Cervical cancer continues to be the fourth most common cancer among women globally, posing a significant health burden in 25 countries.

Primarily caused by high-risk Human Papillomavirus (hrHPV), cervical cancer is a leading global health concern, particularly in resource-limited regions.

Despite ongoing efforts, cervical cancer remains a major public health issue, with mortality rates surpassing the World Health Organization's elimination targets.

Recent research focused on RNA-seq data analysis to condense gene count matrices, aiming to uncover significant genes related to cervical cancer pathways.

The study highlights the limitations of traditional clustering methods in RNA-seq data due to the curse of dimensionality, prompting the use of more advanced techniques.

To address these limitations, the Mclust algorithm was employed for its effectiveness in clustering high-dimensional data, successfully identifying nine optimal clusters.

Among these clusters, Cluster 2 revealed critical epithelial-to-mesenchymal transition (EMT)-related genes that warrant further investigation.

The study stratified tumor budding into high and low groups, identifying 24 EMT-related genes, with five showing significant links to cervical cancer prognosis.

Notably, collagen type VI emerged as a potential gene marker for cervical cancer prognosis, associated with EMT and extracellular matrix degradation.

Methylation analysis indicated that control samples had the highest levels of methylation for key genes, while squamous cervical carcinoma (SCC) samples exhibited the lowest.

This decrease in methylation from normal cytology to SCC suggests that lower methylation levels may indicate abnormal cytology and an increased risk of cancer.

Overall, the study emphasizes the need for further investigation into the role of methylation patterns and gene expression in cervical oncogenesis, supporting their potential as indicators for cancer progression and diagnosis.