ArticleJournal of Cancer2017
Development and Validation of a Serum Biomarker Panel for the Detection of Esophageal Squamous Cell Carcinoma through RNA Transcriptome Sequencing.
Article in Journal of Cancer, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 14 papers, 1 of them a synthesis that pooled it.
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Who cites it
14 citing papers in PubMed, 1 synthesis or guideline pooled it, 26 citations in OpenAlex.
- Identifying Novel Biomarkers Ready for Evaluation in Low-Prevalence Populations for the Early Detection of Upper Gastrointestinal Cancers: A Systematic Review.Advances in therapy · 2021Pooled it
- ADAM12 in a stromal-checkpoint framework: integrating tumor microenvironment remodeling and therapy resistance in solid tumors.Cell communication and signaling : CCS · 2026Review
- An Extracellular Vesicle Protein-Based Machine Learning Framework for Early Detection of Oesophageal Squamous Cell Carcinoma: A Multicentre, Prospective Study.Journal of extracellular vesicles · 2026Article
- Regulation of matrix metalloproteinase-13 in cancer: Signaling pathways and non-coding RNAs in tumor progression and therapeutic targeting.World journal of clinical oncology · 2025Review
- Validation of serum cystatin SN detection for diagnosis and poor prognosis of esophageal squamous cell carcinoma.Frontiers in oncology · 2024Article
- Exosomes in esophageal cancer: a promising frontier for liquid biopsy in diagnosis and therapeutic monitoring.Frontiers in pharmacology · 2024Review
- Identification of TREM2-positive tumor-associated macrophages in esophageal squamous cell carcinoma: implication for poor prognosis and immunotherapy modulation.Frontiers in immunology · 2023Article
- Transforming Diagnosis and Therapeutics Using Cancer Genomics.Cancer treatment and research · 2023Article
- Identity of MMP1 and its effects on tumor progression in head and neck squamous cell carcinoma.Cancer medicine · 2022Article
- Identification of four genes and biological characteristics of esophageal squamous cell carcinoma by integrated bioinformatics analysis.Cancer cell international · 2021Article
- Current Achievements and Applications of Transcriptomics in Personalized Cancer Medicine.International journal of molecular sciences · 2021Review
- Artificial intelligence technique in detection of early esophageal cancer.World journal of gastroenterology · 2020Review
- Integrated Bioinformatics Analysis Identifies Hub Genes Associated with the Pathogenesis and Prognosis of Esophageal Squamous Cell Carcinoma.BioMed research international · 2019Article
- Targeted bisulfite sequencing identified a panel of DNA methylation-based biomarkers for esophageal squamous cell carcinoma (ESCC).Clinical epigenetics · 2017Article
Corrections and comments
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Authors and funding
12 authors at 5 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Serum tumor markers for the diagnosis of esophageal squamous cell carcinoma (ESCC) have low sensitivity. This study aims to identify new serum markers for ESCC diagnosis from RNA sequencing (RNA-seq) data. RNA-seq was performed using six pairs of ESCC and matched normal tissues. The candidates for ESCC were screened from the differentially expressed genes. The candidates were analyzed by ELISA from the serum of a test group and a validation group. Real-time PCR, Western blotting and immunohistochemistry were used to detect the expression of the candidates in tumor cell lines and tumor tissues. Ten genes were selected from the RNA-seq data. Serum levels of ADAM12, CHI3L1, MMP13 and SPP1 were significantly higher in the ESCC patients than in the healthy controls. A diagnostic model combining CHI3L1, MMP13, and SPP1 was established. The area under the curve (AUC) values for serum CHI3L1, MMP13, and SPP1 and the diagnostic model for discriminating ESCC patients from controls were 0.732, 0.881, 0.661 and 0.928, respectively. In the validation cohort, the AUC values were 0.753, 0.789, 0.696 and 0.843, respectively. Moreover, the AUC of the model for classifying patients with early ESCC was 0.918 in the test group and 0.857 in the validation group. Overexpression of CHI3L1, MMP13 and SPP1 was observed in the tumor cell lines and tissues. The diagnostic model composed of CHI3L1, MMP13 and SPP1 discriminates ESCC patients with high sensitivity. Our data highlight the potential of this diagnostic model for the noninvasive diagnosis of ESCC.
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