Ontology highlight
ABSTRACT:
PROVIDER: PRJNA967843 | ENA |
REPOSITORIES: ENA
Dataset's files
| Action | DRS | |||
|---|---|---|---|---|
| SRR24456685_1.fastq.gz | Fastqsanger.gz | |||
| SRR24456685_2.fastq.gz | Fastqsanger.gz | |||
| SRR24456686_1.fastq.gz | Fastqsanger.gz | |||
| SRR24456686_2.fastq.gz | Fastqsanger.gz |
Items per page: 5 1 - 4 of 4 |
Similar Datasets
Project description:Pancreatic ductal adenocarcinoma (PDAC) poses a significant challenge due to its high heterogeneity and aggressiveness. Recognizing the urgency to delineate molecular subtypes, our study focused on the emerging field of lipid metabolism remodeling in PDAC, particularly exploring the prognostic potential and molecular classification associated with fatty acid biosynthesis. Here we identified a 7-gene signature associated with fatty acid biosynthesis-related genes (FRGs), providing a robust tool for survival prediction. The high- FRGs score group displayed a poorer prognosis, lower immune infiltration abundance, and a higher tumor mutational burden. Notably, ACSL5, a key gene in 7-gene signature panel was upregulated in PDAC. The biological function of ACSL5 were validated with in vitro assay. Finally, we unveiled that ACSL5 played a crucial role in pancreatic cancer progression, promoting tumor initiation, metastasis, and immune evasion.
2024-01-16 | GSE253315 | GEO
Project description:Pancreatic cancer is a highly malignant tumor that is well known for its poor prognosis. It has been reported that aspirin can reduce the risk of various cancers, but the potential role of aspirin and gemcitabine in pancreatic cancer pathogenesis and chemotherapy has not been studied. Therefore, we investigated their synergistic anti-tumor effects and explored the potential molecular mechanisms and biological functions underlying their inhibitory effects on the development of pancreatic cancer in the hope of identifying treatment-related or prognostic biomarkers for the effective treatment of pancreatic cancer.
2022-05-31 | PXD031910 | Pride
Project description:<p>Cross-talk among downstream regulatory networks of genome mutations has been the major hypothetical mechanism underlying poor prognosis and low-responsive rates for targeted therapy of pancreatic ductal adenocarcinoma (PDAC). By applying a causal inference-based mutation-upstream-of-the-metabolomic-signature (MUMS), we demonstrated a potential application of prognostic relevant serum metabolomic signature as an indicator to aggregate crosstalk among respective regulatory network downstream of genome mutations, and their collective interference on tumor progression. Moreover, a feature selection of 9-serum metabolites panel also highlights the clinical potential for predicting survival outcomes across multiple PDAC cohorts. Notably, integrating causal inference-based MUMS analysis identified and functional verified GRPEL1 as a novel tumour promoting gene, which shares a common down-stream metabolic signature with mTOR/PI3K/Akt signaling, sensitizing PDAC cells to mTOR inhibition-induced proliferation arrest. Our MUMS based functional cross-talk analysis provided proof-of-concept evidence that serum metabolic signatures reflect crosstalk among tumour mutational landscape, and such co-regulations would provide a novel aspect for identifying neo-targets for targeted therapy and rational based combined therapy design.</p>
2025-10-13 | MTBLS13111 | MetaboLights
Project description:<p>Cross-talk among downstream regulatory networks of genome mutations has been the major hypothetical mechanism underlying poor prognosis and low-responsive rates for targeted therapy of pancreatic ductal adenocarcinoma (PDAC). By applying a causal inference-based mutation-upstream-of-the-metabolomic-signature (MUMS), we demonstrated a potential application of prognostic relevant serum metabolomic signature as an indicator to aggregate crosstalk among respective regulatory network downstream of genome mutations, and their collective interference on tumor progression. Moreover, a feature selection of 9-serum metabolites panel also highlights the clinical potential for predicting survival outcomes across multiple PDAC cohorts. Notably, integrating causal inference-based MUMS analysis identified and functional verified GRPEL1 as a novel tumour promoting gene, which shares a common down-stream metabolic signature with mTOR/PI3K/Akt signaling, sensitizing PDAC cells to mTOR inhibition-induced proliferation arrest. Our MUMS based functional cross-talk analysis provided proof-of-concept evidence that serum metabolic signatures reflect crosstalk among tumour mutational landscape, and such co-regulations would provide a novel aspect for identifying neo-targets for targeted therapy and rational based combined therapy design.</p>
2025-10-13 | MTBLS13127 | MetaboLights
Project description:<p>Cross-talk among downstream regulatory networks of genome mutations has been the major hypothetical mechanism underlying poor prognosis and low-responsive rates for targeted therapy of pancreatic ductal adenocarcinoma (PDAC). By applying a causal inference-based mutation-upstream-of-the-metabolomic-signature (MUMS), we demonstrated a potential application of prognostic relevant serum metabolomic signature as an indicator to aggregate crosstalk among respective regulatory network downstream of genome mutations, and their collective interference on tumor progression. Moreover, a feature selection of 9-serum metabolites panel also highlights the clinical potential for predicting survival outcomes across multiple PDAC cohorts. Notably, integrating causal inference-based MUMS analysis identified and functional verified GRPEL1 as a novel tumour promoting gene, which shares a common down-stream metabolic signature with mTOR/PI3K/Akt signaling, sensitizing PDAC cells to mTOR inhibition-induced proliferation arrest. Our MUMS based functional cross-talk analysis provided proof-of-concept evidence that serum metabolic signatures reflect crosstalk among tumour mutational landscape, and such co-regulations would provide a novel aspect for identifying neo-targets for targeted therapy and rational based combined therapy design.</p>
2025-10-13 | MTBLS13116 | MetaboLights
Project description:Objective: Globally, esophageal cancer is among the most deadly cancer forms. Long non-coding RNAs (lncRNA) are frequently found to have important regulatory roles. We aim to assess the lncRNA expression profile of esophageal squamous cell carcinoma (ESCC) and identify prognosis related lncRNAs. Design: LncRNA expression profiles were studied by microarray in paired tumor and normal tissues from 119 ESCC patients, and validated by qRT-PCR. The 119 patients were subsequently divided randomly into training (n=60) and test (n=59) groups. A prognostic signature was developed from the training group using a random forest supervised classification algorithm and a nearest shrunken centroid algorithm, and validated in test group and further in an independent cohort (n=60). The independence of the signature in survival prediction was evaluated by Multivariable Cox regression analysis. Results: LncRNAs showed significantly altered expression in ESCC tissues. From the training group, we identified a three-lncRNA signature (including the lncRNAs ENST00000435885•1, XLOC_013014, and ENST00000547963•1) which classified the patients into two groups with significantly different overall survival (median survival 19•2 months vs. not reached, p<0•0001). The signature was applied to the test group (median survival 21•5 months vs. not reached, p=0•0030) and independent cohort (median survival 25•8 months vs. not reached, p=0•0187) and showed similar prognostic values in both. Multivariable Cox regression analysis showed that the signature was an independent prognostic factor for ESCC patients. Stratified analysis suggested that the signature was prognostic within clinical stages. Conclusions: Our results suggest that the three-lncRNA signature can serve as a novel biomarker for the prognosis of ESCC patients. Application of it allows for more accurate survival prediction. The lncRNA expression profiles of cancer and adjacent normal tissues form 119 ESCC patients were studied by microarray and an lncRNA signature that can perdict the survival of ESCC patients was identified.
2014-03-07 | E-GEOD-53622 | biostudies-arrayexpress
Project description:Objective: Globally, esophageal cancer is among the most deadly cancer forms. Long non-coding RNAs (lncRNA) are frequently found to have important regulatory roles. We aim to assess the lncRNA expression profile of esophageal squamous cell carcinoma (ESCC) and identify prognosis related lncRNAs. Design: LncRNA expression profiles were studied by microarray in paired tumor and normal tissues from 119 ESCC patients, and validated by qRT-PCR. The 119 patients were subsequently divided randomly into training (n=60) and test (n=59) groups. A prognostic signature was developed from the training group using a random forest supervised classification algorithm and a nearest shrunken centroid algorithm, and validated in test group and further in an independent cohort (n=60). The independence of the signature in survival prediction was evaluated by Multivariable Cox regression analysis. Results: LncRNAs showed significantly altered expression in ESCC tissues. From the training group, we identified a three-lncRNA signature (including the lncRNAs ENST00000435885•1, XLOC_013014, and ENST00000547963•1) which classified the patients into two groups with significantly different overall survival (median survival 19•2 months vs. not reached, p<0•0001). The signature was applied to the test group (median survival 21•5 months vs. not reached, p=0•0030) and independent cohort (median survival 25•8 months vs. not reached, p=0•0187) and showed similar prognostic values in both. Multivariable Cox regression analysis showed that the signature was an independent prognostic factor for ESCC patients. Stratified analysis suggested that the signature was prognostic within clinical stages. Conclusions: Our results suggest that the three-lncRNA signature can serve as a novel biomarker for the prognosis of ESCC patients. Application of it allows for more accurate survival prediction. The lncRNA expression profiles of cancer and adjacent normal tissues form 119 ESCC patients were studied by microarray and an lncRNA signature that can perdict the survival of ESCC patients was identified.
2014-03-07 | E-GEOD-53624 | biostudies-arrayexpress
Project description:We recently established a 67 genes-expression prognostic signature related to genome complexity in sarcomas. We validated this signature in 60 GISTs, determining patient prognosis.
2011-12-16 | E-MTAB-373 | biostudies-arrayexpress
Project description:Objective: Globally, esophageal cancer is among the most deadly cancer forms. Long non-coding RNAs (lncRNA) are frequently found to have important regulatory roles. We aim to assess the lncRNA expression profile of esophageal squamous cell carcinoma (ESCC) and identify prognosis related lncRNAs. Design: LncRNA expression profiles were studied by microarray in paired tumor and normal tissues from 119 ESCC patients, and validated by qRT-PCR. The 119 patients were subsequently divided randomly into training (n=60) and test (n=59) groups. A prognostic signature was developed from the training group using a random forest supervised classification algorithm and a nearest shrunken centroid algorithm, and validated in test group and further in an independent cohort (n=60). The independence of the signature in survival prediction was evaluated by Multivariable Cox regression analysis. Results: LncRNAs showed significantly altered expression in ESCC tissues. From the training group, we identified a three-lncRNA signature (including the lncRNAs ENST00000435885•1, XLOC_013014, and ENST00000547963•1) which classified the patients into two groups with significantly different overall survival (median survival 19•2 months vs. not reached, p<0•0001). The signature was applied to the test group (median survival 21•5 months vs. not reached, p=0•0030) and independent cohort (median survival 25•8 months vs. not reached, p=0•0187) and showed similar prognostic values in both. Multivariable Cox regression analysis showed that the signature was an independent prognostic factor for ESCC patients. Stratified analysis suggested that the signature was prognostic within clinical stages. Conclusions: Our results suggest that the three-lncRNA signature can serve as a novel biomarker for the prognosis of ESCC patients. Application of it allows for more accurate survival prediction.
2014-03-07 | GSE53624 | GEO