Abstract
The SUVmax is a measure of FDG uptake and is related with tumor aggressiveness in thyroid cancer, however, its association with molecular pathways is unclear. Here, we investigated the relationship between SUVmax and gene expression profiles in 80 papillary thyroid cancer (PTC) patients. We conducted an analysis of DEGs and enriched pathways in relation to SUVmax and tumor size. SUVmax showed a positive correlation with tumor size and correlated with glucose metabolic process. The genes that indicate thyroid differentiation, such as SLC5A5 and TPO, were negatively correlated with SUVmax. Unsupervised analysis revealed that SUVmax positively correlated with DNA replication(r = 0.29, p = 0.009), pyrimidine metabolism(r = 0.50, p < 0.0001) and purine metabolism (r = 0.42, p = 0.0001). Based on subgroups analysis, we identified that PSG5, TFF3, SOX2, SL5A5, SLC5A7, HOXD10, FER1L6, and IFNA1 genes were found to be significantly associated with tumor aggressiveness. Both high SUVmax PTMC and macro-PTC are enriched in pathways of DNA replication and cell cycle, however, gene sets for purine metabolic pathways are enriched only in high SUVmax macro-PTC but not in high SUVmax PTMC. Our findings demonstrate the molecular characteristics of high SUVmax tumor and metabolism involved in tumor growth in differentiated thyroid cancer.
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Introduction
Thyroid cancer accounts for the largest portion of endocrine cancers, and its incidence has been increasing in the past decades, with 586,000 cases in 2020 worldwide1. In papillary thyroid cancer (PTC), accounting for 80–85% of all thyroid cancers, current disease staging and therapeutic decision-making are primarily based on tumor size and node metastases2. However, not all cases follow the predicted disease course upon initial staging. For example, during 3 to 10 years of active surveillance for papillary thyroid microcarcinoma (PTMC), which is defined as a tumor of 1.0 cm or less in size, 3.5–14.1% of tumors were found to increase in size by > 3 mm, and up to 1.5% of cases progressed to lymph node metastasis3. Although clinical studies suggest that young age, lymph node metastasis, local invasion to the nerve or trachea, and high-grade malignancy on cytology are risk factors for progression during active surveillance3,4, there is a lack of molecular genetic markers to explain why some tumors progress rapidly5.
Although researchers have sought to establish a relationship between BRAFV600E and thyroid cancer diagnosis or prognosis, the relationship remains inconclusive6,7,8. More recently, the TERT promoter mutation has been found to coexist with the BRAFV600E mutation, which is recognized as an indicator of clinically aggressive tumors9,10,11; however, the prevalence of patients with both mutations is low, and there seems to be no correlation with prognosis, especially in PTMC12,13. Large-scale genomic characterization of PTC uncovered the critical role of genetic alterations that activate the mitogen-activated protein kinase (MAPK), including BRAF and RAS; as well as, gene fusions of protein kinase genes such as RET, NTRK1, and NTRK3 as well as mutations in phosphoinositide 3-kinase AKT (PI3K-AKT) pathways including PTEN, PI3KCA, and AKT1 were identified as the primary molecular aberrations in PTC14,15. However, somatic mutation profiling is still insufficient for risk stratification of patients with PTC.
18F-fluorodeoxyglucose (FDG) PET/CT is increasingly performed for the staging or localization of metastatic disease in patients with various kinds of malignancies16,17. In thyroid cancer, iodine-131-WBS has been useful for determining the differentiation of a tumor on the basis of its avidity to iodine, identifying remnant thyroid tissue, and assessing patients for distant metastatic disease18; most well-differentiated thyroid carcinomas are relatively slow growing and can be FDG-negative19. Recently, however, a “flip-flop phenomenon” has been observed, in that radioiodine uptake in differentiated thyroid carcinoma (DTC) cells decreases when they dedifferentiate while their glucose metabolism generally increases, and 18F-FDG PET/CT has emerged as a powerful tool for predicting recurrence in DTC patients20. Moreover, preoperative SUVmax was found to be related to postoperative recurrence-free survival (RFS)21. It is commonly accepted that a high 18F-FDG uptake reflects the dedifferentiation of thyroid tumors; however, there is little data on the role of 18F-FDG PET/CT in thyroid cancer, and the underlying molecular glucose metabolism mechanisms are not entirely understood.
This study is the first to report findings on 18F-FDG PET/CT in thyroid cancer in conjunction with a gene expression analysis in an attempt to examine the molecular characteristics related to metabolism in thyroid cancer. We investigated the relationship between SUVmax and clinical features of PTC; we also analyzed differentially expressed genes (DEGs) and activated pathways related to SUVmax. The significance of genes and pathways were validated using The Cancer Genome Atlas (TCGA) database.
Results
Enriched DNA replication, pyrimidine and one-carbon metabolism, and cell cycle signaling with reduced thyroid differentiation scores (TDS) in PTCs with high SUVmax
All 80 patients underwent preoperative 18F-FDG-PET/CT (Fig. 1A). As previously reported22,23, SUVmax correlated with tumor size (r = 0.54, p < 0.0001) (Fig. 1B). Since the predictive preoperative SUVmax value for tumor recurrence derived from the ROC curves was 10.15, we divided the subjects into PTCSUV-high (SUVmax > 10) and PTCSUV-low SUVmax ≤ 10) group (Supplementary Fig. 1) and PTCSUV-high revealed the worse prognosis in recurrence free survival analysis (Fig. 1C).
SUVmax were not significantly correlated with GLUTs or glycolysis, however, PTCs with high SUVmax are enriched low TDS score than those with low SUVmax. (A) The design of our studies to verify the metabolic features by SUVmax in PTC (N = 80). (B) The correlation plot between tumor size and SUVmax using Pearson correlation method (r = 0.54, p < 0.0001). (C) The recurrence-free survival probability of low SUVmax (n = 50, blue) and high SUVmax group (n = 30, red). The p = 0.013 is from log-rank test. (D) Correlation plots of SUVmax with GLUTs family and TDS. (E–H) Correlation plots of SUVmax with tumor size, TDS, GSVA score of GLUTs family, and GSVA score Glycolysis. (I) Heatmap of TDS genes using log10TPM in our cohort. Tumors were sorted according to SUVmax. (J,K) Comparison of GSVA for glycolysis or tumor differentiation score (TDS) between PTCSUV-low and PTCSUV-high tumors. (L) Expression of genes encoding SLC2A1, SLC2A2, SLC2A3, SLC2A4, SLC5A5, and TPO between PTCSUV-low and PTCSUV-high tumors. (M) Expression of TDS genes, DIO1, DUOX2, SLC26A4, SLC5A5 and TPO, between PTCSUV-low and PTCSUV-high tumors, and scatter plots of correlation between SUVmax and either SLC5A5 or TPO. In the scatter plots, blue line is drawn using simple linear regression and the gray colored area indicate 95% confidence band.
Since glucose metabolism in thyroid cancer cells is reprogrammed to enhance glucose uptake, glycolysis, and lactate synthesis24 and glucose uptake via glucose transporters (GLUTs) is the first step in producing energy and nucleic acids for cancer survival, we investigated the expressions of GLUTs in relation to SUVmax. Previously, thyroid cancer cells show overexpression of hypoxia-responsive GLUT1 and GLUT3 proteins compared to normal cells25, however, it is not fully understood the relation of GLUTs and PET-CT SUVmax in PTC. We found that GLUT3, GLUT5, and GLUT8-10 were negatively correlated with SUVmax and othter GLUTs were not significantly associated with SUVmax (Fig. 1D). Since many studies have focused on the impact of TDS, and tumor differentiation rate has been shown to correlate with GLUT expression26,27, we also analyzed TDS, GLUTs, and glucolysis according to SUVmax and tumor upon the scores calculated using GSVA (Fig. 1D,E). Gene score of GLUTs family was not significantly associated with SUVmax (r = − 0.20, p = 0.078) (Fig. 1F), however, gene score of glucose metabolic process was significantly associated with SUVmax (r = 0.24, p = 0.036) (Fig. 1G). TDS was not significantly correlated with SUVmax (r = − 0.17, p = 0.138) (Fig. 1H), but The TDS expression heatmap derived from SUVmax showed a tendency for some of TDS gene expression were decreased in the PTCSUV-high group (Fig. 1I). Although the gene score of glucose metabolic process was not significantly changed between PTCSUV-high group and PTCSUV-low group (Fig. 1J), PTCSUV-high group revealed significantly decreased TDS score compared to PTCSUV-low group (Fig. 1K). Although the levels of GLUTs genes, such as SLC2A1, SLC2A2, SLC2A3, and SLC2A4 were not changed, several TDS genes, SLC5A5, TPO, DIO2, and TG, were significantly lower in the PTCSUV-high than in the PTCSUV-low group (Fig. 1L and Supplementary Table 1). Moreover, SUVmax was negatively correlated with several TDS genes, DIO1 (r = − 0.33, p = 0.003), SLC5A5 (r = − 0.33, p = 0.003), and TPO (r = − 0.30, p = 0.007) expression (Fig. 1M).
Next, we performed unsupervised analysis based on GSEA to identify enriched pathways in each subtype. The PTCSUV-high group exhibited enriched DNA replication, ribosome assembly, pyrimidine metabolism, one-carbon pool by folate, purine metabolism, tight junction, adherens junction, and cell cycle processes in the KEGG database (Fig. 2A and Supplementary Table 2). Ribosome biogenesis, DNA replication initiation, DNA-dependent DNA replication, and DNA replication from Gene Ontology Biological Process (GOBP) gene sets were also enriched in the PTCSUV-high group (Supplementary Fig. 2 and Supplementary Table 3). We calculated the scores using GSVA related to various signaling pathways for each tumor sample based on the meaningful GSEA results. We confirmed a positive correlation between SUVmax and DNA replication (r = 0.29, p = 0.009), pyrimidine metabolism signaling (r = 0.50, p < 0.0001), cell cycle (r = 0.24, p = 0.029), and purine metabolism (r = 0.42, p = 0.0001) (Fig. 2B–E). In addition, the expression of genes related to DNA replication, one-carbon metabolism, and cell cycle was higher in the PTCSUV-high group (Fig. 2F). Collectively, our data revealed that SUVmax of thyroid tumor was not correlated with glycolysis, but was significantly related with several molecular pathways, including DNA replication, cell cycle, pyrimidine metabolism and purine metabolism.
PTCs with high SUVmax are enriched with several pathways, such as DNA replication, cell cycle, pyrimidine metabolism, and one carbon pool by folate. (A) Comparison of GSVA based on unsupervised analysis using KEGG analysis between PTCSUV-low and PTCSUV-high tumors. (B–E) Scatter plots of correlation between SUVmax and GSVA score of DNA replication, pyrimidine metabolism, cell cycle, or purine metabolism. In the scatter plots, blue line is drawn using simple linear regression and the gray colored area indicate 95% confidence band. (F) Heatmap of genes related upregulated pathway using log10TPM in our cohort. Log2 fold change and p value were calculated by the comparison of gene expressions between PTCSUV-low and PTCSUV-high tumors.
Importance of SUVmax in predicting tumor aggressiveness after adjustment of tumor size
Since our data revealed the positive correlation of tumor size and SUVmax, we compared to various clinic-pathologic features in relation to tumor size or SUVmax to investigate the pivotal gene signature in relation to SUVmax, independently on tumor size (Table 1). In comparison of PTCSUV-high and PTCSUV-low, PTCSUV-high has more extracapsular invasion (ECI, p = 0.003), gross extrathyroidal extension (gross ETE, p = 0.013), and recurrence (p = 0.026) than PTCSUV-low. Although the PTCSUV-high group had more invasive features, the distribution of subtypes and pathological stage were comparable to those in the PTCSUV-low group (subtype, p = 0.331; pathological stage, p = 0.776). Next, we compared the various clinic-pathologic findings after tumor-size adjustments using ANCOVA or Mantel–Haenszel Chi-square test (Table 1). After adjustment for tumor size, PTCSUV-high has also more extracapsular invasion (ECI, p = 0.013), gross extrathyroidal extension (gross ETE, p = 0.028), and recurrence (p = 0.047) than PTCSUV-low group. These data suggested the need for establishment of the molecular features of SUVmax high tumors and the importance of SUVmax values for predicting cancer progression independent of tumor size. In addition, a comparison of the factors between PTMC and macro-PTC after adjustment to SUVmax revealed that lateral lymph node metastasis (L-LNM, p = 0.019) rate was significantly higher in macro-PTC than PTMC (Supplementary Table 4). Collectively, our clinical data suggested that large tumor was associated with lymph node metastasis after adjustment to SUVmax, and high SUVmax tumor was associated with invasion or extrathyroidal extension after adjustment to tumor size.
To identify the major contributory molecular characteristics according to tumor size and SUVmax in PTC, we divided tumors into PTMC and macro-PTC by 1 cm of the tumor size and further subgrouped using 10 of SUVmax: PTMCSUV-low, PTMCSUV-high, macro-PTCSUV-low, and macro-PTCSUV-high (Supplementary Fig. 1B and Table 2). Consistently with the findings of Table 1, the PTMCSUV-high group exhibited more ECI and gross ETE than the PTMCSUV-low group (Table 2). Moreover, the PTMCSUV-high group exhibited even more gross ETE than the macro-PTCSUV-low group and the ECI and gross ETE rates did not significantly differ between the two SUV-high groups (PTMCSUV-high and macro-PTCSUV-high), suggesting the importance of SUVmax on gross ETE, independently on tumor size. In contrast, significantly less lateral LNM (L-LNM) was observed in the PTMCSUV-high group than that in the macro-PTCSUV-high group (Table 2). Taken together, SUVmax and tumor size were independently correlated with different clinical factors, such as ETE or lymph node metastasis.
DEG and GSEA analyses reveal enriched metabolic pathways in PTMCSUV-high and macro-PTCSUV-high and new genes associated with PTC recurrence
We conducted an analysis of DEGs and enriched pathways between subgroups: (1) PTMCSUV-low vs. PTMCSUV-high, (2) macro-PTCSUV-low vs. macro-PTCSUV-high, and (3) all SUVmax-low vs. SUVmax-high (Supplementary Tables 5–7, 8–10, and 1–3, respectively). The relationship between up- and down- regulated DEGs in each comparison is shown in Supplementary Fig. 3A,B. To identify DEGs related to high SUVmax independent of tumor size, we intersected all three comparisons and found 28 common DEGs (6 upregulated and 22 downregulated) (Supplementary Fig. 3A,B, Supplementary Table 11). Among these, one upregulated DEG (PSG5) and four downregulated DEGs (TFF3, SOX2, SLC5A5, SLC5A7) showed a significant difference in RFS in the GEPIA 2 database (Table 3, Fig. 3A–E). In addition, DEGs from comparison (3) that did not belong to those from comparison (1) or (2) were defined as DEGs related to SUVmax but not tumor size. We identified 16 upregulated DEGs and 60 downregulated DEGs (Supplementary Fig. 3A,B). Among these DEGs, one upregulated (HOXD10) and two downregulated (IFNA1 and FER1L6) DEGs exhibited a significant difference in RFS in the GEPIA 2 database (Table 3, Fig. 3F–H). Based on these results, we identified the up- and down- regulated DEGs related to SUVmax or tumor size and further selected some genes associated with RFS.
Survival plots of genes contributing RFS difference by SUVmax and tumor size. (A–E) Kaplan–Meier plots showing RFS difference from TCGA dataset by expression level of PSG5 (A), TFF3 (B), SOX2 (C), SLC5A5 (D), and SLC5A7 (E) that contribute RFS difference by SUVmax. The high expression of PSG5 (A) and low expression of TFF3 (B), SOX2 (C), SLC5A5 (D), and SLC5A7 (E) have shorter RFS. (F–H) Kaplan–Meier plots showing RFS difference from TCGA dataset by expression level of HOXD10 (F), FER1L6 (G), IFNA1 (H) which were found to contribute to RFS difference by tumor size. The high expression of HOXD10 (F) and low expression of FER1L6 (G) and IFNA1 (H) have shorter RFS.
Our gene ontology analyses identified several gene sets that were shared across the comparisons. The distribution of upregulated KEGG and GOBP genes is shown in the Venn diagrams in Supplementary Fig. 3C,D. In KEGG analyses, genes pivotal for tumor survival and progression were enriched in PTCSUV-high compared to PTCSUV-low (Table 4). Our subgroup analyses revealed enriched DNA replication, cell cycle processes, and ribosome assembly in the SUVmax-high groups compared to the SUVmax-low groups. Interestingly, the gene sets associated with glucose metabolism, such as glycolysis, the citrate cycle (TCA cycle), and the glycolysis offshoot pathway including purine metabolism and the pentose phosphate pathway were enriched in high SUVmax tumors of macro-PTCs but not in those of PTMCs (Table 4). GOBP analyses also revealed that gene sets for DNA replication, cell cycle processes, and ribosome assembly were enriched in the high SUVmax subgroups of both PTMC and macro-PTC. In contrast, gene sets for purine metabolism, glucose import, metabolic processes, and ribose phosphate metabolism were enriched only in high SUVmax tumors of macro-PTCs but not in those of PTMCs (Table 5). These results suggest that purine metabolism, the ribose phosphate pathway, and glucose import may be related to tumor growth rather than SUVmax.
Discussion
In this study, we analyzed the clinicopathological characteristics of PTCSUV-low and PTCSUV-high patient groups and investigated their metabolic features using transcriptomic analysis. We found that SUVmax was positively correlated with tumor size, and the PTCSUV-high group exhibited higher ECI and gross ETE rates than the PTCSUV-low group after adjustments of tumor size. Transcriptomic analysis revealed lower expression of TDS genes in the PTCSUV-high compared to the PTCSUV-low group, and SUVmax was significantly associated with various gene signatures, including DNA replication, pyrimidine metabolism, purine metabolism, and Cell cycle. To determine the molecular characteristics that are independent of tumor size, a DEG analysis of the four tumor size and SUVmax subgroups identified five shared DEGs (upregulated PSG5, and downregulated TFF3, SOX2, SLC5A5, and SLC5A7) that were related to SUVmax and RFS, and three DEGs (upregulated HOXD10 and downregulated IFNA1 and FER1L6) related to tumor size and RFS that were unrelated to SUVmax.
PET/CT plays an important role in the diagnosis, staging, and treatment response assessment of various solid cancers. In thyroid cancer, PET/CT scans are not routinely performed; instead, they are recommended in patients with an aggressive subtype and poorly differentiated thyroid cancer at initial staging and follow-up. Clinical practice is based on the inverse relationship between RAI-avidity and FDG-avidity28, however there was no study focused on the dissection of PET-CT imaging and transcriptomics in thyroid cancer. Tumors with high SUVmax are generally considered to have high glycolytic activity and the Warburg effect explains that aggressive tumors gain energy from aerobic glycolysis rather than from the TCA cycle, producing lactate than pyruvate29. In contrast to the previous study reporting a positive correlation between GLUT3 and GLUT4 protein expression and SUVmax in PTC23, our transcriptomics shows a negative or neutral relationship between GLUT family gene expression and SUVmax, although glycolysis tended to show a positive correlation with SUVmax, suggesting GLUTs gene expression is not directly aligned with glycolysis.
The clinicopathological impact of high SUVmax tumors has been extensively investigated in various tumors30,31. In thyroid cancer, a previous retrospective study of a relatively small number of patients (N = 88) failed to show a difference in SUVmax between the recurrent and non-recurrent group32. However, another retrospective study with an 8-year follow-up period with a large patient size (N = 400) showed a significant survival decrease in patients with high-SUVmax determined on the initial PET/CT scan21. In agreement with a previous study, the PTCSUV-high group in our cohort exhibited lower expression levels of TDS genes, and RFS was shorter in this group than in the PTCSUV-low group. When we focused on PTMC, although ECI and gross ETE rates were higher in patients with PTMCSUV-high than in those with PTMCSUV-low, we could not find a difference in recurrence, primarily due to the small number of patients in these subgroups. Therefore, further studies with larger numbers of patients are required to validate the efficacy of PET/CT scans in predicting recurrence in PTMC.
Tumor progression is accompanied by both the physical growth of the tumor and concomitant metabolic changes, making it challenging to identify the genes and pathways responsible for both these changes separately. Our study revealed five SUVmax-related DEGs (PSG5, TFF3, SOX2, SLC5A5, and SLC5A7) contributing to RFS differences. The PSG5 gene, upregulated in PTCSUV-high regardless of tumor size, was previously reported as a prognostic marker for laryngeal cancer and is known to interact with prognostic lncRNAs in gastric cancer33,34. However, its prognostic role in thyroid cancer has not been determined. Another SUVmax-related gene, TFF3, which is downregulated in PTCSUV-high tumors, plays a role in angiogenesis and tumorigenesis in breast, stomach, and colon cancers. In thyroid cancer, low expression of TFF3 can increase cell proliferation, migration, and invasion via activation of the IL-6/JAK/STAT3 signaling pathway35. These inflammatory pathways lead to high immune cell infiltration around thyroid cancer cells and could serve as a source of increased SUVmax36. High expression of the stemness marker SOX2 is associated with poor prognosis in several solid tumors and is a regulator of GLUT1 expression37,38. In our cohort, the PTCSUV-high group also exhibited low SOX2 expression and relatively low levels of GLUT137,38. SLC5A5 is a well-known marker of thyroid differentiation, and PTC with low expression of SLC5A5 is iodine non-avid and has a poor prognosis39. SLC5A7, which encodes a choline transporter, is downregulated in various solid cancers, and its expression is markedly suppressed in PTCSUV-high40. In colorectal cancer, promoter methylation and the resultant low expression of SLC5A7 are poor prognostic factors as our results.
We also identified three DEGs (HOXD10, IFNA1, and FER1L6) related to tumor size. The expression of HOXD10 is low in PTC, and the HOXD10 gene is hypermethylated in BRAFV600E mutants41. However, PTCSUV-high in our study exhibited high expression levels of HOXD10, which might be related to large tumor size. HOXD10 overexpression has been reported to induce cancer cell proliferation, while low expression induced invasion and metastases in head and neck cancer cell lines, supporting the proliferative role of HOXD10 in cancer42. A previous study showed low expression of FER1L6 in PTC; however, its prognostic significance and mechanism are not fully understood43. IFNA1 has an antitumor effect that inhibits proliferation; thus, low expression of IFNA1 could lead to cancer cell proliferation44.
Several studies have been conducted to understand the metabolic features of high SUVmax tumors using transcriptomic analyses. In breast cancer, the SUV-high-cluster was associated with frequent TP53 mutations and enhanced the expression of downstream glycolysis genes through FOXM1-LDHA45. In multiple myeloma, a negative 18F-FDG PET/CT scan was associated with low expression of hexokinase-2, whereas a positive scan is accompanied by high expression of proliferation genes or GLUT546. In intrahepatic cholangiocarcinoma, cell cycle processes, cell division, and mitosis gene sets were enriched in high SUVmax tumors47. Similarly, in this study, the PTCSUV-high group exhibited enriched gene sets for DNA replication, cell cycle processes, and ribosome assembly, regardless of tumor size, in both KEGG and GOBP analyses. Notably, some gene sets showed differences in SUVmax in macro-PTC but not in PTMC: these were the gene sets for glucose import, glycolysis, citrate cycle TCA cycle, purine metabolism, one-carbon pool by folate, and the pentose phosphate pathway. For tumor growth or proliferation, new macromolecules, such as nucleic acids, lipids, and proteins, are essential, and macro-PTC reprograms and exploits cellular pathways to obtain the materials necessary for proliferation. These cellular pathways could be targets for anticancer therapy, and further studies are needed to assess the precise manipulation of key steps.
In summary, we investigated the clinicopathological and transcriptomic features of PTC based on the SUVmax and tumor size. In PTMC and PTC, tumors with high SUVmax exhibited more capsular invasion and gross ETE than low SUVmax tumors. DEG analyses revealed the genes contributing to RFS and related to SUVmax (PSG5, TFF3, SOX2, SLC5A5, and SLC5A7) and tumor size (HOXD10, IFNA1, and FER1L6). GSEA revealed that gene sets for DNA replication, cell cycle processes, and ribosome assembly were enriched in high SUVmax tumors regardless of tumor size, whereas gene sets for glucose import, glucose metabolic process, purine metabolism, and the pentose phosphate pathway were related to large tumor size.
Our research provides insight into metabolic reprogramming of PTC related to SUVmax, as well as markers to account for SUVmax and tumor size-related RFS. Going beyond the current method of evaluating tumors only by size, using suggested gene biomarkers as well as SUVmax to classify tumors into more diverse subgroups will help predict patient prognosis and pave the way for tailor-made treatment protocols in the future.
Materials and methods
Study population
We retrospectively reviewed 80 patients postoperatively diagnosed with PTC who underwent preoperative 18F-FDG-PET/CT and provided informed consent for collection of fresh frozen thyroid tissue from January 2003 to December 2010. Prior to the 2015 ATA guidelines, total thyroidectomy was performed in patients with the tumor size of 1 cm or more, bilateral multifocality, aggressive variant type, ETE in preoperative radiology (except ETE to only the strap muscle), or N1b lymph node metastasis. 70 patients were received total thyroidectomy and 10 patients were received lobectomy. Among the 10 patients, none underwent recurrence or completion thyroidectomy. All patients who underwent total thyroidectomy were received radioactive iodine (RAI) treatment according to 2009 ATA guideline. All patients were denied the history of diabetes related with glucose signaling. Data were retrospectively collected, including demographic information, laboratory findings, SUVmax, and pathology data. This study was approved by the Institutional Research and Ethics Committee at Chungnam National University Hospital (CNUH-2022-11-004-001) and conducted in accordance with the Declaration of Helsinki. Informed consent was obtained from all individual participants involved in the study. All personal identifiers were removed or disguised to ensure participant anonymity, in line with HIPAA guidelines.
Postoperative follow-up and recurrence
Patients were followed up for 8.8 ± 0.5 years (mean ± SEM). After primary treatment, all patients underwent TSH suppression therapy with thyroid hormone supplementation according to the American Thyroid Association guidelines2. Patients were assessed every 3 months in the first year after surgery, every 6 months for the next 2 years, and annually thereafter. Thyroid ultrasound imaging and thyroid function tests (including thyroglobulin and antithyroglobulin antibodies), were routinely performed at each follow-up consultation. Indeterminate or suspicious thyroid nodules or lymph nodes (LNs) were evaluated by fine-needle aspiration. All six structural recurrence cases were confirmed by cytological analysis, two from the operation bed and four from the lateral LNs. One patient died due to airway obstruction due to the tumor extending significantly into the mediastinum, while the remaining five patients were cured and maintained stable disease after treatment with I-131 100–150 mCi RAI or neck dissection.
RNA extraction for sequencing
To analyze the transcriptome and identify DEGs, RNA was extracted from tumor and paired non-tumor tissue samples. Thyroid samples were isolated from specimens frozen at − 80 °C immediately after thyroidectomy and homogenized using a mortar and pestle; total RNA was extracted using an RNA extraction kit (QIAGEN, Germantown, MD, USA) following the manufacturer’s protocol. All experiments were conducted under clean conditions and equipment was pre-autoclaved. The quality of the extracted RNA was evaluated using the Agilent 2100 Bioanalyzer RNA Nano Chip (Agilent, Santa Clara, CA, USA). The extracted RNA was used to construct RNA libraries using the TruSeq access library or the stranded mRNA LT Sample Preparation Kit (Illumina, San Diego, CA, USA), according to the manufacturer’s protocols. Library quality was analyzed using an Agilent 2100 Bioanalyzer and an Agilent DNA 1000 kit (Agilent, Santa Clara, CA, USA). Samples were sequenced on the Illumina HiSeq 2500 platform (Illumina, San Diego, CA, USA), yielding an average of 38 million paired-end 100 bp reads.
Bioinformatic transcriptome analysis
To analyze the relationship between the thyroid differentiation score (TDS) and SUVmax, we used the “ComplexHeatmap” and “corrplot” R packages with log10 transcript per million (TPM). We calculated the TDS by combining the gene set variation analysis (GSVA) package with the TDS gene list15. To confirm survival probability, we used the “survival” and “survminer” R packages. Additionally, we identified DEGs by subdividing our cohort into two groups based on tumor size and SUVmax. Differential expression analysis was carried out in R using “DESeq2” and enrichment analysis was performed using the “fgsea” R package. Gene sets used for Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway and Gene Ontology enrichment analyses were downloaded from the Gene Set Enrichment Analysis (GSEA) website (https://www.gsea-msigdb.org). DEGs and KEGG pathways with corrected p values < 0.05 were considered statistically significant. We assessed RFS by specific gene expression level using open database from GEPIA 2 which is based on TCGA database.
Statistical analysis
Group data for continuous variables are presented as mean ± standard deviation and, in some cases, as mean ± standard error of the mean, as noted in the footnotes. Categorical variables were presented as numbers and percentages. To compare the means of continuous variables, we used the unpaired Student’s t-tests or Mann–Whitney U test. Chi-square tests or Fisher’s exact test were used to compare the distributions of categorical variables, and Pearson’s correlation analysis was used to evaluate the associations between tumor size and SUVmax. Kaplan–Meier (K–M) survival curves were created to evaluate differences in RFS between the groups, and receiver operating characteristic (ROC) curves were used to determine the cutoff SUVmax value for predicting recurrence. For tumor size- or SUVmax-adjusted statistical analyses, we used ANCOVA, Mantel–Haenszel Chi-square test, and Cox regression analysis. Statistical significance for all analyses was established with a two-tailed p value < 0.05. Statistical analyses/graphs were performed/created using SPSS Version 26.0. (IBM corp., Armonk, NY, USA), R, GraphPad Prism 9.4.1. (GraphPad Software Inc., San Diego, CA, USA), and OriginPro 2021 (OriginLab Corp., Northampton, MA, USA).
Data availability
The accession number for the bulk RNA sequencing dataset is GSE213647 (secure token: avwpkueoznwdzux).
References
Kitahara, C. M. & Sosa, J. A. The changing incidence of thyroid cancer. Nat. Rev. Endocrinol. 12, 646–653 (2016).
Haugen, B. R. et al. 2015 American Thyroid Association management guidelines for adult patients with thyroid nodules and differentiated thyroid cancer: The American Thyroid Association guidelines task force on thyroid nodules and differentiated thyroid cancer. Thyroid 26, 1–133 (2016).
Lee, D. Y. & Jang, P. Risk Factors for tumor size increase during active surveillance of papillary thyroid cancer: Meta-analysis and systematic review. Korean J. Otorhinolaryngol. Head Neck Surg. 64, 914–921 (2021).
Oh, H.-S. et al. Active surveillance of low-risk papillary thyroid microcarcinoma: A multi-center cohort study in Korea. Thyroid 28, 1587–1594 (2018).
Jeon, M. J. et al. Features predictive of distant metastasis in papillary thyroid microcarcinomas. Thyroid 26, 161–168 (2016).
Chen, B., Shi, Y., Xu, Y. & Zhang, J. The predictive value of coexisting BRAFV600E and TERT promoter mutations on poor outcomes and high tumour aggressiveness in papillary thyroid carcinoma: A systematic review and meta-analysis. Clin. Endocrinol. 94, 731–742 (2021).
Xing, M. et al. BRAF mutation predicts a poorer clinical prognosis for papillary thyroid cancer. J. Clin. Endocrinol. Metab. 90, 6373–6379 (2005).
George, J. R. et al. Association of TERT promoter mutation, but not BRAF mutation, with increased mortality in PTC. J. Clin. Endocrinol. Metab. 100, E1550–E1559 (2015).
Xing, M. et al. BRAF V600E and TERT promoter mutations cooperatively identify the most aggressive papillary thyroid cancer with highest recurrence. J. Clin. Oncol. 32, 2718 (2014).
Melo, M. et al. TERT promoter mutations are a major indicator of poor outcome in differentiated thyroid carcinomas. J. Clin. Endocrinol. Metab. 99, E754–E765 (2014).
Liu, R. & Xing, M. TERT promoter mutations in thyroid cancer. Endocr. Relat. Cancer 23, R143 (2016).
Kuchareczko, A. et al. A Significance of concomitant BRAFV600E and TERT mutations in Polish patients with papillary thyroid microcarcinoma: A retrospective cohort study based on 430 cases. Thyroid 32, 1372–1381 (2022).
Song, Y. S. et al. Genomic and transcriptomic characteristics according to size of papillary thyroid microcarcinoma. Cancers 12, 1345 (2020).
Liang, J. et al. Genetic landscape of papillary thyroid carcinoma in the Chinese population. J. Pathol. 244, 215–226 (2018).
Agrawal, N. et al. Integrated genomic characterization of papillary thyroid carcinoma. Cell 159, 676–690 (2014).
Nanni, C. et al. Role of 18F-FDG-PET and PET/CT imaging in thyroid cancer. Biomed. Pharmacother. 60, 409–413 (2006).
Bar-Shalom, R. et al. Clinical performance of PET/CT in evaluation of cancer: Additional value for diagnostic imaging and patient management. J. Nucl. Med. 44, 1200–1209 (2003).
Treglia, G., Bertagna, F., Piccardo, A. & Giovanella, L. 131I whole-body scan or 18FDG PET/CT for patients with elevated thyroglobulin and negative ultrasound?. Clin. Transl. Imaging 1, 175–183 (2013).
Saif, M. W., Tzannou, I., Makrilia, N. & Syrigos, K. Role and cost effectiveness of PET/CT in management of patients with cancer. Yale J. Biol. Med. 83, 53 (2010).
Marcus, C., Whitworth, P. W., Surasi, D. S., Pai, S. I. & Subramaniam, R. M. PET/CT in the management of thyroid cancers. Am. J. Roentgenol. 202, 1316–1329 (2014).
Robbins, R. J. et al. Real-time prognosis for metastatic thyroid carcinoma based on 2-[18F] fluoro-2-deoxy-d-glucose-positron emission tomography scanning. J. Clin. Endocrinol. Metab. 91, 498–505 (2006).
Kim, B. S., Kim, S.-J., Kim, I. J., Pak, K. & Kim, K. Factors associated with positive F-18 flurodeoxyglucose positron emission tomography before thyroidectomy in patients with papillary thyroid carcinoma. Thyroid 22, 725–729 (2012).
Kaida, H. et al. Relationship between clinicopathological factors and fluorine-18-fluorodeoxyglucose uptake in patients with papillary thyroid cancer. Nucl. Med. Commun. 32, 690–698 (2011).
Bao, L. et al. Metabolic reprogramming of thyroid cancer cells and crosstalk in their microenvironment. Front. Oncol. 11, 773028 (2021).
Heydarzadeh, S., Moshtaghie, A. A., Daneshpoor, M. & Hedayati, M. Regulators of glucose uptake in thyroid cancer cell lines. Cell Commun. Signal. 18, 1–16 (2020).
Bongiovanni, M., Paone, G., Ceriani, L. & Pusztaszeri, M. Cellular and molecular basis for thyroid cancer imaging in nuclear medicine. Clin. Transl. Imaging 1, 149–161 (2013).
Hooft, L. et al. [18F] fluorodeoxyglucose uptake in recurrent thyroid cancer is related to hexokinase I expression in the primary tumor. J. Clin. Endocrinol. Metab. 90, 328–334 (2005).
Wang, W. et al. Resistance of [18F]-fluorodeoxyglucose-avid metastatic thyroid cancer lesions to treatment with high-dose radioactive iodine. Thyroid 11, 1169–1175 (2001).
Warburg, O., Wind, F. & Negelein, E. The metabolism of tumors in the body. J. Gen. Physiol. 8, 519 (1927).
Li, D. et al. The correlation between 18F-FDG PET/CT imaging SUVmax of preoperative colon cancer primary lesions and clinicopathological factors. J. Oncol. 2021, 4312296 (2021).
Al-Sarraf, N. et al. Clinical implication and prognostic significance of standardised uptake value of primary non-small cell lung cancer on positron emission tomography: Analysis of 176 cases. Eur. J. Cardio-thorac. Surg. 34, 892–897 (2008).
Gim, H., Lee, D. K., Park, H. S. & Jeong, Y. J. Diagnostic value of SUV in 18F-FDG PET/CT for papillary thyroid cancer. Int. J. Thyroidol. 13, 37–42 (2020).
Su, X. et al. Identification of the prognosis-related lncRNAs and genes in gastric cancer. Front. Genet. 11, 27 (2020).
Yang, D. et al. The prognostic value of tumor mutational burden related 6-gene-based Risk Score in laryngeal cancer patients. BMC Oral Health 22, 1–14 (2022).
Xin, Y. et al. Trefoil factor 3 inhibits thyroid cancer cell progression related to IL-6/JAK/STAT3 signaling pathway. Evid. Based Complement. Alternat. Med. 2021, 2130229 (2021).
Yang, L. et al. Low expression of TFF3 in papillary thyroid carcinoma may correlate with poor prognosis but high immune cell infiltration. Future Oncol. 18, 333–348 (2021).
Grimm, D. et al. Seminars in Cancer Biology 122–153 (Elsevier, 2020).
Hsieh, M.-H. et al. p63 and SOX2 dictate glucose reliance and metabolic vulnerabilities in squamous cell carcinomas. Cell Rep. 28, 1860-1878 e1869 (2019).
Portulano, C., Paroder-Belenitsky, M. & Carrasco, N. The Na+/I− symporter (NIS): Mechanism and medical impact. Endocr. Rev. 35, 106–149 (2014).
Li, M., Sun, Q. & Wang, X. Transcriptional landscape of human cancers. Oncotarget 8, 34534 (2017).
Cao, Y.-M. et al. Aberrant hypermethylation of the HOXD10 gene in papillary thyroid cancer with BRAFV600E mutation. Oncol. Rep. 39, 338–348 (2018).
Hakami, F. et al. The roles of HOXD10 in the development and progression of head and neck squamous cell carcinoma (HNSCC). Br. J. Cancer 111, 807–816 (2014).
Qiu, J. et al. Identification of key genes and miRNAs markers of papillary thyroid cancer. Biol. Res. 51, 45 (2018).
Zhang, K.-J. et al. A potent in vivo antitumor efficacy of novel recombinant type I interferon antitumor effects of recombinant interferon sIFN-I. Clin. Cancer Res. 23, 2038–2049 (2017).
Kim, S.-K. et al. Genomic signature of the standardized uptake value in 18F-Fluorodeoxyglucose positron emission tomography in breast cancer. Cancers 12, 497 (2020).
Alberge, J.-B. et al. Molecular signature of FDG-PET biomarkers in newly diagnosed multiple myeloma patients: A genome-wide transcriptome analysis from the CASSIOPET study. J. Nucl. Med. 63, 1008–1013 (2022).
Ahn, K. S. et al. Genetic features associated with 18F-FDG uptake in intrahepatic cholangiocarcinoma. Ann. Surg. Treat. Res. 96, 153–161 (2019).
Acknowledgements
This research was financially supported by the Ministry of Trade, Industry, and Energy (MOTIE), Korea, under the “System industry Infrastructure and R&D Support Program” (grant number P0009796) supervised by the Korea Institute for Advancement of Technology (KIAT) and National Research Foundation of Korea (NRF) (grant number 2021R1C1C1011183 to YEK; 2022R1I1A1A01071876 to SEL). This work was also supported by the Korea Health Technology R&D Project through the Korea Health Industry Development Institute (KHIDI), funded by the Ministry of Health and Welfare (grant number HR20C0025). This work (research) was supported by Chungnam National University Hospital Research Fund, 2021.
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Conception and design of the study: YE Kang, S Park, BS Koo. Data acquisition: S-H Ju, S Park, SE Lee. Data analysis: S-H Ju, KH Kim, SM Kim. Interpretation of data: SE Lee, S Yi, NR Choi. Software and Visualization: S-K Kim, S-Y Kim, JY Heo. Writing—original draft: S-H Ju. SE Lee. Writing—review and editing: S Park, BS Koo, YE Kang.
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Ju, SH., Lee, S.E., Yi, S. et al. Transcriptomic characteristics according to tumor size and SUVmax in papillary thyroid cancer patients. Sci Rep 14, 11005 (2024). https://doi.org/10.1038/s41598-024-61839-0
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DOI: https://doi.org/10.1038/s41598-024-61839-0
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