Cargando…
Directional association test reveals high-quality putative cancer driver biomarkers including noncoding RNAs
BACKGROUND: Most statistical methods used to identify cancer driver genes are either biased due to choice of assumed parametric models or insensitive to directional relationships important for causal inference. To overcome modeling biases and directional insensitivity, a recent statistical functiona...
Autores principales: | , |
---|---|
Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
BioMed Central
2019
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6936152/ https://www.ncbi.nlm.nih.gov/pubmed/31888644 http://dx.doi.org/10.1186/s12920-019-0565-9 |
_version_ | 1783483694315470848 |
---|---|
author | Zhong, Hua Song, Mingzhou |
author_facet | Zhong, Hua Song, Mingzhou |
author_sort | Zhong, Hua |
collection | PubMed |
description | BACKGROUND: Most statistical methods used to identify cancer driver genes are either biased due to choice of assumed parametric models or insensitive to directional relationships important for causal inference. To overcome modeling biases and directional insensitivity, a recent statistical functional chi-squared test (FunChisq) detects directional association via model-free functional dependency. FunChisq examines patterns pointing from independent to dependent variables arising from linear, non-linear, or many-to-one functional relationships. Meanwhile, the Functional Annotation of Mammalian Genome 5 (FANTOM5) project surveyed gene expression at over 200,000 transcription start sites (TSSs) in nearly all human tissue types, primary cell types, and cancer cell lines. The data cover TSSs originated from both coding and noncoding genes. For the vast uncharacterized human TSSs that may exhibit complex patterns in cancer versus normal tissues, the model-free property of FunChisq provides us an unprecedented opportunity to assess the evidence for a gene’s directional effect on human cancer. RESULTS: We first evaluated FunChisq and six other methods using 719 curated cancer genes on the FANTOM5 data. FunChisq performed best in detecting known cancer driver genes from non-cancer genes. We also show the capacity of FunChisq to reveal non-monotonic patterns of functional association, to which typical differential analysis methods such as t-test are insensitive. Further applying FunChisq to screen unannotated TSSs in FANTOM5, we predicted 1108 putative cancer driver noncoding RNAs, stronger than 90% of curated cancer driver genes. Next, we compared leukemia samples against other samples in FANTOM5 and FunChisq predicted 332/79 potential biomarkers for lymphoid/myeloid leukemia, stronger than the TSSs of all 87/100 known driver genes in lymphoid/myeloid leukemia. CONCLUSIONS: This study demonstrated the advantage of FunChisq in revealing directional association, especially in detecting non-monotonic patterns. Here, we also provide the most comprehensive catalog of high-quality biomarkers that may play a causative role in human cancers, including putative cancer driver noncoding RNAs and lymphoid/myeloid leukemia specific biomarkers. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (10.1186/s12920-019-0565-9) contains supplementary material, which is available to authorized users. |
format | Online Article Text |
id | pubmed-6936152 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2019 |
publisher | BioMed Central |
record_format | MEDLINE/PubMed |
spelling | pubmed-69361522019-12-31 Directional association test reveals high-quality putative cancer driver biomarkers including noncoding RNAs Zhong, Hua Song, Mingzhou BMC Med Genomics Research BACKGROUND: Most statistical methods used to identify cancer driver genes are either biased due to choice of assumed parametric models or insensitive to directional relationships important for causal inference. To overcome modeling biases and directional insensitivity, a recent statistical functional chi-squared test (FunChisq) detects directional association via model-free functional dependency. FunChisq examines patterns pointing from independent to dependent variables arising from linear, non-linear, or many-to-one functional relationships. Meanwhile, the Functional Annotation of Mammalian Genome 5 (FANTOM5) project surveyed gene expression at over 200,000 transcription start sites (TSSs) in nearly all human tissue types, primary cell types, and cancer cell lines. The data cover TSSs originated from both coding and noncoding genes. For the vast uncharacterized human TSSs that may exhibit complex patterns in cancer versus normal tissues, the model-free property of FunChisq provides us an unprecedented opportunity to assess the evidence for a gene’s directional effect on human cancer. RESULTS: We first evaluated FunChisq and six other methods using 719 curated cancer genes on the FANTOM5 data. FunChisq performed best in detecting known cancer driver genes from non-cancer genes. We also show the capacity of FunChisq to reveal non-monotonic patterns of functional association, to which typical differential analysis methods such as t-test are insensitive. Further applying FunChisq to screen unannotated TSSs in FANTOM5, we predicted 1108 putative cancer driver noncoding RNAs, stronger than 90% of curated cancer driver genes. Next, we compared leukemia samples against other samples in FANTOM5 and FunChisq predicted 332/79 potential biomarkers for lymphoid/myeloid leukemia, stronger than the TSSs of all 87/100 known driver genes in lymphoid/myeloid leukemia. CONCLUSIONS: This study demonstrated the advantage of FunChisq in revealing directional association, especially in detecting non-monotonic patterns. Here, we also provide the most comprehensive catalog of high-quality biomarkers that may play a causative role in human cancers, including putative cancer driver noncoding RNAs and lymphoid/myeloid leukemia specific biomarkers. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (10.1186/s12920-019-0565-9) contains supplementary material, which is available to authorized users. BioMed Central 2019-12-30 /pmc/articles/PMC6936152/ /pubmed/31888644 http://dx.doi.org/10.1186/s12920-019-0565-9 Text en © The Author(s) 2019 Open Access This article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver(http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated. |
spellingShingle | Research Zhong, Hua Song, Mingzhou Directional association test reveals high-quality putative cancer driver biomarkers including noncoding RNAs |
title | Directional association test reveals high-quality putative cancer driver biomarkers including noncoding RNAs |
title_full | Directional association test reveals high-quality putative cancer driver biomarkers including noncoding RNAs |
title_fullStr | Directional association test reveals high-quality putative cancer driver biomarkers including noncoding RNAs |
title_full_unstemmed | Directional association test reveals high-quality putative cancer driver biomarkers including noncoding RNAs |
title_short | Directional association test reveals high-quality putative cancer driver biomarkers including noncoding RNAs |
title_sort | directional association test reveals high-quality putative cancer driver biomarkers including noncoding rnas |
topic | Research |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6936152/ https://www.ncbi.nlm.nih.gov/pubmed/31888644 http://dx.doi.org/10.1186/s12920-019-0565-9 |
work_keys_str_mv | AT zhonghua directionalassociationtestrevealshighqualityputativecancerdriverbiomarkersincludingnoncodingrnas AT songmingzhou directionalassociationtestrevealshighqualityputativecancerdriverbiomarkersincludingnoncodingrnas |