identified by gene profiling

作者简介：郑洪雪，1985年生，硕士，主治医师，主要从事小儿内分泌疾病诊治研究。

通信作者：王秀敏，E-mail ：。

Sotos 综合征患儿临床特征及基因变异分析郑洪雪1， 陈　瑶2， 殷丽萍1， 李　辛2， 丁　宇2， 李　娟2， 王秀敏2（1.常州市第一人民医院 苏州大学附属第三医院，江苏 常州 213000；2.上海交通大学医学院附属上海儿童医学中心内分泌代谢科，上海 200127）摘要：目的　分析1例误诊为性早熟的Sotos 综合征患儿的临床特征及基因变异特点。

方法　回顾性分析1例误诊为性早熟的Sotos 综合征患儿的临床资料及相关实验室检查结果。

结果　患儿临床表现为生长过快、发育落后、特殊面容（大头颅、前额突出、下颌窄、高腭弓）。

外院误诊为性早熟，给予醋酸曲普瑞林治疗15个月，生长速度未见减缓。

基因测序显示患儿核受体结合SET 域蛋白1（NSD 1）基因（NM_022455.4）存在“错义变异c.5854C>T （p.Arg1952Trp ）（杂合）”，其父亲携带该位点变异（杂合）。

按照美国医学遗传学和基因组学学会（ACMG ）变异分类标准，归类为“可能致病性变异”。

结论　该患儿确诊为Sotos 综合征，NSD 1基因突变是其致病原因。

Sotos 综合征以身高增长过快为主要表现，伴随骨龄显著提前，不能仅靠性激素激发试验结果与性早熟鉴别，临床应严格评估第二性征发育，避免误诊。

关键词：核受体结合SET 域蛋白1；Sotos 综合征；基因突变；误诊；性早熟Analysis of clinical features and genetic variation of Sotos syndrome ZHENG Hongxue 1，CHEN Yao 2，YIN Liping 2，LI Xin 1，DING Yu 1，LI Juan 1，WANG Xiumin 1.（1.The First People's Hospital of Changzhou ，The Third Affiliated Hospital of Soochow University ，Changzhou 213000，China ；2.Department of Endocrinology and Metabolism ，Shanghai Children's Medical Center ，Shanghai Jiao Tong University School of Medicine ，Shanghai 200127，China ）Abstract ：Objective To analyze clinical characteristics and genetic variation of a case of Sotos syndrome misdiagnosed as precocious puberty. Methods The clinical data and related laboratory test results from one child with Sotos syndrome misdiagnosed as precocious puberty were retrospectively analyzed . Results Clinical manifestations of the child presented overgrowth ，developmental delay ，and typical facial appearance （macrocephaly ，broad forehead ，pointed chin ，high palate ）. The patient was misdiagnosed as precocious puberty in other hospital and treated with triptorelin acetate for 15 months ，but growth rate has not slowed down. Heterozygous missense variants in nuclear receptor-binding SET-domain-containing protein 1（NSD 1）gene was identified in proband by gene sequencing ，which was c.5854C>T （p.Arg1952Trp ）. His father had the same heterozygous mutation. This mutation had been classified to likely pathogenic mutation by American College of Medical Genetics and Genomics （ACMG ） variation classification criteria. Conclusion The diagnosis of Sotos syndrome is confirmed in this child and NSD 1 gene mutation is the cause. Sotos syndrome is characterized by overgrowth and bone age advanced. The results of the provocation test cannot be distinguished from precocious puberty alone. The clinical development of secondary sexual characteristics should be strictly evaluated to avoid misdiagnosis.Key words ：Nuclear receptor-binding SET-domain-containing protein 1；Sotos syndrome ；Gene mutation ；Misdiagnosis ；Precocious puberty文章编号：1673-8640（2021）02-0130-05 中图分类号：R446.1 文献标志码：A DOI ：10.3969/j.issn.1673-8640.2021.02.003Sotos 综合征是一种常染色体显性遗传的过度生长性疾病，人群发病率约为1∶14 000[1]，其诊断标准为特殊面容（额头突起、眼裂下斜、眼距过宽、下颌尖长、高腭弓、 双颞部毛发退化等）、过度生长（头围增大、身高增高，且大于正常同龄儿童的第97百分位数）、骨龄超前、发育迟缓（语言和学习障碍、短期记忆和抽象思维能力缺陷，可伴有不同程度的智力低下等）；此外，还可有早期喂养困难、黄疸、肌张力低下、动作笨拙、协调性差及癫痫等非特异性表现[2]。

弥漫性大B细胞淋巴瘤bcl蒋会勇，陈愉，张三泉，朱梅刚，赵彤【关键词】弥漫Correlation between bcl2/IgH gene rearrangement and GCB molecular subtype in diffuse large Bcell lymphoma【Abstract】AIM: To explore the correlation between bcl2/IgH gene rearrangement and germinal center Bcelllike (GCB) molecular subtype in diffuse large Bcell lymphoma (DLBCL). METHODS: Selfdesigned heminested PCR was used to detect bcl2/IgH gene rearrangement in 60 cases of DLBCL. Positive PCR products were cloned and sequenced. The tissue microarray of 60 specimens of DLBCL was prepared and the expressions of CD20, CD10, Bcl6 and MUM1 were investigated by immunohistochemical SP method. GCB and nongerminal center Bcelllike (nonGCB) were subclassified. RESULTS: Six of the 60 DLBCL were bcl2/IgH positive by conventional PCR method. Selfdesigned heminested PCR was used to amplify 6 positive cases again and bcl2/IgH gene was verified rearrangement in 5 cases by cloning and sequencing and 1 had the gene segment of BAC331191 of human chromosome 19. CD20 waspositive in the 60 DLBCL cases. Positive expression rates of CD10, Bcl6 and MUM1 were %, % and % respectively. 32 %) were GCB and 28 %) were nonGCB. Bcl2/IgH gene rearrangementpositive cases were all GCB subtypes, which were significantly correlated with the expression of CD10 (P= and not with Bcl6 and MUM1. CONCLUSION: Selfdesigned heminested PCR can improve the detection accuracy of bcl2/IgH gene rearrangement. Bcl2/IgH gene rearrangement detection helps the DLBCL determination of GCB subtypes.【Keywords】 lymphoma, Bcell, diffuse large；bcl2; gene rearrangement【摘要】目的: 探讨弥漫性大B细胞淋巴瘤（DLBCL）bcl2/IgH 基因重排与分子亚型生发中心样（GCB）的相关性. 方式:采纳自行设计的半巢式PCR对60例DLBCL的bcl2/IgH基因重排进行了检测，并对阳性产物进行克隆、测序. 同时采纳免疫组化SP法在组织微阵列上同步观测CD20，CD10，Bcl6，黑色素瘤相关抗原（突变体）1 (MUM1)的表达,进行GCB和非生发中心样(nonGCB)分子分类. 结果: 常常规法扩增bcl2/IgH，有6/60例DLBCL bcl2/IgH阳性；在6例阳性样本中,采纳本组设计的半巢式PCR扩增，经克隆及测序证明5例bcl2/IgH 基因重排阳性，1例为人类第19号染色体BAC 331191基因的片段. 60例DLBCL CD20表达全数阳性；CD10，Bcl6， MUM1的阳性表达率别离为%，%，%；GCB 32（%）例，nonGCB 28（%）例. bcl2/IgH基因重排阳性的病例均属GCB分子亚型, 且与CD10表达有显著性相关（P=），而与Bcl6及MUM1表达无相关性. 结论:利用本组设计的半巢式PCR 可提高bcl2/IgH基因重排检测的准确性. bcl2/IgH基因重排的检测可协助确信部份GCB分子亚型的DLBCL.【关键词】弥漫性大B细胞淋巴瘤； bcl2; 基因重排0引言弥漫性大B细胞淋巴瘤（diffuse large Bcell lymphoma, DLBCL）是最多见、高度恶性的非霍奇金淋巴瘤，但新近cDNA芯片的研究结果显示，该瘤存在生发中心样（germinal center Bcelllike，GCB）和非生发中心样（non germinal center Bcelllike, nonGCB）两种分子亚型，GCB的预后明显好于nonGCB类型［1-2］. Hans等［3］以cDNA 芯片为参照标准，发觉利用“套餐”式免疫标记物CD10，Bcl6，黑色素瘤相关抗原（突变体）1 ［melanoma associated antigen(mutated)1, MUM1］也能够对DLBCL进行分子分类. bcl2/IgH基因重排是人类恶性淋巴瘤最多见的染色体异样，存在bcl2基因重排的病例有较好的生存率. 咱们利用组织微阵列(tissue microarray, TMA)技术对60例DLBCL进行分子分类, 采纳PCR技术检测bcl2/IgH基因重排，探讨bcl2/IgH基因重排与分子亚型的相关性，旨在为临床判定DLBCL的预后提供更多的信息.1材料和方式1．1材料1999/2003年诊断明确资料齐全的DLBCL 60（男29，女31）例，中位年龄（16~91）岁，所有标本均为40 g/L中性甲醛固定, 常规石蜡切片HE染色. 由3名专门从事淋巴瘤研究的医师按新的WHO分类方式进行诊断. SP试剂盒购自北京中山生物技术; CD20购于福州Maxim公司；CD10购于美国Zymed公司；Bcl6购于美国Neomarker 公司； MUM1 mAb由意大利Perugia大学血液病研究所Dr. Falini馈赠.1．2方式1．IgH基因重排的检测DNA提取采纳我室成立的石蜡切片刮片法，每例切片1张，厚 3 μm，常规脱蜡、干燥. 用无菌刀片刮入Eppendorf管，加入50 μL消化液(10 mmol/L , 1 mmol/L EDTA, 200 mL/L Tween 20)，同时加入蛋白酶K（终浓度为200 mg/L），56℃水浴孵育留宿，充分消化后，98℃, 10 min灭活蛋白酶K，12 000 g离心10 min，分装上清液，用分光光度计检测DNA的浓度，将浓度调整至200～400 μg/L，-20℃保留备用.1．2．2半巢式PCR检测bcl2/IgH基因重排bcl2/IgH 基因重排的检测采纳半巢式PCR. 第一次扩增PCR引物为：上游:MBR 5′TTAGAGAGTTGCTTTACGTGGCCTG3′; 下游JH1: 5′ACCTGAGGAGACGGTGACC3′. 该对引物为检测bcl2/IgH 基因重排经常使用引物［4］. 利用专业的引物设计软件Primer premier, 咱们在MBR下游设计了第二重引物: Mbr1 5′CAACACAGACCCACCCAGAG3′与原下游引物JH1组成半巢式PCR反映的第二对引物. 反映体系为:10×PCR 缓冲液2 μL, 25 mmol/L MgCl2 μL, 10 mmol/L dNTP μL,上下游引物各μL,Taq DNA聚合酶μL，模板1 μL(浓度200~500 mg/L),三蒸水μL,总反映体系为20 μL. 半巢式PCR的反映体系为10×PCR缓冲液2 μL, 25 mmol/L MgCl2 μL, 10 mmol/L dNTP μL,上下游引物各1 μL, Taq DNA聚合酶μL，模板1 μL,三蒸水μ℃变性5 min，94℃变性30 s，57℃退火45 s, 72℃延伸45 s, 共35个循环，然后，72℃终末延伸10 min，4℃保留30 min. 半巢式PCR 模板为一步法的产物稀释100倍. 产物以20 g/L琼脂糖凝胶电泳, 溴化乙锭（EB）μL染色. 所有PCR操作进程中严格按PCR操作规程进行. 阳性对照采纳经测序证明为基因重排阳性的DNA，阴性对照为双蒸水. PCR阳性产物经TA克隆连于T载体，送上海博亚公司进行测序. 测序采纳3730测序仪，DNA测序技术要紧依据Sanger双脱氧链终止法. 测序结果在基因库中进行比对，以明确是不是为 bcl2/IgH 基因重排片段.制作第一对60例DLBCL蜡块组织的苏木精-伊红染色切片作形态学观看，选择有代表性的肿瘤区域，在相应位置进行标记；然后利用组织微阵列仪（Beecher Instruments, MTA1）制作受体蜡块并打孔，依照原有切片的标记部位，用供体针在蜡块相应部位取样，所用组织芯直径为 mm，将供体组织芯放入受体蜡块. 每例标本在标记区域内随机取2~4条组织芯. 在组织样本填入蜡孔时记录每一个样本在二维阵列中的具体位置，并在必然位置上标记出TMA的方位.免疫组化染色采纳SP法进行免疫组化染色. 以CD20染色来判定每一个组织点的肿瘤细胞数，每例标本以肿瘤细胞数最多的点作分析. 抗体的阳性判定标准为：CD20，CD10阳性定位于细胞膜，Bcl6，MUM1阳性定位于细胞核, >20%的肿瘤细胞染色判为阳性［3］. 采纳CD10， Bcl6和MUM1 3种抗体对DLBCL进行分类［3］. CD10， Bcl6作为生发中心细胞的标志物，CD10+或CD10+/Bcl6+为GCB类；CD10-/Bcl6-为nonGCB类；MUM1作为后生发中心细胞来源的标志物，假设CD10-/Bcl6+，那么用MUM1来分类：MUM1+为nonGCB类，MUM1-为GCB类.统计学处置: 利用SPSS 软件,对数据进行Fisher精准查验. 2结果2．1组织学分型依照WHO新分类方式对DLBCL进行分型，中心母细胞型(centroblastic，CB) 53例%)，免疫母细胞型（immunoblastic，IB）3例%)，间变性大B细胞型（anaplastic，AP) 2例%)，未分型2例%).2．2蛋白表达60例DLBCL患者的组织切片CD20表达全数阳性. CD10， Bcl6， MUM1的阳性表达率别离为%， %， %，其中CD10+ Bcl6+ 14例， CD10+/MUM1+ 13例，Bcl6+/MUM1+ 16例，CD10+/Bcl6+/MUM1+ 8例（图1）. 经统计学分析，年龄性别等因素与CD20, CD10, Bcl6表达无关（表1）.2．3DLBCL TMA分子分类60例DLBCL中， 32例（%）GCB，28例（%）nonGCB. 在32例GCB类中， 26例（%）CD10+，18例（%）Bcl6+，14例（%） CD10+/Bcl6+， 6例%) CD10-/Bcl6+. 在28例nonGCB类中，24例（%）MUM1+，8例（%） MUM1+/Bcl6+.A：细胞膜CD20+ ×100; B：细胞核Bcl6+ ×100; C: 细胞膜CD10+ ×200; D: 细胞核MUM1+ ×100.图1弥漫性大B细胞淋巴瘤免疫组化染色SP2．4基因重排检测因bcl2/IgH基因断裂在不同病例略有不同，因此扩增出的片段为100～300 bp(图2). 经一步法扩增，发觉6例阳性，对阳性样本进行半巢式PCR扩增时发觉5例基因重排阳性，考虑一步法扩增有假阳性，经克隆及测序证明为人类第19号染色体BAC331191基因的片段，其序列为: TTAGAGAGTTGCTTTACGTGGCCTGCCTCTCTCACCTCCCAGGTGAACGGTGTGGACATGAAGCTGCCCGTGGTGCTGGCCAACGGCCAGATCCGTGCCTCCCAGCATGGTTCAGATGTTGTGATTGAGACCGACTTCGGCCTGCGTGTGGCCTACGACCTTGTGTACTATGTGCGGGTCACCGTCTCCTCAGGT, 因此bcl2/IgH基因重排为5例. 基因重排阳性的5例病例均为GCB亚类. CD10阳性及阴性病例bcl2/IgH基因重排的发生率别离为%(5/26)及0%(0/34)，有统计学不同（P=）(表1).表1弥漫性大B细胞淋巴瘤免疫组化与临床资料及bcl2/IgH基因重排关系(略)图2PCR检测bcl2/IgH重排3讨论本组60例DLBCL，CD10的阳性率高于西方Hans等［3］的28%和Colomo等［4］的21%, 可能与本组选用的病例多为CB变型的B细胞淋巴瘤有关. CD10诊断GCB的灵敏性较低，经CD10和Bcl6联合应用，在GCB类中发觉了6例CD10阴性、Bcl6阳性，说明联合应用CD10及Bcl6可提高GCB亚类的检出率. 本组检测MUM1阳性率与文献报导（50%~77%）相近［5-6］. 咱们利用传统引物进行bcl2/IgH基因重排检测发觉的阳性样本，采纳本组设计的半巢式PCR扩增及克隆、测序证明有1例为人类第19号染色体BAC 331191基因的片段，该片段的上下游别离有9个碱基能与传统引物互补配对，因此进行PCR时可能将其扩增出来，而非真正的基因重排. 这可能是传统引物检测重排发生假阳性的分子生物学基础. 结果显示采纳咱们所设计的半巢式PCR 引物检测bcl2/IgH基因重排可有效的幸免假阳性的发生. 在咱们的研究中，所有存在bcl2基因重排的病例均表达CD10，统计学资料说明二者存在相关性，因此通过对bcl2基因重排的检测可确信部份GCB 类的DLBCL. 这部份DLBCL与其它类型的DLBCL相较可能有不同的发病机制，属不同的疾病亚型［7-8］. 正确的检测方式对这种疾病的正确诊断有着重要意义，咱们利用的半巢式PCR提高了诊断重排的准确性.本研究在进行免疫组化进程中，咱们采纳了TMA技术，由于所有标本均在一张切片上进行免疫组化染色，人为误差少，一致性更强，有助于在同一水平进行观看分析和比较研究，因此本研究的免疫组织化学染色结果是靠得住的.【参考文献】［1］ Rosenwald A, Wright G, Chan WC, et al. The use of molecular profiling to predict survival after chemotherapy for diffuse largeBcell lymphoma［J］. N Engl J Med,2002,346(25):1937-1947.［2］ Shipp MA, Ross KN, Tamayo P, et al. Diffuse large Bcell lymphoma outcome prediction by geneexpression profiling and supervised machine learning ［J］. Nat Med, 2002,8(1):68-74.［3］Hans CP, Weisenburger DD, Greiner TC, et al. Confirmation of the molecular classification of diffuse large Bcell lymphoma by immunohistochemistry using a tissue microarray ［J］. Blood, 2004,103(1):275-282.［4］ Colomo L, LopezGuillermo A, Perales M, et al. Clinical impact of the differentiation profile assessed by immunophenotyping in patients with diffuse large Bcell lymphoma ［J］. Blood, 2003,101(1):78-84.［5］Tsuboi K, Iida S, Inagaki H, et al. MUM1/IRF4 expression as a frequent event in mature lymphoid malignancies ［J］. Leukemia, 2000,14(3):449-456.［6］ Natkunam Y, Warnke RA, Montgomery K, et al. Analysis of MUM1/IRF4 protein expression using tissue microarrays and immunohistochemistry ［J］. Mod Pathol, 2001,14(7):686-694.［7］ Alizadeh AA, Eisen MB, Davis RE, et al. Distinct types of diffuse large Bcell lymphoma identified by gene expression profiling ［J］. Nature, 2000,403(6769):503-511.［8］ Huang JZ, Sanger WG, Greiner TC, et al. The t(14,18)defines a unique subset of diffuse large Bcell lymphoma with a germinal center Bcell gene expression profile ［J］. Blood, 2002,99(7):2285-2290.。

PIAS-1基因真核表达质粒的构建与鉴定秦会影;王海琳;刘钰;张培【摘要】Objective To construct interference vector of Plko.1 and PIAS-1.Methods Sequence of siRNA targeted by PIAS-1 was designed and was cloned into the expression vector pLKO. 1.The recombinants were identified by gene sequencing.Results PCR test and gene sequencing a-nalysis showed that the recombinant plasmid was constructed correctly.Conclusion Sequence of siR-NA targeted by PIAS-1 has been constructed successfully,and it provides stable transfection of RNA interference vector for the proliferation and apoptosis of PIAS-1 in cervical cancer.%目的：构建 PIAS-1 siRNA 干扰载体 Plko．1、PIAS-1。

方法设计以PIAS-1基因为靶标的 siRNA 序列，并克隆人载体 Plko．1，用基因测序进行重组克隆验证。

结果PCR 检测证实 siRNA 插入 Plko．1质粒，测序分析证实插入序列正确。

结论成功构建 PIAS-1基因的 siRNA 载体，为研究 PIAS-1基因对宫颈癌细胞增殖凋亡的影响提供了稳定转染的骨架载体。

【期刊名称】《实用临床医药杂志》【年(卷),期】2014(000)016【总页数】4页(P7-10)【关键词】宫颈癌;PIAS-1;质粒 pLKO.1;siRNA【作者】秦会影;王海琳;刘钰;张培【作者单位】兰州大学第一临床学院妇科，甘肃兰州，73000;甘肃省人民医院妇科，甘肃兰州，73000;;【正文语种】中文【中图分类】R73-3RNA干涉(RNAi)由美国科学家Andrew Fire和Craig Mello［1］于1998年正式提出，其原理为利用同源性的双链RNA(dsRNA)诱导序列特异性的目标基因的沉寂，迅速阻断基因活性。

三种常用的缺失值填充方法作者：刘爱鹏来源：《硅谷》2011年第23期摘要：介绍在遇到蛋白质数据链在同源建模中缺失数据需要填充的时候所使用的常用方法，其中包括线性的KNN、SKNN方法和非线性的SVD方法，以及他们相比较起来的优缺点。

关键词：缺失值；KNN；SKNN；SVD中图分类号：TP311.13 文献标识码：A 文章编号：1671－7597（2011）1210188－01在生物学发展中对蛋白质的研究越来越多，各种针对蛋白质的同源建模的结构数据的实验研究也越来越多，可是在我们使用同源建模的方法的时候，由于蛋白质演化或变异的时候将会出现缺失值的情况。

例如经过PCA处理降维处理过的蛋白质链可以分为严格保守部分和非保守部分，严格保守部分基本不缺值，大概占60%左右，而非保守部分则会含有缺失值，当我们填补缺失值后将能够把可以利用的蛋白质数据链的百分比提高到80%左右，所以缺失值的填充问题很重要。

针对生物数据缺失值的填充问题的处理上要与一般的统计方法处理数据的形式不同，需要利用数据之间的关系来准确的，合理的填充缺失值。

近年来，在处理这个问题上出现了一些填充缺失值比较准确地方法，如K个最近邻的缺失值填充法（KNN）、有序的K个最近邻填充法（SKNN）和奇异值分解法（SVD）。

在这里，我分别的简单介绍下这三种方法。

1 KNN算法基于K个最近邻的缺失值填充算法其实是在考虑了生物蛋白质表达数据之间的相关性，因而预测结果较为准确。

通过选定需要多少个最近邻的蛋白质数据链，根据这些个近邻蛋白质链提供的信息，对缺失数据的目标蛋白质链的缺失值进行预测和估计。

首先我们要计算目标蛋白质链（也就是包含有缺失值的链）与其他链之间的欧式距离，然后在所有计算出来的距离中找到距离目标蛋白质链距离最小的K个最近邻的蛋白质链，然后对选择出的K个最近邻蛋白质链赋予相应的权值，其相应位置（即目标链的缺失值位置）的加权平均值即为目标蛋白质缺失值的估计值。

子宫内膜浆液性腺癌组织HER-2的表达及其临床意义研究章杰捷;吕卫国【摘要】目的探讨子宫内膜浆液性腺癌组织人表皮生长因子受体2(HER-2)的表达及其与临床病理特征和预后的关系.方法选取行手术治疗的子宫内膜浆液性腺癌患者51例,采用免疫组织化学染色法检测手术切除的癌组织HER-2表达情况.比较癌组织HER-2阳性表达患者与阴性表达患者临床病理特征与预后.结果子宫内膜浆液性腺癌组织HER-2阳性表达患者1 1例,占21.57％.与HER-2阴性表达患者相比,HER-2阳性表达患者年龄较大,混合型病理组织发生率较高(均P＜0.05).HER-2阳性表达患者5年生存率明显低于阴性表达患者(18.43％ vs 61.98％,P＜0.05).结论子宫内膜浆液性腺癌组织HER-2阳性表达的患者年龄较大,且预后较差.【期刊名称】《浙江医学》【年(卷),期】2018(040)019【总页数】4页(P2119-2121,2125)【关键词】子宫内膜浆液性腺癌;人表皮生长因子受体2;预后【作者】章杰捷;吕卫国【作者单位】310006杭州,浙江大学医学院附属妇产科医院;浙江大学;浙江省肿瘤医院妇瘤科;310006杭州,浙江大学医学院附属妇产科医院【正文语种】中文子宫内膜浆液性腺癌是一种少见的子宫内膜癌组织学亚型，多发生于绝经后妇女，早期即可发生局部浸润与远处转移，其恶性程度高，患者预后差[1]。

人表皮生长因子受体2（HER-2）是一种跨膜酪氨酸激酶受体，可介导细胞信号传递，其过度表达与肿瘤发生、转移、耐药性及预后不良有关[2-4]。

研究发现，西方女性子宫内膜浆液性腺癌组织中存在HER-2过表达情况，并指出HER-2特异性重组人源化单克隆抗体-曲妥珠单抗治疗可能是过表达HER-2的子宫内膜浆液性腺癌患者可选择的治疗新策略[5-6]。

目前关于子宫内膜浆液性腺癌组织HER-2表达情况国内报道少见。

本研究采用免疫组织化学染色法检测子宫内膜浆液性腺癌组织HER-2的表达情况，并分析其与患者临床病理特征和预后的关系，以期为寻找子宫内膜浆液性腺癌治疗新靶点提供参考，现报道如下。

原发性皮肤弥漫性大B细胞淋巴瘤(腿型)2例报道伴文献复习戴向农;叶兴东;邹新青;田歆;林日华;韩建德【摘要】目的:通过探讨原发性皮肤弥漫性大B细胞淋巴瘤临床表现和病理特点,提高对该病的认识,实现早诊早治.方法:对2例疑似原发性弥漫性大B淋巴细胞瘤(腿型)患者进行临床表现分析及组织病理检查,通过检测细胞表面抗原标记确诊,采用利妥昔单抗联合CHOP方案化疗,21天为1个疗程,连续半年结束疗程继续随访.结果:弥漫性大B淋巴细胞瘤进展迅速,可破溃,组织病理检查表现为真皮单核细胞浸润,细胞异型明显,免疫组化表现为B细胞表型,CD20(+),CD79a(+),BCL-2(+),CD10(-),BCL-6(-),采用利妥昔单抗联合CHOP方案治疗,随访2年疗效肯定.结论:利妥昔单抗联合CHOP方案化疗连续6个疗程治疗原发性弥漫性大B细胞淋巴瘤,效果肯定.【期刊名称】《皮肤性病诊疗学杂志》【年(卷),期】2017(024)006【总页数】6页(P389-394)【关键词】B淋巴细胞;淋巴瘤;原发性【作者】戴向农;叶兴东;邹新青;田歆;林日华;韩建德【作者单位】广州市皮肤病防治所皮肤科,广东广州 510095;广州市皮肤病防治所皮肤科,广东广州 510095;广州医科大学肿瘤医院肿瘤内科,广东广州510095;广州市皮肤病防治所皮肤科,广东广州 510095;广州市皮肤病防治所皮肤科,广东广州 510095;中山大学附属第一医院皮肤科,广东广州510080【正文语种】中文【中图分类】R739.5皮肤淋巴瘤是指以皮肤损害为初发或突出表现的淋巴瘤,包括原发于皮肤者，或原发于淋巴结及其他器官而以后发生于皮肤者。

原发于皮肤者约占结外淋巴瘤的第三位[1]。

弥漫性大B细胞淋巴瘤(diffuse large B cell lymphoma，DLBCL)是一类由大B淋巴样细胞呈弥漫性生长构成的B细胞恶性肿瘤，是非霍奇金淋巴瘤(non-Hodgkin lymphoma，NHL)中最常见的临床类型[2]，原发性皮肤弥漫性大B细胞淋巴瘤(腿型)(primary cutaneous diffuse large B-cell lymphoma, leg type, PCLBCL-LT)是原发性皮肤淋巴瘤少见类型，1996年Vermeer等[3]首次报道，近年陆续有报道[4]。

单细胞转录组鉴定炎症细胞因子英文回答：Single-cell transcriptomics has revolutionized our understanding of cellular heterogeneity and has provided valuable insights into various biological processes, including inflammation. In order to identify inflammatory cytokines at the single-cell level, a combination of experimental and computational approaches can be employed.Experimental approaches involve isolating and sequencing the transcriptomes of individual cells. This can be done using methods such as single-cell RNA sequencing (scRNA-seq), which allows for the measurement of gene expression in individual cells. By profiling the transcriptomes of cells before and after exposure to an inflammatory stimulus, differentially expressed genes can be identified. These genes are likely to be involved in the production of inflammatory cytokines.Once the differentially expressed genes are identified, computational methods can be used to further analyze the data and identify the specific inflammatory cytokines produced by different cell types. This can involve clustering analysis to group cells with similar gene expression patterns, as well as gene set enrichment analysis to identify enriched pathways or functions. By comparing the gene expression profiles of different cell clusters, it is possible to infer the cytokines produced by each cluster.For example, let's say we are interested in identifying the inflammatory cytokines produced by macrophages in response to lipopolysaccharide (LPS) stimulation. Weisolate and sequence the transcriptomes of individual macrophages before and after LPS treatment. We identify a set of differentially expressed genes that are upregulated in response to LPS. Using computational methods, we cluster the macrophages based on their gene expression profiles and perform gene set enrichment analysis. We find that acluster of macrophages highly expresses genes associated with the production of pro-inflammatory cytokines such astumor necrosis factor alpha (TNF-α) and interleukin-1 beta (IL-1β). This suggests that t hese macrophages are likelyto be responsible for the production of these cytokines in response to LPS stimulation.中文回答：单细胞转录组学已经彻底改变了我们对细胞异质性的理解，并为各种生物学过程提供了宝贵的见解，包括炎症反应。

免疫学分型在判定弥漫性大B细胞淋巴瘤预后中的临床意义虞海荣;孙振柱【摘要】目的:探讨免疫学分型生发中心B细胞(GCB)亚型和非生发中心B细胞(NGCB)亚型在弥漫性大B细胞淋巴瘤(DLBCL)预后判定中的临床意义.方法:61例DLBCL患者中GCB亚型18例,NGCB亚型43例,分析不同临床特征[年龄、性别、民族、临床分期和国际预后指数(IPI)评分]患者中GCB、NGCB亚型的分布情况.结果:Ⅰ+Ⅱ期、Ⅲ+Ⅳ期患者GCB、NGCB亚型分别为77.8%、41.9%;22.2%、58.1%,两者差异有统计学学意义(P＜0.05);IPI评分0～2分、3～5分,患者GCB、NGCB亚型分别为83.3%、41.9;16.7%、58.1%,两者差异有统计学意义(P＜0.05);汉族、少数民族患者NGCB和GCB亚型分别占72.1%、38.9%;27.9%、61.1%,差异有统计学意义(P＜0.05).结论:DLBCL中的两种免疫学亚型GCB和NGCB可能与患者病情进展、恶性程度有关,可作为判断预后的一种模式.【期刊名称】《新疆医科大学学报》【年(卷),期】2008(031)005【总页数】3页(P559-560,563)【关键词】弥漫性大B细胞淋巴瘤;免疫学亚型;临床特征;预后【作者】虞海荣;孙振柱【作者单位】新疆医科大学,新疆,乌鲁木齐,830000;新疆维吾尔自治区人民医院病理科,新疆,乌鲁木齐,830000【正文语种】中文【中图分类】R392;R733弥漫性大B细胞淋巴瘤( diffuse large B-cell lymphoma， DLBCL)是恶性淋巴瘤中发病最多的类型，占成人非霍奇金淋巴瘤(non-Hodgkin′s lymphoma，NHL)的30%～40%[1]。

目前临床上用国际预后指数 (international prognostic index，IPI)评分对患者的预后进行评估。

gene单词
gene
英[dʒiːn] 美[dʒiːn]
n. 基因，遗传因子
复数genes
短语搭配：
gene expression [化]基因表达；基因表现gene therapy n. 基因治疗
gene engineering 基因工程；遗传工程
gene transfer 基因导入，转基因；基因转移gene mutation 基因突变
gene cloning 基因克隆；基因选殖
gene pool 基因库
gene mapping [遗]基因图谱
major gene 主要基因；知基因；柱因
gene flow 基因流，基因流动；基因怜foreign gene 外源基因；异体基因
reporter gene [生物]报告基因
suicide gene 自杀基因
gene recombination 基因重组；基因重组现象recessive gene 隐性基因
dominant gene 显性基因
gene bank 基因库，基因文库
gene deletion 基因缺失；基因删除（多余基因的除去）
gene library 基因库；基因文库
gene clone 基因克隆
双语例句：
1、The gene for asthma has been identified. 哮喘病的基因已被识别。

2、The gene is activated by a specific protein. 这种基因由一种特异性蛋白激活。

3、The gene is only part of the causation of illness. 基因只是疾病的部分诱因。

第1期PR DM1在弥漫大B细胞淋巴瘤中表达和预后意义的研究局限在弥漫大B细胞淋巴瘤的非生发中心亚型中。

加入美罗华能够下调PRD M1β表达,从而逆转了P RD M1β对化疗病人的负面作用。

参考文献[1]　A liz adeh AA,Eis en MB,Davis RE,et al.D istinct t yp es of d i ffus el arge B-cel l ly mpho m a identifi ed by gene exp ressi on profiling[J].Na2 t ure.2000,403(6769):503-511.[2]　Shap ir o-Shelef M,L in KI,Savits ky D,et al.B li mp-1is requi redfor maintenance of l ong-li ved p las ma cell s i n t he bone marro w[J].JExp M ed.2005,202(11):1471-1476.[3]　Hans CP,Weis enbu rger DD,Grei ner TC,et al.Confir mation of themolecular clas sifi cation of diffu s e l arge B-cell l ym p homa by i m m uno2histoche m istry using a ti s sue m i croarray[J].B lood.2004,103(1):275 -282.[4]　Catt orett G,Angelin-Duclos C,Shaknovi ch R,et al.PRD M1/B li m p-1is exp ress ed in human B-l ymphocytes com m it t ed to the p las ma cell l ineage[J].J Pat hol.2005,206(1):76-86.[5]　Sas aki O,M egu r o K,Tohm iya Y,et al.A ltered exp ressi on of retino2blast oma p r otei n i nteracting zi nc finger gene,R IZ,i n hum an l eukaem i a [J].B r J Haemat ol.2002,119(4):940-948.[6]　Moun i er N,B riere J,Giss el b rech t C,et al.R ituxi mab plus C HOP(R-C HOP)overcomes bcl-2ass ociat ed resistance t o che mo t herapy in elderl y patient s with diffuse large B-cell ly mp homa(DLBCL)[J].B l ood.2003,101(11):4279-4284.风湿性疾病与多药耐药 在风湿性疾病的治疗中,激素、非类固醇类抗炎药、慢作用抗风湿药及免疫抑制剂是重要的治疗手段,并且常为多种药物长期、联合应用。

基于基因芯片探索鼻咽癌的分子靶标刘玉智1，门剑龙2，李杨2△摘要：目的在分子水平探索鼻咽癌的发病机制，筛选鼻咽癌诊断或治疗的潜在分子靶标。

方法在GEO 公共数据库中下载编号为GSE12452和GSE13597的基因芯片数据（包含鼻咽癌和对照鼻炎黏膜组织数据），利用R 编程语言的相关工具包对原始芯片数据进行预处理和差异表达基因的筛选。

利用DAVID 数据库对差异表达基因进行基因GO 功能分析和KEGG 信号通路分析。

取我院鼻咽癌石蜡标本和对照鼻炎黏膜组织新鲜标本各5例，利用real-time PCR 和Western blot 分别检测18个细胞周期相关基因和4种蛋白在两组织中的表达，进一步验证基因芯片的结果。

结果生物信息学分析得到了260个差异表达基因，涉及16个GO 条目和4条信号通路。

18个细胞周期相关的基因中，real-time PCR 和Western blot 进一步确定鼻咽癌组织中有12个基因在mRNA 水平表达上调，4个基因在蛋白水平表达上调。

结论通过对两套鼻咽癌表达谱芯片数据的综合生物信息学分析与分子生物学验证，发现CDC6、CDK1、MCM2和CCNB1这4个可能是鼻咽癌恶性进展相关的关键基因，可作为潜在靶点作进一步的功能研究。

关键词：鼻咽肿瘤；细胞周期；寡核苷酸序列分析；计算生物学；鼻咽癌；GEO 数据库；DAVID 数据库中图分类号：R739.6文献标志码：ADOI:10.11958/20171427Identify the molecular target of nasopharyngeal carcinoma by bioinformatics analysisLIU Yu-zhi 1,MEN Jian-long 2,LI Yang 2△1Department of Otolaryngology,2Precision Medicine Center,Tianjin Medical UniversityGeneral Hospital,Tianjin 300052,China△Corresponding Author E-mail:yangli.tijmu@Abstract:Objective To study the pathogenesis of nasopharyngeal carcinoma and identify potential biomarkers ortherapeutic targets.MethodsMicroarray data (GSE12452and GSE13597)were downloaded from Gene ExpressionOmnibus.Processing of original microarray data and screening of differentially expressed genes were performed throughbioinformatics analysis.Then,GO and KEGG pathway enrichment analysis was performed for these genes using DAVIDdatabase.Real time-PCR and Western blot assay were used to detect the expression levels of the identified genes.Results A total of 260overlap DEGs were obtained including 16GO entries and 4signal pathways.Eighteen potential therapeutic targets that relative to cell cycle were identified by gene enrichment analysis.Expression levels of 12selected genes were confirmed by real-time PCR.Finally,4selected genes were confirmed by Western blot assay.Conclusion Bybioinformatics analysis of two sets of microarray data and molecular biology research,four genes were found including CDC6，CDK1，MCM2and CCNB1,which might be potential key genes that can be developed for therapy targets of NPC in the future.Key words:nasopharyngeal neoplasms;cell cycle;oligonucleotide array sequence analysis;computational biology;nasopharyngeal carcinoma;Gene Expression Omnibus database;DAVID database基金项目：天津市科技计划项目（16KPXMSF00170）；天津医科大学总医院青年孵育基金（ZYYFY2017006）作者单位：1天津医科大学总医院耳鼻喉科（邮编300052），2精准医学中心作者简介：刘玉智（1963），男，主治医师，主要从事耳鼻喉相关肿瘤学研究△通讯作者E-mail:yangli.tijmu@鼻咽癌（nasopharyngeal carcinoma ，NPC ）是一种鳞状细胞癌，常见于鼻咽侧壁咽鼓管口周围［1］。

The New EnglandJournal of MedicineCopyr ight ©2002 by the Massachusetts Medical Societ yA GENE-EXPRESSION SIGNATURE AS A PREDICTOR OF SURVIVALIN BREAST CANCERM ARC J. VAN DE V IJVER, M.D., P H.D., Y UDONG D. H E, P H.D., L AURA J. VAN ’T V EER, P H.D., H ONGYUE D AI, P H.D.,A UGUSTINUS A.M. H ART, M.S C., D ORIEN W. V OSKUIL, P H.D., G EORGE J. S CHREIBER, M.S C., J OHANNES L. P ETERSE, M.D.,C HRIS R OBERTS, P H.D., M ATTHEW J. M ARTON, P H.D., M ARK P ARRISH,D OUWE A TSMA, A NKE W ITTEVEEN,A NNUSKA G LAS, P H.D., L EONIE D ELAHAYE, T ONY VAN DER V ELDE, H ARRYB ARTELINK, M.D., P H.D.,S JOERD R ODENHUIS, M.D., P H.D., E MIEL T. R UTGERS, M.D., P H.D., S TEPHEN H. F RIEND, M.D., P H.D.,AND R ENÉ B ERNARDS, P H.D.A BSTRACTBackground A more accurate means of prognos-tication in breast cancer will improve the selection ofpatients for adjuvant systemic therapy.Methods Using microarray analysis to evaluate ourpreviously established 70-gene prognosis profile, weclassified a series of 295 consecutive patients with pri-mary breast carcinomas as having a gene-expressionsignature associated with either a poor prognosis ora good prognosis. All patients had stage I or II breastcancer and were younger than 53 years old; 151 hadlymph-node–negative disease, and 144 had lymph-node–positive disease. We evaluated the predictivepower of the prognosis profile using univariable andmultivariable statistical analyses.Results Among the 295 patients, 180 had a poor-prognosis signature and 115 had a good-prognosis sig-nature, and the mean (±SE) overall 10-year survivalrates were 54.6±4.4 percent and 94.5±2.6 percent, re-spectively. At 10 years, the probability of remainingfree of distant metastases was 50.6±4.5 percent in thegroup with a poor-prognosis signature and 85.2±4.3percent in the group with a good-prognosis signature.The estimated hazard ratio for distant metastases inthe group with a poor-prognosis signature, as com-pared with the group with the good-prognosis signa-ture, was 5.1 (95 percent confidence interval, 2.9 to9.0; P<0.001). This ratio remained significant when thegroups were analyzed according to lymph-node sta-tus. Multivariable Cox regression analysis showed thatthe prognosis profile was a strong independent factor in predicting disease outcome.Conclusions The gene-expression profile we stud-ied is a more powerful predictor of the outcome of dis-ease in young patients with breast cancer than stand-ard systems based on clinical and histologic criteria. (N Engl J Med 2002;347:1999-2009.)Copyright © 2002 Massachusetts Medical Society.From the Divisions of Diagnostic Oncology (M.J.V., L.J.V., D.W.V., J.L.P., D.A., A.W., A.G., L.D.), Radiotherapy (A.A.M.H., H.B.), Medical Oncology (S.R.), Biometrics (T.V.), Surgical Oncology (E.T.R.), and Molecular Car-cinogenesis (R.B.), Netherlands Cancer Institute, Amsterdam; the Center for Biomedical Genetics, Amsterdam (R.B.); and Rosetta Inpharmatics, Kirk-land, Wash. (Y.D.H., H.D., G.J.S., C.R., M.J.M., M.P., S.H.F.). Address re-print requests to Dr. Bernards at the Division of Molecular Carcinogenesis, Netherlands Cancer Institute, Plesmanlaan 121, 1066 CX Amsterdam, the Netherlands, or at r.bernards@nki.nl.Drs. van de Vijver, He, and van ’t Veer contributed equally to this article.DJUVANT systemic therapy substantiallyimproves disease-free and overall survival inboth premenopausal and postmenopausalwomen up to the age of 70 years with lymph-node–negative or lymph-node–positive breast cancer.1,2 It is generally agreed that patients with poor prognostic features benefit the most from adjuvant therapy.3,4 The main prognostic factors in breast can-cer are age, tumor size, status of axillary lymph nodes, histologic type of the tumor, pathological grade, and hormone-receptor status. A large number of other factors have been investigated for their potential to pre-dict the outcome of disease, but in general, they have only limited predictive power.5Using complementary DNA (cDNA) microarrays to analyze breast-cancer tissue, Perou et al. identified tumors with distinct patterns of gene expression that they termed “basal type” and “luminal type.”6 These subgroups differ with respect to the outcome of dis-ease in patients with locally advanced breast cancer.7 In addition, microarray analysis has been used to dis-tinguish cancers associated with BRCA1 or BRCA2 mutations8,9 and to determine estrogen-receptor sta-tus6,9,10 and lymph-node status.11,12Using inkjet-synthesized oligonucleotide microar-rays, we recently identified a gene-expression profile Athat is associated with prognosis in patients with breast cancer.9 We analyzed only tumors that were less than 5 cm in diameter from lymph-node–negative patients who were younger than 55 years of age. We found that a classification system based on 70 genes outperformed all clinical variables in predicting the likelihood of dis-tant metastases within five years. We estimated that the odds ratio for metastases among tumors with a gene signature associated with a poor prognosis, as com-pared with those having a signature associated with a good prognosis, was approximately 15 using a cross-validation procedure. Even though these results were encouraging, a limitation of the study was that the re-sults were derived from and evaluated in two groups of patients selected on the basis of outcome: distant metastases had developed in one group within five years, and the other group remained disease-free for at least five years. Therefore, to provide a more accurate estimate of the risks of metastases associated with the two gene-expression signatures and to substantiate that the gene-expression profile of breast cancer is a clin-ically meaningful tool, we studied a cohort of 295 young patients with breast cancer, some of whom were lymph-node–negative and some of whom were lymph-node–positive.METHODSSelection of PatientsT umors from a series of 295 consecutive women with breast can-cer were selected from the fresh-frozen–tissue bank of the Nether-lands Cancer Institute according to the following criteria: the tumor was primary invasive breast carcinoma that was less than 5 cm in di-ameter at pathological examination (pT1 or pT2); the apical axillary lymph nodes were tumor-negative, as determined by a biopsy of the infraclavicular lymph nodes; the age at diagnosis was 52 years or younger; the calendar year of diagnosis was between 1984 and 1995; and there was no previous history of cancer, except nonmelanoma skin cancer. All patients had been treated by modified radical mas-tectomy or breast-conserving surgery, including dissection of the axillary lymph nodes, followed by radiotherapy if indicated. Among the 295 patients, 151 had lymph-node–negative disease (results on pathological examination, pN0) and 144 had lymph-node–positive disease (pN+). T en of the 151 patients who had lymph-node–neg-ative disease and 120 of the 144 who had lymph-node–positive dis-ease had received adjuvant systemic therapy consisting of chemo-therapy (90 patients), hormonal therapy (20), or both (20). Sixty-one of the patients with lymph-node–negative disease were also part of the previous study used to establish the prognosis profile.9 All patients were assessed at least annually for a period of at least five years. Follow-up information was extracted from the medical registry of the Netherlands Cancer Institute. The median duration of follow-up was 7.8 years (range, 0.05 to 18.3) for the 207 patients without metastasis as the first event and 2.7 years (range, 0.3 to 14.0) for the 88 patients with metastasis as the first event. The me-dian follow-up among all 295 patients was 6.7 years (range, 0.05 to 18.3). There were no missing data. The study was approved by the medical-ethics committee of the Netherlands Cancer Institute. Clinicopathological variables were determined as described pre-viously.9 The level of expression of estrogen receptors was estimated on the basis of the hybridization results on the microarray experi-ments, which is a reliable assay for estrogen-receptor status.9 On the basis of this assay, there were 69 estrogen-receptor–negative tumors (defined by an intensity ratio of less than ¡0.65 U on a logarithmic scale, corresponding to staining of less than 10 percent of nuclei on immunohistochemical analysis) and 226 estrogen-receptor–positive tumors in the cohort. The histologic grade was assessed according to the method described by Elston and Ellis13; vascular invasion was assessed as absent, minor (one to three vessels), or major (more than three vessels).Isolation of RNA and Microarray Expression ProfilingThe isolation of RNA, labeling of complementary RNA (cRNA), hybridization of labeled cRNA to 25,000-gene arrays, and assess-ment of expression ratios were all performed as previously de-scribed.9,14 In brief, tumor material was snap-frozen in liquid nitro-gen within one hour after surgery. Frozen sections were stained with hematoxylin and eosin; only samples that had more than 50 percent tumor cells were selected. Thirty 30-µm sections were used for the isolation of RNA. T otal RNA was isolated with RNAzolB and dis-solved in RNase-free water. Then 25 µg of total RNA was treated with DNase with use of the Qiagen RNase-free DNase kit and RNeasy spin columns, the RNA was then dissolved in RNase-free water to a final concentration of 0.2 µg per microliter, and cRNA was generated by in vitro transcription with the use of T7 RNA polymerase and 5 µg of total RNA and labeled with Cy3 or Cy5 (Cy Dye, Amersham Pharmacia Biotech). Five micrograms of Cy-labeled cRNA from one breast-cancer tumor was mixed with the same amount of reverse-color Cy-labeled product from a pool that consisted of an equal amount of cRNA from each patient. Labeled cRNAs were fragmented to an average size of approxi-mately 50 to 100 nucleotides by heating the samples to 60°C in the presence of 10 mM zinc chloride and adding a hybridization buffer containing 1 M sodium chloride, 0.5 percent sodium sarcosine, 50 mM morpholino-ethane sulfonic acid (pH 6.5), and formamideFigure 1 (facing page). Pattern of Expression of Genes Used to Determine the Prognosis and Clinical Characteristics of 295 Patients with Breast Cancer.Panel A shows the pattern of expression of the 70 marker genes (also referred to as prognosis-classifier genes9) in a series of 295 consecutive patients with breast carcinomas. Each row represents the prognostic profile of the 70 marker genes for one tumor, and each column represents the relative level of expression of one gene. The tumors are numbered from 1 to 295 on the y axis, and the genes are numbered from 1 to 70 on the x axis. The genes in the horizontal direction are arrayed in the same order as in our previous study.9 Red indicates a high level of expression of messenger RNA (mRNA) in the tumor, as compared with the reference level of mRNA, and green indicates a low level of expression. The dotted line is the previously determined threshold between a good-prognosis signature and a poor-prognosis signature. Tumors are rank-ordered according to their correlation with the previously determined average profile in tumors from patients with a good prognosis. Panel B shows the time in years to distant metastases as a first event for those in whom this occurred, and the total duration of follow-up for all other patients. Panel C shows the lymph-node status (blue marks indicate lymph-node–positive disease, and white lymph-node–negative disease), the number of patients with distant metasta-ses as a first event (blue marks), and the number of patients who died (blue marks).GENE-EXPRESSION SIGNATURE AS A PREDICTOR OF SURVIVAL IN BREAST CANCER150200250100500¡0.500.5201030405060700510Reporter Genes ABCRatio (log scale)L y m p h -N o d e S t a t u sMetastasisTotal follow-upM e t a s t a s i sD e a t hYearsXxxxxxxxT u m o r s w i t h P o o r -P r o g n o s i s S i g n a t u r e T u m o r s w i t h G o o d -P r o g n o s i s S i g n a t u r e(final concentration, 30 percent at 40°C); the final volume was 3 ml. The microarrays included the 24,479 biologic oligonucleotides as well as 1281 control probes. After hybridization, the slides were washed and scanned with a confocal laser scanner (Agilent T echnol-ogies). Fluorescence intensities on scanned images were quantified, and the values were corrected for the background level and nor-malized.Validation StrategyWe wished to investigate the prognostic value of the gene-expres-sion profile in a consecutive series of patients with breast cancer. We included 61 of the 78 patients with lymph-node–negative disease who were involved in the previous study that determined the 70-gene prognosis profile.9 Leaving them out would have resulted in selection bias, since the previous study included a disproportionate-ly large number of patients in whom distant metastases developed within five years. We included these 61 patients in the study, but we used the “leave-one-out” cross-validated classification established in our previous study to predict the outcomes among these patients. In this approach, the classification of the left-out sample was based on its correlation with the mean levels of expression of the remain-ing samples from the patients with a good-prognosis signature, with the sample in question excluded from the gene-selection process.9 This approach minimizes to some extent the possibility of overesti-mating the value of the prognosis profile while it keeps the con-secutive series complete. We also provide validation results taking only the new samples into account.Correlation of the Microarray Data with the Prognosis ProfileFor each of the 234 tumors from patients who were not included in the previous study, we calculated the correlation coefficient of the level of expression of the 70 genes with the previously determined average profile of these genes in tumors from patients with a good prognosis (C1).9 A patient with a correlation coefficient of more than 0.4 (the threshold in the previous study of 78 tumors that re-sulted in a 10 percent rate of false negative results) was then assigned to the group with a good-prognosis signature, and all other patients were assigned to the group with a poor-prognosis signature. For the 61 patients with lymph-node–negative disease who were included in the previous study, we used a cutoff value of 0.55 (corresponding to the threshold that resulted in a 10 percent rate of false negative results in the cross-validated classification in our previous study).9 Study DesignStudy design, patient selection, RNA isolation from tumor ma-terial, histopathological analyses, clinical annotation, and clinical in-terpretation were carried out at the Netherlands Cancer Institute. RNA amplification and microarray hybridization were carried out at Rosetta Inpharmatics. Bioinformatic and statistical analyses were performed jointly by authors at both locations. All raw data were available to all the investigators.Statistical AnalysisIn the analysis of the probability that patients would remain free of distant metastases, we defined distant metastases as a first event to be a treatment failure; data on all other patients were censored on the date of the last follow-up visit, death from causes other than breast cancer, the recurrence of local or regional disease, or the development of a second primary cancer, including contralateral breast cancer. Data on patients were analyzed from the date of sur-gery to the time of the first event or the date on which data were censored, according to the method of Kaplan and Meier, and the curves were compared with use of the log-rank test. Values are expressed as means ±SE, calculated according to the method of Tsiatis.15We used proportional-hazards regression analysis16 to adjust the association between the correlation coefficient (C1) and metastases for other variables. All SEs were calculated with use of the sandwich estimator.17 The histologic grade, extent of vascular invasion, and number of axillary-lymph-node metastases (0 vs. 1 to 3 or 0 vs. »4) were used as variables. The linearity of the relation between the relative hazard ratio and the diameter of the tumor, age, and level of expression of estrogen receptors was tested with use of the Wald test for nonlinear components of restricted cubic splines.18 No evi-dence of nonlinearity was found (P=0.83 for age, P=0.75 for tumor diameter, P=0.65 for the number of positive nodes, and P=0.27 for the level of expression of estrogen receptors). We eval-uated whether the hazard ratio was proportional using the method of Grambsch and Therneau.19 In addition, we determined the dif-ference between the relative hazard ratio before and after five years of follow-up with respect to the prognosis signature using the Wald test. All calculations were performed with the S Plus 2000 or S Plus 6 statistical package.RESULTSCategorization of Gene-Expression SignaturesT otal RNA from each tumor was isolated and used to generate cRNA, which was labeled and hybridized to microarrays containing approximately 25,000 hu-T ABLE 1. A SSOCIATION BETWEEN C LINICAL C HARACTERISTICSAND THE P ROGNOSIS S IGNATURE.C HARACTERISTICP OOR-P ROGNOSISS IGNATURE(N=180)G OOD-P ROGNOSISS IGNATURE(N=115)PV ALUEno. of patients (%)Age<0.001 <40 yr52 (29)11 (10)40–44 yr41 (23)44 (38)45–49 yr55 (31)43 (37)»50 yr32 (18)17 (15)No. of positive nodes0.60 091 (51)60 (52)1–363 (35)43 (37)»426 (14)12 (10)Tumor diameter0.012«20 mm84 (47)71 (62)>20 mm96 (53)44 (38)Histologic grade<0.001 I (good)19 (11)56 (49)II (intermediate)56 (31)45 (39)III (poor)105 (58)14 (12)Vascular invasion0.38 Absent108 (60)77 (67)1–3 Vessels18 (10)12 (10)>3 Vessels54 (30)26 (23)Estrogen-receptor status<0.001 Negative66 (37) 3 (3)Positive114 (63)112 (97)Surgery0.63 Breast-conserving therapy97 (54)64 (56) Mastectomy83 (46)51 (44) Chemotherapy0.79 No114 (63)71 (62)Yes66 (37)44 (38)Hormonal therapy0.63 No157 (87)98 (85)Yes23 (13)17 (15)GENE-EXPRESSION SIGNATURE AS A PREDICTOR OF SURVIVAL IN BREAST CANCERman genes.9 Fluorescence intensities of scanned images were quantified and normalized. We calculated the ra-tio of these values to the intensity of a reference pool made up of equal amounts of cRNA from all tumors.The gene-expression ratios of the previously deter-mined 70 marker genes for all 295 tumors in this study are shown in Figure 1A. The 115 tumors with values above the previously determined threshold 9 were as-signed to the good-prognosis category, and the 180below the threshold were assigned to the poor-prog-nosis category. Figure 1B shows the time to distant metastases as a first event as well as the total duration of follow-up for all patients who did not have distant metastases as a first event. Figure 1C shows lymph-*The patients selected either had had distant metastases as a first event within five years or had remained free of disease for at least five years.†Odds ratios were calculated with use of a two-by-two contingency table. CI denotes confidence interval.‡P values were calculated with use of Fisher’s exact test.§In this analysis, patients who were part of the previous study of gene-expression profiling were excluded from the series of consecutive patients.T ABLE 2. O DDS R ATIO FOR D ISTANT M ETASTASES WITHIN F IVE Y EARS AS A F IRST E VENT ,A CCORDING TO THE P ROGNOSIS S IGNATURE .G ROUP *N O . OF P ATIENTS D ISTANT M ETASTASES WITHIN 5 Y RD ISEASE -FREE>5 Y R O DDS R ATIO (95% CI)†P V ALUE ‡no. of patientsPatients with lymph-node–negative disease <0.001Patients in previous study 7815.0 (3.3–56)Poor-prognosis signature 3118Good-prognosis signature326Consecutive series (new patients only)§6715.3 (1.8–127)0.003Poor-prognosis signature 1123Good-prognosis signature132Patients with lymph-node–positive disease Consecutive series11313.7 (3.1–61)<0.001Poor-prognosis signature 2842Good-prognosis signature241All new patients in the consecutive series 18014.6 (4.3–50)<0.001Poor-prognosis signature 3965Good-prognosis signature373*Distant metastasis was a first event. Plus–minus values are means ±SE.T ABLE 3. R ATE OF O VERALL S URVIVAL AND THE P ROBABILITY T HAT P ATIENTSW OULD R EMAIN F REE OF D ISTANT M ETASTASES AT 5 AND 10 Y EARS ,A CCORDING TO THE P ROGNOSIS S IGNATURE .*G ROUP N O . OF P ATIENTSF REE OF D ISTANT M ETASTASES O VERALL S URVIVAL 5 YR10 YR5 YR10 YRpercentAll patientsGood-prognosis signature 11594.7±2.185.2±4.397.4±1.594.5±2.6Poor-prognosis signature18060.5±3.850.6±4.574.1±3.354.6±4.4Patients with lymph-node–negativediseaseGood-prognosis signature 6093.4±3.286.8±4.896.7±2.396.7±2.3Poor-prognosis signature9156.2±5.544.1±6.371.5±4.849.6±6.1Patients with lymph-node–positivediseaseGood-prognosis signature 5595.2±2.682.7±7.898.2±1.892.0±4.8Poor-prognosis signature8966.3±5.256.7±6.476.5±4.659.5±6.3Figure 2.0.00.20.40.60.81.0120246810YearsAll PatientsP r o b a b i l i t y o f R e m a i n i n g M e t a s t a s i s -f r e eGood signaturePoor signatureP<0.001Good signaturePoor signatureP<0.0010.00.20.40.60.81.0120246810YearsAll PatientsO v e r a l l S u r v i v a lP<0.001120.00.20.40.60.81.0120246810YearsO v e r a l l S u r v i v a lGood signaturePoor signatureP<0.0010.00.20.40.60.81.00246810YearsGood signaturePoor signatureM e t a s t a s i s -f r e eGENE-EXPRESSION SIGNATURE AS A PREDICTOR OF SURVIVAL IN BREAST CANCERP<0.0010.00.20.40.60.81.0120246810YearsLymph-Node–Negative PatientsM e t a s t a s i s -f r e eGood signaturePoor signatureP<0.001Lymph-Node–Negative Patients0.00.20.40.60.81.0120246810YearsO v e r a l l S u r v i v a lGood signaturePoor signature0.00.20.40.60.81.0120246810YearsGene-Expression ProfilingGood signaturePoor signatureP<0.001P=0.050.00.20.40.60.81.0120246810YearsSt. Gallen CriteriaLow riskHigh risk0.00.20.40.60.81.0120246810YearsNIH, High RiskM e t a s t a s i s -f r e eGood signaturePoor signatureP<0.001P<0.001M e t a s t a s i s -f r e eM e t a s t a s i s -f r e eM e t a s t a s i s -f r e e0.00.20.40.60.81.0120246810YearsSt. Gallen, High RiskGood signaturePoor signatureGENE-EXPRESSION SIGNATURE AS A PREDICTOR OF SURVIVAL IN BREAST CANCERP=0.230.00.20.40.60.81.0120246810YearsNIH Consensus CriteriaM e t a s t a s i s -f r e eLow riskHigh riskM e t a s t a s i s -f r e eP=0.050.00.20.40.60.81.0120246810YearsNIH, Low RiskGood signaturePoor signatureM e t a s t a s i s -f r e eGood signaturePoor signature P=0.110.00.20.40.60.81.0120246810YearsSt. Gallen, Low RiskThe prognosis profile was also strongly associated with the outcome in the group of 144 patients with lymph-node–positive disease (Table 3 and Fig. 2E and 2F). In this group, the hazard ratio for distant metastases was 4.5 (95 percent confidence interval, 2.0 to 10.2; P<0.001).Multivariable AnalysisTable 4 shows the results of the multivariable analy-sis of the risk of distant metastases as the first event. The only independent predictive factors were a poor-prognosis signature, a larger diameter of the tumor, and the nonuse of adjuvant chemotherapy. During the period in which these patients were treated, most pre-menopausal patients with lymph-node–positive dis-ease received adjuvant chemotherapy, whereas the ma-jority of patients with lymph-node–negative disease did not receive adjuvant treatment. Patients who re-ceived adjuvant chemotherapy in this series had a high-er likelihood of remaining free of distant metastases (hazard ratio for distant metastases, 0.37; 95 percent confidence interval, 0.20 to 0.66; P<0.001). The poor-prognosis signature was by far the strongest pre-dictor of the likelihood of distant metastases, with an overall hazard ratio of 4.6 (95 percent confidence in-terval, 2.3 to 9.2; P<0.001).DISCUSSIONWe previously identified a gene-expression profile of 70 genes that is associated with the risk of early dis-tant metastases in young patients with lymph-node–negative breast cancer.9 In the present study we tested this profile in a series of 295 consecutive patients who were treated at the hospital of the Netherlands Cancer Institute. The profile performed best as a predictor of the appearance of distant metastases during the first five years after treatment. This finding is not unexpect-ed, since the tumors on which the profile was based had all metastasized within five years. The prognosis profile is also a strong predictor of the development of distant metastases in patients with lymph-node–pos-itive disease. This finding is important, since the pres-ence of lymph-node metastases is by itself a strong predictor of poor survival. Since most patients with lymph-node–positive breast cancer in our study re-ceived adjuvant chemotherapy or hormonal therapy (120 of 144 patients), we could not evaluate the prog-nostic value of the profile in patients with untreated lymph-node–positive disease. There is, however, no indication of an effect of adjuvant chemotherapy on the prognostic value of the profile (data not shown). Figure 3 shows the Kaplan–Meier estimates of the probability that patients would remain free of distant metastases among the 151 patients with lymph-node–negative cancer, according to whether the patients were classified with the use of gene-expression pro-filing (Fig. 3A), the St. Gallen criteria3 (Fig. 3B), or the National Institutes of Health (NIH) consensus criteria4 (Fig. 3C). The St. Gallen and NIH criteria classify patients as at low risk or high risk on the basis of various histologic and clinical characteristics. This comparison shows that the prognosis profile assigned many more patients with lymph-node–negative dis-ease to the low-risk (good-prognosis signature) group than did the traditional methods (40 percent, as com-pared with 15 percent according to the St. Gallen cri-teria and 7 percent according to the NIH criteria). Moreover, low-risk patients identified by gene-expres-sion profiling had a higher likelihood of metastasis-free survival than those classified according to the St. Gallen or NIH criteria, and high-risk patients iden-tified by gene-expression profiling tended to have a higher rate of distant metastases than did the high-risk patients identified by the St. Gallen or NIH criteria. This result indicates that both sets of the currently used criteria misclassify a clinically significant number of patients. Indeed, the high-risk group defined ac-cording to the NIH criteria included many patients who had a good-prognosis signature and a good out-come (Fig. 3E). Conversely, the low-risk group iden-tified by the NIH criteria included patients with a poor-prognosis signature and poor outcome (Fig. 3G). Similar subgroups were identified within the high-risk and low-risk groups identified according to the St. Gallen criteria (Fig. 3D and 3F, respectively). Since both the St. Gallen and the NIH subgroups contain misclassified patients (who can be better identified through the prognosis signature), these patients would be either overtreated or undertreated in current clin-ical practice.Our data indicate that the ability to metastasize to distant sites is an early and inherent genetic property of breast cancer. Our findings argue against the widely accepted idea that metastatic potential is acquired rel-atively late during multistep tumorigenesis.20 If the metastatic ability of breast cancer is determined early in tumorigenesis, early prognostic testing could be un-dertaken, an approach that would clearly be beneficial. On the other hand, an early onset of metastatic capa-bility theoretically limits the benefit of early detection and treatment. Furthermore, our findings suggest that the molecular mechanism leading to hematogenous (distant) metastases is distinct from the mechanism of lymphogenic (regional) spread of tumor cells. Our conclusion that the prognosis profile is independent of lymphogenic metastases is based on its strong pre-dictive power with respect to hematogenous metasta-ses, regardless of the presence or absence of lymph-node involvement.Our data indicate that classification of patients into high-risk and low-risk subgroups on the basis of the prognosis profile may be a useful means of guiding。

BCL-6和BCL-2蛋白在弥漫大B细胞淋巴瘤中的表达及其临床意义孙冠星;曹祥山;李青【摘要】Objective To investigate the relationship between BCL-6 and BCL-2 as well as the clinical features and prognosis of diffuse large B cell lymphoma(DLBCL). Methods Immunohistochemistry stain was used to determine the expression of BCL-6 and BCL-2. Results Positive rates of BCL-6 and BCL-2 were 60. 9%(28/46) and 54. 3%(25/ 46). BCL-6 positive and BCL-2 negative expression showed a trend to better overallsurvival(OS) and progression-free survival(PFS). Conclusion The results suggest significant correlationship among BCL-6. BCL-2 and prognosis. Moreover, BCL-6 and BCL-2 may be the useful prognostic indicators in diffuse large B cell lymphoma.%目的探讨BCL-6和BCL-2蛋白的表达与弥漫大B细胞淋巴瘤(diffuse large B-cell lymphoma,DLBCL)的临床特征及预后之间的关系.方法应用免疫组织化学染色的方法检测BCL-6和BCL-2两种蛋白在46例DLBCL中的表达,并分析它们与DLBCL的临床特征及预后的关系.结果 BCL-6和BCL-2蛋白的阳性表达率分别为60.9％(28/46)和54.3％(25/46),BCL-6阳性组和BCL-2阴性组患者预后较好.结论 BCL-6和BCL-2的蛋白表达与DLBCL预后有一定相关性,可作为判断DLBCL预后的参考指标.【期刊名称】《临床荟萃》【年(卷),期】2012(027)008【总页数】5页(P652-656)【关键词】淋巴瘤,大细胞,弥漫型;原癌基因蛋白质c-bcl-6;原癌基因蛋白c-bcl-2;预后【作者】孙冠星;曹祥山;李青【作者单位】苏州大学附属第三医院常州市第一人民医院血液科,江苏常州213003;苏州大学附属第三医院常州市第一人民医院血液科,江苏常州213003;苏州大学附属第三医院常州市第一人民医院病理科,江苏常州213003【正文语种】中文【中图分类】R733.41弥漫大 B细胞淋巴瘤（diffuse large B-cell lymphoma，DLBCL）是非霍奇金淋巴瘤中发病率最高的亚型，约占到新诊断病例的30%～40%［1］。

［收稿日期］　２０２０－０３－２２［作者简介］　刘艳芝（１９９０－），女（汉族），湖南省株洲市人，主管技师，主要从事临床微生物检验相关研究。

［通信作者］　李军 Ｅ ｍａｉｌ：ｌｉｊｕｎ１９８４１２＠１２６．ｃｏｍ犇犗犐：１０．１２１３８／犼．犻狊狊狀．１６７１－９６３８．２０２０６００２·论著·１例圣乔治教堂诺卡菌眼部感染病例及文献回顾刘艳芝１，李虹玲２，李艳明２，刘清霞２，晏　群２，邹明祥２，刘文恩２，李　军２（１．湖南省职业病防治院检验科，湖南长沙　４１０００７；２．中南大学湘雅医院检验科，湖南长沙　４１０００８）［摘　要］　圣乔治教堂诺卡菌隶属于诺卡菌属，诺卡菌广泛分布于土壤和水中，不属于人体正常菌群，主要通过呼吸道吸入和破损皮肤侵入人体。

诺卡菌病由诺卡菌感染所致，诺卡菌感染的常见部位以肺部和皮肤多见，眼部感染报道较少，圣乔治教堂诺卡菌所致眼部感染的报道更少。

因此，报道某院１例圣乔治教堂诺卡菌眼部感染的病例，并结合国内外文献进行复习，旨在提高临床对诺卡菌病的诊治水平。

［关　键　词］　圣乔治教堂诺卡菌；诺卡菌病；眼部感染［中图分类号］　Ｒ７７２．２犈狔犲犻狀犳犲犮狋犻狅狀狑犻狋犺犖狅犽犪狉犱犻犪犮狔狉犻犪犮犻犵犲狅狉犵犻犮犪：狅狀犲犮犪狊犲狉犲狆狅狉狋犪狀犱犾犻狋犲狉犪 狋狌狉犲狉犲狏犻犲狑犔犐犝犢犪狀 狕犺犻１，犔犐犎狅狀犵 犾犻狀犵２，犔犐犢犪狀 犿犻狀犵２，犔犐犝犙犻狀犵 狓犻犪２，犢犃犖犙狌狀２，犣犗犝犕犻狀犵狓犻犪狀犵２，犔犐犝犠犲狀 犲狀２，犔犐犑狌狀２（１．犇犲狆犪狉狋犿犲狀狋狅犳犆犾犻狀犻犮犪犾犔犪犫狅狉犪狋狅狉狔，犎狌狀犪狀犘狉犲狏犲狀狋犻狅狀犪狀犱犜狉犲犪狋犿犲狀狋犐狀狊狋犻狋狌狋犲犳狅狉犗犮犮狌狆犪狋犻狅狀犪犾犇犻狊犲犪狊犲狊，犆犺犪狀犵狊犺犪４１０００７，犆犺犻狀犪；２．犇犲狆犪狉狋犿犲狀狋狅犳犆犾犻狀犻犮犪犾犔犪犫狅狉犪狋狅狉狔，犡犻犪狀犵狔犪犎狅狊狆犻狋犪犾，犆犲狀狋狉犪犾犛狅狌狋犺犝狀犻狏犲狉狊犻狋狔，犆犺犪狀犵狊犺犪４１０００８，犆犺犻狀犪）［犃犫狊狋狉犪犮狋］　犖狅犽犪狉犱犻犪犮狔狉犻犪犮犻犵犲狅狉犵犻犮犪（犖．犮狔狉犻犪犮犻犵犲狅狉犵犻犮犪）ｂｅｌｏｎｇｓｔｏｔｈｅ犖狅犮犪狉犱犻犪狊狆狆．，犖狅犮犪狉犱犻犪ｄｉｓｔｒｉｂｕｔｅｓｗｉｄｅｌｙｉｎｓｏｉｌａｎｄｗａｔｅｒ，ｉｔｄｏｅｓｎｏｔｂｅｌｏｎｇｔｏｔｈｅｎｏｒｍａｌｆｌｏｒａｏｆｈｕｍａｎｂｏｄｙ，ｍａｉｎｌｙｉｎｖａｄｅｓｈｕｍａｎｂｏｄｙｔｈｒｏｕｇｈｒｅｓｐｉｒａｔｏｒｙｔｒａｃｔｉｎｈａｌａｔｉｏｎａｎｄｄａｍａｇｅｄｓｋｉｎ．Ｎｏｃａｒｄｉｏｓｉｓｉｓｃａｕｓｅｄｂｙ犖狅犮犪狉犱犻犪ｉｎｆｅｃｔｉｏｎ，犖狅犮犪狉犱犻犪ｉｎｆｅｃｔｉｏｎｉｓｃｏｍｍｏｎｉｎｔｈｅｌｕｎｇａｎｄｓｋｉｎ，ｔｈｅｒｅａｒｅｆｅｗｒｅｐｏｒｔｓｏｆｅｙｅｉｎｆｅｃｔｉｏｎ，ｒｅｐｏｒｔｓｏｆｅｙｅｉｎｆｅｃｔｉｏｎｃａｕｓｅｄｂｙ犖．犮狔狉犻犪犮犻 犵犲狅狉犵犻犮犪ｉｓｅｖｅｎｆｅｗｅｒ．Ｔｈｉｓｐａｐｅｒｒｅｐｏｒｔｓａｃａｓｅｏｆｅｙｅｉｎｆｅｃｔｉｏｎｗｉｔｈ犖．犮狔狉犻犪犮犻犵犲狅狉犵犻犮犪，ａｎｄｒｅｖｉｅｗｓｔｈｅｌｉｔｅｒａ ｔｕｒｅａｔｈｏｍｅａｎｄａｂｒｏａｄ，ｓｏａｓｔｏｉｍｐｒｏｖｅｃｌｉｎｉｃａｌｄｉａｇｎｏｓｉｓａｎｄｔｒｅａｔｍｅｎｔｏｆｎｏｃａｒｄｉｏｓｉｓ．［犓犲狔狑狅狉犱狊］　犖狅犽犪狉犱犻犪犮狔狉犻犪犮犻犵犲狅狉犵犻犮犪；ｎｏｃａｒｄｉｏｓｉｓ；ｅｙｅｉｎｆｅｃｔｉｏｎ 诺卡菌病（Ｎｏｃａｒｄｉｏｓｉｓ）是由诺卡菌（犖狅犮犪狉犱犻犪）感染引起的一种急慢性化脓性疾病。

分子生物学论文范文Title: Application of Molecular Biology Techniques in Understanding the Mechanisms of Cellular Reprogramming Abstract:Cellular reprogramming refers to the conversion of one cell type into another, often achieved through the activation or silencing of specific genes. In recent years, molecular biology techniques have been extensively used to unravel the underlying mechanisms involved in cellular reprogramming. This article aims to provide an overview of the diverse molecular biology techniques employed in this field, including DNA sequencing,gene expression analysis, gene editing, and epigenetic profiling. Furthermore, it discusses the impact of these techniques on our understanding of cellular reprogramming and potentialapplications in regenerative medicine and disease modeling. By exploring these cutting-edge molecular biology techniques, wecan decode the intricate processes underlying cellular reprogramming and harness them for therapeutic purposes.1. IntroductionCellular reprogramming is a revolutionary technique that holds immense potential in regenerative medicine. It allows the conversion of differentiated cells into induced pluripotent stem cells (iPSCs), which possess the ability to differentiate intoany cell type. Furthermore, cellular reprogramming provides valuable insights into the mechanisms governing cellularidentity and differentiation. In recent years, advances in molecular biology techniques have greatly contributed to our understanding of cellular reprogramming. This article highlights the key molecular biology techniques utilized in this field and their impact on the field of regenerative medicine.2. DNA Sequencing3. Gene Expression AnalysisGene expression analysis techniques, such as quantitative reverse transcription-polymerase chain reaction (qRT-PCR) and RNA sequencing, have allowed researchers to examine the expression levels of thousands of genes simultaneously. This has provided valuable insights into the molecular events occurring during cellular reprogramming. By characterizing the gene expression profile of iPSCs, researchers have identifiedspecific gene networks and signaling pathways that drive the reprogramming process. Additionally, gene expression analysis has contributed to the identification of key genes responsible for maintaining cellular identity.4. Gene EditingGene editing techniques, such as the clustered regularly interspaced short palindromic repeats (CRISPR)-Cas9 system, have revolutionized the field of molecular biology. They enabletargeted modification of specific genes, enabling researchers to investigate the functional significance of specific genes and genetic variants during cellular reprogramming. Gene editing has been utilized to introduce or eliminate specific transcription factors or epigenetic modifiers to enhance or inhibit reprogramming efficiency. Furthermore, it allows the correction of genetic mutations associated with certain diseases, paving the way for personalized medicine.5. Epigenetic ProfilingEpigenetic modifications play a crucial role in cellular reprogramming by regulating gene expression patterns. Techniques such as chromatin immunoprecipitation sequencing (ChIP-seq) and DNA methylation profiling have been employed to map specific histone modifications and DNA methylation patterns during reprogramming. These techniques have provided insights into the dynamics of chromatin accessibility and epigenetic remodeling that occur during cellular reprogramming.6. Applications in Regenerative Medicine and Disease ModelingThe understanding gained through molecular biology techniques has paved the way for potential therapeutic applications in regenerative medicine and disease modeling. By deciphering the molecular mechanisms underlying cellular reprogramming, researchers can develop strategies to generatepatient-specific iPSCs for transplantation, bypassing the need for immunosuppression. Additionally, cellular reprogramming can be utilized to model diseases in vitro, enabling the study of disease progression and drug screening in a personalized manner.7. ConclusionMolecular biology techniques have significantly advanced our understanding of cellular reprogramming. The integration of DNA sequencing, gene expression analysis, gene editing, and epigenetic profiling has provided valuable insights into the mechanisms governing cellular identity and differentiation. These techniques have not only expanded our knowledge ofcellular reprogramming but also hold great potential in the fields of regenerative medicine and disease modeling. By harnessing the power of molecular biology, we can unlock thefull therapeutic potential of cellular reprogramming in the future.。

delimiter [dɪ'lɪmi:tə] 定界符,分隔符trigger *ˈtrɪgə(r)] 触发器\发射oracle *ˈɒrəkl 圣贤;哲人database *ˈdeɪtəbeɪs] 数据库;资料库;信息库collate [kəˈleɪt] 校对;（装钉）整理general *ˈdʒenrəl] 普遍的; 常规; 上将declare [dɪˈkleə(r)] 声明，发表宣言procedure [prəˈsi:dʒə(r)] 过程，步骤；(走)程序program *ˈprəʊgræm] (电脑)程序; 节目status *ˈsteɪtəs] 状态，地位repeat [rɪˈpi:t] 重复;背诵(存储过程) case (实)例convert [kənˈvɜ:t] 转变，换算 (存储过程) cursor *ˈkɜ:sə(r)] 光标fetch 取来，拿取(存储过程) handler *ˈhændlə(r)] 处理者 (存储过程) continue [kənˈtɪnju:] 持续，逗留，延期truncate [trʌŋˈkeɪt] 缩短,截断，删除affect [əˈfekt] 受影响（错误时）syntax *ˈsɪntæks] 语法（错误时）manual *ˈmænjuəl] 手册，指南（错误时）correspond *ˌkɒrəˈspɒnd] 一致，符合（错误时）version *ˈvɜ:ʃn] 版本，译本default [dɪˈfɔ:lt] 默认，弃权，拖欠incorrect *ˌɪnkəˈrekt] 不正确的，错误的variable *ˈveəriəbl] 变量，可变的，变化的exit *ˈeksɪt] / export *ˈekspɔ:t] 退出,出口 / 输出,出口; display [dɪˈspleɪ] 显示，显示器client *ˈklaɪənt] 客户端，顾客connection [kəˈnekʃn] 连接，联系（错误时）result [rɪˈzʌlt] 结果，后果（错误时）information [ɪnfəˈmeɪʃn] 数据，信息（错误时）schema *ˈski:mə] 概要，图表（错误时）performance [pəˈfɔ:məns] 执行，表现（错误时）flush 冲刷（修改权限刷新）grant [grɑ:nt+授予，同意（修改权）identified [aɪ'dentɪfaɪd] 确认，确定（修改权）revoke [rɪˈvəʊk] 废除，撤销（修改权）deny [dɪˈnaɪ] 拒绝（错误信息时）privilege *ˈprɪvəlɪdʒ] 特权（修改权限刷新）mix / relay *ˈri:leɪ] 混合 / 传递（从服务器）replication *ˌreplɪ'keɪʃn] 复制（主从）master *ˈmɑ:stə(r)+/ slave [sleɪv] 主人 / 奴隶 (主从服务器) host [həʊst] 电脑主机，主持人，主人position [pəˈzɪʃn] 位置compare [kəmˈpeə(r)] 比较，对照escape [ɪˈskeɪp] 逃脱;逃离; 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硫氧还蛋白还原酶1（TrxR1）属于吡啶核苷酸二硫化物氧化还原酶家族，作为目前已知的唯一能够还原硫氧还蛋白（Trx ）的酶，TrxR1可以将氧化型的Trx 还原成还原型Trx ［1］。

还原型的Trx 能够还原多种蛋白的二硫键，进而调控细胞的多种生物进程，如细胞增殖、分化以及凋亡等等［2］。

肿瘤细胞的代谢水平远高于正常细胞，因而在细胞内会产生大量活性氧［3］。

因此，肿瘤细胞往往会通过诱导抗氧化酶的表达来维持自身的氧化还原稳态，而TrxR1是其中一种被诱导的抗氧化酶。

近年来研究发现，TrxR1在多种肿瘤组织和细胞中高表达，如乳腺癌［4］、胃癌［5］、肺癌［6］和肠癌［7］等，并通过蛋白激酶B （PKB,又名AKT ）、丝裂原活化蛋白激酶（MAPK ）及信号传导及转录激活蛋白3（Stat3）等信号通路促进肿瘤细胞增殖、抑制凋亡及诱导耐药，是一个理想的抗肿瘤药物开发靶点［3,8］。

目前靶向TrxR1的药物具有丰富的骨架结构，但尚无进入临床使用的TrxR1靶向药物，其中一大制约因素就是Construction and identification of a HEK293cell line with stable TrxR1overexpressionLÜXiaomei 1,ZHOU Zhiyin 1,ZHU Li 1,ZHOU Ji 2,HUANG Huidan 1,ZHANG Chao 1,LIU Xiaoping 11Center of Drug Screening and Evaluation,Wannan Medical College,Wuhu 241000,China;2Center for Reproductive Medicine,First Affiliated Hospital of Wannan Medical College,Wuhu 241000,China摘要：目的构建稳定过表达硫氧还蛋白还原酶1（TrxR1）的HEK293细胞株，为TrxR1的功能研究以及靶向TrxR1药物筛选提供细胞模型。

基金项目：２０２３年度山西药科职业学院院级课题（２０２３１２８）鸡ＩＬ １５基因的克隆及序列分析张　凤，田艳花，牛四坤，台晶杰，刘文娟通信作者山西药科职业学院，山西太原０３００３１摘　要　目的　利用基因工程的方法，将鸡白细胞介素１５（ＣｈＩＬ １５）基因在原核表达系统中进行表达，为ＣｈＩＬ １５的应用提供科学依据。

方法　根据美国国家生物信息中心（ＮＣＢＩ）ＧｅｎＢａｎｋ中已发表的ＣｈＩＬ １５基因，设计引物，用ＰＣＲ方法扩增目的基因片段。

结果　成功从鸡的脾脏中扩增出含编码区的ＣｈＩＬ １５基因，ＣｈＩＬ １５基因为５８１ｂｐ，通过测序进行比对，表明ＣｈＩＬ １５基因克隆成功。

提取的质粒经酶切鉴定、ＰＣＲ鉴定、测序比对以及编码成氨基酸进行比对均与Ｇｅｎ Ｂａｎｋ中登录号ＡＦ１３９０９７．１基本符合，核苷酸的同源性为９９．８２％。

结论　通过使用ＰＣＲ方法成功扩增出来的ＣｈＩＬ １５基因片段，具有较高的同源性，并与已发表的基因序列相符。

这为进一步研究ＣｈＩＬ １５基因的功能和应用提供了科学依据。

关键词　鸡；ＣｈＩＬ １５；克隆；序列分析Ｃｌｏｎｉｎｇａｎｄｓｅｑｕｅｎｃｉｎｇｏｆｔｈｅｇｅｎｅｅｎｃｏｄｉｎｇｃｈｉｃｋｅｎｉｎｔｅｒｌｅｕｋｉｎ １５ＺＨＡＮＧＦｅｎｇ，ＴＩＡＮＹａｎｈｕａ，ＮＩＵＳｉｋｕｎ，ＴＡＩＪｉｎｇｊｉｅ，ＬＩＵＷｅｎｊｕａｎｃｏｒｒｅｓｐｏｎｄｉｎｇａｕｔｈｏｒＳｈａｎｘｉＰｈａｒｍａｃｅｕｔｉｃａｌＶｏｃａｔｉｏｎａｌＣｏｌｌｅｇｅ，Ｔａｉｙｕａｎ０３００３１，ＣｈｉｎａＡｂｓｔｒａｃｔ　Ｏｂｊｅｃｔｉｖｅ　Ｔｏｅｘｐｒｅｓｓｔｈｅｃｈｉｃｋｅｎｉｎｔｅｒｌｅｕｋｉｎ １５（ＣｈＩＬ １５）ｇｅｎｅｉｎａｐｒｏｋａｒｙｏｔｉｃｅｘｐｒｅｓｓｉｏｎｓｙｓｔｅｍｂｙｍｅａｎｓｏｆｇｅｎｅｔｉｃｅｎｇｉｎｅｅｒｉｎｇｔｏｐｒｏｖｉｄｅａｓｃｉｅｎｔｉｆｉｃｂａｓｉｓｆｏｒｔｈｅａｐｐｌｉｃａｔｉｏｎｏｆＣｈＩＬ １５．Ｍｅｔｈｏｄｓ　ＰｒｉｍｅｒｓｗｅｒｅｄｅｓｉｇｎｅｄｂａｓｅｄｏｎｔｈｅＣＨＥＲ １５ｇｅｎｅｉｎｔｈｅＧｅｎＢａｎｋｏｆｔｈｅＮａｔｉｏｎａｌＣｅｎｔｅｒｆｏｒＢｉｏｌｏｇｉｃａｌＩｎｆｏｒｍａｔｉｏｎ（ＮＣＢＩ），ａｎｄＰＣＲｗａｓｕｔｉｌｉｚｅｄｔｏａｍｐｌｉｆｙｔｈｅｔａｒｇｅｔｇｅｎｅｆｒａｇｍｅｎｔ．Ｒｅｓｕｌｔ　ＴｈｅＣｈＩＬ １５ｇｅｎｅｃｏｎｔａｉｎｉｎｇｔｈｅｃｏｄｉｎｇｒｅｇｉｏｎｗａｓｓｕｃｃｅｓｓｆｕｌｌｙａｍｐｌｉｆｉｅｄｆｒｏｍｔｈｅｓｐｌｅｅｎｏｆｃｈｉｃｋｅｎｓ，ｗｉｔｈａｌｅｎｇｔｈｏｆ５８１ｂｐ．ＳｅｑｕｅｎｃｉｎｇａｎｄｃｏｍｐａｒｉｓｏｎｓｈｏｗｅｄｔｈａｔｔｈｅＣｈＩＬ １５ｇｅｎｅｗａｓｓｕｃｃｅｓｓｆｕｌｌｙｃｌｏｎｅｄ．Ｔｈｅｅｘｔｒａｃ ｔｅｄｐｌａｓｍｉｄｓｗｅｒｅｉｄｅｎｔｉｆｉｅｄｂｙｅｎｚｙｍｅｄｉｇｅｓｔｉｏｎ，ＰＣＲ，ｓｅｑｕｅｎｃｉｎｇ，ａｎｄａｍｉｎｏａｃｉｄｃｏｄｉｎｇａｎｄｗｅｒｅｃｏｎｓｉｓｔｅｎｔｗｉｔｈｔｈｅｒｅｇｉｓｔｒａ ｔｉｏｎｎｕｍｂｅｒＡＦ１３９０９７．１ｉｎＧｅｎＢａｎｋ．Ｔｈｅｎｕｃｌｅｏｔｉｄｅｈｏｍｏｌｏｇｙｗａｓ９９．８２％．Ｃｏｎｃｌｕｓｉｏｎ　ＴｈｅＣｈＩＬ １５ｇｅｎｅｆｒａｇｍｅｎｔｓｕｃ ｃｅｓｓｆｕｌｌｙａｍｐｌｉｆｉｅｄｂｙｔｈｅＰＣＲｍｅｔｈｏｄｈａｓｈｉｇｈｈｏｍｏｌｏｇｙａｎｄｉｓｃｏｎｓｉｓｔｅｎｔｗｉｔｈｔｈｅｐｕｂｌｉｓｈｅｄｇｅｎｅｓｅｑｕｅｎｃｅ．ＴｈｉｓｐｒｏｖｉｄｅｓａｓｃｉｅｎｔｉｆｉｃｂａｓｉｓｆｏｒｆｕｒｔｈｅｒｒｅｓｅａｒｃｈｏｎｔｈｅｆｕｎｃｔｉｏｎａｎｄａｐｐｌｉｃａｔｉｏｎｏｆｔｈｅＣｈＩＬ １５ｇｅｎｅ．Ｋｅｙｗｏｒｄｓ　ｃｈｉｃｋｅｎ；ＣｈＩＬ １５；ｃｌｏｎｉｎｇ；ｓｅｑｕｅｎｃｅａｎａｌｙｓｉｓｄｏｉ：１０．１９５６７／ｊ．ｃｎｋｉ．１００８ ０４１４．２０２４．０４．００１０　引言细胞因子是由细胞分泌的一类具有调节细胞生长、分化和免疫活性的蛋白质。

疾病差异基因表达英文Disease-specific gene expression: A deep dive into the molecular mechanisms.Diseases are often caused by complex interactions between genetic and environmental factors. Among these, the dysregulation of gene expression plays a pivotal role in the pathogenesis of various diseases. Disease-specific gene expression refers to the altered expression patterns of genes that are uniquely associated with a particular disease. Understanding the molecular mechanisms underlying these changes is crucial for developing effective diagnostic tools and therapeutic strategies.The study of disease-specific gene expression involves the analysis of gene expression profiles in diseased tissues or cells compared to their normal counterparts. This can be achieved through various high-throughput techniques such as microarrays, next-generation sequencing, and proteomics. These technologies allow researchers tomeasure the expression levels of thousands of genes simultaneously, providing a comprehensive view of the transcriptional landscape in diseased tissues.One of the key challenges in the field is the identification of disease-specific gene expression signatures. These signatures refer to the unique combinations of genes that are differentially expressed in a particular disease. The identification of these signatures requires the integration of bioinformatics tools and statistical methods to filter out the most relevant genes from the vast amount of data generated by high-throughput techniques.Once identified, disease-specific gene expression signatures can be used for various applications. Firstly, they can serve as biomarkers for disease diagnosis. By measuring the expression levels of specific genes inpatient samples, doctors can accurately diagnose diseases at an early stage, enabling timely treatment and improved patient outcomes.Secondly, disease-specific gene expression signatures can be used to understand the pathogenesis of diseases. By analyzing the functions and interactions of these genes, researchers can gain insights into the molecular mechanisms underlying the development and progression of diseases. This knowledge can then be used to design targeted therapeutic strategies that aim to modulate the expression of these genes.Moreover, disease-specific gene expression signatures can also be used to predict patient outcomes and responses to treatment. By correlating gene expression patterns with clinical outcomes, researchers can identify subgroups of patients who are more likely to respond favorably to a particular treatment. This information can guide clinicians in making personalized treatment decisions for their patients.In addition, disease-specific gene expression signatures can serve as targets for drug discovery and development. By identifying the genes that are dysregulated in a particular disease, researchers can focus theirefforts on developing drugs that can modulate the expression of these genes. This approach has led to the discovery of several novel therapeutics that are currently being used to treat diseases such as cancer, inflammatory diseases, and neurological disorders.In conclusion, the study of disease-specific gene expression has opened new avenues for understanding the pathogenesis of diseases and developing effective diagnostic tools and therapeutic strategies. With the advent of advanced technologies and bioinformatics methods, we are poised to make further progress in this field and bring about improvements in patient care and outcomes.。

CD5阳性的弥漫性大B细胞淋巴瘤临床病理分析韩雯;刘林华;孙振柱;王春;张晓军【摘要】目的探讨CD5阳性的弥漫性大B细胞淋巴瘤(diffuse large B cell lymphoma,DLBCL)的临床病理特征及临床预后意义.方法分析13例CD5阳性的DLBCL的临床资料、组织学特点、免疫表型及临床随访资料,并复习相关文献.结果13例DLBCL组织CD5抗原阳性,阳性反应定位于肿瘤细胞膜,全部表达B系列抗原CD20、CD79a.组织学特征:9例为中心母细胞变异型,2例为免疫母细胞变异型,2例为间变型.治疗方案选用R-CHOP,其中7例完全缓解,2例部分缓解,1例进展;另3例未选择治疗.随访4 ～46个月,3例死亡,9例情况良好,1例失访.结论 CD5阳性的DLBCL临床少见,老人多发,其分期高、预后不良,亟需探寻新的治疗方法延长患者的生存期.【期刊名称】《临床与实验病理学杂志》【年(卷),期】2014(030)003【总页数】4页(P244-247)【关键词】淋巴瘤;大B细胞;CD5;形态学;鉴别诊断;免疫组织化学【作者】韩雯;刘林华;孙振柱;王春;张晓军【作者单位】石河子大学医学院病理学教研室,石河子832000;新疆维吾尔自治区人民医院病理科,乌鲁木齐830001;新疆维吾尔自治区人民医院病理科,乌鲁木齐830001;新疆维吾尔自治区人民医院病理科,乌鲁木齐830001;新疆维吾尔自治区人民医院病理科,乌鲁木齐830001;新疆维吾尔自治区人民医院病理科,乌鲁木齐830001【正文语种】中文【中图分类】R733.4弥漫性大B细胞淋巴瘤(diffuse large B cell lymphoma，DLBCL)是成人中最常见的非霍奇金淋巴瘤(non-Hodgkin’s lymphoma，NHL)的一种类型，侵袭性高。

由于在组织学形态、免疫反应和分子遗传学方面存在显著异质性，其临床预后也不一致。