scholarly journals The Major Prognostic Features of Nuclear ReceptorNR5A2in Infiltrating Ductal Breast Carcinomas

2015 ◽  
Vol 2015 ◽  
pp. 1-14 ◽  
Author(s):  
Li-Yun Chang ◽  
Li-Yu D. Liu ◽  
Don A. Roth ◽  
Wen-Hung Kuo ◽  
Hsiao-Lin Hwa ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Network Analysis ◽  
Poor Prognosis ◽  
Transcriptional Regulatory Network ◽  
Expression Profiles ◽  
Gene Signature ◽  
Cancer Prognosis ◽  
Transcriptional Networks ◽  
Cancer Subtypes ◽  
Prognostic Features

Background. Gene expression profiles of 181 breast cancer samples were analyzed to identify prognostic features of nuclear receptorsNR5A1andNR5A2based upon their associated transcriptional networks.Methods. A supervised network analysis approach was used to build the NR5A-mediated transcriptional regulatory network. Other bioinformatic tools and statistical methods were utilized to confirm and extend results from the network analysis methodology.Results.NR5A2expression is a negative factor in breast cancer prognosis in both ER(−) and ER(−)/ER(+) mixed cohorts. The clinical and cohort significance ofNR5A2-mediated transcriptional activities indicates that it may have a significant role in attenuating grade development and cancer related signal transduction pathways.NR5A2signature that conditions poor prognosis was identified based upon results from 15 distinct probes. Alternatively, the expression ofNR5A1predicts favorable prognosis when concurrentNR5A2expression is low. A favorable signature of eight transcription factors mediated byNR5A1was also identified.Conclusions. Correlation of poor prognosis andNR5A2activity is identified byNR5A2-mediated 15-gene signature.NR5A2may be a potential drug target for treating a subset of breast cancer tumors across breast cancer subtypes, especially ER(−) breast tumors. The favorable prognostic feature ofNR5A1is predicted byNR5A1-mediated 8-gene signature.

scholarly journals A Supervised Network Analysis on Gene Expression Profiles of Breast Tumors Predicts a 41-Gene Prognostic Signature of the Transcription FactorMYBacross Molecular Subtypes

2014 ◽  
Vol 2014 ◽  
pp. 1-19 ◽  
Author(s):  
Li-Yu D. Liu ◽  
Li-Yun Chang ◽  
Wen-Hung Kuo ◽  
Hsiao-Lin Hwa ◽  
King-Jen Chang ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Network Analysis ◽  
Essential Gene ◽  
Expression Profiles ◽  
Gene Expression Profiles ◽  
Prognostic Signature ◽  
Prognostic Features ◽  
Bioinformatic Tools ◽  
Prognostic Feature ◽  
Relevant Gene

Background. MYBis predicted to be a favorable prognostic predictor in a breast cancer population. We proposed to find the inferred mechanism(s) relevant to the prognostic features ofMYBvia a supervised network analysis.Methods. Both coefficient of intrinsic dependence (CID) and Galton Pierson’s correlation coefficient (GPCC) were combined and designated as CIDUGPCC. It is for the univariate network analysis. Multivariate CID is for the multivariate network analysis. Other analyses using bioinformatic tools and statistical methods are included.Results. ARNT2is predicted to be the essential gene partner ofMYB. We classified four prognostic relevant gene subpools in three breast cancer cohorts as feature types I–IV. Only the probes in feature type II are the potential prognostic feature ofMYB. Moreover, we further validated 41 prognosis relevant probes to be the favorable prognostic signature. Surprisingly, two additional family members ofMYBare elevated to promote poor prognosis when both levels ofMYBandARNT2decline. BothMYBL1andMYBL2may partially decrease the tumor suppressive activities that are predicted to be up-regulated byMYBandARNT2.Conclusions. The major prognostic feature ofMYBis predicted to be determined by theMYBsubnetwork (41 probes) that is relevant across subtypes.

scholarly journals Identification of FPR3 as a Unique Biomarker for Targeted Therapy in the Immune Microenvironment of Breast Cancer

2021 ◽  
Vol 11 ◽  
Author(s):  
Jian Qi ◽  
Yu Liu ◽  
Jiliang Hu ◽  
Li Lu ◽  
Zhen Dou ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Network Analysis ◽  
Poor Prognosis ◽  
Enrichment Analysis ◽  
Gene Set Enrichment Analysis ◽  
The Cancer Genome Atlas ◽  
Breast Cancer Patients ◽  
Cancer Subtypes ◽  
Tumor Stages ◽  
Immune Score

Although research into immunotherapy is growing, its use in the treatment of breast cancer remains limited. Thus, identification and evaluation of prognostic biomarkers of tissue microenvironments will reveal new immune-based therapeutic strategies for breast cancer. Using an in silico bioinformatic approach, we investigated the tumor microenvironmental and genetic factors related to breast cancer. We calculated the Immune score, Stromal score, Estimate score, Tumor purity, TMB (Tumor mutation burden), and MATH (Mutant-allele tumor heterogeneity) of Breast cancer patients from the Cancer Genome Atlas (TCGA) using the ESTIMATE algorithm and Maftools. Significant correlations between Immune/Stromal scores with breast cancer subtypes and tumor stages were established. Importantly, we found that the Immune score, but not the Stromal score, was significantly related to the patient's prognosis. Weighted correlation network analysis (WGCNA) identified a pattern of gene function associated with Immune score, and that almost all of these genes (388 genes) are significantly upregulated in the higher Immune score group. Protein-protein interaction (PPI) network analysis revealed the enrichment of immune checkpoint genes, predicting a good prognosis for breast cancer. Among all the upregulated genes, FPR3, a G protein-coupled receptor essential for neutrophil activation, is the sole factor that predicts poor prognosis. Gene set enrichment analysis analysis showed FRP3 upregulation synergizes with the activation of many pathways involved in carcinogenesis. In summary, this study identified FPR3 as a key immune-related biomarker predicting a poor prognosis for breast cancer, revealing it as a promising intervention target for immunotherapy.

scholarly journals Identification of Novel Biomarkers Associated With the Prognosis and Potential Pathogenesis of Breast Cancer via Integrated Bioinformatics Analysis

2021 ◽  
Vol 20 ◽  
pp. 153303382199208 ◽  
Author(s):  
Meng Wu ◽  
Qingdai Li ◽  
Hongbing Wang
Keyword(s):  
Breast Cancer ◽  
Survival Data ◽  
Expression Profiles ◽  
Cancer Prognosis ◽  
Data Sets ◽  
Expression Levels ◽  
Human Protein Atlas ◽  
Novel Biomarkers ◽  
Pathway Analyses ◽  
Cancer Tissues

Background: Breast cancer is the most commonly diagnosed malignancy and a major cause of cancer-related deaths in women globally. Identification of novel prognostic and pathogenesis biomarkers play a pivotal role in the management of the disease. Methods: Three data sets from the GEO database were used to identify differentially expressed genes (DEGs) in breast cancer. Gene Ontology (GO) enrichment and Kyoto Encyclopaedia of Genes and Genomes pathway analyses were performed to elucidate the functional roles of the DEGs. Besides, we investigated the translational and protein expression levels and survival data of the DEGs in patients with breast cancer from the Gene Expression Profiling Interactive Analysis (GEPIA), Oncomine, Human Protein Atlas, and Kaplan Meier plotter tool databases. The corresponding change in the expression level of microRNAs in the DEGs was also predicted using miRWalk and TargetScan, and the expression profiles were analyzed using OncomiR. Finally, the expression of novel DEGs were validated in Chinese breast cancer tissues by RT-qPCR. Results: A total of 46 DEGs were identified, and GO analysis revealed that these genes were mainly associated with biological processes involved in fatty acid, lipid localization, and regulation of lipid metabolism. Two novel biomarkers, ADH1A and IGSF10, and 4 other genes ( APOD, KIT, RBP4, and SFRP1) that were implicated in the prognosis and pathogenesis of breast cancer, exhibited low expression levels in breast cancer tissues. Besides, 14/25 microRNAs targeting 6 genes were first predicted to be associated with breast cancer prognosis. RT-qPCR results of ADH1A and IGSF10 expression in Chinese breast cancer tissues were consistent with the database analysis and showed significant down-regulation. Conclusion: ADH1A, IGSF10, and the 14 microRNAs were found to be potential novel biomarkers for the diagnosis, treatment, and prognosis of breast cancer.

scholarly journals Combination the Gene Expression of Adjacent Normal and Tumor Makes more Robust Gene Signature as Cancer Prognosis Biomarker

2020 ◽  
Author(s):  
Wei Ma ◽  
Dandan Li ◽  
Changjian Zhang ◽  
Ming Xiong ◽  
Yuanyuan Qiao
Keyword(s):  
Expression Profile ◽  
Normal Tissue ◽  
Cox Regression ◽  
Expression Profiles ◽  
Lung Squamous Cell Carcinoma ◽  
Enrichment Analysis ◽  
Gene Signature ◽  
The Cancer Genome Atlas ◽  
Cancer Prognosis ◽  
Adjacent Normal Tissue

Abstract Purpose: We tried to explore new gene signature via the combination of tumor-derived expression profile and the adjacent normal-derived expression profile to find more robust cancer biomarker. Methods: Log2 transformed ratio of tumor tissue and the adjacent normal tissue (Log2TN) expression, tumor-derived expression, and normal-derived expression were used to do univariate Cox regression in The Cancer Genome Atlas (TCGA) lung squamous cell carcinoma (LUSC) respectively. Then, we used factor analysis and least absolute shrinkage and selection operator Cox (LASSO-Cox) to select gene signature in TCGA LUSC for Log2TN, tumor, and adjacent normal respectively.Results: By comparing Log2TN with tumor and adjacent normal in LUSC, we found that genes derived from Log2TN show more robust (p = 0.006 and p = 0.001) and have lower p-values (p < 0.001). Gene signature selected from Log2TN shows the best generalization in the three GEO datasets even though only tumor-derived expression profiles were available in the three datasets. Enrichment analysis showed that the tumor cells mainly focus on proliferation with losing functional of metabolism.Conclusions: These results indicate that (1) Log2TN could get more robust genes and gene signature than tumor-derived expression profiles used traditionally; (2) the adjacent-normal tissue may also play an important role in the progress and outcome of the tumor.Implications for Cancer Survivors: By combined of tumor-derived expression profile and the adjacent normal-derived expression profile, we could find more robust gene signature than traditionally method. Using these robust gene signatures, robust cancer biomarkers could be constructed and will do great help to improve cancer prognosis.

scholarly journals Comprehensive Analysis of the Unfolded Protein Response in Breast Cancer Subtypes

2017 ◽  
pp. 1-9 ◽  
Author(s):  
Dadi Jiang ◽  
Brandon Turner ◽  
Jie Song ◽  
Ruijiang Li ◽  
Maximilian Diehn ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Estrogen Receptor ◽  
Patient Survival ◽  
Gene Signature ◽  
Comprehensive Analysis ◽  
Breast Cancer Subtypes ◽  
Cancer Subtypes ◽  
Unfolded Protein ◽  
The Unfolded Protein Response ◽  
Protein Response

Purpose Triple-negative breast cancers (TNBCs) are associated with a worse prognosis and patients with TNBC have fewer therapeutic options than patients with non-TNBC. Recently, the IRE1α-XBP1 branch of the unfolded protein response (UPR) was implicated in TNBC prognosis on the basis of a relatively small patient population, suggesting the diagnostic and therapeutic value of this pathway in TNBCs. In addition, the IRE1α-XBP1 and hypoxia-induced factor 1 α (HIF1α) pathways have been identified as interacting partners in TNBC, suggesting a novel mechanism of regulation. To comprehensively evaluate and validate these findings, we investigated the relative activities and relevance to patient survival of the UPR and HIF1α pathways in different breast cancer subtypes in large populations of patients. Materials and Methods We performed a comprehensive analysis of gene expression and survival data from large cohorts of patients with breast cancer. The patients were stratified based on the average expression of the UPR or HIF1α gene signatures. Results We identified a strong positive association between the XBP1 gene signature and estrogen receptor–positive status or the HIF1α gene signature, as well as the predictive value of the XBP1 gene signature for survival of patients who are estrogen receptor negative, or have TNBC or HER2+. In contrast, another important UPR branch, the ATF4/CHOP pathway, lacks prognostic value in breast cancer in general. Activity of the HIF1α pathway is correlated with patient survival in all the subtypes evaluated. Conclusion These findings clarify the relevance of the UPR pathways in different breast cancer subtypes and underscore the potential therapeutic importance of the IRE1α-XBP1 branch in breast cancer treatment.

scholarly journals Impact of 70-Gene Signature Use on Adjuvant Chemotherapy Decisions in Patients With Estrogen Receptor–Positive Early Breast Cancer: Results of a Prospective Cohort Study

2017 ◽  
Vol 35 (24) ◽  
pp. 2814-2819 ◽  
Author(s):  
Anne Kuijer ◽  
Marieke Straver ◽  
Bianca den Dekker ◽  
Annelotte C.M. van Bommel ◽  
Sjoerd G. Elias ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Gene Expression ◽  
Estrogen Receptor ◽  
Early Stage ◽  
Expression Profiles ◽  
Gene Expression Profiles ◽  
Gene Signature ◽  
Early Stage Breast Cancer ◽  
Er Positive ◽  
Test Result

Purpose Gene-expression profiles increasingly are used in addition to conventional prognostic factors to guide adjuvant chemotherapy (CT) decisions. The Dutch guideline suggests use of validated gene-expression profiles in patients with estrogen receptor (ER) –positive, early-stage breast cancer without overt lymph node metastases. We aimed to assess the impact of a 70-gene signature (70-GS) test on CT decisions in patients with ER-positive, early-stage breast cancer. Patients and Methods In a prospective, observational, multicenter study in patients younger than 70 years old who had undergone surgery for ER-positive, early-stage breast cancer, physicians were asked whether they intended to administer adjuvant CT before deployment of the 70-GS test and after the test result was available. Results Between October 1, 2013, and December 31, 2015, 660 patients, treated in 33 hospitals, were enrolled. Fifty-one percent of patients had pT1cN0, BRII, HER2-Neu-negative breast cancer. On the basis of conventional clinicopathological characteristics, physicians recommended CT in 270 (41%) of the 660 patients and recommended withholding CT in 107 (16%) of the 660 patients. For the remaining 43% of patients, the physicians were unsure and unable to give advice before 70-GS testing. In patients for whom CT was initially recommended or not recommended, 56% and 59%, respectively, were assigned to a low-risk profile by the 70-GS (κ, 0.02; 95% CI, -0.08 to 0.11). After disclosure of the 70-GS test result, the preliminary advice was changed in 51% of patients who received a recommendation before testing; the definitive CT recommendation of the physician was in line with the 70-GS result in 96% of patients. Conclusion In this prospective, multicenter study in a selection of patients with ER-positive, early-stage breast cancer, 70-GS use changed the physician-intended recommendation to administer CT in half of the patients.

scholarly journals Molecular stratification within triple-negative breast cancer subtypes

2019 ◽  
Vol 9 (1) ◽  
Author(s):  
Dong-Yu Wang ◽  
Zhe Jiang ◽  
Yaacov Ben-David ◽  
James R. Woodgett ◽  
Eldad Zacksenhaus
Keyword(s):  
Breast Cancer ◽  
Triple Negative Breast Cancer ◽  
Poor Prognosis ◽  
Triple Negative ◽  
Cancer Subtypes ◽  
Molecular Stratification ◽  
Hazard Ratios ◽  
Programmed Cell Death 1 ◽  
Tgfβ Signalling ◽  
Survival Differences

AbstractTriple-negative breast cancer (TNBC) has been subdivided into six distinct subgroups: basal-like 1 (BL1), basal-like 2 (BL2), mesenchymal (M), mesenchymal stem–like (MSL), immunomodulatory (IM), and luminal androgen receptor (LAR). We recently identified a subgroup of TNBC with loss of the tumor suppressor PTEN and five specific microRNAs that exhibits exceedingly poor clinical outcome and contains TP53 mutation, RB1 loss and high MYC and WNT signalling. Here, show that these PTEN-low/miRNA-low lesions cluster with BL1 TNBC. These tumors exhibited high RhoA signalling and were significantly stratified on the basis of PTEN-low/RhoA-signalling-high with hazard ratios (HRs) of 8.2 (P = 0.0009) and 4.87 (P = 0.033) in training and test cohorts, respectively. For BL2 TNBC, we identified AKT1 copy gain/high mRNA expression as surrogate for poor prognosis (HR = 3.9; P = 0.02 and HR = 6.1; P = 0.0032). In IM, programmed cell death 1 (PD1) was elevated and predictive of poor prognosis (HR = 5.3; P = 0.01 and HR = 3.5; P < 0.004). Additional alterations, albeit without prognostic power, characterized each subtype including high E2F2 and TGFβ signalling and CXCL8 expression in BL2, high IFNα and IFNγ signalling and CTLA4 expression in IM, and high EGFR signalling in MSL, and may be targeted for therapy. This study identified PTEN-low/RhoA-signalling-high, and high AKT1 and PD1 expression as potent prognostications for BL1, BL2 and IM subtypes with survival differences of over 14, 2.75 and 10.5 years, respectively. This intrinsic heterogeneity could be exploited to prioritize patients for precision medicine.

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