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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:ObjectiveThe prevalence of early-onset colon cancer (EOCC) among individuals below the age of 50 has shown a marked upward trend in recent years. The embryology, clinical symptoms, incidence, molecular pathways, and oncologic outcomes differ between right-sided and left-sided colon cancers. However, the differences have not been fully researched in EOCC. Our study aims to develop and validate prognostic nomograms predicting overall survival (OS) and cancer-specific survival (CSS) for EOCC in different tumor locations based on the Surveillance, Epidemiology, and End Results (SEER) database.MethodsUsing the SEER database, a total of 5,588 patients with EOCC were extracted and divided into development and validation cohorts in a random allocation ratio of 7:3 across three groups. Univariate and multivariate Cox regression analyses were performed to identify independent prognostic factors influencing OS and CSS outcomes. These factors were then utilized to construct nomogram models. The prognostic capabilities of the three models were assessed through various evaluation metrics, including the concordance index (C-index), receiver operating characteristic (ROC) curves, calibration curves, decision curve analysis (DCA), and validation cohorts respectively. Additionally, survival curves of the low- and high-risk groups were calculated using the Kaplan-Meier method together with the log-rank test.ResultsSignificant differences in clinical features were observed between right-sided and left-sided EOCCs, particularly in terms of OS (52 months vs 54 months) as demonstrated by Kaplan-Meier curves. Transverse-sided EOCCs exhibited clinical characteristics similar to right-sided EOCCs, suggesting a potential shared tumor microenvironment and therapeutic considerations. Advanced stage, liver metastasis, poor grade, elevated pretreatment carcinoembryonic antigen (CEA) level, chemotherapy, and perineural invasion were identified as independent prognostic factors across all three tumor locations and were incorporated into the nomogram model. Nomograms were constructed to predict the probability of 3- and 5-year OS and CSS. The C-index and calibration plots showed that the established nomograms had good consistency between actual clinical observations and predicted outcomes. ROC curves with calculated area under the curve (AUC) values exceeded 0.8 for all three groups in both the development and validation cohorts, indicating robust predictive performance for OS and CSS. Furthermore, decision curve analysis (DCA) plots revealed a threshold probability range of 0.1 to 0.9, within which the nomogram model exhibited maximum benefit. Kaplan-Meier curves exhibited significant differences between the low- and high-risk groups in EOCC for all three tumor locations in OS and CSS, further validating the prognostic value of the nomogram models.ConclusionsWe successfully developed three precise nomogram models for EOCCs in different tumor locations, providing valuable support for clinicians in guiding clinical treatments and facilitating further prospective follow-up studies.

Project description:BackgroundPancreatic ductal adenocarcinoma (PDAC) is a rare and refractory malignancy. Early-onset pancreatic cancer (EOPC), defined as pancreatic cancer diagnosed before the age of 50 years, is very rare. Clinical presentation and oncological outcomes of EOPC are confusing according to previous studies.MethodsWe performed a retrospective, population-based study by querying the SEER database to analyze patients with PDAC from 2004 to 2018. Data on demographics, pathological characteristics, treatment patterns, and survival outcomes were compared between EOPC and pancreatic cancer in older patients. Propensity score matching (PSM) was used to minimize the potential bias of baseline characteristics between the two groups. The effect of age on changes in treatment modalities was evaluated using the Cochran-Armitage trend test.ResultsThe entire study enrolled 42,414 patients, including 2,916 (6.9%) patients with EOPC. Patients with EOPC were more likely to be male (56.6% vs. 51.0%, P < 0.001) and more frequently to present with a larger tumor size (40 mm vs. 37 mm, P < 0.001), vascular invasion (28.6% vs. 25.9%, P = 0.022) and distant metastasis (56.2% vs. 50.8%, P < 0.001) compared with older group. However, surgical resection rates (29.3% vs. 28.3%, P = 0.284) were fairly comparable, and most clinicopathologic characteristics were similar in the patients underwent resection. Younger patients had longer 5-year overall survival (6.9% vs. 5.5%, P < 0.001) and 5-year cancer-specific survival (8.4% vs. 7.3%, P < 0.001) among the overall cohort but had comparable prognosis among patients received surgery (both P > 0.05). Similar survival outcomes were obtained after PSM. In addition, operated patients tended to receive fewer systemic treatments at an increasing age (Ptrend < 0.001). The survival analysis, which was stratified by age groups, suggested that younger patients only had a better prognosis than those over 70.ConclusionsPatients with EOPC exhibited an advanced stage and a male predilection at diagnosis in the overall cohort but broadly similar clinicopathologic characteristics in the operated patients. In the surgical cohort, although younger patients were more likely to receive systemic treatment, patients with EOPC presented comparable outcomes compared with elderly patients. We suggest that more research should be conducted to uncover the unique characteristics of EOPC for better clinical management.

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.

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