ArticlePLoS computational biology2021
Analyzing cancer gene expression data through the lens of normal tissue-specificity.
Article in PLoS computational biology, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 17 papers.
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Who cites it
17 citing papers in PubMed, 21 citations in OpenAlex.
- TSProm: deep learning framework to predict tissue-specific regulatory logic.NAR genomics and bioinformatics · 2026Article
- HES1 oscillations are required for cell cycle reentry in oestrogen receptor-positive breast cancer cells.Proceedings of the National Academy of Sciences of the United States of America · 2026Article
- Advancements in DNA methylation technologies and their application in cancer diagnosis.Epigenetics · 2025Review
- TSProm: Deciphering the Genomic Context of Tissue Specificity.bioRxiv : the preprint server for biology · 2025Article
- Novel cancer subtyping method guided by tumor-normal sample in latent space of transcriptomic variational autoencoder.Scientific reports · 2025Article
- Genetic Variants inGenes · 2025Article
- Gene set optimization for cancer transcriptomics using sparse principal component analysis.bioRxiv : the preprint server for biology · 2025Article
- Relevance of Cellular Homeostasis-Related Gene Expression Signatures in Distinct Molecular Subtypes of Breast Cancer.Biomedicines · 2025Article
- Integration of single-cell sequencing and drug sensitivity profiling reveals an 11-gene prognostic model for liver cancer.Human genomics · 2024Article
- Reinventing gene expression connectivity through regulatory and spatial structural empowerment via principal node aggregation graph neural network.Nucleic acids research · 2024Article
- Article
- Tissue-adjusted pathway analysis of cancer (TPAC): A novel approach for quantifying tumor-specific gene set dysregulation relative to normal tissue.PLoS computational biology · 2024Article
- Oncogenic BRCA1,2 Mutations in the Human Lineage-A By-Product of Sexual Selection?Biomedicines · 2023Review
- Expanding PROTACtable genome universe of E3 ligases.Nature communications · 2023Article
- Decreased Gene Expression of Antiangiogenic Factors in Endometrial Cancer: qPCR Analysis and Machine Learning Modelling.Cancers · 2023Article
- Transcriptomic data in tumor-adjacent normal tissues harbor prognostic information on multiple cancer types.Cancer medicine · 2023Article
- Article
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Authors and funding
1 author at 1 institution in 1 country.
Funding
Abstract
The genetic alterations that underlie cancer development are highly tissue-specific with the majority of driving alterations occurring in only a few cancer types and with alterations common to multiple cancer types often showing a tissue-specific functional impact. This tissue-specificity means that the biology of normal tissues carries important information regarding the pathophysiology of the associated cancers, information that can be leveraged to improve the power and accuracy of cancer genomic analyses. Research exploring the use of normal tissue data for the analysis of cancer genomics has primarily focused on the paired analysis of tumor and adjacent normal samples. Efforts to leverage the general characteristics of normal tissue for cancer analysis has received less attention with most investigations focusing on understanding the tissue-specific factors that lead to individual genomic alterations or dysregulated pathways within a single cancer type. To address this gap and support scenarios where adjacent normal tissue samples are not available, we explored the genome-wide association between the transcriptomes of 21 solid human cancers and their associated normal tissues as profiled in healthy individuals. While the average gene expression profiles of normal and cancerous tissue may appear distinct, with normal tissues more similar to other normal tissues than to the associated cancer types, when transformed into relative expression values, i.e., the ratio of expression in one tissue or cancer relative to the mean in other tissues or cancers, the close association between gene activity in normal tissues and related cancers is revealed. As we demonstrate through an analysis of tumor data from The Cancer Genome Atlas and normal tissue data from the Human Protein Atlas, this association between tissue-specific and cancer-specific expression values can be leveraged to improve the prognostic modeling of cancer, the comparative analysis of different cancer types, and the analysis of cancer and normal tissue pairs.
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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.