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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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 |
doi_str_mv | 10.1016/j.compbiomed.2022.106135 |
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fullrecord | <record><control><sourceid>proquest_cross</sourceid><recordid>TN_cdi_proquest_miscellaneous_2718960641</recordid><sourceformat>XML</sourceformat><sourcesystem>PC</sourcesystem><els_id>S0010482522008435</els_id><sourcerecordid>2729950343</sourcerecordid><originalsourceid>FETCH-LOGICAL-c452t-de98d41e52762fca32addff07c58acf19201bdcc84abbc1a7b0a5bba5c70400f3</originalsourceid><addsrcrecordid>eNqFkF1rHCEUhqW0NNskf6EIuenNbI_OOB-XSUjbQKAU2mtxjsfU7axudSZl_31cNiGQm16Jr8_x1YcxLmAtQLSfN2uM293o45bsWoKUJW5Frd6wlei7oQJVN2_ZCkBA1fRSnbAPOW8AoIEa3rOTuhVtO_TDiv24tRRm7zya2cfAo-N_aM_RBOutmYnfU6DMy5aXuineF3DiOzP__mf2mfvAAy0pHgKPJffhjL1zZsp0_rSesl9fbn5ef6vuvn-9vb68q7BRcq4sDb1tBCnZtdKhqaWx1jnoUPUGnRgkiNEi9o0ZRxSmG8GocTQKu_ILcPUp-3S8d5fi34XyrLc-I02TCRSXrGUn-qGFthEFvXiFbuKSQnldoeQwKKibulD9kcIUc07k9C75rUl7LUAftOuNftGuD9r1UXsZ_fhUsIyHs-fBZ88FuDoCVIw8eEo6o6eAZH0inLWN_v8tj4crmXo</addsrcrecordid><sourcetype>Aggregation Database</sourcetype><iscdi>true</iscdi><recordtype>article</recordtype><pqid>2729950343</pqid></control><display><type>article</type><title>Identification of key candidate genes and biological pathways in neuropathic pain</title><source>MEDLINE</source><source>Elsevier ScienceDirect Journals</source><creator>Cui, Chun-Yan ; Liu, Xiao ; Peng, Ming-Hui ; Liu, Qing ; Zhang, Ying</creator><creatorcontrib>Cui, Chun-Yan ; Liu, Xiao ; Peng, Ming-Hui ; Liu, Qing ; Zhang, Ying</creatorcontrib><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><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 & Allied Health Database</collection><collection>Health & Medical Collection</collection><collection>ProQuest Central (purchase pre-March 2016)</collection><collection>Medical Database (Alumni Edition)</collection><collection>Computing Database (Alumni Edition)</collection><collection>ProQuest Pharma Collection</collection><collection>Technology Research Database</collection><collection>ProQuest SciTech Collection</collection><collection>ProQuest Technology Collection</collection><collection>ProQuest Natural Science Collection</collection><collection>Hospital Premium Collection</collection><collection>Hospital Premium Collection (Alumni Edition)</collection><collection>ProQuest Central (Alumni) (purchase pre-March 2016)</collection><collection>Research Library (Alumni Edition)</collection><collection>ProQuest Central (Alumni Edition)</collection><collection>ProQuest Central UK/Ireland</collection><collection>Advanced Technologies & Aerospace Collection</collection><collection>ProQuest Central Essentials</collection><collection>Biological Science Collection</collection><collection>ProQuest Central</collection><collection>Technology Collection</collection><collection>Natural Science Collection</collection><collection>ProQuest One Community College</collection><collection>ProQuest Central Korea</collection><collection>Engineering Research Database</collection><collection>Health Research Premium Collection</collection><collection>Health Research Premium Collection (Alumni)</collection><collection>ProQuest Central Student</collection><collection>Research Library Prep</collection><collection>SciTech Premium Collection</collection><collection>ProQuest Computer Science Collection</collection><collection>Computer Science Database</collection><collection>ProQuest Health & Medical Complete (Alumni)</collection><collection>Nursing & Allied Health Database (Alumni Edition)</collection><collection>ProQuest Biological Science Collection</collection><collection>Computing Database</collection><collection>Health & Medical Collection (Alumni Edition)</collection><collection>Medical Database</collection><collection>Research Library</collection><collection>Biological Science Database</collection><collection>Biochemistry Abstracts 1</collection><collection>Research Library (Corporate)</collection><collection>Nursing & Allied Health Premium</collection><collection>Advanced Technologies & Aerospace Database</collection><collection>ProQuest Advanced Technologies & Aerospace Collection</collection><collection>Biotechnology and BioEngineering Abstracts</collection><collection>ProQuest One Academic Eastern Edition (DO NOT USE)</collection><collection>ProQuest One Academic</collection><collection>ProQuest One Academic UKI Edition</collection><collection>ProQuest Central China</collection><collection>ProQuest Central Basic</collection><collection>MEDLINE - Academic</collection><jtitle>Computers in biology and medicine</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Cui, Chun-Yan</au><au>Liu, Xiao</au><au>Peng, Ming-Hui</au><au>Liu, Qing</au><au>Zhang, Ying</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Identification of key candidate genes and biological pathways in neuropathic pain</atitle><jtitle>Computers in biology and medicine</jtitle><addtitle>Comput Biol Med</addtitle><date>2022-11</date><risdate>2022</risdate><volume>150</volume><spage>106135</spage><pages>106135-</pages><artnum>106135</artnum><issn>0010-4825</issn><issn>1879-0534</issn><eissn>1879-0534</eissn><abstract>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.</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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