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. 2021 Oct 7:11:727752.
doi: 10.3389/fonc.2021.727752. eCollection 2021.

Robust Glycogene-Based Prognostic Signature for Proficient Mismatch Repair Colorectal Adenocarcinoma

Yixi Li  1   2 , Dehua Li  3 , Yang Chen  3 , Yongping Lu  2 , Fangbin Zhou  1 , Chunhong Li  1 , Zhipeng Zeng  1 , Wanxia Cai  1 , Liewen Lin  1 , Qiang Li  4 , Mingjun Ye  2 , Jingjing Dong  2 , Lianghong Yin  2 , Donge Tang  1 , Gong Zhang  3 , Yong Dai  1   5
Affiliations

Robust Glycogene-Based Prognostic Signature for Proficient Mismatch Repair Colorectal Adenocarcinoma

Yixi Li et al. Front Oncol. .

Abstract

Background: Proficient mismatch repair (pMMR) colorectal adenocarcinoma (CRAC) metastasizes to a greater extent than MMR-deficient CRAC. Prognostic biomarkers are preferred in clinical practice. However, traditional biomarkers screened directly from sequencing are often not robust and thus cannot be confidently utilized.

Methods: To circumvent the drawbacks of blind screening, we established a new strategy to identify prognostic biomarkers in the conserved and specific oncogenic pathway and its regulatory RNA network. We performed RNA sequencing (RNA-seq) for messenger RNA (mRNA) and noncoding RNA in six pMMR CRAC patients and constructed a glycosylation-related RNA regulatory network. Biomarkers were selected based on the network and their correlation with the clinicopathologic information and were validated in multiple centers (n = 775).

Results: We constructed a competing endogenous RNA (ceRNA) regulatory network using RNA-seq. Genes associated with glycosylation pathways were embedded within this scale-free network. Moreover, we further developed and validated a seven-glycogene prognosis signature, GlycoSig (B3GNT6, GALNT3, GALNT8, ALG8, STT3B, SRD5A3, and ALG6) that prognosticate poor-prognostic subtype for pMMR CRAC patients. This biomarker set was validated in multicenter datasets, demonstrating its robustness and wide applicability. We constructed a simple-to-use nomogram that integrated the risk score of GlycoSig and clinicopathological features of pMMR CRAC patients.

Conclusions: The seven-glycogene signature served as a novel and robust prognostic biomarker set for pMMR CRAC, highlighting the role of a dysregulated glycosylation network in poor prognosis.

Keywords: biomarker; ceRNA; colorectal cancer; glycogene; glycosylation; mismatch repair.

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Conflict of interest statement

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Figures

Figure 1
Figure 1
Cancer-specific messenger RNAs (mRNAs) in proficient mismatch repair (pMMR) colorectal adenocarcinoma (CRAC) and functional enrichment analysis. (A) Workflow of the experiment process. (B) The representative pattern of mismatch repair (MMR) status for CRAC tissues. (C) Top 16 enriched Gene Ontology (GO) terms of molecular function; y axis represents GO terms, and x axis represents gene number. Color of the bars represent enrichment significance. (D) Top 16 enriched Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways; y axis represents pathway names, and x axis represents rich factor. Size and color of the bubble represent number of differentially expressed mRNAs (DEGs) enriched in the pathway and enrichment significance, respectively.
Figure 2
Figure 2
Construction of glycosylation related competing endogenous RNA (ceRNA) network. (A) The ceRNA network related to glycosylation. Fuchsia v shapes represent microRNA (miRNA); green dots, long noncoding RNA (lncRNA); pink dots, circular RNA (circRNA); yellow dots, messenger RNA (mRNA). (B) Power law scatter plot for ceRNA network. (C) Top 10 mRNA–miRNA interactions of ceRNA network in proficient mismatch repair (pMMR) colorectal adenocarcinoma (CRAC) tissues (CT1-6) and non-tumor tissues (CN1-6) were visualized in heatmap.
Figure 3
Figure 3
GlycoSig model for the prognosis of proficient mismatch repair (pMMR) colorectal adenocarcinoma (CRAC) in The Cancer Genome Atlas (TCGA) dataset. (A) The expression of seven glycogenes in tumor and non-tumor tissues from TCGA database. (B) Forest plots showed the multivariate Cox regression analysis for GlycoSig; *P < 0.05; ***P < 0.001. (C) Kaplan–Meier survival curves of pMMR CRAC patients classified into high- and low-risk groups using the GlycoSig.
Figure 4
Figure 4
The prognostic power of the GlycoSig biomarker set. Receiver operating characteristic (ROC) curve analysis of clinicopathologic information (A) and GlycoSig combined with clinical information (B); Yellow, green, and red dotted lines represent GlycoSig risk score predicting 1-, 3-, and 5-year overall survival. (C) Heatmap of the GlycoSig combined with clinical features and messenger RNA (mRNA) expression of glycogenes in The Cancer Genome Atlas (TCGA) dataset.
Figure 5
Figure 5
Validation of GlycoSig and nomogram for proficient mismatch repair (pMMR) colorectal adenocarcinoma (CRAC). (A) Independent validation of GlycoSig in the GSE39582 set; Kaplan–Meier survival curves of patients classified into high- and low-risk groups using the GlycoSig for overall survival. (B) Nomograms to predict 1-, 3-, and 5-year survival probability in pMMR CRAC.
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