title: A rank-based transcriptional signature for predicting relapse risk of stage II colorectal cancer identified with proper data sources.
abstract: The irreproducibility problem seriously hinders the studies on transcriptional signatures for predicting relapse risk of early stage colorectal cancer (CRC) patients. Through reviewing recently published 34 literatures for the development of CRC prognostic signatures based on gene expression profiles, we revealed a surprising phenomenon that 33 of these studies analyzed CRC samples with and without adjuvant chemotherapy together in the training and/or validation datasets. This data misuse problem could be partially attributed to the unclear and incomplete data annotation in public data sources. Furthermore, all the signatures proposed by these studies were based on risk scores summarized from gene expression levels, which are sensitive to experimental batch effects and risk compositions of the samples analyzed together. To avoid the above-mentioned problems, we carefully selected three qualified large datasets to develop and validate a signature consisting of three pairs of genes. The within-sample relative expression orderings of these gene pairs could robustly predict relapse risk of stage II CRC samples assessed in different laboratories. The transcriptional and functional analyses provided clear evidence that the high risk patients predicted by the proposed signature represent patients with micro-metastases.
authors.affiliation: College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, China.
authors.forename: Wenyuan
authors.initials: W
authors.lastname: Zhao
authors.affiliation: College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, China.
authors.forename: Beibei
authors.initials: B
authors.lastname: Chen
authors.affiliation: College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, China.
authors.forename: Xin
authors.initials: X
authors.lastname: Guo
authors.affiliation: College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, China.
authors.forename: Ruiping
authors.initials: R
authors.lastname: Wang
authors.affiliation: College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, China.
authors.forename: Zhiqiang
authors.initials: Z
authors.lastname: Chang
authors.affiliation: College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, China.
authors.forename: Yu
authors.initials: Y
authors.lastname: Dong
authors.affiliation: College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, China.
authors.forename: Kai
authors.initials: K
authors.lastname: Song
authors.affiliation: College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, China.
authors.forename: Wen
authors.initials: W
authors.lastname: Wang
authors.affiliation: College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, China.
authors.forename: Lishuang
authors.initials: L
authors.lastname: Qi
authors.affiliation: College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, China.
authors.forename: Yunyan
authors.initials: Y
authors.lastname: Gu
authors.affiliation: College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, China.
authors.forename: Chenguang
authors.initials: C
authors.lastname: Wang
authors.affiliation: Department of Pharmaceutical Sciences, University of Pittsburgh, Pittsburgh, PA, USA.|Women's Cancer Research Center, University of Pittsburgh Cancer Institute, Pittsburgh, PA, USA.|Department of Computational & Systems Biology, University of Pittsburgh, Pittsburgh, PA, USA.
authors.forename: Da
authors.initials: D
authors.lastname: Yang
authors.affiliation: College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, China.|Key Laboratory of Ministry of Education for Gastrointestinal Cancer, Department of Bioinformatics, Fujian Medical University, Fuzhou, China.
authors.forename: Zheng
authors.initials: Z
authors.lastname: Guo
mesh_terms: D015179:Colorectal Neoplasms / Q000235:genetics* / Q000473:pathology*; D030541:Databases, Genetic; D066264:Datasets as Topic; D005260:Female; D020869:Gene Expression Profiling; D006801:Humans; D008297:Male; D009364:Neoplasm Recurrence, Local / Q000235:genetics / Q000473:pathology; D009367:Neoplasm Staging; D011379:Prognosis; D015533:Transcriptional Activation
keywords: experimental batch effect; gene expression profiles; gene pairs; prognostic signatures; relative expression