ArticleComputational and mathematical methods in medicine2020
Discovery of Prognostic Signature Genes for Overall Survival Prediction in Gastric Cancer.
Article in Computational and mathematical methods in medicine, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers.
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Who cites it
10 citing papers in PubMed, 16 citations in OpenAlex.
- MPND Loss Epigenetically Activates TGFβ/SMAD3 Signaling to Drive Tumor Progression and Metastasis in Non-Small Cell Lung Cancer.Cancer research · 2026Article
- Research progress and challenges of multimodal deep learning models for prognosis of gastric cancer.Discover oncology · 2026Review
- Article
- Structures of MPND Reveal the Molecular Recognition of Nucleosomes.International journal of molecular sciences · 2023Article
- RAI14 Promotes Melanoma Progression by Regulating the FBXO32/c-MYC Pathway.International journal of molecular sciences · 2022Article
- Models for Predicting Response to Immunotherapy and Prognosis in Patients with Gastric Cancer: DNA Damage Response Genes.BioMed research international · 2022Article
- Cellular Senescence-Related Genes: Predicting Prognosis in Gastric Cancer.Frontiers in genetics · 2022Article
- Pan-Cancer Analysis of OLFML2B Expression and Its Association With Prognosis and Immune Infiltration.Frontiers in genetics · 2022Article
- Construction and validation of a novel coagulation-related 7-gene prognostic signature for gastric cancer.Frontiers in genetics · 2022Article
- High Expression of RAI14 in Triple-Negative Breast Cancer Participates in Immune Recruitment and Implies Poor Prognosis Through Bioinformatics Analyses.Frontiers in pharmacology · 2022Article
Corrections and comments
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Authors and funding
6 authors at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundGastric cancer (GC) is one of the most common malignant tumors in the digestive system with high mortality globally. However, the biomarkers that accurately predict the prognosis are still lacking. Therefore, it is important to screen for novel prognostic markers and therapeutic targets.
methodsWe conducted differential expression analysis and survival analysis to screen out the prognostic genes. A stepwise method was employed to select a subset of genes in the multivariable Cox model. Overrepresentation enrichment analysis (ORA) was used to search for the pathways associated with poor prognosis.
resultsIn this study, we designed a seven-gene-signature-based Cox model to stratify the GC samples into high-risk and low-risk groups. The survival analysis revealed that the high-risk and low-risk groups exhibited significantly different prognostic outcomes in both the training and validation datasets. Specifically,
conclusionIn conclusion, we aimed at developing a model to predict the prognosis of gastric cancer. The predictive model could not only effectively predict the risk of GC but also be beneficial to the development of therapeutic strategies.
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