Evidence map›Paper›PMID 29228676›Full record

ArticleOncotarget2017

Genes with mutation significance were highly associated with the clinical pattern of patients with breast cancer.

Wan-Jun Ding, Tao Zeng, Li-Jun Wang, Hong-Bo Lei, Wei Ge, Zhi Wang

Open access · diamondAbstract read
In one paragraph

Article in Oncotarget, 2017. 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
0.2field-weighted citation impact, top 48% 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

2 citing papers in PubMed, 3 citations in OpenAlex.

  1. Review
  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

6 authors at 2 institutions in 1 country.

Wan-Jun DingDepartment of Oncology, Renmin Hospital, Wuhan University, Wuhan, Hubei 430060, P.R. China.
Tao ZengDepartment of East Campus, Renmin Hospital, Wuhan University, Wuhan, Hubei 430060, P.R. China.
Li-Jun WangDepartment of Breast and Thyroid Surgery, Renmin Hospital, Wuhan University, Wuhan, Hubei 430060, P.R. China.
Hong-Bo LeiDepartment of Oncology, Renmin Hospital, Wuhan University, Wuhan, Hubei 430060, P.R. China.
Wei GeDepartment of Oncology, Renmin Hospital, Wuhan University, Wuhan, Hubei 430060, P.R. China.
Zhi WangDepartment of East Campus, Renmin Hospital, Wuhan University, Wuhan, Hubei 430060, P.R. China.
Wuhan University · CNRenmin Hospital of Wuhan University · CN

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

In the United States, breast cancer is the second leading cause of cancer death in women. Over the past 20 years, breast cancer incidence and mortality rates increased rapidly in developing regions. We aimed to identify the gene mutation patterns that associated with the clinical patterns, including survival status, histo-pathological classes and so forth, of breast cancer. We retrieved 1098 cases of the clinical information, and level-3 legacy data of mRNA expression level, protein expression data and mutation files from GDC data portal. The genes with mutation significance were obtained. We studied the impacts of mutation types on the expression levels of mRNA and protein. Different statistics methods were used to calculate the correlation between the mutation types and the expression data or histo-clinical measures. There were 24 genes with mutation significance identified. The most mutated genes were selected to study the role of specific mutations played on the patients with breast cancer. One interesting finding was the missense mutations on TP53 were related with high expression levels of mRNA and protein. The missense mutations on TP53 were highly related with the morphology, race, ER status, PR status and HER2 Status, while the truncated mutations were only related with the morphology, ER status and PR status. The missense mutation on PIK3CA was highly associated with the morphology, race, ER status and PR status. The mutants with different mutants and the wild type of the most mutated genes had different impacts on the histo-clinical measures that might help personalized therapy.

Indexed as

breast invasive carcinomadriver mutationPIK3CATP53

Identifiers

PMID29228676
PMCPMC5716716
OpenAlexW2760928020

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.