Prevalence Oncogenic Human Papillomavirus in Cervical Cancer Patients in Riau Province Indonesia

The role of long non-coding RNAs and circular RNAs in cervical cancer: modulating miRNA function

Cervical cancer (CC) is a primary global health concern, ranking as the fourth leading cause of cancer-related death in women. Despite advancements in prognosis, long-term outcomes remained poor. Beyond HPV, cofactors like dietary deficiencies, immunosuppression, hormonal contraceptives, co-infections, and genetic variations are involved in CC progression. The pathogenesis of various diseases, including cancer, has brought to light the critical regulatory roles of microRNAs (miRNAs), long non-coding RNAs (lncRNAs), and circular RNAs (circRNAs). The aberrant expression of these miRNAs, lncRNAs, and circRNAs plays a pivotal role in the initiation and progression of CC. This review provides a comprehensive summary of the recent literature regarding the involvement of lncRNAs and circRNAs in modulating miRNA functions in cervical neoplasia and metastasis. Studies have shown that lncRNAs and circRNAs hold great potential as therapeutic agents and innovative biomarkers in CC. However, more clinical research is needed to advance our understanding of the therapeutic benefits of circRNAs and lncRNAs in CC.

SKCV: Stratified K-fold cross-validation on ML classifiers for predicting cervical cancer

Cancer is the unregulated development of abnormal cells in the human body system. Cervical cancer, also known as cervix cancer, develops on the cervix’s surface. This causes an overabundance of cells to build up, eventually forming a lump or tumour. As a result, early detection is essential to determine what effective treatment we can take to overcome it. Therefore, the novel Machine Learning (ML) techniques come to a place that predicts cervical cancer before it becomes too serious. Furthermore, four common diagnosis testing namely, Hinselmann, Schiller, Cytology, and Biopsy have been compared and predicted with four common ML models, namely Support Vector Machine (SVM), Random Forest (RF), K-Nearest Neighbors (K-NNs), and Extreme Gradient Boosting (XGB). Additionally, to enhance the better performance of ML models, the Stratified k-fold cross-validation (SKCV) method has been implemented over here. The findings of the experiments demonstrate that utilizing an RF classifier for analyzing the cervical cancer risk, could be a good alternative for assisting clinical specialists in classifying this disease in advance.