Efficient EEG Feature Learning Model Combining Random Convolutional Kernel with Wavelet Scattering for Seizure Detection.

Automatic seizure detection has significant value in epilepsy diagnosis and treatment. Although a variety of deep learning models have been proposed to automatically learn electroencephalography (EEG) features for seizure detection, the generalization performance and computational burden of such deep models remain the bottleneck of practical application. In this study, a novel lightweight model based on random convolutional kernel transform (ROCKET) is developed for EEG feature learning for seizure detection. Specifically, random convolutional kernels are embedded into the structure of a wavelet scattering network instead of original wavelet transform convolutions. Then the significant EEG features are selected from the scattering coefficients and convolutional outputs by analysis of variance (ANOVA) and minimum redundancy-maximum relevance (MRMR) methods. This model not only preserves the merits of the fast-training process from ROCKET, but also provides insight into seizure detection by retaining only the helpful channels. The extreme gradient boosting (XGboost) classifier was combined with this EEG feature learning model to build a comprehensive seizure detection system that achieved promising epoch-based results, with over 90% of both sensitivity and specificity on the scalp and intracranial EEG databases. The experimental comparisons showed that the proposed method outperformed other state-of-the-art methods for cross-patient and patient-specific seizure detection.

Thioredoxin reductase as a novel biomarker for the diagnosis and efficacy prediction of gastrointestinal malignancy: a large-scale, retrospective study.

BACKGROUND: Our aim was to investigate the rationality and accuracy of plasma TrxR activity as an efficient tool in the early diagnosis of gastrointestinal malignancy, and whether TrxR can be used to evaluate the therapeutic efficacy of gastrointestinal malignancy. METHODS: We enrolled a total of 5091 cases, including 3736 cases in gastrointestinal malignancy, 964 in benign diseases, and 391 cases in healthy controls. We also performed receiver operating characteristic (ROC) analysis to evaluate diagnostic efficiency of TrxR. Finally, we detected pre- and post-treatment level of TrxR and common tumor markers. RESULTS: The plasma TrxR level in patients with gastrointestinal malignancy [8.4 (6.9, 9.7) U/mL] was higher than that in patients with benign disease [5.8 (4.6, 6.9) U/mL] and healthy control [3.5 (1.4, 5.4) U/mL]. Plasma TrxR showed a significant diagnostic advantage with an AUC of 0.897, compared with conventional tumor markers. In addition, the combination of TrxR and conventional tumor markers can further improve the diagnostic efficiency. We derived the optimal cut-off value of plasma TrxR as a diagnostic marker of gastrointestinal malignancy according to Youden index of 6.15 U/mL. After measuring the change trend of TrxR activity and conventional tumor markers before and after anti-tumor treatments, we found that their change trend was generally consistent, and the plasma TrxR activity was significantly decreased in patients treated with chemotherapy, targeted therapy and immunotherapy. CONCLUSIONS: Our findings recommend that plasma TrxR activity could be monitored as an efficient tool for the early diagnosis of gastrointestinal malignancy and as a feasible tool to evaluate the therapeutic effect.

Impact of endogenous glucocorticoid on response to immune checkpoint blockade in patients with advanced cancer.

Background: Previous studies indicate that exogenous use of glucocorticoid (GC) affects immune checkpoint inhibitor (ICI) efficacy. However, there is a paucity of clinical data evaluating the direct impact of endogenous GC on the efficacy for cancer patients with immune checkpoint blockade. Methods: We first compared the endogenous circulating GC levels in healthy individuals and patients with cancer. We next retrospectively reviewed patients with advanced cancer with PD-1/PD-L1 inhibitor alone or combination therapy in a single center. The effects of baseline circulating GC levels on objective response rate (ORR), durable clinical benefit (DCB), progression-free survival (PFS), and overall survival (OS) were analyzed. The association of the endogenous GC levels with circulating lymphocytes, cytokines levels, and neutrophil to lymphocyte ratio, and tumor infiltrating immune cells, were systematically analyzed. Results: The endogenous GC levels in advanced cancer patients were higher than those in early-stage cancer patients as well as healthy people. In the advanced cancer cohort with immune checkpoint blockade (n=130), patients with high baseline endogenous GC levels (n=80) had a significantly reduced ORR (10.0% vs 40.0%; p<0.0001) and DCB (35.0% vs 73.5%, p=0.001) compared to those with low endogenous GC levels (n=50). The increased GC levels was significantly associated with reduced PFS (HR 2.023; p=0.0008) and OS (HR 2.809; p=0.0005). Moreover, statistically significant differences regarding PFS, and OS were also detected after propensity score matching. In a multivariable model, the endogenous GC was identified as an independent indicator for predicting PFS (HR 1.779; p=0.012) and OS (HR 2.468; p=0.013). High endogenous GC levels were significantly associated with reduced lymphocytes (p=0.019), increased neutrophil to lymphocyte ratio (p=0.0009), and increased interleukin-6 levels (p=0.025). Patients with high levels of endogenous GC had low numbers of tumor infiltrating CD3+ (p=0.001), CD8+ T (p=0.059), and CD4+ T (p=0.002) cells, and the numbers of circulating PD-1+ NK cells (p=0.012), and the ratio of CD8+PD-1+ to CD4+PD-1+ (p=0.031) were higher in patients with high levels of endogenous GC compared to low levels of endogenous GC. Conclusion: Baseline endogenous GC increase executes a comprehensive negative effect on immunosurveillance and response to immunotherapy in real-world cancer patients accompanied with cancer progression.

Compact Convolutional Neural Network with Multi-Headed Attention Mechanism for Seizure Prediction.

Epilepsy is a neurological disorder related to frequent seizures. Automatic seizure prediction is crucial for the prevention and treatment of epilepsy. In this paper, we propose a novel model for seizure prediction that incorporates a convolutional neural network (CNN) with multi-head attention mechanism. In this model, the shallow CNN automatically captures the EEG features, and the multi-headed attention focuses on discriminating the effective information among these features for identifying pre-ictal EEG segments. Compared with current CNN models for seizure prediction, the embedded multi-headed attention empowers the shallow CNN to be more flexible, and enables improvement of the training efficiency. Hence, this compact model is more resistant to being trapped in overfitting. The proposed method was evaluated over the scalp EEG data from the two publicly available epileptic EEG databases, and achieved outperforming values of event-level sensitivity, false prediction rate (FPR), and epoch-level F1. Furthermore, our method achieved the stable length of seizure prediction time that was between 14 and 15 min. The experimental comparisons showed that our method outperformed other prediction methods in terms of prediction and generalization performance.

[Analysis of progress characteristics of retinoblastoma based on single cell transcriptome sequencing].

Retinoblastoma (RB) is the most common intraocular malignant tumor in infants and young children. The key causative factors in the progression of RB remain unclear. Therefore, identifying genes closely associated with RB progression may provide important clues for disease diagnosis and gene therapy. However, tumor tissues have strong cellular heterogeneity. There may be significant differences in cell function and gene expression among cells in different pathological states. In this study, we downloaded single-cell transcriptome sequencing data of RB tumors and adjacent tissues from the GEO public database. Subsequently, we analyzed RB tumor transcriptional profiles with different disease duration at the single-cell level and identified cell groups and gene sets potentially associated with RB progression. The results showed that the tumor tissue and the adjacent tissues had overall consistency in the single-cell transcriptional map, but there were obvious differences in the distribution proportions of G1 phase cells, G2 phase cells, and microglia cells of cone precursors in RB tumor and the adjacent tissues. Furthermore, the role of three cell populations in the progression of RB tumors was emphatically analyzed. We found that in the early stage of RB tumors, cone precursor cells proliferated abnormally in G1 phase. With the progression of RB tumors, the proportion of cone precursor cells in G2 phase increased significantly. Meanwhile, the results of differential analysis of microglial populations during RB progression showed that the key genes mainly involved in immune response include RPL23, B2M, and HLA superfamily genes. This study provides new perspectives and data resources for the research of RB pathogenesis and progress.

Both Cross-Patient and Patient-Specific Seizure Detection Based on Self-Organizing Fuzzy Logic.

Automatic epilepsy detection is of great significance for the diagnosis and treatment of patients. Most detection methods are based on patient-specific models and have achieved good results. However, in practice, new patients do not have their own previous EEG data and therefore cannot be initially diagnosed. If the EEG data of other patients can be used to achieve cross-patient detection, and cross-patient and patient-specific experiments can be combined at the same time, this method will be more widely used. In this work, an EEG classification model based on a self-organizing fuzzy logic (SOF) classifier is proposed for both cross-patient and patient-specific seizure detection. After preprocessing, the features of the original EEG signal are extracted and sent to the SOF classifier. This classification model is free from predefined parameters or a prior assumption regarding the EEG data generation model and only stores the key meta-parameters in memory. Therefore, it is very suitable for large-scale EEG signals in cross-patient detection. Selecting different granularity and classification distance in two different experiments after post-processing will achieve the best results. Experiments were conducted using a long-term continuous scalp EEG database and the [Formula: see text]-mean of cross-patient and patient-specific detection reached 83.35% and 92.04%, respectively. A comparison with other methods shows that there is greater performance and generalizability with this method.

Epileptic Seizure Detection with EEG Textural Features and Imbalanced Classification Based on EasyEnsemble Learning.

Imbalance data classification is a challenging task in automatic seizure detection from electroencephalogram (EEG) recordings when the durations of non-seizure periods are much longer than those of seizure activities. An imbalanced learning model is proposed in this paper to improve the identification of seizure events in long-term EEG signals. To better represent the underlying microstructure distributions of EEG signals while preserving the non-stationary nature, discrete wavelet transform (DWT) and uniform 1D-LBP feature extraction procedure are introduced. A learning framework is then designed by the ensemble of weakly trained support vector machines (SVMs). Under-sampling is employed to split the imbalanced seizure and non-seizure samples into multiple balanced subsets where each of them is utilized to train an individual SVM classifier. The weak SVMs are incorporated to build a strong classifier which emphasizes seizure samples and in the meantime analyzing the imbalanced class distribution of EEG data. Final seizure detection results are obtained in a multi-level decision fusion process by considering temporal and frequency factors. The model was validated over two long-term and one short-term public EEG databases. The model achieved a G-mean of 97.14% with respect to epoch-level assessment, an event-level sensitivity of 96.67%, and a false detection rate of 0.86/h on the long-term intracranial database. An epoch-level G-mean of 95.28% and event-level false detection rate of 0.81/h were yielded over the long-term scalp database. The comparisons with 14 published methods demonstrated the improved detection performance for imbalanced EEG signals and the generalizability of the proposed model.

Endostatin reverses immunosuppression of the tumor microenvironment in lung carcinoma.

Endostatin has previously been demonstrated to efficiently inhibit the angiogenesis and growth of endothelial cells. However, the role of endostatin in the tumor microenvironment remains to be elucidated. To investigate the antitumor effect of endostatin in lung cancer, the present study was designed to explore the alterations of microvessel density in Lewis lung cancer models and the expression of vascular endothelial growth factor (VEGF), interleukin (IL)-6, IL-17, interferon (IFN)-γ and hypoxia inducible factor (HIF)-1α, following endostatin therapy. It was demonstrated that the growth and angiogenesis of tumors were markedly suppressed by treatment with endostatin, compared with control group. The microvessel density in mice treated with endostatin was significantly inhibited in a dose-dependent manner. The expression levels of VEGF, IL-6 and IL-17 in tumors were decreased, however IFN-γ and HIF-1α expression levels were increased, following treatment with endostatin. In addition, the proportion of myeloid derived suppressor cells and tumor associated macrophages (TAMs; M2 type) were significantly decreased, whereas those of mature dendritic cells and TAMs (M1 type) were increased, and cluster of differentiation (CD)8+ T cells were recruited to infiltrate the tumors following treatment with endostatin. In addition, the expression levels of IL-6, IL-10, tumor growth factor-ß and IL-17 in tumor tissue were potently decreased with endostatin therapy. These results indicated that endostatin efficiently inhibited tumor angiogenesis and reversed the immunosuppressive microenvironment associated with the presence of tumors.

The expression status of TRX, AR, and cyclin D1 correlates with clinicopathological characteristics and ER status in breast cancer.

BACKGROUND: The ER signaling pathway plays a critical role in breast cancer. ER signaling pathway-related proteins, such as TRX, AR, and cyclin D1, may have an important function in breast cancer. However, the ways that they influence breast cancer development and progression are still unclear. PATIENTS AND METHODS: A total of 101 Chinese female patients diagnosed with invasive ductal breast adenocarcinoma were retrospectively enrolled in the study. The expression levels of TRX, AR, and cyclin D1 were detected by immunohistochemistry and analyzed via correlation with clinicopathological characteristics and the expression status of ER, PR, and HER2. RESULTS: The expression status of TRX, AR, and cyclin D1 was not associated with the patient's age, menopausal status, tumor size, or histological differentiation (P>0.05), but was positively correlated with ER and PR (P<0.001, respectively). Most (66/76, 86.8) TRX-positive patients were also HER2-positive (P=0.003). Of AR- or cyclin D1-positive patients, most had relatively earlier I-II tumor stage (P=0.005 and P=0.047, respectively) and no metastatic lymph node involvement (P=0.008 and P=0.005, respectively). CONCLUSION: TRX was found to be positively correlated with ER and PR expression, whereas it was negatively correlated with HER2 expression. In addition, we found that the positive expression of AR and cyclin D1 was correlated with lower TNM stage and fewer metastatic lymph nodes, and it was more common in ER-positive breast cancer than in the basal-like subtype. This may indicate that AR and cyclin D1 are good predictive and prognostic factors and closely interact with ER signaling pathway. Further studies will be necessary to investigate the response and clinical outcomes of treatment targeting TRX, AR, and cyclin D1.

TXNIP interaction with the Her-1/2 pathway contributes to overall survival in breast cancer.

Previous studies have indicated that Her-2 induction causes a strong decrease in thioredoxin interaction protein (TXNIP) in breast cancer cells. However, little is known regarding the prognostic value of TXNIP in clinical breast cancer patients with anti-Her-2 treatment. Using a tissue microarray, we detected TXNIP and p27 expression in breast cancer tissue, as well as corresponding noncancerous tissues. We found that TXNIP expression was associated with better overall survival (OS) in these 150 breast cancer patients and that TXNIP and Her-2 expression status were significantly inversely correlated (r=-0.334, P<0.001). These results were validated in another 101 breast cancer tissue samples (r=-0.422, P<0.001). Moreover, TXNIP expression increased significantly following treatment of the human breast cancer cell lines BT474 and SK-BR-3 with a Her-1/2 inhibitor. Furthermore, TXNIP transfection induced p27 expression and G1 cell cycle arrest and apoptosis. Taken together, our findings suggest that TXNIP plays a critical role in anti-Her-1/Her-2 treatment and may be a potential prognostic marker in breast cancer.

miR-1470 mediates lapatinib induced p27 upregulation by targeting c-jun.

Our previous study indicated that lapatinib induces p27-dependent G(1) arrest through both transcriptional and post-translational mechanisms. Using miRNA microarray technology and quantitative RT-PCR, we further investigated the potential miRNAs that involved in p27 upregulation and Her-2 signaling pathway alteration with lapatinib treatment. A subset of 7 miRNAs was significantly affected in both 0.5 µM and 2.0 µM and 24 h and 48 h lapatinib treatment. Among them, only miR-1470, miR-126, and miR-1208 were identified in the Her-2 pathway after KEGG pathway analysis. However, luciferase reporter assay confirmed that miR-1470 directly recognized the 3'-untranslated region of c-jun transcripts, which was consistent with TargetScan analysis. miR-1470 significantly decreased c-jun expression, thus miR-1470 may repressc-jun activation of cyclinD1 expression, and consequently promoted the upregulation of p27, a key molecule in the cell cycle arrest. Taken together, the present study provided the first evidences that miR-1470 mediated lapatinib induced p27 upregulation by targeting c-jun.

The regulatory loop of COMP1 and HNF-4-miR-150-p27 in various signaling pathways.

MicroRNAs (miRNAs) are short regulatory RNAs that negatively modulate protein expression at the post-transcriptional level. Additionally, they have been associated with the pathogenesis of a number of types of cancer. In the current study, two target sites for miR-150 were determined within the 3'-untranslated region of p27Kip1 (hereafter referred to as p27) mRNA, and it was determined that ectopic overexpression of miR-150 led directly to p27 downregulation in cancer cells. These findings indicate that miR-150 may be a novel regulator of p27 expression. In the databases of the University of California, Santa Cruz (UCSC) and Match online, two common transcription factors were identified for miR-150 and p27: Cooperates with myogenic proteins 1 (COMP1) and hepatocyte nuclear factor-4 (HNF-4). Using the Database for Annotation, Visualization, and Integrated Discovery (DAVID), it was determined that p27 is involved in pathways regulated by the target genes of miR-150. Therefore, these results suggest that there may be a regulatory loop between COMP1 and HNF-4-miR-150-p27. Additional functional studies are required to understand the molecular basis for the formation of this circuit loop, and provide an insight into the development of innovative therapies targeting specific tumor markers.

MicroRNA-495 induces breast cancer cell migration by targeting JAM-A.

MicroRNAs (miRNAs) are small, non-coding RNAs that function as post-transcriptional regulators of gene expression. The deregulated expression of miRNAs is associated with a variety of diseases, including breast cancer. In the present study, we found that miR-495 was markedly up-regulated in clinical breast cancer samples by quantitative real time-PCR (qRT-PCR). Junctional adhesion molecule A (JAM-A) was predicted to be a potential target of miR-495 by bioinformatics analysis and was subsequently verified by luciferase assay and Western blotting. JAM-A was found to be negatively correlated with the migration of breast cancer cells through loss-of-function and gain-of-function assays, and the inhibition of JAM-A by miR-495 promoted the migration of MCF-7 and MDA-MB-231 cells. Furthermore, overexpression of JAM-A could restore miR-495-induced breast cancer cell migration. Taken together, our findings suggest that miR-495 could facilitate breast cancer progression through the repression of JAM-A, making this miRNA a potential therapeutic target.

Transcriptional regulation of the p73 gene by Nrf-2 and promoter CpG methylation in human breast cancer.

To understand the transcriptional regulation of p73 by promoter methylation and Nrf-2 in breast carcinogenesis, ChIP assay indicated that Nrf-2 can bind to both promoters and can activate the transcription of TAp73 and ΔNp73 in MCF-7 cell line, knockdown of Nrf-2 gene resulted in an abrogation of TAp73 and ΔNp73 expression in the cells transfected with sh-Nrf-2 as well as Nrf-2 knock out mouse model. However, we found Nrf-2 induced ΔNp73 expression was abolished with 5-aza-dC treatment, thus lead to a down-regulated ΔNp73 and an up-regulated TAp73 expression in breast cancer cells lines. Consistent with this model, we detected decreased TAp73 and increased ΔNp73 expression in breast cancer tissue, along with increased TAp73 but decreased ΔNp73 expression in corresponding surrounding noncancerous tissues (NCTs) in a breast cancer tissue assay. A significant inverse correlation was found between TAp73 and ΔNp73 expression in the above tissue-array (P = 0.047) and validated in another set consisting of 128 breast cancer tumor tissue (P = 0.034). Taken together, our findings suggest that Nrf-2 and promoter methylation cooperatively govern the transcriptional regulation of p73, and unbalanced expression of TAp73 and ΔNp73 expression plays a critical role in breast cancer development.

Lin28B expression correlates with aggressive clinicopathological characteristics in breast invasive ductal carcinoma.

Lin28B is a RNA-binding protein that inhibits the let-7 microRNA family and acts as an oncogene in various human malignant diseases. Conversely, the members of let-7 family function as tumor suppressers and are often inactivated in cancers. The interaction of Lin28B/let-7 plays a crucial part of tumorigenesis. In this study, the authors examined the Lin28B expression using immunohistochemistry in 190 breast cancers and analyzed the correlation of Lin28B immunostaining and clinicopathological characteristics. Breast cancer patients previously diagnosed with invasive ductal carcinomas were enrolled in this study. All cases went through surgical procedures as the initial treatment. The characteristics of every case were collected, including tumor size, pathologic grade, metastatic lymphoid nodes, and estrogen receptor α (ERα), progesterone receptor (PR), and HER2 status. The immunostaining was scored by two independent investigators. Eighty-three (43.7%) of 190 cases showed positive expression of Lin28B. Lin28B immunostaining was increased in tumors compared with the adjacent tissues. Overexpression of Lin28B was linked to poor differentiation, advanced-stage disease, and Ki67-positive status (all p<0.05). Besides, Lin28B expression was significantly different among breast cancer subtypes. This study addresses the role of Lin28B in breast cancers and provides insight of its predictive effects in disease development.

Multitargeted antiangiogenic tyrosine kinase inhibitors combined to chemotherapy in metastatic breast cancer: a systematic review and meta-analysis.

PURPOSE: We undertook a meta-analysis of randomized trials to evaluate the efficacy of multitargeted antiangiogenic tyrosine kinase inhibitors (MATKIs) in addition to chemotherapy in metastatic breast cancer. METHODS: PubMed, Web of Knowledge databases and the ASCO meeting abstracts were searched for eligible literature published up to August 30, 2013. The endpoints included progression-free survival (PFS), overall survival (OS), overall response rate (ORR) and toxicities. Pooled hazard ratios (HRs) for survival outcomes and odds ratio (ORs) for dichotomous data with 95 % confidence intervals (CIs) were derived. RESULTS: Eight studies including 2,077 participants were analyzed. Compared to chemotherapy alone, adding MATKIs to chemotherapy resulted in a 14 % risk reduction of PFS events. However, the benefit did not reach statistical significance (HR 0.86; 95 % CI 0.70-1.04, P=0.126). Also, no OS benefit was observed (HR 1.03; 95 % CI 0.89-1.18, P=0.724). The addition of MATKIs significantly increased the ORR (OR 1.57; 95 % CI 1.30-1.91, P=0.000). Subgroup analysis revealed that sorafinib showed a significantly greater effect on PFS in patients with HER2 negative metastatic breast cancer (HR 0.67; 95 % CI 0.55-0.82, P=0.000) in comparison to chemotherapy alone. Additionally, sunitinib seemed to have no substantial efficacy for metastatic breast cancer. Toxicities were more frequent in patients receiving MATKIs. CONCLUSION: Overall, regimens consisting of MATKIs seemed not to be superior to chemotherapy alone in terms of PFS and OS, although significant improvement in ORR was observed. However, the addition of sorafenib significantly improved PFS. Further studies are needed to corroborate this finding.

Risk of hypertension with regorafenib in cancer patients: a systematic review and meta-analysis.

BACKGROUND: Regorafenib is a novel multikinase inhibitor approved for use in metastatic colorectal cancer (mCRC) and locally advanced gastrointestinal stromal tumors (GISTs). Hypertension is one of the major adverse events of this agent, but to date the incidence and risk of hypertension with regorafenib have not been systematically investigated. We have conducted a systematic review and meta-analysis of published clinical trials to determine its overall incidence and risk. METHODS: PubMed, Web of Science and abstracts presented at the American Society of Clinical Oncology annual meetings were searched to identify relevant studies published up to September 9, 2013. Eligible studies were prospective phase II or III clinical trials using regorafenib in cancer patients with data on hypertension available. The incidence and relative risk (RR) of hypertension were calculated using a random-effects model. RESULTS: Data from a total of 1,069 patients (regorafenib n = 750; controls n = 319) from five clinical trials were included for analysis. The overall incidence of all-grade and high-grade hypertension were 44.4 % [95 % confidence interval (CI) 30.8-59.0 %) and 12.5 % (95 % CI 5.2-27.1 %), respectively. The use of regorafenib in cancer patients was associated with a significantly increased risk of all-grade (RR 3.76, 95 % CI 2.35-5.99) and high-grade (RR, 8.39, 95 % CI 3.10-22.71) hypertension. The risk might vary with tumor types (P = 0.000). CONCLUSIONS: Patients with cancer receiving regorafenib have a significantly higher risk of developing hypertension. Close monitoring and appropriate management of this hypertension are strongly recommended.

Quantitative assessment of the association between three polymorphisms in FAS and FASL gene and breast cancer risk.

FAS and FAS ligand (FASL) play crucial roles in apoptotic signaling, and deregulation of this pathway may facilitate carcinogenesis. Studies on the association between the FAS/FASL polymorphisms (FAS-1377G/A rs2234767, FAS-670A/G rs1800682, and FASL-844C/T rs763110) and breast cancer risk have reported inconsistent results. Therefore, to characterize the relationship between those polymorphisms and breast cancer risk, we undertook a meta-analysis of those studies. Several electronic databases were searched for articles on the FAS/FASL polymorphisms and breast cancer risk. The genotype data were extracted; pooled odds ratios (OR) with 95% confidence intervals (CIs) were used to estimate the strength of the association. Five studies were eligible for our meta-analysis. Overall, we observed significant associations of the FAS-1377G/A polymorphism with breast cancer susceptibility (AG vs. GG: OR = 1.15, 95% CI 1.02-1.30; AA vs. GG: OR = 1.39, 95% CI 1.12-1.72; AG/AA vs. GG: OR = 1.18, 95% CI, 1.16-1.32; A vs. G: OR = 1.16, 95% CI 1.06-1.26), but we did not observe significant association of the Fas-670A/G and FasL-844C/T polymorphisms with breast cancer risk. In the subgroup analysis, we observed that the FAS-1377G/A and FASL-844C/T polymorphisms were associated with breast cancer risk in Chinese but not Whites; we still did not observed association of the FAS-670A/G polymorphism with breast cancer risk. Our meta-analysis revealed that FAS-1377G>A polymorphism was associated with an increased risk of breast cancer. FASL-844C>T polymorphism might be associated with a reduced breast cancer risk in Chinese. However, FAS-670A/G had no any effect on breast carcinogenesis.

TCF7L2 rs12255372 (G>T) polymorphism contributes to breast carcinogenesis: evidence from a meta-analysis.

Studies on the association between the TCF7L2 rs12255372 polymorphism and breast cancer risk have reported conflicting results. To characterize the relationship between this polymorphism and breast cancer risk, we conducted a comprehensive literature search for relevant studies and performed a meta-analysis. A total of four studies including 5280 cases and 6026 controls were eligible for our analysis. Overall, we did find that this polymorphism correlates with breast cancer risk [TT versus GG: odds ratio (OR)=1.19, 95% confidence interval (CI)=1.02-1.40; GT versus GG: OR=1.10, 95% CI=1.01-1.19; T versus G: OR=1.12, 95% CI=1.05-1.19]. Furthermore, in the subgroup analysis by ethnicity, we did also find that this polymorphism associated with an increased breast cancer risk in white individuals (T versus G: OR=1.11, 95% CI=1.04-1.18). In summary, this meta-analysis suggests that the rs12255372 T allele is a low-penetrant risk factor for breast carcinogenesis. In the future, larger-scale and more well-designed studies based on homogeneous breast cancer patients are needed to validate our findings, especially in Asians.

3' untranslated region 1630 C>T polymorphism of prohibitin increases risk of breast cancer.

BACKGROUND: Prohibitin 3' untranslated region 1630 C>T (rs6917) polymorphism creates a variant T allele that lacks the antiproliferative activity of the more common functional C allele. Previous studies indicate that women carrying the prohibitin T allele have an increased susceptibility to breast cancer. However, the role of 1630 C>T polymorphism in mRNA expression of prohibitin and its contribution to carcinogenesis in the breast remains controversial. METHODS: Using mRNA expression data from the HapMap online database, we sought an association between prohibitin 1630 C>T polymorphism and its mRNA expression, then conducted a meta-analysis of prohibitin 1630 C>T polymorphism and risk of breast cancer. RESULTS: Although no significant association was found between prohibitin 1630 C>T polymorphism and mRNA expression in lymphoblastoid cell lines from the HapMap database (P trend = 0.543), the present meta-analysis involving 5072 cases and 4796 controls demonstrated that prohibitin 1630 C>T polymorphism was significantly correlated with breast cancer risk in allele contrast model T versus C (odds ratio [OR] 1.09, 95% confidence interval [CI] 1.01-1.18), the homozygote codominant model TT versus CC (OR 1.47, 95% CI 1.12-1.92), and the recessive model TT versus CC/CT (OR 1.45, 95% CI 1.10-1.89). CONCLUSION: Our study indicates that minor allele T of prohibitin 1630 C>T polymorphism is associated with increased susceptibility to breast cancer.