Kazakova Anastasia N, Anufrieva Ksenia S, Ivanova Olga M, Shnaider Polina V, Malyants Irina K, Aleshikova Olga I, Slonov Andrey V, Ashrafyan Lev A, Babaeva Nataliya A, Eremeev Artem V, Boichenko Veronika S, Lukina Maria M, Lagarkova Maria A, Govorun Vadim M, Shender Victoria O, Arapidi Georgij P
Federal Research and Clinical Center of Physical-Chemical Medicine of Federal Medical Biological Agency, Moscow, Russia.
Moscow Institute of Physics and Technology (National Research University), Dolgoprudny, Russia.
Front Cell Dev Biol. 2022 Oct 3;10:825014. doi: 10.3389/fcell.2022.825014. eCollection 2022.
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.
长期以来，癌症相关成纤维细胞（CAFs）一直被认为是肿瘤发生和发展过程中最重要的参与者之一。即便如此，对于肿瘤微环境细胞中CAFs的鉴定仍存在认识不足的情况，因为文献中CAF标记基因的列表差异很大，因此迫切需要找到一种更好的方法来鉴定CAFs的可靠标记物。为此，我们总结了多种肿瘤类型和相应正常组织的大量单细胞RNA测序数据。结果，对于9种不同类型的癌症，我们鉴定出了CAF特异性基因表达特征，并根据人类蛋白质图谱数据库的免疫组化图像分析，发现了10种在肿瘤基质中呈强阳性染色的蛋白质标记物。我们的结果为选择最合适的癌症相关成纤维细胞标记物组合提供了见解。此外，对研究癌症相关成纤维细胞与正常成纤维细胞（NFs）之间差异的不同方法进行比较，说明了从新鲜组织样本中获取的成纤维细胞进行转录组分析的优越性。利用单细胞RNA测序数据，我们确定了正常成纤维细胞与癌症相关成纤维细胞之间基因表达模式的共同差异，这些差异不依赖于肿瘤类型。
