Evidence map›Paper›PMID 36900109›Full record

ArticleDiagnostics (Basel, Switzerland)2023

Identification of Prognostic Biomarkers for Suppressing Tumorigenesis and Metastasis of Hepatocellular Carcinoma through Transcriptome Analysis.

Divya Mishra, Ashish Mishra, Sachchida Nand Rai, Emanuel Vamanu, Mohan P Singh

Full text read
In one paragraph

Article in Diagnostics (Basel, Switzerland), 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 33 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
33citing papers in PubMed, 1 pooled it
–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

33 citing papers in PubMed, 1 synthesis or guideline pooled it.

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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.

Divya MishraCentre of Bioinformatics, Affiliated University of Allahabad, Prayagraj 211002, India.ORCID 0000-0003-2127-6113
Ashish MishraCentre of Bioinformatics, Affiliated University of Allahabad, Prayagraj 211002, India.ORCID 0000-0002-3378-2390
Sachchida Nand RaiCentre of Biotechnology, Affiliated University of Allahabad, Prayagraj 211002, India.ORCID 0000-0001-8418-9549
Emanuel VamanuFaculty of Biotechnology, University of Agricultural Sciences and Veterinary Medicine, 011464 Bucharest, Romania.ORCID 0000-0002-3376-2058
Mohan P SinghCentre of Biotechnology, Affiliated University of Allahabad, Prayagraj 211002, India.ORCID 0000-0002-6236-856X

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Cancer is one of the deadliest diseases developed through tumorigenesis and could be fatal if it reaches the metastatic phase. The novelty of the present investigation is to explore the prognostic biomarkers in hepatocellular carcinoma (HCC) that could develop glioblastoma multiforme (GBM) due to metastasis. The analysis was conducted using RNA-seq datasets for both HCC (PRJNA494560 and PRJNA347513) and GBM (PRJNA494560 and PRJNA414787) from Gene Expression Omnibus (GEO). This study identified 13 hub genes found to be overexpressed in both GBM and HCC. A promoter methylation study showed these genes to be hypomethylated. Validation through genetic alteration and missense mutations resulted in chromosomal instability, leading to improper chromosome segregation, causing aneuploidy. A 13-gene predictive model was obtained and validated using a KM plot. These hub genes could be prognostic biomarkers and potential therapeutic targets, inhibition of which could suppress tumorigenesis and metastasis.

Indexed as

cox regression analysisGEPIAglioblastoma multiforme (GBM)hepatocellular carcinomas (HCC)hub genemetastasisRNA-seq analysis

Identifiers

PMID36900109
PMCPMC10001411

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Textfull text, public
LicenceCC BY
reference markers read2
measurements read30
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.