Jiangxi Province Key Laboratory of Laboratory Medicine, Jiangxi Provincial Clinical Research Center for Laboratory Medicine, Department of Clinical Laboratory, The Second Affiliated Hospital, Jiangxi Medical College, Nanchang University, Nanchang, Jiangxi Provence, China.
Technol Cancer Res Treat. 2024 Jan-Dec;23:15330338241232554. doi: 10.1177/15330338241232554.
Necroptosis is an inflammatory cell death mode, and its association with multiple myeloma (MM) remains unclear.
This prospective study first analyzed the association between necroptosis-related signature as well as prognosis and chemotherapy sensitivity in MM using the necroptosis score. Consensus clustering was used to identify necroptosis-related molecular clusters. Least absolute shrinkage and selection operator analysis and multivariate Cox regression analysis were performed to establish the prognostic model of necroptosis-related genes (NRGs).
A high necroptosis score was associated with poor prognosis and abundant immune infiltration. Two molecular clusters (clusters A and B) significantly differed in terms of prognosis and tumor microenvironment. Cluster B had a worse prognosis and higher tumor marker pathway activity than cluster A. The risk score model based on four NRGs can accurately predict the prognosis of patients with MM, which was validated in two validation cohorts. Receiver operating characteristic curve analysis showed that the area under the curves of the risk score in predicting the 1-, 3-, and 5-year survival rates were 0.710, 0.758, and 0.834, respectively. Further, the activity of pathways related to proliferation and genetic regulation in the high-risk group significantly increased. The drug prediction results showed that the low-risk score group was more sensitive to bortezomib, cytarabine, and doxorubicin than the high-risk score group. Meanwhile, the high-risk score group was more sensitive to lenalidomide and vinblastine than the low-risk score group. Finally, the upregulation of model genes CHMP1A, FAS, JAK3, and HSP90AA1 in clinical samples collected from patients with MM was validated via real-time polymerase chain reaction.
A systematic analysis of NRGs can help identify potential necroptosis-related mechanisms and provide novel biomarkers for MM prognosis prediction, tumor microenvironment evaluation, and personalized treatment planning.
坏死性凋亡是一种炎症细胞死亡方式，其与多发性骨髓瘤（MM）的关系尚不清楚。
本前瞻性研究首先使用坏死性凋亡评分分析了与坏死性凋亡相关的特征以及与 MM 预后和化疗敏感性的关系。采用共识聚类鉴定与坏死性凋亡相关的分子聚类。进行最小绝对收缩和选择算子分析以及多变量 Cox 回归分析，以建立与坏死性凋亡相关基因（NRGs）的预后模型。
高坏死性凋亡评分与预后不良和丰富的免疫浸润有关。两个分子聚类（聚类 A 和 B）在预后和肿瘤微环境方面存在显著差异。与聚类 A 相比，聚类 B 具有更差的预后和更高的肿瘤标志物通路活性。基于四个 NRGs 的风险评分模型可以准确预测 MM 患者的预后，并在两个验证队列中得到验证。受试者工作特征曲线分析表明，风险评分预测 1、3 和 5 年生存率的曲线下面积分别为 0.710、0.758 和 0.834。此外，高危组与增殖和遗传调节相关的通路活性显著增加。药物预测结果表明，与高危组相比，低危评分组对硼替佐米、阿糖胞苷和多柔比星更敏感。同时，与低危评分组相比，高危评分组对来那度胺和长春碱更敏感。最后，通过实时聚合酶链反应验证了从 MM 患者中收集的临床样本中模型基因 CHMP1A、FAS、JAK3 和 HSP90AA1 的上调。
对 NRGs 的系统分析有助于识别潜在的坏死性凋亡相关机制，并为 MM 的预后预测、肿瘤微环境评估和个性化治疗计划提供新的生物标志物。
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