A Novel Hypoxia-Featured Genes Prognostic Model for Identification of Hypoxia Subtypes in Diffuse Large B-Cell Lymphoma.

Diffuse large B-cell lymphoma (DLBCL), known as the predominant type of aggressive B-cell lymphoma, is biologically and clinically heterogeneous. The prognosis of DLBCL is quite different among subtypes. Hypoxia is one of the key elements in tumor microenvironment, promoting tumor progression by means of various mechanisms, such as increased proliferation, altered metabolism, enhanced angiogenesis, and greater migratory capability, among others. The primary purpose of this research is to investigate the connection between hypoxia-featured genes (HFGs), prognosis in DLBCL, and their capacity association with the immune microenvironment. Various hypoxia-associated patterns for DLBCL patients from GEO and TCGA databases were identified by means of an unsupervised consensus clustering algorithm. CIBERSORT and IOBR package is used to identify different immune infiltration status. To develop a predictive model using hypoxia-related genes, we conducted univariate Cox regression, multivariate Cox regression, and LASSO regression assessment. Subsequently, we confirmed the predictive importance of these hypoxia-associated genes, highlighting hypoxia-associated characteristics, and explored the connection between the hypoxia model and the immune environment. Three hypoxia clusters were identified. We also observed that each pattern of hypoxia response was significantly related to different prognoses. It was found that the immune status among hypoxia clusters is different. After developing a prognostic risk model using 5 hypoxia-related genes, we discovered that the risk score is related to immune factors and how effective drugs are in treating DLBCL. In DLBCL patients, varying hypoxia patterns correlate with both prognostic outcomes and the immune microenvironment. Hypoxia-featured genes (HFGs) function as a standalone predictive element in these patients. It is also potentially a reliable indicator for predicting clinical responses to ICI therapy and traditional drugs.