Deciphering Obesity-Related Gene Clusters Unearths SOCS3 Immune Infiltrates and 5mC/m6A Modifiers in Ossification of Ligamentum Flavum Pathogenesis

Abstract

BackgroundOssification of ligamentum flavum (OLF) is an insidious and debilitating heterotopic ossifying disease with etiological heterogeneity and undefined pathogenesis. Obese individuals predispose to OLF, whereas the underlying connections between obesity phenotype and OLF pathomechanism are not fully understood. Therefore, this study aims to explore distinct obesity-related genes and their functional signatures in OLF.MethodsThe transcriptome sequencing data related to OLF were downloaded from the GSE106253 in the Gene Expression Omnibus (GEO) database. The obesity-related differentially expressed genes (ORDEGs) in OLF were screened, and functional and pathway enrichment analysis were applied for these genes. Furthermore, protein-protein interactions (PPI), module analysis, transcription factor enrichment analysis (TFEA), and experiment validation were used to identify hub ORDEGs. The immune infiltration landscape in OLF was depicted, and correlation analysis between core gene SOCS3 and OLF-related infiltrating immune cells (OIICs) as well as 5mC/m6A modifiers in OLF was constructed.ResultsNinety-nine ORDEGs were preliminarily identified, and functional annotations showed these genes were mainly involved in metabolism, inflammation, and immune-related biological functions and pathways. Integrative bioinformatic algorithms determined a crucial gene cluster associated with inflammatory/immune responses, such as TNF signaling pathway, JAK-STAT signaling pathway, and regulation of interferon-gamma-mediated signaling. Eight hub ORDEGs were validated, including 6 down-regulated genes (SOCS3, PPARG, ICAM-1, CCL2, MYC, and NT5E) and 2 up-regulated genes (PTGS2 and VEGFA). Furthermore, 14 differential OIICs were identified by ssGSEA and xCell, and SOCS3 was overlapped to be the core gene, which was associated with multiple immune infiltrates (dendritic cells, macrophage, and T cells) and six m6A modifiers as well as four 5mC regulators in OLF. Reduced SOCS3 and FTO expression and up-regulated DNMT1 level in OLF were validated by Western blotting.ConclusionThis study deciphered immune/inflammatory signatures of obesity-related gene clusters for the first time, and defined SOCS3 as one core gene. The crosstalk between 5mC/m6A methylation may be a key mediator of SOCS3 expression and immune infiltration. These findings will provide more insights into molecular mechanisms and therapeutic targets of obesity-related OLF.

Safety and Efficacy Outcomes Following Spinal Endoscopic Procedures for Thoracic Ligamentous Ossification

“…38,39 Some reports have indicated that a range of factors including genetics, metabolic abnormalities, inflammation, and mechanical stress contribute to TOLF incidence and progression. [40][41][42] Age, body mass index, and smoking have all been identified as independent risk factors associated with TOLF. [43][44][45][46] While overall disease incidence is low, TOLF patients frequently suffer from nerve damage 47,48 developing symptoms that progress with the increasing severity of spinal stenosis and spinal cord compression, culminating in complete paraplegia in some cases.…”

“…Reports focused on OLF are increasing outside of East Asia as reported case numbers continue to climb 38,39 . Some reports have indicated that a range of factors including genetics, metabolic abnormalities, inflammation, and mechanical stress contribute to TOLF incidence and progression 40–42 . Age, body mass index, and smoking have all been identified as independent risk factors associated with TOLF 43–46 .…”

Unearths IFNB1 immune infiltrates in SOP-related ossification of ligamentum flavum pathogenesis

“…The pathological process of OLF was similar to that of endochondral ossification and was characterized by the formation of heterotopic mature bone, which involved several stages including chondrocyte proliferation, fibroblast hyperplasia, osteogenesis, and maturation [ 15 , 16 ]. Since there was a selectively high prevalence of the condition in certain geographic areas, the underlying mechanisms of OLF were initially considered to be primarily linked to congenital (genetic) factors; however, as research progressed, non-genetic (environmental) factors such as age, inflammation, biomechanics, and so on began to emerge, causing OLF to gradually be considered a multifactorial disease [ [3] , [4] , [5] ]. These risk factors largely overlapped with SOP, creating a complex relationship between OLF and SOP.…”

“…To forecast protein interactions and build a PPI network, SODEGs were uploaded to the STRING database ( http://string-db.org ). After taking into account the previous literature and our deliberations [ 3 , 12 , 13 ], an interaction score greater than 0.4 was finally chose to balance between including a sufficient number of potential interactions for downstream analysis and minimizing the inclusion of false positive interactions. The edge-betweenness and random walk methods were subsequently employed to emphasize sub-networks or neighborhoods of the gene connectivity data from the STRING database, and the divided clusters were then depicted by further function enrichment in R software.…”

“…Till now, congenital (genetic factors) and non-genetic factors such as obesity, age, inflammatory responses, biomechanics, etc. have been thought to be implicated in the development of OLF [ [3] , [4] , [5] ]; however, its precise etiology remains vague. Epidemiological research had shown that the OLF were closely related to aging and that older individuals were more likely to experience OLF [ 6 , 7 ].…”