Evidence map›Paper›PMID 41755885›Full record

ArticleInternational journal of genomics2026

Extracellular Matrix-Associated Biomarkers for Hepatocellular Carcinoma: Insights From Machine Learning and Single-Cell Analysis.

Pedram Asadi Sarabi, Elham Rismani, Amir Ali Judaki, Amirhossein Farrokhzad, Zahra Hendi, Moustapha Hassan, Massoud Vosough

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in International journal of genomics, 2026. 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
–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

1 citing paper in PubMed.

  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

7 authors.

Pedram Asadi SarabiDepartment of Regenerative Medicine, Cell Science Research Center, Royan Institute for Stem Cell Biology and Technology, ACECR, Tehran, Iran, acecr.ac.ir.ORCID https://orcid.org/0009-0008-6918-4748
Elham RismaniMolecular Medicine Department, Biotechnology Research Center (BRC), Pasteur Institute of Iran, Tehran, Iran, pasteur.ac.ir.ORCID https://orcid.org/0000-0001-9997-3512
Amir Ali JudakiDepartment of Cellular and Molecular Biology, Faculty of Sciences and Advanced Technology in Biology, University of Science and Culture, Tehran, Iran, usc.ac.ir.ORCID https://orcid.org/0009-0008-3896-2914
Amirhossein FarrokhzadDepartment of Cellular and Molecular Biology, Faculty of Sciences and Advanced Technology in Biology, University of Science and Culture, Tehran, Iran, usc.ac.ir.ORCID https://orcid.org/0009-0000-2726-8849
Zahra HendiDepartment of Cellular and Molecular Biology, Faculty of Sciences and Advanced Technology in Biology, University of Science and Culture, Tehran, Iran, usc.ac.ir.ORCID https://orcid.org/0009-0004-3208-5202
Moustapha HassanExperimental Cancer Medicine, Institution for Laboratory Medicine, and Karolinska University Hospital, Karolinska Institute, Stockholm, Sweden, ki.se.ORCID https://orcid.org/0000-0003-1927-5859
Massoud VosoughDepartment of Regenerative Medicine, Cell Science Research Center, Royan Institute for Stem Cell Biology and Technology, ACECR, Tehran, Iran, acecr.ac.ir.ORCID https://orcid.org/0000-0001-5924-4366

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

The 5-year overall survival rate for hepatocellular carcinoma (HCC) patients remains below 20%. Alterations in the extracellular matrix (ECM) are increasingly recognized as central drivers of HCC initiation and progression. This study applied a system biology framework integrating omics data and machine learning to analyze gene expression and regulatory networks in HCC using The Cancer Genome Atlas. Eight ECM-associated genes (

Indexed as

extracellular matrix remodelinghepatocellular carcinomamachine learningoverall survivalsystems-level biomarker

Identifiers

PMID41755885
PMCPMC12932914

What Socratic 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 Socratic graph.