A Survey on RNA-seq of Liquid Biopsy and ML Models for Cancer Detection

Liquid biopsy for cancer diagnosis in recent times has been under scientific scrutiny due to its non-invasive nature. RNA sequencing (RNA-seq) from liquid biopsies has immense potential for early cancer detection and is yet under research. This study describes the various research studies on RNA-seq of human liquid biopsies and cancer diagnosis using RNA-seq data. It covers several machine learning (ML) and deep learning (DL) models used for the classification and prediction of cancer types. Describing the utilization of RNA-seq data in the DESeq2 algorithm for differentially expressed genes (DEGs) identification, which plays a crucial role in diagnosis. The overall study is to understand the application of tumor-educated platelet (TEP) RNA-seq data for cancer identification.