Evidence map›Paper›PMID 34691238›Full record

ArticleComputational and mathematical methods in medicine2021

An Autophagy-Related Gene-Based Prognostic Risk Signature for Hepatocellular Carcinoma: Construction and Validation.

Rui Feng, Jian Li, Weiling Xuan, Hanbo Liu, Dexin Cheng, Guowei Wang

Open access · hybridAbstract readValidation Study
PubMed Full text at PMC Publisher
In one paragraph

Article in Computational and mathematical methods in medicine, 2021. 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
0.6field-weighted citation impact, top 30% 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

3 citing papers in PubMed, 6 citations in OpenAlex.

  1. Article
  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

6 authors at 3 institutions in 1 country.

Rui FengDepartment of International Medicine, Zhejiang Provincial People's Hospital, People's Hospital of Hangzhou Medical College, Hangzhou City, Zhejiang Province 310000, China.ORCID https://orcid.org/0000-0001-5283-3803
Jian LiDepartment of Interventional Medicine, The Affiliated Hospital of Qingdao University, Qingdao City, Shandong Province 266000, China.ORCID https://orcid.org/0000-0002-2690-7845
Weiling XuanDepartment of Radiology, Xixi Hospital of Hangzhou, Hangzhou City, Zhejiang Province 310000, China.ORCID https://orcid.org/0000-0002-7682-4335
Hanbo LiuDepartment of Vascular Surgery-Center for Vascular Intervention, Zhejiang Provincial People's Hospital, People's Hospital of Hangzhou Medical College, Hangzhou City, Zhejiang Province 310000, China.ORCID https://orcid.org/0000-0002-8978-9700
Dexin ChengDepartment of Vascular Surgery-Center for Vascular Intervention, Zhejiang Provincial People's Hospital, People's Hospital of Hangzhou Medical College, Hangzhou City, Zhejiang Province 310000, China.ORCID https://orcid.org/0000-0002-0829-3018
Guowei WangDepartment of Radiology, Xixi Hospital of Hangzhou, Hangzhou City, Zhejiang Province 310000, China.ORCID https://orcid.org/0000-0003-4980-4563
Zhejiang Provincial People's Hospital · CNHangzhou Xixi hospital · CNQingdao University · CN

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundHepatocellular carcinoma (HCC) is a prevalent primary liver cancer. Treatment is dramatically difficult due to its high complexity and poor prognosis. Due to the disclosed dual functions of autophagy in cancer development, understanding autophagy-related genes devotes into novel biomarkers for HCC.

methodsDifferential expression of genes in normal and tumor groups was analyzed to acquire autophagy-related genes in HCC. These genes were subjected to GO and KEGG pathway analyses. Genes were then screened by univariate regression analysis. The screened genes were subjected to multivariate Cox regression analysis to build a prognostic model. The model was validated by the ICGC validation set.

resultsTo sum up, 42 differential genes relevant to autophagy were screened by differential expression analysis. Enrichment analysis showed that they were mainly enriched in pathways including regulation of autophagy and cell apoptosis. Genes were screened by univariate analysis and multivariate Cox regression analysis to build a prognostic model. The model constituted 6 feature genes: EIF2S1, BIRC5, SQSTM1, ATG7, HDAC1, and FKBP1A. Validation confirmed the accuracy and independence of this model in predicting the HCC patient's prognosis.

conclusionA total of 6 feature genes were identified to build a prognostic risk model. This model is conducive to investigating interplay between autophagy-related genes and HCC prognosis.

Indexed as

AutophagyAutophagy-Related Protein 7Biomarkers, TumorCarcinoma, HepatocellularComputational BiologyEukaryotic Initiation Factor-2Gene Expression ProfilingGene Expression Regulation, NeoplasticGene OntologyHistone Deacetylase 1HumansKaplan-Meier EstimateLiver NeoplasmsNomogramsPrognosisProportional Hazards ModelsATG7 protein, humanAutophagy-Related Protein 7Biomarkers, TumorBIRC5 protein, humanEIF2S1 protein, humanEukaryotic Initiation Factor-2FKBP1A protein, humanHDAC1 protein, humanHistone Deacetylase 1Sequestosome-1 ProteinSQSTM1 protein, humanSurvivinTacrolimus Binding Proteins

Identifiers

PMID34691238
PMCPMC8529386
OpenAlexW3206832409

What OpenQuestion holds

Textmetadata
LicenceCC BY
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.