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Proteomics of PCa under antiandrogen treatment

ABSTRACT: In this study, 33 PCa and 10 control samples were collected, including 9 paired samples, and 4D DIA-based proteomics was employed to scrutinize the proteome alterations in PCa patients. Several potential diagnostic biomarkers were identified at both mRNA and protein levels. Furthermore, to analyze the effect of antiandrogens on proteome of PCa, we divided PCa samples into two groups, treated with bicalutamide and non-treated group. Sixty-eight up-regulated and 59 down-regulated proteins upon bicalutamide treatment were found as significantly differential proteins. In addition, proteomics profiles of PCa cells under enzalutamide treatment were analyzed.

ORGANISM(S): Homo Sapiens

SUBMITTER: Shan Wang

PROVIDER: PXD046769 | iProX | Wed Nov 08 00:00:00 GMT 2023

REPOSITORIES: iProX

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Project description: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 3’mRNA-Seq Library Prep Kit and sequenced on an Illumina NextSeq 500. QuantSeq 3’mRNA-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 3’mRNA-Seq Library Prep Kit and sequenced on an Illumina NextSeq 500. QuantSeq 3’mRNA-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.

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Proteomics of PCa under antiandrogen treatment

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