Published November 2024 | Version v3
Journal article Open

Integrated single-cell transcriptomic analyses identify a novel lineage plasticity-related cancer cell type involved in prostate cancer progression

  • 1. Huazhong University of Science and Technology
  • 2. ROR icon Hainan University

Contributors

Data collector:

Supervisor:

  • 1. Huazhong University of Science and Technology
  • 2. ROR icon Hainan University

Description

Cancer cell plasticity is the ability of neoplastic cells to alter their identity and acquire new biological properties under microenvironmental pressures. In prostate cancer (PCa), lineage plasticity often results in therapy resistance and trans-differentiation to neuroendocrine (NE) lineage. However, identifying the cancer cells harboring lineage plasticity-related status remains challenging. Here, we established a lineage plasticity-related gene signature (LPSig) using 3314 PCa samples from 13 multi-center public transcriptomic datasets of human PCa. Based on LPSig, we identified a previously undefined minority population of lineage plasticity-related PCa cells (LPCs) from a comprehensive single-cell RNA-sequencing (scRNA-seq) meta-atlas consisted of 8 public human PCa scRNA-seq cohorts. Furthermore, in-depth dissection revealed pivotal roles of LPCs in trans-differentiation, tumor recurrence, and poor patient survival during PCa progression. Furthermore, we identified HMMR as a representative cell surface marker for LPCs, which was validated using additional scRNA-seq datasets and multiplexed immunohistochemistry. Moreover, HMMR was transcriptionally inhibited by AR, and was required for the aggressive adenocarcinoma features and NE phenotype. Collectively, our study uncovers a novel population of lineage plasticity-related cells with low AR activity, stemness-like traits, and elevated HMMR expression, that may facilitate poor prognosis in PCa.

Files

README.md

Files (1.8 GB)

Name Size Download all
md5:db030f7edb92e6798b7cee98a90c08e4
47.5 kB Download
md5:2fd0e59831d3f9b26e26dabba35976f5
6.7 kB Download
md5:e8a6419d0f2833755f2e3b833bacafa9
17.6 kB Download
md5:d5afdc9b12248b892c37eab61a4bc1ed
23.7 kB Download
md5:267b552d87c52137798989cb451fdc49
2.2 kB Download
md5:188484e60743ad1bab72d0d0d4500563
18.3 kB Download
md5:4ece3d589ef899e43e4f071d3f1aa1df
6.1 kB Download
md5:c8f827a90fc2d6bc99a41cc6cd68ee58
4.1 kB Preview Download
md5:30da201b81981ddd34bd0895e76ad7ed
3.1 kB Preview Download
md5:21964bec4dc6632a1b6f93e1dfc75fc4
775 Bytes Download
md5:d66066759cfecb24c2a169feae8a4bea
6.5 kB Download
md5:6275ab8ce71d2301564272e331e39549
7.5 kB Download
md5:8296190600ffb9ac25c8d2a0211e77eb
4.0 kB Download
md5:7e8ae524c2c5185bb7639941f9fa4efc
1.1 kB Download
md5:2d51ebbc478a1b1c4506b2090df82ea0
3.4 kB Download
md5:ae097ea10148bbb066d31a1f5312e530
1.2 kB Download
md5:8e0a107ce417ea84a021edd78b12eabe
1.6 GB Download
md5:10a0e1873e8e17bc0a82cbc86c1f1174
205.2 MB Download
md5:0b51f27b571a9b8702d7d9242d002326
3.4 kB Preview Download

Additional details

Software

Programming language
Python , R , Markdown