Kolmogorov–Arnold Network Model Integrated with Hypoxia Risk for Predicting PD-L1 Inhibitor Responses in Hepatocellular Carcinoma
Hepatocellular carcinoma (HCC) is a leading cause of cancer-related deaths, with immunotherapy being a first-line treatment at the advanced stage and beyond. Hypoxia plays a critical role in tumor progression and resistance to therapy. This study develops and validates an artificial intelligence (AI...
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MDPI AG
2025-03-01
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| author | Mohan Huang Xinyue Chen Yi Jiang Lawrence Wing Chi Chan |
| author_facet | Mohan Huang Xinyue Chen Yi Jiang Lawrence Wing Chi Chan |
| author_sort | Mohan Huang |
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| description | Hepatocellular carcinoma (HCC) is a leading cause of cancer-related deaths, with immunotherapy being a first-line treatment at the advanced stage and beyond. Hypoxia plays a critical role in tumor progression and resistance to therapy. This study develops and validates an artificial intelligence (AI) model based on publicly available genomic datasets to predict hypoxia-related immunotherapy responses. Based on the HCC-Hypoxia Overlap (HHO) and immunotherapy response to hypoxia (IRH) genes selected by differential expression and enrichment analyses, a hypoxia model was built and validated on the TCGA-LIHC and GSE233802 datasets, respectively. The training and test sets were assembled from the EGAD00001008128 dataset of 290 HCC patients, and the response and non-response classes were balanced using the Synthetic Minority Over-sampling Technique. With the genes selected via the minimum Redundancy Maximum Relevance and stepwise forward methods, a Kolmogorov–Arnold Network (KAN) model was trained. Support Vector Machine (SVM) combined the Hypoxia and KAN models to predict immunotherapy response. The hypoxia model was constructed using 10 genes (IRH and HHO). The KAN model with 11 genes achieved a test accuracy of 0.7. The SVM integrating the hypoxia and KAN models achieved a test accuracy of 0.725. The established AI model can predict immunotherapy response based on hypoxia risk and genomic factors potentially intervenable in HCC patients. |
| format | Article |
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| institution | Kabale University |
| issn | 2306-5354 |
| language | English |
| publishDate | 2025-03-01 |
| publisher | MDPI AG |
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| series | Bioengineering |
| spelling | doaj-art-43a5eb826dfe4b5ebe1f2d5aea719f2c2025-08-20T03:43:30ZengMDPI AGBioengineering2306-53542025-03-0112332210.3390/bioengineering12030322Kolmogorov–Arnold Network Model Integrated with Hypoxia Risk for Predicting PD-L1 Inhibitor Responses in Hepatocellular CarcinomaMohan Huang0Xinyue Chen1Yi Jiang2Lawrence Wing Chi Chan3Department of Health Technology and Informatics, The Hong Kong Polytechnic University, Hong Kong SAR, ChinaDepartment of Health Technology and Informatics, The Hong Kong Polytechnic University, Hong Kong SAR, ChinaThe Second Affiliated Hospital, School of Medicine, The Chinese University of Hong Kong, Shenzhen 518000, ChinaDepartment of Health Technology and Informatics, The Hong Kong Polytechnic University, Hong Kong SAR, ChinaHepatocellular carcinoma (HCC) is a leading cause of cancer-related deaths, with immunotherapy being a first-line treatment at the advanced stage and beyond. Hypoxia plays a critical role in tumor progression and resistance to therapy. This study develops and validates an artificial intelligence (AI) model based on publicly available genomic datasets to predict hypoxia-related immunotherapy responses. Based on the HCC-Hypoxia Overlap (HHO) and immunotherapy response to hypoxia (IRH) genes selected by differential expression and enrichment analyses, a hypoxia model was built and validated on the TCGA-LIHC and GSE233802 datasets, respectively. The training and test sets were assembled from the EGAD00001008128 dataset of 290 HCC patients, and the response and non-response classes were balanced using the Synthetic Minority Over-sampling Technique. With the genes selected via the minimum Redundancy Maximum Relevance and stepwise forward methods, a Kolmogorov–Arnold Network (KAN) model was trained. Support Vector Machine (SVM) combined the Hypoxia and KAN models to predict immunotherapy response. The hypoxia model was constructed using 10 genes (IRH and HHO). The KAN model with 11 genes achieved a test accuracy of 0.7. The SVM integrating the hypoxia and KAN models achieved a test accuracy of 0.725. The established AI model can predict immunotherapy response based on hypoxia risk and genomic factors potentially intervenable in HCC patients.https://www.mdpi.com/2306-5354/12/3/322hepatocellular carcinomahypoxiaimmunotherapy responseKolmogorov–Arnold networksupport vector machine |
| spellingShingle | Mohan Huang Xinyue Chen Yi Jiang Lawrence Wing Chi Chan Kolmogorov–Arnold Network Model Integrated with Hypoxia Risk for Predicting PD-L1 Inhibitor Responses in Hepatocellular Carcinoma Bioengineering hepatocellular carcinoma hypoxia immunotherapy response Kolmogorov–Arnold network support vector machine |
| title | Kolmogorov–Arnold Network Model Integrated with Hypoxia Risk for Predicting PD-L1 Inhibitor Responses in Hepatocellular Carcinoma |
| title_full | Kolmogorov–Arnold Network Model Integrated with Hypoxia Risk for Predicting PD-L1 Inhibitor Responses in Hepatocellular Carcinoma |
| title_fullStr | Kolmogorov–Arnold Network Model Integrated with Hypoxia Risk for Predicting PD-L1 Inhibitor Responses in Hepatocellular Carcinoma |
| title_full_unstemmed | Kolmogorov–Arnold Network Model Integrated with Hypoxia Risk for Predicting PD-L1 Inhibitor Responses in Hepatocellular Carcinoma |
| title_short | Kolmogorov–Arnold Network Model Integrated with Hypoxia Risk for Predicting PD-L1 Inhibitor Responses in Hepatocellular Carcinoma |
| title_sort | kolmogorov arnold network model integrated with hypoxia risk for predicting pd l1 inhibitor responses in hepatocellular carcinoma |
| topic | hepatocellular carcinoma hypoxia immunotherapy response Kolmogorov–Arnold network support vector machine |
| url | https://www.mdpi.com/2306-5354/12/3/322 |
| work_keys_str_mv | AT mohanhuang kolmogorovarnoldnetworkmodelintegratedwithhypoxiariskforpredictingpdl1inhibitorresponsesinhepatocellularcarcinoma AT xinyuechen kolmogorovarnoldnetworkmodelintegratedwithhypoxiariskforpredictingpdl1inhibitorresponsesinhepatocellularcarcinoma AT yijiang kolmogorovarnoldnetworkmodelintegratedwithhypoxiariskforpredictingpdl1inhibitorresponsesinhepatocellularcarcinoma AT lawrencewingchichan kolmogorovarnoldnetworkmodelintegratedwithhypoxiariskforpredictingpdl1inhibitorresponsesinhepatocellularcarcinoma |