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Data irregularity in cancer genomics studies has been widely observed in the form of outliers and heavy-tailed distributions in the complex traits. In the past decade, robust variable selection methods have emerged as powerful alternatives to the nonrobust ones to identify important genes associated with heterogeneous disease traits and build superior predictive models. In this study, to keep the remarkable features of the quantile LASSO and fully Bayesian regularized quantile regression while overcoming their disadvantage in the analysis of high-dimensional genomics data, we propose the spike-and-slab quantile LASSO through a fully Bayesian spike-and-slab formulation under the robust likelihood by adopting the asymmetric Laplace distribution (ALD). The proposed robust method has inherited the prominent properties of selective shrinkage and self-adaptivity to the sparsity pattern from the spike-and-slab LASSO (Roc̆ková and George, J Am Stat Associat, 2018, 113(521): 431-444). Furthermore, the spike-and-slab quantile LASSO has a computational advantage to locate the posterior modes via soft-thresholding rule guided Expectation-Maximization (EM) steps in the coordinate descent framework, a phenomenon rarely observed for robust regularization with nondifferentiable loss functions. We have conducted comprehensive simulation studies with a variety of heavy-tailed errors in both homogeneous and heterogeneous model settings to demonstrate the superiority of the spike-and-slab quantile LASSO over its competing methods. The advantage of the proposed method has been further demonstrated in case studies of the lung adenocarcinomas (LUAD) and skin cutaneous melanoma (SKCM) data from The Cancer Genome Atlas (TCGA).

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Cell death regulation is essential for tissue homeostasis and its dysregulation often underlies cancer development. Understanding the different pathways of cell death can provide novel therapeutic strategies for battling cancer. This review explores several key cell death mechanisms of apoptosis, necroptosis, autophagic cell death, ferroptosis, and pyroptosis. The research gap addressed involves a thorough analysis of how these cell death pathways can be precisely targeted for cancer therapy, considering tumor heterogeneity and adaptation. It delves into genetic and epigenetic factors and signaling cascades like the phosphatidylinositol 3-kinase/protein kinase B/mammalian target of rapamycin (PI3K/AKT/mTOR) and mitogen-activated protein kinase/extracellular signal-regulated kinase (MAPK/ERK) pathways, which are critical for the regulation of cell death. Additionally, the interaction of the microenvironment with tumor cells, and particularly the influence of hypoxia, nutrient deprivation, and immune cellular interactions, are explored. Emphasizing therapeutic strategies, this review highlights emerging modulators and inducers such as B cell lymphoma 2 (BCL2) homology domain 3 (BH3) mimetics, tumour necrosis factor-related apoptosis-inducing ligand (TRAIL), chloroquine, and innovative approaches to induce ferroptosis and pyroptosis. This review provides insights into cancer therapy's future direction, focusing on multifaceted approaches to influence cell death pathways and circumvent drug resistance. This examination of evolving strategies underlines the considerable clinical potential and the continuous necessity for in-depth exploration within this scientific domain.

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BACKGROUND: Epimedin A (EA) has been shown to suppress extensive osteoclastogenesis and bone resorption, but the effects of EA remain incompletely understood. The aim of our study was to investigate the effects of EA on osteoclastogenesis and bone resorption to explore the corresponding signalling pathways. METHODS: Rats were randomly assigned to the sham operation or ovariectomy group, and alendronate was used for the positive control group. The therapeutic effect of EA on osteoporosis was systematically analysed by measuring bone mineral density and bone biomechanical properties. In vitro, RAW264.7 cells were treated with receptor activator of nuclear factor kappa-B ligand (RANKL) and macrophage colony-stimulating factor (M-CSF) to induce osteoclast differentiation. Cell viability assays, tartrate-resistant acid phosphatase (TRAP) staining, and immunofluorescence were used to elucidate the effects of EA on osteoclastogenesis. In addition, the expression of bone differentiation-related proteins or genes was evaluated using Western blot analysis or quantitative polymerase chain reaction (PCR), respectively. RESULTS: After 3 months of oral EA intervention, ovariectomized rats exhibited increased bone density, relative bone volume, trabecular thickness, and trabecular number, as well as reduced trabecular separation. EA dose-dependently normalized bone density and trabecular microarchitecture in the ovariectomized rats. Additionally, EA inhibited the expression of TRAP and NFATc1 in the ovariectomized rats. Moreover, the in vitro results indicated that EA inhibits osteoclast differentiation by suppressing the TRAF6/PI3K/AKT/NF-κB pathway. Further studies revealed that the effect on osteoclast differentiation, which was originally inhibited by EA, was reversed when the TRAF6 gene was overexpressed. CONCLUSIONS: The findings indicated that EA can negatively regulate osteoclastogenesis by inhibiting the TRAF6/PI3K/AKT/NF-κB axis and that ameliorating ovariectomy-induced osteoporosis in rats with EA may be a promising potential therapeutic strategy for the treatment of osteoporosis.

Asunto(s)

Diferenciación Celular , FN-kappa B , Osteoclastos , Fosfatidilinositol 3-Quinasas , Proteínas Proto-Oncogénicas c-akt , Transducción de Señal , Factor 6 Asociado a Receptor de TNF , Animales , Factor 6 Asociado a Receptor de TNF/metabolismo , Factor 6 Asociado a Receptor de TNF/genética , Osteoclastos/metabolismo , FN-kappa B/metabolismo , Transducción de Señal/efectos de los fármacos , Diferenciación Celular/efectos de los fármacos , Proteínas Proto-Oncogénicas c-akt/metabolismo , Femenino , Fosfatidilinositol 3-Quinasas/metabolismo , Ratas , Ratones , Células RAW 264.7 , Flavonoides/farmacología , Osteogénesis/efectos de los fármacos , Ratas Sprague-Dawley , Osteoporosis/metabolismo , Osteoporosis/etiología , Ovariectomía/efectos adversos , Regulación de la Expresión Génica/efectos de los fármacos , Densidad Ósea/efectos de los fármacos

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BACKGROUND: Bladder cancer is a common malignancy with high recurrence rate. Early diagnosis and recurrence surveillance are pivotal to patients' outcomes, which require novel minimal-invasive diagnostic tools. The urinary microbiome is associated with bladder cancer and can be used as biomarkers, but the underlying mechanism is to be fully illustrated and diagnostic performance to be improved. METHODS: A total of 23 treatment-naïve bladder cancer patients and 9 non-cancerous subjects were enrolled into the Before group and Control group. After surgery, 10 patients from the Before group were further assigned into After group. Void mid-stream urine samples were collected and sent for 16S rDNA sequencing, targeted metabolomic profiling, and flow cytometry. Next, correlations were analyzed between microbiota, metabolites, and cytokines. Finally, receiver operating characteristic (ROC) curves of the urinary biomarkers were plotted and compared. RESULTS: Comparing to the Control group, levels of IL-6 (p < 0.01), IL-8 (p < 0.05), and IL-10 (p < 0.05) were remarkably elevated in the Before group. The α diversity of urine microbiome was also significantly higher, with the feature microbiota positively correlated to the level of IL-6 (r = 0.58, p < 0.01). Significant differences in metabolic composition were also observed between the Before and Control groups, with fatty acids and fatty acylcarnitines enriched in the Before group. After tumor resection, cytokine levels and the overall microbiome structure in the After group remained similar to that of the Before group, but fatty acylcarnitines were significantly reduced (p < 0.05). Pathway enrichment analysis revealed beta-oxidation of fatty acids was significantly involved (p < 0.001). ROC curves showed that the biomarker panel of Actinomycetaceae + arachidonic acid + IL-6 had superior diagnostic performance, with sensitivity of 0.94 and specificity of 1.00. CONCLUSIONS: Microbiome dysbiosis, proinflammatory environment and altered fatty acids metabolism are involved in the pathogenesis of bladder cancer, which may throw light on novel noninvasive diagnostic tool development.

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Disbiosis , Ácidos Grasos , Inflamación , Microbiota , Neoplasias de la Vejiga Urinaria , Humanos , Neoplasias de la Vejiga Urinaria/microbiología , Neoplasias de la Vejiga Urinaria/orina , Inflamación/microbiología , Masculino , Disbiosis/microbiología , Disbiosis/orina , Persona de Mediana Edad , Femenino , Ácidos Grasos/metabolismo , Ácidos Grasos/orina , Curva ROC , Citocinas/metabolismo , ARN Ribosómico 16S/genética , Anciano , Estudios de Casos y Controles

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Objective: Despite the clear association of TyG-BMI with prediabetes and the progression of diabetes, no study to date has examined the relationship between TyG-BMI and the reversal of prediabetes to normoglycemia. Methods: 25,279 participants with prediabetes who had physical examinations between 2010 and 2016 were enrolled in this retrospective cohort study. The relationship between baseline TyG-BMI and regression to normoglycemia from prediabetes was examined using the Cox proportional hazards regression model in this study. Additionally, the nonlinear association between TyG-BMI and the likelihood of regression to normoglycemia was investigated using the Cox proportional hazards regression with cubic spline function. Competing risk multivariate Cox regression analysis was conducted, with progression to diabetes as a competing risk for prediabetes reversal to normoglycemia. Furthermore, subgroup analyses and a series of sensitivity analyses were performed. Results: After adjusting for covariates, the results showed that TyG-BMI was negatively associated with the probability of returning to normoglycemia (per 10 units, HR=0.970, 95% CI: 0.965, 0.976). They were also nonlinearly related, with an inflection point for TyG-BMI of 196.46. The effect size (HR) for TyG-BMI to the right of the inflection point (TyG-BMI ≥ 196.46) and the probability of return of normoglycemia was 0.962 (95% CI: 0.954, 0.970, per 10 units). In addition, the competing risks model found a negative correlation between TyG-BMI and return to normoglycemia (SHR=0.97, 95% CI: 0.96-0.98). Sensitivity analyses demonstrated the robustness of our results. Conclusion: This study demonstrated a negative and nonlinear relationship between TyG-BMI and return to normoglycemia in Chinese adults with prediabetes. Through active intervention, the combined reduction of BMI and TG levels to bring TyG-BMI down to 196.46 could significantly increase the probability of returning to normoglycemia.

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Estado Prediabético , Adulto , Humanos , Estado Prediabético/epidemiología , Índice de Masa Corporal , Estudios de Cohortes , Estudios Retrospectivos , Probabilidad , Glucosa , Triglicéridos , China/epidemiología

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AIMS: The chemotherapy drugs for NSCLC often face the consequences of treatment failure due to acquired drug resistance. Tumor chemotherapy resistance is often accompanied by angiogenesis. Here, we aimed to investigate the effect and underlying mechanisms of ADAM-17 inhibitor ZLDI-8 we found before on angiogenesis and vasculogenic mimicry(VM) in drug-resistant NSCLC. MAIN METHODS: The tube formation assay was used to evaluate angiogenesis and VM. Migration and invasion were assessed with transwell assays in the co-culture condition. To explore the underlying mechanisms of how ZLDI-8 inhibited tubes formation, ELISA assay and western blot assay were preformed. The effects of ZLDI-8 on angiogenesis in vivo were investigated in Matrigel plug, CAM and Rat aortic ring assays. KEY FINDINGS: In the present study, ZLDI-8 significantly inhibited the tube formation of human umbilical vein endothelial cells (HUVECs) in either normal medium or in tumor supernatants. Furthermore, ZLDI-8 also inhibited VM tubes formation of A549/Taxol cells. In the co-culture assay, the interaction between lung cancer cells and HUVECs promotes increased cell migration and invasion, while ZLDI-8 eliminates this promotion. Moreover, the VEGF secretion were decreased by ZLDI-8 and the expression of Notch1, Dll4, HIF1α and VEGF were inhibited by ZLDI-8. In addition, ZLDI-8 can inhibit blood vessel formation in the Matrigel plug, CAM and Rat aortic ring assays. SIGNIFICANCE: ZLDI-8 inhibits angiogenesis and VM in drug-resistant NSCLC through suppressing Notch1-HIF1α-VEGF signaling pathway. This study lays the foundation for the discovery of drugs that inhibit angiogenesis and VM in drug resistant NSCLC.

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Carcinoma de Pulmón de Células no Pequeñas , Neoplasias Pulmonares , Humanos , Ratas , Animales , Neoplasias Pulmonares/tratamiento farmacológico , Neoplasias Pulmonares/patología , Células Endoteliales/patología , Factor A de Crecimiento Endotelial Vascular , Línea Celular Tumoral , Neovascularización Patológica/tratamiento farmacológico , Neovascularización Patológica/patología , Carcinoma de Pulmón de Células no Pequeñas/tratamiento farmacológico , Carcinoma de Pulmón de Células no Pequeñas/patología , Movimiento Celular , Células Endoteliales de la Vena Umbilical Humana/metabolismo , Células Endoteliales de la Vena Umbilical Humana/patología

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BACKGROUND: Allergic bronchopulmonary aspergillosis (ABPA) is an immune-related pulmonary disease caused by sensitization of airway by Aspergillus fumigatus. The disease manifests as bronchial asthma and recurring pulmonary shadows, which may be associated with bronchiectasis. The diagnosis of ABPA mainly depends on serological, immunological, and imaging findings. Pathological examination is not necessary but may be required in atypical cases to exclude pulmonary tuberculosis, tumor, and other diseases through lung biopsy. CASE SUMMARY: An 18-year-old man presented with recurrent wheezing, cough, and peripheral blood eosinophilia. Chest computed tomography showed pulmonary infiltration. There was a significant increase in eosinophils in bronchoalveolar lavage fluid. There was no history of residing in a parasite-endemic area or any evidence of parasitic infection. Pathologic examination of bronchoalveolar lavage fluid excluded fungal and mycobacterial infections. The patient was receiving medication for comorbid diseases, but there was no temporal correlation between medication use and clinical manifestations, which excluded drug-induced etiology. Histopathological examination of lung biopsy specimen showed no signs of eosinophilic granulomatosis with polyangiitis, IgG4-related diseases, or tumors. The diagnosis of ABPA was considered based on the history of asthma and the significant increase in serum Aspergillus fumigatus-specific immunoglobulin (Ig)E. Eosinophil-related diseases were excluded through pathological biopsy, which showed typical pathological manifestations of ABPA. CONCLUSION: The possibility of ABPA should be considered in patients with poorly controlled asthma, especially those with eosinophilia, lung infiltration shadows, or bronchiectasis. Screening for serum IgE, Aspergillus fumigatus-specific IgE and IgG, and alveolar lavage can help avoid misdiagnosis.

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In high-dimensional data analysis, the bi-level (or the sparse group) variable selection can simultaneously conduct penalization on the group level and within groups, which has been developed for continuous, binary, and survival responses in the literature. Zhou et al. (2022) (PMID: 35766061) has further extended it under the longitudinal response by proposing a quadratic inference function-based penalization method in gene-environment interaction studies. This study introduces "springer," an R package implementing the bi-level variable selection within the QIF framework developed in Zhou et al. (2022). In addition, R package "springer" has also implemented the generalized estimating equation-based sparse group penalization method. Alternative methods focusing only on the group level or individual level have also been provided by the package. In this study, we have systematically introduced the longitudinal penalization methods implemented in the "springer" package. We demonstrate the usage of the core and supporting functions, which is followed by the numerical examples and discussions. R package "springer" is available at https://cran.r-project.org/package=springer.

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Gene-environment (G× E) interactions have important implications to elucidate the etiology of complex diseases beyond the main genetic and environmental effects. Outliers and data contamination in disease phenotypes of G× E studies have been commonly encountered, leading to the development of a broad spectrum of robust regularization methods. Nevertheless, within the Bayesian framework, the issue has not been taken care of in existing studies. We develop a fully Bayesian robust variable selection method for G× E interaction studies. The proposed Bayesian method can effectively accommodate heavy-tailed errors and outliers in the response variable while conducting variable selection by accounting for structural sparsity. In particular, for the robust sparse group selection, the spike-and-slab priors have been imposed on both individual and group levels to identify important main and interaction effects robustly. An efficient Gibbs sampler has been developed to facilitate fast computation. Extensive simulation studies, analysis of diabetes data with single-nucleotide polymorphism measurements from the Nurses' Health Study, and The Cancer Genome Atlas melanoma data with gene expression measurements demonstrate the superior performance of the proposed method over multiple competing alternatives.

Asunto(s)

Interacción Gen-Ambiente , Melanoma , Humanos , Teorema de Bayes , Simulación por Computador , Fenotipo , Melanoma/genética

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This work uses surface acoustic waves (SAWs) that are generated by a piezoelectric substrate containing an interdigital transducer (IDT) to which a low voltage of 2 mV was applied at a frequency of 1 kHz to fabricate a polymer-stabilized blue phase liquid crystal (PS-BPLC) layer. The PS-BPLC layer has a more uniform optical microscope (OM) image at a voltage of 2 mV than at zero voltage, and its reflective spectrum exhibits a smaller full width at half maximum (FWHM) at the former than at the latter. The uniform OM image and small FWHM reveal that the lattices in the PS-BPLC layer have monodomain structure. The monodomain PS-BPLC layer is formed because the SAWs cause longitudinal and transverse vibrations of the PS-BPLC lattices in the vertical plane along their traveling direction. The proposed method for fabricating the monodomain PS-BPLC layer using the SAWs has potential for the development of reflective optical devices that consume low power during their fabrication.

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The quantile varying coefficient (VC) model can flexibly capture dynamical patterns of regression coefficients. In addition, due to the quantile check loss function, it is robust against outliers and heavy-tailed distributions of the response variable, and can provide a more comprehensive picture of modeling via exploring the conditional quantiles of the response variable. Although extensive studies have been conducted to examine variable selection for the high-dimensional quantile varying coefficient models, the Bayesian analysis has been rarely developed. The Bayesian regularized quantile varying coefficient model has been proposed to incorporate robustness against data heterogeneity while accommodating the non-linear interactions between the effect modifier and predictors. Selecting important varying coefficients can be achieved through Bayesian variable selection. Incorporating the multivariate spike-and-slab priors further improves performance by inducing exact sparsity. The Gibbs sampler has been derived to conduct efficient posterior inference of the sparse Bayesian quantile VC model through Markov chain Monte Carlo (MCMC). The merit of the proposed model in selection and estimation accuracy over the alternatives has been systematically investigated in simulation under specific quantile levels and multiple heavy-tailed model errors. In the case study, the proposed model leads to identification of biologically sensible markers in a non-linear gene-environment interaction study using the NHS data.

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A dynamical control of the coupling strengths between dressed states and probe photon states is demonstrated with a transmon-like artificial atom coupled to two closely spaced resonant modes. When the atom is driven with one mode, the atom state and driving photon states form the so-called dressed states. Dressed states with sideband index up to 3 were prepared and probed via the strong coupling to the other resonant mode. Spectroscopy reveals that the coupling strengths are "dressed" and can be modulated by the power and sideband index of the driving. The transmission of the probe tone is modulated by the driving microwave amplitude with a Bessel behavior, displaying multi-photon process associated with the inter-atomic level transitions.

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Breast cancer (BrCa) is a heterogeneous disease, which leads to unsatisfactory prognosis in females worldwide. Previous studies have proved that tumor immune microenvironment (TIME) plays crucial roles in oncogenesis, progression, and therapeutic resistance in Breast cancer. However, biomarkers related to TIME features have not been fully discovered. Proteasome activator complex subunit 2 (PSME2) is a member of proteasome activator subunit gene family, which is critical to protein degradation mediated by the proteasome. In the current research, we comprehensively analyzed the expression and immuno-correlations of Proteasome activator complex subunit 2 in Breast cancer. Proteasome activator complex subunit 2 was significantly upregulated in tumor tissues but associated with well prognosis. In addition, Proteasome activator complex subunit 2 was overexpressed in HER2-positive Breast cancer but not related to other clinicopathological features. Interestingly, Proteasome activator complex subunit 2 was positively related to immune-related processes and identified immuno-hot TIME in Breast cancer. Specifically, Proteasome activator complex subunit 2 was positively correlated with immunomodulators, tumor-infiltrating immune cells (TIICs), immune checkpoints, and tumor mutation burden (TMB) levels. Moreover, the positive correlation between Proteasome activator complex subunit 2 and PD-L1 expression was confirmed in a tissue microarray (TMA) cohort. Furthermore, in an immunotherapy cohort of Breast cancer, patients with pathological complete response (pCR) expressed higher Proteasome activator complex subunit 2 compared with those with non-pathological complete response. In conclusion, Proteasome activator complex subunit 2 is upregulated in tumor tissues and correlated with the immuno-hot tumor immune microenvironment, which can be a novel biomarker for the recognition of tumor immune microenvironment features and immunotherapeutic response in Breast cancer.

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Emerging evidence has uncovered that tumor-infiltrating immune cells (TIICs) play significant roles in regulating the tumorigenesis and progression of clear cell renal cell carcinoma (ccRCC). However, the exact composition of TIICs and their prognostic values in ccRCC have not been well defined. A total of 534 ccRCC samples with survival information and TIIC data from The Cancer Genome Atlas (TCGA) dataset were included in our research. The ImmuCellAI tool was employed to estimate the abundance of 24 TIICs and further survival analysis explored the prognostic values of TIICs in ccRCC. In addition, the expression levels of immunosuppressive molecules (PDL1, PD1, LAG3, and CTLA4) in the high- and low-risk groups were explored. Various subtypes of TIICs had distinct infiltrating features and most TIICs exhibited dysregulated abundance between normal and tumor tissues. Moreover, specific kinds of TIICs had encouraging prognostic values in ccRCC. Further analysis constructed a 4-TIICs signature to evaluate the prognosis of ccRCC patients. Cox regression analyses confirmed the independent prognostic role of the signature in ccRCC. Moreover, immunosuppressive molecules, including PD1, LAG3, and CTLA4, were significantly upregulated in the high-risk group and predicted poor prognosis. However, PDL1 was not changed between high- and low-risk groups and could not predict poor prognosis. To sum up, our research explored the landscape of TIICs in ccRCC and established a novel 4-TIIC prognostic signature, which could effectively predict the prognosis for patients with ccRCC. Based on this signature, we also concluded that PDL1 may not predict prognosis in ccRCC.

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Penalized variable selection for high-dimensional longitudinal data has received much attention as it can account for the correlation among repeated measurements while providing additional and essential information for improved identification and prediction performance. Despite the success, in longitudinal studies, the potential of penalization methods is far from fully understood for accommodating structured sparsity. In this article, we develop a sparse group penalization method to conduct the bi-level gene-environment (G × $\times $ E) interaction study under the repeatedly measured phenotype. Within the quadratic inference function framework, the proposed method can achieve simultaneous identification of main and interaction effects on both the group and individual levels. Simulation studies have shown that the proposed method outperforms major competitors. In the case study of asthma data from the Childhood Asthma Management Program, we conduct G × $\times $ E study by using high-dimensional single nucleotide polymorphism data as genetic factors and the longitudinal trait, forced expiratory volume in 1 s, as the phenotype. Our method leads to improved prediction and identification of main and interaction effects with important implications.

Asunto(s)

Asma , Interacción Gen-Ambiente , Asma/genética , Simulación por Computador , Humanos , Estudios Longitudinales , Modelos Genéticos

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We introduce interep, an R package for interaction analysis of repeated measurement data with high-dimensional main and interaction effects. In G × E interaction studies, the forms of environmental factors play a critical role in determining how structured sparsity should be imposed in the high-dimensional scenario to identify important effects. Zhou et al. (2019) (PMID: 31816972) proposed a longitudinal penalization method to select main and interaction effects corresponding to the individual and group structure, respectively, which requires a mixture of individual and group level penalties. The R package interep implements generalized estimating equation (GEE)-based penalization methods with this sparsity assumption. Moreover, alternative methods have also been implemented in the package. These alternative methods merely select effects on an individual level and ignore the group-level interaction structure. In this software article, we first introduce the statistical methodology corresponding to the penalized GEE methods implemented in the package. Next, we present the usage of the core and supporting functions, which is followed by a simulation example with R codes and annotations. The R package interep is available at The Comprehensive R Archive Network (CRAN).

Asunto(s)

Algoritmos , Programas Informáticos , Interpretación Estadística de Datos

RESUMEN

In high-throughput genetics studies, an important aim is to identify gene-environment interactions associated with the clinical outcomes. Recently, multiple marginal penalization methods have been developed and shown to be effective in G×E studies. However, within the Bayesian framework, marginal variable selection has not received much attention. In this study, we propose a novel marginal Bayesian variable selection method for G×E studies. In particular, our marginal Bayesian method is robust to data contamination and outliers in the outcome variables. With the incorporation of spike-and-slab priors, we have implemented the Gibbs sampler based on Markov Chain Monte Carlo (MCMC). The proposed method outperforms a number of alternatives in extensive simulation studies. The utility of the marginal robust Bayesian variable selection method has been further demonstrated in the case studies using data from the Nurse Health Study (NHS). Some of the identified main and interaction effects from the real data analysis have important biological implications.

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High-risk human papillomavirus (HR HPV) causes nearly all cervical cancers, half of which are due to HPV type 16 (HPV16). HPV16 oncoprotein E6 (16E6) binds to NFX1-123, and dysregulates gene expression, but their clinical implications are unknown. Additionally, HPV16 E7's role has not been studied in concert with NFX1-123 and 16E6. HR HPVs express both oncogenes, and transformation requires their expression, so we sought to investigate the effect of E7 on gene expression. This study's goal was to define gene expression profiles across cervical precancer and cancer stages, identify genes correlating with disease progression, assess patient survival, and validate findings in cell models. We analyzed NCBI GEO datasets containing transcriptomic data linked with cervical cancer stage and utilized LASSO analysis to identify cancer-driving genes. Keratinocytes expressing 16E6 and 16E7 (16E6E7) and exogenous NFX1-123 were tested for LASSO-identified gene expression. Ten out of nineteen genes correlated with disease progression, including CEBPD, NOTCH1, and KRT16, and affected survival. 16E6E7 in keratinocytes increased CEBPD, KRT16, and SLPI, and decreased NOTCH1. Exogenous NFX1-123 in 16E6E7 keratinocytes resulted in significantly increased CEBPD and NOTCH1, and reduced SLPI. This work demonstrates the clinical relevance of CEBPD, NOTCH1, KRT16, and SLPI, and shows the regulatory effects of 16E6E7 and NFX1-123.

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Primary tracheobronchial light chain (AL) amyloidosis is a rare and heterogeneous disease characterized by the buildup of amyloid deposits in the airway mucosa. Although its treatment remains challenging, the current view is that the localized form can be treated conservatively due to its slow progression. While radiotherapy has proven effective in treating localized form of the disease, some patients do not respond to local treatment and continue to experience poor quality of life, highlighting the need to explore additional treatment strategies. In this report, we discuss a case of primary tracheobronchial AL amyloidosis with biclonal gammopathy (IgA κ and IgG κ) in a 46-year-old man who was transferred to our hospital due to dyspnea progression over the preceding 3 years. Chest computed tomography revealed irregular tracheobronchial stenosis with wall thickening, and histological examination of the bronchial biopsies confirmed the diagnosis of endobronchial AL amyloidosis. Owing to the poor effect of radiation therapy and treatments for improving airway patency, he was treated with a systemic chemotherapy regimen [cyclophosphamide-bortezomib-dexamethasone (CyBorD)]. We observed substantial improvements in his dyspnea, highlighting the potential of systemic therapy to improve quality of life of patients with tracheobronchial AL amyloidosis. However, the long-term pathological changes associated with local bronchial lesions require further investigation.

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BACKGROUND AND OBJECTIVES: The majority of coronavirus disease 2019 (COVID-19) cases are nonsevere, but severe cases have high mortality and need early detection and treatment. We aimed to develop a nomogram to predict the disease progression of nonsevere COVID-19 based on simple data that can be easily obtained even in primary medical institutions. METHODS: In this retrospective, multicenter cohort study, we extracted data from initial simple medical evaluations of 495 COVID-19 patients randomized (2:1) into a development cohort and a validation cohort. The progression of nonsevere COVID-19 was recorded as the primary outcome. We built a nomogram with the development cohort and tested its performance in the validation cohort. RESULTS: The nomogram was developed with the nine factors included in the final model. The area under the curve (AUC) of the nomogram scoring system for predicting the progression of nonsevere COVID-19 into severe COVID-19 was 0.875 and 0.821 in the development cohort and validation cohort, respectively. The nomogram achieved a good concordance index for predicting the progression of nonsevere COVID-19 cases in the development and validation cohorts (concordance index of 0.875 in the development cohort and 0.821 in the validation cohort) and had well-fitted calibration curves showing good agreement between the estimates and the actual endpoint events. CONCLUSIONS: The proposed nomogram built with a simplified index might help to predict the progression of nonsevere COVID-19; thus, COVID-19 with a high risk of disease progression could be identified in time, allowing an appropriate therapeutic choice according to the potential disease severity.