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Review
. 2025 Mar 20;6(1):18.
doi: 10.1186/s43556-025-00257-8.

Liquid biopsies in cancer

Affiliations
Review

Liquid biopsies in cancer

Hang Yin et al. Mol Biomed. .

Abstract

Cancer ranks among the most lethal diseases worldwide. Tissue biopsy is currently the primary method for the diagnosis and biological analysis of various solid tumors. However, this method has some disadvantages related to insufficient tissue specimen collection and intratumoral heterogeneity. Liquid biopsy is a noninvasive approach for identifying cancer-related biomarkers in peripheral blood, which allows for repetitive sampling across multiple time points. In the field of liquid biopsy, representative biomarkers include circulating tumor cells (CTCs), circulating tumor DNA (ctDNA), and exosomes. Many studies have evaluated the prognostic and predictive roles of CTCs and ctDNA in various solid tumors. Although these studies have limitations, the results of most studies appear to consistently demonstrate the correlations of high CTC counts and ctDNA mutations with lower survival rates in cancer patients. Similarly, a reduction in CTC counts throughout therapy may be a potential prognostic indicator related to treatment response in advanced cancer patients. Moreover, the biochemical characteristics of CTCs and ctDNA can provide information about tumor biology as well as resistance mechanisms against targeted therapy. This review discusses the current clinical applications of liquid biopsy in cancer patients, emphasizing its possible utility in outcome prediction and treatment decision-making.

Keywords: Circulating tumor DNA; Circulating tumor cell; Liquid biopsy; Minimal residual disease; Personalized medicine; Prognosis.

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Conflict of interest statement

Declarations. Ethics approval and consent to participate: Not applicable. Consent for publication: Not applicable. Competing interests: The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

Figures

Fig. 1
Fig. 1
The primary tumor site, due to microenvironmental conditions such as hypoxia, triggers epithelial–mesenchymal transition (EMT). Then, the tumor-related substances are released into the blood. Circulating tumor cells (CTCs) have the capacity to create clusters either among themselves or with other immune cells or cancer-associated fibroblasts. This clustering enhances their metastatic potential, proliferation capability, stemness, and ability to evade the immune system. Ultimately, tumor cells establish a premetastatic niche and colonize a distant site following extravasation
Fig. 2
Fig. 2
Summary of the characteristics of CTCs and ctDNA at different stages of cancer with an emphasis on their complementarity in the field of liquid biopsy. In the process of tumor occurrence and development, tumor cells and their surrounding environment are constantly changing. The differences between early-stage tumors and late-stage tumors, as well as between tumors before and after the acquisition of resistance, can be reflected at the molecular level. The characteristics of CTCs and ctDNA at different stages of the tumor are listed to illustrate the complementarity between the two
Fig. 3
Fig. 3
Summary of tumor treatment targets and resistance mutations based on CTC and ctDNA detection. The tumor types include non-small cell lung cancer (EGFR, ROS1, ALK), colorectal cancer (NRAS), breast cancer (PIK3CA, ESR1), prostate cancer (ARv7), and melanoma (BRAF); PD-L1 is also targeted in various tumors. Through the analysis of ctDNA, it is possible to detect drug resistance-conferring mutations that occur during the treatment process, while CTC analysis can provide insights into changes in protein expression within tumor cells that can be used to infer whether related mutations occur in resistance genes. CTCs and ctDNA are accurate markers for liquid biopsy and provide complementary information
Fig. 4
Fig. 4
A simple summary diagram on the applications of machine learning in the analysis of liquid biopsy data. It shows what datasets are used for machine learning in the contexts of cancer diagnosis, prognosis evaluation, and treatment effect monitoring and lists common types of machine learning models and considerations for data analysis

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