Journal of translational medicine · 2025
Construction of a prostate adenocarcinoma molecular classification: integrating spatial transcriptomics with retrospective cohort validation.
СтатьяВалидационное исследованиеПоддержка исследования: вне правительства СШАНазвание и аннотация: Перевод в очереди
Обработка статьи · этапы, ошибки и управление
Загрузка состояния…
Ошибки и ограничения
История · было → стало
Состояние обновляется каждые 5 секунд, пока блок открыт. Процент показывается только там, где известен объём работы. Пауза останавливает продолжение этапа; текущий запрос к источнику может завершиться.
Lu Bingnan, Liu Yifan, Ji Guo, Yao Yuntao, Yang Zhao, Zhu Bolin, Wang Lei, Dong Keqin, Li Yuanan, Shi Jiaying, He Junzhe, Huang Runzhi, Zhou Wang, Cui Xinming, Pan Xiuwu, Cui Xingang
Аннотация
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.
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.