ArticleJournal of cellular and molecular medicine2024
Cross-modal integration of bulk RNA-seq and single-cell RNA sequencing data to reveal T-cell exhaustion in colorectal cancer.
Article in Journal of cellular and molecular medicine, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
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The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
5 citing papers in PubMed.
- Machine Learning Reveals the Association Between Gene Expression and Immune Infiltration in Colorectal Cancer: A Comprehensive Study From Single-Cell to Survival Analysis.Journal of cellular and molecular medicine · 2026Article
- Decoding epithelial-T cell interactions in colorectal cancer through single-cell and spatial transcriptomics.Discover oncology · 2025Article
- Multidimensional decoding of colorectal cancer heterogeneity: Artificial intelligence-enabled precision exploration of single-cell and spatial transcriptomics.World journal of gastrointestinal oncology · 2025Review
- A comprehensive analysis of gene expression and the immune landscape in gastric cancer through single-cell and multi-omics approaches.Discover oncology · 2024Article
- Cross-modal integration of bulk RNA-seq and single-cell RNA sequencing data to reveal T-cell exhaustion in colorectal cancer.Journal of cellular and molecular medicine · 2024Article
Corrections and comments
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Authors and funding
10 authors.
Funding
Abstract
Colorectal cancer (CRC) is a relatively common malignancy clinically and the second leading cause of cancer-related deaths. Recent studies have identified T-cell exhaustion as playing a crucial role in the pathogenesis of CRC. A long-standing challenge in the clinical management of CRC is to understand how T cells function during its progression and metastasis, and whether potential therapeutic targets for CRC treatment can be predicted through T cells. Here, we propose DeepTEX, a multi-omics deep learning approach that integrates cross-model data to investigate the heterogeneity of T-cell exhaustion in CRC. DeepTEX uses a domain adaptation model to align the data distributions from two different modalities and applies a cross-modal knowledge distillation model to predict the heterogeneity of T-cell exhaustion across diverse patients, identifying key functional pathways and genes. DeepTEX offers valuable insights into the application of deep learning in multi-omics, providing crucial data for exploring the stages of T-cell exhaustion associated with CRC and relevant therapeutic targets.
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What Socratic holds
Registered trials
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 Socratic graph.