Evidence map›Paper›PMID 29348867›Full record

ArticleOncotarget2017

Pleural MAC30 as a prognostic marker in NSCLC with malignant pleural effusion.

Yi Shan, Hui Ding, Junjie Lu, Zhijun Ge, Yongfei Tan

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 3 papers.

0numbers the graph read from it
0cells of the map it votes in
3citing papers in PubMed
–field-weighted citation impact, top 74% 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

3 citing papers in PubMed, 4 citations in OpenAlex.

  1. Article
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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.

Yi Shan *Department of Critical Care Medicine, The Affiliated Yixing Hospital of Jiangsu University Yixing, Jiangsu 214200, China.
Hui Ding *Department of Respiratory, The Affiliated Yixing Hospital of Jiangsu University Yixing, Jiangsu 214200, China.
Junjie LuDepartment of Critical Care Medicine, The Affiliated Yixing Hospital of Jiangsu University Yixing, Jiangsu 214200, China.
Zhijun GeDepartment of Critical Care Medicine, The Affiliated Yixing Hospital of Jiangsu University Yixing, Jiangsu 214200, China.
Yongfei TanDepartment of Cardiac & Thoracic Surgery, The Affiliated Yixing Hospital of Jiangsu University Yixing, Jiangsu 214200, China.
Jiangsu University · CNYixing People's Hospital · CN

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Over-expressed meningioma-associate protein (MAC30) in tissues was associated with malignant tumor differentiation, metastasis and poor prognosis. However, the attention of MAC30 in pleural effusion from lung tumor is insufficient. Our retrospective study was prepared to explore the clinical values on diagnosis and prognosis of MAC30 from malignant pleural effusion (MPE) in non-small cell lung cancer (NSCLC). Levels of MAC30 were confirmed in MPE from 48 NSCLC patients and in benign pleural effusion (BPE) from 45 controls via enzyme-linked immunosorbent assay (ELISA). The association of MAC30 in MPE with clinical significance was further determined. We found that the levels of MAC30 in MPE were obviously higher than those in BPE (p < 0.05). Moreover, with a cutoff point (17.5 ng/ml), we confirmed the sensitivity and specificity of MAC30 for MPE were 82.7% and 85.3% using ROC curve analysis. Indeed, longer overall survival (OS) was present in NSCLC patients with low MAC30 expression in MPE. Multivariate analysis explicated that elevated MAC30 in MPE was an independent prognostic factor for shorter OS of NSCLC. Our data suggests that MAC30 in pleural effusion could be a potential prognostic marker in NSCLC with MPE.

Indexed as

BPEMAC30MPENSCLCOS

Identifiers

PMID29348867
PMCPMC5762552
OpenAlexW2770274142

What OpenQuestion holds

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Registered trials

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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.