR V, et al. Network-based survival analysis reveals subnetwork signatures for predicting outcomes of ovarian cancer treatment ［J］.PLoS Comput Biol,2013,9(3):e1002975.
［7］HYUNHWAN J,SANGSEOB L,KYUBUM W, et al. Integrative network analysis for survival-associated gene-gene interactions across multiple genomic profiles in ovarian cancer ［J］.Journal of ovarian research,2015,8:42.
［8］VER-SSIMO A, OLIVEIRA A L, SAGOT M F, et al. DegreeCox–a network-based regularization method for survival analysis［J］.BMC bioinformatics,2016,17(16):449.
［9］VAM F S, UNG M H, LUO S, et al. Integrative analysis of survivalassociated gene sets in breast cancer［J］.BMC medical genomics,2015,8:11.

［10］张秀秀. 基于（I）SIS的变量选择方法及其在极高维数据生存分析中的应用［D］.太原：山西医科大学,2013.
［11］HUMMEL M, BENTINK S,BERGER H, et al. A biologic definition of Burkitt′s lymphoma from transcriptional and genomic profiling. N Engl J Med 2006 Jun 8;354(23):2419-2430.
［12］RICHTER J, SCHLESNER M, HOFFMANN S, et al. Recurrent mutation of the ID3 gene in Burkitt lymphoma identified by integrated genome, exome and transcriptome sequencing［J］. Nat Genet 2012，44(12):1316-1320.
［13］FAN J, LI R. Variable selection via nonconcave penalized likelihood and its oracle properties［J］.Journal of the American statistical Association,2001,96(456):1348-1360.
［14］GOEMAN J J. L1 penalized estimation in the Cox proportional hazards model［J］.Biometrical journal,2010,52(1):70-84.
［15］FAN J, LV J. Sure independence screening for ultrahigh dimensional feature space［J］.Journal of the Royal Statistical Society: Series B (Statistical Methodology),2008,70(5):849-911.
［16］SEOK J, DAVIS R W, XIAO W. A hybrid approach of gene sets and single genes for the prediction of survival risks with gene expression data［J］. PLoS One,2015,10(5):e0122103.
［17］闫丽娜. 惩罚COX模型和弹性网技术在高维数据生存分析中的应用［D］.太原：山西医科大学,2011.
［18］BARABSI A L. Scalefree networks: a decade and beyond［J］. science,2009,325(5939):412-413.
［19］ZHANG K K, XIANG M, ZHOU L, et al. Gene network and familial analyses uncover a gene network involving Tbx5/Osr1/Pcsk6 interaction in the second heart field for atrial septation［J］.Human molecular genetics,2016,25(6):1140-1151.
［20］YIP D KS, CHAN L L, PANG I K, et al. A network approach to exploring the functional basis of genegene epistatic interactions in disease susceptibility［J］.Bioinformatics,2018,34(10):1741-1749.
［21］PEARL J. Models, reasoning and inference［J］.Cambridge: Cambridge University Press,2000.
［22］PENG J, WANG P, ZHOU N, et al. Partial correlation estimation by joint sparse regression models［J］.Journal of the American Statistical Association,2009,104(486):735-746.
［23］TANG H, XIAO G, BEHRENS C, et al. A 12-gene set predicts survival benefits from adjuvant chemotherapy in non-small cell lung cancer patients［J］.Clinical cancer research,2013,19(6):1577-1586.
［24］WU L, CANDILLE S I, CHOI Y, et al. Variation and genetic control of protein abundance in humans［J］.Nature,2013,499(7456):79-82. 
［25］张秀秀,王慧,田双双,等.高维数据回归分析中基于LASSO的自变量选择［J］.中国卫生统计,2013,30(6):922-926.