DataSheet_1_Association of geriatric nutritional risk index with the risk of osteoporosis in the elderly population in the NHANES.zip

BackgroundOsteoporosis is common in the elderly, and malnutrition is considered a major risk factor for osteoporosis. This study investigated the relationship between the Geriatric Nutrition Risk Index (GNRI) and osteoporosis based on a large cross-sectional study of the National Health and Nutrition Examination Survey (NHANES).MethodsWe included 7405 older adults from NHANES (2005 to 2018) and divided them into the High-GNRI and Low-GNRI groups based on GNRI levels to compare the prevalence of osteoporosis among the two groups. A multi-factor logistic regression analysis was used to determine whether GNRI was an independent risk factor for osteoporosis. Spearman’s rank correlation coefficient was computed to investigate the linear relationship between geriatric nutritional risk index (GNRI) and bone mineral density (BMD) T-score. Finally, a generalized additive model (GAM) revealed whether there was a non-linear relationship between GNRI and osteoporosis.ResultsThe prevalence of osteoporosis was higher in the Low-GNRI group than those in the High-GNRI group (12.2% vs. 8.2%; P = 0.001). Similarly, the femoral neck BMD T-scores (-1.09 ± 1.42 vs. -0.91 ± 1.31; P = 0.003). However, there was no significant difference between Low-GNRI group and High-GNRI group in lumbar BMD T-scores (1.700 ± 1.69 vs 1.85 ± 1.72; P>0.05). The multi-factor logistic regression analysis identified low GNRI as an independent risk factor for osteoporosis (OR: 1.544; 95% CI: 1.179-2.022; P ConclusionGNRI is an independent risk factor for osteoporosis in the elderly and is negatively and non-linearly associated with the risk of osteoporosis in the elderly population.</p

Table_1_Identification of risk model based on glycolysis-related genes in the metastasis of osteosarcoma.xls

BackgroundGlycolytic metabolic pathway has been confirmed to play a vital role in the proliferation, survival, and migration of malignant tumors, but the relationship between glycolytic pathway-related genes and osteosarcoma (OS) metastasis and prognosis remain unclear.MethodsWe performed Gene set enrichment analysis (GSEA) on the osteosarcoma dataset in the TARGET database to explore differences in glycolysis-related pathway gene sets between primary osteosarcoma (without other organ metastases) and metastatic osteosarcoma patient samples, as well as glycolytic pathway gene set gene difference analysis. Then, we extracted OS data from the TCGA database and used Cox proportional risk regression to identify prognosis-associated glycolytic genes to establish a risk model. Further, the validity of the risk model was confirmed using the GEO database dataset. Finally, we further screened OS metastasis-related genes based on machine learning. We selected the genes with the highest clinical metastasis-related importance as representative genes for in vitro experimental validation.ResultsUsing the TARGET osteosarcoma dataset, we identified 5 glycolysis-related pathway gene sets that were significantly different in metastatic and non-metastatic osteosarcoma patient samples and identified 29 prognostically relevant genes. Next, we used multivariate Cox regression to determine the inclusion of 13 genes (ADH5, DCN, G6PD, etc.) to construct a prognostic risk score model to predict 1- (AUC=0.959), 3- (AUC=0.899), and 5-year (AUC=0.895) survival under the curve. Ultimately, the KM curves pooled into the datasets GSE21257 and GSE39055 also confirmed the validity of the prognostic risk model, with a statistically significant difference in overall survival between the low- and high-risk groups (PConclusionsA risk model based on seven glycolytic genes (INSR, FAM162A, GLCE, ADH5, G6PD, SDC3, HS2ST1) can effectively evaluate the prognosis of osteosarcoma, and in vitro experiments also confirmed the important role of INSR in promoting OS migration.</p

Additional file 1 of The global burden and risk factors of cardiovascular diseases in adolescent and young adults, 1990–2019

Supplementary Material

Table_2_Identification of risk model based on glycolysis-related genes in the metastasis of osteosarcoma.xls

BackgroundGlycolytic metabolic pathway has been confirmed to play a vital role in the proliferation, survival, and migration of malignant tumors, but the relationship between glycolytic pathway-related genes and osteosarcoma (OS) metastasis and prognosis remain unclear.MethodsWe performed Gene set enrichment analysis (GSEA) on the osteosarcoma dataset in the TARGET database to explore differences in glycolysis-related pathway gene sets between primary osteosarcoma (without other organ metastases) and metastatic osteosarcoma patient samples, as well as glycolytic pathway gene set gene difference analysis. Then, we extracted OS data from the TCGA database and used Cox proportional risk regression to identify prognosis-associated glycolytic genes to establish a risk model. Further, the validity of the risk model was confirmed using the GEO database dataset. Finally, we further screened OS metastasis-related genes based on machine learning. We selected the genes with the highest clinical metastasis-related importance as representative genes for in vitro experimental validation.ResultsUsing the TARGET osteosarcoma dataset, we identified 5 glycolysis-related pathway gene sets that were significantly different in metastatic and non-metastatic osteosarcoma patient samples and identified 29 prognostically relevant genes. Next, we used multivariate Cox regression to determine the inclusion of 13 genes (ADH5, DCN, G6PD, etc.) to construct a prognostic risk score model to predict 1- (AUC=0.959), 3- (AUC=0.899), and 5-year (AUC=0.895) survival under the curve. Ultimately, the KM curves pooled into the datasets GSE21257 and GSE39055 also confirmed the validity of the prognostic risk model, with a statistically significant difference in overall survival between the low- and high-risk groups (PConclusionsA risk model based on seven glycolytic genes (INSR, FAM162A, GLCE, ADH5, G6PD, SDC3, HS2ST1) can effectively evaluate the prognosis of osteosarcoma, and in vitro experiments also confirmed the important role of INSR in promoting OS migration.</p

Correlation analysis of SD and yield-related traits.

Correlation analysis of SD and yield-related traits.</p

Distribution of Euclidean Distance (ED) association values on chromosomes.

The abscissa is the chromosome name, the colored dots represent the ED values of each SNP site, the black line represents the fitted ED value, and the red dotted line represents the significant association threshold. The higher the ED value is, the greater association of the point, and the stronger the effect.</p

GO classification of differentially expressed genes in buds.

The x-axis represents GO term, and the y-axis represents Gene ratio. Red represents biological processes, Green represents cellular components, Blue represents molecular functions. (DOC)</p

Comparison of DEGs in different tissues.

The x-axis represents different tissues, and the y-axis represents the number of differentially expressed genes. Black indicates down-regulated expressed genes, white indicates up-regulated expressed genes. LMP Bud: bud mixed pool with extremely low SD; HMP Bud: bud mixed pool with extremely high SD, P1:No.935, P2: No.3641. (DOC)</p

Candidate interval gene analysis.

Seed density per silique (SD) is an important agricultural trait and plays an important role in the yield performance of Brassica napus L. (B. napus). In this study, a genetic linkage map was constructed using a double haploid (DH) population with 213 lines derived from a cross between a low SD line No. 935 and a high SD line No. 3641, and a total of 1,098,259 SNP (single-nucleotide polymorphisms) markers and 2,102 bins were mapped to 19 linkage groups. Twenty-eight QTLs for SD were detected on chromosomes A02, A04, A05, A09, C02, C03, C06, and C09 of B. napus, of which eight QTLs were on chromosome A09 and explained 5.89%-13.24% of the phenotypic variation. Furthermore, a consistent QTL for SD on chromosome A09, cqSD-A9a, was identified in four environments by QTL meta-analysis, explaining 10.68% of the phenotypic variation. In addition, four pairs of epistatic interactions were detected in the DH population via QTL epistasis analysis, indicating that SD is controlled not only by additive effects but also by epistatic effects that play an important role in spring B. napus., but with little environmental effect. Moreover, 18 closely linked SSR markers for cqSD-A9a were developed, as a result, it was mapped to a 1.86Mb (7.80–9.66 Mb) region on chromosome A09. A total of 13 differentially expressed genes (DEGs) were screened in the candidate interval by RNA-seq analysis, which were differentially expressed in buds, leaves and siliques both between and siliques both between two parents and two pools of extremely high-SD and low-SD lines in the DH population. Three of 13 DEGs were possible candidate genes that might control SD: BnaA09g14070D, which encodes a callose synthase that plays an important role in development and stress responses; BnaA09g14800D, a plant synaptic protein that encodes a membrane component; and BnaA09g18250D, which is responsible for DNA binding, transcriptional regulation, and sequence-specific DNA binding and is involved in the response to growth hormone stimulation. Overall, these results lay a foundation for fine mapping and gene cloning for SD in B. napus.</div

Genetic linkage map information.

Seed density per silique (SD) is an important agricultural trait and plays an important role in the yield performance of Brassica napus L. (B. napus). In this study, a genetic linkage map was constructed using a double haploid (DH) population with 213 lines derived from a cross between a low SD line No. 935 and a high SD line No. 3641, and a total of 1,098,259 SNP (single-nucleotide polymorphisms) markers and 2,102 bins were mapped to 19 linkage groups. Twenty-eight QTLs for SD were detected on chromosomes A02, A04, A05, A09, C02, C03, C06, and C09 of B. napus, of which eight QTLs were on chromosome A09 and explained 5.89%-13.24% of the phenotypic variation. Furthermore, a consistent QTL for SD on chromosome A09, cqSD-A9a, was identified in four environments by QTL meta-analysis, explaining 10.68% of the phenotypic variation. In addition, four pairs of epistatic interactions were detected in the DH population via QTL epistasis analysis, indicating that SD is controlled not only by additive effects but also by epistatic effects that play an important role in spring B. napus., but with little environmental effect. Moreover, 18 closely linked SSR markers for cqSD-A9a were developed, as a result, it was mapped to a 1.86Mb (7.80–9.66 Mb) region on chromosome A09. A total of 13 differentially expressed genes (DEGs) were screened in the candidate interval by RNA-seq analysis, which were differentially expressed in buds, leaves and siliques both between and siliques both between two parents and two pools of extremely high-SD and low-SD lines in the DH population. Three of 13 DEGs were possible candidate genes that might control SD: BnaA09g14070D, which encodes a callose synthase that plays an important role in development and stress responses; BnaA09g14800D, a plant synaptic protein that encodes a membrane component; and BnaA09g18250D, which is responsible for DNA binding, transcriptional regulation, and sequence-specific DNA binding and is involved in the response to growth hormone stimulation. Overall, these results lay a foundation for fine mapping and gene cloning for SD in B. napus.</div