Lv Jun-Ting, Jiao Yu-Tian, Han Xin-Le, Cao Yang-Jia, Lv Xu-Kun, Du Jun, Hou Jian
Zhuhai Hospital of Integrated Traditional Chinese & Western Medicine, 519000, China.
Department of Clinical Medicine, Shanghai Jiao Tong University School of Medicine, Shanghai, 200025, China.
Heliyon. 2024 Apr 20;10(8):e30123. doi: 10.1016/j.heliyon.2024.e30123. eCollection 2024 Apr 30.
Tumor genetic anomalies and immune dysregulation are pivotal in the progression of multiple myeloma (MM). Accurate patient stratification is essential for effective MM management, yet current models fail to comprehensively incorporate both molecular and immune profiles.
We examined 776 samples from the MMRF CoMMpass database, employing univariate regression with LASSO and CIBERSORT algorithms to identify 15 p53-related genes and six immune cells with prognostic significance in MM. A p53-TIC (tumor-infiltrating immune cells) classifier was constructed by calculating scores using the bootstrap-multicox method, which was further validated externally (GSE136337) and through ten-fold internal cross-validation for its predictive reliability and robustness.
The p53-TIC classifier demonstrated excellent performance in predicting the prognosis in MM. Specifically, patients in the p53/TIC subgroup had the most favorable prognosis and the lowest tumor mutational burden (TMB). Conversely, those in the p53/TIC subgroup, with the least favorable prognosis and the highest TMB, were predicted to have the best anti-PD1 and anti-CTLA4 response rate (40 %), which can be explained by their higher expression of PD1 and CTLA4. The three-year area under the curve (AUC) was 0.80 in the total sample.
Our study highlights the potential of an integrated analysis of p53-associated genes and TIC in predicting prognosis and aiding clinical decision-making in MM patients. This finding underscores the significance of comprehending the intricate interplay between genetic abnormalities and immune dysfunction in MM. Further research into this area may lead to the development of more effective treatment strategies.
肿瘤基因异常和免疫失调在多发性骨髓瘤（MM）的进展中起着关键作用。准确的患者分层对于MM的有效管理至关重要，但目前的模型未能全面纳入分子和免疫特征。
我们检查了MMRF CoMMpass数据库中的776个样本，采用LASSO单变量回归和CIBERSORT算法来识别15个与p53相关的基因和6种在MM中具有预后意义的免疫细胞。通过使用自举多cox方法计算得分构建了一个p53-TIC（肿瘤浸润免疫细胞）分类器，并在外部（GSE136337）以及通过十折内部交叉验证对其预测可靠性和稳健性进行了进一步验证。
p53-TIC分类器在预测MM预后方面表现出色。具体而言，p53/TIC亚组中的患者预后最有利且肿瘤突变负担（TMB）最低。相反，p53/TIC亚组中预后最差且TMB最高的患者预计具有最佳的抗PD1和抗CTLA4反应率（40%），这可以通过其更高的PD1和CTLA4表达来解释。总样本中的三年曲线下面积（AUC）为0.80。
我们的研究强调了对p53相关基因和TIC进行综合分析在预测MM患者预后及辅助临床决策方面的潜力。这一发现强调了理解MM中基因异常与免疫功能障碍之间复杂相互作用的重要性。对该领域的进一步研究可能会导致开发出更有效的治疗策略。
