ArticleComputational and mathematical methods in medicine2021
Bioinformatic Analysis of Prognostic and Immune-Related Genes in Pancreatic Cancer.
Article in Computational and mathematical methods in medicine, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.
What it found
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Who cites it
7 citing papers in PubMed, 10 citations in OpenAlex.
- Article
- Pharmacogenomics-based subtype decoded implications for risk stratification and immunotherapy in pancreatic adenocarcinoma.Molecular medicine (Cambridge, Mass.) · 2025Article
- The role of MYEOV gene: a review and future directions.Frontiers in oncology · 2025Review
- Modeling of new markers for the diagnosis and prognosis of pancreatic cancer based on the transition from inflammation to cancer.Translational cancer research · 2024Article
- Overexpression of ZNF488 supports pancreatic cancer cell proliferation and tumorigenesis through inhibition of ferroptosis via regulating SCD1-mediated unsaturated fatty acid metabolism.Biology direct · 2023Article
- Prognostic biomarkers of pancreatic cancer identified based on a competing endogenous RNA regulatory network.Translational cancer research · 2022Article
- ZNF488 Promotes the Invasion and Migration of Pancreatic Carcinoma Cells through the Akt/mTOR Pathway.Computational and mathematical methods in medicine · 2022Article
Corrections and comments
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Authors and funding
6 authors at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Pancreatic cancer (PC) is a malignant tumor with poor prognosis. The poor effect of surgery and chemotherapy makes the research of immunotherapy target molecules significant. Therefore, identifying the new molecular targets of PC is important for patients. In our study, we systematically analyzed molecular correlates of pancreatic cancer by bioinformatic analysis. We characterized differentially expressed analysis based on the TCGA pancreatic cancer dataset. Then, univariate Cox regression was employed to screen out overall survival- (OS-) related DEGs. Based on these genes, we established a risk signature by the multivariate Cox regression model. The ICGC cohort and GSE62452 cohort were used to validate the reliability of the risk signature. The impact of T lymphocyte-related genes from risk signature was confirmed in PC. Here, we observed the correlation between the T lymphocyte-related genes and the expression level of targeted therapy. We established a five-mRNA (LY6D, ANLN, ZNF488, MYEOV, and SCN11A) prognostic risk signature. Next, we identified ANLN and MYEOV that were associated with T lymphocyte infiltrations (
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