Evidence map›Paper›PMID 39984938›Full record

ArticleJournal of translational medicine2025

Construction of hot tumor classification models in gastrointestinal cancers.

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

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Article in Journal of translational medicine, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.

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4citing papers in PubMed
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1 · What the graph read from it

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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.

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3 · Its place in the literature

Who cites it

4 citing papers in PubMed.

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4 · The record

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5 · Who and what money

Authors and funding

10 authors.

Chien-Jung Huang *Institute of Biomedical Informatics, National Yang Ming Chiao Tung University, Taipei, Taiwan.
Guan-Ting Liu *Department of Life Sciences and Institute of Genome Sciences, National Yang Ming Chiao Tung University, Taipei, Taiwan.
Yi-Chen Yeh *Department of Pathology and Laboratory Medicine, Taipei Veterans General Hospital, Taipei, Taiwan.
Shin-Yi ChungDepartment of Oncology, Taipei Veterans General Hospital, Taipei, Taiwan.
Yu-Chan ChangDepartment of Biomedical Imaging and Radiological Sciences, National Yang Ming Chiao Tung University, Taipei, Taiwan.
Nai-Jung ChiangSchool of Medicine, National Yang Ming Chiao Tung University, Taipei, Taiwan.
Meng-Lun LuDepartment of Oncology, Taipei Veterans General Hospital, Taipei, Taiwan.
Wei-Ning HuangInstitute of Biomedical Informatics, National Yang Ming Chiao Tung University, Taipei, Taiwan.
Ming-Huang ChenSchool of Medicine, National Yang Ming Chiao Tung University, Taipei, Taiwan. mhchen9@vghtpe.gov.tw.ORCID 0000-0003-3187-7393
Yu-Chao WangInstitute of Biomedical Informatics, National Yang Ming Chiao Tung University, Taipei, Taiwan. yuchao@nycu.edu.tw.ORCID 0000-0002-1574-1691

Funding

National Science and Technology Council NSTC 111-2628-E-A49-004-MY2National Science and Technology Council NSTC 112-2221-E-A49-088-MY3National Science and Technology Council NSTC 112-2314-B-075-009-MY3National Science and Technology Council NSTC 112-2813-C-A49-028-E
6 · The paper itself

Abstract

backgroundGastrointestinal (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.

methodsTo 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.

resultsWe 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.

conclusionsIn 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.

Indexed as

Gastrointestinal NeoplasmsModels, BiologicalBiomarkers, TumorGene Expression ProfilingGene Expression Regulation, NeoplasticHumansLymphocytes, Tumor-InfiltratingPrognosisTumor MicroenvironmentBiomarkers, TumorBiomarkerGastrointestinal cancersHot tumorsImmune checkpoint inhibitorTertiary lymphoid structuresTumor-infiltrating lymphocytesTumor microenvironment

Identifiers

PMID39984938
PMCPMC11846462

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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 OpenQuestion graph.