ArticleBioengineered2021
Up-regulation of S100P predicts the poor long-term survival and construction of prognostic signature for survival and immunotherapy in patients with pancreatic cancer.
Article in Bioengineered, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.
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Who cites it
8 citing papers in PubMed, 11 citations in OpenAlex.
- S100P is a core gene for diagnosing and predicting the prognosis of sepsis.Scientific reports · 2025Article
- Structure-activity relationships for the G-quadruplex-targeting experimental drug QN-302 and two analogues probed with comparative transcriptome profiling and molecular modeling.Scientific reports · 2024Article
- QN-302 demonstrates opposing effects between i-motif and G-quadruplex DNA structures in the promoter of the S100P gene.Organic & biomolecular chemistry · 2023Article
- S100P as a potential biomarker for immunosuppressive microenvironment in pancreatic cancer: a bioinformatics analysis and in vitro study.BMC cancer · 2023Article
- The Potent G-Quadruplex-Binding Compound QN-302 Downregulates S100P Gene Expression in Cells and in an In Vivo Model of Pancreatic Cancer.Molecules (Basel, Switzerland) · 2023Article
- The role of BHLHE40 in clinical features and prognosis value of PDAC by comprehensive analysis andFrontiers in oncology · 2023Article
- Pancreatic adenocarcinoma associated immune-gene signature as a novo risk factor for clinical prognosis prediction in hepatocellular carcinoma.Scientific reports · 2022Article
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Authors and funding
7 authors at 4 institutions in 3 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Pancreatic cancer is associated with a high mortality rate, and the prognosis is positively related to immune status. In this study, we constructed a prognostic signature from survival- and immune-related genes (IRGs) to guide treatment and assess prognosis of patients with pancreatic cancer. The transcriptomic data were obtained from The Cancer Genome Atlas (TCGA) database, and IRGs were extracted from the ImmPort database. Univariate and LASSO regression analysis were used to obtain survival-related IRGs. Finally, the prognostic signature was constructed using multivariate regression analysis. The laboratory experiments were conducted to verify the key IRG expression. Immune cells infiltration was analyzed using the CIBERSORT algorithm and TIMER database. Prognostic signature containing four IRGs (ADA2, TLR1, PTPN6, S100P) was constructed with good predictive performance; in particular, S100P played a significant role in the immune microenvironment, and tumorigenesis of pancreatic cancer. Moreover, we found that CD8
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