Comprehensive Single-Cell RNA Sequencing Analysis of Cervical Cancer: Insights Into Tumor Microenvironment and Gene...

Background: Cervical cancer is a complex disease with considerable cellular heterogeneity, which hampers our understanding of its progression and the development of effective treatments. Single-cell RNA sequencing (scRNA-seq)-a technology that enables gene expression analysis at the cellular level-has emerged as an important tool to explore this heterogeneity on a cell-to-cell basis. We perform an analysis on data quality and differential gene expression in cervical cancer via scRNA-seq, giving insights into the tumor microenvironment and likely therapeutic targets. Methods: scRNA-seq for cervical cancer sample and advanced bioinformatics tool for data analysis were utilized. Scatter plots were generated to assess quality control metrics based on mitochondrial gene expression and total RNA count. Cell clustering differential expression analysis identified significant genes in each cell cluster. Gene coexpression networks and modules were performed network analysis. We utilized pseudotime analysis to model the experience of cell state transitions to infer a trajectory and functional enrichment analysis to understand the biological processes involved. Results: scRNA-seq data revealed distinct cluster pattern of high quality gene expression profile. Ultimately, differential expression analysis suggested significant genes: TP53, GNG4, and CCL5 had high degrees of differential expression and potential roles in tumor progression. Some of these gene modules have unique biological functions identified by network analysis, while dynamic changes in gene expression across the trajectory of the pseudotime reveal the differences in gene expression during cell state transition. We next performed functional enrichment analysis which revealed that immune response and metabolic processes play a pivotal role in cervical cancer. Conclusion: Our large scale scRNA-seq of cervical cancer provide insights into cellular heterogeneity and gene expression dynamics within the tumor microenvironment.