An Evaluation of Gene Set Analysis for Biomarker Discovery with Applications to Myeloma Research

Amplification of autocrine motility factor and its receptor in multiple myeloma and other musculoskeletal tumors

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This study is a large scale cohort of the patients with malignant musculoskeletal tumors to determine the expression levels of Autocrine Motility factor (AMF) and its receptor (AMFR).

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We Visualization of amplified Autocrine motility factor (AMF) and its receptor (AMFR) in musculoskeletal tumors.

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A novel software aimed at analyzing numerous cell-to-cell and ligand-to-receptor interactions was developed, which lead to visualization of bone tumor microenvironment.

Autocrine motility factor (AMF: GPI) and its receptor AMFR (AMF Receptor: gp78) regulate the metastatic process. Here, we have tested the expression levels of AMF, AMFR, and AMF × AMFR in 1348 patients with musculoskeletal tumor. The results depicted here identified that multiple myeloma highly express AMF × AMFR value as compared with normal bone samples (p < 0.00001). To visualize the AMF × AMFR autocrine amplification in multiple myeloma microenvironment, we have developed a novel software aimed at analyzing numerous cell-to-cell and ligand-to-receptor interactions, i.e., Environmentome. It has led to the identification that myeloma-associated interactions with normal bone cells including osteoblast, osteoclast, immunological components, and others in a paracrine manner. In conclusion, the data showed that AMF × AMFR amplification is a clinical manifestation in bone microenvironment of multiple myeloma.

Disease Pathway Cut for Multi-Target drugs

Background

Biomarker discovery studies have been moving the focus from a single target gene to a set of target genes. However, the number of target genes in a drug should be minimum to avoid drug side-effect or toxicity. But still, the set of target genes should effectively block all possible paths of disease progression.

Methods

In this article, we propose a network based computational analysis for target gene identification for multi-target drugs. The min-cut algorithm is employed to cut all the paths from onset genes to apoptotic genes on a disease pathway. If the pathway network is completely disconnected, development of disease will not further go on. The genes corresponding to the end points of the cutting edges are identified as candidate target genes for a multi-target drug.

Results and conclusions

The proposed method was applied to 10 disease pathways. In total, thirty candidate genes were suggested. The result was validated with gene set enrichment analysis software, PubMed literature review and de facto drug targets.