Machine Learning Gene Signature to Metastatic ccRCC Based on ceRNA Network.

Clear-cell renal-cell carcinoma (ccRCC) is a silent-development pathology with a high rate of metastasis in patients. The activity of coding genes in metastatic progression is well known. New studies evaluate the association with non-coding genes, such as competitive endogenous RNA (ceRNA). This study aims to build a ceRNA network and a gene signature for ccRCC associated with metastatic development and analyze their biological functions. Using data from The Cancer Genome Atlas (TCGA), we constructed the ceRNA network with differentially expressed genes, assembled nine preliminary gene signatures from eight feature selection techniques, and evaluated the classification metrics to choose a final signature. After that, we performed a genomic analysis, a risk analysis, and a functional annotation analysis. We present an 11-gene signature: SNHG15, AF117829.1, hsa-miR-130a-3p, hsa-mir-381-3p, BTBD11, INSR, HECW2, RFLNB, PTTG1, HMMR, and RASD1. It was possible to assess the generalization of the signature using an external dataset from the International Cancer Genome Consortium (ICGC-RECA), which showed an Area Under the Curve of 81.5%. The genomic analysis identified the signature participants on chromosomes with highly mutated regions. The hsa-miR-130a-3p, AF117829.1, hsa-miR-381-3p, and PTTG1 were significantly related to the patient's survival and metastatic development. Additionally, functional annotation resulted in relevant pathways for tumor development and cell cycle control, such as RNA polymerase II transcription regulation and cell control. The gene signature analysis within the ceRNA network, with literature evidence, suggests that the lncRNAs act as "sponges" upon the microRNAs (miRNAs). Therefore, this gene signature presents coding and non-coding genes and could act as potential biomarkers for a better understanding of ccRCC.

An integrated approach to identify bimodal genes associated with prognosis in câncer.

Bimodal gene expression (where a gene expression distribution has two maxima) is associated with phenotypic diversity in different biological systems. A critical issue, thus, is the integration of expression and phenotype data to identify genuine associations. Here, we developed tools that allow both: i) the identification of genes with bimodal gene expression and ii) their association with prognosis in cancer patients from The Cancer Genome Atlas (TCGA). Bimodality was observed for 554 genes in expression data from 25 tumor types. Furthermore, 96 of these genes presented different prognosis when patients belonging to the two expression peaks were compared. The software to execute the method and the corresponding documentation are available at the Data access section.

Mobile applications for sexual encounters accessed by men who have sex with men: Information on sexually transmitted infection.

We evaluated existing mobile applications (apps) on both Android and iOS (Apple) platforms that are used by men who have sex with men (MSM) to obtain sexual encounters. The word "gay" was used to search for apps in the Apple and Google Play virtual stores. The 10 most downloaded apps were analysed concerning safe sexual practices (SSP) messages. Out of 245 apps selected, 213 were evaluated - 102 for Android and 111 for iOS. Mostly social networks were accessed by MSM of which 112 allow access to people aged 14 and over. Most of the apps could be downloaded in more than two languages. Of the 10 most downloaded and evaluated apps, 5 had no HIV/STI and SSP messages, only 3 contained HIV/STI and SSP messages, and 2 had information about one or the other. Several social networking apps are available, however, there is no information on HIV/STI in the most accessed apps.

A decision tree to improve identification of pathogenic mutations in clinical practice.

BACKGROUND: A variant of unknown significance (VUS) is a variant form of a gene that has been identified through genetic testing, but whose significance to the organism function is not known. An actual challenge in precision medicine is to precisely identify which detected mutations from a sequencing process have a suitable role in the treatment or diagnosis of a disease. The average accuracy of pathogenicity predictors is 85%. However, there is a significant discordance about the identification of mutational impact and pathogenicity among them. Therefore, manual verification is necessary for confirming the real effect of a mutation in its casuistic. METHODS: In this work, we use variables categorization and selection for building a decision tree model, and later we measure and compare its accuracy with four known mutation predictors and seventeen supervised machine-learning (ML) algorithms. RESULTS: The results showed that the proposed tree reached the highest precision among all tested variables: 91% for True Neutrals, 8% for False Neutrals, 9% for False Pathogenic, and 92% for True Pathogenic. CONCLUSIONS: The decision tree exceptionally demonstrated high classification precision with cancer data, producing consistently relevant forecasts for the sample tests with an accuracy close to the best ones achieved from supervised ML algorithms. Besides, the decision tree algorithm is easier to apply in clinical practice by non-IT experts. From the cancer research community perspective, this approach can be successfully applied as an alternative for the determination of potential pathogenicity of VOUS.

A Novel Machine Learning 13-Gene Signature: Improving Risk Analysis and Survival Prediction for Clear Cell Renal Cell Carcinoma Patients.

Patients with clear cell renal cell carcinoma (ccRCC) have poor survival outcomes, especially if it has metastasized. It is of paramount importance to identify biomarkers in genomic data that could help predict the aggressiveness of ccRCC and its resistance to drugs. Thus, we conducted a study with the aims of evaluating gene signatures and proposing a novel one with higher predictive power and generalization in comparison to the former signatures. Using ccRCC cohorts of the Cancer Genome Atlas (TCGA-KIRC) and International Cancer Genome Consortium (ICGC-RECA), we evaluated linear survival models of Cox regression with 14 signatures and six methods of feature selection, and performed functional analysis and differential gene expression approaches. In this study, we established a 13-gene signature (AR, AL353637.1, DPP6, FOXJ1, GNB3, HHLA2, IL4, LIMCH1, LINC01732, OTX1, SAA1, SEMA3G, ZIC2) whose expression levels are able to predict distinct outcomes of patients with ccRCC. Moreover, we performed a comparison between our signature and others from the literature. The best-performing gene signature was achieved using the ensemble method Min-Redundancy and Max-Relevance (mRMR). This signature comprises unique features in comparison to the others, such as generalization through different cohorts and being functionally enriched in significant pathways: Urothelial Carcinoma, Chronic Kidney disease, and Transitional cell carcinoma, Nephrolithiasis. From the 13 genes in our signature, eight are known to be correlated with ccRCC patient survival and four are immune-related. Our model showed a performance of 0.82 using the Receiver Operator Characteristic (ROC) Area Under Curve (AUC) metric and it generalized well between the cohorts. Our findings revealed two clusters of genes with high expression (SAA1, OTX1, ZIC2, LINC01732, GNB3 and IL4) and low expression (AL353637.1, AR, HHLA2, LIMCH1, SEMA3G, DPP6, and FOXJ1) which are both correlated with poor prognosis. This signature can potentially be used in clinical practice to support patient treatment care and follow-up.

A small interfering RNA (siRNA) database for SARS-CoV-2.

Coronavirus disease 2019 (COVID-19) rapidly transformed into a global pandemic, for which a demand for developing antivirals capable of targeting the SARS-CoV-2 RNA genome and blocking the activity of its genes has emerged. In this work, we presented a database of SARS-CoV-2 targets for small interference RNA (siRNA) based approaches, aiming to speed the design process by providing a broad set of possible targets and siRNA sequences. The siRNAs sequences are characterized and evaluated by more than 170 features, including thermodynamic information, base context, target genes and alignment information of sequences against the human genome, and diverse SARS-CoV-2 strains, to assess possible bindings to off-target sequences. This dataset is available as a set of four tables, available in a spreadsheet and CSV (Comma-Separated Values) formats, each one corresponding to sequences of 18, 19, 20, and 21 nucleotides length, aiming to meet the diversity of technology and expertise among laboratories around the world. A metadata table (Supplementary Table S1), which describes each feature, is also provided in the aforementioned formats. We hope that this database helps to speed up the development of new target antivirals for SARS-CoV-2, contributing to a possible strategy for a faster and effective response to the COVID-19 pandemic.

Use of dating sites and applications by women and their risk of sexually transmitted infections: a systematic review and meta-analysis protocol.

INTRODUCTION: The use of social networks has been increasing worldwide. Mobile websites and applications (apps) allow people to network more quickly and have more partners for sex. This can facilitate risky sexual behaviours, such as having multiple partners and unprotected sex, which can lead to a higher incidence of sexually transmitted infections. This systematic review/meta-analysis will assess the effects of the use of dating sites and apps by women on their level of engagement in risky sexual behaviours and their incidence of sexually transmitted infections. METHODS AND ANALYSIS: The Cochrane Central Controlled Trials Registry, ClinicalTrials.gov, MEDLINE, Embase, SciELO, Web of Science, Scopus and Cumulative Index to Nursing & Allied Health Literature will be searched for cross-sectional studies, clinical trials and observational studies published between January 1990 and July 2020. This systematic review and meta-analysis will include studies investigating the use of mobile apps by women, risky sexual behaviour and sexually transmitted infections. The outcome will be an increase in new cases of sexually transmitted infections and HIV among women using dating sites and apps. Three independent reviewers will select the studies and extract data from the original articles. The risk of bias will be assessed using the Cochrane risk of bias tool and Risk Of Bias in Non-randomized Studies of Interventions. Data synthesis will be performed using Review Manager software (RevMan V.5.2.3). To assess heterogeneity, we will compute the I2 statistic. In addition, a quantitative synthesis will be carried out if the included studies are sufficiently homogeneous. ETHICS AND DISSEMINATION: This study will be a review of the published data, and thus ethical approval is not required. The findings of this systematic review will be published in a peer-reviewed journal. PROSPERO REGISTRATION NUMBER: CRD42019120494.

Impact of mobile applications on adherence to cancer treatment: a systematic review and meta-analysis protocol.

INTRODUCTION: The number of patients taking oral chemotherapy is increasing around the world. It is essential to maximise the adherence to oral chemotherapy to improve the overall survival and life expectancy of the patients. In this systematic review and meta-analysis, we aim to evaluate the effectiveness of mobile applications in improving the adherence to oral chemotherapy and adjuvant hormonal therapy in cancer survivors. METHODS AND ANALYSIS: MEDLINE, Embase, LILACS, clinicaltrials.gov, Scopus and the Cochrane Central Register of Controlled Trials will be searched for randomised or quasi-experimental studies published between January 2009 and July 2019. This systematic review and meta-analysis will include studies investigating the use of mobile applications by cancer survivors to aid adherence to oral chemotherapy and adjuvant hormonal therapy. Patient education, reminder tools, calendars, pillboxes and electronic reminders will not be evaluated. The primary outcome will be the improvement in adherence to anticancer drugs. The secondary outcomes will be an improvement in the overall survival and life expectancy, improved quality of life and control of cancer-related symptoms. Three independent reviewers will select the studies and extract data from the original publications. The risk-of-bias will be assessed using the Cochrane risk-of-bias tool. Data synthesis will be performed using the Review Manager software (RevMan V.5.2.3). To assess heterogeneity, we will compute the I2 statistics. Additionally, a quantitative synthesis will be performed if the included studies are sufficiently homogenous. ETHICS AND DISSEMINATION: This study will be a review of the published data, and thus, ethical approval is not required. Findings of this systematic review will be published in a peer-reviewed journal. PROSPERO REGISTRATION NUMBER: CRD42018102172.

Assessment of mobile phone applications for care management in gynecology and obstetrics.

We identified mobile applications (apps) found on digital platforms (iTunes Store and Google Play) that addressed topics about gynecology and obstetrics.

Modeling cancer: integration of "omics" information in dynamic systems.

The last 10 years have seen the rise of many technologies that produce an unprecedented amount of genome-scale data from many organisms. Although the research community has been successful in exploring these data, many challenges still persist. One of them is the effective integration of such data sets directly into approaches based on mathematical modeling of biological systems. Applications in cancer are a good example. The bridge between information and modeling in cancer can be achieved by two major types of complementary strategies. First, there is a bottom-up approach, in which data generates information about structure and relationship between components of a given system. In addition, there is a top-down approach, where cybernetic and systems-theoretical knowledge are used to create models that describe mechanisms and dynamics of the system. These approaches can also be linked to yield multi-scale models combining detailed mechanism and wide biological scope. Here we give an overall picture of this field and discuss possible strategies to approach the major challenges ahead.

Genome-wide identification of cancer/testis genes and their association with prognosis in a pan-cancer analysis.

Cancer/testis (CT) genes are excellent candidates for cancer immunotherapies because of their restrict expression in normal tissues and the capacity to elicit an immune response when expressed in tumor cells. In this study, we provide a genome-wide screen for CT genes with the identification of 745 putative CT genes. Comparison with a set of known CT genes shows that 201 new CT genes were identified. Integration of gene expression and clinical data led us to identify dozens of CT genes associated with either good or poor prognosis. For the CT genes related to good prognosis, we show that there is a direct relationship between CT gene expression and a signal for CD8+ cells infiltration for some tumor types, especially melanoma.

Bioinformatics Analysis of the Human Surfaceome Reveals New Targets for a Variety of Tumor Types.

It is estimated that 10 to 20% of all genes in the human genome encode cell surface proteins and due to their subcellular localization these proteins represent excellent targets for cancer diagnosis and therapeutics. Therefore, a precise characterization of the surfaceome set in different types of tumor is needed. Using TCGA data from 15 different tumor types and a new method to identify cancer genes, the S-score, we identified several potential therapeutic targets within the surfaceome set. This allowed us to expand a previous analysis from us and provided a clear characterization of the human surfaceome in the tumor landscape. Moreover, we present evidence that a three-gene set-WNT5A, CNGA2, and IGSF9B-can be used as a signature associated with shorter survival in breast cancer patients. The data made available here will help the community to develop more efficient diagnostic and therapeutic tools for a variety of tumor types.

Modeling tumor evolutionary dynamics.

Tumorigenesis can be seen as an evolutionary process, in which the transformation of a normal cell into a tumor cell involves a number of limiting genetic and epigenetic events, occurring in a series of discrete stages. However, not all mutations in a cell are directly involved in cancer development and it is likely that most of them (passenger mutations) do not contribute in any way to tumorigenesis. Moreover, the process of tumor evolution is punctuated by selection of advantageous (driver) mutations and clonal expansions. Regarding these driver mutations, it is uncertain how many limiting events are required and/or sufficient to promote a tumorigenic process or what are the values associated with the adaptive advantage of different driver mutations. In spite of the availability of high-quality cancer data, several assumptions about the mechanistic process of cancer initiation and development remain largely untested, both mathematically and statistically. Here we review the development of recent mathematical/computational models and discuss their impact in the field of tumor biology.

Insights into gliomagenesis: systems biology unravels key pathways.

Technological advances have enabled a better characterization of all the genetic alterations in tumors. A picture that emerges is that tumor cells are much more genetically heterogeneous than originally expected. Thus, a critical issue in cancer genomics is the identification of the genetic alterations that drive the genesis of a tumor. Recently, a systems biology approach has been used to characterize such alterations and find associations between them and the process of gliomagenesis. Here, we discuss some implications of this strategy for the development of new therapeutic and diagnostic protocols for cancer.

Regeneration of natural antibody repertoire after massive ablation of lymphoid system: robust selection mechanisms preserve antigen binding specificities.

Natural Abs (NAbs) are Igs present in the serum and body fluids of healthy vertebrate animals, without any previous intentional immunization. NAbs often exhibit autoreactivity but also play an essential role in immunity, being a first line of defense against infectious microorganisms. We have previously analyzed the natural serum IgM Ab repertoire of normal mice, characterizing their reactivity with hundreds/thousands of self Ags; a significant similarity among different individuals was observed, and it was found that many reactivities of NAbs stably kept during adulthood were established early in life, implicating that period as a critical time window in the physiology of NAb repertoire selection. In the work reported here, experiments were conducted to address the role of normal lymphocyte ontogeny to the formation and stability of adult NAb repertoire. The massive destruction of the lymphoid system was promoted in adult mice with gamma-irradiation, and regeneration of hemopoietic tissues was granted by bone marrow or fetal liver inoculum. NAb repertoire regeneration was followed for 60 days after gamma-irradiation in bone marrow or fetal liver chimeric animals. The analysis of serum IgM reactivity with hundreds/thousands of self Ags showed that the NAb repertoire regenerated most of its original format after massive destruction of lymphoid compartments, characterizing autoreactive repertoire selection as a robust biological process. The data also show that regeneration of the NAb repertoire occurred similarly in fetal liver and bone marrow chimeras, although the latter animals poorly reconstituted their CD5(+) B1 cell compartment, suggesting that B1 cells are not essential for natural Ab regeneration.