Evidence map›Paper›PMID 33854986›Full record

ArticleJournal of hepatocellular carcinoma2021

High Expression of PIGC Predicts Unfavorable Survival in Hepatocellular Carcinoma.

Xufeng Guo, Shan Tian, Pan Cao, Yishan Xie, Weiguo Dong

Open access · goldAbstract read
In one paragraph

Article in Journal of hepatocellular carcinoma, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing papers in PubMed
0.1field-weighted citation impact, top 58% 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

2 citing papers in PubMed, 3 citations in OpenAlex.

  1. Article
  2. 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

5 authors at 2 institutions in 1 country.

Xufeng GuoDepartment of Oncology, Renmin Hospital of Wuhan University, Wuhan, People's Republic of China.
Shan TianDepartment of Gastroenterology, Renmin Hospital of Wuhan University, Wuhan, People's Republic of China.
Pan CaoDepartment of Infectious Disease, Renmin Hospital of Wuhan University, Wuhan, People's Republic of China.
Yishan XieDepartment of Oncology, Renmin Hospital of Wuhan University, Wuhan, People's Republic of China.
Weiguo DongDepartment of Gastroenterology, Renmin Hospital of Wuhan University, Wuhan, People's Republic of China.
Wuhan University · CNRenmin Hospital of Wuhan University · CN

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

purposeThe effects of phosphatidylinositol glycan anchor biosynthesis, class C (PIGC), in the progression of liver cancer are unknown. In this study, we attempted to clarify the clinical significance and mechanism of PIGC in hepatocellular carcinoma (HCC). PATIENTS AND

methodsTo explore the expression profiles, DNA methylation, mutation status, clinical relevance, and prognostic value of PIGC in patients with HCC, a series of bioinformatic databases and websites were searched. Moreover, numerous vitro experiments were performed to investigate the mechanism of PIGC in the regulation of cancerous liver cells.

resultsExpression of PIGC mRNA and protein was upregulated in cancerous liver specimens compared with normal liver tissues. High expression of PIGC mRNA was related to higher tumor grade, lymphatic metastasis, advanced TNM stage, and TP53 mutation. High expression of PIGC mRNA predicted more unfavorable overall survival (OS) (HR=1.7, P=0.0028) and disease-free survival (DFS) (HR=1.5, P=0.0067) in patients with liver cancer. The mutation rate of PIGC was 10%, and amplification was the most common mutant type. Expression of

conclusion

Indexed as

cell cyclegene mutationhepatocellular carcinomaPIGCsurvival

Identifiers

PMID33854986
PMCPMC8040696
OpenAlexW3143070200

What OpenQuestion holds

Textmetadata
LicenceCC BY-NC
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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.