Harnessing plasma transcriptomics for non-invasive cancer biomarker identification: a comprehensive review

Plasma transcriptomic profiling has emerged as a promising non-invasive strategy for cancer biomarker discovery, offering a dynamic change in gene expression profiles associated with carcinogenesis. Plasma-based transcriptomic profiling offers a less-invasive alternative for detecting cancer biomarkers compared to tissue-based methods. Unlike traditional tissue biopsies, plasma-based analyses enable easier, repeatable sampling and real-time disease monitoring through liquid biopsy. Despite its clinical potential, plasma transcriptomics remain underutilized due to technical limitations such as RNA degradation, low yield, and lack of standardized protocols. This review presents a comprehensive overview of current transcriptomic technologies including high-throughput sequencing (RNA-seq), single cell RNA sequencing (scRNA-seq), and spatial transcriptomics, and evaluates their suitability for plasma-based applications. This review also discusses the analytical workflows used to process transcriptomic data, including differential expression analysis, pathway enrichment, and protein-protein interaction network mapping, as well as tool for assessing biomarker performance through ROC curves and survival analysis. Furthermore, this review highlights emerging trends in integrative analysis, such as meta-analysis and artificial intelligence (AI)-assisted multiomics integration, which significantly enhance biomarker discovery and therapeutic target identification. The review also outlines critical challenges in plasma transcriptomic profiling and proposes strategies for advancing the field toward clinical translation. Overall, this review aims to equip cancer researchers and clinicians with foundational knowledge and practical insights into plasma-based transcriptomics, reinforcing its potential in precision oncology and non-invasive cancer diagnostic.