ArticleAging2023
A SARS-CoV-2 related signature that explores the tumor microenvironment and predicts immunotherapy response in esophageal squamous cell cancer.
Article in Aging, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 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
6 citing papers in PubMed, 5 citations in OpenAlex.
- Integrative Transcriptomic and Single-Cell Analyses Identify ATP1A1 as a Prognostic and Immune-Associated Factor in Esophageal Cancer.International journal of genomics · 2026Article
- The signature of SARS-CoV-2-related genes predicts the immune therapeutic response and prognosis in breast cancer.BMC medical genomics · 2024Article
- Deciphering the role of sphingolipid metabolism in the immune microenvironment and prognosis of esophageal cancer via single-cell sequencing and bulk data analysis.Discover oncology · 2024Article
- Genetic predisposition to milder forms of COVID-19 may provide some resilience to head and neck cancers.Frontiers in oncology · 2024Article
- Comprehensive analysis of disulfidptosis-related genes and the immune microenvironment in heart failure.Frontiers in cell and developmental biology · 2024Article
- Single-cell transcriptomics reveals heterogeneity in esophageal squamous epithelial cells and constructs models for predicting patient prognosis and immunotherapy.Frontiers in immunology · 2023Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
9 authors at 5 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundThe existing therapeutic approaches for combating tumors are insufficient in completely eradicating malignancy, as cancer facilitates tumor relapse and develops resistance to treatment interventions. The potential mechanistic connection between SARS-CoV-2 and ESCC has received limited attention. Therefore, our objective was to investigate the characteristics of SARS-CoV-2-related-genes (SCRGs) in esophageal squamous cancer (ESCC).
methodsRaw data were obtained from the TCGA and GEO databases. Clustering of SCRGs from the scRNA-seq data was conducted using the Seurat R package. A risk signature was then generated using Lasso regression, incorporating prognostic genes related to SCRGs. Subsequently, a nomogram model was developed based on the clinicopathological characteristics and the risk signature.
resultsEight clusters of SCRGs were identified in ESCC utilizing scRNA-seq data, of which three exhibited prognostic implications. A risk signature was then made up with bulk RNA-seq, which displayed substantial correlations with immune infiltration. The novel signature was verified to have excellent prognostic efficacy.
conclusionThe utilization of risk signatures based on SCRGs can efficiently forecast the prognosis of ESCC. A thorough characterization of the SCRGs signature in ESCC could facilitate the interpretation of ESCC's response to immunotherapy and offer innovative approaches to cancer therapy.
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.