Liu Qian-Qian, Chen Yan, Zhan Zhi-Qing, Wang Hao-Lian, Chen Ying-Xuan
Division of Gastroenterology and Hepatology, Key Laboratory of Gastroenterology and Hepatology, Ministry of Health, State Key Laboratory for Oncogenes and Related Genes, Shanghai Institute of Digestive Disease, Renji Hospital, School of Medicine, Shanghai Jiao Tong University, Shanghai, China.
Department of Gastroenterology, Sichuan Provincial People's Hospital, School of Medicine, University of Electronic Science and Technology of China, Chengdu, China.
J Cancer. 2025 Jun 23;16(9):2946-2958. doi: 10.7150/jca.114813. eCollection 2025.
We constructed a novel biomarker cholesterol (C)-to-natural killer (NK) cell ratio (CNR) to reflect the synergistic effect of cholesterol metabolism and inflammation on colorectal cancer (CRC) outcomes. This study aimed to investigate the clinical significance and predictive value of CNR in CRC and develop a simple and reliable prognostic model for predicting OS in CRC patients. We retrospectively collected the hematology data and medical records of 213 patients with CRC at Renji hospital and the histological data and medical records of 94 patients with CRC included in a tissue microarray. The association between tumor biomarkers and survival was evaluated using the log-rank test. The diagnostic efficacy of CNR was assessed using receiver operating characteristic curves. The overall survival (OS) rates were estimated using the Kaplan-Meier method. Cox proportional hazards regression was employed in both univariate and multivariate analyses to identify independent prognostic factors, which were subsequently utilized to develop a predictive model for OS. The performance of the model was evaluated using the concordance index (C-index) and calibration plots. The patients were stratified based on the total risk scores calculated from the model. The differences in OS among these groups were assessed using the Kaplan-Meier method. The relationship between cholesterol and NK cells was analyzed by investigating the colon cancer datasets TCGA and GSE39582. The TNM stage III-IV CRC group had significantly higher blood levels of cholesterol, triglycerides, low-density lipoprotein cholesterol (LDL-C), CNR, and carcinoembryonic antigen (CEA), and shorter progression-free survival (PFS) than the TNM stage I-II CRC group (all, < 0.05). The blood CNR correlated negatively with PFS ( < 0.001). Elevated tissue CNR levels were an independent risk factor for CRC, where low-tissue CNR patients demonstrated significantly prolonged survival ( < 0.05). The area under the curve for blood CNR was 0.743. The multivariate analyses indicated that tumor location ( = 0.004), TNM stage ( = 0.004), and tissue CNR ( = 0.033) were independent prognostic factors of OS in CRC. The nomogram model was based on these variables and demonstrated good calibration and predictive performance, achieving an excellent C-index of 0.737 [95% confidence interval (CI), 0.674-0.779]. The expression of the key cholesterol biosynthesis players , , and was not significantly associated with NK cell-mediated cytotoxicity-related gene signatures. and were negatively associated with , a phenotypic NK cell marker ( < 0.001). This is the first study to explore the predictive value of CNR in CRC, which was a promising predictor of CRC progression. The developed nomogram model may serve as a reliable tool for predicting survival in patients with CRC, which may complement the TNM staging system.
我们构建了一种新型生物标志物胆固醇（C）与自然杀伤（NK）细胞比值（CNR），以反映胆固醇代谢和炎症对结直肠癌（CRC）预后的协同作用。本研究旨在探讨CNR在CRC中的临床意义和预测价值，并开发一种简单可靠的预后模型来预测CRC患者的总生存期（OS）。我们回顾性收集了上海交通大学医学院附属仁济医院213例CRC患者的血液学数据和病历，以及组织芯片中94例CRC患者的组织学数据和病历。使用对数秩检验评估肿瘤生物标志物与生存之间的关联。使用受试者工作特征曲线评估CNR的诊断效能。使用Kaplan-Meier方法估计总生存率（OS）。在单变量和多变量分析中均采用Cox比例风险回归来识别独立的预后因素，随后利用这些因素开发OS预测模型。使用一致性指数（C-index）和校准图评估模型的性能。根据模型计算的总风险评分对患者进行分层。使用Kaplan-Meier方法评估这些组之间OS的差异。通过研究结肠癌数据集TCGA和GSE39582分析胆固醇与NK细胞之间的关系。与TNM I-II期CRC组相比，TNM III-IV期CRC组的血液中胆固醇、甘油三酯、低密度脂蛋白胆固醇（LDL-C）、CNR和癌胚抗原（CEA）水平显著更高，无进展生存期（PFS）更短（均P < 0.05）。血液CNR与PFS呈负相关（P < 0.001）。组织CNR水平升高是CRC的独立危险因素，组织CNR低的患者生存期显著延长（P < 0.05）。血液CNR的曲线下面积为0.743。多变量分析表明，肿瘤位置（P = 0.004）、TNM分期（P = 0.004）和组织CNR（P = 0.033）是CRC患者OS的独立预后因素。列线图模型基于这些变量构建，具有良好的校准和预测性能，C-index为0.737 [95%置信区间（CI），0.674 - 0.779]。关键胆固醇生物合成因子、和的表达与NK细胞介导的细胞毒性相关基因特征无显著关联。和与表型NK细胞标志物呈负相关（P < 0.001）。这是第一项探索CNR在CRC中预测价值的研究，CNR是CRC进展的一个有前景的预测指标。所开发的列线图模型可作为预测CRC患者生存的可靠工具，可能补充TNM分期系统。
