Evidence map›Paper›PMID 32282693›Full record

Observational studyMedicine2020

A 17-gene expression-based prognostic signature associated with the prognosis of patients with breast cancer: A STROBE-compliant study.

Jin-Xian Qian, Min Yu, Zhe Sun, Ai-Mei Jiang, Bo Long

Abstract readObservational Study
In one paragraph

Observational study in Medicine, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing 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

2 citing papers in PubMed.

  1. Article
  2. 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

5 authors.

Jin-Xian QianDepartment of Breast Surgery, The First Affiliated Hospital of Kunming Medical University, Kunming 650032, Yunnan, People's Republic of China.
Min YuYangtze University, Jingzhou Central Hospital, Galactophore Department, The Second Clinical Medical College, Jingzhou, People's Republic of China.
Zhe SunDepartment of Breast Surgery, The First Affiliated Hospital of Kunming Medical University, Kunming 650032, Yunnan, People's Republic of China.
Ai-Mei JiangDepartment of Breast Surgery, The First Affiliated Hospital of Kunming Medical University, Kunming 650032, Yunnan, People's Republic of China.
Bo LongSchool of Life Sciences, Yunnan University, Kunming 650091, People's Republic of China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Identification of reliable predictive biomarkers for patients with breast cancer (BC).Univariate Cox proportional hazards regression model was conducted to identify genes correlated with the overall survival (OS) of patients in the TCGA-BRCA cohort. Functional enrichment analysis was conducted to investigate the biological meaning of these survival related genes. Then, patients in TCGA-BCRA were randomly divided into training set and test. Least absolute shrinkage and selection operator (LASSO) penalized Cox regression model was performed and the risk score of BC patients in this model was used to build a prognostic signature. The prognostic performance of the signature was evaluated in the training set, test set, and an independent validation set GSE7390.2519 genes were demonstrated to be significantly associated with the OS of BC patients. Functional annotation of the 2519 genes suggested that these genes were associated with immune response and protein synthesis related gene ontology terms and pathways. 17 genes were identified in the LASSO Cox regression model and used to construct a 17-gene signature. Patients in the 17-gene signature low risk group have better OS and event-free survival compared with those in the 17-gene signature high risk group in the TCGA-BRCA cohort. The prognostic role of the 17-gene signature has been confirmed in the validation cohort. Multivariable Cox proportional hazards regression model suggested the 17-gene signature was an independent prognostic factor in BC.The 17-gene signature we developed could successfully classify patients into high- and low-risk groups, indicating that it might serve as candidate biomarker in BC.

Indexed as

Breast NeoplasmsFemaleGene Expression ProfilingHumansPrognosisProportional Hazards ModelsRetrospective Studies

Identifiers

PMID32282693
PMCPMC7220332

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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.