ArticleAnnals of translational medicine2020
A novel workflow for cancer blood biomarker identification.
Article in Annals of translational medicine, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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7 authors at 4 institutions in 1 country.
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Abstract
backgroundOver the last few years, great progress has been made in the development of key technologies to detect peripheral blood-based, tumor-specific biomarkers, such as circulating tumor cells (CTCs) and circulating cell free tumor DNA (ctDNA). Despite the considerable advances and their multiple clinical values, liquid biopsies are challenged by the very low concentrations of CTCs and ctDNA in blood samples. Additionally, blood biomarkers which were found using data-driven methods may only be effective in few datasets.
methodsWe firstly collected the genes which have expression correlations between blood and the other tissues/organs using Genotype-Tissue Expression (GTEx). Survival hazard genes and differential expression genes of each cancer type in The Cancer Genome Atlas (TCGA) were then selected by Cox regression model and Wilcoxon rank sum test, respectively. By combining the P values of two steps, several blood biomarkers can be inferred for each cancer type. After applying these potential blood biomarker sets to 13 datasets of blood samples from solid tumor patients using single sample gene set enrichment analyses (ssGSEA), we got an enrichment score (ES) for each sample.
resultsThe inferred blood biomarker (BB infer) genes showed reliable predictive value in various malignancies. In all the blood samples that were analyzed, the ESs of positive BB Infer genes in cancer patients are higher than healthy people. Conversely, the ESs of negative BB Infer genes in cancer patients are lower than healthy people. Furthermore, lower ES of negative BB infer genes signify the dismal outcome of patients.
conclusionsWe developed a novel solid tumor blood biomarker inference workflow for cancer screening and diagnosis. Moreover, we demonstrated the utility of this inference method in a series of blood sample datasets of solid tumor patients. These results suggested the potential value of this method in the screening, diagnosis and prognosis of cancers.
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