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Int J Biol Sci 2024; 20(15):6038-6055. doi:10.7150/ijbs.103977 This issue Cite

Review

Multi-Omics Approaches to Discover Biomarkers of Thyroid Eye Disease: A Systematic Review

Haiyang Zhang^1,2,3,†, Yuyu Zhou^1,2,3,†, Baiguang Yu^1,2,3,†, Yuyang Deng^1,2,3, Yang Wang^1,2,3, Sijie Fang^1,2,3, Xuefei Song^1,2,3, , Xianqun Fan^1,2,3, , Huifang Zhou^1,2,3,

1. Department of Ophthalmology, Shanghai Ninth People's Hospital, Shanghai Jiao Tong University School of Medicine, No.639, Zhizaoju Road, Huangpu District, Shanghai, China.
2. Shanghai Key Laboratory of Orbital Diseases and Ocular Oncology, Shanghai, China.
3. Center for Basic Medical Research and Innovation in Visual System Diseases, Ministry of Education, Shanghai, China.
†Haiyang Zhang, Yuyu Zhou, and Baiguang Yu contributed equally to this manuscript.

✉ Corresponding authors: Huifang Zhou, fangzzfangedu.cn; Xianqun Fan, fanxqedu.cn; Xuefei Song, songxuefeiedu.cn.

Abstract

Thyroid eye disease (TED) is an organ-specific autoimmune disorder that significantly impacts patients' visual function, appearance, and well-being. Despite existing clinical evaluation methods, there remains a need for objective biomarkers to facilitate clinical management and pathogenesis investigation. Rapid advances in multi-omics technologies have enabled the discovery and development of more informative biomarkers for clinical use. This systematic review synthesizes the current landscape of multi-omics approaches in TED research, highlighting the potential of genomics, transcriptomics, proteomics, metabolomics, and microbiomics to uncover novel biomarkers. Our review encompasses 69 studies involving 1,363 TED patients and 1,504 controls, revealing a wealth of biomarker candidates across various biological matrices. The identified biomarkers reflect alterations in gene expression, protein profiles, metabolic pathways, and microbial compositions, underscoring the systemic nature of TED. Notably, the integration of multi-omics data has been pivotal in enhancing our understanding of TED's molecular mechanisms and identifying diagnostic and prognostic markers with clinical potential.

Keywords: Thyroid eye disease, Biomarker, Multi-omics, Bioinformation

Introduction

Thyroid eye disease (TED), also known as thyroid-associated ophthalmopathy (TAO) and Graves' orbitopathy (GO), is an organ-specific autoimmune disorder characterized by the enlargement of the extraocular muscles and an increase in fatty or connective tissue volume. It is the most frequent extrathyroidal manifestation of Graves' disease (GD) and occasionally occurs in patients with autoimmune thyroiditis and other thyroid diseases, resulting from a combination of genetic and environmental factors [1]. The estimated incidence rate is 4.2 per 100,000 person-years. Female patients outnumber males, and it tends to occur in individuals aged 40-50 years and 60-70 years [2]. The clinical manifestations of TED include upper eyelid retraction, oedema, erythema of the periorbital tissues and conjunctivae, exophthalmos, dry eye, diplopia, and strabismus, significantly affecting the quality of life of patients [3, 4].

The pathogenesis of TED is not yet fully understood, but autoantibodies to the thyroid-stimulating hormone receptor (TSH-R) and insulin-like growth factor-1 receptor (IGF-1R) are thought to play a key role [4, 5]. Current disease activity scoring relies on the European Group on Graves' Orbitopathy (EUGOGO) system for clinical activity scores (CAS), which considers the presence of pain, redness, swelling, and functional impairment of specific ocular and orbital structures [6, 7]. However, the pathological processes of TED extend beyond orbital inflammation and tissue remodeling to include immune and metabolic changes in peripheral blood [8-11]. These changes serve as important biomarkers for a deeper understanding of the disease's complexity. For instance, intraorbital biomarkers may describe the activation of orbital fibroblasts, changes in the fibrosis of extraocular muscles, and the proliferation of adipose tissues [12]. In peripheral blood, biomarkers related to inflammation, immune cell activation, and autoimmune responses may be observed, such as elevated levels of thyroid-stimulating hormone receptor antibodies (TRAb), the presence of specific antibodies, or alterations in the proportions of immune cell subsets [13]. These biomarkers are valuable not only for early diagnosis and assessment of disease activity but also for predicting disease progression and treatment response. Therefore, identifying and validating these biomarkers is crucial.

With the rapid development of high-throughput sequencing technology and the popularization of public databases, multi-omics studies using bioinformatics analysis have been conducted to identify new diagnostic or therapeutic biomarkers for various diseases, including cancers and autoimmune disorders [14, 15]. According to the central dogma of molecular biology, omics fields such as genomics, epigenomics, transcriptomics, proteomics, metabolomics, and lipidomics have a promising role in investigating more biomarkers and comprehensively understanding the molecular mechanisms of TED [16]. For example, several microarray gene profile studies on TED have been performed to identify differentially expressed genes (DEGs) and key pathways, revealing the underlying pathological mechanisms of TED [17-20]. Using mass spectrometry profiling, differences in proteins between TED patients and control subjects can be investigated, potentially serving as biomarkers for aiding the detection and prognosis of TED in the future [21-24]. Furthermore, microbiomics research is indeed crucial as it sheds light on the connection between the gut microbiota and systemic immune responses that may influence the pathogenesis of TED, broadening the understanding of TED beyond the traditional central dogma and opening up new avenues for diagnosis, treatment, and potentially even prevention [25-27].

The emergence of multi-omics technologies offers a new perspective for studying TED, providing a more holistic and integrated view of the disease's pathophysiology. This comprehensive review aims to summarize the current knowledge regarding the application of multi-omics to TED, highlighting the potential for novel biomarker discovery and improved diagnostics.

Methods

This systematic review was conducted following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) 2020 Statement [28]. The review protocol was registered in the International Prospective Register of Systematic Reviews (PROSPERO) platform (registration number CRD42024533793). The protocol is available online at [https://www.crd.york.ac.uk/prospero/display_record.php?ID=CRD42024533793].

Search Strategy

Two independent researchers (HYZ and YYZ) systematically searched the PubMed, Embase, and Web of Science databases from inception to February 10, 2024. All English-language publications were retrieved without restrictions on the country of origin or article type. The search strategy is provided in Supplementary Tables 1-3.

Selection Criteria

We included only original studies, such as cross-sectional, retrospective, and prospective studies. The inclusion criteria were as follows: (a) studies involving patients with a confirmed diagnosis of TED, (b) studies employing any method of sequencing, including epigenomics, genomics, transcriptomics, microbiomics, metabolomics, and proteomics, and (c) studies comparing TED patients with a healthy control (HC) group and/or different subgroups within the TED population, such as those in the active TED phase versus the inactive TED phase. Studies were excluded if they met any of the following conditions: (a) review articles, animal studies, conference abstracts, meeting reports, studies with unclear information, and registry data, (b) studies focusing on diseases other than TED, (c) studies lacking explicit diagnostic criteria for TED, and (d) studies without sequencing results. Two researchers (HYZ and YYZ) independently selected the studies for exclusion. Conflicts were resolved through discussion to reach a consensus or by third-party arbitration.

Data Extraction and Visualization

Data extraction was carried out independently by two researchers and subsequently double-checked. Disputes were resolved through discussion or by consulting a third specialist. Critical characteristics from all included studies were extracted and recorded. The extracted data encompassed the author, year of publication, region, sample type and size, age and sex of each group, sequencing methods, data analysis methods, and main findings.

Risk of Bias and Quality Assessment

The quality of the studies was assessed using the tool described by Lumbreras et al. according to QUADOMICS [29], which was developed to evaluate quality issues specific to omics research. Detailed information is presented in Supplementary Table 4, which considers sixteen criteria. The second and fourteenth items of QUADOMICS did not apply to the included studies, and none of the studies met item 12, which indicates that they interpreted the index test results with knowledge of the reference standard. Two independent authors (YYZ and YYD) conducted the quality assessment, and discrepancies were discussed until a consensus was reached. If consensus could not be achieved, a third specialist was consulted.

Results

Descriptive Characteristics

A total of 412 studies were screened using the described search strategy. At the end of the selection process, 69 studies were included in the systematic review. Details of the screening process are provided in the PRISMA flow diagram (Figure 1). Of the 69 studies, 4 studies (5.80%) utilized genomics and epigenomics, 33 studies (47.83%) utilized transcriptomics, 5 studies (7.25%) utilized metabolomics and lipidomics, 13 studies (18.84%) utilized proteomics, 7 studies (10.14%) utilized microbiomics, 6 studies (8.69%) utilized two omics assays, and 1 study (1.45%) utilized three omics assays. These studies were conducted between 2005 and 2024, with significant contributions from China, the UK, the US, Germany, Korea, and other countries. According to the quality assessment, 8 of the 69 studies received a score of "low" 54 received a score of "moderate" and 7 received a score of "high" Further information about this quality assessment is available in Supplementary Table 4.

 Figure 1 

Flow Diagram of study selection - adapted from the flow diagram template provided by PRISMA 2020.

A total of 69 studies involved 1,363 TED patients and 1,504 controls (including 1,007 healthy controls (HCs) and 497 patients with GD or other diseases). The patients included in this review exhibit low heterogeneity, with ages ranging from 30.0 to 70.3 years and an overall mean age of approximately 48.40 years. The distribution of sex was similar across most studies, with a total of 322 males and 776 females. Although most of the included studies have a higher number of female participants, 2 studies reported a significantly higher proportion of male patients (male/female ratio ≥ 1.5). This data aligns with population statistics for TED.

The included studies analyzed various subgroups of TED. Forty studies compared TED with HC. Nine studies compared TED, GD, and HC. Five studies compared TED and GD. Six studies compared active TED, inactive TED, and HC. Seven studies examined TED based on severity, smoking status, which is a risk factor for the development of TED, and other valuable factors. One study compared TED, nonspecific orbital inflammation, sarcoidosis, and granulomatosis with polyangiitis. Additionally, one study employed 16S rRNA gene sequencing and metabolic network-driven analysis to investigate the association between gut microbiota and TED-related traits.

Genomics and Epigenomics

This systematic review included 5 studies employing genomics and epigenomics. Of these, 1 study (20.0%) utilized genomics, while 4 studies (80.0%) investigated DNA methylation. Among the studies, 3 (60.0%) used orbital tissues as samples, and 2 studies (40.0%) analyzed peripheral blood.

In the genomic and epigenomic analysis of TED, whole-genome sequencing (WGS) and DNA methylation assays were applied, yet no significant genetic variants were identified [30]. For DNA methylation analysis, the sequencing approaches included whole-genome bisulfite sequencing (WGBS) [21], methylation microarrays [31], and reduced representation bisulfite sequencing (RRBS) [32, 33], DNA methylation data were analyzed using various approaches, including differential expression analysis to identify key genes [21], functional and pathway analysis with Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) for gene role classification [21, 31], network analysis to explore gene interactions [21], and epigenetic analysis techniques such as differentially methylated probes and differentially methylated region distribution to pinpoint methylation changes [31-33]. Bioinformatics tools like Ingenuity were used for data integration, while statistical and machine learning methods, including MIRA score calculation and random forest analysis, were employed for pattern recognition and correlation analysis [33], DNA methylation analysis detected various biomarkers. Notably, genes related to inflammation, adipogenesis, and autoimmunity, such as PTPRU and VCAM-1, exhibited significant alterations in methylation levels, potentially playing a key role in the pathogenesis of TED [21, 31]. Pathway analysis and MIRA scoring identified four biological pathways significantly associated with TED, with genes such as LDLR, CDK5, and PIK3CB correlating with the TED phenotype [33]. Additionally, several genomic loci, including CD14, IL17RE, CDK5, DRD4, and ZCCHC6, exhibited significant differences in methylation patterns associated with TED incidence [32].

Transcriptomics

This systematic review included 36 studies employing transcriptomics. Of these, 29 studies (77.8%) analyzed orbital tissues (orbital connective tissues in 27 studies, 75.0%, and extraocular muscles in 2 studies, 5.6%). Six studies (16.7%) used peripheral blood as the sample (peripheral blood mononuclear cells (PBMC) in 4 studies, 11.1%, and serum in 2 studies, 5.6%), and 1 study (2.8%) used thyroid tissue. Regarding sequencing methods, 16 studies (44.4%) utilized RNA-seq, 13 studies (36.1%) employed microarray, 4 studies (11.1%) used single-cell RNA sequencing (scRNA-seq), and 1 study (2.8%) implemented miRNA microarray. Additionally, nanostring and single-nucleus RNA sequencing (snRNA-seq) were each used in 1 study (2.8%). A wide range of data analysis methodologies were employed. DEGs were used in 31 studies (86.1%), KEGG pathway analysis in 20 studies (55.6%), GO analysis in 15 studies (41.7%), gene set enrichment analysis (GSEA) in 7 studies (19.4%), and protein-protein interaction (PPI) networks in 5 studies (13.9%). Cell-cell interaction analysis and principal component analysis (PCA) were each used in 2 studies (5.6%). Other methods, such as miRanda, competing endogenous RNA (ceRNA) network construction, and co-expression analysis, were employed in 1 study each (2.8%).

Most studies utilized a single approach—identifying DEGs in TED patients compared to HCs and conducting functional analysis using GO, KEGG, and GSEA. In studies analyzing orbital tissues, genes involved in antigen presentation, immune adhesion, interferon-γ (IFN-γ) signaling, and immune cell markers for macrophages, B cells, and T cells were found to be upregulated. Additionally, the relaxin signaling pathway, which regulates fibrosis in TED, was active in TED patients. Four studies reported dysregulation of Wnt signaling gene expression, including Wnt5a, secreted frizzled-related proteins, and Dickkopf-related proteins [19, 34-36]. It is well established that the typical Wnt signal regulates the fate of neural crest progenitor cells. Similarly, activation of the Wnt/β-catenin pathway inhibits commitment to the adipocyte lineage while promoting differentiation into bone cells [37, 38]. Two studies used scRNA-seq to analyze peripheral blood mononuclear cells (PBMCs) and found that CD169+ classical monocytes and a novel CD4+ cytotoxic T lymphocyte (CTL) subtype with chemotactic and inflammatory characteristics contributed to hyperinflammation, fibrosis, and adipogenesis in orbital tissues [39, 40]. Additionally, Z Cheng et al. identified the VEGF-A gene as a regulator of the cytotoxic function of CD4+ CTLs in TED [41].

Proteomics

This systematic review included 18 studies employing proteomics. Among these, 5 studies (27.8%) analyzed orbital tissues (orbital connective tissues in 4 studies, 80.0%, and extraocular muscles in 1 study, 20.0%). Four studies (22.2%) analyzed serum, and 9 studies (50.0%) used tear samples. Liquid chromatography-tandem mass spectrometry (LC-MS/MS) was the most used sequencing assay, employed in 11 studies (61.1%). Additionally, 2 studies (11.1%) utilized matrix-assisted laser desorption/ionization time-of-flight mass spectrometry (MALDI-TOF MS), while other studies (27.8%) used Nano LC/QTOF, surface-enhanced laser desorption/ionization time-of-flight mass spectrometry (SELDI-TOF-MS), and proximity extension assays.

Of the five studies that analyzed orbital tissues, four employed LC-MS/MS to examine differentially expressed proteins (DEPs) by comparing TED patients to HCs. These studies found that proteins involved in tissue inflammation, adipose tissue differentiation, and metabolism were typically overexpressed in TED patients [21, 23]. These findings suggest a shift in glycometabolism and lipometabolism in TED orbital tissues, which may help elucidate the underlying pathological mechanisms of TED. In serum samples, DEPs analysis, along with KEGG, GO, and PPI analyses, revealed significantly increased levels of IL6, CSF1, FLT3LG, and C4A in TED patients. These inflammatory proteins may play a crucial role in the pathogenesis of TED and could serve as potential new biomarkers for clinical use [42, 43]. Nine studies analyzed DEPs in tear samples, finding that protective proteins such as PROL1, PRP4, and β2-microglobulin were markedly downregulated, while inflammatory proteins such as lysozyme C and cystatin S were upregulated in the patient group compared to controls [44-47]. Enrichment analysis of the DEPs indicated that pathways related to the immune system, apoptosis, cell cycle, carbohydrate metabolism, and protein synthesis and degradation may be key in TED patients.

Metabolomics and Lipidomics

This systematic review included 9 studies employing metabolomics. Among these, 3 studies (33.3%) used targeted metabolomics, while 6 studies (66.7%) utilized non-targeted metabolomics, including 1 study (11.1%) that specifically used non-targeted lipidomic metabolomics. Of the 9 studies, 3 (33.3%) analyzed orbital connective tissues, 3 (33.3%) used serum, 1 study (11.1%) analyzed feces, 1 study (11.1%) employed tear samples, and 1 study (11.1%) examined both orbital connective tissues and serum.

The non-targeted lipidomic metabolomics study utilized nanoflow ultrahigh pressure liquid chromatography-electrospray ionization tandem mass spectrometry (nUPLC-ESI MS/MS) sequencing, with data analyzed using Student's t-test and PCA. This study identified significantly increased levels of sphingosine-1-phosphate in serum and urine samples, indicating its potential as a biomarker for TED diagnosis. Among the other 6 non-targeted metabolomics studies, 3 studies (50.0%) employed LC-MS sequencing, 2 studies (33.3%) used gas chromatography-time-of-flight mass spectrometry (GC-TOF MS), and 1 study (16.7%) utilized nuclear magnetic resonance (NMR) spectroscopy. Metabolite profiles in TED patients, including fumarate, proline, phenylalanine, and glycerol, suggested a potential metabolic connection between orbital connective tissues and blood metabolites. Notably, cholesterol metabolism was significantly linked to TED pathogenesis.

When comparing TED patients to HCs, metabolites such as short-chain fatty acids, uric acid, uracil, hexose-phosphate, and D-sedoheptulose 1,7-bisphosphate were enriched in TED samples. Combining TED-specific modulators—proline and 1,5-anhydroglucitol—with key metabolites—lycine, glycerol 3-phosphate, and estrone sulfate—substantially improved the biomarker model's ability to discriminate between HCs, GD, and TED groups. All 3 targeted metabolomics studies utilized LC-MS/MS sequencing, with data analyzed using factor analysis, linear regression models, and various univariate analyses. Metabolic profiling revealed significant upregulation of glycolysis-related parameters (F6P/F16BP, AMP/ATP, ADP/ATP, and lactate) in orbital fibroblasts of TED patients compared to HCs. Additionally, active TED patients showed an increased ratio of putrescine to ornithine and spermine in serum compared to inactive TED patients.

Microbiomics

This systematic review included 8 studies that utilized microbiomics. Among these, 6 studies (75.0%) analyzed fecal microbiota, 1 study (12.5%) examined orbital adipose tissues, and 1 study (12.5%) investigated ocular microbiota. Of the studies, 6 (75.0%) employed 16S rRNA sequencing, while 2 studies (25.0%) used 16S rDNA sequencing. The methodologies for identifying biomarkers and analyzing co-occurrence patterns within the microbiota varied. Notably, α- and β-diversity indices were used in 5 studies (62.5%), and random forest analysis was employed in 3 studies (37.5%). Additionally, specific techniques such as KEGG, weighted gene co-expression network analysis (WGCNA), and module-trait association analysis were utilized in 1 study (12.5%).

The collective evidence from these studies suggests that gut microbiota may play a significant role in TED, with specific microbial imbalances potentially contributing to immune dysregulation and disease development. TED patients exhibit decreased bacterial community diversity but show increased proportions of Actinobacteria, Bacillus, Brevundimonas, and a higher Firmicutes-to-Bacteroidetes ratio compared to HCs [48, 49]. Specific taxa, such as Deinococcus-Thermus, Chloroflexi, and various bacterial species, display differential abundance patterns between TED and GD, indicating disease-specific microbial signatures [50]. Further characterization reveals distinct microbiome types unique to TED patients, with taxa like Klebsiella pneumoniae, Paracoccus, and Hemophilus correlating with disease severity [48, 51]. Additionally, s_Prevotella_copri and f_Prevotellaceae show significant correlation with TRAb, suggesting their potential role in TED development [52]. Furthermore, bacterial species such as Bacteroides, Dialister, and Lactobacillus exhibit associations with serum lipopolysaccharide-binding protein concentration, indicating their involvement in TED pathophysiology [53].

Discussion

TED is an autoimmune inflammatory disorder of the orbit, characterized by inflammation and a range of symptoms that can impact vision and appearance. Despite the presence of clinical scoring systems, there remains a critical need for more objective biomarkers to improve early diagnosis, disease monitoring, and therapeutic efficacy evaluation. Recent advancements in high-throughput sequencing technology and the rapid accumulation of omics data have led to increased utilization of genomics, proteomics, and other omics approaches to identify novel targets and biomarkers. Our systematic review comprehensively analyzed studies that employed genomics, epigenomics, transcriptomics, proteomics, metabolomics, lipidomics, and microbiomics to identify potential biomarkers in various biological samples, including orbital tissues, peripheral blood, feces, and tears. The integration of multi-omics data has significantly advanced our understanding of the molecular mechanisms underlying TED and has facilitated the identification of new diagnostic and prognostic biomarkers.

Among the 69 articles included in our study, numerous biomarkers were identified across various mediums, including fecal microbiota, tear fluid, orbital tissues, blood, and thyroid tissues. Connective tissues and extraocular muscles are the primary sites of pathological processes in TED. Studies often focus on exploring disease-specific molecular changes such as inflammation, adipogenesis, and immune responses. For instance, through transcriptomic analysis, researchers have identified the upregulation of genes related to antigen presentation, immune cell adhesion, and interferon-γ signaling in TED patients [19, 36]. Tear samples reflect the health status of the ocular surface. Proteomic analysis has revealed the downregulation of protective proteins and upregulation of inflammatory proteins, which may be associated with ocular surface inflammation in TED patients [54]. Fecal samples are used to investigate the association between gut microbiota and TED, revealing specific microbial imbalances related to immune dysregulation and disease development [50].

Through scRNA-seq, blood samples are utilized to explore the connections between the peripheral immune landscape and TED, such as the activation of CD169⁺ classical monocytes, which may be pivotal in driving the autoimmune reactions characteristic of TED [39]. Combining data from different biological samples can provide a more comprehensive understanding of disease mechanisms. For example, combining orbital tissue and blood samples can reveal both local and systemic changes. Samples that are easily obtainable (such as blood and tears) may aid in developing non-invasive diagnostic tools for disease monitoring and prognosis. Examining biological samples from various sources allows for a more thorough comprehension of TED, spanning from localized tissue effects to broader immune system impacts, ultimately guiding the identification of more effective biomarkers.

In omics analyses, the selection of appropriate control groups is crucial for elucidating biological differences. In this systematic review, many studies compared TED patients with HC and patients with GD to identify biomarkers associated with TED. Comparing TED patients to HC helps reveal disease-specific molecular alterations, with the HC group providing a baseline against which pathological changes in TED patients can be contrasted. For example, significant differences in gene expression, protein levels, metabolites, and microbiota composition between TED patients and HC may shed light on pathogenic mechanisms and disease progression. Conversely, comparisons with GD patients aid in distinguishing the specific manifestations of TED within the context of GD. Although GD is a commonly associated with TED, not all GD patients develop TED. Differentiating TED from GD helps identify molecular signatures unique to TED, which may be related to disease severity, inflammatory responses, or tissue remodeling. Additionally, some studies compared TED with other inflammatory conditions, such as non-specific orbital inflammation, sarcoidosis, and granulomatosis with polyangiitis, or grouped patients based on factors like smoking status and serum lipopolysaccharide-binding protein levels. These comparisons aim to uncover the unique pathological features and influencing factors of TED.

In exploring biomarkers related to TED, the choice of sequencing methods is pivotal, requiring a nuanced understanding of different biological molecular levels as described by the central dogma, which is a principle that describes the flow of genetic information within a biological system [16]. The central dogma was initially proposed by Francis Crick in 1957 and has since been refined. It outlines the flow of genetic information from DNA to RNA to proteins, and the selection of sequencing technology should align with different stages of this information flow. This review includes various sequencing methods, such as WGS, RRBS, and RNA-seq. Each method has its technical advantages and limitations: WGS offers comprehensive genomic information but at a higher cost; RRBS is more cost-effective but provides a narrower scope of information; RNA-seq reveals changes in gene expression, which is crucial for understanding TED's molecular mechanisms. Mass spectrometry is a vital technique for high-throughput sequencing in proteomics. For instance, MALDI-TOF serves as a cost-effective, efficient, and precise analytical tool widely used for microbial species identification and protein profiling in both clinical and research settings. MALDI-TOF can rapidly and accurately identify bacteria or proteins by analyzing their unique molecular fingerprints, thus providing valuable insights into their biological functions and characteristics [55-57]. Within the framework of the central dogma, combining transcriptomics and proteomics methods offers a comprehensive view from gene expression to changes at the protein and metabolite levels. This multi-omics approach elucidates the relationship between gene expression regulation and protein function, leading to a more thorough understanding of the molecular mechanisms underlying TED.

In the realm of TED research, the selection of an appropriate technological approach to address specific scientific inquiries is a critical step. Therefore, the choice of technology should be guided by specific research questions and available biological materials. For instance, while WGS provides comprehensive genomic information, not all studies require such exhaustive analysis [30]; RRBS or targeted gene panels might suffice. Similarly, for researchers interested in immediate molecular responses, RNA-seq may be more appropriate than microarrays. Proteomics can elucidate alterations in protein expression associated with inflammation, adipose tissue differentiation, extracellular matrix remodeling, and metabolism in the orbital tissues of TED patients [21]. Metabolomics can identify metabolic changes related to the pathogenesis of TED, such as alterations in cholesterol metabolism, which may be linked to the inflammatory and immune responses of the disease [24]. Although microbiomics may not be directly involved in the traditional central dogma, it provides a new perspective for understanding the systemic aspects of TED.

Multi-omics sequencing has identified a range of potential biomarkers for TED, offering promising applications for disease identification, course prediction, and treatment response evaluation. For example, Ji et al. explored the diagnostic value of a multi-panel approach combining various biomarkers to differentiate between TED and GD patients [58]. Their study demonstrated an area under the curve range of 0.845 to 0.935, suggesting that such multi-panel approaches could effectively identify individuals with TED. However, the clinical applicability of these findings depends on further validation in larger, more diverse cohorts to ensure their generalizability and reliability. Similarly, Zhang et al. used proteomic and miRNA analyses to identify 20 DEPs, including Zonulin, α-2 macroglobulin, β-2 glycoprotein 1, and fibronectin, which may play roles in diagnosis and prognosis [59]. While clinical practice currently relies on markers such as TBII, TSHR-Ab, TSI and TSAb, additional potential biomarkers that could serve as adjuncts for TED confirmation and prognosis assessment remain to be identified and validated. Despite the progress made in biomarker identification through multi-omics sequencing, realizing their full clinical potential involves overcoming several challenges. Future research should focus on establishing well-defined cohorts, conducting longitudinal studies, and addressing technical, logistical, and economic barriers to the integration of these biomarkers into routine clinical practice.

Recent advancements in multi-omics technologies are enhancing the discovery and potential development of informative biomarkers for clinical practice [60]. These technologies offer valuable insights into the complex pathophysiology of TED and facilitate the identification of new diagnostic and therapeutic biomarkers [17, 21, 61]. Despite the promising prospects, several challenges remain, particularly regarding the translation of research discoveries into clinical applications. A significant issue is the inadequacy of study designs, which often results in low statistical significance and limits the impact of many research efforts. Future research in TED should continue to leverage multi-omics technologies, with a focus on improving sample collection, data standardization, and analytical methods [62]. Additionally, it is crucial to validate the clinical relevance of these biomarkers through larger sample sizes and multicenter studies to ensure their robustness and applicability in clinical settings.

It is important to recognize the limitations and challenges associated with the current body of research. Firstly, there is a need to increase sample sizes to enhance the robustness of findings. Larger sample sizes will improve the statistical power and generalizability of results. Secondly, standardization of biomarker detection methodologies across studies is crucial. Such standardization ensures the reliability and reproducibility of results, facilitating more accurate comparisons and interpretations. Thirdly, the validation of identified biomarkers in independent cohorts remains inadequately addressed. Many studies, according to the QUADOMICS criteria, have used patient populations that are not fully representative, and there has been insufficient validatory estimation and assessment, which may lead to overfitting. Fourthly, the lack of quantitative analysis prevents the performance of a meta-analysis, as the available data is not sufficiently homogeneous to draw meaningful conclusions or make reliable statistical inferences. Future research should focus on overcoming these limitations by developing more representative patient cohorts, implementing standardized biomarker detection protocols, and rigorously validating biomarkers in independent studies. With continued research efforts, the integration of novel biomarkers holds the potential to significantly enhance disease management.

Conclusion

This systematic review has explored the complex landscape of TED through the application of multi-omics sequencing, revealing a wide range of potential biomarkers that could significantly enhance diagnosis, monitoring, and treatment of this multifaceted condition. Despite variations in methodologies and the early stage of biomarker discovery, the evidence collectively highlights the transformative potential of genomics, transcriptomics, proteomics, metabolomics, lipidomics, and microbiomics in unraveling the intricate pathophysiology of TED. Our comprehensive analysis has demonstrated the value of diverse biological samples—such as orbital tissues, peripheral blood, feces, tears, and ocular microbiota—in providing insights into the molecular mechanisms of TED. These biomarkers reflect the systemic nature of the disease and underscore the potential for developing minimally invasive diagnostic approaches. The identification of upregulated genes and proteins associated with inflammation, adipogenesis, and immune dysregulation, along with unique metabolic and microbial signatures, presents promising avenues for future research of TED.

Supplementary Material

Supplementary tables.

Acknowledgements

Funding

This work was supported by the National Natural Science Foundation of China (grant numbers 82388101, 81930024, and 82201235), the Science and Technology Commission of Shanghai (grant number 20DZ2270800), and the Shanghai Key Clinical Specialty, Shanghai Eye Disease Research Center (grant number 2022ZZ01003).

Consent for Publication

All authors approved the final manuscript and the submission to this journal.

Author Contributions

HYZ: conception and design, analysis and interpretation of data, drafting of the manuscript, critical revision of the manuscript for important intellectual content; YYZ: conception and design, acquisition and interpretation of data, drafting of the manuscript; BGY: analysis of data, drafting of the manuscript; YYD, YW, and SJF: analysis and interpretation of data, drafting of the manuscript; HFZ, XQF and XFS: critical revision of the manuscript for important intellectual content, administrative support, obtaining funding, supervision.

Competing Interests

The authors have declared that no competing interest exists.

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Author contact

Corresponding authors: Huifang Zhou, fangzzfangedu.cn; Xianqun Fan, fanxqedu.cn; Xuefei Song, songxuefeiedu.cn.

Received 2024-9-21
Accepted 2024-10-28
Published 2024-11-11

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