Browse
Submit Data
Databases
API
Help

Dataset Information

23 Views

0 Connections

0 Citations

0 Reanalyses

0 Downloads

Omics score: 0

Retinoid X receptor gamma (RXRG) is an independent prognostic biomarker in ER-positive invasive breast cancer.

ABSTRACT:

Background

Retinoid X Receptor Gamma (RXRG) is a member of the nuclear receptor superfamily and plays a role in tumour suppression. This study aims to explore the prognostic significance of RXRG in breast cancer.

Methods

Primary breast cancer tissue microarrays (n = 923) were immuno-stained for RXRG protein and correlated with clinicopathological features, and patient outcome.

Results

Nuclear RXRG expression was significantly associated with smaller tumour size (p = 0.036), lower grade (p < 0.001), lobular histology (p = 0.016), lower Nottingham Prognostic Index (p = 0.04) and longer breast cancer-specific survival (p < 0.001), and longer time to distant metastasis (p = 0.002). RXRG expression showed positive association with oestrogen receptor (ER)-related biomarkers: GATA3, FOXA1, STAT3 and MED7 (all p < 0.001) and a negative correlation with the Ki67 proliferation marker. Multivariate analysis demonstrated RXRG protein as an independent predictor of longer breast cancer-specific survival and distant metastasis-free survival. In the external validation cohorts, RXRG expression was associated with improved patients' outcome (p = 0.025). In ER-positive tumours, high expression of RXRG was associated with better patient outcome regardless of adjuvant systemic therapy. ER signalling pathway was the top predicted master regulator of RXRG protein expression (p = 0.005).

Conclusion

This study provides evidence for the prognostic value of RXRG in breast cancer particularly the ER-positive tumours.

SUBMITTER: Joseph C

PROVIDER: S-EPMC6889395 | biostudies-literature |

REPOSITORIES: biostudies-literature

ACCESS DATA

Json Xml

Similar Datasets

The estrogen receptor coactivator AIB1 is a new putative prognostic biomarker in ER-positive/HER2-negative invasive lobular carcinoma of the breast.

Project description:PurposeAccording to the 2017 St Gallen surrogate definitions of the intrinsic subtypes, Ki67, progesterone receptor (PR) and Nottingham histological grade (NHG) are used for prognostic classification of estrogen receptor (ER) positive/HER2-negative breast cancer into luminal A- or luminal B-like. The aim of the present study was to investigate if additional biomarkers, related to endocrine signaling pathways, e.g., amplified in breast cancer 1 (AIB1), androgen receptor (AR), and G protein-coupled estrogen receptor (GPER), can provide complementary prognostic information in a subset of ER-positive/HER-negative invasive lobular carcinoma (ILC).MethodsBiomarkers from 224 patients were analyzed immunohistochemically on tissue microarray. The primary endpoint was breast cancer mortality (BCM), analyzed with 10- and 25-year follow-up (FU). In addition, the prognostic value of gene expression data for these biomarkers was analyzed in three publicly available ILC datasets.ResultsAIB1 (high vs. low) was associated to BCM in multivariable analysis (adjusted for age, tumor size, nodal status, NHG, Ki67, luminal-like classification, and adjuvant systemic therapy) with 10-year FU (HR 6.8, 95% CI 2.3-20, P = 0.001) and 25-year FU (HR 3.0, 95% CI 1.1-7.8, P = 0.03). The evidence of a prognostic effect of AIB1 could be confirmed by linking gene expression data to outcome in independent publicly available ILC datasets. AR and GPER were neither associated to BCM with 10-year nor with 25-year FU (P > 0.33). Furthermore, Ki67 and NHG were prognostic for BCM at both 10-year and 25-year FU, whereas PR was not.ConclusionsAIB1 is a new putative prognostic biomarker in ER-positive/HER2-negative ILC.

| S-EPMC6533234 | biostudies-literature

G2M Cell Cycle Pathway Score as a Prognostic Biomarker of Metastasis in Estrogen Receptor (ER)-Positive Breast Cancer.

Project description:The vast majority of breast cancer death is a result of metastasis. Thus, accurate identification of patients who are likely to have metastasis is expected to improve survival. The G2M checkpoint plays a critical role in cell cycle. We hypothesized that breast cancer tumors with high activity of G2M pathway genes are more aggressive and likely to metastasize. To test this, we used the single-sample gene set variation analysis method to calculate the score for the Hallmark G2M checkpoint pathway using gene expression data of a total of 4626 samples from 12 human breast cancer cohorts. As expected, a high G2M pathway score correlated with enriched tumor expression of other cell proliferation-related gene sets. The score was significantly associated with clinical aggressive features of tumors and patient survival in estrogen receptor (ER)-positive/human epidermal growth factor receptor 2 (HER2)-negative breast cancer. Interestingly, a high G2M score of metastasis tumors was also significantly associated with worse survival. In primary as well as metastasis tumors with high scores, the infiltration of both pro- and anti-cancerous immune cells increased. Tumor G2M score was also associated with treatment response to systemic chemotherapy in ER-positive/HER2-negative cancer, and was predictive of response to cyclin-dependent kinase inhibition therapy.

| S-EPMC7215898 | biostudies-literature

Prognostic Impact of Progesterone Receptor Status in Chinese Estrogen Receptor Positive Invasive Breast Cancer Patients.

Project description:Estrogen receptor (ER) and progesterone receptor (PR) have been used as indicators of endocrine system status since the mid-1970s in the clinical management of breast cancer. The predictive role of ER in endocrine therapy is undisputed, but the prognostic value of PR is still debated. The aim of this study was to investigate the clinical characteristics and prognosis of ER positive breast cancer with different PR expression levels.A population cohort of 3,030 primary invasive ER positive breast cancer patients from a single cancer center underwent surgery and received adjuvant endocrine therapy from 2004 to 2010. The clinical and biological features of these patients with high PR-expressing tumors were compared with those of patients with low PR-expressing tumors. The follow-up data for disease-free survival (DFS), overall survival (OS), and breast cancer specific survival (BCSS) was obtained from 2,778 patients. Cox regression analysis was used to correlate biomarkers and tumor characteristics with DFS, OS, and BCSS.Tumors with low PR expression had more invasive pathological features and biological indexes than those with high PR expression. Low PR expression was an independent poor prognostic factor for DFS (p=0.014; hazard ratio [HR], 0.781; 95% confidence interval [CI], 0.641-0.950), OS (p=0.002; HR, 0.699; 95% CI, 0.560-0.873), and BCSS (p=0.005; HR, 0.714; 95% CI, 0.566-0.902). Furthermore, in low PR expressing tumors, patients who received chemotherapy had better DFS (p=0.002; HR, 0.449; 95% CI, 0.268-0.751), OS (p<0.001; HR, 0.341; 95% CI, 0.192-0.606), and BCSS (p<0.001; HR, 0.292; 95% CI, 0.156-0.549) than patients who did not received chemotherapy.Patients with ER positive invasive breast cancer with low PR expressing tumors have a worse prognosis than those with high PR expressing tumors, and these patients can benefit from chemotherapy.

| S-EPMC5500399 | biostudies-other

RAB6C is an independent prognostic factor of estrogen receptor-positive/progesterone receptor-negative breast cancer.

Project description:The majority of breast cancer tumors are estrogen receptor-positive (ER+) and can be treated with endocrine therapy. However, certain patients may exhibit a good prognosis without systemic treatment. The aim of the present study was to identify novel prognostic factors for patients with ER+ breast cancer tumors using gene copy data, and to investigate if these factors have prognostic value in subgroups categorized by progesterone receptor status (PR). Public data, including the whole genome gene copy data of 199 systemically untreated patients with ER+ tumors, were utilized in the present study. To assess prognostic value, patients were divided into two groups using the median gene copy number as a cut-off for the SNPs that were the most variable. One SNP was identified, which indicated that the Ras-related protein Rab-6C (RAB6C) gene may exhibit prognostic significance. Therefore, RAB6C protein expression was subsequently investigated in a second independent cohort, consisting of 469 systematically untreated patients (of which 310 were ER+) who received long term follow-up. In the public data set, a distant recurrence risk reduction of 55% was determined for copy numbers above the median value of RAB6C compared with numbers below [multivariable adjusted hazard ratio (HR), 0.45; 95% CI 0.28-0.72; P=0.001)]. It was also more pronounced in the ER+/PR- subgroup (HR, 0.15; 95% CI, 0.05-0.46; P=0.001). In the second cohort, patients of the ER+/PR- subgroup who exhibited high RAB6C expression had a reduced distant recurrence risk (HR, 0.17; 95% CI, 0.05-0.60; P=0.006). However, this was not identified among ER+/PR+ tumors (HR, 1.31; 95% CI, 0.69-2.48; P=0.41). The results of the present study indicated that RAB6C serves as an independent prognostic factor of distant recurrence risk in systemically untreated patients with an ER+/PR- tumor.

| S-EPMC6923975 | biostudies-literature

KCNJ3 is a new independent prognostic marker for estrogen receptor positive breast cancer patients.

Project description:Numerous studies showed abnormal expression of ion channels in different cancer types. Amongst these, the potassium channel gene KCNJ3 (encoding for GIRK1 proteins) has been reported to be upregulated in tumors of patients with breast cancer and to correlate with positive lymph node status. We aimed to study KCNJ3 levels in different breast cancer subtypes using gene expression data from the TCGA, to validate our findings using RNA in situ hybridization in a validation cohort (GEO ID GSE17705), and to study the prognostic value of KCNJ3using survival analysis. In a total of > 1000 breast cancer patients of two independent data sets we showed a) that KCNJ3 expression is upregulated in tumor tissue compared to corresponding normal tissue (p < 0.001), b) that KCNJ3 expression is associated with estrogen receptor (ER) positive tumors (p < 0.001), but that KCNJ3 expression is variable within this group, and c) that ER positive patients with high KCNJ3 levels have worse overall (p < 0.05) and disease free survival probabilities (p < 0.01), whereby KCNJ3 is an independent prognostic factor (p <0.05). In conclusion, our data suggest that patients with ER positive breast cancer might be stratified into high risk and low risk groups based on the KCNJ3 levels in the tumor.

| S-EPMC5356693 | biostudies-literature

MicroRNA-196a is regulated by ER and is a prognostic biomarker in ER+ breast cancer.

Project description:BackgroundMicroRNAs are potent post-transcriptional regulators involved in all hallmarks of cancer. Mir-196a is transcribed from two loci and has been implicated in a wide range of developmental and pathogenic processes, with targets including Hox, Fox, Cdk inhibitors and annexins. Genetic variants and altered expression of MIR196A are associated with risk and progression of multiple cancers including breast cancer, however little is known about the regulation of the genes encoding this miRNA, nor the impact of variants therein.MethodsGenomic data and chromatin interaction analysis were used to discover functional promoter and enhancer elements for MIR196A. Expression data were used to associate MIR196A with mechanisms of resistance, breast cancer subtypes and prognosis.ResultsHere we demonstrate that MIR196A displays complex and dynamic expression patterns, in part controlled by long-range transcriptional regulation between promoter and enhancer elements bound by ERα. Expression of this miRNA is significantly increased in drug-resistant models of hormone-receptor positive disease. The expression of MIR196A also proves to be a robust prognostic factor for patients with advanced and post-menopausal ER+ disease.ConclusionThis work sheds light on the normal and abnormal regulation of MIR196A and provides a novel stratification method for therapeutically resistant breast cancer.

| S-EPMC6461839 | biostudies-literature

The prognostic effects of somatic mutations in ER-positive breast cancer.

Project description:Here we report targeted sequencing of 83 genes using DNA from primary breast cancer samples from 625 postmenopausal (UBC-TAM series) and 328 premenopausal (MA12 trial) hormone receptor-positive (HR+) patients to determine interactions between somatic mutation and prognosis. Independent validation of prognostic interactions was achieved using data from the METABRIC study. Previously established associations between MAP3K1 and PIK3CA mutations with luminal A status/favorable prognosis and TP53 mutations with Luminal B/non-luminal tumors/poor prognosis were observed, validating the methodological approach. In UBC-TAM, NF1 frame-shift nonsense (FS/NS) mutations were also a poor outcome driver that was validated in METABRIC. For MA12, poor outcome associated with PIK3R1 mutation was also reproducible. DDR1 mutations were strongly associated with poor prognosis in UBC-TAM despite stringent false discovery correction (q?=?0.0003). In conclusion, uncommon recurrent somatic mutations should be further explored to create a more complete explanation of the highly variable outcomes that typifies ER+ breast cancer.

| S-EPMC6123466 | biostudies-literature

A consensus prognostic gene expression classifier for ER positive breast cancer.

Project description:BackgroundA consensus prognostic gene expression classifier is still elusive in heterogeneous diseases such as breast cancer.ResultsHere we perform a combined analysis of three major breast cancer microarray data sets to hone in on a universally valid prognostic molecular classifier in estrogen receptor (ER) positive tumors. Using a recently developed robust measure of prognostic separation, we further validate the prognostic classifier in three external independent cohorts, confirming the validity of our molecular classifier in a total of 877 ER positive samples. Furthermore, we find that molecular classifiers may not outperform classical prognostic indices but that they can be used in hybrid molecular-pathological classification schemes to improve prognostic separation.ConclusionThe prognostic molecular classifier presented here is the first to be valid in over 877 ER positive breast cancer samples and across three different microarray platforms. Larger multi-institutional studies will be needed to fully determine the added prognostic value of molecular classifiers when combined with standard prognostic factors.

| S-EPMC1794561 | biostudies-literature

AGR2 and FOXA1 as prognostic markers in ER-positive breast cancer.

Project description:BackgroundThe prognostic role of either forkhead box A1 (FOXA1) or anterior gradient 2 (AGR2) in breast cancer has been found separately. Considering that there were interplays between them depending on ER status, we aimed to assess the statistical interaction between AGR2 and FOXA1 on breast cancer prognosis and examine the prognostic role of the combination of them by ER status.MethodsAGR2 and FOXA1 expression in tumor tissues were evaluated with tissue microarrays by immunohistochemistry in 915 breast cancer patients with follow up data. The expression levels of these two markers were treated as binary variables, and many different cutoff values were tried for each marker. Survival and Cox proportional hazard analyses were used to evaluate the relationship between AGR2, FOXA1 and prognosis, and the statistical interaction between them on the prognosis was assessed on multiplicative scale.ResultsStatistical interaction between AGR2 and FOXA1 on the PFS was significant with all the cutoff points in ER-positive breast cancer patients but not ER-negative ones. Among ER-positive patients, the poor prognostic role of the high level of FOXA1 was significant only in patients with the low level of AGR2, and vice versa. When AGR2 and FOXA1 were considered together, patients with low levels of both markers had significantly longer PFS compared with all other groups.ConclusionsThere was a statistical interaction between AGR2 and FOXA1 on the prognosis of ER-positive breast cancer. The combination of AGR2 and FOXA1 was a more useful marker for the prognosis of ER-positive breast cancer patients.

| S-EPMC10416444 | biostudies-literature

CCNA2 is a prognostic biomarker for ER+ breast cancer and tamoxifen resistance.

Project description:Identification of effective prognostic biomarkers and targets are of crucial importance to the management of estrogen receptor positive (ER+) breast cancer. CCNA2 (also known as CyclinA2) belongs to the highly conserved cyclin family and is significantly overexpressed in various cancer types. In this study, we demonstrated that CCNA2 had significant predictive power in distant metastasis free survival, disease free survival, recurrence free survival and overall survival of ER+ breast cancer patients. We also found that CCNA2 was closely associated with tamoxifen resistance. In addition, gene set enrichment analysis (GSEA) revealed that its expression was positively associated with genes overexpressed in endocrine therapy resistant samples. Finally, though CCNA2-Drug interaction network, we demonstrated the interactions between CCNA2 and several available cancer drugs. Overall, we suggest that CCNA2 is a biomarker for the prognosis of ER+ breast cancer and monitoring of tamoxifen efficacy. It's also a promising target for developing new strategies to prevent or even reverse tamoxifen resistance. Moreover, CCNA2 expression may help monitoring tamoxifen efficacy and directing personalized therapies. Nevertheless, in vivo and in vitro experiments and multi-center randomized controlled clinical trials are still needed before its application in clinical settings.

| S-EPMC3951414 | biostudies-literature

OmicsDI is part of the ELIXIR infrastructure

OmicsDI is an Elixir interoperability service. Learn more ›

Tweets

OmicsDI Databases

PRIDE
PeptideAtlas
MassIVE
JPOST Repository
Physiome Model Repository

EGA
EVA
ENA
LINCS
PAXDB
Cell Collective

MetaboLights
Metabolomics Workbench
MetabolomeExpress
GNPS
BioModels
FAIRDOMHub

ArrayExpress
dbGaP
ExpressionAtlas
GEO
NODE

Information

Databases
Help
API
Contact us
Code on GitHub
Terms of use
Submit Data