Exzellenzcluster Makromolekulare Komplexe
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- RNA-binding protein (1)
- UV crosslink events (1)
- binding sites (1)
- bioinformatics (1)
- cancer (1)
- cancer-associated fibroblasts (1)
- data processing (1)
- gene signature (1)
- iCLIP (1)
- mammary carcinoma (1)
Precise knowledge on the binding sites of an RNA-binding protein (RBP) is key to understanding the complex post-transcriptional regulation of gene expression. This information can be obtained from individual-nucleotide resolution UV crosslinking and immunoprecipitation (iCLIP) experiments. Here, we present a complete data analysis workflow to reliably detect RBP binding sites from iCLIP data. The workflow covers all steps from the initial quality control of the sequencing reads up to peak calling and quantification of RBP binding. For each tool, we explain the specific requirements for iCLIP data analysis and suggest optimised parameter settings.
Cancer-associated fibroblasts (CAFs) in the tumor microenvironment contribute to all stages of tumorigenesis and are usually considered to be tumor-promoting cells. CAFs show a remarkable degree of heterogeneity, which is attributed to developmental origin or to local environmental niches, resulting in distinct CAF subsets within individual tumors. While CAF heterogeneity is frequently investigated in late-stage tumors, data on longitudinal CAF development in tumors are lacking. To this end, we used the transgenic polyoma middle T oncogene-induced mouse mammary carcinoma model and performed whole transcriptome analysis in FACS-sorted fibroblasts from early- and late-stage tumors. We observed a shift in fibroblast populations over time towards a subset previously shown to negatively correlate with patient survival, which was confirmed by multispectral immunofluorescence analysis. Moreover, we identified a transcriptomic signature distinguishing CAFs from early- and late-stage tumors. Importantly, the signature of early-stage CAFs correlated well with tumor stage and survival in human mammary carcinoma patients. A random forest analysis suggested predictive value of the complete set of differentially expressed genes between early- and late-stage CAFs on bulk tumor patient samples, supporting the clinical relevance of our findings. In conclusion, our data show transcriptome alterations in CAFs during tumorigenesis in the mammary gland, which suggest that CAFs are educated by the tumor over time to promote tumor development. Moreover, we show that murine CAF gene signatures can harbor predictive value for human cancer.