ArticleAnnals of translational medicine2022
Screening and verification of potential gene targets in esophageal carcinoma by bioinformatics analysis and immunohistochemistry.
Article in Annals of translational medicine, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
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Who cites it
5 citing papers in PubMed, 5 citations in OpenAlex.
- Homeobox A10 in gastrointestinal malignancies: unraveling metastatic mechanisms and novel therapeutic opportunities.Cancer metastasis reviews · 2026Review
- A prognostic lncRNA signature associated with ribonucleotide reductase predicts overall survival and immune landscape in hepatocellular carcinoma.Frontiers in cell and developmental biology · 2026Article
- Cysteine-rich intestinal protein family: structural overview, functional diversity, and roles in human disease.Cell death discovery · 2025Review
- Identification of potential biomarkers for lung cancer using integrated bioinformatics and machine learning approaches.PloS one · 2025Article
- Deep Proteomics Network and Machine Learning Analysis of Human Cerebrospinal Fluid in Japanese Encephalitis Virus Infection.Journal of proteome research · 2023Article
Corrections and comments
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Authors and funding
4 authors at 3 institutions in 2 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Background: To evaluate the potential of candidate proteins as diagnostic markers or drug targets in esophageal carcinoma (ESCA). Methods: GSE20347, GSE17351, and GSE45670 were downloaded from Gene Expression Omnibus (GEO). Differently expressed genes (DEGs) between ESCA and normal esophageal tissues from patients were obtained. Gene Ontology (GO) analysis and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis were performed. The genes commonly featured in ESCA were screened by least absolute shrinkage and selection operator (LASSO) logistic regression and Boruta feature selection algorithm. The transcriptome data and corresponding clinical data of ESCA were downloaded from The Cancer Genome Atlas (TCGA) public database. Kaplan-Meier survival analysis was used to explore the core genes related to the prognosis of patients. A protein-protein interaction (PPI) network was generated by GeneMANIA to visualize the functional network between genes. Expressions of Results: Out of 11,207 genes, 430 DEGs were identified, including 210 up-regulated genes and 220 down-regulated genes. After taking the intersection of LASSO regression and Boruta algorithm, 15 core genes were identified. Survival analyses demonstrated that low expression of Conclusions: The low CRIP2 expression and high expressions of FOS and HOXA10 are associated with more advanced tumor stage, which may have the potential to be novel biomarkers for treatment selection in ESCA.
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