American Association for Cancer Research
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TABLE 2 from A Systems Biology Approach to Understand the Racial Disparities in Colorectal Cancer

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posted on 2024-01-12, 14:20 authored by Annabelle Nwaokorie, Walter Kolch, Dirk Fey

The top two significant genes associated with overall survival across nine colorectal cancer relevant signaling pathways for both Black/AA and White cohorts from the TCGA, PanCancer Atlas dataset. A Kaplan–Meier estimate, and log-rank test were used to compute the association between patient overall survival and gene expression and report the associated HR, confidence intervals, P-value, P-adj (P-adjusted value), and SE. “Patients” indicates the number of patients for which data were available. “Significant”, indicates the number of significant genes out of the total number of genes for this pathway. The fold change and P-value cutoff used were 0.5 and 0.05, respectively. The genes in bold were found to be common top significant genes in both Black/AA and White cohorts in the associated STN (Supplementary Table S3)



Science Foundation Ireland Precision Oncology Ireland



Racial disparities between Black/African Americans (AA) and White patients in colorectal cancer are an ever-growing area of concern. Black/AA show the highest incidence and have the highest mortality among major U.S. racial groups. There is no definite cause other than possible sociodemographic, socioeconomic, education, nutrition, delivery of healthcare, screening, and cultural factors. A primary limitation in this field is the lack of and small sample size of Black/AA studies. Thus, this study aimed to investigate whether differences in gene expression contribute to this ongoing unanswered racial disparity issue. In this study, we examined transcriptomic data of Black/AA and White patient cohorts using a bioinformatic and systems biology approach. We performed a Kaplan–Meier overall survival analysis between both patient cohorts across critical colorectal cancer signal transduction networks (STN), to determine the differences in significant genes across each cohort. Other bioinformatic analyses performed included PROGENy (pathway responsive genes for activity inference), RNA sequencing differential expression using DESeq2, multivariable-adjusted regression, and other associated Kaplan–Meier analyses. These analyses identified novel prognostic genes independent from each cohort, 176 differentially expressed genes, and specific patient cohort STN survival associations. Despite the overarching limitation, the results revealed several novel differences in gene expression between the colorectal cancer Black/AA and White patient cohorts, which allows one to dive deeper into and understand the behavior on a systems level of what could be driving this racial difference across colorectal cancer. Concretely, this information can guide precision medicine approaches tailored specifically for colorectal cancer racial disparities. The purpose of this work is to investigate the racial disparities in colorectal cancer between Black/AA and White patient cohorts using a systems biology and bioinformatic approach. Our study investigates the underlying biology of each patient cohort. Concretely, the findings of this study include disparity-associated genes and pathways, which provide a tangible starting point to guide precision medicine approaches tailored specifically for colorectal cancer racial disparities.