Evidence map›Paper›PMID 31888644›Full record

ArticleBMC medical genomics2019

Directional association test reveals high-quality putative cancer driver biomarkers including noncoding RNAs.

Hua Zhong, Mingzhou Song

Open access · goldAbstract read
In one paragraph

Article in BMC medical genomics, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing papers in PubMed
0.2field-weighted citation impact, top 52% of its field
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

0 citing papers in PubMed, 3 citations in OpenAlex.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

2 authors at 1 institution in 1 country.

Hua ZhongDepartment of Computer Science, New Mexico State University, University Ave, Las Cruces, 88003, NM, USA.
Mingzhou SongDepartment of Computer Science, New Mexico State University, University Ave, Las Cruces, 88003, NM, USA.
New Mexico State University · US

Funding

Research Education About Cancer and Health (REACH) CoreU54CA132381 · NCI · FRED HUTCHINSON CANCER RESEARCH CENTER · PI KAREN R PETERSON · 2007 to 2026
$25.3M
Undergraduate Student Training in Cancer ResearchU54CA132383 · NCI · NEW MEXICO STATE UNIVERSITY LAS CRUCES · PI MICHELE Irene SHUSTER · 2007 to 2026
$21.4M
NCI NIH HHS U54 CA132381NCI NIH HHS U54 CA132383
6 · The paper itself

Abstract

backgroundMost 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.

resultsWe 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.

conclusionsThis 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.

Indexed as

Biomarkers, TumorChromosomes, HumanDatabases, GeneticGene Expression Regulation, NeoplasticHumansNeoplasmsRNA, UntranslatedTranscription Initiation SiteBiomarkers, TumorRNA, UntranslatedBiomarkerCancer driver geneFunChisqHuman cancerLeukemiaNoncoding RNANon-monotonic directional association

Identifiers

PMID31888644
PMCPMC6936152
OpenAlexW2997330432

What OpenQuestion holds

Textmetadata
LicenceCC BY
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Registered trials

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.