Evidence map›Paper›PMID 29731976›Full record

ArticleOncotarget2018

Predicting high-risk endometrioid carcinomas using proteins.

Di Du, Wencai Ma, Melinda S Yates, Tao Chen, Karen H Lu, Yiling Lu, John N Weinstein, Russell R Broaddus, Gordon B Mills, Yuexin Liu

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Oncotarget, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.

0numbers the graph read from it
0cells of the map it votes in
5citing papers in PubMed
–field-weighted citation impact
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

5 citing papers in PubMed.

  1. Article
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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

10 authors.

Di DuDepartment of Bioinformatics and Computational Biology, The University of Texas MD Anderson Cancer Center, Houston, Texas, USA.
Wencai MaDepartment of Bioinformatics and Computational Biology, The University of Texas MD Anderson Cancer Center, Houston, Texas, USA.
Melinda S YatesDepartment of Gynecologic Oncology and Reproductive Medicine, The University of Texas MD Anderson Cancer Center, Houston, Texas, USA.
Tao ChenEndoscopy Center and Endoscopy Research Institute, Zhongshan Hospital of Fudan University, Shanghai, China.
Karen H LuDepartment of Gynecologic Oncology and Reproductive Medicine, The University of Texas MD Anderson Cancer Center, Houston, Texas, USA.
Yiling LuDepartment of Systems Biology, The University of Texas MD Anderson Cancer Center, Houston, Texas, USA.
John N WeinsteinDepartment of Bioinformatics and Computational Biology, The University of Texas MD Anderson Cancer Center, Houston, Texas, USA.
Russell R BroaddusDepartment of Pathology, The University of Texas MD Anderson Cancer Center, Houston, Texas, USA.
Gordon B MillsDepartment of Systems Biology, The University of Texas MD Anderson Cancer Center, Houston, Texas, USA.
Yuexin LiuDepartment of Bioinformatics and Computational Biology, The University of Texas MD Anderson Cancer Center, Houston, Texas, USA.

Funding

Targeting the PI3K Signaling Pathway in Endometrial CarcinomaP50CA098258 · NCI · UNIVERSITY OF TX MD ANDERSON CAN CTR · PI BODURKA, DIANE · 2003 to 2020
$32.0M
Functional Proteomics by Reverse Phase Protein Array in CancerR50CA221675 · NCI · UNIVERSITY OF TX MD ANDERSON CAN CTR · PI LU, YILING · 2017 to 2021
$726k
NCI NIH HHS P50 CA098258NCI NIH HHS R50 CA221675
6 · The paper itself

Abstract

backgroundThe lethality of endometrioid endometrial cancer (EEC) is primarily attributable to advanced-stage diseases. We sought to develop a biomarker model that predicts EEC surgical stage at the time of clinical diagnosis.

resultsPSES was significantly correlated with surgical stage in the TCGA cohort (

methodsUsing reverse-phase protein array expression profiles of 170 antibodies for 210 EEC cases from TCGA, we constructed a Protein Scoring of EEC Staging (PSES) scheme comprising 6 proteins (3 of them phosphorylated) for surgical stage prediction. We validated and evaluated its diagnostic potential in an independent cohort of 184 EEC cases obtained at MDACC using receiver operating characteristic curve analyses. Kaplan-Meier survival analysis was used to examine the association of PSES score with patient outcome, and Ingenuity pathway analysis was used to identify relevant signaling pathways. Two-sided statistical tests were used.

conclusionsPSES may provide clinically useful prediction of high-risk tumors and offer new insights into tumor biology in EEC.

Indexed as

biomarkerendometrioid carcinomaproteinRPPAstage

Identifiers

PMID29731976
PMCPMC5929419

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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.