Pharmacologic mechanisms mining and prediction of Xiaoer Qixing Cha Formulae in the treatment of infantile functional dyspepsia based on chemical analysis by UPLC-QTOF/MS and interactive network pharmacology

Objective: To explore the main chemical compounds in Xiaoer Qixing Cha Formulae (XQCF), and investigate its mechanisms for the treatment of infantile functional dyspepsia (IFD). Methods: The chemical components were identified by UPLC-QTOF/MS analytic technique. Targets of the compounds were screened from TCMSP and SWISS database, and disease targets were screened from OMIM and TTD online database. Candidate targets of compounds were mapped to the disease targets as predict therapeutic targets for XQCF. Several networks were constructed and analyzed by Cytoscape ver. 3.2.1. Meanwhile, prescription compatibility in XQCF was interpreted from the network perspective based on distribution of the number of targets. Furthermore, Gene Ontology (GO) enrichment analysis and KEGG pathway analysis were operated via Clue Go to illustrate complex relationships between the potential targets and pharmacological mechanisms. Results: A total of fifty-three compounds were recognized or tentatively characterized belonging to XQCF based on MS data and online chemical database. Sixty-three therapeutic targets were screened. AKT1, FOS, SLC6A4, COMT and 5-HT receptors were focused as therapeutic targets of XQCF. Pathways including carbohydrate digestion and absorption, serotonergic synapse, calcium signaling pathway and cAMP signaling pathway were predicted as significant regulatory pathways. The results indicated that the predicted targets and pathways related in brain-gut axis to a great extent, which could be potential pharmacological mechanism of XQCF for the treatment of IFD. Conclusions: The findings in this study provided the experimental and theoretical basis for further research for XQCF. Those also illustrated a reasonable method worth intensive study on pharmacodynamic mechanisms of TCM Formulae.

Multimodal Interactive Network for Sequential Recommendation

Building an effective sequential recommendation system is still a challenging task due to limited interactions among users and items.Recent work has shown the effectiveness of incorporating textual or visual information into sequential recommendation to alleviate the data sparse problem.The data sparse problem now is attracting a lot of attention in both industry and academic community.However,considering interactions among modalities on a sequential scenario is an interesting yet challenging task because of multimodal heterogeneity.In this paper,we introduce a novel recommendation approach of considering both textual and visual information,namely Multimodal Interactive Network(MIN).The advantage of MIN lies in designing a learning framework to leverage the interactions among modalities from both the item level and the sequence level for building an efficient system.Firstly,an item-wise interactive layer based on the encoder-decoder mechanism is utilized to model the item-level interactions among modalities to select the informative information.Secondly,a sequence interactive layer based on the attention strategy is designed to capture the sequence-level preference of each modality.MIN seamlessly incorporates interactions among modalities from both the item level and the sequence level for sequential recommendation.It is the first time that interactions in each modality have been explicitly discussed and utilized in sequential recommenders.Experimental results on four real-world datasets show that our approach can significantly outperform all the baselines in sequential recommendation task.

Ecological network analysis reveals complex responses of tree species life stage interactions to stand variables

Tree interactions are essential for the structure,dynamics,and function of forest ecosystems,but variations in the architecture of life-stage interaction networks(LSINs)across forests is unclear.Here,we constructed 16 LSINs in the mountainous forests of northwest Hebei,China based on crown overlap from four mixed forests with two dominant tree species.Our results show that LSINs decrease the complexity of stand densities and basal areas due to the interaction cluster differentiation.In addition,we found that mature trees and saplings play different roles,the first acting as“hub”life stages with high connectivity and the second,as“bridges”controlling information flow with high centrality.Across the forests,life stages with higher importance showed better parameter stability within LSINs.These results reveal that the structure of tree interactions among life stages is highly related to stand variables.Our efforts contribute to the understanding of LSIN complexity and provide a basis for further research on tree interactions in complex forest communities.

Effect of Apis mellifera on community composition of local pollinator bees and their pollination network in Qinling Mountains and surrounding areas

The Qinling Mountains, known for their rich vegetation and diverse pollinating insects, have seen a significant decline in bee species richness and abundance over recent decades, largely due to the introduction and spread of Apis mellifera. This decline has caused cascading effects on the region's community structure and ecosystem stability. To improve the protection of native bees in the natural and agricultural landscape of the Qinling Mountains and its surrounding areas, we investigated 33 sampling sites within three habitats: forest, forest-agriculture ecotones, and farmland. Using a generalized linear mixing model, t-test, and other data analysis methods, we explored the impact of Apis mellifera on local pollinator bee richness, abundance, and the pollination network in different habitats in these regional areas. The results show that(1)Apis mellifera significantly negatively affects the abundance and richness of wild pollinator bees,while Apis cerana abundance is also affected by beekeeping conditions.(2)There are significant negative effects of Apis mellifera on the community structure of pollinator bees in the Qinling Mountains and its surrounding areas: the Shannon-Wiener diversity index, Pielou evenness index, and Margalef richness index of bee communities at sites with Apis mellifera influence were significantly lower than those at sites without Apis mellifera influence.(3)The underlying driver of this effect is the monopolization of flowering resources by Apis mellifera. This species tends to visit flowering plants with large nectar sources, which constitute a significant portion of the local plant community. By maintaining a dominant role in the bee-plant pollination network, Apis mellifera competitively displaces native pollinator bees, reducing their access to floral resources. This ultimately leads to a reduction in local bee-plant interactions, decreasing the complexity and stability of the pollination network. These findings highlight the need for targeted conservation efforts to protect native pollinator species and maintain the ecological balance in the Qinling Mountains.

Research on Interactive Learning and Teaching based on Computer Network

This paper analyzes the basic interactivity connotation of network teaching from the interactive definition, function, types and level etc., and established the interaction quality evaluation index system from the four angles between student and media, the course content, teacher and peer interaction. Similarly, network instruction interaction also needs to carry on the design from the perspective of instructional design, and take full advantage of the Internet, and finally achieved good network interaction effect. Practice shows that, research on network interactive strategy significance.

Construction of gene/protein interaction networks and enrichment pathway analysis for paroxysmal nocturnal hemoglobinuria and aplastic anemia

Background:To develop a protein-protein interaction network of Paroxysmal nocturnal hemoglobinuria(PNH)and Aplastic anemia(AA)based on genetic genes and to predict pathways underlying the molecular complexes in the network.Methods:In this research,the PNH and AA-related genes were screened through Online Mendelian Inheritance in Man(OMIM).The plugins and Cytoscape were used to search literature and build a protein-protein interaction network.Results:The protein-protein interaction network contains two molecular complexes that are five higher than the correlation integral values.The target genes of this study were obtained:CD59,STAT3,TERC,TNF,AKT1,C5AR1,EPO,IL6,IL10 and so on.We also found that many factors regulate biological behaviors:neutrophils,macrophages,vascular endothelial growth factor,immunoglobulin,interleukin,cytokine receptor,interleukin-6 receptor,tumor necrosis factor,and so on.This research provides a bioinformatics foundation for further explaining the mechanism of common development of both.Conclusion:This indicates that the PNH and AA is a complex process regulated by many cellular pathways and multiple genes.

RNA sequencing of exosomes secreted by fibroblast and Schwann cells elucidates mechanisms underlying peripheral nerve regeneration

Exosomes exhibit complex biological functions and mediate a variety of biological processes,such as promoting axonal regeneration and functional recove ry after injury.Long non-coding RNAs(IncRNAs)have been reported to play a crucial role in axonal regeneration.Howeve r,the role of the IncRNA-microRNAmessenger RNA(mRNA)-competitive endogenous RNA(ceRNA)network in exosome-mediated axonal regeneration remains unclear.In this study,we performed RNA transcriptome sequencing analysis to assess mRNA expression patterns in exosomes produced by cultured fibroblasts(FC-EXOs)and Schwann cells(SCEXOs).Diffe rential gene expression analysis,Gene Ontology analysis,Kyoto Encyclopedia of Genes and Genomes analysis,and protein-protein intera ction network analysis were used to explo re the functions and related pathways of RNAs isolated from FC-EXOs and SC-EXOs.We found that the ribosome-related central gene Rps5 was enriched in FC-EXOs and SC-EXOs,which suggests that it may promote axonal regeneration.In addition,using the miRWalk and Starbase prediction databases,we constructed a regulatory network of ceRNAs targeting Rps5,including 27 microRNAs and five IncRNAs.The ceRNA regulatory network,which included Ftx and Miat,revealed that exsosome-derived Rps5 inhibits scar formation and promotes axonal regeneration and functional recovery after nerve injury.Our findings suggest that exosomes derived from fibro blast and Schwann cells could be used to treat injuries of peripheral nervous system.

Investigating the Impact of Ocean Acidification on Anti-Stress Mechanisms in Sepia esculenta Larvae Based on Transcriptome Profiling

With the rapid development of oil,energy,power and other industries,CO_(2) emissions rise sharply,which will cause a large amount of CO_(2) in the air be absorbed by the ocean and lead to ocean acidification.The growth and development of organisms can be seriously affected by acidified seawater.Sepia esculenta is a mollusk with high nutritional and economic value and is widely cultured in offshore waters of China.Larvae are the early life forms of the organism and are more vulnerable to changes in the external environment.Too low pH will lead to some adverse reactions in larvae,which will affect metabolism,immune response and other life activities.In this study,we sequenced the transcriptome of S.esculenta subjected to acidified seawater stress and identified 1072differentially expressed genes(DEGs).The detected atypical expression of DEGs substantiates cellular malformation and translocation in S.esculenta under low pH stimulation.Simultaneously,this also substantiates the notable impact of ocean acidification on mollusks.These DEGs were used for functional enrichment analysis of GO and KEGG,and the top twenty items of the biological process classification in GO terms and 11 KEGG signaling pathways were significantly enriched.Finally,the constructed proteinprotein interaction network(PPI)was used to analyze protein-protein interactions,and 12 key DEGs and 3 hub genes were identified.The reliability of 12 genes was verified by quantitative RT-PCR.A comprehensive analysis of the KEGG signaling pathway and PPI revealed that ocean acidification leads to abnormalities in lipid metabolism in S.esculenta larvae,which can lead to cancer development and metastasis,accompanied by some degree of inflammation.The results of the study will help to further investigate the physiological processes of S.esculenta when stimulated by ocean acidification,and provide a reference to cope with the captive breeding of S.esculenta affected by acidification.

Essential proteins identification method based on four-order distances and subcellular localization information

Essential proteins are inseparable in cell growth and survival. The study of essential proteins is important for understanding cellular functions and biological mechanisms. Therefore, various computable methods have been proposed to identify essential proteins. Unfortunately, most methods based on network topology only consider the interactions between a protein and its neighboring proteins, and not the interactions with its higher-order distance proteins. In this paper, we propose the DSEP algorithm in which we integrated network topology properties and subcellular localization information in protein–protein interaction(PPI) networks based on four-order distances, and then used random walks to identify the essential proteins. We also propose a method to calculate the finite-order distance of the network, which can greatly reduce the time complexity of our algorithm. We conducted a comprehensive comparison of the DSEP algorithm with 11 existing classical algorithms to identify essential proteins with multiple evaluation methods. The results show that DSEP is superior to these 11 methods.

MINE:A Method of Multi-Interaction Heterogeneous Information Network Embedding

Interactivity is the most significant feature of network data,especially in social networks.Existing network embedding methods have achieved remarkable results in learning network structure and node attributes,but do not pay attention to the multi-interaction between nodes,which limits the extraction and mining of potential deep interactions between nodes.To tackle the problem,we propose a method called Multi-Interaction heterogeneous information Network Embedding(MINE).Firstly,we introduced the multi-interactions heterogeneous information network and extracted complex heterogeneous relation sequences by the multi-interaction extraction algorithm.Secondly,we use a well-designed multi-relationship network fusion model based on the attention mechanism to fuse multiple interactional relationships.Finally,applying a multitasking model makes the learned vector contain richer semantic relationships.A large number of practical experiments prove that our proposed method outperforms existing methods on multiple data sets.

Protein interaction network related to Helicobacter pylori infection response

AIM:To understand the complex reaction of gastric inflammation induced by Helicobacter pylori(H pylori) in a systematic manner using a protein interaction network. METHODS:The expression of genes significantly changed on microarray during H pylori infection was scanned from the web literary database and translated into proteins.A network of protein interactions was constructed by searching the primary interactions of selected proteins.The constructed network was mathematically analyzed and its biological function was examined.In addition,the nodes on the network were checked to determine if they had any further functional importance or relation to other proteins by extending them. RESULTS:The scale-free network showing the relationship between inflammation and carcinogenesis was constructed.Mathematical analysis showed hub and bottleneck proteins,and these proteins were mostly related to immune response.The network contained pathways and proteins related to H pylori infection,such as the JAK-STAT pathway triggered by interleukins.Activation of nuclear factor (NF)-κB,TLR4,and other proteins known to function as core proteins of immune response were also found. These immune-related proteins interacted on the network with pathways and proteins related to the cell cycle,cell maintenance and proliferation,andtranscription regulators such as BRCA1,FOS,REL,and zinc finger proteins.The extension of nodes showed interactions of the immune proteins with cancer- related proteins.One extended network,the core network,a summarized form of the extended network, and cell pathway model were constructed. CONCLUSION:Immune-related proteins activated by H pylori infection interact with proto-oncogene proteins.The hub and bottleneck proteins are potential drug targets for gastric inflammation and cancer.

Spatial Interaction Network Analysis of Crude Oil Trade Relations between Countries along the Belt and Road

Based on the theories and methods of complex network,crude oil trade flows between countries along the Belt and Road(B&R,hereafter)are inserted into the Geo-space of B&R and form a spatial interaction network which takes the countries as nodes and takes the trade relations as edges.The networked mining and evolution analysis can provide important references for the research on trade relations among the B&R countries and the formulation of trade policy.This paper researches and discusses the construction,statistical analysis,top networks and stability of the crude oil trade network between the B&R countries from 2001 to 2020 from the perspectives of Geo-Computation for Social Sciences(GCSS)and spatial interaction.Firstly,evolutions of out-degree,in-degree,out-strength and in-strength of the top 10 countries in the crude oil trade network are computed and analyzed.Secondly,the top network method is used to explore the evolution characteristics of hierarchical structures.And finally,the sequential evolution characteristics of the crude oil trade network stability are analyzed utilizing the network stability measure method based on the trade relationship autocorrelation function.The analysis results show that Russia has the largest out-degree and out-strength,and China has the largest in-degree and in-strength.The crude oil trade volume of the top 10 import and export networks between 2001—2020 accounts for over 90%of the total trade volume of the crude oil trade network,and the proportion remains relatively stable.However,the stability of the network showed strong fluctuations in 2009,2012 and 2014,which may be closely related to major international events in these years,which could furtherly be used to build a correlation model between network volatility and major events.This paper explores how to construct and analyze the spatial interaction network of crude oil trade and can provide references for trade relations research and trade policy formulation of B&R countries.

Transcriptome Profile Based on Protein-Protein Interaction Networks Provides a Set of Core Genes for Understanding the Metabolic Mechanisms of the Egg-Protecting Behavior in Amphioctopus fangsiao

Marine organisms cannot grow and reproduce without proper metabolic regulation.Within a metabolic network,problems with a given link will affect the normal life activities of the organism.Many metabolic mechanisms associated with behaviors of Am-phioctopus fangsiao are still unclear.Moreover,as a factor affecting the normal growth of A.fangsiao,egg protection has rarely been considered in previous behavioral studies.In this research,we analyzed the transcriptome profile of gene expression in A.fangsiao egg-unprotected larvae and egg-protected larvae,and identified 818 differentially expressed genes(DEGs).We used GO and KEGG enrichment analyses to search for metabolism-related DEGs.Protein-protein interaction networks were constructed to examine the interactions between metabolism-related genes.Twenty hub genes with multiple protein-protein interaction relationships or that were involved in multiple KEGG signaling pathways were obtained and verified by quantitative RT-PCR.We first studied the effects of egg protection on the metabolism of A.fangsiao larvae by means of protein-protein interaction networks,and the results provide va-luable gene resources for understanding the metabolism of invertebrate larvae.The data serve as a foundation for further research on the egg-protecting behavior of invertebrates.

Yeast protein-protein interaction network model based on biological experimental data

Duplication and divergence have been widely recognized as the two domi- nant evolutionary forces in shaping biological networks, e.g., gene regulatory networks and protein-protein interaction （PPI） networks. It has been shown that the network growth models constructed on the principle of duplication and divergence can recapture the topo- logical properties of real PPI networks. However, such network models only consider the evolution processes. How to select the model parameters with the real biological experi- mental data has not been presented. Therefore, based on the real PPI network statistical data, a yeast PPI network model is constructed. The simulation results indicate that the topological characteristics of the constructed network model are well consistent with those of real PPI networks, especially on sparseness, scale-free, small-world, hierarchical modularity, and disassortativity.

Analysis of mechanism on Indigo Naturalis in treating chronic myelocytic leukemia based on two-dimentional model of protein-protein interaction network-moleculardocking technique

To explore the molecular mechanism of Ind-igo Naturalis in intervening chronic myelocytic leukemia （CML） under the guidance of protein-protein interaction network, the molecular docking technique and in vitro cell experiment were chosen. CML-related genes were obtained from the online mendelian inheritance in man database （OMIM）, then String 10. 0 was used for text mining and constructing the CML protein-protein interaction network. The interaction data were input in Cytoscape 3. 4. 0 software. Plug-in CentiScaPe 2. 1 was used for implement topology analysis. Small active substances of Indigo Naturalis were obtained from a third-party database, which were optimized by Chemoffice 8. 0 and Sybyl 8. 1, then small molecular ligand library was obtained. The molecular docking was carried out by Surflex-Dock module, the key target was received after scoring. Protein-protein interaction network of CML was constructed, which was consisted of 425 nodes （ proteins） and 2 799 sides （ interactions）. The key gene J.AK2 was got. CML is a polygenic disease and JAK2 is likely to be a key node.

Seasonal variation and host sex affect bat–bat fly interaction networks in the Amazonian savannahs

Bats are the second-most diverse group of mammals in the world,and bat flies are their main parasites.However,significant knowledge gaps remain regarding these antagonistic interactions,especially since diverse factors such as seasonality and host sex can affect their network structures.Here,we explore the influence of such factors by comparing species richness and composition of bat flies on host bats,as well as specialization and modularity of bat–bat fly interaction networks between seasons and adult host sexes.We captured bats and collected their ectoparasitic flies at 10 sampling sites in the savannahs of AmapáState,northeastern region of the Brazilian Amazon.Despite female bats being more parasitized and recording greater bat fly species richness in the wet season,neither relationship was statistically significant.The pooled network could be divided into 15 compartments with 54 links,and all subnetworks comprised>12 compartments.The total number of links ranged from 27 to 48(for the dry and wet seasons,respectively),and female and male subnetworks had 44 and 41 links,respectively.Connectance values were very low for the pooled network and for all subnetworks.Our results revealed higher bat fly species richness and abundance in the wet season,whereas specialization and modularity were higher in the dry season.Moreover,the subnetwork for female bats displayed higher specialization and modularity than the male subnetwork.Therefore,both seasonality and host sex contribute in different ways to bat–bat fly network structure.Future studies should consider these factors when evaluating bat–bat fly interaction networks.

Identification of functional genes regulating gastric cancer progression using integrated bioinformatics analysis

BACKGROUND Gastric cancer(GC)is one of the most common cancers and has a poor prognosis.Treatment of GC has remained unchanged over the past few years.AIM To investigate the potential therapeutic targets and related regulatory biomarkers of GC.METHODS We obtained the public GC transcriptome sequencing dataset from the Gene Expression Omnibus database.The datasets contained 348 GC tissues and 141 healthy tissues.In total,251 differentially expressed genes(DEGs)were identified,including 187 down-regulated genes and 64 up-regulated genes.The DEGs’enriched functions and pathways include Progesterone-mediated oocyte maturation,cell cycle,and oocyte meiosis,Hepatitis B,and the Hippo signaling pathway.Survival analysis showed that BUB1,MAD2L1,CCNA2,CCNB1,and BIRC5 may be associated with regulation of the cell cycle phase mitotic spindle checkpoint pathway.We selected 26 regulated genes with the aid of the protein-protein interaction network analyzed by Molecular Complex Detection.RESULTS We focused on three critical genes,which were highly expressed in GC,but negatively related to patient survival.Furthermore,we found that knockdown of Yu K et al.Biochemical analysis in GC WJCC https://www.wjgnet.com 5024 July 26,2023 Volume 11 Issue 21 BIRC5,TRIP13 or UBE2C significantly inhibited cell proliferation and induced cell apoptosis.In addition,knockdown of BIRC5,TRIP13 or UBE2C increased cellular sensitivity to cisplatin.CONCLUSION Our study identified significantly upregulated genes in GC with a poor prognosis using integrated bioinformatics methods.

Expression Pattern, Interaction Network, and Functional Analysis of the Arabidopsis Botrytis Susceptible1 Interactor

E3 ubiquitin ligases are participated in numerous processes, regulating the response to biotic and abiotic stresses. Botrytis susceptible1 interactor (BOI) is a RING (Really Interesting New Gene)-type E3 ligase that mediates the ubiquitination of BOS1 (Botrytis susceptible1), a transcription factor involved in stress and pathogen responses. Although BOI is an E3 ligase, there are reports to show that BOI interacts with target proteins such as DELLAs or CONSTANS to repress gibberellin responses and flowering without the degradation of the target proteins. In this article, we utilize diversified methods to comprehensively analyze the expression pattern, interaction network and function of BOI gene. Firstly, 1800 bp upstream region of BOI gene from Arabidopsis thaliana (Arabidopsis) genome was isolated, and fused GUS reporter gene. The resulting expression cassette was introduced into wild-type Arabidopsis through Agrobacterium-mediated transformation. The result demonstrated that BOI gene was expressed predominantly in leaves, siliques, young roots, and flowering tissues, indicating that BOI gene may be involved in multiple processes in plant growth and development in Arabidopsis. Besides, eight candidate interacting proteins were obtained from the Arabidopsis cDNA library via yeast two-hybrid technology, including EXO70E2 (AT5G61010), WRKY7 (AT4G24240), WRKY11 (AT4G31550), WRKY17 (AT2G24570), UBP20 (AT4G17895), L5 (AT1G12290), SAUR9 (AT4G36110) and TCP21 (AT5G08330). Functional analysis of these candidate interacting proteins manifested that they related to multiple pathways, including biological and abiotic stress, programmed cell death, protein degradation, material metabolism and transcriptional regulation. In addition, the results of the transient assay proclaimed that BOI protein affects the protein stability of EXO70E2 and L5 through its E3 ubiquitin ligase activity. Our results provide novel clues for a better understanding of molecular mechanisms underlying BOI-mediated regulations.

Cell cycle and complement inhibitors may be specific for treatment of spinal cord injury in aged and young mice: transcriptomic analyses

Previous studies have reported age-specific pathological and functional outcomes in young and aged patients suffering spinal cord injury,but the mechanisms remain poorly understood. In this study, we examined mice with spinal cord injury. Gene expression profiles from the Gene Expression Omnibus database （accession number GSE93561） were used, including spinal cord samples from 3 young injured mice （2–3-months old, induced by Impactor at Th9 level） and 3 control mice （2–3-months old, no treatment）, as well as 2 aged injured mice （15–18-months old, induced by Impactor at Th9 level） and 2 control mice （15–18-months old, no treatment）. Differentially expressed genes （DEGs） in spinal cord tissue from injured and control mice were identified using the Linear Models for Microarray data method,with a threshold of adjusted P 〈 0.05 and ｜logFC（fold change）｜ 〉 1.5. Protein–protein interaction networks were constructed using data from the STRING database, followed by module analysis by Cytoscape software to screen crucial genes. Kyoto encyclopedia of genes and genomes pathway and Gene Ontology enrichment analyses were performed to investigate the underlying functions of DEGs using Database for Annotation, Visualization and Integrated Discovery. Consequently, 1,604 and 1,153 DEGs were identified between injured and normal control mice in spinal cord tissue of aged and young mice, respectively. Furthermore, a Venn diagram showed that 960 DEGs were shared among aged and young mice, while 644 and 193 DEGs were specific to aged and young mice, respectively. Functional enrichment indicates that shared DEGs are involved in osteoclast differentiation, extracellular matrix–receptor interaction, nuclear factor-kappa B signaling pathway, and focal adhesion. Unique genes for aged and young injured groups were involved in the cell cycle （upregulation of PLK1） and complement （upregulation of C3） activation, respectively. These findings were confirmed by functional analysis of genes in modules （common, 4; aged, 2; young, 1） screened from protein–protein interaction networks. Accordingly, cell cycle and complement inhibitors may be specific treatments for spinal cord injury in aged and young mice, respectively.

Bioinformatics analysis of microarray data to explore the key genes involved in HSF4 mutation-induced cataract

AIM： To reveal the mechanisms of heat-shock transcription factor 4 （HSF4） mutation-induced cataract.METHODS： GSE22362, including 3 HSF4-null lens and 3 wild-type lens, was obtained from Gene Expression Omnibus database. After data preprocessing, the differentially expressed genes （DEGs） were identified using the limma package. Based on Database for Annotation, Visualization and Integrated Discovery （DAVID） tool, functional and pathway enrichment analyses were performed for the DEGs. Followed by protein-protein interaction （PPI） network was constructed using STRING database and Cytoscape software. Furthermore, the validated microRNA （miRNA）-DEG pairs were obtained from miRWalk2.0 database, and then miRNA-DEG regulatory network was visualized by Cytoscape software. RESULTS： A total of 176 DEGs were identified in HSF4-null lens compared with wild-type lens. In the PPI network, FBJ osteosarcoma oncogene （FOS）, early growth response 1 （EGR1） and heme oxygenase （decycling） 1 （HMOX1） had higher degrees and could interact with each other. Besides, mmu-miR-15a-5p and mmu-miR-26a-5p were among the top 10 miRNAs in the miRNA-DEG regulatory network. Additionally, mmu-miR-26a-5p could target EGR1 in the regulatory network. CONCLUSION： FOS, EGR1, HMOX1, mmu-miR-26a-5p and mmu-miR-15a-5p might function in the pathogenesis of HSF4 mutation-induced cataract.