Chen Shan, Zhang Jie, Qian Chengjia, Qi Xiaowei, Mao Yong, Lu Tingxun
Department of Oncology, Affiliated Hospital of Jiangnan University, Wuxi, Jiangsu Province, 214122, People's Republic of China.
Department of Gastrointestinal Surgery, Affiliated Hospital of Jiangnan University, Wuxi, Jiangsu Province, 214122, People's Republic of China.
J Inflamm Res. 2023 Sep 22;16:4229-4250. doi: 10.2147/JIR.S422500. eCollection 2023.
We aim to investigate the clinical significance of dynamic changes in the lymphocyte-to-monocyte ratio (LMR) and neutrophil-lymphocyte ratio (NLR) in peripheral blood at different time points combined with CEA in the prediction of postoperative-recurrence-in-patients with colorectal cancer (CRC).
This study collected 357 patients with stage I-III CRC between 2016 and April 2018. The dynamic changes from preoperative to postoperative LMR (p-LMR-p) and NLR (p-NLR-p) were analyzed using COX regression for multivariate analysis. Logistic regression was used to investigate whether the dynamic changes from post-treatment to pre-end of follow-up LMR (p-LMR-f) and NLR (p-NLR-f) were independent risk factors for CRC recurrence and to construct a predictive model. Internal validation using bootstrapping was performed to validate the discrimination ability of the model. The models' discriminative effect, calibration degree, and clinical utility were assessed.
In both the total cohort and the adjuvant therapy group, the dynamic changes of p-LMR-p (High-High vs Low-Low: p=0.006; HR:2.210, 95% CI: 1.256-3.890) were found to be independent prognostic factors for recurrence-free survival (RFS) in CRC patients. Additionally, logistic regression analysis revealed that N stage, CEA, LMR of pre-end of follow-up (f-LMR), and p-LMR-f were independent risk factors for CRC recurrence. In the total cohort, the p-LMR-f had an area under the curve (AUC) of 0.704, with a sensitivity of 64% and a specificity of 75.3%. By combining p-LMR-f with CEA, a predictive model was constructed, which showed an AUC of 0.913 (0.986-0.913) in the total cohort and an AUC of 0.924 (0.902-0.924) in the adjuvant therapy group during internal validation using bootstrapping.
Dynamic changes in LMR can be used to predict the prognosis of CRC and serve as a biomarker for predicting CRC recurrence. Combined with CEA, it can improve the predictive performance for detecting CRC recurrence.
我们旨在研究不同时间点外周血淋巴细胞与单核细胞比值（LMR）和中性粒细胞与淋巴细胞比值（NLR）的动态变化联合癌胚抗原（CEA）在预测结直肠癌（CRC）患者术后复发中的临床意义。
本研究收集了2016年至2018年4月期间的357例I - III期CRC患者。使用COX回归分析术前至术后LMR（p - LMR - p）和NLR（p - NLR - p）的动态变化进行多因素分析。采用逻辑回归研究治疗后至随访结束前LMR（p - LMR - f）和NLR（p - NLR - f）的动态变化是否为CRC复发的独立危险因素，并构建预测模型。采用自抽样法进行内部验证以验证模型的判别能力。评估模型的判别效果、校准程度和临床实用性。
在整个队列和辅助治疗组中，均发现p - LMR - p的动态变化（高 - 高与低 - 低：p = 0.006；HR：2.210，95%CI：1.256 - 3.890）是CRC患者无复发生存（RFS）的独立预后因素。此外，逻辑回归分析显示N分期、CEA、随访结束前LMR（f - LMR）和p - LMR - f是CRC复发的独立危险因素。在整个队列中，p - LMR - f的曲线下面积（AUC）为0.704，敏感性为64%，特异性为75.3%。通过将p - LMR - f与CEA相结合，构建了一个预测模型，在采用自抽样法进行内部验证时，该模型在整个队列中的AUC为0.913（0.986 - 0.913），在辅助治疗组中的AUC为0.924（0.902 - 0.924）。
LMR的动态变化可用于预测CRC的预后，并作为预测CRC复发的生物标志物。与CEA联合使用可提高检测CRC复发的预测性能。
