Evidence map›Paper›PMID 28260099›Full record

ArticleMolecular medicine reports2017

Identification of prognostic genes in kidney renal clear cell carcinoma by RNA‑seq data analysis.

Yanqin Gu, Linfeng Lu, Lingfeng Wu, Hao Chen, Wei Zhu, Yi He

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In one paragraph

Article in Molecular medicine reports, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 40 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
40citing papers in PubMed, 1 pooled it
–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

40 citing papers in PubMed, 1 synthesis or guideline pooled it.

  1. Pooled it
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  9. Targeting MELK in tumor cells and tumor microenvironment: from function and mechanism to therapeutic application.Clinical & translational oncology : official publication of the Federation of Spanish Oncology Societies and of the National Cancer Institute of Mexico · 2025
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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.

Yanqin GuDepartment of Urology, The First Hospital of Jiaxing, Jiaxing, Zhejiang 314001, P.R. China.
Linfeng LuDepartment of Urology, The First Hospital of Jiaxing, Jiaxing, Zhejiang 314001, P.R. China.
Lingfeng WuDepartment of Urology, The First Hospital of Jiaxing, Jiaxing, Zhejiang 314001, P.R. China.
Hao ChenDepartment of Urology, The First Hospital of Jiaxing, Jiaxing, Zhejiang 314001, P.R. China.
Wei ZhuDepartment of Urology, The First Hospital of Jiaxing, Jiaxing, Zhejiang 314001, P.R. China.
Yi HeDepartment of Urology, The First Hospital of Jiaxing, Jiaxing, Zhejiang 314001, P.R. China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

The present study aimed to analyze RNA-seq data of kidney renal clear cell carcinoma (KIRC) to identify prognostic genes. RNA‑seq data were downloaded from The Cancer Genome Atlas. Feature genes with a coefficient of variation (CV) >0.5 were selected using the genefilter package in R. Gene co‑expression networks were constructed with the WGCNA package. Cox regression analysis was performed using the survive package. Furthermore, a functional enrichment analysis was conducted using Database for Annotation, Visualization and Integrated Discovery tools. A total of 533 KIRC samples were collected, from which 6,758 feature genes with a CV >0.5 were obtained for further analysis. The KIRC samples were divided into two sets: The training set (n=319 samples) and the validation set (n=214 samples). Subsequently, gene co‑expression networks were constructed for the two sets. A total of 12 modules were identified, and the green module was significantly associated with survival time. Genes from the green module were revealed to be implicated in the cell cycle and p53 signaling pathway. In addition, a total of 11 hub genes were revealed, and 10 of them (CCNA2, CDC20, CDCA8, GTSE1, KIF23, KIF2C, KIF4A, MELK, TOP2A and TPX2) were validated as possessing prognostic value, as determined by conducting a survival analysis on another gene expression dataset. In conclusion, a total of 10 prognostic genes were identified in KIRC. These findings may help to advance the understanding of this disease, and may also provide potential biomarkers for therapeutic development.

Indexed as

Genes, NeoplasmSequence Analysis, RNAStatistics as TopicCarcinoma, Renal CellCluster AnalysisGene OntologyGene Regulatory NetworksHumansKaplan-Meier EstimateKidney NeoplasmsPrognosisReproducibility of Results

Identifiers

PMID28260099
PMCPMC5364979

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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.