ArticleWorld journal of gastrointestinal oncology2021
Mining The Cancer Genome Atlas database for tumor mutation burden and its clinical implications in gastric cancer.
Article in World journal of gastrointestinal oncology, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
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Who cites it
4 citing papers in PubMed, 7 citations in OpenAlex.
- Continuous prediction for tumor mutation burden based on transcriptional data in gastrointestinal cancers.BMC medical informatics and decision making · 2024Article
- The Immune Cell Infiltration Patterns and Characterization Score in Bladder Cancer to Identify Prognosis.Frontiers in genetics · 2022Article
- Prognostic Significance of Gene Signature of Tertiary Lymphoid Structures in Patients With Lung Adenocarcinoma.Frontiers in oncology · 2021Article
- Identification of a Gene Prognostic Model of Gastric Cancer Based on Analysis of Tumor Mutation Burden.Pathology oncology research : POR · 2021Article
Corrections and comments
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Authors and funding
3 authors at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundTumor mutational burden (TMB) is an important independent biomarker for the response to immunotherapy in multiple cancers. However, the clinical implications of TMB in gastric cancer (GC) have not been fully elucidated.
aimTo explore the landscape of mutation profiles and determine the correlation between TMB and microRNA (miRNA) expression in GC.
methodsGenomic, transcriptomic, and clinical data from The Cancer Genome Atlas were used to obtain mutational profiles and investigate the statistical correlation between mutational burden and the overall survival of GC patients. The difference in immune infiltration between high- and low-TMB subgroups was evaluated by Wilcoxon rank-sum test. Furthermore, miRNAs differentially expressed between the high- and low-TMB subgroups were identified and the least absolute shrinkage and selection operator method was employed to construct a miRNA-based signature for TMB prediction. The biological functions of the predictive miRNAs were identified with DIANA-miRPath v3.0.
resultsC>T single nucleotide mutations exhibited the highest mutation incidence, and the top three mutated genes were
conclusionTMB:
Indexed as
Identifiers
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Registered trials
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