bioRxivBioinformaticsPreprint
An FEV-Associated Metastasis-Initiating-Like State Linked to Tumor Cell Plasticity and Immune Niche Remodeling in Prostate Cancer
Sijia WuiD, Jiangpeng WeiiD, Xiaorui ShiiD, Xin Liu, Xiaobo zhou, Liyu HuangiD
Abstract
Prostate cancer progression is accompanied by dynamic remodeling of malignant cell states, whose metastatic potential and regulatory programs remain poorly defined. Here, we integrated single-cell, spatial, bulk transcriptomic, epigenomic, and experimental data to characterize malignant cell state transitions and identify candidate regulators associated with metastatic progression. A distinct intermediate state exhibits increased copy-number alterations, stemness, proliferation, and MYC activity, together with the highest transcriptional similarity to metastatic tumor cells, and is therefore designated as a metastasis-initiating-like (MIC-like) state. Transcriptional and regulon dynamics along the malignant cell state trajectory highlight FEV as a prominent candidate associated with the MIC-like state, with concordant expression and regulon activity patterns. Functionally, FEV is associated with a hybrid epithelial-mesenchymal phenotype, and its perturbation disrupts this phenotype, accompanied by reduced epithelial and mesenchymal features as well as impaired prostate cancer cell migration and invasion. Beyond this tumor-intrinsic feature, FEV is also associated with reduced cytotoxicity and interferon responses in the tumor microenvironment through its regulation of MIF-family genes. In addition to its potential roles in tumor cell plasticity and immune remodeling, FEV shows a reciprocal association with androgen signaling and distinct expression patterns across treatment response groups, highlighting its potential relevance in therapeutic contexts.
- organism: human
- assay: scRNA-seq
- assay: spatial transcriptomics
- assay: bulk RNA-seq
- assay: DNA methylation
- research focus: cell type annotation
- research focus: trajectory inference
- research focus: differential expression
- research focus: gene regulatory network inference
- approach: statistical model
- approach: machine learning (classical)
- Citations
- 0
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