Abstract
Abnormal lipid metabolism is closely related to the malignant biological behavior of tumor cells. Such abnormal lipid metabolism provides energy for rapid proliferation, and certain genes related to lipid metabolism encode important components of tumor signaling pathways. In this study, we analyzed pancreatic cancer datasets from The Cancer Genome Atlas and searched for prognostic genes related to lipid metabolism in the Molecular Signature Database. A risk score model was built and verified using the GSE57495 dataset and International Cancer Genome Consortium dataset. Four molecular subtypes and 4249 differentially expressed genes (DEGs) were identified. The DEGs obtained by Weighted Gene Coexpression Network Construction analysis were intersected with 4249 DEGs to obtain a total of 1340 DEGs. The final prognosis model included CA8, CEP55, GNB3 and SGSM2, and these had a significant effect on overall survival. The area under the curve at 1, 3 and 5 years was 0.72, 0.79 and 0.87, respectively. These same results were obtained using the validation cohort. Survival analysis data showed that the model could stratify the prognosis of patients with different clinical characteristics, and the model has clinical independence. Functional analysis indicated that the model is associated with multiple cancer‐related pathways. Compared with published models, our model has a higher C‐index and greater risk value. In summary, this four‐gene signature is an independent risk factor for pancreatic cancer survival and may be an effective prognostic indicator.
Highlights
Abnormal lipid metabolism is closely related to the malignant biological behavior of tumor cells
It has been reported that about 50% of patients have confirmed that the cancer has Abbreviations AUC, area under the receiver operating characteristic curve; CI, confidence interval; DEG, differentially expressed gene; FDR, false discovery rate; GEO, Gene Expression Omnibus; GO, Gene Ontology; GSVA, Gene Set Variation Analysis; HR, hazard ratio; ICGC, International Cancer Genome Consortium; KEGG, Kyoto Encyclopedia of Genes and Genomes; KM, Kaplan–Meier; Lasso, least absolute shrinkage and selection operator; NMF, non-negative matrix clustering algorithm; OS, overall survival; RNA-seq, RNA sequencing; ROC, receiver operating characteristic; ssGSEA, single-sample gene set enrichment analysis; TCGA, The Cancer Genome Atlas; TIMER, Tumor Immune Estimation Resource; Weighted Gene Coexpression Network Construction Analysis (WGCNA), Weighted Gene Coexpression Network Construction
FEBS Open Bio published by John Wiley & Sons Ltd on behalf of Federation of European Biochemical Societies
Summary
Abnormal lipid metabolism is closely related to the malignant biological behavior of tumor cells. Our model has a higher C-index and greater risk value This four-gene signature is an independent risk factor for pancreatic cancer survival and may be an effective prognostic indicator. Activated lipid scratch synthesis was found to be associated with poorer prognosis and shorter diseasefree survival in tumor patients [15,16]. A deeper understanding of lipid metabolism-related genes in the prognosis and treatment of pancreatic cancer is needed
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