ArticleHormone and metabolic research = Hormon- und Stoffwechselforschung = Hormones et metabolisme2023
Screening of Therapeutic Targets for Pancreatic Cancer by Bioinformatics Methods.
Article in Hormone and metabolic research = Hormon- und Stoffwechselforschung = Hormones et metabolisme, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
4 citing papers in PubMed, 3 citations in OpenAlex.
- Integrated transcriptomic and molecular docking analysis identifies Rhotekin 2 as a promising therapeutic target for overcoming gemcitabine resistance in cholangiocarcinoma.Biomedical reports · 2026Article
- Investigation of molecular immune regulation mechanisms and therapeutic target prediction in pancreatic cancer based on bioinformatics and genetics.Clinics (Sao Paulo, Brazil) · 2026Article
- Uncovering Overlapping Gene Networks and Potential Therapeutic Targets in Osteoporosis and COVID-19 Through Bioinformatics Analysis.International journal of endocrinology · 2025Article
- Identification of potential therapeutic drugs targeting core genes for systemic lupus erythematosus (SLE) and coexisting COVID-19: Insights from bioinformatic analyses.Immunity, inflammation and disease · 2023Article
Corrections and comments
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Authors and funding
14 authors at 3 institutions in 1 country.
Funding
Abstract
Pancreatic cancer (PC) has the lowest survival rate and the highest mortality rate among all cancers due to lack of effective treatments. The objective of the current study was to identify potential therapeutic targets in PC. Three transcriptome datasets, namely GSE62452, GSE46234, and GSE101448, were analyzed for differentially expressed genes (DEGs) between cancer and normal samples. Several bioinformatics methods, including functional analysis, pathway enrichment, hub genes, and drugs were used to screen therapeutic targets for PC. Fisher's exact test was used to analyze functional enrichments. To screen DEGs, the paired t-test was employed. The statistical significance was considered at p <0.05. Overall, 60 DEGs were detected. Functional enrichment analysis revealed enrichment of the DEGs in "multicellular organismal process", "metabolic process", "cell communication", and "enzyme regulator activity". Pathway analysis demonstrated that the DEGs were primarily related to "Glycolipid metabolism", "ECM-receptor interaction", and "pathways in cancer". Five hub genes were examined using the protein-protein interaction (PPI) network. Among these hub genes, 10 known drugs targeted to the CPA1 gene and CLPS gene were found. Overall, CPA1 and CLPS genes, as well as candidate drugs, may be useful for PC in the future.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.