Gene Expression Correlation for Cancer Diagnosis: A Pilot Study

Abstract

Poor prognosis for late-stage, high-grade, and recurrent cancers has been motivating cancer researchers to search for more efficient biomarkers to identify the onset of cancer. Recent advances in constructing and dynamically analyzing biomolecular networks for different types of cancer have provided a promising novel strategy to detect tumorigenesis and metastasis. The observation of different biomolecular networks associated with normal and cancerous states led us to hypothesize that correlations for gene expressions could serve as valid indicators of early cancer development. In this pilot study, we tested our hypothesis by examining whether the mRNA expressions of three randomly selected cancer-related genesPIK3C3,PIM3, andPTENwere correlated during cancer progression and the correlation coefficients could be used for cancer diagnosis. Strong correlations(0.68≤r≤1.0)were observed betweenPIK3C3andPIM3in breast cancer, betweenPIK3C3andPTENin breast and ovary cancers, and betweenPIM3andPTENin breast, kidney, liver, and thyroid cancers during disease progression, implicating that the correlations for cancer network gene expressions could serve as a supplement to current clinical biomarkers, such as cancer antigens, for early cancer diagnosis.