Methods for multi-omic data integration in cancer research.

Multi-omics data integration is a term that refers to the process of combining and analyzing data from different omic experimental sources, such as genomics, transcriptomics, methylation assays, and microRNA sequencing, among others. Such data integration approaches have the potential to provide a more comprehensive functional understanding of biological systems and has numerous applications in areas such as disease diagnosis, prognosis and therapy. However, quantitative integration of multi-omic data is a complex task that requires the use of highly specialized methods and approaches. Here, we discuss a number of data integration methods that have been developed with multi-omics data in view, including statistical methods, machine learning approaches, and network-based approaches. We also discuss the challenges and limitations of such methods and provide examples of their applications in the literature. Overall, this review aims to provide an overview of the current state of the field and highlight potential directions for future research.

The ROSMAP project: aging and neurodegenerative diseases through omic sciences.

The Religious Order Study and Memory and Aging Project (ROSMAP) is an initiative that integrates two longitudinal cohort studies, which have been collecting clinicopathological and molecular data since the early 1990s. This extensive dataset includes a wide array of omic data, revealing the complex interactions between molecular levels in neurodegenerative diseases (ND) and aging. Neurodegenerative diseases (ND) are frequently associated with morbidity and cognitive decline in older adults. Omics research, in conjunction with clinical variables, is crucial for advancing our understanding of the diagnosis and treatment of neurodegenerative diseases. This summary reviews the extensive omics research-encompassing genomics, transcriptomics, proteomics, metabolomics, epigenomics, and multiomics-conducted through the ROSMAP study. It highlights the significant advancements in understanding the mechanisms underlying neurodegenerative diseases, with a particular focus on Alzheimer's disease.

The flow of reward information through neuronal ensembles in the accumbens.

The nucleus accumbens shell (NAcSh) integrates reward information through diverse and specialized neuronal ensembles, influencing decision-making. By training rats in a probabilistic choice task and recording NAcSh neuronal activity, we found that rats adapt their choices based solely on the presence or absence of a sucrose reward, suggesting they build an internal representation of reward likelihood. We further demonstrate that NAcSh ensembles dynamically process different aspects of reward-guided behavior, with changes in composition and functional connections observed throughout the reinforcement learning process. The NAcSh forms a highly connected network characterized by a heavy-tailed distribution and the presence of neuronal hubs, facilitating efficient information flow. Reward delivery enhances mutual information, indicating increased communication between ensembles and network synchronization, whereas reward omission decreases it. Our findings reveal how reward information flows through dynamic NAcSh ensembles, whose flexible membership adapts as the rat learns to obtain rewards (energy) in an ever-changing environment.

The Molecular Comorbidity Network of Periodontal Disease.

Periodontal disease, a multifactorial inflammatory condition affecting the supporting structures of the teeth, has been increasingly recognized for its association with various systemic diseases. Understanding the molecular comorbidities of periodontal disease is crucial for elucidating shared pathogenic mechanisms and potential therapeutic targets. In this study, we conducted comprehensive literature and biological database mining by utilizing DisGeNET2R for extracting gene-disease associations, Romin for integrating and modeling molecular interaction networks, and Rentrez R libraries for accessing and retrieving relevant information from NCBI databases. This integrative bioinformatics approach enabled us to systematically identify diseases sharing associated genes, proteins, or molecular pathways with periodontitis. Our analysis revealed significant molecular overlaps between periodontal disease and several systemic conditions, including cardiovascular diseases, diabetes mellitus, rheumatoid arthritis, and inflammatory bowel diseases. Shared molecular mechanisms implicated in the pathogenesis of these diseases and periodontitis encompassed dysregulation of inflammatory mediators, immune response pathways, oxidative stress pathways, and alterations in the extracellular matrix. Furthermore, network analysis unveiled the key hub genes and proteins (such as TNF, IL6, PTGS2, IL10, NOS3, IL1B, VEGFA, BCL2, STAT3, LEP and TP53) that play pivotal roles in the crosstalk between periodontal disease and its comorbidities, offering potential targets for therapeutic intervention. Insights gained from this integrative approach shed light on the intricate interplay between periodontal health and systemic well-being, emphasizing the importance of interdisciplinary collaboration in developing personalized treatment strategies for patients with periodontal disease and associated comorbidities.

Antitumoral and Antimetastatic Activity by Mixed Chelate Copper(II) Compounds (Casiopeínas®) on Triple-Negative Breast Cancer, In Vitro and In Vivo Models.

Triple-negative breast cancer (TNBC), accounting for 15-20% of all breast cancers, has one of the poorest prognoses and survival rates. Metastasis, a critical process in cancer progression, causes most cancer-related deaths, underscoring the need for alternative therapeutic approaches. This study explores the anti-migratory, anti-invasive, anti-tumoral, and antimetastatic effects of copper coordination compounds Casiopeína IIIia (CasIIIia) and Casiopeína IIgly (CasIIgly) on MDA-MB-231 and 4T1 breast carcinoma cell lines in vitro and in vivo. These emerging anticancer agents, mixed chelate copper(II) compounds, induce apoptosis by generating reactive oxygen species (ROS) and causing DNA damage. Whole-transcriptome analysis via gene expression arrays indicated that subtoxic concentrations of CasIIIia upregulate genes involved in metal response mechanisms. Casiopeínas® reduced TNBC cell viability dose-dependently and more efficiently than Cisplatin. At subtoxic concentrations (IC20), they inhibited random and chemotactic migration of MDA-MB-231 and 4T1 cells by 50-60%, similar to Cisplatin, as confirmed by transcriptome analysis. In vivo, CasIIIia and Cisplatin significantly reduced tumor growth, volume, and weight in a syngeneic breast cancer model with 4T1 cells. Furthermore, both compounds significantly decreased metastatic foci in treated mice compared to controls. Thus, CasIIIia and CasIIgly are promising chemotherapeutic candidates against TNBC.

Coordinated inflammation and immune response transcriptional regulation in breast cancer molecular subtypes.

Breast cancer, characterized by its complexity and diversity, presents significant challenges in understanding its underlying biology. In this study, we employed gene co-expression network analysis to investigate the gene composition and functional patterns in breast cancer subtypes and normal breast tissue. Our objective was to elucidate the detailed immunological features distinguishing these tumors at the transcriptional level and to explore their implications for diagnosis and treatment. The analysis identified nine distinct gene module clusters, each representing unique transcriptional signatures within breast cancer subtypes and normal tissue. Interestingly, while some clusters exhibited high similarity in gene composition between normal tissue and certain subtypes, others showed lower similarity and shared traits. These clusters provided insights into the immune responses within breast cancer subtypes, revealing diverse immunological functions, including innate and adaptive immune responses. Our findings contribute to a deeper understanding of the molecular mechanisms underlying breast cancer subtypes and highlight their unique characteristics. The immunological signatures identified in this study hold potential implications for diagnostic and therapeutic strategies. Additionally, the network-based approach introduced herein presents a valuable framework for understanding the complexities of other diseases and elucidating their underlying biology.

Transitions in chromatin conformation shaped by fatty acids and the circadian clock underlie hepatic transcriptional reorganization in obese mice.

The circadian clock system coordinates metabolic, physiological, and behavioral functions across a 24-h cycle, crucial for adapting to environmental changes. Disruptions in circadian rhythms contribute to major metabolic pathologies like obesity and Type 2 diabetes. Understanding the regulatory mechanisms governing circadian control is vital for identifying therapeutic targets. It is well characterized that chromatin remodeling and 3D structure at genome regulatory elements contributes to circadian transcriptional cycles; yet the impact of rhythmic chromatin topology in metabolic disease is largely unexplored. In this study, we explore how the spatial configuration of the genome adapts to diet, rewiring circadian transcription and contributing to dysfunctional metabolism. We describe daily fluctuations in chromatin contacts between distal regulatory elements of metabolic control genes in livers from lean and obese mice and identify specific lipid-responsive regions recruiting the clock molecular machinery. Interestingly, under high-fat feeding, a distinct interactome for the clock-controlled gene Dbp strategically promotes the expression of distal metabolic genes including Fgf21. Alongside, new chromatin loops between regulatory elements from genes involved in lipid metabolism control contribute to their transcriptional activation. These enhancers are responsive to lipids through CEBPß, counteracting the circadian repressor REVERBa. Our findings highlight the intricate coupling of circadian gene expression to a dynamic nuclear environment under high-fat feeding, supporting a temporally regulated program of gene expression and transcriptional adaptation to diet.

Differential effects of follicle-stimulating hormone glycoforms on the transcriptome profile of cultured rat granulosa cells as disclosed by RNA-seq.

It has been documented that variations in glycosylation on glycoprotein hormones, confer distinctly different biological features to the corresponding glycoforms when multiple in vitro biochemical readings are analyzed. We here applied next generation RNA sequencing to explore changes in the transcriptome of rat granulosa cells exposed for 0, 6, and 12 h to 100 ng/ml of four highly purified follicle-stimulating hormone (FSH) glycoforms, each exhibiting different glycosylation patterns: a. human pituitary FSH18/21 (hypo-glycosylated); b. human pituitary FSH24 (fully glycosylated); c. Equine FSH (eqFSH) (hypo-glycosylated); and d. Chinese-hamster ovary cell-derived human recombinant FSH (recFSH) (fully-glycosylated). Total RNA from triplicate incubations was prepared from FSH glycoform-exposed cultured granulosa cells obtained from DES-pretreated immature female rats, and RNA libraries were sequenced in a HighSeq 2500 sequencer (2 x 125 bp paired-end format, 10-15 x 106 reads/sample). The computational workflow focused on investigating differences among the four FSH glycoforms at three levels: gene expression, enriched biological processes, and perturbed pathways. Among the top 200 differentially expressed genes, only 4 (0.6%) were shared by all 4 glycoforms at 6 h, whereas 118 genes (40%) were shared at 12 h. Follicle-stimulating hormone glycocoforms stimulated different patterns of exclusive and associated up regulated biological processes in a glycoform and time-dependent fashion with more shared biological processes after 12 h of exposure and fewer treatment-specific ones, except for recFSH, which exhibited stronger responses with more specifically associated processes at this time. Similar results were found for down-regulated processes, with a greater number of processes at 6 h or 12 h, depending on the particular glycoform. In general, there were fewer downregulated than upregulated processes at both 6 h and 12 h, with FSH18/21 exhibiting the largest number of down-regulated associated processes at 6 h while eqFSH exhibited the greatest number at 12 h. Signaling cascades, largely linked to cAMP-PKA, MAPK, and PI3/AKT pathways were detected as differentially activated by the glycoforms, with each glycoform exhibiting its own molecular signature. These data extend previous observations demonstrating glycosylation-dependent distinctly different regulation of gene expression and intracellular signaling pathways triggered by FSH in granulosa cells. The results also suggest the importance of individual FSH glycoform glycosylation for the conformation of the ligand-receptor complex and induced signalling pathways.

Sleep Quality, Nutrient Intake, and Social Development Index Predict Metabolic Syndrome in the Tlalpan 2020 Cohort: A Machine Learning and Synthetic Data Study.

This study investigated the relationship between Metabolic Syndrome (MetS), sleep disorders, the consumption of some nutrients, and social development factors, focusing on gender differences in an unbalanced dataset from a Mexico City cohort. We used data balancing techniques like SMOTE and ADASYN after employing machine learning models like random forest and RPART to predict MetS. Random forest excelled, achieving significant, balanced accuracy, indicating its robustness in predicting MetS and achieving a balanced accuracy of approximately 87%. Key predictors for men included body mass index and family history of gout, while waist circumference and glucose levels were most significant for women. In relation to diet, sleep quality, and social development, metabolic syndrome in men was associated with high lactose and carbohydrate intake, educational lag, living with a partner without marrying, and lack of durable goods, whereas in women, best predictors in these dimensions include protein, fructose, and cholesterol intake, copper metabolites, snoring, sobbing, drowsiness, sanitary adequacy, and anxiety. These findings underscore the need for personalized approaches in managing MetS and point to a promising direction for future research into the interplay between social factors, sleep disorders, and metabolic health, which mainly depend on nutrient consumption by region.

Functional impact of multi-omic interactions in lung cancer.

Lung tumors are a leading cause of cancer-related death worldwide. Lung cancers are highly heterogeneous on their phenotypes, both at the cellular and molecular levels. Efforts to better understand the biological origins and outcomes of lung cancer in terms of this enormous variability often require of high-throughput experimental techniques paired with advanced data analytics. Anticipated advancements in multi-omic methodologies hold potential to reveal a broader molecular perspective of these tumors. This study introduces a theoretical and computational framework for generating network models depicting regulatory constraints on biological functions in a semi-automated way. The approach successfully identifies enriched functions in analyzed omics data, focusing on Adenocarcinoma (LUAD) and Squamous cell carcinoma (LUSC, a type of NSCLC) in the lung. Valuable information about novel regulatory characteristics, supported by robust biological reasoning, is illustrated, for instance by considering the role of genes, miRNAs and CpG sites associated with NSCLC, both novel and previously reported. Utilizing multi-omic regulatory networks, we constructed robust models elucidating omics data interconnectedness, enabling systematic generation of mechanistic hypotheses. These findings offer insights into complex regulatory mechanisms underlying these cancer types, paving the way for further exploring their molecular complexity.

Comorbidity patterns in cardiovascular diseases: the role of life-stage and socioeconomic status.

Cardiovascular diseases stand as a prominent global cause of mortality, their intricate origins often entwined with comorbidities and multimorbid conditions. Acknowledging the pivotal roles of age, sex, and social determinants of health in shaping the onset and progression of these diseases, our study delves into the nuanced interplay between life-stage, socioeconomic status, and comorbidity patterns within cardiovascular diseases. Leveraging data from a cross-sectional survey encompassing Mexican adults, we unearth a robust association between these variables and the prevalence of comorbidities linked to cardiovascular conditions. To foster a comprehensive understanding of multimorbidity patterns across diverse life-stages, we scrutinize an extensive dataset comprising 47,377 cases diagnosed with cardiovascular ailments at Mexico's national reference hospital. Extracting sociodemographic details, primary diagnoses prompting hospitalization, and additional conditions identified through ICD-10 codes, we unveil subtle yet significant associations and discuss pertinent specific cases. Our results underscore a noteworthy trend: younger patients of lower socioeconomic status exhibit a heightened likelihood of cardiovascular comorbidities compared to their older counterparts with a higher socioeconomic status. By empowering clinicians to discern non-evident comorbidities, our study aims to refine therapeutic designs. These findings offer profound insights into the intricate interplay among life-stage, socioeconomic status, and comorbidity patterns within cardiovascular diseases. Armed with data-supported approaches that account for these factors, clinical practices stand to be enhanced, and public health policies informed, ultimately advancing the prevention and management of cardiovascular disease in Mexico.

Topological Data Analysis in Cardiovascular Signals: An Overview.

Topological data analysis (TDA) is a recent approach for analyzing and interpreting complex data sets based on ideas a branch of mathematics called algebraic topology. TDA has proven useful to disentangle non-trivial data structures in a broad range of data analytics problems including the study of cardiovascular signals. Here, we aim to provide an overview of the application of TDA to cardiovascular signals and its potential to enhance the understanding of cardiovascular diseases and their treatment in the form of a literature or narrative review. We first introduce the concept of TDA and its key techniques, including persistent homology, Mapper, and multidimensional scaling. We then discuss the use of TDA in analyzing various cardiovascular signals, including electrocardiography, photoplethysmography, and arterial stiffness. We also discuss the potential of TDA to improve the diagnosis and prognosis of cardiovascular diseases, as well as its limitations and challenges. Finally, we outline future directions for the use of TDA in cardiovascular signal analysis and its potential impact on clinical practice. Overall, TDA shows great promise as a powerful tool for the analysis of complex cardiovascular signals and may offer significant insights into the understanding and management of cardiovascular diseases.

The Role of Transcription Factors in the Loss of Inter-Chromosomal Co-Expression for Breast Cancer Subtypes.

Breast cancer encompasses a diverse array of subtypes, each exhibiting distinct clinical characteristics and treatment responses. Unraveling the underlying regulatory mechanisms that govern gene expression patterns in these subtypes is essential for advancing our understanding of breast cancer biology. Gene co-expression networks (GCNs) help us identify groups of genes that work in coordination. Previous research has revealed a marked reduction in the interaction of genes located on different chromosomes within GCNs for breast cancer, as well as for lung, kidney, and hematopoietic cancers. However, the reasons behind why genes on the same chromosome often co-express remain unclear. In this study, we investigate the role of transcription factors in shaping gene co-expression networks within the four main breast cancer subtypes: Luminal A, Luminal B, HER2+, and Basal, along with normal breast tissue. We identify communities within each GCN and calculate the transcription factors that may regulate these communities, comparing the results across different phenotypes. Our findings indicate that, in general, regulatory behavior is to a large extent similar among breast cancer molecular subtypes and even in healthy networks. This suggests that transcription factor motif usage does not fully determine long-range co-expression patterns. Specific transcription factor motifs, such as CCGGAAG, appear frequently across all phenotypes, even involving multiple highly connected transcription factors. Additionally, certain transcription factors exhibit unique actions in specific subtypes but with limited influence. Our research demonstrates that the loss of inter-chromosomal co-expression is not solely attributable to transcription factor regulation. Although the exact mechanism responsible for this phenomenon remains elusive, this work contributes to a better understanding of gene expression regulatory programs in breast cancer.

Real-World Evaluation of a Pharmacoinvasive Strategy for STEMI in Latin America: A Cost-Effective Approach with Short-Term Benefits.

Purpose: While pharmacoinvasive strategy (PI) is a safe and effective approach whenever access to primary percutaneous intervention (pPCI) is limited, data on each strategy's economic cost and impact on in-hospital stay are scarce. The objective is to compare the cost-effectiveness of a PI with that of pPCI for the treatment of ST-elevation myocardial infarction (STEMI) in a Latin-American country. Patients and Methods: A total of 1747 patients were included, of whom 470 (26.9%) received PI, 433 (24.7%) pPCI, and 844 (48.3%) NR. The study's primary outcome was the incremental cost-effectiveness ratio (ICER) for PI compared with those for pPCI and non-reperfused (NR), calculated for 30-day major cardiovascular events (MACE), 30-day mortality, and length of stay. Results: For PI, the ICER estimates for MACE showed a decrease of $-35.81/per 1% (95 confidence interval, -114.73 to 64.81) compared with pPCI and a decrease of $-271.60/per 1% (95% CI, -1086.10 to -144.93) compared with NR. Also, in mortality, PI had an ICER decrease of $-129.50 (95% CI, -810.57, 455.06) compared to pPCI and $-165.27 (-224.06, -123.52) with NR. Finally, length of stay had an ICER reduction of -765.99 (-4020.68, 3141.65) and -283.40 (-304.95, -252.76) compared to pPCI and NR, respectively. Conclusion: The findings of this study suggest that PI may be a more efficient treatment approach for STEMI in regions where access to pPCI is limited or where patient and system delays are expected.

Exploring disparities and novel insights into metabolic and cardiovascular comorbidities among COVID-19 patients in Mexico.

Introduction: The COVID-19 pandemic, especially its early stages, sparked extensive discussions regarding the potential impact of metabolic and cardiovascular comorbidities on the severity and fatality of SARS-CoV-2 infection, yielding inconclusive outcomes. In this study, we delve into the prevalence of metabolic and cardiovascular comorbidities within COVID-19 patients in Mexico. Methods: Employing a retrospective observational study design, we collected data from official databases encompassing COVID-19 patients admitted to both public and private hospitals in Mexico City. Results: Our investigation unveiled a noteworthy incongruity in the prevalence of metabolic and cardiovascular comorbidities among COVID-19 patients, with a particular emphasis on obesity, hypertension, and diabetes. This incongruity manifests as location-dependent phenomena, where the prevalence of these comorbidities among COVID-19 patients significantly deviates from the reported values for the general population in each specific location. Discussion: These findings underscore the critical importance of screening for metabolic and cardiovascular comorbidities in COVID-19 patients and advocate for the necessity of tailored interventions for this specific population. Furthermore, our study offers insights into the intricate interplay between COVID-19 and metabolic and cardiovascular comorbidities, serving as a valuable foundation for future research endeavors and informing clinical practice.

Computational single cell oncology: state of the art.

Single cell computational analysis has emerged as a powerful tool in the field of oncology, enabling researchers to decipher the complex cellular heterogeneity that characterizes cancer. By leveraging computational algorithms and bioinformatics approaches, this methodology provides insights into the underlying genetic, epigenetic and transcriptomic variations among individual cancer cells. In this paper, we present a comprehensive overview of single cell computational analysis in oncology, discussing the key computational techniques employed for data processing, analysis, and interpretation. We explore the challenges associated with single cell data, including data quality control, normalization, dimensionality reduction, clustering, and trajectory inference. Furthermore, we highlight the applications of single cell computational analysis, including the identification of novel cell states, the characterization of tumor subtypes, the discovery of biomarkers, and the prediction of therapy response. Finally, we address the future directions and potential advancements in the field, including the development of machine learning and deep learning approaches for single cell analysis. Overall, this paper aims to provide a roadmap for researchers interested in leveraging computational methods to unlock the full potential of single cell analysis in understanding cancer biology with the goal of advancing precision oncology. For this purpose, we also include a notebook that instructs on how to apply the recommended tools in the Preprocessing and Quality Control section.

A machine learning approach to personalized predictors of dyslipidemia: a cohort study.

Introduction: Mexico ranks second in the global prevalence of obesity in the adult population, which increases the probability of developing dyslipidemia. Dyslipidemia is closely related to cardiovascular diseases, which are the leading cause of death in the country. Therefore, developing tools that facilitate the prediction of dyslipidemias is essential for prevention and early treatment. Methods: In this study, we utilized a dataset from a Mexico City cohort consisting of 2,621 participants, men and women aged between 20 and 50 years, with and without some type of dyslipidemia. Our primary objective was to identify potential factors associated with different types of dyslipidemia in both men and women. Machine learning algorithms were employed to achieve this goal. To facilitate feature selection, we applied the Variable Importance Measures (VIM) of Random Forest (RF), XGBoost, and Gradient Boosting Machine (GBM). Additionally, to address class imbalance, we employed Synthetic Minority Over-sampling Technique (SMOTE) for dataset resampling. The dataset encompassed anthropometric measurements, biochemical tests, dietary intake, family health history, and other health parameters, including smoking habits, alcohol consumption, quality of sleep, and physical activity. Results: Our results revealed that the VIM algorithm of RF yielded the most optimal subset of attributes, closely followed by GBM, achieving a balanced accuracy of up to 80%. The selection of the best subset of attributes was based on the comparative performance of classifiers, evaluated through balanced accuracy, sensitivity, and specificity metrics. Discussion: The top five features contributing to an increased risk of various types of dyslipidemia were identified through the machine learning technique. These features include body mass index, elevated uric acid levels, age, sleep disorders, and anxiety. The findings of this study shed light on significant factors that play a role in dyslipidemia development, aiding in the early identification, prevention, and treatment of this condition.

Differential effects of follicle-stimulating hormone glycoforms on the transcriptome profile of cultured rat granulosa cells as disclosed by RNA-seq.

It has been documented that variations in glycosylation on glycoprotein hormones, confer distinctly different biological features to the corresponding glycoforms when multiple in vitro biochemical readings are analyzed. We here applied next generation RNA sequencing to explore changes in the transcriptome of rat granulosa cells exposed for 0, 6, and 12 h to 100 ng/ml of four highly purified follicle-stimulating hormone (FSH) glycoforms, each exhibiting different glycosylation patterns: human pituitary FSH18/21 and equine FSH (eqFSH) (hypo-glycosylated), and human FSH24 and chinese-hamster ovary cell-derived human recombinant FSH (recFSH) (fully-glycosylated). Total RNA from triplicate incubations was prepared from FSH glycoform-exposed cultured granulosa cells obtained from DES-pretreated immature female rats, and RNA libraries were sequenced in a HighSeq 2500 sequencer (2 × 125 bp paired-end format, 10-15 × 106 reads/sample). The computational workflow focused on investigating differences among the four FSH glycoforms at three levels: gene expression, enriched biological processes, and perturbed pathways. Among the top 200 differentially expressed genes, only 4 (0.6%) were shared by all 4 glycoforms at 6 h, whereas 118 genes (40%) were shared at 12 h. Follicle-stimulating hormone glycocoforms stimulated different patterns of exclusive and associated up regulated biological processes in a glycoform and time-dependent fashion with more shared biological processes after 12 h of exposure and fewer treatment-specific ones, except for recFSH, which exhibited stronger responses with more specifically associated processes at this time. Similar results were found for down-regulated processes, with a greater number of processes at 6 h or 12 h, depending on the particular glycoform. In general, there were fewer downregulated than upregulated processes at both 6 h and 12 h, with FSH18/21 exhibiting the largest number of down-regulated associated processes at 6 h while eqFSH exhibited the greatest number at 12 h. Signaling cascades, largely linked to cAMP-PKA, MAPK, and PI3/AKT pathways were detected as differentially activated by the glycoforms, with each glycoform exhibiting its own molecular signature. These data extend previous observations demonstrating glycosylation-dependent differential regulation of gene expression and intracellular signaling pathways triggered by FSH in granulosa cells. The results also suggest the importance of individual FSH glycoform glycosylation for the conformation of the ligand-receptor complex and induced signalling pathways.

Methylation-related genes involved in renal carcinoma progression.

Renal carcinomas are a group of malignant tumors often originating in the cells lining the small tubes in the kidney responsible for filtering waste from the blood and urine production. Kidney tumors arise from the uncontrolled growth of cells in the kidneys and are responsible for a large share of global cancer-related morbidity and mortality. Understanding the molecular mechanisms driving renal carcinoma progression results crucial for the development of targeted therapies leading to an improvement of patient outcomes. Epigenetic mechanisms such as DNA methylation are known factors underlying the development of several cancer types. There is solid experimental evidence of relevant biological functions modulated by methylation-related genes, associated with the progression of different carcinomas. Those mechanisms can often be associated to different epigenetic marks, such as DNA methylation sites or chromatin conformation patterns. Currently, there is no definitive method to establish clear relations between genetic and epigenetic factors that influence the progression of cancer. Here, we developed a data-driven method to find methylation-related genes, so we could find relevant bonds between gene co-expression and methylation-wide-genome regulation patterns able to drive biological processes during the progression of clear cell renal carcinoma (ccRC). With this approach, we found out genes such as ITK oncogene that appear hypomethylated during all four stages of ccRC progression and are strongly involved in immune response functions. Also, we found out relevant tumor suppressor genes such as RAB25 hypermethylated, thus potentially avoiding repressed functions in the AKT signaling pathway during the evolution of ccRC. Our results have relevant implications to further understand some epigenetic-genetic-affected roles underlying the progression of renal cancer.

[Social, demographic and morbimortality characteristics of the cases treated for COVID-19 at the Ignacio Chávez National Institute of Cardiology. A descriptive cross-sectional study]. / Características sociales, demográficas y de morbimortalidad de los casos atendidos por COVID-19 en el Instituto Nacional de Cardiología Ignacio Chávez. Un estudio transversal descriptivo.

Introduction: The COVID-19 pandemic brought with it a large number of adverse consequences for public health with serious socioeconomic repercussions. In this study we characterize the social, demographic, morbidity and mortality conditions of individuals treated for COVID-19 in one of the SARS-CoV-2 reference hospitals in Mexico City. Method: A descriptive cross-sectional study was carried out in 259 patients discharged from the Instituto Nacional de Cardiología Ignacio Chávez, between April 11, 2020 and March 14, 2021. A multivariate logistic regression model was used to identify the association between sociodemographic and clinical variables. An optimization was performed using maximum likelihood calculations to choose the best model compatible with the data. The maximum likelihood model was evaluated using ROC curves, goodnessof-fit estimators, and multicollinearity analysis. Statistically significant patterns of comorbidities were inferred by evaluating a hypergeometric test over the frequencies of co-occurrence of pairs of conditions. A network analysis was implemented to determine connectivity patterns based on degree centrality, between comorbidities and outcome variables. Results: The main social disadvantages of the studied population are related to the lack of social security (96.5%) and the lag in housing conditions (81%). Variables associated with the probability of survival were being younger (p < 0.0001), having more durable material goods (p = 0.0034) and avoiding: pneumonia (p = 0.0072), septic shock (p < 0.0001) and acute respiratory failure (p < 0.0001); (AUROC: 91.5%). The comorbidity network for survival cases has a high degree of connectivity between conditions such as cardiac arrhythmias and essential arterial hypertension (Degree Centrality = 90 and 78, respectively). Conclusions: Given that among the factors associated with survival to COVID-19 there are clinical, sociodemographic and social determinants of health variables, in addition to age; It is imperative to consider the various factors that may affect or modify the health status of a population, especially when addressing emerging epidemic phenomena such as the current COVID-19 pandemic.

Introducción: La pandemia de enfermedad por coronavirus 2019 (COVID-19) trajo aparejadas una gran cantidad de consecuencias adversas para la salud pública con serias repercusiones socioeconómicas. En este estudio caracterizamos las condiciones sociales, demográficas y de morbimortalidad de los casos atendidos por COVID-19 en uno de los hospitales de referencia de coronavirus 2 del síndrome respiratorio agudo grave (SARS-CoV-2) en la Ciudad de México. Método: Se llevó a cabo un estudio transversal descriptivo en 259 pacientes egresados del Instituto Nacional de Cardiología Ignacio Chávez, entre el 11 de abril de 2020 y el 14 de marzo de 2021. Se utilizó un modelo de regresión logística multivariante para identificar la asociación entre variables sociodemográficas y clínicas. Se realizó una optimización mediante cálculos de máxima verosimilitud para elegir el mejor modelo compatible con los datos. El modelo de máxima verosimilitud fue evaluado mediante curvas ROC, estimadores de bondad de ajuste y análisis de multicolinealidad. Se infirieron patrones de comorbilidades estadísticamente significativos mediante la evaluación de una prueba hipergeométrica en las frecuencias de coocurrencia de pares de condiciones. Se implementó un análisis de redes para determinar los patrones de conectividad basado en la centralidad de grado, entre algunas comorbilidades y las variables de desenlace. Resultados: Las principales desventajas sociales de la población estudiada se relacionan con la falta de seguridad social (96.5%) y el rezago en las condiciones de vivienda (81%). Las variables asociadas a la probabilidad de sobrevivir fueron tener una menor edad (p < 0.0001), contar con más bienes materiales durables (p = 0.0034) y evitar: la neumonía (p = 0.0072), el choque séptico (p < 0.0001) y la insuficiencia respiratoria aguda (p < 0.0001); (AUROC: 91.5%). Las red de comorbilidades para los casos de supervivencia tienen un alto grado de conectividad entre padecimientos como las arritmias cardiacas e hipertensión arterial esencial (centralidad de grado: 90 y 78 respectivamente). Conclusiones: En vista de que entre los factores asociados a supervivencia existen variables clínicas, sociodemográficas y determinantes sociales de la salud, además de la edad, resulta imperativo considerar los diversos factores que puedan incidir o modificar el estado de salud de una población, sobre todo al abordar los fenómenos epidémicos emergentes como es el caso de la actual pandemia de COVID-19.