Sequencing and Quantitative Analysis of Skin Transcriptomes from Rats with EGFR Inhibitors Induced Rash
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ABSTRACT:
ORGANISM(S): Rattus norvegicus
PROVIDER: GSE199970 | GEO | 2022/04/04
REPOSITORIES: GEO
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2021-07-01 | GSE162734 | GEO
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.
2021-08-22 | GSE182142 | GEO
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 .
2021-04-30 | GSE173575 | GEO
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.
2021-04-30 | GSE173621 | GEO
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.
2021-03-24 | GSE169344 | GEO
Project description: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 .
2021-06-19 | GSE178487 | GEO
Project description:In order to investigate the early pathological alteration of EGFRI-induced rash, we treated rat with vehicle or afatinib for 3 days. We then performed gene expression profiling analysis using data obtained from RNA-Seq of 3 different rat skins in each group
2024-05-20 | GSE201504 | GEO
Project description:Purpose: The goal of this study is to compare NGS-derived wild type and Hnrnpul1 knockout (Hnrnpul1-/-) RAW 264.7 cells transcriptomes with or without LPS stimulation. Methods: Sequancing was performed by Novogene China Co. Ltd. RNA profiles of wild type and Hnrnpul1-/- RAW 264.7 cells as well as LPS stimulated (10 h) wild type and Hnrnpul1-/- RAW 264.7 cells were generated by deep sequencing using Illumina Novaseq 6000. 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. Method of TMM was used to normalize the readcount. Negative binomial distribution model was used to calculate the P value, and FDR was calculated by the method of Benjaminiand Hochberg. Results: Using an optimized data analysis workflow, we mapped about 40 million sequence reads per sample to the mouse genome (GRCm38/mm10). Comparing to wild type RAW 264.7 cells, 237 genes were up-regulated and 181 genes were down-regulated in Hnrnpul1-/- cells. At 10 h following LPS stimulation, 341 genes were up-regulated and 288 genes were down-regulated in Hnrnpul1-/- cells. Genes were pre-ranked according to log2FoldChange(KO/WT) followed by GSEA and 6 gene sets were significantly enriched. Significantly differential genes were undergone GO analysis (biological process) and biological process including cell-cell adhesion, positive regulation of cell activation and regulation of response to external stimulus were enriched. Conclusions: Lacking Hnrnpul1 promotes the expression of inflammatory cytokines in LPS stimulated RAW 264.7 cells.
2022-06-01 | GSE196232 | GEO
Project description:To identify differentially expressed mRNAs after knockout of YTHDF2 in granule cells of mouse hippocampus dentate gyrus, we perfromed RNA-seq. Total RNA from DG deficient in YTHDF2 and the negative control DG were collected and subjected to construct sequencing libraries using NEBNext® Ultra™ RNA Library Prep Kit for Illumina® (NEB). After sequencencing, the filtered reads were mapped to the mouse reference genome (GRCm38) using HISAT2 v2.0.5 with default parameters. The resulting bam files were fed to featureCounts v1.5.0-p3 to count the number of RNA-seq reads, which was further normalized to calculate FPKM. Differential expression analysis of two groups (cKO vs NC) (three biological replicates per group) was performed using the DESeq2 package (version 1.20.0). Altogether 779 mRNA were differentially regulated, among which 536 mRNA were upregulated after YTHDF2 KD. This study provides a gene list which shows differentially expressed mRNAs after knockout of YTHDF2 in granule cells of mouse hippocampus dentate gyrus.
2023-01-19 | GSE171790 | GEO
Project description:Purpose: RNA-Seq uses next-generation sequencing to analyze expression across the transcriptome, enabling to detect known or novel features and quantify RNA. The goal of this study is to compare transcriptome profiles between control and GLUT1-HK2-PFKFB3 overexpressed mouse skeletal muscle. Methods: Total RNA was isolated from the gastrocnemius muscle of chow diet-fed M;G and control mice at the age of 3-month-old using RNAiso Plus (Takara Bio). Three independent pooled samples per group were analyzed. Sequencing libraries were generated using NEBNext® UltraTM RNA Library Prep Kit for Illumina® (NEB, USA) following manufacturer’s recommendations and index codes were added to attribute sequences to each sample. The clustering of the index-coded samples was performed on a cBot Cluster Generation System using TruSeq PE Cluster Kit v3-cBot-HS (Illumia) according to the manufacturer’s instructions. After cluster generation, the library preparations were sequenced on an Illumina Hiseq platform and 125 bp/150 bp paired-end reads were generated. 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. Fragments Per Kb of exon per Million mapped reads (FPKM) were calculated using featureCounts v1.5.0-p3. Results: We mapped about 6 million sequence reads per sample to the mouse genome and identified 54533 transcripts in the gastrocnemius muscle of control and GLUT1-HK2-PFKFB3-overexpressed mice. RNA-seq data confirmed stable expression of 10 known housekeeping genes, and 7 of these were validated with qRT–PCR. A total 664 transtripts were differentially expressed in wildtype and GLUT1-HK2-PFKFB3 overexpressed skeletal muscle with a cutoff of 2.0-fold and p < 0.05 from RNA sequencing analysis. Altered expression of 15 genes was confirmed with qRT–PCR, demonstrating the high degree of sensitivity of the RNA-seq method.
2022-09-01 | GSE157257 | GEO