BackgroundRobust and precise molecular prognostic predictors for luminal breast cancer are required. This study aimed to identify key methylation sites in luminal breast cancer, as well as precise molecular tools for predicting prognosis.MethodsWe compared methylation levels of normal and luminal breast cancer samples from The Cancer Genome Atlas dataset. The relationships among differentially methylated sites, corresponding mRNA expression levels and prognosis were further analysed. Differentially expressed genes in normal and cancerous samples were analysed, followed by the identification of prognostic signature genes. Samples were divided into low- and high-risk groups based on the signature genes. Prognoses of low- and high-risk groups were compared. The Gene Expression Omnibus dataset were used to validate signature genes for prognosis prediction. Prognosis of low- and high-risk groups in Luminal A and Luminal B samples from the TCGA and the Metabric cohort dataset were analyzed. We also analysed the correlation between clinical features of low- and high- risk groups as well as their differences in gene expression.ResultsFourteen methylation sites were considered to be related to luminal breast cancer prognosis because their methylation levels, mRNA expression and prognoses were closely related to each other. The methylation level of SOSTDC1 was used to divide samples into hypo- and hyper-methylation groups. We also identified an mRNA signature, comprising eight transcripts, ESCO2, PACSIN1, CDCA2, PIGR, PTN, RGMA, KLK4 and CENPA, which was used to divide samples into low- and high-risk groups. The low-risk group showed significantly better prognosis than the high-risk group. A correlation analysis revealed that the risk score was an independent prognostic factor. Low- and high- risk groups significantly correlated with the survival ratio in Luminal A samples, but not in Luminal B samples on the basis of the TCGA and the Metabric cohort dataset. Further functional annotation demonstrated that the differentially expressed genes were mainly involved in cell cycle and cancer progression.ConclusionsWe identified several key methylation sites and an mRNA signature for predicting luminal breast cancer prognosis. The signature exhibited effective and precise prediction of prognosis and may serve as a prognostic and diagnostic marker for luminal breast cancer.Electronic supplementary materialThe online version of this article (10.1186/s12885-018-4314-9) contains supplementary material, which is available to authorized users.

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The presented study performed an mtDNA genome-wide association analysis to screen the peripheral blood of breast cancer patients for high-risk germline mutations. Unlike previous studies, which have used breast tissue in analyzing somatic mutations, we looked for germline mutations in our study, since they are better predictors of breast cancer in high-risk groups, facilitate early, non-invasive diagnoses of breast cancer and may provide a broader spectrum of therapeutic options. The data comprised 22 samples of healthy group and 83 samples from breast cancer patients. The sequencing data showed 170 mtDNA mutations in the healthy group and 393 mtDNA mutations in the disease group. Of these, 283 mtDNA mutations (88 in the healthy group and 232 in the disease group) had never been reported in the literature. Moreover, correlation analysis indicated there was a significant difference in 32 mtDNA mutations. According to our relative risk analysis of these 32 mtDNA mutations, 27 of the total had odds ratio values (ORs) of less than 1, meaning that these mutations have a potentially protective role to play in breast cancer. The remaining 5 mtDNA mutations, RNR2-2463 indelA, COX1-6296 C>A, COX1-6298 indelT, ATP6-8860 A>G, and ND5-13327 indelA, whose ORs were 8.050, 4.464, 4.464, 5.254 and 4.853, respectively, were regarded as risk factors of increased breast cancer. The five mutations identified here may serve as novel indicators of breast cancer and may have future therapeutic applications. In addition, the use of peripheral blood samples was procedurally simple and could be applied as a non-invasive diagnostic technique.

We investigated the effects of millimeter wave treatment on the expression of the cell cycle regulating proteins cyclin-dependent kinase 2 (CDK2) and cyclin A in chondrocytes. Knee articular cartilage from SD rats was used to establish cultured primary chondrocytes. After identification using toluidine blue staining, passage 2 chondrocytes were randomly divided into different groups and treated with nocodazole or millimeter wave. The RNA expression of CDK2 and cyclin A was measured using RT-PCR, and their protein levels were detected by Western blotting. Cell cycle analysis showed that nocodazole treatment significantly increased the number of G0/G1 and G2/M stage chondrocytes and decreased the amount of S phase cells. In contrast, millimeter wave treatment significantly decreased the number of G0/G1 and G2/M chondrocytes and increased the number of S phase cells. The mRNA and protein levels of CDK2 and cyclin A consistently demonstrated a reverse trend, with the lowest levels in the chondrocytes treated with nocodazole. The expression of CDK2 and cyclin A was higher in chondrocytes receiving millimeter wave treatment than in untreated cells. In conclusion, millimeter wave treatment induces CDK2 and cyclin A expression, accelerates S-phase entry and G2/M transition and promotes chondrocyte cell cycle progression.

Given that tigecycline-based combination therapy is recognized as a valuable option for the treatment of tigecycline-resistant Acinetobacter baumannii, we conducted this systematic review and meta-analysis to assess the overall evidence of its effectiveness. The synergy rate was defined as the primary outcome that was calculated separately for time-kill, Etest, and checkerboard microdilution methods. The secondary outcomes were bactericidal activity and the efficacy of combination treatment on the development of resistance. In total, 37 published papers and 16 conference proceedings were included. Nine classes consisting of 22 antibiotic types in combination with tigecycline against 1,159 A. baumannii strains were reported in the analysis. For the time-kill studies, combination therapy showed a synergy rate of 37.9% (95% confidence interval [CI], 30.7-46.5); the highest synergy rate was 67.4% (95% CI, 27.3-91.9) for tigecycline in combination with colistin. Moreover, combination with amikacin or colistin could efficiently inhibit the development of tigecycline resistance. Compared with checkerboard microdilution and Etest methods, time-kill studies always showed higher synergy rates. Altogether, these results suggest that the in vitro tigecycline-based combinations resulted in moderate synergy rates and that several combinations could suppress the resistance of A. baumannii to tigecycline, which should be further confirmed in animal models and clinical trials.

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