Evidence map›Paper›PMID 29983594›Full record

ArticleCancer management and research2018

A panel containing PD-1, IL-2Rα, IL-10, and CA15-3 as a biomarker to discriminate breast cancer from benign breast disease.

Chao Liu, Bing Sun, Bin Xu, Xiangying Meng, Lan Li, Yang Cong, Jiannan Liu, Qian Wang, Liang Xuan, Qibin Song and 1 more

Open access · goldAbstract read
In one paragraph

Article in Cancer management and research, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 17 papers, 2 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
17citing papers in PubMed, 2 pooled it
1.3field-weighted citation impact, top 16% 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

17 citing papers in PubMed, 2 syntheses or guidelines pooled it, 26 citations in OpenAlex.

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  6. Epigenetics · 2024
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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

11 authors at 2 institutions in 1 country.

Chao LiuCancer Center, Renmin Hospital of Wuhan University, Wuhan, People's Republic of China, qibinsong@163.com.
Bing SunDepartment of Radiation Oncology, Affiliated Hospital of Academy of Military Medical Sciences, Beijing, People's Republic of China, skywu4923@sina.com.
Bin XuCancer Center, Renmin Hospital of Wuhan University, Wuhan, People's Republic of China, qibinsong@163.com.
Xiangying MengDepartment of Radiation Oncology, Affiliated Hospital of Academy of Military Medical Sciences, Beijing, People's Republic of China, skywu4923@sina.com.
Lan LiCancer Center, Renmin Hospital of Wuhan University, Wuhan, People's Republic of China, qibinsong@163.com.
Yang CongDepartment of Radiation Oncology, Affiliated Hospital of Academy of Military Medical Sciences, Beijing, People's Republic of China, skywu4923@sina.com.
Jiannan LiuDepartment of Radiation Oncology, Affiliated Hospital of Academy of Military Medical Sciences, Beijing, People's Republic of China, skywu4923@sina.com.
Qian WangDepartment of Radiation Oncology, Affiliated Hospital of Academy of Military Medical Sciences, Beijing, People's Republic of China, skywu4923@sina.com.
Liang XuanDepartment of Radiation Oncology, Affiliated Hospital of Academy of Military Medical Sciences, Beijing, People's Republic of China, skywu4923@sina.com.
Qibin SongCancer Center, Renmin Hospital of Wuhan University, Wuhan, People's Republic of China, qibinsong@163.com.
Shikai WuDepartment of Radiation Oncology, Affiliated Hospital of Academy of Military Medical Sciences, Beijing, People's Republic of China, skywu4923@sina.com.
Academy of Military Medical Sciences · CNWuhan University · CN

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Introduction: Programmed cell death protein 1 (PD-1), an immune checkpoint molecule, has recently been recognized as a predictive and prognostic biomarker in several malignant tumors, but its diagnostic value remains largely unknown. We aimed to investigate the differential diagnostic efficiency of PD-1 and other immune molecules and propose a panel of immune molecules combined with cancer antigen 15-3 (CA15-3) to distinguish breast cancer (BC) from benign breast disease (BBD). Patients and methods: Ninety-one eligible BC patients and 31 BBD patients were enrolled. Pretreatment peripheral blood was collected and tested for mRNA expression of PD-1, cytotoxic T lymphocyte antigen 4, forkhead box P3, transforming growth factor beta, interleukin-10 (IL-10), IL-2 receptor alpha (IL-2Rα), and cluster of differentiation 28 by quantitative reverse transcription PCR. Results: The diagnostic areas under curve (AUCs) of PD-1, IL-2Rα, and IL-10 for BC-BBD discrimination were 0.764, 0.758, and 0.743, respectively. The diagnostic efficiencies of these three parameters in distinguishing early-stage or advanced BC from BBD were consistent with a role in BC-BBD discrimination. A panel of PD-1 + IL-10 + IL-2Rα + CA15-3 showed the highest AUC (0.862), with a sensitivity of 0.933 and a specificity of 0.724, for BC-BBD discrimination. In addition, for early-stage BC discrimination, this panel also had the highest AUC (0.811), with a sensitivity of 0.933 and a specificity of 0.614, while for advanced BC discrimination, a panel of PD-1 + IL-10 + CA15-3 exhibited the highest AUC (0.896), with a sensitivity of 0.933 and a specificity of 0.783. Conclusion: These data indicate that the panel containing PD-1, IL-2Rα, IL-10, and CA15-3 can effectively discriminate BC from BBD with a high efficiency. After further confirmation, it could be used to complement conventional imaging modalities, especially in discriminating early-stage BC from BBD.

Indexed as

biomarkerbreast cancerCTLA-4diagnosisIL-2Rαimmune checkpointPD-1

Identifiers

PMID29983594
PMCPMC6027692
OpenAlexW2811429363

What OpenQuestion holds

Textmetadata
LicenceCC BY-NC
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.