Multiple signatures of a disease in potential biomarker space: Getting the signatures consensus and identification of novel biomarkers

Abstract

BackgroundThe lack of consensus among reported gene signature subsets (GSSs) in multi-gene biomarker discovery studies is often a concern for researchers and clinicians. Subsequently, it discourages larger scale prospective studies, prevents the translation of such knowledge into a practical clinical setting and ultimately hinders the progress of the field of biomarker-based disease classification, prognosis and prediction.MethodsWe define all "gene identificators" (gIDs) as constituents of the entire potential disease biomarker space. For each gID in a GSS of interest ("tested GSS"/tGSS), our method counts the empirical frequency of gID co-occurrences/overlaps in other reference GSSs (rGSSs) and compares it with the expected frequency generated via implementation of a randomized sampling procedure. Comparison of the empirical frequency distribution (EFD) with the expected background frequency distribution (BFD) allows dichotomization of statistically novel (SN) and common (SC) gIDs within the tGSS.ResultsWe identify SN or SC biomarkers for tGSSs obtained from previous studies of high-grade serous ovarian cancer (HG-SOC) and breast cancer (BC). For each tGSS, the EFD of gID co-occurrences/overlaps with other rGSSs is characterized by scale and context-dependent Pareto-like frequency distribution function. Our results indicate that while independently there is little overlap between our tGSS with individual rGSSs, comparison of the EFD with BFD suggests that beyond a confidence threshold, tested gIDs become more common in rGSSs than expected. This validates the use of our tGSS as individual or combined prognostic factors. Our method identifies SN and SC genes of a 36-gene prognostic signature that stratify HG-SOC patients into subgroups with low, intermediate or high-risk of the disease outcome. Using 70 BC rGSSs, the method also predicted SN and SC BC prognostic genes from the tested obesity and IGF1 pathway GSSs.ConclusionsOur method provides a strategy that identify/predict within a tGSS of interest, gID subsets that are either SN or SC when compared to other rGSSs. Practically, our results suggest that there is a stronger association of the IGF1 signature genes with the 70 BC rGSSs, than for the obesity-associated signature. Furthermore, both SC and SN genes, in both signatures could be considered as perspective prognostic biomarkers of BCs that stratify the patients onto low or high risks of cancer development.

Circulating miR-16-5p, miR-92a-3p, and miR-451a in Plasma from Lung Cancer Patients: Potential Application in Early Detection and a Regulatory Role in Tumorigenesis Pathways

“…Despite differences in study design, goals, samples, and patients, these three miRNAs were found within the MSC classifier. This finding contrasts with other studies that observed low degrees of overlap between signatures [34]. Our 3-miRNA signature (miR-16-5p, miR-92a-3p, and miR-451a) may be robust for lung cancer early detection, since it was distinctly found in lung cancer patients only, which were mostly of Stage I (62%; 48/78).…”

Identification of disease modules using higher-order network structure

“…Genes MAP2K3 and MAPKAPK3 are found to play a role in BMI ( Bian et al 2013 , Shao et al 2022 ). Other genes in MAPK signaling that are not significant according to the gene scores ( RPS6KA4, DUSP4, MAPKAPK5 ) have also been associated with obesity ( Ow and Kuznetsov 2015 ). EEF2K is also predicted to be a novel target for obesity ( Joshi et al 2021 ).…”

Genome and transcriptome delineation of two major oncogenic pathways governing invasive ductal breast cancer development

“…To test the novelty of genes in 22g-TAG, we compared our 22g-TAG with reference lists of 72 BC gene signatures previously published in other studies and collated by our group [ 36 , 37 ] (including 2 grading signatures from previous studies [ 18 , 20 ]). Only one gene ( CAPN8 ) can be considered a novel IDC-associated gene.…”