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Controlling for cellular heterogeneity using single-cell deconvolution of gene expression reveals novel markers of colorectal tumors exhibiting microsatellite instability
Approximately 15% of colorectal cancer (CRC) cases present with high levels of microsatellite instability (MSI-H). Bulk RNA-sequencing approaches have been employed to elucidate transcriptional differences between MSI-H and microsatellite stable (MSS) CRC tumors. These approaches are frequently conf...
Autores principales: | , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Impact Journals LLC
2021
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8057268/ https://www.ncbi.nlm.nih.gov/pubmed/33889300 http://dx.doi.org/10.18632/oncotarget.27935 |
Sumario: | Approximately 15% of colorectal cancer (CRC) cases present with high levels of microsatellite instability (MSI-H). Bulk RNA-sequencing approaches have been employed to elucidate transcriptional differences between MSI-H and microsatellite stable (MSS) CRC tumors. These approaches are frequently confounded by the complex cellular heterogeneity of tumors. We performed single-cell deconvolution of bulk RNA-sequencing on The Cancer Genome Atlas colon adenocarcinoma (TCGA-COAD) dataset. Cell composition within each dataset was estimated using CIBERSORTx. Cell composition differences were analyzed using linear regression. Significant differences in abundance were observed for 13 of 19 cell types between MSI-H and MSS/MSI-L tumors in TCGA-COAD. This included a novel finding of increased enteroendocrine (q = 3.71E(-06)) and reduced colonocyte populations (q = 2.21E(-03)) in MSI-H versus MSS/MSI-L tumors. We were able to validate some of these differences in an independent biopsy dataset. By incorporating cell composition into our regression model, we identified 3,193 differentially expressed genes (q = 0.05), of which 556 were deemed novel. We subsequently validated many of these genes in an independent dataset of colon cancer cell lines. In summary, we show that some of the challenges associated with cellular heterogeneity can be overcome using single-cell deconvolution, and through our analysis we highlight several novel gene targets for further investigation. |
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