Controlling for cellular heterogeneity using single-cell deconvolution of gene expression reveals novel markers of colorectal tumors exhibiting microsatellite instability.
Controlling for cellular heterogeneity using single-cell deconvolution of gene expression reveals novel markers of colorectal tumors exhibiting microsatellite instability.
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DOI：
10.18632/oncotarget.27935
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发表时间：
2021-04-13
期刊：
影响因子：
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通讯作者：
Casey G
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作者：
Devall MAM;Casey G
大约15%的结直肠癌（CRC）病例存在高水平的微卫星不稳定性（MSI-H）。批量RNA测序方法已被用于阐明MSI-H和微卫星稳定（MSS）CRC肿瘤之间的转录差异。这些方法经常被肿瘤复杂的细胞异质性所混淆。我们对癌症基因组图谱结肠腺癌（TCGA-COAD）数据集进行了批量RNA测序的单细胞去卷积。使用CIBERSORTx估计每个数据集中的细胞组成。使用线性回归分析细胞组成差异。在TCGA-COAD中，在MSI-H和MSS/MSI-L肿瘤之间的19种细胞类型中，观察到13种细胞类型的丰度存在显著差异。这包括MSI-H与MSS/MSI-L肿瘤中肠内分泌增加（q = 3.71E-06）和结肠细胞群减少（q = 2.21E-03）的新发现。我们能够在一个独立的活检数据集中验证其中的一些差异。通过将细胞组成纳入我们的回归模型，我们确定了3，193个差异表达基因（q = 0.05），其中556个被认为是新的。我们随后在结肠癌细胞系的独立数据集中验证了其中许多基因。总之，我们表明，与细胞异质性相关的一些挑战可以使用单细胞去卷积来克服，通过我们的分析，我们强调了几个新的基因靶点，以供进一步研究。
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.