PLX109176

GSE147043: Distinct immune microenvironments between primary and paired metastatic tumors in gastric cancer patients

• Organsim human
• Type RNASEQ
• Target gene
• Project ARCHS4

We performed RNA-seq to investigate the differences in the gene expression profiles between primary gastric cancer (PGC) and paired metastatic gastric cancer (MGC). RNA-sequencing was performed on 7 paired PGC and MGC FFPE tissues. The transcriptome profiling of the two groups (MGC vs PGC) including immune response gene signature was analyzed. Total RNA was isolated using Trizol reagent. Extracted RNA samples were processed using the QuantSeq 3mRNA-Seq Library Prep Kit and sequenced on an Illumina NextSeq 500. QuantSeq 3mRNA-Seq reads were aligned using Bowtie2. The alignment file was used to assemble transcripts and the Read Count data was processed based on quantile normalization method using EdgeR within R. We identified 519 differentially regulated genes between the two sets, 76 of which were significantly up-regulated and 443 significantly down-regulated in MGCs. Among DEGs, 33 immune response genes were selected using QuickGO database (https://www.ebi.ac.uk/QuickGO/), with majority (27/33) of them exhibiting downregulation in the MGC samples. In summary, RNA sequencing data revealed that immune-related gene expression were down-regulated in MGC compared to PGC. We performed RNA-seq to investigate the differences in the gene expression profiles between primary gastric cancer (PGC) and paired metastatic gastric cancer (MGC). RNA-sequencing was performed on 7 paired PGC and MGC FFPE tissues. The transcriptome profiling of the two groups (MGC vs PGC) including immune response gene signature was analyzed. Total RNA was isolated using Trizol reagent. Extracted RNA samples were processed using the QuantSeq 3mRNA-Seq Library Prep Kit and sequenced on an Illumina NextSeq 500. QuantSeq 3mRNA-Seq reads were aligned using Bowtie2. The alignment file was used to assemble transcripts and the Read Count data was processed based on quantile normalization method using EdgeR within R. We identified 519 differentially regulated genes between the two sets, 76 of which were significantly up-regulated and 443 significantly down-regulated in MGCs. Among DEGs, 33 immune response genes were selected using QuickGO database (https://www.ebi.ac.uk/QuickGO/), with majority (27/33) of them exhibiting downregulation in the MGC samples. In summary, RNA sequencing data revealed that immune-related gene expression were down-regulated in MGC compared to PGC. SOURCE: Seung-Myoung Son (da10na13@daum.net) - Chungbuk National University College of Medicin