The considerable burden of colorectal cancer and the rising trend in young adultsemphasize the necessity of understanding its underlying mechanisms, providing newdiagnostic and prognostic markers, and improving therapeutic approaches. Precisionmedicine is a new trend all over the world and identification of novel biomarkers andtherapeutic targets is a step forward towards this trend. In this context, multi-omics dataand integrated analysis are being investigated to develop personalized medicine in themanagement of colorectal cancer. Given the large amount of data from multi-omicsapproach, data integration and analysis is a great challenge. In this Review, wesummarize how statistical and machine learning techniques are applied to analyzemulti-omics data and how it contributes to the discovery of useful diagnostic andprognostic biomarkers and therapeutic targets. Moreover, we discuss the importance ofthese biomarkers and therapeutic targets in the clinical management of colorectal cancerin the future. Taken together, integrated analysis of multi-omics data has great potentialfor finding novel diagnostic and prognostic biomarkers and therapeutic targets, however,there are still challenges to overcome in future studies.

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