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BMC Medical Informatics and Decision Making
Published in: BMC Medical Informatics and Decision Making 1/2020

01-12-2020 | Colon Cancer | Research article

Improving prediction performance of colon cancer prognosis based on the integration of clinical and multi-omics data

Authors: Danyang Tong, Yu Tian, Tianshu Zhou, Qiancheng Ye, Jun Li, Kefeng Ding, Jingsong Li

Published in: BMC Medical Informatics and Decision Making | Issue 1/2020

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Abstract

Background

Colon cancer is common worldwide and is the leading cause of cancer-related death. Multiple levels of omics data are available due to the development of sequencing technologies. In this study, we proposed an integrative prognostic model for colon cancer based on the integration of clinical and multi-omics data.

Methods

In total, 344 patients were included in this study. Clinical, gene expression, DNA methylation and miRNA expression data were retrieved from The Cancer Genome Atlas (TCGA). To accommodate the high dimensionality of omics data, unsupervised clustering was used as dimension reduction method. The bias-corrected Harrell’s concordance index was used to verify which clustering result provided the best prognostic performance. Finally, we proposed a prognostic prediction model based on the integration of clinical data and multi-omics data. Uno’s concordance index with cross-validation was used to compare the discriminative performance of the prognostic model constructed with different covariates.

Results

Combinations of clinical and multi-omics data can improve prognostic performance, as shown by the increase of the bias-corrected Harrell’s concordance of the prognostic model from 0.7424 (clinical features only) to 0.7604 (clinical features and three types of omics features). Additionally, 2-year, 3-year and 5-year Uno’s concordance statistics increased from 0.7329, 0.7043, and 0.7002 (clinical features only) to 0.7639, 0.7474 and 0.7597 (clinical features and three types of omics features), respectively.

Conclusion

In conclusion, this study successfully combined clinical and multi-omics data for better prediction of colon cancer prognosis.
Appendix
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Metadata
Title
Improving prediction performance of colon cancer prognosis based on the integration of clinical and multi-omics data
Authors
Danyang Tong
Yu Tian
Tianshu Zhou
Qiancheng Ye
Jun Li
Kefeng Ding
Jingsong Li
Publication date
01-12-2020
Publisher
BioMed Central
Published in
BMC Medical Informatics and Decision Making / Issue 1/2020
Electronic ISSN: 1472-6947
DOI
https://doi.org/10.1186/s12911-020-1043-1

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