ArticleOncology reports2018
Survival analysis of genome-wide profiles coupled with Connectivity Map database mining to identify potential therapeutic targets for cholangiocarcinoma.
Article in Oncology reports, 2018. 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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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.
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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
7 citing papers in PubMed, 8 citations in OpenAlex.
- A molecular network analysis and in silico docking of beta-eudesmol, atractylodin and hinesol in patients with advance stage intrahepatic cholangiocarcinoma.Scientific reports · 2025Article
- Article
- Mutation spectrum associated with metastasis of advanced cholangiocarcinoma.The Journal of international medical research · 2022Article
- DNA Damage Response Inhibitors in Cholangiocarcinoma: Current Progress and Perspectives.Cells · 2022Review
- Identification and validation of a novel ferroptosis-related gene signature for prognosis and potential therapeutic target prediction in cholangiocarcinoma.Frontiers in immunology · 2022Article
- Potential Molecular Mechanisms and Drugs for Aconitine-Induced Cardiotoxicity in Zebrafish through RNA Sequencing and Bioinformatics Analysis.Medical science monitor : international medical journal of experimental and clinical research · 2020Article
- Comprehensive genomic profile of cholangiocarcinomas in China.Oncology letters · 2020Article
Corrections and comments
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Authors and funding
11 authors at 2 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Cholangiocarcinoma (CCA) is one of the most common epithelial cell malignancies worldwide. However, its prognosis is poor. The aim of the present study was to examine the prognostic landscape and potential therapeutic targets for CCA. RNA sequencing data and clinical information were downloaded from The Cancer Genome Atlas (TCGA) dataset and processed. A total of 172 genes that were significantly associated with overall survival of patients with CCA were identified using the univariate Cox regression method. Bioinformatics tools were applied using the Kyoto Encyclopedia of Genes and Genomes (KEGG) and gene ontology (GO). It was identified that 'Wnt signaling pathway', 'cytoplasm' and 'AT DNA binding' were the three most significant GO categories of CCA survival-associated genes. 'Transcriptional misregulation in cancer' was the most significant pathway identified in the KEGG analysis. Using the Drug-Gene Interaction database, a drug-gene interaction network was constructed, and 31 identified genes were involved in it. The most meaningful potential therapeutic targets were selected via protein-protein and gene-drug interactions. Among these genes, polo-like kinase 1 (PLK1) was identified to be a potential target due to its significant upregulation in CCA. To rapidly find molecules that may affect these genes, the Connectivity Map was queried. A series of molecules were selected for their potential anti-CCA functions. 0297417-0002B and tribenoside exhibited the highest connection scores with PLK1 via molecular docking. These findings may offer novel insights into treatment and perspectives on the future innovative treatment of CCA.
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