Determination of target genes for classified molecular subtypes of triple-negative breast cancer form microarray gene expression profiling: An integrative in silico approach.

BACKGROUND: Highly heterogeneous triple-negative breast cancer (TNBC) has tough clinical features, which were gradually solving and improving in diagnosis by the molecular subtyping of TNBC. AIM: Presently, this study was focused on analyzing the genetic makeup of TNBC subtypes. SETTINGS AND DESIGN: This study explored the MicroArray expression profiling of differentially expressed genes in molecular subtypes BL1, BL2, IM, luminal androgen receptor, M, and mesenchymal stem-like of TNBC by analyzing the Gene Expression Omnibus dataset GSE167213. Various gene ontologies-based protein-protein interaction (PPI) networks were subtyped TNBC genes. The effect of genetic alteration on TNBC cases was also interpreted. MATERIALS AND METHODS: The MicroArray gene expression profiling was done through R programming and subjected to functional annotation through the database for annotation, visualization, and integrated discovery. The PPI networking of functionally associated genes was interpreted by STRING. The survival analysis was done through cBioPortal. STATISTICAL ANALYSIS USED: The t-test was used through R programming to generate the P values for a test of the significance of expressed genes. RESULTS: A total of 54,613 significant probes were analyzed in the TNBC MicroArray dataset. The functional PPI networks of BL1, BL2, and IM upregulated genes showed significant associations. The survival analysis of differentially expressed genes showed the significant prognostic effect of 32 upregulated genes of different subtypes on TNBC cases with genetic alterations, whereas the remaining genes showed no significant effects. CONCLUSION: The output of the present study provided significant target gene panels for different TNBC subtypes, which would add an informative genetic value to TNBC diagnosis.

Cyanobacterial Genomes from a Brackish Coastal Lagoon Reveal Potential for Novel Biogeochemical Functions and Their Evolution.

Cyanobacteria are recognised for their pivotal roles in aquatic ecosystems, serving as primary producers and major agents in diazotrophic processes. Currently, the primary focus of cyanobacterial research lies in gaining a more detailed understanding of these well-established ecosystem functions. However, their involvement and impact on other crucial biogeochemical cycles remain understudied. This knowledge gap is partially attributed to the challenges associated with culturing cyanobacteria in controlled laboratory conditions and the limited understanding of their specific growth requirements. This can be circumvented partially by the culture-independent methods which can shed light on the genomic potential of cyanobacterial species and answer more profound questions about the evolution of other key biogeochemical functions. In this study, we assembled 83 cyanobacterial genomes from metagenomic data generated from environmental DNA extracted from a brackish water lagoon (Chilika Lake, India). We taxonomically classified these metagenome-assembled genomes (MAGs) and found that about 92.77% of them are novel genomes at the species level. We then annotated these cyanobacterial MAGs for all the encoded functions using KEGG Orthology. Interestingly, we found two previously unreported functions in Cyanobacteria, namely, DNRA (Dissimilatory Nitrate Reduction to Ammonium) and DMSP (Dimethylsulfoniopropionate) synthesis in multiple MAGs using nirBD and dsyB genes as markers. We validated their presence in several publicly available cyanobacterial isolate genomes. Further, we identified incongruities between the evolutionary patterns of species and the marker genes and elucidated the underlying reasons for these discrepancies. This study expands our overall comprehension of the contribution of cyanobacteria to the biogeochemical cycling in coastal brackish ecosystems.

Direct Binding Methods to Measure Receptor-Ligand Interactions.

G-protein-coupled receptors (GPCRs) contribute to numerous physiological processes via complex network mechanisms. While indirect signaling assays (Ca2+ mobilization, cAMP production, and GTPγS binding) have been useful in identifying and characterizing downstream signaling mechanisms of GPCRs, these methods lack measurements of direct binding affinities, kinetics, binding specificity, and selectivity that are important parameters in GPCR drug discovery. In comparison to existing direct methods that use radio- or fluorescent labels, label-free techniques can closely emulate the native interactions around binding partners. Surface plasmon resonance (SPR) is a label-free technique that utilizes the refractive index (RI) property and is applied widely in quantitative GPCR-ligand binding kinetics measurement including small molecules screening. However, purified GPCRs are further embedded in a synthetic lipid environment which is immobilized through different tags to the SPR sensor surface, resulting in a non-native environment. Here, we introduced a methodology that also uses the RI property to measure binding interactions in a label-free, immobilization-free arrangement. The free-solution technique is successfully applied in quantifying the interaction of bioactive lipids to cognate lipid GPCRs, which is not purified but rather present in near-native conditions, i.e., in milieu of other cytoplasmic lipids and proteins. To further consider the wide applicability of these free-solution approaches in biomolecular interaction research, additional applications on a variety of receptor-ligand pairs are imperative.

FTY720 requires vitamin B12-TCN2-CD320 signaling in astrocytes to reduce disease in an animal model of multiple sclerosis.

Vitamin B12 (B12) deficiency causes neurological manifestations resembling multiple sclerosis (MS); however, a molecular explanation for the similarity is unknown. FTY720 (fingolimod) is a sphingosine 1-phosphate (S1P) receptor modulator and sphingosine analog approved for MS therapy that can functionally antagonize S1P1. Here, we report that FTY720 suppresses neuroinflammation by functionally and physically regulating the B12 pathways. Genetic and pharmacological S1P1 inhibition upregulates a transcobalamin 2 (TCN2)-B12 receptor, CD320, in immediate-early astrocytes (ieAstrocytes; a c-Fos-activated astrocyte subset that tracks with experimental autoimmune encephalomyelitis [EAE] severity). CD320 is also reduced in MS plaques. Deficiency of CD320 or dietary B12 restriction worsens EAE and eliminates FTY720's efficacy while concomitantly downregulating type I interferon signaling. TCN2 functions as a chaperone for FTY720 and sphingosine, whose complex induces astrocytic CD320 internalization, suggesting a delivery mechanism of FTY720/sphingosine via the TCN2-CD320 pathway. Taken together, the B12-TCN2-CD320 pathway is essential for the mechanism of action of FTY720.

Landscape of In Silico Tools for Modeling Covalent Modification of Proteins: A Review on Computational Covalent Drug Discovery.

Covalent drug discovery has been a challenging research area given the struggle of finding a sweet balance between selectivity and reactivity for these drugs, the lack of which often leads to off-target activities and hence undesirable side effects. However, there has been a resurgence in covalent drug design following the success of several covalent drugs such as boceprevir (2011), ibrutinib (2013), neratinib (2017), dacomitinib (2018), zanubrutinib (2019), and many others. Design of covalent drugs includes many crucial factors, where "evaluation of the binding affinity" and "a detailed mechanistic understanding on covalent inhibition" are at the top of the list. Well-defined experimental techniques are available to elucidate these factors; however, often they are expensive and/or time-consuming and hence not suitable for high throughput screens. Recent developments in in silico methods provide promise in this direction. In this report, we review a set of recent publications that focused on developing and/or implementing novel in silico techniques in "Computational Covalent Drug Discovery (CCDD)". We also discuss the advantages and disadvantages of these approaches along with what improvements are required to make it a great tool in medicinal chemistry in the near future.

Electronic Structure of Heteronuclear Cerium-Platinum Clusters.

Beyond the now well-known strong catalyst-support interactions reported for ceria-supported platinum catalysts, intermetallic Ce-Pt compounds exhibit fascinating properties such as heavy fermion behavior and magnetic instability. Small heterometallic Ce-Pt clusters, which can provide insights into the local features that govern bulk phenomena, have been less explored. Herein, the anion photoelectron spectra of three small mixed Ce-Pt clusters, Ce2OPt-, Ce2Pt-, and Ce3Pt-, are presented and interpreted with supporting density functional theory calculations. The calculations, which are readily reconciled with the experimental spectra, suggest the presence of numerous close-lying spin states, including states in which the Ce 4f electrons are ferromagnetically coupled or antiferromagnetically coupled. The Pt center is consistently in a nominal -2 charge state in all cluster neutrals and anions, giving the Ce-Pt bond ionic character. Ce-Pt bonds are stronger than Ce-Ce bonds, and the O atom in Ce2OPt- coordinates only with the Ce centers. The energy of the singly occupied Ce-local 4f orbitals relative to the Pt-local orbitals changes with cluster composition. Discussion of the results includes potential implications for Ce-rich intermetallic materials.

Novel Antagonist of the Type 2 Lysophosphatidic Acid Receptor (LPA2), UCM-14216, Ameliorates Spinal Cord Injury in Mice.

Spinal cord injuries (SCIs) irreversibly disrupt spinal connectivity, leading to permanent neurological disabilities. Current medical treatments for reducing the secondary damage that follows the initial injury are limited to surgical decompression and anti-inflammatory drugs, so there is a pressing need for new therapeutic strategies. Inhibition of the type 2 lysophosphatidic acid receptor (LPA2) has recently emerged as a new potential pharmacological approach to decrease SCI-associated damage. Toward validating this receptor as a target in SCI, we have developed a new series of LPA2 antagonists, among which compound 54 (UCM-14216) stands out as a potent and selective LPA2 receptor antagonist (Emax = 90%, IC50 = 1.9 µM, KD = 1.3 nM; inactive at LPA1,3-6 receptors). This compound shows efficacy in an in vivo mouse model of SCI in an LPA2-dependent manner, confirming the potential of LPA2 inhibition for providing a new alternative for treating SCI.

PDXNet portal: patient-derived Xenograft model, data, workflow and tool discovery.

We created the PDX Network (PDXNet) portal (https://portal.pdxnetwork.org/) to centralize access to the National Cancer Institute-funded PDXNet consortium resources, to facilitate collaboration among researchers and to make these data easily available for research. The portal includes sections for resources, analysis results, metrics for PDXNet activities, data processing protocols and training materials for processing PDX data. Currently, the portal contains PDXNet model information and data resources from 334 new models across 33 cancer types. Tissue samples of these models were deposited in the NCI's Patient-Derived Model Repository (PDMR) for public access. These models have 2134 associated sequencing files from 873 samples across 308 patients, which are hosted on the Cancer Genomics Cloud powered by Seven Bridges and the NCI Cancer Data Service for long-term storage and access with dbGaP permissions. The portal includes results from freely available, robust, validated and standardized analysis workflows on PDXNet sequencing files and PDMR data (3857 samples from 629 patients across 85 disease types). The PDXNet portal is continuously updated with new data and is of significant utility to the cancer research community as it provides a centralized location for PDXNet resources, which support multi-agent treatment studies, determination of sensitivity and resistance mechanisms, and preclinical trials.

Presentation of potential genes and deleterious variants associated with non-syndromic hearing loss: a computational approach.

Non-syndromic hearing loss (NSHL) is a common hereditary disorder. Both clinical and genetic heterogeneity has created many obstacles to understanding the causes of NSHL. The present study has attempted to ravel the genetic aetiology in NSHL progression and to screen out potential target genes using computational approaches. The reported NSHL target genes (2009-2020) have been studied by analyzing different biochemical and signaling pathways, interpretation of their functional association network, and discovery of important regulatory interactions with three previously established miRNAs in the human inner ear as well as in NSHL such as miR-183, miR-182, and miR-96. This study has identified SMAD4 and SNAI2 as the most putative target genes of NSHL. But pathogenic and deleterious non-synonymous single nucleotide polymorphisms discovered within SMAD4 is anticipated to have an impact on NSHL progression. Additionally, the identified deleterious variants in the functional domains of SMAD4 added a supportive clue for further study. Thus, the identified deleterious variant i.e., rs377767367 (G491V) in SMAD4 needs further clinical validation. The present outcomes would provide insights into the genetics of NSHL progression.

Genetics Landscape of Nonsyndromic Hearing Loss in Indian Populations.

Congenital nonsyndromic hearing loss (NSHL) has been considered as one of the most prevalent chronic disorder in children. It affects the physical and mental conditions of a large children population worldwide. Because of the genetic heterogeneity, the identification of target gene is very challenging. However, gap junction ß-2 ( GJB2 ) is taken as the key gene for hearing loss, as its involvement has been reported frequently in NSHL cases. This study aimed to identify the association of GJB2 mutants in different Indian populations based on published studies in Indian population. This will provide clear genetic fundamental of NSHL in Indian biogeography, which would be helpful in the diagnosis process.

Differential activation mechanisms of lipid GPCRs by lysophosphatidic acid and sphingosine 1-phosphate.

Lysophospholipids are bioactive lipids and can signal through G-protein-coupled receptors (GPCRs). The best studied lysophospholipids are lysophosphatidic acid (LPA) and sphingosine 1-phosphate (S1P). The mechanisms of lysophospholipid recognition by an active GPCR, and the activations of lysophospholipid GPCR-G-protein complexes remain unclear. Here we report single-particle cryo-EM structures of human S1P receptor 1 (S1P1) and heterotrimeric Gi complexes formed with bound S1P or the multiple sclerosis (MS) treatment drug Siponimod, as well as human LPA receptor 1 (LPA1) and Gi complexes in the presence of LPA. Our structural and functional data provide insights into how LPA and S1P adopt different conformations to interact with their cognate GPCRs, the selectivity of the homologous lipid GPCRs for S1P versus LPA, and the different activation mechanisms of these GPCRs by LPA and S1P. Our studies also reveal specific optimization strategies to improve the MS-treating S1P1-targeting drugs.

Ponesimod inhibits astrocyte-mediated neuroinflammation and protects against cingulum demyelination via S1P1 -selective modulation.

Ponesimod is a sphingosine 1-phosphate (S1P) receptor (S1PR) modulator that was recently approved for treating relapsing forms of multiple sclerosis (MS). Three other FDA-approved S1PR modulators for MS-fingolimod, siponimod, and ozanimod-share peripheral immunological effects via common S1P1 interactions, yet ponesimod may access distinct central nervous system (CNS) mechanisms through its selectivity for the S1P1 receptor. Here, ponesimod was examined for S1PR internalization and binding, human astrocyte signaling and single-cell RNA-seq (scRNA-seq) gene expression, and in vivo using murine cuprizone-mediated demyelination. Studies confirmed ponesimod's selectivity for S1P1 without comparable engagement to the other S1PR subtypes (S1P2,3,4,5 ). Ponesimod showed pharmacological properties of acute agonism followed by chronic functional antagonism of S1P1 . A major locus of S1P1 expression in the CNS is on astrocytes, and scRNA-seq of primary human astrocytes exposed to ponesimod identified a gene ontology relationship of reduced neuroinflammation and reduction in known astrocyte disease-related genes including those of immediate early astrocytes that have been strongly associated with disease progression in MS animal models. Remarkably, ponesimod prevented cuprizone-induced demyelination selectively in the cingulum, but not in the corpus callosum. These data support the CNS activities of ponesimod through S1P1 , including protective, and likely selective, effects against demyelination in a major connection pathway of the brain, the limbic fibers of the cingulum, lesions of which have been associated with several neurologic impairments including MS fatigue.

Comprehensive functional network analysis and screening of deleterious pathogenic variants in non-syndromic hearing loss causative genes.

Hearing loss (HL) is a significant public health problem and causes the most frequent congenital disability in developed societies. The genetic analysis of non-syndromic hearing loss (NSHL) may be considered as a complement to the existent plethora of diagnostic modalities available. The present study focuses on exploring more target genes with respective non-synonymous single nucleotide polymorphisms (nsSNPs) involved in the development of NSHL. The functional network analysis and variant study have successfully been carried out from the gene pool retrieved from reported research articles of the last decade. The analyses have been done through STRING. According to predicted biological processes, various variant analysis tools have successfully classified the NSHL causative genes and identified the deleterious nsSNPs, respectively. Among the predicted pathogenic nsSNPs with rsIDs rs80356586 (I515T), rs80356596 (L1011P), rs80356606 (P1987R) in OTOF have been reported in NSHL earlier. The rs121909642 (P722S), rs267606805 (P722H) in FGFR1, rs121918506 (E565A) and rs121918509 (A628T, A629T) in FGFR2 have not been reported in NSHL yet, which should be clinically experimented in NSHL. This also indicates this variant's novelty as its association in NSHL. The findings and the analyzed data have delivered some vibrant genetic pathogenesis of NSHL. These data might be used in the diagnostic and prognostic purposes in non-syndromic congenitally deaf children.

Lysophosphatidic acid (LPA)-antibody (504B3) engagement detected by interferometry identifies off-target binding.

BACKGROUND: Lysophosphatidic acid (LPA) is a bioactive lysophospholipid that acts through its six cognate G protein-coupled receptors. As a family, lysophospholipids have already produced medicines (e.g., sphingosine 1-phosphate) as is being pursued for LPA through the use of specific antibodies that reduce ligand availability. METHODS: The binding properties of a commercially available, reportedly specific, monoclonal LPA antibody named 504B3 that is related to the clinical candidate Lpathomab/LT3015 were reexamined using a free solution assay (FSA) measured in a compensated interferometric reader (CIR). RESULTS: Measurement of 504B3 binding properties with an FSA-CIR approach revealed similar binding affinities for 504B3 against LPA as well as the non-LPA lipids, phosphatidic acid (PA) and lysophosphatidylcholine (LPC). CONCLUSIONS: Antibody binding specificity and sensitivity, particularly involving lipid ligands, can be assessed in solution and without labels using FSA-CIR. These findings could affect interpretations of both current and past basic and clinical studies employing 504B3 and related anti-LPA antibodies.

Bioinformatics tools developed to support BioCompute Objects.

Developments in high-throughput sequencing (HTS) result in an exponential increase in the amount of data generated by sequencing experiments, an increase in the complexity of bioinformatics analysis reporting and an increase in the types of data generated. These increases in volume, diversity and complexity of the data generated and their analysis expose the necessity of a structured and standardized reporting template. BioCompute Objects (BCOs) provide the requisite support for communication of HTS data analysis that includes support for workflow, as well as data, curation, accessibility and reproducibility of communication. BCOs standardize how researchers report provenance and the established verification and validation protocols used in workflows while also being robust enough to convey content integration or curation in knowledge bases. BCOs that encapsulate tools, platforms, datasets and workflows are FAIR (findable, accessible, interoperable and reusable) compliant. Providing operational workflow and data information facilitates interoperability between platforms and incorporation of future dataset within an HTS analysis for use within industrial, academic and regulatory settings. Cloud-based platforms, including High-performance Integrated Virtual Environment (HIVE), Cancer Genomics Cloud (CGC) and Galaxy, support BCO generation for users. Given the 100K+ userbase between these platforms, BioCompute can be leveraged for workflow documentation. In this paper, we report the availability of platform-dependent and platform-independent BCO tools: HIVE BCO App, CGC BCO App, Galaxy BCO API Extension and BCO Portal. Community engagement was utilized to evaluate tool efficacy. We demonstrate that these tools further advance BCO creation from text editing approaches used in earlier releases of the standard. Moreover, we demonstrate that integrating BCO generation within existing analysis platforms greatly streamlines BCO creation while capturing granular workflow details. We also demonstrate that the BCO tools described in the paper provide an approach to solve the long-standing challenge of standardizing workflow descriptions that are both human and machine readable while accommodating manual and automated curation with evidence tagging. Database URL:  https://www.biocomputeobject.org/resources.

Understanding the causes, symptoms and effects of young-onset dementia.

Young-onset dementia refers to dementia that develops before the age of 65 years. It can present with a wide variety of symptoms including cognitive, behavioural, neurological and systemic symptoms, which reflects the wide range of possible causes. Young-onset dementia profoundly affects all aspects of people's lives, including relationships, employment and finances, and it will also profoundly affect relatives and carers. This article outlines the causes, symptoms and effects of young-onset dementia and describes the role of nurses in providing care and support to people with this progressive condition.

Essential interpretations of bioinformatics in COVID-19 pandemic.

The currently emerging pathogen SARS-CoV-2 has produced the global pandemic crisis by causing COVID-19. The unique and novel genetic makeup of SARS-CoV-2 has created hurdles in biological research, due to which the potential drug/vaccine candidates have not yet been discovered by the scientific community. Meanwhile, the advantages of bioinformatics in viral research had created a milestone since last few decades. The exploitation of bioinformatics tools and techniques has successfully interpreted this viral genomics architecture. Some major in silico studies involving next-generation sequencing, genome-wide association studies, computer-aided drug design etc. have been effectively applied in COVID-19 research methodologies and discovered novel information on SARS-CoV-2 in several ways. Nowadays the implementation of in silico studies in COVID-19 research has not only sequenced the SARS-CoV-2 genome but also properly analyzed the sequencing errors, evolutionary relationship, genetic variations, putative drug candidates against SARS-CoV-2 viral genes etc. within a very short time period. These would be very needful towards further research on COVID-19 pandemic and essential for vaccine development against SARS-CoV-2 which will save public health.

BCO App: tools for generating BioCompute Objects from next-generation sequencing workflows and computations.

The BioCompute Object (BCO) standard is an IEEE standard (IEEE 2791-2020) designed to facilitate the communication of next-generation sequencing data analysis with applications across academia, government agencies, and industry. For example, the Food and Drug Administration (FDA) supports the standard for regulatory submissions and includes the standard in their Data Standards Catalog for the submission of HTS data. We created the BCO App to facilitate BCO generation in a range of computational environments and, in part, to participate in the Advanced Track of the precisionFDA BioCompute Object App-a-thon. The application facilitates the generation of BCOs from both workflow metadata provided as plaintext and from workflow contents written in the Common Workflow Language. The application can also access and ingest task execution results from the Cancer Genomics Cloud (CGC), an NCI funded computational platform. Creating a BCO from a CGC task significantly reduces the time required to generate a BCO on the CGC by auto-populating workflow information fields from CGC workflow and task execution results. The BCO App supports exporting BCOs as JSON or PDF files and publishing BCOs to both the CGC platform and to GitHub repositories.

Unlabeled lysophosphatidic acid receptor binding in free solution as determined by a compensated interferometric reader.

Native interactions between lysophospholipids (LPs) and their cognate LP receptors are difficult to measure because of lipophilicity and/or the adhesive properties of lipids, which contribute to high levels of nonspecific binding in cell membrane preparations. Here, we report development of a free-solution assay (FSA) where label-free LPs bind to their cognate G protein-coupled receptors (GPCRs), combined with a recently reported compensated interferometric reader (CIR) to quantify native binding interactions between receptors and ligands. As a test case, the binding parameters between lysophosphatidic acid (LPA) receptor 1 (LPA1; one of six cognate LPA GPCRs) and LPA were determined. FSA-CIR detected specific binding through the simultaneous real-time comparison of bound versus unbound species by measuring the change in the solution dipole moment produced by binding-induced conformational and/or hydration changes. FSA-CIR identified KD values for chemically distinct LPA species binding to human LPA1 and required only a few nanograms of protein: 1-oleoyl (18:1; KD = 2.08 ± 1.32 nM), 1-linoleoyl (18:2; KD = 2.83 ± 1.64 nM), 1-arachidonoyl (20:4; KD = 2.59 ± 0.481 nM), and 1-palmitoyl (16:0; KD = 1.69 ± 0.1 nM) LPA. These KD values compared favorably to those obtained using the previous generation back-scattering interferometry system, a chip-based technique with low-throughput and temperature sensitivity. In conclusion, FSA-CIR offers a new increased-throughput approach to assess quantitatively label-free lipid ligand-receptor binding, including nonactivating antagonist binding, under near-native conditions.

Lauric Acid Modulates Cancer-Associated microRNA Expression and Inhibits the Growth of the Cancer Cell.

BACKGROUND: microRNAs are known to regulate various protein-coding gene expression posttranscriptionally. Fatty acids are cell membrane constituents and are also known to influence the biological activities of the cells like signal transduction, growth and differentiation of the cells, apoptosis induction, and other physiological functions. In our experiments, we used lauric acid to analyse its effects on human cancerous cell lines. OBJECTIVE: Our objective was to speculate the miRNA expression profile in lauric acid treated and untreated cancerous cell lines and further study the metabolic pathways of the targeted tumour suppressor and oncogenes. METHODS: The KB cells and HepG2 cells were treated with lauric acid and miRNA was isolated and the expression of tumour suppressor and oncogenic miRNA was measured by quantitative PCR. The untreated cells were used as control. The metabolic pathways of the target tumour suppressor and oncogenes were examined by GeneMANIA software. RESULTS: Interestingly, the lauric acid treatment suppresses the expression of oncogenic miRNA and significantly upregulated the expression of some tumour suppressor miRNAs. GeneMANIA metabolic pathway revealed that the upregulated tumour suppressor miRNAs regulate several cancer-associated pathways such as DNA damage, signal transduction p53 class mediator, stem cell differentiation, cell growth, cell cycle phase transition, apoptotic signalling pathway, cellular response to stress and radiation, etc. whereas oncogenic miRNAs regulate the cancer-associated pathway like cell cycle phase transition, apoptotic signalling pathway, cell growth, response to oxidative stress, immune response activating cell surface protein signalling pathway, cyclin-dependent protein kinase activity, epidermal growth factor receptor signalling pathways, etc. Conclusion: In our study, we found that lauric acid works as an anticancer agent by altering the expression of miRNAs.