Dataset Information

Microarray gene-expression profiles of 139 pancreatic tumor,102 adjacent non-tumor tissue from patients with pancreatic ductal adenocarcinoma and 3 normal pancreas from donors.

ABSTRACT: In order to identify key pathways associated with disease aggressiveness and therapeutics resistance in the most agrressive subset of PDAC, we analyzed gene expression profiling of tumor and adjacent non-tumor tissues from PDAC cases. Non-negative matrix factorization (NMF) clustering, using gene expression profile form PDAC tumors, revealed three patient subsets. A 142-gene signature specific to the subset with the worst patient survival, predicted prognosis and stratified patients with significantly different survival. Mechanistic and functional analyses of most aggressive subset revealed a HNF1B/Clusterin Axis negatively regulate pancreatic cancer progression and potentially be useful in designing novel strategies to attenuate disease progression. Affymetrix data from from these dataset were partially earlier submited by us as GEO accession#: GSE28735 and GSE 62452. The batch effect between the different sets of data was removed using Partek Genomic Suite and this normalized data was submitted to GEO in this submission.

ORGANISM(S): Homo sapiens

PROVIDER: GSE183795 | GEO | 2022/12/01

REPOSITORIES: GEO

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Project description:Engraftment of primary pancreas ductal adenocarcinomas (PDAC) in mice to generate patient derived xenograft (PDX) models is a promising platform to for biological and therapeutic studies in this disease. However, these models are still incompletely characterized. Here, we measured the impact of the murine environment on the gene expression of the engrafted human tumoral cells. We have analyzed gene expression profiles from 35 new PDX models and compared them with previously published microarray data from PDAC and hepatocellular carcinoma (HCC). Our results showed that PDX models derived from PDAC, or HCC, were clearly different to the cell lines derived from the same cancer tissues. Indeed, PDAC- and HCC-derived cell lines are indistinguishable one from the other based in their gene expression profiles. In contrast, the transcriptomes of PDAC and HCC PDX models are clearly different and more similar to their original tumor than to PDX models from the other tumor type. Interestingly, the main differences between pancreatic PDX models and human PDAC is the expression of genes involved in pathways related with extracellular matrix interactions and cell cycle regulation likely reflecting the adaptations of the tumors to the new environment. Furthermore, most of these differences are detected in the first passages after the tumor engraftment, indicating early phases of the adaptation process. In conclusion, different from conventional cancer cell lines, PDX models of PDAC retain similar gene expression profiles of PDAC. Expression changes are mainly related to genes involved in stromal pathways likely reflecting the adaptation to new environments. We also provide evidence of the stability of gene expression patterns over subsequent passages. We have analyzed gene expression profiles from 35 new PDX models and compared them with previously published in GEO microarray data. We used PDX models, primary tumors and cell lines from PDAC and hepatocellular carcinoma. All these public data were re-process in order to compare with our 35 samples

Dataset Information

Microarray gene-expression profiles of 139 pancreatic tumor,102 adjacent non-tumor tissue from patients with pancreatic ductal adenocarcinoma and 3 normal pancreas from donors.

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