GSAE: an autoencoder with embedded gene-set nodes for genomics functional characterization

Bioinformatics tools have been developed to interpret gene expression data at the gene set level, and these gene set based analyses improve the biologists' capability to discover functional relevance of their experiment design. While elucidating gene set individually, inter gene sets association is rarely taken into consideration. Deep learning, an emerging machine learning technique in computational biology, can be used to generate an unbiased combination of gene set, and to determine the biological relevance and analysis consistency of these combining gene sets by leveraging large genomic data sets. In this study, we proposed a gene superset autoencoder (GSAE), a multi-layer autoencoder model with the incorporation of a priori defined gene sets that retain the crucial biological features in the latent layer. We introduced the concept of the gene superset, an unbiased combination of gene sets with weights trained by the autoencoder, where each node in the latent layer is a superset. Trained with genomic data from TCGA and evaluated with their accompanying clinical parameters, we showed gene supersets' ability of discriminating tumor subtypes and their prognostic capability. We further demonstrated the biological relevance of the top component gene sets in the significant supersets. Using autoencoder model and gene superset at its latent layer, we demonstrated that gene supersets retain sufficient biological information with respect to tumor subtypes and clinical prognostic significance. Superset also provides high reproducibility on survival analysis and accurate prediction for cancer subtypes.Comment: Presented in the International Conference on Intelligent Biology and Medicine (ICIBM 2018) at Los Angeles, CA, USA and published in BMC Systems Biology 2018, 12(Suppl 8):14

Predicting drug response of tumors from integrated genomic profiles by deep neural networks

The study of high-throughput genomic profiles from a pharmacogenomics viewpoint has provided unprecedented insights into the oncogenic features modulating drug response. A recent screening of ~1,000 cancer cell lines to a collection of anti-cancer drugs illuminated the link between genotypes and vulnerability. However, due to essential differences between cell lines and tumors, the translation into predicting drug response in tumors remains challenging. Here we proposed a DNN model to predict drug response based on mutation and expression profiles of a cancer cell or a tumor. The model contains a mutation and an expression encoders pre-trained using a large pan-cancer dataset to abstract core representations of high-dimension data, followed by a drug response predictor network. Given a pair of mutation and expression profiles, the model predicts IC50 values of 265 drugs. We trained and tested the model on a dataset of 622 cancer cell lines and achieved an overall prediction performance of mean squared error at 1.96 (log-scale IC50 values). The performance was superior in prediction error or stability than two classical methods and four analog DNNs of our model. We then applied the model to predict drug response of 9,059 tumors of 33 cancer types. The model predicted both known, including EGFR inhibitors in non-small cell lung cancer and tamoxifen in ER+ breast cancer, and novel drug targets. The comprehensive analysis further revealed the molecular mechanisms underlying the resistance to a chemotherapeutic drug docetaxel in a pan-cancer setting and the anti-cancer potential of a novel agent, CX-5461, in treating gliomas and hematopoietic malignancies. Overall, our model and findings improve the prediction of drug response and the identification of novel therapeutic options.Comment: Accepted for presentation in the International Conference on Intelligent Biology and Medicine (ICIBM 2018) at Los Angeles, CA, USA. Currently under consideration for publication in a Supplement Issue of BMC Genomic

Functional Effects of let-7g Expression in Colon Cancer Metastasis.

MicroRNA regulation is crucial for gene expression and cell functions. It has been linked to tumorigenesis, development and metastasis in colorectal cancer (CRC). Recently, the let-7 family has been identified as a tumor suppressor in different types of cancers. However, the function of the let-7 family in CRC metastasis has not been fully investigated. Here, we focused on analyzing the role of let-7g in CRC. The Cancer Genome Atlas (TCGA) genomic datasets of CRC and detailed data from a Taiwanese CRC cohort were applied to study the expression pattern of let-7g. In addition, in vitro as well as in vivo studies have been performed to uncover the effects of let-7g on CRC. We found that the expression of let-7g was significantly lower in CRC specimens. Our results further supported the inhibitory effects of let-7g on CRC cell migration, invasion and extracellular calcium influx through store-operated calcium channels. We report a critical role for let-7g in the pathogenesis of CRC and suggest let-7g as a potential therapeutic target for CRC treatment

Myopia progression after cessation of atropine in children: a systematic review and meta-analysis

Purpose: To comprehensively assess rebound effects by comparing myopia progression during atropine treatment and after discontinuation.Methods: A systematic search of PubMed, EMBASE, Cochrane CENTRAL, and ClinicalTrials.gov was conducted up to 20 September 2023, using the keywords “myopia," “rebound,” and “discontinue." Language restrictions were not applied, and reference lists were scrutinized for relevant studies. Our study selection criteria focused on randomized control trials and interventional studies involving children with myopia, specifically those treated with atropine or combination therapies for a minimum of 6 months, followed by a cessation period of at least 1 month. The analysis centered on reporting annual rates of myopia progression, considering changes in spherical equivalent (SE) or axial length (AL). Data extraction was performed by three independent reviewers, and heterogeneity was assessed using I2 statistics. A random-effects model was applied, and effect sizes were determined through weighted mean differences with 95% confidence intervals Our primary outcome was the evaluation of rebound effects on spherical equivalent or axial length. Subgroup analyses were conducted based on cessation and treatment durations, dosage levels, age, and baseline SE to provide a nuanced understanding of the data.Results: The analysis included 13 studies involving 2060 children. Rebound effects on SE were significantly higher at 6 months (WMD, 0.926 D/y; 95%CI, 0.288–1.563 D/y; p = .004) compared to 12 months (WMD, 0.268 D/y; 95%CI, 0.077–0.460 D/y; p = .006) after discontinuation of atropine. AL showed similar trends, with higher rebound effects at 6 months (WMD, 0.328 mm/y; 95%CI, 0.165–0.492 mm/y; p < .001) compared to 12 months (WMD, 0.121 mm/y; 95%CI, 0.02–0.217 mm/y; p = .014). Sensitivity analyses confirmed consistent results. Shorter treatment durations, younger age, and higher baseline SE levels were associated with more pronounced rebound effects. Transitioning or stepwise cessation still caused rebound effects but combining optical therapy with atropine seemed to prevent the rebound effects.Conclusion: Our meta-analysis highlights the temporal and dose-dependent rebound effects after discontinuing atropine. Individuals with shorter treatment durations, younger age, and higher baseline SE tend to experience more significant rebound effects. Further research on the rebound effect is warranted.Systematic Review Registration: [https://www.crd.york.ac.uk/prospero/display_record.php?RecordID=463093], identifier [registration number

Gene Regulation, Modulation, and Their Applications in Gene Expression Data Analysis

Common microarray and next-generation sequencing data analysis concentrate on tumor subtype classification, marker detection, and transcriptional regulation discovery during biological processes by exploring the correlated gene expression patterns and their shared functions. Genetic regulatory network (GRN) based approaches have been employed in many large studies in order to scrutinize for dysregulation and potential treatment controls. In addition to gene regulation and network construction, the concept of the network modulator that has significant systemic impact has been proposed, and detection algorithms have been developed in past years. Here we provide a unified mathematic description of these methods, followed with a brief survey of these modulator identification algorithms. As an early attempt to extend the concept to new RNA regulation mechanism, competitive endogenous RNA (ceRNA), into a modulator framework, we provide two applications to illustrate the network construction, modulation effect, and the preliminary finding from these networks. Those methods we surveyed and developed are used to dissect the regulated network under different modulators. Not limit to these, the concept of "modulation" can adapt to various biological mechanisms to discover the novel gene regulation mechanisms

DC-SIGN (CD209) Promoter −336 A/G (rs4804803) Polymorphism Associated with Susceptibility of Kawasaki Disease

Kawasaki disease (KD) is characterized by systemic vasculitis of unknown etiology. High-dose intravenous immunoglobulin (IVIG) is the most effective therapy for KD to reduce the prevalence of coronary artery lesion (CAL) formation. Recently, the α2, 6 sialylated IgG was reported to interact with a lectin receptor, specific intracellular adhesion molecule-3 grabbing nonintegrin homolog-related 1 (SIGN-R1) in mice and dendritic cell-specific intercellular adhesion molecule-3 grabbing nonintegrin (DC-SIGN) in human, and to trigger an anti-inflammatory cascade. This study was conducted to investigate whether the polymorphism of DC-SIGN (CD209) promoter −336 A/G (rs4804803) is responsible for susceptibility and CAL formation in KD patients using Custom TaqMan SNP Genotyping Assays. A total of 521 subjects (278 KD patients and 243 controls) were investigated to identify an SNP of rs4804803, and they were studied and showed a significant association between the genotypes and allele frequency of rs4804803 in control subjects and KD patients (P = 0.004 under the dominant model). However, the promoter variant of DC-SIGN gene was not associated with the occurrence of IVIG resistance, CAL formation in KD. The G allele of DC-SIGN promoter −336 (rs4804803) is a risk allele in the development of KD

Pembuatan Niosom Berbasis Maltodekstrin De 5-10 Dari Pati Singkong (Manihot Utilissima)

Niosomes are non ionic surfactant vesicles that have potential application in the delivery of hydrophobic or amphilic drugs. We developed proniosomes, a dry formulation using a maltodextrin as a carrier coated with non ionic surfactant, which can be used to produce niosomes within a minutes by addition of hot water followed by agitation. A novel method is reported here for rapid preparation of proniosomes with wide range of surfactant loading. Maltodextrin DE 5-10 was hidrolyzed from tapioca starch using Thermamyl L 120 da Novo at 85o C. The result from SEM analyses shown that proniosomes appear very similar to the maltodextrin, but the surface was more smooth. Niosome suspensions which was observed under the optical microscopy and particle size analyzer were evaluated as drug carrier using ibuprofen as a model. The result provide an indication of maltodextrin DE 5-10 from tapioca starch are potentialy carrier in the proniosome preparation which can be used for producing niosomes