ArticleGenome medicine2024
An atlas of cell-type-specific interactome networks across 44 human tumor types.
Article in Genome medicine, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.
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Who cites it
7 citing papers in PubMed, 5 citations in OpenAlex.
- DISCERN: inferring drug sensitivity from single-cell transcriptomes using cell-type-specific genetic interaction networks.Genome medicine · 2026Article
- Integrative network analysis reveals organizational principles of the endocannabinoid system.Journal of cannabis research · 2026Article
- Dynamic tumor microenvironment remodeling in cancer therapy resistance: molecular mechanisms and translational opportunities.Frontiers in cell and developmental biology · 2026Review
- Deciphering cancer therapy resistance via patient-level single-cell transcriptomics with CellResDB.Communications biology · 2025Article
- HCNetlas: A reference database of human cell type-specific gene networks to aid disease genetic analyses.PLoS biology · 2025Article
- Considerations for building and using integrated single-cell atlases.Nature methods · 2025Review
- Mass Spectrometry-Based Proteomics Technologies to Define Endogenous Protein-Protein Interactions and Their Applications to Cancer and Viral Infectious Diseases.Mass spectrometry reviewsReview
Corrections and comments
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Authors and funding
10 authors at 2 institutions in 1 country.
Funding
Abstract
backgroundBiological processes are controlled by groups of genes acting in concert. Investigating gene-gene interactions within different cell types can help researchers understand the regulatory mechanisms behind human complex diseases, such as tumors.
methodsWe collected extensive single-cell RNA-seq data from tumors, involving 563 patients with 44 different tumor types. Through our analysis, we identified various cell types in tumors and created an atlas of different immune cell subsets across different tumor types. Using the SCINET method, we reconstructed interactome networks specific to different cell types. Diverse functional data was then integrated to gain biological insights into the networks, including somatic mutation patterns and gene functional annotation. Additionally, genes with prognostic relevance within the networks were also identified. We also examined cell-cell communications to investigate how gene interactions modulate cell-cell interactions.
resultsWe developed a data portal called CellNetdb for researchers to study cell-type-specific interactome networks. Our findings indicate that these networks can be used to identify genes with topological specificity in different cell types. We also found that prognostic genes can deconvolved into cell types through analyzing network connectivity. Additionally, we identified commonalities and differences in cell-type-specific networks across different tumor types. Our results suggest that these networks can be used to prioritize risk genes.
conclusionsThis study presented CellNetdb, a comprehensive repository featuring an atlas of cell-type-specific interactome networks across 44 human tumor types. The findings underscore the utility of these networks in delineating the intricacies of tumor microenvironments and advancing the understanding of molecular mechanisms underpinning human tumors.
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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.