ArticleFrontiers in bioinformatics2025
Integrated bioinformatics analysis to explore potential therapeutic targets and drugs for small cell carcinoma of the esophagus.
Article in Frontiers in bioinformatics, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 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
3 citing papers in PubMed.
- In-vitro and In-silico Evaluation of Supercritical Extract of Acacia Auriculiformis against Breast Cancer.Cell biochemistry and biophysics · 2026Article
- Esophageal Small Cell Carcinoma: From Bench Discoveries to Bedside Therapeutics.International journal of biological sciences · 2026Review
- Article
Corrections and comments
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Authors and funding
6 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Background: Small cell carcinoma of the esophagus (SCCE) is a rare form of esophageal cancer, which also belongs to the category of neuroendocrine malignant tumors, with a low incidence but high aggressiveness, and a very poor prognosis for patients. Currently, there is a lack of unique staging and treatment guidelines for SCCE. Therefore, a deeper understanding of the therapeutic targets and the mechanisms underlying its occurrence and development is of great importance for early diagnosis, identification of potential therapeutic agents and improvement of the prognosis for patients. Methods: Firstly, the dataset of SCCE was downloaded from the GEO database and GEO2R tool was employed for the analysis of differentially expressed genes (DEGs). GO and KEGG analysis of DEGs were carried out by using the Bioinformatics and OmicStudio tools. Then, up- and down-regulated genes were intersected with the oncogenes and the tumor suppressor genes respectively, to obtain the differentially expressed onco/tumor suppressor genes in SCCE. The STRING database was employed to conduct protein-protein interaction (PPI) analysis of differentially expressed onco/tumor suppressor genes, the network was further constructed in Cytoscape, and hub genes of the network were obtained through the Cytohubba plugin. In addition, miRDB, miRwalk, Targetscan, OncomiR, starbase, Lncbase were used to predict miRNAs and lncRNAs that regulate hub genes, the ceRNA network was built based on this. Transcription factor-miRNA co-regulatory network was analyzed in the NetworkAnalyst database and embellished in Cytoscape. Finally, drugs that may target hub genes were searched through the DGIdb and ConnectivityMAP, and docking verification was performed using Schrodinger. Results: A total of 820 genes were upregulated and 716 were downregulated, of these, 54 were identified as oncogenes and 85 as tumor suppressor genes. Seven hub genes were identified from the PPI network, which were Conclusion:
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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 Socratic graph.