Prognostic value of the miRNA-27a and PPAR/RXRα signaling axis in patients with thyroid carcinoma

Assessment of causal association between differentiated thyroid cancer and disordered serum lipid profile: a Mendelian randomization study

Background

Research has shown that the disordered serum lipid profile may be associated with the risk of differentiated thyroid cancer (DTC). Whether this association reflect causal effect is still unclear. The aim of this study was to evaluate the causality of circulating lipoprotein lipids on DTC.

Methods

Mendelian randomization (MR) analysis was conducted to evaluate the relationship between the circulating lipoprotein lipids and DTC risk using single-nucleotide polymorphisms (SNPs) from a genome-wide association (GWA) study containing a high-incidence Italian population of 690 cases samples with DTC and 497 controls.

Results

Univariate and multivariate mendelian randomization analysis demonstrated that 'total cholesterol', 'HDL cholesterol', 'apolipoprotein B' and 'ratio of apolipoprotein B to apolipoprotein A1' were correlated with DTC. According to sensitivity analysis, our results were reliable. Furthermore, multivariate analysis revealed that there is no causative association between DTC and any of the many cause factors when they interact with one another, suggesting that there was a deep interaction between the four factors, which could affect each other. Finally, the mechanism of the related effects each other as well as the target genes with significant SNP regulatory effects in DTC was explored by conducting functional enrichment analysis and constructing the regulatory networks.

Conclusions

We obtained four exposure factors (total cholesterol, HDL cholesterol, apolipoprotein B and ratio of apolipoprotein B to apolipoprotein A1) closely related to DTC, which laid a theoretical foundation for the treatment of DTC.

Chromatin-Accessible miRNA Regulons Driving Thyroid Tumorigenesis and Progression

BACKGROUND

Although papillary thyroid cancer can remain indolent, associated lymph node metastases and recurrence rates are approximately 50% and 20%, respectively. Omics-based medicine has led to the discovery of predictive biomarkers that can be used to predict tumor progression and clinical outcomes. We aimed to develop a noninvasive omics-driven blood test to allow accurate risk stratification and help tailor individual patient treatment plans.

STUDY DESIGN

RNA sequencing (seq) and microRNA analysis of The Cancer Genome Atlas and Gene Expression Omnibus datasets were employed to identify an epigenetic prognostic panel. Integrated bulk assay for transposase-accessible chromatin-seq and RNA-seq experiments confirmed the results. Sixty-two paired tumor and adjacent control thyroid tissues and 67 blood samples (62 papillary thyroid cancer and 5 controls) were analyzed for validation using sequencing and real-time polymerase chain reaction and correlated to clinical outcomes. A liposome-exosome fusion clustered regularly interspaced short palindromic repeats (CRISPR)-fluorescent detection system miRNA assay was developed. A predictive risk nomogram was generated and tested for performance.

RESULTS

Our miRNA panel (miR-146b-5p and miR-221-3p) from tissue and blood was associated with aggressive features and was located within accessible chromatin regions. The miRNA risk score and prognostic nomogram showed higher accuracy in predicting lymph node metastases (miR-146b: area under the curve [AUC] 0.816, sensitivity 76.9%; miR-221: AUC 0.740, sensitivity 79.5%) and recurrence (miR-146b: AUC 0.921, sensitivity 75.0%; miR-221: AUC 0.756, sensitivity 70.0%; p < 0.001) than staging and American Thyroid Association risk stratification. CRISPR-based miRNA assays showed upregulation in the blood of cancer cohorts.

CONCLUSIONS

CRISPR-based detection of miR-146b and miR-221 in the blood of thyroid cancer patients is a reliable and noninvasive tool for real-time assessment and prognostication that has great potential to provide a direct impact on the care of these patients.

Ferroptosis-related gene signature predicts the prognosis of papillary thyroid carcinoma

Background

Papillary thyroid carcinoma (PTC) is the most common type of thyroid cancer (TC), accounting for more than 80% of all cases. Ferroptosis is a novel iron-dependent and Reactive oxygen species (ROS) reliant type of cell death which is distinct from the apoptosis, necroptosis and pyroptosis. Considerable studies have demonstrated that ferroptosis is involved in the biological process of various cancers. However, the role of ferroptosis in PTC remains unclear. This study aims at exploring the expression of ferroptosis-related genes (FRG) and their prognostic values in PTC.

Methods

A ferroptosis-related gene signature was constructed using lasso regression analysis through the PTC datasets of the Cancer Genome Atlas (TCGA). Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analyses were performed to investigate the bioinformatics functions of significantly different genes (SDG) of ferroptosis. Additionally, the correlations of ferroptosis and immune cells were assessed through the single-sample gene set enrichment analysis (ssGSEA) and CIBERSORT database. Finally, SDG were test in clinical PTC specimens and normal thyroid tissues.

Results

LASSO regression model was utilized to establish a novel FRG signature with 10 genes (ANGPTL7, CDKN2A, DPP4, DRD4, ISCU, PGD, SRXN1, TF, TFRC, TXNRD1) to predicts the prognosis of PTC, and the patients were separated into high-risk and low-risk groups by the risk score. The high-risk group had poorer survival than the low-risk group (p < 0.001). Receiver operating characteristic (ROC) curve analysis confirmed the signature's predictive capacity. Multivariate regression analysis identified the prognostic signature-based risk score was an independent prognostic indicator for PTC. The functional roles of the DEGs in the TGCA PTC cohort were explored using GO enrichment and KEGG pathway analyses. Immune related analysis demonstrated that the most types of immune cells and immunological function in the high-risk group were significant different with those in the low-risk group. Quantitative Real-Time Polymerase Chain Reaction (qRT-PCR) verified the SDG have differences in expression between tumor tissue and normal thyroid tissue. In addition, cell experiments were conducted to observe the changes in cell morphology and expression of signature’s genes with the influence of ferroptosis induced by sorafenib.

Conclusions

We identified differently expressed FRG that may involve in PTC. A ferroptosis-related gene signature has significant values in predicting the patients’ prognoses and targeting ferroptosis may be an alternative for PTC’s therapy.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12935-021-02389-7.

The PPARα and PPARγ Epigenetic Landscape in Cancer and Immune and Metabolic Disorders

Peroxisome proliferator-activated receptors (PPARs) are ligand-modulated nuclear receptors that play pivotal roles in nutrient sensing, metabolism, and lipid-related processes. Correct control of their target genes requires tight regulation of the expression of different PPAR isoforms in each tissue, and the dysregulation of PPAR-dependent transcriptional programs is linked to disorders, such as metabolic and immune diseases or cancer. Several PPAR regulators and PPAR-regulated factors are epigenetic effectors, including non-coding RNAs, epigenetic enzymes, histone modifiers, and DNA methyltransferases. In this review, we examine advances in PPARα and PPARγ-related epigenetic regulation in metabolic disorders, including obesity and diabetes, immune disorders, such as sclerosis and lupus, and a variety of cancers, providing new insights into the possible therapeutic exploitation of PPAR epigenetic modulation.