Integrative omics analysis reveals effective stratification and potential prognosis markers of pan-gastrointestinal cancers

Jiangzhou, Huiting; Zhang, Hang; Sun, Ruiyan; Fahira, Aamir; Wang, Ke; Li, Zhiqiang; Shi, Yongyong; Wang, Zhuo

doi:10.1016/j.isci.2021.102824

Cited by 7 publications

(3 citation statements)

References 59 publications

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“…Hence, they have signi cantly revolutionized our understanding of various cancers' etiology and mechanisms. Undoubtedly, integrated-omics studies give a chance to accelerate diagnosis, estimate prognosis more accurately, and prescribe the most effective and personalized therapy in patients with upper GI cancers (30).…”

Section: Multi-omics Data Integration For Upper Gi Cancersmentioning

confidence: 99%

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Multi-omics data integration in upper gastrointestinal cancers research: A review of concepts, approaches, and application

Mazloomnejad

Ahmadi

Piroozkhah

et al. 2023

Preprint

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Upper gastrointestinal (GI) cancers, including esophageal, gastric, liver, and pancreatic cancers, are a major medical and economic burden worldwide. Despite significant advances in radiotherapy, chemotherapy, and targeted treatments for upper GI cancers in the past decade, a high recurrence rate and poor prognosis are still challenging in upper GI cancer management. This trouble is rooted in the current diagnosis methods and the lack of adequate and reliable diagnostic/prognostic biomarkers. The diagnosis of almost every disease of the upper GI tract still depends on invasive investigations such as endoscopy of the upper GI tract, manometry of the stomach and esophagus, or radiography. Although cancer was considered a single disease in the organ of origin in the past, today, it is accepted that cancer is a heterogeneous disease assuming the same organ of origin. Therefore, to conduct precision/personalized medicine, it seems necessary to have suitable biomarkers to make an accurate diagnosis, appropriate patient classification, prognosis assessment, and drug response in cancers. Systems biology and multi-omics research are strategies adopted to provide genetic and molecular biomarkers in cancer. Toward studying complex biological processes, multi-omics data analysis provides an opportunity to gain a deeper and more comprehensive understanding of cancer development and progression. Multi-omics approaches are new frameworks that integrate omics datasets, including genome, epigenome, transcriptome, proteome, metabolome, and metagenome, on the same set of samples to understand cancer's molecular and clinical characteristics better. Therefore, in this review, we focus on the integrated multi-omics studies conducted on esophageal, gastric, liver, and pancreatic cancers and discuss the results regarding diagnostic and prognostic biomarkers, as well as biomarkers that determine the response to treatment.

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Section: Multi-omics Data Integration For Upper Gi Cancersmentioning

confidence: 99%

“…Considering the complexity and dynamics of the pathogenic process in ESCC, including genetic interaction, epigenetics, gene expression, and proteins, integrative multi-omics studies have contributed signi cantly to comprehending the disease (30,38,39). , HOXC10, LDB2 and PACRG genes loci.…”

Section: Esophageal Cancermentioning

confidence: 99%

Multi-omics data integration in upper gastrointestinal cancers research: A review of concepts, approaches, and application

Mazloomnejad

Ahmadi

Piroozkhah

et al. 2023

Preprint

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“…Bioinformatics approaches for integrative analysis are concurrently expanding with sequential and simultaneous method designs ( Subramanian et al, 2020 ). These methods allow the discovery of complex patterns that would otherwise not be detectable using a single data type, potentially leading to higher diagnostic accuracy ( Herman et al, 2018 ) and data-driven molecular subtyping ( Jiangzhou et al, 2021 ).…”

Section: Introductionmentioning

confidence: 99%

Integration of small RNAs from plasma and cerebrospinal fluid for classification of multiple sclerosis

et al. 2022

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Multiple Sclerosis (MS) is an autoimmune, neurological disease, commonly presenting with a relapsing-remitting form, that later converts to a secondary progressive stage, referred to as RRMS and SPMS, respectively. Early treatment slows disease progression, hence, accurate and early diagnosis is crucial. Recent advances in large-scale data processing and analysis have progressed molecular biomarker development. Here, we focus on small RNA data derived from cell-free cerebrospinal fluid (CSF), cerebrospinal fluid cells, plasma and peripheral blood mononuclear cells as well as CSF cell methylome data, from people with RRMS (n = 20), clinically/radiologically isolated syndrome (CIS/RIS, n = 2) and neurological disease controls (n = 14). We applied multiple co-inertia analysis (MCIA), an unsupervised and thereby unbiased, multivariate method for simultaneous data integration and found that the top latent variable classifies RRMS status with an Area Under the Receiver Operating Characteristics (AUROC) score of 0.82. Variable selection based on Lasso regression reduced features to 44, derived from the small RNAs from plasma (20), CSF cells (8) and cell-free CSF (16), with a marginal reduction in AUROC to 0.79. Samples from SPMS patients (n = 6) were subsequently projected on the latent space and differed significantly from RRMS and controls. On contrary, we found no differences between relapse and remission or between inflammatory and non-inflammatory disease controls, suggesting that the latent variable is not prone to inflammatory signals alone, but could be MS-specific. Hence, we here showcase that integration of small RNAs from plasma and CSF can be utilized to distinguish RRMS from SPMS and neurological disease controls.

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Emerging trends in translational omics

Pandey,

Sahu,

Singh

2024

Integrative Omics

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Integrative omics analysis reveals effective stratification and potential prognosis markers of pan-gastrointestinal cancers

Cited by 7 publications

References 59 publications

Multi-omics data integration in upper gastrointestinal cancers research: A review of concepts, approaches, and application

Multi-omics data integration in upper gastrointestinal cancers research: A review of concepts, approaches, and application

Integration of small RNAs from plasma and cerebrospinal fluid for classification of multiple sclerosis

Emerging trends in translational omics

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