Evidence map›Paper›PMID 34616452›Full record

ArticleJournal of oncology2021

Identification of Prognostic Metabolism-Related Genes in Clear Cell Renal Cell Carcinoma.

Yusa Chen, Yumei Liang, Ying Chen, Shaxi Ouyang, Kanghan Liu, Wei Yin

Abstract read
In one paragraph

Article in Journal of oncology, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 13 papers.

0numbers the graph read from it
0cells of the map it votes in
13citing 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

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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

13 citing papers in PubMed.

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  5. Prognostic role ofHeliyon · 2024
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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.

Yusa ChenDepartment of Nephrology, Hunan Provincial People's Hospital, The First Affiliated Hospital of Hunan Normal University, Changsha 410000, China.
Yumei LiangDepartment of Nephrology, Hunan Provincial People's Hospital, The First Affiliated Hospital of Hunan Normal University, Changsha 410000, China.
Ying ChenDepartment of Nephrology, Hunan Provincial People's Hospital, The First Affiliated Hospital of Hunan Normal University, Changsha 410000, China.
Shaxi OuyangDepartment of Nephrology, Hunan Provincial People's Hospital, The First Affiliated Hospital of Hunan Normal University, Changsha 410000, China.
Kanghan LiuDepartment of Nephrology, Hunan Provincial People's Hospital, The First Affiliated Hospital of Hunan Normal University, Changsha 410000, China.
Wei YinDepartment of Nephrology, Hunan Provincial People's Hospital, The First Affiliated Hospital of Hunan Normal University, Changsha 410000, China.ORCID https://orcid.org/0000-0003-3467-695X

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundClear cell renal cell carcinoma (ccRCC) is a cancer with abnormal metabolism. The purpose of this study was to investigate the effect of metabolism-related genes on the prognosis of ccRCC patients.

methodsThe data of ccRCC patients were downloaded from the TCGA and the GEO databases and clustered using the nonnegative matrix factorization method. The limma software package was used to analyze differences in gene expression. A random forest model was used to screen for important genes. A novel Riskscore model was established using multivariate regression. The model was evaluated based on the metabolic pathway, immune infiltration, immune checkpoint, and clinical characteristics.

resultsAccording to metabolism-related genes, kidney clear cell carcinoma (KIRC) datasets downloaded from TCGA were clustered into two groups and showed significant differences in prognosis and immune infiltration. There were 667 differentially expressed genes between the two clusters, of which 408 were screened by univariate analysis. Finally, 12 differentially expressed genes (

conclusionWe established a new Riskscore model for the prognosis of ccRCC based on metabolism. The genes in the model provided several novel targets for the study of ccRCC.

Identifiers

PMID34616452
PMCPMC8490028

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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.