Bioinformatics analysis identified shared differentially expressed genes as potential biomarkers for Hashimoto's thyroiditis-related papillary thyroid cancer

Clinical and molecular impact of concurrent thyroid autoimmune disease and thyroid cancer: From the bench to bedside

The recent incorporation of immune checkpoint inhibitors targeting the PD-1 (programmed cell death receptor 1) and CTLA-4 (cytotoxic T lymphocyte antigen 4) pathways into the therapeutic armamentarium of cancer has increased the need to understand the correlation between the immune system, autoimmunity, and malignant neoplasms. Both autoimmune thyroid diseases and thyroid cancer are common clinical conditions. The molecular pathology of autoimmune thyroid diseases is characterized by the important impact of the PD-1/PD-L1 axis, an important inhibitory pathway involved in the regulation of T-cell responses. Insufficient inhibitory pathways may prone the thyroid tissue to a self-destructive immune response that leads to hypothyroidism. On the other hand, the PD-1/PD-L1 axis and other co-inhibitory pathways are the cornerstones of the immune escape mechanisms in thyroid cancer, which is a mechanism through which the immune response fails to recognize and eradicate thyroid tumor cells. This common mechanism raises the idea that thyroid autoimmunity and thyroid cancer may be opposite sides of the same coin, meaning that both conditions share similar molecular signatures. When associated with thyroid autoimmunity, thyroid cancer may have a less aggressive presentation, even though the molecular explanation of this clinical consequence is unclear. More studies are warranted to elucidate the molecular link between thyroid autoimmune disease and thyroid cancer. The prognostic impact that thyroid autoimmune disease, especially chronic lymphocytic thyroiditis, may exert on thyroid cancer raises important insights that can help physicians to better individualize the management of patients with thyroid cancer.

Identification and Preliminary Clinical Validation of Key Extracellular Proteins as the Potential Biomarkers in Hashimoto's Thyroiditis by Comprehensive Analysis

Hashimoto's thyroiditis (HT) is an autoimmune disruption manifested by immune cell infiltration in thyroid tissue and the production of antibodies against thyroid-specific antigens, such as the thyroid peroxidase antibody (TPOAb) and thyroglobulin antibody (TGAb). TPOAb and TGAb are commonly used in clinical tests; however, handy indicators of the diagnosis and progression of HT are still scarce. Extracellular proteins are glycosylated and are likely to enter body fluids and become readily available and detectable biomarkers. Our research aimed to discover extracellular biomarkers and potential treatment targets associated with HT through integrated bioinformatics analysis and clinical sample validations. A total of 19 extracellular protein-differentially expressed genes (EP-DEGs) were screened by the GSE138198 dataset from the Gene Expression Omnibus (GEO) database and protein annotation databases. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) were used to analyze the function and pathway of EP-DEGs. STRING, Cytoscape, MCODE, and Cytohubba were used to construct a protein-protein interaction (PPI) network and screen key EP-DEGs. Six key EP-DEGs (CCL5, GZMK, CXCL9, CXCL10, CXCL11, and CXCL13) were further validated in the GSE29315 dataset and the diagnostic curves were evaluated, which all showed high diagnostic accuracy (AUC > 0.95) for HT. Immune profiling revealed the correlation of the six key EP-DEGs and the pivotal immune cells in HT, such as CD8+ T cells, dendritic cells, and Th2 cells. Further, we also confirmed the key EP-DEGs in clinical thyroid samples. Our study may provide bioinformatics and clinical evidence for revealing the pathogenesis of HT and improving the potential diagnosis biomarkers and therapeutic strategies for HT.

Comprehensive analysis of hub biomarkers associated with immune and oxidative stress in Hashimoto's thyroiditis

Hashimoto's thyroiditis (HT) is a type of autoimmune disorder with a complex interplay between immune disorder and oxidative stress (OS). This research aimed to discover biomarkers and potential treatment targets associated with immune and OS dysregulation in HT through integrated bioinformatics analysis and clinical validations. Differential gene expression analysis of GSE138198 dataset from the GEO database identified 1490 differentially expressed genes (DEGs) in HT, including 883 upregulated and 607 downregulated genes. Weighted gene co-expression network analysis explored module genes associated with HT. Overlapping the differentially expressed module genes with immune-related and OS-related genes identified eight differentially expressed module genes associated with immune and OS (DEIOGs) in HT. Protein-protein interaction network analysis identified five hub genes (TNFAIP3, FOS, PTK2B, STAT1, and MMP9). We confirmed four hub genes (TNFAIP3, PTK2B, STAT1 and MMP9) in GSE29315 dataset and clinical thyroid samples, which showed high diagnostic accuracy (AUC >0.7) for HT. The expression of these four genes was positively correlated with serum thyroid peroxidase antibody, thyroglobulin antibody levels, and inflammatory infiltration scores in clinical thyroid samples. Immune profiling revealed distinct profiles in HT, such as B cells memory, monocytes and macrophages. Additionally, all hub genes were inversely associated with monocytes. Further, miRNA-mRNA network analysis was conducted, and a regulatory network comprising four hub genes, 238 miRNAs and 32 TFs was established. These findings suggest that immune cells play a crucial role in the development of HT, and the hub genes TNFAIP3, PTK2B, STAT1, and MMP9 may be key players in HT through immune- and OS-related signaling pathways. Our results may provide valuable insights into the pathogenesis and therapeutic monitoring of HT.

Identification of SERPINA1 promoting better prognosis in papillary thyroid carcinoma along with Hashimoto's thyroiditis through WGCNA analysis

Background

Hashimoto's thyroiditis (HT) is an autoimmune thyroid disease. Papillary thyroid carcinoma (PTC) is the most common endocrine cancer. In recent years the rate of coexistence between PTC and HT has increased but the relationship between them remains unclear, meaning it is necessary to find potential biomarkers for PTC coexistence with HT to predict its potential pathways.

Method

A co-expression network was constructed using the weighted gene co-expression network analysis (WGCNA) in the R package. The modules of PTC associated with HT (PTC-W) were identified from the GSE138198 dataset. Protein-protein interaction network (PPI) was used to screen the hub genes. Immunohistochemical (IHC) analysis was performed to validate the expression of the hub genes in tissues. Clinical data from The Cancer Genome Atlas (TCGA) datasets were used to analyse the prognosis of the hub genes. Gene set enrichment analysis (GSEA) was used to screen potential pathways of PTC-W.

Result

The MEbrown module representing the most significant module, with 958 differentially expressed genes (DEGs), was screened in PTC-W, based on WGCNA analysis. Through PPI, SERPINA1 was identified as a hub gene. Immunostaining validated that SERPINA1 was highly expressed in PTC-W. Moreover, PTC-W expressing SERPINA1 exhibits a better prognosis than PTC without HT (PTC-WO).

Conclusion

Our study demonstrates that SERPINA1 promotes the occurrence of PTC-W, and its prognosis is better than PTC-WO. SERPINA1 promotes a better prognosis for PTC-W, possibly through a tumour inhibition signalling pathway.

Altered profile of glycosylated proteins in serum samples obtained from patients with Hashimoto's thyroiditis following depletion of highly abundant proteins

Objectives

Hashimoto's thyroiditis (HT) is one of the most common autoimmune disorders; however, its underlying pathological mechanisms remain unclear. Although aberrant glycosylation has been implicated in the N-glycome of immunoglobulin G (IgG), changes in serum proteins have not been comprehensively characterized. This study aimed to investigate glycosylation profiles in serum samples depleted of highly abundant proteins from patients with HT and propose the potential functions of glycoproteins for further studies on the pathological mechanisms of HT.

Methods

A lectin microarray containing 70 lectins was used to detect and analyze glycosylation of serum proteins using serum samples (N=27 HT; N=26 healthy control [HC]) depleted of abundant proteins. Significant differences in glycosylation status between HT patients and the HC group were verified using lectin blot analysis. A lectin-based pull-down assay combined with mass spectrometry was used to investigate potential glycoproteins combined with differentially present lectins, and an enzyme-linked immunosorbent assay (ELISA) was used to identify the expression of targeted glycoproteins in 131 patients with papillary thyroid carcinoma (PTC), 131 patients with benign thyroid nodules (BTN) patients, 130 patients with HT, and 128 HCs.

Results

Compared with the HC group, the majority of the lectin binding signals in HT group were weakened, while the Vicia villosa agglutinin (VVA) binding signal was increased. The difference in VVA binding signals verified by lectin blotting was consistent with the results of the lectin microarray. A total of 113 potential VVA-binding glycoproteins were identified by mass spectrometry and classified by gene ontology (GO) and Kyoto encyclopedia of genes and genomes (KEGG) analyses. Using ELISA, we confirmed that lactoferrin (LTF) and mannan-binding lectin-associated serine protease 1 (MASP-1) levels were elevated in the serum of patients with HT and PTC.

Conclusion

Following depletion of abundant proteins, remaining serum proteins in HT patients exhibited lower glycosylation levels than those observed in HCs. An increased level of potential VVA-binding glycoproteins may play an important role in HT development. LTF and MASP-1 expression was significantly higher in the serum of HT and PTC patients, providing novel insight into HT and PTC.

Identification of iron metabolism-related genes as prognostic indicators for papillary thyroid carcinoma: a retrospective study

Background

The thyroid cancer subtype that occurs more frequently is papillary thyroid carcinoma (PTC). Despite a good surgical outcome, treatment with traditional antitumor therapy does not offer ideal results for patients with radioiodine resistance, recurrence, and metastasis. The evidence for the connection between iron metabolism imbalance and cancer development and oncogenesis is growing. Nevertheless, the iron metabolism impact on PTC prognosis is still indefinite.

Methods

Herein, we acquired the medical data and gene expression of individuals with PTC from The Cancer Genome Atlas (TCGA) and the Gene Expression Omnibus (GEO) database. Typically, three predictive iron metabolism-related genes (IMRGs) were examined and employed to build a risk score (RS) model via the least absolute shrinkage and selection operator (LASSO) regression, univariate Cox, and differential gene expression analyses. Then we analyzed somatic mutation and immune cell infiltration among RS groups. We also validated the prognostic value of two IMRGs (SFXN3 and TFR2) by verifying their biological function through in vitro experiments.

Results

Based on RS, all patients with PTC were stratified into low- and high-risk groups, where Kaplan-Meier analysis indicated that disease-free survival (DFS) in the high-risk group was much lower than in the low-risk group (P < 0.0001). According to ROC analysis, the RS model successfully predicted the 1-, 3-, and 5-year DFS of individuals with PTC. Additionally, in the TCGA cohort, a nomogram model with RS was developed and exhibited a strong capability to anticipate PTC patients' DFS. In the high-risk group, the enriched pathological processes and signaling mechanisms were detected utilizing the gene set enrichment analysis (GSEA). Moreover, the high-risk group had a significantly higher level of BRAF mutation frequency, tumor mutation burden, and immune cell infiltration than the low-risk group. In vitro experiments found that silencing SFXN3 or TFR2 significantly reduced cell viability.

Conclusion

Collectively, our predictive model depended on IMRGs in PTC, which could be potentially utilized to predict the PTC patients' prognosis, schedule follow-up plans, and provide potential targets against PTC.

Identifying and analyzing the key genes shared by papillary thyroid carcinoma and Hashimoto's thyroiditis using bioinformatics methods

Background

Hashimoto's thyroiditis (HT) is a chronic autoimmune disease that poses a risk factor for papillary thyroid carcinoma (PTC). The present study aimed to identify the key genes shared by HT and PTC for advancing the current understanding of their shared pathogenesis and molecular mechanisms.

Methods

HT- and PTC-related datasets (GSE138198 and GSE33630, respectively) were retrieved from the Gene Expression Omnibus (GEO) database. Genes significantly related to the PTC phenotype were identified using weighted gene co-expression network analysis (WGCNA). Differentially expressed genes (DEGs) were identified between PTC and healthy samples from GSE33630, and between HT and normal samples from GSE138198. Subsequently, functional enrichment analysis was performed using Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG). Transcription factors and miRNAs regulating the common genes in PTC and HT were forecasted using the Harmonizome and miRWalk databases, respectively, and drugs targeting these genes were investigated using the Drug-Gene Interaction Database (DGIdb). The key genes in both GSE138198 and GSE33630 were further identified via Receiver Operating Characteristic (ROC) analysis. The expression of key genes was verified in external validation set and clinical samples using quantitative real-time polymerase chain reaction (qRT-PCR) and immunohistochemistry (IHC).

Results

In total, 690 and 1945 DEGs were associated with PTC and HT, respectively; of these, 56 were shared and exhibited excellent predictive accuracy in the GSE138198 and GSE33630 cohorts. Notably, four genes, Alcohol Dehydrogenase 1B (ADH1B), Active BCR-related (ABR), alpha-1 antitrypsin (SERPINA1), and lysophosphatidic acid receptor 5 (LPAR5) were recognized as key genes shared by HT and PTC. Subsequently, EGR1 was identified as a common transcription factor regulating ABR, SERPINA1, and LPAR5 expression. These findings were confirmed using qRT-PCR and immunohistochemical analysis.

Conclusion

Four (ADH1B, ABR, SERPINA1, and LPAR5) out of 56 common genes exhibited diagnostic potential in HT and PTC. Notably, this study, for the first time, defined the close relationship between ABR and HT/PTC progression. Overall, this study provides a basis for understanding the shared pathogenesis and underlying molecular mechanisms of HT and PTC, which might help improve patient diagnosis and prognosis.

Clinical significance and diagnostic value of QPCT, SCEL and TNFRSF12A in papillary thyroid cancer

PURPOSE

To identify specific novel genes that could be used as diagnostic and prognostic factors in papillary thyroid carcinoma (PTC).

METHODS

Screening of differential genes by RNA sequencing (RNA-Seq) in normal thyroid, Hashimoto's thyroiditis, PTC combined with Hashimoto's thyroiditis and PTC tissues. The genes QPCT, SCEL and TNFRSF12A were selected by qRT-PCR and immunohistochemical pre-experiments. The GEPIA2 database, qRT-PCR, and immunohistochemical studies were used to confirm the target genes QPCT, SCEL, and TNFRSF12A. ROC curves were used to assess the diagnostic usefulness of these 3 genes for PTC in more detail.

RESULTS

Functional enrichment analysis showed that QPCT, SCEL and TNFRSF12A were enriched in the pathways for peptidyl-pyroglutamic acid biosynthesis, keratinocyte differentiation, WNT signaling, apoptosis. GEPIA2 database analysis revealed that QPCT, SCEL and TNFRSF12A were high in thyroid cancer, and TC patients with lower TNFRSF12A levels had short survival. QPCT, SCEL and TNFRSF12A were elevated in PTC and thyroid adenoma. The mRNA diagnostic values were as follows: for QPCT, AUROC = 0.891, 95% CI, 0.835-0.947; for SCEL, AUROC = 0.921, 95% CI, 0.869-0.974; for TNFRSF12A, AUROC = 0.884, 95% CI, 0.809-0.958. Immunohistochemical results showed that QPCT, SCEL, and TNFRSF12A differed to varying degrees between subgroups of thyroid tissue. SCEL was associated with BRAF V600E mutation status and stratification of recurrence risk, while TNFRSF12A was associated with Cyclin D1. The protein diagnostic values were as follows: for QPCT, AUROC = 0.752, 95% CI, 0.685-0.819; for SCEL, AUROC = 0.715, 95% CI, 0.645-0.784; for TNFRSF12A, AUROC = 0.660, 95% CI, 0.587-0.734.

CONCLUSION

QPCT, SCEL and TNFRSF12A are expected to be diagnostic markers for PTC.