sj-tif-3-jbm-10.1177_03936155221140421 - Supplemental material for Diagnostic and prognostic values of MMP-9 expression in ovarian cancer: A study based on bioinformatics analysis and meta-analysis

Supplemental material, sj-tif-3-jbm-10.1177_03936155221140421 for Diagnostic and prognostic values of MMP-9 expression in ovarian cancer: A study based on bioinformatics analysis and meta-analysis by Changyu Liu, Ying Shen and Qiyan Tan in The International Journal of Biological Markers</p

sj-doc-5-jbm-10.1177_03936155221140421 - Supplemental material for Diagnostic and prognostic values of MMP-9 expression in ovarian cancer: A study based on bioinformatics analysis and meta-analysis

Supplemental material, sj-doc-5-jbm-10.1177_03936155221140421 for Diagnostic and prognostic values of MMP-9 expression in ovarian cancer: A study based on bioinformatics analysis and meta-analysis by Changyu Liu, Ying Shen and Qiyan Tan in The International Journal of Biological Markers</p

Table_2_Identification of a miRNA–mRNA regulatory network for post-stroke depression: a machine-learning approach.XLS

ObjectiveThe study aimed to explore the miRNA and mRNA biomarkers in post-stroke depression (PSD) and to develop a miRNA–mRNA regulatory network to reveal its potential pathogenesis.MethodsThe transcriptomic expression profile was obtained from the GEO database using the accession numbers GSE117064 (miRNAs, stroke vs. control) and GSE76826 [mRNAs, late-onset major depressive disorder (MDD) vs. control]. Differentially expressed miRNAs (DE-miRNAs) were identified in blood samples collected from stroke patients vs. control using the Linear Models for Microarray Data (LIMMA) package, while the weighted correlation network analysis (WGCNA) revealed co-expressed gene modules correlated with the subject group. The intersection between DE-miRNAs and miRNAs identified by WGCNA was defined as stroke-related miRNAs, whose target mRNAs were stroke-related genes with the prediction based on three databases (miRDB, miRTarBase, and TargetScan). Using the GSE76826 dataset, the differentially expressed genes (DEGs) were identified. Overlapped DEGs between stroke-related genes and DEGs in late-onset MDD were retrieved, and these were potential mRNA biomarkers in PSD. With the overlapped DEGs, three machine-learning methods were employed to identify gene signatures for PSD, which were established with the intersection of gene sets identified by each algorithm. Based on the gene signatures, the upstream miRNAs were predicted, and a miRNA–mRNA network was constructed.ResultsUsing the GSE117064 dataset, we retrieved a total of 667 DE-miRNAs, which included 420 upregulated and 247 downregulated ones. Meanwhile, WGCNA identified two modules (blue and brown) that were significantly correlated with the subject group. A total of 117 stroke-related miRNAs were identified with the intersection of DE-miRNAs and WGCNA-related ones. Based on the miRNA-mRNA databases, we identified a list of 2,387 stroke-related genes, among which 99 DEGs in MDD were also embedded. Based on the 99 overlapped DEGs, we identified three gene signatures (SPATA2, ZNF208, and YTHDC1) using three machine-learning classifiers. Predictions of the three mRNAs highlight four miRNAs as follows: miR-6883-5p, miR-6873-3p, miR-4776-3p, and miR-6738-3p. Subsequently, a miRNA–mRNA network was developed.ConclusionThe study highlighted gene signatures for PSD with three genes (SPATA2, ZNF208, and YTHDC1) and four upstream miRNAs (miR-6883-5p, miR-6873-3p, miR-4776-3p, and miR-6738-3p). These biomarkers could further our understanding of the pathogenesis of PSD.</p

sj-tif-2-jbm-10.1177_03936155221140421 - Supplemental material for Diagnostic and prognostic values of MMP-9 expression in ovarian cancer: A study based on bioinformatics analysis and meta-analysis

Supplemental material, sj-tif-2-jbm-10.1177_03936155221140421 for Diagnostic and prognostic values of MMP-9 expression in ovarian cancer: A study based on bioinformatics analysis and meta-analysis by Changyu Liu, Ying Shen and Qiyan Tan in The International Journal of Biological Markers</p

sj-doc-7-jbm-10.1177_03936155221140421 - Supplemental material for Diagnostic and prognostic values of MMP-9 expression in ovarian cancer: A study based on bioinformatics analysis and meta-analysis

Supplemental material, sj-doc-7-jbm-10.1177_03936155221140421 for Diagnostic and prognostic values of MMP-9 expression in ovarian cancer: A study based on bioinformatics analysis and meta-analysis by Changyu Liu, Ying Shen and Qiyan Tan in The International Journal of Biological Markers</p

sj-tif-1-jbm-10.1177_03936155221140421 - Supplemental material for Diagnostic and prognostic values of MMP-9 expression in ovarian cancer: A study based on bioinformatics analysis and meta-analysis

Supplemental material, sj-tif-1-jbm-10.1177_03936155221140421 for Diagnostic and prognostic values of MMP-9 expression in ovarian cancer: A study based on bioinformatics analysis and meta-analysis by Changyu Liu, Ying Shen and Qiyan Tan in The International Journal of Biological Markers</p

sj-doc-4-jbm-10.1177_03936155221140421 - Supplemental material for Diagnostic and prognostic values of MMP-9 expression in ovarian cancer: A study based on bioinformatics analysis and meta-analysis

Supplemental material, sj-doc-4-jbm-10.1177_03936155221140421 for Diagnostic and prognostic values of MMP-9 expression in ovarian cancer: A study based on bioinformatics analysis and meta-analysis by Changyu Liu, Ying Shen and Qiyan Tan in The International Journal of Biological Markers</p

Distribution Features of Skeletal Metastases: A Comparative Study between Pulmonary and Prostate Cancers

<div><p>Bone scintigraphies are widely applied for detecting bone metastases. The aim of this study was to investigate distribution features of bone metastases in pulmonary and prostate cancers. Bone scintigraphies were performed in 460 patients with pulmonary cancer and 144 patients with prostate cancer. Patients were divided into three groups according to the total number of bone metastases: few bone metastases, moderate bone metastases, and extensive bone metastases. We compared the distribution of bone metastases in the two cancers, and analyzed the relationship between the distribution of metastatic lesions and their metastatic patterns. A total of 2279 and 2000 lesions of bone metastases were detected in 258 patients with pulmonary cancer and 102 patients with prostate cancer, respectively. In patients with few bone metastases, the distributions of metastatic lesions in the vertebrae (χ² = 16.0, P = 0.000) and thoracic bones (χ² = 20.7, P = 0.002) were significantly different between pulmonary and prostate cancers. In cases with moderate bone metastases, the distributions in the vertebrae (χ² = 6.6, P = 0.010), pelvis (χ² = 15.1 P = 0.000), and thoracic bones (χ² = 38.8, P = 0.000) were also significantly different between the two cancers. However, in patients with extensive bone metastases, the distributions were very similar. As the total number of bone metastases increased, their distribution in pulmonary cancer did not noticeably change, but the distribution in the vertebrae and thoracic bones of prostate cancer patients significantly changed. Accordingly, the distribution characteristics of bone metastases differed in pulmonary and prostate cancers, mainly in the early stages of metastasis.</p></div

sj-doc-6-jbm-10.1177_03936155221140421 - Supplemental material for Diagnostic and prognostic values of MMP-9 expression in ovarian cancer: A study based on bioinformatics analysis and meta-analysis

Supplemental material, sj-doc-6-jbm-10.1177_03936155221140421 for Diagnostic and prognostic values of MMP-9 expression in ovarian cancer: A study based on bioinformatics analysis and meta-analysis by Changyu Liu, Ying Shen and Qiyan Tan in The International Journal of Biological Markers</p

Table_4_Identification of a miRNA–mRNA regulatory network for post-stroke depression: a machine-learning approach.XLS

ObjectiveThe study aimed to explore the miRNA and mRNA biomarkers in post-stroke depression (PSD) and to develop a miRNA–mRNA regulatory network to reveal its potential pathogenesis.MethodsThe transcriptomic expression profile was obtained from the GEO database using the accession numbers GSE117064 (miRNAs, stroke vs. control) and GSE76826 [mRNAs, late-onset major depressive disorder (MDD) vs. control]. Differentially expressed miRNAs (DE-miRNAs) were identified in blood samples collected from stroke patients vs. control using the Linear Models for Microarray Data (LIMMA) package, while the weighted correlation network analysis (WGCNA) revealed co-expressed gene modules correlated with the subject group. The intersection between DE-miRNAs and miRNAs identified by WGCNA was defined as stroke-related miRNAs, whose target mRNAs were stroke-related genes with the prediction based on three databases (miRDB, miRTarBase, and TargetScan). Using the GSE76826 dataset, the differentially expressed genes (DEGs) were identified. Overlapped DEGs between stroke-related genes and DEGs in late-onset MDD were retrieved, and these were potential mRNA biomarkers in PSD. With the overlapped DEGs, three machine-learning methods were employed to identify gene signatures for PSD, which were established with the intersection of gene sets identified by each algorithm. Based on the gene signatures, the upstream miRNAs were predicted, and a miRNA–mRNA network was constructed.ResultsUsing the GSE117064 dataset, we retrieved a total of 667 DE-miRNAs, which included 420 upregulated and 247 downregulated ones. Meanwhile, WGCNA identified two modules (blue and brown) that were significantly correlated with the subject group. A total of 117 stroke-related miRNAs were identified with the intersection of DE-miRNAs and WGCNA-related ones. Based on the miRNA-mRNA databases, we identified a list of 2,387 stroke-related genes, among which 99 DEGs in MDD were also embedded. Based on the 99 overlapped DEGs, we identified three gene signatures (SPATA2, ZNF208, and YTHDC1) using three machine-learning classifiers. Predictions of the three mRNAs highlight four miRNAs as follows: miR-6883-5p, miR-6873-3p, miR-4776-3p, and miR-6738-3p. Subsequently, a miRNA–mRNA network was developed.ConclusionThe study highlighted gene signatures for PSD with three genes (SPATA2, ZNF208, and YTHDC1) and four upstream miRNAs (miR-6883-5p, miR-6873-3p, miR-4776-3p, and miR-6738-3p). These biomarkers could further our understanding of the pathogenesis of PSD.</p