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The Hypoxic Landscape Stratifies Gastric Cancer Into 3 Subtypes With Distinct M6a Methylation and Tumor Microenvironment Infiltration Characteristics.

ABSTRACT: The interaction between hypoxia and RNA N6-methyladenosine (m6A) is an emerging focus of investigation. However, alterations in m6A modifications at distinct hypoxia levels remain uncharacterized in gastric cancer (GC). Unsupervised hierarchical clustering was performed to stratify samples into different clusters. Differentially expressed gene analysis, univariate Cox proportional hazards regression analysis, and hazard ratio calculations were used to establish an m6A score to quantify m6A regulator modification patterns. After using an algorithm integrating Least absolute shrinkage and selection operator (LASSO) and bootstrapping, we identified the best candidate predictive genes. Thence, we established an m6A-related hypoxia pathway gene prognostic signature and built a nomogram to evaluate its predictive ability. The area under the curve (AUC) value of the nomogram was 0.811, which was higher than that of the risk score (AUC=0.695) and stage (AUC=0.779), suggesting a high credibility of the nomogram. Furthermore, the clinical response of anti-PD-1/CTLA-4 immunotherapy between high- and low-risk patients showed a significant difference. Our study successfully explored a brand-new GC pathological classification based on hypoxia pathway genes and the quantification of m6A modification patterns. Comprehensive immune analysis and validation demonstrated that hypoxia clusters were reliable, and our signature could provide a new approach for clinical decision-making and immunotherapeutic strategies for GC patients.

SUBMITTER: Ning ZK

PROVIDER: S-EPMC9253390 | biostudies-literature |

REPOSITORIES: biostudies-literature

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Project description:ObjectiveEpigenetic aberration plays an important role in the development and progression of hepatocellular carcinoma (HCC). However, the alteration of RNA N6-methyladenosine (m6A) modifications and its role in HCC progression remain unclear. We therefore aimed to provide evidence using bioinformatics analysis.MethodsWe comprehensively analyzed the m6A regulator modification patterns of 605 HCC samples and correlated them with metabolic alteration characteristics. We elucidated 390 gene-based m6A-related signatures and defined an m6Ascore to quantify m6A modifications. We then assessed their values for predicting prognoses and therapeutic responses in HCC patients.ResultsWe identified 3 distinct m6A modification patterns in HCC, and each pattern had distinct metabolic characteristics. The evaluation of m6A modification patterns using m6Ascores could predict the prognoses, tumor stages, and responses to sorafenib treatments of HCC patients. A nomogram based on m6Ascores showed high accuracy in predicting the overall survival of patients. The area under the receiver operating characteristic curve of predictions of 1, 3, and 5-year overall survivals were 0.71, 0.69, and 0.70 in the training cohort, and in the test cohort it was 0.74, 0.75, and 0.71, respectively. M6Acluster C1, which corresponded to hypoactive mRNA methylation, lower expression of m6A regulators, and a lower m6Ascore, was characterized by metabolic hyperactivity, lower tumor stage, better prognosis, and lower response to sorafenib treatment. In contrast, m6Acluster C3 was distinct in its hyperactive mRNA methylations, higher expression of m6A regulators, and higher m6Ascores, and was characterized by hypoactive metabolism, advanced tumor stage, poorer prognosis, and a better response to sorafenib. The m6Acluster, C2, was intermediate between C1 and C3.ConclusionsHCCs harbored distinct m6A regulator modification patterns that contributed to the metabolic heterogeneity and diversity of HCC. Development of m6A gene signatures and the m6Ascore provides a more comprehensive understanding of m6A modifications in HCC, and helps predict the prognosis and treatment response.

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The Hypoxic Landscape Stratifies Gastric Cancer Into 3 Subtypes With Distinct M6a Methylation and Tumor Microenvironment Infiltration Characteristics.

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