Study of metastatic mechanisms revealed by plasma metabolomics analysis in patients with thyroid cancer

【Abstract】Objective: To analyze the mechanism of thyroid metastasis based on plasma metabolomics, to explore the molecular pathways with regulatory effects based on bioinformatics, and to search for functional targeting genes. Methods: A total of 61 patients with thyroid cancer admitted to the hospital from August 2015 to March 2023 were selected as the study subjects, and they were divided into two groups based on whether there was lymph node metastasis after surgeryThe small molecule metabolites were analyzed by HPLC-Q-TOF-MS/MS, and then the metabolomics raw data were preprocessed based on Metaboscape 3.0 software, including normalization, noise removal and peak extraction, and MetaboAnalysis4.0 software was used to find the metabolic pathways related to thyroid metastasis. Results: Among the 61 patients in this group, 32 (52.46%) had metastasis and 29 (47.54%) had no metastasis. A total of 9 potentially differential metabolites were screened, including 5 metabolites with up-regulated expression, namely 2-methylcitric acid, aspartylphenylalanine, vanillic acid, melatonin and leucine, and 4 with down-regulated expression, namely lactose, phosphatidylcholine, glycerophospholipid and 3-glucoside. Cysteine and multiple metabolic pathways, such as methionine metabolism, retinol metabolism, taurine, aminoacyl tRNA biosynthesis, subtaurine metabolism and retention metabolism, are involved in the pathogenesis of thyroid cancer. Conclusion: The metabolic characteristics of patients with metastatic thyroid cancer and those without metastasis are significantly different, and these metabolites can be used as a potential marker to provide a certain reference for clinical treatment and prognosis.

【Keywords】thyroid cancer; metabolomics; Transfer; Metabolic pathways

Thyroid cancer is the most common type of endocrine neoplasm, ranking 9th among all malignancies ^[1], and has a higher proportion of females than ^{males [2].}。 According to relevant statistics, in recent years, the number of thyroid cancer patients in China has gradually increased, and the mortality rate has increased, which seriously endangers the physical and mental health of patients ^[3-6]. From the perspective of histopathology, there are four types of thyroid cancer, namely undifferentiated carcinoma, medullary carcinoma, follicular carcinoma, and papillary carcinoma, of which more than 80% are papillary carcinoma ^[7], and there are certain differences in diagnosis, biological characteristics, prognosis, and clinical manifestations of different pathological types. Studies have shown that the overall prognosis of most patients with thyroid cancer is good, but some patients develop lymph node metastases after surgery, increasing the risk of recurrence ^[8,9]. At present, in the clinical diagnosis of thyroid cancer, fine-needle aspiration cytology and ultrasonography are mainly used to evaluate tumor morphology, but there are still underdiagnoses for thyroid cancer with a high risk of invasion and metastasis ^[10].。 Therefore, it is particularly important to understand and grasp the molecular mechanism of lymph node metastasis of thyroid cancer and accurately assess the risk of lymph node metastasis of thyroid cancer to formulate targeted treatment plans. Metabolomics can quantitatively analyze mixtures with more biological sample components, understand the function and composition of biochemical networks, identify tumor markers, and provide an effective basis for judging the efficacy of tumor diseases. Therefore, the mechanism of metastasis of thyroid cancer was analyzed based on plasma metabolomics, as follows.

Information and Methodology

1.1 General Information

A total of 61 thyroid cancer patients admitted to the hospital from August 2015 to March 2023 were selected as the study subjects, and they were divided into two groups based on whether there was lymph node metastasis after surgery, including 32 patients in the metastasis group, aged 31-70 years, with an average age of (45.8±15.3) years, 13 males and 19 females. There were 29 cases in the non-metastasis group, aged 22-75 years, with an average age of (48.6±14.9) years, 20 females and 9 males.

Inclusion Criteria: (1) meet diagnostic criteria; (2) Age≥ 18 years old; (3) Complete clinical data; (4) Regular review and good compliance.

Exclusion criteria: (1) Long-term users of metabolic drugs; (2) Combined with other malignant tumors; (3) Combined with cardiovascular and cerebrovascular diseases; (4) History of previous surgery for thyroid disease; (5) Those with severe mental abnormalities or impaired consciousness.

1.2 Methods

1.2.1 Specimen collection

In the early morning fasting state, 5ml of elbow venous blood was collected, and after 10min centrifugation at 3000r rpm speed, the upper serum was separated and stored at -80°C for testing.

1.2.2 Sample pretreatment

After thawing the serum specimen at 4 °C, 150 μl of serum was taken, 450 μl of pre-chilled (-20 °C) formaldehyde was added, vortex and mixed evenly, and placed at -20 °C for 60 min. Centrifugation at 14,000 r/min for 15 min at 4 °C, transfer of the supernatant in a new EP tube, storage at -80 °C, and 200 μl of filtered supernatant in the vial and test.

1.2.3 Collect metabolic data

Regarding the separation and detection platform of metabolites, HPLC-MS was the main one, and the data were collected in the naïve positive ion mode, and the phase chromatographic conditions were: 0.1 acetonitrile/water as mobile phase B, 0.1% formic acid/water as mobile phase A, the column temperature was maintained at 40 °C, the injection volume was 3 μl, Acclainm T, RSLC120-C18 column (100*2.1mm), and Table 1 was the gradient elution conditions.

Table 1 Table of chromatographic gradient elution conditions

For mass spectrometry conditions: positive ion mode, electrospray ion source, high purity desolvation and N2 assisted spray ionization, set parameters, drying temperature 200°C, 1.2L/min flow rate, 20-1000m/z scanning range.

1.2.4 Data Processing

The mass spectra are obtained by liquid chromatography mass spectrometer, and Metaboscape 3.0 software is selected for data normalization, extraction, and pre-processing such as noise removal, which can obtain relevant data such as retention time, metabolite information, and mass-to-charge ratio.

1.2.5 Analysis of biological information of differential genes

The GO database was used to analyze the significance enrichment of differential genes from multiple aspects such as molecular function, cell composition and biological process, and the enrichment, false judgment rate and significance analysis results of the classification results were obtained. The KEGG database was used to find the targeted pathways and map the up-and-down differential genes in each pathway, so as to locate the key genes of the key pathways.

1.3 Statistical analysis

The study data were analyzed by SPSS24.0 software, and the mean ± standard deviation (x±s) was used for the continuous data, and the t-test was used to indicate that the data were statistically significant with P<0.05.

outcome

2.1 Comparison of serum metabolites between the two groups

Among the 61 patients in this group, 32 (52.46%) were metastatic and 29 (47.54%) were not metastasized. In the positive ion mode, the peak time of thyroid cancer tissue and metastatic tissue was basically the same, and different peaks had different intensities, indicating that there were more differential metabolites between thyroid cancer and metastatic tissue in positive ion mode. However, the ion maps of the two groups in the negative ion mode were more consistent, and the intensity of the differential peaks was less, suggesting that the two groups had fewer differential metabolites, as shown in Figures 1 and 2.

Fig.1 Positive ion mode

Fig.2. Negative ion mode

2.2 Cluster analysis

In order to analyze the degree of clustering and differentiation of the differential metabolites between the two groups, a heat map of the differential metabolites was made by importing the differential metabolites in the MetaboAnalyst 4.0 software for visualization, and the results are shown in Figure 3.

Figure 3 Heatmap in positive mode

2.3 Principal component analysis

In the metabolite profile analysis, the unsupervised principal component analysis (PCA) method was used, and the results showed that the metabolic profile of the two groups was significantly different, as shown in Figure 4.

Fig.4 Scores of the two groups of metabolic profiling PCA models in positive mode

2.4 Screening and identification of differential metabolites

In this study, a total of 9 potentially differential metabolites were screened, of which 5 metabolites were up-regulated, namely 2-methylcitric acid, aspartylphenylalanine, vanillic acid, melatonin and leucine, and 4 were down-regulated, namely lactose, phosphatidylcholine, glycerophospholipid and 3-glucoside, as shown in Figure 5.

Fig.5 Distribution of differential metabolites

2.5 Metabolic pathway analysis

In the analysis of differential metabolite metabolic pathways, MetaboAnalyst 4.0 was used as the main method, combined with the KEGG database, 26 metabolic pathways were matched, and then different metabolic pathways, including sulfur metabolism, cysteine and methionine metabolism, taurine metabolism and aminoacyl tRNA biosynthesis were screened based on the -log(p) value and pathway impact value >0.5, as shown in Figure 6.

Fig.6 Differential metabolic pathways

discuss

As an emerging discipline, metabolomics has attracted more and more attention for its ability to quantitatively and qualitatively analyze metabolites in vivo, especially smaller molecular metabolites ^[11]. Metabolomics plays an important role in the study of oncological diseases, as it can activate metabolic pathways, such as aerobic digestion and glutamine metabolic decomposition ^[12]. With regard to the pathogenesis of malignancy, altering tumor-specific metabolic small molecules is an important factor ^[13]. For example, Torregrrossa et al. ^[14] reported that compared with benign tumors, the levels of phosphocholine and choline in malignant tumors decreased, while the levels of taurine and lactate increased. Tian et al^{. [15]} pointed out that compared with benign thyroid lesions, the levels of uridine, inosinate, and citric acid in malignant lesions were lower, while the contents of amino acids, lactic acid, glycerophosphocholine, and phosphocholine were higher. Shang et al^{. [16]} suggested that galactoside, melobiose, and melatonin had significant changes in papillary thyroid carcinoma tissues, especially the galactose metabolism pathway was a metabolite with obvious changes. Miccoli et al. ^[17] found that in thyroid tissue, lipid content decreased significantly, while alanine, glutamic acid, glutamine, taurine, lysine, and phenylalanine levels increased. There are also Nagana et al. ^[18] and Lu et al^{. [19].}In the study, it was pointed out that the fatty acids of thyroid tumors decreased, and the expression of alanine, cystine, serine and tyrosine increased. In this study, based on liquid chromatography mass spectrometry, the combination of LC and MS can make up for the shortcomings, which is characterized by convenience, high sensitivity and high specificity. This study found that lactose, glucose and mannose were all differential metabolites in the galactose metabolism pathway, among which lactose and glucose were significantly changed, and their content decreased significantly in the metastatic tissues of thyroid cancer, indicating that tumor energy metabolism may be related to galactose metabolism. It has been pointed out that cancer cells have different growth patterns due to different energy production pathways, which produce a large amount of energy under the action of glycolysis, which is different from the oxidative phosphorylation energy metabolism pathway of normal cells ^[20].。 At the same time, it was found that a variety of fatty acids such as glycerophospholipid and phosphatidylcholine metastasis of thyroid cancer metastasis decreased significantly, suggesting that the occurrence of thyroid cancer metastasis was related to lipid metabolism. The main reason for the analysis is that the growth of cancer cells is related to the fatty acid synthesis pathway, and in cancer cells, fatty acids are consumed too much to meet the needs of proliferation, so the level of fatty acids in the body decreases.

In summary, there are obvious differences in the metabolic characteristics of patients with thyroid cancer metastasis, and it can be used as a potential marker to provide a reference for treatment and prognosis.

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