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
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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)
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