Exploring the Dynamic Changes of Intercellular Connections in Cervical Cancer: Insights From Transcriptomic Data Combined With Single‐Cell Sequencing
Ran Ji, Rui Geng, Zhaoyue Zhang, Feng Gao, Pengpeng Zhang, Ying Sun, Jinhui Liu
TL;DR
This study explores how cells in cervical cancer interact, identifies a new way to predict patient outcomes, and finds that COL4A1 is a promising target for treatment.
Contribution
The study introduces a new prognostic model for cervical cancer and identifies COL4A1 as a novel therapeutic target.
Findings
Cluster 8 of epithelial cells shows lower CNV scores, better prognosis, and interacts with fibroblasts via PTN signaling.
A cervical cancer-related model (CCM) based on Cluster 8 marker genes effectively distinguishes patient prognosis.
COL4A1 knockdown inhibits cervical cancer cell proliferation and metastasis in vitro.
Abstract
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. 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…
Genes, proteins, chemicals, diseases, species, mutations and cell lines named across the full text — each resolved to its canonical identifier and authoritative record.
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Taxonomy
TopicsSingle-cell and spatial transcriptomics · Ferroptosis and cancer prognosis · Cancer Immunotherapy and Biomarkers