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• Different cultivars exhibited different sensitivity to light. • Different phenolics showed different sensitivity to light. • Bagging inhibited the synthesis of most phenolics in peach fruit. • Bagging suppressed the expression of most structural genes and transcription factors. • PpHY5 homolog PpHYH was induced by high light. Bagging is a vital cultivation measure for producing high-quality peach fruit, which affects the appearance and internal quality of the fruit by mainly intervening in its exposure to light. However, it is not entirely clear whether bagging affects the content of phenolic compounds or which compounds are most sensitive to bagging treatment, and the regulatory role of bagging on phenolic synthetic-related genes is also unknown. In this experiment, four peach cultivars, 'BJ', 'XH', 'RG', and 'HYT', were bagged with four different types of bags at 50 days after full bloom, with no bag as control, and all fruits were harvested at the commercial maturation stage. The study found that bagged fruits had higher L*, b*, and h° values but lower a* and C* values than non-bagged fruits. Different cultivars and different phenolics exhibited different sensitivities to bagging treatments. Light intensity not only played a decisive role in the accumulation of anthocyanins in peach fruit but also had a strong and widespread impact on other phenolic substances. Overall, light not onlly significantly promoted the accumulation of cyanidin 3-glucoside, cyanidin 3-rutinoside, epicatechin, and rutin but also increased the accumulation of catechin, chlorogenic acid, and neochlorogenic acid to some extent, while shading by bagging inhibited the accumulation of these phenolics. Correspondingly, high light induced the upregulation of most structural genes and transcription factors, including PpPAL1, PpC3H1, PpC3H2, PpDFR, PpLDOX, PpUFGT, and PpMYB10.1 , while low light inhibited the expression of these genes in the four varieties. In addition, PpHY5 homolog PpHYH was induced by high light, which might participate in the biosynthesis of the light-regulated phenolic in peach fruit. This study lays the foundation for further analysis of the regulation mechanism of light on the synthesis of phenolic compounds in peach fruit. [ABSTRACT FROM AUTHOR]

Background: Mesenchymal stem cell therapy is a new treatment for immune rejection in heart transplantation. The aim of this paper is to investigate the effect of human amniotic mesenchymal stem cells (hAMSCs) on alleviating immune rejection of allogeneic heart transplantation in mice and its possible underlying mechanism. Methods: We injected hAMSCs into cervical ectopic heart transplantation model mice via tail vein to observe the survival time, the pathological changes of donor myocardium, and the fluorescent distribution of hAMSCs after the transplantation. MicroRNAs (miRs) with significantly differential expression were obtained by RNA sequencing and bioinformatic analysis, and a dual luciferase reporter gene assay together with real-time quantitative PCR (qRT-PCR) was performed to verify the relationship between miRs and their targeting genes. Results: The intervention of hAMSCs prolonged the graft survival time and alleviated the pathological damage of the donor heart. The injected hAMSCs were distributed mainly in the liver, spleen, and kidney, only a very small portion in the donor and recipient hearts. In the allogeneic transplantation models, the expression of miR-34b-5p significantly increased after hAMSC treatment. MiR-34b-5p showed a knockdown effect on gene Fc gamma receptor 2B (FCGR2B). Conclusions: hAMSCs can reduce the immune rejection injury after allogeneic heart transplantation. This effect may be associated with the upregulation of miR-34b-5p expression to knock down its targeting gene FCGR2B. [ABSTRACT FROM AUTHOR]

The applications of clustering in biomedical is pervasive and ubiquitous. A typical example is gene expression data analysis, where clustering is emerging as a powerful solution for uncovering cancer-related insights. Neutrosophic c -means (NCM) clustering has advantages over other conventional clustering methods in characterizing the uncertainty and imprecision caused by cluster overlap and identifying outliers. Nonetheless, NCM and its derivatives equally treat the contribution of each attribute to the cluster. In biomedical applications, genes (i.e. attributes) should take different importance in identifying different clusters. In this paper, we first propose a self-adaptive attribute-weighted neutrosophic c -means (AWNCM) clustering method to overcome the above defects. Moreover, a new objective function is designed to obtain optimal neutrosophic partition, cluster centers and attribute weights. Since AWNCM tends to be more effective against spherical data, we further develop a kernelized version of AWNCM, called KAWNCM, in order to better satisfy the clustering of some complex data (i.e. non-spherical data). We employ the iterative optimization strategy to obtain the optimal solutions for AWNCM and KAWNCM. The advantage of AWNCM and KAWNCM is to improve performance by learning the importance of each attribute to the cluster while maintaining an efficient solution to cluster overlap and outliers. Extensive experimental results using synthetic data and gene expression data demonstrate the feasibility and effectiveness of the proposed methods. [ABSTRACT FROM AUTHOR]

Breast cancer, one common malignant tumor all over the world, has a considerably high rate of recurrence, which endangers the health and life of patients. While more and more data have been available, how to leverage the gene expression data to predict the survival risk of cancer patients and identify key genes has become a hot topic for cancer research. Therefore, in this work, we investigate the gene expression and clinical data of breast cancer patients, specifically a novel framework is proposed focusing on the survival risk classification and key gene identification task. We firstly combine the differential expression and univariate Cox regression analysis to achieve dimensional reduction of gene expression data. The median survival time is subsequently proposed as the risk classification threshold and a learning model based on neural network is trained to classify the survival risk of patients. Innovatively, in this work, the activation region visualization technology is selected as the identification tool, which identify 20 key genes related to the survival risk of breast cancer patients. We further analyze the gene function of these 20 key genes based on STRING database. It is critical to learn that, the genetic biomarkers identified in this paper may possess value for the following clinical treatment of breast cancer according to the literature findings. Importantly, the genetic biomarkers identified in this paper may possess value for the following clinical treatment of breast cancer according to the literature findings. Our work accomplishes the objective of proposing a targeted approach to enhancing the survival analysis and therapeutic strategies in breast cancer through advanced computational techniques and gene analysis. [Display omitted] • Development of a Neural Network-Based Model: We developed a novel neural network model that effectively classifies breast cancer survival risk using integrated gene expression and clinical data, enhancing the accuracy of predicting patient outcomes. • Identification of Key Genetic Markers: Utilizing activation region visualization technology, the study successfully identified 20 key genes associated with the survival risk of breast cancer patients, providing potential targets for therapeutic intervention. • Comprehensive Validation with STRING Database: The functions of the identified genes were rigorously analyzed using the STRING database for protein interactions, Gene Ontology (GO) functional enrichment, and KEGG pathway analysis, confirming their biological significance in breast cancer pathogenesis. • Robust Framework for Future Research: The research framework and methodologies established in this study not only advance the understanding of genetic influences on breast cancer but also offer a replicable model for similar genetic studies in other cancers. • Open Access to Research Tools: In a commitment to fostering further scientific discovery, the research tools and neural network models developed in this study have been made openly accessible, allowing for ongoing advancements in the field by the broader scientific community. [ABSTRACT FROM AUTHOR]

Periostin is a matricellular protein known to be alternatively spliced to produce ten isoforms with a molecular weight of 78–91 kDa. Within the extracellular matrix, periostin attaches to cell surfaces to induce signaling via integrin-binding and actively participates in fibrillogenesis, orchestrating the arrangement of collagen in the extracellular environment. In atopic diseases such as atopic dermatitis (AD) and asthma, periostin is known to participate in driving the disease-causing type 2 inflammation. The periostin isoforms expressed in these diseases and the implication of the alternative splicing events are unknown. Here, we present two universal assays to map the expression of periostin isoforms at the mRNA (RT-qPCR) and protein (PRM-based mass spectrometry) levels. We use these assays to study the splicing profile of periostin in AD lesions as well as in in vitro models of AD and asthma. In these conditions, periostin displayed overexpression with isoforms 3 and 5 standing out as highly overexpressed. Notably, isoforms 9 and 10 exhibited a divergent pattern relative to the remaining isoforms. Isoforms 9 and 10 are often overlooked in periostin research and this paper presents the first evidence of their expression at the protein level. This underlines the necessity to include isoforms 9 and 10 in future research addressing periostin splice isoforms. The assays presented in this paper hold the potential to improve our insight into the splicing profile of periostin in tissues and diseases of interest. The application of these assays to AD lesions and in vitro models demonstrated their potential for identifying isoforms of particular significance, warranting a further in-depth investigation. [Display omitted] • The study presents two new methods for mapping and measuring periostin splice isoforms in human samples. • The analyses show that isoforms 3 and 5 are significantly overexpressed while isoforms 9 and 10 are downregulated in the diseases studied. • This is the first experimental proof of the existence of isoforms 9 and 10 at the protein level. • The results demonstrate that these methods have the potential to identify isoforms that are particularly significant. • Our research accurately maps periostin isoform expression in human samples, providing the basis for further understanding. [ABSTRACT FROM AUTHOR]

Expression quantitative trait locus (eQTL) analysis is a useful tool to identify genetic loci that are associated with gene expression levels. Large collaborative efforts such as the Genotype-Tissue Expression (GTEx) project provide valuable resources for eQTL analysis in different tissues. Most existing methods, however, either focus on one tissue at a time, or analyze multiple tissues to identify eQTLs jointly present in multiple tissues. There is a lack of powerful methods to identify eQTLs in a target tissue while effectively borrowing strength from auxiliary tissues. In this paper, we propose a novel statistical framework to improve the eQTL detection efficacy in the tissue of interest with auxiliary information from other tissues. This framework can enhance the power of the hypothesis test for eQTL effects by incorporating shared and specific effects from multiple tissues into the test statistics. We also devise data-driven and distributed computing approaches for efficient implementation of eQTL detection when the number of tissues is large. Numerical studies in simulation demonstrate the efficacy of the proposed method, and the real data analysis of the GTEx example provides novel insights into eQTL findings in different tissues. • Identify eQTLs in a target tissue while effectively borrowing strength from auxiliary tissues. • A novel mFDR-control procedure is proposed to maximize the mTPR. • Propose distributed computing approach for efficient eQTL detection. [ABSTRACT FROM AUTHOR]

Lithium-ion battery is a kind of energy storage devices with complex internal reaction and many factors affecting its performance. Accurate prediction of its SOH (State of Health) is of great significance to prolong its service life and improve safety performance. However, the current prediction for SOH has the difficulties of selecting health factors and using data-driven methods with opaque mechanisms. In this paper, a data-driven model will be established with the capacity change during aging of lithium-ion batteries as a health indicator to realize precise prediction of capacity degradation. Firstly, the feature parameters were extracted and analyzed from the battery cyclic aging test dataset. Two in-situ nondestructive characterization methods, incremental capacity analysis curve and differential thermal voltammetry curve, were utilized to resolve the evolution paths of the feature parameters. After that, a data-driven battery capacity degradation estimation is realized based on the GEP (Gene Expression Programming) algorithm, the performance is compared with the existing vehicle-end and cloud-end models, and a higher-accuracy SOH stacking model is developed with an increase of less than 1 ms in computational time. The results indicated that the GEP model proposed in this paper has obvious advantages in terms of physical explain-ability, computational efficiency and robustness. • Establishing a high-precision battery SOH estimation model based on the GEP algorithm. • The relationship between ICA and DTV curves with aging evolution laws is revealed. • Fusion of GEP model with existing vehicle-end and cloud-end models using stacking strategy. [ABSTRACT FROM AUTHOR]

Genomic biomarkers of cancer are based on changes in nucleic acids, which include abnormal expression levels of some miRNAs, point mutations in DNA sequences, and altered levels of DNA methylation. The presence of tumor-related nucleic acids in body fluids (blood, saliva, or urine) makes it possible to achieve a non-invasive early-stage cancer diagnosis. Currently existing techniques for the discovery of nucleic acids require complex, time-consuming, costly assays and have limited multiplexing abilities. Surface-enhanced Raman spectroscopy (SERS) is a vibrational spectroscopy technique that is able to provide molecular specificity combined with trace sensitivity. SERS has gained research attention as a tool for the detection of nucleic acids because of its promising potential: label-free SERS can decrease the complexity of assays currently used with fluorescence-based detection due to the absence of the label, while labeled SERS may outperform the gold standard in terms of the multiplexing ability. The first papers about SERS-based methods for the measurement of genomic biomarkers were written in 2008, and since then, more than 150 papers have been published. The aim of this paper is to review and evaluate the proposed SERS-based methods in terms of their level of development and their potential for liquid biopsy application, as well as to contribute to their further evolution by attracting research attention to the field. This goal will be reached by grouping, on the basis of their experimental protocol, all the published manuscripts on the topic and evaluating each group in terms of its limit of detection and applicability to real body fluids. Thus, the methods are classified according to their working principles into five main groups, including capture-based, displacement-based, sandwich-based, enzyme-assisted, and specialized protocols. [Display omitted] • SERS-based detection of miRNA level changes, DNA point mutations, and DNA methylation level alterations in body fluids have good potential for early-stage cancer diagnosis. • Biological protocols designed for the selective detection of the target DNAs/miRNAs have been reviewed and grouped into the following categories: capture, displacement, sandwich, cleavage, and specific protocols. • The protocols have been arranged according to the increasing complexity of the assays and evaluated in terms of their performance for liquid biopsy application. As a result, one-to-step protocols have shown good LODs, applicability in body fluids, and multiplexing abilities, especially for miRNA detection. • In general, labeled protocols outperform label-free assays by reaching the LODs as low as 10 − 16 M and detecting the target miRNA/DNA in the complex matrices, such as total RNAs/DNAs and real body fluids. However, the performance of the label-free protocols can be improved by developing highly sensitive SERS substrates. • Thus, there is a potential to develop relatively simple SERS-based protocols for liquid biopsy applications with promising LODs and multiplexing abilities, but more work should be conducted in this direction. [ABSTRACT FROM AUTHOR]

With the depletion of non-renewable fossil fuels, there has been an increasing emphasis on renewable biomass. Penicillium oxalicum is notable for its exceptional capacity to secrete a diverse array of enzymes that degrade plant polysaccharides into monosaccharides. These valuable monosaccharides can be harnessed in the production of bioethanol and other sustainable forms of energy. By enhancing the production of plant-polysaccharide-degrading enzymes (PPDEs) in P. oxalicum , we can optimize the utilization of plant biomass. This paper presents recent advances in augmenting PPDE expression in P. oxalicum through genetic engineering strategies involving protoplast preparation, transformation, and factors influencing PPDE gene expression. [ABSTRACT FROM AUTHOR]

Triclosan (TCS) is a widely used antimicrobial, antifungal, and antiviral agent. To date, it has been reported that TCS can enter the human body and disrupt hormonal homeostasis. Therefore, the aim of our paper was to evaluate the impact of TCS on astrocytes, i.e. a crucial population of cells responsible for steroid hormone production. Our data showed that, in mouse primary astrocyte cultures, TCS can act as an endocrine disrupting chemical through destabilization of the production or secretion of progesterone (P 4), testosterone (T), and estradiol (E 2). TCS affects the mRNA expression of enzymes involved in neurosteroidogenesis, such as Cyp17a1 , 17β-Hsd, and Cyp19a1. Our data showed that a partial PPARγ agonist (honokiol) prevented changes in Cyp17a1 mRNA expression caused by TCS. Similarly, honokiol inhibited TCS-stimulated P 4 release. However, rosiglitazone (classic PPARγ agonist) or GW9662 (PPARγ antagonist) had a much stronger effect. Therefore, we believe that the changes observed in the P 4 , T, and E 2 levels are a result of dysregulation of the activity of the aforementioned enzymes, whose expression can be affected by TCS through a Pparγ-dependent pathway. TCS was found to decrease the aryl hydrocarbon receptor (AhR) and Sirtuin 3 protein levels, which may be the result of the activation of the these proteins. Since our study showed dysregulation of the production or secretion of neurosteroids in astrocytes, it can be concluded that TCS reaching the brain may contribute to the development of neurodegenerative diseases in which an abnormal amount of neurosteroids is observed. [Display omitted] • TCS increases progesterone and testosterone release from astrocytes in vitro. • TCS decreases testosterone and estradiol production in astrocytes in vitro. • TCS decreases 17β-Hsd and Cyp19a1 mRNA expression in astrocytes in vitro. • TCS decreases AhR and Sirt3 protein expression in astrocytes in vitro. [ABSTRACT FROM AUTHOR]

• Host DOT1L expression is upregulated on L. donovani infection. • DOT1L transcriptionally regulates HDAC1 expression, which negatively modulates the expression of defensin genes onon L. donovani infection. • HDAC1 regulates the expression of DOT1L, thus, creating a feedback loop on L. donovani infection. Leishmania donovani , a protozoan parasite, causes visceral leishmaniasis. The parasite modifies the global gene expressions of the host genome, facilitating its survival within the host. Thus, the host epigenetic modulators play important roles in host-pathogen interaction and host epigenetic modification in response to infection. Previously, we had reported that the host epigenetic modulator, histone deacetylase 1 (HDAC1) expression was upregulated on Leishmania donovani infection. This upregulation led to the repression of host defensin genes in response to the infection. In this paper, we have investigated the interplay between the host DOT1L, a histone methyltransferase, and HDAC1 in response to Leishmania donovani infection. We show that the expression of DOT1L is upregulated both at transcript and protein level following infection leading to increase in H3K79me, H3K79me2, and H3K79me3 levels. ChIP experiments showed that DOT1L regulated the expression of HDAC1. Downregulation of DOT1L using siRNA resulted in decreased expression of HDAC1 and increased transcription of defensin genes and thereby, lower parasite load. In turn, HDAC1 regulates the expression of DOT1L on Leishmania donovani infection as downregulation of HDAC1 using siRNA led to reduced expression of DOT1L. Thus, during Leishmania donovani infection, an interplay between DOT1L and HDAC1 regulates the expression of these two histone modifiers leading to downregulation of defensin gene expression. [ABSTRACT FROM AUTHOR]

Sugarcane, a prominent global crop utilized for sugar, bioethanol, and renewable bioenergy production, holds significant importance in Colombia. In 2022, it contributed to the production of 2.1 million tons of sugar, 347 million liters of bioethanol, and 1745 GWh of electrical energy. The cultivation of sugarcane worldwide faces vulnerability to drought stress induced by climate change, which significantly affects yields. Understanding how plants respond to drought involves complex interactions among genes, morphology, physiology, and biochemistry. However, these factors are often analyzed separately using methods such as Differentially Expressed Genes, comparative physiology, or metabolomics, thereby restricting the potential for broader insights that could arise from their further integration. This paper uses an improved version of Control-Stress data Integration with Overlapping Clustering (CSI-OC), a methodology that provides a comprehensive perspective by integrating diverse data types, in which a Lasso-based network helps in pinpointing stress-responsive genes. The objective of this study is to utilize CSI-OC to identify genes relevant to drought stress in Colombian sugarcane cultivars. To accomplish this goal, the study analyzes both leaf and root expression data, alongside four physiological parameters associated with leaf responses across different levels of drought stress. Computational evaluation indicates that the datasets are effectively processed using the CSI-OC workflow. This methodology demonstrates good performance in identifying genes strongly correlated with both the stress condition and the considered phenotypic traits. As stress levels increase, the number of genes selected by CSI-OC displays a contrasting pattern between leaves and roots. This observation implies a coordinated cascade of gene responses from leaves to roots with escalating stress levels, indicating a holistic adaptation strategy within the plant. Overall, the findings of this study underscore the effectiveness of CSI-OC as a comprehensive approach for identifying pertinent genes linked to drought stress in sugarcane. [Display omitted] • Stress genes were found via interactions in overlapping modules influencing phenotype. • Genes can be stress-relevant even without differential expression or hub node status. • Merging control-stress data for Lasso-based networks boosts large dataset efficiency. • Findings suggest coordinated gene response from leaves to roots with increased stress. [ABSTRACT FROM AUTHOR]

[Display omitted] Healthy aging results in cardiac structural and electrical remodeling that increase susceptibility to cardiovascular diseases. Relaxin has shown broad cardioprotective effects including anti-fibrotic, anti-arrhythmic and anti-inflammatory outcomes in multiple models. This paper focuses on the cardioprotective effects of Relaxin in a rat model of aging. Sustained atrial or ventricular fibrillation are readily induced in the hearts of aged but not young control animals. Treatment with Relaxin suppressed this arrhythmogenic response by increasing conduction velocity, decreasing fibrosis and promoting substantial cardiac remodeling. Relaxin treatment resulted in a significant increase in the levels of: Nav1.5, Cx43, βcatenin and Wnt1 in rat hearts. In isolated cardiomyocytes, Relaxin increased Nav1.5 expression. These effects were mimicked by CHIR 99021, a pharmacological activator of canonical Wnt signaling, but blocked by the canonical Wnt inhibitor Dickkopf1. Relaxin prevented TGF-β-dependent differentiation of cardiac fibroblasts into myofibroblasts while increasing the expression of Wnt1; the effects of Relaxin on cardiac fibroblast differentiation were blocked by Dickkopf1. RNASeq studies demonstrated reduced expression of pro-inflammatory cytokines and an increase in the expression of α- and β-globin in Relaxin-treated aged males. Relaxin reduces arrhythmogenicity in the hearts of aged rats by reduction of fibrosis and increased conduction velocity. These changes are accompanied by substantial remodeling of the cardiac tissue and appear to be mediated by increased canonical Wnt signaling. Relaxin also exerts significant anti-inflammatory and anti-oxidant effects in the hearts of aged rodents. The mechanisms by which Relaxin increases the expression of Wnt ligands, promotes Wnt signaling and reprograms gene expression remain to be determined. [ABSTRACT FROM AUTHOR]

Knowledge discovery from big datasets is one of the most important challenges in the pattern recognition field. More important than this is how much the extracted information and created models are reliable. Studies have shown that these models are usually highly dependent on the samples, features, data, and structure of the models. In general, the issue of stability is very important in creating models. This paper presents a method that not only considers the effect of different known classifiers but also tries to achieve a stable model for separating samples by combining different feature selection methods and considering the criterion of stability. Briefly, our contributions to the proposed method include 1) analyzing the ability of features in sample classification individually with different well-known classifiers, 2) estimating the probability of the features that could be selected in high-impact sets of features, and 3) applying the stability concept to increase the weight of the robust sets of the features. The proposed algorithm is used to select the high-impact genes of microarray datasets. Three high-dimensional gene expressions of cancerous tissues are used as benchmarks. The results obtained show relative superiority compared to other methods. [ABSTRACT FROM AUTHOR]

To explore the expression of HAUS6 in squamous cell carcinoma of the tongue (TSCC) and its relationship with the clinicopathological features of patients, and to further provide new ideas and therapeutic targets for curing TSCC. The Cancer Genome Atlas (TCGA) database was used to screen for differentially expressed genes (DEGs) between TSCC and normal tissues and survival analysis. DEGs of HAUS6 were screened and analyzed for GO, KEGG and GSEA enrichment. Exploring the correlation of HAUS6 with immune cell infiltration and immune checkpoint-related genes. The expression of HAUS6 in tumor and paraneoplastic tissues was confirmed by immunohistochemistry and Western Blot. Analysis of the TCGA database results showed that expression of HAUS6 mRNA was significantly enhanced and correlated with overall survival (OS, p < 0.05) in TSCC. HAUS6 expression correlated with the level of immune cell infiltration and immune checkpoint-related genes. Immunohistochemistry and Western Blot confirmed that the expression level of HAUS6 protein was significantly higher in tumor tissues than in paraneoplastic tissues, and that tumor size and hypo-differentiation were higher in the HAUS6 high expression group than in the low expression group in TSCC (p < 0.05). In conclusion, these analyses suggest that HAUS6 can act as an independent predictor of prognosis (p < 0.05) and high HAUS6 expression is strongly associated with poor prognosis. • The link between TSCC differential gene HAUS6 expression and its clinicopathologic features and prognosis was identified by TCGA. • In this paper, the HAUS6 gene was studied for the first time in TSCC. • HAUS6 was found to be highly expressed in tumour tissues and was strongly associated with poor prognosis. • The combination of bioinformatics and experiments provided a potential target for the diagnosis and treatment of TSCC. [ABSTRACT FROM AUTHOR]

Aquaculture has emerged as a crucial sector in many countries. In the Recirculating Aquaculture System (RAS), Dissolved Oxygen (DO) levels are critical to the health of aquatic animals. As DO sensors are costly, a number of studies have proposed a soft sensor technique utilizing machine learning for estimating DO levels in water. However, the existing research work mainly focuses on black-box approaches, which do not provide numerical analysis between the DO levels and the related parameters. To solve this issue, a sequential Genetic Programming (GP) approach with an evolutionary refinement method is proposed to generate a mathematical expression that represents DO levels in water. In particular, a coarse mathematical model is generated using GP and subsequently fine-tuned using the Covariance Matrix Adaptation Evolution Strategy (CMA-ES). As a study case, the Klamath River dataset is used to generate the model. The evaluation of our proposed method uses datasets from both the Klamath River and the Fanno Creek. Two models are generated in this paper; one model uses six features, while the other only employs three. The results indicate that the model with six features exhibits relatively higher accuracy. However, it is worth noting that a smaller dataset of features is also capable of achieving generalization of the model. [ABSTRACT FROM AUTHOR]

[Display omitted] • PTEN gene expression is epigenetically controlled by circular RNAs from transcription to post-translation. • Most regulatory circRNAs exert their function through sponging miRNAs that target PTEN. • CircPTENs directly derived from PTEN mRNA negatively affect cancer progression. • CircPTENs suppress tumors by regulating PTEN function or affecting TGF-β and AKT pathways. Epigenetic regulation of gene expression, without altering the DNA sequence, is involved in many normal cellular growth and division events, as well as diseases such as cancer. Epigenetics is no longer limited to DNA methylation, and histone modification, but regulatory non-coding RNAs (ncRNAs) also play an important role in epigenetics. Circular RNAs (circRNAs), single-stranded RNAs without 3′ and 5′ ends, have recently emerged as a class of ncRNAs that regulate gene expression. CircRNAs regulate phosphatase and tensin homolog (PTEN) expression at various levels of transcription, post-transcription, translation, and post-translation under their own regulation. Given the importance of PTEN as a tumor suppressor in cancer that inhibits one of the most important cancer pathways PI3K/AKT involved in tumor cell proliferation and survival, significant studies have been conducted on the regulatory role of circRNAs in relation to PTEN. These studies will be reviewed in this paper to better understand the function of this protein in cancer and explore new therapeutic approaches. [ABSTRACT FROM AUTHOR]

A makiang seed powder (MSP) feeding trial was performed on Nile tilapia (Oreochromis niloticus) for eight weeks to evaluate the effects on growth performance, immune response, and gene expression. Three hundred fingerlings (13.20 ± 0.04 g) were fed five diets with various amounts of MSP at 0, 5, 10, 20 and 40 g kg−1. After four and eight weeks of the feeding trial, growth rates and immune responses were tested, while relative immune and antioxidant gene expression were performed after eight weeks of MSP feeding. Accordingly, the growth indices (SGR, WG) and immune activity (skin mucus and serum lysozyme, skin mucus and serum peroxidase, alternative complement, phagocytosis and respiratory burst activities) in Nile tilapia were significantly increased in the treated diets compared with the control diet (P < 0.05). The maximum (P ≤ 0.05) growth and immune activity values were noted in fish fed 5 g kg−1 MSP. IL1, IL8, LBP, GPX, GSTa and GSR gene expressions were also shown to be upregulated in fish on MSP diets, with the highest value reported in fish fed 5 g kg−1 MSP. Finally, MSP has the potential to be employed as a feed supplement in the biofloc system for the cultivation of Nile tilapia. Visual representation of the results obtained in our paper by supplementing Nile Tilapia diet with MSP for a period of 8 weeks. Image created with biorender.com. [Display omitted] • MSP is a promising feed additive for the aquacultural practices of Nile tilapia. • MSP5 (5 g kg−1) can enhance growth performance and feed conversion ratio. • MSP5 boosts the skin mucus and serum immune responses of Nile tilapia. • MSP5 induces increased expression of immune response and antioxidant-relate genes. [ABSTRACT FROM AUTHOR]

[Display omitted] This review signifies the role of circular RNAs (circRNAs) in tuberculosis (TB) and lung cancer (LC), focusing on pathogenesis, diagnosis, and treatment. CircRNAs, a newly discovered type of non-coding RNA, have emerged as key regulators of gene expression and promising biomarkers in various bodily fluids due to their stability. The current review discusses circRNA biogenesis, highlighting their RNase-R resistance due to their loop forming structure, making them effective biomarkers. It details their roles in gene regulation, including splicing, transcription control, and miRNA interactions, and their impact on cellular processes and diseases. For LC, the review identifies circRNA dysregulation affecting cell growth, motility, and survival, and their potential as therapeutic targets and biomarkers. In TB, it addresses circRNAs' influence on host anti-TB immune responses, proposing their use as early diagnostic markers. The paper also explores the interplay between TB and LC, emphasizing circRNAs as dual biosignatures, and the necessity for differential diagnosis. It concludes that no single circRNA biomarker is universally applicable for both TB and LC. Ultimately, the review highlights the pivotal role of circRNAs in TB and LC, encouraging further research in biomarker identification and therapeutic development concomitant for both diseases. [ABSTRACT FROM AUTHOR]

In recent years, there has been a growing interest in modulating the performance of probiotic, mainly Lactic Acid Bacteria (LAB), in the field of probiotic food. Attenuation, induced by sub-lethal stresses, delays the probiotic metabolism, and induces a metabolic shift as survival strategy. In this paper, RNA sequencing was used to uncover the transcriptional regulation in Lacticaseibacillus casei ATCC 393 after ultrasound-induced attenuation. Six (T) and 8 (ST) min of sonication induced a significant differential expression of 742 and 409 genes, respectively. We identified 198 up-regulated and 321 down-regulated genes in T, and similarly 321 up-regulated and 249 down-regulated in ST. These results revealed a strong defensive response at 6 min, followed by adaptation at 8 min. Ultrasound attenuation modified the expression of genes related to a series of crucial biomolecular processes including membrane transport, carbohydrate and purine metabolism, phage-related genes, and translation. Specifically, genes encoding PTS transporters and genes involved in the glycolytic pathway and pyruvate metabolism were up-regulated, indicating an increased need for energy supply, as also suggested by an increase in the transcription of purine biosynthetic genes. Instead, protein translation, a high-energy process, was inhibited with the down-regulation of ribosomal protein biosynthetic genes. Moreover, phage-related genes were down-regulated suggesting a tight transcriptional control on DNA structure. The observed phenomena highlight the cell need of ATP to cope with the multiple ultrasound stresses and the activation of processes to stabilize and preserve the DNA structure. Our work demonstrates that ultrasound has remarkable effects on the tested strain and elucidates the involvement of different pathways in its defensive stress-response and in the modification of its phenotype. [ABSTRACT FROM AUTHOR]

High dimensional and small samples characterize gene expression data and contain a large number of genes unrelated to disease. Feature selection improves the efficiency of disease diagnosis by selecting a small number of important genes. Unfortunately, existing algorithms do not consider the correlation between features, and search algorithms tend to fall into the local optimal solution in the feature search process. To this end, this paper proposes a feature filter and group evolution hybrid feature selection algorithm (FG-HFS) for high-dimensional gene expression data. Unlike existing algorithms, we propose using spectral clustering to group redundant features into a group. Then, we propose a redundant feature filter algorithm. According to the principle of approximate Markov blanket, grouped feature groups are filtered to delete these redundant features. Among them, filtered features are evenly divided by density according to the feature exponential strategy. Most importantly, we propose using the group evolution multi-objective genetic algorithm to search the filtered feature subsets and evaluate the candidate feature subsets according to the in-group and out-group so as to select the feature subsets with the highest accuracy and the least number. Experimental results show that the average accuracy (ACC) and Matthews correlation coefficient (MCC) indexes of the selected feature subsets (FSs) by the FG-HFS algorithm on 5 gene expression datasets are 92.76% and 88.76%, respectively, which are significantly better than the existing algorithms. In addition, the FSs and ACC/FSs indexes of the FG-HFS algorithm are also better than the existing algorithms, which fully proves the superiority of the FG-HFS algorithm. More importantly, the Wilcoxon and Friedman statistical experiments results show that the feature selection effect of FG-HFS algorithm is significantly better than that of existing algorithms, no matter in pairwise comparison or multiple comparison. • We propose using spectral clustering to group the features so that the in-group feature similarity is extremely high. • We propose a redundant feature filter algorithm since existing algorithms cannot filter redundant features. • we propose using the group evolution multi-objective genetic algorithm to search the filtered feature subsets. [ABSTRACT FROM AUTHOR]

CircRNAs are a class of covalently closed non-coding RNA formed by linking the 5′ terminus and the 3′ terminus after reverse splicing. CircRNAs are widely found in eukaryotes, and they are highly conserved, with spatio-temporal expression specificity and stability. CircRNAs can act as miRNA sponges to regulate the expression of downstream target genes, regulating the transcription of parental genes and some can even be translated into peptides or proteins. Research on circRNAs in plants is still in its infancy compared to that in animals. With the deepening of research, the results of a variety of plant circRNAs suggest that they play an important role in growth and development, and tolerance towards abiotic stresses such as salt, drought, low temperature, high temperature and other adverse environments. In this review paper, we elaborated the molecular characteristics, mechanism of action, function and bioinformatics databases of plant circRNAs, combined with the progress of circRNA research in animals, discussed the potential mechanism of action of plant circRNAs, and proposed the unsolved problems and prospects for future application of plant circRNAs. [Display omitted] [ABSTRACT FROM AUTHOR]

Estradiol (E2), an endocrine disruptor, acts by mimicking or interfering with the normal physiological functions of natural hormones within organisms, leading to issues such as endocrine system disruption. Notably, seasonal fluctuations in environmental temperature may influence the degradation speed of estradiol (E2) in the natural environment, intensifying its potential health and ecological risks. Therefore, this study aims to explore how bacteria can degrade E2 under low-temperature conditions, unveiling their resistance mechanisms, with the goal of developing new strategies to mitigate the threat of E2 to health and ecological safety. In this paper, we found that Rhodococcus equi DSSKP-R-001 (R-001) can efficiently degrade E2 at 30 °C and 10 °C. Six genes in R-001 were shown to be involved in E2 degradation by heterologous expression at 30 °C. Among them, 17β-HSD, KstD2, and KstD3, were also involved in E2 degradation at 10 °C; KstD was not previously known to degrade E2. RNA-seq was used to characterize differentially expressed genes (DEGs) to explore the stress response of R-001 to low-temperature environments to elucidate the strain's adaptation mechanism. At the low temperature, R-001 cells changed from a round spherical shape to a long rod or irregular shape with elevated unsaturated fatty acids and were consistent with the corresponding genetic changes. Many differentially expressed genes linked to the cold stress response were observed. R-001 was found to upregulate genes encoding cold shock proteins, fatty acid metabolism proteins, the ABC transport system, DNA damage repair, energy metabolism and transcriptional regulators. In this study, we demonstrated six E2 degradation genes in R-001 and found for the first time that E2 degradation genes have different expression characteristics at 30 °C and 10 °C. Linking R-001 to cold acclimation provides new insights and a mechanistic basis for the simultaneous degradation of E2 under cold stress in Rhodococcus adaptation. [Display omitted] • A Rhodococcus strain was found to efficiently degrade E2 at both room and low temperatures. • The KstD genes were first proven to degrade E2 through heterologous expression, and six E2 degradation genes exhibited different expression characteristics at 30°C and 10°C. • The adaptation mechanisms of Rhodococcus strains exposed to low temperatures for decreasing E2 were analyzed for the first time. [ABSTRACT FROM AUTHOR]

For the diagnosis and outcome prediction of gastric cancer (GC), machine learning methods based on whole slide pathological images (WSIs) have shown promising performance and reduced the cost of manual analysis. Nevertheless, accurate prediction of GC outcome may rely on multiple modalities with complementary information, particularly gene expression data. Thus, there is a need to develop multimodal learning methods to enhance prediction performance. In this paper, we collect a dataset from Ruijin Hospital and propose a multimodal learning method for GC diagnosis and outcome prediction, called GaCaMML, which is featured by a cross-modal attention mechanism and Per-Slide training scheme. Additionally, we perform feature attribution analysis via integrated gradient (IG) to identify important input features. The proposed method improves prediction accuracy over the single-modal learning method on three tasks, i.e., survival prediction (by 4.9% on C-index), pathological stage classification (by 11.6% on accuracy), and lymph node classification (by 12.0% on accuracy). Especially, the Per-Slide strategy addresses the issue of a high WSI-to-patient ratio and leads to much better results compared with the Per-Person training scheme. For the interpretable analysis, we find that although WSIs dominate the prediction for most samples, there is still a substantial portion of samples whose prediction highly relies on gene expression information. This study demonstrates the great potential of multimodal learning in GC-related prediction tasks and investigates the contribution of WSIs and gene expression, respectively, which not only shows how the model makes a decision but also provides insights into the association between macroscopic pathological phenotypes and microscopic molecular features. [Display omitted] • GaCaMML outperforms the single-modal learning method across three tasks on accuracy. • Per-Slide strategy enhances prediction results by optimizing WSI-to-patient ratio. • WSI features prevail in gastric cancer outcome prediction for most samples. • Certain genes like MMPs and TP53 significantly impact prediction results. [ABSTRACT FROM AUTHOR]

Since scientific investigations have demonstrated that aberrant expression of miRNAs brings about the incidence of numerous intricate diseases, precise determination of miRNA-disease relationships greatly contributes to the advancement of human medical progress. To tackle the issue of inefficient conventional experimental approaches, numerous computational methods have been proposed to predict miRNA-disease association with enhanced accuracy. However, constructing miRNA-gene-disease heterogeneous network by incorporating gene information has been relatively under-explored in existing computational techniques. Accordingly, this paper puts forward a technique to predict miRNA-disease association by applying autoencoder and implementing random walk on miRNA-gene-disease heterogeneous network(AE-RW). Firstly, we integrate association information and similarities between miRNAs, genes, and diseases to construct a miRNA-gene-disease heterogeneous network. Subsequently, we consolidate two network feature representations extracted independently via an autoencoder and a random walk procedure. Finally, deep neural network(DNN) are utilized to conduct association prediction. The experimental results demonstrate that the AE-RW model achieved an AUC of 0.9478 through 5-fold CV on the HMDD v3.2 dataset, outperforming the five most advanced existing models. Additionally, case studies were implemented for breast and lung cancer, further validated the superior predictive capabilities of our model. [Display omitted] • The topology of the network becomes more informative when the gene data is combined. • Feature extraction employs autoencoders and random walk from diverse perspectives. • The results of the experiments reveal that our model is better than the previous models. [ABSTRACT FROM AUTHOR]

• The review describes the role of amiRNAs in the improvement of horticultural crops. • Recent trends and implications of amiRNA demonstrate a significant role in the development of desirable horticultural crops, including biotic and abiotic resistance plants. • The review also highlights the future prospects of amiRNAs for the development of desirable horticultural plants. Gene silencing by artificial microRNAs (amiRNAs) is one of the most crucial methods for the development of desired horticultural plants. amiRNAs are highly specific, around 21 nt long, and designed to repress gene expression post-transcriptionally. A number of studies have shown that amiRNA technology can be used as a cutting-edge biotechnological tool for horticulture crop improvement. This is a potent and reliable method that is rapidly being applied for improving useful traits in horticultural plants, including fruits, vegetables, and ornamentals, as well as unveiling new insights into metabolic pathways and gene functions across numerous disciplines. Keeping with the growing interest in amiRNA application, this review paper emphasizes the background of artificial miRNA in general and how the technology has contributed to orchestrating features like disease resistance, productivity, and quality in horticultural plants. In the available literature, amiRNA's benefits, limitations, and possibilities for the future are also examined. As far as the authors are aware, no prior review has rigorously examined how amiRNA can potentially be applied for gene regulations and agronomic trait improvement in horticultural crops. In general, this review enhances the technology's worth in plant science and may be useful to academics and researchers who are implementing amiRNA for accelerating breeding programs and developmental studies in crop plants. [Display omitted] [ABSTRACT FROM AUTHOR]

The tons of keratin waste are produced by the poultry and meat industry which is an insoluble and protein-rich material found in hair, feathers, wool, and some epidermal wastes. These waste products could be degraded and recycled to recover protein, which can save our environment. One of the potential strategy to achieve this target is use of microbial biotreatment which is more convenient, cost-effective, and environment-friendly by formulating hydrolysate complexes that could be administered as protein supplements, bioactive peptides, or animal feed ingredients. Keratin degradation shows great promise for long-term protein and amino acid recycling. According to the MEROPS database, known keratinolytic enzymes currently belong to at least 14 different protease families, including S1, S8, S9, S10, S16, M3, M4, M14, M16, M28, M32, M36, M38, and M55. In addition to exogenous attack (proteases from families S9, S10, M14, M28, M38, and M55), the various keratinolytic enzymes also function via endo-attack (proteases from families S1, S8, S16, M4, M16, and M36). Biotechnological methods have shown great promise for enhancing keratinase expression in different strains of microbes and different protein engineering techniques in genetically modified microbes such as bacteria and some fungi to enhance keratinase production and activity. Some microbes produce specific keratinolytic enzymes that can effectively degrade keratin substrates. Keratinases have been successfully used in the leather, textile, and pharmaceutical industries. However, the production and efficiency of existing enzymes need to be optimized before they can be used more widely in other processes, such as the cost-effective pretreatment of chicken waste. These can be improved more effectively by using various biotechnological applications which could serve as the best and novel approach for recycling and degrading biomass. This paper provides practical insights about molecular strategies to enhance keratinase expression to effectively utilize various poultry wastes like feathers and feed ingredients like soybean pulp. Furthermore, it describes the future implications of engineered keratinases for environment friendly utilization of wastes and crop byproducts for their better use in the poultry feed industry. [ABSTRACT FROM AUTHOR]

• MMODE is developed as a new hybrid gene selection method for tumor identification. • MMODE combines multi-tasking and multi-objective frameworks. • MMODE uses a new elite strategy and a new guidance strategy. • MMODE selects a few genes and achieves high classification accuracy. A key preprocessing step in tumor recognition based on microarray expression profile data and machine learning is to identify tumor marker genes. Gene selection aims to select the most relevant gene subset from the original ultra-high dimensional microarray expression profile data to improve tumor identification performance. Inspired by evolutionary multitasking (EMT) and multi-objective optimization, this paper puts forward a novel multitasking multi-objective differential evolution gene selection algorithm (MMODE) which uses new elite and guidance strategies to select the best gene subsets. MMODE initializes two different populations according to different filtering criteria to increase the diversity of the search. These two populations guide their respective populations to search in the optimal direction through knowledge transfer in the evolutionary process. In addition, MMODE employs new elite and guidance strategies that enables individuals to narrow the search range and jump out of local optima. The proposed algorithm is validated on 13 publicly available microarray expression datasets in comparison with state-of-the-art gene selection algorithms. The experimental results show that MMODE can find smaller gene subsets and achieve higher classification accuracy compared with other algorithms. [ABSTRACT FROM AUTHOR]

RNA-seq is often used for early accurate diagnosis and related gene screening of liver cancer, significantly improving patients' survival rates. Popular diagnostic methods based on machine learning often ignore genes with insignificant differential expression in RNA-seq and fail to characterize the overlapping group effect triggered by a few genes participating in multiple biological pathways. This paper aimed to solve the above problems by developing an adaptive logistic regression via integrating gene mutation and RNA-seq (ALRIGMR). A new data integration strategy was proposed to highlight genes with high mutation rates and insignificant differential expression. The local maximal quasi-clique merger (lmQCM) was used for the overlapping grouping, which was proved to be superior to the weighted gene co-expression network analysis (WGCNA). Relying on differential expression and mutational information, a new criterion for evaluating gene significance was proposed. ALRIGMR achieved a diagnosis accuracy of 88.4% on the external validation set, which is 23.0%, 53.8%, 26.9%, 15.3%, 11.5%, 7.6%, 3.8%, and 7.6% higher than that of eight methods. Five insignificant differentially expressed genes, TP53 , TTN , MUC16 , ABCA13 , and RYR2 were screened, which were confirmed to be closely associated with liver cancer. • A new data integration strategy based on p -value and fold change is proposed. • lmQCM is used to implement overlapping grouping. • A new criterion for evaluating gene significance is proposed. • Ablation analysis confirms that the criterion can improve the model performance. • ALRIGMR has better diagnosis performance than the other eight methods. [ABSTRACT FROM AUTHOR]

DNA-modifying enzymes act as critical regulators in a wide range of genetic functions (e.g., DNA damage & repair, DNA replication), and their aberrant expression may interfere with regular genetic functions and induce various malignant diseases including cancers. DNA-modifying enzymes have emerged as the potential biomarkers in early diagnosis of diseases and new therapeutic targets in genomic research. Consequently, the development of highly specific and sensitive biosensors for the detection of DNA-modifying enzymes is of great importance for basic biomedical research, disease diagnosis, and drug discovery. Single-molecule fluorescence detection has been widely implemented in the field of molecular diagnosis due to its simplicity, high sensitivity, visualization capability, and low sample consumption. In this paper, we summarize the recent advances in single-molecule counting-based biosensors for DNA-modifying enzyme (i.e, alkaline phosphatase, DNA methyltransferase, DNA glycosylase, flap endonuclease 1, and telomerase) assays in the past four years (2019 − 2023). We highlight the principles and applications of these biosensors, and give new insight into the future challenges and perspectives in the development of single-molecule counting-based biosensors. [Display omitted] • We review single-molecule counting-based biosensors for DNA-modifying enzyme detection. • The principles and applications of these biosensors are highlighted. • The performances of different biosensors are compared. • We discuss the future challenges and perspectives in this field. [ABSTRACT FROM AUTHOR]

Prostaglandins (Pgs) are eicosanoid lipid mediators detected in all vertebrates, in some marine invertebrates, macroalgae and in diatoms, a class of eukaryotic microalgae composing the phytoplankton. The enzymes involved in the Pgs pathway were found to be differentially expressed in two strains of the diatom Skeletonema marinoi , named FE7 and FE60, already known to produce different levels of oxylipins, a class of secondary metabolites involved in the defence of diatoms against copepod predation, with FE7 being higher producer than FE60. In the present study we investigated the response of genes involved in the production of oxylipins and Pgs, evaluating their expression after the exposure to the copepod Temora stylifera. Our results highlighted a grazer feeding preference for FE60, the strain having low oxylipins content and reduced expression of Pgs enzymes, and an impact on the gene expression of the enzymes involved in oxylipins (i.e. lipoxygenase) and Pgs (i.e. cyclooxygenase) biosynthesis, especially in FE7. A time course evaluation of the gene expression over 24 h showed an upregulation of the essential enzyme in the Pgs pathway, the cyclooxygenase, in FE60 after 6 h of exposure to the grazer, differently from FE7 where no upregulation of gene expression in the presence of copepods was revealed. These results provide preliminary indications regarding the existence of a complex involvement of the Pgs pathway in the prey-predator interaction that requires further investigations. • The copepod grazer T. stylifera shows different ingestion rates on two S. marinoi strains, with a preference for the one with lower oxylipin content. • S. marinoi strains responded differently to the presence of copepods in terms of expression of the oxylipins enzymes. • This paper provides indications regarding the existence of a complex Pgs involvement in the prey-predator interaction. [ABSTRACT FROM AUTHOR]

A real-valued information system (RVIS) is an information system (IS) whose information values are real numbers. If the objects, attributes and information values of a RVIS change to cells, genes and gene expression values where gene expression data is single cell RNA-seq (scRNA) data, respectively, then this RVIS is referred to as a single cell gene space (s c g -space). Unsupervised gene selection becomes very challenging due to a lack of decision information, which is to select the optimal gene subset that can maintain learning ability without decision information. However, little research has been done on unsupervised gene selection. Uncertainty measurement is a tool of gene selection. In view of this, this paper studies uncertainty measurement in an s c g -space via Gaussian kernel and explores its application for unsupervised gene selection. In the first place, the distance between two cells in a given subspace is constructed. In the next place, the fuzzy T c o s -equivalence relation induced by this subspace is obtained employing Gaussian kernel. After that, measures of uncertainty for an s c g -space are investigated. Lastly, gene selection algorithms in an s c g -space are presented by using the proposed information entropy and information granularity. The presented algorithms are applied to clustering analyses of scRNA data. Multiple publicly available scRNA data sets are employed to evaluate the gene selection performances of the presented algorithms, while two commonly-used clustering methods, kmeans and AGNES, are utilized to obtain four metrics such as Silhouette Coefficient (S C), Davies–Bouldin Index (D B I), Fowlkes and Mallows Index (F M I), Normalized Mutual Information (N M I). The clustering results demonstrated that the presented algorithms can lower significantly the number genes selected, achieve the better S C , D B I , F M I and N M I. They also show that the presented algorithms are superior to raw data and PCA and NMF regardless of using kmeans or AGNES clustering. This also indirectly demonstrates that the granulation measure and information entropy can effectively evaluate the uncertainty of an s c g -space. [ABSTRACT FROM AUTHOR]

Highly accurate detection of pulsars is mandatory. With the application of machine learning (ML) algorithms, the detection of pulsars can certainly be improved if the dataset is balanced. In this paper, the publicly available dataset (HTRU2) is highly imbalanced so various balancing methods were applied. The balanced dataset was used in genetic programming symbolic classifier (GPSC) to obtain symbolic expressions (SEs) that can detect pulsars with high classification accuracy. To find the optimal combination of GPSC hyperparameters the random hyperparameter search (RHS) method was developed and applied. The GPSC was trained using 5-fold cross-validation so after each training a total of 5 SEs were obtained. The best set of SEs are selected based on their classification performance and all of them are applied on the original dataset. The best classification accuracy (ACC), the area under receiver operating characteristic (AUC), precision, recall, and f1-score were achieved in the case of the dataset balanced with the AllKNN method i.e. all mean evaluation metric values are equal to 0.995. The ensemble consisted of 25 SEs that achieved the A C C = 0. 978 , A U C = 0. 9452 , P r e c i s i o n = 0. 905 , R e c a l l = 0. 9963 , and F 1 − S c o r e = 0. 94877 , on the original dataset. [ABSTRACT FROM AUTHOR]

Epigenomic imbalance drives abnormal transcriptional processes, promoting the onset and progression of cancer. Although defective gene regulation generally affects carcinogenesis and tumor suppression networks, tumor immunogenicity and immune cells involved in antitumor responses may also be affected by epigenomic changes, which may have significant implications for the development and application of epigenetic therapy, cancer immunotherapy, and their combinations. Herein, we focus on the impact of epigenetic regulation on tumor immune cell function and the role of key abnormal epigenetic processes, DNA methylation, histone post-translational modification, and chromatin structure in tumor immunogenicity, and introduce these epigenetic research methods. We emphasize the value of small-molecule inhibitors of epigenetic modulators in enhancing antitumor immune responses and discuss the challenges of developing treatment plans that combine epigenetic therapy and immunotherapy through the complex interaction between cancer epigenetics and cancer immunology. In this review, we mainly review the effects of epigenetic modifications on chromosomal structure, thereby affecting gene expression and their functional realization. Combining cutting-edge epigenetic methods, we summarize in detail how DNA methylation, histone modification, and chromatin 3D structure affect the tumor microenvironment through immune cells to regulate tumorigenesis and tumor metastasis. At the same time, we summarize the effects of genomic topology on immune cells and tumors. This review provides detailed and in-depth methods and insights in epigenetics and tumor immunity, providing doctors and cancer patients with new ideas. [Display omitted] • This paper mainly studies the effect of Epigenetics regulation on tumor immune cell function. • The key abnormal Epigenetics processes include DNA methylation, histone modification and their influence in Chromatin structure to modulate tumor immune cells. • Summarized the research methods of these epigenetic modifications for the antitumor immunity. • The value of epigenetic modulators and small molecule inhibitors in enhancing anti-tumor immune response was emphasized. • The challenges of developing a combination of epigenetic and immunotherapy regimens were discussed. [ABSTRACT FROM AUTHOR]

Despite recent breakthroughs of immune-checkpoint inhibitors in the management of bladder cancer (BC), only 20% of patients with non-muscle invasive BC (NMIBC) completely respond, leaving room for improvement. Due to challenges in developing a preclinical model which reflects the heterogeneity and biology of bladder tumors, patient-derived xenograft (PDX) models were created. PDX models involve implanting tumor extracted from patients into immunocompromised mice, which therefore keep the morphology and genomic fidelity of their parental patient cancers. The state of immunodeficiency, however, precludes study of immunotherapy and consequently humanized mice models engrafted with a human immune system were developed, though this led to spontaneous xenogenic graft vs host disease (GVHD). As a potential solution to GVHD in humanized mouse models, this paper describes successful creation of a double-humanized mouse (DHM) by simultaneously engrafting human immune cells and human tumors from the same donor. BC tissue was processed +/- digested into single-cell suspensions (SCS, F0 tissue) and injected orthotopically or subcutaneously into flanks of NSG™ mice. Subsequent tumor growth (PDX-F1) was measured, and PDX-F1 tumors reaching >200mm3 were processed into SCS and reimplanted subcutaneously into a new NSG™ (PDX-F2). Continuous passages (PDX-Fn) were repeated.; Extra SCS-F1 and Fn was analyzed for human epithelial cell adhesion molecule (EpCAM) expression. From an individual F1, a stable cell line (PDX257S) was cultured, and also analyzed for EpCAM expression by flow cytometry. For the DHM model, autologous γδ T-cells expanded in vitro from PDX257S donors were injected with PDX257S cells (effector: target = 2:1) into the NSG™ flanks and were observed for body condition/hematuria for 45 days. Total RNA-sequencing was performed on F0, PDX-Fn and PDX257S cell line. Tumor growth curves were analyzed with 2-way analysis of variance (ANOVA). F0 tissue was obtained from 60 patients. Tumor acceptance in orthotopic injection was not observed in the first 11 suitable tumors, and this method was ceased. Subcutaneous tumor acceptance rate improved with digestion and processing of F0 rather than processing alone (11% vs 5%). EpCAM expression was 98% vs 70% in PDX257S vs its F1 tissue, and < 30% in F1 tissue from other donors that did not yield further passages. In the DHM, while tumor growth was significantly reduced compared to control group after 45 days (mean vol 200 mm3 vs 580 mm3, p=0.02) post injection, it was not completely suppressed. RNA sequencing revealed increased expression of genes including BCL2L1, KRAS, MYC, E2F1, and E2F4 in both PDX257 subcutaneous tumor and cell line compared with donor tumor tissue, and very low/no expression of CDKN2A and FHIT genes in both PDX257 cell line and donor tumor tissue. Creation of a double-humanized mouse model is feasible, and tumor growth is not completely suppressed with the addition of autologous γδ T-cells. PDX257S tumor and cell line is associated with increased EpCAM expression and on RNA sequencing, increased expression of BCL2L1, KRAS, MYC, E2F1, and E2F4. [ABSTRACT FROM AUTHOR]

MBIP is a component of the Ada2A containing complex (ATAC) and has been identified as a susceptibility gene in several cancers. However, the role and molecular mechanism of MBIP in esophageal squamous cell carcinoma (ESCC) remain unclear. Our finding indicated that the expression level of MBIP in ESCC was higher than that in normal tissue (P < 0.05) based on the data from the Cancer Gene Atlas (TCGA) and Gene Expression Omnibus (GEO). Kaplan-Meier analysis showed that high MBIP expression was closely associated with deeper invasion and worse prognosis. Transwell assay and mouse xenograft assay demonstrated that MBIP overexpression promoted migration and invasion in vitro and in vivo, while MBIP knockdown played the opposite role. Furthermore, the results of RNA-seq, qRT-PCR, western blotting and rescue experiments revealed that MBIP promoted epithelial-mesenchymal transition (EMT) via the phosphorylation JNK/p38 in ESCC. Our study indicates that MBIP plays a significant role in the prognosis and metastasis of ESCC, suggesting that MBIP might serve as an ESCC prognostic biomarker. • This paper firstly reports the expression level of MBIP in ESCC is higher than that in normal tissue. • High MBIP expression was associated with deeper invasion and worse prognosis. The effect of MBIP on ESCC metastasis was proven in vivo and in vitro. • MBIP promoted the EMT process in ESCC by activating the phosphorylation of JNK/p38 pathway. • MBIP may serve as a prognostic biomarker for ESCC and BIRB796 may offer theoretical evidence for drug selection in ESCC. [ABSTRACT FROM AUTHOR]

Microtia is one of the most common craniofacial birth defects worldwide, and its primary clinical manifestation is auricle deformity. Epigenetic factors are known to contribute to the etiology of microtia, yet the involvement of circular RNAs (circRNAs) in human auricle development and their association with microtia remains poorly understood. In this study, we aimed to analyze differentially expressed circRNAs and explore their functional implications in isolated microtia. By employing circRNA microarray analysis and bioinformatics approaches, we identified 340 differentially expressed circRNAs in auricle cartilage of patients with isolated microtia, comprising 152 upregulated and 188 downregulated circRNAs. A circRNA-mRNA co-expression network was constructed, followed by gene ontology analysis and Kyoto Encyclopedia of Genes and Genomes pathway enrichment analysis. Subsequently, we selected four significantly upregulated circRNAs from the co-expression network based on their association with cartilage development and validated their expressions in 30 isolated microtia and 30 control clinical auricle cartilage samples. Among these circRNAs, circCOL1A2, the most significantly upregulated circRNA, was selected as a representative circRNA for investigating its role in isolated microtia. Overexpression of circCOL1A2 significantly inhibited chondrocyte proliferation and chondrogenic differentiation of human mesenchymal stem cells. Additionally, circCOL1A2 upregulated Dermatan Sulfate Epimerase Like (DSEL) expression by sponging miR-637 through the competing endogenous RNA (ceRNA) mechanism. Notably, the downregulation of DSEL attenuated the inhibitory effect of circCOL1A2 overexpression on cell proliferation and chondrogenic differentiation. Collectively, these findings highlight the involvement of circCOL1A2 in the pathogenesis of isolated microtia and emphasize the potential significance of dysregulated circRNAs in disease development. [Display omitted] • This paper is the first to report the expression profile of circular RNA in human auricle cartilage. • CircCOL1A2 inhibited chondrocyte proliferation and chondrogenic differentiation of human mesenchymal stem cells. • CircCOL1A2 sponged miR-637 to upregulate DSEL expression. • Downregulation of DSEL attenuated the inhibitory effect on cell proliferation and chondrogenic differentiation. • Dysregulation of circRNAs is a novel pathogenic mechanism of isolated microtia. [ABSTRACT FROM AUTHOR]

Connarus semidecandrus Jack (Family: connaraceae) is a medicinal plant known for its wide distribution throughout Southeast Asia. Renowned for its diverse therapeutic properties, it has been traditionally used for treating fever, skin irritation, and colic. Numerous individuals suffer from skin issues, including wrinkles, hyperpigmentation, and inflammation, due to environmental factors. Although many drugs are available to treat skin problems, chemical drugs have many shortcomings and side effects. Therefore, natural products are attractive potential medicines for alleviating skin troubles. We recently showed that Connarus semidecandrus Jack ethanol extract (Cs-EE) has anti-alopecia potential. This paper aims to explore the potential skin-protective effects and underlying molecular mechanisms of Connarus semidecandrus Jack in UVB-induced human keratinocytes (HaCaT). Before utilization, Cs-EE was dissolved in dimethyl sulfoxide (DMSO) and was preserved at a temperature of −20 °C. The phytochemical constituents of Cs-EE were detected by gas chromatography–mass spectrometry analysis (GC-MS). Sequentially, HaCaT cells were exposed to varying concentrations of Cs-EE prior to ultraviolet B (UVB) irradiation. Evaluations of cellular responses in HaCaT cells, including assessments of cell viability, deoxyribonucleic acid (DNA) damage, and gene and protein expressions, were carried out. To explore the specific signaling pathway involved, we conducted a luciferase assay in addition to validating these pathways using Western blot analysis. Nitric oxide (NO) and intracellular reactive oxygen species were decreased. Melanin production through the activation of melanocytes by α-melanocyte-stimulating hormone (MSH) was also inhibited by Cs-EE. Furthermore, the mRNA expression levels of key factors such as cyclooxygenase-2 (COX-2), tumor necrosis factor-α (TNF-α), interleukin 6 (IL-6), MMP-1, MMP-3, and MMP-9 exhibited a remarkable decrease. In addition, the phosphorylation of TAK1 within the signaling cascade exhibited a decline, and the activities of the transcription factor AP-1 were decreased according to a luciferase reporter assay. Taken together, these findings suggest that the anti-inflammatory, anti-aging, and anti-apoptotic effects of Cs-EE indicate the compound's potential usefulness as a natural component in pharmaceutical and cosmetic products. [Display omitted] • Connarus semidecandrus Jack ethanol extract (Cs-EE) shows radical scavenging activity. • Cs-EE reduces UVB-induced cell death and apoptosis. • Cs-EE reduces the expression of genes involved in UVB-induced inflammatory response and wrinkle formation. • Cs-EE downregulates α-MSH-induced melanogenic responses of melanocyte. • Cs-EE inhibits AP-1 activity via suppression of TAK1/MKK3/6/p38 pathway. [ABSTRACT FROM AUTHOR]

The existent algebraic models of the genetic code contribute to the understanding of the physio-chemical characteristics of the amino acids. However, the process of translating a gene into a phenotype is highly complex. Moreover, the intricacy of gene expression gets further multiplied due to the biases in the codon usage. This paper explores an algebraic structure called module on the set of codons as well as on that of RNA sequences. We study the potential implications of these structures on gene expression and the G C content of an RNA sequence. The base order { C , U , G , A } appears to possess greater biological significance than many of the orders previously studied. We have developed a novel algorithm to generate RNA sequences with high G C content, aiming to enhance the thermostability of biomolecules. The insights gained from this investigation may have applications in biomolecular modeling and docking, protein engineering, drug development, and related fields. • A module structure is constructed on genetic code and studied its implications. • Developed a novel algorithm to increase the G C content of RNA sequences. • This study may be utilized in computational protein engineering. [ABSTRACT FROM AUTHOR]

Protein arginine methyltransferase 5 (PRMT5), a type II arginine methyltransferase, controls arginine dimethylation of a variety of substrates. While many papers have reported the function of mammalian PRMT5, it remains unclear how PRMT5 functions in chicken cells. In this study, we found that chicken (ch) PRMT5 is widely expressed in a variety of chicken tissues and is distributed in both the cytoplasm and the nucleus. Ectopic expression of chPRMT5 significantly suppresses chIFN-β activation induced by chMDA5. In addition, a prmt5 gene-deficient DF-1 cell line was constructed using CRISPR/Cas9. In comparison with the wild-type cells, the prmt5−/− DF-1 cells displays normal morphology and maintain proliferative capacity. Luciferase reporter assay and overexpression showed that prmt5−/− DF-1 cells had increased IFN-β production. With identified chicken PRMT5 and CRISPR/Cas9 knockout performed in DF-1 cells, we uncovered a functional link of chPRMT5 in suppression of IFN-β production and interferon-stimulated gene expression. [ABSTRACT FROM AUTHOR]

Carotenoid cleavage oxygenase (CCO) plays a pivotal role in various biological activities, including antioxidant and immune functions in animals. This paper investigates the evolution and expression of CCO genes based on three chordates and 27 arthropods. Aquatic animals exhibit a higher abundance of CCO genes. Despite this, research on CCO in crustaceans has been notably limited, with a complete absence of any previous studies on the CCO genes for the Chinese mitten crab (Eriocheir sinensis). In this study, six CCO genes were identified in the E. sinensis genome database. Results reveal that the evolution of the CCO gene family in Crustacea is primarily characterized by purifying selection, with a preference for employing similar codons. EsCCO1 and EsCCO3 were mainly expressed in the epidermal layer, and EsCCO4 was mainly expressed in the hindgut. Meanwhile, EsCCO5 and EsCCO6 were mainly expressed in the hepatopancreas and endometrium. A notable detail that different EsCCO genes demonstrate distinct expression patterns within various tissues of E. sinensis. The findings of this study offer fundamental insights that could serve as a basis for further exploration into the functions and regulatory mechanisms of CCO genes in crustacean species. [ABSTRACT FROM AUTHOR]

Endometriosis is an estrogen-dependent chronic inflammatory disorder involving the placement and growth of endometrial tissue outside the uterine cavity. It is the most common multifactorial disease that affects the life quality of women in reproductive age. Due to its multicomponent nature, early diagnosis of the disease is challenging. Since many genetic, epigenetic alterations and non-genetic factors contribute to the pathology of endometriosis, devising a drug therapy that directly acts on the ectopic tissue is extremely difficult. Endometriosis is a hormone-driven disease with estrogen considered as a primary driver for the development of endometriotic lesions. This study aims to identify biosignatures involved in endometriosis with and without gonadotropin releasing hormone agonists (GnRHa). GnRHa is a short peptide analog of GnRH that causes inhibition of estrogen and androgen synthesis. Microarray based-gene expression profiling was performed on total RNA extracted from endometriotic tissue samples with and without GnRHa-treated patients already published in our previous paper. The untreated group were considered as the control. Genes were then selected for validation by quantitative real-time polymerase chain reaction (qRT-PCR). qRT-PCR analysis confirmed significant downregulation in(p < 0.05) expression of DARC (p = 0.0042), CDH1 (p = 0.0027), CDH5 (p = 0.0283), ATP2A3 (p < 0.001), RGS5 (p = 0.0032), and CD36 (p = 0.0162) in endometriosis patients treated with GnRHa analogs. Although, CTNNAL1 (p = 0.0136) also showed significant results but there was upregulation in their expression levels after GnRHa treatment. Thus, an altered expression of these genes makes them a possible candidate determinant of endometriosis treated with GnRHa. [Display omitted] [ABSTRACT FROM AUTHOR]

Across the globe, cardiovascular disease (CVD) is the leading cause of mortality. According to reports, around 6.2 million people in the United states have heart failure. Current standards of care for heart failure can delay but not prevent progression of disease. Gene therapy is one of the novel treatment modalities that promises to fill this limitation in the current standard of care for Heart Failure. In this paper we performed an extensive search of the literature on various advances made in gene therapy for heart failure till date. We review the delivery methods, targets, current applications, trials, limitations and feasibility of gene therapy for heart failure. Various methods have been employed till date for administering gene therapies including but not limited to arterial and venous infusion, direct myocardial injection and pericardial injection. Various strategies such as AC6 expression, S100A1 protein upregulation, VEGF-B and SDF-1 gene therapy have shown promise in recent preclinical trials. Furthermore, few studies even show that stimulation of cardiomyocyte proliferation such as through cyclin A2 overexpression is a realistic avenue. However, a considerable number of obstacles need to be overcome for gene therapy to be part of standard treatment of care such as definitive choice of gene, gene delivery systems and a suitable method for preclinical trials and clinical trials on patients. Considering the challenges and taking into account the recent advances in gene therapy research, there are encouraging signs to indicate gene therapy for heart failure to be a promising treatment modality for the future. However, the time and feasibility of this option remains in a situation of balance. [ABSTRACT FROM AUTHOR]

Enzymatic browning causes a decline in consumer's acceptance of fresh-cut products. Previous study showed reactive oxygen species (ROS) is associated with enzymatic browning, and prohibitin (PHB) is involved in ROS formation. However, whether PHB influences the enzymatic browning is still unknown. In this paper, changes of StPHB3 transcript levels after cutting were investigated, then stphb3 mutants were constructed through CRISPR/Cas9 and the browning difference between stphb3 mutants and wild type was compared, finally the mechanism of PHB influencing the browning was explored. The results showed that the transcript levels of StPHB3 were enhanced in potato (cultivar Desiree) tubers after fresh cutting. Mutation of StPHB3 caused lower browning, less production of soluble quinones, decreased malondialdehyde (MDA) content, increased 1,1-Diphenyl-2-picrylhydrazyl (DPPH) inhibition, and increased total phenol contents. Additionally, polyphenol oxidase (PPO) activity and StPOT32 gene transcript level were also reduced after disturbing StPHB3 gene expression. StPHB3 was found to be located within the chloroplast same as the predominant location and activation site of PPO protein. Yeast two-hybrid (Y2H) and Co-Immunoprecipitation (Co-IP) assays showed StPHB3 interacted with PPO. And mature PPO contents were decreased in stphb3. These results suggest that suppression of StPHB3 can alleviate the browning of fresh-cut potatoes by enhancing the antioxidant activity and inhibiting PPO activation. [ABSTRACT FROM AUTHOR]

Breast cancer remains a leading cause of death for women worldwide, and new treatment strategies are needed. There are innumerable anomalous genes that are responsible for the multi-factorial carcinogenesis pathway. Although several disease-causing mutations have been detected, therapy frequently focuses on attenuating the manifestation of the disease rather than harmonizing the mutation in the target area. The advent of CRISPR-Cas9 technology has revolutionized genome editing, allowing for precise and efficient manipulation of gene expression. The purpose of this review paper is to summarize recent progress in the use of CRISPR-based approaches to target key signaling pathways associated with breast cancer progression. The first section introduces basic concepts of CRISPR technology, focusing on its application in genome editing and transcriptional regulation followed by an overview of aspects involving complex signaling pathways in breast cancer such as P13K/AKT/mTOR, EPK/MAPK and Wnt/β catenin. An extensive literature search using PubMed and Google Scholar is performed for information retrieval. Further, the role of CRISPR-based interventions in regulating gene expression revealed, altered pathway activity and potential therapeutic consequences are discussed. This review will be a valuable addition to providing comprehensive knowledge of CRISPR-Cas-mediated therapeutic targeting in breast cancer. [ABSTRACT FROM AUTHOR]

• SOTA gene expression prediction model using single local image from histological image. • Consistent and outstanding performance across various dataset from diverse organs and can be adapted for bulk RNA-seq. • Derive crucial multimodal biomarker information from easily accessible pathological images. • Introduced a vision backbone that combines CNN, GNN, and transformer architectures for image analysis. • Accurately predicted genes are strongly related to cancer immunology. Inferring gene expressions from histopathological images has long been a fascinating yet challenging task, primarily due to the substantial disparities between the two modality. Existing strategies using local or global features of histological images are suffering model complexity, GPU consumption, low interpretability, insufficient encoding of local features, and over-smooth prediction of gene expressions among neighboring sites. In this paper, we develop TCGN (Transformer with Convolution and Graph-Node co-embedding method) for gene expression estimation from H&E-stained pathological slide images. TCGN comprises a combination of convolutional layers, transformer encoders, and graph neural networks, and is the first to integrate these blocks in a general and interpretable computer vision backbone. Notably, TCGN uniquely operates with just a single spot image as input for histopathological image analysis, simplifying the process while maintaining interpretability. We validate TCGN on three publicly available spatial transcriptomic datasets. TCGN consistently exhibited the best performance (with median PCC 0.232). TCGN offers superior accuracy while keeping parameters to a minimum (just 86.241 million), and it consumes minimal memory, allowing it to run smoothly even on personal computers. Moreover, TCGN can be extended to handle bulk RNA-seq data while providing the interpretability. Enhancing the accuracy of omics information prediction from pathological images not only establishes a connection between genotype and phenotype, enabling the prediction of costly-to-measure biomarkers from affordable histopathological images, but also lays the groundwork for future multi-modal data modeling. Our results confirm that TCGN is a powerful tool for inferring gene expressions from histopathological images in precision health applications. [Display omitted] [ABSTRACT FROM AUTHOR]

Spatial transcriptomics (ST) is essential for understanding diseases and developing novel treatments. It measures the gene expression of each fine-grained area (i.e., different windows) in the tissue slide with low throughput. This paper proposes an exemplar guided graph network dubbed EGGN to accurately and efficiently predict gene expression from each window of a tissue slide image. We apply exemplar learning to dynamically boost gene expression prediction from nearest/similar exemplars of a given tissue slide image window. Our framework has three main components connected in a sequence: (i) an extractor to structure a feature space for exemplar retrievals; (ii) a graph construction strategy to connect windows and exemplars as a graph; (iii) a graph convolutional network backbone to process window and exemplar features, and a graph exemplar bridging block to adaptively revise the window features using its exemplars. Finally, we complete the gene expression prediction task with a simple attention-based prediction block. Experiments on standard benchmark datasets indicate the superiority of our approach when compared with past state-of-the-art methods. We release our code at https://github.com/Yan98/EGN. [Display omitted] • An exemplar guided graph network to predict gene expression for slide image windows. • A graph construction strategy to connect windows and exemplars. • A graph exemplar bridging block to revise the window feature by using exemplars. • State-of-the-art experimental results on two standard benchmark datasets. [ABSTRACT FROM AUTHOR]

• The first attempt to design a GEP-based codematrix generation algorithm. The individual structure of GEP allows the codematrix to be optimized in a much faster and more effective way. • The feature selection scheme is combined with the design of codematrix, to accomplish the feature selection and codematrix optimization simultaneously. • A greedy strategy-based adaptive optimization method to further improve the individual's quality to deal with hard classes by appending proper columns to the codematrix. The multi-class classification task is one of the most common tasks in machine learning. As a typical solution based on a partitioning strategy, Error-Correcting Output Codes (ECOC) can transform a multi-class classification problem into multiple binary classification problems. The key of ECOC is to construct an effective codematrix to represent a set of class decomposition schemes, which transforms a multiclass problem into a group of binary class problems. Consequently, the design of a fast and effective ECOC codematrix generation method is of great research significance and value for solving multi-class classification problems. In ECOC algorithms, the design of codematrix is treated as a combination problem between different code columns, in which the evolutionary algorithm shows a great advantage. Based on this consideration, the Gene Expression Programming (GEP) is applied to search for the codematrix with high performance because its expressive tree structure makes it well represent codematrcies for subsequent optimization operations. This paper proposes an adaptive ECOC algorithm based on Gene Expression Programming (GEP) and similarity measurement matrix, named GEP-ECOC. In our GEP, each individual represents a set of columns to form a random ECOC codematrix, which is optimized in the evolutionary process. Meanwhile, the crossover and mutation operations are modified to include a legality checking process to ensure that the generated codematrix satisfies the ECOC constraints. The GEP-based ECOC codematrix generation algorithm can quickly produce a codematrix with better performance, which ensures the efficiency of the algorithm to a certain extent. In addition, an adaptive algorithm based on a similarity measurement matrix is proposed to add new columns to the current codematrix, aiming to better handle hard classes. Our algorithm is compared with other algorithms on various data sets, and the experimental results confirm that our GEP-ECOC can balance the efficiency and performance of the algorithm and achieve higher performance. [ABSTRACT FROM AUTHOR]

Cancer is a multifaceted, complex disease characterized by unchecked cell growth, genetic mutations, and dysregulated signalling pathways. These factors eventually cause evasion of apoptosis, sustained angiogenesis, tissue invasion, and metastasis, which makes it difficult for targeted therapeutic interventions to be effective. MicroRNAs (miRNAs) are essential gene expression regulators linked to several biological processes, including cancer and inflammation. The NF-κB signalling pathway, a critical regulator of inflammatory reactions and oncogenesis, has identified miR-155 as a significant participant in its modulation. An intricate network of transcription factors known as the NF-κB pathway regulates the expression of genes related to inflammation, cell survival, and immunological responses. The NF-κB pathway's dysregulation contributes to many cancer types' development, progression, and therapeutic resistance. In numerous cancer models, the well-studied miRNA miR-155 has been identified as a crucial regulator of NF-κB signalling. The p65 subunit and regulatory molecules like IκB are among the primary targets that miR-155 directly targets to alter NF-κB activity. The molecular processes by which miR-155 affects the NF-κB pathway are discussed in this paper. It also emphasizes the miR-155's direct and indirect interactions with important NF-κB cascade elements to control the expression of NF-κB subunits. We also investigate how miR-155 affects NF-κB downstream effectors in cancer, including inflammatory cytokines and anti-apoptotic proteins. [ABSTRACT FROM AUTHOR]