A Combined four-mRNA Signature Associated with Lymphatic Metastasis for Prognosis of Colorectal Cancer

Abstract

Background: Colorectal cancer (CRC) is one of the most common malignant tumors in the world. Lymph node metastasis (LNM) is a common mode of metastasis of CRC. However, the combined mRNA biomarkers associated with LNM of CRC that can effectively predict CRC prognosis have not been reported yet. Methods: To identify biomarkers that are associated with LNM, we collected data from the The Cancer Genome Atlas (TCGA) database. The edgeR package was searched to seek LNM-related genes by comparisons between cancer samples and normal colorectal tissues and between LNM and non-LNM (NLNM) of CRC. Univariate and multivariate regression analysis of genes in the intersection to build gene signature associated with independent prognosis of CRC, and then verified by Kaplan-Meier curve and log-rank test, receiver operating characteristic (ROC) curve was used to determine the efficiency of survival prediction of our four-mRNA signature. Finally, the potential molecular mechanisms and properties of these gene signature were also explored with functional and pathway enrichment analysis. Results: 329 mRNAs were up-regulated in CRC tissues with LNM, and 8461 mRNAs were up-regulated in CRC tissues, the intersection is 100 mRNAs. After univariate and multivariate Cox regression analysis of 100 mRNAs, a novel four LNM related mRNAs (EPHA8, KRT85, GABRA3, and CLPSL1) were screened as independent prognostic indicators of CRC. Surprisingly, the four-mRNA signature can predict the prognosis of CRC patients independently of clinical factors andthe area under the curve (AUC) of the ROC is 0.730. The novel four-mRNA signature was used to identify high and low-risk groups. Stratified analysis indicated the risk score based on four-mRNA signature was an independent prognostic indicator for female, T3+T4, N1+N2 ,stage III+IV and patients with no new tumor event. Functional annotation of this risk model in high-risk patients revealed that pathways associated with neuroactive ligand-receptor interaction, estrogen signaling pathway, and steroid hormone biosynthesis. Conclusions: By conducting TCGA data mining, our study demonstrated that a four-mRNA signature associated with LNM can be used as a combined biomarker for independent prognosis of CRC.

Keywords: biomarker; colorectal cancer; lymph node metastasis; mRNA; prognosis.

Figure 1

DEGs associated with LNM. (A) study design. (B) The volcano map of DEGs in CRC tissues with LNM vs NLNM, the red represents the up-regulated genes. (C) the intersection of the up-regulated genes in LNM vs NLNM tissues and CRC vs non-cancer tissues.

Figure 2

Risk score analysis of the four-mRNAs signature of CRC. (A) The heat map of five genes in CRC patients. Each column represents a patient and each row represents a gene. The expression levels of genes are displayed in different colors. From blue to red, the expression is gradually increasing. (B) The coefficients of the four genes, red for positive numbers and blue for negative numbers. (C-E)The distribution of high and low risk scores of four mRNAs in entire TCGA set (N=614), TCGA test set (N=322) and validation set (N=292). (F-H) Survival time and status of patients based on the high and low risk scores of four mRNAs in entire TCGA set (N=614)、TCGA test set (N=322) and validation set (N=292). (I-K) The heat map of four genes in entire TCGA set (N=614), TCGA test set (N=322) and validation set (N=292).

Figure 3

Univariate and multivariate Cox regression analysis of OS. (A) Distribution of the clinic pathological parameters including age, T, N, M, stage, residual tumor and neoplasm cancer status in CRC patients with low-risk score to high-risk score. (B) univariate Cox regression analysis of OS. (C) multivariate Cox regression analysis of OS.

Figure 4

Kaplan-Meier survival analysis of CRC patients in TCGA data. (A) Risk score predicts survival of patients with CRC in entire TCGA set (N=614). (B) ROC curves of the four-mRNA signature in CRC (AUC = 0.730). (C) Risk score predicts survival of patients with CRC in TCGA test set (N=322). (D) Risk score predicts survival of patients with CRC in TCGA validation set (N=292).

Figure 5

Kaplan-Meier curves predicts patient survival with clinical features for the patients divided into high and low risk scores. (A) gender. (B) T stage. (C) N stage. (D) stage.( E) new tumor event after initial treatment.

Figure 6

Functional enrichment analysis. (A) KEGG analysis of up-regulated genes in high risk score group. (B) GO analysis of up-regulated genes in high risk score group.