Construction of hot tumor classification models in gastrointestinal cancers
Abstract Background Gastrointestinal (GI) cancers account for more than one-third of cancer-related mortality, and the prognosis for late-stage patients remains poor. Immunotherapy has been proven to extend the survival of patients at advanced stages; however, challenges persist in patient selection...
| Main Authors: | , , , , , , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
BMC
2025-02-01
|
| Series: | Journal of Translational Medicine |
| Subjects: | |
| Online Access: | https://doi.org/10.1186/s12967-025-06230-x |
| _version_ | 1826585703944814592 |
|---|---|
| author | Chien-Jung Huang Guan-Ting Liu Yi-Chen Yeh Shin-Yi Chung Yu-Chan Chang Nai-Jung Chiang Meng-Lun Lu Wei-Ning Huang Ming-Huang Chen Yu-Chao Wang |
| author_facet | Chien-Jung Huang Guan-Ting Liu Yi-Chen Yeh Shin-Yi Chung Yu-Chan Chang Nai-Jung Chiang Meng-Lun Lu Wei-Ning Huang Ming-Huang Chen Yu-Chao Wang |
| author_sort | Chien-Jung Huang |
| collection | DOAJ |
| description | Abstract Background Gastrointestinal (GI) cancers account for more than one-third of cancer-related mortality, and the prognosis for late-stage patients remains poor. Immunotherapy has been proven to extend the survival of patients at advanced stages; however, challenges persist in patient selection and overcoming drug resistance. Tumor-infiltrating lymphocytes (TILs) and tertiary lymphoid structures (TLS) in the tumor microenvironment (TME) have been found to be associated with anti-tumor immune responses. ‘Hot tumors’ with high levels of infiltration tend to respond better to immune checkpoint inhibitor (ICI) therapy, making them potential biomarkers for ICI treatment. Methods To explore potential biomarkers for predicting immunotherapy response and prognosis in GI cancers, we downloaded the gene expression profiles of seven GI cancers from The Cancer Genome Atlas (TCGA) database and characterized their TME, classifying the samples into hot/cold tumor subgroups. Furthermore, we developed a computational framework to construct cancer-specific hot tumor classification models with only a few genes. External independent datasets and qPCR experiments were used to verify the performance of our few-gene models. Results We constructed cancer-specific few-gene models to identify hot tumors for GI cancers with only two to nine genes. The results showed that B cells are important for hot tumor determination, and the identified hot tumors are significantly associated with TLS. They not only overexpress TLS marker genes but are also associated with the presence of TLS in whole-slide images. Further, a two-gene qPCR model was developed to effectively distinguish between hot and cold tumor subgroups in cholangiocarcinoma, providing an opportunity for stratifying patients with hot tumors in clinical settings. Conclusions In conclusion, our established few-gene models, which can be easily integrated into clinical practice, can distinguish hot and cold tumor subgroups, and may serve as potential biomarkers for predicting ICI response. |
| first_indexed | 2025-03-14T15:57:52Z |
| format | Article |
| id | doaj.art-3b936deb59b744438b0583c4d803acbe |
| institution | Directory Open Access Journal |
| issn | 1479-5876 |
| language | English |
| last_indexed | 2025-03-14T15:57:52Z |
| publishDate | 2025-02-01 |
| publisher | BMC |
| record_format | Article |
| series | Journal of Translational Medicine |
| spelling | doaj.art-3b936deb59b744438b0583c4d803acbe2025-02-23T12:40:36ZengBMCJournal of Translational Medicine1479-58762025-02-0123111510.1186/s12967-025-06230-xConstruction of hot tumor classification models in gastrointestinal cancersChien-Jung Huang0Guan-Ting Liu1Yi-Chen Yeh2Shin-Yi Chung3Yu-Chan Chang4Nai-Jung Chiang5Meng-Lun Lu6Wei-Ning Huang7Ming-Huang Chen8Yu-Chao Wang9Institute of Biomedical Informatics, National Yang Ming Chiao Tung UniversityDepartment of Life Sciences and Institute of Genome Sciences, National Yang Ming Chiao Tung UniversityDepartment of Pathology and Laboratory Medicine, Taipei Veterans General HospitalDepartment of Oncology, Taipei Veterans General HospitalDepartment of Biomedical Imaging and Radiological Sciences, National Yang Ming Chiao Tung UniversitySchool of Medicine, National Yang Ming Chiao Tung UniversityDepartment of Oncology, Taipei Veterans General HospitalInstitute of Biomedical Informatics, National Yang Ming Chiao Tung UniversitySchool of Medicine, National Yang Ming Chiao Tung UniversityInstitute of Biomedical Informatics, National Yang Ming Chiao Tung UniversityAbstract Background Gastrointestinal (GI) cancers account for more than one-third of cancer-related mortality, and the prognosis for late-stage patients remains poor. Immunotherapy has been proven to extend the survival of patients at advanced stages; however, challenges persist in patient selection and overcoming drug resistance. Tumor-infiltrating lymphocytes (TILs) and tertiary lymphoid structures (TLS) in the tumor microenvironment (TME) have been found to be associated with anti-tumor immune responses. ‘Hot tumors’ with high levels of infiltration tend to respond better to immune checkpoint inhibitor (ICI) therapy, making them potential biomarkers for ICI treatment. Methods To explore potential biomarkers for predicting immunotherapy response and prognosis in GI cancers, we downloaded the gene expression profiles of seven GI cancers from The Cancer Genome Atlas (TCGA) database and characterized their TME, classifying the samples into hot/cold tumor subgroups. Furthermore, we developed a computational framework to construct cancer-specific hot tumor classification models with only a few genes. External independent datasets and qPCR experiments were used to verify the performance of our few-gene models. Results We constructed cancer-specific few-gene models to identify hot tumors for GI cancers with only two to nine genes. The results showed that B cells are important for hot tumor determination, and the identified hot tumors are significantly associated with TLS. They not only overexpress TLS marker genes but are also associated with the presence of TLS in whole-slide images. Further, a two-gene qPCR model was developed to effectively distinguish between hot and cold tumor subgroups in cholangiocarcinoma, providing an opportunity for stratifying patients with hot tumors in clinical settings. Conclusions In conclusion, our established few-gene models, which can be easily integrated into clinical practice, can distinguish hot and cold tumor subgroups, and may serve as potential biomarkers for predicting ICI response.https://doi.org/10.1186/s12967-025-06230-xTumor microenvironmentTertiary lymphoid structuresTumor-infiltrating lymphocytesImmune checkpoint inhibitorGastrointestinal cancersHot tumors |
| spellingShingle | Chien-Jung Huang Guan-Ting Liu Yi-Chen Yeh Shin-Yi Chung Yu-Chan Chang Nai-Jung Chiang Meng-Lun Lu Wei-Ning Huang Ming-Huang Chen Yu-Chao Wang Construction of hot tumor classification models in gastrointestinal cancers Journal of Translational Medicine Tumor microenvironment Tertiary lymphoid structures Tumor-infiltrating lymphocytes Immune checkpoint inhibitor Gastrointestinal cancers Hot tumors |
| title | Construction of hot tumor classification models in gastrointestinal cancers |
| title_full | Construction of hot tumor classification models in gastrointestinal cancers |
| title_fullStr | Construction of hot tumor classification models in gastrointestinal cancers |
| title_full_unstemmed | Construction of hot tumor classification models in gastrointestinal cancers |
| title_short | Construction of hot tumor classification models in gastrointestinal cancers |
| title_sort | construction of hot tumor classification models in gastrointestinal cancers |
| topic | Tumor microenvironment Tertiary lymphoid structures Tumor-infiltrating lymphocytes Immune checkpoint inhibitor Gastrointestinal cancers Hot tumors |
| url | https://doi.org/10.1186/s12967-025-06230-x |
| work_keys_str_mv | AT chienjunghuang constructionofhottumorclassificationmodelsingastrointestinalcancers AT guantingliu constructionofhottumorclassificationmodelsingastrointestinalcancers AT yichenyeh constructionofhottumorclassificationmodelsingastrointestinalcancers AT shinyichung constructionofhottumorclassificationmodelsingastrointestinalcancers AT yuchanchang constructionofhottumorclassificationmodelsingastrointestinalcancers AT naijungchiang constructionofhottumorclassificationmodelsingastrointestinalcancers AT menglunlu constructionofhottumorclassificationmodelsingastrointestinalcancers AT weininghuang constructionofhottumorclassificationmodelsingastrointestinalcancers AT minghuangchen constructionofhottumorclassificationmodelsingastrointestinalcancers AT yuchaowang constructionofhottumorclassificationmodelsingastrointestinalcancers |