Evidence map›Paper›PMID 33352742›Full record

ArticleGenes2020

OSmfs: An Online Interactive Tool to Evaluate Prognostic Markers for Myxofibrosarcoma.

Huimin Li, Longxiang Xie, Qiang Wang, Yifang Dang, Xiaoxiao Sun, Lu Zhang, Yali Han, Zhongyi Yan, Huan Dong, Hong Zheng and 3 more

Open access · goldAbstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Genes, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.

0numbers the graph read from it
0cells of the map it votes in
3citing papers in PubMed
0.3field-weighted citation impact, top 37% 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

3 citing papers in PubMed, 4 citations in OpenAlex.

  1. Article
  2. Review
  3. 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

13 authors at 2 institutions in 2 countries.

Huimin LiCell Signal Transduction Laboratory, Bioinformatics Center, Henan Provincial Engineering Center for Tumor Molecular Medicine, Institute of Biomedical Informatics, School of Software, School of Basic Medical Sciences, Henan University, Kaifeng 475001, China.ORCID 0000-0001-8751-7501
Longxiang XieCell Signal Transduction Laboratory, Bioinformatics Center, Henan Provincial Engineering Center for Tumor Molecular Medicine, Institute of Biomedical Informatics, School of Software, School of Basic Medical Sciences, Henan University, Kaifeng 475001, China.ORCID 0000-0001-6825-9690
Qiang WangCell Signal Transduction Laboratory, Bioinformatics Center, Henan Provincial Engineering Center for Tumor Molecular Medicine, Institute of Biomedical Informatics, School of Software, School of Basic Medical Sciences, Henan University, Kaifeng 475001, China.
Yifang DangCell Signal Transduction Laboratory, Bioinformatics Center, Henan Provincial Engineering Center for Tumor Molecular Medicine, Institute of Biomedical Informatics, School of Software, School of Basic Medical Sciences, Henan University, Kaifeng 475001, China.
Xiaoxiao SunCell Signal Transduction Laboratory, Bioinformatics Center, Henan Provincial Engineering Center for Tumor Molecular Medicine, Institute of Biomedical Informatics, School of Software, School of Basic Medical Sciences, Henan University, Kaifeng 475001, China.
Lu ZhangCell Signal Transduction Laboratory, Bioinformatics Center, Henan Provincial Engineering Center for Tumor Molecular Medicine, Institute of Biomedical Informatics, School of Software, School of Basic Medical Sciences, Henan University, Kaifeng 475001, China.
Yali HanCell Signal Transduction Laboratory, Bioinformatics Center, Henan Provincial Engineering Center for Tumor Molecular Medicine, Institute of Biomedical Informatics, School of Software, School of Basic Medical Sciences, Henan University, Kaifeng 475001, China.
Zhongyi YanCell Signal Transduction Laboratory, Bioinformatics Center, Henan Provincial Engineering Center for Tumor Molecular Medicine, Institute of Biomedical Informatics, School of Software, School of Basic Medical Sciences, Henan University, Kaifeng 475001, China.
Huan DongCell Signal Transduction Laboratory, Bioinformatics Center, Henan Provincial Engineering Center for Tumor Molecular Medicine, Institute of Biomedical Informatics, School of Software, School of Basic Medical Sciences, Henan University, Kaifeng 475001, China.
Hong ZhengCell Signal Transduction Laboratory, Bioinformatics Center, Henan Provincial Engineering Center for Tumor Molecular Medicine, Institute of Biomedical Informatics, School of Software, School of Basic Medical Sciences, Henan University, Kaifeng 475001, China.
Yongqiang LiCell Signal Transduction Laboratory, Bioinformatics Center, Henan Provincial Engineering Center for Tumor Molecular Medicine, Institute of Biomedical Informatics, School of Software, School of Basic Medical Sciences, Henan University, Kaifeng 475001, China.
Wan ZhuDepartment of Anesthesia, Stanford University, Stanford, CA 94305, USA.
Xiangqian GuoCell Signal Transduction Laboratory, Bioinformatics Center, Henan Provincial Engineering Center for Tumor Molecular Medicine, Institute of Biomedical Informatics, School of Software, School of Basic Medical Sciences, Henan University, Kaifeng 475001, China.ORCID 0000-0001-6645-5786
Henan University · CNStanford University · US

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Myxofibrosarcoma is a complex genetic disease with poor prognosis. However, more effective biomarkers that forebode poor prognosis in Myxofibrosarcoma remain to be determined. Herein, utilizing gene expression profiling data and clinical follow-up data of Myxofibrosarcoma cases in three independent cohorts with a total of 128 Myxofibrosarcoma samples from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) databases, we constructed an easy-to-use web tool, named Online consensus Survival analysis for Myxofibrosarcoma (OSmfs) to analyze the prognostic value of certain genes. Through retrieving the database, users generate a Kaplan-Meier plot with log-rank test and hazard ratio (HR) to assess prognostic-related genes or discover novel Myxofibrosarcoma prognostic biomarkers. The effectiveness and availability of OSmfs were validated using genes in ever reports predicting the prognosis of Myxofibrosarcoma patients. Furthermore, utilizing the cox analysis data and transcriptome data establishing OSmfs, seven genes were selected and considered as more potentially prognostic biomarkers through overlapping and ROC analysis. In conclusion, OSmfs is a promising web tool to evaluate the prognostic potency and reliability of genes in Myxofibrosarcoma, which may significantly contribute to the enrichment of novelly potential prognostic biomarkers and therapeutic targets for Myxofibrosarcoma.

Indexed as

InternetSoftwareArea Under CurveBase SequenceBiomarkers, TumorDatasets as TopicFibromaFibrosarcomaGene OntologyHumansKaplan-Meier EstimatePrognosisProportional Hazards ModelsROC CurveSurvival AnalysisBiomarkers, TumorMyxofibrosarcomaOSmfsprognostic markerstool

Identifiers

PMID33352742
PMCPMC7766036
OpenAlexW3116452481

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.