Identification of novel neuroblastoma biomarkers in urine samples

Scoring system for diagnosis and pretreatment risk assessment of neuroblastoma using urinary biomarker combinations

The urinary catecholamine metabolites, homovanillic acid (HVA) and vanillylmandelic acid (VMA), are used for the adjunctive diagnosis of neuroblastomas. We aimed to develop a scoring system for the diagnosis and pretreatment risk assessment of neuroblastoma, incorporating age and other urinary catecholamine metabolite combinations. Urine samples from 227 controls (227 samples) and 68 patients with neuroblastoma (228 samples) were evaluated. First, the catecholamine metabolites vanillactic acid (VLA) and 3-methoxytyramine sulfate (MTS) were identified as urinary marker candidates through comprehensive analysis using liquid chromatography-mass spectrometry. The concentrations of these marker candidates and conventional markers were then compared among controls, patients, and numerous risk groups to develop a scoring system. Participants were classified into four groups: control, low risk, intermediate risk, and high risk, and the proportional odds model was fitted using the L2-penalized maximum likelihood method, incorporating age on a monthly scale for adjustment. This scoring model using the novel urine catecholamine metabolite combinations, VLA and MTS, had greater area under the curve values than the model using HVA and VMA for diagnosis (0.978 vs. 0.964), pretreatment risk assessment (low and intermediate risk vs. high risk: 0.866 vs. 0.724; low risk vs. intermediate and high risk: 0.871 vs. 0.680), and prognostic factors (MYCN status: 0.741 vs. 0.369, histology: 0.932 vs. 0.747). The new system also had greater accuracy in detecting missing high-risk neuroblastomas, and in predicting the pretreatment risk at the time of screening. The new scoring system employing VLA and MTS has the potential to replace the conventional adjunctive diagnostic method using HVA and VMA.

Systems Biology in Cancer Diagnosis Integrating Omics Technologies and Artificial Intelligence to Support Physician Decision Making

Cancer is the second major cause of disease-related death worldwide, and its accurate early diagnosis and therapeutic intervention are fundamental for saving the patient's life. Cancer, as a complex and heterogeneous disorder, results from the disruption and alteration of a wide variety of biological entities, including genes, proteins, mRNAs, miRNAs, and metabolites, that eventually emerge as clinical symptoms. Traditionally, diagnosis is based on clinical examination, blood tests for biomarkers, the histopathology of a biopsy, and imaging (MRI, CT, PET, and US). Additionally, omics biotechnologies help to further characterize the genome, metabolome, microbiome traits of the patient that could have an impact on the prognosis and patient's response to the therapy. The integration of all these data relies on gathering of several experts and may require considerable time, and, unfortunately, it is not without the risk of error in the interpretation and therefore in the decision. Systems biology algorithms exploit Artificial Intelligence (AI) combined with omics technologies to perform a rapid and accurate analysis and integration of patient's big data, and support the physician in making diagnosis and tailoring the most appropriate therapeutic intervention. However, AI is not free from possible diagnostic and prognostic errors in the interpretation of images or biochemical-clinical data. Here, we first describe the methods used by systems biology for combining AI with omics and then discuss the potential, challenges, limitations, and critical issues in using AI in cancer research.

Development of computational models using omics data for the identification of effective cancer metabolic biomarkers

Identification of novel biomarkers has been an active area of study for the effective diagnosis, prognosis and treatment of cancers. Among various types of cancer biomarkers, metabolic biomarkers, including enzymes, metabolites and metabolic genes, deserve attention as they can serve as a reliable source for diagnosis, prognosis and treatment of cancers. In particular, efforts to identify novel biomarkers have been greatly facilitated by a rapid increase in the volume of multiple omics data generated for a range of cancer cells. These omics data in turn serve as ingredients for developing computational models that can help derive deeper insights into the biology of cancer cells, and identify metabolic biomarkers. In this review, we provide an overview of omics data generated for cancer cells, and discuss recent studies on computational models that were developed using omics data in order to identify effective cancer metabolic biomarkers.