Haiyan Sun, Mingwei He, Jinlei Pang, Xiangfei Guo, Yansong Huo, Jun Ma
{"title":"椎间盘退变中的乳酸代谢：通过生物信息学揭示新的机制","authors":"Haiyan Sun, Mingwei He, Jinlei Pang, Xiangfei Guo, Yansong Huo, Jun Ma","doi":"10.1002/jsp2.70126","DOIUrl":null,"url":null,"abstract":"<div>\n \n \n <section>\n \n <h3> Background</h3>\n \n <p>Intervertebral disc degeneration (IDD) is a widespread issue associated with chronic lumbar pain and disability. This study aimed to identify lactate metabolism-related genes in IDD and elucidate their mechanistic roles in disease progression.</p>\n </section>\n \n <section>\n \n <h3> Methods</h3>\n \n <p>IDD datasets were analyzed using R packages GEOquery, sva, and limma for data retrieval, batch correction, and normalization. Differential gene expression analysis identified significant genes between IDD and control groups, from which lactate metabolism-related differentially expressed genes (LMRDEGs) were derived. Relationships among the LMRDEGs were assessed using Spearman's correlation analysis, and functional enrichment was conducted using ClusterProfiler. Gene set enrichment analysis identified biological processes associated with IDD. Diagnostic models were assessed using receiver operating characteristic (ROC) curve. Immune cell infiltration and correlations with core genes were analyzed via the CIBERSORT algorithm. Regulatory networks were constructed, and reverse transcription quantitative polymerase chain reaction (RT-qPCR) was employed to validate the expression of hub LMRDEGs in IDD.</p>\n </section>\n \n <section>\n \n <h3> Results</h3>\n \n <p>A total of 1325 differentially expressed genes were identified, yielding seven LMRDEGs: <i>TGFβ2</i>, <i>GSR</i>, <i>MB</i>, <i>MMP2</i>, <i>SLC16A7</i>, <i>PER2</i>, and <i>STAT3</i>, which are enriched in blood circulation regulation and hypoxic response, as well as pathways like AGE–RAGE signaling in diabetic complications. ROC analysis indicated potential hub genes (<i>MMP2, MB, TGFβ2</i>, and <i>PER2</i>), while immune infiltration analysis uncovered significant variations in immune cell distribution. RT-qPCR confirmed <i>MMP2</i>, <i>MB</i>, and <i>SLC16A7</i> as molecular indicators reflecting lactate metabolism abnormalities in IDD.</p>\n </section>\n \n <section>\n \n <h3> Conclusion</h3>\n \n <p>This study clarifies how lactate metabolism contributes to IDD through molecular mechanisms and its interplay with immunological features, providing a theoretical basis for understanding the early pathogenesis of IDD.</p>\n </section>\n </div>","PeriodicalId":14876,"journal":{"name":"JOR Spine","volume":"8 4","pages":""},"PeriodicalIF":3.9000,"publicationDate":"2025-10-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/jsp2.70126","citationCount":"0","resultStr":"{\"title\":\"Lactate Metabolism in Intervertebral Disc Degeneration: Unveiling Novel Mechanisms Through Bioinformatics\",\"authors\":\"Haiyan Sun, Mingwei He, Jinlei Pang, Xiangfei Guo, Yansong Huo, Jun Ma\",\"doi\":\"10.1002/jsp2.70126\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<div>\\n \\n \\n <section>\\n \\n <h3> Background</h3>\\n \\n <p>Intervertebral disc degeneration (IDD) is a widespread issue associated with chronic lumbar pain and disability. This study aimed to identify lactate metabolism-related genes in IDD and elucidate their mechanistic roles in disease progression.</p>\\n </section>\\n \\n <section>\\n \\n <h3> Methods</h3>\\n \\n <p>IDD datasets were analyzed using R packages GEOquery, sva, and limma for data retrieval, batch correction, and normalization. Differential gene expression analysis identified significant genes between IDD and control groups, from which lactate metabolism-related differentially expressed genes (LMRDEGs) were derived. Relationships among the LMRDEGs were assessed using Spearman's correlation analysis, and functional enrichment was conducted using ClusterProfiler. Gene set enrichment analysis identified biological processes associated with IDD. Diagnostic models were assessed using receiver operating characteristic (ROC) curve. Immune cell infiltration and correlations with core genes were analyzed via the CIBERSORT algorithm. Regulatory networks were constructed, and reverse transcription quantitative polymerase chain reaction (RT-qPCR) was employed to validate the expression of hub LMRDEGs in IDD.</p>\\n </section>\\n \\n <section>\\n \\n <h3> Results</h3>\\n \\n <p>A total of 1325 differentially expressed genes were identified, yielding seven LMRDEGs: <i>TGFβ2</i>, <i>GSR</i>, <i>MB</i>, <i>MMP2</i>, <i>SLC16A7</i>, <i>PER2</i>, and <i>STAT3</i>, which are enriched in blood circulation regulation and hypoxic response, as well as pathways like AGE–RAGE signaling in diabetic complications. ROC analysis indicated potential hub genes (<i>MMP2, MB, TGFβ2</i>, and <i>PER2</i>), while immune infiltration analysis uncovered significant variations in immune cell distribution. 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Lactate Metabolism in Intervertebral Disc Degeneration: Unveiling Novel Mechanisms Through Bioinformatics
Background
Intervertebral disc degeneration (IDD) is a widespread issue associated with chronic lumbar pain and disability. This study aimed to identify lactate metabolism-related genes in IDD and elucidate their mechanistic roles in disease progression.
Methods
IDD datasets were analyzed using R packages GEOquery, sva, and limma for data retrieval, batch correction, and normalization. Differential gene expression analysis identified significant genes between IDD and control groups, from which lactate metabolism-related differentially expressed genes (LMRDEGs) were derived. Relationships among the LMRDEGs were assessed using Spearman's correlation analysis, and functional enrichment was conducted using ClusterProfiler. Gene set enrichment analysis identified biological processes associated with IDD. Diagnostic models were assessed using receiver operating characteristic (ROC) curve. Immune cell infiltration and correlations with core genes were analyzed via the CIBERSORT algorithm. Regulatory networks were constructed, and reverse transcription quantitative polymerase chain reaction (RT-qPCR) was employed to validate the expression of hub LMRDEGs in IDD.
Results
A total of 1325 differentially expressed genes were identified, yielding seven LMRDEGs: TGFβ2, GSR, MB, MMP2, SLC16A7, PER2, and STAT3, which are enriched in blood circulation regulation and hypoxic response, as well as pathways like AGE–RAGE signaling in diabetic complications. ROC analysis indicated potential hub genes (MMP2, MB, TGFβ2, and PER2), while immune infiltration analysis uncovered significant variations in immune cell distribution. RT-qPCR confirmed MMP2, MB, and SLC16A7 as molecular indicators reflecting lactate metabolism abnormalities in IDD.
Conclusion
This study clarifies how lactate metabolism contributes to IDD through molecular mechanisms and its interplay with immunological features, providing a theoretical basis for understanding the early pathogenesis of IDD.
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