Dataset Information

Prognostic analysis of very early onset pancreatic cancer: a population-based analysis.

ABSTRACT: Background:We aimed to use competing risk model to assess whether very early onset pancreatic cancer (VEOPC ) (<45 years) had a worse prognosis than older pancreatic cancer (PC) patients, and to build a competing risk nomogram for predicting the risk of death of VEOPC. Methods:We selected pancreatic adenocarcinoma (PDAC) patients as our cohort from the Surveillance, Epidemiology, and End Results (SEER) database. The impact of cancer specific death was estimated by competing risk analysis. Multivariate Fine-Gray regression for proportional hazards modeling of the subdistribution hazard (SH) model based nomogram was constructed, which was internally validated by discrimination and calibration with 1,000 bootstraps. Results:Our cohort included 1,386 VEOPC patients and 53,940 older patients. We observed that in unresectablePDAC patients, VEOPC had better cancer specific survival (CSS) than each older group (45-59 years, 60-69 years, 70-79 years and >79 years). There was no significant prognostic difference between VEOPC and each older group in resectablePDAC. Our competing nomogram showed well discrimination and calibration by internal validation. Conclusion:For unresectable PDAC patients, VEOPC had better CSS than older patients. Our competing risk nomogram might be an easy-to-use tool for the specific death prediction of VEOPC patients with PDAC.

SUBMITTER: Dai D

PROVIDER: S-EPMC7017800 | biostudies-literature | 2020

REPOSITORIES: biostudies-literature

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Prognostic analysis of very early onset pancreatic cancer: a population-based analysis.

Dai Dongjun D Wang Yanmei Y Hu Xinyang X Jin Hongchuan H Wang Xian X

PeerJ 20200210

<h4>Background</h4>We aimed to use competing risk model to assess whether very early onset pancreatic cancer (VEOPC ) (<45 years) had a worse prognosis than older pancreatic cancer (PC) patients, and to build a competing risk nomogram for predicting the risk of death of VEOPC.<h4>Methods</h4>We selected pancreatic adenocarcinoma (PDAC) patients as our cohort from the Surveillance, Epidemiology, and End Results (SEER) database. The impact of cancer specific death was estimated by competing risk a ...[more]

PMID: 32095324

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Project description:BACKGROUND:Gene expression profiles from early-onset breast cancer and normal tissues were analyzed to explore the genes and prognostic factors associated with breast cancer. METHODS:GSE109169 and GSE89116 were obtained from the database of Gene Expression Omnibus. We firstly screened the differentially expressed genes between tumor samples and normal samples from patients with early-onset breast cancer. Based on database for annotation, visualization and intergrated discovery (DAVID) tool, functional analysis was calculated. Transcription factor-target regulation and microRNA-target gene network were constructed using the tool of transcriptional regulatory relatitionships unraveled by sentence-based text mining (TRRUST) and miRWalk2.0, respectively. The prognosis-related survival information was compiled based on The Cancer Genome Atlas breast cancer clinical data. RESULTS:A total of 708 differentially expressed genes from GSE109169 data sets and 358 differentially expressed genes from GSE89116 data sets were obtained, of which 122 common differentially expressed genes including 102 uniformly downregulated genes and 20 uniformly upregulated genes were screened. Protein-protein interaction network with a total of 83 nodes and 157 relationship pairs was obtained, and genes in protein-protein interaction, such as peroxisome proliferator-activated receptor γ, FGF2, adiponectin, and PCK1, were recognized as key nodes in protein-protein interaction. In total, 66 transcription factor-target relationship pairs were obtained, and peroxisome proliferator-activated receptor γ was the only one downregulated transcription factor. MicroRNA-target gene network contained 368 microRNA-target relationship pairs. Moreover, 16 differentially expressed genes, including 2 upregulations and 14 downregulations, were related to a significant correlation with the prognosis, including SQLE and peroxisome proliferator-activated receptor γ. CONCLUSIONS:SQLE and peroxisome proliferator-activated receptor γ might be important prognostic factors in breast cancers, and adiponectin might be important in breast cancer pathogenesis regulated by peroxisome proliferator-activated receptor γ.

Dataset Information

Prognostic analysis of very early onset pancreatic cancer: a population-based analysis.

Publications

Prognostic analysis of very early onset pancreatic cancer: a population-based analysis.

Similar Datasets

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