Development of a prognostic nomogram for patients with malignant mesothelioma with bone metastasis

Abstract Malignant mesothelioma (MM) is a rare aggressive tumor, and bone metastasis often occurs in later stages of this disease. This study aimed to establish a nomogram to predict the prognosis of bone metastasis of patients with MM. Data from the Surveillance, Epidemiology, and End Results database were screened and retrieved. This study included 311 patients with MM with bone metastases. Prognostic factors were analyzed using the Kaplan–Meier method and Cox proportional hazards model. A nomogram for overall survival (OS) was established and evaluated using statistically significant prognostic factors, and cancer-specific survival (CSS) analysis was performed to investigate its prognostic factors. In addition, the metastasis patterns of patients with MM were investigated, and the effects of different sites of metastasis on survival were compared using the Kaplan–Meier method. Age, sex, histological type, and chemotherapy were identified as the independent risk factors for OS. The 1-, 2-, and 3-year areas under the curve of the nomogram were 0.792, 0.774, and 0.928, and 0.742, 0.733, and 0.733 in the training and validation sets, respectively. Compared to OS, histological type, radiotherapy, and chemotherapy were independent risk factors for CSS. Different metastatic sites in MM have significantly different effects on prognosis

Genome-wide DNA methylation profile analysis identifies differentially methylated loci associated with ankylosis spondylitis

Abstract Background Ankylosing spondylitis (AS) is a chronic rheumatic and autoimmune disease. Little is known about the potential role of DNA methylation in the pathogenesis of AS. This study was undertaken to explore the potential role of DNA methylation in the genetic mechanism of AS. Methods In this study, we compared the genome-wide DNA methylation profiles of peripheral blood mononuclear cells (PBMCs) between five AS patients and five healthy subjects, using the Illumina Infinium HumanMethylation450 BeadChip. Quantitative real-time reverse transcription-polymerase chain reaction (qRT-PCR) was performed to validate the relevance of the identified differentially methylated genes for AS, using another independent sample of five AS patients and five healthy subjects. Results Compared with healthy controls, we detected 1915 differentially methylated CpG sites mapped to 1214 genes. The HLA-DQB1 gene achieved the most significant signal (cg14323910, adjusted P = 1.84 × 10–6, β difference = 0.5634) for AS. Additionally, the CpG site cg04777551 of HLA-DQB1 presented a suggestive association with AS (adjusted P = 1.46 × 10–3, β difference = 0.3594). qRT-PCR observed that the mRNA expression level of HLA-DQB1 in AS PBMCs was significantly lower than that in healthy control PBMCs (ratio = 0.48 ± 0.10, P < 0.001). Gene Ontology (GO) and KEGG pathway enrichment analysis of differentially methylated genes identified four GO terms and 10 pathways for AS, functionally related to antigen dynamics and function. Conclusions Our results demonstrated the altered DNA methylation profile of AS and implicated HLA-DQB1 in the development of AS

A Genomewide Integrative Analysis of GWAS and eQTLs Data Identifies Multiple Genes and Gene Sets Associated with Obesity

To identify novel susceptibility genes and gene sets for obesity, we conducted a genomewide expression association analysis of obesity via integrating genomewide association study (GWAS) and expression quantitative trait loci (eQTLs) data. GWAS summary data of body mass index (BMI) and waist-to-hip ratio (WHR) was driven from a published study, totally involving 339,224 individuals. The eQTLs dataset (containing 927,753 eQTLs) was obtained from eQTLs meta-analysis of 5,311 subjects. Integrative analysis of GWAS and eQTLs data was conducted by SMR software. The SMR single gene analysis results were further subjected to gene set enrichment analysis (GSEA) for identifying obesity associated gene sets. A total of 13,311 annotated gene sets were analyzed in this study. SMR single gene analysis identified 20 BMI associated genes (TUFM, SPI1, APOB48R, etc.). Also 3 WHR associated genes were detected (CPEB4, WARS2, and L3MBTL3). The significant association between Chr16p11 and BMI was observed by GSEA (FDR adjusted p value = 0.040). The TGCTGCT, MIR-15A, MIR-16, MIR-15B, MIR-195, MIR-424, and MIR-497 (FDR adjusted p value = 0.049) gene set appeared to be linked with WHR. Our results provide novel clues for the genetic mechanism studies of obesity. This study also illustrated the good performance of SMR for susceptibility gene mapping

Integrating Genome-Wide Association and eQTLs Studies Identifies the Genes and Gene Sets Associated with Diabetes

Aim. To identify novel candidate genes and gene sets for diabetes. Methods. We performed an integrative analysis of genome-wide association studies (GWAS) and expression quantitative trait loci (eQTLs) data for diabetes. Summary data was driven from a large-scale GWAS of diabetes, totally involving 58,070 individuals. eQTLs dataset included 923,021 cis-eQTL for 14,329 genes and 4,732 trans-eQTL for 2,612 genes. Integrative analysis of GWAS and eQTLs data was conducted by summary data-based Mendelian randomization (SMR). To identify the gene sets associated with diabetes, the SMR single gene analysis results were further subjected to gene set enrichment analysis (GSEA). A total of 13,311 annotated gene sets were analyzed in this study. Results. SMR analysis identified 6 genes significantly associated with fasting glucose, such as C11ORF10 (p value = 6.04 × 10−8), MRPL33 (p value = 1.24 × 10−7), and FADS1 (p value = 2.39 × 10−7). Gene set analysis identified HUANG_FOXA2_TARGETS_UP (false discovery rate = 0.047) associated with fasting glucose. Conclusion. Our study provides novel clues for clarifying the genetic mechanism of diabetes. This study also illustrated the good performance of SMR approach and extended it to gene set association analysis for complex diseases

Integrating genome-wide DNA methylation and mRNA expression profiles identified different molecular features between Kashin-Beck disease and primary osteoarthritis

Abstract Background Kashin-Beck disease (KBD) is an endemic osteochondropathy of unknown etiology. Osteoarthritis (OA) is a form of degenerative joint disease sharing similar clinical manifestations and pathological changes to articular cartilage with KBD. Methods A genome-wide DNA methylation profile of articular cartilage from five KBD patients and five OA patients was first performed using the Illumina Infinium HumanMethylation450 BeadChip. Together with a previous gene expression profiling dataset comparing KBD cartilage with OA cartilage, an integrative pathway enrichment analysis of the genome-wide DNA methylation and the mRNA expression profiles conducted in articular cartilage was performed by InCroMAP software. Results We identified 241 common genes altered in both the DNA methylation profile and the mRNA expression profile of articular cartilage of KBD versus OA, including CHST13 (NM_152889, fold-change = 0.5979, P methy = 0.0430), TGFBR1 (NM_004612, fold-change = 2.077, P methy = 0.0430), TGFBR2 (NM_001024847, fold-change = 1.543, P methy = 0.037), TGFBR3 (NM_001276, fold-change = 0.4515, P methy = 6.04 × 10−4), and ADAM12 (NM_021641, fold-change = 1.9768, P methy = 0.0178). Integrative pathway enrichment analysis identified 19 significant KEGG pathways, including mTOR signaling (P = 0.0301), glycosaminoglycan biosynthesis-chondroitin sulfate/dermatan sulfate (P = 0.0391), glycosaminoglycan biosynthesis-keratan sulfate (P = 0.0278), and PI3K-Akt signaling (P = 0.0243). Conclusion This study identified different molecular features between Kashin-Beck disease and primary osteoarthritis and provided novel clues for clarifying the pathogenetic differences between KBD and OA

Additional file 3: Table S1. of Genome-wide DNA methylation profile analysis identifies differentially methylated loci associated with ankylosis spondylitis

Presenting significantly differentially methylated CpG sites between AS patients and healthy controls. (XLSX 167 kb

Additional file 1: Figure S1. of Genome-wide DNA methylation profile analysis identifies differentially methylated loci associated with ankylosis spondylitis

Showing Pearson correlation coefficient plot of the genome-wide DNA methylation study results. X axis, average β values in cases; Y axis, average β values in controls. (JPEG 104 kb

Additional file 2: of Integrating genome-wide DNA methylation and mRNA expression profiles identified different molecular features between Kashin-Beck disease and primary osteoarthritis

Table S2. Pathway enrichment analysis results of genome-wide DNA methylation profiling. (DOCX 16 kb

Additional file 1: of Integrating genome-wide DNA methylation and mRNA expression profiles identified different molecular features between Kashin-Beck disease and primary osteoarthritis

Table S1. The common significant genes detected by both DNA methylation and mRNA expression profiling studies. (DOCX 26 kb