Evidence map›Paper›PMID 27495091›Full record

ArticleMedicine2016

Method for discriminating synchronous multiple lung cancers of the same histological type: miRNA expression analysis.

Xudong Zhou, Long Tian, Jun Fan, Yutian Lai, Shuangjiang Li, Guowei Che, Jian Huang

Abstract read
In one paragraph

Article in Medicine, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
2citing papers in PubMed, 1 pooled it
–field-weighted citation impact
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, 1 synthesis or guideline pooled it.

  1. Pooled it
  2. Multiple primary lung cancer: a rising challenge.Journal of thoracic disease · 2019
    Review
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

7 authors.

Xudong ZhouDepartment of Thoracic Surgery, West-China Hospital, Sichuan University, Chengdu, P.R. China.
Long Tian
Jun Fan
Yutian Lai
Shuangjiang Li
Guowei Che
Jian Huang

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

With the development of imaging technology, an increasing number of synchronous multiple lung cancers (SMLCs) have been diagnosed in recent years. Patients with >1 tumor are diagnosed with either synchronous multiple primary lung cancers (SMPLCs) or other primary tumors and metastases. Clinical guidelines, histological characteristics, and molecular diagnostics have been used to discriminate SMPLCs from other multiple lung cancers. However, there is still ambiguity in the diagnosis of SMPLCs of the same histological type. We enrolled 24 patients with the same histological type of SMLCs and assessed their status using established clinical guidelines, comprehensive histological subtyping, and molecular analysis. The sum value of the differential microRNA (miRNA) expression profiles (ΔΔCt) with matched tumors was evaluated to discriminate SMPLCs of the same histological type from metastases. Twelve patients with lymph node metastases were included for comparison, and the sum value of the ΔΔCt of 5 miRNAs between primary tumors and lymph node metastases was <9. Patients definitively diagnosed with SMPLCs by integrated analysis were also classified as SMPLCs by miRNA analysis; 6 patients showed conflicting diagnoses by integrated and miRNA analysis and 14 were given the same classification. Analysis of miRNA expression profiles is considered to be a useful tool for discriminating SMPLCs from intrapulmonary metastases.

Indexed as

AdenocarcinomaAgedAged, 80 and overCarcinoma, Squamous CellCase-Control StudiesDiagnosis, DifferentialErbB ReceptorsFemaleGene Expression ProfilingHumansLung NeoplasmsMaleMicroRNAsMiddle AgedMutationNeoplasms, Multiple PrimaryEGFR protein, humanErbB ReceptorsMicroRNAs

Identifiers

PMID27495091
PMCPMC4979845

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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.