Evidence map›Paper›PMID 26169172›Full record

ArticleBMC cancer2015

Pan-cancer analysis of TCGA data reveals notable signaling pathways.

Richard Neapolitan, Curt M Horvath, Xia Jiang

Open access · goldAbstract read
In one paragraph

Article in BMC cancer, 2015. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 24 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
24citing papers in PubMed, 1 pooled it
3.8field-weighted citation impact, top 6% 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

24 citing papers in PubMed, 1 synthesis or guideline pooled it, 39 citations in OpenAlex.

  1. Pooled it
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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

3 authors at 2 institutions in 1 country.

Richard NeapolitanDepartment of Preventive Medicine, Northwestern University Feinberg School of Medicine, Chicago, Il, USA. richard.neapolitan@northwestern.edu.
Curt M HorvathDepartment of Molecular Biosciences, Northwestern University, Evanston, Illinois, USA. horvath@northwestern.edu.
Xia JiangDepartment of Biomedical Informatics, University of Pittsburgh, Pittsburgh, PA, USA. xij6@pitt.edu.
Northwestern University · USUniversity of Pittsburgh · US

Funding

A New Generation Clinical Decision Support SystemR01LM011663 · NLM · UNIVERSITY OF PITTSBURGH AT PITTSBURGH · PI JIANG, XIA, NEAPOLITAN, RICHARD E · 2014 to 2017
$2.0M
Detecting Genome Wide Epistasis with Efficient Bayesian Network LearningR00LM010822 · NLM · UNIVERSITY OF PITTSBURGH AT PITTSBURGH · PI JIANG, XIA · 2012 to 2014
$565k
NLM NIH HHS R00 LM010822NLM NIH HHS R00LM010822NLM NIH HHS R01 LM011663NLM NIH HHS R01LM011663
6 · The paper itself

Abstract

backgroundA signal transduction pathway (STP) is a network of intercellular information flow initiated when extracellular signaling molecules bind to cell-surface receptors. Many aberrant STPs have been associated with various cancers. To develop optimal treatments for cancer patients, it is important to discover which STPs are implicated in a cancer or cancer-subtype. The Cancer Genome Atlas (TCGA) makes available gene expression level data on cases and controls in ten different types of cancer including breast cancer, colon adenocarcinoma, glioblastoma, kidney renal papillary cell carcinoma, low grade glioma, lung adenocarcinoma, lung squamous cell carcinoma, ovarian carcinoma, rectum adenocarcinoma, and uterine corpus endometriod carcinoma. Signaling Pathway Impact Analysis (SPIA) is a software package that analyzes gene expression data to identify whether a pathway is relevant in a given condition.

methodsWe present the results of a study that uses SPIA to investigate all 157 signaling pathways in the KEGG PATHWAY database. We analyzed each of the ten cancer types mentioned above separately, and we perform a pan-cancer analysis by grouping the data for all the cancer types.

resultsIn each analysis several pathways were found to be markedly more significant than all the other pathways. We call them notable. Research has already established a connection between many of these pathways and the corresponding cancer type. However, some of our discovered pathways appear to be new findings. Altogether there were 37 notable findings in the separate analyses, 26 of them occurred in 7 pathways. These 7 pathways included the 4 notable pathways discovered in the pan-cancer analysis. So, our results suggest that these 7 pathways account for much of the mechanisms of cancer. Furthermore, by looking at the overlap among pathways, we identified possible regions on the pathways where the aberrant activity is occurring.

conclusionsWe obtained 37 notable findings concerning 18 pathways. Some of them appear to be new discoveries. Furthermore, we identified regions on pathways where the aberrant activity might be occurring. We conclude that our results will prove to be valuable to cancer researchers because they provide many opportunities for laboratory and clinical follow-up studies.

Indexed as

GenomicsSignal TransductionCluster AnalysisComputational BiologyDatabases, GeneticFemaleHumansMaleNeoplasmsProtein Interaction MappingProtein Interaction Maps

Identifiers

PMID26169172
PMCPMC4501083
OpenAlexW1606499663

What OpenQuestion holds

Textmetadata
LicenceCC BY
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.