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33647fb9-cf85-4ff9-babc-8575b526543c - samples

ABSTRACT: Archival de-identified formalin-fixed paraffin-embedded RCC tumor tissue blocks from nephrectomy or tumor biopsy were processed as per below and the same sections were used for both DNA and RNA extractions. For WES (ACE version 3; Illumina NovaSeq), samples were profiled using Personalis ACE Cancer Exome (Personalis, Inc, Menlo Park, CA) Whole-transcriptome profiles were generated by RNA-seq (Accuracy and Content Enhanced (ACE) version 3; Illumina NovaSeq) using Personalis ACE Cancer Transcriptome (Personalis, Inc, Menlo Park, CA ) Of the 615 patients in the intent-to-treat population in S-TRAC trial, 193 individual specimens were available for molecular profiling, of which 171 (27.8%) (sunitinib, n = 91; placebo, n = 80) returned results for the WES analysis, and 133 (21.6%) (sunitinib, n = 72; placebo, n = 61) returned results for the GES analysis. Of the 138 WTS samples with data, replicates for two patients were summarized by median expression, and three samples were excluded from the final analysis due to low counts.

PROVIDER: EGAD00001009294 | EGA |

REPOSITORIES: EGA

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Project description:Purpose: The goal of this study are to investigate the TRPM1-regulated genes using RNAseq to compare the transcriptome profiling between 661W cells expressing TRPM1 and control vectors Methods: RNAs were isolatedusing the RNeasy Mini kit (Qiagen). RNA-seq libraries were prepared using the KAPA mRNA HyperPrep Kit (KAPA Biosystems, Roche, Basel, Switzerland) and validated using the Qsep 100 DNA/RNA Analyzer (BiOptic Inc., Taiwan). Libraries were sequenced on a NovaSeq 6000 sequencer (Illumina, CA, USA). Clean reads were aligned to the mouse genome (GRCm38) using HISAT2 (version 2.1.0) after removing low-quality reads. The differential expression of genes between TRPM1-overexpression and control cells was computed using the fragments per kilobase of transcript per million mapped reads calculated by featureCounts (version 2.0.0). Raw read counts were imported into edgeR (version 3.28.1) and analyzed by using R package of DESeq (version 1.40.0). Genes with false discovery rate (FDR) p-value < 0.05 adjusted by using Benjamini–Hochberg (BH) method were considered as differentially expressed genes (DEGs). Gene set enrichment analysis of the genes differentially expressed upon TRPM1 expression was done using the Gene Set Knowledgebase（GSKB）hallmark gene sets. Results: We had 40,922,040 clean reads in the control group and 44,244,608 clean reads in the TRPM1-overexpressing group. We mapped 43,253, 668 (97.76%) sequence reads in the control group and 39,942,649 (97.6%) sequence reads in the TRPM1-overexpressing group. We identified 16,014 transcripts. 76 transcripts showed differential expression between the vector control group and TRPM1-expressing group, with a fold change ≥1.5 and p value <0.01. Gene set enrichment analysis of the genes differentially expressed upon TRPM1 expression uncovered several TRPM1-regulated genes that may contribute to photoreceptor function, such as retina morphogenesis and JAK-STAT cascade. Conclusions: Our study represents the genes associated with TRPM1 overexpression in 661W photoreceptor cells using RNA-seq approach. The overexpression of TRPM1 may contribute to regulate the photoreceptor morphogenesis and function

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33647fb9-cf85-4ff9-babc-8575b526543c - samples

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