Evidence map›Paper›PMID 32218835›Full record

ArticleOncology letters2020

Gene expression profiling identified TP53

Jia'Nan Cheng, Xiaofang Ding, Shouxia Xu, Bo Zhu, Qingzhu Jia

Open access · diamondAbstract read
In one paragraph

Article in Oncology letters, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 15 papers.

0numbers the graph read from it
0cells of the map it votes in
15citing papers in PubMed
1.2field-weighted citation impact, top 19% 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

15 citing papers in PubMed, 19 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

5 authors at 2 institutions in 1 country.

Jia'Nan ChengInstitute of Cancer, Xinqiao Hospital, Third Military Medical University, Chongqing Key Laboratory of Tumor Immunotherapy, Chongqing 400037, P.R. China.
Xiaofang DingInstitute of Cancer, Xinqiao Hospital, Third Military Medical University, Chongqing Key Laboratory of Tumor Immunotherapy, Chongqing 400037, P.R. China.
Shouxia XuInstitute of Cancer, Xinqiao Hospital, Third Military Medical University, Chongqing Key Laboratory of Tumor Immunotherapy, Chongqing 400037, P.R. China.
Bo ZhuInstitute of Cancer, Xinqiao Hospital, Third Military Medical University, Chongqing Key Laboratory of Tumor Immunotherapy, Chongqing 400037, P.R. China.
Qingzhu JiaInstitute of Cancer, Xinqiao Hospital, Third Military Medical University, Chongqing Key Laboratory of Tumor Immunotherapy, Chongqing 400037, P.R. China.
Army Medical University · CNXinqiao Hospital · CN

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Triple-negative breast cancer (TNBC) accounts for 15-30% of all breast cancer cases and is clinically difficult to treat due to the lack of hormone or human epidermal growth factor receptor 2 receptors, which are usually targeted by the most successful therapeutic approaches. Immune checkpoint inhibitors (ICIs) have offered long-term survival benefits in several types of solid tumors, however with low response rates. Thus, there is an urgent need to develop feasible biomarkers for identifying patients with TNBC, who are responsive. The present study demonstrated that the immune microenvironment of TNBC has the highest expression of immunoregulatory molecules among all pathologic types. The tumor mutation burden (TMB) of TNBC was not strongly correlated with cytolytic activity and showed no significant associations with different degrees of immune cell infiltration and TMB. The machine learning method divided patients with TNBC into two groups characterized by 'hot' and 'cold' tumors, according to whether immune-associated genes were highly expressed, and different responses to immunotherapy were seen between these two groups. Furthermore, patients with a TP53

Indexed as

biomarkerimmune checkpoint inhibitorsTP53MutPIK3CAWildtriple-negative breast cancer

Identifiers

PMID32218835
PMCPMC7068237
OpenAlexW3004647644

What OpenQuestion holds

Textmetadata
LicenceCC BY-NC-ND
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.