Integrative Analysis to Identify Race-Associated Metabolite Biomarkers for Hepatocellular Carcinoma

Unveiling Endogenous Serum Peptides as Potential Biomarkers for Hepatocellular Carcinoma in Patients with Liver Cirrhosis

Hepatocellular carcinoma (HCC) is a leading cause of cancer-related deaths worldwide, mainly associated with liver cirrhosis. Current diagnostic methods for HCC have limited sensitivity and specificity, highlighting the need for improved early detection and intervention. In this study, we used a comprehensive approach involving endogenous peptidome along with bioinformatics analysis to identify and evaluate potential biomarkers for HCC. Serum samples from 40 subjects, comprising 20 HCC cases and 20 patients with liver cirrhosis (CIRR), were analyzed. Among 2568 endogenous peptides, 67 showed significant differential expression between the HCC vs CIRR. Further analysis revealed three endogenous peptides (VMHEALHNHYTQKSLSLSPG, NRFTQKSLSLSPG, and SARQSTLDKEL) that showed far better performance compared to AFP in terms of area under the receiver operating characteristic curve (AUC), showcasing their potential as biomarkers for HCC. Additionally, endogenous peptide IAVEWESNGQPENNYKT that belongs to the precursor protein Immunoglobulin heavy constant gamma 4 was detected in 100% of the HCC group and completely absent in the CIRR group, suggesting a promising diagnostic biomarker. Gene ontology and pathway analysis revealed the potential involvement of these dysregulated peptides in HCC. These findings provide valuable insights into the molecular basis of HCC and may contribute to the development of improved diagnostic methods and therapeutic targets for HCC.

A Bayesian two-step integrative procedure incorporating prior knowledge for the identification of miRNA-mRNAs involved in hepatocellular carcinoma

Recent studies have confirmed the role of miRNA regulation of gene expression in oncogenesis for various cancers. In parallel, prior knowledge about relationships between miRNA and mRNA have been accumulated from biological experiments or statistical analyses. Improved identification of disease-associated miRNA-mRNA pairs may be achieved by incorporating prior knowledge into integrative genomic analyses. In this study we focus on 39 patients with hepatocellular carcinoma (HCC) and 25 patients with liver cirrhosis and use a flexible Bayesian two-step integrative method. We found 66 significant miRNA-mRNA pairs, several of which contain molecules that have previously been identified as potential biomarkers. These results demonstrate the utility of the proposed approach in providing a better understanding of relationships between different biological levels, thereby giving insights into the biological mechanisms underlying the diseases, while providing a better selection of biomarkers that may serve as diagnostic, prognostic, or therapeutic biomarker candidates.

Multi-agent Feature Selection for Integrative Multi-omics Analysis

Multiomics data integration is key for cancer prediction as it captures different aspects of molecular mechanisms. Nevertheless, the high-dimensionality of multi-omics data with a relatively small number of patients presents a challenge for the cancer prediction tasks. While feature selection techniques have been widely used to tackle the curse of dimensionality of multi-omics data, most existing methods have been applied to each type of omics data separately. In this paper, we propose a multi-agent architecture for feature selection, called MAgentOmics, to consider all omics data together. MAgentOmics extends the ant colony optimization algorithm to multi-omics data, which iteratively builds candidate solutions and evaluates them. Moreover, a new fitness function is introduced to assess the candidate feature subsets without using prediction target such as survival time of patients. Therefore, it can be considered as an unsupervised method. We evaluate the performance of MAgentOmics on the TCGA ovarian cancer multi-omics data from 176 patients using a 5-fold cross-validation. The results demonstrate that the integration power of MAgentOmics is relatively better than the state-of-the-art supervised multi-view method. The code is publicly available at https://github.com/SinaTabakhi/MAgentOmics. Clinical relevance- Discovering knowledge in existing multi-omics datasets through better feature selection enhances the clinical understanding of cancers and speeds-up decision-making in the clinic.