Dataset Information

Identification of a Hypoxia-Related Molecular Classification and Hypoxic Tumor Microenvironment Signature for Predicting the Prognosis of Patients with Triple-Negative Breast Cancer.

ABSTRACT:

Purpose

The hypoxic tumor microenvironment was reported to be involved in different tumorigenesis mechanisms of triple-negative breast cancer (TNBC), such as invasion, immune evasion, chemoresistance, and metastasis. However, a systematic analysis of the prognostic prediction models based on multiple hypoxia-related genes (HRGs) has not been established in TNBC before in the literature. We aimed to develop and verify a hypoxia gene signature for prognostic prediction in TNBC patients.

Methods

The RNA sequencing profiles and clinical data of TNBC patients were generated from the TCGA, GSE103091, and METABRIC databases. The TNBC-specific differential HRGs (dHRGs) were obtained from differential expression analysis of hypoxia cultured TNBC cell lines compared with normoxic cell lines from the GEO database. Non-negative matrix factorization (NMF) method was then performed on the TNBC patients using the dHRGs to explore a novel molecular classification on the basis of the dHRG expression patterns. Prognosis-associated dHRGs were identified by univariate and multivariate Cox regression analysis to establish the prognostic risk score model.

Results

Based on the expressions of 205 dHRGs, all the patients in the TCGA training cohort were categorized into two subgroups, and the patients in Cluster 1 demonstrated worse OS than those in Cluster 2, which was validated in two independent cohorts. Additionally, the effects of somatic copy number variation (SCNV), somatic single nucleotide variation (SSNV), and methylation level on the expressions of dHRGs were also analyzed. Then, we performed Cox regression analyses to construct an HRG-based risk score model (3-gene dHRG signature), which could reliably discriminate the overall survival (OS) of high-risk and low-risk patients in TCGA, GSE103091, METABRIC, and BMCHH (qRT-PCR) cohorts.

Conclusions

In this study, a robust predictive signature was developed for patients with TNBC, indicating that the 3-gene dHRG model might serve as a potential prognostic biomarker for TNBC.

SUBMITTER: Sun X

PROVIDER: S-EPMC8416750 | biostudies-literature |

REPOSITORIES: biostudies-literature

ACCESS DATA

Similar Datasets

Project description:BackgroundN6-methylation (m6A) modification of RNA has been found to have essential effects on aspects of the tumor microenvironment (TME) including hypoxia status and mobilization of immune cells. However, there are no studies to explore the combined effect of m6A modification and hypoxia on molecular heterogeneity and TME of triple-negative breast cancer (TNBC).MethodsWe collected The Cancer Genome Atlas (TCGA-TNBC, N=139), the Molecular Taxonomy of Breast Cancer International Consortium (METABRIC-TNBC, N=297), the GSE103091, GSE21653, and GSE135565 series from the Gene Expression Omnibus (GEO-TNBC, N=247), and FUSCCTNBC (N=245) for our study. The non-negative matrix factorization algorithm was used to cluster TNBC samples. Immune cell infiltration was analyzed by the CIBERSORT algorithm. The enrichment scores were calculated by single-sample gene set enrichment analysis(ssGSEA) to characterize TME in TNBC samples. Immunohistochemistry (IHC) and qRT-PCR were performed to detect the gene expression.ResultsBased on the expression of m6A-related genes, we identified three distinct m6A clusters (denoted A, B, and C) in TNBC samples. Comparing the TME characteristics among the three clusters, we observed that cluster C was strongly related to hypoxia status and immune suppression, whereas clusters A and B displayed more immune cell infiltration. Therefore, we combine m6A and hypoxia related genes to classify two m6A-hypoxia clusters of TNBC and screened six prognostic genes by LASSO-Cox regression to construct a m6A-hypoxia signature(MHPS), which divided TNBC samples into high- and low-risk groups. We identified different TME features, immune cell infiltration between the two groups, and a better immunotherapy response was observed in the low-risk group. A nomogram was constructed with tumor size, lymph node, and risk score to improve clinical application of MHPS.ConclusionWe identified distinct TME characteristics of TNBC based on three different m6A modification patterns. Then, we constructed a specific m6A-hypoxia signature for TNBC to evaluate risk and predict immunotherapy response of patients, to enable more accurate treatment in the future.