Comprehensive transcriptome and scRNA-seq analyses uncover the expression and underlying mechanism of SYNJ2 in papillary thyroid carcinoma.

Synaptojanin 2 (SYNJ2) has crucial role in various tumors, but its role in papillary thyroid carcinoma (PTC) remains unexplored. This study first detected SYNJ2 protein expression in PTC using immunohistochemistry method and further assessed SYNJ2 mRNA expression through mRNA chip and RNA sequencing data and its association with clinical characteristics. Additionally, KEGG, GSVA, and GSEA analyses were conducted to investigate potential biological functions, while single-cell RNA sequencing data were used to explore SYNJ2's underlying mechanisms in PTC. Meanwhile, immune infiltration status in different SYNJ2 expression groups were analyzed. Besides, we investigated the immune checkpoint gene expression and implemented drug sensitivity analysis. Results indicated that SYNJ2 is highly expressed in PTC (SMD = 0.66 [95% CI: 0.17-1.15]) and could distinguish between PTC and non-PTC tissues (AUC = 0.74 [0.70-0.78]). Furthermore, the study identified 134 intersecting genes of DEGs and CEGs, mainly enriched in the angiogenesis and epithelial-mesenchymal transition (EMT) pathways. Subsequent analysis showed the above pathways were activated in PTC epithelial cells. PTC patients with high SYNJ2 expression showed higher sensitivity to the six common drugs. Summarily, SYNJ2 may promote PTC progression through angiogenesis and EMT pathways. High SYNJ2 expression is associated with better response to immunotherapy and chemotherapy.

Yang YP ，Huang ZG ，Luo JY ，He J ，Shi L ，Chen G ，Chen SY ，Deng YW ，Yang YJ ，Tang YJ ，Pang YY ... -  《-》

The expression of CCL17 and potential prognostic value on tumor immunity in thyroid carcinoma based on bioinformatics analysis.

Although CCL17 has been reported to exert a vital role in many cancers, the related studies in the thyroid carcinoma have never reported. As a chemokine, CCL17 plays a positive role by promoting the infiltration of immune cells into the tumor microenviroment (TME) to influence tumor invasion and metastasis. Therefore, this study is aimed to investigate the association of CCL17 level with potential prognostic value on tumor immunity in the thyroid carcinoma (THCA) based on the bioinformatics analysis. GEPIA database was applied to analyze CCL17 mRNA expression in THCA data from TCGA database. Through the collection of the data, totally 500 tumor and 57 normal tissue samples were taken for the study. According to survival status and survival time in 500 tumor samples and CCL17 expression from RNA-seq data, all patients were categorized as high- expression (n = 64) and low-expression (n = 436) groups using X-tile program. Next, the association of CCL17 with survival in the thyroid carcinoma patients was examined by using the Kaplan-Meier plotter database. Then, weighted gene co-expression network (WGCNA) was employed to analyze the 1424 DEGs to classify 9 modules. Besides, STRING database was used to obtain the hub genes. GO and KEGG database were employed to explore blue module genes enrichment situations. In addition, TISIDB was used to analyze the relationship of CCL17 expression with tumor-infiltrating lymphocytes proportion, immunostimulators, and major histocompatibility complexes in THCA. The correlation of CCL17 with 22 TIIC subtypes was evaluated by ESTIMATE and CIBERSORT databases. The association of CCL17 level with gene marker of immune cells in THCA was analyzed by GEPIA and TIMER databases. Finally, immunohistochemistry was applied to validate CCL17 expression in 21 tumor and para-carcinoma tissue samples. CCL17 expression in tumors was significantly up-regulated relative to non-carcinoma samples. Patients from CCL17 high-expression group had significantly decreased overall survival compared with low-expression group, which has a significantly importantly potential prognostic value. Moreover, CCL17 and clinical characteristics were analyzed, suggesting that CCL17 expression significantly increased among patients of advanced stage, with advanced T classification, advanced N classification, and higher CCR4 expression. Based on WGCNA, expression of 1424 DEGs in blue module with 258 genes was negatively related to dismal survival and clinical lymph node metastasis in THCA patients. Moreover, CCR4 and CCL17 genes were identified as hub genes within blue module. CCL17 high-expression had greater ImmuneScore, StromalScore and ESTIMATEScore, while lower TumorPurity compared to the CCL17 low-expression. Then, GO and KEGG database were used to analyze blue module genes enrichment situations. The result showed that genes in blue module were associated with cytokine-cytokine receptor interaction, chemokine, and PI3K - Akt pathways. The results of tumor-infiltrating lymphocytes proportion, immunostimulators, and major histocompatibility complexes were significantly positive in CCL17 high-expression. Our findings showed that B cells naïve, T cells CD4 memory resting, T cells CD8, T cells regulatory (Tregs), and dendritic cells resting were the main immune components of THCA tumor microenvironment (TME). CCL17 high-expression in TC was significantly positively related to expression of immune cell gene markers. The result of immunohistochemistry demonstrated that CCL17 expression in tumor tissues significantly increased compared with para-carcinoma tissues. CCL17 high-expression was significantly positively associated with age and advanced N classification, suggesting that CCL17 could accelerate tumor progression by promoting the lymph node metastasis. CCL17 high-expression in THCA tumor microenvironment (TME) accelerates local infiltration of immune cells and enhances anticancer immunity, resulting in worse survival of patients and exerting potential prognostic value on tumor immunity in THCA.

Gu X ，Chen B ，Zhang S ，Zhai X ，Hu Y ，Ye H ... -  《Scientific Reports》

Revolutionary multi-omics analysis revealing prognostic signature of thyroid cancer and subsequent in vitro validation of SNAI1 in mediating thyroid cancer progression through EMT.

Thyroid carcinoma (TC), the most commonly diagnosed malignancy of the endocrine system, has witnessed a significant rise in incidence over the past few decades. The integration of scRNA-seq with other sequencing approaches offers researchers a distinct perspective to explore mechanisms underlying TC progression. Therefore, it is crucial to develop a prognostic model for TC patients by utilizing a multi-omics approach. We acquired and processed transcriptomic data from the TCGA-THCA dataset, including mRNA expression profiles, lncRNA expression profiles, miRNA expression profiles, methylation chip data, gene mutation data, and clinical data. We constructed a tumor-related risk model using machine learning methods and developed a consensus machine learning-driven signature (CMLS) for accurate and stable prediction of TC patient outcomes. 2 strains of undifferentiated TC cell lines and 1 strain of PTC cell line were utilized for in vitro validation. mRNA, protein levels of hub genes, epithelial-mesenchymal transition (EMT)-associated phenotypes were detected by a series of in vitro experiments. We identified 3 molecular subtypes of TC based on integrated multi-omics clustering algorithms, which were associated with overall survival and displayed distinct molecular features. We developed a CMLS based on 28 hub genes to predict patient outcomes, and demonstrated that CMLS outperformed other prognostic models. TC patients of relatively lower CMLS score had significantly higher levels of T cells, B cells, and macrophages, indicating an immune-activated state. Fibroblasts were predominantly enriched in the high CMLS group, along with markers associated with immune suppression and evasion. We identified several drugs that could be suitable for patients with high CMLS, including Staurosporine_1034, Rapamycin_1084, gemcitabine, and topotecan. SNAI1 was elevated in both undifferentiated TC cell lines, comparing to PTC cells. Knockdown of SNAI1 reduced the cell proliferation and EMT phenotypes of undifferentiated TC cells. Our findings highlight the importance of multi-omics analysis in understanding the molecular subtypes and immune characteristics of TC, and provide a novel prognostic model and potential therapeutic targets for this disease. Moreover, we identified SNAI1 in mediating TC progression through EMT in vitro.

Jin X ，Fu C ，Qi J ，Chen C ... -  《-》

FOXE1 Gene is a Probable Tumor Suppressor Gene with Decreased Expression as Papillary Thyroid Cancers Grow, and is Absent in Anaplastic Thyroid Cancers.

Papillary thyroid carcinoma (PTC), the most prevalent cancer of the thyroid, is more common in women than in men. To uncover the expression profile of FOXE1 gene in PTC tumor etiology. Microarray and RNA sequencing data on PTC in humans were analyzed. Eleven PTC tumor tissue samples and their neighboring normal tissue samples were collected. RT-qPCR was performed. Data normality, ROC construction, and logistic regression analysis were conducted. PTC tumors, normal tissues surrounding tumors, patients of different sexes and ages, metastasizing tumors, and tumor variants were assessed for FOXE1 expression. Eleven PTC tissues were obtained from seven women and four men. Among the PTC subtypes, there were two FV-PTCs, four C-PTCs, one microcarcinoma, and four tissues with an unknown subtype. FOXE1 gene expression was significantly increased in PTC tumors with dimensions less than 10 mm (relative expression = 14.437, p = 0.050). A significant increase in FOXE1 gene expression was observed in the normal tissue adjacent to the tumor, which was less than 10 mm in size, compared to the normal tissue adjacent to the tumor, which was larger than 10 mm (relative expression = 41.760, p = 0.0001). Females diagnosed with PTC showed a significant reduction in FOXE1 mRNA levels compared to their male counterparts (relative expression = 0.081, p = 0.042). In contrast to adjacent normal tissue, there was a significant reduction in FOXE1 gene expression in FV-PTC (relative expression = 0.044 and p = 0.0001). PTC tumors under 10mm had higher FOXE1 gene expression than larger tumors; normal tissue adjacent to smaller tumors also had higher FOXE1 expression. Females with PTC, regardless of their subtype, expressed less FOXE1 mRNA than males. FV-PTC tissues exhibited lower expression of FOXE1 mRNA than their adjacent normal tissues.

Hajian R ，Javadirad SM ，Kolahdouzan M 《-》

Biomarkers associated with papillary thyroid carcinoma and Hashimoto's thyroiditis: Bioinformatic analysis and experimental validation.

Hashimoto's thyroiditis (HT) is widely recognized as a risk factor for papillary thyroid carcinoma (PTC). This study aimed to identify key targets involved in the progression of HT to PTC. Microarray datasets (GSE138198) for PTC, HT, and PTC with HT in the background (PTC-W) were obtained from the Gene Expression Omnibus (GEO) database. Differentially expressed genes (DEGs) were identified and analyzed between normal and diseased groups. Functional enrichment analysis was performed using Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG). Protein-protein interaction (PPI) network analysis was conducted to identify hub genes, which were validated through qPCR and immunohistochemical (IHC) analysis. ROC analysis was then carried out based on the expression levels of hub genes in clinical plasma samples. A total of 78 shared DEGs were identified from the GEO dataset. GO and KEGG analyses highlighted pathways such as epithelial-to-mesenchymal transition (EMT) and PI3K-Akt signaling. The analysis of immune cell subtypes showed that the hub genes were commonly associated with various immune cells, particularly dendritic cells (DC) and macrophages. Ten hub genes-LYZ, FCER1G, CCL18, CXCL9, ALOX5, TYROBP, C1QB, CTSS, MET, and FAM20A-were identified from the PPI network. qPCR and IHC confirmed the overexpression of MET and FAM20A in PTC-W. The area under the curve (AUC) of the ROC analysis was 0.889 for MET and 0.825 for FAM20A. This study identified two hub genes, MET and FAM20A, with potential diagnostic value in HT and PTC.

Li B ，Cai Z ，Zhang Y ，Chen R ，Tang S ，Kong F ，Li W ，Ding L ，Chen L ，Xu H ... -  《-》