Translational Bioinformatics and Clinical Research (Biomedical) Informatics

The role of IRAK1 in breast cancer patients treated with neoadjuvant chemotherapy

Background

IRAK1 has been shown to be abnormally expressed in a set of tumors leading to tumorigenesis and progression. IRAK1 is a therapeutic target that drives breast cancer metastasis and resistance to paclitaxel. However, the exact role of IRAK1 in neoadjuvant chemotherapy (NCT) for breast cancer remains unclear. The aim of this study was to investigate the relationship between the expression of IRAK1 and the clinicopathological parameters and survival prognosis of breast cancer patients treated with NCT.

Patients and methods

Based on the clinical data and mRNA microarray data from 1,085 breast cancer patients in The Cancer Genome Atlas, the correlation between IRAK1 expression and clinicopathological parameters of breast cancer was analyzed. Immunohistochemistry was performed to evaluate the expression of IRAK1. The Human Protein Atlas and the String database were used to analyze the expression of IRAK1 protein and its interaction with altered neighboring proteins in breast cancer. IRAK1 alteration was analyzed in cBioPortal database. GEO enrichment of IRAK1 was performed using WEB-based Gene SeT AnaLysis Toolkit.

Results

The expression of IRAK1 was significantly downregulated following NCT. The decreased expression of IRAK1 following NCT was positively correlated with reduced tumor size. Finally, survival analysis confirmed that a shorter survival period was correlated to higher expression of IRAK1 both before and after NCT.

Conclusion

These findings advanced our understanding about the expression pattern of IRAK1 in breast cancer, especially in those patients who were treated with NCT, suggesting that IRAK1 could be used as a prognostic indicator, as well as a potential indicator for evaluating NCT efficacy for breast cancer patients.

Omics-Based Strategies in Precision Medicine: Toward a Paradigm Shift in Inborn Errors of Metabolism Investigations

The rise of technologies that simultaneously measure thousands of data points represents the heart of systems biology. These technologies have had a huge impact on the discovery of next-generation diagnostics, biomarkers, and drugs in the precision medicine era. Systems biology aims to achieve systemic exploration of complex interactions in biological systems. Driven by high-throughput omics technologies and the computational surge, it enables multi-scale and insightful overviews of cells, organisms, and populations. Precision medicine capitalizes on these conceptual and technological advancements and stands on two main pillars: data generation and data modeling. High-throughput omics technologies allow the retrieval of comprehensive and holistic biological information, whereas computational capabilities enable high-dimensional data modeling and, therefore, accessible and user-friendly visualization. Furthermore, bioinformatics has enabled comprehensive multi-omics and clinical data integration for insightful interpretation. Despite their promise, the translation of these technologies into clinically actionable tools has been slow. In this review, we present state-of-the-art multi-omics data analysis strategies in a clinical context. The challenges of omics-based biomarker translation are discussed. Perspectives regarding the use of multi-omics approaches for inborn errors of metabolism (IEM) are presented by introducing a new paradigm shift in addressing IEM investigations in the post-genomic era.