Abstract
Background
The diagnostic and prognostic value of the leucine-rich alpha-2-glycoprotein 1 (LRG1) gene in thyroid cancer remains unclear. Using the Cancer Genome Atlas (TCGA) database, we conducted a bioinformatics analysis to determine the role of LRG1 in thyroid cancer.
Methods
Data from 512 patients with thyroid cancer and 59 normal individuals were collected from TCGA database. The Kruskal–Wallis test and logistic analysis were used to examine the relationship between LRG1 expression and clinicopathologic characteristics. Cox regression and Kaplan–Meier analysis were used to determine the predictive value of LRG1 on clinical outcomes. Single-sample gene set enrichment analysis (ssGSEA) was used to reveal associations between LRG1 expression and immune infiltration levels in thyroid cancer.
Results
LRG1 was highly expressed in thyroid cancer (P < 0.001) and could effectively distinguish tumor tissue (area under the curve = 0.875) from normal tissue. Moreover, LRG1 was significantly correlated with pathological N stage (odds ratio (OR) = 2.411 (1.659–3.505), P < 0.001). Kaplan–Meier survival analysis revealed that patients with high LRG1 expression had better overall survival (hazard ratio (HR) = 0.30, P = 0.038). Cox regression analysis indicated that pathological M stage was a risk factor for progression-free interval (HR = 5.964 (2.010–17.694), P < 0.001). Using ssGSEA, we found that LRG1 expression was positively correlated with the number of T helper 1 cells (R = 0.435, P < 0.001), dendritic cells (R = 0.442, P < 0.001), and macrophages (R = 0.459, P < 0.001).
Conclusion
LRG1 may be an important biomarker for predicting the prognosis of thyroid cancer and represent a suitable target for immunotherapy associated with immune infiltration.
Introduction
Thyroid cancer is one of the most common malignant tumors of the endocrine system, accounting for 2% of all malignant tumors. The incidence of thyroid cancer has rapidly increased in recent decades, which is a significant concern (1, 2). In 2020, the incidence of thyroid cancer was 1–10 cases in women and 1–3 cases in men per 100,000 individuals worldwide. The prevalence of thyroid disease in the United States has been reported to be higher in women than in men. Although the proportion of affected men is low, they are at higher risk of dying once they develop the disease (3). According to the American Thyroid Association, it is estimated that approximately 20 million Americans have thyroid disease. In 2022 alone, there were 43,800 new cases of thyroid cancer in the USA, comprising >10,000 men and >30,000 women. Approximately 2230 people eventually died from thyroid cancer (4). In China, there are approximately 200 million people with thyroid dysfunction, and its incidence has rapidly increased in recent years. Notably, patients with thyroid cancer are younger compared with those with other cancers; thus, thyroid health concerns should be considered seriously (5). At present, researchers are working to find biomarkers related to thyroid cancer, hoping to guide clinical treatment. For example, if a biomarker can be found that is associated with tumor metastasis. Doctors can then formulate follow-up chemotherapy or radiation strategies based on the expression of the marker in the patient's thyroid cancer tissue. Therefore, the identification of biomarkers that can accurately predict the prognosis of thyroid cancer has become an important area of research.
Leucine-rich alpha-2-glycoprotein 1 (LRG1) is an important member of the leucine repeat protein family (6). It has an important regulatory role in many biological processes, such as inflammation, angiogenesis, fibrosis, cell adhesion, apoptosis, and cell viability. While exploring the various pathogenic mechanisms of LRG1, its potential as a biomarker for the diagnosis, prognosis, and monitoring of disease occurrence has also been examined. For example, LRG1 expression is increased in the serum of patients with pancreatic cancer is associated with advanced stage tumors and poor prognosis in patients with pancreatic cancer (7). Another study reported that the expression of LRG1 in colorectal cancer tissues was significantly higher than that in normal tissues. With the transition from normal tissue to colorectal cancer tissue, the expression of LRG1 gradually increases (8). LRG1 may be an independent risk factor for the prognosis of patients with colorectal cancer (9). Therefore, LRG1 represents a potential biomarker for tumor prognosis and requires further study.
Using the Cancer Genome Atlas (TCGA) database, we established a correlation between LRG1 and thyroid cancer and analyzed the possibility of using LRG1 to predict the prognosis of thyroid cancer. RNA sequencing (RNA-seq) data for thyroid tumors in TCGA was analyzed for LRG1 expression differences in thyroid cancer patients and normal tissues. Differences in LRG1 expression in each group and the correlation between LRG1 expression and prognosis were also analyzed. In addition, the correlation between LRG1 expression and immune infiltration was determined as a possible mechanism for LRG1 involvement in the progression of thyroid cancer. Our results may provide an important target for the prognosis and treatment of thyroid cancer.
Methods
RNA-seq data and bioinformatics analysis
This study used data downloaded from the TCGA database (https://portal.gdc.cancer.gov). RNA-seq data from the TCGA–thyroid carcinoma (THCA) project was subjected to STAR workflow and extracted in TPM format to obtain gene expression and clinical data. Data processing and visualization were performed using R software (R version 4.2.1), wherein clinically uninformative and duplicate data were removed. Based on the characteristics of the data format, appropriate statistical methods (R package ‘stats’ and R package ‘car’) were selected for statistical analysis where applicable, and the R package ‘ggplot2’ was used to visualize the data. This study was conducted in accordance with the Declaration of Helsinki (revised in 2013) and complied with the publication guidelines provided by TCGA. It did not include any research on humans or animals by the authors.
Receiver operating characteristic curve analysis
Data processing and visualization were performed using R software (R version 4.2.1). We used the R package ‘pROC [1.18.0]’ to conduct ROC analysis of the previously processed data. The results were visualized using the R package ’ggplot2’.
Kaplan–Meier curve analysis
Data processing and visualization were performed using R software (R version 4.2.1). We used the R package ‘survival’ to conduct KM cure analysis on the previously processed data. The results were visualized by the R package ‘survminer’.
Immune infiltration analysis
The immune infiltration matrix data were obtained from the cloud database from Xiantao Academic (https://www.xiantao.love). The correlation between the main variables and the immune infiltration matrix data in the preprocessed data was analyzed by R software (R version 4.2.1). The results were visualized using the R package ‘ggplot2’.
Statistical analysis
All statistical analyses were performed using R software (R version 4.2.1). The Kruskal–Wallis test was used to compare the expression of LRG1 in thyroid cancer and normal tissues. P < 0.05 was considered to indicate statistical significance. The following P-values were considered: *P < 0.05, **P < 0.01, and ***P < 0.001.
Results
Differences in LRG1 expression between thyroid cancer and normal tissues
To confirm an association between LRG1 expression and thyroid cancer, we collected gene expression data for 512 thyroid cancer patients and 59 normal subjects from the TGCA database. The results indicated that the expression of the LRG1 gene in patients with thyroid cancer was significantly higher compared with that in normal subjects and the difference was statistically significant (P < 0.001, Fig. 1A). Next, we selected 59 thyroid cancer patients and 59 normal controls for a paired comparison analysis. The results indicated that LRG1 expression was significantly increased in thyroid cancer patients (P < 0.001, Fig. 1B). The correlation between LRG1 expression and thyroid cancer was rigorously demonstrated by two comparative methods. Finally, an ROC curve was used to analyze the efficacy of LRG1 in differentiating tumor from nontumor tissues. The area under the curve for LRG1 was 0.859 (CI = 0.829–0.890), indicating a good value for distinguishing tumor from nontumor tissues based on LRG1 expression (Fig. 1C).
Differential expression of LRG1 in thyroid cancer and various other cancers. (A) Difference in LRG1 expression between patients with thyroid cancer and normal individuals. (B) Difference in LRG1 expression between patients with thyroid cancer and paired adjacent normal samples. (C) ROC curve showing the efficiency of LRG1 in distinguishing thyroid cancer tissues from normal tissues. (D) Differential expression of LRG1 in various cancers. (E–G) The high expression of LRG1 in thyroid cancer was examined by multiple groups of independent samples (the location of the LRG1 was circled in the figure). ACC, adrenocortical carcinoma; BLCA, bladder urothelial carcinoma; BRCA, breast invasive carcinoma; CESC, cervical squamous cell carcinoma and endocervical adenocarcinoma; CHOL, cholangiocarcinoma; COAD, colon adenocarcinoma; DLBC, lymphoid neoplasm diffuse large B-cell lymphoma; ESCA, esophageal carcinoma; GBM, glioblastoma multiforme; HNSC, head and neck squamous cell carcinoma; KICH, kidney chromophobe; KIRC, kidney renal clear cell carcinoma; KIRP, kidney renal papillary cell carcinoma; LAML, acute myeloid leukemia; LGG, brain lower grade glioma; LIHC, liver hepatocellular carcinoma; LUAD, lung adenocarcinoma; LUSC, lung squamous cell carcinoma; MESO, mesothelioma; OV, ovarian serous cystadenocarcinoma; PAAD, pancreatic adenocarcinoma; PCPG, pheochromocytoma and paraganglioma; PRAD, prostate adenocarcinoma; READ, rectum adenocarcinoma; SARC, sarcoma; SKCM, skin cutaneous melanoma; STAD, stomach adenocarcinoma; TGCT, testicular germ cell tumors; THCA, thyroid carcinoma; THYM, thymoma; UCEC, uterine corpus endometrial carcinoma; UCS, uterine carcinosarcoma; UVM, uveal melanoma. *P < 0.05, **P < 0.01, and ***P < 0.001.
Citation: Endocrine Connections 13, 1; 10.1530/EC-23-0418
LRG1 expression in 33 common tumors was analyzed (Fig. 1D) and found to be significantly overexpressed in 11 tumor types, including breast invasive carcinoma (BRCA) (P < 0.001), cholangiocarcinoma (CHOL) (P < 0.001), head and neck squamous cell carcinoma (HNSC) (P < 0.001), kidney chromophobe (KICH) (P < 0.001), kidney renal clear cell carcinoma (KIRC) (P < 0.001), liver hepatocellular carcinoma (LIHC) (P < 0.001), lung adenocarcinoma (LUAD) (P < 0.01), lung squamous cell carcinoma (LUSC) (P < 0.001), stomach adenocarcinoma (STAD) (P < 0.05), thyroid carcinoma (THCA) (P < 0.001), and uterine corpus endometrial carcinoma (UCEC) (P < 0.001).
To verify the validity of our conclusions, we selected independent samples from three separate studies to verify the differences in LRG1 gene expression (all samples were from human thyroid tissue). The data in Fig. 1E, from a published paper by Handkiewicz-Junak, D. (10), included 51 normal thyroid tissues and 31 papillary thyroid cancer tissues, and the statistical results show that log fold change = 1.6801, P < 0.001. The data in Fig. 1F, from a published paper by Tarabichi, M. (11), included 30 normal thyroid tissues and 32 papillary thyroid cancer tissues, and the statistical results show that log fold change = 0.9857, P < 0.001. The data in Fig. 1G, from a published paper by Lee, S. (12), included eight normal thyroid tissues and eight papillary thyroid cancer tissues, and the statistical results show that log fold change = 0.9857, P < 0.001. Therefore, the LRG1 gene is reliable as a biomarker for thyroid cancer.
Relationship between LRG1 expression and clinicopathologic characteristics of thyroid cancer
Baseline data of 512 patients with thyroid cancer obtained from the TCGA database were statistically analyzed (Table 1). The data were divided into two groups: 256 samples of patients with thyroid cancer with relatively low LRG1 expression and 256 samples of those with relatively high LRG1 expression. There was no significant difference in sex (P = 0.766) or age (P = 0.063) between the two groups. Regarding different TNM stages, there was no significant difference in the number of patients with T stage (P = 0.554) or M stage (P = 0.844) between the two groups; however, there were significant differences in the number of patients with different N stages (P < 0.001). The number of patients in the two groups differed significantly with respect to the pathological stage (P < 0.001), histological type (P < 0.001), and history of thyroid disease (P < 0.001).
Correlation between LRG1 expression and clinicopathologic characteristics of thyroid cancer.
| Characteristics | LRG1 expression | P | |
|---|---|---|---|
| Low (n = 256) | High (n = 256) | ||
| Gender, n (%) | 0.766 | ||
| Male | 71 (13.9%) | 68 (13.3%) | |
| Female | 185 (36.1%) | 188 (36.7%) | |
| Age, n (%) | 0.063 | ||
| ≤45 | 111 (21.7%) | 132 (25.8%) | |
| >45 | 145 (28.3%) | 124 (24.2%) | |
| Pathologic T stage, n (%) | 0.554 | ||
| T1 | 72 (14.1%) | 71 (13.9%) | |
| T2 | 91 (17.8%) | 78 (15.3%) | |
| T3 | 82 (16.1%) | 93 (18.2%) | |
| T4 | 10 (2%) | 13 (2.5%) | |
| Pathologic N stage, n (%) | <0.001 | ||
| N0 | 134 (29%) | 95 (20.6%) | |
| N1 | 86 (18.6%) | 147 (31.8%) | |
| Pathologic M stage, n (%) | 0.844 | ||
| M0 | 133 (45.1%) | 153 (51.9%) | |
| M1 | 5 (1.7%) | 4 (1.4%) | |
| Pathologic stage, n (%) | <0.001 | ||
| Stage I | 142 (27.8%) | 146 (28.6%) | |
| Stage II | 37 (7.3%) | 15 (2.9%) | |
| Stage III | 57 (11.2%) | 56 (11%) | |
| Stage IV | 18 (3.5%) | 39 (7.6%) | |
| Histological type, n (%) | <0.001 | ||
| Classical | 158 (30.9%) | 208 (40.6%) | |
| Follicular | 86 (16.8%) | 15 (2.9%) | |
| Tall cell | 8 (1.6%) | 28 (5.5%) | |
| Other | 4 (0.8%) | 5 (1%) | |
| Thyroid gland disorder history, n (%) | <0.001 | ||
| Lymphocytic thyroiditis | 45 (9.9%) | 29 (6.4%) | |
| Nodular hyperplasia | 45 (9.9%) | 23 (5.1%) | |
| Other, specify | 17 (3.7%) | 9 (2%) | |
| Normal | 121 (26.7%) | 165 (36.3%) | |
A logistic regression analysis was performed to determine the correlation between LRG1 expression and clinicopathologic characteristics of thyroid cancer (Table 2). Similar to the baseline statistics, logistic regression analysis revealed no significant differences in sex (P = 0.776), age (P = 0.063), T stage (P = 0.204), or M stage (P = 0.594) between the two groups. In addition, no significant difference was observed between the stage III and stage IV and stage I and stage II (P = 0.070). Further, the analysis revealed a significant difference between samples with N1 stage and those with N0 stage (odds ratio (OR) = 2.411 (1.659–3.505), P < 0.001). In addition, a significant difference was observed between patients with and without a history of thyroid disease (OR = 2.392 (1.616–3.541), P < 0.001).
LRG1 expression associated with clinicopathologic characteristics (logistic regression) in thyroid cancer.
| Characteristics | Total (n) | OR (95% CI) | P |
|---|---|---|---|
| Gender (female vs male) | 512 | 1.061 (0.719–1.567) | 0.766 |
| Age (>45 vs ≤45) | 512 | 0.719 (0.508–1.019) | 0.063 |
| Pathologic T stage (T3 and T4 vs T1 and T2) | 510 | 1.260 (0.882–1.801) | 0.204 |
| Pathologic N stage (N1 vs N0) | 462 | 2.411 (1.659–3.505) | <0.001 |
| Pathologic M stage (M1 vs M0) | 295 | 0.695 (0.183–2.643) | 0.594 |
| Pathologic stage (stage III and stage IV vs stage I and stage II) | 510 | 1.408 (0.973–2.039) | 0.070 |
| Thyroid gland disorder history (normal vs lymphocytic thyroiditis and nodular hyperplasia and other, specify) | 454 | 2.392 (1.616–3.541) | <0.001 |
Finally, to verify the relationship between LRG1 expression and different pathological stages of thyroid cancer, we analyzed the expression of LRG1 in thyroid cancer samples in different pathological stages. The results revealed that LRG1 expression was significantly higher in thyroid cancer samples with three T stages than in normal thyroid tissues; however, no significant difference was noted among the samples with three T stages (Fig. 2A). LRG1 expression was significantly higher in thyroid cancer samples with both N stages than in normal thyroid tissues. Similarly, there were significant differences between samples with N0 stage and N1 stage (Fig. 2B). LRG1 expression was significantly higher in samples with two M stages than in normal thyroid tissues; however, there was no significant difference between the samples with two M stages (Fig. 2C). Moreover, LRG1 expression was significantly higher in samples with four pathological stages of thyroid cancer than in normal thyroid tissues. There was a significant difference between samples with stage I and stage II as well as between samples with stage II and stage III; however, there were no significant differences between the other groups (Fig. 2D).
Differential LRG1 expression associated with clinicopathologic characteristics of thyroid cancer. (A) Difference in LRG1 expression among different T stages. (B) Difference in LRG1 expression among different N stages. (C) Difference in LRG1 expression among different M stages. (D) Difference in LRG1 expression among different pathologic stages. *P < 0.05, **P < 0.01, and ***P < 0.001.
Citation: Endocrine Connections 13, 1; 10.1530/EC-23-0418
Role of LRG1 expression in the survival of patients with thyroid cancer
A Kaplan–Meier survival curve was used to evaluate the prognostic value of LRG1 expression in thyroid cancer. The overall survival (OS) curve showed that patients with high LRG1 expression had a significantly higher survival rate compared with those with low expression, and the difference was statistically significant (HR = 0.30, 95% CI: 0.10–0.94, P = 0.038) (Fig. 3A). A similar result was observed for the progression-free interval (PFI) (HR = 0.89, 95% CI: 0.52–1.52, P = 0.680), although the difference between the two survival curves was not significant (Fig. 3B).
Overall survival curve (A) and progression-free interval curve (B) of patients with thyroid cancer in TCGA database.
Citation: Endocrine Connections 13, 1; 10.1530/EC-23-0418
To better predict the time length without recurrence of thyroid cancer after treatment, a Cox regression analysis was performed with PFI as a dependent variable (Table 3). Based on the results of a univariate analysis, only pathological stage had a significant correlation with PFI, including pathologic T1 and T2 vs T3 and T4 (HR = 2.475 (1.431–4.278), P = 0.001), pathologic M0 vs M1 (OR = 7.305 (2.780–19.197), P < 0.001), and pathologic stage I and stage II vs stage III and stage IV (OR= 2.619 (1.533–4.476), P < 0.001). Finally, we performed a multivariate analysis of the data, which indicated that only pathologic M0 vs M1 (OR = 5.964 (2.010–17.694), P < 0.001) was significantly associated with PFI.
Univariate and multivariate Cox regression analyses of prognostic factors for PFI in thyroid cancer.
| Characteristics | Total (n) | Univariate analysis | Multivariate analysis | ||
|---|---|---|---|---|---|
| Hazard ratio (95% CI) | P | Hazard ratio (95% CI) | P | ||
| Gender | 512 | ||||
| Male | 139 | Reference | Reference | ||
| Female | 373 | 0.586 (0.337–1.018) | 0.058 | 0.886 (0.404–1.945) | 0.763 |
| Age | 512 | ||||
| ≤45 | 243 | Reference | Reference | ||
| >45 | 269 | 1.612 (0.931–2.791) | 0.088 | 1.346 (0.424–4.269) | 0.614 |
| Pathologic T stage | 510 | ||||
| T1 and T2 | 312 | Reference | Reference | ||
| T3 and T4 | 198 | 2.475 (1.431–4.278) | 0.001 | 1.217 (0.467–3.172) | 0.687 |
| Pathologic N stage | 462 | ||||
| N0 | 229 | Reference | Reference | ||
| N1 | 233 | 1.636 (0.924–2.895) | 0.091 | 0.953 (0.423–2.147) | 0.907 |
| Pathologic M stage | 295 | ||||
| M0 | 286 | Reference | Reference | ||
| M1 | 9 | 7.305 (2.780–19.197) | <0.001 | 5.964 (2.010–17.694) | 0.001 |
| Pathologic stage | 510 | ||||
| Stage I and stage II | 340 | Reference | Reference | ||
| Stage III and stage IV | 170 | 2.619 (1.533–4.476) | <0.001 | 2.341 (0.633–8.663) | 0.203 |
| Histological type | 512 | ||||
| Classical | 366 | Reference | |||
| Follicular | 101 | 0.578 (0.245–1.363) | 0.210 | ||
| Tall cell | 36 | 2.030 (0.908–4.538) | 0.085 | ||
| Other | 9 | 1.021 (0.140–7.433) | 0.984 | ||
| Thyroid gland disorder history | 454 | ||||
| Lymphocytic thyroiditis | 74 | Reference | |||
| Nodular hyperplasia | 68 | 0.861 (0.273–2.714) | 0.798 | ||
| Other, specify | 26 | 1.599 (0.468–5.465) | 0.454 | ||
| Normal | 286 | 1.064 (0.469–2.415) | 0.881 | ||
Correlation between LRG1 expression and immune infiltration
Using the single-sample gene set enrichment analysis (ssGSEA) algorithm, we determined the correlation between LRG1 expression and 24 immune cell types associated with immune infiltration and then visualized the results. The visualization in Fig. 4A shows that the size of each circle represents the correlation between the enrichment of this immune cell and LRG1, while the color of the circle represents the P-value. The distance from the circle to the baseline also indicates the correlation between the enrichment of the immune cell and LRG1, as does the size of the circle. Subsequently, we selected the top 3 immune cells with the strongest correlation and significant differences in P-values for analysis. A positive correlation was noted between LRG1 expression and the immune infiltration of T helper 1 (Th1) cells (R = 0.435, P < 0.001), dendritic cells (DCs) (R = 0.442, P < 0.001), and macrophages (R = 0.459, P < 0.001) (Fig. 4B, C, and D). Subsequent statistical analysis revealed that the infiltration levels of Th1, DCs, and macrophages increased significantly in samples with high LRG1 expression compared with those in samples with low LRG1 expression (Fig. 4E, F, and G).
Relationship between LRG1 expression and immune infiltration in the thyroid cancer microenvironment. (A) Correlation between the relative abundance of 24 immune cells and LRG1 expression. The size of the dots indicates the absolute value of Spearman’s correlation coefficient R. (B–D) Correlation between LRG1 expression and infiltration levels of Th1 cells, DC, and macrophages. (E–G) Correlation between high and low LRG1 expression and the infiltration levels of Th1 cells, DC, and macrophages. *P < 0.05, **P < 0.01, and ***P < 0.001.
Citation: Endocrine Connections 13, 1; 10.1530/EC-23-0418
Discussion
To date, only a few reports have described the role of LRG1 in disease progression; however, LRG1 plays an important role in the occurrence and development of tumors. For example, a study revealed that inhibiting LRG1 normalizes blood vessels in tumor tissues and enhances the anticancer efficacy of immunotherapy (13); however, the relationship between LRG1 and thyroid cancer as well as its impact on prognosis has not been thoroughly examined. Therefore, we determined the role of LRG1 in prognosis prediction and immune infiltration of thyroid cancer, which may represent a new biomarker for the diagnosis and treatment of thyroid cancer.
Based on bioinformatics analysis, we revealed that LRG1 was significantly overexpressed in patients with thyroid cancer. The results were further validated using a more rigorous paired comparison analysis. ROC analysis also confirmed that LRG1 is a useful biomarker for differentiating thyroid cancer tissues from normal tissues. Subgroup analysis revealed that LRG1 expression was significantly correlated with pathologic TNM stage, histological type, and history of thyroid disease. Finally, we performed a more detailed analysis of LRG1 expression and pathologic TNM stage and reported that patients with N1 stage had significantly increased LRG1 expression. Therefore, LRG1 is a useful biomarker for predicting thyroid cancer, and the severity of thyroid cancer can be preliminarily assessed by the level of LRG1 expression.
Based on KM curve, the survival rate of patients with high LRG1 expression was significantly higher compared with that of patients with low expression, and the difference was statistically significant. Therefore, the LRG1 gene may represent an important biomarker for predicting the prognosis of thyroid cancer. In addition, the recurrence-free interval of the high LRG1 expression group was longer compared with that of the low expression group, but the difference was not statistically significant. That suggests that LRG1 expression is not useful for predicting the recurrence of thyroid cancer after treatment. Next, we performed a Cox regression analysis to identify factors that could predict PFI. We found that pathological stage had a significant impact on the recurrence of thyroid cancer following treatment. In particular, pathological M1 stage had a significant impact on tumor recurrence in a multivariate analysis. Thus, the combination of LRG1 expression and pathological stage can effectively predict the prognosis of thyroid cancer.
Finally, we performed ssGSEA and Spearman correlation to reveal an association between LRG1 expression and immune infiltration levels in thyroid cancer. Th1, DCs, and macrophages were positively correlated with LRG1 expression. Regarding immune infiltration into the tumor microenvironment, a previous study revealed that a high proportion of Th1 cells can significantly improve the survival rate of patients with cancer (14). Moreover, DCs and regulatory T cells are involved in the immune escape of thyroid cancer (15). High expression of macrophages and CXCL16 in thyroid cancer is associated with angiogenesis-related genes and an invasive phenotype, which may have a significant impact on the prognosis of thyroid cancer (16). Therefore, LRG1 may play an important role in mediating the immune response against cancer; however, its underlying mechanism warrants further investigation.
In the beginning, we found that LRG1 gene expression was significantly higher in thyroid cancer patients compared to normal people. And LRG1 expression is even higher in some patients with more severe clinicopathological stages. This makes us think that the high expression of LRG1 gene may be one of the pathogenesis of thyroid cancer, and the higher the expression level indicates the more serious the disease. However, the subsequent analysis showed us that our previous conjecture was wrong. As the analysis shows, patients with high LRG1 expression had a higher survival rate. Therefore, LRG1 gene should not be the pathogenesis of thyroid cancer. The expression of LRG1 gene should be an important means of body resistance to thyroid cancer. At the same time, when the clinicopathological stage of thyroid cancer is more severe, the body tries to further increase the expression level of LRG1, so as to better resist thyroid cancer. The most direct evidence for this conjecture is the correlation between LRG1 and immune infiltration. Immune infiltration is one of the important methods of body to resist tumor. Only immune cells infiltrate into tumor tissue can kill tumor cells. Our analysis found that after the body increased the expression of LRG1 gene, it could significantly promote the infiltration of Th1, DC, and macrophage cells. These three types of immune cells are the key to killing tumor cells. In conclusion, the LRG1 gene can predict the prognosis of thyroid cancer and has the potential to be developed as a novel therapeutic target.
Conclusion
LRG1 may be an important biomarker for screening and predicting thyroid cancer. Furthermore, LRG1 expression in thyroid cancer tissues can effectively predict the survival rate of patients. In addition, the expression of LRG1 combined with the pathological stage of thyroid cancer can provide meaningful evidence for its recurrence in patients following treatment. Thus, LRG1 expression is strongly correlated with immune infiltration and may be an important immunotherapeutic target for thyroid cancer.
Declaration of interest
There is no conflict of interest that could be perceived as prejudicing the impartiality of the study reported.
Funding
This study did not receive any specific grant from any funding agency in the public, commercial, or not-for-profit sector.
References
- 1↑
Shah JP. Thyroid carcinoma: epidemiology, histology, and diagnosis. Clinical Advances in Hematology and Oncology 2015 13 3–6.
- 2↑
Taylor PN, Albrecht D, Scholz A, Gutierrez-Buey G, Lazarus JH, Dayan CM, & Okosieme OE. Global epidemiology of hyperthyroidism and hypothyroidism. Nature Reviews Endocrinology 2018 14 301–316. (https://doi.org/10.1038/nrendo.2018.18)
- 3↑
Pizzato M, Li M, Vignat J, Laversanne M, Singh D, La Vecchia C, & Vaccarella S. The epidemiological landscape of thyroid cancer worldwide: GLOBOCAN estimates for incidence and mortality rates in 2020. The Lancet Diabetes and Endocrinology 2022 10 264–272. (https://doi.org/10.1016/S2213-8587(2200035-3)
- 4↑
Gordon AJ, Dublin JC, Patel E, Papazian M, Chow MS, Persky MJ, Jacobson AS, Patel KN, Suh I, Morris LGT, et al.American Thyroid Association guidelines and national trends in management of papillary thyroid carcinoma. JAMA Otolaryngology–Head and Neck Surgery 2022 148 1156–1163. (https://doi.org/10.1001/jamaoto.2022.3360)
- 5↑
Du L, Li R, Ge M, Wang Y, Li H, Chen W, & He J. Incidence and mortality of thyroid cancer in China, 2008–2012. Chinese Journal of Cancer Research 2019 31 144–151. (https://doi.org/10.21147/j.issn.1000-9604.2019.01.09)
- 6↑
O'Donnell LC, Druhan LJ, & Avalos BR. Molecular characterization and expression analysis of leucine-rich alpha2-glycoprotein, a novel marker of granulocytic differentiation. Journal of Leukocyte Biology 2002 72 478–485. (https://doi.org/10.1189/jlb.72.3.478)
- 7↑
Xie ZB, Zhang YF, Jin C, Mao YS, & Fu DL. LRG-1 promotes pancreatic cancer growth and metastasis via modulation of the EGFR/p38 signaling. Journal of Experimental and Clinical Cancer Research 2019 38 75. (https://doi.org/10.1186/s13046-019-1088-0)
- 8↑
Zhang J, Zhu L, Fang J, Ge Z, & Li X. LRG1 modulates epithelial-mesenchymal transition and angiogenesis in colorectal cancer via HIF-1alpha activation. Journal of Experimental and Clinical Cancer Research 2016 35 29. (https://doi.org/10.1186/s13046-016-0306-2)
- 9↑
Zhang Q, Huang R, Tang Q, Yu Y, Huang Q, Chen Y, Wang G, & Wang X. Leucine-rich alpha-2-glycoprotein-1 is up-regulated in colorectal cancer and is a tumor promoter. OncoTargets and Therapy 2018 11 2745–2752. (https://doi.org/10.2147/OTT.S153375)
- 10↑
Handkiewicz-Junak D, Swierniak M, Rusinek D, Oczko-Wojciechowska M, Dom G, Maenhaut C, Unger K, Detours V, Bogdanova T, Thomas G, et al.Gene signature of the post-Chernobyl papillary thyroid cancer. European Journal of Nuclear Medicine and Molecular Imaging 2016 43 1267–1277. (https://doi.org/10.1007/s00259-015-3303-3)
- 11↑
Tarabichi M, Saiselet M, Tresallet C, Hoang C, Larsimont D, Andry G, Maenhaut C, & Detours V. Revisiting the transcriptional analysis of primary tumours and associated nodal metastases with enhanced biological and statistical controls: application to thyroid cancer. British Journal of Cancer 2015 112 1665–1674. (https://doi.org/10.1038/bjc.2014.665)
- 12↑
Lee S, Bae JS, Jung CK, & Chung WY. Extensive lymphatic spread of papillary thyroid microcarcinoma is associated with an increase in expression of genes involved in epithelial-mesenchymal transition and cancer stem cell-like properties. Cancer Medicine 2019 8 6528–6537. (https://doi.org/10.1002/cam4.2544)
- 13↑
O'Connor MN, Kallenberg DM, Camilli C, Pilotti C, Dritsoula A, Jackstadt R, Bowers CE, Watson HA, Alatsatianos M, Ohme J, et al.LRG1 destabilizes tumor vessels and restricts immunotherapeutic potency. Med 2021 2 1231–1252.e10. (https://doi.org/10.1016/j.medj.2021.10.002)
- 14↑
Nizri E, Greenman-Maaravi N, Bar-David S, Ben-Yehuda A, Weiner G, Lahat G, & Klausner J. Analysis of histological and immunological parameters of metastatic lymph nodes from colon cancer patients reveals that T-helper 1 type immune response is associated with improved overall survival. Medicine (Baltimore) 2016 95 e5340. (https://doi.org/10.1097/MD.0000000000005340)
- 15↑
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)
- 16↑
Kim MJ, Sun HJ, Song YS, Yoo SK, Kim YA, Seo JS, Park YJ, & Cho SW. CXCL16 positively correlated with M2-macrophage infiltration, enhanced angiogenesis, and poor prognosis in thyroid cancer. Scientific Reports 2019 9 13288. (https://doi.org/10.1038/s41598-019-49613-z)
