Knockdown of ITGA2 Promotes Pyroptosis in Thyroid Cancer by Regulating the DNA Damage Response

Abstract

Background:

The most common endocrine cancer, thyroid carcinoma (TC), has a dismal prognosis when it reaches an advanced stage. Integrin α-2 (ITGA2) has been implicated in cancer progression, influencing both DNA damage and repair mechanisms. However, it is unknown how ITGA2 influences these processes in TC.

Methods:

ITGA2 was identified as a key prognostic gene for TC from the Cancer Genome Atlas-thyroid carcinoma (THCA), GSE3678, GSE29265, and GSE33630 datasets. Functional assays were used to evaluate the impact of ITGA2 knockdown on cell viability, migration, apoptosis, invasion, pyroptosis (N-terminal fragment of GSDME, GSDME-N), and cytotoxicity (Lactate dehydrogenase, LDH). DNA damage markers (phosphorylated histone H2AX on serine 139 (γ-H2AX), phosphorylated ataxia telangiectasia mutated (p-ATM), phosphorylated checkpoint kinase 2 (p-CHK2)) and the level of Reactive Oxygen Species (ROS) were used to assess oxidative stress. The impact of ITGA2 inhibition on Wnt/β-catenin signaling was evaluated, and a mouse xenograft model assessed tumor growth in vivo.

Results:

ITGA2 was significantly overexpressed in TC. Knockdown of ITGA2 significantly reduced cell viability, migration, and invasion, while promoting pyroptosis by upregulating cleaved-poly(ADP-ribose) polymerase (PARP) and GSDME-N. ITGA2 silencing also increased LDH activity, enhanced the expression of DNA damage markers (p-ATM, γ-H2AX, p-CHK2), and increased ROS levels. Furthermore, suppression of ITGA2 activity attenuated the Wnt/β-catenin pathway by reducing the levels of MYC proto-oncogene, bHLH transcription factor (C-myc), CD44 molecule (CD44), slug, snail, β-catenin, and wingless-type MMTV integration site family, member 1 (Wnt-1). ITGA2 silencing significantly inhibited tumor growth in a mouse model.

Conclusion:

ITGA2 promotes TC progression by regulating the DNA damage response and inhibiting pyroptosis. Knockdown of ITGA2 increases oxidative stress, exacerbates DNA damage, and inhibits the Wnt/β-catenin pathway, indicating it may have potential as a treatment target in TC.

Graphical abstract

Keywords

thyroid cancer / ITGA2 / Wnt/β-catenin signaling pathway / DNA damage / pyroptosis

1. Introduction

Thyroid cancer (TC) is the most common endocrine malignancy globally and shows an increasing incidence [1]. Early intervention and the use of various therapeutic approaches, including surgical procedures, radioactive iodine treatment, and thyroid hormone therapy, have improved the outcomes for many TC patients. Patients with advanced or metastatic illness, on the other hand, continue to face bleak prospects [2]. DNA damage is pivotal in TC progression due to its activation of critical pathways, such as pyroptosis and apoptosis [3]. Apoptosis is a non-inflammatory, programmed mode of cellular demise, whereas pyroptosis is a pro-inflammatory process that entails inflammasome activation and the cleavage of gastrin [4, 5]. Minor DNA damage can be repaired, but severe or persistent damage triggers cell death pathways [6]. The pro-inflammatory effects of pyroptosis may contribute to the aggressive behavior of specific TC subtypes [7]. Therefore, it is essential to comprehend the connection between DNA damage and cell death in TC in order to create innovative treatment approaches [8].

Integrin alpha-2 (ITGA2) is a transmembrane receptor that forms the \alpha2\beta1 integrin complex. Recent data also indicates that ITGA2 is essential in the DNA damage response. Specifically, the overexpression of ITGA2 negatively affects the process of DNA repair, primarily by interfering with the non-homologous end-joining (NHEJ) mechanism, which is crucial for the repair of double-strand DNA breaks [9]. This disruption increases genomic instability and sensitizes tumor cells to treatments such as radiotherapy. Zhou et al. [10] showed that ITGA2 overexpression in pancreatic cancer exacerbates DNA damage and enhances radiation sensitivity. The Wnt/\beta-catenin signaling cascade is a well-known regulator of cell proliferation, differentiation, and stem cell maintenance. This pathway has attracted significant attention for its potential involvement in facilitating DNA repair mechanisms [11]. Dysregulation of the Wnt/\beta-catenin pathway inhibits cellular repair systems, increasing genomic instability [12]. ITGA2 may influence cancer progression by directly impairing DNA repair and connecting with the Wnt/\beta-catenin signaling pathway. In hepatocellular carcinoma, the transcription factor TCF7L1 modulates ITGA2 expression, thereby establishing a direct connection to the Wnt/\beta-catenin signaling axis and influencing tumorigenic processes such as expansion and dissemination [13].

ITGA2 plays a complex function in cancer development. This includes the disruption of DNA repair processes, enhancement of genomic instability, and stimulation of cancer-promoting pathways like Wnt/\beta-catenin. Targeting of ITGA2 could therefore be a promising therapeutic strategy for cancers with aberrant DNA damage response and signaling pathways, including TC. This study aimed to investigate the function of ITGA2 in TC by integrating a bioinformatics analysis with functional assays. To further understand the molecular pathways underlying TC advancement, we investigated the impact of ITGA2 knockdown on tumor cell behavior, pyroptosis, as well as DNA damage response.

2. Materials and Methods

2.1 Extraction and Analysis of TC Data

Three TC datasets (GSE29265, GSE3678, GSE33630) were collected from the Gene Expression Omnibus database (GEO; https://www.ncbi.nlm.nih.gov/gds/). The GSE3678 dataset comprises 7 TC samples and 7 paired control samples, the GSE29265 dataset contains 29 TC samples and 20 control samples, while the GSE33630 dataset contains 60 TC samples and 45 control samples. Furthermore, information collected The Cancer Genome Atlas (TCGA; https://portal.gdc.cancer.gov/) database, which included 59 control samples and 512 TC samples, was also used. To identify differentially expressed genes (DEGs) in the datasets, the ASSISTANT for Clinical Bioinformatic platform (https://www.aclbi.com/static/index.html#/) was utilized. A fold-change (FC) threshold of >2 indicated upregulation of DEGs, whereas a FC threshold of 0.5 indicated downregulation of DEGs. The criterion for statistical significance was set at a p-value of less than 0.05.

2.2 Identification of Overlapping DEGs and Building Protein-Protein Interaction (PPI) Networks

Overlapping DEGs from the four TC datasets were identified using a bioinformatics platform (https://bioinformatics.psb.ugent.be/webtools/Venn/). Intersection analysis of both upregulated and downregulated DEGs was used to obtain DEGs that overlapped. The Search Tool for the Retrieval of Interacting Genes/Proteins (STRING) database (https://string-db.org/) was used to identify PPI networks among overlapping DEGs. Further exploration of potential functional associations and regulatory networks of candidate genes was carried out by constructing PPI networks. Analysis was performed using the Molecular Complex Detection (MCODE) algorithm. For visualization, the Cytoscape open-source software platform (version 3.7.1; Cytoscape Consortium, San Diego, CA, USA) was applied to the resulting PPI network.

2.3 Building of a Predictive Model and Analysis of Survival Using LASSO Regression of the Overlapping DEGs

Overlapping DEGs were analyzed with the “glmnet” package in R software. Tuning parameters were determined through a 10-fold cross-validation process, and the Least Absolute Shrinkage and Selection Operator (LASSO) model was applied [14, 15, 16]. To find the most predictive genes, we employed the ideal \lambda, which represents the minimal adjusted parameter criteria. These formed the basis of the predictive model. To find the most predictive genes, we employed the ideal \lambda, which represents the minimal adjusted parameter criteria. Kaplan-Meier (KM) analysis estimated overall survival (OS) probabilities, and the log-rank test evaluated differences between survival curves. To evaluate relative risk, the hazard ratio (HR) for the high-risk group was computed [17]. The “timeROC” tool was used to generate receiver operating characteristic (ROC) curves, and the area under the curve [18] was calculated to determine how well the model predicted one-, three-, and five-year survival rates. Higher area under the curve (AUC) values showed improved prediction accuracy.

2.4 Analysis of Prognostic Genes, and Nomogram Construction for Survival Prediction

Correlation analysis for significant prognostic genes was performed using R software. Single-factor and multi-factor Cox regression analyses were conducted on signature genes and clinical variables (age, gender, pathological TNM (pTNM) stage) using the “forest plot” package. For each variable, the 95% confidence intervals (CIs) [19], p-values, and HRs were computed. Statistically significant variables (p 0.05) in survival analysis were found as crucial prognostic factors. The “regression modeling strategies (RMS)” program was used to create nomograms that predicted the probability of one-, three-, and five-year survival. The concordance index was calculated to assess model accuracy. Calibration curves were generated, and the closer the alignment of the survival prediction curve to the calibration curve, the better the model performance. Gene expression levels were evaluated using R and visualized with boxplots to compare tumor and paired control groups from the TCGA-thyroid carcinoma (THCA), GSE3678, GSE29265, and GSE33630 datasets.

2.5 Cell Lines and Culture

Otwo Biotech (Shenzhen, China) provided the normal thyroid cell line (Nthy-ori3-1), the TC cell line TPC-1, and CAL-62. The selection of TPC-1 (papillary carcinoma) and CAL-62 (mesenchymal carcinoma) was based on the unequivocal identification of their molecular features in a previous study [20] and is widely used in studies of thyroid cancer migration, DNA damage, and cell death [21]. The two cell lines can be studied to cover the heterogeneity of the main subtypes of thyroid cancer. In a humidified 37 °C atmosphere with 5% CO₂, cells were cultivated in Dulbecco’s Modified Eagle Medium (DMEM; Procell; cat. no. PM150210B; Wuhan, China) supplemented with 10% fetal bovine serum (FBS) (Gibco; Thermo Fisher Scientific, Inc.; cat. no. 16000044; Waltham, MA, USA) and 1% penicillin-streptomycin (Beyotime; cat. no. C0222; Shanghai, China). We declare that all cell lines used in this study tested for mycoplasma contamination using PCR-based detection methods, and were confirmed to be free of contamination (Supplementary Fig. 1). All cell lines were obtained from OTWO (Huatuo Biotech, China). The supplier verified the authenticity of these cell lines using short tandem repeat (STR) profiling.

2.6 Cell Transfection

To conduct transient transfection, TC cells were seeded in 24-well plates at a density of 2 \times 10⁵ per well. ITGA2 was knocked down utilizing two different small interfering RNAs (siRNAs) that target it. Lipofectamine 3000 (Invitrogen; Thermo Fisher Scientific, Inc.; cat. no. L3000150; MA, USA) was used to transfect TC cells for a given period of time, as indicated by the manufacturer. si-ITGA2 siRNA sequence information: si-ITGA2-1: 5^′-UUCUCAGGAAAGCCACUCCAGGGUG-3^′ (Sense Sequence), 5^′-UUCUCAGCAGAAGGCCACUCCAG-3^′ (Antisense Sequence); si-ITGA2-2, 5^′-GAGCCUUUAGGAGCCAUCUCCAGUA-3^′, 5^′-UACUGGAGAUGGCUCCUAAAGGCUC-3^′ (Antisense Sequence); si-NC (non-targeting control) siRNA sequences: 5^′-CACAGGUGGUGUUUCGUCCACCGAA-3^′ (Sense Sequence), 5^′-UUCGGUGGACGAAACACCACCUGUG-3^′ (Antisense Sequence).

2.7 Quantitative Real-time Polymerase Chain Reaction (qRT-PCR)

qRT-PCR was carried out as previously mentioned [22]. The relative expression of genes was determined using the 2^{-Δ⁢Δ⁢CT} technique, and the levels were normalized to glyceraldehyde-3-phosphate dehydrogenase (GAPDH). The primer sequences utilized in the qRT-PCR tests are listed in Table 1.

2.8 Western Blot (WB) Assay

To extract protein lysates from TC cells, a combination of protease inhibitor cocktail (CoWin Biosciences; cat. no. CW2200S; Nanjing, China) and phosphatase inhibitors cocktail (CoWin Biosciences; cat. no. CW2383S; Nanjing, China) was combined with RIPA lysis solution (Solarbio; cat. no. R0020; Beijing, China). Before the protein was separated using 10% SDS-PAGE and then transferred onto PVDF membranes (Beyotime; cat. no. FFP22; Beijing, China), the protein concentration was measured using a BCA Protein Assay Kit (Beyotime; cat. no. P0012; Beijing, China). Primary antibodies were then administered overnight at 4 °C after the membranes had been blocked for an hour at room temperature using 5% skim milk. These included antibodies to ITGA2 (cat. no. ab181548), BCL2-associated X protein (Bax) (cat. no. ab32503), caspase-3 (cat. no. ab32351), caspase-9 (cat. no. ab32539), BCL2 apoptosis regulator (Bcl-2) (cat. no. ab182858), poly(ADP-ribose) polymerase (PARP) (cat. no. ab191217), cleaved-PARP (cat. no. ab32561), GSDME-full length (GSDME-FL) (cat. no. ab215191), N-terminal fragment of GSDME (GSDME-N) (cat. no. ab222408), phosphorylated histone H2AX on serine 139 (\gamma-H2AX) (cat. no. ab81299), MYC proto-oncogene, bHLH transcription factor (C-myc) (cat. no. ab32072), CD44 molecule (CD44) (cat. no. ab51037), \beta-catenin (cat. no. ab32572), slug (cat. no. ab51772), snail (cat. no. ab216347), phosphorylated checkpoint kinase 2 (p-CHK2) (cat. no. ab85743), and phosphorylated ataxia telangiectasia mutated (p-ATM) (cat. no. ab36810) (Abcam; Cambridge, UK; 1:1000), as well as wingless-type MMTV integration site family, member 1 (Wnt-1) (Wuhan Sanying; cat. no. 27935-1-AP; Wuhan, China; 1:1000). Following membrane washing, the proper secondary antibodies (Kangcheng; cat. no. KC-RB-035; Shanghai, China; 1:5000) were added. The protein bands were seen using an enhanced chemiluminescence kit (Tiangen; cat. no. PA110; Beijing, China), and the data were recorded and analyzed using the ChemiDoc Imaging System (Bio-Rad, Shanghai, China). The loading control was GAPDH (Abcam; cat. no. ab8245; 1:1000).

2.9 Cell Counting Kit-8 (CCK-8) Assay

The CCK-8 analysis method (KeyGEN; cat. no. KGA317; Nanjing, China) was used to assess cell viability. At a density of 5 \times 10³ cells per well, TC cells were seeded onto 96-well plates. A microplate reader (Kehua Technologies, Shanghai, China) was used to measure the absorbance at 450 nm after the CCK-8 reagent was added to each well at 0, 1, 2, 3, 4, and 5 days.

2.10 Flow Cytometry

Trypsin-EDTA (Absin; cat. no. abs42104714; Shanghai, China) was used to separate TC cells, and then washing with phosphate-buffered saline (PBS; Beyotime; cat. no. C0221B; Shanghai, China). As advised by the manufacturer, the cells were labeled with Annexin V (BD Biosciences; cat. no. 563973; San Jose, CA, USA) and propidium iodide (PI) (BD Biosciences; cat. no. 556547; San Jose, CA, USA). Viable, necrotic, and apoptotic cells were then separated using a flow cytometer (Jiyuan, Guangzhou, China). FlowJo software (version 7.6, FlowJo, Hangzhou, China) was utilized for processing the flow cytometry data.

2.11 Transwell Assays

Transwells were utilized to monitor cell invasion and migration. Transfected TC cells were placed in serum-free media in the top compartment of the Transwell instrument.

Subsequently, the medium in the lower compartment was enriched with 10% FBS. After a 24-hour incubation period, the cells were fixed with 4% paraformaldehyde (Wuhan Servicebio Biotechnology Co., Ltd.; cat. no. G1101; Wuhan, China) and stained with DAPI (Abcam; cat. no. ab104139; Cambridge, UK). The migration of cells into the bottom compartment was then assessed using inverted microscopy. The upper chamber was initially covered with matrigel for the cell invasion studies, and the same steps as for cell migration were taken.

2.12 Enzyme-linked Immunosorbent Assay (ELISA)

A suitably diluted quantity of cell culture supernatant was then added to the wells of ELISA plates that had previously been coated with IL-1\beta antibodies (Wuhan Sanying; cat. no. 16806-1-AP; Wuhan, China). The chromogenic substrate was added after the enzyme-linked secondary antibody had been incubated and cleaned. The reaction was subsequently terminated and absorbance at the specified wavelength was measured using a microplate reader. The absorbance results were compared to a standard curve created with known quantities of IL-1\beta in order to ascertain the amount of IL-1\beta present in each sample.

2.13 Xenograft Model

To create the xenograft model, TC cells transfected with si-NC and si-ITGA2-1 were suspended in serum-free media. Subcutaneous injections of a 100 µL solution containing 5 \times 10⁶ cells were then administered to the flanks of ten 6-week-old female BALB/c nude mice (n = 3 per group). Subsequently, calipers were used to measure the tumor dimensions every week for 28 days. The formula used to compute the tumor volume was 0.52 \times length \times width². An intraperitoneal dose of sodium pentobarbital (150 mg/kg, 50 mg/mL) was administered to euthanize the animals after 4 weeks. Tumors were then surgically excised, weighed, and analyzed histologically. All animal procedures were approved by the Animal Welfare and Ethics Committee of the Laboratory Animal Science Department, Fudan University (No. 2024-MHYY-304), and were conducted following institutional ethical guidelines and complied with the 3R (Replacement, Reduction, Refinement) principles.

2.14 Biochemical Assays for Oxidative Stress and Cytotoxicity Markers

Lactate dehydrogenase (LDH; cat. no. P0395S) activity and reactive oxygen species (ROS; cat. no. S0035) levels were assessed following the manufacturer’s instructions. The corresponding detection kits were used, respectively (Beyotime Institute of Biotechnology, Jiangsu, China).

2.15 Immunohistochemistry

After embedding the subcutaneous tumor tissues of mice in sections as required, the oven temperature was set to 65 °C and baked at a constant temperature for 120 mins. This was performed according to the immunohistochemistry kit (Maxim; cat. no. Kit9710; Fuzhou, China). Sections were incubated with an anti-ITGA2 primary antibody (Abcam, cat. no. ab181548; 1:500 dilution) overnight at 4 °C, followed by detection using the HRP-conjugated secondary antibody (Kangcheng; cat. no. KC-RB-035; 1:250 dilution, Shanghai, China). The complete tissue sample was scanned at low magnification (\times10) to evaluate staining, and it was verified at high magnification (\times20).

2.16 Statistical Analysis

Data was analyzed using the R program (version 4.2.2, R Foundation for Statistical Computing, Vienna, Austria). Three duplicates of each experiment were conducted, and the mean \pm standard deviation (SD) was used to report the data. Tukey’s post-hoc test was used for pairwise comparisons after one-way ANOVA was used for multi-group comparisons. Statistical significance was defined as a p-value of less than 0.05.

3. Results

3.1 Identification of DEGs in TC, and Construction of the PPI Network

The number of up-regulated DEGs found in the TCGA-THCA, GSE3678, GSE29265 and GSE33630 datasets was 557, 210, 247 and 430, respectively, while the number of down-regulated DEGs was 746, 209, 295 and 404, respectively (Fig. 1A–D). An integrated bioinformatics platform found that 95 up-regulated DEGs as well as 130 down-regulated DEGs overlapped across all four datasets (Fig. 1E,F). Further analysis of these overlapping DEGs was performed by constructing a PPI network. This identified 14 genes using the MCODE plugin (Fig. 1G).

3.2 Analysis of Three Critical Prognostic Genes in TC

As shown in Fig. 2A,B, three critical prognostic genes were identified from the 14 genes based on the ideal lambda value (\lambdamin = 0.014), giving the following risk score: (–0.1389) \times EFEMP1 + (–0.1195) \times EGR2 + (–0.2028) \times ITGA2. In terms of mortality and survival, the high-risk group did better than the low-risk group (Fig. 2C). Survival analysis of these 3 genes using the KM method showed a significantly lower OS in the high-risk group, with an HR of 6.078 (>1) (Fig. 2D). Additionally, ROC curve analysis showed the predictive ability of this model was strongest at one year of follow-up, with an area under the curve (AUC) of 0.755 (Fig. 2E).

3.3 Association Analysis of ITGA2 Gene Expression and Prognosis of Thyroid Cancer Patients

Cox regression analysis and survival analysis of key prognostic factors in TC patients. The results of univariate Cox regression analysis for the 3 key candidate genes (EFEMP1, EGR2, and ITGA2) showed significant prognostic correlations, indicating that the expression levels of these genes were significantly correlated with the survival prognosis of patients (Fig. 3A). After adjusting for potential confounders, the results of multivariate Cox regression analysis for the 3 key candidate genes indicated that the ITGA2 gene still showed a significant prognostic correlation, suggesting that ITGA2 may be an independent prognostic factor (Fig. 3B). The column-line graph demonstrated a prognostic model constructed based on ITGA2 expression levels and other clinicopathologic features (Fig. 3C). The analysis showed that the predictive ability of the model was statistically significant. The calibration curves further validated the predictive accuracy of the model, showing good agreement between the predicted 1-, 3-, and 5-year survival probabilities and the actual observations (Fig. 3D). Notably, most of the actual observations in Fig. 3D are clustered near the upper-right corner, which reflects the overall high survival probability in the TC cohort. The expression level of the ITGA2 gene was significantly correlated with the prognosis of patients with thyroid cancer.

3.4 Up-regulation of ITGA2 Gene Expression in Thyroid Cancer and Evaluation of its Diagnostic Potential

In this study, we analyzed the expression pattern of the ITGA2 gene in thyroid cancer and its potential as a diagnostic marker. The volcano map show that the expression level of ITGA2 was significantly up-regulated in tumor samples compared to normal samples in the TCGA database (Fig. 4A). In addition, box line plots revealed that the expression levels of ITGA2 were significantly higher in tumor samples than in normal samples in the GSE3678, GSE29265, and GSE33630 datasets, especially in the GSE29265 and GSE33630 datasets (Fig. 4B–D). Further, ROC curve analysis was performed to evaluate the potential of ITGA2 gene expression as a diagnostic marker (Fig. 4E–H). The results showed that the AUC value of ITGA2 was as high as 0.887 in the TCGA dataset, indicating a very high diagnostic accuracy. In the GSE3678 and GSE33630 datasets, the AUC values of ITGA2 were 0.898 and 0.954, respectively, which also showed good diagnostic performance. The AUC value of ITGA2 in the GSE29265 dataset was 0.907, further confirming its potential as a diagnostic marker. We analyzed the effect of ITGA2 gene expression on disease-free survival in patients using the TCGA dataset. Analysis of the Clinical BioLetter House platform (n = 512) showed that high ITGA2 expression was significantly associated with lower disease-free survival (Fig. 4I), a finding supported by results from the GEPIA database (n = 500) (Supplementary Fig. 2A). However, no significant association between ITGA2 expression and disease-free survival was found when the TCGA dataset (n = 497) was analyzed by the cBioPortal platform (Supplementary Fig. 2B). Different analytical methods or platform differences may cause this discrepancy.

3.5 ITGA2 is Highly Expressed in TC Cell Lines and Affects Cell Viability

qRT-PCR and WB methods were used to investigate ITGA2 expression in TC cell lines TPC-1 and CAL-62, and normal thyroid cells (Nthy-ori3-1). Significant up-regulation of ITGA2 expression was observed in TC cell lines (Fig. 5A–C). Further, qRT-PCR and WB assays confirmed the efficient knockdown of ITGA2 by si-ITGA2-1 in TC cell lines (Fig. 5D–F). Cell viability declined significantly following ITGA2 knockdown in TC cell lines, as shown by the CCK-8 assay (Fig. 5G,H). Therefore, changes in ITGA2 expression were associated with altered biological behavior in TC cells, further supporting the important role of this key gene.

3.6 ITGA2 Knockdown Inhibits the Growth of TC Cells and Induces Apoptosis

Transwell tests demonstrated that ITGA2 knockdown dramatically decreased the TC cells’ migration and invasion capacity (Fig. 6A,B). Flow cytometry analysis also demonstrated a marked increase in the apoptosis rate of TC cells with silenced ITGA2 (Fig. 6C,D). qRT-PCR analysis showed that ITGA2 knockdown in TPC-1 cells led to up-regulation of the pro-apoptotic genes Caspase-3, Bax, and Caspase-9 and down-regulation of the anti-apoptotic gene Bcl-2 (Fig. 6E). This was further confirmed at the protein level by WB analysis. Cells with ITGA2 knockdown showed significantly reduced expression of anti-apoptotic proteins relative to the control group but increased expression of a pro-apoptotic marker (Fig. 6G,I). In CAL-62 cells, ITGA2 knockdown resulted in a significant up-regulation of Caspase-3 and Bax expression, whereas the changes in Bcl-2 and Caspase-9 expression were not significant compared with the control (Fig. 6F,H,J). This suggests that in CAL-62 cells, ITGA2 knockdown may promote apoptosis mainly by affecting Caspase-3 and Bax, but not through the Bcl-2 and Caspase-9 pathways. We hypothesize that this difference may reflect the diversity of responses to ITGA2 knockdown in different thyroid cancer cell lines or suggest that CAL-62 cells may be more dependent on the Caspase-3 and Bax-mediated apoptotic pathways.

3.7 ITGA2 Knockdown in TC Cells Induces Pyroptosis and Inflammation

WB analysis showed that the expression of proteins related to apoptosis, pro-inflammatory cytokines and pyroptosis was significantly changed in TC cell lines after ITGA2 knockdown. Specifically, the full-length (FL) forms of PARP and GSDME were reduced. In contrast, the cleaved forms (cleaved-PARP and GSDME-N), as well as the pro-inflammatory cytokine IL-18, were significantly up-regulated in the si-ITGA2-1 group as compared to the control group (Fig. 7A–C). In addition, LDH activity in TC cells was notably increased after ITGA2 knockdown, indicating increased cytotoxicity (Fig. 7D). ELISA assay also showed that IL-1\beta levels were significantly higher in si-ITGA2-1-treated cells compared to the control group (Fig. 7E). These results indicate that ITGA2 knockdown in TC cells induces pyroptosis and promotes inflammatory responses.

3.8 Knockdown of ITGA2 in TC Cells Increases DNA Damage Response and ROS Generation

WB analysis revealed that ITGA2 knockdown significantly upregulated the expression of key DNA damage response markers (\gamma-H2AX, p-CHK2, and p-ATM) compared to the si-NC control (Fig. 8A–C). Additionally, ITGA2 knockdown caused ROS to increase significantly in TC cell lines, as measured by the fluorescence intensity (Fig. 8D). These findings indicate that ITGA2 knockdown induces DNA damage and oxidative stress in TC cells.

3.9 Knockdown of ITGA2 Inhibits TC Stemness by Suppressing the Wnt/\beta-catenin Pathway and Suppresses Tumor Growth In Vivo

qRT-PCR analysis revealed that ITGA2 knockdown in TC cells markedly decreased the expression of genes associated with the Wnt/\beta-catenin signaling pathway, such as snail, c-Myc, CD44, Wnt-1, slug, and \beta-catenin (Fig. 9A,B). WB assays further confirmed the down-regulation of these genes at the protein level (Fig. 9C–E), suggesting that ITGA2 knockdown inhibits stemness in TC cells by impeding the Wnt/\beta-catenin signaling pathway. In vivo, tumorigenesis assays were performed by injecting si-ITGA2-1-transfected TPC-1 cells into the flanks of mice. After four weeks, the ITGA2 knockdown group’s tumor weight and volume were noticeably lower than those of the control group (Fig. 10A–C). ITGA2 is localized in the cytoplasm, the, immunohistochemical analysis further confirmed that ITGA2 expression was down-regulated in tumors from the ITGA2 knockdown group (Fig. 10D).

4. Discussion

With a rising prevalence globally, TC is the most common endocrine cancer. The most common kind of TC, papillary thyroid carcinoma (PTC), has few effective treatment options for advanced or metastatic TC, despite advancements in screening and therapy. The discovery of novel therapy targets and prognostic indicators is therefore essential for improving patient survival. Through a bioinformatics analysis of the TCGA-THCA, GSE3678, GSE29265, and GSE33630 datasets, the present study identified three crucial prognostic genes in TC: EFEMP1, EGR2, and ITGA2. EGR2 has become one of the most critical tumor suppressor genes in PTC. Qiu et al. [23] reported that EGR2 shows elevated transcriptional activity in PTC tissues in comparison with normal controls, and is involved in regulating the cell cycle and degradation of the extracellular matrix. EFEMP1 was also identified as another critical player in the progression of PTC. Li et al. [24] reported that EFEMP1 was a hub protein involved in tumor development, with its upregulation correlating with disease progression. The significant expression of EFEMP1 in PTC tissues indicates that it may serve as a target for therapy. Finally, our nomogram analysis identified ITGA2 as a hub gene in TC. Expression analysis confirmed that ITGA2 was significantly overexpressed in tumor samples from the TCGA-THCA, GSE3678, GSE29265, and GSE33630 datasets, thus supporting a role in TC progression. ITGA2 is a transmembrane receptor involved in cell adhesion and migration. As such, it is a potential target for upcoming treatment approaches and may aid in tumor invasion and metastasis.

Apoptosis and pyroptosis represent two different types of programmed cell death. Apoptosis involves the caspase-3, Bax, Bcl-2, and caspase-9 proteins, which control mitochondrial permeability and initiate the cell death cascade [25]. Additionally, cleaved-PARP plays a critical role in DNA degradation during apoptosis [26]. In contrast, pyroptosis is driven by gastrin proteins, particularly GSDME. These form membrane pores, leading to inflammatory cell death [27]. While apoptosis typically maintains cellular homeostasis, pyroptosis triggers a robust immune response due to its inflammatory nature [28]. Several studies have shown that both apoptotic and pyroptotic pathways can influence tumor progression in TC. For example, Zhou J et al. [29] showed that naringin promotes apoptosis by simultaneously increasing the levels of caspase-3, Bax, and cleaved-caspase-3, while decreasing the levels of anti-apoptotic proteins such as Bcl-2. Similarly, Zhou X et al. [30] discovered that downregulating SPTBN2 causes cell cycle arrest by increasing the expression of cleaved caspase-3 and Bax and decreasing that of Bcl-2, which results in apoptosis. Importantly, Hu et al. [31] demonstrated that Alantolactone triggers GSDME-dependent pyroptosis and apoptosis in anaplastic TC, thus highlighting the dual role of cell death pathways in tumor suppression. The present study further highlights the importance of pyroptosis in TC. We observed that silencing of ITGA2 inhibited cell growth and simultaneously induced apoptosis. Moreover, ITGA2 knockdown induced pyroptosis, as seen by elevated levels of cleaved-GSDME, LDH and IL-1\beta release. This was accompanied by a notable decrease in pro-survival proteins such as PARP and GSDME-FL. Our findings implicate ITGA2 in pyroptosis and apoptosis, suggesting it may be a possible therapeutic target in TC, particularly for strategies that induce pyroptosis.

DNA damage is a critical event that can lead to genomic instability and promote cancer development. It initiates a cellular response involving essential proteins such as \gamma-H2AX, p-CHK2, and p-ATM [32]. \gamma-H2AX marks DNA double-strand breaks, thus serving as an early signal for repair processes [33]. ATM kinase phosphorylates substrates like CHK2, thereby activating DNA repair pathways and inducing cell cycle arrest [34]. When these mechanisms fail or are overwhelmed, the damaged cells may evade repair and proliferate uncontrollably, contributing to tumorigenesis. The regulation of DNA damage response proteins is therefore essential for preventing cancer progression and maintaining genomic integrity. Several previous investigations have focused on the function of DNA damage in TC. Xu et al. [35] showed that YM155 induced significant DNA damage in ATC through oxidative stress. This was marked by increased \gamma-H2AX, causing apoptosis and cell cycle arrest. Similarly, Wu et al. [36] demonstrated that paclitaxel (PTX) combined with recombinant adenovirus-p53 increased DNA damage in PTC. This resulted in elevated p-ATM and \gamma-H2AX levels, thereby improving the anti-tumor efficacy of PTX. Furthermore, Wang et al. [37] reported that inhibition of SGLT2 by canagliflozin induced DNA damage via activation of the ATM/CHK2 pathway, resulting in G1/S phase arrest and increased apoptosis. Our findings support these observations, as ITGA2 knockdown in TC cells significantly increased the levels of DNA damage indicators, including \gamma-H2AX, p-ATM, and p-CHK2. Additionally, the silencing of ITGA2 resulted in elevated ROS levels, indicating that oxidative stress contributes to the enhanced DNA damage response. These findings underline the role of ITGA2 in DNA damage repair as well as highlight its possible use as a therapeutic target for enhancing DNA damage-induced cancer cell death.

Cell migration and proliferation are regulated by the Wnt/\beta-catenin signaling system. Wnt proteins, including Wnt-1, bind to Frizzled receptors, causing \beta-catenin to stabilize and aggregate in the cytoplasm [38]. After stabilizing, \beta-catenin moves to the nucleus, where it attaches itself to T cell factor/lymphoid enhancer-binding factor (TCF/LEF) transcription factors and activates target genes that are involved in cell adhesion and proliferation, including CD44 and C-myc [39]. Additionally, \beta-catenin affects the expression of other transcription factors, including snail and slug, which are necessary for epithelial-mesenchymal transition (EMT) and, consequently, the facilitation of invasion and migration of cells [17]. TC is one of the several cancer forms that are frequently associated with dysregulation of this system. Gilbert-Sirieix et al. [40] shown that the Wnt/\beta-catenin pathway influences the course of PTC by upregulating TTF-1 expression. Zhang et al. [41] demonstrated that miR-200c affects the Wnt/\beta-catenin signaling cascade by targeting parathyroid hormone-like hormone (PTHLH) and modulating the growth, and EMT of ATC cells. Our research showed that ITGA2 knockdown leads to significantly reduced expression of CD44, C-myc, Wnt-1, slug, snail, and \beta-catenin in TC cells, as well as markedly lower tumor weight and volume in a xenograft model. These results imply that ITGA2 is essential for regulating tumor development and the Wnt/\beta-catenin signaling pathway.

In the discussion section of this study, we first need to address a key issue: the differences in the results of the analysis of the relationship between ITGA2 expression and disease-free survival in thyroid cancer patients by different analytical platforms. While the Clinical BioLetter House platform analysis showed that high ITGA2 expression was significantly associated with lower disease-free survival, the cBioPortal platform did not find a significant association between ITGA2 expression and disease-free survival when analyzed. This discrepancy could be due to several factors, including differences in analysis methods or platform-specific biases. This finding suggests that we must consider the effects of differences in analytical techniques and platforms when interpreting bioinformatics data.

Furthermore, the role of ITGA2 in thyroid cancer cells may be more complex than previously thought. Our results suggest that ITGA2 may play a role in multiple cell death pathways, not only apoptosis. Although WB experiments showed significant changes in the levels of apoptosis-related markers after ITGA2 inhibition, the increase in apoptosis rate was not as substantial as expected. This suggests that ITGA2 may regulate the apoptotic process by affecting multiple signaling pathways rather than direct induction. In addition, we observed that ITGA2 inhibition may involve other types of cell death, such as pyroptosis, which may somewhat mask the increase in apoptosis.

ITGA2 may play different roles in different biological contexts. In some cases, ITGA2 may promote tumor growth primarily by inhibiting apoptosis, whereas in others, it may act through mechanisms such as promoting cell migration or angiogenesis. Our findings highlight the multifaceted nature of ITGA2 in thyroid cancer and the multiple biological processes that may be involved in tumor progression. These findings provide new directions for future studies, including further exploration of the specific mechanisms of ITGA2 in apoptosis regulation and how ITGA2 interacts with other cell death pathways.

In summary, our findings suggest that ITGA2 may play a role in multiple cell death pathways and emphasize the importance of considering differences in analysis methods and platforms when interpreting bioinformatics data. Future studies should include cross-platform data validation and in-depth biological mechanism studies to ensure the accuracy and clinical relevance of the findings.

5. Conclusion

This study emphasizes the importance of ITGA2 in the development of TC, as well as its potential as a therapy target. ITGA2 knockdown reduced TC cell viability, migration, as well as the promotion of apoptosis and pyroptosis. Furthermore, ITGA2 silencing enhanced the DNA damage response and increased ROS production, contributing to elevated cellular stress. ITGA2 knockdown also repressed the Wnt/\beta-catenin signaling pathway resulting in reduced cancer stemness, as demonstrated by decreased tumor growth in vivo. These results imply that ITGA2 could be a viable treatment target for the induction of DNA damage and pyroptosis in TC cells, as well as for the disruption of stemness.

Availability of Data and Materials

The datasets used and analyzed during the current study are available from the corresponding authors upon reasonable request.

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Funding

2024 National Traditional Chinese Medicine Comprehensive Reform Pilot Zone “Advanced Talents in Integrated Traditional Chinese and Western Medicine” Project(PDZY-2024-0708)