ArticleOncotarget2017
Identification of candidate genes related to pancreatic cancer based on analysis of gene co-expression and protein-protein interaction network.
Article in Oncotarget, 2017. 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, 11 citations in OpenAlex.
- Transcriptome analysis of the procession from chronic pancreatitis to pancreatic cancer and metastatic pancreatic cancer.Scientific reports · 2021Article
- Locally Adjust Networks Based on Connectivity and Semantic Similarities for Disease Module Detection.Frontiers in genetics · 2021Article
- A Novel IGLC2 Gene Linked With Prognosis of Triple-Negative Breast Cancer.Frontiers in oncology · 2021Article
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Corrections and comments
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Authors and funding
3 authors at 2 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Pancreatic cancer (PC) is one of the most common causes of cancer mortality worldwide. As the genetic mechanism of this complex disease is not uncovered clearly, identification of related genes of PC is of great significance that could provide new insights into gene function as well as potential therapy targets. In this study, we performed an integrated network method to discover PC candidate genes based on known PC related genes. Utilizing the subnetwork extraction algorithm with gene co-expression profiles and protein-protein interaction data, we obtained the integrated network comprising of the known PC related genes (denoted as seed genes) and the putative genes (denoted as linker genes). We then prioritized the linker genes based on their network information and inferred six key genes (
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Registered trials
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