Evidence map›Paper›PMID 33842641›Full record

ReviewAnnals of translational medicine2021

The predictive value of microRNAs for pathological response after neoadjuvant treatment in esophageal squamous cell carcinoma: a systematic review.

Dong Lin, Xiaosang Chen, Lijie Tan

Open access · diamondAbstract readReview
In one paragraph

Review in Annals of translational medicine, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing papers in PubMed
0.4field-weighted citation impact, top 40% 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

1 citing paper in PubMed, 2 citations in OpenAlex.

  1. Article
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

3 authors at 1 institution in 1 country.

Dong LinDepartment of Thoracic Surgery, Zhongshan Hospital of Fudan University, Shanghai, China.
Xiaosang ChenDepartment of Thoracic Surgery, Zhongshan Hospital of Fudan University, Shanghai, China.
Lijie TanDepartment of Thoracic Surgery, Zhongshan Hospital of Fudan University, Shanghai, China.
Fudan University · CN

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Neoadjuvant treatment followed by esophagectomy has been the standard strategy for resectable locally advanced esophageal squamous cell carcinoma (ESCC). Pathological response after neoadjuvant treatment is of vital importance in the determination of long-term survival. Due to the involvement of microRNAs (miRNAs) in ESCC, some studies have proposed miRNA models to predict the pathological response. We aimed to summarize current studies on the predictive value of the miRNA models. We searched the relevant studies on PubMed, Web of Science and Cochrane Library up to February 14, 2020, using the following search term: (esophageal OR esophagus OR oesophageal OR oesophagus) AND (miR OR miRNA OR microRNA) AND (neoadjuvant OR preoperative OR induction). The initial search retrieved 206 studies. We briefly summarized the involvement of miRNAs in the origin, development and chemo- and radioresistance in ESCC. Then, 9 studies were enrolled in the systematic review. A great heterogeneity was observed across these studies. Of the 6 studies with diagnostic tests, the area under curve varied a lot. Although much evidence demonstrated the correlation between miRNAs and pathological response after in ESCC, the current studies has not established any promising models. A well-designed prospective study is essential to investigate the potential predictive models for pathological response after neoadjuvant treatment in ESCC.

Indexed as

Esophageal squamous cell carcinoma (ESCC)microRNAs (miRNA)pathological responsepredictive

Identifiers

PMID33842641
PMCPMC8033340
OpenAlexW3119093247

What OpenQuestion holds

Textmetadata
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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.