Li Jingru, Wang Jingting, Cao Bangwei
Department of Oncology, Beijing Friendship Hospital, Capital Medical University, Beijing, China.
Front Oncol. 2024 Sep 24;14:1448966. doi: 10.3389/fonc.2024.1448966. eCollection 2024.
Analyzing the impact of peripheral lipid levels on the efficacy of immune checkpoint inhibitor therapy in non-small cell lung cancer (NSCLC) patient populations and exploring whether it can serve as a biomarker for broadening precise selection of individuals benefiting from immunotherapy.
We retrospectively collected clinical data from 201 cases of NSCLC patients receiving immune checkpoint inhibitor therapy. The clinical information included biochemical indicators like total cholesterol, triglycerides, high-density lipoprotein (HDL), and low-density lipoprotein (LDL). We utilized machine learning algorithms and Cox proportional hazards regression models to investigate independent predictors for both short-term and long-term efficacy of immunotherapy. Additionally, we concurrently developed a survival prediction model. Analyzing the Genes of Patients with Treatment Differences to Uncover Mechanisms.
Correlation analysis revealed a significant positive association between HDL and ORR, DCR, and PFS. T-test results indicated that the high-HDL group exhibited higher DCR (81.97% vs. 45.57%) and ORR (61.48% vs. 16.46%). Kruskal-Wallis test showed that the high-HDL group had a longer median PFS (11 months vs. 6 months). Utilizing six machine learning algorithms, we constructed models to predict disease relief and stability. The model built using the random forest algorithm demonstrated superior performance, with AUC values of 0.858 and 0.802. Furthermore, both univariate and multivariate Cox analyses identified HDL and LDL as independent risk factors for predicting PFS. In patients with poor immunotherapy response, there is upregulation of BCL2L11, AKT1, and LMNA expression.
HDL and LDL are independent factors influencing the survival prognosis of NSCLC patients undergoing immune checkpoint inhibitor therapy. HDL is expected to become new biomarkers for predicting the immunotherapy efficacy in patients with NSCLC. In patients with poor immunotherapy response, upregulation of the LMNA gene leads to apoptosis resistance and abnormal lipid metabolism.
分析外周血脂水平对非小细胞肺癌（NSCLC）患者群体免疫检查点抑制剂治疗疗效的影响，并探讨其是否可作为生物标志物，以扩大精准选择从免疫治疗中获益个体的范围。
我们回顾性收集了201例接受免疫检查点抑制剂治疗的NSCLC患者的临床数据。临床信息包括总胆固醇、甘油三酯、高密度脂蛋白（HDL）和低密度脂蛋白（LDL）等生化指标。我们利用机器学习算法和Cox比例风险回归模型来研究免疫治疗短期和长期疗效的独立预测因素。此外，我们同时开发了一个生存预测模型。分析治疗差异患者的基因以揭示机制。
相关性分析显示HDL与客观缓解率（ORR）、疾病控制率（DCR）和无进展生存期（PFS）之间存在显著正相关。t检验结果表明，高HDL组的DCR（81.97%对45.57%）和ORR（61.48%对16.46%）更高。Kruskal-Wallis检验显示，高HDL组的中位PFS更长（11个月对6个月）。利用六种机器学习算法，我们构建了预测疾病缓解和稳定的模型。使用随机森林算法构建的模型表现出卓越性能，AUC值分别为0.858和0.802。此外，单因素和多因素Cox分析均将HDL和LDL确定为预测PFS的独立危险因素。在免疫治疗反应较差的患者中，BCL2L11、AKT1和LMNA表达上调。
HDL和LDL是影响接受免疫检查点抑制剂治疗的NSCLC患者生存预后的独立因素。HDL有望成为预测NSCLC患者免疫治疗疗效的新生物标志物。在免疫治疗反应较差的患者中，LMNA基因上调导致抗凋亡和脂质代谢异常。
