Evidence map›Paper›PMID 32939184›Full record

ArticleCancer cell international2020

MiR-1231 decrease the risk of cancer-related mortality in patients combined with non-small cell lung cancer and diabetes mellitus.

Jing Li, Jialiang Xu, Zhijun Cao, Shouzuo Du, Luyu Zhang

Open access · goldAbstract read
In one paragraph

Article in Cancer cell international, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing papers in PubMed
0.3field-weighted citation impact, top 49% 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

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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, 5 citations in OpenAlex.

  1. Review
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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.

Jing Li *Department of Medicine, Respiratory, Emergency and Intensive Care Medicine, The Affiliated Dushu Lake Hospital of Soochow University, Suzhou, China.
Jialiang Xu *Department of Cardiovascular, The First Affiliated Hospital of Soochow University, Suzhou, China.
Zhijun Cao *Department of Urology, The Ninth People's Hospital of Suzhou, Suzhou, China.
Shouzuo DuDepartment of Endocrinology, Suzhou Xiangcheng People's Hospital, Suzhou, China.
Luyu ZhangDepartment of Endocrinology, Suzhou Xiangcheng People's Hospital, Suzhou, China.ORCID 0000-0003-4524-9224
Soochow University · CNThe Fifth People’s Hospital of Suzhou · CN

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundNon-small cell lung cancer (NSCLC) is a deadly human malignancy, and previous studies support the contribution of microRNAs (miRNAs) to cancer assessment. It has been reported that miR-1231 can be used as a biomarker to assess prognosis in different cancers. However, the prognostic value of miR-1231 in NSCLC patients with comorbid diabetes mellitus (DM) remains unclear. The present study evaluated the risk factors for NSCLC with DM and developed a predictive model for it.

methodsA real-world study was conducted, including data from 108 patients with NSCLC combined with DM from April 1, 2010, to June 1, 2015. MiR-1231 was recorded during hospital admission. Cox-proportional hazards model was applied for survival analysis of risk factors for cancer-related mortality and to create nomograms for prediction. The accuracy of the model was evaluated by C-index and calibration curves.

resultsThe mortality rate in the high miR-1231 level (≥ 1.775) group was 57.4%. On the basis of univariate analysis, we put factors (P < 0.05) into multivariate regression models, and high miR-1231 levels (P < 0.001, HR = 0.57), surgery (P < 0.001, HR = 0.37) and KPS score > 80 (P = 0.01, HR = 0.47) had a better prognosis and were considered as independent protective factors. These independently relevant factors were used to create nomograms to predict long-term patient survival. Nomogram showed good accuracy in risk estimation with a guide-corrected C-index of 0.691.

conclusionMiR-1231 reduced the risk of cancer-related death in patients with combined NSCLC and DM. Nomogram based on multivariate analysis showed good accuracy in estimating the overall risk of death.

Indexed as

Diabetes mellitusmiR-1231Non-small cell lung cancerOverall survivalThe prediction model

Identifiers

PMID32939184
PMCPMC7487554
OpenAlexW3083907708

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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.