An integrated landscape of protein expression in human cancer.
Ontology highlight
ABSTRACT:
SUBMITTER: Jarnuczak AF
PROVIDER: S-EPMC8065022 | biostudies-literature |
REPOSITORIES: biostudies-literature
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Project description:Simple Summary Proteins are among the most fundamental building blocks and molecular players behind the functions of cells and tissues. Their abundance and interaction patterns shape, to a large extent, what happens at the cellular and organ levels. This is also true regarding tumor tissues. In this work, we explored the patterns of abundance and co-occurrence of a large number of proteins in breast cancer cells and their healthy counterparts. We discovered the main differences and tried to see whether those differences may be associated with relevant aspects of the biology of these tumors. Our final goal is to provide information to empower cancer clinicians and pharmacologists to develop better diagnostic, prognostic, and therapeutic tools. Abstract Breast cancer is a complex phenotype (or better yet, several complex phenotypes) characterized by the interplay of a large number of cellular and biomolecular entities. Biological networks have been successfully used to capture some of the heterogeneity of intricate pathophenotypes, including cancer. Gene coexpression networks, in particular, have been used to study large-scale regulatory patterns. Ultimately, biological processes are carried out by proteins and their complexes. However, to date, most of the tumor profiling research has focused on the genomic and transcriptomic information. Here, we tried to expand this profiling through the analysis of open proteomic data via mutual information co-expression networks’ analysis. We could observe that there are distinctive biological processes associated with communities of these networks and how some transcriptional co-expression phenomena are lost at the protein level. These kinds of data and network analyses are a broad resource to explore cellular behavior and cancer research.
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Project description:BACKGROUND:Gene expression can be influenced by DNA methylation 1) distally, at regulatory elements such as enhancers, as well as 2) proximally, at promoters. Our current understanding of the influence of distal DNA methylation changes on gene expression patterns is incomplete. Here, we characterize genome-wide methylation and expression patterns for ~?13 k genes to explore how DNA methylation interacts with gene expression, throughout the genome. RESULTS:We used a linear mixed model framework to assess the correlation of DNA methylation at ~?400 k CpGs with gene expression changes at ~?13 k transcripts in two independent datasets from human blood cells. Among CpGs at which methylation significantly associates with transcription (eCpGs), >?50% are distal (>?50 kb) or trans (different chromosome) to the correlated gene. Many eCpG-transcript pairs are consistent between studies and ~?90% of neighboring eCpGs associate with the same gene, within studies. We find that enhancers (P?<?5e-18) and microRNA genes (P?=?9e-3) are overrepresented among trans eCpGs, and insulators and long intergenic non-coding RNAs are enriched among cis and distal eCpGs. Intragenic-eCpG-transcript correlations are negative in 60-70% of occurrences and are enriched for annotated gene promoters and enhancers (P?<?0.002), highlighting the importance of intragenic regulation. Gene Ontology analysis indicates that trans eCpGs are enriched for transcription factor genes and chromatin modifiers, suggesting that some trans eCpGs represent the influence of gene networks and higher-order transcriptional control. CONCLUSIONS:This work sheds new light on the interplay between epigenetic changes and gene expression, and provides useful data for mining biologically-relevant results from epigenome-wide association studies.
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Project description:Microarray technology enables a standardized, objective assessment of oncological diagnosis and prognosis. However, such studies are typically specific to certain cancer types, and the results have limited use due to inadequate validation in large patient cohorts. Discovery of genes commonly regulated in cancer may have an important implication in understanding the common molecular mechanism of cancer.We described an integrated gene-expression analysis of 2,186 samples from 39 studies to identify and validate a cancer type-independent gene signature that can identify cancer patients for a wide variety of human malignancies. The commonness of gene expression in 20 types of common cancer was assessed in 20 training datasets. The discriminative power of a signature defined by these common cancer genes was evaluated in the other 19 independent datasets including novel cancer types. QRT-PCR and tissue microarray were used to validate commonly regulated genes in multiple cancer types. We identified 187 genes dysregulated in nearly all cancerous tissue samples. The 187-gene signature can robustly predict cancer versus normal status for a wide variety of human malignancies with an overall accuracy of 92.6%. We further refined our signature to 28 genes confirmed by QRT-PCR. The refined signature still achieved 80% accuracy of classifying samples from mixed cancer types. This signature performs well in the prediction of novel cancer types that were not represented in training datasets. We also identified three biological pathways including glycolysis, cell cycle checkpoint II and plk3 pathways in which most genes are systematically up-regulated in many types of cancer.The identified signature has captured essential transcriptional features of neoplastic transformation and progression in general. These findings will help to elucidate the common molecular mechanism of cancer, and provide new insights into cancer diagnostics, prognostics and therapy.
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Project description:BackgroundThe BRAF protein kinase is widely studied as a cancer driver and therapeutic target. However, the regulation of its expression is not completely understood.ResultsTaking advantage of the RNA-seq data of more than 4800 patients belonging to 9 different cancer types, we show that BRAF mRNA exists as a pool of 3 isoforms (reference BRAF, BRAF-X1, and BRAF-X2) that differ in the last part of their coding sequences, as well as in the length (BRAF-ref: 76 nt; BRAF-X1 and BRAF-X2: up to 7 kb) and in the sequence of their 3'UTRs. The expression levels of BRAF-ref and BRAF-X1/X2 are inversely correlated, while the most prevalent among the three isoforms varies from cancer type to cancer type. In melanoma cells, the X1 isoform is expressed at the highest level in both therapy-naïve cells and cells with acquired resistance to vemurafenib driven by BRAF gene amplification or expression of the ?[3-10] splicing variant. In addition to the BRAF-ref protein, the BRAF-X1 protein (the full length as well as the ?[3-10] variant) is also translated. The expression levels of the BRAF-ref and BRAF-X1 proteins are similar, and together they account for BRAF functional activities. In contrast, the endogenous BRAF-X2 protein is hard to detect because the C-terminal domain is selectively recognized by the ubiquitin-proteasome pathway and targeted for degradation.ConclusionsBy shedding light on the repertoire of BRAF mRNA and protein variants, and on the complex regulation of their expression, our work paves the way to a deeper understanding of a crucially important player in human cancer and to a more informed development of new therapeutic strategies.
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