基于SEER数据库四肢骨肉瘤肺转移风险与临床预测模型的建立与验证

目的 构建列线图预测四肢骨肉瘤患者肺转移风险.方法 从美国国立癌症研究所的监测、流行病学、结果数据库(surveillance,epidemiology,and end results,SEER)数据库中,收集2010年至2016年诊断的四肢骨肉瘤患者共713例,根据有无发生肺转移分为转移组(135例)和非转移组(57...     

列线图在骨肿瘤疾病的应用与研究进展

随着对循证医学和精确医学的重视,数据的价值越来越受到重视.如今大数据的获取与信息的传递较以往有迅速的发展,个性化的医疗也成为可能 [1].临床预测模型 ( clinical prediction models,CPMs ) 作为一种评估风险和获益的工具,可以为临床医生和患者以及从事公共卫生事业的行政管理人员带来更加直观...     

非小细胞肺癌患者发生脑转移风险列线图预测模型的建立

目的:基于非小细胞肺癌(non-small cell lung cancer,NSCLC)患者发生脑转移风险的影响因素分析,建立列线图预测模型。方法:选取2011年06月至2015年06月在本院确诊为NSCLC的患者452例作为研究对象,收集患者的临床资料并进行随访,如果出现脑转移情况或5年随访时间已满,则随访终止,通过单因素和多因素的Cox回归分析得出发生脑转移风险的影响因素,在此基础上根据筛选出的变量建立列线图预测模型。结果:Cox回归最终筛选出的变量为TNM分期、分化程度、病理类型、CA125水平和淋巴结转移数目,建立的列线图预测模型C-index为0.801（95%CI:0.778~0.882）。结论:本研究建立的NSCLC患者发生脑转移风险的列线图预测模型,可以根据多因素分析结果中有意义的变量,较为准确的预测患者的预后情况,临床应用前景较好。     

预测上皮性卵巢癌复发机器学习模型及列线图的建立

目的:本研究拟基于机器学习及Cox回归开发上皮性卵巢癌复发机器学习模型及列线图。方法:回顾性分析2010年01月至2020年12月于云南省肿瘤医院确诊739例Ⅲ-Ⅳ期EOC患者的医疗记录。收集患者的基本信息、手术、化疗细节和预后结果。使用单多因素逻辑回归及Cox回归筛选变量,使用5种机器学习算法基于单多因素逻辑回归的结果构建预测模型,采用10折交叉验证方法评估模型性能。基于Cox回归结果开发列线图。结果:739例患者中,399（54.0%）例最终发生了复发,340（46.0%）例未复发。复发患者分期以ⅢC期为主,占59.1%,病理类型以浆液性癌为主,占91.0%。单多因素逻辑回归显示围手术期化疗周期、术后残余病灶、手术方式、新辅助化疗是与复发独立相关的4个变量,基于这些变量和FIGO分期建立5个机器学习模型,XGBoost在识别复发病例方面表现最佳,AUC为0.775。Cox回归分析显示,术前局部灌注化疗、残余病灶直径、围手术期化疗周期、手术方式是影响复发的独立危险因素,基于上述因素开发了晚期上皮性卵巢癌患者复发的预测列线图。结论:机器学习模型和列线图可早期识别卵巢癌复发,通过早期识别可改善晚期卵巢癌预后。     

FN1基因表达与人纤维肉瘤肺转移的相关性

目的研究纤维连接蛋白1(FN1)基因表达与人纤维肉瘤肺转移的相关性。方法将HT1080细胞经裸鼠尾静脉注射接种,采集肿瘤浸润组与正常对照组肺组织RNA,合成cDNA,以含1176个人肿瘤相关基因的芯片行cDNA微阵列分析,以实时定量RT-PCR法测定两组FN1表达。利用激光捕获法采集实验组小鼠肿瘤浸润肺组织及周围组织RNA,分别以实时定量RT-PCR法测定FN1表达。结果肺转移组与对照组相比,表达差异基因31个,占2.3%,其中表达下调的基因占0.3%,表达上调的基因占2.3%,FN1表达明显上调。肿瘤浸润肺组织与周围组织相比,FN1表达明显上调。结论FN1基因高表达可能与人纤维肉瘤肺转移有关。     

结肠癌淋巴结转移的风险基因及列线图预测模型的构建

目的 寻找结肠癌淋巴结转移的相关风险基因,并构建由基因组成的列线图（nomogram）预测模型。 方法 从TCGA和GEO数据库下载基因测序数据,利用差异分析和LASSO回归方法筛选基因。利用赤池信息准则确定最优的nomogram模型,ROC曲线、校准曲线及拟合优度检验评估模型预测的准确性,决策曲线分析评估临床应用价值。 结果 通过筛选得到11个有效预测结肠癌淋巴结转移的基因。由年龄、病理T分期、TH、CDH4、PNMA6A、TNNC1、KIR2DL4、STUM、SFTA2构成的nomogram模型具有最小的AIC值（440.4）。内部评估模型AUC值为0.800,外部验证AUC值为0.664,校准度及拟合优度均较佳。临床决策曲线分析法评估基于nomogram模型的风险判断可以带来临床获益。结论 共筛选出11个结肠癌淋巴结转移的风险基因。构建的nomogram预测模型的一致性和区分度良好,可帮助评估患者淋巴结转移状态。     

间叶性软骨肉瘤肺转移误诊为肺非典型类癌一例报告

病例介绍 患者,女,33岁.2005年5月初无明显诱因出现咳嗽,痰中带血,伴轻度胸闷.胸部CT示"右肺门占位性病变"(图1、2),支纤镜检示"右主支气管腔内新生物".活检病理为"小细胞癌伴大片坏死,不排除淋巴瘤可能".在全麻下行"右肺下叶切除术",术后病理为:右肺非典型类癌伴支气管淋巴结1/5转移.免疫组化NSE+,PCK-,SYN-.     

甲氨蝶呤与重组人肿瘤坏死因子对软骨肉瘤的作用机制

目的 探讨甲氨蝶呤(MTX)、重组人肿瘤坏死因子(rhTNF)对软骨肉瘤的抑制作用及机制.方法 采用裸鼠软骨肉瘤细胞株皮下接种模型,将实验动物随机分为四组:rhTNF组(n=8),rhTNF+MTX组(n=8),MTX组(n=8),空白对照组(n=6),分别注射rhTNF和(或)MTX.观察肿瘤的形成,记录肿瘤体积、瘤重,计算抑瘤率,通过HE染色观察肿瘤的组织学变化,应用免疫组织化学法检测瘤组织中bcl-2、bax、血管内皮生长因子(VEGF)等的表达及细胞凋亡情况.结果 rhTNF、rhTNF+MTX组肿瘤重量和体积小于MTX组、空白对照组(P＜0.05),MTX组肿瘤重量与对照组相比差异无统计学意义(P＞0.05);rhTNF、rhTNF+MTX、MTX三组肿瘤中bcl-2表达低于空白对照组、bax表达高于空白对照组(P＜0.05);rhTNF、rhTNF+MTX组中VEGF的表达低于MTX、空白对照组(P＜0.05),上述指标在rhTNF、rhTNF+MTX组间差异无统计学意义(P＞0.05).结论 rhTNF能显著抑制荷瘤小鼠体内软骨肉瘤的生长,MTX对体内软骨肉瘤的抑制作用不明显,联合应用可增强rhTNF的抑瘤效应,其机制可能与下调bcl-2及VEGF、上调bax的表达水半有关.     

结肠癌根治术后复发转移风险模型的初步建立及评估

目的初步建立并评估一个结肠癌术后复发转移的风险预测模型。方法回顾分析2009年6月至2013年6月间收治的240例结肠癌患者临床资料,通过Logistic回归分析建立模型,受试者工作特征(ROC)曲线评估模型。结果模型公式为:Ln[p/(1-P)]=3.63×淋巴结转移+2.64×B期+3.20×C期-2.63。模型受试者工作特征(ROC)曲线下面积为0.92,模型风险预测值为0.68。结论该模型能在一定程度上预测结肠癌根治术后复发转移风险。     

Brain metastasis detection using machine learning: a systematic review and meta-analysis

BackgroundAccurate detection of brain metastasis (BM) is important for cancer patients. We aimed to systematically review the performance and quality of machine-learning-based BM detection on MRI in the relevant literature.MethodsA systematic literature search was performed for relevant studies reported before April 27, 2020. We assessed the quality of the studies using modified tailored questionnaires of the Quality Assessment of Diagnostic Accuracy Studies 2 (QUADAS-2) criteria and the Checklist for Artificial Intelligence in Medical Imaging (CLAIM). Pooled detectability was calculated using an inverse-variance weighting model.ResultsA total of 12 studies were included, which showed a clear transition from classical machine learning (cML) to deep learning (DL) after 2018. The studies on DL used a larger sample size than those on cML. The cML and DL groups also differed in the composition of the dataset, and technical details such as data augmentation. The pooled proportions of detectability of BM were 88.7% (95% CI, 84–93%) and 90.1% (95% CI, 84–95%) in the cML and DL groups, respectively. The false-positive rate per person was lower in the DL group than the cML group (10 vs 135, P < 0.001). In the patient selection domain of QUADAS-2, three studies (25%) were designated as high risk due to non-consecutive enrollment and arbitrary exclusion of nodules.ConclusionA comparable detectability of BM with a low false-positive rate per person was found in the DL group compared with the cML group. Improvements are required in terms of quality and study design.     

高转移性人肺腺癌细胞株SPC-A-1BM的建立及其特性分析

目的:建立高转移性人肺腺癌细胞株SPC-A-1BM及其免疫缺陷小鼠转移动物模型。方法:将人肺腺癌细胞株SPC-A-1经免疫缺陷小鼠血道或肺原位接种后形成转移,在放射性核素示踪下找到骨转移病灶,然后切除病变骨组织进行体外培养获得转移性肺癌细胞,用这些癌细胞重复以上循环10次,获得高转移肺癌细胞。采用荧光定量PCR方法检测亲代细胞和高转移细胞的基因表达。结果:获得以骨转移为主,肺、肾上腺、淋巴结等多脏器转移的人肺腺癌转移细胞株(SPC-A-1BM)及其免疫缺陷小鼠动物模型。定量PCR检测显示SPC-A-1BM的上皮生长因子受体(EGFR/HER)家族、血管内皮生长因子(VEGF)家族和3个抗凋亡蛋白的基因较原代细胞均有不同程度的变化。结论:SPC-A-1BM是以骨转移为主的高转移性人肺腺癌细胞株,该细胞株及其转移动物模型为肺癌转移的生物学研究提供了一个良好的技术平台。     

小鼠乳腺癌4T1-luc细胞实验性肺转移模型的建立及其评价

目的:用萤火虫荧光素酶标记的小鼠乳腺癌细胞4T1（4T1-luc）建立可量化评估的小鼠乳腺癌实验性肺转移模型,为研究乳腺癌肺转移机制提供依据。方法:给10只BALB/c小鼠尾静脉注射105个/只4T1-luc细胞,小动物活体成像系统监测肿瘤细胞在体内的生长过程,21天处死小鼠,计算肺表面转移结节,并将转移肺组织做病理切片、HE染色及镜检评价模型。结果:接种第5天,小动物活体成像可见肺转移。21天取材,实验组小鼠全部出现肺转移灶。切片、HE染色及镜检提示符合肺转移瘤模型。结论:应用4T1-luc成功建立了小鼠乳腺癌实验性肺转移模型。     

非小细胞肺癌纵隔淋巴结转移风险模型的构建及预测效能的初步验证

目的:研究非小细胞肺癌(NSCLC)纵隔淋巴结转移的风险模型及预测效能.方法:回顾性分析2016年9月至2018年9月本院120例NSCLC患者临床资料,根据是否发生纵隔淋巴结转移将患者分为观察组(33例,发生纵隔淋巴结转移)和对照组(87例,未发生纵隔淋巴结转移).采用Cox风险模型分析NSCLC发生纵隔淋巴结转移的...     

Diagnostic genes and immune infiltration analysis of colorectal cancer determined by LASSO and SVM machine learning methods: a bioinformatics analysis

BackgroundGenetic factors account for approximately 35% of colorectal cancer risk. The specificity and sensitivity of previous diagnostic biomarkers for colorectal cancer could not meet the need of clinical application. The expanding scale and inherent complexity of biological data have encouraged a growing use of machine learning to build informative and predictive models of the underlying biological processes. The aim of this study is to identify diagnostic genes of colorectal cancer by using machine learning methods.MethodsThe {"type":"entrez-geo","attrs":{"text":"GSE41328","term_id":"41328"}}GSE41328 and {"type":"entrez-geo","attrs":{"text":"GSE106582","term_id":"106582"}}GSE106582 data sets were downloaded from the Gene Expression Omnibus (GEO) database. The gene expression differences between colon cancer and normal tissues were analyzed. The key colorectal cancer genes were screened and validated by Least Absolute Shrinkage and Selection Operator (LASSO) and Support Vector Machine (SVM) regression. Immune cell infiltration and the correlation with the key genes in patients with colon cancer were further analyzed by CIBERSORT.ResultsEleven key genes were identified as biomarkers for colon cancer, namely ASCL2, BEST4, CFD, DPEPCFD, FOXQ1, TRIB3, KLF4, MMP7, MMP11, PYY, and PDK4. The mean area under the receiver operating characteristic (ROC) curve (AUC) of all 11 genes for colon cancer diagnosis were 0.94 with a range of 0.91–0.97. In the validation set, the expression of the 11 key genes was significantly different between colon cancer and normal subjects (P<0.05) and the mean AUCs were 0.82 with a range of 0.70–0.88. Immune cell infiltration analyses demonstrated that the relative quantity of plasma cells, T cells, B cells, NK cells, MO, M1, Dendritic cells resting, Mast cells resting, Mast cells activated, and Neutrophils in the tumor group were significantly different to the normal group.ConclusionsASCL2, BEST4, CFD, DPEPCFD, FOXQ1, TRIB3, KLF4, MMP7, MMP11, PYY, and PDK4 were identified as the key genes for colon cancer diagnosis. These genes are expected to become novel diagnostic markers and targets of new pharmacotherapies for colorectal cancer.     

Involvement of parathyroid hormone-related protein in experimental cachexia induced by a human lung cancer-derived cell line established from a bone metastasis specimen.

Cachexia often causes deterioration in the quality of life in cancer patients; however, its mechanism remains poorly understood. Cachexia has often been observed in experimental animals with bone metastases, and parathyroid hormone-related protein (PTHrP) plays an important role in the formation of such metastases. We therefore investigated the possible involvement of PTHrP in an experimental cachexia model using human lung-cancer cells (HARA-B). HARA-B cells produce a high amount of PTHrP but no TNF-alpha, IL-6 or leukemia inhibitory factor. The s.c. inoculation of HARA-B cells into nude mice caused reductions in body weight, adipose tissue weight, muscle weight and serum glucose levels. Serum levels of calcium and PTHrP increased. Neutralization of PTHrP with antibody caused rapid weight gain along with a rapid decrease in serum calcium levels. Our findings suggest that PTHrP plays an important role in the development of cancer cachexia. PTHrP therefore is a possible target molecule for the treatment of cancer cachexia.     

LIMK-1／cofilin在姜黄素抑制人肺癌细胞增殖和转移中的作用

目的探讨姜黄素对人肺癌细胞株（801D）增殖和转移的影响,并通过检测姜黄素对细胞内LIMK-1／cofilin蛋白表达及细胞骨架重组的影响,揭示LIMK-1／cofilin在姜黄素抑制肺癌转移中的作用。方法应用MTT法检测姜黄素对人肺癌细胞株增殖能力的影响,体外侵袭实验和迁移实验观察姜黄素对肺癌细胞转移能力的影响。Western blot法检测姜黄素对与细胞骨架重组相关的LIMK-1／cofilin及磷酸化LIMK-1／cofilin蛋白表达的影响。激光共聚焦扫描显微镜观察姜黄素对细胞骨架重组的影响。结果姜黄素能够抑制人肺癌细胞801D增殖,抑制率均随处理浓度增大和作用时间延长而增加。与对照组比较,10p,mol／L、20μmol／L的姜黄素处理24小时后的801D细胞体外侵袭和迁移能力均显著下降（P〈0．01）。姜黄素能显著下调LIMK-1／cofilin蛋白的磷酸化水平（P〈0．05）,并能影响细胞内微丝骨架的结构和分布。结论姜黄素抑制肺癌细胞体外增殖和转移能力与姜黄素调控LIMK-1／cofilin信号通路与肺癌细胞微丝骨架结构有关。