Multi-omics approaches in cancer research with applications in tumor subtyping, prognosis, and diagnosis.
Otília Menyhárt,Balázs Győrffy +1 more
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TLDR
Multi-omics data integration strategies across different cellular function levels, including genomes, epigenomes, transcriptomes, proteomes, metabolomes, and microbiomes offer unparalleled opportunities to understand the underlying biology of complex diseases, such as cancer.Abstract:
While cost-effective high-throughput technologies provide an increasing amount of data, the analyses of single layers of data seldom provide causal relations. Multi-omics data integration strategies across different cellular function levels, including genomes, epigenomes, transcriptomes, proteomes, metabolomes, and microbiomes offer unparalleled opportunities to understand the underlying biology of complex diseases, such as cancer. We review some of the most frequently used data integration methods and outline research areas where multi-omics significantly benefit our understanding of the process and outcome of the malignant transformation. We discuss algorithmic frameworks developed to reveal cancer subtypes, disease mechanisms, and methods for identifying driver genomic alterations and consider the significance of multi-omics in tumor classifications, diagnostics, and prognostications. We provide a comprehensive summary of each omics strategy's most recent advances within the clinical context and discuss the main challenges facing their clinical implementations. Despite its unparalleled advantages, multi-omics data integration is slow to enter everyday clinics. One major obstacle is the uneven maturity of different omics approaches and the growing gap between generating large volumes of data compared to data processing capacity. Progressive initiatives to enforce the standardization of sample processing and analytical pipelines, multidisciplinary training of experts for data analysis and interpretation are vital to facilitate the translatability of theoretical findings.read more
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Integration strategies of multi-omics data for machine learning analysis.
TL;DR: In this article, the authors focus on challenges and existing multi-omics integration strategies by paying special attention to machine learning applications and summarize the most recent data integration methods/ frameworks into five different integration strategies: early, mixed, intermediate, late and hierarchical.
Journal ArticleDOI
Machine Learning: A New Prospect in Multi-Omics Data Analysis of Cancer
Babak Arjmand,Shayesteh Kokabi Hamidpour,Akram Tayanloo-Beik,Parisa Goodarzi,Hamid Reza Aghayan,Hossein Adibi,Bagher Larijani +6 more
TL;DR: It can be concluded that the use of artificial intelligence technologies such as machine learning can have revolutionary roles in the fight against cancer.
Journal ArticleDOI
Integration of transcriptomics, proteomics, and metabolomics data to reveal HER2-associated metabolic heterogeneity in gastric cancer with response to immunotherapy and neoadjuvant chemotherapy
Qihang Yuan,Da Wei Deng,Chen Pan,Jie Ren,Tianfu Wei,Zeming Wu,Biao Zhang,Shuangyue Li,Peiyuan Yin,Dong Zhang +9 more
TL;DR: Transcriptomic and proteomic analyses highlighted the close association of HER-2 level with the immune status and metabolic features of patients with GC and the co-expressed relationship between alanine, aspartate and glutamate and glycolysis/gluconeogenesis metabolisms and HER2 level in GC.
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Establishment of a Prognostic Model for Hepatocellular Carcinoma Based on Endoplasmic Reticulum Stress-Related Gene Analysis.
Peng Liu,Jinhong Wei,Feiyu Mao,Zechang Xin,Heng Duan,Yan Du,Xiaodong Wang,Zhennan Li,Jian-Jun Qian,Jie Yao +9 more
TL;DR: In this paper, a 5-gene signature (HDGF, EIF2S1, SRPRB, PPP2R5B and DDX11) was created and had power as a prognostic biomarker for HCC.
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Structurally related (-)-epicatechin metabolites and gut microbiota derived metabolites exert genomic modifications via VEGF signaling pathways in brain microvascular endothelial cells under lipotoxic conditions: Integrated multi-omic study.
Karla F Corral-Jara,Saivageethi Nuthikattu,John Rutledge,Amparo C Villablanca,Reedmond Fong,Christian Heiss,Javier I. Ottaviani,Dragan Milenkovic +7 more
TL;DR: In this paper , the authors used a multi-omic approach (transcriptomics of mRNA, miRNAs and lncRNAs, and proteomics) to provide novel in-depth insights into molecular mechanisms of how metabolites affect brain endothelial cells under lipid-stressed (as a model of BBB dysfunction) at physiological concentrations.
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TL;DR: It is reported that high-grade serous ovarian cancer is characterized by TP53 mutations in almost all tumours (96%); low prevalence but statistically recurrent somatic mutations in nine further genes including NF1, BRCA1,BRCA2, RB1 and CDK12; 113 significant focal DNA copy number aberrations; and promoter methylation events involving 168 genes.