Identification of key candidate genes and biological pathways in neuropathic pain

Neuropathic pain is a common chronic pain, characterized by spontaneous pain and mechanical allodynia. The incidence of neuropathic pain is on the rise due to infections, higher rates of diabetes and stroke, and increased use of chemotherapy drugs in cancer patients. At present, due to its pathophys...

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Veröffentlicht in:Computers in biology and medicine 2022-11, Vol.150, p.106135, Article 106135
Hauptverfasser: Cui, Chun-Yan, Liu, Xiao, Peng, Ming-Hui, Liu, Qing, Zhang, Ying
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Liu, Xiao
Peng, Ming-Hui
Liu, Qing
Zhang, Ying
description Neuropathic pain is a common chronic pain, characterized by spontaneous pain and mechanical allodynia. The incidence of neuropathic pain is on the rise due to infections, higher rates of diabetes and stroke, and increased use of chemotherapy drugs in cancer patients. At present, due to its pathophysiological process and molecular mechanism remaining unclear, there is a lack of effective treatment and prevention methods in clinical practice. Now, we use bioinformatics technology to integrate and filter hub genes that may be related to the pathogenesis of neuropathic pain, and explore their possible molecular mechanism by functional annotation and pathway enrichment analysis. The expression profiles of GSE24982, GSE2884, GSE2636 and GSE30691 were downloaded from the Gene Expression Omnibus(GEO)database, and these datasets include 93 neuropathic pain Rattus norvegicus and 59 shame controls. After the four datasets were all standardized by quantiles, the differentially expressed genes (DEGs) between NPP Rattus norvegicus and the shame controls were finally identified by the robust rank aggregation (RRA) analysis method. In order to reveal the possible underlying biological function of DEGs, the Gene Ontology (GO) functional annotation and Kyoto Encyclopedia of Genes and Genomes (KEGG) Pathway enrichment analysis of DEGs were performed. In addition, a Protein-protein Interaction (PPI) network was also established. At the end of our study, a high throughput sequencing dataset GSE117526 was used to corroborate our calculation results. Through RRA analysis of the above four datasets GSE24982, GSE2884, GSE2636, and GSE30691, we finally obtained 231 DEGs, including 183 up-regulated genes and 47 down-regulated genes. Arranging 231 DEGs in descending order according to |log2 fold change (FC)|, we found that the top 20 key genes include 14 up-regulated genes and 6 down-regulated genes. The most down-regulated hub gene abnormal expressed in NPP was Egf17 (P-value = 0.008), Camk2n2 (P-value = 0.002), and Lep (P-value = 0.02), and the most up-regulated hub gene abnormal expressed in NPP was Nefm (P-value = 1.08E-06), Prx (P-value = 2.68E-07), and Stip1 (P-value = 4.40E-07). In GO functional annotation analysis results, regulation of ion transmembrane transport (GO:0034765; P-value = 1.45E-09) was the most remarkable enriched for biological process, synaptic membrane (GO:0097060; P-value = 2.95E-08) was the most significantly enriched for cellular component, channel activi
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The incidence of neuropathic pain is on the rise due to infections, higher rates of diabetes and stroke, and increased use of chemotherapy drugs in cancer patients. At present, due to its pathophysiological process and molecular mechanism remaining unclear, there is a lack of effective treatment and prevention methods in clinical practice. Now, we use bioinformatics technology to integrate and filter hub genes that may be related to the pathogenesis of neuropathic pain, and explore their possible molecular mechanism by functional annotation and pathway enrichment analysis. The expression profiles of GSE24982, GSE2884, GSE2636 and GSE30691 were downloaded from the Gene Expression Omnibus(GEO)database, and these datasets include 93 neuropathic pain Rattus norvegicus and 59 shame controls. After the four datasets were all standardized by quantiles, the differentially expressed genes (DEGs) between NPP Rattus norvegicus and the shame controls were finally identified by the robust rank aggregation (RRA) analysis method. In order to reveal the possible underlying biological function of DEGs, the Gene Ontology (GO) functional annotation and Kyoto Encyclopedia of Genes and Genomes (KEGG) Pathway enrichment analysis of DEGs were performed. In addition, a Protein-protein Interaction (PPI) network was also established. At the end of our study, a high throughput sequencing dataset GSE117526 was used to corroborate our calculation results. Through RRA analysis of the above four datasets GSE24982, GSE2884, GSE2636, and GSE30691, we finally obtained 231 DEGs, including 183 up-regulated genes and 47 down-regulated genes. Arranging 231 DEGs in descending order according to |log2 fold change (FC)|, we found that the top 20 key genes include 14 up-regulated genes and 6 down-regulated genes. The most down-regulated hub gene abnormal expressed in NPP was Egf17 (P-value = 0.008), Camk2n2 (P-value = 0.002), and Lep (P-value = 0.02), and the most up-regulated hub gene abnormal expressed in NPP was Nefm (P-value = 1.08E-06), Prx (P-value = 2.68E-07), and Stip1 (P-value = 4.40E-07). In GO functional annotation analysis results, regulation of ion transmembrane transport (GO:0034765; P-value = 1.45E-09) was the most remarkable enriched for biological process, synaptic membrane (GO:0097060; P-value = 2.95E-08) was the most significantly enriched for cellular component, channel activity (GO:0015267; P-value = 2.44E-06) was the most prominent enriched for molecular function. In KEGG pathway enrichment analysis results, the top three notable enrichment pathways were Neuroactive ligand-receptor interaction (rno04080; P-value = 3.46E-08), Calcium signaling pathway (rno04020; P-value = 5.37E-05), and Osteoclast differentiation (rno04380; P-value = 0.000459927). Cav1 and Lep appeared in the top 20 genes in both RRA analysis and PPI analysis, while Nefm appeared in RRA analysis and datasets GSE117526 validation analysis, so we finally identified these three genes as hub genes. Our research identified the hub genes and signal pathways of neuropathic pain, enriched the pathophysiological mechanism of neuropathic pain to some extent, and provided a possible basis for the targeted therapy of neuropathic pain. •Neuropathic pain is a polygenic disease.•Robust Rank Aggregation (RRA) method was used to identify hub genes of neuropathic pain.</description><identifier>ISSN: 0010-4825</identifier><identifier>ISSN: 1879-0534</identifier><identifier>EISSN: 1879-0534</identifier><identifier>DOI: 10.1016/j.compbiomed.2022.106135</identifier><identifier>PMID: 36166989</identifier><language>eng</language><publisher>United States: Elsevier Ltd</publisher><subject>Acids ; Animals ; Annotations ; Bioinformatics ; Biological activity ; Calcium channels (voltage-gated) ; Calcium signalling ; Channel gating ; Chemotherapy ; Chronic illnesses ; Chronic pain ; Computational Biology - methods ; Databases, Genetic ; Datasets ; Diabetes mellitus ; Diabetic neuropathy ; Encyclopedias ; Enrichment ; Gene expression ; Gene Expression Profiling - methods ; Genes ; Genomes ; Hub genes ; Humans ; Hybridization ; Methods ; Microarray data analysis ; Neuralgia - genetics ; Neuropathic pain ; Next-generation sequencing ; Ontology ; Osteoclastogenesis ; Pain ; Pain perception ; Pathogenesis ; Protein interaction ; Protein Interaction Maps - genetics ; Protein-protein interaction (PPI) network analysis ; Proteins ; Quantiles ; Rats ; Rattus norvegicus ; Robust rank aggregation ; Signal transduction</subject><ispartof>Computers in biology and medicine, 2022-11, Vol.150, p.106135, Article 106135</ispartof><rights>2022 The Authors</rights><rights>Copyright © 2022 The Authors. Published by Elsevier Ltd.. All rights reserved.</rights><rights>2022. The Authors</rights><lds50>peer_reviewed</lds50><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c452t-de98d41e52762fca32addff07c58acf19201bdcc84abbc1a7b0a5bba5c70400f3</citedby><cites>FETCH-LOGICAL-c452t-de98d41e52762fca32addff07c58acf19201bdcc84abbc1a7b0a5bba5c70400f3</cites></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktohtml>$$Uhttps://www.sciencedirect.com/science/article/pii/S0010482522008435$$EHTML$$P50$$Gelsevier$$Hfree_for_read</linktohtml><link.rule.ids>314,776,780,3537,27901,27902,65534</link.rule.ids><backlink>$$Uhttps://www.ncbi.nlm.nih.gov/pubmed/36166989$$D View this record in MEDLINE/PubMed$$Hfree_for_read</backlink></links><search><creatorcontrib>Cui, Chun-Yan</creatorcontrib><creatorcontrib>Liu, Xiao</creatorcontrib><creatorcontrib>Peng, Ming-Hui</creatorcontrib><creatorcontrib>Liu, Qing</creatorcontrib><creatorcontrib>Zhang, Ying</creatorcontrib><title>Identification of key candidate genes and biological pathways in neuropathic pain</title><title>Computers in biology and medicine</title><addtitle>Comput Biol Med</addtitle><description>Neuropathic pain is a common chronic pain, characterized by spontaneous pain and mechanical allodynia. The incidence of neuropathic pain is on the rise due to infections, higher rates of diabetes and stroke, and increased use of chemotherapy drugs in cancer patients. At present, due to its pathophysiological process and molecular mechanism remaining unclear, there is a lack of effective treatment and prevention methods in clinical practice. Now, we use bioinformatics technology to integrate and filter hub genes that may be related to the pathogenesis of neuropathic pain, and explore their possible molecular mechanism by functional annotation and pathway enrichment analysis. The expression profiles of GSE24982, GSE2884, GSE2636 and GSE30691 were downloaded from the Gene Expression Omnibus(GEO)database, and these datasets include 93 neuropathic pain Rattus norvegicus and 59 shame controls. After the four datasets were all standardized by quantiles, the differentially expressed genes (DEGs) between NPP Rattus norvegicus and the shame controls were finally identified by the robust rank aggregation (RRA) analysis method. In order to reveal the possible underlying biological function of DEGs, the Gene Ontology (GO) functional annotation and Kyoto Encyclopedia of Genes and Genomes (KEGG) Pathway enrichment analysis of DEGs were performed. In addition, a Protein-protein Interaction (PPI) network was also established. At the end of our study, a high throughput sequencing dataset GSE117526 was used to corroborate our calculation results. Through RRA analysis of the above four datasets GSE24982, GSE2884, GSE2636, and GSE30691, we finally obtained 231 DEGs, including 183 up-regulated genes and 47 down-regulated genes. Arranging 231 DEGs in descending order according to |log2 fold change (FC)|, we found that the top 20 key genes include 14 up-regulated genes and 6 down-regulated genes. The most down-regulated hub gene abnormal expressed in NPP was Egf17 (P-value = 0.008), Camk2n2 (P-value = 0.002), and Lep (P-value = 0.02), and the most up-regulated hub gene abnormal expressed in NPP was Nefm (P-value = 1.08E-06), Prx (P-value = 2.68E-07), and Stip1 (P-value = 4.40E-07). In GO functional annotation analysis results, regulation of ion transmembrane transport (GO:0034765; P-value = 1.45E-09) was the most remarkable enriched for biological process, synaptic membrane (GO:0097060; P-value = 2.95E-08) was the most significantly enriched for cellular component, channel activity (GO:0015267; P-value = 2.44E-06) was the most prominent enriched for molecular function. In KEGG pathway enrichment analysis results, the top three notable enrichment pathways were Neuroactive ligand-receptor interaction (rno04080; P-value = 3.46E-08), Calcium signaling pathway (rno04020; P-value = 5.37E-05), and Osteoclast differentiation (rno04380; P-value = 0.000459927). Cav1 and Lep appeared in the top 20 genes in both RRA analysis and PPI analysis, while Nefm appeared in RRA analysis and datasets GSE117526 validation analysis, so we finally identified these three genes as hub genes. Our research identified the hub genes and signal pathways of neuropathic pain, enriched the pathophysiological mechanism of neuropathic pain to some extent, and provided a possible basis for the targeted therapy of neuropathic pain. •Neuropathic pain is a polygenic disease.•Robust Rank Aggregation (RRA) method was used to identify hub genes of neuropathic pain.</description><subject>Acids</subject><subject>Animals</subject><subject>Annotations</subject><subject>Bioinformatics</subject><subject>Biological activity</subject><subject>Calcium channels (voltage-gated)</subject><subject>Calcium signalling</subject><subject>Channel gating</subject><subject>Chemotherapy</subject><subject>Chronic illnesses</subject><subject>Chronic pain</subject><subject>Computational Biology - methods</subject><subject>Databases, Genetic</subject><subject>Datasets</subject><subject>Diabetes mellitus</subject><subject>Diabetic neuropathy</subject><subject>Encyclopedias</subject><subject>Enrichment</subject><subject>Gene expression</subject><subject>Gene Expression Profiling - methods</subject><subject>Genes</subject><subject>Genomes</subject><subject>Hub genes</subject><subject>Humans</subject><subject>Hybridization</subject><subject>Methods</subject><subject>Microarray data analysis</subject><subject>Neuralgia - genetics</subject><subject>Neuropathic pain</subject><subject>Next-generation sequencing</subject><subject>Ontology</subject><subject>Osteoclastogenesis</subject><subject>Pain</subject><subject>Pain perception</subject><subject>Pathogenesis</subject><subject>Protein interaction</subject><subject>Protein Interaction Maps - genetics</subject><subject>Protein-protein interaction (PPI) network analysis</subject><subject>Proteins</subject><subject>Quantiles</subject><subject>Rats</subject><subject>Rattus norvegicus</subject><subject>Robust rank aggregation</subject><subject>Signal transduction</subject><issn>0010-4825</issn><issn>1879-0534</issn><issn>1879-0534</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2022</creationdate><recordtype>article</recordtype><sourceid>EIF</sourceid><sourceid>8G5</sourceid><sourceid>BENPR</sourceid><sourceid>GUQSH</sourceid><sourceid>M2O</sourceid><recordid>eNqFkF1rHCEUhqW0NNskf6EIuenNbI_OOB-XSUjbQKAU2mtxjsfU7axudSZl_31cNiGQm16Jr8_x1YcxLmAtQLSfN2uM293o45bsWoKUJW5Frd6wlei7oQJVN2_ZCkBA1fRSnbAPOW8AoIEa3rOTuhVtO_TDiv24tRRm7zya2cfAo-N_aM_RBOutmYnfU6DMy5aXuineF3DiOzP__mf2mfvAAy0pHgKPJffhjL1zZsp0_rSesl9fbn5ef6vuvn-9vb68q7BRcq4sDb1tBCnZtdKhqaWx1jnoUPUGnRgkiNEi9o0ZRxSmG8GocTQKu_ILcPUp-3S8d5fi34XyrLc-I02TCRSXrGUn-qGFthEFvXiFbuKSQnldoeQwKKibulD9kcIUc07k9C75rUl7LUAftOuNftGuD9r1UXsZ_fhUsIyHs-fBZ88FuDoCVIw8eEo6o6eAZH0inLWN_v8tj4crmXo</recordid><startdate>202211</startdate><enddate>202211</enddate><creator>Cui, Chun-Yan</creator><creator>Liu, Xiao</creator><creator>Peng, Ming-Hui</creator><creator>Liu, Qing</creator><creator>Zhang, Ying</creator><general>Elsevier Ltd</general><general>Elsevier Limited</general><scope>6I.</scope><scope>AAFTH</scope><scope>CGR</scope><scope>CUY</scope><scope>CVF</scope><scope>ECM</scope><scope>EIF</scope><scope>NPM</scope><scope>AAYXX</scope><scope>CITATION</scope><scope>3V.</scope><scope>7RV</scope><scope>7X7</scope><scope>7XB</scope><scope>88E</scope><scope>8AL</scope><scope>8AO</scope><scope>8FD</scope><scope>8FE</scope><scope>8FG</scope><scope>8FH</scope><scope>8FI</scope><scope>8FJ</scope><scope>8FK</scope><scope>8G5</scope><scope>ABUWG</scope><scope>AFKRA</scope><scope>ARAPS</scope><scope>AZQEC</scope><scope>BBNVY</scope><scope>BENPR</scope><scope>BGLVJ</scope><scope>BHPHI</scope><scope>CCPQU</scope><scope>DWQXO</scope><scope>FR3</scope><scope>FYUFA</scope><scope>GHDGH</scope><scope>GNUQQ</scope><scope>GUQSH</scope><scope>HCIFZ</scope><scope>JQ2</scope><scope>K7-</scope><scope>K9.</scope><scope>KB0</scope><scope>LK8</scope><scope>M0N</scope><scope>M0S</scope><scope>M1P</scope><scope>M2O</scope><scope>M7P</scope><scope>M7Z</scope><scope>MBDVC</scope><scope>NAPCQ</scope><scope>P5Z</scope><scope>P62</scope><scope>P64</scope><scope>PQEST</scope><scope>PQQKQ</scope><scope>PQUKI</scope><scope>PRINS</scope><scope>Q9U</scope><scope>7X8</scope></search><sort><creationdate>202211</creationdate><title>Identification of key candidate genes and biological pathways in neuropathic pain</title><author>Cui, Chun-Yan ; Liu, Xiao ; Peng, Ming-Hui ; Liu, Qing ; Zhang, Ying</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c452t-de98d41e52762fca32addff07c58acf19201bdcc84abbc1a7b0a5bba5c70400f3</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2022</creationdate><topic>Acids</topic><topic>Animals</topic><topic>Annotations</topic><topic>Bioinformatics</topic><topic>Biological activity</topic><topic>Calcium channels (voltage-gated)</topic><topic>Calcium signalling</topic><topic>Channel gating</topic><topic>Chemotherapy</topic><topic>Chronic illnesses</topic><topic>Chronic pain</topic><topic>Computational Biology - methods</topic><topic>Databases, Genetic</topic><topic>Datasets</topic><topic>Diabetes mellitus</topic><topic>Diabetic neuropathy</topic><topic>Encyclopedias</topic><topic>Enrichment</topic><topic>Gene expression</topic><topic>Gene Expression Profiling - methods</topic><topic>Genes</topic><topic>Genomes</topic><topic>Hub genes</topic><topic>Humans</topic><topic>Hybridization</topic><topic>Methods</topic><topic>Microarray data analysis</topic><topic>Neuralgia - genetics</topic><topic>Neuropathic pain</topic><topic>Next-generation sequencing</topic><topic>Ontology</topic><topic>Osteoclastogenesis</topic><topic>Pain</topic><topic>Pain perception</topic><topic>Pathogenesis</topic><topic>Protein interaction</topic><topic>Protein Interaction Maps - genetics</topic><topic>Protein-protein interaction (PPI) network analysis</topic><topic>Proteins</topic><topic>Quantiles</topic><topic>Rats</topic><topic>Rattus norvegicus</topic><topic>Robust rank aggregation</topic><topic>Signal transduction</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Cui, Chun-Yan</creatorcontrib><creatorcontrib>Liu, Xiao</creatorcontrib><creatorcontrib>Peng, Ming-Hui</creatorcontrib><creatorcontrib>Liu, Qing</creatorcontrib><creatorcontrib>Zhang, Ying</creatorcontrib><collection>ScienceDirect Open Access Titles</collection><collection>Elsevier:ScienceDirect:Open Access</collection><collection>Medline</collection><collection>MEDLINE</collection><collection>MEDLINE (Ovid)</collection><collection>MEDLINE</collection><collection>MEDLINE</collection><collection>PubMed</collection><collection>CrossRef</collection><collection>ProQuest Central (Corporate)</collection><collection>Nursing &amp; 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The incidence of neuropathic pain is on the rise due to infections, higher rates of diabetes and stroke, and increased use of chemotherapy drugs in cancer patients. At present, due to its pathophysiological process and molecular mechanism remaining unclear, there is a lack of effective treatment and prevention methods in clinical practice. Now, we use bioinformatics technology to integrate and filter hub genes that may be related to the pathogenesis of neuropathic pain, and explore their possible molecular mechanism by functional annotation and pathway enrichment analysis. The expression profiles of GSE24982, GSE2884, GSE2636 and GSE30691 were downloaded from the Gene Expression Omnibus(GEO)database, and these datasets include 93 neuropathic pain Rattus norvegicus and 59 shame controls. After the four datasets were all standardized by quantiles, the differentially expressed genes (DEGs) between NPP Rattus norvegicus and the shame controls were finally identified by the robust rank aggregation (RRA) analysis method. In order to reveal the possible underlying biological function of DEGs, the Gene Ontology (GO) functional annotation and Kyoto Encyclopedia of Genes and Genomes (KEGG) Pathway enrichment analysis of DEGs were performed. In addition, a Protein-protein Interaction (PPI) network was also established. At the end of our study, a high throughput sequencing dataset GSE117526 was used to corroborate our calculation results. Through RRA analysis of the above four datasets GSE24982, GSE2884, GSE2636, and GSE30691, we finally obtained 231 DEGs, including 183 up-regulated genes and 47 down-regulated genes. Arranging 231 DEGs in descending order according to |log2 fold change (FC)|, we found that the top 20 key genes include 14 up-regulated genes and 6 down-regulated genes. The most down-regulated hub gene abnormal expressed in NPP was Egf17 (P-value = 0.008), Camk2n2 (P-value = 0.002), and Lep (P-value = 0.02), and the most up-regulated hub gene abnormal expressed in NPP was Nefm (P-value = 1.08E-06), Prx (P-value = 2.68E-07), and Stip1 (P-value = 4.40E-07). In GO functional annotation analysis results, regulation of ion transmembrane transport (GO:0034765; P-value = 1.45E-09) was the most remarkable enriched for biological process, synaptic membrane (GO:0097060; P-value = 2.95E-08) was the most significantly enriched for cellular component, channel activity (GO:0015267; P-value = 2.44E-06) was the most prominent enriched for molecular function. In KEGG pathway enrichment analysis results, the top three notable enrichment pathways were Neuroactive ligand-receptor interaction (rno04080; P-value = 3.46E-08), Calcium signaling pathway (rno04020; P-value = 5.37E-05), and Osteoclast differentiation (rno04380; P-value = 0.000459927). Cav1 and Lep appeared in the top 20 genes in both RRA analysis and PPI analysis, while Nefm appeared in RRA analysis and datasets GSE117526 validation analysis, so we finally identified these three genes as hub genes. Our research identified the hub genes and signal pathways of neuropathic pain, enriched the pathophysiological mechanism of neuropathic pain to some extent, and provided a possible basis for the targeted therapy of neuropathic pain. •Neuropathic pain is a polygenic disease.•Robust Rank Aggregation (RRA) method was used to identify hub genes of neuropathic pain.</abstract><cop>United States</cop><pub>Elsevier Ltd</pub><pmid>36166989</pmid><doi>10.1016/j.compbiomed.2022.106135</doi><oa>free_for_read</oa></addata></record>
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subjects Acids
Animals
Annotations
Bioinformatics
Biological activity
Calcium channels (voltage-gated)
Calcium signalling
Channel gating
Chemotherapy
Chronic illnesses
Chronic pain
Computational Biology - methods
Databases, Genetic
Datasets
Diabetes mellitus
Diabetic neuropathy
Encyclopedias
Enrichment
Gene expression
Gene Expression Profiling - methods
Genes
Genomes
Hub genes
Humans
Hybridization
Methods
Microarray data analysis
Neuralgia - genetics
Neuropathic pain
Next-generation sequencing
Ontology
Osteoclastogenesis
Pain
Pain perception
Pathogenesis
Protein interaction
Protein Interaction Maps - genetics
Protein-protein interaction (PPI) network analysis
Proteins
Quantiles
Rats
Rattus norvegicus
Robust rank aggregation
Signal transduction
title Identification of key candidate genes and biological pathways in neuropathic pain
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