Identification of a Transcription Factor Signature That Can Predict Breast Cancer Survival.

BACKGROUND: The expression pattern of transcription factors (TFs) can be used to develop potential prognostic biomarkers for cancer. In this study, we aimed to identify and validate a TF signature for predicting disease-free survival (DFS) of breast cancer (BRCA) patients. METHODS: Lasso and the Cox regression analyses were applied to construct a TF signature based on a gene expression dataset from TCGA. The prognosis value of the TF signature was investigated in the TCGA database, and its reliability was further validated in 3 independent datasets from Gene Expression Omnibus (GEO). The prognosis performance of the TF signature was compared with 4 previously published gene signatures. To investigate the association between the TF signature and hallmarks of cancer, Gene Set Enrichment Analysis (GSEA) was carried out. The correlations of the TF signature and the levels of immune infiltration were also investigated. RESULTS: An 11-TF prognostic signature was constructed with good survival prediction performance for BRCA patients. By using the risk score model based on the 11-TF signature, BRCA patients were stratified into low- and high-risk groups and showed good and poor disease-free survival (DFS), respectively. The risk score was an independent prediction indicator when adjusting for other clinicopathological factors. Furthermore, the 11-TF signature had a better survival prediction performance compared to 4 previously published gene signatures. Moreover, the risk score was a cancer hallmark. Finally, a high-risk score was associated with higher infiltration of M0 and M2 macrophages and was associated with a lower infiltration of resting memory CD4+ T cells and CD8+ T cells. CONCLUSION: The findings in this study identified and validated a novel prognostic TF signature, which is an independent biomarker for the prediction of DFS in BRCA patients.

Effect of verapamil on the pharmacokinetics of hydroxycamptothecin and its potential mechanism.

Context: Hydroxycamptothecin (HCPT) has antitumor activity in various cancers, but its poor bioavailability and efflux limit its clinical application. Verapamil has been demonstrated to improve the bioavailability of many drugs. However, the effect of verapamil on the pharmacokinetics of HCPT was not clear.Objective: The effect of verapamil on the pharmacokinetics of HCPT was investigated to clarify the drug-drug interaction between HCPT and verapamil.Materials and methods: The pharmacokinetic profiles of oral administration of HCPT (50 mg/kg) in two group of Sprague-Dawley rats (six rats each), with pre-treatment of verapamil (10 mg/kg/day) for 7 days were investigated, with the group without verapamil pre-treatment as control. Additionally, the metabolic stability and transport of HCPT in the presence or absence of verapamil were also investigated with the employment of the rat liver microsomes and Caco-2 cell transwell model.Results: Verapamil significantly increased the peak plasma concentration (from 91.97 ± 11.30 to 125.30 ± 13.50 ng/mL), and decrease the oral clearance (from 63.85 ± 10.79 to 32.95 ± 6.17 L/h/kg). The intrinsic clearance rate was also significantly decreased (from 39.49 ± 0.42 to 28.64 ± 0.30 µL/min/mg protein) by the preincubation of verapamil. The results of Caco-2 cell transwell experiments showed the efflux of HCPT was inhibited by verapamil, as the efflux ratio decreased from 1.82 to 1.21.Discussion and conclusions: The system exposure of HCPT was increased by verapamil. Verapamil may exert this effect through inhibiting the activity of CYP3A4 or P-gp, which are related to the metabolism and transport of HCPT.

Comprehensive multigene mutation spectra of breast cancer patients from Northeast China obtained using the Ion Torrent sequencing platform.

The objectives of the present study were to obtain the multigene mutation spectra of female breast cancer patients in Northeast China, to explore the correlation between mutations and clinicopathological characteristics, and to identify genetic mutations that correlate with the prognosis and survival of breast cancer patients. An Ion Torrent sequencing platform was used to detect mutations, including 31 known gene mutations associated with breast cancer, in 621 specimens from 286 breast cancer patients. A total of 286 patients were enrolled in this study. Eleven harmful/pathogenic gene mutations were found in 54.2% (155/286) of the patients, and 179 somatic nonsynonymous mutations were detected. Approximately 5.6% (16/286) of the patients carried two or more gene mutations. Among the 11 pathogenic gene mutations, those in PIK3CA were the most common and were detected in 65.4% (117/179) of the patients; TP53 gene mutations were the second most common and were detected in 20.7% (37/179) of the patients. Additional mutations were found in AKT (14/179; 7.8%) and PTEN (4/179; 2.2%), and mutations in the remaining 7 genes were each detected in approximately 0.6% (1/179) of the patients. Excluding 6 cases of breast ductal carcinoma in situ, the remaining 280 breast cancer cases were divided into four groups by molecular subtype, and the mutation frequencies of the 11 breast cancer­associated genes differed among the four groups. Furthermore, these 280 breast cancer cases were divided into two clinically relevant therapeutic groups: the HR+/HER2­ and triple­negative groups. The triple­negative group had a high frequency of TP53 mutations (21.8%) and a low frequency of PIK3CA mutations (21.8%), whereas the HR+/HER2­ group harbored TP53 mutations at a low frequency (10.1%) and PIK3CA mutations at a high frequency (50.0%). Cancerous, paracancerous, and normal tissues were collected from 72 patients and subjected to next­generation sequencing. The types and frequencies of somatic nonsynonymous mutations differed among the three studied tissue types, reflecting the genetic heterogeneity of different tissues from the same individual. In addition, tissues from 70 patients (excluding 2 patients with ductal carcinoma in situ) were divided into four groups according to molecular subtype, and the gene mutation frequencies in cancerous, paracancerous, and normal tissues differed among the four groups. After normalization, gene mutations were detected at a higher rate in cancerous tissues than in paracancerous and normal tissues in all groups, except for the HER2­positive group (which had a small sample size). In addition, Cox multivariate analyses of clinicopathological data, gene sequencing results, and 5­year survival rates of the 286 patients showed that gene mutations in the PTEN­PI3K/AKT signaling pathway were independently associated with a poor prognosis (P<0.05). In conclusion, mutations in the PTEN­PI3K/AKT signaling pathway may be valuable in the prediction of the prognosis and survival of breast cancer patients.

MicroRNA-206 inhibits metastasis of triple-negative breast cancer by targeting transmembrane 4 L6 family member 1.

Purpose: Breast cancer (BC) is a common malignancy in women, but the survival rate for BC is not very encouraging. Especially for triple-negative breast cancer (TNBC), a kind of breast cancer that does not have any of the receptors that are commonly found in BC. We investigated the impact of microRNA-206 (miR-206) on transmembrane 4 L6 family member 1 (TM4SF1) in TNBC for therapeutic purpose. Patients and methods: Twenty BC tissues from diagnosed BC patients were analyzed via real-time PCR and Western blotting for expression of TM4SF1 and miR-206. The expression of TM4SF1 was studied in relationship with miR-206 in MDA-MB-231 cells. The biological impact of TM4SF1 and miR-206 on MDA-MB-231 cells and BALB/c nude mice model was studied using proliferation, transwell migration, and invasion assays both in vitro and in vivo. Results: The expression of TM4SF1 in BC tissues was significantly higher than that in adjacent normal breast tissues. In contrast, miR-206 showed a decreased expression level in BC tissues, especially for subtype basal like. Overexpression of miR-206 in MDA-MB-231 cells by transfecting miR-206 resulted in downregulation of TM4SF1. In contrast, knockdown miR-206 expression reversed miR-206-mediated phenotype in MDA-MB-231 cells. Expression level of TM4SF1 in MDA-MB-231 cells was associated with cell migration and invasion capabilities in vitro. Breast tumor burden was correlated with the expression level of TM4SF1 in vivo. Conclusion: Taken together, our results showed the involvement of TM4SF1 in TNBC migration and invasion. miR-206 negatively regulated gene expression of TM4SF1. These findings indicate that miR-206 could be used as a potential therapeutic agent for TNBC.

Identification of dysregulated microRNAs associated with diagnosis and prognosis in triple­negative breast cancer: An in silico study.

Triple­negative breast cancer (TNBC) is a highly aggressive subtype of breast cancer with limited treatment options combined with poor rates of survival. Given the lack of appropriate prognostic biomarkers for TNBC patients, the present study aimed to identify potential dysregulated miRNAs capable of providing a diagnosis and predicting overall survival for TNBC patients. A total of 289 miRNAs were aberrantly regulated in TNBC tissue compared to adjacent, non­cancerous tissues and 96 microRNAs (miRNAs) in TNBC compared with non­triple­negative breast cancer (nTNBC) samples. Receiver operating characteristic (ROC) curve analysis suggested that 4 miRNAs (hsa­miR­10a, hsa­miR­18a, hsa­miR­135b and hsa­miR­577) had diagnostic value [area under curve (AUC) >0.8]. A 4­miRNA signature consisting of hsa­miR­148b, hsa­miR­203a, hsa­miR­203b and hsa­miR­3922 was constructed for prediction of prognosis. A multivariate Cox's proportional hazards regression model indicated that the 4­miRNA signature was an independent prognostic factor of other clinical variables in patients with TNBC. Functional analysis of the target genes of the miRNA signature demonstrated that the prolactin signaling pathway and miRNAs in cancer were significantly enriched. In conclusion, the results in the present study may highlight efficient biomarkers for the diagnosis of TNBC and its prognosis. In­depth exploitation of these miRNAs will help define and develop novel molecular therapeutic strategies and improve prognosis for TNBC patients.

Comprehensive analysis of novel three-long noncoding RNA signatures as a diagnostic and prognostic biomarkers of human triple-negative breast cancer.

Currently, traditional predictors of prognosis (tumor size, nodal status, progesterone receptor [PR], estrogen receptor [ER], or human epidermal growth factor receptor-2 [HER2]) are insufficient for precise survival prediction for triple-negative breast cancer (TNBC). Long noncoding RNAs (lncRNAs) have been observed to exert critical functions in cancer, including in TNBC. Nevertheless, systematically tracking expression-based lncRNA biomarkers based on the sequence data for the prediction of prognosis in TNBC has not yet been investigated. To ascertain whether biomarkers exist that can distinguish TNBC from adjacent normal tissue or nTNBC, we implemented a comprehensive analysis of lncRNA expression profiles and clinical data of 1097 BC samples from The Cancer Genome Atlas database. A total of 1510 differentially expressed lncRNAs in normal and TNBC samples were extracted. Similarly, 672 differentially expressed lncRNAs between nTNBC and TNBC samples were detected. The receiver operating characteristic curve analysis indicated that three upregulated lncRNAs (AC091043.1, AP000924.1, and FOXCUT) may be of strong diagnostic value for predicting the existence of TNBC in the training and validation sets (area under the curve (AUC > 0.85). Kaplan-Meier analysis demonstrated that the other three lncRNAs (AC010343.3, AL354793.1, and FGF10-AS1) were associated with the prognosis of TNBC patients (P < 0.05). We used the three overall survival (OS)-related lncRNAs to establish a three-lncRNA signature. Multivariate Cox regression analysis suggested that the three-lncRNA signature was a prognostic factor independent of other clinical variables ( P < 0.01) for predicting OS in TNBC patients that could be utilized to classify patients into high- or low-risk subgroups. Our results might provide efficient signatures for clinical diagnosis and prognostic evaluation of TNBC.

Systematic analysis of lncRNA-miRNA-mRNA competing endogenous RNA network identifies four-lncRNA signature as a prognostic biomarker for breast cancer.

BACKGROUND: Increasing evidence has underscored the role of long non-coding RNAs (lncRNAs) acting as competing endogenous RNAs (ceRNAs) in the development and progression of tumors. Nevertheless, lncRNA biomarkers in lncRNA-related ceRNA network that can predict the prognosis of breast cancer (BC) are still lacking. The aim of our study was to identify potential lncRNA signatures capable of predicting overall survival (OS) of BC patients. METHODS: The RNA sequencing data and clinical characteristics of BC patients were obtained from the Cancer Genome Atlas database, and differentially expressed lncRNA (DElncRNAs), DEmRNAs, and DEmiRNAs were then identified between BC and normal breast tissue samples. Subsequently, the lncRNA-miRNA-mRNA ceRNA network of BC was established, and the gene oncology enrichment analyses for the DEmRNAs interacting with lncRNAs in the ceRNA network was implemented. Using univariate and multivariate Cox regression analyses, a four-lncRNA signature was developed and used for predicting the survival in BC patients. We applied receiver operating characteristic analysis to assess the performance of our model. RESULTS: A total of 1061 DElncRNAs, 2150 DEmRNAs, and 82 DEmiRNAs were identified between BC and normal breast tissue samples. A lncRNA-miRNA-mRNA ceRNA network of BC was established, which comprised of 8 DEmiRNAs, 48 DElncRNAs, and 10 DEmRNAs. Further gene oncology enrichment analyses revealed that the DEmRNAs interacting with lncRNAs in the ceRNA network participated in cell leading edge, protease binding, alpha-catenin binding, gamma-catenin binding, and adenylate cyclase binding. A univariate regression analysis of the DElncRNAs revealed 7 lncRNAs (ADAMTS9-AS1, AC061992.1, LINC00536, HOTAIR, AL391421.1, TLR8-AS1 and LINC00491) that were associated with OS of BC patients. A multivariate Cox regression analysis demonstrated that 4 of those lncRNAs (ADAMTS9-AS1, LINC00536, AL391421.1 and LINC00491) had significant prognostic value, and their cumulative risk score indicated that this 4-lncRNA signature independently predicted OS in BC patients. Furthermore, the area under the curve of the 4-lncRNA signature associated with 3-year survival was 0.696. CONCLUSIONS: The current study provides novel insights into the lncRNA-related ceRNA network in BC and the 4 lncRNA biomarkers may be independent prognostic signatures in predicting the survival of BC patients.

Downregulation of the long non-coding RNA ZFAS1 is associated with cell proliferation, migration and invasion in breast cancer.

Long non-coding RNAs (lncRNAs) are non­coding RNAs that are >200 nucleotides in length. Recent studies have identified a number of lncRNAs with critical roles in various biological processes including tumorigenesis. Zinc finger antisense 1 (ZFAS1) is a lncRNA that has recently been reported to be involved in the progression of several human cancers. However, the biological function of ZFAS1 in breast cancer remains to be elucidated. In order to determine the effect of ZFAS1 in breast cancer cells, reverse transcription­quantitative polymerase chain reaction (RT­qPCR) was performed to measure ZFAS1 expression in cells from breast cancer cell lines. In addition, gain­of­function experiments were performed in vitro to investigate the biological role of ZFAS1. The results revealed that ZFAS1 expression was significantly downregulated in breast cancer cell lines when compared with the levels in controls. In vitro experiments also demonstrated that ZFAS1 overexpression significantly suppressed cell proliferation by causing cell cycle arrest and inducing apoptosis in breast cancer cells. Further functional assays indicated that ZFAS1 overexpression inhibited cell migration and invasion by regulating epithelial­mesenchymal transition. These findings indicated that the lncRNA ZFAS1 may be a tumor suppressor in breast cancer, and thus, may serve as a potential therapeutic target for patients with breast cancer.