What question did this study set out to answer?

This research aims to investigate intercellular interactions in cervical cancer and develop a prognostic model based on transcriptomic and single-cell sequencing data.

January 25, 2026Open Access

Exploring the Dynamic Changes of Intercellular Connections in Cervical Cancer: Insights From Transcriptomic Data Combined With Single‐Cell Sequencing

Key Points

This research aims to investigate intercellular interactions in cervical cancer and develop a prognostic model based on transcriptomic and single-cell sequencing data.
Used InferCNV to determine CNV scores and ssGSE for sample abundance calculations.
Conducted CellChat and pseudotime analyses to observe intercluster interactions.
Established a prognostic model using LASSO and Cox analysis, focusing on copy number variation and tumor microenvironment (TME).
Performed assays (CCK‐8, EdU, etc.) to examine the role of COL4A1 in cervical cancer.
Calculated drug sensitivity in risk groups using the OncoPredict tool.
Identified nine clusters of epithelial cells, with Cluster 8 showing a lower CNV score and better prognosis.
Developed a cervical cancer–related model (CCM) based on Cluster 8 marker genes, effectively distinguishing prognosis.
Found significant differences in tumor mutational burden (TMB), immune cell infiltration, and ESTIMATE scores across groups.
Nine drugs were suggested for better treatment outcomes in low-risk patients.
COL4A1 knockdown decreased proliferation and metastasis of cervical cancer cells.

Abstract

Background As a common gynecological malignancy, cervical cancer has a rising incidence rate and mortality, which has brought huge pressure to global public health. Although immunotherapy has been applied in clinical practice, its therapeutic effect is still far from satisfactory. Methods InferCNV was used to calculate the CNV score and the ssGSE, which is an algorithm to calculate the abundance of samples. CellChat analysis and pseudotime analysis were used to observe the evolution and interaction relationships between different clusters. Establish a prognostic model for CC patients using univariate, LASSO, and Cox analysis, and evaluate copy number variation and TME in low‐risk groups. Finally, ssGSEA was applied to calculate the relationship between the hallmark gene sets and immune cycle steps and to calculate drug sensitivity in different risk groups using “oncopredict.” A series of experiments including CCK‐8 assay, clone formation, EdU assay, and Transwell assay were performed to detect the role of COL4A1 in CC. Results The epithelial cells were divided into nine clusters. Among them, Cluster 8 has a lower CNV score, a lower degree of variation, and a better prognosis. After that, Cluster 8 sends a signal to fibroblasts through the PTN signaling pathway. A cervical cancer–related model (CCM) was constructed based on the marker genes of Cluster 8, and it can effectively distinguish the prognosis. There is a great difference in standardized TMB, immune cell infiltration, and ESTIMATE scores between the groups. Nine drugs were identified which may achieve better therapeutic effects when applied to low‐risk patients. Finally, knockdown of COL4A1 inhibits the proliferation and metastatic ability of CC cells. Conclusion Our study revealed different interactions between subgroups in the tumor microenvironment of CC epithelial cells. We established an effective prognostic model. Ultimately, through a series of in vitro function experiments, COL4A1 was recognized as a new potential target for the therapeutic intervention of CC.

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