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![Principal component analysis (PCA) and Overall Survival Kaplan-Meier Curve. PCA plot of the nonsynonymous mutations data in the TCGA COADREAD dataset [13]. (A) The red circle represents the patient projected onto the PC space. (B) PC1 vs. number of mutations, showing that the variation in the dataset is mostly attributed to the mutational burden. (C) The red line represents the low-risk (111 patients), and the black line represents the high-risk patients (112 patients) in the COADREAD TCGA cohort [13]. Cut-off mode was set as median risk from Cox regression.](publication/381508218/figure/fig2/AS:11431281252497119@1718744964351/Principal-component-analysis-PCA-and-Overall-Survival-Kaplan-Meier-Curve-PCA-plot-of.png)
Principal component analysis (PCA) and Overall Survival Kaplan-Meier Curve. PCA plot of the nonsynonymous mutations data in the TCGA COADREAD dataset [13]. (A) The red circle represents the patient projected onto the PC space. (B) PC1 vs. number of mutations, showing that the variation in the dataset is mostly attributed to the mutational burden. (C) The red line represents the low-risk (111 patients), and the black line represents the high-risk patients (112 patients) in the COADREAD TCGA cohort [13]. Cut-off mode was set as median risk from Cox regression.
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Early-onset colorectal cancer (EOCRC), defined as colorectal cancer in individuals under 50 years of age, has shown an alarming increase in incidence worldwide. We report a case of a twenty-four-year-old female with a strong family history of colorectal cancer (CRC) but without an identified underlying genetic predisposition syndrome. Two years aft...
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Context 1
... a comparison of overlap might not be enough to understand how this patient fits in within the distribution of the dataset. For this reason, we performed a principal component analysis (PCA) (Figure 3A,B). tations and clinical factors to be prognostically relevant, including tumour size, age of the patient and KRAS, TP53, ZIC1, KDM5C, THBS1, CDH13, DMTB1 and NDRG4 gene mutations (Supplementary Table S1). ...
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... and clinical factors to be prognostically relevant, including tumour size, age of the patient and KRAS, TP53, ZIC1, KDM5C, THBS1, CDH13, DMTB1 and NDRG4 gene mutations (Supplementary Table S1). We assessed the overall survival of patients from the TCGA COADREAD dataset stratified with the Cox multivariate model ( Figure 3C). The patient was classified as having low risk of cancer-related mortality by the model, i.e., lower than 98% of the patients in the COADREAD cohort. ...
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... the COADREAD dataset, most of the variance of the non-synonymous mutations was attributed to the number of mutations ( Figure 3A). The first PCA was strongly correlated with mutation burden ( Figure 3B), and it explained over 20% of the variance. ...
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... the COADREAD dataset, most of the variance of the non-synonymous mutations was attributed to the number of mutations ( Figure 3A). The first PCA was strongly correlated with mutation burden ( Figure 3B), and it explained over 20% of the variance. The tumour in this report fits in with the patients with a significantly lower mutation burden. ...
Context 5
... survival analysis revealed a number of mutations and clinical factors to be prognostically relevant, including tumour size, age of the patient and KRAS, TP53, ZIC1, KDM5C, THBS1, CDH13, DMTB1 and NDRG4 gene mutations (Supplementary Table S1). We assessed the overall survival of patients from the TCGA COADREAD dataset stratified with the Cox multivariate model ( Figure 3C). The patient was classified as having low risk of cancer-related mortality by the model, i.e., lower than 98% of the patients in the COADREAD cohort. ...
