Skip to main page content
U.S. flag

An official website of the United States government

Dot gov

The .gov means it’s official.
Federal government websites often end in .gov or .mil. Before sharing sensitive information, make sure you’re on a federal government site.

Https

The site is secure.
The https:// ensures that you are connecting to the official website and that any information you provide is encrypted and transmitted securely.

Access keys NCBI Homepage MyNCBI Homepage Main Content Main Navigation
. 2025 Jun 21;15(13):1579.
doi: 10.3390/diagnostics15131579.

Integrating Clinical and Transcriptomic Profiles Associated with Vitamin D to Enhance Disease-Free Survival in Cervical Cancer Recurrence Using the CatBoost Algorithm

Geeitha Senthilkumar  1 , Renuka Pitchaimuthu  1 , Seshathiri Dhanasekaran  2 , Prabu Sankar Panneerselvam  3
Affiliations

Integrating Clinical and Transcriptomic Profiles Associated with Vitamin D to Enhance Disease-Free Survival in Cervical Cancer Recurrence Using the CatBoost Algorithm

Geeitha Senthilkumar et al. Diagnostics (Basel). .

Abstract

Background/Objectives: Cervical cancer is a leading cancer-related cause of death among women, with recurrence being a serious clinical issue. Recent evidence demonstrates that long non-coding RNAs (lncRNAs) affect cancer recurrence. This research investigates vitamin D's regulatory actions in the recurrence of cervical cancer, centering on the involvement of lncRNA. Clinical data on 738 patients shows that greater serum vitamin D levels are linked to reduced recurrence rates and enhanced disease-free survival (DFS). Methods: A transcriptomic analysis of CaSki cervical cancer cells using data from the GEO dataset GSE267715 identified that vitamin D controls genes that prevent cervical cancer recurrence. Machine learning predictors CatBoost, LightGBM, Extra Trees, and Logistic Regression and feature selection methods such as ANOVA F-test, mutual information, Chi-squared test, and Recursive Feature Elimination (RFE) are used to identify predictors of recurrence, evaluating model performance using accuracy, precision, recall, ROC AUC, confusion matrices, and ROC curves. Result: CatBoost performs the best overall, producing an accuracy of 95.27%. CatBoost provided an ROC AUC of 0.9930, a precision of 0.9296, and a recall of 0.9706, and this implies a significant trade-off between the ability to detect metastatic cases correctly. Conclusions: These data identify the therapeutic potential of vitamin D as a regulatory compound and lncRNA as a potential therapeutic target in the recurrence of cervical cancer.

Keywords: cervical cancer recurrence; disease-free survival; long non-coding RNA; machine learning predictors; vitamin D.

PubMed Disclaimer

Conflict of interest statement

The authors declare no conflicts of interest.

Figures

Figure 1
Figure 1
Distribution of FIGO stages among cervical cancer patients.
Figure 2
Figure 2
Recurrence rate by symptom type.
Figure 3
Figure 3
Correlation matrix.
Figure 4
Figure 4
Proposed model architecture.
Figure 5
Figure 5
Visualization of the classification report.
Figure 6
Figure 6
ROC curves for all models.
Figure 7
Figure 7
Confusion matrix of classification models.
Figure 8
Figure 8
KDE plot of vitamin D by recurrence.
Figure 9
Figure 9
Box plot of vitamin D level by recurrence status.
Figure 10
Figure 10
Kaplan–Meier curve for disease-free survival.
Figure 11
Figure 11
Volcano plot (fold change vs. p-value).
Figure 12
Figure 12
Mean difference plot (mean expression vs. fold change).
Figure 13
Figure 13
UMAP plot (sample clustering).
Figure 14
Figure 14
Boxplot of the expression distribution across samples.
Figure 15
Figure 15
Density plot of expression intensity.
Figure 16
Figure 16
Mean variance plot.
Figure 17
Figure 17
Volcano plot of calcitriol responsive lncRNA in cervical cancer.
See this image and copyright information in PMC

References

    1. Tanim M.T.H., Nath S.D., Khan S.F., Khan A., Sajib A.A. Transcriptomes of cervical cancer provide novel insights into dysregulated pathways, potential therapeutic targets, and repurposed drugs. Cancer Treat. Res. Commun. 2024;39:100808. doi: 10.1016/j.ctarc.2024.100808. - DOI - PubMed
    1. Singh D., Vignat J., Lorenzoni V., Eslahi M., Ginsburg O., Lauby-Secretan B., Arbyn M., Basu P., Bray F., Vaccarella S. Global estimates of incidence and mortality of cervical cancer in 2020: A baseline analysis of the WHO Global Cervical Cancer Elimination Initiative. Lancet Glob. Health. 2023;11:e197–e206. doi: 10.1016/S2214-109X(22)00501-0. - DOI - PMC - PubMed
    1. Buskwofie A., David-West G., Clare C.A. A review of cervical cancer: Incidence and disparities. J. Natl. Med. Assoc. 2020;112:229–232. doi: 10.1016/j.jnma.2020.03.002. - DOI - PubMed
    1. Hu Z., Ma D. The precision prevention and therapy of HPV-related cervical cancer: New concepts and clinical implications. Cancer Med. 2018;7:5217–5236. doi: 10.1002/cam4.1501. - DOI - PMC - PubMed
    1. Murillo R., Herrero R., Sierra M.S., Forman D. Cervical cancer in Central and South America: Burden of disease and status of disease control. Cancer Epidemiol. 2016;44((Suppl. 1)):S121–S130. doi: 10.1016/j.canep.2016.07.015. - DOI - PubMed

LinkOut - more resources