# Data Mining and Biochemical Profiling Reveal Novel Biomarker Candidates in Alzheimer’s Disease

## TL;DR

This study uses data mining and biochemical analysis to find new Alzheimer’s disease biomarkers linked to mitochondrial and iron metabolism.

## Contribution

A novel data mining and biochemical analysis approach is introduced for identifying AD-related proteins.

## Key findings

- Several potential new biomarkers for Alzheimer’s disease were identified.
- Defective protein synthesis in poorly oxygenated brain tissue was suggested.
- The method can be applied to biomarker discovery in other diseases.

## Abstract

The search for the biomarkers of Alzheimer’s disease (AD) may prove essential in the diagnosis and prognosis of the pathology, and the differential expression of key proteins may assist in identifying new therapeutic targets. In this proof-of-concept (POC) study, a new approach of data mining and matching combined with the biochemical analysis of proteins was applied to AD investigation. Three influential online open databases (UniProt, AlzGene, and Allen Human Brain Atlas) were explored to identify the genes and encoded proteins involved in AD linked to mitochondrial and iron dysmetabolism. The databases were searched using specific keywords to collect information about protein composition, and function, and meta-analysis data about their correlation with AD. The extracted datasets were matched to yield a list of relevant proteins in AD. The biochemical analysis of their amino acid content suggested a defective synthesis of these proteins in poorly oxygenated brain tissue, supporting their relevance in AD progression. The result of our POC study revealed several potential new markers of AD that deserve further molecular and clinical investigation. This novel database search approach can be a valuable strategy for biomarker search that can be exploited in many diseases.