Evidence map›Paper›PMID 26606135›Full record

ArticlePloS one2015

Comparison of the Prognostic Utility of the Diverse Molecular Data among lncRNA, DNA Methylation, microRNA, and mRNA across Five Human Cancers.

Li Xu, Liang Fengji, Liu Changning, Zhang Liangcai, Li Yinghui, Li Yu, Chen Shanguang, Xiong Jianghui

Erratum issuedOpen access · goldAbstract readComparative Study
PubMed Full text at PMC Publisher
In one paragraph

Article in PloS one, 2015. The graph could read no effect estimate from its abstract, so it casts no vote on the map. An erratum has been issued. Cited by 10 papers.

0numbers the graph read from it
0cells of the map it votes in
10citing papers in PubMed
0.9field-weighted citation impact, top 27% 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

10 citing papers in PubMed, 19 citations in OpenAlex.

  1. Article
  2. Computational Analysis of High-Dimensional DNA Methylation Data for Cancer Prognosis.Journal of computational biology : a journal of computational molecular cell biology · 2022
    Review
  3. Review
  4. Article
  5. Article
  6. Review
  7. Article
  8. Article
  9. Article
  10. Article
4 · The record

Corrections and comments

5 · Who and what money

Authors and funding

8 authors at 4 institutions in 2 countries.

Li XuSchool of life science and biotechnology, Harbin Institute of Technology, Harbin, Hei Longjiang Province, China.
Liang FengjiState Key Laboratory of Space Medicine Fundamentals and Application, Space Institute of Southern China, China Astronaut Research and Training Center, Beijing, China.
Liu ChangningXishuangbanna Tropical Botanical Garden, Chinese Academy of Sciences, Yunnan, China.
Zhang LiangcaiDepartment of statistics, Rice University, Houston, Texas, United States of America.
Li YinghuiSchool of life science and biotechnology, Harbin Institute of Technology, Harbin, Hei Longjiang Province, China.
Li YuSchool of life science and biotechnology, Harbin Institute of Technology, Harbin, Hei Longjiang Province, China.
Chen ShanguangSchool of life science and biotechnology, Harbin Institute of Technology, Harbin, Hei Longjiang Province, China.
Xiong JianghuiState Key Laboratory of Space Medicine Fundamentals and Application, Space Institute of Southern China, China Astronaut Research and Training Center, Beijing, China.
Harbin Institute of Technology · CNChina Astronaut Research and Training Center · CNRice University · USXishuangbanna Tropical Botanical Garden · CN

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

introductionAdvances in high-throughput technologies have generated diverse informative molecular markers for cancer outcome prediction. Long non-coding RNA (lncRNA) and DNA methylation as new classes of promising markers are emerging as key molecules in human cancers; however, the prognostic utility of such diverse molecular data remains to be explored. MATERIALS AND

methodsWe proposed a computational pipeline (IDFO) to predict patient survival by identifying prognosis-related biomarkers using multi-type molecular data (mRNA, microRNA, DNA methylation, and lncRNA) from 3198 samples of five cancer types. We assessed the predictive performance of both single molecular data and integrated multi-type molecular data in patient survival stratification, and compared their relative importance in each type of cancer, respectively. Survival analysis using multivariate Cox regression was performed to investigate the impact of the IDFO-identified markers and traditional variables on clinical outcome.

resultsUsing the IDFO approach, we obtained good predictive performance of the molecular datasets (bootstrap accuracy: 0.71-0.97) in five cancer types. Impressively, lncRNA was identified as the best prognostic predictor in the validated cohorts of four cancer types, followed by DNA methylation, mRNA, and then microRNA. We found the incorporating of multi-type molecular data showed similar predictive power to single-type molecular data, but with the exception of the lncRNA + DNA methylation combinations in two cancers. Survival analysis of proportional hazard models confirmed a high robustness for lncRNA and DNA methylation as prognosis factors independent of traditional clinical variables.

conclusionOur study provides insight into systematically understanding the prognostic performance of diverse molecular data in both single and aggregate patterns, which may have specific reference to subsequent related studies.

Indexed as

Biomarkers, TumorBreast NeoplasmsColonic NeoplasmsDNA MethylationFemaleGene Expression ProfilingHumansLung NeoplasmsMicroRNAsOvarian NeoplasmsPredictive Value of TestsPrognosisProportional Hazards ModelsRNA, Long NoncodingRNA, MessengerSurvival AnalysisBiomarkers, TumorMicroRNAsRNA, Long NoncodingRNA, Messenger

Identifiers

PMID26606135
PMCPMC4659652
OpenAlexW2180719045

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

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.