Identification of shared key genes and pathways in osteoarthritis and sarcopenia patients based on bioinformatics analysis

Abstract

Objective

Osteoarthritis (OA) and sarcopenia are significant health concerns in the elderly, substantially impacting their daily activities and quality of life. However, the relationship between them remains poorly understood. This study aims to uncover common biomarkers and pathways associated with both OA and sarcopenia.

Methods

Gene expression profiles related to OA and sarcopenia were retrieved from the Gene Expression Omnibus (GEO) database. Differentially expressed genes (DEGs) between disease and control groups were identified using R software. Common DEGs were extracted via Venn diagram analysis. Gene ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analyses were conducted to identify biological processes and pathways associated with shared DEGs. Protein-protein interaction (PPI) networks were constructed, and candidate hub genes were ranked using the maximal clique centrality (MCC) algorithm. Further validation of hub gene expression was performed using 2 independent datasets. Receiver operating characteristic (ROC) curve analysis was used to evaluate the predictive value of key genes for OA and sarcopenia. Mouse models of OA and sarcopenia were established. Hematoxylin-eosin and Safranin O/Fast Green staining were used to validate the OA model. The sarcopenia model was validated via rotarod testing and quadriceps muscle mass measurement. Real-time reverse transcription PCR (real-time RT-PCR) was employed to assess the mRNA expression levels of candidate key genes in both models. Gene set enrichment analysis (GSEA) was conducted to identify pathways associated with the selected shared key genes in both diseases.

Results

A total of 89 common DEGs were identified in the gene expression profiles of OA and sarcopenia, including 76 upregulated and 13 downregulated genes. These 89 DEGs were significantly enriched in protein digestion and absorption, the PI3K-Akt signaling pathway, and extracellular matrix-receptor interaction. PPI network analysis and MCC algorithm analysis of the 89 common DEGs identified the top 17 candidate hub genes. Based on the differential expression analysis of these 17 candidate hub genes in the validation datasets, AEBP1 and COL8A2 were ultimately selected as the common key genes for both diseases, both of which showed a significant upregulation trend in the disease groups (all P<0.05). The value of area under the curve (AUC) for AEBP1 and COL8A2 in the OA and sarcopenia datasets were all greater than 0.7, indicating that both genes have potential value in predicting OA and sarcopenia. Real-time RT-PCR results showed that the mRNA expression levels of AEBP1 and COL8A2 were significantly upregulated in the disease groups (all P<0.05), consistent with the results observed in the bioinformatics analysis. GSEA revealed that AEBP1 and COL8A2 were closely related to extracellular matrix-receptor interaction, ribosome, and oxidative phosphorylation in OA and sarcopenia.

Conclusion

AEBP1 and COL8A2 have the potential to serve as common biomarkers for OA and sarcopenia. The extracellular matrix-receptor interaction pathway may represent a potential target for the prevention and treatment of both OA and sarcopenia.

Among the spectrum of diseases associated with aging, sarcopenia, and osteoarthritis (OA) have emerged as significant health concerns for the elderly. These diseases significantly affect the daily activities of living (ADL) and the overall quality of life (QOL) of older adults^[1]. To alleviate the physical and economic burdens on the elderly, targeted therapeutic strategies and preventive measures for OA and sarcopenia have become a focal point of research.

Sarcopenia, initially defined in 1989, refers to the age-related loss of skeletal muscle strength and function^[2-3]. The prevalence of sarcopenia is high among the elderly, with reports indicating that it affects up to 29% of residents in senior communities and 33% of patients in long-term care facilities^[4]. Sarcopenia can lead to a cascade of negative outcomes, including falls, fractures, hospitalizations, disabilities, and even fatalities, significantly impairing the quality of life and the ability to live independently among the elderly^[5-6] .Sarcopenia has a complex pathogenesis, potentially related to factors such as reduced satellite cell numbers and aging, mitochondrial dysfunction, the loss of motor neurons and reduced activity of neuromuscular junctions, endocrine alterations, and weight loss with decreased appetite, or a combination of these factors^[7].

OA, similar to sarcopenia, poses a formidable health challenge for the elderly. This degenerative joint disease is characterized by the progressive degeneration of cartilage, inflammation of the synovial membrane, formation of osteophytes, and subchondral bone sclerosis^[8]. Clinical symptoms associated with OA include pain, stiffness, and joint functional limitations^[9]. A recent study^[10] indicates that OA impacts approximately 7.6% of the global population, with a projected increase of 60% to 100% by 2050. OA does not exhibit any early symptoms^[11].

OA and sarcopenia often occur concurrently, with sarcopenia potentially increasing the risk of developing OA, and vice versa^[12-14].A cross-sectional study^[15] based on the US National Health and Nutrition Examination Survey (NHANES) found that sarcopenia is associated with the occurrence of OA in adults. Another systematic review and meta-analysis^[16] indicated that sarcopenic obesity and low skeletal muscle mass index are related to an increased risk of knee OA. A recent bidirectional Mendelian randomization study^[17] assessing the causal relationship between sarcopenia and knee OA also showed that individuals with sarcopenia are more likely to develop knee OA. Decreased muscle strength, a primary characteristic of sarcopenia, is also considered a major risk factor for OA^[18]. Reduced lower limb muscle strength may lead to decreased joint stability and altered lower limb alignment, thereby increasing joint stress and the risk of OA. Particularly in obese individuals, this increased load can further exacerbate the progression of OA through metabolic disorders and chronic inflammation^[19]. Conversely, the occurrence of OA may also promote the development of sarcopenia. For example, a study^[20] of women over 70 years old in a German community found that those with hip or lower limb OA have an increased risk of sarcopenia. Individuals with OA may reduce physical activity to avoid pain and stiffness, leading to physical inactivity and a sedentary lifestyle. This can result in decreased energy expenditure, muscle atrophy, and fat accumulation, thereby promoting the development of sarcopenia^[21].

Increased fat mass (with decreased lean body mass) is an important factor in explaining the potential link between sarcopenia and OA. The various cytokines secreted by adipose tissue (such as inflammatory factors, leptin, and adiponectin) may cause systemic low-grade inflammation, leading to reduced muscle mass and cartilage destruction^[22]. On the other hand, extracellular matrix (ECM) remodeling is closely related to the progression of both sarcopenia and OA. Changes in the skeletal muscle ECM partially account for the deteriorated muscle function during aging, playing a potential role in the pathogenesis of sarcopenia^[23]. Meanwhile, increased catabolism of the articular cartilage ECM is a key factor in the development and progression of OA^[24].

Given the shared characteristics of inflammatory response and ECM remodeling, we hypothesize that OA and sarcopenia may share common molecular mechanisms. This study aims to discover shared biomarkers and biological pathways for OA and sarcopenia, to better elucidate their common pathological mechanisms, and to provide the theoretical basis for the early diagnosis, prevention, and treatment of these 2 diseases.

1. Materials and methods

The workflow of this study is depicted in Figure 1.

Figure 1. Flowchart of this research DEGs: Differentially expressed genes; GO: Gene Ontology; KEGG: Kyoto Encyclopedia of Genes and Genomes; PPI: Protein-protein interaction; ROC: Receiver operating characteristic; GSEA: Gene set enrichment analysis.

1.1. Ethics statement

All animal experiments were approved by the Animal Care and Use Committee of the Second Xiangya Hospital (Approval Number: 20240013) and were conducted in compliance with the National Institutes of Health Guidelines for the Care and Use of Laboratory Animals.

1.2. Data acquisition

The gene expression data were acquired from the Gene Expression Omnibus (GEO) database. We utilized “Osteoarthritis” or “Sarcopenia” as the search keywords within the GEO database. The screening criteria are as follows: 1) The organism was restricted to Homo sapiens; 2) the datasets had to consist of messenger RNA transcriptome data; 3) each dataset should encompass more than 20 samples. In accordance with these criteria, we selected the OA expression profile dataset GSE114007 and the sarcopenia expression profile dataset GSE111017. The GSE114007 dataset comprised 18 healthy knee cartilage tissue samples and 20 OA-affected samples. The GSE111017 dataset is a SuperSeries that includes 3 SubSeries: GSE11106, GSE111010, and GSE111016, encompassing a total of 62 muscle biopsy samples from healthy subjects and 33 muscle biopsy samples from sarcopenic subjects.

1.3. Data normalization and identification of differentially expressed genes

For the SuperSeries GSE111017, which includes the series GSE11106, GSE111010, and GSE111016, the datasets were merged into a single dataset. Given that all 3 sequences employ high-throughput sequencing methods that measure the absolute values of gene expression, and exhibit essentially uniform sequencing depths, the “DESeq2” software package in R software (version 4.2.1) was utilized to screen for differentially expressed genes (DEGs), and the variance-stabilizing transformation (VST) function was applied for data normalization. For the GSE114007 dataset, data normalization and analysis of DEGs were performed directly. The criteria for selection were set at |log₂(Fold change)|>0.58 and an adjusted P≤0.05. We then used Venn diagram analysis to identify common DEGs. A hypergeometric distribution test was employed to determine the P-value for the Venn diagram analysis.

1.4. Functional and pathway enrichment analysis

Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analyses of common DEGs were conducted to clarify their potential functions. The 3 main categories of GO enrichment includes biological processes (BP), cellular components (CC), and molecular function (MF). The online annotation tool Database for Annotation, Visualization, and Integrated Discovery (DAVID) was employed to retrieve the results of the GO and KEGG enrichment analyses. The visualization of these results was facilitated through ImageGP.

1.5. Constructing PPI network and identifying candidate hub genes

The protein-protein interaction (PPI) network was used to delve into the relationships between DEGs at the protein level and to identify the hub genes. The PPI network of DEGs was constructed using the Search Tool for the Retrieval of Interacting Genes (STRING) database, with a minimum interaction score set at medium confidence (0.400). Cytoscape (version 3.10.0) was utilized to screen for and visualize candidate hub genes. The CytoHubba plugin within Cytoscape was employed to assign and rank each gene using topological network algorithms, specifically the maximal clique centrality (MCC) algorithm, to identify important nodes.

1.6. Identification of shared key genes

Two additional datasets were used to confirm the candidate hub genes identified via Cytoscape. The GSE51588 dataset comprises expression profiles of 10 normal controls and 40 OA samples, while the GSE38718 dataset includes 14 normal controls and 8 sarcopenia samples. After data normalization, the Wilcoxon test was conducted to assess the differences between control and case samples.

1.7. Description of diagnostic value

All samples in 4 datasets (GSE114007, GSE111017, GSE51588, and GSE38718) were divided into 2 categories: Healthy group and disease group. Subsequently, we utilized the “pROC” package to conduct receiver operating characteristic (ROC) analysis, aiming to assess the predictive potential of our key genes. A value of area under the curve (AUC)>0.7 signifies good predictive power.

1.8. Construction of OA and sarcopenia mouse models

To validate the bioinformatics analysis, we established mouse models for OA and sarcopenia. Experimental animals were procured from Hunan SJA Laboratory Animal Co., Ltd. The mice were allowed to acclimate to the environment for one week before the experimental procedures.

Osteoarthritis-like changes in the knee joint were induced by destabilization of the medial meniscus (DMM) combined with anterior cruciate ligament transection (ACLT). Six-week-old mice were randomly assigned to either a sham operation group (n=6) or an OA group (n=6). The DMM+ACLT surgery was performed as follows: Mice were anesthetized with pentobarbital (1%, 1 mg/0.1 mL), secured once they reached a comatose state, and the hair around the knee joint was shaved and disinfected. An incision was made in the skin and muscle of the medial patellar ligament using a surgical blade to open the joint capsule. The anterior cruciate ligament and a portion of the medial meniscus were transected with ophthalmic scissors. Antibiotics were applied to the wound, and the incision was sutured. In the sham operation group, the joint capsule was opened, but the ACLT and meniscus were left intact, with all other procedures consistent with those of the OA group. Four weeks post-surgery, all mice were sacrificed, and the knee joints and articular cartilage were harvested.

18-month-old C57BL/6J mice, serving as a naturally aged model, were used as the sarcopenia samples (n=5). Muscle function was assessed using the rotarod test, where the rod’s speed incrementally increases from 0 to 30 r/min, with the test lasting for 5 min. The mouse was placed on the rod, and the time interval from the moment when the rotational speed of the rod reached 30 r/min to the instant when the mouse fell off was recorded. The quadriceps muscle mass was measured post-mortem, and samples were collected for subsequent experiments.

1.9. Histological analysis

Hematoxylin-eosin (HE) staining and safranin O-fast green staining were employed to assess pathological changes in the knee joint. Mouse knee joints were fixed in 4% paraformaldehyde for over 24 hours, subsequently decalcified with an ethylenediaminetetra- acetic acid (EDTA) decalcifying solution (G1105, Servicebio, Wuhan, China) for 2 weeks, and then embedded in paraffin. Sections with a thickness of 5 mm were cut from the paraffin blocks and mounted onto glass slides. These prepared tissue sections were stained with HE and safranin O-fast green following the manufacturer’s instructions. The structure and morphology of the knee joint were examined using an inverted microscope (Olympus, Japan).

1.10. Real-time reverse transcription PCR

Total RNA from cartilage and muscle tissue was extracted using TRIzol reagent (AM9738, Thermo Fisher, America) and reverse-transcribed into complementary DNA (cDNA) using a reverse transcription-premixed kit (AG11728, AG, Changsha, China). Real-time reverse transcription PCR (real-time RT-PCR) was conducted with SYBR Green Pro Taq HS Premix (AG11701, AG, Changsha, China) and specific primers. The primer sequences utilized in this study are detailed in Table 1. The relative expression level of the target gene was calculated using the 2^-ΔΔCt method, with GAPDH as the reference gene for normalizing the results.

Table 1.

Primer sequences of AEBP1 and COL8A2

Gene name	NCBI gene ID	Forward primer (5'-3')	Reverse primer (5'-3')
AEBP1	11 568	TTGGAAACGCTGGATCGGTTA	CTTGACCTTGCCAGGCATTT
COL8A2	329 941	GCCCTGGATGAGACTGGTATC	CGGTCAAACCTAACCGGCAT

1.11. Immune cell infiltration analysis

To assess immune function and inflammatory infiltration profiles, we employed single-sample gene set enrichment analysis (ssGSEA) using the “GSVA” package on OA and sarcopenia datasets. The gene sets for 28 types of immune cells can be downloaded from the Tumor and Immune System Interactions Database (TISIDB)^[25].

1.12. Gene set enrichment analysis

Gene set enrichment analysis (GSEA) was conducted to analyze biological and signaling pathways, with the aim of elucidating the roles of selected key genes in the pathogenesis of OA and sarcopenia. Disease samples were stratified into high and low expression groups based on the median expression levels of the key genes. The analysis of DEGs was conducted between the high and low expression groups. Subsequently, the list of DEGs was subjected to GSEA to infer the potential signaling pathways in which the key genes may be implicated. Enrichment results with a P<0.05 were considered to indicate statistically significant differences.

1.13. Statistics analysis

Data processing and analysis were conducted using GraphPad Prism 9.0 software. Data were expressed as the mean±standard error of the mean (SEM). An independent two-sample t-test was utilized to assess differences between 2 groups. A P<0.05 was considered statistically significant.

2. Results

2.1. Common DEGs

The box plots before and after normalization of the 2 datasets are presented in Supplementary Figure 1(https://doi.org/10.57760/sciencedb.xbyxb.00091). As depicted in the volcano plot of Figure 2A, the GSE114007 dataset revealed 3 927 DEGs, comprising 2 129 up-regulated and 1 798 down-regulated DEGs. In the comparison between the sarcopenia group and the control group in GSE111017, 860 DEGs were identified, including 686 up-regulated and 174 down-regulated genes (Figure 2B). Venn diagram analysis results indicated an overlap of 89 common DEGs between GSE114007 and GSE111017, with 76 commonly up-regulated genes (P=8.71e-5) and 13 commonly down-regulated genes (P=0.001; Figure 2C and 2D).

Figure 2. Screening for common DEGs.

A: Volcano plot of DEGs for OA and control groups; B: Volcano plot of DEGs for sarcopenia and control groups; C and D: Venn diagram analysis results of 89 common DEGs in both OA and sarcopenia. FC: Fold change.

2.2. Functional and pathway enrichment analysis of DEGs

As shown by the results of GO analysis on the 89 common DEGs, the BP were highly enriched in signal transduction, cell adhesion, extracellular matrix organization and ossification (Figure 3A). Regarding CC, the genes were primarily associated with the extracellular region, plasma membrane, and extracellular space (Figure 3B). In terms of MF, the DEGs were enriched in functions like extracellular matrix structural constituent, calcium ion binding, and receptor binding (Figure 3C). Additionaly, KEGG enrichment analysis revealed that the genes were primarily enriched in pathways related to protein digestion and absorption, the PI3K-Akt signaling pathway, and ECM receptor interaction (Figure 3D). The comprehensive results of the GO and KEGG enrichment analyses are detailed in Supplementary Table 1 (https://doi.org/10.57760/sciencedb.xbyxb.00091).

Figure 3. Functional and pathway enrichment for 89 common DEGs.

A-C: GO enrichment analysis in biological process (A), cellular component (B), and molecular function (C). D: KEGG pathway analysis map of common DEGs. ECM: Extracellular matrix.

2.3. PPI network

Utilizing the interactions of common DEGs, the PPI networks were generated using STRING, comprising 83 nodes and 90 edges, with the PPI enrichment P<1.0e-16 (Supplementary Figure 2A). Based on the results of the MCC algorithm from the Cytohubba plugin in Cytoscape software, 17 candidate hub genes selected were COL1A1, POSTN, SPP1, ELN, CDH1, FGF18, SERPINE1, THBS2, EFEMP1, COL11A1, OGN, TWIST1, AEBP1, TNFSF11, FLG, TNFRSF11B, and COL8A2 (Supplementary Figure 2B). Detailed results of the PPI network construction and CytoHubba analysis are presented in Supplementary Table 2 (https://doi.org/10.57760/sciencedb.xbyxb.00091).

2.4. Shared key genes

Compared to the normal control group, the analysis of DEGs revealed that of the 17 candidate hub genes, only AEBP1 and COL8A2 exhibited significant differences in both the OA and sarcopenia groups, with up-regulated expression in the disease groups (Figure 4A-4D).

Figure 4. Determination of shared key genes.

A and B: Expression of AEBP1 (A) and COL8A2 (B) in OA samples was statistically higher than in healthy controls within GSE51588 dataset. C and D: Expression of AEBP1 (C) and COL8A2 (D) in sarcopenia samples was statistically higher than in healthy controls within GSE38718 dataset.

2.5. ROC curve of shared key genes

For OA, the AUC values for AEBP1 and COL8A2 in the GSE114007 dataset were 0.900 (95% CI 0.794-1.000) and 0.814 (95% CI 0.678-0.950), respectively (Figure 5A), and in the GSE51588 dataset, they were 0.790 (95% CI 0.660-0.920) and 0.960 (95% CI 0.904-1.000), respectively (Figure 5B). For sarcopenia, the AUC values for AEBP1 and COL8A2 in the GSE111017 dataset were 0.734 (95% CI 0.628-0.840) and 0.754 (95% CI 0.653-0.855), respectively (Figure 5C), and in the GSE38718 dataset, they were 0.920 (95% CI 0.801-1.000) and 0.830 (95% CI 0.596-1.000), respectively (Figure 5D). The AUC values for both AEBP1 and COL8A2 in these 4 datasets exceeded 0.7, indicating their potential utility in predicting both OA and sarcopenia.

Figure 5. Predictive power of shared key genes.

A: ROC curves of AEBP1 and COL8A2 in dataset GSE114007; B: ROC curves of AEBP1 and COL8A2 in dataset GSE51588. C: ROC curves of AEBP1 and COL8A2 in dataset GSE111017; D: ROC curves of AEBP1 and COL8A2 in dataset GSE38718.

2.6. Experimental verification

The results of HE and safranin O-fast green staining are shown in Figure 6A. In the sham operation group, the cartilage surface appeared smooth, with normal cartilage thickness, a clear tide line, intact cartilage structure, and healthy chondrocytes. In contrast, the OA group exhibited joint space narrowing, a rough and uneven cartilage surface, reduced surface chondrocyte, a blurred tide line, and incomplete cartilage structure. The results of the rotarod test are presented in Figure 6B, where the sarcopenia group showed a significantly shorter persistence time compared to the control group on the rotating rod. Moreover, the quadriceps femoris muscle mass was markedly reduced in the sarcopenia group relative to the control group (Figure 6C). The RNA expression levels of AEBP1 and COL8A2 in OA cartilage were significantly higher compared to the sham operation group (P<0.05, Figure 6D). Similarly, the expression levels of AEBP1 and COL8A2 in the quadriceps femoris of the sarcopenia group were significantly higher than those in the young control group (P<0.05, Figure 6E).

Figure 6. Experimental verification of key genes in the OA and sarcopenia mouse model.

A: HE and safranin O-fast green staining of knee joint control and OA groups. B and C: Duration of the roteted test and muscle mass of quadriceps in control and sarcopenia groups. D: Relative gene expression level of AEBP1 and COL8A2 in cartilage tissues from control and OA groups (n=3). E: Relative gene expression level of AEBP1 and COL8A2 in quadriceps muscles from control and sarcopenia groups (n=3).

2.7. Immune cell infiltration analysis

The box plot depicting differential immune cell infiltration revealed significant differences in the penetration of immature dendritic cells, macrophages, T follicular helper cells, gamma delta T cells, type 17 T helper cells, natural killer T cells, regulatory T cells, and activated dendritic cells between OA samples and normal controls (Supplementary Figure 3A, https://doi. org/10.57760/sciencedb.xbyxb.00091). Sarcopenia samples and healthy controls exhibited significant differences in the penetration of type 17 T helper cells, activated CD8 T cells, and monocytes (Supplementary Figure 3B, https://doi.org/10.57760/sciencedb.xbyxb.00091).

In the OA dataset GSE114007, the key genes were positively correlated with T follicular helper cells (AEBP1, adjusted P=5.97E-03; COL8A2, adjusted P=0.028; Supplementary Figure 3C). In the sarcopenia dataset GSE111017, AEBP1 and COL8A2 showed strong correlations with central memory CD8 T cells (AEBP1, adjusted P=1.70E-04; COL8A2, adjusted P=4.62E-05), mast cells (AEBP1, adjusted P=2.18E-06; COL8A2, adjusted P=2.90E-05), central memory CD4 T cells (AEBP1, adjusted P=1.87E-03; COL8A2, adjusted P=4.67E-04), T follicular helper cells (AEBP1, adjusted P=3.81E-03; COL8A2, adjusted P=1.77E-03), and type 17 T helper cells (AEBP1, adjusted P=3.27E-03; COL8A2, adjusted P=1.25E-04; Supplementary Figure 3D, https://doi.org/10.57760/sciencedb.xbyxb.00091). These findings suggest that AEBP1 and COL8A2 may be associated with the composition of these immune cells, with type 17 T helper cells may play a key role in the immune processes in which AEBP1 and COL8A2 affect the progression of OA and sarcopenia.

2.8. Biological processes and functions of shared key genes

GSEA indicated that AEBP1 and COL8A2 were significantly associated with signaling pathways such as ECM-receptor interaction, ribosome, and oxidative phosphorylation (Figure 7A-7D). The detailed results of the GSEA are presented in Supplementary Table 3 (https://doi.org/10.57760/sciencedb.xbyxb.00091).

Figure 7. GSEA enrichment of key genes.

A and B: GSEA analyses on AEBP1 (A) and COL8A2 (B) in OA samples from the GSE114007 dataset; C and D: GSEA analyses on AEBP1 (C) and COL8A2 (D) in sarcopenia samples from the GSE111017 dataset.

3. Discussion

OA and sarcopenia, prevalent among the aging population, adversely affect quality of life, physical function, and mortality rates^[26-27]. An increasing number of studies indicate a strong relationship between OA and sarcopenia; however, the mechanisms have not been fully elucidated. To explore the potential co-pathogenesis of OA and sarcopenia and to provide a theoretical basis for their early diagnosis, prevention, and treatment, this study utilized bioinformatics approaches and experimental validation to investigate the common key genes and associated pathways between these 2 diseases. This study indicates that AEBP1 and COL8A2 are significantly upregulated in individuals with OA or sarcopenia. These genes may be integral to the shared pathogenesis of both diseases and could potentially serve as biomarkers for OA, sarcopenia, and their associated complications. The enrichment analysis results suggest that multiple signaling pathways are closely associated with the expression of AEBP1 and COL8A2, with a particularly notable correlation in the ECM-receptor interaction pathway.

The ECM is a key regulator of tissue organization and homeostasis. The development and progression of various diseases are associated with abnormal ECM remodeling^[28]. In articular cartilage, chondrocytes are tasked with producing the majority of the ECM, which is primarily constituted by collagen networks and proteoglycans, notably aggrecan^[28-29]. The synthesis of ECM is a vital mechanism by which chondrocytes maintain cartilage homeostasis, thereby preserving the structural and functional integrity of the cartilage. Conversely, this capability of chondrocytes may be diminished or absent in patients with OA^[30]. The original publication providing GSE114007 dataset also indicated that active ECM remodeling is a feature of OA pathogenesis^[31]. The skeletal muscle ECM is similarly composed mainly of collagens and other connective tissue elements, including elastin and proteoglycans^[32]. The ECM serves multiple functions within muscle tissue, including providing structural support for muscle fibers, nerves, and blood vessels, as well as facilitating communication with other cells^[33]. Age-related remodeling of the ECM is a hallmark pathological change observed in sarcopenia^[32]. Cells and tissues maintain their structural and functional integrity through ECM-receptor interaction^[34], which can modulate cellular behavior and is essential for cell-to-cell communication, cell proliferation, adhesion, and migration^[35].

AEBP1 encodes aortic carboxypeptidase-like protein (ACLP), which is involved in the organization and remodeling of the ECM through its direct binding to collagens (such as types I, III, and V) and its regulation of collagen fiber formation. ACLP also plays diverse roles in development, tissue repair, and fibrosis. AEBP1 contains several distinct domains(including a discoidin domain), which are thought to mediate various protein-protein interactions within the ECM. Studies have also found that individuals with AEBP1 mutations exhibit clinical phenotypes including OA, suggesting that AEBP1 may be implicated in the pathogenesis of OA^[36]. Obesity is a well-known risk factor for OA and sarcopenia^[37-38]. AEBP1 interacts with ERK1/2 to modulate MAPK activation, thereby inhibiting adipocyte differentiation, a process crucial for adipogenesis. This indicates that AEBP1 and MAPK are key regulators of adipogenesis^[39]. Zhang, et al^[40] similarly reported that AEBP1 plays a pivotal role in regulating obesity by targeting adipose tissue and lipid metabolism pathways, thereby influencing central obesity. Beyond its role in adipogenesis, AEBP1 is also associated with metabolic and inflammatory processes. A study^[41] demonstrated that AEBP1 can activate the NF-κB signaling pathway by binding to IκBα, thereby promoting the expression of inflammatory factors and exerting a pro-inflammatory effect. Elevated AEBP1 expression levels are associated with OA progression, consistent with our findings. AEBP1 exacerbates inflammation by reducing IκBα activity in chondrocytes, activating the NF-κB signaling pathway, and upregulating the expression of inflammatory factors such as TNF-α and IL-6 ^[42] . It also promotes ECM degradation by modulating the expression of MMP13, ADAMTS5, and ADAMTS4, thereby contributing to OA development. This study suggests that AEBP1 may regulate ECM homeostasis by promoting inflammation, thereby participating in OA progression. Additionally, AEBP1 is highly expressed in macrophages and has been shown to induce the expression of various inflammatory mediators, including IL-6, TNFα, MCP-1, and iNOS, and to enhance the inflammatory responsiveness of macrophages by activating NF-κB ^[43]. Based on our immune infiltration results, macrophage infiltration levels are correlated with AEBP1 and COL8A2 expression in both OA and sarcopenia, further indicating AEBP1’s involvement in the inflammatory response of these diseases. Obesity can also lead to increased pro-inflammatory factors and reduced physical activity, triggering systemic low-grade inflammation, which in turn promotes the occurrence of sarcopenia and OA^[44-45]. Moreover, AEBP1 may act as a negative regulator of skeletal muscle cell differentiation by transcriptionally repressing myogenesis-related genes^[46], and abnormal myogenesis is one of the important mechanisms underlying sarcopenia^[47]. In summary, AEBP1 may influence the pathogenesis and progression of OA and sarcopenia by modulating adipogenesis and inflammatory responses.

COL8A2 encodes the alpha-2 chain of type VIII collagen, a key ECM protein involved in the construction and maintenance of the ECM and in interactions with other ECM molecules^[48]. COL8A2 plays a regulatory role in cell proliferation and migration and is crucial for cell structure and integrity^[49]. Type VIII collagen is expressed in various tissues, including the heart, brain, liver, lung, muscle, and certain cartilages, and its overexpression is known to promote angiogenesis, tissue remodeling, and fibrosis^[50]. Consistent with our findings, studies^[51-52] have also shown increased COL8A2 expression in patients with OA. The type VIII collagen encoded by COL8A2 appears to be secreted by rapidly proliferating cells, which are more widely expressed in OA cartilage, potentially explaining the increased expression of COL8A2 in OA cartilage. A research^[53] has demonstrated significant age-related differential expression of COL8A2 in bone marrow-derived mesenchymal stem cells (bmMSCs), with upregulation of COL8A2 expression as age increases. Given that bmMSCs are a major source of chondrocytes, COL8A2 may be associated with age-related pathological changes in OA. Moreover, angiogenesis is intimately linked with chronic inflammation, playing a crucial role in enhancing and sustaining inflammatory responses^[54]. In adults, normal articular cartilage is avascular; while angiogenesis within the cartilage and the formation of osteophytes are significant pathological features of OA^[54]. Consequently, the expression of COL8A2 may be indirectly associated with the development of OA. Similarly, given its association with inflammation, COL8A2 may also have a role in sarcopenia. In addition, COL8A2 is a component of the ECM, which, as a scaffold for cell-matrix interactions, plays a crucial role in many physiological activities within skeletal muscle tissue, and muscle aging is significantly influenced by the ECM^[23]. This may suggest that COL8A2 indirectly affects muscle structure and function by influencing the integrity and function of the ECM, thereby participating in the pathogenesis of sarcopenia. However, the precise mechanisms through which COL8A2 influences OA and sarcopenia remain elusive and warrant further investigation.

This study has several limitations. Firstly, this study lacks human clinical sample data and validation at the protein expression level to confirm the shared key genes and relevant pathways identified. Future research should collect human cartilage and muscle tissues and validate the changes in protein expression of AEBP1 and COL8A2 through immunohistochemistry and Western blotting experiments. Secondly, this study does not encompass patients with both OA and sarcopenia. Patients with comorbidities may exhibit unique gene expression patterns and biological characteristics that are not present in patients with only one of the conditions. The absence of data from comorbid patients may lead to the omission of critical biological information, thereby affecting the comprehensiveness and accuracy of the results. However, no existing dataset currently includes patients suffering from both conditions. Future research should aim to incorporate patients with both diseases to provide a more comprehensive understanding. Thirdly, the sample size of the datasets was relatively small. This may lead to insufficient statistical power, reducing the ability to detect significant results and potentially raising concerns about the representativeness of the data. In future research, we plan to increase the sample size by leveraging the latest data from other public databases or by conducting multicenter studies. This approach will enhance the reliability and applicability of our bioinformatics analyses, thereby providing stronger support for clinical diagnosis and treatment.

This research explores AEBP1 and COL8A2 as potential biomarkers for the diagnosis and treatment of OA and sarcopenia, utilizing bioinformatics methods. The ECM-receptor interaction, a significant pathway, could potentially serve as a target for the prevention and treatment of OA and sarcopenia.

Funding Statement

This work was supported by the National Natural Science Foundation of China (82060418).

Conflict of Interest

The authors declare that they have no conflicts of interest to disclose.

AUTHORS’CONTRIBUTIONS

SUN Yuyan Research design, data analysis, paper writing and revision; LUO Ziyu Data analysis and revision; LING Huixian, WU Sha, SHEN Hongwei, FU Yuanyuan, NGO Thainamanh, and WANG Wen Paper modification; KONG Ying Research design, paper supervision and revision. The final version of the manuscript has been approved and read by all authors

Note

http://xbyxb.csu.edu.cn/xbwk/fileup/PDF/202503430.pdf