Additional file 1: of MicroRNA transcriptome analysis of porcine vital organ responses to immunosuppressive porcine cytomegalovirus infection

Figure S1. Length distribution of total sRNAs. Figure S2. Pie charts of sRNA percentages. Figure S3. Top 10 miRNA reads. Figure S4. GO functional enrichment annotations for the target genes of DE miRNAs. Table S1. Overview of miRNA high-throughput sequencing data. Table S2. Expression profiles of miRNAs. Table S3. Differentially expressed miRNAs. Table S4. Target prediction for DE miRNAs. Table S5. Integrated expression analysis of DE miRNAs and their target mRNA. Table S6. DE miRNAs in vivo and vitro under viral infection. Table S7. Primers used for stem-loop qRT-PCR. (DOCX 2983 kb

Table_2_Identification and experimental validation of a tumor-infiltrating lymphocytes–related long noncoding RNA signature for prognosis of clear cell renal cell carcinoma.xlsx

Clear cell renal cell carcinoma (ccRCC) is a common aggressive malignant tumor of the urinary system. Given the heterogeneity of the tumor microenvironment, immunotherapy may not fully exert its role in the treatment of advanced patients. Long noncoding RNA (lncRNA) has been reported to be critically associated with the differentiation and maturation of tumor-infiltrating lymphocytes (TILs), which work against tumor cells. In this study, we identified 10 TIL-related lncRNAs (AL590094.1, LINC02027, LINC00460, AC147651.1, AC026401.3, LINC00944, LINC01615, AP000439.2, AL162586.1, and AC084876.1) by Pearson correlation, univariate Cox regression, Lasso regression, and multivariate Cox regression based on The Cancer Genome Atlas (TCGA) database. A risk score model was established based on these lncRNAs. Next, a nomogram was constructed to predict the overall survival. By employing differentially expressed genes (DEGs) between groups with high and low risk scores, gene ontology (GO) enrichment analysis was performed to identify the major biological processes (BP) related to immune DEGs. We analyzed the mutation data of the groups and demonstrated that SETD2 and BAP1 had the highest mutation frequency in the high-risk group. The “CIBERSORT” R package was used to detect the abundance of TILs in the groups. The expression of lymphocyte markers was compared. We also determined the expression of two lncRNAs (AC084876.1 and AC026401.3) and their relationship with lymphocyte markers in the kidney tissue of ccRCC patients and showed that there was a positive correlation between AC084876.1 and FoxP3. Proliferation, migration, and invasion of AC084876.1-downregulated ccRCC cell lines were inhibited, and the expression of PD-L1 and TGF-β secretion decreased. To our knowledge, this is the first bioinformatics study to establish a prognostic model for ccRCC using TIL-related lncRNAs. These lncRNAs were associated with T-cell activities and may serve as biomarkers of disease prognosis.</p

Table_5_Identification and experimental validation of a tumor-infiltrating lymphocytes–related long noncoding RNA signature for prognosis of clear cell renal cell carcinoma.xlsx

Clear cell renal cell carcinoma (ccRCC) is a common aggressive malignant tumor of the urinary system. Given the heterogeneity of the tumor microenvironment, immunotherapy may not fully exert its role in the treatment of advanced patients. Long noncoding RNA (lncRNA) has been reported to be critically associated with the differentiation and maturation of tumor-infiltrating lymphocytes (TILs), which work against tumor cells. In this study, we identified 10 TIL-related lncRNAs (AL590094.1, LINC02027, LINC00460, AC147651.1, AC026401.3, LINC00944, LINC01615, AP000439.2, AL162586.1, and AC084876.1) by Pearson correlation, univariate Cox regression, Lasso regression, and multivariate Cox regression based on The Cancer Genome Atlas (TCGA) database. A risk score model was established based on these lncRNAs. Next, a nomogram was constructed to predict the overall survival. By employing differentially expressed genes (DEGs) between groups with high and low risk scores, gene ontology (GO) enrichment analysis was performed to identify the major biological processes (BP) related to immune DEGs. We analyzed the mutation data of the groups and demonstrated that SETD2 and BAP1 had the highest mutation frequency in the high-risk group. The “CIBERSORT” R package was used to detect the abundance of TILs in the groups. The expression of lymphocyte markers was compared. We also determined the expression of two lncRNAs (AC084876.1 and AC026401.3) and their relationship with lymphocyte markers in the kidney tissue of ccRCC patients and showed that there was a positive correlation between AC084876.1 and FoxP3. Proliferation, migration, and invasion of AC084876.1-downregulated ccRCC cell lines were inhibited, and the expression of PD-L1 and TGF-β secretion decreased. To our knowledge, this is the first bioinformatics study to establish a prognostic model for ccRCC using TIL-related lncRNAs. These lncRNAs were associated with T-cell activities and may serve as biomarkers of disease prognosis.</p

Table_6_Identification and experimental validation of a tumor-infiltrating lymphocytes–related long noncoding RNA signature for prognosis of clear cell renal cell carcinoma.xlsx

Clear cell renal cell carcinoma (ccRCC) is a common aggressive malignant tumor of the urinary system. Given the heterogeneity of the tumor microenvironment, immunotherapy may not fully exert its role in the treatment of advanced patients. Long noncoding RNA (lncRNA) has been reported to be critically associated with the differentiation and maturation of tumor-infiltrating lymphocytes (TILs), which work against tumor cells. In this study, we identified 10 TIL-related lncRNAs (AL590094.1, LINC02027, LINC00460, AC147651.1, AC026401.3, LINC00944, LINC01615, AP000439.2, AL162586.1, and AC084876.1) by Pearson correlation, univariate Cox regression, Lasso regression, and multivariate Cox regression based on The Cancer Genome Atlas (TCGA) database. A risk score model was established based on these lncRNAs. Next, a nomogram was constructed to predict the overall survival. By employing differentially expressed genes (DEGs) between groups with high and low risk scores, gene ontology (GO) enrichment analysis was performed to identify the major biological processes (BP) related to immune DEGs. We analyzed the mutation data of the groups and demonstrated that SETD2 and BAP1 had the highest mutation frequency in the high-risk group. The “CIBERSORT” R package was used to detect the abundance of TILs in the groups. The expression of lymphocyte markers was compared. We also determined the expression of two lncRNAs (AC084876.1 and AC026401.3) and their relationship with lymphocyte markers in the kidney tissue of ccRCC patients and showed that there was a positive correlation between AC084876.1 and FoxP3. Proliferation, migration, and invasion of AC084876.1-downregulated ccRCC cell lines were inhibited, and the expression of PD-L1 and TGF-β secretion decreased. To our knowledge, this is the first bioinformatics study to establish a prognostic model for ccRCC using TIL-related lncRNAs. These lncRNAs were associated with T-cell activities and may serve as biomarkers of disease prognosis.</p