Evidence map›Paper›PMID 29463849›Full record

ArticleScientific reports2018

Reconstruction of kidney renal clear cell carcinoma evolution across pathological stages.

Shichao Pang, Yidi Sun, Leilei Wu, Liguang Yang, Yi-Lei Zhao, Zhen Wang, Yixue Li

Abstract read
In one paragraph

Article in Scientific reports, 2018. 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
–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

3 citing papers in PubMed.

  1. Observational
  2. Article
  3. Article
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

7 authors.

Shichao PangDepartment of Statistics, School of Mathematical Sciences, Shanghai Jiao Tong University, Shanghai, 200240, China.
Yidi SunKey Lab of Computational Biology, CAS-MPG Partner Institute for Computational Biology, Shanghai Institutes for Biological Sciences, Chinese Academy of Sciences, Shanghai, P.R. China.
Leilei WuDepartment of Bioinformatics and Biostatistics, MOE LSB and LSC, State Key Laboratory of Microbial Metabolism, Joint International Research Laboratory of Metabolic & Developmental Sciences, School of Life Sciences and Biotechnology, Shanghai Jiao Tong University, Shanghai, 200240, China.
Liguang YangKey Lab of Computational Biology, CAS-MPG Partner Institute for Computational Biology, Shanghai Institutes for Biological Sciences, Chinese Academy of Sciences, Shanghai, P.R. China.
Yi-Lei ZhaoDepartment of Bioinformatics and Biostatistics, MOE LSB and LSC, State Key Laboratory of Microbial Metabolism, Joint International Research Laboratory of Metabolic & Developmental Sciences, School of Life Sciences and Biotechnology, Shanghai Jiao Tong University, Shanghai, 200240, China. yileizhao@sjtu.edu.cn.ORCID 0000-0003-4687-7847
Zhen WangKey Lab of Computational Biology, CAS-MPG Partner Institute for Computational Biology, Shanghai Institutes for Biological Sciences, Chinese Academy of Sciences, Shanghai, P.R. China. zwang01@sibs.ac.cn.
Yixue LiDepartment of Bioinformatics and Biostatistics, MOE LSB and LSC, State Key Laboratory of Microbial Metabolism, Joint International Research Laboratory of Metabolic & Developmental Sciences, School of Life Sciences and Biotechnology, Shanghai Jiao Tong University, Shanghai, 200240, China. yxli@sibs.ac.cn.ORCID 0000-0002-1198-7176

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Although numerous studies on kidney renal clear cell carcinoma (KIRC) were carried out, the dynamic process of tumor formation was not clear yet. Inadequate attention was paid on the evolutionary paths among somatic mutations and their clinical implications. As the tumor initiation and evolution of KIRC were primarily associated with SNVs, we reconstructed an evolutionary process of KIRC using cross-sectional SNVs in different pathological stages. KIRC driver genes appeared early in the evolutionary tree, and the genes with moderate mutation frequency showed a pattern of stage-by-stage expansion. Although the individual gene mutations were not necessarily associated with survival outcome, the evolutionary paths such as VHL-PBRM1 and FMN2-PCLO could indicate stage-specific prognosis. Our results suggested that, besides mutation frequency, the evolutionary relationship among the mutated genes could facilitate to identify novel drivers and biomarkers for clinical utility.

Indexed as

CarcinogenesisMutationCarcinoma, Renal CellCross-Sectional StudiesDisease ProgressionEvolution, MolecularHumansKidney NeoplasmsNeoplasm StagingPolymorphism, Single NucleotideSurvival Analysis

Identifiers

PMID29463849
PMCPMC5820260

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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.