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

Table_1_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

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

Table_3_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

Table_1_NcRNA-regulated CAPZA1 associated with prognostic and immunological effects across lung adenocarcinoma.docx

Recent discoveries have suggested that the F-actin capping protein α1 subunit (CAPZA1) in various human tumors could play a significantly important role in regulating cell proliferation, metastasis, and epithelial–mesenchymal transition. However, the immune-regulating role of CAPZA1 in the initiation and development of lung adenocarcinoma (LUAD) remains unclear. In our research, we first found that CAPZA1 serves as an oncogene in pan-cancers from the TCGA data and higher CAPZA1 expression process unfavorably prognostic value in LUAD based on starBase database, PrognoScan, and LOGpc database. Then, in our analyses, lncRNAs AC026356.1 in LUAD acted as a competitive endogenous RNA (ceRNA) of miR-30d-5p, which might be the possible regulatory miRNA of CAPZA1 based on the starBase database. Finally, we confirmed that CAPZA1 expression had a tightly positive correlation with immune infiltration cells, immune infiltration markers, TMB, MSI, immune score, stromal score, and immune checkpoints, indicating that CAPZA1 was a markedly reliable therapeutic target for immunological antitumor strategies. In conclusion, our investigations revealed that CAPZA1 might function as an immune-associated biomarker in the development and treatment of LUAD, thereby acting as a promising prognostic and therapeutic target against LUAD.</p

Table_2_NcRNA-regulated CAPZA1 associated with prognostic and immunological effects across lung adenocarcinoma.docx

Recent discoveries have suggested that the F-actin capping protein α1 subunit (CAPZA1) in various human tumors could play a significantly important role in regulating cell proliferation, metastasis, and epithelial–mesenchymal transition. However, the immune-regulating role of CAPZA1 in the initiation and development of lung adenocarcinoma (LUAD) remains unclear. In our research, we first found that CAPZA1 serves as an oncogene in pan-cancers from the TCGA data and higher CAPZA1 expression process unfavorably prognostic value in LUAD based on starBase database, PrognoScan, and LOGpc database. Then, in our analyses, lncRNAs AC026356.1 in LUAD acted as a competitive endogenous RNA (ceRNA) of miR-30d-5p, which might be the possible regulatory miRNA of CAPZA1 based on the starBase database. Finally, we confirmed that CAPZA1 expression had a tightly positive correlation with immune infiltration cells, immune infiltration markers, TMB, MSI, immune score, stromal score, and immune checkpoints, indicating that CAPZA1 was a markedly reliable therapeutic target for immunological antitumor strategies. In conclusion, our investigations revealed that CAPZA1 might function as an immune-associated biomarker in the development and treatment of LUAD, thereby acting as a promising prognostic and therapeutic target against LUAD.</p

Image_2_NcRNA-regulated CAPZA1 associated with prognostic and immunological effects across lung adenocarcinoma.tif

Recent discoveries have suggested that the F-actin capping protein α1 subunit (CAPZA1) in various human tumors could play a significantly important role in regulating cell proliferation, metastasis, and epithelial–mesenchymal transition. However, the immune-regulating role of CAPZA1 in the initiation and development of lung adenocarcinoma (LUAD) remains unclear. In our research, we first found that CAPZA1 serves as an oncogene in pan-cancers from the TCGA data and higher CAPZA1 expression process unfavorably prognostic value in LUAD based on starBase database, PrognoScan, and LOGpc database. Then, in our analyses, lncRNAs AC026356.1 in LUAD acted as a competitive endogenous RNA (ceRNA) of miR-30d-5p, which might be the possible regulatory miRNA of CAPZA1 based on the starBase database. Finally, we confirmed that CAPZA1 expression had a tightly positive correlation with immune infiltration cells, immune infiltration markers, TMB, MSI, immune score, stromal score, and immune checkpoints, indicating that CAPZA1 was a markedly reliable therapeutic target for immunological antitumor strategies. In conclusion, our investigations revealed that CAPZA1 might function as an immune-associated biomarker in the development and treatment of LUAD, thereby acting as a promising prognostic and therapeutic target against LUAD.</p

Image_1_NcRNA-regulated CAPZA1 associated with prognostic and immunological effects across lung adenocarcinoma.tif

Recent discoveries have suggested that the F-actin capping protein α1 subunit (CAPZA1) in various human tumors could play a significantly important role in regulating cell proliferation, metastasis, and epithelial–mesenchymal transition. However, the immune-regulating role of CAPZA1 in the initiation and development of lung adenocarcinoma (LUAD) remains unclear. In our research, we first found that CAPZA1 serves as an oncogene in pan-cancers from the TCGA data and higher CAPZA1 expression process unfavorably prognostic value in LUAD based on starBase database, PrognoScan, and LOGpc database. Then, in our analyses, lncRNAs AC026356.1 in LUAD acted as a competitive endogenous RNA (ceRNA) of miR-30d-5p, which might be the possible regulatory miRNA of CAPZA1 based on the starBase database. Finally, we confirmed that CAPZA1 expression had a tightly positive correlation with immune infiltration cells, immune infiltration markers, TMB, MSI, immune score, stromal score, and immune checkpoints, indicating that CAPZA1 was a markedly reliable therapeutic target for immunological antitumor strategies. In conclusion, our investigations revealed that CAPZA1 might function as an immune-associated biomarker in the development and treatment of LUAD, thereby acting as a promising prognostic and therapeutic target against LUAD.</p

Image1_Integrative analyses of prognosis, tumor immunity, and ceRNA network of the ferroptosis-associated gene FANCD2 in hepatocellular carcinoma.tif

Extensive evidence has revealed that ferroptosis plays a vital role in HCC development and progression. Fanconi anemia complementation group D2 (FANCD2) has been reported to serve as a ferroptosis-associated gene and has a close relationship with tumorigenesis and drug resistance. However, the impact of the FANCD2-related immune response and its mechanisms in HCC remains incompletely understood. In the current research, we evaluated the prognostic significance and immune-associated mechanism of FANCD2 based on multiple bioinformatics methods and databases. The results demonstrated that FANCD2 was commonly upregulated in 15/33 tumors, and only the high expression of FANCD2 in HCC was closely correlated with worse clinical outcomes by OS and DFS analyses. Moreover, ncRNAs, including two major types, miRNAs and lncRNAs, were closely involved in mediating FANCD2 upregulation in HCC and were established in a ceRNA network by performing various in silico analyses. The DUXAP8-miR-29c-FANCD2 and LINC00511-miR-29c-FANCD2 axes were identified as the most likely ncRNA-associated upstream regulatory axis of FANCD2 in HCC. Finally, FANCD2 expression was confirmed to be positively related to HCC immune cell infiltration, immune checkpoints, and IPS analysis, and GSEA results also revealed that this ferroptosis-associated gene was primarily involved in cancer-associated pathways in HCC. In conclusion, our investigations indicate that ncRNA-related modulatory overexpression of FANCD2 might act as a promising prognostic and immunotherapeutic target against HCC.</p

Image2_Integrative analyses of prognosis, tumor immunity, and ceRNA network of the ferroptosis-associated gene FANCD2 in hepatocellular carcinoma.tif

Extensive evidence has revealed that ferroptosis plays a vital role in HCC development and progression. Fanconi anemia complementation group D2 (FANCD2) has been reported to serve as a ferroptosis-associated gene and has a close relationship with tumorigenesis and drug resistance. However, the impact of the FANCD2-related immune response and its mechanisms in HCC remains incompletely understood. In the current research, we evaluated the prognostic significance and immune-associated mechanism of FANCD2 based on multiple bioinformatics methods and databases. The results demonstrated that FANCD2 was commonly upregulated in 15/33 tumors, and only the high expression of FANCD2 in HCC was closely correlated with worse clinical outcomes by OS and DFS analyses. Moreover, ncRNAs, including two major types, miRNAs and lncRNAs, were closely involved in mediating FANCD2 upregulation in HCC and were established in a ceRNA network by performing various in silico analyses. The DUXAP8-miR-29c-FANCD2 and LINC00511-miR-29c-FANCD2 axes were identified as the most likely ncRNA-associated upstream regulatory axis of FANCD2 in HCC. Finally, FANCD2 expression was confirmed to be positively related to HCC immune cell infiltration, immune checkpoints, and IPS analysis, and GSEA results also revealed that this ferroptosis-associated gene was primarily involved in cancer-associated pathways in HCC. In conclusion, our investigations indicate that ncRNA-related modulatory overexpression of FANCD2 might act as a promising prognostic and immunotherapeutic target against HCC.</p