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Osteoporosis (OP) is significant and debilitating comorbidity of chronic obstructive pulmonary disease (COPD). We hypothesize that genetic variance identified with OP may also play roles in COPD. We have conducted a large-scale relation data analysis to explore the genes implicated with either OP or COPD, or both. Each gene linked to OP but not to COPD was further explored in a mega-analysis and partial mega-analysis of 15 independently collected COPD RNA expression datasets, followed by gene set enrichment analysis (GSEA) and literature-based pathway analysis to explore their functional linked to COPD. A multiple linear regression (MLR) model was built to study the possible influence of sample size, population region, and study date on the gene expression data in COPD. At the first step of the analysis, we have identified 918 genes associated with COPD, 581 with OP, and a significant overlap (P<2.30e-140; 210 overlapped genes). Partial mega-analysis showed that, one OP gene, GPNMB presented significantly increased expression in COPD patients (P-value = 0.0018; log fold change = 0.83). GPNMB was enriched in multiple COPD pathways and plays roles as a gene hub formulating multiple vicious COPD pathways included gene MMP9 and MYC. GPNMB could be a novel gene that plays roles in both COPD and OP. Partial mega-analysis is valuable in identify case-specific genes for COPD. [ABSTRACT FROM AUTHOR]

Esophageal squamous cell carcinoma (ESCC) and esophageal adenocarcinoma (EA) are the two main subtypes of esophageal cancer. Genetics underpinnings of EA are substantially less understood than that of ESCC. A large-scale relation data analysis was conducted to explore the genes implicated with either EA or ESCC, or both. Each gene linked to ESCC but not EA was further explored in mega-analysis of six independently collected EA RNA expression datasets. Amultiple linear regression (MLR)model was built to study the possible influence of sample size, population region, and study date on the gene expression data in EA. Finally, a functional pathway analysis was conducted to identify the possible linkage between EA and the genes identified as novel significant contributors. We have identified 276 genes associated with EA, 1088 with ESCC, with a significant (P<5.14e-143) overlap between these two gene groups (n=157). Mega-analysis showed that two ESCC-related genes, UGT2B17 and MIR224, were significantly associated with EA (P-value <1e-10), with multiple connecting pathways revealed by functional analysis. ESCC and EA share some common pathophysiological pathways. Further study of UGT2B17 and MIR224, which are differentially dysregulated in ESCC and EA tumors, is warranted. Enhanced expression of UGT2B17 and the lack of miR-224 signaling may contribute to the responsiveness of EA to the male sex steroids. [ABSTRACT FROM AUTHOR]