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. 2020 Sep 2:8:e9847.
doi: 10.7717/peerj.9847. eCollection 2020.

Prognostic implications of metabolism-associated gene signatures in colorectal cancer

Yandong Miao #  1 , Qiutian Li #  2 , Jiangtao Wang  1 , Wuxia Quan  3 , Chen Li  4 , Yuan Yang  1 , Denghai Mi  1   5
Affiliations

Prognostic implications of metabolism-associated gene signatures in colorectal cancer

Yandong Miao et al. PeerJ. .

Abstract

Colorectal cancer (CRC) is one of the most common and deadly malignancies. Novel biomarkers for the diagnosis and prognosis of this disease must be identified. Besides, metabolism plays an essential role in the occurrence and development of CRC. This article aims to identify some critical prognosis-related metabolic genes (PRMGs) and construct a prognosis model of CRC patients for clinical use. We obtained the expression profiles of CRC from The Cancer Genome Atlas database (TCGA), then identified differentially expressed PRMGs by R and Perl software. Hub genes were filtered out by univariate Cox analysis and least absolute shrinkage and selection operator Cox analysis. We used functional enrichment analysis methods, such as Gene Ontology, Kyoto Encyclopedia of Genes and Genomes, and Gene Set Enrichment Analysis, to identify involved signaling pathways of PRMGs. The nomogram predicted overall survival (OS). Calibration traces were used to evaluate the consistency between the actual and the predicted survival rate. Finally, a prognostic model was constructed based on six metabolic genes (NAT2, XDH, GPX3, AKR1C4, SPHK1, and ADCY5), and the risk score was an independent prognostic prognosticator. Genetic expression and risk score were significantly correlated with clinicopathologic characteristics of CRC. A nomogram based on the clinicopathological feature of CRC and risk score accurately predicted the OS of individual CRC cancer patients. We also validated the results in the independent colorectal cancer cohorts GSE39582 and GSE87211. Our study demonstrates that the risk score is an independent prognostic biomarker and is closely correlated with the malignant clinicopathological characteristics of CRC patients. We also determined some metabolic genes associated with the survival and clinical stage of CRC as potential biomarkers for CRC diagnosis and treatment.

Keywords: Bioinformatic analysis; Biomarker; Colorectal cancer; GEO; Metabolic gene; TCGA.

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Conflict of interest statement

The authors declare there are no competing interests.

Figures

Figure 1
Figure 1. A flow chart of the study design and analysis.
Figure 2
Figure 2. Establishment of prognostic metabolic gene signature by univariate and LASSO Cox regression analysis.
(A–D) The process of constructing the signature containing six metabolic genes. (A) The HR, 95% CI calculated by univariate Cox regression. A coefficient (D) profile plot was generated against the log (lambda) sequence (B). (C) Selection of the optimal parameter (lambda) in the LASSO model for colorectal cancer. (E) Genetic alteration of the six genes in the colorectal cancer cohort. X axis represents cancer type, sky blue indicates COAD, light blue indicates READ. The left Y axis represents ratio of gene mutation, right Y axis represents gene names. Dark blue, cyan, and pink small rectangles indicate the type of gene mutation. HR, hazard ratios; CI, confidence intervals; COAD, colon adenocarcinoma; READ, rectum adenocarcinoma.
Figure 3
Figure 3. Traits of the prognostic metabolism-associated genes signature.
Heatmap of the metabolism-associated gene expression profiles in prognostic signature for TCGA-CRC (A) and GEO-CRC (B). The distribution of risk score and patient’s survival time of TCGA-CRC (C) and GEO-CRC (D). The black dotted line is the optimum cutoff dividing patients into low-risk and high-risk groups. The red curve represents high risk and the blue curve represents low risk. The distribution of survival status of TCGA-CRC (E) and GEO-CRC (F). The dots indicate the survival status, the red dot indicates the death of the patient and the blue dot indicates alive. (G, H) Univariate Cox regression analysis. Forest plot of the association between risk factors and survival of TCGA-CRC (G) and GEO-CRC (H). TCGA, the Cancer Genome Atlas database; GEO, Gene Expression Omnibus; CRC, colorectal cancer.
Figure 4
Figure 4. Metabolism-associated gene signature was significantly associated with survival in colorectal cancer.
(A, B) Multivariate Cox regression analysis. The risk score was an independent prognostic element in TCGA-CRC (A) and GEO-CRC (B). (C, D) Kaplan-Meier survival analysis of CRC patients ranked by the median risk score. The X axis represents the survival time (year) of the CRC patient; the Y axis represents the survival probability of the CRC patient. The high-risk score was related to poor OS in TCGA-CRC (C) and GEO-CRC (D). ROC analysis of the sensitivity and specificity of the OS for the combination of risk score and clinical characteristics in TCGA-CRC (E, G, I) and GEO-CRC (F, H, J). TCGA, the Cancer Genome Atlas database; GEO, Gene Expression Omnibus; CRC, colorectal cancer; ROC, receiver operating characteristic; OS, overall survival; AUC, area under curve; T, primary tumor; M, distant metastasis; N, regional lymph nodes.
Figure 5
Figure 5. Risk score and metaboli sm-genes associated with the clinicopathological features of CRC.
Box-plot showed that there was a significant association between metabolic genes expression, risk score, and clinicopathological features in the TCGA dataset (A–K) and GEO dataset (L–AA). In the TCGA dataset, the expression of NAT2 related to the group of the stage (A), N-stage (B), and M-stage (C). The expression of ADCY5 related to stage (D) and N-stage (E); SPHK1 (F), GPX3 (G) associated with T-stage; Risk score associated with stage (H), T-stage (I), N-stage (J), and M-stage (K). In the GEO dataset, the expression of NAT2 related to a group of age (l) and M-stage (M). The expression of XDH related to a group of age (N) and M-stage (O). The expression of ADCY5 associated with age (P) and M-stage (Q); SPHK1 associated with stage (R), T-stage (S), and N-stage (T); GPX3 associated with stage (U), T-stage (V), and N-stage (W); AKR1C4 had a correlation with stage (X) and N-stage (Y). Risk score associated with age (Z) and M-stage (AA).
Figure 6
Figure 6. GO, KEGG, and GSEA analysis.
(A) GO analysis of six PRMGs, on the left of circle chart is the gene, up-regulation was red, and down-regulation was blue, on the right of circle chart is different GO terms, and the genes linked via ribbons to their assigned terms (p < 0.05). (B) KEGG pathway of PRMGs. (C) GSEA analysis of the differentially expressed genes between high and low-risk groups. Green line chart representation enrichment profile, horizontal axis is each gene under the KEGG pathway, and the vertical axis is the corresponding accumulated enrichment score. The peak in the line graph is the enrichment score of this pathway, and the gene before the peak is the core gene of the pathway; Hits representation mark the genes of the pathway with black lines; Ranking metric scores indicates the distribution of rank values of all genes in the pathway. (D) Multiple GSEA analyses of the differentially expressed genes between high and low-risk groups. GO, gene ontology, KEGG, kyoto encyclopedia of genes and genomes, GSEA, gene set enrichment analysis. PRMGs, prognosis-related metabolic genes.
Figure 7
Figure 7. The nomogram to anticipate prognostic probabilities in CRC.
(A) The nomogram for predicting 1-, 3- and 5-year OS of CRC by clinical-pathological features and Risk score. The 1-, 3- and 5- year calibration curves of TCGA-CRC (B, D, F) and GEO-CRC (C, E, G). X axis represents predicted survival time and Y axis indicates actual survival time. TCGA, the Cancer Genome Atlas database; GEO, Gene Expression Omnibus; CRC, colorectal cancer. OS, overall survival.
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