Proteomic profile of acute myeloid leukaemia: A review update

Proteome analysis is a complex and dynamic process that encompasses several analytical platforms that include protein sequencing, structural or expression proteomics, protein modification, sub-cellular protein localization, protein-protein interaction and biological functional proteomics. In fact, expression proteomics is extensively applied in a majority of biomarker detection studies because it provides a detailed overview of differentially expressed proteins in cellular pathways and disease processes. Proteomics are also effective and dynamic in protein-protein interactions and cross-talks between interacting molecules of the cell. Proteomics has evolved into a crucial tool used to investigate the biochemical changes that possibly lead to development of cancer biomarkers. This review draws attention to the progress and advancements in cancer proteomics technology with the aim of simplifying the understanding of the mechanisms underlying the disease and to contribute to detection of biomarkers in addition to the development of novel treatments. Given that proteome is a dynamic entity of cellular functions in health and disease, it is capable of reflecting the immediate environmental state of cells and tissues as shown in this review. The review shows the possibility of elucidating the pathophysiology of acute myeloid leukaemia (AML) through proteome expressions, thus confirming the viability of proteome analysis in profiling AML.

Pathway analysis for identification of potential biomarkers in severe cutaneous drug hypersensitivity reactions

Purpose: To construct a cluster model or a gene signature for Stevens-Johnson syndrome (SJS) using pathways analysis in order to identify some potential biomarkers that may be used for early detection of SJS and epidermal necrolysis (TEN) manifestations. Methods: Gene expression profiles of GSE12829 were downloaded from Gene Expression Omnibus database. A total of 193 differentially expressed genes (DEGs) were obtained. We applied these genes to geneMANIA database, to remove ambiguous and duplicated genes, and after that, characterized the gene expression profiles using geneMANIA, DAVID, REACTOME, STRING and GENECODIS which are online software and databases. Results: Out of 193 genes, only 91 were used (after removing the ambiguous and duplicated genes) for topological analysis. It was found by geneMANIA database search that majority of these genes were coexpressed yielding 84.63 % co-expression. It was found that ten genes were in Physical interactions comprising almost 14.33 %. There were < 1 % pathway and genetic interactions with values of 0.97 and 0.06 %, respectively. Final analyses revealed that there are two clusters of gene interactions and 13 genes were shown to be in evident relationship of interaction with regards to hypersensitivity. Conclusion: Analysis of differential gene expressions by topological and database approaches in the current study reveals 2 gene network clusters. These genes are CD3G, CD3E, CD3D, TK1, TOP2A, CDK1, CDKN3, CCNB1, and CCNF. There are 9 key protein interactions in hypersensitivity reactions and may serve as biomarkers for SJS and TEN. Pathways related gene clusters has been identified and a genetic model to predict SJS and TEN early incidence using these biomarker genes has been developed.