Identification and Validation of a Novel Six-Gene Prognostic Signature of Stem Cell Characteristic in Colon Cancer

Abstract

Cancer stem cells play crucial roles in the development of colon cancer (COAD). This study tried to explore new markers for predicting the prognosis of colon cancer based on stem cell-related genes. In our study, 424 COAD samples from TCGA were divided into three subtypes based on 412 stem cell-related genes; there were significant differences in prognosis, clinical characteristics, and immune scores between these subtypes. 694 genes were screened between subgroups. Subsequently a six-gene signature (DYDC2, MS4A15, MAGEA1, WNT7A, APOD, and SERPINE1) was established. This model had strong robustness and stable predictive performance in cohorts of different platforms. Taken together, the six-gene signature constructed in this study could be used as a novel prognostic marker for COAD patients.

Keywords: colon adenocarcinoma; molecular subtype; prognostic marker; six-gene signature; stem cell.

Figure 1

(A) Consensus map of NMF clustering; (B) heat map of clustering of 412 prognosis-related genes (C) PFS prognostic survival curve of different molecular subtypes; (D) comparison of different subtypes and CMS, where different colors represent different subtypes. The coordinates represent the percentage of samples.

Figure 2

(A) Change trajectory of each independent variable; the horizontal axis represents the log value of the independent variable lambda, and the vertical axis represents the coefficient of the independent variable; (B) the confidence interval at different value of lambda.

Figure 3

(A) Risk score, survival time and survival status and expression of six-gene in the training cohort; (B) ROC curve and AUC of six-gene signature classification; (C) KM survival curve distribution of six-gene signature in the training cohort.

Figure 4

(A) Risk score, survival time and survival status and expression of the six genes in the internal validation cohort; (B) ROC curve and AUC of the six-gene signature classification; (C) KM survival curve distribution of six-gene signature in validation cohort.

Figure 5

(A) ROC curve of GSE39582 external validation cohort; (B) KM curve of the six-gene signature in GSE39582 external validation cohort; (C) ROC curve of GSE17536 external validation cohort; (D) KM curve of the six-gene signature in GSE17536 external validation cohort.

Figure 6

Prognostic survival curves of different clinical characteristics. (A) age; (B) gender; (C) Clinical Stage; (D) T stage; (E) N stage; (F) M stage; (G) Lymphatic invasion; (H) Venous invasion; (I) MSI; The abscissa represents survival time, and the ordinate represents Survival rate.

Figure 7

(A) Forest map of univariate survival analysis; (B) Forest map of multivariate survival analysis, where orange red represents significant PFS correlation.

Figure 8

(A) Clustering of correlation coefficients between KEGG pathways with correlation to Risk score greater than 0.35 and between Risk scores; (B) Changes in ssGSEA scores of KEGG pathways with correlation to risk score greater than 0.35 in each sample, the horizontal axis represents the samples, and the risk scores increase from left to right.

Figure 9

(A) AUC curve of Zuo model in TCGA training cohort; (B) KM curve of Zuo model in TCGA training cohort; (C) AUC curve of Kim model in TCGA training cohort; (D) KM curve of Kim model in TCGA training cohort. (E) C-index score of three models.

Figure 10

(A) Expression levels of six genes quantified using qPCR in 60 paired normal tissues and colon cancer tissues. *P < 0.05. (B) Kaplan–Meier curves of OS in COAD patients based on risk score.

Figure 11

Methodology flow chart.