ArticleTranslational cancer research2020
Predictions for high
Article in Translational cancer research, 2020. 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.
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.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
7 citing papers in PubMed, 13 citations in OpenAlex.
- COL10A1 beyond skeletal development: a hypertrophic chondrocyte-specific collagen emerging as a potential biomarker and tumor microenvironment regulator in solid cancers.American journal of cancer research · 2026Review
- Oncogenic mechanisms of COL10A1 in cancer and clinical challenges (Review).Oncology reports · 2024Review
- SHFM1 deficiency suppresses esophageal squamous cell carcinomas progression via modulating NF‑κB signaling and enhancing nature killer cell‑mediated tumor surveillance.Experimental and therapeutic medicine · 2023Article
- High expression COL10A1 promotes breast cancer progression and predicts poor prognosis.Heliyon · 2022Article
- Bioinformatics-based identification of miRNAs, mRNA, and regulatory signaling pathways involved in esophageal squamous cell carcinoma.Gastroenterology and hepatology from bed to bench · 2022Article
- High-Intensity Aerobic Exercise Suppresses Cancer Growth by Regulating Skeletal Muscle-Derived Oncogenes and Tumor Suppressors.Frontiers in molecular biosciences · 2022Article
- Factors to be considered in designing frameworks for automated bioinformatics pipelines-a perspective based on application setting.Translational cancer research · 2020Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
5 authors at 3 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundEsophageal squamous cell carcinoma (ESCC) is one of the most common malignant neoplasms of the digestive tract worldwide. The lack of key molecular biomarkers is associated with the poor prognosis in ESCC patients. The present study was aimed to identify candidate genes for diagnostic, prognostic, and therapeutic applications in ESCC by bioinformatics.
methodsTwo datasets of ESCC (GSE20347 and GSE70409) from gene expression omnibus (GEO) were analyzed using GEO2R online tool to identify the differentially expressed genes (DEGs). Subsequently, functions and pathways enrichment analyses of DEGs and their protein-protein interaction (PPI) network analyses were performed. When key DEGs were identified, their relationship with ESCC prognosis was further validated.
resultsThere were 134 commonly changed DEGs (33 up-regulated and 101 down-regulated) from GSE20347 and GSE70409 datasets were identified using integrated bioinformatical analysis. Gene ontology (GO) and pathway enrichment analysis was performed to annotate genes and gene products, highlight biological processes (BPs) and systemic functional information. Through the PPI network and cluster analysis, two clusters containing 21 key DEGs were detected and 14 of them were validated based on TCGA and GTEx data. Among these key DEGs,
conclusionsIn conclusion, a total of 14 key DEGs and outcome in ESCC were identified by integrated bioinformatics analyses.
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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.