Characterization of the crotonylation landscape in gastric cancer: a machine learning-derived prognostic signature and the oncogenic role of TIMP1
4 affiliations
- 1Department of Anorectal, Macheng Hospital of Traditional Chinese Medicine
- 2Department of Hepatobiliary Surgery, Renmin Hospital of Wuhan University
- 3Department of Obstetrics & Gynecology, Renmin Hospital of Wuhan University
- 4Department of Pathology, Renmin Hospital of Wuhan University
Abstract
Background Gastric cancer (GC) remains a lethal malignancy with high heterogeneity and poor prognosis. Crotonylation, a key post-translational modification, contributes significantly to oncogenic processes and immune system interactions; however, its prognostic significance in gastric cancer is not fully understood. Methods We integrated single-cell RNA sequencing and bulk transcriptomics to evaluate cell-specific crotonylation activity and utilized WGCNA to identify core crotonylation-related gene modules. A machine learning-derived crotonylation-related prognostic signature (CRGS) was established and validated across independent cohorts. Functional enrichment, immune infiltration, and immunotherapy response analyses were conducted. Furthermore, the expression and oncogenic roles of a key risk gene, TIMP1, were experimentally validated using qRT-PCR, colony formation, and Transwell migration/invasion assays. Results Single-cell analysis revealed significantly elevated crotonylation activity in epithelial cells. The machine learning-derived CRGS successfully stratified patients into distinct risk groups, with high-risk patients showing a severe survival disadvantage. The nomogram integrating the CRGS and clinical factors demonstrated robust prognostic accuracy. Functional analysis indicated that high-risk patients were enriched in tumor-promoting pathways, while low-risk patients exhibited enhanced immune infiltration and metabolic homeostasis. The low-risk group also exhibited elevated tumor mutational burden, stronger cancer-immunity cycle activity, and better predicted response to immune checkpoint inhibitors. Experimental validation confirmed that TIMP1 was heavily upregulated in GC cell lines. Silencing TIMP1 significantly suppressed GC cell proliferation, colony formation, migration, and invasion. Conclusions Our study establishes a reliable, clinically translatable CRGS for risk stratification and immunotherapy prediction, while identifying TIMP1 as a potential therapeutic target and a key oncogenic factor in vitro in GC.
Related
- An Exploratory Multiome Analysis of a BCG-Associated HSPC Signature: Trained-Immunity Marker in Blood and an Adverse Prognostic Association in Tumour
- Prognostic value of melatonin-related signature genes in lung adenocarcinoma
- Development and External Validation of an M2 Macrophage-Related Prognostic Signature in Breast Cancer Using Multi-Omics Data and Machine Learning.
- Integrating ferroptosis and the tumor-immune gene mutations interface: a novel lenvatinib resistance prognostic signature with YWHAB-related immunotherapeutic correlates in hepatocellular carcinoma
- Longitudinal Metaproteomic and Lysine Crotonylation Remodeling Defines Clinical Response to Fecal Microbiota Transplantation in Ulcerative Colitis
BibTeX
@article{gao2026characterization,
title = {{Characterization of the crotonylation landscape in gastric cancer: a machine learning-derived prognostic signature and the oncogenic role of TIMP1}},
author = {Ji Gao and Yuefeng Zhang and Min Zhou and Xiaoli Tian},
journal = {Human Genomics},
year = {2026},
month = jul,
doi = {10.1186/s40246-026-01018-6},
url = {https://doi.org/10.1186/s40246-026-01018-6},
note = {CC BY},
}
RIS
TY - JOUR
TI - Characterization of the crotonylation landscape in gastric cancer: a machine learning-derived prognostic signature and the oncogenic role of TIMP1
AU - Ji Gao
AU - Yuefeng Zhang
AU - Min Zhou
AU - Xiaoli Tian
PY - 2026
DA - 2026/07/01
JO - Human Genomics
DO - 10.1186/s40246-026-01018-6
UR - https://doi.org/10.1186/s40246-026-01018-6
AB - Background Gastric cancer (GC) remains a lethal malignancy with high heterogeneity and poor prognosis. Crotonylation, a key post-translational modification, contributes significantly to oncogenic processes and immune system interactions; however, its prognostic significance in gastric cancer is not fully understood. Methods We integrated single-cell RNA sequencing and bulk transcriptomics to evaluate cell-specific crotonylation activity and utilized WGCNA to identify core crotonylation-related gene modules. A machine learning-derived crotonylation-related prognostic signature (CRGS) was established and validated across independent cohorts. Functional enrichment, immune infiltration, and immunotherapy response analyses were conducted. Furthermore, the expression and oncogenic roles of a key risk gene, TIMP1, were experimentally validated using qRT-PCR, colony formation, and Transwell migration/invasion assays. Results Single-cell analysis revealed significantly elevated crotonylation activity in epithelial cells. The machine learning-derived CRGS successfully stratified patients into distinct risk groups, with high-risk patients showing a severe survival disadvantage. The nomogram integrating the CRGS and clinical factors demonstrated robust prognostic accuracy. Functional analysis indicated that high-risk patients were enriched in tumor-promoting pathways, while low-risk patients exhibited enhanced immune infiltration and metabolic homeostasis. The low-risk group also exhibited elevated tumor mutational burden, stronger cancer-immunity cycle activity, and better predicted response to immune checkpoint inhibitors. Experimental validation confirmed that TIMP1 was heavily upregulated in GC cell lines. Silencing TIMP1 significantly suppressed GC cell proliferation, colony formation, migration, and invasion. Conclusions Our study establishes a reliable, clinically translatable CRGS for risk stratification and immunotherapy prediction, while identifying TIMP1 as a potential therapeutic target and a key oncogenic factor in vitro in GC.
SN - 1479-7364
ER -