Identification of aging-related biomarkers and immune infiltration characteristics in osteoarthritis based on bioinformatics analysis and machine learning.
Osteoarthritis (OA) is a degenerative disease closely related to aging. Nevertheless, the role and mechanisms of aging in osteoarthritis remain unclear. This study aims to identify potential aging-related biomarkers in OA and to explore the role and mechanisms of aging-related genes and the immune microenvironment in OA synovial tissue.
Normal and OA synovial gene expression profile microarrays were obtained from the Gene Expression Omnibus (GEO) database and aging-related genes (ARGs) from the Human Aging Genomic Resources database (HAGR). Gene Ontology (GO), Kyoto Encyclopedia of Genes and Genomes (KEGG), Disease Ontology (DO), and Gene set variation analysis (GSVA) enrichment analysis were used to uncover the underlying mechanisms. To identify Hub ARDEGs with highly correlated OA features (Hub OA-ARDEGs), Weighted Gene Co-expression Network Analysis (WGCNA) and machine learning methods were used. Furthermore, we created diagnostic nomograms and receiver operating characteristic curves (ROC) to assess Hub OA-ARDEGs' ability to diagnose OA and predict which miRNAs and TFs they might act on. The Single sample gene set enrichment analysis (ssGSEA) algorithm was applied to look at the immune infiltration characteristics of OA and their relationship with Hub OA-ARDEGs.
We discovered 87 ARDEGs in normal and OA synovium samples. According to functional enrichment, ARDEGs are primarily associated with inflammatory regulation, cellular stress response, cell cycle regulation, and transcriptional regulation. Hub OA-ARDEGs with excellent OA diagnostic ability were identified as MCL1, SIK1, JUND, NFKBIA, and JUN. Wilcox test showed that Hub OA-ARDEGs were all significantly downregulated in OA and were validated in the validation set and by qRT-PCR. Using the ssGSEA algorithm, we discovered that 15 types of immune cell infiltration and six types of immune cell activation were significantly increased in OA synovial samples and well correlated with Hub OA-ARDEGs.
Synovial aging may promote the progression of OA by inducing immune inflammation. MCL1, SIK1, JUND, NFKBIA, and JUN can be used as novel diagnostic biomolecular markers and potential therapeutic targets for OA.
Zhou J
，Huang J
，Li Z
，Song Q
，Yang Z
，Wang L
，Meng Q
... -
《Frontiers in Immunology》
Identification of autophagy-related genes in neuropathic pain through bioinformatic analysis.
Neuropathic pain (NP) is one of the most common types of chronic pain and significantly compromises the quality of life. Autophagy is an intracellular catabolic process that is required to maintain cellular homeostasis in response to various stresses. The role of autophagy-related genes in the diagnosis and treatment of neuropathic pain remains unclear.
We identified autophagy-related differentially expressed genes (ARDEGs) and differentially expressed miRNAs (DE-miRNAs) in neuropathic pain by bioinformatics analysis of the GSE145226 and GSE145199 datasets. These ARDEGs and their co-expressed genes were subjected to Gene Ontology (GO), Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analysis, Gene Set Enrichment Analysis (GSEA) and friends analysis. Meanwhile, we constructed TFs-ARDEGs, miRNA-ARDEGs regulatory network through ChIPBase database and the HTFtarget database, multiMir R package. Finally, we performed immune infiltration analysis of ARDEGs by Single Sample Gene Set Enrichment Analysis (ssGSEA).
We identified 2 potential autophagy-related differentially expressed genes (Sirt2 and ST7) that may be closely associated with the pathogenesis of neuropathic pain. GO, KEGG and GSEA analysis revealed that these two ARDEGs were mainly enriched in pyridine nucleotide metabolic process, nicotinamide nucleotide metabolic process, Nicotinate and nicotinamide metabolism, NF-κB pathway, KRAS signaling, P53 pathway. In the TFs-ARDEGs and miRNA-ARDEGs regulatory network, miR-140-5p and Cebpb were predicted to be as crucial regulators in the progression of NP. For the ssGSEA results, Sirt2 was positively correlated with Eosinophil and Effector memory CD8+ T cell infiltration, which suggested that it may be involved in the regulation of neuroimmune-related signaling.
Two autophagy-related differentially expressed genes, especially Sirt2, may be potential biomarkers for NP, providing more evidence about the crucial role of autophagy in neuropathic pain.
Tian S
，Wu L
，Zheng H
，Zhong X
，Yu X
，Wu W
... -
《HEREDITAS》
Machine learning and experimental validation identified autophagy signature in hepatic fibrosis.
The molecular mechanisms of hepatic fibrosis (HF), closely related to autophagy, remain unclear. This study aimed to investigate autophagy characteristics in HF.
Gene expression profiles (GSE6764, GSE49541 and GSE84044) were downloaded, normalized, and merged. Autophagy-related differentially expressed genes (ARDEGs) were determined using the limma R package and the Wilcoxon rank sum test and then analyzed by GO, KEGG, GSEA and GSVA. The infiltration of immune cells, molecular subtypes and immune types of healthy control (HC) and HF were analyzed. Machine learning was carried out with two methods, by which, core genes were obtained. Models of liver fibrosis in vivo and in vitro were constructed to verify the expression of core genes and corresponding immune cells.
A total of 69 ARDEGs were identified. Series functional cluster analysis showed that ARDEGs were significantly enriched in autophagy and immunity. Activated CD4 T cells, CD56bright natural killer cells, CD56dim natural killer cells, eosinophils, macrophages, mast cells, neutrophils, and type 17 T helper (Th17) cells showed significant differences in infiltration between HC and HF groups. Among ARDEGs, three core genes were identified, that were ATG5, RB1CC1, and PARK2. Considerable changes in the infiltration of immune cells were observed at different expression levels of the three core genes, among which the expression of RB1CC1 was significantly associated with the infiltration of macrophage, Th17 cell, natural killer cell and CD56dim natural killer cell. In the mouse liver fibrosis experiment, ATG5, RB1CC1, and PARK2 were at higher levels in HF group than those in HC group. Compared with HC group, HF group showed low positive area in F4/80, IL-17 and CD56, indicating decreased expression of macrophage, Th17 cell, natural killer cell and CD56dim natural killer cell. Meanwhile, knocking down RB1CC1 was found to inhibit the activation of hepatic stellate cells and alleviate liver fibrosis.
ATG5, RB1CC1, and PARK2 are promising autophagy-related therapeutic biomarkers for HF. This is the first study to identify RB1CC1 in HF, which may promote the progression of liver fibrosis by regulating macrophage, Th17 cell, natural killer cell and CD56dim natural killer cell.
Huang Y
，Luo W
，Yang Z
，Lan T
，Wei X
，Wu H
... -
《Frontiers in Immunology》
ResearchGate
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钛学术
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