Evidence mapPaperPMID 41044669Full record

ArticleCancer cell international2025

Integrating bulk RNA-seq, scRNA-seq, and spatial transcriptomics data to identify novel post-translational modification-related molecular subtypes and therapeutic responses in hepatocellular carcinoma.

Shiling Chen, Yunjie Li, Jichang Hu, Heli Li, Chen Hu, Jinzhu Zhao, Hong Qian, Shuya Bai, Zhouping Tang, Yangyang Feng

Abstract read
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Article in Cancer cell international, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.

0numbers the graph read from it
0cells of the map it votes in
5citing 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

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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

5 citing papers in PubMed.

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4 · The record

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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

10 authors.

Shiling Chen *Department of Neurology, Tongji Hospital of Tongji Medical College, Huazhong University of Science and Technology, Wuhan, 430030, Hubei, China.
Yunjie Li *Division of Child Healthcare, Department of Pediatrics, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, 430030, China.
Jichang HuDepartment of Pathophysiology, School of Basic Medicine, Key Laboratory of Education Ministry of China for Neurological Disorders, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China.
Heli LiDivision of Child Healthcare, Department of Pediatrics, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, 430030, China.
Chen HuDivision of Child Healthcare, Department of Pediatrics, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, 430030, China.
Jinzhu ZhaoDivision of Child Healthcare, Department of Pediatrics, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, 430030, China.
Hong QianDivision of Child Healthcare, Department of Pediatrics, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, 430030, China.
Shuya BaiDepartment of Gastroenterology and Hepatology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, 430030, China.
Zhouping TangDepartment of Neurology, Tongji Hospital of Tongji Medical College, Huazhong University of Science and Technology, Wuhan, 430030, Hubei, China.
Yangyang FengDivision of Child Healthcare, Department of Pediatrics, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, 430030, China. yangyanggfeng@163.com.

Funding

National Natural Science Foundation of China 82203835
6 · The paper itself

Abstract

backgroundHepatocellular carcinoma (HCC) poses considerable difficulties regarding the prognosis and the assessment of treatment efficacy. Additionally, while it is recognized that post-translational modification (PTM) plays a crucial role in modulating HCC progression, their specific prognostic implications in HCC have not been thoroughly investigated.

methods21 types of PTM (acetylation, succinylation, malonylation, crotonylation, β-hydroxybutyrylation, lactylation, palmitoylation, myristoylation, SUMOylation, NEDDylation, ISGylation, ATG8ylation, FAT10ylation, UFMylation, methylation, glycosylation, biotinylation, S-nitrosylation, phosphorylation, ubiquitination, deubiquitination) were generated consensus cluster. Then, WGCNA was utilized to identify module genes. Finally, a machine learning approach was employed to create PTM.score.

resultsThis analysis revealed two distinct subtypes of PTMs, each characterized by unique molecular signatures. By integrating different categories of genes, including prognosis-related DEGs, module genes, and PTM-related genes, 15 hub genes were identified, and a PTM.score was developed. PTM.score was rigorously validated across independent external cohorts (TCGA-LIHC, LIRI-JP, GSE10143, GSE14520, GSE27150, GSE36376, and GSE76427) and an in-house cohort, demonstrating its reliability and potential applicability. In addition, patients categorized with a low PTM.score displayed a TME that was more actively engaged, which corresponded with a poor prognosis. Furthermore, these patients demonstrated a high level of responsiveness to immunotherapy interventions. Furthermore, an examination using scRNA-seq and spatial transcriptomics indicated that patients with low PTM.score exhibited heightened cell proliferation and malignancy.

conclusionThis novel PTM-related prognostic signature could effectively assess the prognosis and therapeutic responses of HCC patients, providing new perspectives for individualized treatment for the patient population.

Indexed as

Hepatocellular carcinomaImmunotherapyMulti-omics analysisPost-translational modification

Identifiers

PMID41044669
PMCPMC12495838

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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.