ArticleOncology letters2018
Identification and validation of a Hedgehog pathway-based 3-gene prognostic signature for gastric cancers.
Article in Oncology letters, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers, 1 of them a synthesis that pooled it.
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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.
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Who cites it
3 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Systematic Review of Prognostic Gene Signature in Gastric Cancer Patients.Frontiers in bioengineering and biotechnology · 2020Pooled it
- Construction of a prognostic model via WGCNA combined with the LASSO algorithm for stomach adenocarcinoma patients.Frontiers in genetics · 2024Article
- Molecular Typing of Gastric Cancer Based on Invasion-Related Genes and Prognosis-Related Features.Frontiers in oncology · 2022Article
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Authors and funding
3 authors.
Funding
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Abstract
Developing prognostic factors for patients with gastric cancer (GC) is crucial for the accurate identification of subgroups with distinct clinical outcomes and the development of effective treatment strategies. The aim of this study was to determine novel gene expression signatures from the hedgehog (Hh) signaling pathway as predictors of risk with biological significance. The Cancer Genome Atlas (TCGA) GC (STAD) cohort was used as the training dataset to select for significant prognostic Hh genes. Three Hh genes, indian hedgehog (IHH), patched 1 (PTCH1) and smoothened frizzled class receptor (SMO), were identified to be significant prognostic factors. On this basis, a 3-Hh-gene set was constructed and the high-risk patients of the training cohort were distinguished against low-risk cases [hazard ratio (HR)=1.73, 95% confidence interval (CI)=1.26-2.39, P=0.00069]. Then the gene signature was externally validated in a combined dataset from Gene Expression Omnibus (n=631), and experimentally confirmed in an independent cohort of 126 clinical GC samples by immunohistochemistry (IHC). Validation in the combined GEO dataset yielded consistent results (HR=1.45, 95% CI=1.17-1.81, P=0.00068), and remained significant for stages I-III, HER2-positive and surgery alone subgroups. Subsequently, we further demonstrated that this mRNA-based gene set could be successfully transferred to an IHC-based signature in our local cohort (HR=2.04, 95% CI=1.09-3.82, P=0.02). In addition, this signature served as an independent prognostic indicator for overall survival in the multivariate Cox analysis (HR=2.133, 95% CI=1.110-4.099, P=0.02). In conclusion, we successfully generated a stable III-Hh-gene model with the ability to separate patients into prognostic subgroups, which may have notable biological importance and be easily utilized clinically.
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