Evidence map›Paper›PMID 31496805›Full record

ArticleCancer management and research2019

Identification of a novel DNA repair-related prognostic signature predicting survival of patients with hepatocellular carcinoma.

Na Li, Lan Zhao, Chunyan Guo, Chang Liu, Yongyu Liu

Open access · goldAbstract read
In one paragraph

Article in Cancer management and research, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 35 papers.

0numbers the graph read from it
0cells of the map it votes in
35citing papers in PubMed
7.5field-weighted citation impact, top 2% 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

35 citing papers in PubMed, 46 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

5 authors at 2 institutions in 1 country.

Na LiDepartment of Central Laboratory, Shenyang Tenth People's Hospital, Shenyang Chest Hospital, Shenyang, Liaoning, People's Republic of China.
Lan ZhaoDepartment of Pharmacology, School of Pharmacy, China Medical University, Shenyang, Liaoning, People's Republic of China.
Chunyan GuoDepartment of Pharmacy, Shenyang Tenth People's Hospital, Shenyang Chest Hospital, Shenyang, Liaoning, People's Republic of China.
Chang LiuDepartment of Thoracic Surgery, Shenyang Tenth People's Hospital, Shenyang Chest Hospital, Shenyang, Liaoning, People's Republic of China.
Yongyu LiuDepartment of Thoracic Surgery, Shenyang Tenth People's Hospital, Shenyang Chest Hospital, Shenyang, Liaoning, People's Republic of China.
Shenyang Ninth People's Hospital · CNChina Medical University · CN

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

purposeHepatocellular carcinoma (HCC) is the sixth most lethal neoplasm worldwide. Traditional biomarkers often exploit the relationship between a certain gene and cancer progression, but they cannot predict patient survival or prognosis accurately. We aim to construct a new DNA repair-related gene signature that combines several genes to improve prognosis prediction in HCC.

methodsWe selected an HCC mRNA sequencing (mRNA-seq) dataset (n=365) from The Cancer Genome Atlas (TCGA), and gene set enrichment analysis (GSEA) was used to explore bioinformatics information and further screen genes. We then built a gene signature based on the Cox proportional hazards regression model.

resultsGSEA revealed that the hallmark DNA repair gene set was significantly upregulated in the tumor phenotype. A set of seven genes, namely,

conclusionWe developed and identified a seven-gene signature related to the DNA repair process that can predict survival in HCC. It can be used as an effective classification tool and to guide clinical treatment.

Indexed as

DNA repairhepatocellular carcinomamRNAsprognosticsurvival

Identifiers

PMID31496805
PMCPMC6689532
OpenAlexW2965853671

What OpenQuestion holds

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Registered trials

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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.