Sarcopenia is a disease characterized by the gradual loss of skeletal muscle mass, strength, and function, typically associated with aging (Cruz-Jentoft et al., 2019). This common age-related condition significantly impacts an individual’s physical health and overall quality of life (Cruz-Jentoft and Sayer, 2019). Sarcopenia becomes more prevalent with advancing age, with its incidence rising significantly among the elderly population. It affects both men and women, but its prevalence is particularly high among older adults and individuals with certain underlying health conditions (Pascual-Fernández et al., 2020). Therefore, identifying new biomarkers and uncovering immune mechanisms are crucial for prevention and treatment.

Inflammation is defined as a series of tissue responses triggered by injury, which is closely associated with various diseases (Franceschi and Campisi, 2014). Numerous studies have demonstrated that inflammation ultimately influences the mass, strength, and function of skeletal muscle by regulating protein synthesis and degradation within these muscles (Franceschi and Campisi, 2014; Zembron-Lacny et al., 2019). Tumor necrosis factor α (TNF-α) plays a pivotal role in the degradation of muscle proteins via the nuclear factor-κB (NF-κB) signaling pathway (Hirata et al., 2022). Interleukin 6 (IL-6)(IL-6), a pro-inflammatory cytokine, mediates processes that lead to either reduction or stabilization of muscle atrophy (Hirata et al., 2022). Additionally, interleukin 1 (IL-1) and IL-18 are also implicated in inflammation-mediated muscle atrophy (McBride et al., 2017; Dalle et al., 2017). While inflammation is a well-known contributor to muscle degradation, recent studies have begun to explore more specific inflammatory mechanisms such as pyroptosis, which may provide new insights into the molecular underpinnings of sarcopenia.

Pyroptosis involves apoptotic cells characterized by programmed cell death and is linked to inflammatory processes (Picca and Calvani, 2021). This phenomenon is typically initiated through two molecular pathways: one classical pathway mediated by Caspase-1 and another non-classical pathway involving Caspases 4/5/11, culminating in pyroptosis executed by members of the gasdermin protein family (Rosenberg, 1997; Ibebunjo et al., 2013; Rudolf et al., 2014). Both in vivo and in vitro investigations have indicated that activation of the NLRP3 inflammasome induces pyroptosis while promoting activation of the ubiquitin-proteasome system (UPS), resulting in muscle proteolysis and subsequent muscle atrophy (You et al., 2023). Factors related to inflammation and pyroptosis may serve as molecular markers for early diagnosis of muscular atrophy since their levels increase with age-related changes in muscle.

Currently, there are no predictive models based on the characteristics of IPRGs in musculoskeletal aging. Combining inflammation, pyroptosis, and immune infiltration analysis can more accurately identify diagnostic biomarkers. In this study, we systematically analyzed the expression of IPRGs, which play pivotal roles in inflammation-mediated muscle atrophy, along with their relationship to immune infiltration. We identified six key characteristic genes and constructed a diagnostic model, validated through RT-qPCR in a sarcopenia cell model, to support the early diagnosis of sarcopenia.

Sarcopenia has emerged as a significant health concern due to an aging population; thus, early diagnosis of muscular atrophy has become imperative. In recent years, accurately predicting sarcopenia through screening relevant genes as diagnostic biomarkers has gained critical importance.

Although the pathogenesis of musculoskeletal aging is not fully understood, inflammation, pyroptosis, and immune infiltration in skeletal muscle cells are believed to play significant roles in its molecular mechanisms. Recent studies have indicated that the NLRP3 inflammasome and pyroptosis contribute to muscle dysfunction by reducing glycolytic potential and decreasing muscle fiber size (McBride et al., 2017). Additionally, chronic inflammation is linked to key characteristics of sarcopenia, such as increased skeletal muscle wasting, loss of strength, and functional impairment (Dalle et al., 2017; Picca and Calvani, 2021). Therefore, we hypothesize that inflammation and pyroptosis may be central mechanisms in musculoskeletal aging. To date, no studies have specifically explored the relationship between inflammation and pyroptosis in sarcopenia.

In this study, we developed and validated a predictive model for sarcopenia through bioinformatics analysis, which led to the identification of potential biomarkers. These findings are further supported by the confirmation of these biomarkers in follow-up experiments. Our study identified potential biomarkers for sarcopenia using LASSO analysis and experimental validation.

Firstly, we identified DEGs in the combined dataset through differential expression analysis. Next, we applied a LASSO regression model to 37 common genes related to inflammation and pyroptosis to screen for potential hub genes closely associated with the development of sarcopenia. GO enrichment analysis revealed that these DEGs are primarily involved in oxidative and metabolic processes within mitochondria. KEGG pathway enrichment analysis also indicated that these DEGs are strongly linked to pathways associated with various neurodegenerative diseases. Previous studies have shown that aging leads to gradual disorders in the neuromuscular system (Rosenberg, 1997). The neuromuscular junction (NMJ), a central component of this system, is a crucial synapse connecting motor nervous system excitability with skeletal muscle contraction (Ibebunjo et al., 2013; Rudolf et al., 2014; Punga and Ruegg, 2012). Recent research has increasingly highlighted the NMJ’s role in sarcopenia development (Gonzalez-Freire et al., 2014; Pannérec et al., 2016; Monti et al., 2021). On one hand, the NMJ is rich in mitochondria that supply the energy needed for neuromuscular transmission in the form of ATP (Anagnostou and Hepple, 2020; Zhou, 2021; Spendiff et al., 2016). On the other hand, mitochondria depend on PGC-1α as a cofactor for their transcriptional activities and normal metabolic functions (Austin and St-Pierre, 2012; St-Pierre et al., 2006). Furthermore, correlation analyses of the six hub genes with immune cells revealed strong associations with monocytes, macrophages, and mast cells, consistent with observations from DEGs and immune cell correlation analyses. In 2021, Afandy et al. found significantly higher MCP-1 levels in the sarcopenia group compared to the non-sarcopenia group (Afandy et al., 2021). MCP-1 promotes the migration and infiltration of monocytes to inflammation sites, contributing to muscle atrophy (Franceschi and Campisi, 2014; Bettcher et al., 2019; Curtis et al., 2015; Sell et al., 2006). Several studies have demonstrated that macrophages play a role in the regeneration and repair of aging-associated skeletal muscle (Sell et al., 2006; Sousa-Victor et al., 2015; Tidball, 2017; Fuchs and Blau, 2020; Zhang et al., 2020), exerting anti-inflammatory effects, clearing dead cells, and facilitating tissue repair through altered polarization states (Tidball, 2017; Fuchs and Blau, 2020). Additionally, a study revealed a significant increase in mast cells in the skeletal muscle of mice with malignant disease (Widner et al., 2021). These findings are consistent with our correlation analysis results.

The highlight of our study is the identification of six potential diagnostic markers for sarcopenia: BTG2, FOXO3, AQP9, SCN1B, CYCS, and GPC3.

Protein BTG2, also known as B-cell translocation gene 2, BTG family member 2, NGF-inducible anti-proliferative protein PC3, or NGF-inducible protein TIS21, plays a crucial role in regulating cell senescence, differentiation, and various other biological processes (Mauxion et al., 2009; Wheaton et al., 2010; Passeri et al., 2006). It can be induced by p53 to inhibit the cell cycle (Zhang et al., 2011), and its expression can be upregulated by stimuli such as IL-6 and growth factors (Yuniati et al., 2019). Previous studies have shown that miR-103-3p and miR-222-3p may influence the proliferation and differentiation of C2C12 myoblasts by targeting BTG2 (He et al., 2023; Yang et al., 2019). Peng et al. demonstrated that BTG2 could be a target for muscle aging by regulating MuSCs senescence (Peng et al., 2023). Our findings that BTG2 expression is significantly increased in the muscle atrophy group align with these studies, although the precise mechanism by which BTG2 regulates muscle atrophy requires further investigation.

FOXO family proteins, which are known for their highly conserved structural domains, are involved in crucial intracellular processes, including cell cycle regulation, oxidative stress response, inflammation, apoptosis, and energy metabolism (Dijkers et al., 2000a; Medema et al., 2000; Furukawa-Hibi et al., 2002; Kops et al., 2002; Ogg et al., 1997; Brunet et al., 1999; Dijkers et al., 2000b; Li et al., 2010). In mammals, the primary FOXO family members are FOXO1, FOXO3, FOXO4, and FOXO6 (Cao et al., 2023). Notably, FOXO3 is predominantly expressed in skeletal muscle (Curtis et al., 2015). Numerous studies have demonstrated that FOXO3 enhances the expression of atrogin-1 and MuRF1 through the IGF1-PI3K-AKT signaling pathway, leading to muscle atrophy (Sandri et al., 2004; Kamei et al., 2004; Southgate et al., 2009; Liu et al., 2007; Schiaffino et al., 2013). Additionally, activation of AMPK has been shown to promote muscle atrophy by increasing FOXO3 expression (Romanello et al., 2010; Nakashima and Yakabe, 2007; Sanchez et al., 2012). Interestingly, oxidative stress induction contributes to the activation of FOXO3 in models of disuse-mediated muscle atrophy (Suzuki et al., 2007; Piétri-Rouxel et al., 2010; Levine et al., 2008). It has also been reported that PGC-1α can work in conjunction with FOXO3 to mitigate muscle atrophy (Puigserver et al., 2003; Wu et al., 1999; Geng et al., 2011; Wenz et al., 2009). Aquaporins (AQPs) are a class of membrane channel proteins classified into water-selective channel proteins and aquaglyceroporins (Agre, 2004; Ishibashi et al., 1997; Ishibashi et al., 1998; Kishida et al., 2000). As a member of the latter group, AQP9 is primarily expressed in hepatocytes and plays a crucial role in gluconeogenesis and lipid metabolism by transporting glycerol (Trinchese et al., 2023; Inoue et al., 2009). Yang et al. first identified AQP9 expression in rat skeletal muscle in 2000 (Leek et al., 2012), with subsequent studies by Wang et al. and Inoue et al. confirming its presence in human skeletal muscle as well (Inoue et al., 2009; Yang et al., 2000; Wang et al., 2003). Although no reports have yet linked AQP9 directly to muscle atrophy, the skeletal muscle, as the largest endocrine organ in the body, also exhibits glycerol kinase activity (Inoue et al., 2009). Ren et al. suggested that the PI3K-AKT signaling pathway inhibits AQP9 (Ren and Wang, 2018), hinting at a potentially unexplored relationship between AQP9 and muscle atrophy that merits further investigation.

SCN1B, encoding the β1 and β1B subunits of voltage-gated sodium channels, is implicated in epilepsy and arrhythmia syndromes (O'Malley and Isom, 2015; Cervantes et al., 2022). It is widely recognized that muscle strength is influenced by both skeletal muscle and neurological factors (Arnold and Clark, 2023). The neuromuscular junction plays a critical role by converting electrical signals from presynaptic neurons into chemical signals that trigger muscle fiber contraction. Thus, SCN1B may impact muscle force production by regulating sodium influx and action potential generation. There is a clear positive relationship between SCN1B and muscle strength, consistent with our findings.

CYCS encodes cytochrome c, a mitochondrial membrane protein crucial for oxidative phosphorylation and apoptosis (Earnshaw, 1999; Liu et al., 1996; Kluck et al., 1997; Zou et al., 1997). Huang et al. demonstrated that overexpression of Mdfi (Myod family inhibitor) increased CYCS expression, promoting differentiation in C2C12 cells (Huang et al., 2021). Conversely, Kan et al. observed a significant decrease in CYCS expression with skeletal muscle aging (Kan et al., 2021). Baechler et al. identified that mitochondrial autophagy can activate CYCS to support myogenic differentiation (Baechler et al., 2019). These studies collectively highlight a strong association between CYCS and sarcopenia, although the underlying mechanisms warrant further investigation.

GPC3, a member of the glypican family, is typically expressed in embryonic tissues and various organs, and is notably overexpressed in hepatocellular carcinoma (Zhou et al., 2018; Wang et al., 2014; Xu et al., 2021; Qin et al., 2020). A study published in 2014 showed that GPC3 expression decreases with age in mouse skeletal muscle (Jones et al., 2014). However, our study did not find a significant difference in GPC3 expression between sarcopenia and control groups. This discrepancy may be due to the limited sample size; we plan to expand our sample and validate these results in future experiments and additional models.

In summary, our study developed and validated a risk-prediction model for sarcopenia, offering a precise biological tool for diagnosing sarcopenia in primary healthcare settings. By accurately identifying these potential biomarkers, the model aims to reduce instances of missed and misdiagnosed sarcopenia, thereby allowing for timely early intervention.

In this study, we identified correlations between inflammation and pyroptosis-related genes, finding that 37 genes were differentially expressed in the sarcopenia group compared to controls. This indicates a significant interaction between inflammation and pyroptosis in the development of sarcopenia. Through bioinformatics analysis, we screened six IPRGs as potential diagnostic biomarkers for sarcopenia. We then developed a novel nomogram model based on these IPRGs, which demonstrated high diagnostic performance. The ROC curve analysis confirmed the significant predictive value of these biomarkers. Additionally, validation in a sarcopenia cell model supported the reliability of these six characteristic genes, reinforcing their potential as diagnostic biomarkers for sarcopenia.

The statistical analysis was performed using R software (version4.2.0) and GraphPad Prism (Version 9.0). Continuous variables were expressed as mean ± SD or median (quartile). The Student’s t-test and Mann–Whitney test were used to compare continuous variables with and without normal distribution, respectively. Categorical variables were presented as counts (percentages) and analyzed using the chi-square test. All statistical p-values were two-sided, with p < 0.05 considered statistically significant.