RNA-seq analysis of A549 cells based on DSTY knockdown
Ontology highlight
ABSTRACT: Purpose: Analysis of the regulatory network involved in DSTY in lung cancer cells. Methods: mRNA profiles of A549 knockdown cell group and A549 NC group were generated by deep sequencing, using Illumina Novaseq platform. Index of the reference genome was built using Hisat2 v2.0.5 and paired-end clean reads were aligned to the reference genome using Hisat2 v2.0.5. featureCounts v1.5.0-p3 was used to count the reads numbers mapped to each gene. And then FPKM of each gene was calculated based on the length of the gene and reads count mapped to this gene. Differential expression analysis of two conditions/groups (two biological replicates per condition) was performed using the DESeq2 R package (1.16.1). The resulting P-values were adjusted using the Benjamini and Hochberg’s approach for controlling the false discovery rate .Genes with an adjusted P-value <0.05 found by DESeq2 were assigned as differentially expressed. Clusterprofiler software was used to perform GO function enrichment analysis and KEGG pathway enrichment analysis of the differential gene sets. Results: We identified 2555 differential genes, of which 1444 were up-regulated and 1111 were down-regulated in A549 sh1 VS NC. In GO enrichment analysis,the biological process is mainly in the cell ions homeostasis and the molecular function is mainly in inflammation response,chemotaxis,innate immune response, and extracellular matrix. KEGG analysis shows that differential genes are enriched in cytokine-cytokine receptor interaction ,JAK-STAT signaling pathway and PI3K-Akt signaling pathways.We identified 2600 differential genes, of which 1523 were up-regulated and 1077 were down-regulated in A549 sh4 VS NC. In GO enrichment analysis,the biological process is mainly in the cell ions homeostasis and the molecular function is mainly in chemokine receptor binding,nic hydroxy compound metabolic process,alcohol metabolic process, and extracellular matrix. KEGG analysis shows that differential genes are enriched in cytokine-cytokine receptor interaction ,glutathione metabolism and chemical carcinogenesis. Conclusion: Based on RNA-seq analysis, the regulatory network involved in DSTY in Lung cancer .
Project description:Purpose: Analysis of the regulatory network involved in SIK2 in breast cancer cells activated by the Wnt/β-catenin pathway. Methods: mRNA profiles of BT549-shSIK2 and BT549-Scrambled group that Wnt3a treated for 2 hours were generated by deep sequencing, using Illumina Novaseq platform. Index of the reference genome was built using Hisat2 v2.0.5 and paired-end clean reads were aligned to the reference genome using Hisat2 v2.0.5. featureCounts v1.5.0-p3 was used to count the reads numbers mapped to each gene. And then FPKM of each gene was calculated based on the length of the gene and reads count mapped to this gene. Differential expression analysis of two conditions/groups (two biological replicates per condition) was performed using the DESeq2 R package (1.16.1). The resulting P-values were adjusted using the Benjamini and Hochberg’s approach for controlling the false discovery rate . Genes with an adjusted P-value <0.05 found by DESeq2 were assigned as differentially expressed. Clusterprofiler software was used to perform GO function enrichment analysis and KEGG pathway enrichment analysis of the differential gene sets. Results: We identified 1709 differential genes, of which 1147 were up-regulated and 562 were down-regulated. In GO enrichment analysis, the biological process is mainly in the cell ions homeostasis, the cell components are mainly enriched on the cell surface, and the molecular function is mainly in the transmembrane signaling receptor activity. KEGG analysis shows that differential genes are enriched in Axon guidance and Nicotinate and nicotinamide metabolism pathways. Conclusion: Based on RNA-seq analysis, the regulatory network involved in SIK2 in breast cancer response to Wnt signaling is depicted.
Project description:Purpose: Analysis of the regulatory network involved in PPDPF in colon cancer cells. Methods: mRNA profiles of HT8-cas1 and HT8 group were generated by deep sequencing, using Illumina Novaseq platform. Index of the reference genome was built using Hisat2 v2.0.5 and paired-end clean reads were aligned to the reference genome using Hisat2 v2.0.5. featureCounts v1.5.0-p3 was used to count the reads numbers mapped to each gene. And then FPKM of each gene was calculated based on the length of the gene and reads count mapped to this gene. Differential expression analysis of two conditions/groups (two biological replicates per condition) was performed using the DESeq2 R package (1.16.1). The resulting P-values were adjusted using the Benjamini and Hochberg’s approach for controlling the false discovery rate .Genes with an adjusted P-value <0.05 found by DESeq2 were assigned as differentially expressed. Clusterprofiler software was used to perform GO function enrichment analysis and KEGG pathway enrichment analysis of the differential gene sets. Results: We identified 3233 differential genes, of which 1877 were up-regulated and 1356 were down-regulated. In GO enrichment analysis,the biological process is mainly in the cell ions homeostasis and the molecular function is mainly in viral life cycle,response to virus,neutrophil activation, and the transmembrane signaling receptor activity. KEGG analysis shows that differential genes are enriched in Epstein-Barr virus infection ,Glutathione metabolism and Proteasome pathways. Conclusion: Based on RNA-seq analysis, the regulatory network involved in PPDPF in colon cancer .
Project description:Purpose: Analysis of the regulatory network involved in PANK1 in liver cancer cells . Methods: mRNA profiles of Huh7-shPANK1 and Huh7-shNC group were generated by deep sequencing, using Illumina Novaseq platform. Index of the reference genome was built using Hisat2 v2.0.5 and paired-end clean reads were aligned to the reference genome using Hisat2 v2.0.5. featureCounts v1.5.0-p3 was used to count the reads numbers mapped to each gene. And then FPKM of each gene was calculated based on the length of the gene and reads count mapped to this gene. Differential expression analysis of two conditions/groups (two biological replicates per condition) was performed using the DESeq2 R package (1.16.1). The resulting P-values were adjusted using the Benjamini and Hochberg’s approach for controlling the false discovery rate . Genes with an adjusted P-value <0.05 found by DESeq2 were assigned as differentially expressed. Clusterprofiler software was used to perform GO function enrichment analysis and KEGG pathway enrichment analysis of the differential gene sets. Results: We identified 681 differential genes, of which 441 were up-regulated and 240 were down-regulated. In GO enrichment analysis, the biological process is mainly in the cell ions homeostasis and the molecular function is mainly in the defense response to virus、defense response to other organism and receptor ligand activity. KEGG analysis shows that differential genes are enriched in Systemic lupus erythematosus , Alcoholism and influenza A pathways. Conclusion: Based on RNA-seq analysis, the regulatory network involved in PANK1 in liver cancer is depicted.
Project description:Purpose: Analysis of the regulatory network involved in NME7 in liver cancer cells . Methods: mRNA profiles of Huh7-shNME7 and Huh7-shCtrl group were generated by deep sequencing, using Illumina Novaseq platform. Index of the reference genome was built using Hisat2 v2.0.5 and paired-end clean reads were aligned to the reference genome using Hisat2 v2.0.5. featureCounts v1.5.0-p3 was used to count the reads numbers mapped to each gene. And then FPKM of each gene was calculated based on the length of the gene and reads count mapped to this gene. Differential expression analysis of two conditions/groups (two biological replicates per condition) was performed using the DESeq2 R package (1.16.1). The resulting P-values were adjusted using the Benjamini and Hochberg’s approach for controlling the false discovery rate . Genes with an adjusted P-value <0.05 found by DESeq2 were assigned as differentially expressed. Clusterprofiler software was used to perform GO function enrichment analysis and KEGG pathway enrichment analysis of the differential gene sets. Results: We identified 873 differential genes, of which 420 were up-regulated and 453 were down-regulated. In GO enrichment analysis, the biological process is mainly in purine nucleoside triphosphate metabolic process and oxidative phosphorylation process. KEGG analysis shows that differential genes are enriched in thermogenesis and aminoacyl-tRNA biosynthesis . Conclusion: Based on RNA-seq analysis, the regulatory network involved in NME7 in liver cancer is depicted.
Project description:Intervention type:DRUG. Intervention1:Huaier, Dose form:GRANULES, Route of administration:ORAL, intended dose regimen:20 to 60/day by either bulk or split for 3 months to extended term if necessary. Control intervention1:None.
Primary outcome(s): For mRNA libraries, focus on mRNA studies. Data analysis includes sequencing data processing and basic sequencing data quality control, prediction of new transcripts, differential expression analysis of genes. Gene Ontology (GO) and the KEGG pathway database are used for annotation and enrichment analysis of up-regulated genes and down-regulated genes.
For small RNA libraries, data analysis includes sequencing data process and sequencing data process QC, small RNA distribution across the genome, rRNA, tRNA, alignment with snRNA and snoRNA, construction of known miRNA expression pattern, prediction New miRNA and Study of their secondary structure Based on the expression pattern of miRNA, we perform not only GO / KEGG annotation and enrichment, but also different expression analysis.. Timepoint:RNA sequencing of 240 blood samples of 80 cases and its analysis, scheduled from June 30, 2022..
Project description:Intervention type:DRUG
Name of intervention:Huaier
Dose form / Japanese Medical Device Nomenclature:GRANULES
Route of administration / Site of application:ORAL
Dose per administration:20?
g
Dosing frequency / Frequency of use:OTHER, SPECIFY
20g? per day
Planned duration of intervention:3 months to extending if necessary
Intended dose regimen:20 to 60/day by either bulk or split for 3 months to extended term if necessary
detailes of teratment arms:hepatocellular carcinoma, breast cancer, colorectal cancer and related gastrointestinal cancers, urologic cancers including prostate cancer, pancreas cancer, and lung cancer, etc.
Comparative intervention name:None
Dose form / Japanese Medical Device Nomenclature:
Route of administration / Site of application:
Dose per administration:
Dosing frequency / Frequency of use:
Planned duration of intervention:
Intended dose regimen:
Primary outcome(s): For mRNA libraries, focus on mRNA studies. Data analysis includes sequencing data processing and basic sequencing data quality control, prediction of new transcripts, differential expression analysis of genes. Gene Ontology (GO) and the KEGG pathway database are used for annotation and enrichment analysis of up-regulated genes and down-regulated genes.
For small RNA libraries, data analysis includes sequencing data process and sequencing data process QC, small RNA distribution across the genome, rRNA, tRNA, alignment with snRNA and snoRNA, construction of known miRNA expression pattern, prediction New miRNA and Study of their secondary structure Based on the expression pattern of miRNA, we perform not only GO / KEGG annotation and enrichment, but also different expression analysis.
Study Design: Comparative test, None, No, open(masking not used), EXPLORATORY
Project description:Macrophages possess the hallmark feature of plasticity, allowing them to undergo a dynamic transition between M1 and M2 polarized phenotypes. The aim of the present study was to screen for differentially expressed genes (DEGs) that were associated with macrophage polarization. The transcription profiles of three M1 and three M2 samples were obtained using microarray analysis. Based on the threshold of fold-change >2.0 and a p-value < 0.05, we screened a total of 1,253 DEGs, of which 696 were upregulated and 557 downregulated in M1 macrophages compared to that in M2 macrophages. Gene Ontology (GO) function and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analyses were also performed. A gene-gene interaction network of the DEGs was constructed using the STRING database. GO annotation identified three categories, which included cellular component (CC), molecular function (MF), and biological process (BP), with 34 and 40 enrichment terms consisting of upregulated and downregulated DEGs, respectively. GO enrichment analysis of DEGs was mainly associated with protein binding, response to stimulus, cell differentiation, and regulation of biological process. KEGG enrichment identified 15 and four pathways involving upregulated and downregulated DEGs, respectively. Signaling pathway analysis showed that these DEGs were mainly involved in apoptosis, HIF-1 signaling pathway, innate immune system, TNF signaling pathway, cytokine-cytokine receptor interaction, and other signal transduction pathways. Interaction network analysis indicated that Tnf, Il6, Il1b, Socs3, Nos2, Hif1a, and other genes may play key roles in macrophage polarization. This study provides new insights into the role of genes in macrophage differentiation and polarization.
Project description:There had a total number of 804 genes with transcript difference, including 482 up-regulated genes and 322 down-regulated genes. Enrichment analysis of those 804 different transcript gene sets was performed to explore the biological effects and the corresponding pathways that are significantly associated on GO and KEGG (Padj <0.05 as the threshold for significant enrichment). In the GO functional enrichment analysis, all differential genes were significantly enriched into 65 GO Terms, among them there had 50 GO terms belonging to BP (Biological Process). Among these BP 50 terms, organonitrogen compound metabolic process (44 genes), organonitrogen compound biosynthetic process (44 genes), and oxidation-reduction process (58 genes) had most different transcript gene sets. While in the MF (Molecular Function), there had 3 GO terms involved transcript changes, which including coenzyme binding (31 genes), structural constituent of ribosome (17 genes) and structural molecule activity (17 genes). In the KEGG pathways enrichment analysis, all differential genes were significantly enriched mainly into 4 pathways, including efq03010 (Ribosome, 30 genes), efq01212 (Fatty acid metabolism, 10 genes), efq00061 (Fatty acid biosynthesis, 10 genes), and efq02010 (ABC transporters, 19 genes), where efq03010 (Ribosome), efq01212 (Fatty acid metabolism), efq00061 (Fatty acid biosynthesis) were up-transcripted pathways, and efq02010 (ABC transporters) was the down-transcripted pathway. Based on transcriptome data analysis in selenium-enriched E. durans A8-1 compared with E. durans A8-1, a total of 19 genes were screened for significant up- transcripted that may be associated with selenium metabolic processes
Project description:Total RNA was extracted from HepG2 cells with sh-NC (n = 3) or sh-LINC01977 (n = 3). RNA samples were analyzed by RNA sequencing based on the manufacturer’s protocols. Briefly, Illumina HiSeq 4000 platform was used to sequence the RNA samples for the subsequent generation of raw data. R package was utilized to select genes with significantly differential expression based on fold change ≥2.0 and P≤0.05 between sh-NC and sh-LINC01977 cells. KEGG pathway and GSEA enrichment analysis were used for functional pathway analysis.
Project description:Total RNA was extracted from Hep3B cells with sh-NC (n = 3) or sh-lnc-CTHCC (n = 3). RNA samples were analyzed by RNA sequencing based on the manufacturer’s protocols. Briefly, Illumina HiSeq 4000 platform was used to sequence the RNA samples for the subsequent generation of raw data. R package was utilized to select genes with significantly differential expression based on fold change ≥2.0 and P≤0.05 between sh-NC and sh-lnc-CTHCC cells. KEGG pathway and GSEA enrichment analysis were used for functional pathway analysis.