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. 2024 Aug 28;15(1):376.
doi: 10.1007/s12672-024-01255-y.

Establishment of a prognostic model for pancreatic cancer based on mitochondrial metabolism related genes

Qinwen Ba  1 , Xiong Wang  2 , Yanjun Lu  3
Affiliations

Establishment of a prognostic model for pancreatic cancer based on mitochondrial metabolism related genes

Qinwen Ba et al. Discov Oncol. .

Abstract

Aim: Pancreatic ductal adenocarcinoma (PAAD) is recognized as an exceptionally aggressive cancer that both highly lethal and unfavorable prognosis. The mitochondrial metabolism pathway is intimately involved in oncogenesis and tumor progression, however, much remains unknown in this area. In this study, the bioinformatic tools have been used to construct a prognostic model with mitochondrial metabolism-related genes (MMRGs) to evaluate the survival, immune status, mutation profile, and drug sensitivity of PAAD patients.

Method: Univariate Cox regression and LASSO regression were used to screen the differentially expressed genes (DEGs), and multivariate Cox regression was used to develop the risk model. Kaplan-Meier estimator was employed to identify MMRGs signatures associated with overall survival (OS). ROC curves were utilized to evaluate the model's performance. Maftools, immunedeconv and CIBERSORT R packages were applied to analyze the gene mutation profiles and immune status. The corresponding sensitivity to pharmaceutical agents was assessed using oncoPredict R packages.

Results: A prognostic model with five MMRGs was developed, which defined the patients as high-risk showed lower survival rates. There was good consistency among individuals categorized as high-risk, showing elevated rates of genetic alterations, particularly in the TP53 and KRAS genes. Furthermore, these patients exhibited increased levels of immunosuppression, characterized by an increased presence of macrophages, neutrophils, Th2 cells, and regulatory T cells. Additionally, high-risk patients showed increased sensitivity to Sabutoclax and Venetoclax.

Conclusion: By utilizing a gene signature associated with mitochondrial metabolism, a prognostic model has been established which could be a highly efficient method for predicting the outcomes of PAAD patients.

Keywords: Bioinformatic; Mitochondrial metabolism related genes; Pancreatic ductal adenocarcinoma; Risk model.

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Conflict of interest statement

The authors declare no competing interests.

Figures

Fig. 1
Fig. 1
The general workflow of the current study. MMRGs: mitochondrial metabolism-related genes; OS: overall survival
Fig. 2
Fig. 2
Screening of MMRGs-related DEGs in PAAD. A An intersection of prognostic MMRGs and DEGs was conducted based on their significance difference analysis, resulting in a Venn diagram with the jvenn online tool. B, C LASSO coefficient profiles were created to identify five MMRGs-related DEGs. D, E Univariate and multivariate Cox regression analyses of the association connection DEGs with OS, and a forest plot illustrating the results
Fig. 3
Fig. 3
Assessment of the risk score model in PAAD. A The distribution of risk scores and patients, along with the expression profiles of genes within the TCGA-PAAD cohort. B Survival plots were created for patients categorized as high risk and low risk using Kaplan–Meier survival analysis. C ROC curves were plotted to evaluate the model's predictive performance over 1, 3, and 5 years using the timeROC (v0.4) R package
Fig. 4
Fig. 4
The validation of the risk model in PAAD was broadened to external cohorts: A, B ICGC-PACA-CA and C, D ICGC-PAAD-US. Kaplan–Meier analysis was constructed to create survival curves that stratified patients based on high and low risk, along with, the distribution of survival, expression in the subgroup patients
Fig. 5
Fig. 5
A nomogram was developed which integrated clinical parameters alongside the calculated risk score. A To evaluate its precision within TCGA-PAAD patients, the nomogram plot was generated based on clinical factors and the risk score. B Calibration plot of the nomogram in TCGA-PAAD patients, calibration plots were drawn using the rms (v6.6–0) R package. C A decision curve analysis (DCA) was used to compare the predictive performance of the nomogram against conventional medical factors such as age, N stage, T stage, and tumor grade. The DCA curve was drawn using the ggDCA (v1.2) R package. D, E The results from univariate and multivariate Cox regression analyses provided substantial evidence supporting the risk score as an independent prognostic factor
Fig. 6
Fig. 6
Comparing the molecular characteristics between high- and low-risk patients. A Firstly, the volcano plot displayed the differentially enriched KEGG pathways in high- versus low-risk PAAD patients using ggplot2 R package. B The bar plot showcasing the top 4 positively and negatively enriched KEGG pathways in high-risk patients
Fig. 7
Fig. 7
Comparing mutational landscapes between high- and low-risk groups. A, B The top 5 mutated genes in low-risk group, and the top 7 mutated genes in high-risk group. C, D A forest plot was used to emphasize the significant differences in gene mutations between high- and low-risk patients by employing Fisher's test. E An enrichment analysis of significantly different mutated genes was conducted through Enrichr to explore the top 10 GO biological process terms
Fig. 8
Fig. 8
Exploration of the relationship between drug sensitivity and risk score was conducted using two different statistical analyses. A Spearman correlation between drug IC50 values and risk scores. B Wilcoxon tests utilized to evaluate variances in drug IC50 values
Fig. 9
Fig. 9
Distinguishing the immune profiles between high- and low-risk groups. A Comparing stromal, immune, and estimate scores across the two groups using the Wilcoxon test. B, C Comparing the immune infiltration and the immune function scores between these groups using Wilcoxon test. The gene set enrichment was performed using the GSVA (v1.46.0) R pack, the box plots were drawn using the ggpubr (v0.6.0) R package. D The infiltration levels of immune cells with the immunedeconv and CIBERSORT R packages. The spearman correlation between the infiltration ratio and risk score was performed using the psych (v2.3.3) R package. E The distribution of immune subtypes in PAAD patients using the ImmuneSubtypeClassifier (v0.1.0) R package and plotted using the ggplot2 R package
Fig. 10
Fig. 10
The validation of risk gene expression within PAAD tissues involved two main analyses. A Comparing mRNA levels of risk genes using the Wilcoxon test in the TCGA-GTEx cohort. B Kaplan–Meier survival curves constructed for high- and low-risk patients
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