A set of molecular biomarkers as an indicator of cancer risk.

person Sonalika Singhal University of North Dakota, Grand Forks, ND info_outline Sonalika Singhal, Kevin Gardner, Sandeep K. Singhal, Donald Sens

Authors person Sonalika Singhal University of North Dakota, Grand Forks, ND info_outline Sonalika Singhal, Kevin Gardner, Sandeep K. Singhal, Donald Sens Organizations University of North Dakota, Grand Forks, ND, Department of Pathology and Cell Biology, Columbia University Medical Center, New York, NY, UND School of Medicine and Health Sciences, Grand Forks, ND Abstract Disclosures Research Funding No funding sources reported Background: Biomarkers are molecular indicators of a biological status and are often used to determine the presence or absence of cancer Molecular changes in both in of cancer cells and their surrounding environment including, blood other body fluids, and tissues that react to the cancer provide biomarker materials that may be effective predictor of disease risk or disease outcome. Effective biomarkers for the disease associated risk and early detection of cancer improves potential patient outcome. An emerging theme is to develop multi-omics panels of biomarker with sufficient sensitivity and specificity for the pre-symptomatic detection of cancer. We applied approximately 300 genomics and proteomics-based genomics (PbG) biomarkers, to identify a panel of biomarkers associated with different types of cancers using patient and donor RNA samples collected from platelets Platelet RNA mirrors the RNA of nucleated cells and can be an important indicator of tissue states particularly in under pro-inflammatory conditions [PMC9555784]. Methods: Publicly available RNA-seq data of N = 2351 blood platelet samples, (including n= 390 asymptomatic controls) from 18 different tumor types was analyzed to identify the panel of biomarkers associated with highest risk cancers. Approximately 300 genomic and PbG signatures (developed from gene expression-based signatures on patients stratified according to protein expression (IHC) and survival. The predictive value of each gene signature (including commercial once ROR, OncotypeDx, etc.) was evaluated by logistic regression modeling in univariate and multivariate settings. Results: Out of 300 biomarkers, we found a set of PbG biomarkers including ratio of BRG1 expression between nuclear and cytoplasm, cytoplasmic C-terminal-binding protein 2 (CtBP2) in express in cytoplasm and ratio of nucleus and cytoplasm, Nucleus Tripartite motif-containing 28 (TRIM28), Vascular endothelial growth factor (VEGF), Mitogen-activated protein kinases (MAPK), androgen receptor (AR) express in Nuclear vs cytoplasm, Glycoprotein 78 (Gp78) in cytoplasm, E2F3, and a member of the epidermal growth factor (EGF) receptor family (ERBB2) are highly predictive (0.77 (0.76 ~ 0.79)) of cancer associated risk on a multivariate training/validation setting. The BRG1 biomarker has the highest odds ratio (11.33) within model, known for coordinating and maintaining key signaling pathways that promote oncogenesis in different cancer types including leukemia, breast, and prostate cancer. Cancer Risk model = 3.4 - 1.01 ERBB2 + 0.3 GP78_Cyto - 3.2 AR_Nucl + 0.7 CtBP2_NucVsCyto - 0.3 E2F3 - 0.99 AR_NucVsCyto + 0.87 VEGF - 1.9 MAPK + 2.43 BRG1_NucVsCyto + 1.35 TRIM28_Nucl + 1.23 CtBP2_Cyto. Conclusions: Utilizing novel genomic, and proteomic technologies, risk prediction and early detection of cancer could identify patients at a time when outcomes are superior and treatment is manageable. Further evaluation of this model is recommended.