Novel therapeutic clues in thyroid carcinomas: The role of targeting cancer stem cells

Anti-papillary thyroid carcinoma effects of dioscorea bulbifera L. through ferroptosis and the PI3K/AKT pathway based on network pharmacology and experimental validation

ETHNOPHARMACOLOGICAL RELEVANCE

Papillary thyroid carcinoma (PTC) is the predominant form of thyroid cancer with a rising global incidence. Despite favorable prognoses, a significant recurrence rate persists. Dioscorea bulbifera L. (DBL), a traditional Chinese medicine, has been historically used for thyroid-related disorders. However, its therapeutic effects and mechanisms of action on PTC remain unclear.

AIM OF THE STUDY

To explore the potential therapeutic effects, principal active components, and molecular mechanisms of DBL in the treatment of PTC through network pharmacology and molecular docking, with experimental validation conducted to corroborate these findings.

MATERIALS AND METHODS

The Traditional Chinese Medicine Systems Pharmacology Database (TCMSP) was utilized as a systematic tool for collecting and screening the phytochemical components of DBL, and for establishing associations between these components and molecular targets. Based on this, network data was visually processed using Cytoscape software (version 3.8.0). Concurrently, precise molecular docking studies of the principal active components of DBL and their corresponding targets were conducted using Autodock software. Additionally, PTC-related genes were selected through the GeneCards and GEO databases. We further employed the DAVID bioinformatics resources to conduct comprehensive Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analyses on the intersecting genes between DBL and PTC. These analyses aid in predicting the potential therapeutic actions of DBL on PTC and its mechanisms of action. To validate these findings, corresponding in vitro experimental studies were also conducted.

RESULTS

In this investigation, 14 bioactive compounds of DBL and 195 corresponding molecular targets were identified, with 127 common targets shared between DBL and PTC. Molecular docking revealed strong binding affinities between major bioactive compounds and target proteins. GO enrichment analysis unveiled key processes involved in DBL's action. KEGG analysis highlighted DBL's modulation of the PI3K/AKT signaling pathway. Experimental outcomes demonstrated DBL's potential in inhibiting PTC cell proliferation and migration, suppressing PI3K/AKT pathway activation, and promoting ferroptosis.

CONCLUSION

In conclusion, DBL offers a multifaceted therapeutic approach for PTC, targeting multiple molecular entities and influencing diverse biological pathways. Network pharmacology and molecular docking shed light on DBL's potential utility in PTC treatment, substantiated by experimental validation. This study contributes valuable insights into using DBL as a promising therapeutic agent for PTC management.

Multi-omics analysis-based macrophage differentiation-associated papillary thyroid cancer patient classifier

BACKGROUND

The reclassification of Papillary Thyroid Carcinoma (PTC) is an area of research that warrants attention. The connection between thyroid cancer, inflammation, and immune responses necessitates considering the mechanisms of differential prognosis of thyroid tumors from an immunological perspective. Given the high adaptability of macrophages to environmental stimuli, focusing on the differentiation characteristics of macrophages might offer a novel approach to address the issues related to PTC subtyping.

METHODS

Single-cell RNA sequencing data of medullary cells infiltrated by papillary thyroid carcinoma obtained from public databases was subjected to dimensionality reduction clustering analysis. The RunUMAP and FindAllMarkers functions were utilized to identify the gene expression matrix of different clusters. Cell differentiation trajectory analysis was conducted using the Monocle R package. A complex regulatory network for the classification of Immune status and Macrophage differentiation-associated Papillary Thyroid Cancer Classification (IMPTCC) was constructed through quantitative multi-omics analysis. Immunohistochemistry (IHC) staining was utilized for pathological histology validation.

RESULTS

Through the integration of single-cell RNA and bulk sequencing data combined with multi-omics analysis, we identified crucial transcription factors, immune cells/immune functions, and signaling pathways. Based on this, regulatory networks for three IMPTCC clusters were established.

CONCLUSION

Based on the co-expression network analysis results, we identified three subtypes of IMPTCC: Immune-Suppressive Macrophage differentiation-associated Papillary Thyroid Carcinoma Classification (ISMPTCC), Immune-Neutral Macrophage differentiation-associated Papillary Thyroid Carcinoma Classification (INMPTCC), and Immune-Activated Macrophage differentiation-associated Papillary Thyroid Carcinoma Classification (IAMPTCC). Each subtype exhibits distinct metabolic, immune, and regulatory characteristics corresponding to different states of macrophage differentiation.

Antineoplastic Activity of Pazopanib in Anaplastic Thyroid Cancer in Primary Culture

Anaplastic thyroid cancer (ATC) is a rare and rapidly fatal human cancer. Its usual treatment includes the combination of surgery, external hyperfractionated radiation therapy, and chemotherapy. These treatments permit achieving about 6-10 months of median survival. For this reason, it is challenging to predict the ATC patient clinical therapy responsiveness. Pazopanib is a multitarget tyrosine kinase inhibitor of VEGF receptors, PDGF, and c-Kit. Until now, the effect of pazopanib in primary human ATC cells (pATC) has not been reported in the literature. The aim of our study was to evaluate in vitro the antineoplastic effect of pazopanib in pATC. Surgical thyroidal tissues were collected from five patients with ATC, from thyroid biopsy at the moment of first surgical operation. An inhibition of proliferation, migration, and invasion, and an increase in apoptosis were demonstrated upon treating pATC cells with pazopanib (p < 0.05). Moreover, pazopanib was able to significantly decrease the VEGF expression in pATC cells (p < 0.05). To conclude, in this study, we demonstrate the antineoplastic activity of the antiangiogenic inhibitor, pazopanib, in human pATC in vitro.

Machine learning for identifying benign and malignant of thyroid tumors: A retrospective study of 2,423 patients

Thyroid tumors, one of the common tumors in the endocrine system, while the discrimination between benign and malignant thyroid tumors remains insufficient. The aim of this study is to construct a diagnostic model of benign and malignant thyroid tumors, in order to provide an emerging auxiliary diagnostic method for patients with thyroid tumors. The patients were selected from the Chongqing General Hospital (Chongqing, China) from July 2020 to September 2021. And peripheral blood, BRAFV600E gene, and demographic indicators were selected, including sex, age, BRAFV600E gene, lymphocyte count (Lymph#), neutrophil count (Neu#), neutrophil/lymphocyte ratio (NLR), platelet/lymphocyte ratio (PLR), red blood cell distribution width (RDW), platelets count (PLT), red blood cell distribution width-coefficient of variation (RDW-CV), alkaline phosphatase (ALP), and parathyroid hormone (PTH). First, feature selection was executed by univariate analysis combined with least absolute shrinkage and selection operator (LASSO) analysis. Afterward, we used machine learning algorithms to establish three types of models. The first model contains all predictors, the second model contains indicators after feature selection, and the third model contains patient peripheral blood indicators. The four machine learning algorithms include extreme gradient boosting (XGBoost), random forest (RF), light gradient boosting machine (LightGBM), and adaptive boosting (AdaBoost) which were used to build predictive models. A grid search algorithm was used to find the optimal parameters of the machine learning algorithms. A series of indicators, such as the area under the curve (AUC), were intended to determine the model performance. A total of 2,042 patients met the criteria and were enrolled in this study, and 12 variables were included. Sex, age, Lymph#, PLR, RDW, and BRAFV600E were identified as statistically significant indicators by univariate and LASSO analysis. Among the model we constructed, RF, XGBoost, LightGBM and AdaBoost with the AUC of 0.874 (95% CI, 0.841-0.906), 0.868 (95% CI, 0.834-0.901), 0.861 (95% CI, 0.826-0.895), and 0.837 (95% CI, 0.802-0.873) in the first model. With the AUC of 0.853 (95% CI, 0.818-0.888), 0.853 (95% CI, 0.818-0.889), 0.837 (95% CI, 0.800-0.873), and 0.832 (95% CI, 0.797-0.867) in the second model. With the AUC of 0.698 (95% CI, 0.651-0.745), 0.688 (95% CI, 0.639-0.736), 0.693 (95% CI, 0.645-0.741), and 0.666 (95% CI, 0.618-0.714) in the third model. Compared with the existing models, our study proposes a model incorporating novel biomarkers which could be a powerful and promising tool for predicting benign and malignant thyroid tumors.

Identification of ferroptosis genes in immune infiltration and prognosis in thyroid papillary carcinoma using network analysis

BACKGROUND

Papillary thyroid carcinoma (PTC) is the most common thyroid cancer. While many patients survive, a portion of PTC cases display high aggressiveness and even develop into refractory differentiated thyroid carcinoma. This may be alleviated by developing a novel model to predict the risk of recurrence. Ferroptosis is an iron-dependent form of regulated cell death (RCD) driven by lethal accumulation of lipid peroxides, is regulated by a set of genes and shows a variety of metabolic changes. To elucidate whether ferroptosis occurs in PTC, we analyse the gene expression profiles of the disease and established a new model for the correlation.

METHODS

The thyroid carcinoma (THCA) datasets were downloaded from The Cancer Genome Atlas (TCGA), UCSC Xena and MisgDB, and included 502 tumour samples and 56 normal samples. A total of 60 ferroptosis related genes were summarised from MisgDB database. Gene set enrichment analysis (GSEA) and Gene set variation analysis (GSVA) were used to analyse pathways potentially involving PTC subtypes. Single sample GSEA (ssGSEA) algorithm was used to analyse the proportion of 28 types of immune cells in the tumour immune infiltration microenvironment in THCA and the hclust algorithm was used to conduct immune typing according to the proportion of immune cells. Spearman correlation analysis was performed on the ferroptosis gene expression and the correlation between immune infiltrating cells proportion. We established the WGCNA to identify genes modules that are highly correlated with the microenvironment of immune invasion. DEseq2 algorithm was further used for differential analysis of sequencing data to analyse the functions and pathways potentially involving hub genes. GO and KEGG enrichment analysis was performed using Clusterprofiler to explore the clinical efficacy of hub genes. Univariate Cox analysis was performed for hub genes combined with clinical prognostic data, and the results was included for lasso regression and constructed the risk regression model. ROC curve and survival curve were used for evaluating the model. Univariate Cox analysis and multivariate Cox analysis were performed in combination with the clinical data of THCA and the risk score value, the clinical efficacy of the model was further evaluated.

RESULTS

We identify two subtypes in PTC based on the expression of ferroptosis related genes, with the proportion of cluster 1 significantly higher than cluster 2 in ferroptosis signature genes that are positively associated. The mutations of Braf and Nras are detected as the major mutations of cluster 1 and 2, respectively. Subsequent analyses of TME immune cells infiltration indicated cluster 1 is remarkably richer than cluster 2. The risk score of THCA is in good performance evaluated by ROC curve and survival curve, in conjunction with univariate Cox analysis and multivariate Cox analysis results based on the clinical data shows that the risk score of the proposed model could be used as an independent prognostic indicator to predict the prognosis of patients with papillary thyroid cancer.

CONCLUSIONS

Our study finds seven crucial genes, including Ac008063.2, Apoe, Bcl3, Acap3, Alox5ap, Atxn2l and B2m, and regulation of apoptosis by parathyroid hormone-related proteins significantly associated with ferroptosis and immune cells in PTC, and we construct the risk score model which can be used as an independent prognostic index to predict the prognosis of patients with PTC.

Clinical Indications for Treatment with Multi-Kinase Inhibitors in Patients with Radioiodine-Refractory Differentiated Thyroid Cancer

Differentiated thyroid cancer is usually a slow-growing disease, even if the patients develop distant metastasis. For recurrent or metastatic disease, radioactive iodine therapy is a standard treatment. However, the disease gradually progresses in some of the patients and can ultimately develop into life-threatening conditions. For patients with progressive radioactive iodine-refractory differentiated thyroid cancer (RR-DTC), multi-kinase inhibitors (MKIs) including sorafenib and lenvatinib prolonged progression-free survival compared with placebo in pivotal randomized phase 3 trials, although the benefit in overall survival has not been clearly confirmed, possibly because the patients who received placebo were permitted to cross-over to lenvatinib upon disease progression. Moreover, the adverse events related to MKIs were not negligible. Therefore, the optimal timing of MKI initiation has long been controversial, and physicians should consider various patient and disease factors. Herein, we comprehensively review the clinical factors that can be helpful in determining the initiation of MKIs for patients with RR-DTC.

The Identification of Three Key Genes Related to Stemness in Thyroid Carcinoma through Comprehensive Analysis

BACKGROUND

Tumor heterogeneity imposes great challenges on cancer treatment. Cancer stem cells (CSCs) are a leading factor contributing to tumor occurrence. However, the mechanisms underlying the growth of thyroid cancer (TCHA) are still unclear.

METHODS

Key genes regulating the characteristics of THCA, such as stemness were identified by combining gene expressions of samples downloaded from the Cancer Genome Atlas (TCGA) and were used to establish an mRNA expression stemness index (mRNAsi) through machine learningbased methods. The relationships of mRNAsi, THCA clinical features and molecular subtypes were analyzed. Weighted Gene Co-Expression Network Analysis (WGCNA) was performed to obtain mRNAsi-related gene modules and determine mRNAsi-related differentially co-expressed genes. Key genes related to mRNAsi were screened by protein interaction network. Functional analysis was conducted and expressions of key genes were verified in multiple external data sets.

RESULTS

The mRNAsi score, which was found to be lower in the TCHA tissues than that in normal tissues (p<0.05), was positively correlated with a slow progression of tumor prognosis (p=0.0085). We screened a total of 83 differentially co-expressed genes related to mRNAsi and multiple tumor pathways such as apoptosis, tight junction, cytokine-cytokine receptor interaction, and cAMP signaling pathway (p<0.05). Finally, 28 protein interaction networks incorporating 32 genes were established, and 3 key genes were identified through network mining. 3 core genes were finally determined, as their low expressions were strongly correlated with the progression of THCA.

CONCLUSION

The study found that NGF, FOS, and GRIA1 are closely related to the characteristics of THCA stem cells. These genes, especially FOS, are highly indicative of the prognosis of THCA patients. Thus, screening therapy could be used to inhibit the stemness of TCHA.

Progression Risk Assessment of Post-surgical Papillary Thyroid Carcinoma Based on Circular RNA-Associated Competing Endogenous RNA Mechanisms

Background: Accurate risk assessment of post-surgical progression in papillary thyroid carcinoma (PTC) patients is critical. Exploring key differentially expressed mRNAs (DE-mRNAs) regulated by differentially expressed circular RNAs (circRNAs) via the ceRNA mechanism could help establish a novel assessment tool. Methods: ceRNA network was established based on differentially expressed RNAs and correlation analysis. DE-mRNAs within the ceRNA network associated with progression-free interval (PFI) of PTC were identified to construct a prognostic ceRNA regulatory subnetwork. least absolute shrinkage and selection operator (LASSO)-Cox regression was applied to identify hub DE-mRNAs and establish a novel DE-mRNA signature in predicting PFI of PTC. Results: Six hub DE-mRNAs, namely, CLCNKB, FXBO27, FXYD6, RIMS2, SPC24, and CDKN2A, were identified to be most significantly related to the PFI of PTC, and a prognostic DE-mRNA signature was proposed. A nomogram incorporating the DE-mRNA signature and clinical parameters was established to improve the progression risk assessment in post-surgical PTC, which was superior to the American Thyroid Association risk stratification system and distant Metastasis, patient Age, Completeness of resection, local Invasion, and tumor Size (MACIS) score American Joint Committee on Cancer staging system. Conclusions: Based on the circRNA-associated ceRNA RNA mechanism, a DE-mRNA signature and prognostic nomogram was established, which may improve the progression risk assessment in post-surgical PTC.

Molecular targets of tyrosine kinase inhibitors in thyroid cancer

Thyroid cancer (TC) is the eighth most frequently diagnosed cancer worldwide with a rising incidence in the past 20 years. Surgery is the primary strategy of therapy for patients with medullary TC (MTC) and differentiated TC (DTC). In DTC patients, radioactive iodine (RAI) is administered after thyroidectomy. Neck ultrasound, basal and thyroid-stimulating hormone-stimulated thyroglobulin are generally performed every three to six months for the first year, with subsequent intervals depending on initial risk assessment, for the detection of possible persistent/recurrent disease during the follow up. Distant metastases are present at the diagnosis in ∼5 % of DTC patients; up to 15 % of patients have recurrences during the follow up, with a survival reduction (70 %-50 %) at 10-year. During tumor progression, the iodide uptake capability of DTC cancer cells can be lost, making them refractory to RAI, with a negative impact on the prognosis. Significant advances have been done recently in our understanding of the molecular pathways implicated in the progression of TCs. Several drugs have been developed, which inhibit signaling kinases or oncogenic kinases (BRAF^V600E, RET/PTC), such as those associated with Platelet-Derived Growth Factor Receptor and Vascular Endothelial Growth Factor Receptor. Tyrosine kinase receptors are involved in cancer cell proliferation, angiogenesis, and lymphangiogenesis. Several tyrosine kinase inhibitors (TKIs) are emerging as new treatments for DTC, MTC and anaplastic TC (ATC), and can induce a clinical response and stabilize the disease. Lenvatinib and sorafenib reached the approval for RAI-refractory DTC, whereas cabozantinib and vandetanib for MTC. These TKIs extend median progression-free survival, but do not increase the overall survival. Severe side effects and drug resistance can develop in TC patients treated with TKIs. Additional studies are needed to identify a potential effective targeted therapy for aggressive TCs, according to their molecular characterization.

Identification and Validation of Novel Genes in Anaplastic Thyroid Carcinoma via Bioinformatics Analysis

Purpose

The conventional interventions of anaplastic thyroid carcinoma (ATC) patients are mainly through surgery, chemotherapy, and radiotherapy; however, it is hardly to improve survival rate. We aimed to investigate the differential expressed genes (DEGs) between ATC and normal thyroid gland through bioinformatics analysis of the microarray datasets and find new potential therapeutic targets for ATC.

Methods

Microarray datasets GSE9115, GSE29265, GSE33630, GSE53072, and GSE65144 were downloaded from Gene Expression Omnibus (GEO) database. Compared with the normal tissue, GEO2R was conducted to screen the DEGs in each chip under the condition of |log FC| > l, adjusted P‐values (adj. P) < 0.05. The Retrieval of Interacting Genes (STRING) database was used to calculate PPI networks of DEGs with a combined score >0.4 as the cut-off criteria. The hub genes in the PPI network were visualized and selected according to screening conditions in Cytoscape software. In addition, the novel genes in ATC were screened for survival analysis using Kaplan–Meier plotter from those hub genes and validated by RT-qPCR.

Results

A total of 284 overlapping DEGs were obtained, including 121 upregulated and 161 downregulated DEGs. A total of 232 DEGs were selected by STRING database. The 50 hub genes in the PPI network were chosen according to three screening conditions. In addition, the Kaplan–Meier plotter database confirmed that high expressions of ANLN, CENPF, KIF2C, TPX2, and NDC80 were negatively correlated with poor overall survival of ATC patients. Finally, RT-qPCR experiments showed that KIF2C and CENPF were significantly upregulated in ARO cells and CAL-62 cells when compared to Nthy-ori 3–1 cells, TPX2 was upregulated only in CAL-62 cells, while ANLN and NDC80 were obviously decreased in ARO cells and CAL-62 cells.

Conclusion

Our study suggested that CENPF, KIF2C, and TPX2 might play a significant role in the development of ATC, which could be further explored as potential biomarkers for the treatment of ATC.

Identification of a Recurrence Signature and Validation of Cell Infiltration Level of Thyroid Cancer Microenvironment

Though many patients with thyroid cancer may be indolent, there are still about 50% lymph node metastases and 20% the recurrence rates. There is still no ideal method to predict its relapse. In this study, we analyzed the gene transcriptome profiles of eight Gene Expression Omnibus (GEO), and next screened 77 commonly differential expressed genes. Next, Least Absolute Shrinkage and Selection Operator (LASSO) regression model was performed and seven genes (i.e., FN1, PKIA, TMEM47, FXYD6, SDC2, CD44, and GGCT) were then identified, which is highly associated with recurrence data from the Cancer Genome Atlas (TCGA) database. These patients were then divided into low and high-risk groups with specific risk-score formula. Univariate and multivariate Cox regression further revealed that the 7-mRNA signature plays a functional causative role independent of clinicopathological characteristics. The 7-mRNA-signature integrated nomogram showed better discrimination, and decision curve analysis demonstrated that it is clinically useful. Besides, patient with lower risk score shows a relatively lower level of activated dendritic cells (DCs), resting DCs, regulatory T cells and γδT cells, and process of DCs apoptotic. In conclusion, our present immune-related classifier could produce a potential tool for predicting early-relapse.

Cancer Stem Cells in Thyroid Tumors: From the Origin to Metastasis

Thyroid tumors are extremely heterogeneous varying from almost benign tumors with good prognosis as papillary or follicular tumors, to the undifferentiated ones with severe prognosis. Recently, several models of thyroid carcinogenesis have been described, mostly hypothesizing a major role of the thyroid cancer stem cell (TCSC) population in both cancer initiation and metastasis formation. However, the cellular origin of TCSC is still incompletely understood. Here, we review the principal epigenetic mechanisms relevant to TCSC origin and maintenance in both well-differentiated and anaplastic thyroid tumors. Specifically, we describe the alterations in DNA methylation, histone modifiers, and microRNAs (miRNAs) involved in TCSC survival, focusing on the potential of targeting aberrant epigenetic modifications for developing novel therapeutic approaches. Moreover, we discuss the bidirectional relationship between TCSCs and immune cells. The cells of innate and adaptive response can promote the TCSC-driven tumorigenesis, and conversely, TCSCs may favor the expansion of immune cells with protumorigenic functions. Finally, we evaluate the role of the tumor microenvironment and the complex cross-talk of chemokines, hormones, and cytokines in regulating thyroid tumor initiation, progression, and therapy refractoriness. The re-education of the stromal cells can be an effective strategy to fight thyroid cancer. Dissecting the genetic and epigenetic landscape of TCSCs and their interactions with tumor microenvironment cells is urgently needed to select more appropriate treatment and improve the outcome of patients affected by advanced differentiated and undifferentiated thyroid cancers.

Epithelial-to-mesenchymal transition in thyroid cancer: a comprehensive review

The Metastatic progression of solid tumors, such as thyroid cancer is a complex process which involves various factors. Current understanding on the role of epithelial-mesenchymal transition (EMT) in thyroid carcinomas suggests that EMT is implicated in the progression from follicular thyroid cancer (FTC) and papillary thyroid cancer (PTC) to poorly differentiated thyroid carcinoma (PDTC) and anaplastic thyroid cancer (ATC). According to the literature, the initiation of the EMT program in thyroid epithelial cells elevates the number of stem cells, which contribute to recurrent and metastatic diseases. The EMT process is orchestrated by a complex network of transcription factors, growth factors, signaling cascades, epigenetic modulations, and the tumor milieu. These factors have been shown to be dysregulated in thyroid carcinomas. Therefore, molecular interferences restoring the expression of tumor suppressors, or thwarting overexpressed oncogenes is a hopeful therapeutic method to improve the treatment of progressive diseases. In this review, we summarize the recent findings on EMT in thyroid cancer focusing on the main role-players and regulators of this process in thyroid tumors.

Nevirapine inhibits migration and invasion in dedifferentiated thyroid cancer cells

Background

Metastatic or recurrent thyroid cancer often behaves aggressively, and approximately two‐thirds of patients present with radioiodine resistance. Effective therapies to suppress thyroid cancer metastasis are urgently needed. Nevirapine has been proved to suppress tumor growth and induce differentiation in several tumor cells, but has not previously been evaluated in metastasis of thyroid cancer. The present study aimed to investigate the effect of nevirapine on migration and invasion in dedifferentiated thyroid cancer cells.

Methods

Human dedifferentiated thyroid cancer cell line (WRO 82‐1) was subject to real‐time quantitative PCR, western blot and transwell migration/invasion assays. The liver metastasis in tumor xenografts of nude mice was subject to hematoxylin‐eosin (HE) staining.

Results

Nevirapine significantly repressed cell migration and invasion in WRO 82‐1 cells, and surprisingly significantly decreased liver metastatic tumor in the nude mouse model of dedifferentiated thyroid cancer compared with that of the control. Moreover, nevirapine significantly decreased the expression of IL‐6 mRNA and phosphorylation of JAK2 (Y1007+Y1008) and STAT3 (Tyr 705) in WRO 82‐1 cells compared with those in control cells.

Conclusion

Our findings suggest that nevirapine significantly repressed migration and invasion/metastasis in WRO 82‐1 cells and tumor xenografts, which may be related to inhibition of IL‐6/STAT3 signaling pathway. It promises great potential as a novel therapy for thyroid cancer, especially for those patients with metastasis.

Correlations of lncRNAs with cervical lymph node metastasis and prognosis of papillary thyroid carcinoma

Background

Cervical lymph node metastasis is an important prognostic indicator for papillary thyroid carcinoma (PTC) and affects treatment strategies for PTC. lncRNAs essentially contribute to the biological functions of tumors. This study aimed to identify the lncRNAs associated with cervical lymph node metastasis and prognosis of PTC and their potential pathophysiological mechanisms.

Materials and methods

PTC-associated lncRNAs were selected from The Cancer Genome Atlas database, and correlations among lncRNAs, lymph node metastasis, tumor staging, and prognosis of PTC were analyzed in silico. These correlations were then validated through quantitative reverse transcription PCR (qRT-PCR) and immunohistochemistry (IHC).

Results

In silico analysis showed that FAM95B1 and UCA1 were significantly correlated with cervical lymph node metastasis, tumor staging, and PTC prognosis (P<0.05). qRT-PCR analysis revealed high UCA1 expression in PTC tissues and correlations between UCA1 expression levels and cervical lymph node metastasis and tumor staging in PTC, that is, higher UCA1 expression resulted in poorer PTC prognosis. IHC analysis revealed that a high expression of UCA1 was accompanied by a high expression of metastasis-related proteins (MMP-2 and MMP-9), thereby validating the correlation of UCA1 expression with metastasis.

Conclusion

FAM95B1 and UCA1 expression was significantly correlated with the occurrence and progression of PTC. The expression levels of UCA1 significantly affected the prognosis of PTC patients and were significantly correlated with tumor staging and cervical lymph node metastasis.

Identification of a Five-Gene Signature and Establishment of a Prognostic Nomogram to Predict Progression-Free Interval of Papillary Thyroid Carcinoma

Background: The incidence of papillary thyroid carcinoma (PTC) is high and increasing worldwide. Although prognosis is relatively good, it is important to select the minority of patients with poorer prognosis to avoid side effects associated with unnecessary over-treatment in low-risk patients; this requires accurate prognostic predictions. Materials and Methods: Six PTC expression datasets were obtained from the gene expression omnibus (GEO) database. Level 3 mRNA expression and clinicopathological data were obtained from The Cancer Genome Atlas Thyroid Cancer (TCGA-THCA) database. Through integrated analysis of these datasets, highly reliable differentially-expressed genes (DEGs) between tumor and normal tissue were identified and lasso Cox regression was applied to identify DEGs related to the progression-free interval (PFI) and to establish a prognostic gene signature. The performance of a five-gene signature was evaluated based on a Kaplan-Meier curve, receiver operating characteristic (ROC), and Harrell's concordance index (C-index). Multivariate Cox regression analysis was used to identify factors associated with PTC prognosis. Finally, a prognostic nomogram was established based on the TCGA-THCA dataset. Results: A novel five-gene signature was established to predict the PTC PFI, which included PLP2, LYVE1, FABP4, TGFBR3, and FXYD6, and the ROC curve and C-index showed good performance in both training and validation datasets. This could classify patients into high- and low-risk groups with distinct PFIs and differentiate PTC tumors from normal tissue. Univariate Cox regression revealed that this signature was an independent prognostic factor for PTC. The established nomogram, incorporating the prognostic gene signature and clinical parameters, was able to predict the PFI with high efficiency. The gene signature-based nomogram was superior to the American Thyroid Association (ATA) risk stratification to predict PTC PFI. Conclusions: Our study identified a five-gene signature and established a prognostic nomogram, which were reliable in predicting the PFI of PTC; this could be beneficial for individualized treatment and medical decision making.

Novel therapies against aggressive differentiated thyroid carcinomas

The incidence of thyroid cancer (TC) is increasing. Although the majority of these cancers have a good prognosis, 10% of these will develop local recurrence and/or distant metastases. Conventional cytotoxic chemotherapy has been largely replaced by molecular-target therapies, but it can still have a role. Two tyrosine kinase inhibitors have been approved for the treatment of advanced differentiated TC. They significantly improve progression-free survival, but at the cost of frequent and potentially serious adverse effects. At the moment, there are multiple clinical trials with other tyrosine kinase inhibitors and other drugs. We present a review of the current standard of care and what is up to come in the treatment of advanced TC.