Evidence map›Paper›PMID 40610613›Full record

ArticleScientific reports2025

Machine learning-assisted multi-dimensional transcriptomic analysis of cytoskeleton-related molecules and their relationship with prognosis in hepatocellular carcinoma.

Yuxuan Li, Mingbo Cao, Xiaorui Su, Gaoyuan Yang, Yupeng Ren, Zhiwei He, Zheng Shi, Ziyi Hu, Guirong Liang, Qi Zhang and 2 more

Abstract read
In one paragraph

Article in Scientific reports, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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1 · What the graph read from it

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2 · The registry

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3 · Its place in the literature

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4 · The record

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5 · Who and what money

Authors and funding

12 authors.

Yuxuan Li *Department of Hepatobiliary Surgery, The Third Affiliated Hospital, Sun Yat-Sen University, Guangzhou, 510630, China.
Mingbo Cao *Department of Hepatobiliary Surgery, The Third Affiliated Hospital, Sun Yat-Sen University, Guangzhou, 510630, China.
Xiaorui Su *Department of Hepatobiliary & Pancreatic Surgery, The Third Affiliated Hospital, Sun Yat-Sen University, Guangzhou, 510630, China.
Gaoyuan YangDepartment of Hepatobiliary Surgery, The Third Affiliated Hospital, Sun Yat-Sen University, Guangzhou, 510630, China.
Yupeng RenDepartment of Hepatobiliary Surgery, The Third Affiliated Hospital, Sun Yat-Sen University, Guangzhou, 510630, China.
Zhiwei HeDepartment of Hepatobiliary Surgery, The Third Affiliated Hospital, Sun Yat-Sen University, Guangzhou, 510630, China.
Zheng ShiDepartment of Hepatobiliary Surgery, The Third Affiliated Hospital, Sun Yat-Sen University, Guangzhou, 510630, China.
Ziyi HuDepartment of Hepatobiliary & Pancreatic Surgery, The Third Affiliated Hospital, Sun Yat-Sen University, Guangzhou, 510630, China.
Guirong LiangDepartment of Hepatobiliary & Pancreatic Surgery, The Third Affiliated Hospital, Sun Yat-Sen University, Guangzhou, 510630, China.
Qi ZhangBiotherapy Centre, The Third Affiliated Hospital, Sun Yat-Sen University, Guangzhou, 510630, China. zhangq27@mail.sysu.edu.cn.
Zhicheng YaoDepartment of Hepatobiliary & Pancreatic Surgery, The Third Affiliated Hospital, Sun Yat-Sen University, Guangzhou, 510630, China. yaozhch2@mail.sysu.edu.cn.
Meihai DengDepartment of Hepatobiliary Surgery, The Third Affiliated Hospital, Sun Yat-Sen University, Guangzhou, 510630, China. dengmeih@mail.sysu.edu.cn.

Funding

Beijing Xisike Clinical Oncology Research Foundation Y-Roche2019/2-0041Guangdong Basic and Applied Basic Research Foundation 2023A1515220090National Natural Science Foundation Cultivation Project of the Third Affiliated Hospital of Sun Yat-sen University 2020GZRPYMS11Science and Technology Planning Project of Guangzhou city 202102010171Science and Technology Planning Project of Guangzhou city 2023A03J0211
6 · The paper itself

Abstract

Hepatocellular carcinoma (HCC) is a leading cause of cancer-related death worldwide, with a poor prognosis due to its aggressive nature and limited treatment options. Cytoskeletal dynamics play a critical role in tumor progression, but the prognostic and therapeutic potential of cytoskeleton-related genes in HCC remains underexplored. In this study, transcriptomic data from the TCGA-LIHC dataset were used to identify differentially expressed cytoskeleton-related genes associated with overall survival (OS). Prognostic models were constructed using LASSO regression and random forest algorithms, and validated in two independent cohorts (ICGC LIRI-JP and CHCC-HBV). Single-cell sequencing (scRNA-seq) and spatial transcriptomics analyses explored the expression and functional roles of key genes, while drug screening and molecular docking identified potential therapeutic agents, followed by in vitro and in vivo validation. The analysis identified 110 cytoskeleton-related DEGs, with 13 significantly associated with OS. A robust five-gene prognostic model (ARPC1A, CCNB2, CKAP5, DCTN2, TTK) was developed using LASSO regression and validated across cohorts. The model was integrated into a clinical nomogram, demonstrating good calibration and utility. Single-cell and spatial transcriptomics revealed high expression of the five genes in malignant tissues and their association with immunosuppressive microenvironments. High-risk scores correlated with TP53 mutations. Drug screening identified irinotecan and sorafenib as potential agents targeting TTK, with combined treatment significantly inhibiting tumor growth in vitro and in vivo. This study highlights the prognostic and therapeutic significance of cytoskeleton-related genes in HCC. The five-gene model provides a reliable tool for risk stratification, and the irinotecan-sorafenib combination shows promise as a therapeutic strategy.

Indexed as

Carcinoma, HepatocellularCytoskeletonGene Expression ProfilingLiver NeoplasmsMachine LearningTranscriptomeBiomarkers, TumorFemaleGene Expression Regulation, NeoplasticHumansMalePrognosisSorafenibBiomarkers, TumorSorafenibCytoskeletonHepatocellular carcinomaMolecular dockingPrognostic predictive modelTranscriptome

Identifiers

PMID40610613
PMCPMC12229654

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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 OpenQuestion graph.