Dataset Information

Identification of an extracellular vesicle-related gene signature in the prediction of pancreatic cancer clinical prognosis.

ABSTRACT: Although extracellular vesicles (EVs) in body fluid have been considered to be ideal biomarkers for cancer diagnosis and prognosis, it is still difficult to distinguish EVs derived from tumor tissue and normal tissue. Therefore, the prognostic value of tumor-specific EVs was evaluated through related molecules in pancreatic tumor tissue. NA sequencing data of pancreatic adenocarcinoma (PAAD) were acquired from The Cancer Genome Atlas (TCGA) and International Cancer Genome Consortium (ICGC). EV-related genes in pancreatic cancer were obtained from exoRBase. Protein-protein interaction (PPI) network analysis was used to identify modules related to clinical stage. CIBERSORT was used to assess the abundance of immune and non-immune cells in the tumor microenvironment. A total of 12 PPI modules were identified, and the 3-PPI-MOD was identified based on the randomForest package. The genes of this model are involved in DNA damage and repair and cell membrane-related pathways. The independent external verification cohorts showed that the 3-PPI-MOD can significantly classify patient prognosis. Moreover, compared with the model constructed by pure gene expression, the 3-PPI-MOD showed better prognostic value. The expression of genes in the 3-PPI-MOD had a significant positive correlation with immune cells. Genes related to the hypoxia pathway were significantly enriched in the high-risk tumors predicted by the 3-PPI-MOD. External databases were used to verify the gene expression in the 3-PPI-MOD. The 3-PPI-MOD had satisfactory predictive performance and could be used as a prognostic predictive biomarker for pancreatic cancer.

SUBMITTER: Xu D

PROVIDER: S-EPMC7724614 | biostudies-literature | 2020 Dec

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

Identification of an extracellular vesicle-related gene signature in the prediction of pancreatic cancer clinical prognosis.

Xu Dafeng D Wang Yu Y Zhou Kailun K Wu Jincai J Zhang Zhensheng Z Zhang Jiachao J Yu Zhiwei Z Liu Luzheng L Liu Xiangmei X Li Bidan B Zheng Jinfang J

Bioscience reports 20201201 12

Although extracellular vesicles (EVs) in body fluid have been considered to be ideal biomarkers for cancer diagnosis and prognosis, it is still difficult to distinguish EVs derived from tumor tissue and normal tissue. Therefore, the prognostic value of tumor-specific EVs was evaluated through related molecules in pancreatic tumor tissue. NA sequencing data of pancreatic adenocarcinoma (PAAD) were acquired from The Cancer Genome Atlas (TCGA) and International Cancer Genome Consortium (ICGC). EV-r ...[more]

PMID: 33169793

Similar Datasets

Project description:BackgroundThis study aimed to get a deeper insight into new osteosarcoma (OS) signature based on bone morphogenetic proteins (BMPs)-related genes and to confirm the prognostic pattern to speculate on the overall survival among OS patients.MethodsFirstly, pathway analyses using Gene Ontology (GO) and the Kyoto Encyclopedia of Genes and Genomes (KEGG) were managed to search for possible prognostic mechanisms attached to the OS-specific differentially expressed BMPs-related genes (DEBRGs). Secondly, univariate and multivariate Cox analysis was executed to filter the prognostic DEBRGs and establish the polygenic model for risk prediction in OS patients with the least absolute shrinkage and selection operator (LASSO) regression analysis. The receiver operating characteristic (ROC) curve weighed the model's accuracy. Thirdly, the GEO database (GSE21257) was operated for independent validation. The nomogram was initiated using multivariable Cox regression. Immune infiltration of the OS sample was calculated. Finally, the three discovered hallmark genes' mRNA and protein expressions were verified.ResultsA total of 46 DEBRGs were found in the OS and control samples, and three prognostic DEBRGs (DLX2, TERT, and EVX1) were screened under the LASSO regression analyses. Multivariate and univariate Cox regression analysis were devised to forge the OS risk model. Both the TARGET training and validation sets indicated that the prognostic biomarker-based risk score model performed well based on ROC curves. In high- and low-risk groups, immune cells, including memory B, activated mast, resting mast, plasma, and activated memory CD4 + T cells, and the immune, stromal, and ESTIMATE scores showed significant differences. The nomogram that predicts survival was established with good performance according to clinical features of OS patients and risk scores. Finally, the expression of three crucial BMP-related genes in OS cell lines was investigated using quantitative real-time polymerase chain reaction (qRT-PCR) and western blotting (WB).ConclusionThe new BMP-related prognostic signature linked to OS can be a new tool to identify biomarkers to detect the disease early and a potential candidate to better treat OS in the future.

Project description:BackgroundPrevious studies have found that the microenvironment of cervical cancer (CESC) affects the progression and treatment of this disease. Thus, we constructed a multigene model to assess the survival of patients with cervical cancer.MethodsWe scored 307 CESC samples from The Cancer Genome Atlas (TCGA) and divided them into high and low matrix and immune scores using the ESTIMATE algorithm for differential gene analysis. Cervical cancer patients were randomly divided into a training group, testing group and combined group. The multigene signature prognostic model was constructed by Cox analyses. Multivariate Cox analysis was applied to evaluate the significance of the multigene signature for cervical cancer prognosis. Prognosis was assessed by Kaplan-Meier curves comparing the different groups, and the accuracy of the prognostic model was analyzed by receiver operating characteristic-area under the curve (ROC-AUC) analysis and calibration curve. The Tumor Immune Estimation Resource (TIMER) database was used to analyze the relationship between the multigene signature and immune cell infiltration.ResultsWe obtained 420 differentially expressed genes in the tumor microenvironment from 307 patients with cervical cancer. A three-gene signature (SLAMF1, CD27, SELL) model related to the tumor microenvironment was constructed to assess patient survival. Kaplan-Meier analysis showed that patients with high risk scores had a poor prognosis. The ROC-AUC value indicated that the model was an accurate predictor of cervical cancer prognosis. Multivariate cox analysis showed the three-gene signature to be an independent risk factor for the prognosis of cervical cancer. A nomogram combining the three-gene signature and clinical features was constructed, and calibration plots showed that the nomogram resulted in an accurate prognosis for patients. The three-gene signature was associated with T stage, M stage and degree of immune infiltration in patients with cervical cancer.ConclusionsThis research suggests that the developed three-gene signature may be applied as a biomarker to predict the prognosis of and personalized therapy for CESC.

Dataset Information

Identification of an extracellular vesicle-related gene signature in the prediction of pancreatic cancer clinical prognosis.

Publications

Identification of an extracellular vesicle-related gene signature in the prediction of pancreatic cancer clinical prognosis.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets