RPSA gene mutants associated with risk of colorectal cancer among the chinese population

Nuclear RPSA senses viral nucleic acids to promote the innate inflammatory response

Innate sensors initiate the production of type I interferons (IFN-I) and proinflammatory cytokines to protect host from viral infection. Several innate nuclear sensors that mainly induce IFN-I production have been identified. Whether there exist innate nuclear sensors that mainly induce proinflammatory cytokine production remains to be determined. By functional screening, we identify 40 S ribosomal protein SA (RPSA) as a nuclear protein that recognizes viral nucleic acids and predominantly promotes proinflammatory cytokine gene expression in antiviral innate immunity. Myeloid-specific Rpsa-deficient mice exhibit less innate inflammatory response against infection with Herpes simplex virus-1 (HSV-1) and Influenza A virus (IAV), the viruses replicating in nucleus. Mechanistically, nucleus-localized RPSA is phosphorylated at Tyr204 upon infection, then recruits ISWI complex catalytic subunit SMARCA5 to increase chromatin accessibility of NF-κB to target gene promotors without affecting innate signaling. Our results add mechanistic insights to an intra-nuclear way of initiating proinflammatory cytokine expression in antiviral innate defense.

Green tea (Camellia sinensis) for the prevention of cancer

BACKGROUND

This review is an update of a previously published review in the Cochrane Database of Systematic Reviews (2009, Issue 3).Tea is one of the most commonly consumed beverages worldwide. Teas from the plant Camellia sinensis can be grouped into green, black and oolong tea, and drinking habits vary cross-culturally. C sinensis contains polyphenols, one subgroup being catechins. Catechins are powerful antioxidants, and laboratory studies have suggested that these compounds may inhibit cancer cell proliferation. Some experimental and nonexperimental epidemiological studies have suggested that green tea may have cancer-preventative effects.

OBJECTIVES

To assess possible associations between green tea consumption and the risk of cancer incidence and mortality as primary outcomes, and safety data and quality of life as secondary outcomes.

SEARCH METHODS

We searched eligible studies up to January 2019 in CENTRAL, MEDLINE, Embase, ClinicalTrials.gov, and reference lists of previous reviews and included studies.

SELECTION CRITERIA

We included all epidemiological studies, experimental (i.e. randomised controlled trials (RCTs)) and nonexperimental (non-randomised studies, i.e. observational studies with both cohort and case-control design) that investigated the association of green tea consumption with cancer risk or quality of life, or both.

DATA COLLECTION AND ANALYSIS

Two or more review authors independently applied the study criteria, extracted data and assessed methodological quality of studies. We summarised the results according to diagnosis of cancer type.

MAIN RESULTS

In this review update, we included in total 142 completed studies (11 experimental and 131 nonexperimental) and two ongoing studies. This is an additional 10 experimental and 85 nonexperimental studies from those included in the previous version of the review. Eleven experimental studies allocated a total of 1795 participants to either green tea extract or placebo, all demonstrating an overall high methodological quality based on 'Risk of bias' assessment. For incident prostate cancer, the summary risk ratio (RR) in the green tea-supplemented participants was 0.50 (95% confidence interval (CI) 0.18 to 1.36), based on three studies and involving 201 participants (low-certainty evidence). The summary RR for gynaecological cancer was 1.50 (95% CI 0.41 to 5.48; 2 studies, 1157 participants; low-certainty evidence). No evidence of effect of non-melanoma skin cancer emerged (summary RR 1.00, 95% CI 0.06 to 15.92; 1 study, 1075 participants; low-certainty evidence). In addition, adverse effects of green tea extract intake were reported, including gastrointestinal disorders, elevation of liver enzymes, and, more rarely, insomnia, raised blood pressure and skin/subcutaneous reactions. Consumption of green tea extracts induced a slight improvement in quality of life, compared with placebo, based on three experimental studies. In nonexperimental studies, we included over 1,100,000 participants from 46 cohort studies and 85 case-control studies, which were on average of intermediate to high methodological quality based on Newcastle-Ottawa Scale 'Risk of bias' assessment. When comparing the highest intake of green tea with the lowest, we found a lower overall cancer incidence (summary RR 0.83, 95% CI 0.65 to 1.07), based on three studies, involving 52,479 participants (low-certainty evidence). Conversely, we found no association between green tea consumption and cancer-related mortality (summary RR 0.99, 95% CI 0.91 to 1.07), based on eight studies and 504,366 participants (low-certainty evidence). For most of the site-specific cancers we observed a decreased RR in the highest category of green tea consumption compared with the lowest one. After stratifying the analysis according to study design, we found strongly conflicting results for some cancer sites: oesophageal, prostate and urinary tract cancer, and leukaemia showed an increased RR in cohort studies and a decreased RR or no difference in case-control studies.

AUTHORS' CONCLUSIONS

Overall, findings from experimental and nonexperimental epidemiological studies yielded inconsistent results, thus providing limited evidence for the beneficial effect of green tea consumption on the overall risk of cancer or on specific cancer sites. Some evidence of a beneficial effect of green tea at some cancer sites emerged from the RCTs and from case-control studies, but their methodological limitations, such as the low number and size of the studies, and the inconsistencies with the results of cohort studies, limit the interpretability of the RR estimates. The studies also indicated the occurrence of several side effects associated with high intakes of green tea. In addition, the majority of included studies were carried out in Asian populations characterised by a high intake of green tea, thus limiting the generalisability of the findings to other populations. Well conducted and adequately powered RCTs would be needed to draw conclusions on the possible beneficial effects of green tea consumption on cancer risk.

Identification of key regulators in prostate cancer from gene expression datasets of patients

Identification of key regulators and regulatory pathways is an important step in the discovery of genes involved in cancer. Here, we propose a method to identify key regulators in prostate cancer (PCa) from a network constructed from gene expression datasets of PCa patients. Overexpressed genes were identified using BioXpress, having a mutational status according to COSMIC, followed by the construction of PCa Interactome network using the curated genes. The topological parameters of the network exhibited power law nature indicating hierarchical scale-free properties and five levels of organization. Highest degree hubs (k ≥ 65) were selected from the PCa network, traced, and 19 of them was identified as novel key regulators, as they participated at all network levels serving as backbone. Of the 19 hubs, some have been reported in literature to be associated with PCa and other cancers. Based on participation coefficient values most of these are connector or kinless hubs suggesting significant roles in modular linkage. The observation of non-monotonicity in the rich club formation suggested the importance of intermediate hubs in network integration, and they may play crucial roles in network stabilization. The network was self-organized as evident from fractal nature in topological parameters of it and lacked a central control mechanism.

QuaDMutEx: quadratic driver mutation explorer

BACKGROUND

Somatic mutations accumulate in human cells throughout life. Some may have no adverse consequences, but some of them may lead to cancer. A cancer genome is typically unstable, and thus more mutations can accumulate in the DNA of cancer cells. An ongoing problem is to figure out which mutations are drivers - play a role in oncogenesis, and which are passengers - do not play a role. One way of addressing this question is through inspection of somatic mutations in DNA of cancer samples from a cohort of patients and detection of patterns that differentiate driver from passenger mutations.

RESULTS

We propose QuaDMutEx, a method that incorporates three novel elements: a new gene set penalty that includes non-linear penalization of multiple mutations in putative sets of driver genes, an ability to adjust the method to handle slow- and fast-evolving tumors, and a computationally efficient method for finding gene sets that minimize the penalty, through a combination of heuristic Monte Carlo optimization and exact binary quadratic programming. Compared to existing methods, the proposed algorithm finds sets of putative driver genes that show higher coverage and lower excess coverage in eight sets of cancer samples coming from brain, ovarian, lung, and breast tumors.

CONCLUSIONS

Superior ability to improve on both coverage and excess coverage on different types of cancer shows that QuaDMutEx is a tool that should be part of a state-of-the-art toolbox in the driver gene discovery pipeline. It can detect genes harboring rare driver mutations that may be missed by existing methods. QuaDMutEx is available for download from https://github.com/bokhariy/QuaDMutEx under the GNU GPLv3 license.

Identifying and analyzing different cancer subtypes using RNA-seq data of blood platelets

Detection and diagnosis of cancer are especially important for early prevention and effective treatments. Traditional methods of cancer detection are usually time-consuming and expensive. Liquid biopsy, a newly proposed noninvasive detection approach, can promote the accuracy and decrease the cost of detection according to a personalized expression profile. However, few studies have been performed to analyze this type of data, which can promote more effective methods for detection of different cancer subtypes. In this study, we applied some reliable machine learning algorithms to analyze data retrieved from patients who had one of six cancer subtypes (breast cancer, colorectal cancer, glioblastoma, hepatobiliary cancer, lung cancer and pancreatic cancer) as well as healthy persons. Quantitative gene expression profiles were used to encode each sample. Then, they were analyzed by the maximum relevance minimum redundancy method. Two feature lists were obtained in which genes were ranked rigorously. The incremental feature selection method was applied to the mRMR feature list to extract the optimal feature subset, which can be used in the support vector machine algorithm to determine the best performance for the detection of cancer subtypes and healthy controls. The ten-fold cross-validation for the constructed optimal classification model yielded an overall accuracy of 0.751. On the other hand, we extracted the top eighteen features (genes), including TTN, RHOH, RPS20, TRBC2, in another feature list, the MaxRel feature list, and performed a detailed analysis of them. The results indicated that these genes could be important biomarkers for discriminating different cancer subtypes and healthy controls.

The importance of being (slightly) modified: The role of rRNA editing on gene expression control and its connections with cancer

In human ribosomal RNAs, over 200 residues are modified by specific, RNA-driven enzymatic complexes or stand-alone, RNA-independent enzymes. In most cases, modification sites are placed in specific positions within important functional areas of the ribosome. Some evidence indicates that the altered control in ribosomal RNA modifications may affect ribosomal function during mRNA translation. Here we provide an overview of the connections linking ribosomal RNA modifications to ribosome function, and suggest how aberrant modifications may affect the control of the expression of key cancer genes, thus contributing to tumor development. In addition, the future perspectives in this field are discussed.

Pathways to Specialized Ribosomes: The Brussels Lecture

"Specialized ribosomes" is a topic of intense debate and research whose provenance can be traced to the earliest days of molecular biology. Here, the history of this idea is reviewed, and critical literature in which the specialized ribosomes have come to be presently defined is discussed. An argument supporting the evolution of a variety of ribosomes with specialized functions as a consequence of selective pressures acting on a near-infinite set of possible ribosomes is presented, leading to a discussion of how this may also serve as a biological buffering mechanism. The possible relationship between specialized ribosomes and human health is explored. A set of criteria and possible approaches are also presented to help guide the definitive identification of "specialized" ribosomes, and this is followed by a discussion of how synthetic biology approaches might be used to create new types of special ribosomes.

High SHIP2 expression indicates poor survival in colorectal cancer

SH2-containing inositol 5′-phosphatase 2 (SHIP2), which generally regulates insulin signaling, cytoskeleton remodeling, and receptor endocytosis, has been suggested to play a significant role in tumor development and progression. However, the associations between SHIP2 expression and the clinical features to evaluate its clinicopathologic significance in colorectal cancer (CRC) have not been determined yet. In the present study, one-step quantitative real-time polymerase chain reaction (qPCR) test and immunohistochemistry (IHC) analysis with CRC tissue microarrays (TMA) were employed to evaluate the mRNA and protein expression of SHIP2 in CRC. The results showed that SHIP2 expression in the mRNA and protein levels was significantly higher in CRC tissues than that in corresponding noncancerous tissues (both P < 0.05). The expression of SHIP2 protein in CRC was related to lymph node metastasis (P = 0.036), distant metastasis (P = 0.001), and overall survival (P = 0.009). Kaplan-Meier method and Cox multifactor analysis suggested that high SHIP2 protein level (P = 0.040) and positive distant metastasis (P = 0.048) were critically associated with the unfavorable survival of CRC patients. The findings suggested that SHIP2 may be identified as a useful prognostic marker in CRC and targeting CRC may provide novel strategy for CRC treatment.

Cancer prediction based on radical basis function neural network with particle swarm optimization

This paper addresses cancer prediction based on radial basis function neural network optimized by particle swarm optimization. Today, cancer hazard to people is increasing, and it is often difficult to cure cancer. The occurrence of cancer can be predicted by the method of the computer so that people can take timely and effective measures to prevent the occurrence of cancer. In this paper, the occurrence of cancer is predicted by the means of Radial Basis Function Neural Network Optimized by Particle Swarm Optimization. The neural network parameters to be optimized include the weight vector between network hidden layer and output layer, and the threshold of output layer neurons. The experimental data were obtained from the Wisconsin breast cancer database. A total of 12 experiments were done by setting 12 different sets of experimental result reliability. The findings show that the method can improve the accuracy, reliability and stability of cancer prediction greatly and effectively.