Developing metabolic gene signatures to predict intrahepatic cholangiocarcinoma prognosis and mining a miRNA regulatory network

Abstract

Background: Metabolic disturbance is closely correlated with intrahepatic cholangiocarcinoma (IHCC), and we aimed to identify metabolic gene marker for the prognosis of IHCC.

Methods: We obtained expression and clinical data from 141 patients with IHCC from public databases. Prognostic metabolic genes were selected using univariate Cox regression analysis. Unsupervised cluster analysis was applied to identify IHCC subtypes, and CIBERSORT was used for immune infiltration analysis of different subtypes. Then, the metabolic gene signature was screened using multivariate Cox regression analysis and the LASSO algorithm. The prognostic potential and regulatory network of the metabolic gene signature were further investigated.

Results: We screened 228 prognosis-related metabolic genes. Based on their expression levels, IHCC samples were divided into two subtypes, which showed significant differences in survival and immune cell infiltration. After LASSO analysis, eight metabolic genes including CYP19A1, SCD5, ACOT8, SRD5A3, MOGAT2, PFKFB3, PPARGC1B, and RPL17 were identified as the optimal genes for the prognosis signature. The prognostic model had excellent predictive abilities, with areas under the receiver-operating characteristic curves over 0.8. A nomogram model was also established based on two independent prognostic clinical factors (pathologic stage and prognostic model), and the generated calibration curves and c-indexes determined its excellent accuracy and discriminative ability to predict 1- and 5-year survival status (c-indexes>0.7). Finally, we found that miR-26a-5p, miR-27a-3p, and miR-27b-3p were the upstream regulators that mediate the involvement of gene signatures in metabolic pathways.

Conclusion: We developed eight metabolic gene signatures to predict IHCC prognosis and proposed potential upstream regulatory axes of gene signatures.

Keywords: intrahepatic cholangiocarcinoma; metabolism; miRNA-mRNA regulatory network; prognostic model.

FIGURE 1

Flowchart describing this study

FIGURE 2

PPI and enrichment analyses of 228 prognosis‐related metabolic genes. (A) Construction of the PPI network. The larger the number of nodes, the higher the number of connections. The redder the nodes, the smaller the p value. (B and C) Top 20 GO‐BP (B) and top 17 KEGG pathways (C) ranking by FDR from small to large. The x‐axis indicates the fold enrichment, whereas the y‐axis indicates the terms of the GO‐BP or KEGG pathways

FIGURE 3

Unsupervised cluster analysis to identify IHCC subtypes. (A) Bidirectional hierarchical clustering heatmap based on the expression levels of 228 prognosis‐related metabolic genes. The green and purple bars indicate cluster 1 and 2, respectively. (B) The KM curve shows the differences in survival between samples from clusters 1 and 2. The red and green lines indicate cluster 1 and 2, respectively

FIGURE 4

Immune cells with significant differences in infiltration abundances between samples from cluster 1 and 2

FIGURE 5

Screening of an optimal gene set associated with prognosis and identifying the metabolic gene signatures to predict the IHCC prognosis. (A) The forest plot shows the coefficients of eight metabolic gene signatures identified using the LASSO algorithm. (B) KM curves show the differences in survival status between high‐expression and low‐expression groups; samples are divided into groups according to the median expression values of eight metabolic gene signatures. The green and red lines indicate the high‐expression and low‐expression groups, respectively

FIGURE 6

Construction and verification of the prognostic models in the training set, validation set, and entire sample set. (A–C) The distributions of PS and survival time, as well as the changes in expression level of the eight gene signature in the training set (A), validation set (B), and entire sample set (C). (D–F) KM curves and ROC curves created to evaluate the predictive abilities of PS‐based prognostic models in the training set (D), validation set (E), and entire sample set (F)

FIGURE 7

Construction and validation of a nomogram prediction model. (A) The forest plot shows that the pathologic stage and PS status are two independent prognostic clinical factors of IHCC. (B) A nomogram model based on two independent prognostic clinical factors built to predict the 1‐, 3‐, and 5‐year survival probabilities of IHCC patients. (C) Calibration curves and c‐indexes were analyzed to evaluate the predictive ability of the nomogram model. The x‐axis indicates the predicted survival status, and the y‐axis indicates the actual survival status. The blue, red, and black lines indicate 1‐, 3‐, and 5‐year survival status, respectively, along with the calculated c‐indexes

FIGURE 8

Construction of a miRNA regulatory network based on metabolic gene signatures. (A) A miRNA‐mRNA regulatory network based on DEmiRNAs of IHCC and prognosis‐related metabolic genes. The triangles and circles indicate miRNAs and mRNAs, respectively. The red lines indicate the relationship between the upstream miRNAs and prognostic metabolic gene signatures. (B) GO‐BP function and KEGG pathway enrichment analyses of mRNAs in the miRNA‐mRNA regulatory network. The x‐axis indicates the fold enrichment, and the y‐axis indicates the terms of GO functions and KEGG pathways. (C) Construction of miRNA‐mRNA‐pathway regulatory axes based on metabolic gene signatures of IHCC prognosis