<HashMap><database>biostudies-literature</database><scores/><additional><omics_type>Unknown</omics_type><volume>2023</volume><submitter>Chen W</submitter><pubmed_abstract>&lt;h4>Background&lt;/h4>Systemic lupus erythematosus (SLE) is an autoimmune disease with strong heterogeneity, leading to variable clinical symptoms, which makes diagnosis and activity evaluation difficult.&lt;h4>Methods&lt;/h4>The original dataset of GSE88884 was analyzed to screen differentially expressed genes (DEGs) of SLE and the correlation between DEGs and clinical parameters (SLEDAI, anti-dsDNA, C3, and C4). The result was validated by microarray GSE121239 and SLE patients with RT-qPCR. Next, receiver operator characteristic (ROC) analysis, correlation analysis, and ordinal logistic regression were applied, respectively, to evaluate the capability of diagnosis and prediction of the candidate biomarker. Subsequently, the biological functions of the candidate biomarker were investigated through</pubmed_abstract><journal>Disease markers</journal><pagination>8564650</pagination><full_dataset_link>https://www.ebi.ac.uk/biostudies/studies/S-EPMC9842429</full_dataset_link><repository>biostudies-literature</repository><pubmed_title>Integrative Bioinformatics Analysis Identifies DDX60 as a Potential Biomarker for Systemic Lupus Erythematosus.</pubmed_title><pmcid>PMC9842429</pmcid><pubmed_authors>Luo WQ</pubmed_authors><pubmed_authors>Wang Q</pubmed_authors><pubmed_authors>Zhang XF</pubmed_authors><pubmed_authors>Chen W</pubmed_authors><pubmed_authors>Zhou DH</pubmed_authors><pubmed_authors>Wen CP</pubmed_authors><pubmed_authors>Huang L</pubmed_authors><pubmed_authors>Li ZY</pubmed_authors><pubmed_authors>Li L</pubmed_authors></additional><is_claimable>false</is_claimable><name>Integrative Bioinformatics Analysis Identifies DDX60 as a Potential Biomarker for Systemic Lupus Erythematosus.</name><description>&lt;h4>Background&lt;/h4>Systemic lupus erythematosus (SLE) is an autoimmune disease with strong heterogeneity, leading to variable clinical symptoms, which makes diagnosis and activity evaluation difficult.&lt;h4>Methods&lt;/h4>The original dataset of GSE88884 was analyzed to screen differentially expressed genes (DEGs) of SLE and the correlation between DEGs and clinical parameters (SLEDAI, anti-dsDNA, C3, and C4). The result was validated by microarray GSE121239 and SLE patients with RT-qPCR. Next, receiver operator characteristic (ROC) analysis, correlation analysis, and ordinal logistic regression were applied, respectively, to evaluate the capability of diagnosis and prediction of the candidate biomarker. Subsequently, the biological functions of the candidate biomarker were investigated through</description><dates><release>2023-01-01T00:00:00Z</release><publication>2023</publication><modification>2025-04-04T14:28:06.178Z</modification><creation>2025-04-04T14:28:06.178Z</creation></dates><accession>S-EPMC9842429</accession><cross_references><pubmed>36655136</pubmed><doi>10.1155/2023/8564650</doi></cross_references></HashMap>