Evidence map›Paper›PMID 30852945›Full record

ArticleTechnology in cancer research & treatment2019

Comprehensive Analysis of Driver Genes in Personal Genomes of Clear Cell Renal Cell Carcinoma.

Jin Li, Liping Guo, Li Chai, Zisheng Ai

Open access · goldAbstract read
In one paragraph

Article in Technology in cancer research & treatment, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.

0numbers the graph read from it
0cells of the map it votes in
4citing papers in PubMed
0.5field-weighted citation impact, top 31% 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

4 citing papers in PubMed, 6 citations in OpenAlex.

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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

4 authors at 2 institutions in 1 country.

Jin Li1 Department of Geriatrics, Shanghai Tenth People's Hospital, Tongji University School of Medicine, Shanghai, China.
Liping Guo2 Department of Nephrology, The Shanghai Ninth People's Hospital, Shanghai, China.
Li Chai3 Department of Nuclear Medicine, Shanghai Tenth People's Hospital, Tongji University School of Medicine, Shanghai, China.
Zisheng Ai4 Department of Medical Statistics, School of Medicine, Tongji University, Shanghai, China.ORCID 0000-0002-3575-6217
Tongji University · CNShanghai Ninth People's Hospital · CN

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

aimTo characterize personal driver genes in clear cell renal cell carcinoma independent of somatic mutation frequencies.

methodsPersonal cancer driver genes were predicted by Integrated CAncer GEnome Score in 417 patients with clear cell renal cell carcinoma using 26 786 somatic mutations from The Cancer Genome Atlas, followed by an integrated investigation on personal driver genes.

resultsA total of 233 personal driver genes were determined by Integrated CAncer GEnome Score. The coexpression network analysis found 5 coexpressed modules. The blue module was significantly negatively correlated with all 5 clinical features, including cancer stage, lymph node metastasis, distant metastasis, age, and survival status (death). CTNNB1, TGFBR2, KDR, FLT1, and INSR were the hub genes in the blue module. The expression of 79 personal driver genes was significantly associated with clinical outcomes of patients with clear cell renal cell carcinoma.

conclusionsThe set of personal driver genes sheds insights into the tumorigenesis of clear cell renal cell carcinoma and paves the way for developing personalized medicine for clear cell renal cell carcinoma.

Indexed as

Genome, HumanMutationPrecision MedicineBiomarkers, TumorCarcinoma, Renal CellGene Expression Regulation, NeoplasticHumansKidney NeoplasmsPrognosisSurvival RateBiomarkers, Tumorclear cell renal cell carcinomaiCAGESpersonal driver gene

Identifiers

PMID30852945
PMCPMC6413433
OpenAlexW2921115743

What OpenQuestion holds

Textmetadata
LicenceCC BY-NC
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.