Dataset Information

Gene signature associated with neuro-endocrine activity predicting prognosis of pancreatic carcinoma.

ABSTRACT:

Background

Genomic analysis is the promising tool to clear understanding of the tumorigenesis and guide molecular classification for pancreatic cancer. Our purpose was to develop a critical predictive model for prognosis in pancreatic carcinoma, based on the genomic data.

Methods

The online The Cancer Genome Atlas (TCGA) and International Cancer Genome Consortium (ICGC) datasets were queried as training and validation cohorts for comprehensive bioinformatic analysis. We applied Lasso and multivariate Cox regression to shrink genes and construct predictive model.

Results

A four genes model (DNAH10: HR = 0.71, 95% CI = 0.57-0.88, HSBP1L1: HR = 1.51, 95% CI = 1.18-1.92, KIAA0513: HR = 0.69, 95% CI = 0.50-0.96, and MRPL3: HR = 3.73, 95% CI = 2.03-6.86), was proposed and validated. The C-index was 0.73 (95% CI: 0.7-0.77). Patients in high-risk and low-risk group, stratified by model, suffered significantly different overall survival time (15.1 vs. 49.3 months, p < 0.0001 in TCGA; 423 vs. 618 days, p = 0.038 in ICGC). Taken clinical parameters into consideration, the risk-score was independent marker in clinical subpopulation. To explore the molecular mechanisms, 579 differential expression genes (DEG) in two groups were identified by edgeR. Functional enrichment of DEG indicated neuro-endocrine activity was the potential mechanism for the discrepant prognosis.

Conclusion

A specific four genes signature with the ability to predicted survival of pancreatic carcinoma was generated, which may indicate the connection between neuro-endocrine activity and patients' prognosis.

SUBMITTER: Chen K

PROVIDER: S-EPMC6625361 | biostudies-literature | 2019 Jul

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

Gene signature associated with neuro-endocrine activity predicting prognosis of pancreatic carcinoma.

Chen Ke K He Yiping Y Liu Yuan Y Yang Xiujiang X

Molecular genetics & genomic medicine 20190517 7

<h4>Background</h4>Genomic analysis is the promising tool to clear understanding of the tumorigenesis and guide molecular classification for pancreatic cancer. Our purpose was to develop a critical predictive model for prognosis in pancreatic carcinoma, based on the genomic data.<h4>Methods</h4>The online The Cancer Genome Atlas (TCGA) and International Cancer Genome Consortium (ICGC) datasets were queried as training and validation cohorts for comprehensive bioinformatic analysis. We applied La ...[more]

PMID: 31102348

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Project description:Background: Colorectal carcinoma (CRC) is one of the most prevalent malignancies globally. Ferroptosis, a novel type of cell death, is critical in the development and treatment of tumors. Objective: This study was designed to establish a genetic signature for ferroptosis which has a predictive effect on the outcomes and immunotherapeutic response of CRC. Methods: Data of CRC patients were retrieved from TCGA and GEO databases. The genes associated with ferroptosis were obtained from GeneCards. The genetic signature for ferroptosis was identified by performing Cox regression analysis. Kaplan-Meier and ROC analysis were performed to assess the prognosis role of the genetic signature. CIBERSORT tool was used to identify a potential association of the genetic signature with the immune cells. The potential immunotherapeutic signatures and drug sensitivity prediction targeting this signature were also discussed. Immunohistochemistry was used to detect expression of ferroptosis-associated genes in CRC tissues and adjacent tissues. Results: A ferroptosis-associated gene signature comprised of three genes (CDKN2A, FDFT1, and ACSL6) was developed for prediction of prognosis and evaluation of immune responses in CRC. Patients in the high-risk group tended to have a poor prognosis. In CRC, the ferroptosis-associated gene signature may function as independent predictors. Additionally, the expressional levels of the immune checkpoint proteins PD-L1 and CTLA-4 were substantially increased in the high-risk group. Moreover, we can distinguish between patients based on their immunotherapeutic responses more effectively if we categorize them by this signature. Additionally, candidate compounds were identified for the differentiation of CRC subtypes. Conclusion: The ferroptosis-associated gene signature identified in this study is effective in predicting the prognosis and evaluating immunotherapeutic response in CRC patients, and provides us with novel insights into the potential effect of ferroptosis targeted treatment on CRC.

Dataset Information

Gene signature associated with neuro-endocrine activity predicting prognosis of pancreatic carcinoma.

Background

Methods

Results

Conclusion

Publications

Gene signature associated with neuro-endocrine activity predicting prognosis of pancreatic carcinoma.

Similar Datasets

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