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Links between melanoma germline risk loci, driver genes and comorbidities: insight from a tissue-specific multi-omic analysis.

ABSTRACT: Genome-wide association studies (GWAS) have associated 76 loci with the risk of developing melanoma. However, understanding the molecular basis of such associations has remained a challenge because most of these loci are in non-coding regions of the genome. Here, we integrated data on epigenomic markers, three-dimensional (3D) genome organization, and expression quantitative trait loci (eQTL) from melanoma-relevant tissues and cell types to gain novel insights into the mechanisms underlying melanoma risk. This integrative approach revealed a total of 151 target genes, both near and far away from the risk loci in linear sequence, with known and novel roles in the etiology of melanoma. Using protein-protein interaction networks, we identified proteins that interact-directly or indirectly-with the products of the target genes. The interacting proteins were enriched for known melanoma driver genes. Further integration of these target genes into tissue-specific gene regulatory networks revealed patterns of gene regulation that connect melanoma to its comorbidities. Our study provides novel insights into the biological implications of genetic variants associated with melanoma risk.

SUBMITTER: Pudjihartono M

PROVIDER: S-EPMC10994230 | biostudies-literature | 2024 Apr

REPOSITORIES: biostudies-literature

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Links between melanoma germline risk loci, driver genes and comorbidities: insight from a tissue-specific multi-omic analysis.

Pudjihartono Michael M Golovina Evgeniia E Fadason Tayaza T O'Sullivan Justin M JM Schierding William W

Molecular oncology 20240203 4

Genome-wide association studies (GWAS) have associated 76 loci with the risk of developing melanoma. However, understanding the molecular basis of such associations has remained a challenge because most of these loci are in non-coding regions of the genome. Here, we integrated data on epigenomic markers, three-dimensional (3D) genome organization, and expression quantitative trait loci (eQTL) from melanoma-relevant tissues and cell types to gain novel insights into the mechanisms underlying mela ...[more]

PMID: 38308491

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Project description:BackgroundInflammation is a core element of many different, systemic and chronic diseases that usually involve an important autoimmune component. The clinical phase of inflammatory diseases is often the culmination of a long series of pathologic events that started years before. The systemic characteristics and related mechanisms could be investigated through the multi-omic comparative analysis of many inflammatory diseases. Therefore, it is important to use molecular data to study the genesis of the diseases. Here we propose a new methodology to study the relationships between inflammatory diseases and signalling molecules whose dysregulation at molecular levels could lead to systemic pathological events observed in inflammatory diseases.ResultsWe first perform an exploratory analysis of gene expression data of a number of diseases that involve a strong inflammatory component. The comparison of gene expression between disease and healthy samples reveals the importance of members of gene families coding for signalling factors. Next, we focus on interested signalling gene families and a subset of inflammation related diseases with multi-omic features including both gene expression and DNA methylation. We introduce a phylogenetic-based multi-omic method to study the relationships between multi-omic features of inflammation related diseases by integrating gene expression, DNA methylation through sequence based phylogeny of the signalling gene families. The models of adaptations between gene expression and DNA methylation can be inferred from pre-estimated evolutionary relationship of a gene family. Members of the gene family whose expression or methylation levels significantly deviate from the model are considered as the potential disease associated genes.ConclusionsApplying the methodology to four gene families (the chemokine receptor family, the TNF receptor family, the TGF- β gene family, the IL-17 gene family) in nine inflammation related diseases, we identify disease associated genes which exhibit significant dysregulation in gene expression or DNA methylation in the inflammation related diseases, which provides clues for functional associations between the diseases.

Project description:Colon cancer onset is strongly associated with the differences in microbial taxa in the gastrointestinal tract. Although recent studies highlight the role of individual taxa, the effect of a complex gut microbiome (GM) on the metabolome and host transcriptome is still unknown. We used a multi-omics approach to determine how differences in the GM affect the susceptibility to adenoma development in a rat model of human colon cancer. Ultra-high performance liquid chromatography mass spectrometry of feces collected prior to observable disease onset identified putative metabolite profiles that likely predict future disease severity. Transcriptome analyses performed after disease onset from normal colonic epithelium and tumor tissues show a correlation between GM and host gene expression. Integrated pathway analyses of the metabolome and transcriptome based on putatively identified metabolic features indicate that bile acid biosynthesis is enriched in rats with high tumors along with increased fatty acid metabolism and mucin biosynthesis. Targeted pyrosequencing of the Pirc allele indicates that the GM alters the mechanism of adenoma development and may drive an epigenetic pathway of tumor suppressor silencing. This study reveals how untargeted metabolomics identifies signatures of susceptibility and integrated analyses uncover pathways of differential mechanisms of loss of tumor suppressor gene function and for potential prevention and therapeutic intervention. IMPORTANCE The association between the gut microbiome and colon cancer is significant but difficult to test in model systems. This study highlights the association of differences in the pathogen-free gut microbiome to changes in the host transcriptome and metabolome that correlate with colon adenoma initiation and development in a rat genetic model of early colon cancer. The utilization of a multi-omics approach integrating metabolomics and transcriptomics reveals differences in pathways including bile acid biosynthesis and fatty acid metabolism. The study also shows that differences in gut microbiomes significantly alter the mechanism of adenoma formation, shifting from genetic changes to epigenetic changes that initiate the early loss of tumor suppressor function. These findings enhance our understanding of the gut microbiome's role in colon cancer susceptibility, offer insights into potential biomarkers and therapeutic targets, and may pave the way for future prevention and intervention strategies.

Project description:High body mass index (BMI) is a causal risk factor for endometrial cancer but the tumor molecular mechanisms affected by adiposity and their therapeutic relevance remain poorly understood. Here we characterize the tumor multi-omic landscape of endometrial cancers that have developed on a background of lifelong germline genetic exposure to elevated BMI. We built a polygenic score (PGS) for BMI in women using data on independent, genome-wide significant variants associated with adult BMI in 434,794 women. We performed germline (blood) genotype quality control and imputation on data from 354 endometrial cancer cases from The Cancer Genome Atlas (TCGA). We assigned each case in this TCGA cohort their genetically predicted life-course BMI based on the BMI PGS. Multivariable generalized linear models adjusted for age, stage, microsatellite status and genetic principal components were used to test for associations between the BMI germline PGS and endometrial cancer tumor genome-wide genomic, transcriptomic, proteomic, epigenomic and immune traits in TCGA. High BMI germline PGS was associated with (i) upregulated tumor gene expression in the IL6-JAK-STAT3 pathway (FDR=4.2×10-7); (ii) increased estimated intra-tumor activated mast cell infiltration (FDR=0.008); (iii) increased single base substitution (SBS) mutational signatures 1 (FDR=0.03) and 5 (FDR=0.09) and decreased SBS13 (FDR=0.09), implicating age-related and APOBEC mutagenesis, respectively; and (iv) decreased tumor EGFR protein expression (FDR=0.07). Alterations in IL6-JAK-STAT3 signaling gene and EGFR protein expression were, in turn, significantly associated with both overall survival and progression-free interval. Thus, we integrated germline and somatic data using a novel study design to identify associations between genetically predicted lifelong exposure to higher BMI and potentially actionable endometrial cancer tumor molecular features. These associations inform our understanding of how high BMI may influence the development and progression of this cancer, impacting endometrial tumor biology and clinical outcomes.

Dataset Information

Links between melanoma germline risk loci, driver genes and comorbidities: insight from a tissue-specific multi-omic analysis.

Publications

Links between melanoma germline risk loci, driver genes and comorbidities: insight from a tissue-specific multi-omic analysis.

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