Circulating MicroRNAs as Non-Invasive Biomarkers for Early Detection of Non-Small-Cell Lung Cancer

BackgroundDetection of lung cancer at an early stage by sensitive screening tests could be an important strategy to improving prognosis. Our objective was to identify a panel of circulating microRNAs in plasma that will contribute to early detection of lung cancer.ResultsWe identified a panel of 24 microRNAs with optimum classification performance. The combination of these 24 microRNAs alone could discriminate lung cancer cases from non-cancer controls with an AUC of 0.92 (95% CI: 0.87-0.95). This classification improved to an AUC of 0.94 (95% CI: 0.90-0.97) following addition of sex, age and smoking status to the model. Internal validation of the model suggests that the discriminatory power of the panel will be high when applied to independent samples with a corrected AUC of 0.78 for the 24-miRNA panel alone.ConclusionOur 24-microRNA predictor improves lung cancer prediction beyond that of known risk factors.     

Meta-analysis of gene expression data: a predictor-based approach

MOTIVATION: With the increasing availability of cancer microarray data sets there is a growing need for integrative computational methods that evaluate multiple independent microarray data sets investigating a common theme or disorder. Meta-analysis techniques are designed to overcome the low sample size typical to microarray experiments and yield more valid and informative results than each experiment separately. RESULTS: We propose a new meta-analysis technique that aims at finding a set of classifying genes, whose expression level may be used to answering the classification question in hand. Specifically, we apply our method to two independent lung cancer microarray data sets and identify a joint core subset of genes which putatively play an important role in tumor genesis of the lung. The robustness of the identified joint core set is demonstrated on a third unseen lung cancer data set, where it leads to successful classification using very few top-ranked genes. Identifying such a set of genes is of significant importance when searching for biologically meaningful biomarkers. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.     

Use of RNA isolated from feces as a promising tool for the early detection of colorectal cancer

Colorectal cancer is one of the most common forms of cancer worldwide. Early detection would allow patients to be treated surgically and halt the progression of the disease; however, the current methods of early detection are invasive (colonoscopy and sigmoidoscopy) or have low sensitivity (fecal occult blood test). The altered expression of genes in stool samples of patients with colorectal cancer can be determined by RT-PCR. This is a noninvasive and highly sensitive technique for colorectal cancer screening. According to information gathered in this review and our own experience, the use of fecal RNA to determine early alterations in gene expression due to malignancy appears to be a promising alternative to the current detection methods and owing to its low cost could be implemented in public health services.     

肺癌诊断方法的比较与研究进展

近年来,随着周围环境的恶化,肺癌的发病率最高并且呈现逐年上升的趋势,且其早期表现隐匿,很容易被忽视,很多患者确诊时已属晚期,丧失去了治疗的最佳时机。.本文主要总结和比较了几种传统的肺癌诊断方法如X线片、CT、MRI、PET-CT,并介绍了几种肺癌的最新的诊断技术,比如肺泡灌洗液或血清肿瘤标记物的联合检测、呼出气中的有机化合物的构成分析以及纤维支气管镜镜检技术等。上述方法诊断肺癌的敏感性和特异性各有优势,临床医生在临床工作中应合理应用上述检测方法,必要时联用多种检测手段,尤其要重视支气管肺泡灌洗液癌胚抗原如Cyfra21-1和CEA的联合检测,利用其较高的敏感性以提早发现和诊断肺癌。     

痰液肺癌细胞Ras基因和P63基因的表达与分子生物学意义

目的研究骨髓间充质干细胞分化为心肌细胞过程中Notch表达的研究。方法别用免疫细胞化学方法和Western-blot方法检测167例患者痰液中Ras基因和P63基因的表达情况,分析其对早期肺癌诊断的意义。结果肺癌患者痰液中的Ras基因在痰脱落细胞中有明显高表达,阳性率为75%。P63基因在肺癌痰细胞中阳性表达率为34.78%。结论利用Ras基因标记物可以标记出痰液中的肺非典型增生细胞和肺癌细胞,有利于肺癌的早期发现和诊断,并且可以作为肺癌的普查指标或手段。     

Cancer screening by systemic administration of a gene delivery vector encoding tumor-selective secretable biomarker expression

Cancer biomarkers facilitate screening and early detection but are known for only a few cancer types. We demonstrated the principle of inducing tumors to secrete a serum biomarker using a systemically administered gene delivery vector that targets tumors for selective expression of an engineered cassette. We exploited tumor-selective replication of a conditionally replicative Herpes simplex virus (HSV) combined with a replication-dependent late viral promoter to achieve tumor-selective biomarker expression as an example gene delivery vector. Virus replication, cytotoxicity and biomarker production were low in quiescent normal human foreskin keratinocytes and high in cancer cells in vitro. Following intravenous injection of virus >90% of tumor-bearing mice exhibited higher levels of biomarker than non-tumor-bearing mice and upon necropsy, we detected virus exclusively in tumors. Our strategy of forcing tumors to secrete a serum biomarker could be useful for cancer screening in high-risk patients, and possibly for monitoring response to therapy. In addition, because oncolytic vectors for tumor specific gene delivery are cytotoxic, they may supplement our screening strategy as a "theragnostic" agent. The cancer screening approach presented in this work introduces a paradigm shift in the utility of gene delivery which we foresee being improved by alternative vectors targeting gene delivery and expression to tumors. Refining this approach will usher a new era for clinical cancer screening that may be implemented in the developed and undeveloped world.     

Early detection of colon cancer: new tests on the horizon

This year, the American Cancer Society reported that the rate of decline in both the incidence and mortality of colorectal cancer has increased over the last two decades. This success is felt to be attributable to the early detection and treatment of colonic adenomas and early-stage colorectal cancers. However, the current recommended 'menu of options' for screening is limited by poor patient acceptance, low sensitivity, and both high cost and poor accessibility for application to a large general screening population (colonoscopy). Computerized tomography and magnetic resonance colonography offer an alternative method for the identification of polyps and early lesions in certain patients, but have cost, access, and acceptance limitations that are similar to those of colonoscopy; thus, they present similar barriers to their use in broad population screening. These limitations provide a strong rationale for the development of early colorectal cancer detection biomarkers that are simple to use and are cost effective. A successful biomarker or biomarker panel, coupled with the colonoscopic follow-up of only those patients with positive results, would reduce the burden and morbidity associated with the screening of colonoscopy. This would most likely result in enhanced adherence to colorectal screening, as well as a dramatic reduction in the incidence and mortality rates of colorectal cancer. In this paper, we review recent advances in the discovery of potential colorectal cancer biomarkers. Their applicability to clinical population screening will require large prospective validation.     

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。     

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。     

DNA Methylation Markers in Lung Cancer

Lung cancer is the most common cancer and the leading cause of cancer-related morbidity and mortality worldwide. As early symptoms of lung cancer are minimal and non-specific, many patients are diagnosed at an advanced stage. Despite a concerted effort to diagnose lung cancer early, no biomarkers that can be used for lung cancer screening and prognosis prediction have been established so far. As global DNA demethylation and gene-specific promoter DNA methylation are present in lung cancer, DNA methylation biomarkers have become a major area of research as potential alternative diagnostic methods to detect lung cancer at an early stage. This review summarizes the emerging DNA methylation changes in lung cancer tumorigenesis, focusing on biomarkers for early detection and their potential clinical applications in lung cancer.     

应用双向热循环消减SELEX技术筛选肺癌血清核酸适配体

肺癌是发病率和死亡率较高的恶性肿瘤。现阶段,用于肺癌早期诊断的血清肿瘤标志物因其特异性与敏感性均较低,严重影响肺癌的临床诊断和治疗。本文用双向热循环消减指数富集的配基进化(systematic evolution of ligands by exponential enrichment, SELEX)技术,筛选肺癌和非癌血清标志物的核酸适配体,建立肺癌的检测方法,提高诊断和治疗效率。实验用环氧基琼脂磁珠为筛选介质,以非癌混合血清、肺癌混合血清作为双向靶标。应用热循环消减SELEX技术,经19轮筛选分别获得非癌和肺癌血清的特异性核酸适配体。通过高通量测序,得到 40条非癌核酸适配体序列和 41条肺癌核酸适配体序列。从肺癌与非癌血清特异性核酸适配体序列中分别挑选出高丰度的 4条序列,合成后制成检测试剂,经临床血清验证,阳性率为 90%。该检测方法检测灵敏度高,为肺癌的早期诊断和治疗提供了新的分子识别元件。     

Identifying circulating miRNA biomarkers for early diagnosis and monitoring of lung cancer

Liquid biopsy refers to the sampling, screening, and detecting potential biomarkers in unique liquid samples for clinical use. Lung cancer is one of the most highly frequent cancer subtypes, which is hard to be early diagnosed and monitored by radiological and histopathological evaluation that are the most general and accurate methods. Circulating miRNA is a potential clinical examination index for tumor detection and monitoring tumorigenesis progression using liquid biopsy. However, recognizing and validating the unique clinical values of each candidate circulating miRNA is expensive and time consuming. In this study, we presented a novel computational approach for identifying significant circulating miRNAs that may be applied to early screening, diagnosis, and constant monitoring of lung cancer progression. This approach incorporated several machine learning algorithms and was applied on the expression profiles of circulating miRNAs on lung cancer patients and control samples. In brief, a powerful feature selection method, minimum redundancy maximum relevance, was adopted to evaluate the importance of all features, resulting in a feature list. Then, incremental feature selection incorporating random forest followed to extract key circulating miRNAs. At the same time, an efficient classifier with MCC 0.740 was built. Top five circulating miRNAs, including miR-92a, miR-140-5p, miR-331-3p, miR-223, miR-374a, were analyzed and confirmed that they participated in the pathogenesis of lung cancer, indicating their significant prognosis power in lung cancer.     

Screening for fetal malformations using ultrasound and measurements of alpha-fetoprotein in maternal serum.

Fetal ultrasound combined with semiquantitative measurements of alpha-fetoprotein in maternal serum was used for early detection of neural tube defects and omphalocele in 10 147 pregnancies. The accurate assessment of gestational age, obtained by ultrasound, facilitated evaluation of alpha-fetoprotein concentrations in selecting cases for amniocentesis. The advantage of screening with two independent methods is suggested by the finding that eight out of 10 cases with malformations (spina bifida, encephalocele, anencephalus, omphalocele) were detected when both methods were used. Screening by routine ultrasound alone detected only four malformations and by measurement of alpha-fetoprotein alone only seven. The results suggest that, in a low risk population, ultrasound should be combined with the measurement of alpha-fetoprotein in screening for neural tube defects. Measurement of alpha-fetoprotein is indispensable in detection of the small neural tube defects, where the fetus would survive with severe sequelae. The semi-quantitative analysis of alpha-fetoprotein that may be used in combination with ultrasound examination is of negligible cost.     

Systematic benchmarking of microarray data classification: assessing the role of non-linearity and dimensionality reduction

MOTIVATION: Microarrays are capable of determining the expression levels of thousands of genes simultaneously. In combination with classification methods, this technology can be useful to support clinical management decisions for individual patients, e.g. in oncology. The aim of this paper is to systematically benchmark the role of non-linear versus linear techniques and dimensionality reduction methods. RESULTS: A systematic benchmarking study is performed by comparing linear versions of standard classification and dimensionality reduction techniques with their non-linear versions based on non-linear kernel functions with a radial basis function (RBF) kernel. A total of 9 binary cancer classification problems, derived from 7 publicly available microarray datasets, and 20 randomizations of each problem are examined. CONCLUSIONS: Three main conclusions can be formulated based on the performances on independent test sets. (1) When performing classification with least squares support vector machines (LS-SVMs) (without dimensionality reduction), RBF kernels can be used without risking too much overfitting. The results obtained with well-tuned RBF kernels are never worse and sometimes even statistically significantly better compared to results obtained with a linear kernel in terms of test set receiver operating characteristic and test set accuracy performances. (2) Even for classification with linear classifiers like LS-SVM with linear kernel, using regularization is very important. (3) When performing kernel principal component analysis (kernel PCA) before classification, using an RBF kernel for kernel PCA tends to result in overfitting, especially when using supervised feature selection. It has been observed that an optimal selection of a large number of features is often an indication for overfitting. Kernel PCA with linear kernel gives better results.     

Markers of small cell lung cancer

Lung cancer is the number one cause of cancer death; however, no specific serum biomarker is available till date for detection of early lung cancer. Despite good initial response to chemotherapy, small-cell lung cancer (SCLC) has a poor prognosis. Therefore, it is important to identify molecular markers that might influence survival and may serve as potential therapeutic targets. The review aims to summarize the current knowledge of serum biomarkers in SCLC to improve diagnostic efficiency in the detection of tumor progression in lung cancer. The current knowledge on the known serum cytokines and tumor biomarkers of SCLC is emphasized. Recent findings in the search for novel diagnostic and therapeutic molecular markers using the emerging genomic technology for detecting lung cancer are also described. It is believed that implementing these new research techniques will facilitate and improve early detection, prognostication and better treatment of SCLC.     

Quantitative analysis of DNA methylation after whole bisulfitome amplification of a minute amount of DNA from body fluids

Cell-free circulating DNA isolated from the plasma of individuals with cancer has been shown to harbor cancer-associated changes in DNA methylation, and thus it represents an attractive target for biomarker discovery. However, the reliable detection of DNA methylation changes in body fluids has proven to be technically challenging. Here we describe a novel combination of methods that allows quantitative and sensitive detection of DNA methylation in minute amounts of DNA present in body fluids (quantitative Methylation Analysis of Minute DNA amounts after whole Bisulfitome Amplification, qMAMBA). This method involves genome-wide amplification of bisulphite-modified DNA template followed by quantitative methylation detection using pyrosequencing and allows analysis of multiple genes from a small amount of starting DNA. To validate our method we used qMAMBA assays for four genes and LINE1 repetitive sequences combined with plasma DNA samples as a model system. qMAMBA offered high efficacy in the analysis of methylation levels and patterns in plasma samples with extremely small amounts of DNA and low concentrations of methylated alleles. Therefore, qMAMBA will facilitate methylation studies aiming to discover epigenetic biomarkers, and should prove particularly valuable in profiling a large sample series of body fluids from molecular epidemiology studies as well as in tracking disease in early diagnostics.     

Genomic and proteomic biomarkers for cancer: A multitude of opportunities

Biomarkers are molecular indicators of a biological status, and as biochemical species can be assayed to evaluate the presence of cancer and therapeutic interventions. Through a variety of mechanisms cancer cells provide the biomarker material for their own detection. Biomarkers may be detectable in the blood, other body fluids, or tissues. The expectation is that the level of an informative biomarker is related to the specific type of disease present in the body. Biomarkers have potential both as diagnostic indicators and monitors of the effectiveness of clinical interventions. Biomarkers are also able to stratify cancer patients to the most appropriate treatment. Effective biomarkers for the early detection of cancer should provide a patient with a better outcome which in turn will translate into more efficient delivery of healthcare. Technologies for the early detection of cancer have resulted in reductions in disease-associated mortalities from cancers that are otherwise deadly if allowed to progress. Such screening technologies have proven that early detection will decrease the morbidity and mortality from cancer. An emerging theme in biomarker research is the expectation that panels of biomarker analytes rather than single markers will be needed to have sufficient sensitivity and specificity for the presymptomatic detection of cancer. Biomarkers may provide prognostic information of disease enabling interventions using targeted therapeutic agents as well as course-corrections in cancer treatment. Novel genomic, proteomic and metabolomic technologies are being used to discover and validate tumor biomarkers individually and in panels.     

Application of biomarkers in cancer risk management: evaluation from stochastic clonal evolutionary and dynamic system optimization points of view

Aside from primary prevention, early detection remains the most effective way to decrease mortality associated with the majority of solid cancers. Previous cancer screening models are largely based on classification of at-risk populations into three conceptually defined groups (normal, cancer without symptoms, and cancer with symptoms). Unfortunately, this approach has achieved limited successes in reducing cancer mortality. With advances in molecular biology and genomic technologies, many candidate somatic genetic and epigenetic "biomarkers" have been identified as potential predictors of cancer risk. However, none have yet been validated as robust predictors of progression to cancer or shown to reduce cancer mortality. In this Perspective, we first define the necessary and sufficient conditions for precise prediction of future cancer development and early cancer detection within a simple physical model framework. We then evaluate cancer risk prediction and early detection from a dynamic clonal evolution point of view, examining the implications of dynamic clonal evolution of biomarkers and the application of clonal evolution for cancer risk management in clinical practice. Finally, we propose a framework to guide future collaborative research between mathematical modelers and biomarker researchers to design studies to investigate and model dynamic clonal evolution. This approach will allow optimization of available resources for cancer control and intervention timing based on molecular biomarkers in predicting cancer among various risk subsets that dynamically evolve over time.     

Identification of new candidate drugs for lung cancer using chemical–chemical interactions,chemical–protein interactions and a K-means clustering algorithm

Lung cancer, characterized by uncontrolled cell growth in the lung tissue, is the leading cause of global cancer deaths. Until now, effective treatment of this disease is limited. Many synthetic compounds have emerged with the advancement of combinatorial chemistry. Identification of effective lung cancer candidate drug compounds among them is a great challenge. Thus, it is necessary to build effective computational methods that can assist us in selecting for potential lung cancer drug compounds. In this study, a computational method was proposed to tackle this problem. The chemical–chemical interactions and chemical–protein interactions were utilized to select candidate drug compounds that have close associations with approved lung cancer drugs and lung cancer-related genes. A permutation test and K-means clustering algorithm were employed to exclude candidate drugs with low possibilities to treat lung cancer. The final analysis suggests that the remaining drug compounds have potential anti-lung cancer activities and most of them have structural dissimilarity with approved drugs for lung cancer.