Evidence map›Paper›PMID 32760725›Full record

ArticleFrontiers in cell and developmental biology2020

A Novel Ten-Gene Signature Predicting Prognosis in Hepatocellular Carcinoma.

Taicheng Zhou, Zhihua Cai, Ning Ma, Wenzhuan Xie, Chan Gao, Mengli Huang, Yuezong Bai, Yangpeng Ni, Yunqiang Tang

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Frontiers in cell and developmental biology, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 57 papers.

0numbers the graph read from it
0cells of the map it votes in
57citing 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

57 citing papers in PubMed.

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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

9 authors.

Taicheng ZhouDepartment of Gastroenterological Surgery and Hernia Center, The Sixth Affiliated Hospital of Sun Yat-sen University, Guangdong Institute of Gastroenterology, Guangdong Provincial Key Laboratory of Colorectal and Pelvic Floor Diseases, Supported by National Key Clinical Discipline, Guangzhou, China.
Zhihua CaiDepartment of Oncology, The Affiliated Cancer Hospital of Guangzhou Medical University, Guangzhou, China.
Ning MaDepartment of Gastroenterological Surgery and Hernia Center, The Sixth Affiliated Hospital of Sun Yat-sen University, Guangdong Institute of Gastroenterology, Guangdong Provincial Key Laboratory of Colorectal and Pelvic Floor Diseases, Supported by National Key Clinical Discipline, Guangzhou, China.
Wenzhuan XieThe Medical Department, 3D Medicines Inc., Shanghai, China.
Chan GaoThe Medical Department, 3D Medicines Inc., Shanghai, China.
Mengli HuangThe Medical Department, 3D Medicines Inc., Shanghai, China.
Yuezong BaiThe Medical Department, 3D Medicines Inc., Shanghai, China.
Yangpeng NiDepartment of Oncology, Jieyang People's Hospital, Sun Yat-sen University, Jieyang, China.
Yunqiang TangDepartment of Hepatic-Biliary Surgery, The Affiliated Cancer Hospital of Guangzhou Medical University, Guangzhou, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Hepatocellular carcinoma (HCC) has a dismal long-term outcome. We aimed to construct a multi-gene model for prognosis prediction to inform HCC management. The cancer-specific differentially expressed genes (DEGs) were identified using RNA-seq data of paired tumor and normal tissue. A prognostic signature was built by LASSO regression analysis. Gene set enrichment analysis (GSEA) was performed to further understand the underlying molecular mechanisms. A 10-gene signature was constructed to stratify the TCGA and ICGC cohorts into high- and low-risk groups where prognosis was significantly worse in the high-risk group across cohorts (

Indexed as

expressionhepatocellular carcinomaprognosisrisk stratificationsignature

Identifiers

PMID32760725
PMCPMC7372135

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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.