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脂代谢异常与肿瘤标志物联合检测在胃癌早期诊断中的应用研究

Combined Analysis of Dyslipidemia and Tumor Markers for the Diagnosis of Gastric Cancer

  • 摘要:
    目的  探讨血脂谱相关指标联合肿瘤标志物在胃癌(gastric cancer, GC)患者血清中水平的变化及对GC的筛查价值。
    方法  连续选取2025年5月–2025年9月于上海交通大学医学院附属仁济医院西院确诊的GC患者100例(Ⅰ/Ⅱ期54例,Ⅲ/Ⅳ期46例)作为GC组,同期体检健康者100例作为健康对照(HC)组。采用全自动生化分析仪及电化学发光仪检测血清9项血脂指标〔高密度脂蛋白胆固醇(HDL-C)、低密度脂蛋白胆固醇(LDL-C)、总胆固醇(TC)、甘油三酯(TG)、小而密低密度脂蛋白胆固醇(sdLDL-C)、载脂蛋白(Apo)A1、ApoB、ApoC2、ApoC3〕及5项肿瘤标志物〔癌胚抗原(CEA)、糖类抗原(CA)19-9、CA50、CA242、CA72-4〕。采用Mann–Whitney U检验比较组间差异,采用逐步法Fisher判别算法建立基于血脂及肿瘤标志物的筛查模型和受试者工作特征曲线(ROC)分析评估诊断效能,连续选取2025年9月–2025年12月就诊的30例GC患者及30例健康者作为筛查模型的验证集,验证模型的诊断效能。
    结果  与HC组相比,GC组ApoA1、ApoC3、TC、HDL-C、LDL-C及sdLDL-C指标水平降低(P<0.05),CEA、CA50指标水平升高(P<0.05)。GC组Ⅲ、Ⅳ期ApoA1、ApoB、TC、HDL-C、LDL-C、sdLDL-C水平均低于Ⅰ、Ⅱ期(P<0.05)。ROC分析显示,单项指标中HDL-C诊断效能最高,曲线下面积(AUC)为0.797〔95%置信区间(CI):0.734~0.850〕,灵敏度为78%,特异度为74%。ApoA1、ApoC3、HDL-C、LDL-C、TC、sdLDL-C、CEA、CA50及年龄构成的筛查模型AUC为0.940(95%CI:0.891~0.971),灵敏度为83.75%,特异度为92.5%,诊断效能优于任一单项生物标志物。
    结论  ApoA1、ApoC3、HDL-C、LDL-C、TC、sdLDL-C、CEA、CA50及年龄组成的GC筛查模型可为临床辅助诊断提供参考。

     

    Abstract:
    Objective  To investigate changes in serum lipid profile parameters combined with tumor markers in gastric cancer (GC) patients and their value in GC screening.
    Methods  A total of 100 patients diagnosed with GC at Renji Hospital (West) between May and September 2025 were consecutively enrolled as the GC group (54 cases in stage Ⅰ/Ⅱ and 46 cases in stage Ⅲ/Ⅳ). Additionally, 100 age- and sex-matched healthy individuals undergoing routine physical examinations were included as the healthy control (HC) group. The serum levels of nine lipid indicators (high-density lipoprotein cholesterol HDL-C, low-density lipoprotein cholesterol LDL-C, total cholesterol TC, triglycerides TG, small and dense low-density lipoprotein cholesterol sdLDL-C, apolipoprotein Apo A1, ApoB, ApoC2, and ApoC3) and five tumor markers (carcinoembryonic antigen CEA, carbohydrate antigen CA 19-9, CA50, CA242, and CA72-4) were measured using an automatic biochemical analyzer and an electrochemiluminescence instrument. Intergroup differences were analyzed using the Mann–Whitney U test. A stepwise Fisher discriminant analysis was used to establish a screening model based on lipid profiles and tumor markers. Diagnostic performance was evaluated using receiver operating characteristic (ROC) curve analysis, and the model's diagnostic performance was validated using 30 GC patients and 30 healthy controls consecutively enrolled from September to December 2025.
    Results  Compared with the HC group, the GC group showed significantly lower levels of ApoA1, ApoC3, TC, HDL-C, LDL-C, and sdLDL-C (P < 0.05), while CEA and CA50 levels were significantly higher (P < 0.05). Patients with stage Ⅲ-Ⅳ GC had lower levels of ApoA1, ApoB, TC, HDL-C, LDL-C, and sdLDL-C compared to those with stage Ⅰ-Ⅱ GC (P < 0.05). ROC analysis showed that among individual indicators, HDL-C had the highest diagnostic performance, with an area under the curve (AUC) of 0.797 (95% CI: 0.734-0.850), sensitivity of 78%, and specificity of 74%. The screening model including ApoA1, ApoC3, HDL-C, LDL-C, TC, sdLDL-C, CEA, CA50, and age achieved an AUC of 0.940 (95% CI: 0.891-0.971), with 83.75% sensitivity and 92.5% specificity, outperforming any single biomarker in diagnostic efficacy.
    Conclusion  The combined panel of ApoA1, ApoC3, HDL-C, LDL-C, TC, sdLDL-C, CEA, CA50 and age offers a potential auxiliary tool for detecting gastric cancer.

     

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