Pan-cancer onco-signatures reveal a novel mitochondrial subtype of luminal breast cancer with specific regulators

被引:0
|
作者
Simeone, Ines [1 ,2 ]
Ceccarelli, Michele [1 ,3 ]
机构
[1] Univ Naples Federico II, Dept Elect Engn & Informat Technol, Via Claudio 21, I-80128 Naples, Italy
[2] Ist Italiano Tecnol IIT, Ctr Genom Sci IITSEMM, Via Adamello 16, I-20139 Milan, Italy
[3] BIOGEM Inst Mol Biol & Genet, Via Camporeale, I-83031 Ariano Irpino, Italy
关键词
Onco-signatures; Pan-cancer; Breast cancer disease; TCGA; Gene set enrichment analysis; Normalized enrichment score; Luminal breast tumor subtype; EMT; Hsa-miR-135-5p; TDMD; EXPRESSION; GENE; MICRORNA; DEREGULATION; DEGRADATION;
D O I
10.1186/s12967-023-03907-z
中图分类号
R-3 [医学研究方法]; R3 [基础医学];
学科分类号
1001 ;
摘要
BackgroundSomatic alterations in cancer cause dysregulation of signaling pathways that control cell-cycle progression, apoptosis, and cell growth. The effect of individual alterations in these pathways differs between individual tumors and tumor types. Recognizing driver events is a complex task requiring integrating multiple molecular data, including genomics, epigenomics, and functional genomics. A common hypothesis is that these driver events share similar effects on the hallmarks of cancer. The availability of large-scale multi-omics studies allows for inferring these common effects from data. Once these effects are known, one can then deconvolve in every individual patient whether a given genomics alteration is a driver event.MethodsHere, we develop a novel data-driven approach to identify shared oncogenic expression signatures among tumors. We aim to identify gene onco-signature for classifying tumor patients in homogeneous subclasses with distinct prognoses and specific genomic alterations. We derive expression pan-cancer onco-signatures from TCGA gene expression data using a discovery set of 9107 primary pan-tumor samples together with respective matched mutational data and a list of known cancer-related genes from COSMIC database.ResultsWe use the derived ono-signatures to state their prognostic significance and apply them to the TCGA breast cancer dataset as proof of principle of our approach. We uncover a "mitochondrial" sub-group of Luminal patients characterized by its biological features and regulated by specific genetic modulators. Collectively, our results demonstrate the effectiveness of onco-signatures-based methodologies, and they also contribute to a comprehensive understanding of the metabolic heterogeneity of Luminal tumors.ConclusionsThese findings provide novel genomics evidence for developing personalized breast cancer patient treatments. The onco-signature approach, demonstrated here on breast cancer, is general and can be applied to other cancer types.
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页数:14
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