ABSTRACT
Lung adenocarcinoma (LUAD) is a significant planetary health challenge with its high morbidity and mortality rate, not to mention the marked interindividual variability in treatment outcomes and side effects. There is an urgent need for robust systems biomarkers that can help with early cancer diagnosis, prediction of treatment outcomes, and design of precision/personalized medicines for LUAD. The present study aimed at systems biomarkers of LUAD and deployed integrative bioinformatics and machine learning tools to harness gene expression data. Predictive models were developed to stratify patients based on prognostic outcomes. Importantly, we report here several potential key genes, for example, PMEL and BRIP1, and pathways implicated in the progression and prognosis of LUAD that could potentially be targeted for precision/personalized medicine in the future. Our drug repurposing analysis and molecular docking simulations suggested eight drug candidates for LUAD such as heat shock protein 90 inhibitors, cardiac glycosides, an antipsychotic agent (trifluoperazine), and a calcium ionophore (ionomycin). In summary, this study identifies several promising leads on systems biomarkers and drug candidates for LUAD. The findings also attest to the importance of integrative bioinformatics, structural biology and machine learning techniques in biomarker discovery, and precision oncology research and development.
Subject(s)
Adenocarcinoma of Lung , Biomarkers, Tumor , Computational Biology , Lung Neoplasms , Machine Learning , Humans , Biomarkers, Tumor/genetics , Computational Biology/methods , Adenocarcinoma of Lung/genetics , Adenocarcinoma of Lung/drug therapy , Adenocarcinoma of Lung/metabolism , Lung Neoplasms/genetics , Lung Neoplasms/drug therapy , Lung Neoplasms/metabolism , Molecular Docking Simulation , Drug Repositioning/methods , Gene Expression Regulation, Neoplastic/drug effects , Trifluoperazine/pharmacology , Prognosis , Adenocarcinoma/genetics , Adenocarcinoma/drug therapy , Adenocarcinoma/metabolism , Gene Expression Profiling/methods , Drug Discovery/methodsABSTRACT
BACKGROUND: Most human diseases are accompanied by systems changes. Systems biomarkers should reflect such changes. The phosphorylation and dephosphorylation of biomolecules maintain human homeostasis. However, the systems biomarker characteristics of circulating alkaline phosphatase, a routine blood test conducted for many human diseases, have never been investigated. METHOD: This study retrieved the circulating alkaline phosphatase (ALP) activities from patients with 48 clinically confirmed diseases and healthy individuals from the database of our hospital during the past five years. A detailed analysis of the statistical characteristics of ALP was conducted, including quantiles, receiving operator curve (ROC), and principal component analysis. RESULTS: Among the 48 diseases, 45 had increased, and three had decreased median levels of ALP activities compared to the healthy control. Preeclampsia, hepatic encephalopathy, pancreatic cancer, and liver cancer had the highest median values, whereas nephrotic syndrome, lupus erythematosus, and nephritis had decreased median values compared to the healthy control. Further, area under curve (AUC) values were ranged between 0.61 and 0.87 for 19 diseases, and the ALP activities were the best systems biomarker for preeclampsia (AUC 0.87), hepatic encephalopathy (AUC 0.87), liver cancer (AUC 0.81), and pancreatic cancer (AUC 0.81). CONCLUSIONS: Alkaline phosphatase was a decent systems biomarker for 19 different types of human diseases. Understanding the molecular mechanisms of over-up-and-down-regulation of ALP activities might be the key to understanding the whole-body systems' reactions during specific disease progression.