Evidence map›Paper›PMID 39430152›Full record

ArticleIranian journal of public health2024

A Bioinformatics-Based Approach to Discover Novel Biomarkers in Hepatocellular Carcinoma.

Amir Shourideh, Reza Maddah, Bahareh Shateri Amiri, Zarrin Basharat, Marzieh Shadpirouz

Abstract read
In one paragraph

Article in Iranian journal of public health, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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

0 citing papers in PubMed.

No citing paper in PubMed yet.

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.

Amir ShouridehFaculty of Pharmacy, Eastern Mediterranean University, Famagusta, Cyprus.
Reza MaddahDepartment of Bioprocess Engineering, Institute of Industrial and Environmental Biotechnology, National Institute of Genetic Engineering and Biotechnology, Tehran, Iran.
Bahareh Shateri AmiriDepartment of Internal Medicine, School of Medicine Hazrat-e Rasool General Hospital Iran University of Medical Sciences, Tehran, Iran.
Zarrin BasharatAlpha Genomics Private Limited, Islamabad 45710, Pakistan.
Marzieh ShadpirouzDepartment of Applied Mathematics, Faculty of Mathematical Sciences, Shahrood University of Technology, Semnan, Iran.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Background: Liver hepatocellular carcinoma (LIHC) is a common cancer with a poor prognosis and high recurrence rate. We aimed to identify potential biomarkers for LIHC by investigating the involvement of hub genes, microRNAs (miRNAs), transcription factors (TFs), and protein kinases (PKs) in its occurrence. Methods: we conducted a bioinformatics analysis using microarray datasets, the TCGA-LIHC dataset, and text mining to identify differentially expressed genes (DEGs) associated with LIHC. They then performed functional enrichment analysis and gene-disease association analysis. The protein-protein interaction network of the genes was established, and hub genes were identified. The expression levels and survival analysis of these hub genes were evaluated, and their association with miRNAs, TFs, and PKs was assessed. Results: The analysis identified 122 common genes involved in LIHC pathogenesis. Ten hub genes were filtered out, including Conclusion: Identifying potential biomarkers for LIHC can aid in the design of targeted treatments and improve the survival of LIHC patients. The findings of this study provide a basis for further research in the field of LIHC and contribute to the understanding of its molecular pathogenesis.

Indexed as

GenesHepatocellular carcinomaMolecular pathwaySystems biology

Identifiers

PMID39430152
PMCPMC11488550

What Socratic holds

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LicenceCC BY-NC
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Registered trials

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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 Socratic graph.