Association of <em>microRNA-499</em> rs3746444 Polymorphism with Cancer Risk: Evidence from 7188 Cases and 8548 Controls

<div><h3>Background</h3><p>Owing to inconsistent and inconclusive results, we performed a meta-analysis to derive a more precise estimation of the association between <em>miR-499</em> rs3746444 polymorphism and cancer risk.</p> <h3>Methodology/Principal Findings</h3><p>A systematic search of the Pubmed, Excerpta Medica Database (Embase) and Chinese Biomedical Literature Database (CBM) databases was performed with the last search updated on May 6, 2012. The odds ratio (OR) and its 95% confidence interval (95%CI) were used to assess the strength of the association. A total of 15 independent studies including 7,188 cases and 8,548 controls were used in the meta-analysis. In the present meta-analysis, we found a significant association between <em>miR-499</em> rs3746444 polymorphism and cancer risk in the overall analysis (G versus A: OR = 1.10, 95%CI 1.01–1.19, <em>P</em> = 0.03; GG+AG versus AA: OR = 1.15, 95%CI 1.02–1.30, <em>P</em> = 0.02; GG versus AG+AA: OR = 1.07, 95%CI 0.89–1.28, <em>P</em> = 0.50; GG versus AA: OR = 1.13, 95%CI 0.98–1.31, <em>P</em> = 0.09; AG versus AA: OR = 1.16, 95%CI 1.02–1.33, <em>P</em> = 0.03). In the subgroup analysis by ethnicity, <em>miR-499</em> rs3746444 polymorphism was significantly associated with cancer risk in Asian population. In the subgroup analysis by cancer types, <em>miR-499</em> rs3746444 polymorphism was significantly associated with breast cancer.</p> <h3>Conclusions/Significance</h3><p>This meta-analysis suggests a significant association between <em>miR-499</em> rs3746444 polymorphism and cancer risk. Large-scale and well-designed case-control studies are necessary to validate the risk identified in the present meta-analysis.</p> </div

Raw data from Soy protein isolate-carboxymethyl cellulose conjugates with pH sensitivity for sustained avermectin release

Graft ratio, FTIR, TG, DTG , DSC, Size distribution in SEM images,EE, Zeta Potential, DLS Size, anti - UV, Liquid holding capacity, sustain release and toxicity test dat

Meta-analysis of <i>miR-499</i> rs3746444 polymorphism with cancer susceptibility.^*

*<p>OR, odds ratio; vs, versus; R, random effect model; F, fixed effect model.</p

Image_6_Machine learning models based on immunological genes to predict the response to neoadjuvant therapy in breast cancer patients.pdf

Breast cancer (BC) is the most common malignancy worldwide and neoadjuvant therapy (NAT) plays an important role in the treatment of patients with early BC. However, only a subset of BC patients can achieve pathological complete response (pCR) and benefit from NAT. It is therefore necessary to predict the responses to NAT. Although many models to predict the response to NAT based on gene expression determined by the microarray platform have been proposed, their applications in clinical practice are limited due to the data normalization methods during model building and the disadvantages of the microarray platform compared with the RNA-seq platform. In this study, we first reconfirmed the correlation between immune profiles and pCR in an RNA-seq dataset. Then, we employed multiple machine learning algorithms and a model stacking strategy to build an immunological gene based model (Ipredictor model) and an immunological gene and receptor status based model ICpredictor model) in the RNA-seq dataset. The areas under the receiver operator characteristic curves for the Ipredictor model and ICpredictor models were 0.745 and 0.769 in an independent external test set based on the RNA-seq platform, and were 0.716 and 0.752 in another independent external test set based on the microarray platform. Furthermore, we found that the predictive score of the Ipredictor model was correlated with immune microenvironment and genomic aberration markers. These results demonstrated that the models can accurately predict the response to NAT for BC patients and will contribute to individualized therapy.</p

Image_7_Machine learning models based on immunological genes to predict the response to neoadjuvant therapy in breast cancer patients.pdf

Breast cancer (BC) is the most common malignancy worldwide and neoadjuvant therapy (NAT) plays an important role in the treatment of patients with early BC. However, only a subset of BC patients can achieve pathological complete response (pCR) and benefit from NAT. It is therefore necessary to predict the responses to NAT. Although many models to predict the response to NAT based on gene expression determined by the microarray platform have been proposed, their applications in clinical practice are limited due to the data normalization methods during model building and the disadvantages of the microarray platform compared with the RNA-seq platform. In this study, we first reconfirmed the correlation between immune profiles and pCR in an RNA-seq dataset. Then, we employed multiple machine learning algorithms and a model stacking strategy to build an immunological gene based model (Ipredictor model) and an immunological gene and receptor status based model ICpredictor model) in the RNA-seq dataset. The areas under the receiver operator characteristic curves for the Ipredictor model and ICpredictor models were 0.745 and 0.769 in an independent external test set based on the RNA-seq platform, and were 0.716 and 0.752 in another independent external test set based on the microarray platform. Furthermore, we found that the predictive score of the Ipredictor model was correlated with immune microenvironment and genomic aberration markers. These results demonstrated that the models can accurately predict the response to NAT for BC patients and will contribute to individualized therapy.</p

Growth of islets in each group after 4 days of cultivation (×40).

<p>(AO/EB staining, a–d: MCT group, Ficoll-400 group, 1077 group, and HPU group, respectively).</p

GPC test results of CMC from Soy protein isolate-carboxymethyl cellulose conjugates with pH sensitivity for sustained avermectin release

Cirrus GPC Sample Injection Repor

Image_9_Machine learning models based on immunological genes to predict the response to neoadjuvant therapy in breast cancer patients.pdf

Breast cancer (BC) is the most common malignancy worldwide and neoadjuvant therapy (NAT) plays an important role in the treatment of patients with early BC. However, only a subset of BC patients can achieve pathological complete response (pCR) and benefit from NAT. It is therefore necessary to predict the responses to NAT. Although many models to predict the response to NAT based on gene expression determined by the microarray platform have been proposed, their applications in clinical practice are limited due to the data normalization methods during model building and the disadvantages of the microarray platform compared with the RNA-seq platform. In this study, we first reconfirmed the correlation between immune profiles and pCR in an RNA-seq dataset. Then, we employed multiple machine learning algorithms and a model stacking strategy to build an immunological gene based model (Ipredictor model) and an immunological gene and receptor status based model ICpredictor model) in the RNA-seq dataset. The areas under the receiver operator characteristic curves for the Ipredictor model and ICpredictor models were 0.745 and 0.769 in an independent external test set based on the RNA-seq platform, and were 0.716 and 0.752 in another independent external test set based on the microarray platform. Furthermore, we found that the predictive score of the Ipredictor model was correlated with immune microenvironment and genomic aberration markers. These results demonstrated that the models can accurately predict the response to NAT for BC patients and will contribute to individualized therapy.</p

Table_6_Machine learning models based on immunological genes to predict the response to neoadjuvant therapy in breast cancer patients.docx

Breast cancer (BC) is the most common malignancy worldwide and neoadjuvant therapy (NAT) plays an important role in the treatment of patients with early BC. However, only a subset of BC patients can achieve pathological complete response (pCR) and benefit from NAT. It is therefore necessary to predict the responses to NAT. Although many models to predict the response to NAT based on gene expression determined by the microarray platform have been proposed, their applications in clinical practice are limited due to the data normalization methods during model building and the disadvantages of the microarray platform compared with the RNA-seq platform. In this study, we first reconfirmed the correlation between immune profiles and pCR in an RNA-seq dataset. Then, we employed multiple machine learning algorithms and a model stacking strategy to build an immunological gene based model (Ipredictor model) and an immunological gene and receptor status based model ICpredictor model) in the RNA-seq dataset. The areas under the receiver operator characteristic curves for the Ipredictor model and ICpredictor models were 0.745 and 0.769 in an independent external test set based on the RNA-seq platform, and were 0.716 and 0.752 in another independent external test set based on the microarray platform. Furthermore, we found that the predictive score of the Ipredictor model was correlated with immune microenvironment and genomic aberration markers. These results demonstrated that the models can accurately predict the response to NAT for BC patients and will contribute to individualized therapy.</p

Insulin secretion stimulated by different glucose levels in each group.

Insulin secretion stimulated by different glucose levels in each group.</p