It is well known that tumors arising in the pancreas constitute one of the most prevalent malignancies affecting the upper gastrointestinal (GI) tract, following esophageal and gastric cancers. Moreover, it is one of the most difficult malignancies to manage due to its rapid progression and resistance to conventional treatments (1–3). The early stages of the disease typically show minimal or no symptoms (1, 2). Despite the introduction of new therapeutic approaches, the overall prognosis is generally unfavorable due to the aggressive nature of the tumor and remarkable recurrence rates. This emphasizes the importance of the crucial need for developing more effective diagnostic methods (3–6).

In clinical diagnostics for upper gastrointestinal malignancies, traditional serum protein markers such as carcinoembryonic antigen (CEA) and carbohydrate antigen 19.9 (CA19.9) are increasingly being recognized. However, these markers have important limitations, especially in terms of sensitivity and specificity (4, 7). Therefore, circulating tumor DNA (ctDNA) and cell-free DNA (cfDNA) can be used to detect the dynamic and genomic alterations associated with pancreatic malignancies (5, 6).

Liquid biopsy techniques such as ctDNA and cfDNA are promising methods for improving diagnostic accuracy and treatment strategies (8). A growing evidence base emphasizes the superiority of liquid biopsies compared to traditional tissue biopsies. Their noninvasive nature, increased safety, and improved ability to manage the complexities of intratumor heterogeneity are well documented (5, 6, 9). Additionally, it has been suggested that ctDNA provides faster and more accurate predictions than radiological imaging (6, 9). Furthermore, these studies also highlight the ability of ctDNA to detect real-time tumor dynamics with advances in technology.

Previous reports of primary research on the diagnostic accuracy of ctDNA and CA 19.9 have presented various contradictory findings. Several studies have proposed that CA 19.9 has notably greater diagnostic accuracy than ctDNA (10, 11). These studies also suggest that CA 19.9 may be more reliable. It has been well validated in many patient groups and is commonly used in clinical practice. Conversely, other studies propose no or negligible diagnostic difference between them (12, 13). Notably, numerous factors may have contributed to these inconsistent results, including the methodologies of the studies, the specific patient populations in the studies, and the technological platforms. Therefore, it is important and necessary to conduct more robust large-scale comparative studies, especially meta-analyses, to assess the current landscape associated with these biomarkers.

Herein, we performed a comprehensive meta-analysis to evaluate ctDNA and cfDNA as potential diagnostic and prognostic biomarkers for pancreatic malignancies.

A total of 751 papers were identified through structured literature searches of the PubMed/Medline (n = 132), Scopus (n = 357), and WoS (n = 262) databases. After eliminating irrelevant and duplicate records, the full texts of the remaining 79 studies were meticulously examined in detail. A total of 20 studies were evaluated for further analysis. After detailed quality assessment, one study was excluded from the meta-analysis because of an insufficient quality score (REMARK score < 5.5) (20). After all the reviews were completed, a total of 18 articles (10–12, 21–35) that met the criteria for the current meta-analysis were included in the study. The PRISMA flowchart, which maps out the number of records identified, included, and excluded, as well as the reasons for exclusions, is presented visually in Figure 1 .

This current meta-analysis provides a comprehensive evaluation of ctDNA and cfDNA as potential diagnostic and prognostic biomarkers for pancreatic malignancies. Hence, this study provides a comprehensive perspective on research suggesting that liquid biopsies may be a potential instrument for the diagnosis and prognosis of pancreatic malignancies.

Reports of previous studies evaluating ctDNA and CA 19.9 for diagnostic accuracy have provided conflicting reports on the topic. In a study conducted by Chen et al., it was reported that the diagnostic accuracy of CA 19.9 was greater than that of ctDNA (21). Similarly, other studies conducted with various sample sizes have also reported that CA 19.9 has significantly greater diagnostic accuracy than ctDNA (10, 11, 32). However, several studies have proposed that the diagnostic accuracy of ctDNA is greater than that of CA 19.9 or that there is no significant difference between them (12, 13, 26). The observed discrepancy may be attributed to varying ctDNA detection techniques and patient cohorts. In the present meta-analysis, 12 primary studies provided information on the diagnostic accuracy of ctDNA against CA 19-9 (10–12, 21–27, 31–35). The detailed analysis revealed that CA19–9 offers greater diagnostic accuracy than ctDNA across all disease stages. Although the diagnostic accuracy of ctDNA is lower than that of the CA19–9 protein biomarker, the development of high-sensitivity and multigene NGS techniques is important for diagnosing several cancers, including pancreatic malignancies. It is critical to highlight that CA19–9 analysis has now become complementary to ctDNA analysis. The integration of both biomarkers in diagnostic and prognostic assessments offers a more comprehensive understanding of pancreatic cancer progression. While ctDNA provides real-time insights into tumor dynamics and genetic alterations, CA19–9 serves as an established marker that can enhance diagnostic accuracy when used alongside ctDNA. Taken together, these biomarkers should be considered for monitoring treatment response, detecting early relapses, and developing personalized treatment strategies, especially when surgical resection is not an option. In terms of cfDNA, reports from a total of three studies (28–30) were obtained in our study. The diagnostic accuracy of cfDNA was significantly lower than that of CA19.9 in the pooled analysis. However, the number of studies included in the meta-analysis was relatively small, and there were few statistical analyses. Therefore, it is essential to conduct more studies with larger sample sizes focusing on cfDNA to better understand its potential role in diagnosing pancreatic cancer.

Various studies have investigated the prognostic value of ctDNA in patients with early-stage or advanced-stage pancreatic cancer. In a study focusing on patients with resectable and detectable ctDNA, patients who were ctDNA-positive had a significantly worse prognosis (OS: 13.6 months) than those who were ctDNA-negative (OS: 27.6 months) (22). Similarly, in another study conducted by Chen et al., the K-ras mutation found in plasma DNA served as a predictive biomarker for poor prognosis in patients with unresectable pancreatic cancer (21). These findings suggest that it could serve as a valuable tool for determining the prognosis and developing personalized treatment strategies in patients with pancreatic cancer who cannot undergo surgical removal. In contrast to prior studies, another study indicated that the presence of ctDNA was not significantly associated with survival outcomes in a cohort of pancreatic cancer patients at all disease stages (36). This dissimilarity underlines the variability and complexity in the prognostic value of ctDNA across different patient populations and disease stages. The findings also suggest that the role of ctDNA in prognosis may be influenced by various factors, such as cancer stage, treatment methods, and individual patient characteristics. In this study, patients who were ctDNA positive were found to have poorer OS. Our meta-analysis highlights the prognostic clinical value of ctDNA in pancreatic malignancies, increasing the evidence level of existing prior studies. Therefore, these findings could have significant implications for patient management, influence treatment strategies, potentially guide treatment decisions, and enable more personalized therapeutic approaches for ctDNA-positive patients.

The potential role of ctDNA in screening and diagnosing malignant diseases remains unclear. Notably, the current situation in ctDNA parallels concerns regarding the use of CA19–9, another biomarker, which is a screening test for cancer according to international guidelines (37). There are several challenges to the widespread adoption of ctDNA. These challenges are related to the specificity, sensitivity, and variability of ctDNA levels across different cancers and cancer stages. Additionally, cost-effectiveness, ethical considerations, and the need for a comprehensive understanding of its implications across various types of malignancies contribute to the ongoing discussion. However, CA19–9 has limited application due to similar issues. Therefore, the future role of ctDNA in early cancer detection and screening has yet to be determined, pending further research and consensus in the scientific community.

Our meta-analysis had several limitations that should be discussed and highlighted. In this meta-analysis, a substantial amount of heterogeneity was observed, which can be attributed to large variations in the study populations and the methods used for detecting ctDNA. This variability may affect the consistency and comparability of the results, making it difficult to draw definitive conclusions from the pooled data. We utilized a random effects model in our statistical analyses to accommodate the variations among different patient groups. However, this approach does not eliminate the potential bias introduced by the diverse study populations. In particular, the limited number of studies examining the association between ctDNA and OS limits the generalizability of the results. This limited number of studies also restricted our capacity to conduct subgroup analyses, which could have provided deeper insights into the effects of ctDNA across different patient subpopulations or stages of the disease. Future studies with larger sample sizes and more homogeneous patient groups are needed to verify the findings of this meta-analysis and refine the understanding of the prognostic value of ctDNA in pancreatic cancer patients.

Moreover, the detection rate of ctDNA is affected by several uncertain factors, such as the tumor’s ability to release ctDNA into the bloodstream. This capability varies with the tumor’s size, type, stage, metabolic activity, and surrounding tissue environment. Another crucial factor is the rate at which ctDNA is cleared from the blood. This process is influenced by physiological factors such as degradation by nucleases and removal by organs such as the liver and kidneys. These variables contribute to the observed differences in ctDNA levels among patients and can significantly impact biomarker sensitivity and specificity in cancer detection and monitoring. Hence, the need for further understanding of these biological mechanisms in the context of this topic should be highlighted.

Despite these limitations, pooling data from primary studies has significantly increased the level of evidence. Therefore, this paper provides a strong perspective for future studies.

Taken together, the findings of this meta-analysis indicate that CA19.9 still provides greater diagnostic accuracy across all disease stages than KRAS mutations in ctDNA or cfDNA. However, the presence of detectable levels of ctDNA was associated with worse patient outcomes regarding OS in pancreatic malignancies. Recognizing the prognostic significance of ctDNA could significantly influence treatment decisions, enabling healthcare providers to tailor more personalized and effective therapeutic approaches. As the field of oncology continues to evolve, the role of these biomarkers in improving diagnosis, prognosis, and treatment monitoring may become more important. It is highly recommended that future research continue to investigate the potential role of ctDNA and cfDNA as biomarkers in larger samples and multicenter studies.

The raw data supporting the conclusions of this article will be made available by the authors, without undue reservation.

MA: Conceptualization, Data curation, Formal analysis, Funding acquisition, Investigation, Methodology, Project administration, Resources, Software, Supervision, Validation, Visualization, Writing – original draft, Writing – review & editing. Aİ: Data curation, Investigation, Resources, Validation, Visualization, Writing – original draft, Writing – review & editing. YB: Conceptualization, Investigation, Methodology, Project administration, Resources, Software, Supervision, Validation, Writing – original draft, Writing – review & editing. NO: Conceptualization, Data curation, Funding acquisition, Investigation, Methodology, Project administration, Software, Supervision, Validation, Visualization, Writing – original draft, Writing – review & editing.