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bioRxiv · 10.1101/2022.10.12.511912

Prioritizing prognostic-associated subpopulations and individualized recurrence risk signatures from single-cell transcriptomes of colorectal cancer

Tong, M.·Lin, Y.·Yang, W.·Song, J.·Zhang, Z.·Xie, J.·Tian, J.·Luo, S.·Liang, C.·Huang, J.·Yu, R.
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Abstract

Colorectal cancer (CRC) is one of the most common gastrointestinal malignancies. There are few recurrence risk signatures for CRC patients. Single-cell RNA-sequencing (scRNA-seq) provides a high resolution platform for prognostic signature detection. However, scRNA-seq is not practical in large cohorts due to its high cost and most single-cell experiments lack clinical phenotype information. Few studies have been reported to use external bulk transcriptome with survival time to guide the detection of key cell subtypes in scRNA-seq data. We proposed a data analysis framework to prioritize prognostic-associated subpopulations based on relative expression orderings (REOs). Cell type specific gene pairs (C-GPs) were identified to evaluate prognostic value for each cell type. We found REOs-based signatures could accurately classify most cell subtypes. C-GPs achieves higher precision compared with four current methods. Moreover, we developed single-cell gene pair signatures to predict recurrence risk for patients individually. Fibro_SGK1 cells and IgA+ IGLC2+ B cells were novel prognostic-associated subpopulations. A user-friendly toolkit, scRankXMBD (https://github.com/xmuyulab/scRank-XMBD), was developed to enable implementation of this framework. Our work facilitate the application of the rank-based method in scRNA-seq data for prognostic biomarker discovery and precision oncology.

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Tong, M., Lin, Y., Yang, W., Song, J., Zhang, Z., Xie, J., Tian, J., Luo, S., Liang, C., Huang, J., Yu, R.. 2022-10-16. Prioritizing prognostic-associated subpopulations and individualized recurrence risk signatures from single-cell transcriptomes of colorectal cancer. https://doi.org/10.1101/2022.10.12.511912

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