UCA1及其剪接变异体与膀胱癌

尿路上皮癌胚抗原1 (urothelial carcinoma antigen 1,UCA1) 在人膀胱移行细胞癌细胞系 BLZ-211 中包含3个剪接变异体：UCA1、UCA1a 和 UCA1b. 我们以往的研究表明, UCA1、UCA1a 均属于长非编码 RNA (long non-coding RNA,lncRNA),它们之间具有一段长 1 265 bp 的共同序列. 组织表达谱分析表明,它们具有相似的组织表达模式,提示它们可能与胚胎发育和膀胱癌发生发展密切相关. 异位表达UCA1基因、UCA1a 基因均可以促进人膀胱癌细胞生长,增强细胞的恶性表型,使其体外增殖、迁移、侵袭、抗凋亡能力明显增加,裸鼠致瘤能力明显增强,表明它们在膀胱癌的发生发展中均起到了重要的促进作用. 本文将从基因结构、组织表达谱及机制功能等不同角度系统地阐述UCA1基因及其剪接变异体在膀胱癌中的研究现状与进展.     

Lipoprotein-associated phospholipase A2, and subsequent cardiovascular events and mortality among patients with coronary heart disease

Objective: The objective of this study is to evaluate the relevance of Lp-PLA₂ to risk prediction among coronary heart disease (CHD) patients.

Methods: Lp-PLA₂ activity was measured in 2538 CHD patients included in the Bezafibrate Infarction Prevention (BIP) study.

Results: Adjusting for patient characteristics and traditional risk factors, 1 standard deviation of Lp-PLA₂ was associated with a hazard ratio (HR) of 1.12 (95% confidence interval (CI): 1.00–1.25) for mortality and 1.03 (0.93–1.14) for cardiovascular events. Lp-PLA₂ did not significantly improve model discrimination, or calibration nor result in noteworthy reclassification.

Conclusions: Our results do not support added value of Lp-PLA₂ for predicting cardiovascular events or mortality among CHD patients beyond traditional risk factor.     

后GWAS时代结直肠癌致病SNP功能机制的研究进展

结直肠癌(colorectal cancer, CRC)是受遗传与环境因素共同影响的复杂疾病,其中遗传因素发挥重要作用。至今,全基因组关联研究(genome-wide association studies, GWAS)已经发现了大量与结直肠癌风险相关的遗传变异。随之而来的后GWAS时代,越来越多的研究侧重于利用多组学数据和功能实验对潜在的致病位点进行解析。分析表明绝大多数风险单核苷酸多态性(single nucleotide polymorphism,SNP)位于非编码区,可能通过影响转录因子结合、表观遗传修饰、染色质可及性、基因组高级结构等,调控靶基因表达。本文对后GWAS时代结直肠癌致病位点的机制研究进行综述,阐述了后GWAS对于理解结直肠癌分子机制的重要意义,并探讨了结直肠癌GWAS的应用和前景,为实现GWAS成果转化提供参考。     

Identification of an alternative form of caspase-9 in human gastric cancer cell lines: a role of a caspase-9 variant in apoptosis resistance

Overcoming apoptosis resistance to chemotherapy and radiation may lead to a reduction in gastric cancer death. We hypothesize that the apoptotic machinery in gastric cancer cells is dependent upon specific cellular conditions. In the course of our study of the expression of apoptosis-related genes in human gastric cancer cell lines, we have identified a cDNA clone which predicts an alternative form of caspase-9. The caspase-9 variant, which we designated as caspase-9 beta, retained a truncated structure of native caspase-9 without its catalytic domain and was expressed in seven cell lines from human gastric cancer. Among the cell lines examined, MKN-28 cells, which exhibited the most resistance against apoptotic stimuli, expressed the highest level of caspase-9 beta. The induction of apoptosis by staurosporine or actinomycin D was markedly suppressed in caspase-9 beta-transfected HeLa cells. These results are consistent with our hypothesis that the caspase-9 beta may be an endogenous dominant-negative molecule which attenuates apoptotic activity in human gastric cancer cells.     

Genetic polymorphisms and microRNAs: new direction in molecular epidemiology of solid cancer

MicroRNAs (miRNAs) are small non-coding RNAs, which regulate gene expression. Single nucleotide polymorphisms (SNPs) may occur in miRNA biogenesis pathway genes, primary miRNA, pre-miRNA or a mature miRNA sequence. Such polymorphisms may be functional with respect to biogenesis and actions of mature miRNA. Specific SNPs were identified in predicted miRNA target sites within 3' untranslated regions of mRNAs. These SNPs have a potential to affect the efficiency of miRNA binding to the target sites or can create or disrupt binding sites. Resulting gene dysregulation may involve changes in phenotype and may eventually prove critical for the susceptibility to cancer and its onset as well as for estimates of prognosis and therapy response. In this review, we provide a comprehensive list of potentially functional miRNA-related SNPs and summarize their importance as candidate cancer biomarkers.     

Advancement in Deep Learning Methods for Diagnosis and Prognosis of Cervical Cancer

Cervical cancer is the leading cause of death in women, mainly in developing countries, including India. Recent advancements in technologies could allow for more rapid, cost-effective, and sensitive screening and treatment measures for cervical cancer. To this end, deep learning-based methods have received importance for classifying cervical cancer patients into different risk groups. Furthermore, deep learning models are now available to study the progression and treatment of cancerous cervical conditions. Undoubtedly, deep learning methods can enhance our knowledge toward a better understanding of cervical cancer progression. However, it is essential to thoroughly validate the deep learning-based models before they can be implicated in everyday clinical practice. This work reviews recent development in deep learning approaches employed in cervical cancer diagnosis and prognosis. Further, we provide an overview of recent methods and databases leveraging these new approaches for cervical cancer risk prediction and patient outcomes. Finally, we conclude the state-of-the-art approaches for future research opportunities in this domain.     

A Comprehensive Genetic Approach for Improving Prediction of Skin Cancer Risk in Humans

Prediction of genetic risk for disease is needed for preventive and personalized medicine. Genome-wide association studies have found unprecedented numbers of variants associated with complex human traits and diseases. However, these variants explain only a small proportion of genetic risk. Mounting evidence suggests that many traits, relevant to public health, are affected by large numbers of small-effect genes and that prediction of genetic risk to those traits and diseases could be improved by incorporating large numbers of markers into whole-genome prediction (WGP) models. We developed a WGP model incorporating thousands of markers for prediction of skin cancer risk in humans. We also considered other ways of incorporating genetic information into prediction models, such as family history or ancestry (using principal components, PCs, of informative markers). Prediction accuracy was evaluated using the area under the receiver operating characteristic curve (AUC) estimated in a cross-validation. Incorporation of genetic information (i.e., familial relationships, PCs, or WGP) yielded a significant increase in prediction accuracy: from an AUC of 0.53 for a baseline model that accounted for nongenetic covariates to AUCs of 0.58 (pedigree), 0.62 (PCs), and 0.64 (WGP). In summary, prediction of skin cancer risk could be improved by considering genetic information and using a large number of single-nucleotide polymorphisms (SNPs) in a WGP model, which allows for the detection of patterns of genetic risk that are above and beyond those that can be captured using family history. We discuss avenues for improving prediction accuracy and speculate on the possible use of WGP to prospectively identify individuals at high risk.     

Statistical models for low dose exposure

Extrapolation of health risks from high to low doses has received a considerable amount of attention in carcinogenic risk assessment over decades. Fitting statistical dose-response models to experimental data collected at high doses and use of the fitted model for estimating effects at low doses lead to quite different risk predictions. Dissatisfaction with this procedure was formulated both by toxicologists who saw a deficit of biological knowledge in the models as well as by risk modelers who saw the need of mechanistically-based stochastic modeling. This contribution summarizes the present status of low dose modeling and the determination of the shape of dose-response curves. We will address the controversial issues of the appropriateness of threshold models, the estimation of no observed adverse effect levels (NOAEL), and their relevance for low dose modeling. We will distinguish between quantal dose-response models for tumor incidence and models of the more informative age/time dependent tumor incidence. The multistage model and the two-stage model of clonal expansion are considered as dose-response models accounting for biological mechanisms. Problems of the identifiability of mechanisms are addressed, the relation between administered dose and effective target dose is illustrated by examples, and the recently proposed Benchmark Dose concept for risk assessment is presented with its consequences for mechanistic modeling and statistical estimation.     

Polygenic risk prediction: why and when out-of-sample prediction R2 can exceed SNP-based heritability

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Tenascin-C and integrins in cancer

Tenascin-C (TNC) is highly expressed in cancer tissues. Its cellular sources are cancer and stromal cells, including fibroblasts/myofibroblasts, and also vascular cells. TNC expressed in cancer tissues dominantly contains large splice variants. Deposition of the stroma promotes the epithelial-mesenchymal transition, proliferation, and migration of cancer cells. It also facilitates the formation of cancer stroma including desmoplasia and angiogenesis. Integrin receptors that mediate the signals of TNC have also been discussed.     

Predicting all‐cause mortality from basic physiology in the Framingham Heart Study

Using longitudinal data from a cohort of 1349 participants in the Framingham Heart Study, we show that as early as 28–38 years of age, almost 10% of variation in future lifespan can be predicted from simple clinical parameters. Specifically, we found diastolic and systolic blood pressure, blood glucose, weight, and body mass index (BMI) to be relevant to lifespan. These and similar parameters have been well‐characterized as risk factors in the relatively narrow context of cardiovascular disease and mortality in middle to old age. In contrast, we demonstrate here that such measures can be used to predict all‐cause mortality from mid‐adulthood onward. Further, we find that different clinical measurements are predictive of lifespan in different age regimes. Specifically, blood pressure and BMI are predictive of all‐cause mortality from ages 35 to 60, while blood glucose is predictive from ages 57 to 73. Moreover, we find that several of these parameters are best considered as measures of a rate of ‘damage accrual’, such that total historical exposure, rather than current measurement values, is the most relevant risk factor (as with pack‐years of cigarette smoking). In short, we show that simple physiological measurements have broader lifespan‐predictive value than indicated by previous work and that incorporating information from multiple time points can significantly increase that predictive capacity. In general, our results apply equally to both men and women, although some differences exist.