Dataset Information

Identification of functional pathways and molecular signatures in neuroendocrine neoplasms by multi-omics analysis.

ABSTRACT:

Background

Neuroendocrine neoplasms (NENs) represent a heterogeneous class of rare tumors with increasing incidence. They are characterized by the ability to secrete peptide hormones and biogenic amines but other reliable biomarkers are lacking, making diagnosis and identification of the primary site very challenging. While in some NENs, such as the pancreatic ones, next generation sequencing technologies allowed the identification of new molecular hallmarks, our knowledge of the molecular profile of NENs from other anatomical sites is still poor.

Methods

Starting from the concept that NENs from different organs may be clinically and genetically correlated, we applied a multi-omics approach by combining multigene panel testing, CGH-array, transcriptome and miRNome profiling and computational analyses, with the aim to highlight common molecular and functional signatures of gastroenteropancreatic (GEP)-NENs and medullary thyroid carcinomas (MTCs) that could aid diagnosis, prognosis and therapy.

Results

By comparing genomic and transcriptional profiles, ATM-dependent signaling emerged among the most significant pathways at multiple levels, involving gene variations and miRNA-mediated regulation, thus representing a novel putative druggable pathway in these cancer types. Moreover, a set of circulating miRNAs was also selected as possible diagnostic/prognostic biomarkers useful for clinical management of NENs.

Conclusions

These findings depict a complex molecular and functional landscape of NENs, shedding light on novel therapeutic targets and disease biomarkers to be exploited.

SUBMITTER: Melone V

PROVIDER: S-EPMC9258165 | biostudies-literature |

REPOSITORIES: biostudies-literature

ACCESS DATA

Similar Datasets

Project description:BackgroundRett syndrome (RTT) is a neurodevelopmental disorder mainly caused by mutations in the methyl-CpG-binding protein 2 gene (MECP2). MeCP2 is a multi-functional protein involved in many cellular processes, but the mechanisms by which its dysfunction causes disease are not fully understood. The duplication of the MECP2 gene causes a distinct disorder called MECP2 duplication syndrome (MDS), highlighting the importance of tightly regulating its dosage for proper cellular function. Additionally, some patients with mutations in genes other than MECP2 exhibit phenotypic similarities with RTT, indicating that these genes may also play a role in similar cellular functions. The purpose of this study was to characterise the molecular alterations in patients with RTT in order to identify potential biomarkers or therapeutic targets for this disorder.MethodsWe used a combination of transcriptomics (RNAseq) and proteomics (TMT mass spectrometry) to characterise the expression patterns in fibroblast cell lines from 22 patients with RTT and detected mutation in MECP2, 15 patients with MDS, 12 patients with RTT-like phenotypes and 13 healthy controls. Transcriptomics and proteomics data were used to identify differentially expressed genes at both RNA and protein levels, which were further inspected via enrichment and upstream regulator analyses and compared to find shared features in patients with RTT.ResultsWe identified molecular alterations in cellular functions and pathways that may contribute to the disease phenotype in patients with RTT, such as deregulated cytoskeletal components, vesicular transport elements, ribosomal subunits and mRNA processing machinery. We also compared RTT expression profiles with those of MDS seeking changes in opposite directions that could lead to the identification of MeCP2 direct targets. Some of the deregulated transcripts and proteins were consistently affected in patients with RTT-like phenotypes, revealing potentially relevant molecular processes in patients with overlapping traits and different genetic aetiology.ConclusionsThe integration of data in a multi-omics analysis has helped to interpret the molecular consequences of MECP2 dysfunction, contributing to the characterisation of the molecular landscape in patients with RTT. The comparison with MDS provides knowledge of MeCP2 direct targets, whilst the correlation with RTT-like phenotypes highlights processes potentially contributing to the pathomechanism leading these disorders.

Project description:Multi-omics data integration has emerged as a promising approach to identify patient subgroups. However, in terms of grouping genes (or gene products) into co-expression modules, data integration methods suffer from two main drawbacks. First, most existing methods only consider genes or samples measured in all different datasets. Second, known molecular interactions (e.g., transcriptional regulatory interactions, protein-protein interactions and biological pathways) cannot be utilized to assist in module detection. Herein, we present a novel data integration framework, Correlation-based Local Approximation of Membership (CLAM), which provides two methodological innovations to address these limitations: 1) constructing a trans-omics neighborhood matrix by integrating multi-omics datasets and known molecular interactions, and 2) using a local approximation procedure to define gene modules from the matrix. Applying Correlation-based Local Approximation of Membership to human colorectal cancer (CRC) and mouse B-cell differentiation multi-omics data obtained from The Cancer Genome Atlas (TCGA), Clinical Proteomics Tumor Analysis Consortium (CPTAC), Gene Expression Omnibus (GEO) and ProteomeXchange database, we demonstrated its superior ability to recover biologically relevant modules and gene ontology (GO) terms. Further investigation of the colorectal cancer modules revealed numerous transcription factors and KEGG pathways that played crucial roles in colorectal cancer progression. Module-based survival analysis constructed four survival-related networks in which pairwise gene correlations were significantly correlated with colorectal cancer patient survival. Overall, the series of evaluations demonstrated the great potential of Correlation-based Local Approximation of Membership for identifying modular biomarkers for complex diseases. We implemented Correlation-based Local Approximation of Membership as a user-friendly application available at https://github.com/free1234hm/CLAM.