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.

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.

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.

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.

Prediction of the anti-inflammatory mechanisms of curcumin by module-based protein interaction network analysis

Curcumin,the medically active component from Curcuma longa(Turmeric),is widely used to treat inflammatory diseases.Protein interaction network(PIN) analysis was used to predict its mechanisms of molecular action.Targets of curcumin were obtained based on ChE MBL and STITCH databases.Protein–protein interactions(PPIs) were extracted from the String database.The PIN of curcumin was constructed by Cytoscape and the function modules identified by gene ontology(GO) enrichment analysis based on molecular complex detection(MCODE).A PIN of curcumin with 482 nodes and 1688 interactions was constructed,which has scale-free,small world and modular properties.Based on analysis of these function modules,the mechanism of curcumin is proposed.Two modules were found to be intimately associated with inflammation.With function modules analysis,the anti-inflammatory effects of curcumin were related to SMAD,ERG and mediation by the TLR family.TLR9 may be a potential target of curcumin to treat inflammation.

Protein interaction networks:centrality,modularity,dynamics,and applications

In the post-genomic era,proteomics has achieved significant theoretical and practical advances with the development of high-throughput technologies.Especially the rapid accumulation of protein-protein interactions(PPIs)provides a foundation for constructing protein interaction networks(PINs),which can furnish a new perspective for understanding cellular organizations,processes,and functions at network level.In this paper,we present a comprehensive survey on three main characteristics of PINs:centrality,modularity,and dynamics.1)Different centrality measures,which are used to calculate the importance of proteins,are summarized based on the structural characteristics of PINs or on the basis of its integrated biological information;2)Different modularity definitions and various clustering algorithms for predicting protein complexes or identifying functional modules are introduced;3)The dynamics of proteins,PPIs and sub-networks are discussed,respectively.Finally,the main applications of PINs in the complex diseases are reviewed,and the challenges and future research directions are also discussed.

Exploring Mechanisms of Panax notoginseng Saponins in Treating Coronary Heart Disease by Integrating Gene Interaction Network and Functional Enrichment Analysis

Objective： To investigate the mechanisms of Panax notoginseng saponins（PNS） in treating coronary heart disease（CHD） by integrating gene interaction network and functional enrichment analysis. Methods： Text mining was used to get CHD and PNS associated genes. Gene–gene interaction networks of CHD and PNS were built by the Gene MANIA Cytoscape plugin. Advanced Network Merge Cytoscape plugin was used to analyze the two networks. Their functions were analyzed by gene functional enrichment analysis via DAVID Bioinformatics. Joint subnetwork of CHD network and PNS network was identified by network analysis. Results： The 11 genes of the joint subnetwork were the direct targets of PNS in CHD network and enriched in cytokine-cytokine receptor interaction pathway. PNS could affect other 85 genes by the gene–gene interaction of joint subnetwork and these genes were enriched in other 7 pathways. The direct mechanisms of PNS in treating CHD by targeting cytokines to relieve the inflammation and the indirect mechanisms of PNS in treating CHD by affecting other 7 pathways through the interaction of joint subnetwork of PNS and CHD network. The genes in the 7 pathways could be potential targets for the immunologic adjuvant, anticoagulant, hypolipidemic, anti-platelet and anti-hypertrophic activities of PNS. Conclusion： The key mechanisms of PNS in treating CHD could be anticoagulant and hypolipidemic which are indicated by analyzing biological functions of hubs in the merged network.

Proteomic dissection of biological pathways/processes through profiling protein-protein interaction networks

Cellular functions, either under the normal or pathological conditions or under different stresses, are the results of the coordinated action of multiple proteins interacting in macromolecular complexes or assemblies. The precise determination of the specific composition of protein complexes, especially using scalable and high-throughput methods, represents a systematic approach toward revealing particular cellular biological functions. In this regard, the direct profiling protein-protein interactions (PPIs) represent an efficient way to dissect functional pathways for revealing novel protein functions. In this review, we illustrate the technological evolution for the large-scale and precise identification of PPIs toward higher physiologically relevant accuracy. These techniques aim at improving the efficiency of complex pull-down, the signal specificity and accuracy in distinguishing specific PPIs, and the accuracy of identifying physiological relevant PPIs. A newly developed streamline proteomic approach for mapping the binary relationship of PPIs in a protein complex is introduced.

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-protein interactions: Methods, databases, and applications in virus-host study

Almost all the cellular processes in a living system are controlled by proteins:They regulate gene expression,catalyze chemical reactions,transport small molecules across membranes,and transmit signal across membranes.Even,a viral infection is often initiated through virus-host protein interactions.Protein-protein interactions(PPIs)are the physical contacts between two or more proteins and they represent complex biological functions.Nowadays,PPIs have been used to construct PPI networks to study complex pathways for revealing the functions of unknown proteins.Scientists have used PPIs to find the molecular basis of certain diseases and also some potential drug targets.In this review,we will discuss how PPI networks are essential to understand the molecular basis of virus-host relationships and several databases which are dedicated to virus-host interaction studies.Here,we present a short but comprehensive review on PPIs,including the experimental and computational methods of finding PPIs,the databases dedicated to virus-host PPIs,and the associated various applications in protein interaction networks of some lethal viruses with their hosts.

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.

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.

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.

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.

Spatial Humanities and Geo-computation for Social Sciences:Advances and Applications

Humanities and Social Sciences(HSS) are undergoing the transformation of spatialization and quantification. Geo-computation, with geoinformatics(including RS: Remote Sensing;GIS: Geographical Information System;GNSS: Global Navigation Satellite System), provides effective computational and spatialization methods and tools for HSS. Spatial Humanities and Geo-computation for Social Sciences(SH&GSS) is a field coupling geo-computation, and geoinformatics, with HSS. This special issue accepted a set of contributions highlighting recent advances in methodologies and applications of SH&GSS, which are related to sentiment spatial analysis from social media data, emotional change spatial analysis from news data, spatial analysis of social media related to COVID-19, crime spatiotemporal analysis, “double evaluation” for Land Use/Land Cover(LUCC), Specially Protected Natural Areas(SPNA) analysis, editing behavior analysis of Volunteered Geographic Information(VGI), electricity consumption anomaly detection, First and Last Mile Problem(FLMP) of public transport, and spatial interaction network analysis for crude oil trade network. Based on these related researches, we aim to present an overview of SH&GSS, and propose some future research directions for SH&HSS.

Bioinformatics prediction of potential mechanisms and biomarkers underlying dilated cardiomyopathy

BACKGROUND Heart failure is a health burden responsible for high morbidity and mortality worldwide, and dilated cardiomyopathy(DCM) is one of the most common causes of heart failure. DCM is a disease of the heart muscle and is characterized by enlargement and dilation of at least one ventricle alongside impaired contractility with left ventricular ejection fraction < 40%. It is also associated with abnormalities in cytoskeletal proteins, mitochondrial ATP transporter, microvasculature, and fibrosis. However, the pathogenesis and potential biomarkers of DCM remain to be investigated.AIM To investigate the candidate genes and pathways involved in DCM patients.METHODS Two expression datasets(GSE3585 and GSE5406) were downloaded from the Gene Expression Omnibus database. The differentially expressed genes(DEGs) between the DCM patients and healthy individuals were identified using the R package “linear models for microarray data.” The pathways with common DEGs were analyzed via Gene Ontology(GO), Kyoto Encyclopedia of Genes and Genomes(KEGG), and gene set enrichment analyses. Moreover, a protein-protein interaction network(PPI) was constructed to identify the hub genes and modules. The MicroRNA Database was applied to predict the microRNAs(miRNAs) targeting the hub genes. Additionally, immune cell infiltration in DCM was analyzed using CIBERSORT.RESULTS In total, 97 DEGs(47 upregulated and 50 downregulated) were identified. GO analysis showed that the DEGs were mainly enriched in “response to growth factor,” “extracellular matrix,” and “extracellular matrix structural constituent.” KEGG pathway analysis indicated that the DEGs were mainly enriched in “protein digestion and absorption” and “interleukin 17(IL-17) signaling pathway.” The PPI network suggested that collagen type Ⅲ alpha 1 chain(COL3A1) and COL1A2 contribute to the pathogenesis of DCM. Additionally, visualization of the interactions between miRNAs and the hub genes revealed that hsa-miR-5682 and hsa-miR-4500 interacted with both COL3A1 and COL1A2, and thus these miRNAs might play roles in DCM. Immune cell infiltration analysis revealed that DCM patients had more infiltrated plasma cells and fewer infiltrated B memory cells, T follicular helper cells, and resting dendritic cells.CONCLUSION COL1A2 and COL3A1 and their targeting miRNAs, hsa-miR-5682 and hsa-miR-4500, may play critical roles in the pathogenesis of DCM, which are closely related to the IL-17 signaling pathway and acute inflammatory response. These results may provide useful clues for the diagnosis and treatment of DCM.

Key genes of deficiency of liver and kidney type chronic pain through bioinformatics analyses

Objective To screen the key genes of chronic pain and provide a reference for the treatment of chronic pain.Methods We performed comprehensive bioinformatics analysis by screening chronic primary pain-related datasets to obtain differentially expressed genes(DEGs)and then imported DEGs into the DAVID database for Gene Ontology(GO)and Kyoto Encyclopedia of Genes and Genomes(KEGG)enrichment analysis.Gene Set Enrichment Analysis(GESA)analysis was done by GSEA_4.1.0 software.At the same time,we imported the intersecting genes into the STRING database and processed them by Cytoscape_3.8.1 software to obtain the protein-protein interaction(PPI)network and the central gene.Results As a result,a total of 54 DEGs were screened,including 47 up-regulated genes,1 down-regulated gene,and 6 genes that were expressed differently in different datasets.23 GO terms and 8 KEGG pathways were enriched by DAVID.PPI network analysis found that SPI1,STAT3,TNFRSF1B,PTGS2,and CXCL1 genes interacted more strongly with other genes,and were predicted to be key genes in chronic primary pain.Conclusion Our results suggested that 5 DEGs,STAT3,SPI1,TNFRSF1B,PTGS2,and CXCL1,have the potential to be used as prognostic and predictive markers for the clinical management of patients with this disease.