Evidence map›Paper›PMID 26539246›Full record

ArticleComputational and mathematical methods in medicine2015

A Five-Gene Signature Predicts Prognosis in Patients with Kidney Renal Clear Cell Carcinoma.

Yueping Zhan, Wenna Guo, Ying Zhang, Qiang Wang, Xin-jian Xu, Liucun Zhu

Abstract read
In one paragraph

Article in Computational and mathematical methods in medicine, 2015. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 22 papers.

0numbers the graph read from it
0cells of the map it votes in
22citing papers in PubMed
–field-weighted citation impact
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

22 citing papers in PubMed.

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  11. Genomic profiling in renal cell carcinoma.Nature reviews. Nephrology · 2020
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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

6 authors.

Yueping ZhanSchool of Life Sciences, Shanghai University, Shanghai 200444, China.
Wenna GuoSchool of Life Sciences, Shanghai University, Shanghai 200444, China.
Ying ZhangYangzhou Breeding Biological Agriculture Technology Co. Ltd., Yangzhou 225200, China.
Qiang WangState Key Laboratory of Pharmaceutical Biotechnology, School of Life Sciences, Nanjing University, Nanjing 210093, China.
Xin-jian XuDepartment of Mathematics, Shanghai University, Shanghai 200444, China.
Liucun ZhuSchool of Life Sciences, Shanghai University, Shanghai 200444, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Kidney renal clear cell carcinoma (KIRC) is one of the most common cancers with high mortality all over the world. Many studies have proposed that genes could be used to predict prognosis in KIRC. In this study, RNA expression data from next-generation sequencing and clinical information of 523 patients downloaded from The Cancer Genome Atlas (TCGA) dataset were analyzed in order to identify the relationship between gene expression level and the prognosis of KIRC patients. A set of five genes that significantly associated with overall survival time was identified and a model containing these five genes was constructed by Cox regression analysis. By Kaplan-Meier and Receiver Operating Characteristic (ROC) analysis, we confirmed that the model had good sensitivity and specificity. In summary, expression of the five-gene model is associated with the prognosis outcomes of KIRC patients, and it may have an important clinical significance.

Indexed as

AdultAgedAged, 80 and overBiomarkers, TumorCarcinoma, Renal CellCation Transport ProteinsComputational BiologyFemaleFerroportinGene Expression ProfilingHumansKaplan-Meier EstimateKidney NeoplasmsMaleMannosidasesMembrane ProteinsBiomarkers, TumorCation Transport ProteinsCKAP4 protein, humanCRPPA protein, humanFerroportinMAN2A2 protein, humanMannosidasesMembrane ProteinsNucleotidyltransferasesOTOF protein, human

Identifiers

PMID26539246
PMCPMC4619904

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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.