title: Identifying the gene signatures from gene-pathway bipartite network guarantees the robust model performance on predicting the cancer prognosis.
abstract: For the purpose of improving the prediction of cancer prognosis in the clinical researches, various algorithms have been developed to construct the predictive models with the gene signatures detected by DNA microarrays. Due to the heterogeneity of the clinical samples, the list of differentially expressed genes (DEGs) generated by the statistical methods or the machine learning algorithms often involves a number of false positive genes, which are not associated with the phenotypic differences between the compared clinical conditions, and subsequently impacts the reliability of the predictive models. In this study, we proposed a strategy, which combined the statistical algorithm with the gene-pathway bipartite networks, to generate the reliable lists of cancer-related DEGs and constructed the models by using support vector machine for predicting the prognosis of three types of cancers, namely, breast cancer, acute myeloma leukemia, and glioblastoma. Our results demonstrated that, combined with the gene-pathway bipartite networks, our proposed strategy can efficiently generate the reliable cancer-related DEG lists for constructing the predictive models. In addition, the model performance in the swap analysis was similar to that in the original analysis, indicating the robustness of the models in predicting the cancer outcomes.
authors.affiliation: Biogas Institute of Ministry of Agriculture, Chengdu 610041, China.
authors.forename: Li
authors.initials: L
authors.lastname: He
authors.affiliation: College of Chemistry, Sichuan University, Chengdu 610064, China.
authors.forename: Yuelong
authors.initials: Y
authors.lastname: Wang
authors.affiliation: College of Chemistry, Sichuan University, Chengdu 610064, China.
authors.forename: Yongning
authors.initials: Y
authors.lastname: Yang
authors.affiliation: College of Chemistry, Sichuan University, Chengdu 610064, China.
authors.forename: Liqiu
authors.initials: L
authors.lastname: Huang
authors.affiliation: College of Chemistry, Sichuan University, Chengdu 610064, China.
authors.forename: Zhining
authors.initials: Z
authors.lastname: Wen
mesh_terms: D000465:Algorithms; D001185:Artificial Intelligence; D020869:Gene Expression Profiling / Q000379:methods; D015972:Gene Expression Regulation, Neoplastic; D053263:Gene Regulatory Networks; D006801:Humans; D009363:Neoplasm Proteins / Q000096:biosynthesis*; D009369:Neoplasms / Q000235:genetics* / Q000473:pathology; D020411:Oligonucleotide Array Sequence Analysis / Q000379:methods*; D011379:Prognosis; D060388:Support Vector Machine
chemical_list: D009363:Neoplasm Proteins