Tumour heterogeneity is an obstacle to effective breast cancer diagnosis and therapy. DNA methylation is an important regulator of gene expression, thus characterizing tumour heterogeneity by epigenetic features can be clinically informative. In this study, we explored specific prognosis‐subtypes based on DNA methylation status using 669 breast cancers from the TCGA database. Nine subgroups were distinguished by consensus clustering using 3869 CpGs that significantly influenced survival. The specific DNA methylation patterns were reflected by different races, ages, tumour stages, receptor status, histological types, metastasis status and prognosis. Compared with the PAM50 subtypes, which use gene expression clustering, DNA methylation subtypes were more elaborate and classified the Basal‐like subtype into two different prognosis‐subgroups. Additionally, 1252 CpGs (corresponding to 888 genes) were identified as specific hyper/hypomethylation sites for each specific subgroup. Finally, a prognosis model based on Bayesian network classification was constructed and used to classify the test set into DNA methylation subgroups, which corresponded to the classification results of the train set. These specific classifications by DNA methylation can explain the heterogeneity of previous molecular subgroups in breast cancer and will help in the development of personalized treatments for the new specific subtypes.

A combination of computational tools will be useful in mining candidate genes for periodontitis. These theoretical results provide new clues for experimental biologists to plan targeted experiments.

Human housekeeping genes are often confused with essential human genes, and several studies regard both types of genes as having the same level of evolutionary conservation. However, this is not necessarily the case. To clarify this, we compared the differences between human housekeeping genes and essential human genes with respect to four aspects: the evolutionary rate (dN/dS), protein sequence identity, single-nucleotide polymorphism (SNP) density and level of linkage disequilibrium (LD). The results showed that housekeeping genes had lower evolutionary rates, higher sequence identities, lower SNP densities and higher levels of LD compared with essential genes. Together, these findings indicate that housekeeping and essential genes are two distinct types of genes, and that housekeeping genes have a higher level of evolutionary conservation. Therefore, we suggest that researchers should pay careful attention to the distinctions between housekeeping genes and essential genes. Moreover, it is still controversial whether we should substitute human orthologs of mouse essential genes for human essential genes. Therefore, we compared the evolutionary features between human orthologs of mouse essential genes and human housekeeping genes and we got inconsistent results in long-term and short-term evolutionary characteristics implying the irrationality of simply replacing human essential genes with human orthologs of mouse essential genes.

ObjectiveCandidate gene association studies and genome-wide association studies (GWAs) have identified a large number of single nucleotide polymorphisms (SNPs) loci affecting susceptibility to rheumatoid arthritis (RA). However, for the same locus, some studies have yielded inconsistent results. To assess all the available evidence for association, we performed a meta-analysis on previously published case-control studies investigating the association between SNPs and RA.MethodsTwo hundred and sixteen studies, involving 125 SNPs, were reviewed. For each SNP, three genetic models were considered: the allele, dominant and recessive effects models. For each model, the effect summary odds ratio (OR) and 95% CIs were calculated. Cochran’s Q-statistics were used to assess heterogeneity. If the heterogeneity was high, a random effects model was used for meta-analysis, otherwise a fixed effects model was used.ResultsThe meta-analysis results showed that: (1) 30, 28 and 26 SNPs were significantly associated with RA (P<0.01) for the allele, dominant, and recessive models, respectively. (2) rs2476601 (PTPN22) showed the strongest association for all the three models: OR = 1.605, 95% CI: 1.540–1.672, P<1.00E−15 for the T-allele; OR = 1.638, 95% CI: 1.565–1.714, P<1.00E−15 for the T/T+T/C genotype and OR = 2.544, 95% CI: 2.173–2.978, P<1.00E−15 for the T/T genotype. (3) Only 23 (18.4%), 13 (10.4%) and 15 (12.0%) SNPs had high heterogeneity (P<0.01) for the three models, respectively. (4) For some of the SNPs, there was no publication bias according to Funnel plots and Egger’s regression tests (P<0.01). For the other SNPs, the associations were tested in only a few studies, and may have been subject to publication bias. More studies on these loci are required.ConclusionOur meta-analysis provides a comprehensive evaluation of the RA association studies from the past two decades. The detailed meta-analysis results are available at: http://210.46.85.180/DRAP/index.php/Metaanalysis/index.

Prostate cancer (PC) is a common malignant tumor that occurs in the prostate epithelial cells. It is generally considered to be caused by both genetic and environmental factors. To identify the genetic risk factors of PC in Chinese population, we carried out a genome-wide haplotype-based association study. The 33 Chinese PC cases were from the public GEO database (GSE18333), and the 139 Chinese controls (CHB) were from the HapMap project. Our analysis included three stages: (1) identifying the linkage disequilibrium (LD) blocks and performing genome-wide haplotype association scan, (2) mapping PC-risk haplotypes to PC candidate genes, and (3) prioritizing PC candidate genes based on their similarity to known PC susceptibility genes. The results showed that (1) 749 haplotypes were significantly associated with PC (P < 1E-5). (2) Then, we mapped these significant haplotypes to genes and got 454 PC candidate genes. (3) After prioritizing the candidate genes based on their similarity to known PC susceptibility genes, we found that seven novel PC susceptibility genes including BLM, RPS6KA2, FRK, ERBB4, RBL1, PAK7, and ERBB2IP. Among the seven genes, BLM gene ranked first (P = 1.89E-04). A haplotype GGTTACCCCTC (rs2270131, rs2073919, rs11073953, rs12592875, rs16944863, rs2238337, rs414634, rs401549, rs17183344, rs16944884, and rs16944888) on chromosome 15q26.1 had significant association with PC (P = 2.37E-11). To our knowledge, this is the first genetic association study to show the significant association between BLM gene and PC susceptibility in Chinese population.