BackgroundEvidence on the benefits of combining cyclooxygenase-2 inhibitor (COX-2) in treating non-small cell lung cancer (NSCLC) is still controversial. We investigated the efficacy and safety profile of cyclooxygenase-2 inhibitors in treating NSCLC.MethodsThe first meta-analysis of eligible studies was performed to assess the effect of COX-2 inhibitors for patients with NSCLC on the overall response rate (ORR), overall survival (OS), progression-free survival (PFS), one-year survival, and toxicities. The fixed-effects model was used to calculate the pooled RR and HR and between-study heterogeneity was assessed. Subgroup analyses were conducted according to the type of COX-2 inhibitors, treatment pattern, and treatment line.ResultsNine randomized clinical trials, comprising 1679 patents with NSCLC, were included in the final meta-analysis. The pooled ORR of patients who have NSCLC with COX-2 inhibitors was significantly higher than that without COX-2 inhibitors. In subgroup analysis, significantly increased ORR results were found on celecoxib (RR = 1.29, 95% CI: 1.09, 1.51), rofecoxib (RR = 1.61, 95% CI: 1.14, 2.28), chemotherapy (RR = 1.40, 95% CI: 1.20, 1.63), and first-line treatment (RR = 1.39, 95% CI: 1.19, 1.63). However, COX-2 inhibitors had no effect on the one-year survival, OS, and PFS. Increased RR of leucopenia (RR = 1.21, 95% CI: 1.01, 1.45) and thrombocytopenia (RR = 1.36, 95% CI: 1.06, 1.76) suggested that COX-2 inhibitors increased hematologic toxicities (grade ≥ 3) of chemotherapyConclusionsCOX-2 inhibitors increased ORR of advanced NSCLC and had no impact on survival indices, but it may increase the risk of hematologic toxicities associated with chemotherapy.
Centrosome defects can result in aneuploidy and genomic instability, and have important implications for breast cancer development. The Aurora-A and BRCA1 proteins interact and both are strongly involved in centrosome regulation. Genetic variants in these two genes may have an effect on breast cancer development. Here, we report a comprehensive single nucleotide polymorphism (SNP) and haplotype-tagging association study on these two genes in 1334 breast cancer cases and 1568 unaffected controls among the Chinese Han population. Apart from a missense SNP, rs2273535 (Phe31Ile), and a probable risk SNP, rs2064863, six htSNPs were analysed in three high-LD blocks of AURKA spanning from 10 kb upstream to 2 kb downstream of AURKA. For BRCA1, six htSNPs were analysed in a large high-LD region covering 98 kb (10 kb was extended to each end of BRCA1). The results showed that four SNPs in AURKA (data in recessive model, rs2273535: OR = 2.19, 95% CI = 1.03-4.66, p = 0.0422; rs2298016: OR = 0.38, 95% CI = 0.18-0.82, p = 0.0141; rs6024836: OR = 1.54, 95% CI = 1.18-2.00, p = 0.0014; rs10485805: OR = 0.68, 95% CI = 0.47-0.98, p = 0.0380) and one SNP in BRCA1 (rs3737559, dominant model OR = 1.35, 95% CI = 1.11-1.64, p = 0.0030) were associated with breast cancer susceptibility. After correction for multiple comparisons (FDR = 0.05), only rs6024836 and rs3737559 remained significant. Two haplotypes (CC of block 2, OR = 20.74, 95% CI = 4.35-98.88, p = 0.0001; GG of block 3, OR = 1.32, 95% CI = 1.12-1.56, p = 0.0010) and one diplotype (AG-GG of block 3, OR = 1.63, 95% CI = 1.18-2.26, p = 0.0031) within AURKA showed strong associations with breast cancer risk. One haplotype of BRCA1 (CTGTTG, OR = 1.30, 95% CI = 1.06-1.59, p = 0.0118) was also associated with breast cancer risk. However, women harbouring both at-risk genotypes of Aurora-A and BRCA1 were at a slightly increased risk compared with those harbouring either at-risk variant alone. Common genetic variants in the AURKA and BRCA1 genes may contribute to breast cancer development.
The CCNB1 and CDK1 genes encode the proteins of CyclinB1 and CDK1 respectively, which interact with each other and are involved in cell cycle regulation, centrosome duplication and chromosome segregation. This study aimed to investigate whether the genetic variants in these two genes may affect breast cancer (BC) susceptibility, progression, and survival in Chinese Han population using haplotype-based analysis. A total of ten tSNPs spanning from 2kb upstream to 2kb downstream of these genes were genotyped in 1204 cases and 1204 age-matched cancer-free controls. The haplotype blocks were determined according to our genotyping data and linkage disequilibrium (LD) status of these SNPs. For CCNB1, rs2069429 was significantly associated with increased BC susceptibility under recessive model (OR=2.352, 95%CI=1.480-3.737), so was the diplotype TAGT/TAGT (OR=1.947 95%CI=1.154-3.284, P=0.013). In addition, rs164390 was associated with Her2-negative BC. For CDK1, rs2448343 and rs1871446 were significantly associated with decreased BC risk under dominant models, so was the haplotype ATATT. These two SNPs also showed a dose-dependent effect on BC susceptibility. Using stratified association analysis, we found that women with the heterozygotes or minor allele homozygotes of rs2448343 had much less BC susceptibility among women with BMI<23. In CDK1, three closely located SNPs, rs2448343, rs3213048 and rs3213067, were significantly associated with tumor’s PR status: the heterozygotes of rs2448343 were associated with PR-positive tumors, while the minor allele homozygotes of rs3213048 and heterozygotes of rs3213067 were associated with PR-negative BC tumors. In survival analysis, rs1871446 was associated with unfavorable event-free survival under recessive model, so was the CDK1 diplotype ATATG/ATATG, which carried the minor allele homozygote of rs1871446. Our study indicates that genetic polymorphisms of CCNB1 and CDK1 are related to BC susceptibility, progression, and survival in Chinese Han women. Further studies need to be performed in other populations as an independent replication to verify these results.
Obesity, alcohol abuse, type 2 diabetes mellitus and hyperlipidemia may be independent risk factors of fatty liver. The mildly abnormal hepatic functions can be found in patients with fatty liver. More obvious damages of liver function with AST/ALT usually more than 2 were noted in patients with AFL.
Breast segmentation and mass detection in medical images are important for diagnosis and treatment follow-up. Automation of these challenging tasks can assist radiologists by reducing the high manual workload of breast cancer analysis. In this paper, deep convolutional neural networks (DCNN) were employed for breast segmentation and mass detection in dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI). First, the region of the breasts was segmented from the remaining body parts by building a fully convolutional neural network based on U-Net++. Using the method of deep learning to extract the target area can help to reduce the interference external to the breast. Second, a faster region with convolutional neural network (Faster RCNN) was used for mass detection on segmented breast images. The dataset of DCE-MRI used in this study was obtained from 75 patients, and a 5-fold cross validation method was adopted. The statistical analysis of breast region segmentation was carried out by computing the Dice similarity coefficient (DSC), Jaccard coefficient, and segmentation sensitivity. For validation of breast mass detection, the sensitivity with the number of false positives per case was computed and analyzed. The Dice and Jaccard coefficients and the segmentation sensitivity value for breast region segmentation were 0.951, 0.908, and 0.948, respectively, which were better than those of the original U-Net algorithm, and the average sensitivity for mass detection achieved 0.874 with 3.4 false positives per case.
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