Novel insights into the pathogenesis of thyroid eye disease through ferroptosis-related gene signature and immune infiltration analysis

Screening of ferroptosis-related DEGs

To investigate whether ferroptosis is involved in the pathogenesis of TED, we first obtained the union set of genes associated with ferroptosis from both the Homo sapiens organism and the FerrDb V2 database (http://www.zhounan.org/ferrdb/current/operations/download.html) [32]. Then, ferroptosis-related DEGs (F-DEGs) were screened out via the intersection with the DEG set of TED.

Expression analysis and diagnostic value assessment

We assessed the differential expression of F-DEGs in 14 normal and 12 TED tissues, with data normalized and log2 transformed. To assess the diagnostic capability of F-DEGs in TED, we used receiver operating characteristic (ROC) curves to perform a series of analyses. To measure the diagnostic value, the area under the curve (AUC) was calculated. An AUC>0.9 was defined as high accuracy, 0.7<AUC≤0.9 was defined as medium accuracy, and 0.5<AUC≤0.7 was defined as low accuracy [33].

Construction of the F-DEG-related diagnostic model

Based on the expression profiles above, we performed univariate binary logistic regression analysis using the glmnet package [34] in R to further screen variables. Then, the variables were included in the multivariate analysis and the model correlation test if they met the threshold of p < 0.01. F-DEGs were chosen for the diagnostic model, and their odds ratio (OR) was visualized by a forest diagram [35]. On this basis, we constructed an F-DEG-related diagnostic nomogram for TED patients. The model was calibrated [36] and evaluated by employing diagnostic ROC and decision curve analysis (DCA) to verify its precision and robustness. The concordance index (C-Index) and Akaike information criterion (AIC) [37, 38] were subsequently computed. Furthermore, we applied LASSO regression [35, 39], which is a supervised machine learning method, to identify the most relevant variables for diagnostic model establishment. Calibration, diagnostic ROC, and DCA were performed to verify the model’s accuracy and reliability.

Gene enrichment analysis

We conducted Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis, as well as Gene Ontology (GO) analysis, for all DEGs using the “tidyr”, “ggplot2”, and “clusterProfiler” packages in R (version 4.0.3, www.r-project.org) [30]. Bubble diagrams and circle plots were applied for visualization.

GSEA and GSVA

To further elucidate the mechanistic differences between the TED and normal groups, we included all the DEGs in gene set enrichment analysis (GSEA) [40]. GESA offered three benefits over conventional enrichment: 1) genes were ranked according to their expression levels; 2) the object of analysis was a gene set rather than a single gene; and 3) the genes were compared to a predefined gene set. According to the annotation and description, we selected and downloaded reference data for two gene sets, “c2.cp.kegg.v2022.1.Hs.symbols.gmt” and “c5.go.all.v2022.1.Hs.symbols.gmt”, from the Human Molecular Signatures Database (MSigDB) [41]. The results are displayed as biological process (BP), cell component (CC), molecular function (MF), and KEGG. For visualization, classic and ridge diagrams were employed, with the clusterProfiler package [42] in R used to calculate the normalized enrichment score (NES) and false discovery rate (FDR).

Gene set variation analysis (GSVA), a nonparametric unsupervised algorithm, transforms a gene-sample expression matrix into a specific gene set-sample expression matrix [43]. Using this strategy, we sorted the genes based on their cumulative density distribution and computed the rank statistics for further downstream analysis. The MSigDB gene sets “c2.cp.kegg.v2022.1.Hs.symbols.gmt” and “c5.go.all.v2022.1.Hs.symbols.gmt” were incorporated in GSVA, and the route of enrichment differences between the test and control groups were compared as a result.

WGCNA

Weighted gene correlation network analysis (WGCNA) is defined as a systematic biological technique for characterizing gene relationship patterns across samples. By examining the interrelation between the gene set and its associations with other phenotypes, it may be used to identify gene sets with significant covariation as well as possible biomarker genes or therapeutic _targets [44]. We used this method to build a gene co-expression network and discovered essential modules consisting of the interconnected gene sets in both TED and normal samples.

Protein-protein interaction (PPI) network and hub gene identification

We extracted the most relevant gene modules from WGCNA and used them to interact with DEGs. The DEG collection was then imported into the “multiple proteins” module of the STRING database (http://string-db.org/) [45], which allows functional proteomic interaction analysis. The main parameters were set as follows: active interaction sources (“Text mining and Experiments and Databases”), max number of interactors to show [“1st shell: no more than 50 interactors”], minimum required interaction score [“medium confidence (0.400)”] and others were left at default. Next, we built the TED-related PPI network using Cytoscape (version 3.9.1) [46, 47], a free program for network data integration and visualization. We investigated the TED-related hub genes further in Cytoscape using the MCC algorithm of the CytoHubba module [48]. The top ten hub gene networks were categorized, as were the top five hub gene networks discovered by the MCODE module [49].

We used ridge diagrams to show the distribution of the aforesaid hub genes (H-DEGs) in TED and normal tissues according to their expression levels. Moreover, the diagnostic capability of these genes was evaluated by employing ROC and AUC.

Correlation analysis of immune cell infiltration

While the importance of the immune response in TED has been acknowledged, limited research has delved into the involvement of immune cells and inflammatory factors in the disease and their specific pathways. It is crucial to conduct immune infiltration studies at TED to uncover novel mechanisms and potential therapeutic _targets related to immunity. To estimate the abundance of different cell types within a mixed population based on gene expression profiles, we utilized CIBERSORTx, an analytical tool (https://cibersortx.stanford.edu/) [50]. This program was employed to calculate the levels of 22 immune cell types in both the TED and normal groups. The distribution and infiltration abundance of these 22 immune cells between the test and control groups were visually represented using box plots and histograms. Additionally, we examined how F-DEGs and H-DEGs relate to immunocytes as well as their interaction with both TED and normal tissues.

DEG identification and correlation analysis based on thyroid samples

Aside from lacrimal gland samples, we further included thyroid samples in this study by downloading the raw data from dataset {"type":"entrez-geo","attrs":{"text":"GSE9340","term_id":"9340"}}GSE9340 [17]. After the data normalization procedure, 10 TED and 8 normal samples were included in the subsequent analysis. GEO2R was utilized to screen the top DEGs with a threshold of |log2-fold change (FC)| ≥ 1 and a p-value < 0.05. We utilized Pearson’s method to determine the correlation coefficients and depict the association based on the data matrix to further investigate the correlation of the screened DEGs between thyroid and lacrimal gland tissues.

Experimental validation of DEG expression and immune infiltration

Based on the orbital CT scans obtained from both the TED and normal groups, a thorough comparison was made to evaluate the degree of adipose infiltration and ocular muscle hypertrophy in each group. To further investigate these findings, 23 pairs of periorbital adipose samples were selected for real-time quantitative PCR (RT-qPCR) and western blotting (WB) analyses (details of characteristics were summarized in Supplementary Tables 1, 2). These tissue samples were taken via endoscopic resection and immediately stored in liquid nitrogen to maintain their integrity and biological activity.

RT-qPCR: A prior protocol [51] was followed for tissue isolation and total RNA extraction. Briefly, quality-controlled RNA was converted to cDNA using the PrimeScript™ RT reagent kit with gDNA Eraser (cat#RR047A; Takara, Japan) and amplified using the SYBR® Premix Ex Taq™ II kit (cat#RR820A; Takara) on the ABI PRISM® 7500 Sequence Detection System (Applied Biosystems, CA, USA) following the manufacturer’s protocols. Table 1 displays the primer sequences generated by Primer 6.0 (Applied Biosystems, CA, USA). The mixture was incubated for 30 seconds at 95° C, followed by 40 cycles of 5 seconds at 95° C and 34 seconds at 60° C. The 2^−ΔΔCT approach was used to perform semiquantitative gene expression analysis with normalized levels of the housekeeping gene glyceraldehyde 3-phosphate dehydrogenase (GAPDH).

WB: Using radioimmunoprecipitation assay (RIPA) buffer (Beyotime Biotechnology, Shanghai, China) and a BCA protein assay kit (Beyotime Biotechnology, Shanghai, China), the total proteins were extracted and quantitated. _targeted proteins were separated using 10% sodium dodecyl sulfate-polyacrylamide gel electrophoresis (SDS-PAGE) and subsequently transferred to polyvinylidene difluoride (PVDF) membranes (Millipore, MA, USA). The membranes were blocked for 2 hours at ambient temperature using a 5% fat-free milk solution in Tris-buffered saline containing 0.05% Tween-20. Subsequently, the membranes were subjected to an overnight incubation at 4° C with primary antibodies (details of antibodies were summarized in Supplementary Table 3). Corresponding secondary antibodies were then coincubated at room temperature for 1 hour after being appropriately diluted in PBS. The protein bands were visualized using chemiluminescence and quantified using ImageJ software, following coating with the ECL assay kit (EpiZyme, China).

Immunohistochemistry (IHC): Immunohistochemical analysis was employed to investigate immune cell infiltration in six pairs of TED patients’ periorbital lipid tissues. The standard procedures were performed as published [52]. Frozen sections were fixed in methanol for 20 minutes in total. Following antigen retrieval and suppression of endogenous peroxidase activity, the sections were sealed for 30 minutes at room temperature with 3% BSA (Sigma, CA, USA). After the blocking solution was removed, preconfigured primary antibodies were added to the sections for overnight coincubation at 4° C. Subsequently, the sections were incubated at room temperature for 50 minutes with the corresponding secondary antibodies (details of antibodies were summarized in Supplementary Table 3). For section staining, DAB chromogenic solution (Genetech, Shanghai, China) and hematoxylin were used (the nucleus of hematoxylin stained is blue, and the positive expression of DAB is brownish yellow). An imaging system (Leica DFC450C, Leica, Shanghai, China) was used for image acquisition and analysis.

Statistical analysis

R software (version 4.2.1) was used for all statistical analyses and graphing. The t-test and one-way ANOVA were employed to assess variables with a normal distribution, while nonparametric tests were utilized for variables that did not follow a normal distribution. Survival analysis was conducted using the log-rank test and Cox regression, whereas Pearson’s correlation and Spearman’s rank correlation test were employed for correlation analysis. Statistical significance was defined as a p-value of 0.05. Correlation ranges were established as follows: 0.00-0.10 (negligible), 0.10-0.39 (weak), 0.40-0.69 (moderate), 0.70-0.89 (strong), and 0.90-1.00 (extremely strong) [53].

Data normalization

To comprehensively evaluate and analyse variations in gene expression, we combined and merged two distinct datasets. We implemented a standardized process to remove batch effects and normalize the original data, enhancing the accuracy and efficiency of subsequent data analysis. The processed data exhibited significantly superior standardized signal intensity compared to the initial data processing stage (Figure 2A, ​,2B).2B). Using principal component analysis (PCA), the standardized data exhibited improved within-group repeatability and between-group discriminability (Figure 2C, ​,2D).2D). The uniform manifold approximation and projection (UMAP), a technique for reducing dimensions, showed consistent results regarding the data distribution as above (Figure 2E, ​,2F2F).

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Figure 2

Screening of ferroptosis-related DEGs (F-DEGs) in the lacrimal gland. (A, B) Box plot of gene expression before and after batch effect removal in the integrated GEO data; (C, D) PCA of gene expression before and after batch effect removal in the integrated GEO data; (E, F) UMAP of gene expression before and after batch effect removal in the integrated GEO data; (G) Heatmap of DEGs; (H) Volcano plots of DEGs; (I) Venn diagram of ferroptosis-related genes; (J) Venn diagram of the intersection of ferroptosis-related genes and DEGs. DEGs, differentially expressed genes; GEO, Gene Expression Omnibus; PCA, principal component analysis; UMPA, uniform manifold approximation and projection.

F-DEG screening

A total of 12 TED samples and 14 control samples comprised the consolidated data. After examining the variations in gene expression among the two groups, 414 DEGs were screened by applying the filter criterion |Log₂FC| > 1 and p < 0.05. The results are depicted using a heatmap (Figure 2G), which displays the panorama and clusters of DEGs, with 73 upregulated genes and 341 downregulated genes further visualized as a volcano diagram (Figure 2H).

Through the integration of ferroptosis driver, ferroptosis suppressor, and ferroptosis marker gene sets, 322 ferroptosis-related genes were obtained (Figure 2I). By further intersecting with the DEGs above, we identified 11 ferroptosis-related DEGs (F-DEGs) (Figure 2J). The expression patterns of these 11 F-DEGs were detailed and are shown in a violin plot (CP, CDH1, TFAP2A, MDM4, TF, DNAJB6, OSBPL9, IDH1, MYCN, PEX3, and EMC2) (Figure 3A). MYCN stood out as a gene that was specifically overexpressed in the TED group (p < 0.001), while the majority of genes showed increased expression in the normal group (all p < 0.05) (Figure 3A).

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Figure 3

Comprehensive evaluation of F-DEGs. (A) Gene expression analysis of F-DEGs between TED and normal groups; (B) Diagnostic value analysis of F-DEGs; (C) Univariate analysis of F-DEGs; (D) Multivariate analysis of F-DEGs. *P < 0.05, **P < 0.01, ***P < 0.001.

Clinical value assessment of F-DEGs

Additionally, we conducted an evaluation to ascertain the diagnostic value of the F-DEGs for TED. The findings showed that every single F-DEG exhibited exceptional diagnostic performance for TED (TF: AUC = 0.774, DNAJB6: AUC = 0.911, OSBPL9: AUC = 0.881, IDH1: AUC = 0.845, CP: AUC = 0.821, CDH1: AUC = 0.821, TFAP2A: AUC = 0.792, MDM4: AUC = 0.827, MYCN: AUC = 0.899, PEX3: AUC = 0.935, and EMC2: AUC = 0.845) (Figure 3B).

We further performed a univariate logistic analysis of F-DEGs to examine their associations with TED diagnosis. In the diagnosis of individuals with TED, MYCN was identified as a significantly important independent factor (OR = 14.831, 95% confidence interval (CI) = 12.938-16.723, p = 0.005). The examination of other F-DEGs, on the other hand, revealed a possibly unfavorable effect on the diagnosis of TED (all OR < 1, p < 0.05) (Figure 3C). Genes that had a statistical significance of p < 0.01 were chosen for inclusion in the multivariate Cox regression analysis. EMC2 potentially had a favorable association with TED (OR = 4.459, 95% CI = 0.446-8.472). PEX3 (OR =0.003, 95% CI = -6.285-6.292), DNAJB6 (OR = 0.616, 95% CI = -5.291-6.523), OSBPL9 (OR = 0.064, 95% CI = -7.218-7.347), and MYCN (OR = 0.066, 95% CI = -8.304-8.435), on the other hand, revealed an inverse correlation (Figure 3D). However, no statistical significance supported these correlations (all p > 0.05).

Establishment and validation of the diagnostic prediction model

Based on the above results, we first integrated the five most statistically relevant genes and constructed a ferroptosis-related nomogram for TED risk prediction (Figure 4A). PEX3 made the greatest contribution and was recognized as an important independent diagnostic factor in addition to the other four factors. This model demonstrated good accuracy and robustness in providing diagnostic prediction scores for TED patients’ clinical episodes (C-index = 0.976, 95% CI = 0.926-1.026, AIC = 23.220) (Figure 4B). We supplementally employed a time-dependent ROC curve to evaluate the diagnostic precision of this model (AUC = 0.976, 95% CI = 0.925-1.000) (Figure 4C) and performed DCA to assess the practicality of this model (AIC = 15.220) (Figure 4D).

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Figure 4

Establishment of a ferroptosis-related TED model. (A) Diagnostic nomogram of ferroptosis-related TED; (B) Nomogram calibration curve of the diagnostic model; (C) ROC curve of the diagnostic model; (D) Decision curve for evaluating the net benefits of the nomogram; (E) Further identification of F-DEGs via LASSO regression; (F) Trajectory chart of LASSO regression; (G) Diagnostic nomogram of ferroptosis-related TED by LASSO; (H–J) Nomogram calibration curve, ROC curve, and decision curve for evaluating the precision and robustness of the diagnostic model. *P < 0.05, **P < 0.01, ***P < 0.001.

Furthermore, we adopted a machine learning strategy (LASSO regression) to filter the 11 F-DEGs for the most diagnostically valuable genes. The LASSO coefficients were calculated using a tenfold cross-verification procedure and presented as a variable trajectory diagram (Figure 4E, ​,4F).4F). We subsequently established a new F-DEG-related diagnostic nomogram that included PEX3 and CP, both of which contributed equally to this prediction model (Figure 4G). This model’s excellent predictive ability was confirmed (C-index = 0.982, 95% CI = 0.943-1.021, AIC = 15.433) (Figure 4H). The model’s precision and dependability were further confirmed by the time-dependent ROC curve (AUC = 0.982, 95% CI = 0.943-1.000) (Figure 4I) and DCA (AIC = 13.433) (Figure 4J).

Biological enrichment analysis

To delve deeper into the molecular mechanism of DEGs in TED, we employed GO and KEGG enrichment analyses. By setting a significance threshold of p < 0.05, we successfully identified the top 10 correlated pathways of BP and CC, the top 5 correlated pathways of MF, and the top 3 correlated pathways of KEGG. “Regulation of protein transport”, “Golgi vesicle transport”, “protein folding”, “endoplasmic reticulum to Golgi vesicle-mediated transport”, “protein _targeting to membrane”, “endoplasmic reticulum organization”, “vesicle budding from membrane”, “COPII-coated vesicle budding”, “vesicle coating”, and “protein insertion into ER membrane by stop-transfer membrane-anchor sequence” of BP (all p < 0.001) were mainly involved in TED (Figure 5A). Consistently, CC pathways were also primarily associated with protein coating and membrane transport (all p < 0.001) (Figure 5B). Regarding MF, we noticed an enrichment of DEGs in the following pathways: “cadherin binding”, “ribonucleoprotein complex binding”, “chaperone binding”, “protein carrier chaperone”, and “membrane insertase activity” (all p < 0.001) (Figure 5C). The KEGG pathways showed the strongest association with “protein processing in endoplasmic reticulum”, “protein export”, and “proteasome” (all p < 0.001) (Figure 5C).

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Figure 5

GO and KEGG enrichment analysis. (A) Bubble diagram of BP enrichment analysis; (B) Bubble diagram of CC enrichment analysis; (C) Bubble diagram of MF enrichment analysis; (D) Chord diagram of KEGG pathway enrichment analysis. BP, biological process; CC, cell component; MF, molecular function. *P < 0.05, **P < 0.01, ***P < 0.001.

We employed another algorithm to explore the between-group differences in DEG-enriched pathways. The enriched bioprocesses were ranked according to their enrichment score (Figure 6A). The top 6 GO pathways were involved in “ATP synthesis coupled electron transport” (NES = 3.298, p adj < 0.001), “polysomal ribosome” (NES = 3.508, p adj < 0.001), “cytosolic small ribosomal subunit” (NES = 3.765, p adj < 0.001), “cytoplasmic translation” (NES = 3.958, p adj < 0.001), “cytosolic large ribosomal subunit” (NES = 4.016, p adj < 0.001), and “cytosolic ribosome” (NES = 4.412, p adj < 0.001) (Figure 6B). On the other hand, the top 6 KEGG pathways included “Alzheimer’s disease” (NES = 2.715, p.adj < 0.001), “Huntington’s disease” (NES = 2.904, p.adj < 0.001), “spliceosome” (NES = 2.957, p.adj < 0.001), “oxidative phosphorylation” (NES = 3.169, p.adj < 0.001), “Parkinson’s disease” (NES = 3.422, p.adj < 0.001), and “ribosome” (NES = 4.614, p.adj < 0.001) (Figure 6C, ​,6D6D).

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Figure 6

GSEA and GSVA. (A) GSEA-GO of DEGs; (B) ridge plot of top 6 GSEA-GO; (C) GSEA-KEGG of DEGs; (D) ridge plot of top 6 GSEA-KEGG; (E) heatmap of functional scores of GSVA-KEGG; (F) heatmap of functional scores of GSVA-GO. *P < 0.05, **P < 0.01, ***P < 0.001.

The possible trend of association between enriched pathways and TED was discovered further with the use of GSVA. In TED samples, the KEGG pathways “proteasome”, “spliceosome”, “protein export”, and “limonene and pinene degradation” were all restricted (Figure 6E). The findings additionally indicated that TED exhibited primarily positive associations with neural bioprocesses such as “cerebral cortex GABAergic interneuron”, “neuropeptide hormone activity”, and similar processes. Conversely, TED displayed negative correlations with protein and chromatid bioprocesses such as “protein localization to nucleoplasm” and “regulation of sister chromatid cohesion” (Figure 6F).

WGCNA and hub gene network

To identify gene sets with high synergistic variation, we subjected all genes to WGCNA. We excluded the bottom 50% of the genes with the lowest median absolute deviation (MAD), eliminated any genes or samples that were outliers, and built a co-expression network that follows a scale-free pattern (Figure 7A). Sample and gene clusters are displayed in Figure 7B, ​,7C,7C, respectively. Then, to consolidate similarity and decrease redundancy, we assessed the dissimilarity of module eigengenes and merged some modules. Following these steps, we successfully obtained a total of eight co-expression modules. Based on the correlation analysis, the blue module containing 4591 genes was most negatively correlated with TED (cor = -0.66, p = 2.7e-4), whereas the skyblue module, which had 3775 genes, was most positively correlated with TED (cor = 0.67, p = 1.9e-4) (Figure 7D). Furthermore, we evaluated the correlation between gene significance (GS) and module membership (MM). To be consistent, a high degree of synergy of genes within each module was observed, especially in the skyblue (cor = 0.76, p < 0.001) (Figure 7E) and blue modules (cor = 0.72, p < 0.001) (Figure 7E).

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Figure 7

WGCNA. (A) WGCNA of soft threshold screening; (B) Sample clustering and disease type; (C) Co-expression gene clustering; (D) Correlation analysis between co-expression gene modules and clinical characteristics; (E) Correlation analysis between MM and GS in the sky blue module; (F) Correlation analysis between MM and GS in the blue module. MM, module membership; GS, gene significance. *P < 0.05, **P < 0.01, ***P < 0.001.

Next, we intersected the above two gene set modules with the DEGs and obtained 83 DEGs for the skyblue module and 188 DEGs for the blue module (Figure 8A). Based on the evidence from text mining, experiments, and databases, we constructed DEG-related PPI networks of 50 predicted functional partners (Figure 8B). Additionally, we selected the whole interaction network and the top 2 clusters from the DEGs to mine for potential hub genes. Using the MCC algorithm, the top 10 hub genes, including PSMA1, PSMA4, PSMA3, PSMA12, PSMA14, PSMA6, ITCH, SNRPG, SRSF1, and RBM25, were screened from the whole interaction network (Figure 8C). The top 5 hub genes of Cluster 1 (score = 7.000) were PSMA1, PSMA4, PSMA3, PSMA12, and ITCH (Figure 8D). Meanwhile, the top 5 hub genes of YKT6, COPB2, USO1, LMAN1, and RAB1A were included in Cluster 2 (score = 5.600) (Figure 8E).

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Figure 8

PPI construction and hub gene identification. (A) Venn diagram of intersection of DEGs and most significant module genes; (B) PPI network of key genes in blue module; (C) Top 10 hub genes by MCC algorithm; (D) Top 5 hub genes of cluster 1; (E) Top 5 hub genes of cluster 2; (F) 15 hub genes expression distribution in normal samples; (G) 15 hub genes expression distribution in TED samples; (H) ROC curve of 15 hub genes. *P < 0.05, **P < 0.01, ***P < 0.001.

A total of 15 hub gene-related DEGs (H-DEGs) were integrated. Ridge diagrams fully displayed the distribution differences of H-DEG expression in the TED (p = 7.8e-61) (Figure 8F) and normal groups (p = 1.8e-18) (Figure 8G). We further performed ROC curves to evaluate the diagnostic capacity of H-DEGs in TED. A remarkable accuracy of all H-DEGs in diagnosis was observed: SRSF1 (AUC = 0.917), SNRPG (AUC = 0.869), RBM25 (AUC = 0.887), PSMD6 (AUC = 0.929), PSMD14 (AUC = 0.905), COPB2 (AUC = 0.863), LMAN1 (AUC = 0.875), RAB1A (AUC = 0.851), YKT6 (AUC = 0.863), USO1 (AUC = 0.899), ITCH (AUC = 0.905), PSMA1 (AUC = 0.911), PSMA4 (AUC = 0.893), PSMA3 (AUC = 0.917), and PSMA12 (AUC = 0.905) (Figure 8H).

Immune infiltration analysis

We obtained 22 immune cell infiltration scores for lacrimal samples via the CIBERSORT algorithm. Regardless of whether TED or normal samples were taken, the component of plasma cells was the highest (Figure 9A). According to the infiltration abundance, the TED group had a higher degree of B-cell memory (p < 0.01), CD8 T cells (p < 0.001), and regulatory T cells (Tregs) (p < 0.01) infiltration than the normal group. In contrast, the infiltration levels of plasma cells (p < 0.01) and resting memory CD4 T cells (p < 0.05) were higher in the normal group than in the TED group (Figure 9B). Furthermore, we examined the interactions of immune cells in lacrimal tissue, as well as their interactions with F-DEGs and H-DEGs. In the TED group, there was a strong positive correlation between M1 macrophages and naive B cells, resting mast cells and M2 macrophages, and activated mast cells and resting NK cells, while there was a strong negative correlation between resting memory CD4 T cells and plasma cells (all p < 0.001) (Figure 9C). In the normal group, we found a strong positive correlation between activated memory CD4 T cells/nerve follicular helper T cells and naive B cells, neutrophils and resting mast cells and a strong negative correlation between plasma cells and naive B cells/activated memory CD4 T cells (all p < 0.001) (Figure 9D). Furthermore, the findings demonstrated a close relationship between F-DEGs and immune cell infiltration. In particular, CDH1 and TFAP2A were strongly negatively correlated with resting mast cells (all p < 0.001). There was also a strong positive link between resting NK cells and MDM4, as well as CD8 T cells and CDH1 (all p < 0.01) (Figure 9E). In the case of H-DEGs, no significantly positive correlation with immune cells was identified; however, a significantly negative connection between PSMD14 and activated dendritic cells was observed (p < 0.01) (Figure 9F).

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Figure 9

Immune infiltration analysis. (A) Accumulative immune cell concentrations in the TED and normal groups; (B) Analysis of different immune cell infiltration between TED and normal groups; (C) Correlation analysis of immune cell infiltration in the TED group; (D) Correlation analysis of immune cell infiltration in the normal group; (E) Correlation analysis between F-DEGs and immune cells; (F) Correlation analysis between 15 hub genes and immune cells. *P < 0.05, **P < 0.01, ***P < 0.001.

Correlation analysis of DEGs in thyroid samples

By following the aforementioned data processing procedure, we obtained standardized thyroid tissue data from the {"type":"entrez-geo","attrs":{"text":"GSE9340","term_id":"9340"}}GSE9340 dataset. This dataset consisted of samples from 10 patients with TED and 8 samples from healthy controls (Figure 10A). DEGs were identified and chosen for further analysis based on the preestablished threshold (Figure 10B). Upon conducting an interaction analysis, we discovered that the genes identified in {"type":"entrez-geo","attrs":{"text":"GSE9340","term_id":"9340"}}GSE9340, {"type":"entrez-geo","attrs":{"text":"GSE105149","term_id":"105149"}}GSE105149, and 58331 and the ferroptosis gene set did not have any overlapping genes (Figure 10C). Overexpression of MYH11 (p = 8.5e-3) and APOD (p = 0.01) was observed in the TED group, while downregulated expression of EGR2 (p = 0.02) was observed in the TED group (Figure 10D).

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Figure 10

Screening of DEGs in thyroid. (A) Box plot of gene expression in TED-related datasets; (B) Screening of DEGs in thyroid samples; (C) Venn diagram of interaction among TED-related datasets and ferroptosis-related genes; (D) DEG expression levels between TED and normal groups; (E) Correlation analysis between DEGs in thyroid and F-DEGs; (F) Correlation analysis between DEGs in thyroid and hub genes. *P < 0.05, **P < 0.01, ***P < 0.001.

We identified these three DEGs as T-DEGs and explored whether they were potentially related to F-DEGs and H-DEGs. The results showed that EGR2 was significantly negatively correlated with IDH1 (r = -0.68), PEX3 (r = -0.68), and EMC2 (r = -0.71) (all p < 0.05) (Figure 10E). Turning to H-DEGs, we observed a significant positive correlation between MYH11 and PSMA4 (r = 0.64, p < 0.05). In addition, EGR2 was negatively correlated with all H-DEGs, especially COPB2 (r = -0.82, p < 0.01), YKT6 (r = -0.85, p < 0.001), PSMD6 (r = -0.79, p < 0.01), and PSMD14 (r = -0.79, p < 0.01) (Figure 10F).