Medullary thyroid carcinoma (MTC) is a rare disease with few genetic drivers, and the etiology specific to each known susceptibility mutation remains unknown. Exploiting multilayer genomic data, we focused our interest on the role of aberrant DNA methylation in MTC development. We performed genome-wide DNA methylation profiling assessing more than 27,000 CpGs in the largest MTC series reported to date, comprising 48 molecularly characterized tumors. mRNA and miRNA expression data were available for 33 and 31 tumors, respectively. Two human MTC cell lines and 101 paraffin-embedded MTCs were used for validation. The most distinctive methylome was observed for -related tumors. Integration of methylation data with mRNA and miRNA expression data identified genes negatively regulated by promoter methylation. These findings were confirmed for , and miR-10a, -30a, and -200c. The mutation-specific aberrant methylation of , and was validated in 25 independent MTCs by bisulfite pyrosequencing. The methylome and transcriptome data underscored JAK/Stat pathway involvement in MTCs. Immunostaining [immunohistochemistry (IHC)] for the active form of signaling effector STAT3 was performed in a series of 101 MTCs. As expected, positive IHC was associated with -bearing tumors ( < 0.02). Pharmacologic inhibition of STAT3 activity increased the sensitivity to vandetanib of the -positive MTC cell line, MZ-CRC-1. Multilayer OMIC data analysis uncovered methylation hallmarks in genetically defined MTCs and revealed JAK/Stat signaling effector STAT3 as a potential therapeutic target for the treatment of MTCs. .
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http://dx.doi.org/10.1158/1078-0432.CCR-16-0947 | DOI Listing |
J Transl Med
November 2024
Molecular Imaging Facility, Experimental Pharmacology & Translational Science Department, Chiesi Farmaceutici S.P.A, 43122, Parma, Italy.
Background: Drug discovery strongly relies on the thorough evaluation of preclinical experimental studies. In the context of pulmonary fibrosis, micro-computed tomography (µCT) and histology are well-established and complementary tools for assessing, in animal models, disease progression and response to treatment. µCT offers dynamic, real-time insights into disease evolution and the effects of therapies, while histology provides a detailed microscopic examination of lung tissue.
View Article and Find Full Text PDFNat Commun
July 2024
Cancer Epigenetics and Nanomedicine Laboratory, Centro de Investigación en Nanomateriales y Nanotecnología-Consejo Superior de Investigaciones Científicas (CINN-CSIC), Universidad de Oviedo, 33011, Oviedo, Spain.
NPJ Precis Oncol
June 2024
Department of Medical Oncology, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, People's Republic of China.
Fetal adenocarcinoma of the lung (FLAC) is a rare form of lung adenocarcinoma and was divided into high-grade (H-FLAC) and low-grade (L-FLAC) subtypes. Despite the existence of some small case series studies, a comprehensive multi-omics study of FLAC has yet to be undertaken. In this study, we depicted the multi-omics landscapes of this rare lung cancer type by performing multi-regional sampling on 20 FLAC cases.
View Article and Find Full Text PDFNeuron
August 2024
Brain Development and Disease Laboratory, Istituto Italiano di Tecnologia, Genova 16163, Italy; Dulbecco Telethon Institute, Rome 00185, Italy. Electronic address:
Down syndrome (DS) is the most common genetic cause of cognitive disability. However, it is largely unclear how triplication of a small gene subset may impinge on diverse aspects of DS brain physiopathology. Here, we took a multi-omic approach and simultaneously analyzed by RNA-seq and proteomics the expression signatures of two diverse regions of human postmortem DS brains.
View Article and Find Full Text PDFIEEE J Biomed Health Inform
May 2024
Advancements in single-cell technologies concomitantly develop the epigenomic and transcriptomic profiles at the cell levels, providing opportunities to explore the potential biological mechanisms. Even though significant efforts have been dedicated to them, it remains challenging for the integration analysis of multi-omic data of single-cell because of the heterogeneity, complicated coupling and interpretability of data. To handle these issues, we propose a novel self-representation Learning-based Multi-omics data Integrative Clustering algorithm (sLMIC) for the integration of single-cell epigenomic profiles (DNA methylation or scATAC-seq) and transcriptomic (scRNA-seq), which the consistent and specific features of cells are explicitly extracted facilitating the cell clustering.
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