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Abstract
Background Pancreatic adenocarcinoma (PAAD) is a highly aggressive and malignant cancer type characterized by few early symptoms and high mortality. Mitochondrial dynamics-related genes (MDRGs) can affect various processes during cancer development directly or indirectly. The area of MDRGs as biomarkers remains unexplored in PAAD.
Methods The sequencing data and clinical information were from TCGA and GEO databases. The MDRGs were collected from MitoCarta, GO, and KEGG databases. The differentially expressed genes and overall survival-associated genes were selected with the R package ‘limma’ and univariate Cox regression. The prognostic signature was developed using lasso regression and multiple machine learning algorithms from the Python ‘scikit-survival’ library. The R package ‘CIBERSORT’ was used to evaluate the tumor microenvironment. The R package ‘oncoPredict’ was used to predict the IC50 score. The R package ‘maftools’ was used to visualize the somatic mutation.
Results We obtained 37 genes through the intersection of differentially expressed genes (IDDF2024-ABS-0067 Figure 1. Identification of the 37 MDRGs (A)), overall survival-associated genes, and MDRGs (IDDF2024-ABS-0067 Figure 1. Identification of the 37 MDRGs (B)). GO and KEGG analysis of the 37 genes were performed (IDDF2024-ABS-0067 Figure 1. Identification of the 37 MDRGs (C-D)). A 12-gene prognostic signature was developed based on the 37 genes by multiple machine-learning algorithms (IDDF2024-ABS-0067 Figure 2. Construction of the machine learning signature (A)). The KM curves of the high-and low-risk groups demonstrated significant stratification in both the TCGA cohort and GSE62452 cohort (IDDF2024-ABS-0067 Figure 2. Construction of the machine learning signature (B-C)). Univariate and multiple Cox analysis validated the prognostic ability of the 12 genes and highlighted FAM73B as a potential biomarker (IDDF2024-ABS-0067 Figure 2. Construction of the machine learning signature (D-E)). The infiltration level of macrophages, monocytes, and CD8+ T cells showed significant differences between the high- and low-risk groups. The high-and low-risk groups exhibited distinct sensitivity to Gemcitabine and Paclitaxel (IDDF2024-ABS-0067 Figure 3. Immune infiltration, drug sensitivity, and somatic mutational features of high-and low-risk groups (A-D)). The somatic mutation between the groups was visualized in IDDF2024-ABS-0067 Figure 3. Immune infiltration, drug sensitivity, and somatic mutational features of high-and low-risk groups (E).
Identification of the 37 MDRGs
Construction of the machine learning signature
Immune infiltration, drug sensitivity, and somatic mutational features of high-and low-risk groups
Conclusions We developed an MDRG signature based on machine learning algorithms, providing new insights into the prognosis, drug sensitivity, tumor microenvironment, and somatic mutation for PAAD management.