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利用基于铁死亡的特征来表征多发性骨髓瘤的异质性并优化治疗。

Leveraging a disulfidptosis-based signature to characterize heterogeneity and optimize treatment in multiple myeloma.

作者信息

Zhang Bingxin, Zheng Dong, Zhu Shuxia, Zhang Xinyi, Wang Quanqiang, Lin Zhili, Zheng Ziwei, Zhou Shujuan, Chen Zixing, Zheng Sisi, 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

机构信息

Department of Hematology, The First Affiliated Hospital of Wenzhou Medical University, Wenzhou, Zhejiang, China.

Department of Hepatobiliary Surgery, The Second Affiliated Hospital and Yuying Children's Hospital of Wenzhou Medical University, Wenzhou, Zhejiang, China.

出版信息

Front Immunol. 2025 Apr 16;16:1559317. doi: 10.3389/fimmu.2025.1559317. eCollection 2025.

DOI:10.3389/fimmu.2025.1559317
PMID:40308607
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC12041008/
Abstract

BACKGROUND

Disulfidptosis is an emerging type of programmed cell death related to ROS accumulation and aberrant disulfide bond formation. Multiple myeloma (MM) is the second most prevalent hematologic malignancy characterized by a high synthesis rate of disulfide bond-rich proteins and chronic oxidative stress. However, the relationship between disulfidptosis and MM is still unclear.

METHODS

Using the non-negative matrix factorization and lasso algorithm, we constructed the disulfidptosis-associated subtypes and the prognostic model on the GEO dataset. We further explored genetic mutation mapping, protein-protein interactions, functional enrichment, drug sensitivity, drug prediction, and immune infiltration analysis among subtypes and risk subgroups. To improve the clinical benefits, we combined risk scores and clinical metrics to build a nomogram. Finally, experiments examined the expression patterns of disulfidptosis-related genes (DRGs) in MM.

RESULTS

By cluster analysis, we obtained three subtypes with C2 having a worse prognosis than C3. Consistently, C2 exhibited significantly lower sensitivity to doxorubicin and lenalidomide, as well as a higher propensity for T-cell depletion and a non-responsive state to immunotherapy. Similarly, in the subsequent prognostic model, the high-scoring group had a worse prognosis and a higher probability of T-cell dysfunction, immunotherapy resistance, and cancer cell self-renewal. DRGs and risk genes were widely mutated in cancers. Subtypes and risk subgroups differed in ROS metabolism and the p53 signaling pathway. We further identified eight genes differentially expressed in risk subgroups as drug targets against MM. Then 27 drugs targeting the high-risk group were predicted. Based on the DRGs and risk genes, we constructed the miRNA and TF regulatory networks. The nomogram of combined ISS, age, and risk score showed good predictive performance. qRT-PCR of cell lines and clinical specimens provided further support for prognostic modeling.

CONCLUSION

Our research reveals the prognostic value of disulfidptosis in MM and provides new perspectives for identifying heterogeneity and therapeutic targets.

摘要

背景

双硫死亡是一种新出现的程序性细胞死亡类型,与活性氧积累和异常二硫键形成有关。多发性骨髓瘤(MM)是第二常见的血液系统恶性肿瘤,其特征是富含二硫键的蛋白质合成率高和慢性氧化应激。然而,双硫死亡与MM之间的关系仍不清楚。

方法

我们使用非负矩阵分解和套索算法,在GEO数据集上构建了双硫死亡相关亚型和预后模型。我们进一步探索了亚型和风险亚组之间的基因突变图谱、蛋白质-蛋白质相互作用、功能富集、药物敏感性、药物预测和免疫浸润分析。为了提高临床效益,我们结合风险评分和临床指标构建了列线图。最后,实验检测了MM中双硫死亡相关基因(DRGs)的表达模式。

结果

通过聚类分析,我们获得了三个亚型,其中C2的预后比C3差。一致地,C2对阿霉素和来那度胺的敏感性显著较低,T细胞耗竭的倾向较高,对免疫治疗无反应。同样,在随后的预后模型中,高分组预后较差,T细胞功能障碍、免疫治疗耐药和癌细胞自我更新的可能性较高。DRGs和风险基因在癌症中广泛突变。亚型和风险亚组在活性氧代谢和p53信号通路方面存在差异。我们进一步确定了在风险亚组中差异表达的八个基因作为抗MM的药物靶点。然后预测了27种针对高危组的药物。基于DRGs和风险基因,我们构建了miRNA和TF调控网络。结合国际分期系统(ISS)、年龄和风险评分的列线图显示出良好的预测性能。细胞系和临床标本的qRT-PCR为预后模型提供了进一步支持。

结论

我们的研究揭示了双硫死亡在MM中的预后价值,并为识别异质性和治疗靶点提供了新的视角。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/8fc3/12041008/147f7fb80b76/fimmu-16-1559317-g012.jpg
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https://cdn.ncbi.nlm.nih.gov/pmc/blobs/8fc3/12041008/3968670ed3ca/fimmu-16-1559317-g005.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/8fc3/12041008/78b01a9fdbd5/fimmu-16-1559317-g006.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/8fc3/12041008/8437594c08df/fimmu-16-1559317-g007.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/8fc3/12041008/4540b8469b3c/fimmu-16-1559317-g008.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/8fc3/12041008/b5138b6ba709/fimmu-16-1559317-g009.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/8fc3/12041008/0285fcb3a47b/fimmu-16-1559317-g010.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/8fc3/12041008/219f3824c023/fimmu-16-1559317-g011.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/8fc3/12041008/147f7fb80b76/fimmu-16-1559317-g012.jpg

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