摘要
1研究背景和目的
宫颈癌(Cervical Carcinoma)是女性生殖系统最常见的恶性肿瘤，在全球女性恶性肿瘤中占第二位，仅次于乳腺癌，每年全球新增宫颈癌病例约49万多例，其中80％在发展中国家。其预后与临床分期密切相关，中晚期患者5年生存率仅为10％左右，而早期患者5年生存率则可达到90％以上。宫颈癌的复发和转移不仅与临床分期有关，同时亦与临床治疗方案密切相关。如何提高早期诊断率、正确制定治疗方案和评价治疗效果、估计预后以及预防复发显得优为重要，也是本研究的目的。宫颈上皮组织的癌变过程是一个多阶段进行性过程，经历了宫颈上皮内瘤变(Cervical Intraepithelial Neoplasia，CIN)，包括CINⅠ级：轻度不典型增生；CINⅡ级：中度不典型增生；以及CINⅢ级：重度不典型增生和原位癌。宫颈上皮内瘤变是与宫颈浸润癌密切相关的一组癌前病变，它反映了宫颈癌发生发展中的连续过程。在这个过程中可有逆向性转变，而且每个阶段大约要经历5-10年的时间，是宫颈癌的预防及早期诊断和治疗的理论基础和依据。
在宫颈癌的致病因素中，研究最多的是人乳头状瘤病毒(human papilloma virus，HPV)感染，研究表明高危型HPV感染是宫颈癌发生发展中的重要因素，然而单纯的HPV感染不足以引起宫颈癌的发生，HPV感染是宫颈癌发生的基础，加之其它因素如：不良性行为等共同作用下，最终导致宫颈癌的发生。从生物学的角度而言，在宫颈癌发生发展过程中存在多个基因的表达异常。人类染色体中的遗传信息的传递需经转录合成mRNA，再经翻译过程得到相关的蛋白质而实现，也就是说实现基因功能表达的最终物质是蛋白质，因此，直接测定蛋白质的水平和活性是了解细胞活动的最好办法。近年来一种新的可高通量检测血清或组织中的蛋白质技术，表面加强激光解吸电离飞行时间质谱(surface-enhanced laser desorption／ionization-time of flight-mass spectrometry，SELDI-TOF-MS)，发展迅速，这一技术不但可高通量检测蛋白质，而且结合生物信息学技术还可得到用于样本分类的蛋白质组合，标本可用少量甚至微量的血清、组织等，目前已广泛应用于临床肿瘤的研究，如消化系统、呼吸系统以及泌尿生殖系统的恶性肿瘤。但对于宫颈癌的研究报道甚少，用宫颈癌组织做标本进行此项研究的报道有一家，而用血清做标本进行宫颈癌研究的目前尚未见报道。
为了能够建立用于宫颈癌和癌前病变的诊断与宫颈癌筛查的血清蛋白质组合，进一步了解宫颈癌的发生机制，本研究采用SELDI-TOF-MS技术对宫颈癌与健康女性的血清蛋白质质谱变化进行研究，进行差异表达的蛋白质筛选，然后在宫颈癌前病变患者、宫颈癌行广泛子宫切除加盆腔淋巴结清扫术后以及其后进行随访的患者血清中，进行同组蛋白质含量的t检验，以了解它们在无瘤—有瘤—无瘤生存模式全过程中的变化，以期筛选出能够监测宫颈癌全过程的蛋白质组合，如宫颈癌的早期诊断、治疗效果的评价以及预后的评估。
2材料与方法
2．1研究对象
样本来自郑州大学第一附属医院，入选要求：病理资料完整，无放疗或化疗等针对宫颈癌的治疗。
2．1．1血清标本
①宫颈癌49例，其中鳞癌45例(91.84％)，腺癌4例(8.16％)：FIGO分期Ⅰb-Ⅱa期39例(79.59％)，Ⅱa期以上10例(20.41％)。中位年龄47岁(25岁-75岁)。以及年龄相配的健康女性71例。②宫颈上皮内瘤变(cervical intraepithelial neoplasia，CIN)67例，其中CINⅠ-Ⅱ级35例，CINⅢ级32例。③宫颈癌Ⅰb—Ⅱa期行广泛子宫切除和盆腔淋巴结清扫手术后10天者24例，术后3月复诊者22例，术后1年随访者18例。
2．1．2组织标本
宫颈癌组织33例，正常宫颈上皮组织29例。
2．2标本收集
2．2．1血清标本
清晨空腹静脉血3ml，静置30min，2000rpm，离心20min，每管100μl分装，-80℃冰箱保存。
2．2．2组织标本
宫颈癌行广泛子宫全切加盆腔淋巴结清扫手术，标本离体后立即剖开宫颈，清理宫颈黏液，靠近癌组织基底部取材，同时在距癌组织边缘≥1cm处选取正常宫颈上皮，初步切碎后用0.25％胰蛋白酶消化30分钟，Hank's液冲洗，900rpm离心10min，去除上清夜，重复冲洗离心共3次，行细胞涂片常规病理检查，同时行细胞记数，要求目标细胞(正常宫颈上皮细胞或宫颈癌细胞)≥85％，细胞计数1×10~7／ml，做好标记，置于-80℃保存。
2．3研究方法
2．3．1芯片准备
将IMAC-Cu芯片(immobilized metal affinity capture arrays-Cu，IMAC-Cu)置于操作平台，每孔加50μl 100mmol／L硫酸铜，振荡5min，倒除硫酸铜，去离子水冲洗5次，甩干；每孔中加入50μl的100mmol／L醋酸钠(PH4．0)使之中和，振荡5min，去离子水冲洗5次，甩干；每孔加入结合缓冲液150μl(pH7．0)使之平衡，振荡器震荡5min后除去缓冲液，重复一次。
2．3．2血清样本的处理
样本冰盒上融化，以10μl血清加20％μl(1：2)的比例加入U9缓冲液混匀，4℃震荡30min，使蛋白质变性，取10μl变性后的样品加120μl的结合缓冲液，4℃震荡30min备用。
2．3．3组织样本处理
样本冰盒上融化，加入50μl细胞裂解液，4℃震荡30min，15000rpm，4℃离心10min，取上清夜20μl，以1：2的比例加入U9缓冲液，4℃震荡30min，使蛋白质变性，取10μl变性后的样品加120μl的结合缓冲液，4℃震荡30min备用。
2．3．4蛋白质结合反应
在处理好的芯片中每孔加入50μl处理好的样品(血清／组织)，4℃震荡60min；每孔加入150μl结合缓冲液，振荡5min，弃去缓冲液，重复操作一次；每孔分两次加入能量吸收分子饱和溶液，每次0.5μl，两次之间允许各孔风干。
2．3．5数据收集和生物信息学处理
将芯片放入质谱阅读仪检测，在ProteinChip software(Ciphergen，Inc)支持下，设定读片程序。在样本芯片阅读检测前，用加有All-in-one标准蛋白质的NP20芯片校正质谱仪，使分子量检测误差＜0.1％，再以质控蛋白芯片做重复性检测，质谱峰值强度(高度)的变异系数控制在15％以下。设定SELDI-TOF-MS激光强度为170，诊断率为6，优化分子量范围为2000—20000Da，最高分子量为50，000Da，行低、高能量两次检测，平均收取90个激光点。利用Biomarker Wizard Software(类似统计学软件)进行噪音过滤以及统计学分析，组间相同质荷比(mass to charge，M／Z)的蛋白质峰平均强度值做组间t检验，得出该蛋白质在两样本之间表达的差异。采用Biomarker Patterns Software(数据挖掘软件)建立决策树分类模型，即：将初步筛选出的蛋白质质谱信息资料输入这个软件系统，软件读取，建立最大决策树分类模型及源于该模型的较小决策树，决策树会依据质谱峰强度的不同将样本分到不同的两个节点内，而后每一个节点内的样本又会根据一个新的质谱峰的强度值再分类，当一个样本中的质谱峰强度小于或等于这个选定的阈值时，就被分到左边的节点内，否则分到右侧，直到所有输入的样本都被分到终节点为止，进入模型分类时错误率最低的质谱峰被选为分类变量。用10倍交叉验证(10-fold cross validation)方法对决策树进行验证，找出错误率最低的决策树分类模型，同时得到该分类模型对样本的诊断率(敏感度)和排除率(特异度)。
2．3．6差异表达蛋白质的t检验
将从宫颈癌患者与健康女性血清中筛选出的具有分类意义的差异表达蛋白质作为一组，同其从CINⅠ-Ⅱ级、CINⅢ级、宫颈癌手术治疗后以及其后随访各组的SELDI-TOF-MS检测中得到的原始数据进行t检验(SPSS 11．0)
3结果
3．1宫颈癌患者与健康女性血清蛋白质质谱分析及分类意义蛋白质的筛选
宫颈浸润癌患者与正常对照组中，共有47种蛋白质质谱峰强度差异有显著性(P＜0.01)，其中具有诊断分类价值(分类权重在95％以上)的蛋白质有6种，其M／Z为(以相对分类权重排序)：M8929.31，M7930.52，M9127.31，M8141.01，M7963.06，M9280.63，它们的相对分类权重分别为：100、98.25、98.25、98.12、97.35和97.00。这6种蛋白质在宫颈癌患者中全部为低表达，其平均含量在正常对照组明显高于宫颈浸润癌患者(P＜0.01)。M／Z为M8929.31的蛋白质被自动作为分类变量建立的决策树分类模型，敏感性为97.96％(48／49)，特异性为98.59％(70／71)。
3．2 6种蛋白质在宫颈癌前病变血清中的变化
与健康女性相比，在宫颈癌前病变中存在多种蛋白质的表达异常，包括从宫颈癌患者与健康女性血清中筛选出的M／Z为M8929.31，M7930.52，M9127.31，M8141.01，M7963.06，M9280.63的6种差异表达蛋白质。这6种蛋白质在宫颈癌前病变中的含量明显高于宫颈癌，而低于正常健康女性，含量由正常对照、CINⅠ-Ⅱ级、CINⅢ级、宫颈癌依次依P＜0.01水平降低。
3．3 6种蛋白质在宫颈癌术后及随访患者血清中的变化
3．3．1 6种蛋白质在宫颈癌术后第十天时血清中的变化
宫颈癌行广泛子宫切除加盆腔淋巴结清扫手术治疗后的第十天，除M／Z为M9280.63的蛋白质含量较手术前无明显差别外(0.6307±0.5789／0.4084±0.3098，P=0.083)，M／Z为M8929.31，M7930.52，M9127.31，M8141.01和M7963.06的蛋白质含量则较手术前明显回升(P＜0.01)，但与健康女性相比仍有较大差距(P＜0.01)。
3．3．2 6种蛋白质在宫颈癌术后3月时血清中的变化
宫颈癌在手术治疗后3月复诊时，M／Z为M8929.31，M7930.52，M9127.31，M8141.01，M7963.06，M9280.63的6种差异表达蛋白质的含量，继续回升，明显高于手术后第十天时的水平(P＜0.01)，包括M／Z为M9280.63的蛋白质。
3．3．3 6种蛋白质在宫颈癌术后1年时血清中的变化
M／Z为M8929.31，M7930.52，M9127.31，M8141.01，M7963.06，M9280.63的6种差异表达蛋白质的含量，在手术治疗后的1年内的时间里继续回升，与手术后3月复诊时的水平相比明显升高(P＜0.01)。与健康女性相比，M／Z为M8929.31的血清蛋白质含量尚未恢复正常(11.9831±8.1697／19.88395=13.3494，P=0.003)，M／Z为M7930.52，M9127.31，M8141.01，M7963.06，M9280.63的血清蛋白质含量均已恢复正常，已无统计学意义(P＞0.05)。
3．4宫颈癌组织与正常宫颈上皮中蛋白质质谱的变化
宫颈癌细胞与正常宫颈上皮细胞对照共有72种蛋白质质谱峰强度值比较差异有显著性(P＜0.01)，有分类意义的蛋白质有8种，质荷比为：M5929.87，M3630.52，M10335.39，M6793.00，M7365.78，M4498.06，M8856.45，M9586.27，其中M5929.87，M3630.52，M10335.39，M6793.00，M7365.78，M4498.06，在宫颈癌组织中低表达，M8856.45，M9586.27在宫颈癌组织中高表达。用M5929.87，M3630.52和M10335.39建立的决策树分类模型的敏感性为93.94％(31／33)，特异性为96.55％(28／29)。
4结论
4．1宫颈癌和健康对照组血清蛋白质资料分析
质荷比为M8929.31，M7930.52，M9127.31，M8141.01，M7963.06，M9280.63的一组蛋白质与宫颈癌的发生密切相关，在宫颈癌患者血清中低表达，推测它们可能是正常宫颈上皮的具有生理功效的一组蛋白质或多肽，也可能是一组重要的保护因素或抑癌因子。这组蛋白质的表达变化，反映了宫颈癌的重要特征性分子学改变，有可能成为与宫颈癌相关的重要生物学标记物。
4．2宫颈癌前病变组血清蛋白质资料分析
质荷比为M8929.31，M7930.52，M9127.31，M8141.01，M7963.06，M9280.63的一组蛋白质含量，由正常对照、CINⅠ-Ⅱ级、CINⅢ级到宫颈癌，依次依P＜0.01水平降低，推测它们与宫颈癌的发生发展密切相关，验证了它们可能是正常宫颈上皮的具有生理功效的一组蛋白质或多肽，也可能是一组重要的保护因素，或抑癌因子。提示：经过更多的资料补充和研究后可成为用于宫颈癌前病变的筛选以及宫颈癌早期诊断的一组生物学指标。
4．3宫颈癌术后及随访组血清蛋白质资料分析
质荷比为M8929.31，M7930.52，M9127.31，M8141.01，M7963.06和9280.63的一组蛋白质，其含量在宫颈癌手术治疗后逐渐回升，在手术后1年内的时间里基本恢复至正常水平，提示：经过大样本随访资料补充和宫颈癌治疗后复发及转移患者此组血清蛋白质含量的比较研究，有可能成为制定宫颈癌治疗方案的依据、评价宫颈癌治疗效果以及判断预后使用的一组生物学指标。
4．4宫颈癌组织与正常宫颈上皮组织资料分析
利用SELDI-TOF-MS技术从宫颈癌组织和正常宫颈上皮组织中筛选出的具有分类意义的8种蛋白质，经过进一步研究以及与宫颈癌血清蛋白质质谱技术共同应用，有可能筛选出更为简单实用能够应用于临床的免疫组化指标。
4．5本研究资料的综合分析
质荷比为M8929.31，M7930.52，M9127.31，M8141.01，M7963.06和9280.63的一组蛋白质，其血清含量由健康女性、CINⅠ-Ⅱ级、CINⅢ级到宫颈癌，依次依P＜0.01水平降低，至宫颈癌时降低到最低水平，在宫颈癌手术治疗后逐渐回升，在手术后的1年时间里基本恢复至正常水平。撇开它们在宫颈癌分子水平研究中的作用与地位，如果经济允许，可将它们从蛋白质数据库中查出对应的蛋白质，然后进行进一步的蛋白质检测与鉴定，同时进行大样本的测试，并增加宫颈癌治疗后的复发和转移患者此组血清蛋白质的监测，以及进行生存资料的完善与补充，相信能够将本研究用于临床，更希望简单、快捷的试剂盒会问世。
1 Background and aims
Cervical carcinoma is a common malignant tumors ranking second only to breast cancer as the most common malignancy among women worldwide. The estimate annual number of new cases of cervical cancer worldwide is more than 49 000, however, there are more than 80 percent of new cases in developing countries. The prognosis of cervical carcinoma is closely concerned with the clinical features such as clinical stage. The five-year survival rate in the early stage of cervical carcinoma is above 90 percent, while there are only about 10 percent for the middle and late stage of cervical cancer. The recurrent and metastasis of cervical carcinoma is not only relevant to the clinical stage but to the treatment scheme and so on. So it is important that how to improve the early diagnostic rate, how to make the treatment scheme and assessment the treatment effect, how to know the prognosis and prevent it from recurrence, which is the aims for our research. The cervical carcinogenesis is a multistage and progressive process. It begins from precancerous lesions, a group of lesions including the Cervical Intraepithelial Neoplasia (CIN)Ⅰ—mild dysplasia, CINⅡ—moderate dysplasia, CINⅢ—sever dysplasia and carcinoma in situ, to invasive cervical carcinoma. It is a progressive nature but some of cases can be regression. It gaves us a theory basis to early diagnosis and threatment for the cervical cancer because it would take about 5 to 10 years time for every step.
Enormous efforts have been undertaken concerning the etiology of cervical carcinoma, yet it is still not very clearly solved. The most important thing is that infection with specific subtypes of Human Papilloma Virus (HPV) has been strongly implicated in cervical carcinogenesis. However, HPV infection alone is insufficient for malignant transformation. It is a basic factor for cervical carcinogenesis but has to act with others such as an abnormal sexual action. Biologically, the cervical carcinogenesis is a complex course with multigene changes. However, genetic changes may not reflect the stage and progression of disease directly and objectively because proteins carry out most of the cellular functions based on mRNA. Therefore, the direct measurement of protein levels and activities within the cell is the most determinant of overall cellular function. Recently, a novel proteomics technique, surface-enhanced laser desorption/ionization-time of flight-mass spectrometry (SELDI-TOF-MS), has been developed quickly. It can not only map protein profiles with a high throughput but also provide a protein pattern to discriminate the patients from healthy coupling with bio-information and needs low amounts of samples, such as serum, tissue and etc. It has been widely used in tumor research, such as cancers in digestive system, respiratory system, urinary and genital system, and etc, but there was a few report for cervical cancer, there was only one report by using tissue sample, nothing for using serum sample.
To structure serum protein patterns for identifying cervical carcinoma and cervical precancerous and further to explore the mechanisms of cervical carcinogenesis, the SELDI-TOF-MS technique was employed to detect the changes of serum proteomics on cervical cancer and controls. The proteins that can discriminate the patients from normal were picked up and then compared them with the samples of cervical precancerous lesion, post-operation with radical hysterectomy and pelvic lymphadenectomy, and review patients by t test. It means that the whole course of cervical carcinogenesis and recover, a typical survival model—nontumortumor-nontumor of cervical cancer, was observed so that the biomarkers (proteins) those can be used for screening, early diagnosis, assessment treatment effect and prognosis would be found out.
2 Material and Methods
2.1 Study population
The samples with pathological diagnosis and without radiotherapy or chemotherapy were obtained from the First Affiliated Hospital, Zhengzhou University.
2.1.1 Serum sample
Serum samples enrolled in this study were of①49 patients with cervical cancer and 71 age-matched healthy women;②67 cases of cervical intraepithelial neoplasia(CIN) including 35 patients with CINⅠandⅡ, and 32 patients with CINⅢ;③24 patients with cervical cancer those serum samples were collected on the 10th day after radical hysterectomy and pelvic lymphadenectomy; 22 review patients at the time of 3-month after operation; 18 follow-up patients at the time of 1-year after operation.
2.1.2 Tissue sample
33 cervical cancerous tissues and 29 normal cervical epithelial tissues were obtained in this study.
2.2 Sample collection
2.2.1 Serum sample
3 ml fast blood was collected and stood for 30 min, and then centrifuged at 2000rpm for 10 min. All serum samples were aliquoted into 100μl and stored at-80℃until use.
2.2.2 Tissue sample
A piece of the cervical cancer specimen near the cancer floor was collected immediately after radical hysterectomy and pelvic lymphadenectomy. Normal cervical tissue was obtained at same time at the place not less 1cm from the tumor margin. They were cut and digested by 0.25% trypsinase. Then they were centrifuged at 900rpm after washing, repeated this step for 3 times. A cell smear slide was made for pathological diagnosis. The target cells, cervical carcinoma cells or normal cervical epithelial cells, were estimated about≥85%, the cell counting was to 1×10~7/ml. The cell samples were stored at-80℃until use.
2.3 Methods
2.3.1 Preparation of protein chip
An Eight-spot immobilized metal affinity capture array-Cu (IMAC-Cu) chip was put onto a bioprocessor. The spots were activated with 50μL of 100 mmol/L CuSO_4 and vortexed for 5 min, followed by a deionized water rinse for 5 times, then 50μl of 100 mmol/L sodium acetate buffer (pH 4.0) was added to each array and shaken for 5 min, followed by a deionized water rinse again. The activated array surfaces were equilibrated with 150μL of binding buffer (pH 7.0), agitated for 5 min, twice.
2.3.2 Preparation of serum samples
Serum samples were thawed and diluted 10μl to 20μl (1:2) with U9 buffer, vortexed at 4℃for 30 min. Then it was diluted 1:12 in binding buffer, vortexed at 4℃for 30 min.
2.3.3 Preparation of tissue sample
Tissue specimen was thawed and add 50ul cellular splitting fluid, vortexed at 4℃for 30 min, centrifuged at 15000rpm at 4℃for 10min. The supernatants were mixed with U9 buffer (1:2), vortexed at 4℃for 30 min. Then it was diluted 1:12 in binding buffer, vortexed at 4℃for 30 min.
2.3.4 Reaction of binding proteins
50μl of diluted sample was applied onto the array surface and shaken at 4℃for 60 min. Then the chips were washed twice with 150μl of binding buffer for 5 min each wash cycle. The chips were removed from the bioprocessor, air-dried. Before SELDI-TOF MS analysis, 0.5μl of a saturated EAM solution was applied onto each spot twice and air-dried between each EAM application.
2.3.5 Data collecting and analysis of bioinformation
Chips were placed into the Protein Biological SystemⅡmass spectrometer reader (PBSⅡ, Ciphergen Biosystems, Inc). The spectra were calibrated by using the All-in-1 NP 20 protein molecular mass standard. In ProteinChip Software the protein reader program was set. The mass accuracy was calibrated to less than 0.1%. The coefficient of variance for peak height was less than 15%. Data were collected twice by averaging 90 laser shots, a detector sensitivity 6 and an optimized range of 2 000—20000 Da with a highest mass of 50 000 Da. Biomarker Wizard Software, similar as a statistic software, is used for analyzing the differences of the intensity for each labeled peak between the two samples by using t test. Biomarker Pattern Software, a data digging software, is for constructing a decision tree classification model with ten-fold cross validation. The model was set up to split the data set into two nodes based on the intensities of peaks. At each node a peak intensity threshold was set. If the peak intensity of a sample were lower than or equal to the threshold, this sample would be divided into the left-side node. Otherwise, the sample would go to the right-side node. The process would go on until a sample entered a terminal node. The model would be used that yielded the least classification error. Specificity and sensitivity were respectively calculated as the proportion of the number of disease samples correctly identified to the total number of control samples.
2.3.6 t test for the significant expressive proteins
The significant expressive proteins from samples of cervical carcinoma and controls were compared with samples of cervical precancerous lesion, post operation with radical hysterectomy and pelvic lymphadenectomy, and groups of review patients by t test with SPSS Software 11.0.
3 Results
3.1 Serum proteomic profiling analysis on cervical carcinoma
47 proteins were detected with a significant level of P＜0.01 from cervical cancer patients and normal controls. 6 proteins with m/z value of M8929.31, M7930.52, M9127.31, M8141.01, M7963.06 and M9280.63 had high score(＞95%) in building a model of decision tree classification algorithm for cervical cancer detection. The relative splitting score was 100, 98.25, 98.25, 98.12, 97.35, and 97 respectively. The sensitivity and specificity of m/z value of M8929.31 were 97.96% (48/49) and 98.59% (70/71) respectively.
3.2 Analysis of the 6 proteins on cervical precancerous lesions
Many proteins had changed in cervical cancer initiation and development. The 6 significant expressive proteins from cervical cancer with mass to charge value of M8929.31, M7930.52, M9127.31, M8141.01, M7963.06 and M9280.63 appeared to be down regulated in patients with cervical precancerous lesions. The intensities were orderly reducing from controls, CINⅠ-Ⅱ,CINⅢto cervical cancer in a level of P＜0.01.
3.3 Analysis of the 6 proteins on post-operation and review patients
3.3.1 Analysis of the 6 proteins on post-operation (10th day)
The levels of 6 proteins with mass to charge value of M8929.31, M7930.52, M9127.31, M8141.01, M7963.06 and M9280.63 were compared, the 10th day after operation to cervical cancer, and had gradually retrieved in a level of P＜0.01 after surgery, except the level of m/z value of 9280.63 which was no significant difference (0.6307±0.5789/0.4084±0.3098, P=0.083). However, they were still much lower than normal (P＜0.01).
3.3.2 Analysis of the 6 proteins on the review patients at the time of 3 month post-operation
Comparing the intensities of the 6 proteins with mass to charge value of M8929.31, M7930.52, M9127.31, M8141.01, M7963.06 and M9280.63, 3 month post-operation with the 10th day, they had gradually retrieved in a level of P＜0.01 after surgery, including M9280.63.
3.3.3 Analysis of the 6 proteins on review patients with cervical cancer at the time of 1 year post-operation
The 6 proteins with mass to charge value of M8929.31, M7930.52, M9127.31, M8141.01, M7963.06 and M9280.63 had continually climbed in a year after operation. The intensity of them was much higher than the time of 3 month post-operation (P＜0.01). It was almost same as normal (P＞0.05) but M8929.31(11.9831±8.1697/19.8839±13.3494,P=0.003)
3.4 Tissue proteomic analysis on cervical cancer
72 proteins were marked out with a significant level of P<0.01 from cervical cancer tissue and normal cervical epithelial tissue. 8 proteins with m/z value of M5929.87, M3630.52, M10335.39, M6793.00, M7365.78, M4498.06, M8856.45, M9586.27 had high score in building a model of decision tree classification algorithm for cervical cancer detection. M5929.87, M3630.52, M10335.39, M6793.00, M7365.78 and M4498.06 appeared to be down regulated but M8856.45 and M9586.27 were found at high intensity in patients with cervical carcinoma. The sensitivity and specificity of m/z value of M5929.87, M3630.52 and M10335.39 were 93.94% (31/33) and 96.55% (28/29) respectively.
4 Conclusions
4.1 Serum proteomic analysis on cervical cancer
A group of proteins with m/z value of M8929.31, M7930.52, M9127.31, M8141.01, M7963.06 and M9280.63 are closely concerned with invasive cervical cancer. It suggests that they may be a group of proteins or peptides with physiological function and a group of protector in normal cervical epithelial tissues because they appeared to be down regulated in cervical carcinoma. Changes of the proteins would stand for changes in the level of cellular in cervical cancer. They will probably be a group of biomarkers for cervical cancer.
4.2 Serum proteomic analysis on cervical precancerous lesion
The intensity of the 6 significant expressive proteins from cervical cancer with mass to charge value of M8929.31, M7930.52, M9127.31, M8141.01, M7963.06 and M9280.63 was orderly reducing from controls, CINⅠ-Ⅱ,CINⅢto cervical cancer in a level of P＜0.01. It confirms the hypothesis that they may be a group of proteins or peptides with physiological function or a group of protector in healthy. They would be a group of potential screening and diagnostic biomarkers for precancerous lesion and cervical carcinoma.
4.3 Serum proteomic analysis on post-operations and reviews
The level of the 6 proteins with mass to charge value of M8929.31, M7930.52, M9127.31, M8141.01, M7963.06 and M9280.63 was orderly retrieved in a level of P＜0.01 from cervical cancer, the tenth day, 3 month to 1 year after radical hysterectomy and pelvic lymphadenectomy. It strongly suggests that they would be a group of biomarkers for assessment treatment effect and prognosis of cervical cancer after researching more cases.
4.4 Tissue proteomic analysis on cervical cancer
There were 8 proteins with m/z value of M5929.87, M3630.52, M10335.39, M6793.00, M7365.78, M4498.06, M8856.45 and M9586.27 with a model of decision tree classification algorithm for cervical cancer detection. It suggests that the immunohistochemical biomarkers for cervical cancer would be found out from them.
4.5 Comprehensive analysis for the data
The intensity of the 6 significant expressive proteins from cervical cancer with m/z value of M8929.31, M7930.52, M9127.31, M8141.01, M7963.06 and M9280.63 was orderly reducing from controls, CINⅠ-Ⅱ,CINⅢto cervical cancer in a level of P＜0.01 and retrieved in a level of P＜0.01 from cervical cancer, the tenth day, 3 month to 1 year after radical hysterectomy and pelvic lymphadenectomy in order. It strongly suggests that we should research them more for clinical use if there is no economic problem they should be looked for from the protein database house with protein analysis tools and then be identified. We really hope that the simple test boxes will be born for clinical use after they are confirmed by clinical research with enough samples.
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