Dataset Information

Prognostic assessment capability of a five-gene signature in pancreatic cancer: a machine learning based-study.

ABSTRACT:

Background

A prognostic assessment method with good sensitivity and specificity plays an important role in the treatment of pancreatic cancer patients. Finding a way to evaluate the prognosis of pancreatic cancer is of great significance for the treatment of pancreatic cancer.

Methods

In this study, GTEx dataset and TCGA dataset were merged together for differential gene expression analysis. Univariate Cox regression and Lasso regression were used to screen variables in the TCGA dataset. Screening the optimal prognostic assessment model is then performed by gaussian finite mixture model. Receiver operating characteristic (ROC) curves were used as an indicator to assess the predictive ability of the prognostic model, the validation process was performed on the GEO datasets.

Results

Gaussian finite mixture model was then used to build 5-gene signature (ANKRD22, ARNTL2, DSG3, KRT7, PRSS3). Receiver operating characteristic (ROC) curves suggested the 5-gene signature performed well on both the training and validation datasets.

Conclusions

This 5-gene signature performed well on both our chosen training dataset and validation dataset and provided a new way to predict the prognosis of pancreatic cancer patients.

SUBMITTER: Zhang X

PROVIDER: S-EPMC10007739 | biostudies-literature | 2023 Mar

REPOSITORIES: biostudies-literature

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Publications

Prognostic assessment capability of a five-gene signature in pancreatic cancer: a machine learning based-study.

Zhang Xuanfeng X Yang Lulu L Zhang Dong D Wang Xiaochuan X Bu Xuefeng X Zhang Xinhui X Cui Long L

BMC gastroenterology 20230311 1

<h4>Background</h4>A prognostic assessment method with good sensitivity and specificity plays an important role in the treatment of pancreatic cancer patients. Finding a way to evaluate the prognosis of pancreatic cancer is of great significance for the treatment of pancreatic cancer.<h4>Methods</h4>In this study, GTEx dataset and TCGA dataset were merged together for differential gene expression analysis. Univariate Cox regression and Lasso regression were used to screen variables in the TCGA d ...[more]

PMID: 36906533

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Project description:BackgroundDetermining the prognosis of hormone receptor positive (HR+) breast cancer (BC), which accounts for 80% of all BCs, is critical in improving survival outcomes. Stratifying individuals at high risk of BC-related mortality and improving prognosis has been the focus of research for over a decade. However, these tools are not universal as they are limited to clinical factors. We hypothesized that a new framework for predicting prognosis in HR+ BC patients can develop using artificial intelligence.MethodsA total of 2,338 HR+ human epidermal growth factor receptor 2 negative (HER2-) BC cases were analyzed from Molecular Taxonomy of Breast Cancer International Consortium (METABRIC), The Cancer Genome Atlas (TCGA), and Gene Expression Omnibus (GEO) cohorts. Groups were then divided into high- and low-risk categories utilizing a recurrence prediction model (RPM). An RPM was created by extracting nine prognosis-related genes from over 18,000 genes using a logistic progression model.ResultsRisk classification by RPM was significantly stratified in both the discovery cohort and validation cohort. In the time-dependent area under the curve analysis, there was some variation depending on the cohort, but accuracy was found to decline significantly after about 10 years. Cell cycle related gene sets, MYC, and PI3K-AKT-mTOR signaling were enriched in high-risk tumors by the Gene Set Enrichment Analysis. High-risk tumors were associated with high levels of immune cells from the lymphoid and myeloid lineage and immune cytolytic activity, as well as low levels of stem cells and stromal cells. High-risk tumors were also associated with poor therapeutic effects of chemotherapy and endocrine therapy.ConclusionsThis model was able to stratify prognosis in multiple cohorts. This is because the model reflects major BC therapeutic target pathways and tumor immune microenvironment and, further is supported by the therapeutic effect of chemotherapy and endocrine therapy.

Dataset Information

Prognostic assessment capability of a five-gene signature in pancreatic cancer: a machine learning based-study.

Background

Methods

Results

Conclusions

Publications

Prognostic assessment capability of a five-gene signature in pancreatic cancer: a machine learning based-study.

Similar Datasets

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