Enhancing outcome prediction of concurrent chemoradiation treatment in patients with locally advanced cervical cancer through plasma extracellular vesicle proteomics
Issued Date
2024-08-30
Resource Type
eISSN
24058440
Scopus ID
2-s2.0-85201708117
Journal Title
Heliyon
Volume
10
Issue
16
Rights Holder(s)
SCOPUS
Bibliographic Citation
Heliyon Vol.10 No.16 (2024)
Suggested Citation
Leetanaporn K., Chiangjong W., Roytrakul S., Molika P., Janmunee N., Atjimakul T., Hanprasertpong J., Navakanitworakul R. Enhancing outcome prediction of concurrent chemoradiation treatment in patients with locally advanced cervical cancer through plasma extracellular vesicle proteomics. Heliyon Vol.10 No.16 (2024). doi:10.1016/j.heliyon.2024.e36374 Retrieved from: https://repository.li.mahidol.ac.th/handle/123456789/100692
Title
Enhancing outcome prediction of concurrent chemoradiation treatment in patients with locally advanced cervical cancer through plasma extracellular vesicle proteomics
Corresponding Author(s)
Other Contributor(s)
Abstract
Most patients with locally advanced cervical cancer (LACC) are primarily treated using concurrent chemoradiation (CCRT); however, LACC lacks reliable predictive biomarkers. Extracellular vesicles (EVs) could define the dynamic biological response to CCRT. However, the relationship between EVs and the therapeutic response to LACC is unestablished. Thus, we aimed to determine the relationship of plasma EVs pre- and post-CCRT in 62 patients with LACC. For proteomic analyses, EVs were isolated using ultracentrifugation (UC) with size exclusion chromatography or UC alone. We found that plasma particle concentration was significantly increased post-treatment in non-responders. After CCRT, there was a decrease in proteins related to serine protease and fibrinogen, which contribute to tumor microenvironment alteration. This reduction also extended to proteins involved in innate immune and viral immune responses, correlating with reduced tumor burden. Sparse partial least squares discriminant analysis revealed 8, 13, and 19 proteins at diagnosis, one month, and three months, respectively, influencing the CCRT response. Among these, FIBG, TFR1, HBA, and FINC are prognostic markers according to The Cancer Genome Atlas tissue gene expression database. Our discriminant model demonstrated excellent specificity and negative predictive value, underscoring the model's reliability in determining responsiveness to CCRT and highlighting the potential clinical applicability of EVs in improving outcomes in LACC.