Potential of Proliferative Markers in Pancreatic Cancer Management: A Systematic Review

IF 2.1 Q2 MEDICINE, GENERAL & INTERNAL

Health Science Reports Pub Date : 2025-03-05 DOI:10.1002/hsr2.70412

Aryan Salahi-Niri, Paniz Zarand, Negar Mansouri, Parvaneh Rastgou, Omid Yazdani, Romina Esbati, Fatemeh Shojaeian, Behnaz Jahanbin, Zhaleh Mohsenifar, Hamid Asadzadeh Aghdaei, Farid Azmoudeh Ardalan, Seyed Amir Ahmad Safavi-Naini

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Abstract

Background and Aims

Pancreatic cancer is an aggressive malignancy with poor prognosis and limited treatment options. Chemotherapy remains a primary therapeutic approach, but patient responses vary significantly, emphasizing the need for reliable biomarkers. This review explores the potential role of proliferative markers, including Ki-67, PCNA, Cyclin D1, and PHH3, as predictive and prognostic indicators in pancreatic cancer management, aiming to enhance personalized treatment strategies.

Methods

We conducted a narrative review by searching Scopus, PubMed, and Google Scholar for studies focusing on Ki-67, PCNA, Cyclin D1, and PHH3 in relation to pancreatic cancer and chemotherapy. The literature was reviewed to evaluate the role of these markers in predicting chemotherapy response, tumor progression, and overall patient survival.

Results

The review highlights the clinical significance of these markers. Ki-67 and PCNA are associated with cell proliferation, while Cyclin D1 regulates cell cycle progression and PHH3 is linked to mitotic activity. High expression levels of these markers often correlate with increased tumor aggressiveness and poorer patient outcomes. Moreover, they show promise in predicting chemotherapy response, which can inform tailored therapeutic strategies. However, challenges remain, including standardization of detection methods and determination of optimal cutoff values.

Conclusion

Proliferative markers such as Ki-67, PCNA, Cyclin D1, and PHH3 hold potential as predictive and prognostic tools in pancreatic cancer management. Their integration into clinical practice could improve the accuracy of treatment decisions and enhance patient outcomes. Further research and validation are necessary to overcome existing challenges and optimize their application in personalized oncology.

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