Identification of common mechanisms and biomarkers for dermatomyositis and atherosclerosis based on bioinformatics analysis

BackgroundDermatomyositis (DM) is a kind of dermatologically associated autoimmune disease that is notably associated with an increased risk of concurrent malignancies, although the underlying mechanisms remain to be fully elucidated. The purpose of this investigation was to examine the immunological parallels between DM and nasopharyngeal carcinoma (NPC), with the aim of identifying pivotal biomarkers that could facilitate a deeper understanding and enhance the predictive capabilities of NPC in DM patients.MethodData for DM and NPC were sourced from the Gene Expression Omnibus (GEO) database. Immune infiltration was analyzed using the “cibersort” R package, differentially expressed genes (DEGs) were identified with the “limma” package, and functional pathways were investigated through Kyoto Encyclopedia of Genes and Genomes (KEGG) and Gene Ontology (GO) analyses. Characteristic genes were determined by Utilizing Protein–Protein Interaction (PPI) and Least Absolute Shrinkage and Selection Operator (LASSO), and their features were validated using the GSE53819 dataset.ResultsIn comparison to normal samples, significant infiltration of macrophage M1 was observed in both DM and NPC. The analysis revealed 77 DEGs in DM and 1051 DEGs in NPC, with 22 genes found to be co‐DEGs. Following PPI and LASSO analysis, six distinctive genes were retained. Notably, CCL8, IFIH1, CXCL10, and CXCL11 exhibited optimal diagnostic efficacy for NPC and displayed significant correlation with macrophage M1 infiltration within the carcinoma.ConclusionFour characteristic genes, CCL8, IFIH1, CXCL10, and CXCL11 are risk factors for both DM and NPC. They exhibit a robust correlation with the incidence of NPC and offer a commendable diagnostic efficacy. Furthermore, they may serve as prospective predictive biomarkers for the emergence of NPC in DM.

Section: Methodsmentioning

confidence: 99%

Identification of shared immune infiltration characteristic molecules in dermatomyositis and nasopharyngeal carcinoma using bioinformatics

Kai,

Huang,

et al. 2024

“…Bioinformatics has been widely applied to data mining, which revealed great significance in exploring the pathogenesis and precise treatment strategies. 14 , 15 To address these unmet needs and improve the diagnosis of cSLE, our study aims to identify novel biomarkers by employing machine learning algorithms and comprehensive immune infiltration analysis. We utilized the Gene Expression Omnibus (GEO) database to investigate differentially expressed genes (DEGs) between cSLE patients and healthy controls, and subsequently identified hub DEGs as potential diagnostic biomarkers.…”

Section: Introductionmentioning

confidence: 99%

Identification of novel biomarkers for childhood‐onset systemic lupus erythematosus using machine learning algorithms and immune infiltration analysis

Deng,

Sun

2024

BackgroundChildhood‐onset systemic lupus erythematosus (cSLE) is a chronic autoimmune disease that is often more severe than adult‐onset SLE and is challenging to diagnose due to its variable presentation and lack of specific diagnostic tests.ObjectivesThis study aimed to identify potential diagnostic biomarkers for cSLE by analyzing differentially expressed genes (DEGs) using machine learning algorithms.MethodsIn this study, we utilized the Gene Expression Omnibus database to investigate the DEGs between cSLE and normal samples, conducting a functional enrichment analysis on DEGs. Subsequently, we employed machine learning algorithms, including Least Absolute Shrinkage and Selection Operator regression and Support Vector Machine‐Recursive Feature Elimination, to identify hub DEGs, which serve as crucial biomarkers. We delved into the role of these hub DEGs in the pathogenesis of the disease and the correlation between these hub DEGs and immune infiltration by comprehensive immune infiltration analysis using the CIBERSORT algorithm.ResultsWe identified 110 DEGs in cSLE, including 95 upregulated and 15 downregulated genes. Functional annotation revealed that these DEGs were involved in immune response processes, viral defense mechanisms, and regulation of interferon responses. Machine learning algorithms identified CCR1 and SAMD9L as hub DEGs, which were validated in multiple datasets and demonstrated high diagnostic value for cSLE. Mechanistic exploration suggested that CCR1 and SAMD9L are involved in immune response modulation, particularly in interferon signaling and the innate immune system. Assessment of immune cell infiltration revealed significant differences in immune cell composition between cSLE patients and healthy controls, with cSLE patients exhibiting a higher proportion of neutrophils. Moreover, CCR1 and SAMD9L expression levels showed positive correlations with neutrophil infiltration and other immune cell types.ConclusionCCR1 and SAMD9L were identified as potential diagnostic biomarkers for cSLE using machine learning and were validated in multiple datasets. These findings provide novel insights into the biological underpinnings of cSLE.

The diagnostic value and associated molecular mechanism study for fibroblast‐related mitochondrial genes on keloid

Wei,

2024

PurposeThis study aims to reveal the mechanism of fibroblast‐related mitochondrial genes on keloid formation and explore promising signature genes for keloid diagnosis.MethodThe distribution of fibroblasts between the keloid sample and control sample based on three keloid datasets, followed by the differentially expressed genes (DEGs) investigation and associated enrichment analysis. Then, hub genes were explored based on DEGs, mitochondrial genes from an online database, as well as fibroblast‐related genes that were revealed by WCGNA. Subsequently, signature genes were screened through machine learning, and their diagnostic value was validated by nomogram. Moreover, the targeted drugs and related transcriptional regulation of these genes were analyzed. Finally, the verification analysis was performed on signature genes using qPCR analysis.ResultA total of totally 329 DEGs were revealed based on three datasets, followed by enrichment analysis. WGCNA revealed a total of 258 fibroblast‐related genes, which were primarily assembled in functions like muscle tissue development. By using machine learning, we screened four signature genes (ACSF2, ALDH1B1, OCIAD2, and SIRT4) based on eight hub genes (fibroblast‐related mitochondrial genes). Nomogram and validation analyses confirmed the well‐diagnostic performance of these four genes in keloid. Immune infiltration and drug correlation analyses showed that SIRT4 was significantly associated with immune cell type 2 T helper cells and molecular drug cyclosporin. All these findings provided new perspectives for the clinical diagnosis and therapy of keloid.ConclusionThe fibroblast‐related mitochondrial genes including SIRT4, OCIAD2, ALDH1B1, and ACSF2 were novel signature genes for keloid diagnosis, offering novel targets and strategies for diagnosis and therapy of keloid.