The latest advances in liquid biopsy for lung cancer—a narrative review

Introduction

Lung cancer is one of the most common cancers worldwide and a leading cause of cancer-related death (1). Smoking rates have decreased in some regions but environmental pollution has increased, and the incidence of lung cancer has decreased in certain areas but continues to increase in others (2). As lung cancer patients are often diagnosed at an advanced stage, the survival rate of lung cancer patients remains relatively low, emphasizing the critical importance of early detection and treatment (3).

Traditional lung cancer diagnosis relies on tissue biopsy, which is considered the gold standard for diagnosis. However, this method is inherently invasive, carries risks and is limited in its ability to capture the spatial, temporal, and dynamic aspects of tumors. In recent years, liquid biopsy has emerged as a non-invasive diagnostic method that has significantly advanced the early detection, treatment selection, and disease monitoring of lung cancer. Liquid biopsy enables the analysis of tumor biomarkers, cell fragments, deoxyribonucleic acid (DNA), ribonucleic acid (RNA), and other components in bodily fluids (4). It has shown promising capabilities in cancer prediction, diagnosis, and prognosis assessment, and offers a convenient and low-risk diagnostic approach for lung cancer patients (5).

This review aims to comprehensively discuss the latest developments in liquid biopsy for lung cancer, including the technological principles, clinical applications, prospects, and challenges. First, we introduce the concept and significance of liquid biopsy and its importance in lung cancer diagnosis and treatment. Second, we delve into the technological principles involved in liquid biopsy, including the detection methods for circulating tumor DNA (ctDNA), circulating tumor cells (CTCs), circulating tumor RNA (ctRNA), extracellular vesicles (EVs), tumor metabolites, tumor-educated platelets (TEPs), and plasma protein biomarkers (6-9).

In terms of clinical applications, we focus on the use of liquid biopsy in early diagnosis, treatment selection, and disease monitoring. Additionally, we explore recent research advances, including the integration of multi-omics technologies, the application of artificial intelligence (AI) to liquid biopsy, the effect of the tumor microenvironment, and the association between liquid biopsy and immunotherapy.

Finally, we discuss the prospects and challenges of liquid biopsy, including future trends, technological limitations and challenges, and obstacles in clinical practice. By conducting a comprehensive review of these areas, we aim to provide readers with a thorough understanding of the latest developments and potential applications of liquid biopsy in lung cancer, thereby driving further research and clinical practice in this field. We present this article in accordance with the Narrative Review reporting checklist (available at https://tlcr.amegroups.com/article/view/10.21037/tlcr-24-828/rc).

Methods

The literature search strategy is summarized in Table 1. Studies were selected based on their relevance, methodological rigor, and contribution to the field. The selection process was conducted independently by two reviewers with consensus achieved through discussion.

Technical principles

Liquid biopsy uses tumor biomarkers, cell fragments, DNA, RNA, and other components present in bodily fluids to diagnose cancer. Among these, ctDNA, CTCs, ctRNA, EVs, tumor metabolites, TEPs, and plasma protein markers have become focal points of research. The development of these techniques offers new possibilities for the early detection of lung cancer, and these techniques could help to guide treatment selection and monitor disease progression (Figure 1).

Figure 1 The application of liquid biopsy technology in the early diagnosis of lung cancer.

ctDNA

ctDNA refers to small fragments of DNA shed by tumor cells into the bloodstream. These fragments carry genetic mutations or alterations specific to the tumor from which they originate. The detection and analysis of ctDNA through liquid biopsy techniques offer a non-invasive method for assessing tumor characteristics and monitoring disease progression (10). The presence of ctDNA in the bloodstream provides valuable information about the genetic makeup of the tumor, including mutations, copy number variations, and other genomic alterations. By analyzing ctDNA, clinicians can identify specific mutations associated with cancer development and progression, monitor the treatment response of patients, and detect the emergence of drug-resistant mutations.

One of the most significant applications of ctDNA in oncogene-driven non-small cell lung cancer (NSCLC) is the detection of epidermal growth factor receptor (EGFR) mutation (11). For instance, ctDNA can be used to identify the T790M resistence mutation in patients who have developed resistance to first-and second-generation tyrosine kinase inhibition (TKI) (12). The ability to non-invasively monitor EGFR mutations through ctDNA anlysis allows for timely and targeted adjustments in therapy, which can significantly improve patient outcomes (13). According to John et al., the recent ADAURA trial, which evaluated the efficacy of osimertinib in patients with EGFR-mutated NSCLC, further highlighted the importance of ctDNA. Data from this trial demonstrated that ctDNAcould be used to monitor minimal residual disease and predict recurrence, emphasizing its role in guiding post-surgical therapy (14).

Additionally, ctDNA analysis is gaining traction in the management of anaplastic lymphoma kinase-positive (ALK-positive) NSCLC (15). The CUBIK study, conducted by the Spanish Lung Cancer Group (GECP), is specifically designed to assess the utility of ctDNA in monitoring treatment response during brigatinib therapy. This study underscores the expanding role of ctDNA not just in EGFR-mutated cases but also in other oncogene-driven subtypes of NSCLC, providing a broader scope for personalized medicine (16).

Beyond its application in targeted therapy, ctDNA is increasingly recognized as a validated biomarker for monitoring multi-drug resistance (MDR) in NSCLC, particularly after chemoimmunotherapy. Clinical trials such as Provencio et al. and Forde et al. have demonstrated that ctDNA can be used to detect emerging drug resistance early and potentially correlate with overall survival outcomes. The ability to track these changes non-invasively enables clinicians to make earlier, more informed treatment decisions, thereby improving the chances of long-term survival for patients (17,18).

Previous study has indicated that detectable pre-treatment KRASG12C ctDNA could serve as an adverse prognostic marker, while the ctDNA clearance rate during treatment serves as a marker of treatment response (19). Additionally, KRAS amplification is a potential mechanism of resistance to Sotorasib (20). Identifying patients with a favorable prognosis helps to optimize the timing of treatment initiation, and identifying patients at risk of early progression allows for earlier treatment decisions.

One key advantage of ctDNA analysis is its potential for early cancer detection. Tumor cells continuously release ctDNA into the bloodstream, and ctDNA analysis has emerged as a promising non-invasive tool for tumor typing. Even in the early stages of the disease, when tumors are small or before clinical symptoms manifest, ctDNA can be detected and analyzed to identify the presence of cancer (21). Further, ctDNA analysis enables clinicians to practice personalized medicine by identifying actionable mutations to guide treatment decisions. For example, based on specific genetic alterations detected in ctDNA, targeted therapies can be selected, which could potentially lead to more effective and tailored treatment strategies (22).

CTCs

CTCs are a form of tumor cells that detach from the primary tumor and enter the bloodstream. These cells retain many characteristics of the primary tumor, including surface markers and genetic variations (23). However, CTCs may undergo morphological and phenotypic changes, such as epithelial-to-mesenchymal transition (EMT), which enhances their ability to survive in the circulatory system and evade immune detection. These adaptations increase their potential to seed distant metastases (24). CTC detection and analysis are performed using liquid biopsy techniques, providing valuable, non-invasive insights into tumor biology, treatment response, and disease progression. However, in early-stage NSCLC, the role of CTCs as a prognostic biomarker remains an area of active research. While the presence of CTCs can indicate a higher risk of metastasis and poorer prognosis, it is still unclear whether all detected CTCs possess metastatic potential, which raises challenges in their clinical interpretation and utility (25).

Highly sensitive detection methods are required, as CTCs are typically present in very low numbers within the bloodstream. Once isolated, CTCs offer a wealth of information, including tumor type, molecular characteristics, and heterogeneity. Their presence can also inform on the tumor’s invasiveness and likely prognosis, although the precise prognostic significance in early-stage NSCLC is not yet fully established.

In clinical practice, CTC detection serves several purposes. It can aid in the early diagnosis of certain cancers, such as breast and prostate cancer, and is increasingly being explored in lung cancer. Additionally, CTC counts can be monitored over time to assess treatment efficacy and adapt therapeutic strategies accordingly. While promising, more research is needed to clarify the role of CTCs in early-stage NSCLC, particularly concerning their ability to predict metastasis and guide treatment decisions.

ctRNA

ctRNA refers to RNA molecules released into the bloodstream by tumor cells. Similar to ctDNA, ctRNA can be detected and analyzed using liquid biopsy techniques. The presence of ctRNA provides information on tumor gene expression, which can provide insights into the biological characteristics and activity status of the tumor (26).

ctRNA analysis can reveal the gene expression patterns of tumor cells, including overexpressed or underexpressed genes. These expression patterns may be associated with tumor development, invasiveness, and treatment response. By analyzing ctRNA, clinicians can obtain information about tumor molecular characteristics, which can be used to guide treatment selection and prognosis assessment (27).

Similar to ctDNA and CTCs, the ctRNA detection and analysis also require highly sensitive techniques. However, ctRNA analysis has some unique advantages. For example, ctRNA analysis can provide real-time gene expression information that reflects changes in tumor activity status. Additionally, ctRNA analysis can identify tumor-specific RNA biomarkers that can be used for early cancer diagnosis and prognosis assessment.

In clinical applications, ctRNA detection can be used in several ways. First, ctRNA can serve as an adjunct cancer diagnostic marker, especially when tumor biomarkers or imaging findings are inconclusive. Second, the analysis of ctRNA can be used to assess treatment efficacy and prognosis. By monitoring changes in ctRNA levels during treatment, treatment responsiveness can be evaluated, and treatment plans adjusted accordingly.

EVs

EVs are small vesicles released by cells into bodily fluids, ranging in diameter from 30 to 1,000 nanometers. Encased in a double-layered lipid membrane, EVs contain various biomolecules, such as proteins, nucleic acids, and lipids (28). EVs play crucial roles in intercellular communication by delivering bioactive molecules contained within their cargo, thereby transmitting signals and information between different tissues and organs in the body.

In the field of cancer research, EVs continue to garner increasing attention. EVs released by tumor cells carry significant information about tumor development and progression (29), including information about genetic expression patterns, protein compositions, and microRNA. The analysis of the biomolecules in EVs provides insights into the biological characteristics, metastatic tendencies, and treatment responsiveness of tumors.

Specifically, in ALK-positive NSCLC, recent study suggests that EVs could play a significant role in the disease’s biology and treatment response (30). EVs may carry ALK fusion proteins, which could potentially be used as biomarkers to monitor disease progression and response to ALK inhibitors. The ability to non-invasively detect and analyze these ALK-specific EVs could provide clinicians with crucial information for personalizing therapy and monitoring treatment efficacy.

Moreover, in the context of liquid biopsy, EVs are emerging as promising non-invasive biomarkers. They are relatively easy to detect in bodily fluids and can carry tumor-specific information that is critical for diagnosis and monitoring. Beyond their role as biomarkers, EVs are also involved in modulating the tumor microenvironment. They act as mediators of communication between tumor cells and surrounding tissues, promoting tumor growth, invasion, and metastasis. This dual role of EVs—both as carriers of tumor information and as active participants in tumor progression—highlights their potential in advancing our understanding of tumor biology and in developing novel therapeutic strategies for ALK-positive NSCLC (31).

Tumor metabolites

Tumor metabolites refer to specific metabolic products produced in tumor cells, which differ from those released in normal cells, and which can be released into the bloodstream or other bodily fluids. Tumor metabolites include metabolic byproducts, metabolic enzymes, and intermediate products in metabolic pathways. They differ from the metabolic products of normal cells. Tumor cells exhibit aberrant metabolic activity, to support their growth, proliferation, and metastasis (32).

Abnormal glucose metabolism is typical in tumor metabolism. Known as the “Warburg effect”, tumor cells preferentially use glycolysis for energy production over the conventional oxidative phosphorylation process. This metabolic pattern leads to the accumulation of large amounts of lactate and other metabolites in tumor cells. Additionally, tumor cells may also exhibit abnormal lipid metabolism, amino acid metabolism, nucleotide metabolism, and other characteristics, resulting in the production of metabolites distinct from those of normal cells.

Tumor metabolites have significant potential applications in liquid biopsy. By analyzing tumor metabolites in bodily fluid samples, information about the biological characteristics and metabolic activities of tumors can be obtained. This information can be used for early cancer diagnosis, disease monitoring, and prognosis assessment. For example, the characteristic patterns of certain tumor metabolites may be associated with specific types of cancer, and thus could serve as biomarkers for early cancer detection. Various methods may be used to detect tumor metabolites, such as mass spectrometry analysis, spectroscopic techniques, and electrochemical sensors. Through these techniques, tumor-specific metabolites can be identified and quantified, enabling the monitoring and assessment of lung cancer progression.

TEPs

TEPs are platelets whose functions have been altered by tumor cells in the tumor microenvironment. Tumor cells influence the biological characteristics of surrounding platelets by secreting various factors and signaling molecules, endowing these platelets with enhanced abilities to promote tumor growth, invasion, and metastasis.

Certain characteristics of TEPs have led to their application in liquid biopsy. First, TEPs release specific molecular markers such as RNA and proteins, which can be detected and used for the diagnosis and disease monitoring of lung cancer (33). Second, the characteristics of TEPs reflect the status of the tumor microenvironment, including tumor growth, invasion, and metastasis (34,35). Therefore, TEP analysis can provide clues about the biological characteristics of tumors and help guide the development of tumor treatment strategies.

Recent study suggests that TEPs may have a role in ALK+ NSCLC, where ALK gene rearrangements are a key driver of tumor progression. The functional alterations in TEPs might be influenced by the unique tumor microenvironment of ALK+ NSCLC, potentially offering new insights into tumor biology and treatment strategies. Evidence regarding the specific role of TEPs in ALK+ NSCLC needs to be reviewed to better understand their potential diagnostic and therapeutic implications.

Plasma protein biomarkers

Plasma protein biomarkers refer to specific proteins present in the plasma that play crucial roles in the occurrence, development, and treatment response of lung cancer. These proteins may be secreted by tumor cells or generated due to changes in the tumor microenvironment.

Plasma protein biomarkers have been applied in the liquid biopsy analysis of lung cancer in a number of ways. First, as some proteins exhibit specificity or significant elevation in the plasma of lung cancer patients, they can serve as diagnostic biomarkers. For example, proteins such as carcinoembryonic antigen and NSCLC-associated antigen are often elevated in the plasma of lung cancer patients and can be used as diagnostic indicators (36). Second, as the levels of plasma protein biomarkers can reflect the disease status of lung cancer patients, including disease progression and treatment effects (37), monitoring changes in these proteins can enable the timely assessment of treatment efficacy and the prediction of disease progression, which in turn can be used to make adjustments to subsequent treatment strategies. Third, as the levels of certain plasma protein biomarkers are closely related to the prognosis of lung cancer patients, monitoring changes in these proteins can enable clinicians to predict patient survival and disease progression, and thus provide better guidance about treatment decisions (38). Fourth, as the expression levels of some proteins are associated with the sensitivity of lung cancer to specific treatment methods, detecting these proteins can assist clinicians to select the most suitable treatment regimen and improve treatment outcomes (39).

Integration of multi-omics technologies

The integration of multi-omics technologies enables the comprehensive analysis of tumor features. Specifically, the integration of various techniques, such as genomics, transcriptomics, and proteomics, enables a thorough understanding of the molecular characteristics of tumor samples. Genomics identifies tumor-related genetic mutations and chromosomal rearrangements at the DNA level; transcriptomics reveals gene expression patterns and pathway activation statuses; while proteomics detects changes in protein levels. By integrating these data, a more comprehensive understanding of tumor biology is achieved, providing crucial insights into tumor initiation and progression (40).

The integration of multi-omics technologies also enhances the precision of personalized treatment strategies. Molecular information from tumor samples can be analyzed to identify molecular feature differences among patients, which in turn can enable tailored treatment designs. For instance, targeted treatment strategies based on individual genetic mutations or immunotherapy approaches targeting specific gene expression levels can be developed. This precision medicine approach maximizes treatment effectiveness while minimizing adverse reactions (41).

Additionally, the integration of multi-omics technologies enables the discovery and exploration of new treatment targets and drug resistance mechanisms. Conducting analyses at different molecular levels can lead to the identification of novel biological features of tumors and key molecules associated with tumor growth, metastasis, and resistance to treatment. Further, it can reveal tumor resistance mechanisms to different treatment methods, providing clues for the development of new strategies that can overcome resistance.

Application of AI in liquid biopsy

AI can be applied in liquid biopsy to identify tumor biomarkers. Using machine-learning and deep-learning techniques, AI can rapidly and accurately analyze large-scale liquid biopsy data to identify potential tumor biomarkers. These biomarkers may include ctDNA, CTCs, and ctRNA, whose variations are closely associated with tumor occurrence, development, and treatment response. AI can be used to discover hidden patterns and rules from vast data, enhancing the efficiency and accuracy of tumor biomarker identification (42).

AI can also be applied in liquid biopsy for disease prediction. AI uses liquid biopsy data to predict the occurrence of lung cancer. By analyzing patient biomarkers, combined with clinical data and imaging results, models can be established to predict the risk of lung cancer, tumor growth, and metastatic tendencies. These predictive models enable the early detection of potential lung cancer lesions, offering a valuable window for early intervention and treatment (43).

Additionally, AI can be applied in liquid biopsy to assess the treatment response. Specifically, AI can be used to evaluate patients’ responses to treatment by analyzing liquid biopsy data. Monitoring changes in tumor biomarkers can be used to promptly detect treatment responses, and can assist physicians to adjust treatment plans. For patients receiving immunotherapy, AI can be used to assess immunotherapy responses by analyzing changes in ctDNA, which in turn can guide treatment adjustments and optimization.

Finally, AI can be applied in liquid biopsy to support personalized medical decision making. Indeed, the application of AI can provide crucial support for personalized medical decision making. By integrating clinical data, liquid biopsy data, and patients’ genetic information, AI can offer personalized diagnostic and treatment recommendations to physicians. This can help physicians to formulate more precise treatment plans, thereby improving treatment outcomes and patient survival rates.

Influence of tumor microenvironment factors

The complexity of the tumor microenvironment poses new challenges and opportunities for interpreting and applying liquid biopsy. Microenvironment factors include immune cells, stromal cells, and blood vessels, which play vital roles in regulating tumor growth, metastasis, and treatment response (44). In liquid biopsy, microenvironment factors may affect the expression and secretion of tumor biomarkers in bodily fluids, thereby influencing the accuracy and reliability of liquid biopsy results.

Recent study has shown that immune cells in the tumor microenvironment have a significant effect on liquid biopsy results (45). Changes in the activity status and concentration of immune cells may affect the release and circulating levels of ctDNA, CTCs, and other biomarkers in bodily fluids, thereby interfering with the interpretation of liquid biopsy results. Additionally, stromal cells, such as fibroblasts and tumor-associated macrophages, may affect the molecular characteristics of liquid biopsy samples by secreting cytokines and regulating extracellular matrix changes (46).

To address the effect of microenvironment factors on liquid biopsy, researchers are actively seeking appropriate solutions and management strategies. Specifically, researchers are attempting to develop new liquid biopsy techniques to better capture and analyze the influence of microenvironment factors on liquid biopsy results. They are also seeking to establish more comprehensive and accurate analysis frameworks that incorporate microenvironment factors into the data analysis and interpretation of liquid biopsy, thereby improving the diagnostic and predictive accuracy.

Association between liquid biopsy and immunotherapy

Discovery of new treatment targets

The integration of multi-omics technologies enables scientists to identify new treatment targets closely related to tumor development and progression. By analyzing genomics, transcriptomics, and proteomics data, genes and signaling pathways that are highly expressed or mutated in tumor cells can be identified as potential therapeutic targets. For example, if a key gene related to tumor proliferation and metastasis (such as an EGFR mutation) is found to be abnormally expressed, it could serve as a target for therapy, leading to the development of drugs that inhibit tumor growth and spread (47). Techniques such as next-generation sequencing (NGS) and digital PCR (dPCR) are commonly used to detect such mutations in ctDNA, providing precise insights into molecular changes that may guide therapeutic strategies. However, further research is needed to verify the applicability of these targets across different tumor subtypes and to explore how to effectively translate these findings into clinical strategies (48).

Revealing drug resistance mechanisms

The integration of multi-omics technologies also plays a crucial role in uncovering the mechanisms of tumor resistance to various treatment modalities. By comparing molecular differences between resistant and sensitive tumor samples, researchers can identify biomarkers, signaling pathways, and regulatory factors associated with resistance. For example, the T790M mutation in ctDNA, commonly detected using dPCR or NGS, has been found to confer resistance to EGFR inhibitors in NSCLC (49). This highlights how specific mutations can be identified through liquid biopsy and illustrates its role in monitoring resistance mechanisms and guiding treatment adjustments. Furthermore, LC-MS is increasingly being used to analyze protein expression and metabolic changes in resistant tumors, further broadening the understanding of resistance pathways.

As a non-invasive and repeatable diagnostic tool, liquid biopsy holds significant promise in the early detection of lung cancer. By analyzing circulating tumor biomarkers such as ctDNA, CTCs, and ctRNA, liquid biopsy can help physicians identify the presence and subtype of lung cancer, providing a basis for timely treatment interventions. CTC isolation technologies, such as microfluidics-based platforms and EpCAM-based assays, are commonly employed to capture CTCs for downstream analysis. Furthermore, monitoring changes in these biomarkers during treatment with NGS or real-time PCR allows for the evaluation of patient response and the adjustment of treatment plans in real time (50).

Early detection and monitoring of treatment response

Liquid biopsy technologies, such as dPCR and NGS, are frequently used for ctDNA analysis due to their high sensitivity in detecting low-frequency mutations. As a result, liquid biopsy plays a critical role in early lung cancer detection by identifying genetic alterations and aiding in the assessment of tumor burden. These technologies have also been employed to track dynamic changes in CTCs and ctRNA, providing valuable insights into treatment response. Droplet digital PCR (ddPCR), for instance, is highly sensitive and can detect minimal residual disease during treatment, making it a key tool for monitoring tumor progression or regression.

However, the sensitivity and specificity of these methods vary across A study, influenced by differences in sample collection, processing techniques, and the choice of molecular markers. While technologies like ddPCR provide high precision, variations in biomarker selection and assay protocols can result in discrepancies between studies. Thus, continued validation and the development of standardized methods are critical to ensuring the reliability and reproducibility of results across clinical settings.

Challenges of liquid biopsy technology

The field of liquid biopsy is rapidly evolving, with the development of more advanced techniques to improve sensitivity, efficiency, and cost-effectiveness. NGS and dPCR continue to be key technologies, allowing for the detection of a wide range of mutations, including those present at low frequencies. Additionally, advances in microfluidics and CTC enrichment platforms have enabled more efficient isolation of CTCs for molecular profiling. In the future, these advancements will help expand the clinical utility of liquid biopsy, particularly for early-stage lung cancer detection and monitoring therapeutic responses (51).

Despite these promising developments, challenges remain, especially in terms of the complexity of data analysis and the identification of rare mutations. NGS technologies generate vast amounts of sequencing data, which requires sophisticated bioinformatics tools for interpretation. This adds to the technical complexity and increases costs, limiting widespread clinical adoption. Moreover, the lack of standardized procedures across different laboratories continues to affect the comparability and reliability of results. For example, methods used to process ctDNA or isolate CTCs can vary significantly, leading to inconsistencies in outcomes. The development of universally accepted protocols for sample collection, processing, and data analysis is essential to overcome these challenges.

Another challenge facing liquid biopsy is the high cost associated with some of the advanced platforms, such as NGS. Although costs are expected to decrease as technologies mature, the financial barrier currently restricts the widespread use of liquid biopsy in clinical practice. Reducing these costs through innovation and increasing access to high-throughput sequencing technologies will be crucial to expanding the clinical application of liquid biopsy.

Challenges in clinical applications

As clinical medicine advances rapidly, new technologies and treatments continually emerge, bringing with them increasing challenges. One of the most pressing issues is laboratory standardization, which is crucial for ensuring the reliability and comparability of test results. For liquid biopsy, this includes standardizing protocols for PCR-based assays, CTC isolation technologies, and NGS platforms to ensure consistency across different laboratories. For example, the adoption of ACMG-ClinGen standards for CNV interpretation has been an important step toward improving consistency, but additional work is needed to implement standardized guidelines for liquid biopsy technologies. Collaboration between laboratories and external quality assessments (EQA) are also vital for improving the reproducibility of results.

Construction of clinical decision support systems (CDSS)

The integration of AI into liquid biopsy platforms is another area that holds great promise. AI-based CDSS platforms can assist clinicians in interpreting complex liquid biopsy data, offering real-time analysis of ctDNA, CTCs, and other biomarkers. However, building effective systems requires overcoming challenges related to data standardization and integration. The heterogeneity of data generated by various liquid biopsy technologies (e.g., NGS, ddPCR, microfluidics-based CTC isolation) complicates the process of creating unified databases that can be leveraged by AI for decision-making. Ensuring that these systems are validated and clinically relevant will be crucial in their adoption.

Future directions and research prospects

Application of single-cell sequencing

Single-cell sequencing, a powerful emerging technology, provides insights into cellular heterogeneity that bulk sequencing methods cannot achieve. Single-cell RNA sequencing (scRNA-seq), in particular, can reveal gene expression profiles of individual cells within the tumor microenvironment, offering valuable information for personalized therapies. Single-cell DNA sequencing can also identify rare tumor mutations at early stages, which is key for early diagnosis and treatment planning. As the costs of these technologies continue to decrease, their clinical utility is expected to expand, particularly for tracking tumor evolution and understanding treatment resistance at the single-cell level.

Integration of liquid biopsy and AI

The combination of liquid biopsy and AI presents new opportunities for cancer detection, monitoring, and treatment planning. AI can be applied to large datasets generated by NGS or ddPCR to identify potential biomarkers and improve diagnostic accuracy. For instance, AI models trained on liquid biopsy data have already shown promise in early cancer detection, particularly for lung and colorectal cancers. In the future, AI-powered systems will likely play a key role in analyzing liquid biopsy data for real-time prognosis and treatment monitoring, driving advancements in precision medicine.

Conclusions

The results of liquid biopsy technology need to be compared and validated against traditional tissue biopsy results to ensure consistency and reliability. This requires the establishment of corresponding clinical data and research evidence to strengthen the position and reliability of liquid biopsy technology in clinical practice.

Acknowledgments

Funding: None.

Footnote

Reporting Checklist: The authors have completed the Narrative Review reporting checklist. Available at https://tlcr.amegroups.com/article/view/10.21037/tlcr-24-828/rc

Peer Review File: Available at https://tlcr.amegroups.com/article/view/10.21037/tlcr-24-828/prf

Conflicts of Interest: All authors have completed the ICMJE uniform disclosure form (available at https://tlcr.amegroups.com/article/view/10.21037/tlcr-24-828/coif). Y.G. receives support for attending meetings and/or travel from European Society for Medical Oncology (ESMO). The other authors have no conflicts of interest to declare.

Ethical Statement: The authors are accountable for all aspects of the work in ensuring that questions related to the accuracy or integrity of any part of the work are appropriately investigated and resolved.

Open Access Statement: This is an Open Access article distributed in accordance with the Creative Commons Attribution-NonCommercial-NoDerivs 4.0 International License (CC BY-NC-ND 4.0), which permits the non-commercial replication and distribution of the article with the strict proviso that no changes or edits are made and the original work is properly cited (including links to both the formal publication through the relevant DOI and the license). See: https://creativecommons.org/licenses/by-nc-nd/4.0/.

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