ArticleMolecular biotechnology2023
Characterization of the Immune Infiltration Landscape and Identification of Prognostic Biomarkers for Esophageal Cancer.
Article in Molecular biotechnology, 2023. 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
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
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
3 citing papers in PubMed, 3 citations in OpenAlex.
- Macrophage marker gene-driven prognostic models for esophageal cancer: integrating multi-omics analysis and therapeutic strategies.3 Biotech · 2025Article
- Activated Mast Cells Combined with NRF2 Predict Prognosis for Esophageal Cancer.Journal of oncology · 2023Article
- Characterization of the Immune Microenvironmental Landscape of Lung Squamous Cell Carcinoma with Immune Cell Infiltration.Disease markers · 2022Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
10 authors at 2 institutions in 1 country.
Funding
Abstract
Immunotherapy is an effective treatment for esophageal cancer (ESCA) patients. However, there are no dependable markers for predicting prognosis and immunotherapy responses in ESCA. Our study aims to explore immune gene prognostic models and markers in ESCA as well as predictors for immunotherapy. The expression profiles of ESCA were obtained from The Cancer Genome Atlas (TCGA), the Gene Expression Omnibus (GEO), and International Cancer Genome Consortium (ICGC) databases. Cox regression analysis was performed to construct an immune gene prognostic model. ESCA was grouped into three immune cell infiltration (ICI) clusters by CIBERSORT algorithm. The immunotherapy response of patients in different ICI score clusters was also compared. The copy number variations, somatic mutations, and single nucleotide polymorphisms were analyzed. Enrichment analyses were also performed. An immune gene prognostic model was successfully constructed. The ICI score may be used as a predictor independent of tumor mutation burden. Enrichment analyses showed that the differentially expressed genes were mostly enriched in microvillus and the KRAS and IL6/JAK/STAT3 pathways. The top eight genes with the highest mutation frequencies in ESCA were identified and all related to the prognosis of ESCA patients. Our study established an effective immune gene prognostic model and identified markers for predicting the prognosis and immunotherapy response of ESCA patients.
Indexed as
Identifiers
What OpenQuestion holds
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.