Evidence map›Paper›PMID 26884679›Full record

ArticleCancer informatics2016

Toward Signaling-Driven Biomarkers Immune to Normal Tissue Contamination.

John C Stansfield, Matthew Rusay, Roger Shan, Conor Kelton, Daria A Gaykalova, Elana J Fertig, Joseph A Califano, Michael F Ochs

Open access · goldAbstract read
In one paragraph

Article in Cancer informatics, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
9citing papers in PubMed, 1 pooled it
0.8field-weighted citation impact, top 25% 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

9 citing papers in PubMed, 1 synthesis or guideline pooled it, 12 citations in OpenAlex.

  1. Pooled it
  2. Article
  3. Review
  4. Cancers · 2025
    Article
  5. Epigenetic silencing ofOncology reports · 2025
    Article
  6. Article
  7. Review
  8. Article
  9. Article
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

8 authors at 2 institutions in 1 country.

John C StansfieldDepartment of Mathematics and Statistics, The College of New Jersey, Ewing, NJ, USA.
Matthew RusayDepartment of Mathematics and Statistics, The College of New Jersey, Ewing, NJ, USA.
Roger ShanDepartment of Mathematics and Statistics, The College of New Jersey, Ewing, NJ, USA.
Conor KeltonDepartment of Mathematics and Statistics, The College of New Jersey, Ewing, NJ, USA.
Daria A GaykalovaDepartment of Otolaryngology-Head and Neck Surgery, Johns Hopkins Medical Institutions, Baltimore, MD, USA.
Elana J FertigDivision of Oncology Biostatistics and Bioinformatics, Johns Hopkins School of Medicine, Baltimore, MD, USA.
Joseph A CalifanoDepartment of Otolaryngology-Head and Neck Surgery, Johns Hopkins Medical Institutions, Baltimore, MD, USA.; Milton J. Dance Jr. Head and Neck Center, Greater Baltimore Medical Center, Baltimore, MD, USA.
Michael F OchsDepartment of Mathematics and Statistics, The College of New Jersey, Ewing, NJ, USA.
College of New Jersey · USJohns Hopkins University · US

Funding

TUMOR SUPPRESOR GENE PROMOTER HYPERMETHYLATION FOR DETECTION OF HEAD AND NECKP50DE019032 · NIDCR · JOHNS HOPKINS UNIVERSITY · PI SIDRANSKY, DAVID · 2007 to 2016
$27.1M
Dynamical Models of Cetuximab Resistance in HNSCC Based on Serial Genomics DataR01CA177669 · NCI · JOHNS HOPKINS UNIVERSITY · PI FERTIG, ELANA · 2014 to 2019
$1.8M
Modeling Transcriptional Reprogramming by Markov Chain Monte Carlo SamplingR01LM011000 · NLM · JOHNS HOPKINS UNIVERSITY · PI OCHS, MICHAEL F · 2012 to 2014
$886k
NCI NIH HHS R01 CA177669NIDCR NIH HHS P50 DE019032NLM NIH HHS R01 LM011000
6 · The paper itself

Abstract

The goal of this study was to discover a minimally invasive pathway-specific biomarker that is immune to normal cell mRNA contamination for diagnosing head and neck squamous cell carcinoma (HNSCC). Using Elsevier's MedScan natural language processing component of the Pathway Studio software and the TRANSFAC database, we produced a curated set of genes regulated by the signaling networks driving the development of HNSCC. The network and its gene targets provided prior probabilities for gene expression, which guided our CoGAPS matrix factorization algorithm to isolate patterns related to HNSCC signaling activity from a microarray-based study. Using patterns that distinguished normal from tumor samples, we identified a reduced set of genes to analyze with Top Scoring Pair in order to produce a potential biomarker for HNSCC. Our proposed biomarker comprises targets of the transcription factor (TF) HIF1A and the FOXO family of TFs coupled with genes that show remarkable stability across all normal tissues. Based on validation with novel data from The Cancer Genome Atlas (TCGA), measured by RNAseq, and bootstrap sampling, the biomarker for normal vs. tumor has an accuracy of 0.77, a Matthews correlation coefficient of 0.54, and an area under the curve (AUC) of 0.82.

Indexed as

biomarkersbiostatisticscancergene expression profiling

Identifiers

PMID26884679
PMCPMC4750896
OpenAlexW2269242719

What OpenQuestion holds

Textmetadata
Read underepoch 390

Registered trials

None linked

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.