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Peer-reviewedCC BY 4.0Frontiers in Oncology

Integrated bioinformatics and machine learning identify key hub genes in early-onset gastric cancer

  • Shaoxiong Yi1,2
  • Qiuxia Chen3
  • Jiaxin Wu1,2
  • Haobin Hou1,2
  • Huanjie Chen4
  • Weiliang Zhang3
  • Kaiyu Zhu5
  • Baoyi Liu1,2
  • Peiwen Liang1,2
  • Shijun Zhang3
  • Dongjie Yang1,2
5 affiliations
  1. 1Digestive Medicine Center, the Seventh Affiliated Hospital, Sun Yat-sen University
  2. 2Guangdong Provincial Key Laboratory of Digestive Cancer Research
  3. 3Department of Traditional Chinese Medicine, The First Affiliated Hospital, Sun Yat-sen University
  4. 4Department of General Surgery, The First Affiliated Hospital of Shantou University Medical College
  5. 5Center for Gastrointestinal Surgery, the First Affiliated Hospital, Sun Yat-sen University
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Abstract

Background: Early-onset gastric cancer (EOGC) represents a clinically and biologically distinct subtype of gastric cancer characterized by unique molecular features. However, its underlying molecular mechanisms remain unclear. This study aimed to identify EOGC-associated hub genes using an integrated bioinformatics and machine learning framework.

Methods: Differentially expressed genes (DEGs) between EOGC and late-onset gastric cancer (LOGC) were analyzed using functional enrichment, weighted gene co-expression network analysis (WGCNA), least absolute shrinkage and selection operator (LASSO) regression, and random forest algorithms. Immune infiltration was characterized using CIBERSORT. Candidate hub genes were validated using an independent external cohort, clinical specimens, single-cell transcriptomic datasets, and in vitro functional assays. A five-gene logistic regression model was established and evaluated using ROC-AUC, calibration analysis, and bootstrap resampling.

Results: Transcriptomic analysis identified 227 DEGs, and WGCNA identified seven co-expression modules, with the green module showing the strongest association with EOGC. Machine learning identified five hub genes: CCR7, CD1D, PTGER2, PIK3CG, and EBI3. The combined model showed good discriminatory performance in the training, internal validation, and independent external validation cohorts (AUC = 0.909, 0.859, and 0.802, respectively). Bootstrap resampling yielded AUC estimates of 0.908, 0.852, and 0.789, respectively, supporting model stability. All five genes showed higher expression in EOGC and were validated at the mRNA and protein levels. Immune profiling and single-cell analysis revealed an immune-associated molecular phenotype and distinct expression patterns across immune cell subsets. Silencing the hub genes suppressed gastric cancer cell proliferation and migration in vitro .

Conclusion: We identified five EOGC-associated hub genes and revealed an immune-associated molecular landscape. The five-gene model represents an EOGC-associated molecular signature distinguishing EOGC from LOGC rather than a stand-alone clinical diagnostic tool. These findings provide insights into the molecular and immune biology of EOGC and may inform future studies of molecular stratification and treatment-related outcomes.

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Cite

BibTeXRIS
BibTeX
@article{yi2026integrated,
  title = {{Integrated bioinformatics and machine learning identify key hub genes in early-onset gastric cancer}},
  author = {Shaoxiong Yi and Qiuxia Chen and Jiaxin Wu and Haobin Hou and Huanjie Chen and Weiliang Zhang and Kaiyu Zhu and Baoyi Liu and Peiwen Liang and Shijun Zhang and Dongjie Yang},
  journal = {Frontiers in Oncology},
  year = {2026},
  month = oct,
  doi = {10.3389/fonc.2026.1951255},
  url = {https://www.frontiersin.org/articles/10.3389/fonc.2026.1951255/full},
  note = {CC BY 4.0},
}
RIS
TY  - JOUR
TI  - Integrated bioinformatics and machine learning identify key hub genes in early-onset gastric cancer
AU  - Shaoxiong Yi
AU  - Qiuxia Chen
AU  - Jiaxin Wu
AU  - Haobin Hou
AU  - Huanjie Chen
AU  - Weiliang Zhang
AU  - Kaiyu Zhu
AU  - Baoyi Liu
AU  - Peiwen Liang
AU  - Shijun Zhang
AU  - Dongjie Yang
PY  - 2026
DA  - 2026/10/14
JO  - Frontiers in Oncology
DO  - 10.3389/fonc.2026.1951255
UR  - https://www.frontiersin.org/articles/10.3389/fonc.2026.1951255/full
AB  - Background: Early-onset gastric cancer (EOGC) represents a clinically and biologically distinct subtype of gastric cancer characterized by unique molecular features. However, its underlying molecular mechanisms remain unclear. This study aimed to identify EOGC-associated hub genes using an integrated bioinformatics and machine learning framework.  Methods: Differentially expressed genes (DEGs) between EOGC and late-onset gastric cancer (LOGC) were analyzed using functional enrichment, weighted gene co-expression network analysis (WGCNA), least absolute shrinkage and selection operator (LASSO) regression, and random forest algorithms. Immune infiltration was characterized using CIBERSORT. Candidate hub genes were validated using an independent external cohort, clinical specimens, single-cell transcriptomic datasets, and in vitro functional assays. A five-gene logistic regression model was established and evaluated using ROC-AUC, calibration analysis, and bootstrap resampling.  Results: Transcriptomic analysis identified 227 DEGs, and WGCNA identified seven co-expression modules, with the green module showing the strongest association with EOGC. Machine learning identified five hub genes: CCR7, CD1D, PTGER2, PIK3CG, and EBI3. The combined model showed good discriminatory performance in the training, internal validation, and independent external validation cohorts (AUC = 0.909, 0.859, and 0.802, respectively). Bootstrap resampling yielded AUC estimates of 0.908, 0.852, and 0.789, respectively, supporting model stability. All five genes showed higher expression in EOGC and were validated at the mRNA and protein levels. Immune profiling and single-cell analysis revealed an immune-associated molecular phenotype and distinct expression patterns across immune cell subsets. Silencing the hub genes suppressed gastric cancer cell proliferation and migration in vitro .  Conclusion: We identified five EOGC-associated hub genes and revealed an immune-associated molecular landscape. The five-gene model represents an EOGC-associated molecular signature distinguishing EOGC from LOGC rather than a stand-alone clinical diagnostic tool. These findings provide insights into the molecular and immune biology of EOGC and may inform future studies of molecular stratification and treatment-related outcomes.
SN  - 2234-943X
ER  -