# Exploratory unsupervised machine learning of angiogenesis biomarkers in a phase II advanced cervical cancer trial of radiochemotherapy with or without neoadjuvant chemotherapy

**Authors:** Roberto J. Arai, Thiago R. da Costa, Renata Colombo Bonadio, Silvaneide Ferreira, Laura Sichero, Hugo P. Monteiro, Arnold Stern, Maria Del Pilar Estevez-Diz

PMC · DOI: 10.1016/j.clinsp.2025.100723 · Clinics · 2025-07-30

## TL;DR

The study found that changes in angiogenesis biomarkers after chemoradiation, not baseline levels, are linked to better survival in cervical cancer patients.

## Contribution

The study introduces the use of machine learning and PCA to analyze dynamic biomarker changes post-chemoradiation in cervical cancer.

## Key findings

- Baseline angiogenic biomarkers showed limited predictive value for survival outcomes.
- CRT modulated key biomarkers like VEGF-A, HB-EGF, and angiopoietin-2, which were associated with improved survival.
- NAC did not induce significant biomarker changes or clinical benefit compared to CRT alone.

## Abstract

•Baseline angiogenic biomarkers showed a weak predictive value by the ROC curve.•CRT changes VEGF-A, HB-EGF, angiopoietin-2, and HGF.•These alterations are not observed after NAC.•Biomarker patterns post-CRT were associated with improved outcomes by PCA.•ML may aid future stratification of angiogenesis profiles.

Baseline angiogenic biomarkers showed a weak predictive value by the ROC curve.

CRT changes VEGF-A, HB-EGF, angiopoietin-2, and HGF.

These alterations are not observed after NAC.

Biomarker patterns post-CRT were associated with improved outcomes by PCA.

ML may aid future stratification of angiogenesis profiles.

In a prospective, randomized phase II study, exploratory data analysis was conducted to evaluate angiogenesis-associated plasma protein levels in patients with locally advanced cervical cancer

Participants were divided into two groups: Group A received neoadjuvant cisplatin and gemcitabine treatment (NAC) followed by chemoradiation with cisplatin and brachytherapy (CRT), while Group B received only CRT. Plasma samples were collected from patients in Group A at three time points: baseline, after NAC, and after CRT. Group B patients had samples taken at two time points: baseline and after CRT. The study analyzed an angiogenesis-associated panel of plasma proteins, including angiopoietin-2, G-CSF, endothelin-1, FGF-1, FGF-2, follistatin, IL-8, HGF, EGF, HB-EGF, PLGF, VEGF-A, VEGF-C, and VEGF- D. Receiver Operating Characteristic (ROC) analyses assessed the predictive value of baseline biomarkers for 12, 24, and 36-month survival outcomes. Additionally, Principal Component Analysis (PCA) was applied to post-CRT biomarker changes to identify coordinated modulation patterns. PCA was based on normalized delta values and eigenvector loadings, enabling identification of biomarkers aligned with Progression-Free Survival (PFS) and Overall Survival (OS).

Significant differences were observed in the levels of HB-EGF, IL-8, PLGF, and VEGF-C between Groups A and B following CRT. Additionally, angiopoietin-2 levels showed a significant increase in Group B only. NAC treatment in Group A appeared to downregulate IL-8. CRT induced significant changes in HB-EGF, IL-8, PLGF, and VEGF-C levels in both groups. Patients in Group B demonstrated improved PFS and OS compared to those in Group A. Despite these differences in survival outcomes, the authors observed no significant intergroup differences in the tested biomarkers after completion of CRT. ROC analysis of baseline angiogenesis biomarkers demonstrated limited predictive sensitivity for survival outcomes. However, PCA of biomarker changes following CRT highlighted VEGF-A, HB-EGF, and angiopoietin-2 as variables associated with PFS and OS.

Baseline biomarker levels were not predictive of long-term outcomes. In contrast, CRT alone modulated key angiogenic biomarkers, and post-CRT biomarker changes were associated with improved survival. Such biomarker alterations were not observed following NAC, which was not associated with clinical benefit in this study. These findings underscore the value of dynamic biomarker evaluation and highlight how treatment strategies differentially impact biomarker profiles in advanced cervical cancer.

## Linked entities

- **Proteins:** VEGFA (vascular endothelial growth factor A), HBEGF (heparin binding EGF like growth factor), ANGPT2 (angiopoietin 2), HGF (hepatocyte growth factor), CSF3 (colony stimulating factor 3), FGF1 (fibroblast growth factor 1), FGF2 (fibroblast growth factor 2), LOC5564573 (agrin), CXCL8 (C-X-C motif chemokine ligand 8), HGF (hepatocyte growth factor), EGF (epidermal growth factor), PGF (placental growth factor), VEGFC (vascular endothelial growth factor C), VEGFD (vascular endothelial growth factor D)
- **Chemicals:** cisplatin (PubChem CID 5460033), gemcitabine (PubChem CID 60750)
- **Diseases:** cervical cancer (MONDO:0002974)

## Full-text entities

- **Genes:** VEGFA (vascular endothelial growth factor A) [NCBI Gene 7422] {aka L-VEGF, MVCD1, VEGF, VPF}, ANGPT2 (angiopoietin 2) [NCBI Gene 285] {aka AGPT2, ANG2, LMPHM10}, FGF1 (fibroblast growth factor 1) [NCBI Gene 2246] {aka AFGF, ECGF, ECGF-beta, ECGFA, ECGFB, FGF-1}, EDN1 (endothelin 1) [NCBI Gene 1906] {aka ARCND3, ET1, HDLCQ7, PPET1, QME}, FST (follistatin) [NCBI Gene 10468] {aka FS}, VEGFC (vascular endothelial growth factor C) [NCBI Gene 7424] {aka Flt4-L, LMPH1D, LMPHM4, VRP}, PGF (placental growth factor) [NCBI Gene 5228] {aka D12S1900, PGFL, PIGF, PLGF, PlGF-2, SHGC-10760}, VEGFD (vascular endothelial growth factor D) [NCBI Gene 2277] {aka FIGF, VEGF-D}, EGF (epidermal growth factor) [NCBI Gene 1950] {aka HOMG4, URG}, HGF (hepatocyte growth factor) [NCBI Gene 3082] {aka DFNB39, F-TCF, HGFB, HPTA, SF}, FGF2 (fibroblast growth factor 2) [NCBI Gene 2247] {aka BFGF, FGF-2, FGFB, HBGF-2}, CSF3 (colony stimulating factor 3) [NCBI Gene 1440] {aka C17orf33, CSF3OS, GCSF}, HBEGF (heparin binding EGF like growth factor) [NCBI Gene 1839] {aka DTR, DTS, DTSF, HEGFL}, CXCL8 (C-X-C motif chemokine ligand 8) [NCBI Gene 3576] {aka GCP-1, GCP1, IL8, LECT, LUCT, LYNAP}
- **Diseases:** cervical cancer (MESH:D002583)
- **Chemicals:** cisplatin (MESH:D002945), gemcitabine (MESH:D000093542)
- **Species:** Homo sapiens (human, species) [taxon 9606]

## Full text

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## Figures

6 figures with captions in the complete paper: https://tomesphere.com/paper/PMC12332904/full.md

## References

34 references — full list in the complete paper: https://tomesphere.com/paper/PMC12332904/full.md

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Source: https://tomesphere.com/paper/PMC12332904