利用组学数据建立针对四种女性癌症的基于机器学习方法的生存预测模型

乳腺癌、宫颈鳞状细胞癌、子宫内膜癌、卵巢癌是女性常见的癌症.由于癌症的恶性发展并缺乏有效的早期诊疗手段,这些癌症已成为当今世界女性患者的头号杀手.为了探索高通量组学数据能否促进癌症患者的预后,本研究利用美国癌症基因组图谱项目中四种女性癌症的1861个样本的临床数据和多维组学数据(包括DNA甲基化、mRNA表达、miRNA表达和基于芯片的蛋白表达组学数据),建立了Cox比例风险模型和随机生存森林模型用来回顾性地预测患者的生存率.本研究发现,在宫颈鳞状细胞癌中,通过整合临床与DNA甲基化及miRNA表达组学数据建立的模型,生存预测能力显著高于仅使用临床数据的模型(一致性指数c-index中位数提高了8.73%～15.03%).本研究虽然验证了有些组学数据对特定癌症生存模型的预测能力有提升作用,但也存在着相对于临床数据,组学数据对模型的预测能力无显著提升的情况.这些结果为系统地开展基于癌症基因组学的生存预测研究及提升临床生存分析的预测准确性提供了有用经验.     

NSCLC分类及生存分析预测的全基因组特征基因识别

基于NSCLC(非小细胞肺癌)子类分类在临床和生物医学研究方面的意义,利用全基因组基因表达水平(GE)和甲基化(ME)水平的微阵列数据对NSCLC子类分类进行全基因组特征基因识别分析。针对全基因组微阵列数据的高噪声、超高维小样本特性,利用弹性正交贝叶斯算法对全基因组基因进行递归筛选,识别分类精度最优的特征基因集。以TCGA的490的基因表达数据和378个甲基化数据为例,分别识别出52个GE特征基因和25个ME特征基因,相应的分类准确率分别为99%和98%。结合特征基因和临床数据建立的多变量Cox模型明确说明了特征基因在病人生存分析方面的重要作用:仅利用相应的基因表达数据和甲基化数据即可对病人样本的"高/低风险"进行正确分类,显著性水平均低于0.05。特征基因参与的代谢通路与p53、TGF-beta、Wnt等重要的癌症分类和发展的代谢通路的密切关系进一步证实了特征基因对NSCLC分类的重要性。     

肺鳞状细胞癌发生的早期标志物及肿瘤预测模型

选取癌症基因组图谱数据库的肺鳞状细胞癌(Lung Squamous Cell Carcinoma,LUSC)样本作为数据集,在全基因组的水平上研究肺鳞状细胞癌病人从正常到发病I期基因表达的变化,寻找与LUSC发病密切相关的早期标志物,并建立一种基于早期标志基因的肿瘤预测模型。方法 采用模式识别分类法和基因通路和功能分析相结合的筛选方法,对LUSC的早期标志物进行识别,并运用Fisher判别建立肿瘤预测模型。得到12个LUSC的早期标志物,分别是CLDN18, CD34, ESAM, JAM2, CDH5, F11, F8, CFD, MRC1, MARCO, SFTPA2 和 SFTPA1,机器学习建模后对LUSC早期癌症样本和正常肺组织样本的分类精度达到了98%以上。由基因SFTPA1和ESAM建立的LUSC早期肿瘤预测模型,对正常肺组织和LUSC肿瘤Ⅰ期样本的分类敏感性和特异性分别为99.18%和100%,并且独立验证集的分类准确率也在90%以上。结论 筛选出的12个早期分子标志物有望成为LUSC诊断的标志分子,并且建立的肿瘤预测模型具有极高的准确性,可以为LUSC的发生机理研究以及早期肿瘤预测提供帮助。     

宫颈鳞状细胞癌中ADAM17 的表达及其临床意义

目的:探讨去整合素-金属蛋白酶17(ADAM17)在宫颈鳞状细胞癌中的表达及其临床病理意义。方法:运用免疫组织化学法分别检测正常宫颈上皮、宫颈鳞状细胞癌及宫颈上皮内瘤样变组织中ADAM17的表达,并分析其与宫颈癌临床分期及淋巴结转移的相关性。结果:ADAM17在正常宫颈上皮组织切片中无明显表达,在宫颈上皮内瘤样变组织中少部分表达,呈浅黄色,在宫颈鳞状细胞癌中癌细胞大量表达,ADAM17表达呈棕褐色,数量较多且浓染。不同临床分期的宫颈鳞状细胞癌组织中ADAM17的阳性表达率比较存在显著统计学差异(P0.05),且随临床分期的上升,ADAM17的表达逐渐升高;有淋巴结转移的宫颈鳞状细胞癌组织中ADAM17的阳性表达率显著高于无淋巴结转移的宫颈鳞状细胞癌组织,差异具有统计学意义(P0.05)。结论:ADAM17蛋白在宫颈鳞状细胞癌组织中呈异常高表达,且与宫颈鳞状细胞癌的临床分期和淋巴结转移密切相关,通过检测ADAM17蛋白的表达可能有助于宫颈鳞状细胞癌的诊断、治疗和预后预测。     

共刺激分子B7-H4在宫颈癌中的表达及意义

目的:肿瘤微环境中免疫共刺激分子B7-H4与其配体结合后可提供免疫抑制信号,调控肿瘤组织中的免疫应答。本研究探讨B7-H4、Fas及Caspase-3裂解片段在宫颈鳞状细胞癌中的表达及其与临床病理因素的关系,分析其参与肿瘤免疫逃逸的机制。方法:应用免疫组织化学SP法检测23例正常宫颈上皮、38例宫颈上皮内瘤变(CIN)和132例宫颈鳞状细胞癌组织中B7-H4、Fas及Caspase-3裂解片段的表达水平,分析其与宫颈癌各临床病理因素的相关性。结果:B7-H4在正常宫颈上皮组织中不表达,在CIN组织中微弱表达,在宫颈鳞状细胞癌组织中高表达。B7-H4表达与肿瘤的临床分期、淋巴结转移、原发肿瘤大小和肿瘤浸润深度有关,B7-H4与Fas蛋白表达呈现负相关,与Caspase-3裂解片段存在共表达关系。结论:B7-H4在宫颈鳞状细胞癌中过表达可引起Fas蛋白表达下调和Caspase-3裂解片段增多,抑制肿瘤细胞发生凋亡,参与肿瘤逃避宿主的免疫监视,从而促发宫颈癌的发生和发展。阻断B7-H4通路途径,有望成为宫颈鳞状细胞癌治疗的新靶点。     

舌鳞状细胞癌相关长链非编码RNA的筛选与分析

[目的]获得舌鳞状细胞癌组织与正常舌组织中差异表达的长链非编码RNA,并进行功能注释和参与的生理过程进行初步分析。[方法]利用GEO数据库中GES13601的原始芯片数据,采用affymatrix软件套件对芯片数据进行重新均一化和标准化,并筛选差异表达的长链非编码RNA,查找其参与的生理过程。[结果]舌鳞状细胞癌组织与正常舌组织差异表达的基因个数为5 613个。其中在癌组织中上调的为2 951个,下调的共2 662个。从中筛选到长链非编码RNA 38个,上调19个,下调19个。其中的某些参与涉及肌肉、淋巴生长和癌细胞发送等生理过程,但是大部分的功能未知,尚需进一步研究。[结论]筛选到舌鳞状细胞癌相关的38个长链非编码RNA,为寻找早期舌鳞状细胞癌标志物提供了新的参考。     

基于基因突变频率识别低级别脑胶质瘤驱动基因

癌症通常由基因变异的累积所驱动,有效地识别癌症的驱动突变是一个巨大的挑战。目前已有方法更多是通过将基因组区域中观察到的突变率与背景突变率(BMR)预期的突变率进行比较或功能影响测试来识别驱动基因,该驱动基因本质上是存在统计异常的基因。而且并未对已有明确分类的癌症的子类之间驱动基因进行研究。本文引入关联规则算法,探寻发生该基因突变诱使病人患该子类低级别脑胶质瘤的有效规则,将突变数据与患癌结果通过算法建立关系,再通过支持度、置信度和提升度这三个指标对产生的规则进行筛选和评估,来预测候选驱动基因以及类间驱动基因差异。最后利用491例低级别脑胶质瘤体细胞突变数据,得到22个与结果存在关联的驱动基因及其所属的子类,敏感性和假阳性结果优于目前已有的单一算法,且22个基因均具有重要的生物学功能。同时建立了基于22个基因的低级别脑胶质瘤子类识别方法,模型总体准确率达98.99%,方法可有效区分三子类。     

SC-BPSO:肝癌分类中一种融合过滤器的二进制粒子群算法特征的选择方法

癌症的早期诊断能够显著提高癌症患者的存活率,在肝细胞癌患者中这种情况更加明显。机器学习是癌症分类中的有效工具。如何在复杂和高维的癌症数据集中,选择出低维度、高分类精度的特征子集是癌症分类的难题。本文提出了一种二阶段的特征选择方法SC-BPSO:通过组合Spearman相关系数和卡方独立检验作为过滤器的评价函数,设计了一种新型的过滤器方法——SC过滤器,再组合SC过滤器方法和基于二进制粒子群算法(BPSO)的包裹器方法,从而实现两阶段的特征选择。并应用在高维数据的癌症分类问题中,区分正常样本和肝细胞癌样本。首先,对来自美国国家生物信息中心(NCBI)和欧洲生物信息研究所(EBI)的130个肝组织microRNA序列数据(64肝细胞癌,66正常肝组织)进行预处理,使用MiRME算法从原始序列文件中提取microRNA的表达量、编辑水平和编辑后表达量3类特征。然后,调整SC-BPSO算法在肝细胞癌分类场景中的参数,选择出关键特征子集。最后,建立分类模型,预测结果,并与信息增益过滤器、信息增益率过滤器、BPSO包裹器特征选择算法选出的特征子集,使用相同参数的随机森林、支持向量机、决策树、KNN四种分类器分类,对比分类结果。使用SC-BPSO算法选择出的特征子集,分类准确率高达98.4%。研究结果表明,与另外3个特征选择算法相比,SC-BPSO算法能有效地找到尺寸较小和精度更高的特征子集。这对于少量样本高维数据的癌症分类问题可能具有重要意义。     

肺鳞状细胞癌癌症发展模式识别分类模型及特征基因识别

本文利用先进的生物信息学方法,首次从全基因组水平综合基因表达、甲基化水平和拷贝数变异三类数据,寻找与肺鳞状细胞癌(LUSC)发生和发展密切相关的特征基因,为进一步解释其内在机理、开发新的靶向药物和治疗手段提供更加深入的理论依据.为克服全基因组数据超高维高噪声小样本特性对机器学习算法性能的影响,防止信息饱和现象的干扰,本文创新性地组合应用4种特征基因筛选方法,分别从特异性、相关性、生物学功能和对肿瘤分类模型的贡献等多个方面,通过迭代降维技术递归筛选真正的特征基因.研究中,我们以TCGA(The Cancer Genome Atlas project)数据库中的LUSCⅠ~Ⅲ期病人样本为例,对其基因表达数据(GE)、基因甲基化数据(ME)以及拷贝数变异数据(CNV)进行分析.结果筛选出67个GE特征基因,对3类样本分类的平均准确率达到86.29%,70个ME特征基因,相应的分类准确率为90.92%,31个CNV特征基因,相应的分类准确率为69.16%.KEGG(Kyoto Encyclopedia of Genes and Genomes)和IPA(Ingenuity Pathway Analysis)对上述3类特征基因集在代谢通路水平和基因调控网络水平上的分析,证明了其在调控水平上的密切关系.同时也表明,识别的特征基因与LUSC肿瘤进展之间有着重要的直接关系,这对了解肿瘤机理以及新靶向治疗的发展非常重要.     

基于生物信息学方法识别非小细胞肺癌(NSCLC)潜在治疗靶基因

本研究运用生物信息学方法识别非吸烟女性非小细胞肺癌(NSCLC)潜在的靶基因,并从分子水平探索其潜在的发病机制。从GEO数据库下载非吸烟女性非小细胞肺癌相关基因芯片数据集,经癌症组和癌旁对照组差异表达基因识别,并利用R软件对差异基因进行层次聚类分析,DAVID进行基因本体(gene ontology)和KEGG通路富集分析,STRING和Cytoscape软件构建蛋白-蛋白交互(PPI)网络,以及运用PASTAA分析,识别NSCLC相关转录因子,构建转录因子-基因共表达网络。结果表明,185个基因在NSCLC中差异表达,其中40个上调,145个下调;通过PASTAA分析识别出5个NSCLC基因相关转录因子。差异基因与胶原分解代谢过程、炎症反应的正调控等生物过程密切相关,基因的产物主要参与蛋白质细胞外基质、胶原三聚体等细胞组分,且主要发挥调节金属内肽酶活性、肝素结合和调节受体活性等分子功能;KEGG通路富集分析表明差异基因显著富集到胞外基质-受体信号通路、粘着斑信号通路、PPAR信号通和PI3K-Akt信号通路等,与非小细胞肺癌的发生发展密切相关。通过生物信息学方法,最终筛选到4个NSCLC关键基因:IL6、MMP1、COL1A1、CD36,其可能是非吸烟女性NSCLC潜在的治疗靶点。     

A comprehensive analysis of IDO1 expression with tumour‐infiltrating immune cells and mutation burden in gynaecologic and breast cancers

Gynaecologic and breast cancers share some similarities at the molecular level. The aims of our study are to highlight the similarities and differences about IDO1, an important immune‐related gene in female cancers. The NGS data from TCGA of cervical squamous cell carcinoma (CESC), ovarian serous cystadenocarcinoma (OV), uterine corpus endometrial carcinoma (UCEC), uterine carcinosarcoma (UCS) and breast invasive carcinoma (BRCA) were analysed to identify molecular features, and clinically significant and potential therapeutic targets of IDO1. We found IDO1 was significantly up‐regulated in four gynaecologic cancers and breast cancer. According to breast cancer PAM50 classification scheme, IDO1 expression was higher in tumours of basal than other subtypes and showed better survival prognosis in BRCA and OV. Through immune infiltration analysis, we found a strong correlation between IDO1 and immune cell populations especially for dendritic cells and T cells. In addition, we investigated the association between IDO1 and tumour mutation burden (TMB) and found that IDO1 was significantly correlated with TMB in BRCA and CESC. GSVA revealed that hallmarks significantly correlated with IDO1 were involved in interferon gamma response, allograft rejection and inflammatory response. We also found PD‐L1 and LAG3 were highly positive related to IDO1 in gynaecologic cancers when comparing with their corresponding normal tissues. Our results indicated that IDO1 participated in anti‐tumour immune process and is correlated with mutation burden. These findings may expand our outlook of potential anti‐IDO1 treatments.     

Molecular Subtypes in Head and Neck Cancer Exhibit Distinct Patterns of Chromosomal Gain and Loss of Canonical Cancer Genes

Head and neck squamous cell carcinoma (HNSCC) is a frequently fatal heterogeneous disease. Beyond the role of human papilloma virus (HPV), no validated molecular characterization of the disease has been established. Using an integrated genomic analysis and validation methodology we confirm four molecular classes of HNSCC (basal, mesenchymal, atypical, and classical) consistent with signatures established for squamous carcinoma of the lung, including deregulation of the KEAP1/NFE2L2 oxidative stress pathway, differential utilization of the lineage markers SOX2 and TP63, and preference for the oncogenes PIK3CA and EGFR. For potential clinical use the signatures are complimentary to classification by HPV infection status as well as the putative high risk marker CCND1 copy number gain. A molecular etiology for the subtypes is suggested by statistically significant chromosomal gains and losses and differential cell of origin expression patterns. Model systems representative of each of the four subtypes are also presented.     

A Hybrid Deep Learning Model for Predicting Molecular Subtypes of Human Breast Cancer Using Multimodal Data

BackgroundThe prediction of breast cancer subtypes plays a key role in the diagnosis and prognosis of breast cancer. In recent years, deep learning (DL) has shown good performance in the intelligent prediction of breast cancer subtypes. However, most of the traditional DL models use single modality data, which can just extract a few features, so it cannot establish a stable relationship between patient characteristics and breast cancer subtypes.DatasetWe used the TCGA-BRCA dataset as a sample set for molecular subtype prediction of breast cancer. It is a public dataset that can be obtained through the following link: https://portal.gdc.cancer.gov/projects/TCGA-BRCAMethodsIn this paper, a Hybrid DL model based on the multimodal data is proposed. We combine the patient's gene modality data with image modality data to construct a multimodal fusion framework. According to the different forms and states, we set up feature extraction networks respectively, and then we fuse the output of the two feature networks based on the idea of weighted linear aggregation. Finally, the fused features are used to predict breast cancer subtypes. In particular, we use the principal component analysis to reduce the dimensionality of high-dimensional data of gene modality and filter the data of image modality. Besides, we also improve the traditional feature extraction network to make it show better performance.ResultsThe results show that compared with the traditional DL model, the Hybrid DL model proposed in this paper is more accurate and efficient in predicting breast cancer subtypes. Our model achieved a prediction accuracy of 88.07% in 10 times of 10-fold cross-validation. We did a separate AUC test for each subtype, and the average AUC value obtained was 0.9427. In terms of subtype prediction accuracy, our model is about 7.45% higher than the previous average.     

免疫细胞浸润在非小细胞肺癌诊断与预后中的应用

免疫细胞浸润对癌症的诊断与预后有着重要意义。文中收集TCGA数据库已收录的非小细胞肺癌肿瘤与正常组织基因表达数据,利用CIBERSORT工具得到22种免疫细胞占比来评估免疫细胞浸润情况。以22种免疫细胞占比为特征,用机器学习方法构建了非小细胞肺癌肿瘤与正常组织的分类模型,其中随机森林方法构建的模型分类效果AUC=0.987、敏感性0.98及特异性0.84。并且用随机森林方法构建的肺腺癌和肺鳞癌肿瘤组织分类模型效果AUC=0.827、敏感性0.75及特异性0.77。用LASSO回归筛选22种免疫细胞特征,保留8种强相关特征组成的免疫细胞评分结合临床特征构建了非小细胞肺癌预后模型。经评估及验证,预后模型C-index=0.71并且3年和5年的校准曲线拟合良好,可以对预后风险度进行准确预测。本研究基于免疫细胞浸润所构建的分类模型与预后模型,旨在对非小细胞肺癌的诊断与预后研究提供新的策略。     

MicroRNA-181a enhances the chemoresistance of human cervical squamous cell carcinoma to cisplatin by targeting PRKCD

MicroRNAs(miRNAs) are involved in regulating the response of cancer cells to various therapeutic interventions, but their involvement in the chemoresistance of human cervical squamous cell carcinoma is not fully understood. We found miR-181a was significantly up-regulated in specimens from patients with chemoresistant cervical squamous cell carcinoma. In this study, we aimed to clarify the role of miR-181a in regulating the chemoresistance of cervical cancer. Two human cervical squamous cancer cell lines, SiHa and Me180, were used. Enforced expression of miR-181a enhanced chemoresistance to cisplatin in cervical cancer cells through apoptosis reversion. In a nude mouse xenograft model, the overexpression of miR-181a markedly inhibited the therapeutic response to cisplatin. PRKCD, a target gene of miR-181a and a promoter of apoptosis, was negatively regulated by miR-181a. We found that the effect of miR-181a on chemoresistance was mediated by PRKCD. Additionally, silencing of PRKCD yielded an effect similar to that of miR-181a up-regulation and inhibited apoptosis in cervical cancer cells. Our findings suggest that miR-181a may function as an oncogene and induce chemoresistance in cervical squamous cell carcinoma cells at least in part by down-regulating PRKCD, thus may provide a biomarker for predicting chemosensitivity to cisplatin in patients with cervical squamous cancer.     

1235例妇科门诊患者高危型HPV感染状况及亚型分布调查研究

目的了解本地区妇科门诊患者宫颈高危型HPV感染状况及亚型分布,为今后的宫颈癌前病变、宫颈癌防治提供临床依据。方法采用基因芯片技术对1 235例妇科门诊患者进行HPV筛查,筛查出的阳性患者应用流式荧光杂交法进行高危型HPV亚型检测,分析比较宫颈炎、宫颈鳞癌及宫颈腺癌患者高危型HPV感染情况及亚型分布差异。结果六安市金安区妇幼保健院妇科门诊患者HPV感染率高达60%,其中高危型HPV感染率为43. 2%,主要以HPV16、HPV18为主;低危型HPV感染率为30.0%,主要以HPV11为主;单一感染阳性率为34. 1%,而混合型感染高达65. 9%,且两者均以HPV16型和HPV18型为主。宫颈炎患者HPV16型、HPV18型及HPV16 ＋ HPV18型的检出率明显低于宫颈鳞癌和宫颈腺癌患者,其中宫颈腺癌患者HPV16 ＋ HPV18型混合感染率最高。结论妇科门诊患者HPV感染率较高,宫颈癌患者HPV16及18型检出率显著高于宫颈炎患者,加强HPV高危基因型的监测有助于预警宫颈癌尤其是宫颈腺癌的发生。     

Identification of Logic Relationships between Genes and Subtypes of Non-Small Cell Lung Cancer

Non-small cell lung cancer (NSCLC) has two major subtypes: adenocarcinoma (AC) and squamous cell carcinoma (SCC). The diagnosis and treatment of NSCLC are hindered by the limited knowledge about the pathogenesis mechanisms of subtypes of NSCLC. It is necessary to research the molecular mechanisms related with AC and SCC. In this work, we improved the logic analysis algorithm to mine the sufficient and necessary conditions for the presence states (presence or absence) of phenotypes. We applied our method to AC and SCC specimens, and identified lower and higher logic relationships between genes and two subtypes of NSCLC. The discovered relationships were independent of specimens selected, and their significance was validated by statistic test. Compared with the two earlier methods (the non-negative matrix factorization method and the relevance analysis method), the current method outperformed these methods in the recall rate and classification accuracy on NSCLC and normal specimens. We obtained biomarkers. Among biomarkers, genes have been used to distinguish AC from SCC in practice, and other six genes were newly discovered biomarkers for distinguishing subtypes. Furthermore, NKX2-1 has been considered as a molecular target for the targeted therapy of AC, and other genes may be novel molecular targets. By gene ontology analysis, we found that two biological processes (‘epidermis development’ and ‘cell adhesion’) were closely related with the tumorigenesis of subtypes of NSCLC. More generally, the current method could be extended to other complex diseases for distinguishing subtypes and detecting the molecular targets for targeted therapy.     

Comparison of diversity of torque teno virus 1 in different mucosal tissues and disorders

Diversity of TTV1 was assessed in the head and neck region in patients with potentially malignant (oral lichen planus, oral leukoplakia) and malignant lesions (oral and laryngeal squamous cell cancers) and was compared to that found in the uterine cervix (cervical atypia and cervical cancer) by directly sequencing the NG061-063 segment of ORF1. These sequences were classified by the formerly used genogroup-genotype system as well as by the newly accepted species classification by aligning with the corresponding region of the type sequences of the 29 TTV species. All sequences obtained during the study clustered together with the TTV1 type sequence; to express diversity within TTV1, genotypes and subtypes of the former classification were used.The commonest subtypes were 2c followed by 2b, 1a and 1b. Subtypes 2b and 2c were evenly distributed among cervical samples; subtype 1a was more frequent in patients with cervical atypia or cancer. Subtypes 2c was more frequent than 2b in head and neck lesions. In conclusion, genotype and even subtype distribution may be important in association with diseases, therefore using this classification for characterization of intraspecies diversity of TTV1 is proposed.     

肿瘤突变特征与病理分型的关联研究

准确评估肿瘤的病理亚型对诊断、治疗和预后至关重要。以往病理亚型的诊断主要依赖HE染色法和免疫组织化学法,而随着测序技术的不断发展,对患者进行基因型和表型特点的个体分析成为可能,将肿瘤病理分型与基因分型结合用于疾病分型、诊治选择和疗效判断的精准医学研究逐渐兴起。不同病理亚型的肿瘤细胞来源、致癌因素和临床表型均不尽相同,其在基因组上会留下特异“印迹”,即突变特征。本研究通过整合癌症基因组数据库(The Cancer Genome Atlas, TCGA)中肾癌、肺癌和食管癌的外显子测序数据,分别对3种肿瘤通过肿瘤基因突变特征进行肿瘤病理分型聚类和预测。首先通过非监督聚类方法将3种肿瘤分别按照24种突变特征进行聚类分析,其次通过随机森林法从24种突变特征中进一步选择对于区分不同病理亚型有显著性的突变特征并进行聚类分析,构建突变特征对3种肿瘤病理亚型的分型模型。在肾癌中,该模型准确率达到了100% (95% confidence interval (CI): 0.93~1.00),肺癌和食管癌中分别达到了78% (95% CI: 0.66~0.86)和84% (95% CI: 0.60~0.97)。以上研究结果表明,突变特征作为新型分子标记物,对肿瘤的病理分型、诊断,尤其是早诊具有一定的参考意义。