ArticleFrontiers in cell and developmental biology2022
Deeper insights into transcriptional features of cancer-associated fibroblasts: An integrated meta-analysis of single-cell and bulk RNA-sequencing data.
Article in Frontiers in cell and developmental biology, 2022. 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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Who cites it
5 citing papers in PubMed.
- Transcriptomic Meta-Analysis as a Framework for Robust Cross-Study Biological Inference.International journal of molecular sciences · 2026Review
- Blood and urine-based biomarkers in prostate cancer: Current advances, clinical applications, and future directions.The journal of liquid biopsy · 2025Review
- Advances in precision oncology using patient-derived organoids and functional biomaterials.Frontiers in cell and developmental biology · 2025Review
- Exploring the diversity of cancer-associated fibroblasts: insights into mechanisms of drug resistance.Frontiers in cell and developmental biology · 2024Review
- Single-cell omics traces the heterogeneity of prostate cancer cells and the tumor microenvironment.Cellular & molecular biology letters · 2023Review
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16 authors.
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Abstract
Cancer-associated fibroblasts (CAFs) have long been known as one of the most important players in tumor initiation and progression. Even so, there is an incomplete understanding of the identification of CAFs among tumor microenvironment cells as the list of CAF marker genes varies greatly in the literature, therefore it is imperative to find a better way to identify reliable markers of CAFs. To this end, we summarized a large number of single-cell RNA-sequencing data of multiple tumor types and corresponding normal tissues. As a result, for 9 different types of cancer, we identified CAF-specific gene expression signatures and found 10 protein markers that showed strongly positive staining of tumor stroma according to the analysis of IHC images from the Human Protein Atlas database. Our results give an insight into selecting the most appropriate combination of cancer-associated fibroblast markers. Furthermore, comparison of different approaches for studying differences between cancer-associated and normal fibroblasts (NFs) illustrates the superiority of transcriptome analysis of fibroblasts obtained from fresh tissue samples. Using single-cell RNA sequencing data, we identified common differences in gene expression patterns between normal and cancer-associated fibroblasts, which do not depend on the type of tumor.
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