Evidence map›Paper›PMID 32646427›Full record

ArticleBMC medical informatics and decision making2020

A prognostic index based on a fourteen long non-coding RNA signature to predict the recurrence-free survival for muscle-invasive bladder cancer patients.

Xiaolong Zhang, Meng Zhang, Xuanping Zhang, Xiaoyan Zhu, Jiayin Wang

Open access · goldAbstract read
In one paragraph

Article in BMC medical informatics and decision making, 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 50% 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

2 citing papers in PubMed, 5 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

5 authors at 3 institutions in 1 country.

Xiaolong ZhangSchool of Computer Science and Technology, Shaanxi Engineering Research Center of Medical and Health Big Data, Xi'an Jiaotong University, Xi'an, China.
Meng ZhangSchool of Medicine, Shenzhen University, Shenzhen, China.
Xuanping ZhangSchool of Computer Science and Technology, Shaanxi Engineering Research Center of Medical and Health Big Data, Xi'an Jiaotong University, Xi'an, China.
Xiaoyan ZhuSchool of Computer Science and Technology, Shaanxi Engineering Research Center of Medical and Health Big Data, Xi'an Jiaotong University, Xi'an, China.
Jiayin WangSchool of Computer Science and Technology, Shaanxi Engineering Research Center of Medical and Health Big Data, Xi'an Jiaotong University, Xi'an, China. wangjiayin@mail.xjtu.edu.cn.
Xi'an Jiaotong University · CNAnhui Medical University · CNShenzhen University · CN

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundBladder cancer (BC) is regarded as one of the most fatal cancer around the world. Nevertheless, there still lack of sufficient markers to predict the prognosis of BC patients. Herein, we aim to establish a prognosis predicting signature based on long-noncoding RNA (lncRNA) for the invasive BC patients.

methodsThe lncRNA expression profile was downloaded from The Cancer Genome Atlas (TCGA) database, along with the correlated clinicopathological information. The univariate Cox regression test was employed to screen out the recurrence-free survival (RFS)-related lncRNAs. Then, the LASSO method was conducted to construct the signature based on these RFS-related lncRNA candidates. Genes correlated with these fourteen lncRNAs were extracted from the mRNA expression profile, with the Pearson correlation coefficient > 0.60 or < - 0.40. Subsequently, the Proteomap pathway enrichment analyses were conducted to classify the function of these correlated genes. Furthermore, the multivariate analyses were executed to reveal the independent role of the proposed signature with the clinicopathological features.

resultsWe established an lncRNA-based RFS predicting signature by the LASSO Cox regression test, and proved its usage and stability on both the training and validation cohorts by the Kaplan-Meier and receiver operating characteristic (ROC) curves. Notably, the multivariate Cox regression analysis found that our classifier was an independent indicator for muscle-invasive BC patients rather than sex, age and tumor grade, with higher predictive value than the existing ones. Besides, we did the pathway analyses for these genes that highly correlated with the proposed fourteen lncRNAs, as well as the differentially expressed genes (DEGs) derived from the high-risk vs. low-risk groups, and the recurrence vs. non-recurrence groups, respectively. Notably, these results were consistent, and these genes were mostly enriched in the transcription factors, G protein-coupled receptors, MAPK signaling pathways, which were proved significantly associated with tumor progression and drug resistance.

conclusionsOur results suggested that the fourteen-lncRNA-based RFS predicting signature is an independent indicator for BC patients. Further prospective studies with more samples are needed to verify our findings.

Indexed as

RNA, Long NoncodingUrinary Bladder NeoplasmsBiomarkers, TumorGene Expression ProfilingGene Expression Regulation, NeoplasticHumansKaplan-Meier EstimateMusclesPrognosisProspective StudiesBiomarkers, TumorRNA, Long NoncodingClinical decision-supportingLong non-coding RNAMuscle-invasive bladder cancerPrognosis signature

Identifiers

PMID32646427
PMCPMC7346316
OpenAlexW3040757483

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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.