Evidence map›Paper›PMID 29228584›Full record

ArticleOncotarget2017

Combination of CALR and PDIA3 is a potential prognostic biomarker for non-small cell lung cancer.

Ke Wang, Hao Li, Ruo Chen, Yang Zhang, Xiu-Xuan Sun, Wan Huang, Huijie Bian, Zhi-Nan Chen

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 12 papers.

0numbers the graph read from it
0cells of the map it votes in
12citing papers in PubMed
1.0field-weighted citation impact, top 26% 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

12 citing papers in PubMed, 27 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

8 authors at 1 institution in 1 country.

Ke WangDepartment of Cell Biology, National Translational Science Center for Molecular Medicine, Fourth Military Medical University, Xi'an, P.R. China.
Hao LiDepartment of Cell Biology, National Translational Science Center for Molecular Medicine, Fourth Military Medical University, Xi'an, P.R. China.
Ruo ChenDepartment of Cell Biology, National Translational Science Center for Molecular Medicine, Fourth Military Medical University, Xi'an, P.R. China.
Yang ZhangDepartment of Cell Biology, National Translational Science Center for Molecular Medicine, Fourth Military Medical University, Xi'an, P.R. China.
Xiu-Xuan SunDepartment of Cell Biology, National Translational Science Center for Molecular Medicine, Fourth Military Medical University, Xi'an, P.R. China.
Wan HuangDepartment of Cell Biology, National Translational Science Center for Molecular Medicine, Fourth Military Medical University, Xi'an, P.R. China.
Huijie BianDepartment of Cell Biology, National Translational Science Center for Molecular Medicine, Fourth Military Medical University, Xi'an, P.R. China.
Zhi-Nan ChenDepartment of Cell Biology, National Translational Science Center for Molecular Medicine, Fourth Military Medical University, Xi'an, P.R. China.
Air Force Medical University · CN

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Proteomic-based approaches for biomarker discovery are promising strategies used in cancer research. In this study, we performed quantitative proteomic analysis on 16 paired samples of non-small cell lung cancer (NSCLC) and adjacent non-tumor lung tissues using label-free quantitative proteomics and liquid chromatography-tandem mass spectrometry/mass spectrometry (LC-MS/MS) to identify differentially expressed proteins. A total of 91 proteins were differentially expressed in NSCLC compared with adjacent non-tumor lung tissues among 4047 identified proteins (fold change > 1.5 or < 0.67,

Indexed as

biomarkerCALRnon-small cell lung cancerPDIA3proteomics

Identifiers

PMID29228584
PMCPMC5722536
OpenAlexW2626911614

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.