Link to bioRxiv paper:
http://biorxiv.org/cgi/content/short/2023.04.11.536461v1?rss=1
Authors: Luo, G., Letterio, J.
Abstract:
Background: There is a need for new methods to select and analyze cutoffs employed to define genes that are most prognostic significant and impactful. We designed LOCC, a novel tool to visualize and score continuous variables for a dichotomous outcome. Methods: We analyzed TCGA hepatocellular carcinoma gene expression and patient data using LOCC. Analysis of E2F1, TP53, and a previously published gene signature were performed to demonstrate the utility of LOCC. Results: LOCC demonstrated that high E2F1 expression and low TP53 expression were associated with worse prognosis in hepatocellular carcinoma. Analysis of a previously published gene signature showed large differences in LOCC scoring. Optimization of the gene signature by selecting a subset of genes shows similar significance and hazard ratio stratification of the risk groups. Conclusions: LOCC is a novel tool for defining prognostic significance that aids our understanding and selection of cutoffs, particularly for gene expression analysis in cancer. Impact: LOCC can be used in prognosis studies to select and comprehend variables and cutoffs.
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