Machine learning methods for prediction of cancer driver genes: a survey paper.

Identifying the genes and mutations that drive the emergence of tumors is a critical step to improving our understanding of cancer and identifying new directions for disease diagnosis and treatment. Despite the large volume of genomics data, the precise detection of driver mutations and their carrying genes, known as cancer driver genes, from the millions of possible somatic mutations remains a challenge. Computational methods play an increasingly important role in discovering genomic patterns associated with cancer drivers and developing predictive models to identify these elements. Machine learning (ML), including deep learning, has been the engine behind many of these efforts and provides excellent opportunities for tackling remaining gaps in the field. Thus, this survey aims to perform a comprehensive analysis of ML-based computational approaches to identify cancer driver mutations and genes, providing an integrated, panoramic view of the broad data and algorithmic landscape within this scientific problem. We discuss how the interactions among data types and ML algorithms have been explored in previous solutions and outline current analytical limitations that deserve further attention from the scientific community. We hope that by helping readers become more familiar with significant developments in the field brought by ML, we may inspire new researchers to address open problems and advance our knowledge towards cancer driver discovery.

Graph neural networks for clinical risk prediction based on electronic health records: A survey.

OBJECTIVE: This study aims to comprehensively review the use of graph neural networks (GNNs) for clinical risk prediction based on electronic health records (EHRs). The primary goal is to provide an overview of the state-of-the-art of this subject, highlighting ongoing research efforts and identifying existing challenges in developing effective GNNs for improved prediction of clinical risks. METHODS: A search was conducted in the Scopus, PubMed, ACM Digital Library, and Embase databases to identify relevant English-language papers that used GNNs for clinical risk prediction based on EHR data. The study includes original research papers published between January 2009 and May 2023. RESULTS: Following the initial screening process, 50 articles were included in the data collection. A significant increase in publications from 2020 was observed, with most selected papers focusing on diagnosis prediction (n = 36). The study revealed that the graph attention network (GAT) (n = 19) was the most prevalent architecture, and MIMIC-III (n = 23) was the most common data resource. CONCLUSION: GNNs are relevant tools for predicting clinical risk by accounting for the relational aspects among medical events and entities and managing large volumes of EHR data. Future studies in this area may address challenges such as EHR data heterogeneity, multimodality, and model interpretability, aiming to develop more holistic GNN models that can produce more accurate predictions, be effectively implemented in clinical settings, and ultimately improve patient care.

Meta-analysis of Transcriptomic Data from Lung Autopsy and Cellular Models of SARS-CoV-2 Infection.

Severe COVID-19 is a systemic disorder involving excessive inflammatory response, metabolic dysfunction, multi-organ damage, and several clinical features. Here, we performed a transcriptome meta-analysis investigating genes and molecular mechanisms related to COVID-19 severity and outcomes. First, transcriptomic data of cellular models of SARS-CoV-2 infection were compiled to understand the first response to the infection. Then, transcriptomic data from lung autopsies of patients deceased due to COVID-19 were compiled to analyze altered genes of damaged lung tissue. These analyses were followed by functional enrichment analyses and gene-phenotype association. A biological network was constructed using the disturbed genes in the lung autopsy meta-analysis. Central genes were defined considering closeness and betweenness centrality degrees. A sub-network phenotype-gene interaction analysis was performed. The meta-analysis of cellular models found genes mainly associated with cytokine signaling and other pathogen response pathways. The meta-analysis of lung autopsy tissue found genes associated with coagulopathy, lung fibrosis, multi-organ damage, and long COVID-19. Only genes DNAH9 and FAM216B were found perturbed in both meta-analyses. BLNK, FABP4, GRIA1, ATF3, TREM2, TPPP, TPPP3, FOS, ALB, JUNB, LMNA, ADRB2, PPARG, TNNC1, and EGR1 were identified as central elements among perturbed genes in lung autopsy and were found associated with several clinical features of severe COVID-19. Central elements were suggested as interesting targets to investigate the relation with features of COVID-19 severity, such as coagulopathy, lung fibrosis, and organ damage.

Broken silence: 22,841 predicted deleterious synonymous variants identified in the human exome through computational analysis.

Synonymous single nucleotide variants (sSNVs) do not alter the primary structure of a protein, thus it was previously accepted that they were neutral. Recently, several studies demonstrated their significance to a range of diseases. Still, variant prioritization strategies lack focus on sSNVs. Here, we identified 22,841 deleterious synonymous variants in 125,748 human exomes using two in silico predictors (SilVA and CADD). While 98.2% of synonymous variants are classified as neutral, 1.8% are predicted to be deleterious, yielding an average of 9.82 neutral and 0.18 deleterious sSNVs per exome. Further investigation of prediction features via Heterogeneous Ensemble Feature Selection revealed that impact on amino acid sequence and conservation carry the most weight for a deleterious prediction. Thirty nine detrimental sSNVs are not rare and are located on disease associated genes. Ten distinct putatively non-deleterious sSNVs are likely to be under positive selection in the North-Western European and East Asian populations. Taken together our analysis gives voice to the so-called silent mutations as we propose a robust framework for evaluating the deleteriousness of sSNVs in variant prioritization studies.

Brazilian women in Bioinformatics: Challenges and opportunities.

Bioinformatics is a growing research field that received great notoriety in the years of the COVID-19 pandemic. It is a very integrative area, comprising professionals from science, technology, engineering, and mathematics (STEM). In agreement with the other STEM areas, several women have greatly contributed to bioinformatics ascension; however, they had to surpass prejudice and stereotypes to achieve recognition and leadership positions, a path that studies have demonstrated to be more comfortable to their male colleagues. In this review, we discuss the several difficulties that women in STEM, including bioinformatics, surpass during their careers. First, we present a historical context on bioinformatics and the main applications for this area. Then, we discuss gender disparity in STEM and present the challenges that still contribute to women's inequality in STEM compared to their male colleagues. We also present the opportunities and the transformation that we can start, acting in academia, inside the family and school environments, and as a society, hence contributing to gender equality in STEM. Finally, we discuss specific challenges in the bioinformatics field and how we can act to overcome them, especially in low and middle-income countries, such as Brazil.

Economic Cost of the Waste of Anti-inflammatory and Analgesic Drugs in Mexico City.

Anti-inflammatory and analgesic medications (AAMs) are widely used in Mexico and the rest of the world. Their excessive acquisition can lead to waste, representing an unnecessary expense for families and the public health system. The aim of this study was to estimate the economic cost of the waste of unused AAMs collected by the National System for the Collection of Residues of Containers and Medications (SINGREM, the acronym in Spanish) in Mexico City during 2019. Data from SINGREM on discarded AAMs in Mexico City were classified by the type and quantity of drug, pharmaceutical dosage form, origin, dose, and the complete or incomplete condition of the package. The unitary cost for each medication was based on public tenders of the Mexican Social Security Institute (IMSS) for the public sector and the prices in large drug store franchises for the private sector. A decision-making model was constructed to appraise the total cost of discarded AAMs. The economic cost of the 48924 units of discarded AAMs in SINGREM containers in Mexico City during 2019 was approx. USD$143500, of which over USD$127000 corresponded to the private health sector. The current findings evidence an enormous accumulation of unneeded or expired AAMs in Mexico City. According to the present data, the cost of such waste is substantial. The estimated cost was 8-fold higher for discarded medications originating from the private versus the public healthcare sector. It is important to implement measures to prevent this waste and increase awareness of the consequences of inadequate drug disposal.

The Phytochemical α-Mangostin Inhibits Cervical Cancer Cell Proliferation and Tumor Growth by Downregulating E6/E7-HPV Oncogenes and KCNH1 Gene Expression.

Cervical cancer is the fourth most common cancer among women worldwide. The main factor associated with the onset and progression of this neoplasia is the human papillomavirus (HPV) infection. The HPV-oncogenes E6 and E7 are critical drivers of cellular transformation, promoting the expression of oncogenes such as KCNH1. The phytochemical α-mangostin (AM) is a potent antineoplastic and antiviral compound. However, its effects on HPV oncogenes and KCNH1 gene expression remain unknown. This study evaluated the effects of AM on cell proliferation, cell cycle distribution and gene expression, including its effects on tumor growth in xenografted mice. AM inhibited cell proliferation in a concentration-dependent manner, being the most sensitive cell lines those with the highest number of HPV16 copies. In addition, AM promoted G1-cell cycle arrest in CaSki cells, while led to cell death in SiHa and HeLa cells. Of interest was the finding of an AM-dependent decreased gene expression of E6, E7 and KCNH1 both in vitro and in vivo, as well as the modulation of cytokine expression, Ki-67, and tumor growth inhibition. On these bases, we suggest that AM represents a good option as an adjuvant for the treatment and prevention of cervical cancer.

A New Strategy for the Old Challenge of Thalidomide: Systems Biology Prioritization of Potential Immunomodulatory Drug (IMiD)-Targeted Transcription Factors.

Several molecular mechanisms of thalidomide embryopathy (TE) have been investigated, from anti-angiogenesis to oxidative stress to cereblon binding. Recently, it was discovered that thalidomide and its analogs, named immunomodulatory drugs (IMiDs), induced the degradation of C2H2 transcription factors (TFs). This mechanism might impact the strict transcriptional regulation of the developing embryo. Hence, this study aims to evaluate the TFs altered by IMiDs, prioritizing the ones associated with embryogenesis through transcriptome and systems biology-allied analyses. This study comprises only the experimental data accessed through bioinformatics databases. First, proteins and genes reported in the literature as altered/affected by the IMiDs were annotated. A protein systems biology network was evaluated. TFs beta-catenin (CTNNB1) and SP1 play more central roles: beta-catenin is an essential protein in the network, while SP1 is a putative C2H2 candidate for IMiD-induced degradation. Separately, the differential expressions of the annotated genes were analyzed through 23 publicly available transcriptomes, presenting 8624 differentially expressed genes (2947 in two or more datasets). Seventeen C2H2 TFs were identified as related to embryonic development but not studied for IMiD exposure; these TFs are potential IMiDs degradation neosubstrates. This is the first study to suggest an integration of IMiD molecular mechanisms through C2H2 TF degradation.

Intratumoral Treatment with 5-Androstene-3ß, 17α-Diol Reduces Tumor Size and Lung Metastasis in a Triple-Negative Experimental Model of Breast Cancer.

Breast cancer treatment failure is related to low response rates, high costs, and long-term toxicities. Thus, it is necessary to find less toxic, cheaper, and more effective treatments. In situ administration ensures drug delivery to tumor cells and decreases systemic toxic effects. The androstene-3ß, 17α-diol (α-AED) reduces breast tumor cell proliferation and is an ideal candidate to treat mammary tumors. This study aims to identify the in vitro and in vivo effects of α-AED on a triple-negative mammary tumor model. An in vitro biphasic steroid effect was observed in mouse and human mammary tumor cells treated with α-AED. In this sense, cells treated with higher doses (100 and 200 µM) showed an antiproliferative effect. The α-AED administrated intratumorally reduced average tumor weight and increased the percentage of natural killer cells (NK), plasmatic, and plasmablast cells in mice tumors. Of note, VEGF levels in all α-AED-treated tumors was lower than in the control and vehicle groups. The tumor in situ increased response was reflected systemically by higher anti-4T1 IgG concentration in serum from α-AED-treated mice, but no other associated systemic changes were detected. The reduction in tumor size for the local injection of α-AED is associated with the anti-proliferative effect of this steroid, and the lower local levels of VEGF may be related to the imperceptible macroscopic metastasis in α-AED-treated mice. The above suggests that α-AED may be used in clinical studies to prove its efficacy as an alternative breast tumor treatment or in conjunction with already established therapies.

The Endocrine Disruptor Compound Bisphenol-A (BPA) Regulates the Intra-Tumoral Immune Microenvironment and Increases Lung Metastasis in an Experimental Model of Breast Cancer.

Breast cancer (BC) metastasis represents the main physiopathology leading to poor prognosis and death. Bisphenol A (BPA) is a pollutant, classified as an endocrine-disrupting chemical compound with estrogenic properties, their exposure in the early stages of neonatal life leads to an increase in the size and weight of breast tumors and induces cellular changes in the tumoral immune microenvironment where cytokines play a key role. Thus, we used female BALB/c mice exposed neonatally to a single dose of BPA. Once mice reached sexual maturity, a mammary tumor was induced, injecting 4T1 cells in situ. After 25 days of injection, we evaluated endocrine alterations, cytokine expression, tissue alterations denoted by macro or micro-metastasis in the lung, and cell infiltration induced by metastasis. We found that BPA neonatal treatment did not show significant endocrine alterations. Noteworthy, BPA led to an augmented rate of metastasis to the lung associated with higher intratumoral expression of IL-1ß, IL-6, IFN-γ, TNF-α, and VEGF. Our data suggest that cytokines are key players in the induction of BC metastasis and that BPA (an environmental pollutant) should be considered as a risk factor in the clinical history of patients as a possible inductor of BC metastasis.

Potential Estrogenic Properties of 17ß-Hydroxy-ethylimine Estradiol Derivative Targeted to Menopause Stage.

BACKGROUND/AIM: Hormone replacement therapy during menopause increases the risk of thromboembolic diseases and cancer, so safety alternative therapeutic strategies are needed. 17ß-Aminoestrogens are a synthetic estrogens group that possess mild anticoagulant activity that contrasts with the pro-coagulant effects showed by estradiol's (E2) in rodents. Being considered an alternative to conventional hormone replacement therapy during menopause without thrombogenic risks producing. The present study aimed to determine the estrogenic profile and anxiolytic activity of 17ß-[hydroxy-ethylimine]-1,3,5(10)-estratrien-3-ol (IE2), a related compound unknown until now. METHODS: IE2 was assessed in immature rats by uterotrophic assay administering IE2, E2, or vehicle. In ovariectomized adult Wistar rats (Ovx) to facilitating the lordotic behavior compared with E2, estradiol benzoate, or vehicle. The effect of IE2 on anxiety was estimated in Ovx animals treated with IE2, E2, or vehicle group and evaluated in the elevated plus-maze model. RESULTS AND CONCLUSION: IE2 produced an uterotrophic effect, lordotic behavior, and anxiolytic effect in a dose-dependent manner, similar to E2. IE2 depicted estrogenicity, indicating potential clinical use as hormone replacement therapy during menopause.

Combinations of Calcitriol with Anticancer Treatments for Breast Cancer: An Update.

Preclinical, clinical, and epidemiological studies indicate that vitamin D3 (VD) deficiency is a risk factor for the development of breast cancer. Underlying mechanisms include the ability of calcitriol to induce cell differentiation, inhibit oncogenes expression, and modify different signaling pathways involved in the control of cell proliferation. In addition, calcitriol combined with different kinds of antineoplastic drugs has been demonstrated to enhance their beneficial effects in an additive or synergistic fashion. However, a recognized adjuvant regimen based on calcitriol for treating patients with breast cancer has not yet been fully established. Accordingly, in the present work, we review and discuss the preclinical and clinical studies about the combination of calcitriol with different oncological drugs, aiming to emphasize its main therapeutic benefits and opportunities for the treatment of this pathology.

Gene Expression Analysis Platform (GEAP): A highly customizable, fast, versatile and ready-to-use microarray analysis platform.

There are still numerous challenges to be overcome in microarray data analysis because advanced, state-of-the-art analyses are restricted to programming users. Here we present the Gene Expression Analysis Platform, a versatile, customizable, optimized, and portable software developed for microarray analysis. GEAP was developed in C# for the graphical user interface, data querying, storage, results filtering and dynamic plotting, and R for data processing, quality analysis, and differential expression. Through a new automated system that identifies microarray file formats, retrieves contents, detects file corruption, and solves dependencies, GEAP deals with datasets independently of platform. GEAP covers 32 statistical options, supports quality assessment, differential expression from single and dual-channel experiments, and gene ontology. Users can explore results by different plots and filtering options. Finally, the entire data can be saved and organized through storage features, optimized for memory and data retrieval, with faster performance than R. These features, along with other new options, are not yet present in any microarray analysis software. GEAP accomplishes data analysis in a faster, straightforward, and friendlier way than other similar software, while keeping the flexibility for sophisticated procedures. By developing optimizations, unique customizations and new features, GEAP is destined for both advanced and non-programming users.

The chemical environmental pollutants BPA and BPS induce alterations of the proteomic profile of different phenotypes of human breast cancer cells: A proposed interactome.

Breast cancer is one of the most common malignancies and the second leading cause of death in women. Despite efforts for its early detection, its worldwide incidence continues to increase. Thus, identification of risk factors for its development and new targets for its therapy are of vital importance. Environmental pollutants derived from human activity have been associated with predisposition to the development of cancer. Bisphenol A (BPA) is an endocrine disruptor compound (EDC) widely used in the manufacture of polycarbonates, and it has affinity for the estrogen receptor (ER). Scientific evidence has proposed an association between increased incidence of breast cancer and BPA exposure at lower doses. Among worldwide concerns with BPA exposure, different industries proceeded to replace BPA with analogs such as bisphenol S (BPS), which is now employed in products labelled as BPA-free. Nevertheless, recent studies exhibit that its exposure results in altered mammary gland development and morphogenesis; and promotes breast cancer cell proliferation. Of note, most of the effects of both BPA and BPS have been performed in estrogen-dependent breast cancer models. However, gaps in knowledge still exist on the roles and mechanisms that both compounds, specifically BPS, may play in cancer initiation and development in hormone-dependent and other types of breast cancer. Thus, the aim of the present study was to deepen the understanding of biological targets modulated by these ubiquitous pollutants in different breast cancer cell lines, representing two scenarios of this pathology: hormone-dependent and hormone-independent breast cancer. Results point out that both compounds induced proliferation in ER positive cells, not showing this effect in the ER-negative breast cancer cells. Different targets modified at the proteomic level in both breast cancer scenarios were also identified. Stem cell markers (eg. CD44) and invasion proteins (eg. MMP-14) were importantly increased by BPA and BPS in ER-positive breast cancer cells. In contrast, growth factors and associated receptors such as EGFR and TGF-ß were induced by BPS in the ER-negative breast cancer cells; both pollutants induced an increase of vascular endothelial growth factor (VEGF) protein secretion. This finding suggests that the use of BPS must be considered with more caution than BPA, since it can act independently of the presence of the hormonal receptor. These findings show new evidence that BPA and BPS exposure can contribute to breast cancer development and progression. Our results suggest that both BPA and BPS must be considered equally as outstanding risk factors for this pathology.

Perinatal exposure to bisphenol A increases in the adulthood of the offspring the susceptibility to the human parasite Toxocara canis.

Bisphenol A, a very widespread environmental pollutant and endocrine disruptor compound, can interact with several steroid receptors, particularly with estrogen ones. In different studies, it has observed that the endocrine disruption during critical periods of development can trigger alterations in the immune response during the adult life. Male Wistar rats were exposed indirectly to BPA at a dose of 250 µg/kg day during the perinatal period (from day 5 of pregnancy until day 21 postnatal), At the 60 days of age, the adulthood, animals were infected with larvated eggs of the Toxocara canis, and were sacrificed at 7 days post-infection. Parasitic loads in the lung and in the liver were analyzed by artificial digestion. Furthermore, immune cell subpopulations (macrophages, NK cells, Tγδ, total T cells, T helper, T cytotoxic, and B lymphocytes) present in spleen, peripheral and mesenteric lymph nodes were analyzed by flow cytometry. The expression of Th1 and Th2 cytokines at the splenic level was determined by real-time quantitative PCR. Finally, the titers of specific antibodies against to the parasite were analyzed by ELISA. The BPA treatment administrated in the perinatally stage favors a significant increase of the percentage of Toxocara canis larvae in the lungs and liver in the adulthood. Additionally, the exposure to this compound caused a dramatically decrease in the production of specific antibodies against to this parasite, downregulating together Th2 cytokines (IL-4, IL-5 and IL-13), meanwhile upregulated Th1 cytokines (IFN-γ and TNF-α). Perinatal exposure to BPA affects the performance of the immune response during adult life, modifying both cytokines and antibodies production by these cells, which favors the susceptibility to infections, specifically toxocariosis.

How to make more from exposure data? An integrated machine learning pipeline to predict pathogen exposure.

Predicting infectious disease dynamics is a central challenge in disease ecology. Models that can assess which individuals are most at risk of being exposed to a pathogen not only provide valuable insights into disease transmission and dynamics but can also guide management interventions. Constructing such models for wild animal populations, however, is particularly challenging; often only serological data are available on a subset of individuals and nonlinear relationships between variables are common. Here we provide a guide to the latest advances in statistical machine learning to construct pathogen-risk models that automatically incorporate complex nonlinear relationships with minimal statistical assumptions from ecological data with missing data. Our approach compares multiple machine learning algorithms in a unified environment to find the model with the best predictive performance and uses game theory to better interpret results. We apply this framework on two major pathogens that infect African lions: canine distemper virus (CDV) and feline parvovirus. Our modelling approach provided enhanced predictive performance compared to more traditional approaches, as well as new insights into disease risks in a wild population. We were able to efficiently capture and visualize strong nonlinear patterns, as well as model complex interactions between variables in shaping exposure risk from CDV and feline parvovirus. For example, we found that lions were more likely to be exposed to CDV at a young age but only in low rainfall years. When combined with our data calibration approach, our framework helped us to answer questions about risk of pathogen exposure that are difficult to address with previous methods. Our framework not only has the potential to aid in predicting disease risk in animal populations, but also can be used to build robust predictive models suitable for other ecological applications such as modelling species distribution or diversity patterns.

ASTEMIZOLE, AN INHIBITOR OF ETHER-À-GO-GO-1 POTASSIUM CHANNEL, INCREASES THE ACTIVITY OF THE TYROSINE KINASE INHIBITOR GEFITINIB IN BREAST CANCER CELLS.

BACKGROUND: Expression and activity of the potassium channel ether-à-go-go-1 (EAG1) are strongly related to carcinogenesis and tumor progression, which can be exploited for therapeutic purposes. EAG1 activity may be reduced by preventing its phosphorylation with epidermal growth factor receptor (EGFR) kinase inhibitors and by astemizole, which blocks the channel pore and downregulates its gene expression. OBJECTIVE: We aimed to study the potential cooperative antiproliferative effect of the EGFR inhibitor gefitinib and the EAG1-blocker astemizole, in breast cancer cells. MATERIALS AND METHODS: The cells were characterized by immunocytochemistry. Inhibitory concentrations were determined by non-linear regression analysis using dose-response curves. The nature of the pharmacological effect was evaluated by the combination index equation while cell cycle analysis was studied by flow cy-tometry. RESULTS: Astemizole and gefitinib inhibited cell proliferation in a concentration-dependent manner, with inhibitory concentrations (IC 50) values of 1.72 µM and 0.51 µM, respectively. All combinations resulted in a synergistic antiproliferative effect. The combination of astemizole and gefitinib diminished the percentage of cells in G2/M and S phases, while increased accumulation in G0/G1 of the cell cycle. CONCLUSIONS: Astemizole and gefitinib synergistically inhibited proliferation in breast cancer cells expressing both EGFR and EAG1. Our results suggest that the combined treatment increased cell death by targeting the oncogenic activity of EAG1.

Alelos HLA más comunes y asociados con riesgo o protección en enfermedad renal crónica de etiología no determinada.

INTRODUCTION: Chronic kidney disease accounts for part of overall health expenditure; a potential etiology is related to variations, absence or presence of some human leukocyte antigen (HLA) alleles. METHOD: An analysis of HLA reports of 1965 kidney recipients with no determined etiology, and 1361 kidney donors was performed. It was carried out with Luminex based in cell flow fluorometry for the A, B, DRB1 and DQA loci. An analysis was performed with contingency tables in order to determine the odds ratio (OR) and confidence intervals (CI). Quantitative analysis was also carried out. RESULTS: Of the 101 alleles found, 13 showed association, 7 with risk for chronic kidney disease, with the most significant being HLA-DR17 with an OR of 3.91 (95 % CI = 2.96-5.17) and the one with the highest significance for protection being HLA-DR9, with an OR of 0.043 (95 % CI = 0.005-0.3224). CONCLUSIONS: It is necessary to understand that kidney diseases can be associated with yet unknown immune processes, where the association of the absence or presence of any allele should be known.

INTRODUCCIÓN: La enfermedad renal crónica representa parte del gasto en salud en general; una potencial etiología es la relacionada con variaciones, ausencia o presencia de algunos alelos del human leucocyte antigen (HLA). MÉTODO: Se realizó el análisis de 1965 reportes de HLA sin etiología determinada y de 1361 donadores renales. Se llevó a cabo tecnología Luminex con base en fluorimetría de flujo celular para los locus A, B, DRB1 y DQA. Se realizó análisis con tablas de contingencia para determinar razón de momios (RM) e intervalos de confianza (IC). Se efectuó análisis cuantitativo. RESULTADOS: De 101 alelos encontrados, 13 presentaron asociación, siete con riesgo para enfermedad renal crónica, de los cuales el más significativo fue HLA-DR17, con RM = 3.91 (IC 95 % = 2.96-5.17), y el de mayor significación de protección fue HLA-DR9, con RM = 0.043 (IC 95 % = 0.005-0.3224). CONCLUSIONES: Es necesario entender que las enfermedades renales pueden estar ligadas a procesos inmunológicos, en los que se tiene que conocer la asociación de la ausencia o presencia de algún alelo.

The role of protein intrinsic disorder in major psychiatric disorders.

Although new candidate genes for Autism Spectrum Disorder (ASD), Schizophrenia (SCZ), Attention-Deficit/Hyperactivity Disorder (ADHD), and Bipolar Disorder (BD) emerged from genome-wide association studies (GWAS), their underlying molecular mechanisms remain poorly understood. Evidences of the involvement of intrinsically disordered proteins in diseases have grown in the last decade. These proteins lack tridimensional structure under physiological conditions and are involved in important cellular functions such as signaling, recognition and regulation. The aim of the present study was to identify the role and abundance of intrinsically disordered proteins in a set of psychiatric diseases and to test whether diseases are different regarding protein intrinsic disorder. Our hypothesis is that differences across psychiatric illnesses phenotypes and symptoms may arise from differences in intrinsic protein disorder content and properties of each group. A bioinformatics prediction of intrinsic disorder was performed in proteins retrieved based on top findings from GWAS, Copy Number Variation and candidate gene investigations for each disease. This approach revealed that about 80% of studied proteins presented long stretches of disorder. This amount was significantly higher than that observed in general eukaryotic proteins, and those involved in cardiovascular diseases. These results suggest that proteins with intrinsic disorder are a common feature of neurodevelopment and synaptic transmission processes which are potentially involved in the etiology of psychiatric diseases. Moreover, we identified differences between ADHD and ASD when the binary prediction of structure and putative binding sites were compared. These differences may be related to variation in symptom complexity between both diseases. © 2016 Wiley Periodicals, Inc.

Antihormonal agents as a strategy to improve the effect of chemo-radiation in cervical cancer: in vitro and in vivo study.

BACKGROUND: Over the past few years, the concurrent use of cisplatin-based chemotherapy and radiation therapy has dramatically improved the local response and increased overall survival in early-stage cervical cancer. However, for the advanced stages of the disease this standard treatment has proved insufficient. We investigated the capacity of Mifepristone and ICI 182,780, which are anti-progestin and anti-estrogen drugs, respectively, to act as chemo-radiosensitizing agents in cervical cancer cells and cervix xenografts. METHODS: The effect of chemo-radiation alone or combined with Mifepristone or ICI 182,780 was evaluated in HeLa cells and with tumor growth in cervix xenografts. After concomitant chemo-radiotherapy, the effect of each of these antihormonal agents on apoptosis (determined by Annexing V assay) and the cell cycle phases were determined by flow cytometry. The expression of angiogenic factor VEGF in tumor samples was determined using quantitative RT-PCR analysis of VEGF gene expression. RESULTS: Compared to radiation alone or radiation/cisplatin therapy, there was significantly higher cytotoxicity and a greater antitumoral effect with the combined application of radiation/cisplatin and Mifepristone or ICI 182,780. Analyses of the apoptosis and cell cycle demonstrated changes only with ICI, not with Mifepristone, when was applied in combination with radiation/cisplatin. The analysis of VEGF mRNA expression levels in tumors at the end of the study demonstrated a significant inhibition, compared to radiation only or the radiation/cisplatin treatment, after concurrent chemo-radiotherapy and each one of the antihormonal drugs. CONCLUSION: Mifepristone and ICI 182,780 may be potentially promising chemo-radiosensitizing compounds to be used in combination with ionizing irradiation and cisplatin in the treatment of patients with advanced cervical cancer.