Evidence map›Paper›PMID 32655798›Full record

ArticleAmerican journal of translational research2020

Identifying drug candidates for hepatocellular carcinoma based on differentially expressed genes.

Jiyuan Xing, Qingmiao Shi, Junjie Zhao, Zujiang Yu

Open access · greenAbstract read
In one paragraph

Article in American journal of translational research, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing papers in PubMed
0.2field-weighted citation impact, top 57% 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

1 citing paper in PubMed, 2 citations in OpenAlex.

  1. 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

4 authors at 1 institution in 1 country.

Jiyuan XingGene Hospital of Henan Province, Precision Medicine Center, The First Affiliated Hospital of Zhengzhou University Zhengzhou 450052, Henan, China.
Qingmiao ShiGene Hospital of Henan Province, Precision Medicine Center, The First Affiliated Hospital of Zhengzhou University Zhengzhou 450052, Henan, China.
Junjie ZhaoDepartment of Pharmacy, The First Affiliated Hospital of Zhengzhou University Zhengzhou 450052, Henan, China.
Zujiang YuGene Hospital of Henan Province, Precision Medicine Center, The First Affiliated Hospital of Zhengzhou University Zhengzhou 450052, Henan, China.
First Affiliated Hospital of Zhengzhou University · CN

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

The prognosis for patients with advanced hepatocellular carcinoma (HCC) is extremely poor, mainly due to rapid progression and a paucity of effective drugs. Genome-wide analysis allows for potential drugs to be explored based on differentially expressed genes (DEGs). However, drug candidates and DEGs in HCC are largely unknown. In this study, we investigated DEGs and prognostication using The Cancer Genome Atlas (TCGA), the International Cancer Genome Consortium (ICGC), the Gene Expression Omnibus (GEO), and immunohistochemical staining. Protein-protein interaction networks between DEGs were also analyzed to clarify 12 hub genes and query online databases for potential HCC therapeutic drugs. We found that 885 of 3219 DEGs from a TCGA dataset were associated with prognosis. We clarified 12 hub genes that were overexpressed in tumor samples and significantly associated with poor overall survival (OS) in HCC patients. These findings were validated using GEO and ICGC cohorts. Moreover, promising drug candidates targeted against HCC were predicted using online databases. Collectively, the upregulation of 12 hub genes was associated with poor prognosis for patients with HCC, and focusing on their expression may advance efforts towards targeted HCC therapies.

Indexed as

differential gene expressionHepatocellular carcinomahub genesprognosis

Identifiers

PMID32655798
PMCPMC7344051
OpenAlexW3042957368

What OpenQuestion holds

Textmetadata
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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.