Table_1_Characterization of Dysregulated lncRNA-Associated ceRNA Network Reveals Novel lncRNAs With ceRNA Activity as Epigenetic Diagnostic Biomarkers for Osteoporosis Risk.XLSX

The altered expression of long non-coding RNAs (lncRNAs) has been implicated in the development and human diseases. However, functional roles and regulatory mechanisms of lncRNA as competing endogenous RNAs (ceRNAs) in osteoporosis and their potential clinical implication for osteoporosis risk are largely unexplored. In this study, we performed integrated analysis for paired expression profiles and regulatory relationships of dysregulated lncRNAs, mRNAs, and miRNAs based on “ceRNA hypothesis,” and constructed an osteoporosis-related dysregulated miRNA-mediated lncRNA–mRNA ceRNA network (DysCeNet) composed of 105 nodes (including eight miRNAs, 24 mRNAs, and 73 lncRNAs) and 515 edges. Functional analysis suggested that the DysCeNet was involved in known osteoporosis or bone metabolism-related biological processes and pathways. Then, we performed random forest-based feature selection for 73 lncRNAs with ceRNA activity and identified 25 of 73 lncRNAs as potential diagnostic biomarkers. A random forest-based classifier composed of 25 lncRNA biomarkers (RF-25lncRNA) was developed for predicting osteoporosis risk. Performance evaluation with the leave-one-out cross-validation (LOOCV) procedure showed that the RF-25lncRNA achieved a good performance in distinguishing high- and low-bone mineral density (BMD) subjects in different osteoporosis datasets. Our study for the first time revealed a global view of lncRNA-associated ceRNA regulation in osteoporosis and provided novel lncRNAs with ceRNA activity as candidate epigenetic diagnostic biomarkers for early detection of osteoporosis risk.</p

Table_2_Characterization of Dysregulated lncRNA-Associated ceRNA Network Reveals Novel lncRNAs With ceRNA Activity as Epigenetic Diagnostic Biomarkers for Osteoporosis Risk.XLSX

The altered expression of long non-coding RNAs (lncRNAs) has been implicated in the development and human diseases. However, functional roles and regulatory mechanisms of lncRNA as competing endogenous RNAs (ceRNAs) in osteoporosis and their potential clinical implication for osteoporosis risk are largely unexplored. In this study, we performed integrated analysis for paired expression profiles and regulatory relationships of dysregulated lncRNAs, mRNAs, and miRNAs based on “ceRNA hypothesis,” and constructed an osteoporosis-related dysregulated miRNA-mediated lncRNA–mRNA ceRNA network (DysCeNet) composed of 105 nodes (including eight miRNAs, 24 mRNAs, and 73 lncRNAs) and 515 edges. Functional analysis suggested that the DysCeNet was involved in known osteoporosis or bone metabolism-related biological processes and pathways. Then, we performed random forest-based feature selection for 73 lncRNAs with ceRNA activity and identified 25 of 73 lncRNAs as potential diagnostic biomarkers. A random forest-based classifier composed of 25 lncRNA biomarkers (RF-25lncRNA) was developed for predicting osteoporosis risk. Performance evaluation with the leave-one-out cross-validation (LOOCV) procedure showed that the RF-25lncRNA achieved a good performance in distinguishing high- and low-bone mineral density (BMD) subjects in different osteoporosis datasets. Our study for the first time revealed a global view of lncRNA-associated ceRNA regulation in osteoporosis and provided novel lncRNAs with ceRNA activity as candidate epigenetic diagnostic biomarkers for early detection of osteoporosis risk.</p

The allele combinations of seven polymorphisms under study between breast cancer patients and controls.

<p>*Alleles were arranged according to rs1799782, rs25487, rs3218536, rs861539, rs1800975, rs1760944 and rs1130409.</p

Genotype distributions and allele frequencies of seven polymorphisms under study between patients and controls and their risk prediction for breast cancer under three genetic models of inheritance.

<p><i>Abbreviations</i>: W/M, wild allele/mutant allele; OR, odds ratio; 95% CI, 95% confidence interval. P for χ² test was calculated based on the 3×2 contingency tables for genotype comparisons and on the 2×2 contingency tables for allele comparisons. *Controlling for age at enrollment.</p

The Relationship between Seven Common Polymorphisms from Five DNA Repair Genes and the Risk for Breast Cancer in Northern Chinese Women

<div><p>Background</p><p>Converging evidence supports the central role of DNA damage in progression to breast cancer. We therefore in this study aimed to assess the potential interactions of seven common polymorphisms from five DNA repair genes (XRCC1, XRCC2, XRCC3, XPA and APEX1) in association with breast cancer among Han Chinese women.</p><p>Methodology/Principal Findings</p><p>This was a case-control study involving 606 patients diagnosed with sporadic breast cancer and 633 age- and ethnicity-matched cancer-free controls. The polymerase chain reaction - ligase detection reaction method was used to determine genotypes. All seven polymorphisms were in accordance with Hardy-Weinberg equilibrium in controls. Differences in the genotypes and alleles of XRCC1 gene rs25487 and XPA gene rs1800975 were statistically significant between patients and controls, even after the Bonferroni correction (P<0.05/7). Accordingly, the risk for breast cancer was remarkably increased for rs25487 (OR = 1.28; 95% CI: 1.07–1.51; P = 0.006), but decreased for rs1800975 (OR = 0.77; 95% CI: 0.67–0.90; P = 0.001) under an additive model at a Bonferroni corrected alpha of 0.05/7. Allele combination analysis showed higher frequencies of the most common combination C-G-G-C-G-G-G (alleles in order of rs1799782, rs25487, rs3218536, rs861539, rs1800975, rs1760944 and rs1130409) in controls than in patients (P_Sim = 0.002). In further interaction analysis, two-locus model including rs1800975 and rs25487 was deemed as the overall best model with the maximal testing accuracy of 0.654 and the cross-validation consistency of 10 out of 10 (P = 0.001).</p><p>Conclusion</p><p>Our findings provide clear evidence that XRCC1 gene rs25487 and XPA gene rs1800975 might exert both independent and interactive effects on the development of breast cancer among northern Chinese women.</p></div

Summary of multifactor dimensionality reduction (MDR) analysis.

<p><i>Abbreviations</i>: CVC, cross-validation consistency. *The overall best MDR model.</p

The baseline characteristics of all study participants.

<p>Data were expressed as mean ± standard deviation unless otherwise indicated.</p