European journal of medical research · 2025
Development of a cuproptosis-related prognostic signature to reveal heterogeneity of the immune microenvironment and drug sensitivity in acute lymphoblastic leukemia.
СтатьяНазвание и аннотация: Перевод пока не подготовлен
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.
Zhang Bingxin, Zhu Shuxia, Zheng Dong, Zhang Xinyi, Xie Wenxia, Zhou Shujuan, Zheng Sisi, Wang Quanqiang, Lin Zhili, Zheng Ziwei, Chen Zixing, Lan Enqing, Cui Luning, Ying Hansen, Zhang Yu, Lin Xuanru, Zhuang Qiang, Qian Honglan, Hu Xudong, Zhuang Yan, Zhang Qianying, Jin Zhouxiang, Jiang Songfu, Ma Yongyong
Аннотация
BACKGROUND: Cuproptosis is a brand-new copper-dependent type of cell death that has been linked to various tumors. However, the relationship between cuproptosis and acute lymphoblastic leukemia (ALL) remains to be further elaborated.
METHODS: In ALL, 12 cuproptosis-related genes (CRGs) were analyzed at genetic and single-cell levels. Two molecular clusters were identified using "ConsensusClusterPlus". With the least absolute shrinkage and selection operator, a prognostic signature was built based on cuproptosis. The prognosis, clinical parameters, biological function, immune cell infiltration, therapy sensitivities, and transcription factor regulation of the clusters and risk subsets were further compared. Kaplan Meier curves, time-ROC curves, and nomogram were employed to evaluate the accuracy of the signature. Lastly, qRT-PCR was used to detect prognostic genes in cell lines and clinical samples.
RESULTS: CRGs exhibited extensive genetic variations and heterogeneous expression profiles in ALL. Single-cell analysis demonstrated that CRGs were strongly correlated with the biological characteristics of cancer cells. Two clusters and risk subgroups with distinct clinicopathological features, prognoses, biological functions, and drug sensitivities were identified. The cuproptosis signature was crucial in characterizing tumor immune landscape and cancer cell self-renewal ability. Furthermore, we explored that subtype A and high-scoring groups were more sensitive to immunotherapy. Multiple drugs with higher sensitivity among high-risk subgroups have been predicted. Nomograms demonstrated the clinical applicability of cuproptosis in risk assessment. The model was further validated in the verification cohort, our clinical specimens, and cell lines.
CONCLUSIONS: The cuproptosis-based model can characterize the tumor microenvironment, forecast survival results, and aid in improving risk assessment and personalized therapy options in ALL.
METHODS: In ALL, 12 cuproptosis-related genes (CRGs) were analyzed at genetic and single-cell levels. Two molecular clusters were identified using "ConsensusClusterPlus". With the least absolute shrinkage and selection operator, a prognostic signature was built based on cuproptosis. The prognosis, clinical parameters, biological function, immune cell infiltration, therapy sensitivities, and transcription factor regulation of the clusters and risk subsets were further compared. Kaplan Meier curves, time-ROC curves, and nomogram were employed to evaluate the accuracy of the signature. Lastly, qRT-PCR was used to detect prognostic genes in cell lines and clinical samples.
RESULTS: CRGs exhibited extensive genetic variations and heterogeneous expression profiles in ALL. Single-cell analysis demonstrated that CRGs were strongly correlated with the biological characteristics of cancer cells. Two clusters and risk subgroups with distinct clinicopathological features, prognoses, biological functions, and drug sensitivities were identified. The cuproptosis signature was crucial in characterizing tumor immune landscape and cancer cell self-renewal ability. Furthermore, we explored that subtype A and high-scoring groups were more sensitive to immunotherapy. Multiple drugs with higher sensitivity among high-risk subgroups have been predicted. Nomograms demonstrated the clinical applicability of cuproptosis in risk assessment. The model was further validated in the verification cohort, our clinical specimens, and cell lines.
CONCLUSIONS: The cuproptosis-based model can characterize the tumor microenvironment, forecast survival results, and aid in improving risk assessment and personalized therapy options in ALL.