Metabolomic study of serum in patients with invasive ductal breast carcinoma with LC-MS/MS approach

Screening the critical protein subnetwork to delineate potential mechanisms and protective agents associated with arsenic-induced cutaneous squamous cell carcinoma: A toxicogenomic study

Recent studies show that complex mechanisms are involved in arsenic-induced malignant transformation of cells. This study aimed to decipher molecular mechanisms associated with arsenic-induced cutaneous squamous cell carcinoma (cSCC) and suggest potential protective factors. RNA-seq-based differentially expressed genes between arsenic-exposed human keratinocytes (HaCaT) and controls were used to construct a protein-protein interaction (PPI) network and discover critical subnetwork-based mechanisms. Protective compounds against arsenic toxicity were determined and their target interactions in the core sub-network were identified by the comparative toxicogenomic database (CTD). The binding affinity between the effective factor and target was calculated by molecular docking. A total of 15 key proteins were screened out as critical arsenic-responsive subnetwork (FN1, IL-1A, CCN2, PECAM1, FGF5, EDN1, FGF1, PXDN, DNAJB9, XBP1, ERN1, PDIA4, DNAJB11, FOS, PDIA6) and 7 effective protective agents were identified (folic acid, quercetin, zinc, acetylcysteine, methionine, catechin, selenium). The GeneMANIA predicted detailed interactions of the subnetwork and revealed terms related to unfolded protein response as the main processes. FN1, IL1A and CCN2, as top significant genes, had good docking affinity with folic acid and quercetin, as selected key compounds. Integration of gene expression and protein-protein interaction related to arsenic exposure in cSCC explored the potential mechanisms and protective agents.

A journey from omics to clinicomics in solid cancers: Success stories and challenges

The word 'cancer' encompasses a heterogenous group of distinct disease types characterized by a spectrum of pathological features, genetic alterations and response to therapies. According to the World Health Organization, cancer is the second leading cause of death worldwide, responsible for one in six deaths and hence imposes a significant burden on global healthcare systems. High-throughput omics technologies combined with advanced imaging tools, have revolutionized our ability to interrogate the molecular landscape of tumors and has provided unprecedented understanding of the disease. Yet, there is a gap between basic research discoveries and their translation into clinically meaningful therapies for improving patient care. To bridge this gap, there is a need to analyse the vast amounts of high dimensional datasets from multi-omics platforms. The integration of multi-omics data with clinical information like patient history, histological examination and imaging has led to the novel concept of clinicomics and may expedite the bench-to-bedside transition in cancer. The journey from omics to clinicomics has gained momentum with development of radiomics which involves extracting quantitative features from medical imaging data with the help of deep learning and artificial intelligence (AI) tools. These features capture detailed information about the tumor's shape, texture, intensity, and spatial distribution. Together, the related fields of multiomics, translational bioinformatics, radiomics and clinicomics may provide evidence-based recommendations tailored to the individual cancer patient's molecular profile and clinical characteristics. In this chapter, we summarize multiomics studies in solid cancers with a specific focus on breast cancer. We also review machine learning and AI based algorithms and their use in cancer diagnosis, subtyping, prognosis and predicting treatment resistance and relapse.

Environmental Planning and Non-Communicable Diseases: A Systematic Review on the Role of the Metabolomic Profile

Non-communicable diseases (NCDs) are the major cause of death worldwide and have economic, psychological, and social impacts. Air pollution is the second, contributing to NCDs-related deaths. Metabolomics are a useful diagnostic and prognostic tool for NCDs, as they allow the identification of biomarkers linked to emerging pathologic processes. The aim of the present study was to review the scientific literature on the application of metabolomics profiling in NCDs and to discuss environmental planning actions to assist healthcare systems and public managers based on early metabolic diagnosis. The search was conducted following PRISMA guidelines using Web of Science, Scopus, and PubMed databases with the following MeSH terms: "metabolomics" AND "noncommunicable diseases" AND "air pollution". Twenty-nine studies were eligible. Eleven involved NCDs prevention, eight addressed diabetes mellitus, insulin resistance, systemic arterial hypertension, or metabolic syndrome. Six studies focused on obesity, two evaluated nonalcoholic fatty liver disease, two studied cancer, and none addressed chronic respiratory diseases. The studies provided insights into the biological pathways associated with NCDs. Understanding the cost of delivering care where there will be a critical increase in NCDs prevalence is crucial to achieving universal health coverage and improving population health by allocating environmental planning and treatment resources.

Small molecule metabolites: discovery of biomarkers and therapeutic targets

Metabolic abnormalities lead to the dysfunction of metabolic pathways and metabolite accumulation or deficiency which is well-recognized hallmarks of diseases. Metabolite signatures that have close proximity to subject's phenotypic informative dimension, are useful for predicting diagnosis and prognosis of diseases as well as monitoring treatments. The lack of early biomarkers could lead to poor diagnosis and serious outcomes. Therefore, noninvasive diagnosis and monitoring methods with high specificity and selectivity are desperately needed. Small molecule metabolites-based metabolomics has become a specialized tool for metabolic biomarker and pathway analysis, for revealing possible mechanisms of human various diseases and deciphering therapeutic potentials. It could help identify functional biomarkers related to phenotypic variation and delineate biochemical pathways changes as early indicators of pathological dysfunction and damage prior to disease development. Recently, scientists have established a large number of metabolic profiles to reveal the underlying mechanisms and metabolic networks for therapeutic target exploration in biomedicine. This review summarized the metabolic analysis on the potential value of small-molecule candidate metabolites as biomarkers with clinical events, which may lead to better diagnosis, prognosis, drug screening and treatment. We also discuss challenges that need to be addressed to fuel the next wave of breakthroughs.