ArticleCell reports. Medicine2023
TimiGP: Inferring cell-cell interactions and prognostic associations in the tumor immune microenvironment through gene pairs.
Article in Cell reports. Medicine, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.
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Who cites it
8 citing papers in PubMed.
- Machine learning-enhanced discovery of a basement membrane-related gene signature in glioblastoma via single-cell and Spatial transcriptomics.Journal of translational medicine · 2025Article
- Spatial Proximity of Immune Cell Pairs to Cancer Cells in the Tumor Microenvironment as Biomarkers for Patient Stratification.Cancers · 2025Article
- New Insights and Implications of Cell-Cell Interactions in Developmental Biology.International journal of molecular sciences · 2025Review
- Applications of gene pair methods in clinical research: advancing precision medicine.Molecular biomedicine · 2025Review
- Large-scale bulk and single-cell RNA sequencing combined with machine learning reveals glioblastoma-associated neutrophil heterogeneity and establishes a VEGFABiology direct · 2025Article
- Article
- Identification of a stromal immunosuppressive barrier orchestrated by SPP1Frontiers in immunology · 2025Article
- TimiGP-Response: the pan-cancer immune landscape associated with response to immunotherapy.bioRxiv : the preprint server for biology · 2024Article
Corrections and comments
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Authors and funding
8 authors.
Funding
Abstract
Determining the prognostic association of different immune cell types in the tumor microenvironment is critical for understanding cancer biology and developing new therapeutic strategies. However, this is challenging in certain cancer types, where the abundance of different immune subsets is highly correlated. In this study, we develop a computational method named TimiGP to overcome this challenge. Based on bulk gene expression and survival data, TimiGP infers cell-cell interactions that reveal the association between immune cell relative abundance and prognosis. As demonstrated in metastatic melanoma, TimiGP prioritizes immune cells critical in prognosis based on the identified cell-cell interactions. Highly consistent results are obtained by TimiGP when applied to seven independent melanoma datasets and when different cell-type marker sets are used as inputs. Additionally, TimiGP can leverage single-cell RNA sequencing data to delineate the tumor immune microenvironment at high resolutions across a wide range of cancer types.
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Registered trials
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