胰腺癌早期检测的生物标志物（英文）

胰腺癌症是最难诊断和治疗的恶性肿瘤之一,其特点是发病隐匿、进展迅速、预后差。目前,手术治疗仍然是首选治疗方法。然而由于缺乏早期症状,大约70%的患者在确诊时已经出现局部扩散或远端转移,从而无法进行手术治疗。由此看来,早期检测是提高患者治疗效果和预后的有效途径。临床上使用的成像方法 （CT、MRI、EUS等）通常无法检测早期病变,并且很容易受到操作员的影响。常规临床标志物如CA19-9、CA125、CA242和CEA受到限制,其敏感性或特异性不令人满意。因此,寻找新的具有高敏感性和特异性的标志物是实现胰腺癌早期检测的关键。近年来,对生物标志物的广泛研究主要集中在遗传学、转录组学和蛋白质组学上。特别是由microRNA(miRNA)、long non-coding RNA(lncRNA)和circRNA(circRNA)组成的非蛋白质编码RNA(non-protein coding RNA,ncRNA)为胰腺癌的早期检测提出了许多新思路。然而,其中绝大多数仍处于实验室研究阶段。而一项成熟的生物标志物研究应该整合基因组学、转录组学、蛋白质组学或代谢组学的数据,并结合患者的个体特征（如体重指数...     

Integration of IgA and IgG Autoantigens Improves Performance of Biomarker Panels for Early Diagnosis of Lung Cancer

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Highlights

• •HuProt array-based identification of autoantigens in serum of early lung cancer.
• •Independent validation of early lung cancer biomarker candidates with ELISA.
• •Bioinformatics-aided identification of a biomarker panel.
• •Independent verification of the panel with ELISA and immunohistochemistry.

     

循环miRNA 表达谱在肺癌早期诊断、判断预后和指导化疗中的应用

MicroRNA(miRNA)是一种高保守,长度大概21-23个核苷酸,非蛋白编码RNA,起着调节基因表达的作用。近年来有关miRNA与肺癌的关系已经得到证实,并且成为当前研究的热点。miRNA能整体调节基因表达,这使得miRNA表达谱在作为生物信号方面比蛋白编码基因更具有提示作用。最近发现miRNA以被保护的状态存在于循环血液中,这使得miRNA表达的发现具有非侵袭性、重现性以及易检测性。研究显示血浆miRNA表达谱可作为肺癌生物信号分子,在肺癌早期诊断、判断预后和指导化疗药物应用等方面具有重要作用。本文将对血浆miRNA与肺癌的研究进展,以及在肺癌早期诊断、判断预后和指导化疗药物应用等方面作一综述。     

QCMAP: An Interactive Web‐Tool for Performance Diagnosis and Prediction of LC‐MS Systems

The increasing role played by liquid chromatography‐mass spectrometry (LC‐MS)‐based proteomics in biological discovery has led to a growing need for quality control (QC) on the LC‐MS systems. While numerous quality control tools have been developed to track the performance of LC‐MS systems based on a pre‐defined set of performance factors (e.g., mass error, retention time), the precise influence and contribution of the performance factors and their generalization property to different biological samples are not as well characterized. Here, a web‐based application (QCMAP) is developed for interactive diagnosis and prediction of the performance of LC‐MS systems across different biological sample types. Leveraging on a standardized HeLa cell sample run as QC within a multi‐user facility, predictive models are trained on a panel of commonly used performance factors to pinpoint the precise conditions to a (un)satisfactory performance in three LC‐MS systems. It is demonstrated that the learned model can be applied to predict LC‐MS system performance for brain samples generated from an independent study. By compiling these predictive models into our web‐application, QCMAP allows users to benchmark the performance of their LC‐MS systems using their own samples and identify key factors for instrument optimization. QCMAP is freely available from: http://shiny.maths.usyd.edu.au/QCMAP/ .