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Quantification of hypoxia-related gene expression as a potential approach for clinical outcome prediction in breast cancer.

ABSTRACT: Breast cancers are solid tumors frequently characterized by regions with low oxygen concentrations. Cellular adaptations to hypoxia are mainly determined by "hypoxia inducible factors" that mediate transcriptional modifications involved in drug resistance and tumor progression leading to metastasis and relapse occurrence. In this study, we investigated the prognostic value of hypoxia-related gene expression in breast cancer. A systematic review was conducted to select a set of 45 genes involved in hypoxia signaling pathways and breast tumor progression. Gene expression was quantified by RT-qPCR in a retrospective series of 32 patients with invasive ductal carcinoma. Data were analyzed in relation to classical clinicopathological criteria and relapse occurrence. Coordinated overexpression of selected genes was observed in high-grade and HER2+ tumors. Hierarchical cluster analysis of gene expression significantly segregated relapsed patients (p = 0.008, Chi2 test). All genes (except one) were up-regulated and six markers were significantly expressed in tumors from recurrent patients. The expression of this 6-gene set was used to develop a basic algorithm for identifying recurrent patients according to a risk score of relapse. Analysis of Kaplan-Meier relapse-free survival curves allowed the definition of a threshold score of 2 (p = 0.021, Mantel-Haenszel test). The risk of recurrence was increased by 40% in patients with a high score. In addition to classical prognostic factors, we showed that hypoxic markers have potential prognostic value for outcome and late recurrence prediction, leading to improved treatment decision-making for patients with early-stage invasive breast cancer. It will be necessary to validate the clinical relevance of this prognostic approach through independent studies including larger prospective patient cohorts.

SUBMITTER: El Guerrab A

PROVIDER: S-EPMC5400273 | biostudies-literature | 2017

REPOSITORIES: biostudies-literature

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Quantification of hypoxia-related gene expression as a potential approach for clinical outcome prediction in breast cancer.

El Guerrab Abderrahim A Cayre Anne A Kwiatkowski Fabrice F Privat Maud M Rossignol Jean-Marc JM Rossignol Fabrice F Penault-Llorca Frédérique F Bignon Yves-Jean YJ

PloS one 20170421 4

Breast cancers are solid tumors frequently characterized by regions with low oxygen concentrations. Cellular adaptations to hypoxia are mainly determined by "hypoxia inducible factors" that mediate transcriptional modifications involved in drug resistance and tumor progression leading to metastasis and relapse occurrence. In this study, we investigated the prognostic value of hypoxia-related gene expression in breast cancer. A systematic review was conducted to select a set of 45 genes involved ...[more]

PMID: 28430808

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Project description:Purpose: The hypoxic tumor microenvironment was reported to be involved in different tumorigenesis mechanisms of breast cancer (BC). We aimed to establish a hypoxia-related gene signature to identify a new BC subtype through the clustering analysis and explore potential compounds targeting the BC subtypes. Methods: Gene expression data and clinical features of BC and adjacent non-tumor tissues were downloaded from the Cancer Genome Atlas-Breast cancer (TCGA-BRCA) database. We comprehensively revealed the activity changes of Gene Ontology (GO) biological processes (BP) gene sets in BC by gene set variation analysis (GSVA) and identified three hypoxia-related BC subtypes. We then matched the differentially expressed gene profile of each subtype with the gene profile in CMap database to identify the potential agents targeting the BC subtypes. Results: 562 of Gene Ontology biological processes gene sets significantly correlated with hypoxia score in breast cancer. 969 BC patients were clustered into three subtypes based on the enrichment score of hypoxia-associated gene sets. Subtype 1 patients displayed better survival than subtype 2 and 3. KEGG pathway enrichment analysis of each subtype was performed based on the unique differential expression genes profile. In subtype 1, the upregulated genes were associated with lipid and amino acid metabolism regulation; in subtype 2, the upregulated genes were associated with metabolic energy regulation, while in subtype 3, the upregulated genes were associated with apoptosis and protein process. Using the CMap database, 55, 111 and 63 compounds were identified, targeting subtype 1, 2, and 3, respectively. Conclusion: In this study, novel hypoxia-related subtypes were developed for patients with BC. In addition, biological processes associated with differential expression genes profile and potential therapeutic target compounds were identified in each subtype. The new classification might provide a better understanding of the role of hypoxia in breast cancer and more individualized treatment for patients.

Project description:BackgroundIncreasing evidence has indicated that the nuclear receptor subfamily 3 group C member 2 (NR3C2) may be associated with tumorigenesis and patient prognosis for certain types of tumors. However, the clinical significance of NR3C2 is unclear in invasive breast carcinoma (BRCA).MethodsWe used bioinformatics to broadly investigate and obtain a deeper understanding of the prognostic significance between NR3C2 and BRCA. RNA-sequencing data and clinical information of patients with BRCA from the Cancer Genome Atlas database were collected for subsequent analysis. The diagnostic efficacy of NR3C2 was evaluated by calculating the receiver operating characteristic curve. The prognostic value of NR3C2 was evaluated by Kaplan-Meier analysis and Cox regression analysis for patients with BRCA. Moreover, the OSbrca database was used to validate NR3C2 as a prognostic biomarker for BRCA. Gene set enrichment analysis (GSEA) and tumor immune infiltration analysis were conducted to explore the molecular mechanism of NR3C2 in BRCA.ResultsThe expression level of NR3C2 in BRCA tissues decreased compared to that in normal breast tissues (P < 0.001). NR3C2 presented good diagnostic efficacy (AUC = 0.908). Moreover, the expression of NR3C2 was verified using the Oncomine database. High expression of NR3C2 was statistically associated with prolonged overall survival (HR = 0.65, 95% CI: 0.47-0.91, and P = 0.012), progression-free interval (HR = 0.68, 95% CI: 0.49-0.95, and P = 0.024), and disease-specific survival (HR = 0.57, 95% CI: 0.36-0.89, and P = 0.015) for patients with BRCA. Besides, the prognostic value of NR3C2 was verified by the OSbrca database. GSEA results suggested that enriched pathways included neuroactive ligand-receptor interaction, focal adhesion, and ECM-receptor interaction. NR3C2 expression was moderately correlated with mast cells and some T cell subsets in BRCA.ConclusionNR3C2 is a potential prognostic biomarker that could help clinicians develop more appropriate treatment plans for individual patients with BRCA.

Project description:IntroductionBreast cancer is the most common form of cancer worldwide and a serious threat to women. Hypoxia is thought to be associated with poor prognosis of patients with cancer. Long non-coding RNAs are differentially expressed during tumorigenesis and can serve as unambiguous molecular biomarkers for the prognosis of breast cancer.MethodsHere, we accessed the data from The Cancer Genome Atlas for model construction and performed Gene Ontology and Kyoto Encyclopedia of Genes and Genomes analyses to identify biological functions. Four prognostic hypoxia-related lncRNAs identified by univariate, LASSO, and multivariate Cox regression analyses were used to develop a prognostic risk-related signature. Kaplan-Meier and receiver operating characteristic curve analyses were performed, and independent prognostic factor analysis and correlation analysis with clinical characteristics were utilized to evaluate the specificity and sensitivity of the signature. Survival analysis and receiver operating characteristic curve analyses of the validation cohort were operated to corroborate the robustness of the model.ResultsOur results demonstrate the development of a reliable prognostic gene signature comprising four long non-coding RNAs (AL031316.1, AC004585.1, LINC01235, and ACTA2-AS1). The signature displayed irreplaceable prognostic power for overall survival in patients with breast cancer in both the training and validation cohorts. Furthermore, immune cell infiltration analysis revealed that B cells, CD4 T cells, CD8 T cells, neutrophils, and dendritic cells were significantly different between the high-risk and low-risk groups. The high-risk and low-risk groups could be precisely distinguished using the risk signature to predict patient outcomes.DiscussionIn summary, our study proves that hypoxia-related long non-coding RNAs serve as accurate indicators of poor prognosis and short overall survival, and are likely to act as potential targets for future cancer therapy.

Project description:Clinical guidelines for breast cancer treatment differ in their selection of patients at a high risk of recurrence who are eligible to receive adjuvant systemic treatment (AST). The 70-gene signature is a molecular tool to better guide AST decisions. The aim of this study was to evaluate whether adding the 70-gene signature to clinical risk prediction algorithms can optimize outcome prediction and consequently treatment decisions in early stage, node-negative breast cancer patients. A 70-gene signature was available for 427 patients participating in the RASTER study (cT1-3N0M0). Median follow-up was 61.6 months. Based on 5-year distant-recurrence free interval (DRFI) probabilities survival areas under the curve (AUC) were calculated and compared for risk estimations based on the six clinical risk prediction algorithms: Adjuvant! Online (AOL), Nottingham Prognostic Index (NPI), St. Gallen (2003), the Dutch National guidelines (CBO 2004 and NABON 2012), and PREDICT plus. Also, survival AUC were calculated after adding the 70-gene signature to these clinical risk estimations. Systemically untreated patients with a high clinical risk estimation but a low risk 70-gene signature had an excellent 5-year DRFI varying between 97.1 and 100 %, depending on the clinical risk prediction algorithms used in the comparison. The best risk estimation was obtained in this cohort by adding the 70-gene signature to CBO 2012 (AUC: 0.644) and PREDICT (AUC: 0.662). Clinical risk estimations by all clinical algorithms improved by adding the 70-gene signature. Patients with a low risk 70-gene signature have an excellent survival, independent of their clinical risk estimation. Adding the 70-gene signature to clinical risk prediction algorithms improves risk estimations and therefore might improve the identification of early stage node-negative breast cancer patients for whom AST has limited value. In this cohort, the PREDICT plus tool in combination with the 70-gene signature provided the best risk prediction.

Project description:BackgroundAssessment of tissue hypoxia at the bedside has yet to be translated into daily clinical practice in septic shock patients. Perfusion markers are surrogates of deeper physiological phenomena. Lactate-to-pyruvate ratio (LPR) and the ratio between veno-arterial PCO2 difference and Ca-vO2 (ΔPCO2/Ca-vO2) have been proposed as markers of tissue hypoxia, but they have not been compared in the clinical scenario. We studied acute septic shock patients under resuscitation. We wanted to evaluate the relationship of these hypoxia markers with clinical and biochemical markers of hypoperfusion during septic shock resuscitation.MethodsSecondary analysis of a randomized controlled trial. Septic shock patients were randomized to fluid resuscitation directed to normalization of capillary refill time (CRT) versus normalization or significant lowering of lactate. Multimodal assessment of perfusion was performed at 0, 2, 6 and 24 hours, and included macrohemodynamic and metabolic perfusion variables, CRT, regional flow and hypoxia markers. Patients who attained their pre-specified endpoint at 2-hours were compared to those who did not.ResultsForty-two patients were recruited, median APACHE-II score was 23 [15-31] and 28-day mortality 23%. LPR and ΔPCO2/Ca-vO2 ratio did not correlate during early resuscitation (0-2 h) and the whole study period (24-hours). ΔPCO2/Ca-vO2 ratio derangements were more prevalent than LPR ones, either in the whole cohort (52% vs. 23%), and in association with other perfusion abnormalities. In patients who reached their resuscitation endpoints, the proportion of patients with altered ΔPCO2/Ca-vO2 ratio decreased significantly (66% to 33%, P=0.045), while LPR did not (14% vs. 25%, P=0.34).ConclusionsHypoxia markers did not exhibit correlation during resuscitation in septic shock patients. They probably interrogate different pathophysiological processes and mechanisms of dysoxia during early septic shock. Future studies should better elucidate the interaction and clinical role of hypoxia markers during septic shock resuscitation.

Project description:BackgroundAn imbalance of intracellular iron metabolism can lead to the occurrence of ferroptosis. Ferroptosis can be a factor in the remodeling of the immune microenvironment and can affect the efficacy of cancer immunotherapy. How to combine ferroptosis-promoting modalities with immunotherapy to suppress triple-negative breast cancer (TNBC) has become an issue of great interest in cancer therapy. However, potential biomarkers related to iron metabolism and immune regulation in TNBC remain poorly understand.MethodsWe constructed an optimal prognostic TNBC-IMRGs (iron metabolism and immune-related genes) signature using least absolute shrinkage and selection operator (LASSO) cox regression. Survival analysis and ROC curves were analyzed to identify the predictive value in a training cohort and external validation cohorts. The correlations of gene signature with ferroptosis regulators and immune infiltration are also discussed. Finally, we combined the gene signature with the clinical model to construct a combined model, which was further evaluated using a calibration curve and decision curve analysis (DCA).ResultsCompared with the high-risk group, TNBC patients with low-risk scores had a remarkably better prognosis in both the training set and external validation sets. Both the IMRGs signature and combined model had a high predictive capacity, 1/3/5- year AUC: 0.866, 0.869, 0.754, and 1/3/5-yaer AUC: 0.942, 0.934, 0.846, respectively. The calibration curve and DCA also indicate a good predictive performance of the combined model. Gene set enrichment analysis (GSEA) suggests that the high-risk group is mainly enriched in metabolic processes, while the low-risk group is mostly clustered in immune related pathways. Multiple algorithms and single sample GSEA further show that the low-risk score is associated with a high tumor immune infiltration level. Differences in expression of ferroptosis regulators are also observed among different risk groups.ConclusionsThe IMRGs signature based on a combination of iron metabolism and immune factors may contribute to evaluating prognosis, understanding molecular characteristics and selecting treatment options in TNBC.

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Quantification of hypoxia-related gene expression as a potential approach for clinical outcome prediction in breast cancer.

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Quantification of hypoxia-related gene expression as a potential approach for clinical outcome prediction in breast cancer.

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