Abstract
Background
Collagen type VIII α 1 chain (COL8A1), a collagen type VIII protein, has been suggested to exert various functions in progression of multiple cancers. However, the effect of COL8A1 in papillary thyroid cancer (PTC) has not been elucidated.
Methods
The Cancer Genome Atlas (TCGA) databases were applied to investigate the COL8A1 expression and its clinical significance in PTC. The COL8A1 expression level was further validated using Gene Expression Omnibus (GEO) data and clinical paired PTC tissues. Additionally, the Kaplan–Meier curve was used to analyze the prognosis. The cell’s migrative and invasive abilities were evaluated by wound healing assay and Transwell assay. CCK8 assays were used to evaluate the proliferation of PTC cells. Western blotting was conducted to explore the potential mechanisms involved in the pro-tumor role of COL8A1. The correlation between immune cell infiltration and COL8A1 was analyzed using the Tumor Immune Estimation Resource (TIMER) database and the single-sample GSEA (ssGSEA) method.
Results
We found that COL8A1 was upregulated in PTC (P < 0.05). High COL8A1 expression level was significantly associated with advanced T stage (P < 0.01), N stage (P < 0.001) and poor prognosis (P = 0.0142) in PTC. Furthermore, cell migration and invasion were significantly reduced following COL8A1 knockdown (P < 0.001). Mechanistic studies demonstrated that the epithelial-to-mesenchymal transition (EMT) related proteins (FN1, MMP9, MMP7, ZEB2 and Twist1) and phosphorylation of AKT and ERK were obviously down-regulated after COL8A1 knockdown (P < 0.01). Moreover, COL8A1 expression was correlated with immune cell infiltration.
Conclusion
Our study demonstrates that COL8A1 may function as an oncogene and a potential prognostic biomarker for PTC patients.
Introduction
Thyroid cancer is the most common endocrine malignant tumor, with a rapid increase in recent decades (1). Papillary thyroid cancer (PTC) is the most common type of thyroid cancer, accounting for about 80–90% of all cases (2). The prognosis of most PTC is usually good, with a 5-year survival rate exceeding 98% (3). However, approximately 30% of patients with advanced PTC suffer local recurrence or distant metastasis, which worsens the prognosis and increases the risk of death (4). The 5-year survival rate of these patients with progressive PTC is only 53.3% (5). Hence, it is necessary to explore prognostic genetic biomarkers for predicting the prognosis and developing more effective therapeutic strategies against PTC.
Collagen type VIII α 1 chain (COL8A1), secreted by corneal and vascular endothelial cells (6), is a vital component of the extracellular matrix (ECM) (7). COL8A1 has been shown to be involved in numerous physiological processes and multiple diseases, including cell proliferation (8), migration (9), angiogenesis (10) and age-related macular degeneration (11). Recently, limited studies revealed that COL8A1 played crucial roles in tumorigenesis. For example, COL8A1 was found to be upregulated in non-small cell lung cancer (NSCLC), and its high expression contributed to NSCLC proliferation and invasion (12). Moreover, elevated COL8A1 promoted gastric cancer cell proliferation (13). COL8A1 was associated with tumor stage and prognosis in bladder cancer patients (14). Zhao et al. discovered that knockdown of COL8A1 inhibited the proliferation and invasion of hepatocellular carcinoma cells (15). However, the roles of COL8A1 in PTC and its molecular mechanisms underlying PTC tumorigeneses remain largely unclear.
In this research, the expression of COL8A1 in PTC was investigated utilizing The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) data. And the result was validated in paired clinical PTC tissues and adjacent normal tissues using qRT-PCR. Next, we explored the association between COL8A1 expression level and clinicopathological data. We performed a GSEA analysis to explore the potential regulatory role by COL8A1 in PTC. Additionally, we carried out a series of in vitro experiments to verify the effect of COL8A1 and the involved mechanism in PTC cells. Finally, we demonstrated the associations between COL8A1 expression and immune infiltrates using the TIMER database and ssGSEA method.
Materials and methods
Data acquisition
The RNA-seq data of PTC and normal samples were downloaded from the TCGA database (https://cancergenome.nih.gov/). Moreover, the clinical data including age, gender, TNM stage, T stage, N stage, M stage, extrathyroidal extension, histological types, disease-free time and status of these patients were also downloaded. Briefly, a total of 495 PTC tissues and 59 matched normal control tissues were available for COL8A1 expression level analysis. In the clinical data, 346 patients including disease-free time and status were evaluated. Four datasets (GSE33630, GSE60542, GSE29265 and GSE3678), downloaded from the GEO database (https://www.ncbi.nlm.nih.gov/gds), were used as verification the expression of COL8A1. We also downloaded the recurrence risk data for TCGA-THCA samples from the supplementary materials of Nishant’s article (16) and analyzed the association between COL8A1 expression and the risk of recurrence.
Analysis of the relationship between COL8A1 expression and clinical pathologic characteristics
To analyze the relationship between COL8A1 expression and clinical pathologic characteristics, the patients were divided into a low-expression group (≤ median of COL8A1) and a high-expression group (> median of COL8A1).
Analysis of the prognostic role of COL8A1
We adopted the X-tile program (Robert L Camp, Yale University, New Haven, CT, USA) to get the optimal COL8A1 cutoff point, and used Kaplan–Meier analysis to evaluate the relationship between COL8A1 expression and disease-free interval (DFI). DFI refers to the period between the completion of removal or elimination of the tumor and the diagnosis of recurrence.
Analysis of the thyroid differentiation genes
We analyzed the association between COL8A1 expression and the expression levels of 16 thyroid differentiation genes (PAX8, TG, TPO, TSHR, NKX2-1, SLC5A5, SLC5A8, SLC26A4, GLIS3, FOXE1, THRA, THRB, DUOX1, DUOX2, DIO1 and DIO2) using Spearman’s correlation analysis. For the correlation analyses, multiple testing correction was performed using the Benjamini-Hochberg false discovery rate method. Thyroid differentiation score (TDS) was calculated based on mRNA expression levels of 16 thyroid metabolism and function genes using samples from the TCGA-THCA dataset. TDS was downloaded from the supplementary materials of Nishant’s article (16) and the relationship between COL8A1 and TDS was then analyzed.
Analysis of COL8A1 expression and mutational profiles
We downloaded 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 association between COL8A1 expression and the mutational profiles.
Methylation analysis of COL8A1
To determine the molecular mechanism underlying increased COL8A1 expression in PTC, we analyzed the methylation levels of COL8A1 in PTC by the University of Alabama at Birmingham cancer data analysis portal (UALCAN) database (http://ualcan.path.uab.edu). In addition, we used HM450 methylation data from the cBio Cancer Genomics Portal (cBioPortal) online database (http://www.cbioportal.org/) to assess the association between COL8A1 mRNA expression and DNA methylation. The website included methylation data from the probes with the strongest correlation between the methylation signal and the gene's expression in the study and averaged their methylation values. We further analyzed HM450 methylation data from TCGA by mapping CpG islands within 1,500 base pairs (bp) ahead of transcription start sites (TSS) of COL8A1 and assessing the difference in methylation values between PTC and normal thyroid tissues. The calculation of beta values is a common way to measure DNA methylation, which are bounded variables of the form M/(M+U) that are generated by Illumina’s 450k BeadChip array (17).
Human tissue samples
A total of 14 pairs of PTC and adjacent tissues were obtained from patients at the First Affiliated Hospital of Sun Yat-sen University (Guangzhou, China). All tissue samples were acquired from surgery and immediately frozen in liquid nitrogen and stored at −80°C until the extraction of total RNA or protein. Written informed consent was obtained from patients. The study protocol was approved by the Ethics Committee of the First Affiliated Hospital of Sun Yat-sen University (no. 2021087).
RNA isolation and real-time polymerase chain reaction (RT-qPCR)
TRIzol was used to extract total RNA from thyroid cancer and paired normal thyroid tissues or PTC cells according to the manufacturer’s protocol (Invitrogen). The cDNA synthesis was performed using the PrimeScript RT reagent kit (Takara Bio). Quantitative PCR (qPCR) was performed using SYBR Green qPCR Master Mix (Takara Bio) according to the manufacturer’s instructions. GAPDH was used as control. The PCR experiments were repeated three times. The relative gene expression levels were calculated using the 2−∆∆Ct method. The primers used were listed as below: COL8A1 forward, 5′-CAGACAGAACTACAACCCGCA-3′ and reverse, 5′-TTGAATAGAGCAACCCA CACG-3′; GAPDH forward, 5′-GACTCATGACCACAGTCCATGC-3′ and reverse, 5′-AGAGGCAGGGATGATGTTCTG-3′.
Western blotting (WB)
Total protein was extracted from PTC cell lines using radioimmunoprecipitation assay (RIPA) lysis buffer, and the relative concentration of the protein samples was acquired using a BCA kit (KeyGEN, Nanjing, China). The 30 µg protein was separated by sodium dodecyl sulfate-polyacrylamide gel (SDS/PAGE). Then protein was transferred to a polyvinylidene fluoride (PVDF) membrane. The membrane was probed with primary antibodies against COL8A1 (ProteinTech Group, Chicago, IL, USA), FN1, matrix metalloproteinase 9 (MMP9), MMP7, zinc finger E-box binding homeobox 2 (ZEB2), Twist1 (ProteinTech Group, Chicago, IL, USA), AKT, phosphor-AKT (p-AKT), ERK, phosphor-ERK (p- ERK) and GAPDH antibody (Cell Signaling Technology) overnight at 4°C, and then incubated with secondary antibody (Epizime, Shanghai, China) for 1 h at room temperature. Protein was visualized using enzyme-linked chemiluminescence detection (ECL) kit (Beyotime Biotechnology, Beijing, China).
Cell culture and transfection
The BCPAP cell line was purchased from the Chinese Academy of Sciences Cell Bank (Shanghai, China). The TPC1 cell line was purchased from Procell Life Science & Technology Co., Ltd (Wuhan, China). Both cell lines were cultured in Dulbecco’s Modified Eagle’s medium (DMEM) with 10% fetal bovine serum (FBS). Then, small interfering RNAs (siRNAs) and siRNA control, synthesized by RIBOBIO (RiboBio Co., Guangzhou, China), were used to knockdown the expression of COL8A1. The sequences were as follows: si-COL8A1-1, GGGTTGCTCTATTCAAGAA; COL8A1-2, GCAGTAT GGCAAAGA GTAT. Lipofectamine 2000 (Invitrogen) was used for transient transfection.
Wound healing assay
Cell migration was determined by using the wound healing assay. Briefly, the indicated cells were plated in a 6-well plate and allowed to reach 90% confluence. Then a single linear scratch in the cell monolayer was created by a 200 μL pipette tip. Cells were gently washed with 1× PBS. After 24 h, the images of the scratched area were taken.
Transwell assay
For Transwell migration assay, cells were seeded in the upper chambers with a serum-free medium. And the lower chamber was filled with a migration-inducing medium (with 10% FBS). After 24 h incubation, the cells on the upper chambers were removed with cotton swabs after 48 h. Cells on the bottom were stained with 0.1% crystal violet.
For Matrigel invasion assay, the top chambers were coated with Matrigel matrix (BD Biosciences, CA, USA) and then transfected cells were suspended in a serum-free medium. Medium containing 10% FBS was added to the lower chambers. The cells on the upper surface were removed 24 h later and the invaded cells on the lower surface were stained with 0.1% crystal violet. The stained cells were visualized and counted using a microscope.
Cell proliferation assay
Cell proliferation was determined by cell counting kit-8 (CCK8, Beyotime, China) according to the manufacturer's instructions. The indicated cells were seeded in 96-well plates. After incubating for 0, 1, 2, 3 and 4 days, 20 μL CCK-8 solution was added to each well. The absorbance was measured at 450 nm to determine cell proliferation rate and a growth curve was drawn.
Gene set enrichment analysis (GSEA)
The Hallmark and Kyoto Encyclopedia of Genes and Genomes (KEGG, http://www.genome.jp/kegg/) gene sets were performed in GSEA software. The P value was adjusted by Benjamini–Hochberg method. The adjusted P value < 0.05 and a false discovery rate (FDR) < 0.25 were considered as statistical significance.
Immune cell infiltration analysis
The association between COL8A1 expression and tumor-infiltrating immune cells (TIICs) was determined in PTC using Tumor Immune Estimation Resource (TIMER). TIMER is a web tool for the analysis of TIICs and gene correlation in diverse cancer types. TIICs included B cells, CD4+ T cells, CD8+ T cells, neutrophils, macrophages and dendritic cells. Moreover, the single sample gene set enrichment analysis (ssGSEA) was performed to evaluate the immune status of patients in high and low COL8A1 expression groups based on the gene expressions of 16 immune cells. The ssGSEA method is a proposed algorithm for counting immune cell subsets using RNA samples from various tissue types (including solid tumors) (18). It has less noise and unknown mixture content than other methods, and the cell types are closely related. In this study, the ssGSEA method was used to calculate the absolute enrichment fraction of 16 immune cells in TCGA-THCA patients. The 16 immune cell-associated gene sets were acquired from the work of Yin et al. (19) and included in Supplementary Table 1 (see section on supplementary materials given at the end of this article). The ssGSEA analysis was performed using the R package ‘GSVA’.
Statistical analysis
Statistical analysis was performed using SPSS 26.0 (SPSS Inc.) and GraphPad Prism 8.4.0 software. Differences between groups were estimated with Student's t test, paired t-test and one-way ANOVA. Relationships between categorical variables were analyzed using the chi-square test. Survival curves were generated using the Kaplan–Meier method. The correlation analysis was conducted using Spearman's correlation. P < 0.05 was considered as statistically significant.
Results
COL8A1 is upregulated in thyroid cancer
To analyze the mRNA expression of COL8A1 in thyroid cancer, we downloaded the RNAseq data of thyroid cancer from the TCGA database. And we determined that COL8A1 was significantly higher in PTC specimens than in normal tissues (P < 0.0001; Fig. 1A). Moreover, COL8A1 expression was upregulated in PTC tissues compared to adjacent noncancerous tissues (P < 0.0001; Fig. 1B). Subsequently, we validated the COL8A1 expression level using four GEO datasets. The results suggested that COL8A1 was overexpressed in PTC tissues (P < 0.0001; Fig. 1C-F). To further validate COL8A1 expression in PTC, we examined the mRNA expression levels of COL8A1 in 14 paired clinical PTC and normal tissues by PCR. Our results showed that COL8A1 mRNA was strongly upregulated in PTC tissues compared to that in normal tissues (P < 0.05, Fig. 1G). Collectively, these results revealed that the up-regulated COL8A1 might function as a potential oncogene in PTC.
COL8A1 is upregulated in PTC. (A) COL8A1 expression levels in PTC tissues (n = 495) and normal thyroid tissues (n = 59) in The Cancer Genome Atlas (TCGA) cohort. (B) COL8A1 expression levels in paired PTC and adjacent noncancerous tissues (n = 59) in TCGA cohort. (C) COL8A1 expression levels in PTC tissues (n = 49) and normal thyroid tissues (n = 45) in GSE33630 from the Gene Expression Omnibus (GEO) database. (D) COL8A1 expression levels in PTC tissues (n = 33) and normal thyroid tissues (n = 30) in GSE60542. (E) COL8A1 expression levels in paired PTC and adjacent noncancerous tissues (n = 20) in GSE29265. (F) COL8A1 expression levels in paired PTC and adjacent noncancerous tissues (n = 7) in GSE3678. (G) Validation of COL8A1 expression by quantitative real-time PCR (qPCR) in 14 paired clinical PTC and normal tissues. Data are expressed as mean ± s.d. ****P < 0.0001; PTC, papillary thyroid carcinoma.
Citation: Endocrine Connections 13, 12; 10.1530/EC-24-0279
The correlation of COL8A1 expression with clinicopathological characteristics in PTC patients
To evaluate the relationship between COL8A1 expression and clinical data, the characteristics of 495 PTC patients, including age, gender, tumor lymph nodes metastasis (TNM) stage, T stage, N classification and M classification were downloaded from the TCGA database. Then these patients were divided into a high-expression group and a low-expression group based on the median expression level of COL8A1. The detailed results of clinicopathological differences between the two groups are summarized in Table 1. Using the t test, the increased COL8A1 expression was significantly correlated with increased TNM stage (P = 0.0218), T (P = 0.005), N stage (P < 0.0001) and extrathyroidal invasion (P < 0.0001). Moreover, the COL8A1 level of follicular variant subtypes was lower than that of classic and tall cell variant subtypes (P < 0.0001). However, no correlation was found between COL8A1 expression and age (P = 0.2757), gender (P = 0.0557) and M stage (P = 0.2582) (Fig. 2A–H). We also analyzed the association between COL8A1 expression and the risk of recurrence. The results showed that the COL8A1 level of the high and intermediate risk group was higher than that of the low risk group (P < 0.05 and P < 0.0001, Fig. 2I). Therefore, the increased COL8A1 in PTC was significantly correlated with a high risk of recurrence.
Association of COL8A1 expression with clinicopathological characteristics and prognosis in PTC. (A) Expression of COL8A1 between the different ages of PTC based on TCGA database. (B) Expression of COL8A1 between different genders of PTC based on TCGA database. (C) Expression of COL8A1 between different tumor lymph nodes metastasis (TNM) stages of PTC based on TCGA database. (D) Expression of COL8A1 between different T stages of PTC based on TCGA database. (E) Expression of COL8A1 between different N stages of PTC based on TCGA database. (F) Expression of COL8A1 between different M stages of PTC based on TCGA database. (G) Expression of COL8A1 between different extrathyroidal extension status of PTC based on TCGA database. (H) Expression of COL8A1 between different histological types of PTC based on TCGA database. (I) Expression of COL8A1 between different risks of recurrence in PTC based on TCGA database. (J) Kaplan–Meier analysis of disease-free interval (DFI) in PTC patients with high or low COL8A1 expression in the TCGA datasets. P ≥ 0.05 was considered not significant (ns); *P < 0.05, **P < 0.01, ****P < 0.0001.
Citation: Endocrine Connections 13, 12; 10.1530/EC-24-0279
The relationship between COL8A1 and clinicopathologic characteristics in TCGA cohort.
| Clinicopathologic variables | COL8A1 | P | ||
|---|---|---|---|---|
| Total | Low | High | ||
| Age | ||||
| <45 yr | 223 | 104 | 119 | 0.163 |
| ≥45 yr | 272 | 144 | 128 | |
| Gender | ||||
| Male | 130 | 73 | 57 | 0.108 |
| Female | 365 | 175 | 190 | |
| TNM stage | ||||
| I and II | 328 | 173 | 155 | 0.098 |
| III and IV | 165 | 74 | 91 | |
| T classification | ||||
| T1 and T2 | 307 | 172 | 135 | 4.73E-04* |
| T3 and T4 | 186 | 74 | 112 | |
| N classification | ||||
| N0 | 226 | 137 | 89 | 6.17E-07* |
| N1 | 219 | 81 | 138 | |
| M classification | ||||
| M0 | 277 | 131 | 146 | 0.319 |
| M1 | 9 | 6 | 3 | |
*P < 0.05.
COL8A1 expression is associated with poor prognosis in PTC patients
We next investigated the relationship between COL8A1 expression and clinical outcomes in PTC patients via Kaplan–Meier survival analysis. The high COL8A1 expression group showed shorter DFI time than the low COL8A1 group (P = 0.0142, Fig. 2J). The above data indicate that high COL8A1 expression was significantly associated with poor prognosis.
The association between COL8A1 expression and thyroid differentiation genes and BRAF/RAS mutation in TCGA
Given that loss of expression of thyroid differentiation markers is a key event during PTC oncogenesis, we analyzed the association between COL8A1 expression and 16 thyroid differentiation genes using TCGA-THCA data. As shown in Fig. 3A, we identified that COL8A1 expression was negatively associated with paired box 8 (PAX, r = −0.53, P = 2.19E−37), thyroglobulin (TG, r = −0.45, P = 4.91E-26), thyroperoxidase (TPO, r = −0.58, P = 3.42E-45), thyrotropin receptor (TSHR, r = −0.10, P = 0.0285), NK2 homeobox 1 (NKX2-1, r = −0.11, P = 0.0179), sodium iodide symporter (SLC5A5, r = −0.23, P = 2.91E-07), apical iodide transporter (SLC5A8, r = −0.54, P = 3.34E-38), pendrin (SLC26A4, r = −0.50, P = 1.68E-31), glis family zinc finger 3 (GLIS3, r = −0.14, P = 2.01E-03), forkhead box E1 (FOXE1, r = −0.22, P = 9.05E-07), thyroid hormone receptor alpha (THRA, r = −0.26, P = 4.11E-09), dual oxidase 1 (DUOX1, r = −0.26, P = 4.00E-09), dual oxidase 2 (DUOX2, r = −0.29, P = 5.60E-11), iodothyronine deiodinase 1 (DIO1, r = −0.59, P = 1.10E-45) and iodothyronine deiodinase 2 (DIO2, r = −0.44, P = 2.27E-24). While COL8A1 expression was positively associated with thyroid hormone receptor beta (THRB, r = 0.22, P = 7.15E-07).
COL8A1 is positively correlated with thyroid dedifferentiation degree and BRAFV600E mutation in PTC. (A) The correlation between COL8A1 expression and 16 thyroid differentiation genes was analyzed using TCGA-THCA data. (B) The correlation between COL8A1 expression and the thyroid differentiation score (TDS) was evaluated using TCGA-THCA cohort. (C) Heatmap of COL8A1 expression in TCGA PTC patients and their BRAF/RAS mutation status. P < 0.05 was considered significant.
Citation: Endocrine Connections 13, 12; 10.1530/EC-24-0279
Thyroid cell differentiation and function loss, leading to impaired iodine trapping, is a significant clinical feature of radioiodine-refractory thyroid cancer (20). In the Cancer Genome Atlas (TCGA) study, the expression of 16 thyroid metabolism and function genes (PAX8, TG, TPO, TSHR, NKX2-1, SLC5A5, SLC5A8, SLC26A4, GLIS3, FOXE1, THRA, THRB, DUOX1, DUOX2, DIO1, and DIO2) were analyzed and found to be highly correlated across the cohort, and produced a single metric, designated the thyroid differentiation score (TDS) (16). The 16 genes are mainly iodine metabolism genes and thyroid transcription factors (TTFs) (21), which can represent the differentiation status of thyroid cancer at a certain level. TDS, along with the 16 thyroid differentiation genes, have been widely utilized by other studies to evaluate the thyroid cancer differentiation status (22, 23, 24). Thus, we used TDS and the 16 genes to evaluate the association of COL8A1 expression level and differentiation status of thyroid cancer. The result showed that COL8A1 was negatively correlated with TDS (r = −0.61, P < 2.2E-16, Fig. 3B). Basing the above results, the upregulated COL8A1 may affect PTC dedifferentiation.
Given the high frequency and oncogenic role of BRAFV600E and RAS mutations in PTC (16), we investigated the relationship between COL8A1 expression and BRAF/RAS mutation. This analysis revealed that COL8A1 expression was positively related to BRAFV600E mutation (P = 2.00E-35), and negatively associated with BRAF-RAS score and RAS mutation (P < 0.001 and P = 1.66E-22, Fig. 3C). The above data suggest that the upregulated COL8A1 may be driven by BRAFV600E mutation in PTC.
COL8A1 is upregulated in PTC by promoter methylation
Epigenetic mechanisms including DNA methylation play a vital role in the activation of oncogenes and silencing of tumor suppressors (25). To further reveal the upstream of COL8A1, the promotor methylation level of COL8A1 was investigated. The results from the UALCAN database showed that the DNA methylation of COL8A1 in PTC tissues is much lower than that in normal tissues (P < 0.05, Fig. 4A). Furthermore, the cBioPortal database was used to assess the association between COL8A1 expression and DNA methylation. The result suggested that the level of COL8A1 promoter methylation was negatively correlated with COL8A1 expression (r = −0.58, P = 9.91E-45, Fig. 4B). We also observed that the DNA methylation of most individual sites in COL8A1 promoter was lower in PTC samples compared with normal samples (P < 0.05, Fig. 4C). Our data indicate that promoter methylation may be the main cause of the increased COL8A1 in PTC.
COL8A1 is upregulated in PTC by promoter methylation. (A) The promoter methylation level of COL8A1 in normal tissues and PTC tissues from the University of Alabama at Birmingham cancer data analysis portal (UALCAN) database. (B) Correlation analysis of COL8A1 mRNA expression with COL8A1 promoter methylation status using the cBio Cancer Genomics Portal (cBioPortal) online database. (C) Methylation status of individual CpG sites at the COL8A1 promoter in normal and PTC samples using TCGA-THCA datasets. P ≥ 0.05 was considered not significant (ns), P < 0.05 was considered significant. *P < 0.05.
Citation: Endocrine Connections 13, 12; 10.1530/EC-24-0279
Knockdown of COL8A1 suppresses thyroid cancer cell migration and invasion in vitro
To further explore the effects of COL8A1 on PTC, we first conducted GSEA and enriched the ‘Hallmark Epithelial–Mesenchymal Transition’ and ‘KEGG Cell Adhesion Molecules’ gene sets in the samples with the COL8A1 highly expressed using TCGA data. The results indicated that COL8A1 expression may be involved in the EMT process (Fig. 5A). The enriched gene sets in detail are shown in Supplementary Table 2. In addition, correlation analysis was performed to determine the correlations between COL8A1 expression and EMT markers. As shown in Fig. 5B, COL8A1 was positively associated with the expression of FN1, MMP2, MMP7, MMP9, ICAM1, SNAI2, TWIST1 and ZEB2. Thus, we next examined the effect of COL8A1 on PTC migration and invasion in vitro. We knocked down the expression of COL8A1 using siRNA in BCPAP and TPC1 cells. Then WB was conducted to test the knockdown efficiency. As shown in Fig. 5C, siRNA transfection significantly reduced the expression of COL8A1 (P < 0.01). The wound-healing and Transwell assays were used to validate the role of COL8A1 on PTC cell migration and invasion. The wound-healing assay showed that knockdown of COL8A1 suppressed the migration ability of PTC cells (P < 0.001, Fig. 5D). The Transwell assay revealed that knockdown of COL8A1 significantly suppressed PTC cell migration and invasion (P < 0.001, Fig. 5E). In addition, CCK8 assays were performed to examine the regulatory effects of COL8A1 on the proliferative ability of PTC cells. The CKK8 assays showed that COL8A1 knockdown has no effect on BCPAP and TPC1 cell growth when compared to negative control (P > 0.05, Supplementary Figure 1). These data suggest that COL8A1 exerts an oncogene function in PTC by promoting cell migration and invasion.
Knockdown of COL8A1 inhibits PTC cell migration and invasion. (A) GSEA results of the Hallmark and KEGG gene sets for high COL8A1 expression group. (B) Correlation analysis of COL8A1 expression with epithelial-to-mesenchymal transition (EMT)-related makers expression using GSE33630 dataset. (C) Verification of the knockdown effect by Western blot (WB). The histogram shows the relative expression levels of COL8A1. (D) Wound-healing assay of BCPAP and TPC1 cells transfected with COL8A1 siRNA or NC. The histogram shows the quantification of wound healing assays. (E) Transwell of migration and invasion assay of BCPAP and TPC1 cells transfected with COL8A1 siRNA or NC. The histogram shows the quantification of Transwell migration and invasion assays. **P < 0.01, ***P < 0.001 and ****P < 0.0001.
Citation: Endocrine Connections 13, 12; 10.1530/EC-24-0279
Knockdown of COL8A1 inhibits EMT and the ERK and AKT signaling pathways
To better study the underlying mechanisms of COL8A1-mediated cell invasion in PTC, we then tested the protein expression levels of EMT markers in TPC1 and BACAP cells following transfection with siRNA targeting COL8A1 by WB. The expression levels of FN1, MMP9, MMP7, ZEB2 and Twist1 were reduced in COL8A1 knockdown cell lines (P < 0.01, Fig. 6A). These findings demonstrate that COL8A1 knockdown may inhibit the invasion of PTC cells by modulating EMT.
Knockdown of COL8A1 suppresses EMT, ERK and AKT signal pathways in PTC cells. (A) The protein expressions of FN1, MMP7, MMP9, ZEB2 and Twist1 in TPC1 and BCPAP cells after COL8A1 silencing were examined by Western blot. The histogram shows the relative expression levels of proteins. (B) The protein expression of p-AKT, AKT, p-ERK1/2 and ERK1/2 in TPC1 and BCPAP cells after COL8A1 silencing was detected by Western blot. The histogram shows the relative expression levels of proteins. P ≥ 0.05 was considered not significant (ns), P < 0.05 was considered significant. **P < 0.01, ***P < 0.001 and ****P < 0.0001.
Citation: Endocrine Connections 13, 12; 10.1530/EC-24-0279
Since the ERK and AKT pathways play essential roles in cancer cell invasion, we examined the expression of phosphorylation of ERK1/2 and AKT in PTC cells with knock down of COL8A1 by WB. As shown in Fig. 6B, the expression of phosphorylation of AKT and ERK reduced in the COL8A1 knockdown group (P < 0.01), yet the total amount of AKT and ERK remained constant in each group of TPC1 and BCPAP cells. Here, the data indicate that COL8A1 promotes PTC cell invasion, at least partially, through activation of ERK and AKT signaling pathways.
The correlation between COL8A1 expression and immune cell infiltration in PTC
Given the critical roles of the tumor immune microenvironment on the development of cancer, we further explored the correlation between COL8A1 expression and immune infiltration levels in PTC using the TIMER database. The results showed that COL8A1 expression was significantly positively correlated with CD8+ T cells, macrophages, neutrophils and dendritic cells in PTC (P < 0.0001; Fig. 7A). Furthermore, ssGSEA analyses were applied to assess the association between COL8A1 expression and 16 immune cells in PTC patients. The result showed that the infiltration level of most immune cells in high COL8A1 expression group was higher than that in low COL8A1 group (P < 0.05; Fig. 7B). Our results indicate that COL8A1 is closely linked with immune cell infiltration.
COL8A1 was correlated with immune infiltration. (A) The correlation of COL8A1 expression with tumor purity and six immune infiltrates (B cells, CD8+ T cells, CD4+ T cells, macrophages, neutrophils and dendritic cells) was estimated by TIMER in PTC. (B) The association between COL8A1 expression and 16 types of immune cells was evaluated by ssGSEA in PTC. *P < 0.05; ns, not significant.
Citation: Endocrine Connections 13, 12; 10.1530/EC-24-0279
Discussion
In the present study, we demonstrated that COL8A1 was upregulated and associated with poor prognosis in PTC. Moreover, we discovered that the knockdown of COL8A1 inhibited PTC cell invasion and migration in vitro. Silencing COL8A1 inhibited PTC cell EMT and suppressed the ERK and AKT pathways. Finally, COL8A1 positively correlates with infiltrating immune cells. These findings suggest that COL8A1 may function as an oncogene in PTC.
Previous studies have proved that high expression of COL8A1 acts as an oncogene in multiple types of cancers. For example, it was also reported that the expression of COL8A1 was upregulated in hepatocellular carcinoma (HCC) tissues (26). In renal cell carcinoma, COL8A1 was also proved to be overexpressed (27). Shang et al. discovered that upregulated COL8A1 was correlated with the progression and prognosis of human colon adenocarcinoma through focal adhesion-related pathways (28). A recent study revealed that COL8A1 was highly expressed in gastric cancer and patients with high expression of COL8A1 had poor prognosis (29). However, to the best of our knowledge, studies have not focused on COL8A1 in PTC. In our present study, we first determined the expression pattern and prognostic value of COL8A1 in PTC patients. By using the TCGA database, we found that the expression of COL8A1 was increased in PTC. Further GEO, PCR validation results also showed that COL8A1 was upregulated in PTC. And increased COL8A1 expression was linked to the T stage and N stage. High COL8A1 expression levels were strongly related to poor DFI in PTC patients. Furthermore, the promoter methylation participated in the upregulation of COL8A1 in PTC. Taken together, these results indicated that COL8A1 might serve as an oncogene and a candidate prognostic marker for PTC.
The cellular dedifferentiation is a distinctive feature of disease progression. Dedifferentiation is often involved in thyroid cancer progression (30). Tumor dedifferentiation contributed to more aggressive growth, metastatic spread and resistance to treatments, which led to poor prognosis in thyroid cancer patients (31). Considering that COL8A1 promoted PTC cell invasion and metastasis, we hypothesized that COL8A1 expression might correlate with the differentiation level of PTC. Therefore, we further explored the correlation between COL8A1 expression and thyroid differentiation genes. Consistent with our hypothesis, a negative correlation was found between COL8A1 expression and thyroid differentiation genes. Based on these above results, we conclude that COL8A1 might promote PTC progression by inducing dedifferentiation.
COL8A1 has been reported to promote tumor progression in several cancers. For example, Ma et al. reported that COL8A1 enhanced cell proliferation, invasion and in vivo tumorigenicity in hepatocellular carcinoma (32). Moreover, the silence of COL8A1 obviously inhibited gastric cancer cell proliferation, migration and invasion (13). A recent study focusing on breast cancer demonstrated that COL8A1 upregulation promoted the migration of breast cancer through mediating ECM-receptor interaction (33). To further explore the biological effect of COL8A1 in PTC, we first conducted a GSEA analysis. The results showed that COL8A1 expression was positively associated with EMT and cell-adhesion molecules. Moreover, the correlation analysis showed that COL8A1 was positively associated with EMT marker levels. Therefore, the effects of COL8A1 on PTC migration, invasion and proliferation were examined. The results showed that overexpression of COL8A1 enhanced the PTC cell invasion and migration, whereas it had no effect on cell proliferation. In accordance with previous findings from other cancers, we first proved the pro-metastatic role of COL8A1 in PTC.
Multiple studies have indicated that EMT participates in the PTC progression and metastasis(34). In the process of EMT, epithelial cells lose their cell polarity and transform to mesenchymal cells. EMT is characterized by the downregulation of epithelial markers, including E-cadherin, and the acquisition of mesenchymal markers, such as N-cadherin and Vimentin. The EMT was regulated by many transcription factors, such as SNAI1, SNAI2, ZEB1, ZEB2 and TWIST1 (35). FN1, MMP-7 and MMP-9 are also known as pro-EMT biomarkers (36, 37). Here, we further explored whether EMT mediated the effect of COL8A1 on PTC cell migration and invasion. And we found that knockdown of COL8A1 reduced the protein expression levels of FN1, MMP9, MMP7, ZEB2 and Twist1. Our data suggest that COL8A1 may be engaged in the progression of PTC by promoting the EMT process.
It is well known that ERK and AKT signaling pathways are the cardinal signaling programs mediating thyroid cancer progression (38). Previous studies also have shown that activation of these two pathways promote PTC cell proliferation, migration and invasion (39, 40). Thus, we speculate the inhibitory effect of COL8A1 knockdown on PTC invasion relates to the ERK and AKT signaling. As expected, we found that knockdown of COL8A1 markedly decreased p-ERK and p-AKT expression. Thus, our results revealed that COL8A1 accelerated PTC progression through ERK and AKT signaling pathways.
An increasing amount of evidence shows that immune cell infiltration status has prognostic value in many types of cancers. We investigated the association between the infiltrating level of immune cells and COL8A1 using TIMER in PTC. Our results showed that COL8A1 expression was significantly positively correlated with CD8+ T cells, macrophages, neutrophils and dendritic cells in PTC. Moreover, the results obtained from ssGSEA showed that the infiltration level of most immune cells in high COL8A1 expression group was higher than that in low COL8A1 group. A previous study indicated that the enrichment of CD8+ T cells was associated with recurrence in differentiated thyroid cancer (DTC) patients (41). As for macrophages, it was reported that tumor-associated macrophage density was higher in PTC compared with benign thyroid lesions, and positively associated with lymph node metastasis and the TNM stage (42). In addition, macrophages promoted invasiveness of PTC cell lines through CXCL8 secretion (43). Galdiero et al. found that Tumor-infiltrating neutrophils positively correlated with tumor size in human TC specimens (44). Studies also showed that the increased dendritic cells in PTC were associated with the advanced T stage (45, 46). Therefore, the expression of COL8A1 is closely related to immune cell infiltration, suggesting that COL8A1 may regulate the progression of PTC by affecting the activation of the tumor immune response.
We first report the expression pattern and molecular functions of COL8A1 in PTC. Our results also suggest that COL8A1 is a promising therapeutic target for PTC. The present study still has several limitations. First, the precise mechanism of how COL8A1 regulates ERK and AKT signaling needs further exploration. Second, the role of COL8A1 on PTC cell migration and invasion needs to be verified in vivo. Finally, more experiments need to be conducted to explore the regulatory mechanism by which COL8A1 affects immune infiltration.
Conclusion
In conclusion, we reported for the first time that COL8A1 expression was significantly upregulated in PTC. High expression of COL8A1 was significantly correlated with T stage, N stage and poor prognosis in PTC patients. Moreover, knockdown of the expression of COL8A1 could attenuate PTC invasion and migration in vitro. Mechanically, knockdown of COL8A1 inhibited PTC cell EMT, as well as ERK and AKT signaling pathways. In addition, COL8A1 was shown to be correlated with immune infiltration in PTC. Taken together, COL8A1 was identified as a promising biomarker for the prognosis prediction of PTC and might be a potential therapeutic target for the treatment of PTC.
Supplementary materials
This is linked to the online version of the paper at https://doi.org/10.1530/EC-24-0279.
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 statement
The funding for this project was provided by the National Natural Science Foundation of China (no. 82073050); Guangdong Basic and Applied Basic Research Foundation (no. 2019A1515012046); Guangzhou Technology Project (no. 202102080311); and Guangzhou 2023 Basic and Applied Basic Research Project (no. 2023A04J2190).
Ethical Approval
The Ethics Committee approval was obtained from the Ethics Committee of the First Affiliated Hospital of Sun Yat-sen University (No. 2021087).
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) , GSE60542 (https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE60542), GSE29265 (https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE29265) and GSE3678 (https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE3678).
Author contribution statement
WL, JC: Performed the experiments; Analyzed and interpreted the data; Wrote the paper.; HL: Performed the experiments; Wrote the paper.; PZ: Performed the experiments; Wrote the paper.; HG, YL: Conceived and designed the experiments; Analyzed and interpreted the data; Contributed reagents, materials, analysis tools or data; Wrote the paper.
References
- 1↑
Sung H, Ferlay J, Siegel RL, Laversanne M, Soerjomataram I, Jemal A, & Bray F. Global cancer statistics 2020: GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 countries. CA 2021 71 209–249. (https://doi.org/10.3322/caac.21660)
- 2↑
Grimm D. Cell and molecular biology of thyroid disorders 2.0. International Journal of Molecular Sciences 2021 22. (https://doi.org/10.3390/ijms22041990)
- 3↑
Cuomo F, Giani C, & Cobellis G. The role of the kinase inhibitors in thyroid cancers. Pharmaceutics 2022 14. (https://doi.org/10.3390/pharmaceutics14051040)
- 4↑
Magarey MJR, & Freeman JL. Recurrent well-differentiated thyroid carcinoma. Oral Oncology 2013 49 689–694. (https://doi.org/10.1016/j.oraloncology.2013.03.434)
- 5↑
Omry-Orbach G. Risk stratification in differentiated thyroid cancer: an ongoing process. Rambam Maimonides Medical Journal 2016 7. (https://doi.org/10.5041/RMMJ.10230)
- 6↑
Sage H, Trueb B, & Bornstein P. Biosynthetic and structural properties of endothelial cell type VIII collagen. Journal of Biological Chemistry 1983 258 13391–13401. (https://doi.org/10.1016/S0021-9258(17)44129-9)
- 7↑
Kapoor R, Sakai LY, Funk S, Roux E, Bornstein P, & Sage EH. Type VIII collagen has a restricted distribution in specialized extracellular matrices. Journal of Cell Biology 1988 107 721–730. (https://doi.org/10.1083/jcb.107.2.721)
- 8↑
Li X, Wang Z, Tong H, Yan Y, & Li S. Effects of COL8A1 on the proliferation of muscle-derived satellite cells. Cell Biology International 2018 42 1132–1140. (https://doi.org/10.1002/cbin.10979)
- 9↑
Shuttleworth CA. Type VIII collagen. International Journal of Biochemistry and Cell Biology 1997 29 1145–1148. (https://doi.org/10.1016/s1357-2725(97)00033-2)
- 10↑
Buga AM, Margaritescu C, Scholz CJ, Radu E, Zelenak C, & Popa-Wagner A. Transcriptomics of post-stroke angiogenesis in the aged brain. Frontiers in Aging Neuroscience 2014 6 44. (https://doi.org/10.3389/fnagi.2014.00044)
- 11↑
Corominas J, Colijn JM, Geerlings MJ, Pauper M, Bakker B, Amin N, Lores Motta L, Kersten E, Garanto A, Verlouw JAM, et al.Whole-exome sequencing in age-related macular degeneration identifies rare variants in COL8A1, a component of Bruch's membrane. Ophthalmology 2018 125 1433–1443. (https://doi.org/10.1016/j.ophtha.2018.03.040)
- 12↑
Zan X, Li S, Wei S, Gao L, Zhao L, Yan X, Zhao Y, Shi J, Wang Y, Liu R, et al.COL8A1 promotes NSCLC progression through IFIT1/IFIT3-mediated EGFR activation. Frontiers in Oncology 2022 12 707525. (https://doi.org/10.3389/fonc.2022.707525)
- 13↑
Zhou J, Song Y, Gan W, Liu L, Chen G, Chen Z, Luo G, Zhang L, Zhang G, Wang P, et al.Upregulation of COL8A1 indicates poor prognosis across human cancer types and promotes the proliferation of gastric cancer cells. Oncology Letters 2020 20 34. (https://doi.org/10.3892/ol.2020.11895)
- 14↑
Di Y, Chen D, Yu W, & Yan L. Bladder cancer stage-associated hub genes revealed by WGCNA co-expression network analysis. Hereditas 2019 156 7. (https://doi.org/10.1186/s41065-019-0083-y)
- 15↑
Zhao Y, Jia L, Mao X, Xu H, Wang B, & Liu Y. siRNA-targeted COL8A1 inhibits proliferation, reduces invasion and enhances sensitivity to D-limonence treatment in hepatocarcinoma cells. IUBMB Life 2009 61 74–79. (https://doi.org/10.1002/iub.151)
- 16↑
Cancer Genome Atlas Research Network. Integrated genomic characterization of papillary thyroid carcinoma. Cell 2014 159 676–690. (https://doi.org/10.1016/j.cell.2014.09.050)
- 17↑
Weinhold L, Wahl S, Pechlivanis S, Hoffmann P, & Schmid M. A statistical model for the analysis of beta values in DNA methylation studies. BMC Bioinformatics 2016 17 480. (https://doi.org/10.1186/s12859-016-1347-4)
- 18↑
Bindea G, Mlecnik B, Tosolini M, Kirilovsky A, Waldner M, Obenauf AC, Angell H, Fredriksen T, Lafontaine L, Berger A, et al.Spatiotemporal dynamics of intratumoral immune cells reveal the immune landscape in human cancer. Immunity 2013 39 782–795. (https://doi.org/10.1016/j.immuni.2013.10.003)
- 19↑
He Y, Jiang Z, Chen C, & Wang X. Classification of triple-negative breast cancers based on Immunogenomic profiling. Journal of Experimental and Clinical Cancer Research 2018 37 327. (https://doi.org/10.1186/s13046-018-1002-1)
- 20↑
Fuziwara CS, & Kimura ET. MicroRNAs in thyroid development, function and tumorigenesis. Molecular and Cellular Endocrinology 2017 456 44–50. (https://doi.org/10.1016/j.mce.2016.12.017)
- 21↑
Fernandez LP, Lopez-Marquez A, & Santisteban P. Thyroid transcription factors in development, differentiation and disease. Nature Reviews. Endocrinology 2015 11 29–42. (https://doi.org/10.1038/nrendo.2014.186)
- 22↑
Boucai L, Seshan V, Williams M, Knauf JA, Saqcena M, Ghossein RA, & Fagin JA. Characterization of subtypes of BRAF-mutant papillary thyroid cancer defined by their thyroid differentiation score. Journal of Clinical Endocrinology and Metabolism 2022 107 1030–1039. (https://doi.org/10.1210/clinem/dgab851)
- 23↑
Nam M, Yang W, Kim HS, Park J, Park G, Kim S, Song S, Ock CY, Wang VG, Chuang JH, et al.Papillary thyroid cancer immune phenotypes via tumor-infiltrating lymphocyte spatial analysis. Endocrine-Related Cancer 2023 30. (https://doi.org/10.1530/ERC-23-0110)
- 24↑
Suh HY, Choi H, Paeng JC, Cheon GJ, Chung JK, & Kang KW. Comprehensive gene expression analysis for exploring the association between glucose metabolism and differentiation of thyroid cancer. BMC Cancer 2019 19 1260. (https://doi.org/10.1186/s12885-019-6482-7)
- 25↑
Trimarchi MP, Mouangsavanh M, & Huang THM. Cancer epigenetics: a perspective on the role of DNA methylation in acquired endocrine resistance. Chinese Journal of Cancer 2011 30 749–756. (https://doi.org/10.5732/cjc.011.10128)
- 26↑
Bai KH, He SY, Shu LL, Wang WD, Lin SY, Zhang QY, Li L, Cheng L, & Dai YJ. Identification of cancer stem cell characteristics in liver hepatocellular carcinoma by WGCNA analysis of transcriptome stemness index. Cancer Medicine 2020 9 4290–4298. (https://doi.org/10.1002/cam4.3047)
- 27↑
Boguslawska J, Kedzierska H, Poplawski P, Rybicka B, Tanski Z, & Piekielko-Witkowska A. Expression of genes involved in cellular adhesion and extracellular matrix remodeling correlates with poor survival of patients with renal cancer. Journal of Urology 2016 195 1892–1902. (https://doi.org/10.1016/j.juro.2015.11.050)
- 28↑
Shang J, Wang F, Chen P, Wang X, Ding F, Liu S, & Zhao Q. Co-expression network analysis identified COL8A1 is associated with the progression and prognosis in human colon adenocarcinoma. Digestive Diseases and Sciences 2018 63 1219–1228. (https://doi.org/10.1007/s10620-018-4996-5)
- 29↑
She Y, Zhao X, Wu P, Xue L, Wan S, Zhang L, Li C, Cai H, & Li Y. COL8A1 predicts the clinical prognosis of gastric cancer and is related to epithelial-mesenchymal transition. BioMed Research International 2022 2022 7567447. (https://doi.org/10.1155/2022/7567447)
- 30↑
Nikitski AV, Rominski SL, Condello V, Kaya C, Wankhede M, Panebianco F, Yang H, Altschuler DL, & Nikiforov YE. Mouse model of thyroid cancer progression and dedifferentiation driven by STRN-ALK expression and loss of p53: evidence for the existence of two types of poorly differentiated carcinoma. Thyroid 2019 29 1425–1437. (https://doi.org/10.1089/thy.2019.0284)
- 31↑
Feng F, Han H, Wu S, & Wang H. Crosstalk between abnormal TSHR signaling activation and PTEN/PI3K in the dedifferentiation of thyroid cancer cells. Frontiers in Oncology 2021 11 718578. (https://doi.org/10.3389/fonc.2021.718578)
- 32↑
Ma ZH, Ma JH, Jia L, & Zhao YF. Effect of enhanced expression of COL8A1 on lymphatic metastasis of hepatocellular carcinoma in mice. Experimental and Therapeutic Medicine 2012 4 621–626. (https://doi.org/10.3892/etm.2012.652)
- 33↑
Peng W, Li JD, Zeng JJ, Zou XP, Tang D, Tang W, Rong MH, Li Y, Dai WB, Tang ZQ, et al.Clinical value and potential mechanisms of COL8A1 upregulation in breast cancer: a comprehensive analysis. Cancer Cell International 2020 20 392. (https://doi.org/10.1186/s12935-020-01465-8)
- 34↑
Shakib H, Rajabi S, Dehghan MH, Mashayekhi FJ, Safari-Alighiarloo N, & Hedayati M. Epithelial-to-mesenchymal transition in thyroid cancer: a comprehensive review. Endocrine 2019 66 435–455. (https://doi.org/10.1007/s12020-019-02030-8)
- 35↑
Lamouille S, Xu J, & Derynck R. Molecular mechanisms of epithelial-mesenchymal transition. Nature Reviews: Molecular Cell Biology 2014 15 178–196. (https://doi.org/10.1038/nrm3758)
- 36↑
Liang H, Yu M, Yang R, Zhang L, Zhang L, Zhu D, Luo H, Hong Y, Yu T, Sun J, et al.A PTAL-miR-101-FN1 axis promotes EMT and invasion-metastasis in serous ovarian cancer. Molecular Therapy Oncolytics 2020 16 53–62. (https://doi.org/10.1016/j.omto.2019.12.002)
- 37↑
Feng X, & Xu ES. Alectinib and lorlatinib function by modulating EMT-related proteins and MMPs in NSCLC metastasis. Bosnian Journal of Basic Medical Sciences 2021 21 331–338. (https://doi.org/10.17305/bjbms.2020.5066)
- 38↑
Xing M. Molecular pathogenesis and mechanisms of thyroid cancer. Nature Reviews. Cancer 2013 13 184–199. (https://doi.org/10.1038/nrc3431)
- 39↑
Yu ST, Zhong Q, Chen RH, Han P, Li SB, Zhang H, Yuan L, Xia TL, Zeng MS, & Huang XM. CRLF1 promotes malignant phenotypes of papillary thyroid carcinoma by activating the MAPK/ERK and PI3K/AKT pathways. Cell Death and Disease 2018 9 371. (https://doi.org/10.1038/s41419-018-0352-0)
- 40↑
Zhang L, Liu L, He X, Shen Y, Liu X, Wei J, Yu F, & Tian J. CHIP promotes thyroid cancer proliferation via activation of the MAPK and AKT pathways. Biochemical and Biophysical Research Communications 2016 477 356–362. (https://doi.org/10.1016/j.bbrc.2016.06.101)
- 41↑
Cunha LL, Marcello MA, Nonogaki S, Morari EC, Soares FA, Vassallo J, & Ward LS. CD8+ tumour-infiltrating lymphocytes and COX2 expression may predict relapse in differentiated thyroid cancer. Clinical Endocrinology 2015 83 246–253. (https://doi.org/10.1111/cen.12586)
- 42↑
Qing W, Fang WY, Ye L, Shen LY, Zhang XF, Fei XC, Chen X, Wang WQ, Li XY, Xiao JC, et al.Density of tumor-associated macrophages correlates with lymph node metastasis in papillary thyroid carcinoma. Thyroid 2012 22 905–910. (https://doi.org/10.1089/thy.2011.0452)
- 43↑
Fang W, Ye L, Shen L, Cai J, Huang F, Wei Q, Fei X, Chen X, Guan H, Wang W, et al.Tumor-associated macrophages promote the metastatic potential of thyroid papillary cancer by releasing CXCL8. Carcinogenesis 2014 35 1780–1787. (https://doi.org/10.1093/carcin/bgu060)
- 44↑
Galdiero MR, Varricchi G, Loffredo S, Bellevicine C, Lansione T, Ferrara AL, Iannone R, di Somma S, Borriello F, Clery E, et al.Potential involvement of neutrophils in human thyroid cancer. PLoS One 2018 13 e0199740. (https://doi.org/10.1371/journal.pone.0199740)
- 45↑
Bergdorf K, Ferguson DC, Mehrad M, Ely K, Stricker T, & Weiss VL. Papillary thyroid carcinoma behavior: clues in the tumor microenvironment. Endocrine-Related Cancer 2019 26 601–614. (https://doi.org/10.1530/ERC-19-0074)
- 46↑
Yu H, Huang X, Liu X, Jin H, Zhang G, Zhang Q, & Yu J. Regulatory T cells and plasmacytoid dendritic cells contribute to the immune escape of papillary thyroid cancer coexisting with multinodular non-toxic goiter. Endocrine 2013 44 172–181. (https://doi.org/10.1007/s12020-012-9853-2)
