多指标联合检测的液态悬浮芯片技术在卵巢恶性肿瘤诊断中的应用

摘要

目的：探讨多指标联合检测的液态悬浮芯片技术在卵巢恶性肿瘤诊断中的应用。方法收集2003年9月—2009年10月广西医科大学附属肿瘤医院收治的卵巢恶性肿瘤患者119例以及2007年4月—2010年9月本院收治的其他上皮性恶性肿瘤包括乳腺癌、肝癌、肺癌患者（均为女性）各40例（恶性肿瘤组），盆腔良性肿瘤患者204例（良性肿瘤组），以2008年在本院体检的健康妇女120例为正常对照组。（1）采用血清抗原抗体（包括CCL18、CXCL1抗原以及C1D、TM4SF1、FXR1、TIZ IgG型自身抗体）联合的液态悬浮芯片技术检测3组妇女血清CCL18、CXCL1抗原以及C1D、TM4SF1、FXR1、TIZ IgG型自身抗体的含量。（2）采用多因素logistic回归法逐步筛选相关性较好的指标并建立回归模型[即卵巢恶性肿瘤诊断模型和卵巢上皮性癌（卵巢癌）诊断模型]，对模型进行数学验证和临床验证，并与血清CA125单独检测的诊断效能进行比较。（3）比较血清抗原抗体联合检测、自身抗体谱（包括C1D、TM4SF1、FXR1、TIZ IgG型自身抗体）联合检测、CA125单独检测对早期（Ⅰ～Ⅱ期）卵巢恶性肿瘤的诊断效能。（4）比较血清抗原抗体联合检测、CA125单独检测对不同病理类型和病理分化程度的卵巢恶性肿瘤的诊断阳性率。结果（1）恶性肿瘤组患者血清CCL18、CXCL1抗原以及C1D、TM4SF1、FXR1、TIZ IgG型身抗体的含量分别为（228±100）μg/L、（247±84）ng/L、（140±95）μg/L、（182±148）μg/L、（230±106）μg/L、（190±98）μg/L，明显高于良性肿瘤组和正常对照组（P均<0.01）。（2）经logistic回归法逐步筛选，建立血清抗原抗体联合检测的卵巢恶性肿瘤诊断模型和血清自身抗体谱联合检测的卵巢癌诊断模型，其模型分别为：logit（P）=-11.151+0.008× C1D+0.011× TM4SF1+0.011×TIZ-0.008×FXR1+0.021×CCL18+0.200×CXCL1，logit（P）=-5.137+0.013×C1D+0.014×TM4SF1+0.060×TIZ-0.060×FXR1，当P>0.5时预报为卵巢恶性肿瘤（或卵巢癌），P≤0.5时预报为卵巢良性肿瘤（或卵巢良性上皮性肿瘤）。血清抗原抗体联合检测、血清自身抗体谱联合检测分别诊断卵巢恶性肿瘤、卵巢癌的P值分别为0.828、0.734。血清抗原抗体联合检测诊断卵巢恶性肿瘤的敏感度（90.6%）明显高于血清CA125单独检测（60.0%，P=0.005），血清自身抗体谱联合检测诊断卵巢癌的敏感度（75.8%）也明显高于血清CA125单独检测（66.7%，P=0.016）；而其特异度分别比较，差异均无统计学意义（P>0.05）。（3）血清抗原抗体联合检测、自身抗体谱联合检测、CA125单独检测诊断早期卵巢恶性肿瘤的敏感度分别为74.2%、69.4%、39.7%，特异度分别为93.6%、80.3%、60.3%，血清抗原抗体联合检测对早期卵巢恶性肿瘤的诊断效能明显优于血清自身抗体谱联合检测、CA125单独检测（P均<0.05）。（4）血清抗原抗体联合检测病理类型为上皮性卵巢恶性肿瘤（即卵巢癌）的诊断阳性率（75.2%）明显高于血清CA125单独检测（65.7%，P=0.023）；而血清抗原抗体联合检测卵巢浆液性癌、黏液性癌（P=0.196、P=0.602）和高分化、中～低分化卵巢恶性肿瘤（P=0.089、P=0.169）的诊断阳性率分别与血清CA125单独检测比较均无明显差异。结论本研究建立的血清抗原抗体联合检测的卵巢恶性肿瘤诊断模型和自身抗体谱联合检测的卵巢癌诊断模型分别对卵巢恶性肿瘤、卵巢癌的诊断效能均优于血清CA125单独检测，且前者对早期卵巢恶性肿瘤的诊断效能也优于血清CA125单独检测。%Objective To investigates the diagnostic value of combined detection serum CCL18, CXCL1 antigen, C1D, TM4SF1, FXR1, TIZ IgG autoantibody by suspension array for ovarian cancer. Methods Suspension array was used to detect CCL18, CXCL1 antigen, C1D, TM4SF1, FXR1, TIZ IgG autoantibody in 120 cases of healthy women, 204 cases of patients with benign pelvic tumors, 119 cases of pelvic malignant tumor patients, and 40 cases with breast cancer, lung cancer oroliver cancer, respectively. Constructed diagnosis model of combined detection six biomarkers for diagnosis of ovarian malignant tumor. Constructed diagnosis model of combined detection autoantibodies to diagnose epithelial ovarian cancer. Analysed the value of detecting six biomarkers for diagnosis of ovarian malignant tumor and detecting autoantibodies for diagnosis of epithelial ovarian cancer. Analysed diagnostic value of detecting six biomarkers to diagnose stageⅠandⅡepithelial ovarian cancer. Compared diagnostic value of detecting six biomarkers in diagnosis of tissue types and pathologic grading with that of CA125. Results Model of combined detecting six biomarkers to diagnose ovarian malignant tumor was logit(P)=-11.151+0.008×C1D+0.011 × TM4SF1+0.011 × TIZ-0.008 × FXR1+0.021 × CCL18+0.200 × CXCL1. Model of combined detection autoantibodies to diagnose epithelial ovarian cancer was logit(P)=-5.137+0.013 × C1D+0.014 × TM4SF1+0.060 × TIZ-0.060 × FXR1. Sensitivity and specificity of detecting six biomarker to diagnose ovarian malignant tumor was 90.6% and 98.7%. Sensitivity and specificity of detecting autoantibodies to diagnose epithelial ovarian cancer was 75.8%and 96.7%. Combined detection for six biomarkers to diagnose serous and mucinous ovarian cancer was statistically no better than those of CA125 (P=0.196 and P=0.602, respectively);there was significantly difference in diagnosis of ovarian cancer (P=0.023), and there was no significantly difference in diagnosis of different pathological grading (P=0.089 and P=0.169, respectively). Conclusions Constructing diagnosis model of combined detection six biomarker to diagnose ovarian malignant tumor and constructed diagnosis model of combined detectionautoantibodies to diagnose epithelial ovarian cancer. Combined detection six biomarkers to diagnose serous and mucinous ovarian tumors is better than that of CA125.