4.7 Article

Identification of a distinct luminal subgroup diagnosing and stratifying early stage prostate cancer by tissue-based single-cell RNA sequencing

期刊

MOLECULAR CANCER
卷 19, 期 1, 页码 -

出版社

BMC
DOI: 10.1186/s12943-020-01264-9

关键词

Prostate cancer; Single-cell RNA sequencing; Diagnosis and stratification biomarker; HPN

资金

  1. Science and Technology Foundation of Shenzhen [JCYJ20180305164128430, GJHZ20180928171602104]
  2. Shenzhen Economic and Information Committee Innovation Chain and Industry Chain integration special support plan project [20180225112449943]
  3. Basic and Applied Basic Research of Guangdong Province [2019B1515120033]
  4. Guangdong Provincial Natural Science Foundation [2018A030313743]
  5. Shenzhen Public Service Platform on Tumor Precision Medicine and Molecular Diagnosis

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Background The highly intra-tumoral heterogeneity and complex cell origination of prostate cancer greatly limits the utility of traditional bulk RNA sequencing in finding better biomarker for disease diagnosis and stratification. Tissue specimens based single-cell RNA sequencing holds great promise for identification of novel biomarkers. However, this technique has yet been used in the study of prostate cancer heterogeneity. Methods Cell types and the corresponding marker genes were identified by single-cell RNA sequencing. Malignant states of different clusters were evaluated by copy number variation analysis and differentially expressed genes of pseudo-bulks sequencing. Diagnosis and stratification of prostate cancer was estimated by receiver operating characteristic curves of marker genes. Expression characteristics of marker genes were verified by immunostaining. Results Fifteen cell groups including three luminal clusters with different expression profiles were identified in prostate cancer tissues. The luminal cluster with the highest copy number variation level and marker genes enriched in prostate cancer-related metabolic processes was considered the malignant cluster. This cluster contained a distinct subgroup with high expression level of prostate cancer biomarkers and a strong distinguishing ability of normal and cancerous prostates across different pathology grading. In addition, we identified another marker gene, Hepsin (HPN), with a 0.930 area under the curve score distinguishing normal tissue from prostate cancer lesion. This finding was further validated by immunostaining of HPN in prostate cancer tissue array. Conclusion Our findings provide a valuable resource for interpreting tumor heterogeneity in prostate cancer, and a novel candidate marker for prostate cancer management.

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