The differential expression of ARHGAP36 mRNA between thyroid carcinoma and adjacent normal tissues was analyzed using TCGA-THCA tumor data (8) and GTEx normal tissue data (9) (accession no. phs000424.v8.p2) through the GSEA website [www.gsea-msigdb.org (10)]. The limma R package was used for statistical analysis and the Benjamini-Hochberg false discovery rate (FDR) correction (significance threshold: |log2(fold change)|>1 and FDR-adjusted P<0.05) was used as the significance cut-off. The UALCAN database [http://ualcan.path.uab.edu (11)] was used to evaluate the expression of ARHGAP36 in TCGA-THCA cohort across various factors, including sample types, cancer stages, patient demographics, histological characteristics and nodal metastasis status, applying one-way ANOVA with Tukey's post-hoc test for multi-group comparisons and unpaired Student's t-test for binary classifications. The LinkedOmics database [www.linkedomics.org (12)] was used to identify genes correlated with ARHGAP36 using Pearson's correlation coefficient, with |r| >0.3 and FDR <0.5 defined as significant; the top 50 positively/negatively correlated genes were visualized in heatmaps. The Metascape database [https://metascape.org (13)] was used to perform Gene Ontology (GO) process enrichment analysis for ARHGAP36 and its associated genes, highlighting key biological processes, via hypergeometric testing with Benjamini-Hochberg correction, requiring a minimum enrichment factor of 1.5, P<0.01 and FDR <0.05. The TIMER database [https://cistrome.shinyapps.io/timer/ (14)] was used to investigate the relationships between ARHGAP36 and tumor immune components, including purity, B cells, CD8⁺ T cells, CD4⁺ T cells, macrophages, neutrophils and dendritic cells based on TCGA-THCA RNA-seq data, employing a deconvoluted gene expression algorithm with tumor purity-adjusted partial correlation. Protein-protein interaction (PPI) analyses were performed by the Metascape analysis tool. The TISCH database (http://tisch.comp-genomics.org/), a publicly available resource that provides integrated single-cell RNA sequencing data from multiple tumor types, including THCA. This platform allows for cell-type-specific gene expression analysis and immune landscape profiling.

BHT101 and BCPAP cells (obtained from The Cell Bank of Type Culture Collection of The Chinese Academy of Sciences) were cultured in DMEM [Ubi Biotech (Shanghai) Co., Ltd.] supplemented with 10% fetal bovine serum [FBS; cat. no. U11-020A; Ubi Biotech (Shanghai) Co., Ltd.]. The culture medium was further enriched with 1% penicillin-streptomycin solution and 0.25 µg/ml amphotericin B to prevent contamination. Cells were maintained at 37˚C in a humidified incubator with 5% CO₂. When the cell density reached 80-90% confluency, cells were detached using 0.25% trypsin-EDTA solution and centrifuged at 111 x g at 37˚C for 5 min to collect the cell pellet.

The specific siRNA targeting ARHGAP36 were designed and synthesized (Shanghai Jima Pharmaceutical Technology Co., Ltd.) to optimize knockdown efficiency. Prior to transfection, cells were seeded into 6-well plates at a density of 5x10⁵ cells/ml with 1.5 ml of culture medium per well and incubated at 37˚C with 5% CO₂ overnight to achieve 70-80% confluency. A total of 16 h after seeding, cells were divided into two groups: A negative control (NC) group and a si-ARHGAP36 group. Each siRNA was diluted in Opti-MEM (Thermo Fisher Scientific, Inc) to a final concentration of 100 nM in a 250 µl volume and incubated at room temperature for 5 min. In a separate tube, 5 µl of Lipofectamine 2000 transfection reagent (Invitrogen; Thermo Fisher Scientific, Inc.) was added to 250 µl of Opti-MEM and incubated at room temperature for 5 min. The diluted siRNA solution was then gently combined with the Lipofectamine 2000 mixture and incubated at room temperature for 20 min to allow for siRNA-lipid complex formation. Next, 500 µl of this siRNA-lipid complex was carefully added to each well containing the cells, followed by gentle rocking of the plate to ensure even distribution. A total of 6 h post-transfection, the medium was aspirated and 1.5 ml of fresh complete medium was added to each well. The cells were collected 48 h after transfection for western blot analysis to assess the ARHGAP36 protein expression levels and evaluate knockdown efficiency. All procedures were conducted under sterile conditions to prevent contamination, freshly prepared reagents were used to ensure optimal transfection efficiency and strict adherence to timing at each step was maintained to ensure reproducibility. The siRNA sequences were as follows: ARHGAP36 siRNA, sense 5'-GCGGGUCAGCUCCGAGAAA-3' and antisense 5'-UUUUCGGUCAGGGGCCGC-3'; NC siRNA, sense 5'-UUCUCCGAACGUGUCACGU-3' and antisense, 5'-ACGUGACACGUUCGGAGAA-3'.

Cells were lysed using RIPA buffer (Thermo Fisher Scientific, Inc.) supplemented with protease inhibitors to ensure complete protein extraction. Protein concentrations were determined using the BCA Protein Assay Kit (Pierce; Thermo Fisher Scientific, Inc.) according to the manufacturer's instructions. For SDS-PAGE, 100 µg of total protein from each sample was separated on a 10% polyacrylamide gel at a constant voltage of 120 V for 2 h. Proteins were then transferred onto PVDF membranes (MilliporeSigma) using a semi-dry transfer system at 25 V for 1 h. The membranes were then blocked with 5% non-fat milk in 1X TBST (0.1% Tween) for 2 h at room temperature to reduce non-specific binding. Subsequently, the membranes were incubated overnight at 4˚C with primary antibodies against ARHGAP36 (1:2,000; cat. no. PA5-31619; Thermo Fisher Scientific, Inc.) and GAPDH (loading control; 1:2,000; cat. no. ab8245; Abcam). After washing with TBST, the membranes were incubated with horseradish peroxidase-conjugated anti-mouse (1:5,000; cat. no. 58802; Cell Signaling Technology, Inc.) and anti-rabbit (1:5,000; cat. no. 7074; Cell Signaling Technology, Inc.) secondary antibodies for 1 h at room temperature. Protein bands were visualized using an enhanced chemiluminescence detection kit (Thermo Fisher Scientific, Inc.) and imaged with a Tanon 5200 system. Densitometric analysis of the blots was performed using ImageJ software (v1.53; National Institutes of Health) to semi-quantify the protein expression levels.

For the plate colony formation assay, BHT101 and BCPAP cells (1x10³ per well) were seeded into 6-well plates and allowed to adhere overnight. The next day, the medium was replaced with DMEM containing 10% FBS and 1% penicillin-streptomycin. Cells were cultured for 1 month at 37˚C in a humidified 5% CO₂ incubator, with media changes every 3 days. At the end of the culture period, colonies were fixed with ice-cold 100% methanol for 10 min, stained with 0.5% crystal violet solution for 20 min at 37˚C and then washed with distilled water. The plates were air-dried and colonies consisting of >50 cells in each well were manually counted.

To assess the migratory capacity of BHT101 and BCPAP cells, a scratch wound healing assay was performed. Cells were seeded at a density of 1x10⁶ cells per well in 6-well plates and cultured to confluency in DMEM supplemented with 10% FBS and 1% penicillin-streptomycin. A uniform scratch was created using a sterile 200 µl pipette tip and debris was removed by washing with PBS. The medium was then replaced with DMEM containing 1% FBS to minimize proliferation. Images of the scratch wounds were captured immediately (0 h) and again at 24 h post-scratch using an inverted light microscope. Wound closure was quantified by measuring the distance between the edges of the scratch at multiple points, and image analysis software (version 2.3; Olympus Corporation) was used to calculate the average wound width and percentage of closure.

To evaluate cell migration and invasion, BHT101 and BCPAP cells (2x10⁵ in 200 µl serum-free DMEM) were seeded into the top chamber of 24-well Transwell inserts with 8 µm pores. The lower chamber contained 600 µl of DMEM with 10% FBS as a chemoattractant. For invasion assays, the top chamber was pre-coated with Matrigel at 4˚C overnight. After a 24-h incubation at 37˚C in 5% CO₂, the cells that migrated or invaded through the membrane were fixed with 4% paraformaldehyde at room temperature for 15 min. After fixation, the cells were stained with 0.5% crystal violet at 37˚C for 20 min. Images were captured under an Olympus BX50 light microscope (Olympus Corporation). Migrated or invaded cells were quantified by counting five random fields per membrane.

Data were analyzed using Pearson's or Spearman's tests for correlation analysis, and group differences were assessed using an unpaired Student's t-test (GraphPad Prism v6.0; Dotmatics). Results are presented as the mean ± SD, and P<0.05 was considered to indicate a statistically significant difference.

ARHGAP36 expression was analyzed across multiple databases to assess its potential role in THCA progression. The GEPIA results showed higher ARHGAP36 mRNA levels in TCGA tumor tissues versus normal tissues (Fig. 1A and B; P<0.01). The UALCAN results also confirmed elevated ARHGAP36 expression in TCGA-THCA samples (Fig. 1C; P<0.01). GEPIA further revealed similar findings in the THCA dataset (Fig. 1D; P<0.05). These consistent results across multiple datasets strongly indicate that ARHGAP36 levels are markedly elevated in THCA samples. This suggests a potential role for ARHGAP36 in the progression and pathogenesis of THCA, positioning it as a candidate biomarker for further investigation.

UALCAN data revealed a comprehensive association between ARHGAP36 expression and various clinical parameters in THCA. THCA tumor tissues exhibited significantly higher ARHGAP36 levels compared with normal tissues (Fig. 2A; P<0.001). ARHGAP36 expression progressively increased from stage 1 to 4 disease (Fig. 2B; P<0.001), indicating a potential role in disease progression. Elevated ARHGAP36 expression was also observed across different demographic and histological subtypes. Specifically, significant increases were noted in various ethnic groups (Fig. 2C; P<0.001), female patients (Fig. 2D; P<0.001), younger patients (Fig. 2E; P<0.001) and distinct THCA subtypes (Fig. 2F; P<0.001). Additionally, lymph node metastasis was associated with higher ARHGAP36 expression (Fig. 2G; P<0.001), suggesting its involvement in aggressive tumor behavior. These findings highlight the broad relevance of ARHGAP36 in THCA, spanning multiple clinical and demographic factors. The consistent elevation of ARHGAP36 across these diverse parameters underscores its potential as a biomarker for disease progression.

To investigate the functional role of ARHGAP36 in THCA, ARHGAP36 expression was knocked down in BCPAP and BHT101 cells using siRNA (designated as BCPAP/si-ARHGAP36 and BHT101/si-ARHGAP36). Western blot assays showed the effectiveness of the siRNA transfection and confirmed the significant decrease in ARHGAP36 protein levels compared with the control (Fig. 3A and B; P<0.01). Plate colony formation assays revealed significantly reduced proliferation in these cells compared with controls (Fig. 3C and F; P<0.01). Apoptosis assays also showed increased cell death upon ARHGAP36 knockdown (Fig. 3G-J; P<0.01). These findings highlight the critical role of ARHGAP36 in promoting THCA cell survival and suggest that its downregulation can significantly inhibit tumor cell proliferation while enhancing apoptosis.

Wound healing assays demonstrated a reduced migration in ARHGAP36 knockdown cells (Fig. 4A-D; P<0.05). Transwell migration assays also showed significantly fewer si-ARHGAP36 cells traversing through Transwell inserts (Fig. 4E-H; P<0.05). Invasion assays revealed similar results, with significantly decreased invasive behavior (Fig. 4E-H; P<0.05). These findings collectively indicate that the downregulation of ARHGAP36 expression impairs THCA cell migration and invasion, highlighting its critical role in facilitating these processes. This evidence supports the potential of ARHGAP36 as a therapeutic target to inhibit tumor metastasis in THCA.

To explore the genes co-expressed with ARHGAP36, the LinkedOmics database was utilized. A total of 2,436 genes were found to be positively correlated and 1,764 genes were negatively correlated with ARHGAP36 (Fig. 5A). Heatmaps identified the top 50 genes most strongly correlated with ARHGAP36 expression (Fig. 5B and C). Key positively correlated genes included immunoglobulin superfamily member 1 (IGSF1; Fig. 5D; ρ=0.853, P=4.5x10^-145), dihydropyrimidinase like 3 (DPYSL3; Fig. 5E; ρ=0.604, P=6.63x10^-52) and zinc finger CCHC-type containing 12 (ZCCHC12; Fig. 5F; ρ=0.55, P=1.44x10^-41), indicating strong associations between these genes and ARHGAP36. Negatively correlated genes included SET domain containing 3 actin N3(tau)-histidine methyltransferase, COX15 and ring finger protein 157 (Fig. 5G-I), highlighting potential antagonistic relationships in THCA pathogenesis. These findings provide insights into the molecular network surrounding ARHGAP36, suggesting its involvement in complex regulatory pathways that influence THCA progression. This co-expression analysis supports the hypothesis that ARHGAP36 plays a role in THCA by interacting with key regulatory genes.

To gain deeper insights into the biological processes associated with ARHGAP36, a Metascape analysis was conducted. This analysis revealed that ARHGAP36-associated genes are significantly enriched in processes such as ‘Pancreatic cancer subtypes,’ ‘Prolactin signaling pathway’ and ‘T cell mediated immunity’ (Fig. 6A). PPI networks further supported these findings, illustrating the interconnectivity between ARHGAP36 and its associated genes (Fig. 6B and C). These networks highlight key pathways and interactions that may underline the functional role of ARHGAP36 in THCA. The analysis revealed that ARHGAP36 may be intricately involved in multiple signaling pathways critical for THCA development and progression. Notably, it appears to interact with components of the MAPK pathway, which is known to play a pivotal role in cell proliferation, differentiation and survival (15). These results suggest that ARHGAP36 is involved in diverse biological processes beyond THCA, potentially influencing pathways related to other cancer types and immune responses. The enrichment in T cell-mediated immunity is particularly noteworthy, indicating a potential role for ARHGAP36 in modulating the tumor microenvironment and immune evasion mechanisms.

To investigate the relationship between ARHGAP36 expression and immune cell infiltration in THCA, an analysis using the TIMER database was conducted. TIMER analysis indicated a significant but weak correlation between ARHGAP36 expression and CD4⁺ T cell infiltration (Fig. 7D; cor =0.295, P=3.04x10^-11). Additional weak correlations were observed with neutrophils (Fig. 7F) and dendritic cells (Fig. 7G), further supporting the possible role of ARHGAP36 in modulating immune cell infiltration. These findings suggest that ARHGAP36 may influence the tumor microenvironment by affecting the recruitment and activity of various immune cells. The positive correlation with CD4⁺ T cells is particularly noteworthy, as it highlights the potential involvement of ARHGAP36 in adaptive immunity and immune evasion mechanisms in THCA. These results collectively underscore the importance of ARHGAP36 in shaping the immune landscape of THCA, positioning it as a potential target for immunotherapeutic strategies.

To further explore the role of ARHGAP36 in the tumor microenvironment, its expression was analyzed using the TISCH database. This analysis revealed that ARHGAP36 is expressed in epithelial cells within THCA tissues and exhibits a weak positive correlation with several immune checkpoint molecules, including PDCD-1 (programmed cell death protein 1; PD-1), lymphocyte-activation gene 3 (LAG3), CD274 (programmed death-ligand 1; PD-L1) and cytotoxic T-lymphocyte-associated protein 4 (CTLA4) (Fig. 8A-F). These findings suggest that ARHGAP36 may facilitate immune escape in THCA by interacting with these key immune checkpoints. The observed weak correlations imply that ARHGAP36 could contribute to the suppression of immune surveillance and the establishment of an immunosuppressive tumor microenvironment. The positive weak correlations with PD-1, LAG3, PD-L1 and CTLA4 highlight the potential of ARHGAP36 as a mediator of immune evasion mechanisms in THCA. This evidence supports the hypothesis that targeting ARHGAP36 might enhance the efficacy of immunotherapies aimed at overcoming immune resistance in THCA.