Bioinformatics Datasets Research Articles

Engineering quantitative stomatal trait variation and local adaptation potential by cis-regulatory editing.

Cis-regulatory element editing can generate quantitative trait variation that mitigates extreme phenotypes and harmful pleiotropy associated with coding sequence mutations. Here, we applied a multiplexed CRISPR/Cas9 approach, informed by bioinformatic datasets, to generate genotypic variation in the promoter of OsSTOMAGEN, a positive regulator of rice stomatal density. Engineered genotypic variation corresponded to broad and continuous variation in stomatal density, ranging from 70% to 120% of wild-type stomatal density. This panel of stomatal variants was leveraged in physiological assays to establish discrete relationships between stomatal morphological variation and stomatal conductance, carbon assimilation and intrinsic water use efficiency in steady-state and fluctuating light conditions. Additionally, promoter alleles were subjected to vegetative drought regimes to assay the effects of the edited alleles on developmental response to drought. Notably, the capacity for drought-responsive stomatal density reprogramming in stomagen and two cis-regulatory edited alleles was reduced. Collectively our data demonstrate that cis-regulatory element editing can generate near-isogenic trait variation that can be leveraged for establishing relationships between anatomy and physiology, providing a basis for optimizing traits across diverse environments.

A proactive grey wolf optimization for improving bioinformatic systems with high dimensional data

This paper introduces a new methodology for optimization problems, combining the Grey Wolf Optimizer (GWO) with Simi-stochastic search processes. Intelligent optimizations represent an advanced approach in machine learning and computer applications, aiming to reduce the number of features used in the classification process. Optimizing bioinformatics datasets is crucial for information systems that classify data for intelligent tasks. The proposed A-Proactive Grey Wolf Optimization (A-GWO) solves stagnation in GWO by applying a dual search with a Simi-stochastic search. This target is achieved by distributing the population into two groups using a different search technique. The model's performance is evaluated using two benchmarks: the Evolutionary Computation Benchmark (CEC 2005) and seven popular biological datasets. A-GWO demonstrates highly improved efficiency in comparision to the original GWO and Particle Swarm Optimization (PSO). Specifically, it enhances exploration in 66% of CEC functions and achieves high accuracy in 70% of biological datasets.

AMDHD1 acts as a tumor suppressor and contributes to activation of TGF-β signaling pathway in cholangiocarcinoma.

Cholangiocarcinoma (CCA) is a malignant tumor of the digestive system, characterized by its aggressive behavior and the absence of effective therapeutic biomarkers. Although recent studies have implicated AMDHD1 in tumor formation, its role in CCA development has been insufficiently explored. We utilized multiple bioinformatic datasets alongside 108 clinical samples to examine AMDHD1 expression in CCA. Then, in vitro and in vivo experiments were conducted to assess its impact on tumor growth and metastasis. Furthermore, proteomic analysis and immunoprecipitation mass spectrometry were employed to identify the downstream effectors of AMDHD1. We discovered that AMDHD1 was down-regulated in CCA and this down-regulation was associated with adverse clinicopathological features and prognosis. We also demonstrated that overexpression of AMDHD1 hindered G1/S progression in the cell cycle and promoted apoptosis, thereby inhibiting tumor growth and metastasis. Mechanistically, we found that AMDHD1 operated in a TGF-β-dependent manner and the inhibition of TGF-β signaling abrogated the effect of AMDHD1 overexpression on CCA cells. Specifically, AMDHD1 inhibited the ubiquitination and degradation of the SMAD4 protein through binding to the MH2 domain and synergistically enhanced SMAD2/3 phosphorylation, which activated of TGF-β signaling pathway and resulted in the suppression of CCA cell proliferation and migration. Our study identifies AMDHD1 as a significant prognostic biomarker and a tumor suppressor in CCA. It underscores the pivotal role of the AMDHD1/TGF-β signaling pathway in the development and progression of CCA.

Ubiquitin-specific protease 1 facilitates hepatocellular carcinoma progression by modulating mitochondrial fission and metabolic reprogramming via cyclin-dependent kinase 5 stabilization.

Although deubiquitinases (DUBs) have been well described in liver tumorigenesis, their potential roles and mechanisms have not been fully understood. In this study, we identified ubiquitin-specific protease 1 (USP1) as an oncogene with essential roles during hepatocellular carcinoma (HCC) progression. USP1, with elevated expression levels and clinical significance, was identified as a hub DUB for HCC in multiple bioinformatics datasets. Functionally, USP1 overexpression significantly enhanced the malignant behaviors in HCC cell lines and spheroids in vitro, as well as the zebrafish model and the xenograft model in vivo. In contrast, genetic ablation or pharmacological inhibition of USP1 dramatically impaired the phenotypes of HCC cells. Specifically, ectopic USP1 enhanced aggressive properties and metabolic reprogramming of HCC cells by modulating mitochondrial dynamics. Mechanistically, USP1 induced mitochondrial fission by enhancing phosphorylation of Drp1 at Ser616 via deubiquitination and stabilization of cyclin-dependent kinase 5 (CDK5), which could be degraded by the E3 ligase NEDD4L. The USP1/CDK5 modulatory axis was activated in HCC tissues, which was correlated with poor prognosis of HCC patients. Furthermore, Prasugrel was identified as a candidate USP1 inhibitor for targeting the phenotypes of HCC by an extensive computational study combined with experimental validations. Taken together, USP1 induced malignant phenotypes and metabolic reprogramming by modulating mitochondrial dynamics in a CDK5-mediated Drp1 phosphorylation manner, thereby deteriorating HCC progression.

Prognostic relevance and validation of ARPC1A in the progression of low-grade glioma.

Low-grade glioma (LGG) is a grade II-III glioma accompanied by distinct clinical and molecular characteristics and the studies related to its prognosis are still unclear. The objective of this study is to explore the involvement of mitochondrial-related genes SLBP, COMMD7, LSM4, TOMM34, RPP40, FKBP1A, ARPC1A, and TBCA for the prognosis of LGG. We detected differences in the expression of some of the genes by analyzing the bioinformatics dataset and combining it with RT-PCR experiments. Subsequently, a nomogram was constructed and validated for the clinical relevance of risk factors such as age, WHO grade, IDH mutation status, Ch.1p19q co-deletion status, and high and low expression of ARPC1A to predict the 1-, 3-, 5-year overall survival and prognostic relevance of ARPC1A. Gene set enrichment analysis was performed for the relevant datasets pertinent to the expression of ARPC1A to elucidate the cancer-promoting pathways involved in the LGG through KEGG and GO analysis. Transfection assays, CCK-8 assays, and flow cytometry were used to determine the proliferation rate, and apoptosis rate of the HS683 and SW1783 cell lines respectively. Western blotting was used to examine the involvement of the cancer-promoting activity of ARPC1A through MAPK signaling. In this study, the prognostic value of ARPC1A in LGG was found by bioinformatics analysis combined with experimental approach analysis and may be a significant independent risk factor. ARPC1A fosters a higher LGG proliferation rate that may control the MAP kinase signaling and could be a prominent biomarker for LGG. Future studies are warranted to explore its clinical implications.

Use of directed quasi-metric distances for quantifying the information of gene families

A large hindrance to analyzing information in genetic or protein sequence data has been a lack of a mathematical framework for doing so. In this paper, we present a multinomial probability space X as a general foundation for multicategory discrete data, where categories refer to variants/alleles of biosequences. The external information that is infused in order to generate a sample of such data is quantified as a distance on X between the prior distribution of data and the empirical distribution of the sample. A number of distances on X are treated. All of them have an information theoretic interpretation, reflecting the information that the sampling mechanism provides about which variants that have a selective advantage and therefore appear more frequently compared to prior expectations. This includes distances on X based on mutual information, conditional mutual information, active information, and functional information. The functional information distance is singled out as particularly useful. It is simple and has intuitive interpretations in terms of 1) a rejection sampling mechanism, where functional entities are retained, whereas non-functional categories are censored, and 2) evolutionary waiting times. The functional information is also a quasi-metric on X, with information being measured in an asymmetric, mountainous landscape. This quasi-metric property is also retained for a robustified version of the functional information distance that allows for mutations in the sampling mechanism. The functional information quasi-metric has been applied with success on bioinformatics data sets, for proteins and sequence alignment of protein families.

Cost-aware optimal resource provisioning Map-Reduce scheduler for hadoop framework

<p><span lang="EN-US">Distributed data processing model has been one of the primary components in the case of data-intensive applications; furthermore, due to advancements in technologies, there has been a huge volume of data generation of diverse nature. Hadoop map reduce framework is responsible for adopting the ease of deployment mechanism in an open-source framework. The existing Hadoop MapReduce framework possesses high makespan time and high Input/Output overhead and it mainly affects the cost of a model. Thus, this research work presents an optimized cost aware resource provisioning MapReduce model also known as the cost-effective resource provisioning MapReduce (CRP-MR) model. CRP-MR model introduces the two integrated approaches to minimize the cost; at first, this model presents the optimal resource optimization and optimal Input/Output optimization cleansing in the Hadoop MapReduce (HMR) scheduler. CRP-MR is evaluated considering the bioinformatics dataset and CRP-MR performs better than the existing model. </span></p>

Improved nonparametric survival prediction using CoxPH, Random Survival Forest & DeepHit Neural Network

In recent times, time-to-event data such as time to failure or death is routinely collected alongside high-throughput covariates. These high-dimensional bioinformatics data often challenge classical survival models, which are either infeasible to fit or produce low prediction accuracy due to overfitting. To address this issue, the focus has shifted towards introducing a novel approaches for feature selection and survival prediction. In this article, we propose a new hybrid feature selection approach that handles high-dimensional bioinformatics datasets for improved survival prediction. This study explores the efficacy of four distinct variable selection techniques: LASSO, RSF-vs, SCAD, and CoxBoost, in the context of non-parametric biomedical survival prediction. Leveraging these methods, we conducted comprehensive variable selection processes. Subsequently, survival analysis models—specifically CoxPH, RSF, and DeepHit NN—were employed to construct predictive models based on the selected variables. Furthermore, we introduce a novel approach wherein only variables consistently selected by a majority of the aforementioned feature selection techniques are considered. This innovative strategy, referred to as the proposed method, aims to enhance the reliability and robustness of variable selection, subsequently improving the predictive performance of the survival analysis models. To evaluate the effectiveness of the proposed method, we compare the performance of the proposed approach with the existing LASSO, RSF-vs, SCAD, and CoxBoost techniques using various performance metrics including integrated brier score (IBS), concordance index (C-Index) and integrated absolute error (IAE) for numerous high-dimensional survival datasets. The real data applications reveal that the proposed method outperforms the competing methods in terms of survival prediction accuracy.

Abstract PO2-09-05: Building genomic and transcriptomic data among African American and Black patients with triple-negative inflammatory breast cancer

Abstract Background: Inflammatory breast cancer (IBC) is an aggressive breast cancer associated with poor response and early metastases. It represents a health disparity, with higher incidence and worse outcomes in Black compared to White patients. Further, IBC is enriched for the more aggressive triple-negative receptor subtype, TN-IBC, and this subtype is more prevalent among Black IBC patients. It is critical to identify genetic and molecular features that may guide better treatments for these aggressive cancers across races, and thus imperative to identify and increase racial representation among bioinformatics datasets. We previously performed comprehensive genomic and transcriptomic analyses for TN-IBC patient samples and here examine the differences among self-identified African American or Black (AA/B) and other races among IBC patients. Methods: As previously described, we collected matched blood and baseline tumor samples before treatment from 19 patients with primary TN-IBC. We performed whole exome and RNA sequencing (RNA-Seq) on these samples and compared mutations and differentially expressed genes (DEGs). Results were compared between 3 AA/B patient tissues vs the remaining 16 (Other) cases. Results: Mutations shared by all AA/B patients included amplification of the five most commonly mutated hallmark genes in the complete cohort, ARNT, BCL9, DDR2, FCGR2B and LMNA (all 100% in AA/B patients versus all 50% in the Other cohort). All AA/B patients had a Notch1 mutation (two deletions and one missense mutation, 100% AA/B vs 6% Other (one deletion)). Comparing differentially expressed genes and gene set enrichment analyses of the Broad molecular signatures database (MSigDB) demonstrates the majority of DEGs are downregulated in these AA/B patients. Six of the top 20 downregulated gene sets from the MSigDB C2 group relate to breast cancer subtype and among this TNIBC cohort AA/B tumor expression was downregulated for luminal and normal subtype related genes in multiple gene sets and enriched in the Lien metaplastic related genes. In the MSigDB C5 sets, over half of the top 20 enriched gene ontology sets among AA/B patients related to chromosomes, chromatin, spindle assembly or histone binding, while 16 of the top 20 downregulated enriched sets related to immune function. Multiple KEGG pathways enriched in AA/B cases related to RNA processing and expression and DNA replication and repair. Lastly, hallmark gene sets enriched in AA/B patients were cell cycle related, and targets of Myc, Hedgehog, B-catenin, TGFb, and TNFa via NFKb, while top significantly downregulated sets related to metabolism and immune response. Conclusion: While findings from a small sample size can only be considered hypothesis generating, some patterns emerge from the data among this rare cohort of AA/B TN IBC patients. These tumors appear to be devoid of luminal signals, characterized by cell cycle and chromatin remodeling signals, enriched for signaling typical of stem cell self-renewal, and perhaps more immune-deprived than other TN-IBC. Additional work to build racially diverse IBC transcriptomic data are needed to develop strategies to understand and improve outcomes for these patients. Citation Format: Wencai Ma, Li Zhao, Gayathri Devi, Savitri Krishnamurthy, Larry Coffer, Angela Alexander, Megumi Kai, Xiaoping Wang, Huong Le-Petross, Miral Patel, Bisrat Debeb, Chandra Bartholomeusz, Jianhua Zhang, Xingzhi Song, Andrew Futreal, Naoto Ueno, Rachel Layman, Anthony Lucci, Jing Wang, Wendy Woodward. Building genomic and transcriptomic data among African American and Black patients with triple-negative inflammatory breast cancer [abstract]. In: Proceedings of the 2023 San Antonio Breast Cancer Symposium; 2023 Dec 5-9; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2024;84(9 Suppl):Abstract nr PO2-09-05.

CREDO: a friendly Customizable, REproducible, DOcker file generator for bioinformatics applications

BackgroundThe analysis of large and complex biological datasets in bioinformatics poses a significant challenge to achieving reproducible research outcomes due to inconsistencies and the lack of standardization in the analysis process. These issues can lead to discrepancies in results, undermining the credibility and impact of bioinformatics research and creating mistrust in the scientific process. To address these challenges, open science practices such as sharing data, code, and methods have been encouraged.ResultsCREDO, a Customizable, REproducible, DOcker file generator for bioinformatics applications, has been developed as a tool to moderate reproducibility issues by building and distributing docker containers with embedded bioinformatics tools. CREDO simplifies the process of generating Docker images, facilitating reproducibility and efficient research in bioinformatics. The crucial step in generating a Docker image is creating the Dockerfile, which requires incorporating heterogeneous packages and environments such as Bioconductor and Conda. CREDO stores all required package information and dependencies in a Github-compatible format to enhance Docker image reproducibility, allowing easy image creation from scratch. The user-friendly GUI and CREDO's ability to generate modular Docker images make it an ideal tool for life scientists to efficiently create Docker images. Overall, CREDO is a valuable tool for addressing reproducibility issues in bioinformatics research and promoting open science practices.

Fractal feature selection model for enhancing high-dimensional biological problems

The integration of biology, computer science, and statistics has given rise to the interdisciplinary field of bioinformatics, which aims to decode biological intricacies. It produces extensive and diverse features, presenting an enormous challenge in classifying bioinformatic problems. Therefore, an intelligent bioinformatics classification system must select the most relevant features to enhance machine learning performance. This paper proposes a feature selection model based on the fractal concept to improve the performance of intelligent systems in classifying high-dimensional biological problems. The proposed fractal feature selection (FFS) model divides features into blocks, measures the similarity between blocks using root mean square error (RMSE), and determines the importance of features based on low RMSE. The proposed FFS is tested and evaluated over ten high-dimensional bioinformatics datasets. The experiment results showed that the model significantly improved machine learning accuracy. The average accuracy rate was 79% with full features in machine learning algorithms, while FFS delivered promising results with an accuracy rate of 94%.

NCAPD2 is a favorable predictor of prognostic and immunotherapeutic biomarker for multiple cancer types including lung cancer

BackgroundNon-SMC condensin I complex subunit D2 (NCAPD2) belongs to the chromosomal structural maintenance family. While the different contribution of NCAPD2 to chromosome in mitosis have been thoroughly investigated, much less is known about the expression of NCAPD2 in pan-cancer. Thus, we used a bioinformatics dataset to conduct a pan-cancer analysis of NCAPD2 to determine its regulatory role in tumors.MethodsMultiple online databases were analyzed NCAPD2 gene expression, protein level, patient survival and functional enrichment in pan-cancer. Genetic alteration and tumor stemness of NCAPD2 were analyzed using cBioPortal and SangerBox. The GSCA and CellMiner were used to explore the relationship between NCAPD2 and drug sensitivity. The diagnostic value of prognosis was evaluated by ROC curve. Subsequently, the immune infiltration level and immune subtype of NCAPD2 in lung adenocarcinoma (LUAD) and lung squamous cell carcinoma (LUSC) were analyzed using TIMER1 and TISIDB.ResultsNCAPD2 gene expression was significantly higher in most cancers and associated with clinical stage and poor prognosis. Genomic heterogeneity of NCAPD2 promoted the occurrence and development of tumors. GO enrichment analysis suggested NCAPD2 might be involved in DNA repair and immune response. NCAPD2 was involved in immune infiltration of LUAD and LUSC. ROC curves showed that NCAPD2 has important prognosis diagnostic value in LUAD and LUSC. Moreover, NCAPD2 was drug sensitive to topotecan, which may be an optimize immunotherapy.ConclusionsIt was found that NCAPD2 was overexpressed in pan-cancers, which was associated with poor outcomes. Importantly, NCAPD2 could be a diagnostic marker and an immune related biomarker for LUAD and LUSC.

Prediction of miRNAs and diseases association based on sparse autoencoder and MLP.

Introduction: MicroRNAs (miRNAs) are small and non-coding RNA molecules which have multiple important regulatory roles within cells. With the deepening research on miRNAs, more and more researches show that the abnormal expression of miRNAs is closely related to various diseases. The relationship between miRNAs and diseases is crucial for discovering the pathogenesis of diseases and exploring new treatment methods. Methods: Therefore, we propose a new sparse autoencoder and MLP method (SPALP) to predict the association between miRNAs and diseases. In this study, we adopt advanced deep learning technologies, including sparse autoencoder and multi-layer perceptron (MLP), to improve the accuracy of predicting miRNA-disease associations. Firstly, the SPALP model uses a sparse autoencoder to perform feature learning and extract the initial features of miRNAs and diseases separately, obtaining the latent features of miRNAs and diseases. Then, the latent features combine miRNAs functional similarity data with diseases semantic similarity data to construct comprehensive miRNAs-diseases datasets. Subsequently, the MLP model can predict the unknown association among miRNAs and diseases. Result: To verify the performance of our model, we set up several comparative experiments. The experimental results show that, compared with traditional methods and other deep learning prediction methods, our method has significantly improved the accuracy of predicting miRNAs-disease associations, with 94.61% accuracy and 0.9859 AUC value. Finally, we conducted case study of SPALP model. We predicted the top 30 miRNAs that might be related to Lupus Erythematosus, Ecute Myeloid Leukemia, Cardiovascular, Stroke, Diabetes Mellitus five elderly diseases and validated that 27, 29, 29, 30, and 30 of the top 30 are indeed associated. Discussion: The SPALP approach introduced in this study is adept at forecasting the links between miRNAs and diseases, addressing the complexities of analyzing extensive bioinformatics datasets and enriching the comprehension contribution to disease progression of miRNAs.

An empirical study of the impact of biological information dissemination in social media on public science literacy

Abstract In this paper, we first establish a locally converged bioinformatics dataset based on gradient sampling and design an optimal data mining control model to improve the accuracy of bioinformatics big data feature mining. The performance of the Compressive Tracking algorithm and Online Bosting algorithm is compared with the mining error as a test index. At the same time, we propose a social media information dissemination algorithm applicable to large-scale social network datasets, taking the degree value of each node as the node’s full influence and comparing and analyzing the dissemination influence of BP-IM, RAND and MC-CELF algorithms. Finally, taking public health big data as the research object, the least squares regression method was used to analyze the influence of the amount of public attention to bioinformatics scientific knowledge on their scientific literacy in different media. The results showed that there was a significant positive correlation between scientific literacy and willingness to engage in science participation behavior on social media when the amount of public attention to scientific information was β =0225, p <0.01. When more people are interested in scientific knowledge of bioinformatics on social media, their scientific literacy will improve.

TRIM28 Regulates DHCR7-Mediated Cholesterol Biosynthesis to Promote Survival in B Cell Lymphoma

Background: TRIM28, a member of the tripartite motif-containing (TRIM) protein family, is a critical regulator of gene expression, DNA damage response and protein degradation, but little is known about its role in lipid metabolism. Dysregulation of cholesterol metabolism has been implicated in the pathogenesis of diffuse large B-cell lymphoma (DLBCL). However, the role of TRIM28 in lymphomagenesis and its clinical relevance remain unclear. Methods: We searched public databases to characterize the gene expression pattern, clinical parameters, and survival data of TRIM28 in B-cell lymphoma, and immunohistochemistry (IHC) was used to confirm TRIM28 expression. Then, we knocked down TRIM28 by lentiviral shRNA transduction in several B-cell lymphoma lines to determine the effect of TRIM28-KD on cell proliferation, cell cycle progression, apoptosis and drug resistance. In addition, a lymphoma xenograft mouse model was established to test whether TRIM28-KD inhibits lymphoma growth in vivo. Using RNA-seq, we aim to identify novel transcriptional targets and elucidate the mechanisms by which TRIM28 promotes survival in B-cell lymphoma. Results: Analysis of bioinformatic datasets and clinical samples indicated that TRIM28 expression was significantly upregulated in B-cell lymphoma compared to normal tissues, and high expression of TRIM28 was closely associated with disease progression and poor prognosis in patients with B-cell lymphoma. Knockdown of TRIM28 inhibited lymphoma cell proliferation, induced cell cycle arrest at G0/G1, increased cell apoptosis, and enhanced the sensitivity of lymphoma cells to chemotherapeutic agents. Moreover, TRIM28-KD significantly suppressed lymphoma and prolonged mouse survival in DLBCL xenograft models. The RNA-seq with TRIM28-KD vs. control revealed differentially expressed genes, many of which were enriched in lipid metabolism-related pathways. DHCR7, a critical terminal enzyme in cholesterol biosynthesis, was identified as a potential transcriptional target of TRIM28. TRIM28-KD inhibited DHCR7 expression, which was associated with reduced total cholesterol levels. DHCR7-KD impaired cell proliferation, whereas overexpression of DHCR7 rescued the phenotypes of TRIM28-KD in lymphoma cells. Conclusions: TRIM28 drives lymphomagenesis through upregulating DHCR7-mediated cholesterol biosynthesis, which may serve as a novel therapeutic target for B-cell lymphoma.

Computational Technology Based Biology Project For Remote Learning During Covid-19 Crisis

Abstract: In the many months following the C o r o n a v i r u s f l a r e - u p , a l a r g e n u m b e r o f Biotechnology Engineering students throughout the world are compelled to switch over from conventional lab based learning to computational technology based learning. This uncommon interchange enormously affected the method of learning project courses. Specifically, biological science projects which require the students and staff to work in the actual lab was affected. B. Tech. Biotechnology students and a staff f r o m o u r i n s t i t u t i o n u t i l i z e d p r e s e n t d a y computational bioinformatics tool, data sets and programming to study an issue identified as betalactam resistance in a bacterium. The students utilized accessible present day computational tool, data sets and virtual products to complete the project work. This technique for doing the final year B. Tech. project has brought about the consummation of the course effectively. This procedure of utilization of freelyavailable computational tool, information databases and programming software will benefit the students worldwide to complete project based engineering courses effectively. Keywords: in silico, project, workstation, COVID- 19, bioinformatics

Local causal structure learning with missing data

Local causal structure learning aims to discover and distinguish the direct causes and direct effects of a target variable. However, the state-of-the-art algorithms for local causal structure learning fail to perform well when dealing with missing data. The general approach is to fill in the missing data using imputation techniques before learning the local causal structure, but this method suffers from problems such as low accuracy, low efficiency, and instability. To address these issues, we propose a novel method for local causal structure learning with missing data, named misLCS. Firstly, we design an iterative data imputation method to obtain the complete and correct data from the missing data. Then, misLCS adopts a data subset strategy to get a data subset that variables are closely related to the target variable. Thirdly, within this data subset, misLCS constructs the local causal skeleton of the target variable using a mutual information-based feature selection method and orients the direction of edges using conditional independence tests and Meek rules. Finally, misLCS updates the missing data in preparation for the next iteration. This procedure continues until the direct causes and direct effects of the target variable have been identified. Our experiments on seven benchmark Bayesian networks and a real-world bioinformatics dataset, with a number of variables from 11 to 801, demonstrate that our algorithm achieves better accuracy than the existing local causal structure learning algorithms.

Drug metabolism-related gene ABCA1 augments temozolomide chemoresistance and immune infiltration abundance of M2 macrophages in glioma

Gliomas are the most prevalent primary tumor in the central nervous system, with an abysmal 5-year survival rate and alarming mortality. The current standard management of glioma is maximum resection of tumors followed by postoperative chemotherapy with temozolomide (TMZ) or radiotherapy. Low chemosensitivity of TMZ in glioma treatment eventuates limited therapeutic efficacy or treatment failure. Hence, overcoming the resistance of glioma to TMZ is a pressing question. Our research centered on identifying the drug metabolism-related genes potentially involved in TMZ-treated resistance of glioma through several bioinformatics datasets and cell experiments. One efflux transporter, ATP-binding cassette transporter subfamily A1 (ABCA1), was discovered with an upregulated expression level and signaled poor clinical outcomes for glioma patients. The transcript level of ABCA1 significantly elevated across the TMZ-resistant glioma cells in contrast with non-resistant cells. Over-expressed ABCA1 restrained the drug activity of TMZ, and ABCA1 knockdown improved the treatment efficacy. Meanwhile, the results of molecular docking between ABCA1 protein and TMZ showed a high binding affinity. Additionally, co-expression and immunological analysis revealed that ABCA1 facilitates the immune infiltration of M2 macrophages in glioma, thereby stimulating tumor growth and aggravating the poor survival of patients. Altogether, we discovered that the ABCA1 transporter was involved in TMZ chemoresistance and the immune infiltration of M2 macrophages in glioma. Treatment with TMZ after ABCA1 knockdown enhances the chemosensitivity, suggesting that inhibition of ABCA1 may be a potential strategy for improving the therapeutic efficacy of gliomas.

Expression signature and prognostic value of CREC gene family in human colorectal cancer

Colorectal cancer (CRC) is one of the malignant tumors with the highest morbidity and mortality and poor prognosis. The mammalian gene family of Cab45/reticulocalbin/ERC-45/calumenin (CREC) consists of RCN1, RCN2, RCN3, SDF4 and CALU. Although CREC family members have been associated with CRC, the expression pattern, prognostic value, and the role of CREC family in CRC remain unclear. In this study, the expression, survival and biological functions of CREC family in CRC were determined via bioinformatic datasets analysis and experimental verification on clinical CRC specimen. Bioinformatic analysis showed that the expression levels of most CREC family genes were higher in CRC tissues than in normal colorectal tissues. The qPCR and western blot results also revealed that the transcriptional and protein levels of CREC family were elevated in CRC tissues compared with adjacent tissues. Besides, CREC family was significantly correlated with advanced tumor stage and poor prognosis of CRC patients. The expression levels of CREC family had correlations with genomic mutation and methylation, and with the infiltration levels of CD4 + T cells, macrophages, neutrophils, and dendritic cells in the microenvironment of CRC. Functional networks enrichment analysis indicated that the genes of CREC family were essential factors for CRC metastasis. Collectively, these findings suggest that CREC family might be potential targets for the treatment of CRC and candidate prognostic markers for CRC patients.

PICOTEES: a privacy-preserving online service of phenotype exploration for genetic-diagnostic variants from Chinese children cohorts

The growth in biomedical data resources has raised potential privacy concerns and risks of genetic information leakage. For instance, exome sequencing aids clinical decisions by comparing data through web services, but it requires significant trust between users and providers. To alleviate privacy concerns, the most commonly used strategy is to anonymize sensitive data. Unfortunately, studies have shown that anonymization is insufficient to protect against reidentification attacks. Recently, privacy-preserving technologies have been applied to preserve application utility while protecting the privacy of biomedical data. We present the PICOTEES framework, a privacy-preserving online service of phenotype exploration for genetic-diagnostic variants (https://birthdefectlab.cn:3000/). PICOTEES enables privacy-preserving queries of the phenotype spectrum for a single variant by utilizing trusted execution environment technology, which can protect the privacy of the user's query information, backend models, and data, as well as the final results. We demonstrate the utility and performance of PICOTEES by exploring a bioinformatics dataset. The dataset is from a cohort containing 20,909 genetic testing patients with 3,152,508 variants from the Children's Hospital of Fudan University in China, dominated by the Chinese Han population (>99.9%). Our query results yield a large number of unreported diagnostic variants and previously reported pathogenicity.