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

Liu, Chang; Pan, Yu; Li, Qinyu; Zhang, Yifan

doi:10.7150/ijms.63402

Cited by 13 publications

(9 citation statements)

References 53 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The present study also investigated whether CCL21 and ZCCHC12 were common down- or upregulated genes, respectively, in the 4 subclusters. The results supported evidence from previous observations, including those of Liu et al , who found that the expression of CCL21 is lower in PTC tissues than in Hashimoto’s thyroiditis tissues ( 31 ), and Smallridge et al , who reported that CCL21 is associated with lymphocyte infiltration and is differentially overexpressed in BRAF-wild-type tumors compared to BRAF V600E-mutation-harboring tumors ( 32 ).…”

Section: Discussionsupporting

confidence: 91%

Identification and validation of subclusters of papillary thyroid carcinoma based on Human Phenotype Ontology

Xuan¹,

Hu²,

Xu³

et al. 2023

Gland Surg

View full text Add to dashboard Cite

Background The increase in the diagnosis of papillary thyroid carcinoma (PTC) has prompted researchers to establish a diagnostic model and identify functional subclusters. The Human Phenotype Ontology (HPO) platform is widely available for differential diagnostics and phenotype-driven investigations based on next-generation sequence-variation data. However, a systematic and comprehensive study to identify and validate PTC subclusters based on HPO is lacking. Methods We first used the HPO platform to identify the PTC subclusters. An enrichment analysis was then conducted to examine the key biological processes and pathways associated with the subclusters, and a gene mutation analysis of the subclusters was conducted. For each subcluster, the differentially expressed genes (DEGs) were selected and validated. Finally, a single-cell RNA-sequencing data set was used to verify the DEGs. Results In our study, 489 PTC patients from The Cancer Genome Atlas (TCGA) were included. Our analysis demonstrated that distinct subclusters of PTC are associated with different survival times and have different functional enrichment, and that C-C motif chemokine ligand 21 ( CCL21 ) and zinc finger CCHC-type containing 12 ( ZCCHC12 ) were the common down- and upregulated genes, respectively, in the 4 subclusters. Additionally, 20 characteristic genes were identified in the 4 subclusters, some of which have previously been reported to have roles in PTC. Further, we found that these characteristic genes were mainly expressed in thyrocytes, endothelial cells, and fibroblasts, and were rarely expressed in immune cells. Conclusions We first identified subclusters in PTC based on HPO and found that patients with distinct subclusters have different prognoses. We then identified and validated the characteristic genes in the 4 subclusters. These findings are expected to serve as a crucial reference that will improve our understanding of PTC heterogeneity and the use of novel targets.

show abstract

Section: Discussionsupporting

confidence: 91%

Identification and validation of subclusters of papillary thyroid carcinoma based on Human Phenotype Ontology

Xuan¹,

Hu²,

Xu³

et al. 2023

Gland Surg

View full text Add to dashboard Cite

show abstract

“…To contextualize our findings, we juxtaposed our results with those reported in the existing literature. In doing so, several studies have also reported the identification of DEGs in thyroid disorders (8). For instance, He et al, identified a similar number of DEGs in a transcriptome analysis of thyroid cancer patients, highlighting the consistency with our findings (9).…”

Section: Discussionsupporting

confidence: 87%

Utilizing Bioinformatics Approaches to Conduct a Comparative Analysis of the Thyroid Transcriptome in Thyroid Disorders

De Oliveira Andrade,

Matos de Oliveira,

Vinhaes Bittencourt

et al. 2024

Rev.ACE

View full text Add to dashboard Cite

Introduction: This study aims to identify common gene expression patterns and dysregulated pathways in various thyroid disorders by leveraging publicly available transcriptomic datasets. The integration of other omics data, when possible, will allow us to uncover potential molecular drivers and biomarkers associated with specific thyroid dysfunctions. However, there are still gaps in the analysis of the transcriptomes of the various thyroid disorders. Objective: To conduct a comparative analysis of the thyroid transcriptome in thyroid disorders using bioinformatics approaches. Methods: We retrieved publicly available gene expression datasets related to the thyroid from the European Nucleotide Archive. Data preprocessing involved conducting quality control, trimming reads, and aligning them to a reference genome. Differential expression analysis was performed using bioinformatics packages, and finally, a functional enrichment analysis was conducted to gain insights into the biological processes. Network analysis was conducted to explore interactions and regulatory relationships among differentially expressed genes (DEGs). Results: Our analysis included a total of 18 gene expression datasets, of which 15 were selected based on inclusion criteria and quality assessment. Numerous genes exhibiting differential expression (P < 0.01) were discerned, and their significance was systematically ranked. Functional enrichment analysis revealed numerous biological processes associated with the differentially expressed genes, providing insights into the molecular mechanisms of thyroid disorders. Network analysis using Cytoscape software revealed potential interactions among differentially expressed genes and identified key hub genes and potential therapeutic targets. Conclusion: This study demonstrates an accessible methodology for conducting a comparative analysis of the thyroid transcriptome in different disorders without the need for thyroid tissue samples. The integration of bioinformatics approaches provides a comprehensive understanding of the molecular mechanisms underlying thyroid diseases.

show abstract

“…SERPINA1, a protein associated with inflammatory disease, organismal injury, and abnormal thyroid function, is significantly lower in hyperthyroidism samples than in normal thyroid samples ( 64 , 65 ). As a hub gene for PTC-DEGs, SERPINA1 is potential diagnostic gene for PTC and is related to the extracellular matrix pathway ( 66 – 68 ). Our findings further suggest that SERPINA1 serves as an important cross-linking gene involved in the pathogenesis of HT and PTC.…”

Section: Discussionmentioning

confidence: 99%

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

et al. 2023

View full text Add to dashboard Cite

BackgroundHashimoto’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.MethodsHT- 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).ResultsIn 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.ConclusionFour (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.

show abstract

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

Cited by 13 publications

References 53 publications

Identification and validation of subclusters of papillary thyroid carcinoma based on Human Phenotype Ontology

Identification and validation of subclusters of papillary thyroid carcinoma based on Human Phenotype Ontology

Utilizing Bioinformatics Approaches to Conduct a Comparative Analysis of the Thyroid Transcriptome in Thyroid Disorders

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

Contact Info

Product

Resources

About