Abstract
Background
Filamin A (FLNA) is a member of the filamin family and has been found to be critical for the progression of several cancers. However, its biological function in papillary thyroid cancer (PTC) remains largely unexplored.
Methods
Data from The Cancer Genome Atlas (TCGA) databases were utilized to analyze the FLNA expression level and its influence on the clinical implications of patients with PTC. Gene Expression Omnibus (GEO) and qRT-PCR was used to verify the expression levels of FLNA in PTC. Kaplan–Meier survival analysis was conducted to evaluate the prognostic value of FLNA in PTC. Transwell assays and wound healing were performed to examine the biological function of FLNA knockdown in PTC cells. Gene set enrichment analysis (GSEA) and Western blotting were conducted to investigate the potential mechanisms underlying the role of FLNA in PTC progression. In addition, the relationship between FLNA expression and the tumor immune microenvironment (TME) in PTC was explored.
Results
FLNA was significantly upregulated in PTC tissues. High expression levels of FLNA was correlated with advanced TNM stage, T stage, and N stage, as well as poor disease-free interval (DFI) and progression-free interval (PFI) time in PTC patients. Moreover, we found that FLNA knockdown inhibited the migration and invasion of PTC cells. Mechanistically, FLNA knockdown inhibited epithelial–mesenchymal transition (EMT) in PTC and affected the activation of the FAK/AKT signaling pathway. In addition, FLNA expression was associated with TME in PTC.
Conclusion
FLNA may be regarded as a new therapeutic target for PTC patients.
Introduction
Thyroid cancer is the most common cancer in the endocrine system around the globe, being classified into three main histological subtypes: differentiated (papillary and follicular thyroid cancer), undifferentiated (poorly differentiated and anaplastic thyroid cancer), and medullary thyroid cancer (1). Papillary thyroid cancer (PTC) is the most common subtype of thyroid cancer, which approximately accounts for more than 80% of all thyroid cancers (2, 3). The majority of PTCs have a favorable prognosis, with a 10-year survival rate of PTC patients up to 95% (4). However, the recurrence rate of PTC with locoregional lymph node metastases varies from 4% to 32% (5). PTC recurrence significantly impacts quality of life, with the 5-year survival rate being significantly reduced (6). Therefore, it is critical to explore the underlying molecular mechanism of PTC pathogenesis, which may help predict prognosis and develop effective therapeutic strategies.
Filamin A (FLNA), a member of filamin family, is an actin-binding protein encoded by X-linked genes (7). FLNA acts as a cellular scaffold and a connector between receptor signaling and the cytoskeleton. Existing studies have confirmed that more than 90 proteins can interact with FLNA, and these proteins are widely involved in many biological processes, such as cell signaling, cell migration and adhesion, receptor activation, and transcriptional regulation (8). Emerging data show that FLNA plays a dual role in cancers, and it may function as a potential oncogene or tumor suppressor (9). Several studies suggest that FLNA expression is upregulated and plays a promotional role in a variety of cancers, such as cervical cancer (10) and pancreatic cancer (11, 12). Whereas FLNA serves as a tumor suppressor in melanoma cells (13) and colorectal adenocarcinoma (14). The controversial effect of FLNA on the tumorigenic process and underlying mechanisms need to be fully studied in various types of cancers. Sun et al. have investigated the expression pattern and biological function of FLNA in PTC (15). However, the biological function of FLNA in PTC remains largely unclear.
In our present research, we aimed to study the expression pattern and functional role of FLNA in PTC. We also explored the potential molecular mechanisms involved in the effect of FLNA on PTC cells.
Materials and methods
Data collection
The mRNA expression and clinical profiles of PTC patients (495 PTC tissues and 59 paired normal tissues) were downloaded from the The Cancer Genome Atlas (TCGA) database (http://cancergenome.nih.gov/). The corresponding follow-up clinical information was also downloaded to analyze the correlation between FLNA expression and the clinical pathologic features, including age, gender, TNM stage, T stage, N stage, M stage, disease-free interval (DFI) time, progression-free interval (PFI) time, and status. The validation datasets (GSE33630 and GSE29265) were downloaded from GEO database (https://www.ncbi.nlm.nih.gov/gds), which included 49 PTC tissues and 45 normal thyroid tissues, as well as 20 PTC tissues and 20 normal adjacent thyroid tissues, respectively.
Analysis of the relationship between FLNA expression and clinical pathologic characteristics
In order to study the relationship between FLNA expression and clinical pathologic characteristics, the patients were divided into a low-expression group and a high-expression group based on the median of FLNA expression level.
Analysis of FLNA expression and mutational profile
To evaluate the relationship between FLNA expression levels and the mutational profiles in PTC, we downloaded the BRAF-RAS score, BRAF mutation status, and RAS mutation status for TCGA-THCA samples from the supplementary materials of Nishant’s article (16) and analyzed the relationship.
Analysis of the prognostic role of FLNA
The DFI and PFI data downloaded from the TCGA database were used to investigate the relationship between FLNA expression and PTC patients’ prognosis. After using an X-tile (Robert L Camp, Yale University, New Haven, CT, USA) for separating patients into high or low FLNA expressions, the Kaplan–Meier curve methods were applied to produce survival curves.
Clinical specimens
In this study, 14 pairs of PTC and adjacent normal tissues were collected from the patients who underwent tumor resection surgery at the First Affiliated Hospital of Sun Yat-sen University (Guangzhou, China). The tissue samples were frozen in liquid nitrogen and stored at −80°C for RNA detection. The study was approved by the Ethics Committee of the First Affiliated Hospital of Sun Yat-sen University. Written informed consent was obtained from all patients.
RNA isolation and quantitative real-time polymerase chain reaction
Total RNA was extracted from PTC and paired normal thyroid tissues or PTC cells using TRIzol reagent according to the manufacturer’s instructions (Invitrogen) and reversely transcribed into cDNA using the PrimeScript RT Reagent Kit (Takara Bio, Otsu, Shiga, Japan). Quantitative real-time polymerase chain reaction (qRT-PCR) was subsequently conducted using SYBR Green qPCR Master Mix (Takara Bio) with GAPDH as control. The expression of the target gene was calculated by the 2−ΔΔCt method. The sequences of primers were as follows: FLNA forward, 5′-GCCTGAAAATCCCTGAAATTAGC-3′ and reverse, 5′-TGACTGTGTGTGTGCCCATCT-3′; GAPDH forward, 5′-GACTCATGACCACAGTCCATGC-3′ and reverse, 5′-AGAGGCAGGGATGATGTTCTG-3′.
Cell culture
The TPC1 cell line was purchased from Procell Life Science & Technology Co. Ltd (Wuhan, China). The BCPAP cell line was purchased from the Chinese Academy of Sciences Cell Bank (Shanghai, China). Both cell lines were cultured in DMEM (Sigma) containing 10% fetal bovine serum (FBS) at 37°C incubator with 5% CO2.
Cell transfection
siRNAs targeting FLNA and siRNA control were designed and synthesized by RIBOBIO (RiboBio Co., Guangzhou, China). The sequences were as follows: si-FLNA-1, 5′-GGCCAACGTTGGTAGTCAT-3′; si-FLNA-2,5′-CAGTCAGCGTGAAGTACAA-3′. The siRNAs or siRNA control were transfected into TPC1 and BCPAP cells using the Lipofectamine 2000 (Invitrogen).
Transwell assay
Transwell assay was conducted with 24-well Transwell chambers (Corning) to assess cell migration and invasion ability. Transfected TPC1 and BCPAP cells were added to the upper chambers either with or without the Matrigel matrix (BD Biosciences) in a serum-free culture medium. The lower chambers were filled with a culture medium containing 10% FBS. After 24 h of incubation, cells on the surface of the upper membrane were scraped off. The invaded cells on the lower side of the chamber were fixed with methanol and then stained with 0.1% crystal violet. The cells were photographed and counted under a microscope (Olympus). Each experiment was repeated three times.
Wound healing assay
A wound healing assay was conducted to detect the effect of FLNA on PTC cell migration. Indicated cells were seeded into six-well plates after reaching 90% confluence. Then, the cells were scratched straightly by a 200 μL pipette tip. The suspension cells were washed with PBS buffer. Photographs of the scratched area were taken after 24 h under a microscope (Olympus).
Gene set enrichment analysis
Gene set enrichment analysis (GSEA) was performed to further investigate the potential biological signaling pathways involved in FLNA regulating PTC progression using the R package ‘ClusterProfile’. The Hallmark gene sets were downloaded from the Molecular Signatures Database (MSigDB, https://www.gsea-msigdb.org/gsea/msigdb/index.jsp). Gene sets with an adjusted P-value <0.05 and false discovery rate (FDR) <0.25 were considered as significantly enriched.
Western blotting
Total protein was isolated from indicated cells by RIPA lysis buffer, and the concentration of the protein sample was detected with a BCA kit (KeyGEN, Nanjing, China). Equal amounts of the protein (30 μg) were separated by SDS/PAGE and then transferred onto PVDF membranes. The membranes were blocked with 5% milk and subsequently incubated with primary antibodies at 4°C overnight. Primary antibodies contain anti-FLNA (ProteinTech Group, Chicago, IL, USA), anti-N-cadherin (ProteinTech Group), anti-Vimentin (ProteinTech Group), anti-zinc finger E-box binding homeobox 2 (ZEB2) (ProteinTech Group), anti-Snail1 (ProteinTech Group), anti-matrix metalloproteinase 9 (MMP9) (ProteinTech Group), anti-phosphorylated (9)-FAK (Cell Signaling Technology), anti-FAK (Cell Signaling Technology), anti-Akt (Cell Signaling Technology), anti-p-Akt (Cell Signaling Technology), and anti-β-catenin (ProteinTech Group). After incubation with secondary antibodies (Epizime, China) for 2 h at room temperature, the protein bands were visualized using enzyme-linked chemiluminescence detection (ECL) Kit (Beyotime Biotechnology, Beijing, China).
Correlation analysis of FLNA and tumor immune microenvironment
The correlation between FLNA and tumor-infiltrating immune cells (TIICs) was analyzed by the Tumor Immune Estimation Resource (TIMER). TIMER is a widely used tool for analyzing gene expression and infiltration of six immune cell types (B cells, CD4+ T cells, CD8+ T cells, neutrophils, macrophages, and dendritic cells). For further investigation, we downloaded TCGA cell state assignments from Carcinoma EcoTyper (17) and estimated the association between FLNA expression and cell states (S) in PTC.
Statistical analysis
All statistical data were analyzed using SPSS 26.0 (SPSS Inc.) or GraphPad Prism 8.4.0 (GraphPad Software Inc.). Independent student’s t-test, paired t-test, and one-way ANOVA were conducted to compare the differences between/among groups. The chi-square test was used to test the relationships between the expression of FLNA and the clinical pathological characteristics of PTC patients. Kaplan–Meier method was performed to assess differences in prognosis between the two patient groups. Pearson’s correlation coefficient analysis was used to evaluate the correlation. P < 0.05 was defined to be statistically significant.
Results
FLNA is highly expressed in PTC
First, to investigate the expression levels of FLNA in PTC, we compared the difference in FLNA expression between 495 PTC tissues and 59 normal thyroid tissues using TCGA data. As shown in Fig. 1A, FLNA expression was notably upregulated in PTC tissues compared to normal tissues (P < 0.05). Furthermore, FLNA expression was higher in PTC tissues than in paired normal thyroid tissues (P < 0.05; Fig. 1B). Next, we validated the expression of FLNA in PTC using GSEA33630 and GSE29265 datasets. The results revealed that FLNA was significantly increased in PTC tissues compared to normal tissues (Fig. 1C and D). We further collected 14 pairs of PTC tissues and adjacent normal tissues, and qRT-PCR was used to verify the differential expression of FLNA. We found that FLNA was highly expressed in PTC tissues compared with paired normal tissues (P < 0.05, Fig. 1E). Altogether, FLNA expression is elevated in PTC.
FLNA is highly expressed in PTC. (A) The mRNA expression of FLNA in PTC tissues (n = 495) was higher than that in normal tissues (n = 59) in TCGA datasets. (B) The mRNA expression of FLNA in PTC tissues was higher than that in matched-adjacent normal tissues in TCGA datasets (n = 59). (C) The mRNA expression of FLNA in PTC tissues (n = 49) and normal thyroid tissues (n = 45) in GSE33630. (D) The mRNA expression of FLNA in paired PTC and adjacent noncancerous tissues (n = 20) in GSE29265. (E) The mRNA expression of FLNA in PTC tissues (n = 14) and paired adjacent normal tissues (n = 14) was assessed by RT-qPCR. *P < 0.05.
Citation: Endocrine Connections 13, 6; 10.1530/EC-24-0034
High expression of FLNA correlates with more aggressive clinical pathologic features in PTC patients
We further analyzed the relationship between FLNA expression and clinical pathologic features in PTC patients using the TCGA dataset by chi-square test. The median expression level of FLNA was used as a cut-off value to divide the PTC patients into two groups: high-expression group and low-expression group. As shown in Table 1, high FLNA expression was associated with increased TNM stage, T stage, and N stage (all P < 0.05). In general, the expression level of FLNA was higher in patients with more advanced TNM stage (stage I+II vs III+IV, P < 0.05, Fig. 2C), T stage (T1+2 vs T3+4, P < 0.05, Fig. 2D), and N stage (N0 vs N1, P < 0.05, Fig. 2E). However, we did not observe a distinct association between FLNA expression and age (P > 0.05, Fig. 2A), gender (P > 0.05, Fig. 2B), and M stage (P > 0.05, Fig. 2F) in PTC patients.
The associations between FLNA expression and clinicopathological features. (A) age; (B) gender; (C) TNM stage; (D) T stage; (E) N stage; (F) M stage; (G) heatmap of FLNA expression in TCGA PTC patients and their BRAF/RAS mutation status. *P < 0.05; ns, not significant.
Citation: Endocrine Connections 13, 6; 10.1530/EC-24-0034
The relationship between FLNA expression and clinicopathologic characteristics in TCGA cohort.
| Clinicopathologic variables | Total | FLNA | P | |
|---|---|---|---|---|
| Low | High | |||
| Age | ||||
| <45 years | 223 | 117 | 106 | 0.341 |
| ≥45 years | 272 | 131 | 141 | |
| Gender | ||||
| Male | 130 | 62 | 68 | 0.522 |
| Female | 365 | 186 | 179 | |
| TNM stage | ||||
| I and II | 328 | 183 | 145 | 0.000* |
| III and IV NA |
165 2 |
64 1 |
101 1 |
|
| T classification | ||||
| T1 and T2 | 307 | 166 | 141 | 0.024* |
| T3 and T4 | 186 | 81 | 105 | |
| Tx | 2 | 1 | 1 | |
| N classification | ||||
| N0 | 226 | 138 | 88 | 0.000* |
| N1 | 219 | 83 | 136 | |
| Nx | 50 | 27 | 23 | |
| M classification | ||||
| M0 | 277 | 132 | 145 | 0.322 |
| M1 NA Mx |
9 1 208 |
6 1 109 |
3 0 99 |
|
*P < 0.05.
The two main driver events of PTC, BRAF V600E and RAS mutations, play the oncogenic roles by activating distinct intracellular signaling pathways (16). To further understand the driver event related to FLNA expression, we analyzed the relationship between FLNA expression and BRAF/RAS mutation. As shown in Fig. 2G, FLNA expression was positively correlated with BRAFV600E mutation and negatively associated with BRAF-RAS score and RAS mutation in PTC. The results illustrate that FLNA overexpression is related to BRAFV600E mutation. Thus, FLNA expression is associated with many clinical–pathological features in PTC.
High expression of FLNA correlates with poor prognosis in PTC
To further analyze the prognostic value of FLNA in PTC patients, the Kaplan–Meier method and the long-rank test were performed. The results showed that PTC patients with high levels of FLNA exhibited poorer PFI (P = 0.0082, Fig. 3A). Moreover, high expression of FLNA was significantly associated with reduced DFI (P = 0.0244, Fig. 3B) in patients with PTC. These results intimate that FLNA may function as a prognostic factor in PTC.
High FLNA expression predicts a poor prognosis in PTC. Kaplan–Meier survival analysis revealed that PTC patients with a high expression of FLNA had a poor prognosis in PFI (A) and DFI (B) according to TCGA.
Citation: Endocrine Connections 13, 6; 10.1530/EC-24-0034
Knockdown of FLNA inhibits the migration and invasion of PTC cells
We further evaluated the effect of FLNA on PTC cell migration and invasion. We first transfected two specific siRNAs targeting FLNA into TPC1 and BCPAP cells. WB was used to test the knockdown efficiency. As shown in Fig. 4A, FLNA expression was successfully knocked down in TPC1 and BCPAP cells (P < 0.05). Transwell assay revealed that FLNA knockdown remarkably suppressed the migration and invasion of TPC1 and BCPAP cells compared to the control group (Fig. 4B). In addition, the wound healing assay showed that silencing FLNA significantly inhibited the migration of TPC1 and BCPAP cells (Fig. 4C). Collectively, our findings suggest that FLNA promotes the migration and invasion of PTC cells.
The effect of FLNA expression on PTC cell migration and invasion. (A) The expression levels of FLNA in TPC1 and BCPAP cells transfected with two specific siRNAs against FLNA were evaluated by WB. (B) Transwell assay showed that knockdown of FLNA significantly inhibited the migration and invasion of TPC1 and BCPAP cells. (C) Wound healing assay revealed that the knockdown of FLNA suppressed the migration of TPC1 and BCPAP cells. Data are shown as mean ± s.d. *P < 0.05.
Citation: Endocrine Connections 13, 6; 10.1530/EC-24-0034
Knockdown of FLNA suppresses epithelial–mesenchymal transition in PTC cells
To identify the potential mechanisms of FLNA regulatory effects on PTC cell migration and invasion, we conducted GSEA to analyze the enriched signaling pathways in FLNA high and low expression groups. GSEA analysis identified that EMT-related signaling was mainly enriched in the FLNA high expression group (Fig. 5A). The result indicates that FLNA may impact PTC progression through regulating EMT. In addition, we detected the protein expression levels of EMT-related proteins by WB. As illustrated in Fig. 5B, the protein expression levels of EMT-inducing factors such as N-cadherin, vimentin, ZEB2, Snail1, and MMP9 were significantly decreased in the si-FLNA group compared with the si-NC group. Thus, FLNA overexpression may promote PTC invasion and migration by modulating EMT.
Knockdown of FLNA suppresses EMT in PTC cells. (A) Enrichment plot from the GSEA. (B) The protein expression levels of N-cadherin, vimentin, ZEB2, Snail1, MMP9, and GAPDH were evaluated by WB. GAPDH was used as a control.
Citation: Endocrine Connections 13, 6; 10.1530/EC-24-0034
Knockdown of FLNA inhibits the FAK/AKT signaling pathway in PTC
The above results indicated that FLNA regulated the EMT of PTC cells; however, the underlying mechanisms remained to be established. The FAK/AKT signaling pathway was well-known as one of the most frequently dysregulated pathways in cancer. We further evaluated the effect of FLNA knockdown on the FAK/AKT signaling pathway by WB. The results indicated that knockdown of FLNA markedly reduced the expression levels of p-FAK and p-AKT, but total FAK and AKT expression levels had no significant change (Fig. 6). Taken together, the results suggest that FLNA may regulate PTC progression via activating the FAK/AKT signaling pathway.
FLNA promotes PTC cell EMT by activating the FAK/AKT pathway. The protein expression levels of p-FAK, total FAK, p-AKT, and total AKT in FLNA-knockdown TPC1 and BCPAP cells and control cells were detected by WB.
Citation: Endocrine Connections 13, 6; 10.1530/EC-24-0034
Correlation between FLNA expression and TME in PTC
Increasing evidence suggested that the immune microenvironment might play a critical role in PTC invasion. Thus, we explored the correlation between FLNA expression and immune cell infiltration using TIMER. It was found that FLNA expression was positively correlated with CD8+ T cells (r = 0.168, P < 0.05), dendritic cells (r = 0.781, P < 0.001), and neutrophils (r = 0.646, P < 0.001), whereas negatively correlated with B cells (r = −0.232, P < 0.05). There was no significant correlation between FLNA expression level and tumor purity (r = −0.158, P > 0.05), CD4+ T cells (r = −0.016, P > 0.05), and macrophages (r = −0.138, P > 0.05) (Fig. 7A).
FLNA is correlated with the TME in PTC. (A) The correlation of FLNA expression with tumor purity and six types of immune infiltrates (B cells, CD8+ T cells, CD4+ T cells, dendritic cells, macrophages, and neutrophils) was estimated by TIMER in PTC. (B) The association between FLNA expression and cell states was analyzed using data downloaded from Carcinoma EcoTyper. *P < 0.05; ns, not significant.
Citation: Endocrine Connections 13, 6; 10.1530/EC-24-0034
We further downloaded TCGA cell state assignments from Carcinoma EcoTyper and analyzed the association between the expression of FLNA and cell states. We found that FLNA expression was significantly correlated with the majority of the immune cell states (Fig. 7B). These data suggest that FLNA is associated with the TME in PTC.
Discussion
In this study, we showed that FLNA was overexpressed in PTC. High FLNA expression was significantly associated with increased TNM stage, T stage, and N stage, as well as poor DFI and PFI in patients with PTC. Biological experiments demonstrated that FLNA knockdown inhibits PTC cell migration and invasion through modulating FAK/AKT pathway. In addition, FLNA expression was associated with the TME in PTC.
FLNA was first identified as a non-muscle actin filament cross-linking protein (18). FLNA interacts with integrins, transmembrane receptor complexes, and second messengers, thereby regulating cell shape change and motility (19). The expression pattern and clinical significance of FLNA in cancer remain debatable. Recent studies have revealed that FLNA is upregulated in multiple human malignancies and plays a vital role in cancer progression. For example, FLNA was significantly overexpressed in peripheral cholangiocarcinomas (20). Jin et al. reported that FLNA was significantly upregulated in cervical cancer tissues, and FLNA expression levels were associated with lymph node metastasis, parametrial invasion, and poor survival (10). Research by Tian et al. showed that FLNA was overexpressed in breast cancer and associated with advanced-stage, lymph node metastasis, and vascular or neural invasion (21). On the contrary, another study observed a significant decrease in FLNA levels in invasive breast cancer compared with benign disease and in lymph node-positive compared with lymph node-negative breast cancer using human tissue microarrays (22). Sun et al. reported that FLNA was significantly downregulated in gastric cancer, and loss of FLNA expression was associated with poor overall survival (23). In this study, we demonstrated that FLNA expression was upregulated in PTC using TCGA data. Using GEO data and human PTC tissue samples, the upregulated expression of FLNA was further confirmed. High FLNA expression was associated with advanced TNM stage, T stage, and N stage. Moreover, FLNA was highly expressed in PTC patients carrying BRAF mutations. Survival analysis showed the prognostic value of FLNA in PTC patients. High FLNA expression was associated with low DFI and PFI in patients with PTC. These results suggest that upregulated FLNA in PTC may act as a prognostic predictor.
Increasing studies have demonstrated that FLNA has both oncogenic and suppressive roles in tumor progression. It has been reported that FLNA silencing increased cell adhesion and decreased cell migration in pulmonary neuroendocrine tumors (24). In bladder cancer, chromium (VI) promotes EMT by regulating FLNA, and inhibiting the expression of FLNA might suppress cancer progression (25). Contrastingly, results obtained in renal cell carcinoma showed that FLNA suppressed cell migration and invasion by promoting the degradation of MMP-9 (26). Interestingly, FLNA in the cytoplasm promoted prostate cancer cell invasion, whereas its expression in the nucleus suppressed cell invasion (27). The opposite functions of FLNA in cancer development might depend on its subcellular localization (8). Subsequently, the biological function of upregulated FLNA in PTC was investigated. Our results showed that the knockdown of FLNA significantly suppressed the migration and invasion abilities of PTC cells. These results confirmed the carcinogenic role of FLNA in PTC.
EMT is a process in which epithelial cells lose their polarity and acquire mesenchymal features. EMT is often characterized by the loss of the epithelial marker E-cadherin, with upregulation of the mesenchymal markers N-cadherin, vimentin, and MMP9. Moreover, EMT leads to the activation of EMT-associated transcription factors, such as the ZEB and Snail families (28, 29). Studies have reported that EMT plays a crucial role in regulating thyroid cancer cell invasion and metastasis (30). Here, we found that FLNA was positively associated with EMT in PTC through public database analysis and in vitro experiments. The GSEA results showed that FLNA expression was positively correlated with EMT pathway. In vitro experiments, knocking down the expression of FLNA significantly suppressed the mesenchymal markers in PTC cells, including N-cadherin, vimentin, ZEB2, Snail1, and MMP9. These results indicate that FLNA may influence PTC progression via EMT signaling.
Accumulating studies have revealed that the FAK/AKT pathway participates in the process of cancer progression. Recently, several studies have reported that FAK/AKT signaling is closely related to EMT and thereby promotes cancer migration and invasion. For example, cycloartocarpin inhibited non-small-cell lung cancer cell migration through the suppression of EMT and FAK/AKT signaling (31). Li et al. showed that IGSF9 inhibited breast cancer metastasis through EMT process mediated via FAK/AKT signaling (32). In thyroid cancer, EMT promoted by CRABP2 was mediated at least partly by the FAK/AKT pathway (33). Herein, we also evaluated the effect of FLNA expression on the FAK/AKT signaling pathway. As expected, the knockdown of FLNA significantly reduced the protein expression levels of p-FAK and p-AKT. These results indicate that FLNA induces EMT in PTC partly through activating the FAK/AKT pathway.
Tumor-associated immune cells are involved in regulating tumor growth and metastasis (34). Hence, we analyzed the relationship between FLNA expression and immune differences in PTC. In our study, TIMER analysis revealed that FLNA expression was positively correlated with CD8+ T cells, dendritic cells, and neutrophils, whereas negatively correlated with B cells. The role of CD8+ T cells in TC remains controversial. Cunha et al. showed that higher levels of CD8+ T cells were associated with recurrence in differentiated thyroid cancer (DTC) patients (35). However, another research suggested that CD8+ T cells were associated with improved disease-free survival in DTC patients (36). Previous studies also reported that dendritic cells were positively correlated with T stage in PTC patients (37, 38). It was reported that tumor-infiltrating neutrophils positively correlated with tumor size in TC patients (39). B cells showed significant predictive accuracy for lymph node metastases and positive correlation with favorable prognosis in PTC. patients (40). For further investigation, we downloaded TCGA cell state assignments from Carcinoma EcoTyper and analyzed the association between the expression of FLNA and cell states. The results showed that FLNA expression was significantly correlated with the majority of the immune cell states. Collectively, we conclude that FLNA may be involved in the progression of PTC via regulating the immune microenvironment.
Collectively, our results reveal the oncogene function of FLNA in PTC. Moreover, the knockdown of FLNA inhibits the invasion and migration of PTC cells, which is partly due to the regulation of the FAK/AKT pathway. Therefore, our study provides a potential therapeutic target for PTC.
Declaration of interest
The authors declare that there is no conflict of interest that could be perceived as prejudicing the impartiality of the study reported.
Funding
The funding for this project was provided by the National Natural Science Foundation of China (nos. 82200876 and 82201551) and Guangzhou 2023 Basic and Applied Basic Research Project (nos. 2023A04J2190 and 2023A04J2196).
Ethical approval
The Ethics Committee approval was obtained from the Ethics Committee of the First Affiliated Hospital of Sun Yat-sen University.
Data availability statement
The datasets analyzed for this study can be found in the TCGA (https://cancergenome.nih.gov/), GSE33630 (https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE33630) and GSE29265 (https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE29265).
Author contribution statement
WL and YZ performed the experiments; analyzed and interpreted the data; and wrote the paper. YG, PZ, and JJ performed the experiments and wrote the paper. HG and YL conceived and designed the experiments; analyzed and interpreted the data; contributed reagents, materials, analysis tools or data; and wrote the paper.
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