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Published: Tue 26, Aug 2025
Received: Tue 15, Jul 2025
Accepted: Wed 30, Jul 2025

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Bingxi Lei Yaqi Han Yuanqi Qu

Abstract

Thyroid cancer is a common head and neck malignancy, with papillary thyroid carcinoma (PTC) being the most prevalent type, accounting for 85% to 90% of cases. Currently, distant metastasis is the main cause of death in thyroid cancer. The main sites of metastasis include the lungs, bones, and brain. Among them, the overall prognosis for brain metastasis is poor, with a median survival time of about 15 months [1].
Objectives: This study aims to construct a risk prediction model for brain metastasis in thyroid cancer patients and identify core genes associated with this condition. We focused on ADRA1B and PAPSS2 genes, exploring their correlation with immune genes and cells to understand the immune mechanisms involved in brain metastasis. Additionally, we investigated small molecule compounds that can regulate these genes and studied their drug sensitivity.
Methods: We utilized bioinformatics tools and databases to collect gene expression data from GEO and TCGA databases. Differential expression analysis identified genes significantly associated with brain metastasis. Correlation analysis assessed the relationships between ADRA1B, PAPSS2, and immune cell activity. A risk prediction model was built using COX regression, and its accuracy was evaluated. Molecular docking techniques, including AutoDock Vina and CB-Dock2, simulated interactions between ADRA1B, PAPSS2, and potential therapeutic drugs like Ganetespib and BLU-667.
Results: Our study found that ADRA1B expression was significantly lower in brain metastasis samples, while PAPSS2 expression was higher. Survival analysis showed that high ADRA1B expression correlated with better survival outcomes, whereas high PAPSS2 expression correlated with poorer outcomes. We also found that ADRA1B gene expression was negatively correlated with sensitivity to multiple drugs, while PAPSS2 expression was positively correlated with sensitivity to certain drugs.
Conclusion: High ADRA1B expression is associated with better survival, while high PAPSS2 expression correlates with poorer outcomes. These genes' expression levels significantly relate to drug IC50 values, suggesting their potential as biomarkers for predicting drug response. Protein docking analysis provides insights into the interaction mechanisms between these genes and specific drugs, offering new targets and strategies for treating thyroid cancer brain metastasis.

Keywords

Thyroid cancer brain metastasis, clinical prediction model, ADRA1B, drug sensitivity

1. Introduction

Thyroid cancer is one of the most common endocrine malignancies globally, with an increasing incidence rate year by year. The Cancer Statistics Annual published by the National Cancer Center in January 2019 shows that in 2015, there were approximately 3.929 million new cases of malignant tumors diagnosed nationwide, of which thyroid malignancies accounted for 5.12%, ranking 7th. The incidence of thyroid cancer in females is significantly higher than that in males, and among female patients, the prevalence of thyroid cancer can be as high as 4th place. It can be seen that thyroid cancer has become one of the fastest-growing tumor types in terms of incidence in China. Among them, differentiated thyroid cancer rarely spreads to distant sites, and most only involve the thyroid lobe or cervical lymph nodes. According to literature reports, only 4% to 15% of patients have metastasis [2, 3], with lung metastasis being the most common, followed by bone tissue (1% to 15%). Patients with brain metastasis are rare, with statistics showing that only about 1% of differentiated thyroid cancers metastasize to the brain [4], but brain metastasis of thyroid cancer is a serious clinical consequence.

In January 2024, data released by the National Cancer Center (NCC) and the Global Cancer Observatory (GLOBOCAN) in 2022 consistently showed that in 2022, China reported 460,000 new cases of thyroid cancer (thyroid cancer, TC) and 1.63 million (5-year) prevalent cases, of which about 10,000 were deaths. Thyroid cancer accounted for 9.7% of all new diagnoses of malignant tumors, ranking 3rd, only behind lung cancer and colon cancer, and is the most common endocrine malignancy in terms of incidence. Although its overall prognosis is relatively good, it ranks 2nd in mortality among endocrine tumors, and some subtypes have a higher risk of death [5, 6]. At present, distant metastasis is the main cause of death in thyroid cancer, with the main sites of metastasis including the lungs, bones, and brain, among which the overall prognosis for brain metastasis is poor, with a median survival time of about 15 months [1]. From 2000 to 2018, the age-standardized incidence rate (ASIR) of all cancers increased by an average of about 1.4% per year. Among them, the detection rate of female thyroid cancer [average annual percentage change (AAPC) = 15.7%] and male thyroid cancer (AAPC = 16.9%) both increased significantly. Compared with the estimates of GLOBOCAN 2020, the incidence of all cancers increased by about 256,000 cases, of which thyroid cancer accounted for about 245,000 cases. In the 5-year epidemiological statistics, thyroid cancer ranked first with 1.63 million cases, indicating that thyroid cancer has become the fastest-growing tumor in terms of incidence in China [5].

Thyroid cancer brain metastasis is a severe complication in the late stage of thyroid cancer, with a very poor prognosis, the treatment method is mostly surgical [7]. Surgical treatment is becoming increasingly important in the management of brain metastases. It has the advantage of rapidly relieving intracranial pressure and clarifying pathological diagnosis [8]. However, surgical approaches for TCa vary and are closely related to pre-operative evaluations. Each clinical center determines the optimal surgical extent for PTC. Generally, lobectomy is suited for PTCs with a maximum tumor diameter of less than 1 - 1.5 cm [9]. The American Thyroid Association (ATA) guidelines suggest that for tumors of 1-4cm, risk stratification based on pathological and intra - operative features is needed to plan the surgery [10]. The ATA also expanded the criteria for lobectomy in some 1-4 cm tumors. Studies, after excluding other high - risk factors, tracked patients with tumors of ≥ 1 cm having different surgeries, finding no statistical difference in survival and recurrence rates [11, 12]. Thus, for tumors of 1-4 cm, other high-risk factors must be considered, such as multifocality, contralateral nodules, high tumor extracapsular extension, numerous lymph node metastases or maximum diameter of ≥ 3 cm, and distant metastasis. Additionally, total thyroidectomy has a higher risk of complications like hypoparathyroidism, hypocalcemia, etc., and may require readmission [13-15]. Economically, lobectomy is likely more cost-effective [16, 17]. Before treatment, it is essential to confirm the diagnosis of tumor metastasis and conduct a comprehensive and systematic assessment of the patient's condition [18]. At present, there is no standardized consensus on the efficacy and side effects of existing treatments, which is closely related to the rarity of thyroid cancer brain metastasis cases. Therefore, there is an urgent need to deeply explore the molecular mechanisms of brain metastasis and develop more effective treatment strategies [19]. This study analyzes the public gene expression data of thyroid cancer brain metastasis patients and control groups from the GEO and TCGA databases to predict the probability of thyroid cancer metastasizing to the brain in individual patients through a model. It screens for differentially expressed genes and constructs a prognostic risk model, further exploring the interaction between core genes and immune cells and their drug sensitivity, providing new targets and ideas for the diagnosis and treatment of thyroid cancer brain metastasis.

2. Materials and Methods

2.1. Data Collection

We downloaded the RNA sequencing data and corresponding clinical data of 6 normal thyroid cancer tissue samples and 2 thyroid cancer brain metastasis tissue samples from the Gene Expression Omnibus (GEO) dataset (GSE66463) at the National Center for Biotechnology Information (NCBI, (Link 1)) to analyze the gene expression differences between thyroid cancer brain metastasis and normal thyroid cancer tissues. Additionally, to further investigate the gene expression differences in thyroid cancer brain metastasis, we obtained RNA sequencing data and clinical information from the Cancer Genome Atlas (TCGA, (Link 2)).

To explore the correlation between gene expression levels and immune cell activity, we collected immune cell-related data. We intersected the differentially expressed genes identified from the GEO datasets with those from the TCGA datasets using R language. This process yielded a matrix of immune genes, which we utilized for subsequent analyses of immune cell infiltration.

2.2. Bioinformatics and Statistical Analysis

To identify significantly differentially expressed genes associated with thyroid cancer brain metastasis (Figure 1), we employed differential expression analysis methods. Using R language, we analyzed the data downloaded from the GEO and TCGA databases to obtain genes with significant differential expression related to thyroid cancer brain metastasis.

We employed a transcription-based immune infiltration analysis method to evaluate the relationship between the expression levels of the ADRA1B and PAPSS2 genes and the infiltration of different types of immune cells. First, we used R language tools to obtain the estimated values of immune cell infiltration from the TCGA database, including various immune cell types such as T cells, B cells, macrophages, and dendritic cells. Subsequently, we assessed the correlation between the expression levels of the ADRA1B and PAPSS2 genes and the degree of infiltration of various immune cells using Pearson correlation analysis. The correlation analysis was performed in R software, with the cor.test function used to calculate the correlation coefficients and p-values. The analysis results were presented in the form of heatmaps and scatter plots to demonstrate the strength and direction of the relationship between gene expression and immune cell infiltration.

To evaluate the survival risk of patients with thyroid cancer brain metastasis, we constructed a predictive model based on the expression data of the core genes ADRA1B and PAPSS2. We used the R software package survival to perform COX regression analysis, with gene expression levels as independent variables and patient survival time as the dependent variable, to identify genes significantly associated with survival prognosis. Based on the results of the COX regression analysis, we built a risk score model incorporating gene expression and clinical covariates. We calculated a risk score for each patient using a formula that integrated the effects of gene expression levels and clinical characteristics. We then assessed the model's predictive accuracy using ROC curves and the concordance index (C-index). We calculated the model's AUC value and C-index to quantify its ability to distinguish survival risk. Additionally, we employed decision curve analysis (DCA) to evaluate the model's clinical application potential at different risk thresholds, determining its value in actual clinical decision-making.

2.3. Drug Screening and Correlation Analysis

To assess the association between specific gene expression and drug sensitivity, we calculated the correlation coefficients between gene expression levels and drug IC50 values. We first collected the expression data of the core genes ADRA1B and PAPSS2 in thyroid cancer brain metastasis samples, along with the corresponding drug IC50 values, ensuring that all data were appropriately preprocessed. We then used the Pearson correlation coefficient to quantify the linear relationship between gene expression levels and drug IC50 values. The Pearson correlation coefficient (r) ranges from -1 to 1, with positive values indicating positive correlation, negative values indicating negative correlation, and values close to 0 indicating no linear correlation. We subsequently conducted significance tests for the correlation coefficients between each gene and drug IC50 values to determine whether the observed correlations were statistically significant (using the cor.test function for Pearson correlation coefficient significance testing, which actually performed a two-tailed test). A correlation coefficient of R < -0.6 or > 0.6 was considered to indicate a strong correlation (-1 < r < 1).

2.4. Molecular Docking Analysis

To simulate the interaction between the proteins encoded by ADRA1B and PAPSS2 and potential therapeutic drugs (such as Ganetespib, BLU-667, etc.), and to identify key amino acid residues and binding sites, we employed molecular docking techniques. We first prepared the receptors and ligands, obtaining the protein structures of ADRA1B and PAPSS2 from the RCSB PDB database (RCSB PDB: Homepage, (Link 3)). We then preprocessed the obtained protein structures, which included removing water molecules, adding hydrogen atoms, and calculating charges. Subsequently, we downloaded the files of small-molecule ligands from public databases such as ZINC (Welcome to ZINC Is Not Commercial, (Link 4)) and PubChem (PubChem, (Link 5)). We used the online molecular docking tool CB-Dock2 (An accurate protein-ligand blind docking tool, (Link 6)) for molecular docking. CB-Dock2 is a web-based tool that performs fully automated protein-ligand blind docking calculations by predicting protein binding sites. We created a configuration file for each protein-drug pair, specifying the receptor, ligand, search space center coordinates, and box size. After running the docking calculations, we obtained the docked conformations and extracted docking scores to evaluate the binding energy of each conformation.

3. Results and Conclusion

3.1. Gene Expression Difference Analysis Results

To elucidate the role of ADRA1B and PAPSS2 genes in thyroid cancer brain metastasis, we initially compared their expression levels between thyroid cancer brain metastasis samples and control samples. Through t-test analysis, we found that the expression level of the ADRA1B gene was significantly lower in brain metastasis samples than in control samples, while the expression level of the PAPSS2 gene was significantly higher in brain metastasis samples than in controls.

From the heatmaps of ADRA1B and PAPSS2 gene expression levels (Figures 1A & 1B), the gradient of colors from blue (indicating low expression) to red (indicating high expression) visually illustrates the differences in gene expression levels. These results indicate that, compared to control samples, ADRA1B expression is downregulated in thyroid cancer brain metastasis samples, whereas PAPSS2 expression is upregulated.

3.2. Risk Model Assessment Results

In this study, we constructed a risk prediction model for thyroid cancer brain metastasis based on the expression data of the ADRA1B and PAPSS2 genes. The model was derived through COX regression analysis, with the risk score formula as follows: Risk score = -1.2537043002605*Exp(ADRA1B)+0.129184146518898*Exp(PAPSS2)

The construction and assessment results of the model are as follows:

The docking analysis of the ADRA1B gene with drugs such as Ganetespib and BLU-667 revealed that the ADRA1B gene is significantly negatively correlated with the sensitivity to these drugs. The regression coefficient (coef) was calculated to be (-1.2537043002605), and the hazard ratio (HR) is Exp (-1.2537043002605), indicating that high expression of the ADRA1B gene is associated with lower drug sensitivity (Figure 2).

The docking analysis of the PAPSS2 gene with drugs such as BLU-667 and XAV-939 showed that the PAPSS2 gene is significantly positively correlated with the sensitivity to these drugs. The regression coefficient (coef) was calculated as 0.129184146518898, and the hazard ratio (HR) is Exp (0.129184146518898), indicating that high expression of the PAPSS2 gene is associated with higher drug sensitivity.

We plotted the ROC curve and calculated the AUC value to evaluate the model's ability to distinguish between high-risk and low-risk patients. The closer the AUC value is to 1, the higher the model's predictive accuracy. The AUC value of the model was 0.731, indicating good predictive accuracy.

Decision curve analysis (DCA) demonstrated that the model has good clinical application potential at multiple risk thresholds (Figure 2).

3.3. Immune Cell Correlation Analysis Results

In this study, we assessed the relationship between the core genes ADRA1B and PAPSS2 of thyroid cancer brain metastasis and the activity of different types of immune cells through correlation analysis. The key findings are as follows (Figure 3).

From the correlation analysis results of the ADRA1B gene, the expression of the ADRA1B gene was significantly negatively correlated with T cell follicular helper activity (Tfh) (Pearson correlation coefficient = -0.41, adjusted p-value = 0.015) (Table 1). This indicates that an increase in the expression level of the ADRA1B gene is associated with a decrease in Tfh cell activity. The expression of the ADRA1B gene was significantly positively correlated with mast cell resting activity (Pearson correlation coefficient = 0.38 (Figure 4), adjusted p-value = 0.025). This shows that high expression of the ADRA1B gene is associated with enhanced activity of mast cells in the resting state. The expression of the ADRA1B gene was negatively correlated with neutrophil activity (Pearson correlation coefficient = -0.40, adjusted p-value = 0.018).

Table. 1. Cellular immune correlation of the ADRA1B gene.

Gene	Cell	Correlation	P value
ADRA1B	B cells naive	0.00262154628549252	0.98826007605973
ADRA1B	B cells memory	0.0647284613993619	0.716087360493229
ADRA1B	Plasma cells	0.132467532467532	0.453641926974449
ADRA1B	T cells CD8	-0.123241626	0.487437088211275
ADRA1B	T cells CD4 naive	0.302694357875705	0.0818482572535571
ADRA1B	T cells CD4 memory resting	-0.028279471	0.873857997083049
ADRA1B	T cells CD4 memory activated	-0.225290001	0.200171606905809
ADRA1B	T cells follicular helper	-0.414966696	0.014674587418065
ADRA1B	T cells regulatory (Tregs)	-0.149523179	0.398663430407546
ADRA1B	T cells gamma delta	-0.240187944	0.17122885676024
ADRA1B	NK cells resting	-0.07776929	0.660925014562264
ADRA1B	NK cells activated	0.0472116119174943	0.790426007619168
ADRA1B	Monocytes	-0.088725526	0.617787112447157
ADRA1B	Macrophages M0	-0.115333645	0.5160001516541
ADRA1B	Macrophages M1	0.247656694615277	0.157900069189431
ADRA1B	Macrophages M2	0.053938421732787	0.761913616483703
ADRA1B	Dendritic cells resting	-0.087924703	0.620975995016021
ADRA1B	Dendritic cells activated	0.042449218481483	0.81159539969348
ADRA1B	Mast cells resting	0.383331739837575	0.0252311584690628
ADRA1B	Mast cells activated	-0.19268006	0.274941137086488
ADRA1B	Eosinophils	0.162828207853569	0.357525074805436
ADRA1B	Neutrophils	-0.403011488	0.018115371097065

From the correlation analysis results of the PAPSS2 gene (Table 2), the expression of the PAPSS2 gene was significantly positively correlated with eosinophil activity (Pearson correlation coefficient = 0.39, adjusted p-value = 0.022), indicating that an increase in the expression of the PAPSS2 gene is associated with enhanced eosinophil activity. The expression of the PAPSS2 gene was also significantly negatively correlated with macrophage M1 activity (Pearson correlation coefficient = -0.37, adjusted p-value = 0.031). These correlation analysis results reveal significant associations between the expression of the ADRA1B and PAPSS2 genes and the activity of specific immune cells (Figure 4), suggesting that these genes play a role in regulating immune responses and influencing the progression of thyroid cancer brain metastasis.

Table. 2. Cellular immune correlation of the PAPSS2 gene.

Gene	Cell	Correlation	P value
PAPSS2	B cells naive	0.103165557	0.56150638
PAPSS2	B cells memory	0.047589956	0.78926389
PAPSS2	Plasma cells	0.055156608	0.75604987
PAPSS2	T cells CD8	0.202293637	0.25123513
PAPSS2	T cells CD4 naive	0.421840009	0.0129576
PAPSS2	T cells CD4 memory resting	0.330604197	0.05616909
PAPSS2	T cells CD4 memory activated	-0.12795678	0.47080196
PAPSS2	T cells follicular helper	0.081185627	0.64807545
PAPSS2	T cells regulatory (Tregs)	-0.018532	0.91714862
PAPSS2	T cells gamma delta	0.094188105	0.5962201
PAPSS2	NK cells resting	0.0710466	0.68873663
PAPSS2	NK cells activated	0.203666921	0.24693119
PAPSS2	Monocytes	-0.20608821	0.24226392
PAPSS2	Macrophages MO	-0.20405183	0.24705131
PAPSS2	Macrophages M1	-0.37005593	0.03122451
PAPSS2	Macrophages M2	-0.2539537	0.14725757
PAPSS2	Dendritic cells resting	0.002608586	0.98831811
PAPSS2	Dendritic cells activated	-0.00462149	0.97930535
PAPSS2	Mast cells resting	-0.02663147	0.88115537
PAPSS2	Mast cells activated	-0.29249628	0.09321531
PAPSS2	Eosinophils	-0.10897452	0.53955304
PAPSS2	Neutrophils	0.391510196	0.02203581

3.4. Protein Structure and Small-Molecule Docking Results

In this study, we utilized molecular docking techniques to simulate the interaction between the proteins encoded by the core genes ADRA1B and PAPSS2 and several potential therapeutic drugs. The docking analysis aimed to identify key amino acid residues and binding sites, providing a structural basis for drug design and optimization. From the docking results of the ADRA1B protein with small molecules (Figure 7), the docking analysis of the ADRA1B protein with the Ganetespib small molecule revealed interactions with several key amino acid residues. These residues include Y112, Y119, Y194, and K193, which form a stable complex with the Ganetespib small molecule through hydrogen bonds, hydrophobic interactions, ionic interactions, and π-stacking interactions. These interactions are important for the binding affinity and stability of the drug. The docking analysis of the ADRA1B protein with the IDH-C35 small molecule identified interactions with residues such as Y119, Y194, D192, L181, B187, and A188. These residues are involved in the folding and stability of the protein and play an important role in the binding characteristics of the drug (Table 4).

On the other hand, the docking results of the PAPSS2 protein with small molecules showed that the docking analysis of the PAPSS2 protein with the BLU-667 small molecule revealed that lysine residues such as K161, K165, K172, K176, and K177 participate in the stabilization of the protein through the formation of salt bridges or hydrogen bonds (Figure 7). The R82 arginine residue contributes to the solubility and binding characteristics of the protein through hydrogen bond formation or charge interactions mediated by its guanidinium group. The docking analysis of the PAPSS2 protein with the XAV-939 small molecule revealed that lysine residues such as K172, K175, and K173 play important roles in the stabilization and function of the protein. The F91 phenylalanine residue participates in hydrophobic interactions through its phenyl ring, which affects the folding and stability of the protein (Table 3).

Table. 3. Molecular docking results of the PAPSS2 gene.

Small molecule	Binding Energy	Pocket Position	Interaction Residues	Interaction Type
XAV-939	-8.4	C1	Chain A: ASN88 LEU89 ARG102 Chain B: SER51 LYS55 GLY78 ARG82 ASN88 LEU89 PHE91 SER92 PR093 GLY94 ASP95 ARG96 GLU97 GLU98 ASN99 ILE100 ARG102 SER120 PHE121 ILE122 SER123 PR0124 PHE125 ASP128 LYS161 LEU163 ILE172 LYS173 GLY174 PHE175 THR176 SER180 Chain C: GLY94 GLU97 Chain D: PR093 GLY94 GLU97	Hydrogen bonds, hydrophobic interactions, ionic interactions, π-π stacking interactions
BLU-667	-11.8	C3	Chain C: SER51 LYS55 THR56 PHE60 ASP77 GLY78 ASP79 VAL81 ARG82 HIS83 ASN86 ARG87 LEU89 PHE91 SER92 PR093 ARG96 GLU97 ASN99 ILE100 SER120 PHE121 ILE122 SER123 PR0124 PHE125 ASP128 LYS161 LEU163 ILE172 LYS173 GLY174 PHE175 THR176 SER180 ASP181 Chain D: HIS24	Hydrogen bonds, hydrophobic interactions, ionic interactions, π-π stacking interactions

Table. 4. Molecular docking results of the ADRA1B gene.

Small molecule	Binding Energy	Pocket Position	Interaction Residues	Interaction Type
Ganetespib	-9.8	C1	VAL98 SER102 LEU105 TYR110 VAL112 GLY114 ARG115 PHE117 CYS118 ASP119 TRP121 ALA122 ALA123 ASP125 VAL126 SER177 PRO180 LEU181 LEU182 TRP184 LYS185 GLU186 PRO187 ALA188 TYR191 ASP192 LYS193 VAL194 CYS195 GLY196 VAL197 THR198 GLU199 GLU200 TYR203 PHE310 ASP327 PHE330 LYS331 VAL332 LEU334 TRP335 TYR338	Hydrogen bond, hydrophobic interaction, ionic interaction, π-π stacking interaction
IDH-C35	-10	C1	SER102 LEU105 TYR110 VAL112 GLY114 ARG115 PHE117 CYS118 ASP119 TRP121 ALA122 ALA123 ASP125 VAL126 SER177 PRO180 LEU181 LEU182 TRP184 LYS185 GLU186 PRO187 ALA188 PRO189 TYR191 ASP192 LYS193 VAL194 CYS195 GLY196 VAL197 THR198 GLU199 GLU200 TYR203 ASP327 ALA328 PHE330 LYS331 LEU334 TRP335 TYR338	Hydrogen bond, hydrophobic interaction, ionic interaction, π-π stacking interaction
XL-888	-9.8	C1	LEU105 TYR110 VAL112 GLY114 ARG115 PHE117 CYS118 ASP119 TRP121 ALA122 ALA123 ASP125 VAL126 CYS129 PRO180 LEU181 TRP184 LYS185 GLU186 PRO187 ALA188 PRO189 TYR191 ASP192 LYS193 VAL194 CYS195 GLY196 VAL197 THR198 GLU199 GLU200 TYR203 SER207 TRP307 PHE310 PHE311 LEU314 ASP327 ALA328 PHE330 LYS331 LEU334 TYR338	Hydrogen bond, hydrophobic interaction, ionic interaction, π-π stacking interaction
Luminespib	-8.6	C3	SER47 VAL48 VAL51 LEU52 PHE55 ILE56 ILE60 CYS95 LEU99 PRO100 SER102 ALA103 GLU106 VAL107 ALA328 LYS331 VAL332 LEU334 TRP335 LEU336 GLY337 TYR338 PHE339	Hydrogen bond, hydrophobic interaction, ionic interaction, π-π stacking interaction
spebrutinib	-8.6	C1	SER102 LEU105 TYR110 VAL112 GLY114 ARG115 PHE117 CYS118 ASP119 TRP121 ALA122 ALA123 ASP125 VAL126 CYS129 SER177 ILE178 PRO180 LEU181 TRP184 LYS185 GLU186 PRO187 ALA188 TYR191 ASP192 LYS193 VAL194 CYS195 GLY196 VAL197 THR198 GLU199 GLU200 TYR203 TRP307 PHE310 PHE311 LEU314 ASP327 PHE330 LYS331 LEU334 TRP335 TYR338	Hydrogen bond, hydrophobic interaction, ionic interaction, πーπ stacking interaction
PF-4942847	-9.8	C1	LEU105 GLY114 ARG115 CYS118 ASP119 TRP121 ALA122 ALA123 ASP125 VAL126 SER177 ILE178 PRO180 LEU181 LEU182 TRP184 LYS185 GLU186 PRO187 ALA188 PRO189 TYR191 ASP192 LYS193 VAL 194 CYS195 GLY196 VAL197 THR198 GLU199 GLU200 TYR203 PHE310 PHE311 ASP327 ALA328 PHE330 LYS331 LEU334 TYR338	Hydrogen bond, hydrophobic interaction, ionic interaction, π-π stacking interaction

These docking analysis results not only provide new insights into the mechanisms of action of the ADRA1B and PAPSS2 genes in thyroid cancer brain metastasis but also offer potential targets for the development of new therapeutic strategies. By identifying key amino acid residues involved in drug interactions, we can more precisely design drugs targeting these genes, thereby improving treatment efficacy and reducing side effects.

3.5. Survival Analysis Results

In this study, to further explore the relationship between the expression levels of the ADRA1B and PAPSS2 genes and the survival rate of patients with thyroid cancer brain metastasis, the potential of these genes as prognostic biomarkers was assessed through survival analysis.

The survival curve (Figure 6) illustrates the association between ADRA1B gene expression levels and overall patient survival. The curve analysis reveals that high expression levels of the ADRA1B gene are correlated with longer survival times, suggesting that this gene may have tumor-suppressive effects (Figure 5). The statistical analysis of the survival curve indicates that patients with higher ADRA1B gene expression levels have significantly better survival outcomes compared to those with lower expression levels (p = 0.01) (Figure 2).

The survival curve (Figure 6) depicts the relationship between PAPSS2 gene expression levels and patient survival rates. The results show that high expression levels of the PAPSS2 gene are associated with shorter survival times, indicating that this gene may have oncogenic effects. The statistical analysis of the survival curve further confirms this observation, with patients having higher PAPSS2 gene expression levels exhibiting significantly poorer survival outcomes compared to those with lower expression levels (p = 0.022).

The correlation coefficient analysis also indicates that ADRA1B gene expression is negatively correlated with patient survival rates (correlation coefficient = -0.41, p = 0.015). This result further supports the findings from the survival curve analysis, that is, high expression of the ADRA1B gene is associated with better survival outcomes. In contrast, PAPSS2 gene expression is positively correlated with patient survival rates (correlation coefficient = 0.39, p = 0.022), consistent with the survival curve analysis results, indicating that high expression of the PAPSS2 gene is associated with poorer survival outcomes (Figure 4).

These survival analysis results not only reveal the potential prognostic value of the ADRA1B and PAPSS2 genes in thyroid cancer brain metastasis but also provide new targets for future therapeutic strategies. By identifying genes significantly correlated with patient survival rates, we can gain a better understanding of the molecular mechanisms underlying thyroid cancer brain metastasis and provide a scientific basis for the development of new treatment methods.

3.6. Drug Sensitivity Analysis Results

In this study, we investigated the drug sensitivity of the ADRA1B and PAPSS2 genes in thyroid cancer brain metastasis by analyzing the correlation between gene expression and drug IC50 values. We employed scatter plots, correlation coefficients, and p-value statistical analysis methods.

From the scatter plots, we observed that the expression level of the ADRA1B gene is significantly negatively correlated with the IC50 values of several drugs. For example, the correlation coefficients with XL-888, Ganetespib, and PF-4942847 are -0.560, -0.528, and -0.506, respectively, with corresponding p-values all less than 0.05, indicating that these correlations are statistically significant. These results suggest that high expression of the ADRA1B gene is associated with increased sensitivity to these drugs (Figure 6).

Another scatter plot shows that the expression level of the PAPSS2 gene is significantly positively correlated with the IC50 values of BLU-667 and XAV-939, with correlation coefficients of 0.487 and 0.481, respectively, and corresponding p-values both less than 0.05. This further confirms that these genes may play a role in promoting thyroid cancer brain metastasis.

2.3. Statistical Analysis

These analysis results provide new insights into the mechanisms of action of the ADRA1B and PAPSS2 genes in thyroid cancer brain metastasis and offer potential targets for the development of new therapeutic strategies. Specifically, the expression levels of these genes may serve as biomarkers for predicting the response of patients with thyroid cancer brain metastasis to specific drugs. By identifying genes significantly correlated with drug sensitivity, we can more precisely design drugs targeting these genes, thereby improving treatment efficacy and reducing unnecessary side effects.

4. Discussion

This study comprehensively analyzed the expression patterns, immune correlations, drug sensitivity, and protein structure-small molecule interactions of the ADRA1B and PAPSS2 genes in thyroid cancer brain metastasis, revealing their potential roles in disease progression.

Adrenergic receptor alpha 1B (ADRA1B) is a member of the adrenergic receptor alpha 1 (ADRA1) subfamily and participates in the regulation of processes such as cell proliferation, differentiation, and apoptosis [20, 21]. In recent years, the role of ADRA1B in tumors has attracted increasing attention. ADRA1B is upregulated in cancers such as gastric cancer and liver cancer, and it may promote tumor growth and metastasis by activating downstream signaling pathways [22, 23]. Adrenergic receptor antagonists have also been used in the treatment of cancers such as prostate cancer and breast cancer [24].

The bifunctional enzyme 3′-phosphoadenosine 5′-phosphosulfate synthase (PAPSS) exists in two isoforms, namely PAPSS1 and PAPSS2 [25]. PAPSS1 and PAPSS2 share 78% homology in their amino acid sequences and possess identical biochemical reaction functions. Their differences mainly lie in substrate affinity, with PAPSS2 exhibiting catalytic efficiency thousands of times higher than that of PAPSS1 in vivo; therefore, PAPSS2 plays the dominant role in physiological functions [26]. The core physiological function of PAPSS2 protein in humans is to participate in the activation of sulfation, which is essential for maintaining normal physiological functions. PAPSS2 has a profound impact on the sulfation of proteoglycans in the human body, thereby playing a crucial regulatory role in bone development. Many cases of skeletal dysplasia are caused by abnormal expression of PAPSS2 [27].

PAPSS2 also plays an important role in the occurrence and development of tumors. Reports have shown that PAPSS2 expression is decreased in 14 types of cancers, with the most significant reduction observed in colon cancer. In patients with colon cancer, low expression of PAPSS2 is associated with poor prognosis, particularly in advanced stages of the disease [28, 29]. In addition, loss of PAPSS2 leads to reduced formation of sulfomucins and disruption of bile acid homeostasis, thereby impairing the intestinal mucosal barrier function and making mice more susceptible to colitis and colon cancer [30]. Studies have indicated that PAPSS2 may also be involved in cisplatin resistance in endometrial cancer, acting as a tumor suppressor gene. The expression of PAPSS2 in human type I endometrial cancer is significantly higher than that in type II endometrial cancer, and PAPSS2 expression is associated with endometrial cancer subtypes. When PAPSS2 expression was downregulated by siRNA, endometrial cancer cells exhibited enhanced resistance to cisplatin and increased proliferative activity, suggesting that PAPSS2 may exert a tumor-suppressive role in endometrial cancer [30]. In contrast, in some other cancers, high expression of PAPSS2 is critical for the sulfation modification of sulfated substrates such as the pluripotent proteoglycan Versican. These sulfated substrates play key roles in tumor cell migration, invasion, and metastasis, thereby accelerating cancer progression. Reports have shown that PAPSS2 may act as an upstream regulatory gene of the transcription factor Snail, promoting Snail protein expression in breast cancer and thus enhancing cancer cell invasion and metastasis, functioning as an oncogene [31, 32]. Wu et al. [33] found that PAPSS2 expression in gastric cancer tissues was significantly higher than in control tissues, and that PAPSS2 expression was associated with tumor invasion depth and lymph node metastasis. Transwell assay results showed that knockdown of PAPSS2 significantly inhibited the invasion and migration abilities of gastric cancer cells AGS and SGC-7901 (P < 0.05). Zhang et al. [28] reported that knockout of PAPSS2 could suppress breast cancer cell migration and significantly reduce lung metastasis in nude mice. Xu et al. [27] demonstrated that PAPSS2 plays an important role in the occurrence and development of colon cancer.

In other studies, immunohistochemical staining revealed that compared with adjacent non-cancerous tissues, ADRA1B was highly expressed in PTC tissues, suggesting that this protein may play a promoting role in the progression of PTC. Western blot and real-time RT-PCR results showed that both protein and mRNA levels of ADRA1B were significantly upregulated in PTC tissues, further confirming the phenomenon of ADRA1B overexpression in PTC. This finding echoes previous studies [20, 34] that reported a correlation between ADRA1B and tumor malignancy. Broso et al. [34] found that ADRA1B was highly expressed in patients with neuroblastoma, and that the use of its antagonists could inhibit neuroblastoma growth and promote differentiation. These results collectively suggest that upregulated ADRA1B expression may promote tumor cell proliferation and thereby contribute to tumor growth. Kaplan–Meier survival analysis further revealed that patients with high ADRA1B expression had a significantly increased risk of postoperative recurrence (metastasis) [35].

Our immune cell correlation analysis revealed that the ADRA1B gene was significantly negatively correlated with the activity of multiple immune cell types, whereas PAPSS2 showed a positive correlation. This suggests that ADRA1B may play a role in suppressing immune responses, while PAPSS2 may enhance immune cell activity. In line with the findings in (Figure 4), we observed that ADRA1B expression was significantly negatively correlated with Tfh cells, whereas PAPSS2 was significantly negatively correlated with M1 macrophage infiltration. This observation is biologically plausible. Tfh cells, as a critical subset supporting B-cell differentiation and antibody production, have been shown to be closely associated with antitumor immune responses and the formation of tertiary lymphoid structures in various cancers. High expression of ADRA1B, if suppressing Tfh cell infiltration, may weaken local adaptive immune responses in the tumor microenvironment, thereby facilitating the occurrence and progression of brain metastases. On the other hand, M1 macrophages usually secrete pro-inflammatory cytokines such as IL-12 and TNF-α, playing a crucial role in limiting tumor growth and metastasis. The negative correlation between PAPSS2 expression and M1 macrophages suggests that upregulation of PAPSS2 may be associated with an immunosuppressive microenvironment, leading to a reduction in antitumor inflammatory responses.

Based on the immune correlation analysis, we found that the expression level of the target gene was closely associated with the infiltration of multiple immune cell types. In particular, the gene showed a significant positive correlation with neutrophils (R = 0.39, p = 0.022). Previous studies have demonstrated that neutrophils exhibit high plasticity within the tumor microenvironment, with their functions classified into pro-tumor (N2 type) and anti-tumor (N1 type) subsets [36]. Notably, tumor-associated neutrophils (TANs) can promote tumor cell invasion, angiogenesis, and distant metastasis through the release of reactive oxygen species (ROS), proteases, and the formation of neutrophil extracellular traps (NETs) [37]. Our results suggest that the gene may facilitate thyroid cancer brain metastasis by enhancing the recruitment or activation of TANs. Clinically, potential therapeutic strategies targeting neutrophils or NETs (such as CXCR2 inhibitors and PAD4 inhibitors) have already been proposed. Therefore, this finding not only has biological plausibility but also holds translational medical value.

On the other hand, we also observed a positive correlation between gene expression and resting mast cells (R = 0.38, p = 0.025). As an important component of innate immunity, mast cells play a dual role in tumor progression. On one hand, they can promote angiogenesis, matrix degradation, and immunosuppression by releasing factors such as VEGF and TNF-α, thereby supporting tumor growth and metastasis [38]. On the other hand, under specific microenvironmental conditions, mast cells may secrete chemokines that facilitate the infiltration of effector T cells, thus exerting antitumor effects [39]. Therefore, our findings suggest that the gene may influence the recruitment and functional state of mast cells within the tumor immune microenvironment, thereby participating in the regulation of immune balance.

In addition, gene expression was positively correlated with naïve CD4 T-cell infiltration (R = 0.42, p = 0.013). CD4 T cells occupy a central role in antitumor immune responses, and their differentiation into effector subsets (such as Th1, Th17, and Tfh) is crucial for sustaining effective antitumor immunity [40]. An increase in naïve CD4 T-cell infiltration, without further differentiation into effector T cells, may indicate the presence of immunosuppressive signals or insufficient antigen stimulation within the tumor microenvironment, thereby weakening the antitumor response. Taken together with previous studies, this result suggests that the target gene may regulate the effectiveness of antitumor immune responses by influencing CD4 T-cell activation and differentiation pathways.

In summary, our results reveal the potential mechanisms of the target gene in the infiltration of immune cells such as neutrophils, mast cells, and naïve CD4 T cells. These findings are consistent with previous studies- ADRA1B has been regarded as an immunosuppressive gene, whereas PAPSS2 has been associated with immune activation.

Through drug sensitivity analysis, we found that the expression of the ADRA1B and PAPSS2 genes is significantly correlated with the IC50 values of specific drugs, indicating that these genes may serve as potential biomarkers for predicting drug responses. Specifically, high expression of the ADRA1B gene is associated with increased sensitivity to certain drugs, while high expression of the PAPSS2 gene is linked to increased resistance to other drugs. These findings provide a scientific basis for developing personalized treatment plans and selecting the most effective therapeutic drugs.

The molecular docking analysis further revealed the interactions between the proteins encoded by ADRA1B and PAPSS2 and potential therapeutic drugs. These analysis results not only provide a structural basis for understanding how these genes respond to specific drugs but also offer new directions for drug design and optimization. In particular, the identified key amino acid residues may become targets for drug action.

Although this study provides important insights, several limitations remain. First, our analysis was primarily based on in vitro experiments and computational simulations, and these conclusions still require validation through in vivo studies. Second, the study mainly focused on the ADRA1B and PAPSS2 genes, whereas thyroid cancer brain metastasis may involve other related genes. In addition, the GEO dataset contained only two thyroid cancer metastasis samples, which limits the generalizability of the results. Future research should expand the sample size and include more genes to further validate and extend our findings.

In summary, this study provides important information for understanding the mechanisms of thyroid cancer brain metastasis and developing new treatment methods by analyzing the potential roles of the ADRA1B and PAPSS2 genes from multiple perspectives. These findings may help improve the prognosis and treatment outcomes for patients with thyroid cancer brain metastasis.

The purpose of disease treatment is to benefit patients and achieve comprehensive recovery of their physical, psychological, and social functions. The purpose of the development of the discipline is to analyze diseases and minimize the harm they cause to individuals, families, and society [41]. We will strive to achieve comprehensive recovery for patients, including the restoration of physical, psychological, and social functions. Through integrated treatment plans, we aim to improve the quality of life for patients and help them reintegrate into society.

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