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Updated: Aug 18, 2026
Microarray-based Identification of Individual HERV Loci Expression: Application to Biomarker Discovery in Prostate Cancer
Published on: November 2, 2013
Construction of a prostate adenocarcinoma molecular classification: integrating spatial transcriptomics with
Bingnan Lu1, Yifan Liu1, Guo Ji2
1Department of Urology, Xinhua Hospital Affiliated to Shanghai Jiao Tong University School of Medicine, Shanghai, 200092, China.
Background:
Prostate adenocarcinoma (PRAD) is a biologically heterogeneous disease threatening the health of elderly males worldwide, and some PRAD subtypes with certain molecular landscape are always associated with poor prognosis. A more precise molecular classification system for PRAD is urgently needed.
Methods:
Through spatial transcriptome analysis, we identified different malignant cell/spot types in PRAD. Then, Monocle 2 analysis was applied to identify malignant cell fates and differentiation-related genes. Together with the prognosis-related genes identified through Kaplan-Meier analysis and univariate Cox regression in TCGA-PRAD cohort, we defined malignant cell differentiation-related prognostic genes (MDPGs). Based on MDPGs, we constructed a malignant cell differentiation-based PRAD classification (MDPC) using the ConsensusClusterPlus algorithm. Then, we explored multi-omics correlations of MDPC, and constructed the regulation networks of MDPC as well as the prognostic prediction model. Finally, we validated the prognostic prediction value of MDPC through immunohistochemical staining and follow-up of a retrospective cohort.
Results:
Three malignant spot types were identified through spatial transcriptome analysis. Then, we defined 33 MDPGs and successfully constructed MDPC with three different subtypes (DPP4+MSMB+ MDPC, NHP2+NVL+ MDPC, COL1A1+MYLK+ MDPC). Apart from the correlations with tumor genomics, immunomics, MDPC also harbored convincing prognostic prediction value. In our cohort, COL1A1+MYLK+ MDPC served as an independent risk factor of OS (hazard ratio (HR) = 20.720, P-value = 0.0018) and PFS (HR = 117.00, P-value = 0.0036), and was closely correlated with Gleason grade, WHO/ISUP grade, radiotherapy, chemotherapy, endocrinotherapy, bone metastasis before treatment, and progression after treatment.
Conclusion:
We successfully constructed MDPC with validated prognostic prediction value. This classification system provided clinicians with an effective tool to stratify PRAD patients, identifying high-risk individuals, recognizing patients prone to develop bone metastasis, and offering opportunities for early intervention to improve patients' prognosis.
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