ArticleFrontiers in genetics2020
Identification of a Ubiquitination-Related Gene Risk Model for Predicting Survival in Patients With Pancreatic Cancer.
Article in Frontiers in genetics, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers.
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Who cites it
11 citing papers in PubMed.
- DNA hypermethylation of nonspecific cytotoxic cell receptor protein 1 and poor prognosis of pancreatic cancer.Oncology letters · 2026Article
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- Research advancements on the involvement of E3 ubiquitin ligase UBR5 in gastrointestinal cancers.Heliyon · 2024Review
- Identification and characterization of putative biomarkers and therapeutic axis in Glioblastoma multiforme microenvironment.Frontiers in cell and developmental biology · 2023Article
- Combined targeting of KRT23 and NCCRP1 as a potential novel therapeutic approach for the treatment of triple-negative breast cancer.Gland surgery · 2022Article
- Article
- The Establishment and Experimental Verification of an lncRNA-Derived CD8+ T Cell Infiltration ceRNA Network in Colorectal Cancer.Clinical Medicine Insights. Oncology · 2022Article
- Identification of the similarly expressed genes in patients with polycystic ovary syndrome and transsexuals.Medicine · 2021Article
- Ubiquitin Conjugating Enzyme E2 H (UBE2H) Is Linked to Poor Outcomes and Metastasis in Lung Adenocarcinoma.Biology · 2021Article
- A Novel Gene Prognostic Signature Based on Differential DNA Methylation in Breast Cancer.Frontiers in genetics · 2021Article
- Construction and validation of predictive model based on endoplasmic reticulum stress-related genes for triple-negative breast cancer.Cell cycle (Georgetown, Tex.)Article
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4 authors.
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Abstract
Pancreatic cancer is known as "the king of cancer," and ubiquitination/deubiquitination-related genes are key contributors to its development. Our study aimed to identify ubiquitination/deubiquitination-related genes associated with the prognosis of pancreatic cancer patients by the bioinformatics method and then construct a risk model. In this study, the gene expression profiles and clinical data of pancreatic cancer patients were downloaded from The Cancer Genome Atlas (TCGA) database and the Genotype-tissue Expression (GTEx) database. Ubiquitination/deubiquitination-related genes were obtained from the gene set enrichment analysis (GSEA). Univariate Cox regression analysis was used to identify differentially expressed ubiquitination-related genes selected from GSEA which were associated with the prognosis of pancreatic cancer patients. Using multivariate Cox regression analysis, we detected eight optimal ubiquitination-related genes (RNF7, NPEPPS, NCCRP1, BRCA1, TRIM37, RNF25, CDC27, and UBE2H) and then used them to construct a risk model to predict the prognosis of pancreatic cancer patients. Finally, the eight risk genes were validated by the Human Protein Atlas (HPA) database, the results showed that the protein expression level of the eight genes was generally consistent with those at the transcriptional level. Our findings suggest the risk model constructed from these eight ubiquitination-related genes can accurately and reliably predict the prognosis of pancreatic cancer patients. These eight genes have the potential to be further studied as new biomarkers or therapeutic targets for pancreatic cancer.
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