Evidence map›Paper›PMID 32252627›Full record

ArticleBMC genomics2020

A qualitative transcriptional signature to reclassify histological grade of ER-positive breast cancer patients.

Jing Li, Wenbin Jiang, Qirui Liang, Guanghao Liu, Yupeng Dai, Hailong Zheng, Jing Yang, Hao Cai, Guo Zheng

Open access · goldAbstract read
In one paragraph

Article in BMC genomics, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.

0numbers the graph read from it
0cells of the map it votes in
3citing papers in PubMed
0.1field-weighted citation impact, top 64% 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

3 citing papers in PubMed, 5 citations in OpenAlex.

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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

9 authors at 2 institutions in 1 country.

Jing LiFujian Key Laboratory of Medical Bioinformatics, Department of Bioinformatics, The School of Basic Medical Sciences, Fujian Medical University, Fuzhou, 350122, China.
Wenbin JiangFujian Key Laboratory of Medical Bioinformatics, Department of Bioinformatics, The School of Basic Medical Sciences, Fujian Medical University, Fuzhou, 350122, China.
Qirui LiangFujian Key Laboratory of Medical Bioinformatics, Department of Bioinformatics, The School of Basic Medical Sciences, Fujian Medical University, Fuzhou, 350122, China.
Guanghao LiuFujian Key Laboratory of Medical Bioinformatics, Department of Bioinformatics, The School of Basic Medical Sciences, Fujian Medical University, Fuzhou, 350122, China.
Yupeng DaiFujian Key Laboratory of Medical Bioinformatics, Department of Bioinformatics, The School of Basic Medical Sciences, Fujian Medical University, Fuzhou, 350122, China.
Hailong ZhengFujian Key Laboratory of Medical Bioinformatics, Department of Bioinformatics, The School of Basic Medical Sciences, Fujian Medical University, Fuzhou, 350122, China.
Jing YangFujian Key Laboratory of Medical Bioinformatics, Department of Bioinformatics, The School of Basic Medical Sciences, Fujian Medical University, Fuzhou, 350122, China.
Hao CaiMedical Big Data and Bioinformatics Research Center, First Affiliated Hospital of Gannan Medical University, Ganzhou, Jiangxi, China. caihao093@163.com.
Guo ZhengFujian Key Laboratory of Medical Bioinformatics, Department of Bioinformatics, The School of Basic Medical Sciences, Fujian Medical University, Fuzhou, 350122, China. guoz@ems.hrbmu.edu.cn.
Fujian Medical University · CNFirst Affiliated Hospital of Gannan Medical University · CN

Funding

Joint Technology Innovation Fund of Fujian Province 2016Y9044Joint Technology Innovation Fund of Fujian Province 2017Y9109National Natural Science Foundation of China 61602119National Natural Science Foundation of China 81872396 and 81602738National Natural Science Foundation of China 81903186
6 · The paper itself

Abstract

backgroundHistological grade (HG) is commonly adopted as a prognostic factor for ER-positive breast cancer patients. However, HG evaluation methods, such as the pathological Nottingham grading system, are highly subjective with only 50-85% inter-observer agreements. Specifically, the subjectivity in the pathological assignment of the intermediate grade (HG2) breast cancers, comprising of about half of breast cancer cases, results in uncertain disease outcomes prediction. Here, we developed a qualitative transcriptional signature, based on within-sample relative expression orderings (REOs) of gene pairs, to define HG1 and HG3 and reclassify pathologically-determined HG2 (denoted as pHG2) breast cancer patients.

resultsFrom the gene pairs with significantly stable REOs in pathologically-determined HG1 (denoted as pHG1) samples and reversely stable REOs in pathologically-determined HG3 (denoted as pHG3) samples, concordantly identified from seven datasets, we extracted a signature which could determine the HG state of samples through evaluating whether the within-sample REOs match with the patterns of the pHG1 REOs or pHG3 REOs. A sample was classified into the HG3 group if at least a half of the REOs of the 10 gene pairs signature within this sample voted for HG3; otherwise, HG1. Using four datasets including samples of early stage (I-II) ER-positive breast cancer patients who accepted surgery only, we validated that this signature was able to reclassify pHG2 patients into HG1 and HG3 groups with significantly different survival time. For the original pHG1 and pHG3 patients, the signature could also more accurately and objectively stratify them into distinct prognostic groups. And the up-regulated and down down-regulated genes in HG1 compared with HG3 involved in cell proliferation and extracellular signal transduction pathways respectively. By comparing with existing signatures, 10-GPS was with prognostic significance and was more aligned with survival of patients especially for pHG2 samples.

conclusionsThe transcriptional qualitative signature can provide an objective assessment of HG states of ER-positive breast cancer patients, especially for reclassifying patients with pHG2, to assist decision making on clinical therapy.

Indexed as

Gene Regulatory NetworksBiomarkers, TumorBreast NeoplasmsComputational BiologyDatabases, GeneticFemaleGene Expression ProfilingGene Expression Regulation, NeoplasticHumansKaplan-Meier EstimateNeoplasm GradingPrognosisReceptors, EstrogenSurvival AnalysisBiomarkers, TumorReceptors, EstrogenER-positive breast cancerGene expressionHistological gradeSurvival analysis

Identifiers

PMID32252627
PMCPMC7132979
OpenAlexW3014341361

What OpenQuestion holds

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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.