Key Interactions Research Articles

Compound extreme climate events intensify yield anomalies of winter wheat in France

Abstract Compound extreme climate events (ECEs) are increasingly recognized for their potential to exacerbate food insecurity risks beyond those posed by isolated events. The notion of ‘compound event’ encompasses not only co-occurring ECEs but also multiple ECEs across (different) growth stages (mECEs). The additional effects of these mECEs on crop yield, particularly considering various types of ECEs and regional scales, remain poorly understood. To close this knowledge gap, we consider droughts, pluvials, heatwaves, and coldwaves, and further identify which types of compound events have additional effects on winter wheat yield in France, using statistical methods and datasets encompassing 94 counties over a 68-year period. Our results indicate co-occurring drought heatwaves in summer and spring, along with co-occurring pluvial heatwaves and pluvial coldwaves in winter, have negative additional effects on yield compared with single ECEs. We further identify the types of mECEs that have intensified effects, with the majority showing negative effects on yield. Key interactions leading to intensified yield loss include droughts in winter or spring combined with summer co-occurring drought heatwaves, pluvials across multiple growth stages, pluvials combined with coldwaves, and the transition between droughts and pluvials, with the most severe anomaly attaining −17.2%. Coldwaves are the main ECE related to intensified yield increases, while their frequency is decreasing. Overall, this study stresses the interactions among ECEs on crop yield, and the identified types of mECEs could serve as foundational information for designing control experiments and improving process-based crop models.

Rational design, synthesis, and biophysical characterization of a peptidic MDM2-MDM4 interaction inhibitor

In recent years, the restoration of p53 physiological functions has become an attractive therapeutic approach to develop novel and efficacious cancer therapies. Among other mechanisms, the oncosuppressor protein p53 is functionally regulated by MDM2 through its E3 ligase function. MDM2 promotes p53 ubiquitination and degradation following homodimerization or heterodimerization with MDM4. Recently, we discovered Pep3 (1, Pellegrino et al., 2015), a novel peptidic inhibitor of MDM2 dimerization able to restore p53 oncosuppressive functions both in vitro and in vivo. In this work, we were able to identify the key interactions between peptide 1 and MDM2 RING domain and to design peptide 2, a truncated version of 1 that is still able to bind MDM2. Integrating both computational and biophysical techniques, we show that peptide 2 maintains the conserved peptide 1-MDM2 interactions and is still able to bind to full-length MDM2.

Alpha-Glucosidase Inhibitory Effects of Flavonoids, Phenolic Acids and Iridoids Isolated From Vinca Soneri: In Vitro and In Silico Perspectives.

Various Vinca species have been traditionally used for their antihypertensive, sedative, and hemostatic properties, as well as for treating diabetes. In this study, some flavonoids, phenolic acids and iridoids were isolated from an endemic Vinca species, Vinca soneri for the first time. α-Glucosidase inhibitory effects of the isolates were tested and kaempferol-3-O-α-rhamnopyranosyl (1→6) β-galactopyranoside (1) was found to be the most active one with an IC50 value of 285.73 ±7.35 μM. Enzyme kinetic assay revealed that it inhibited α-glucosidase in competitive manner. Molecular geometry of 1 was predicted and Frontier molecular orbital analysis was performed using Density Functional Theory (DFT) calculations. Molecular docking and MM-GBSA calculations predicted good fit for 1 in the enzyme active site and key interactions with the catalytic residues. As a result, current study identifies 1 as a promising competitive α-glucosidase inhibitor to be developed as a potential antidiabetic drug candidate.

Synthesising the Relationships Between Food Web Structure and Robustness.

For many decades, ecologists have sought to understand the extent to which species losses lead to secondary extinctions-that is, the additional loss of species that occurs when resources or key interactions are lost (i.e. robustness). In particular, ecologists aim to identify generalisable rules that explain which types of food webs are more or less robust to secondary extinctions. Food web structure, or the patterns formed by species and their interactions, has been extensively studied as a potential factor that influences robustness to species loss. We systematically reviewed 28 studies to identify the relationships between food web structure and robustness to species loss and how the conclusions depend on methodological differences. Contrary to popular belief and theory, we found relatively consistent, positive relationships between connectance and robustness, among other generalities. Yet, we also found that conflicting conclusions about structure-robustness relationships can be, in part, attributed to differences in the type of data that studies use, particularly studies that use empirical data versus those generated from theoretical models. This review points towards a need to standardise methodology to answer the open question of whether robustness and its relationship with food web structure and to provide applicable insights for managing complex systems.

Solvation and its influence on the electronic structure and pharmacological activity of 2-fluoro-6-trifluromethyl acetophenone

The molecular framework and vibrational spectra of 2′-Fluoro-6′-Trifluoromethyl Acetophenone (MA3) in diverse solvent environments are being studied using quantum chemistry methods like DFT. The structure was successfully optimized using gas phase and solvent phases such as ethanol, diethyl sulfoxide, chloroform, and diethyl ether. Using FT-Raman and FT-IR techniques, both theoretical and experimental scenarios were examined. The IEFPCM model was used to explore the electronic properties, including HOMO-LUMO gaps, UV–Vis spectra, NBO, Mulliken analysis, and MEP, in various solvents. The TD-DFT approach and IEFPCM model were utilized to replicate the UV spectra. The Fukui function helped locate reactive sites and understand chemical interactions. Comprehensive topological analysis using Multiwfn – 3.8 suite integrated RDG, ALIE, ELF, and LOL to identify key binding sites and weak interactions. The molecular docking studies highlighted the compound’s binding affinities with target proteins, showcasing its potential pharmacological applications. This research underscores the critical role of solvation in determining the electronic and structural properties of MA3, paving the way for its application in drug development and other fields.

SpaGRA: Graph augmentation facilitates domain identification for spatially resolved transcriptomics

Recent advances in spatially resolved transcriptomics (SRT) have provided new opportunities for characterizing spatial structures of various tissues. Graph-based geometric deep learning have gained widespread adoption for spatial domain identification tasks. Currently, most methods define adjacency relation between cells or spots by their spatial distance in SRT data, which overlooks key biological interactions like gene expression similarities, and leads to inaccuracies in spatial domain identification. To tackle this challenge, we propose a novel method, SpaGRA (https://github.com/sunxue-yy/SpaGRA), for automatic multi-relationship construction based on graph augmentation. SpaGRA uses spatial distance as prior knowledge and dynamically adjusts edge weights with multi-head graph attention networks (GATs). This helps SpaGRA to uncover diverse node relationships and enhance message passing in geometric contrastive learning. Additionally, SpaGRA uses these multi-view relationships to construct negative samples, addressing sampling bias posed by random selection. Experimental results show that SpaGRA demonstrates superior domain identification performance on multiple datasets generated from different protocols. Using SpaGRA, we analyzed the functional regions in the mouse hypothalamus, identified key genes related to heart development in mouse embryos, and observed cancer-associated fibroblasts enveloping cancer cells in the latest Visium HD data. Overall, SpaGRA can effectively characterize spatial structures across diverse SRT datasets.

Climate change stands as the new challenge for whale watching and North Pacific gray whales (Eschrichtius robustus) in Bahia Magdalena, Mexico, after their recovery from overexploitation

IntroductionSocial-ecological systems (SES) recognize the intricate relationship between human activities and the environment and advocate for comprehensive approaches to address complex environmental issues. This study investigates the factors influencing whale watching, particularly the gray whale (Eschrichtius robustus) in northern Mexico, after significant recovery following overexploitation. Despite reaching a peak in 2015, the eastern North Pacific gray whales experienced unusual mortality events (UME), the most recent from 2019 to 2023, leading to a population decline and historically low calf production in 2022. This decline is evident in the reduced presence of whales and calves in winter breeding lagoons. Concurrently, whale watching has become a significant tourist attraction in these areas.MethodsOur objective was to develop a mental model of the SES of gray whale watching, integrating ecological and socioeconomic data to identify key variables and interactions that support system resilience. From an ecological perspective, we analyzed the long-term temporal trends of eight years of gray whale counts in the southernmost breeding and nursing lagoons within the Bahía Magdalena–Bahía Almejas Complex, Mexico. Additionally, we incorporated the current discussion in the literature about the potential impact of global climate change on gray whale populations. In the socioeconomic subsystem, we used participatory methods, including interviews, surveys, and workshops with government officials, tourism operators, and visitors. We also added on-site assessments of compliance with welfare regulations to understand tourism dynamics. ResultsOur findings identified as main external stressors the changes in ice levels in feeding areas and sea warming in breeding areas, and as internal stressors the compliance with official regulations and the number of vessels observing whales at the same time. The key socioeconomic factor of the system was tourist satisfaction, influenced by factors such as the quality of the information provided by operators. DiscussionUltimately, our mental model provides a framework for further exploration of relevant interactions and trajectories, offering insights for developing effective management strategies.

The molecular structure of an axle-less F1-ATPase

F1Fo ATP synthase is a molecular rotary motor that can generate ATP using a transmembrane proton motive force. Isolated F1-ATPase catalytic cores can hydrolyse ATP, passing through a series of conformational states involving rotation of the central γ rotor subunit and the opening and closing of the catalytic β subunits. Cooperativity in F1-ATPase has long thought to be conferred through the γ subunit, with three key interaction sites between the γ and β subunits being identified. Single molecule studies have demonstrated that the F1 complexes lacking the γ axle still “rotate” and hydrolyse ATP, but with less efficiency. We solved the cryogenic electron microscopy structure of an axle-less Bacillus sp. PS3 F1-ATPase. The unexpected binding-dwell conformation of the structure in combination with the observed lack of interactions between the axle-less γ and the open β subunit suggests that the complete γ subunit is important for coordinating efficient ATP binding of F1-ATPase.

Cofactor binding triggers rapid conformational remodelling of the active site of a methyltransferase ribozyme

The methyltransferase ribozyme SMRZ-1 utilizes S-adenosyl-methionine (SAM) and Cu (II) ions to methylate RNA. Comparison of the SAM bound and unbound RNA structures has shown a conformational change in the RNA. However, the contribution of specific interactions and the role of a pseudo-triplex motif in the catalytic centre on the methylation reaction is not completely understood. In this study, we have used atomic substitutions and mutational analysis to investigate the reaction specificity and the key interactions required for catalysis. Substitution of the fluorescent nucleotide 2-aminopurine within the active ribozyme enabled the conformational dynamics of the RNA upon co-factor binding to be explored using fluorescence spectroscopy. We show that fast co-factor binding (t1/2 ∼ 0.7 seconds) drives a conformational change in the RNA to facilitate methyl group transfer. The importance of stacking interactions at the pseudo-triplex motif and chelation of the Cu (II) ion were shown to be essential for SAM binding.

Role of Apolipoprotein E in Alzheimer's Disease Pathogenesis, Prognosis and Treatment.

Alzheimer's disease (AD) is an incurable and progressive neurodegenerative disease with increasing prevalence worldwide. Previous trials of anti-amyloid and anti-tau immunotherapy indicate that additional research needs to be conducted on other mechanisms to find curative or disease-modifying therapy. This review focuses on apolipoprotein E (ApoE), a critical protein in brain lipid metabolism that acts specifically in the clearance and transport of lipids and cholesterol. The ApoE4 allele confers substantial gene dose-dependent risk of developing AD and lowers the age of onset of AD, although the mechanisms of influence remain incompletely understood. The other isoforms bring different levels of AD risk. ApoE2 is protective while ApoE3 is the most common isoform and is considered neutral. An overview is presented of the latest information on the role of ApoE in AD pathogenesis with an emphasis on pathways that are involved in AD development and interactions with crucial processes in different cell types in the brain. Elucidating the key interactions of ApoE with multiple aspects of brain function can be useful for designing novel ApoE-targeted therapeutic approaches.

Enhancing Quality of Life in Multiple Sclerosis Patients Through Coadministration of FDA-Approved Immunomodulators and Melatonin

Objective: The primary goal of this study was to assess the binding characteristics and affinities of melatonin and certain U.S. Food and Drug Administration (FDA)-approved lipophilic drugs used in the treatment of multiple sclerosis (MS) with the brain and muscle Arnt-like 1 (BMAL1) clock protein. Additionally, the study aimed to investigate their potential as modulators of BMAL1 activity for therapeutic applications in MS.Methods: Molecular docking and simulation studies were conducted to investigate the interactions between the BMAL1 protein and the selected agents as ligands. Docking simulations were performed using AutoDock Vina, and key interactions were analyzed with Biovia Discovery Studio Visualizer. A molecular dynamics simulation was conducted using GROMACS 2020.4 and the binding free energies were calculated using the molecular mechanics/Poisson-Boltzmann surface area (MM/PBSA) method. The pharmacokinetic properties of the ligands were predicted using the BOILED-Egg model implemented in the SwissADME web tool.Results: Melatonin demonstrated the highest binding affinity (-8.5 kcal/mol) and most favorable binding free energy (-245.26±1.27 kJ/mol) with BMAL1, suggesting its potential synergistic activity with other FDA-approved drugs in MS therapy. The pharmacokinetic analysis indicated that all three ligands were likely to cross the blood-brain barrier and exhibit high gastrointestinal absorption, with teriflunomide and melatonin potentially achieving a better profile.Conclusion: This study highlights the potential of concomitant use of melatonin to modulate BMAL1 activity, providing a foundation for developing targeted chronotherapeutic strategies in MS treatment.

Deciphering the PI3K-Akt pathway in lung cancer

This computational study explores the immune landscape of early-stage lung adenocarcinoma through single-cell RNA sequencing data analysis. By leveraging paired single-cell data, key insights were gained into the gene expression profiles of tumor samples, focusing on potential therapeutic targets and pathway interactions involved in cancer progression.Single-cell RNA sequencing data from non-lymphocytic immune cells isolated from stage IA lung adenocarcinoma and adjacent normal tissue were computationally processed. Data quality was assessed using sequence quality control tools, and reads were aligned to the hg38 reference genome. Differential gene expression analysis was conducted to identify upregulated and downregulated features. Reactome pathway analysis complemented with literature review was employed to scrutinize potential relationships and pathway interactions. Correlation analysis highlighted key genes involved in tumor progression, including Thrombospondin, HTR6 (5-hydroxytryptamine receptor 6), and LCK.Distinct gene expression patterns were identified in tumor samples, with strong positive correlations between Thrombospondin, HTR6, and LCK. These genes were found to interact within the PI3K-Akt pathway, suggesting their involvement in tumor proliferation and survival. Pathway analysis further revealed molecular crosstalk between these genes and other metabolic processes, offering potential avenues for therapeutic intervention.This computational analysis of single-cell RNA sequencing data identified key gene expression signatures and pathway interactions in early-stage lung adenocarcinoma. The findings, particularly the roles of Thrombospondin, HTR6, and LCK in the PI3K-Akt pathway, provide new insights into potential therapeutic targets for lung cancer treatment.

Molecular Interactions Governing the Rat Aryl Hydrocarbon Receptor Activities of Polycyclic Aromatic Compounds and Predictive Model Development.

Polycyclic aromatic compounds (PACs) exhibit rat aryl hydrocarbon receptor (rAhR) activities, leading to diverse biological or toxic effects. In this study, the key amino residues and molecular interactions that govern the rAhR activity of PACs were investigated using in silico strategies. The homology model of rAhR was first docked with 90 PACs to yield complexes, and the results of the molecular dynamics simulations of 16 typical complexes showed that the binding energies of the complexes range from -7.37 to -26.39 kcal/mol. The major contribution to the molecular interaction comes from van der Waals forces, and Pro295 and Arg316 become the key residues involved in most complexes. Two QSAR models were further developed to predict the rAhR activity of PACs (in terms of log IEQ for PACs without halogen substitutions and log%-TCDD-max for halogenated PACs). Both models have good predictive ability, robustness, and extrapolation ability. Molecular polarizability, electronegativity, size, and nucleophilicity are identified as the important factors affecting the rAhR activity of PACs. The developed models could be employed to predict the rAhR activity of other reactive PACs. This work provides insight into the mechanisms and interactions of the rAhR activity of PACs and assists in the assessment of their fate and risk in organisms.

Recursive dynamics of GspE through machine learning enabled identification of inhibitors

Type II secretion System has been increasingly recognized as a key driver of virulence in many pathogenic bacteria including Achromobacter xylosoxidans. ATPase GspE is the powerhouse of the T2SS. It powers the entire secretion process by binding with ATP and hydrolyzing it. Therefore, targeting it was thought to have a profound effect on the normal functioning of the whole T2SS. A. xylosoxidans is a Gram-negative bacterium that poses a rising concern to immunocompromised people. It is responsible for many opportunistic infections mostly in people with cystic fibrosis. Due to its intrinsic and acquired resistance mechanisms, it is challenging to treat. In this current study, an extensive machine learning-enabled computational investigation was carried out. Drug libraries were screened using machine learning random forest algorithm trained on non-redundant dataset of 8722 antibacterial compounds with reported IC50 values. Active compounds were then further subjected to molecular docking. To unravel the dynamics and better understand the stability of complexes, the top complexes were subjected to MD Simulations followed by various post-simulation analyses including Trajectory analysis, Atom Contacts, SASA, Hydrogen Bond, RDF, binding free energy calculations, PCA, and AFD analysis. Findings from the study unanimously unveiled Asinex-BAS00263070–28551 as the best inhibitor as it instigated the recursive dynamics of the target by making key hydrogen bond interactions with Walker A motif, suggesting it could serve as the promising drug candidate against GspE. Further experimental in-vivo and in-vitro validation is still required to authenticate the therapeutic effects of these drugs.

Structural insights into the human P2X1 receptor and ligand interactions.

The P2X1 receptor is a trimeric ligand-gated ion channel that plays an important role in urogenital and immune functions, offering the potential for new drug treatments. However, progress in this area has been hindered by limited structural information and a lack of well-characterised tool compounds. In this study, we employ cryogenic electron microscopy (cryo-EM) to elucidate the structures of the P2X1 receptor in an ATP-bound desensitised state and an NF449-bound closed state. NF449, a potent P2X1 receptor antagonist, engages the receptor distinctively, while ATP, the endogenous ligand, binds in a manner consistent with other P2X receptors. To explore the molecular basis of receptor inhibition, activation, and ligand interactions, key residues involved in ligand and metal ion binding were mutated. Radioligand binding assays with [3H]-α,β-methylene ATP and intracellular calcium ion influx assays were used to evaluate the effects of these mutations. These experiments validate key ligand-receptor interactions and identify conserved and non-conserved residues critical for ligand binding or receptor modulation. This research expands our understanding of the P2X1 receptor structure at a molecular level and opens new avenues for in silico drug design targeting the P2X1 receptor.

Mechanism of non-competitive inhibition of the SARS-CoV-2 3CL protease dimerization: Therapeutic and clinical promise of the lichen secondary metabolite perlatolinic acid

In response to the COVID-19 pandemic, identifying effective treatments against SARS-CoV-2 has become of utmost importance. This study elucidates the mechanism by which perlatolinic acid, a lichen-derived secondary metabolite, non-competitively inhibits the dimerization of the SARS-CoV-2 3CL protease, a pivotal enzyme in the virus lifecycle. Utilising a combination of kinetic parameter determination, inhibition assays, and molecular docking studies, we demonstrate that perlatolinic acid effectively disrupts the enzymatic function by binding at the dimer interface with a measured Ki value of 0.67 μM, thereby impeding the protease catalytic activity essential for viral replication. Molecular docking studies further corroborate the binding specificity of perlatolinic acid to the dimer interface, which is attributed to the loss of key interactions essential for dimerization, consequently impairing catalytic activity, highlighting its potential as a scaffold for developing broad-spectrum antiviral drugs.Despite a dose-dependent cytotoxicity of perlatolinic acid, its TC50 is approximately 43 times higher than the Ki value.Our findings suggest that perlatolinic acid holds significant promise as a lead compound for the development of new therapeutics against COVID-19, warranting further investigation and clinical evaluation.In conclusion, the study sheds light on the therapeutic potential of natural compounds in combating SARS-CoV-2, paving the way for the exploration of lichen secondary metabolites as a reservoir of potential antiviral agents.

Cryo-EM structure of small-molecule agonist bound delta opioid receptor-Gi complex enables discovery of biased compound

Delta opioid receptor (δOR) plays a pivotal role in modulating human sensation and emotion. It is an attractive target for drug discovery since, unlike Mu opioid receptor, it is associated with low risk of drug dependence. Despite its potential applications, the pharmacological properties of δOR, including the mechanisms of activation by small-molecule agonists and the complex signaling pathways it engages, as well as their relation to the potential side effects, remain poorly understood. In this study, we use cryo-electron microscopy (cryo-EM) to determine the structure of the δOR-Gi complex when bound to a small-molecule agonist (ADL5859). Moreover, we design a series of probes to examine the key receptor-ligand interaction site and identify a region involved in signaling bias. Using ADL06 as a chemical tool, we elucidate the relationship between the β-arrestin pathway of the δOR and its biological functions, such as analgesic tolerance and convulsion activities. Notably, we discover that the β-arrestin recruitment of δOR might be linked to reduced gastrointestinal motility. These insights enhance our understanding of δOR’s structure, signaling pathways, and biological functions, paving the way for the structure-based drug discovery.

Microbial inoculation accelerates rice straw decomposition by reshaping structure and function of lignocellulose-degrading microbial consortia in paddy fields

Inoculating lignocellulose-degrading microorganisms can accelerate straw decomposition in paddy field; however, the relationship between indigenous and inoculated microorganisms remains unclear. This study explored the effects of microbial inoculation on straw decomposition, microbial community, lignocellulose-degrading consortia, and associated functional genes. After inoculation, straw degradation rate increased by up to 4.9 %, and the rice yield increased by 790 kg/ha. Microbial inoculation restructured soil microbial community, influencing key taxa and interactions within the microbial network. A lignocellulose-degrading consortia consisting 37 genera was established, with a notable increase in the relative abundance of lignocellulose-degrading bacteria following inoculation. Among them, Pseudarthrobacter, with high lignin-degrading enzyme activity, emerged as a key genus after inoculation. Additionally, the abundance of lignin-degrading enzyme genes also increased significantly after inoculation. These findings offer new insights into how microbial inoculation accelerates the in situ decomposition of rice straw by reshaping the structure and function of lignocellulose-degrading consortia within the soil ecosystem.

Structural basis for the access and binding of resolvin D1 (RvD1) to formyl peptide receptor 2 (FPR2/ALX), a class A GPCR.

Inflammation is essential to the body's defense against tissue injury and microbial invasion. However, uncontrolled inflammation is highly detrimental and can result in chronic inflammatory diseases such as asthma, cancer, obesity, and diabetes. An increasing body of evidence suggests that specialized pro-resolving lipid mediators (SPMs), such as resolvins, are actively involved in critical cellular events that drive the resolution of inflammation and a return to homeostasis. An imbalance caused by insufficient SPMs can result in the unsuccessful resolution of inflammation. The D-series resolvins (metabolites of docosahexaenoic acid), such as resolvin D1 (RvD1) and resolvin D2 (RvD2), carry out their pro-resolving functions by directly binding to class A G protein-coupled receptors FPR2/ALXR and GPR32, and GPR18, respectively. We recently demonstrated that RvD1 and RvD2 preferentially partition and accumulate at the polar headgroup regions of the membrane. However, the mechanistic detail of how RvD1 gains access to the FPR2 binding site from a surrounding membrane environment remains unknown. In this study, we used classical MD and well-tempered metadynamics simulations to examine the structural basis for the access and binding of RvD1 to its target receptor from aqueous and membrane environments. The results offer valuable insights into the access path, potential binding pose, and key residue interactions essential for the access and binding of RvD1 to FPR2/ALXR and may help in identifying small molecule therapeutics as a possible treatment for inflammatory disorders.

Identification of miR-20a as a Diagnostic and Prognostic Biomarker in Colorectal Cancer: MicroRNA Sequencing and Machine Learning Analysis.

The differential expression of miRNAs, a key regulator in many cell signaling pathways, has been studied in various malignancies and may have an important role in cancer progression, including colorectal cancer (CRC). The present study used machine learning and gene interaction study tools to explore the prognostic and diagnostic value of miRNAs in CRC. Integrative analysis of 353 CRC samples and normal tissue data was obtained from the TCGA database and further analyzed by R packages to define the deferentially expressed miRNAs (DEMs). Furthermore, machine learning and Kaplan Meier survival analysis helped better specify the significant prognostic value of miRNAs. A combination of online databases was then used to evaluate the interactions between target genes, their molecular pathways, and the correlation between the DEMs. The results indicated that miR-19b and miR-20a have a significant prognostic role and are associated with CRC progression. The ROC curve analysis discovered that miR-20a alone and combined with other miRNAs, including hsa-mir-21 and hsa-mir-542, are diagnostic biomarkers in CRC. In addition, 12 genes, including NTRK2, CDC42, EGFR, AGO2, PRKCA, HSP90AA1, TLR4, IGF1, ESR1, SMAD2, SMAD4, and NEDD4L, were found to be the highest score targets for these miRNAs. Pathway analysis identified the two correlated tyrosine kinase and MAPK signaling pathways with the key interaction genes, i.e., EGFR, CDC42, and HSP90AA1. To better define the role of these miRNAs, the ceRNA network, including lncRNAs, was also prepared. In conclusion, the combination of R data analysis and machine learning provides a robust approach to resolving complicated interactions between miRNAs and their targets.