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The main types of thyroid neoplasms, follicular adenoma (FA), follicular thyroid carcinoma (FTC), classical and follicular variants of papillary carcinoma (clPTC and fvPTC), and anaplastic thyroid carcinoma (ATC), differ in prognosis, progression rate and metastatic behaviour. Specific patterns of lncRNAs involved in the development of clinical and morphological features can be presumed. LncRNA landscapes within distinct benign and malignant histological variants of thyroid neoplasms were not investigated. The aim of the study was to discover long noncoding RNA landscapes common and specific to major benign and malignant histological subtypes of thyroid neoplasms. LncRNA expression in FA, FTC, fvPTC, clPTC and ATC was analysed with comprehensive microarray and RNA-Seq datasets. Putative biological functions were evaluated via enrichment analysis of coexpressed coding genes. In the results, lncRNAs common and specific to FTC, clPTC, fvPTC, and ATC were identified. The discovered lncRNAs are putatively involved in L1CAM interactions, namely, pre-mRNA processing (lncRNAs specific to FTC); PCP/CE and WNT pathways (lncRNAs specific to fvPTC); extracellular matrix organization (lncRNAs specific to clPTC); and the cell cycle (lncRNAs specific to ATC). Known oncogenic and suppressor lncRNAs (RMST, CRNDE, SLC26A4-AS1, NR2F1-AS1, and LINC00511) were aberrantly expressed in thyroid carcinomas. These findings enhance the understanding of lncRNAs in the development of subtype-specific features in thyroid cancer. [ABSTRACT FROM AUTHOR]

Cell transmembrane receptors and extracellular matrix components play a pivotal role in regulating cell activity and providing for the concerted integration of cells in the tissue structures. We have assessed DNA methylation in the promoter regions of eight integrin genes, two nidogen genes, and the dystroglycan gene in normal breast tissues and breast carcinomas (BC). The protein products of these genes interact with the basement membrane proteins LAMA1, LAMA2, and LAMB1; abnormal hypermethylation of the LAMA1, LAMA2, and LAMB1 promoters in BC has been described in our previous publications. In the present study, the frequencies of abnormal promoter hypermethylation in BC were 13% for ITGA1, 31% for ITGA4, 4% for ITGA7, 39% for ITGA9, 38% for NID1, and 41% for NID2. ITGA2, ITGA3, ITGA6, ITGB1, and DAG1 promoters were nonmethylated in normal and BC samples. ITGA4, ITGA9, and NID1 promoter hypermethylation was associated with the HER2 positive tumors, and promoter hypermethylation of ITGA1, ITGA9, NID1 and NID2 was associated with a genome-wide CpG island hypermethylated BC subtype. Given that ITGA4 is not expressed in normal breast, one might suggest that its abnormal promoter hypermethylation in cancer is non-functional and is thus merely a passenger epimutation. Yet, this assumption is not supported by our finding that it is not associated with a hypermethylated BC subtype. ITGA4 acquires expression in a subset of breast carcinomas, and methylation of its promoter may be preventive against expression in some tumors. Strong association of abnormal ITGA4 hypermethylation with the HER2 positive tumors (p = 0.0025) suggests that simultaneous presence of both HER2 and integrin α4 receptors is not beneficial for tumor cells. This may imply HER2 and integrin α4 signaling pathways interactions that are yet to be discovered. [ABSTRACT FROM AUTHOR]

Despite the advantages of neoadjuvant chemotherapy (NACT), associated toxicity is a serious complication that renders monitoring of the patients' response to NACT highly important. Thus, prediction of tumor response to treatment is imperative to avoid exposure of potential non-responders to deleterious complications. We have performed genome-wide analysis of DNA methylation by XmaI-RRBS and selected CpG dinucleotides differential methylation of which discriminates luminal B breast cancer samples with different sensitivity to NACT. With this data, we have developed multiplex methylation sensitive restriction enzyme PCR (MSRE-PCR) protocol for determining the methylation status of 10 genes (SLC9A3, C1QL2, DPYS, IRF4, ADCY8, KCNQ2, TERT, SYNDIG1, SKOR2 and GRIK1) that distinguish BC samples with different NACT response. Analysis of these 10 markers by MSRE-PCR in biopsy samples allowed us to reveal three top informative combinations of markers, (1) IRF4 and C1QL2; (2) IRF4, C1QL2, and ADCY8; (3) IRF4, C1QL2, and DPYS, with the areas under ROC curves (AUCs) of 0.75, 0.78 and 0.74, respectively. A classifier based on IRF4 and C1QL2 better meets the diagnostic panel simplicity requirements, as it consists of only two markers. Diagnostic accuracy of the panel of these two markers is 0.75, with the sensitivity of 75% and specificity of 75%. [ABSTRACT FROM AUTHOR]