Dataset Information

Seven-gene signature on tumor microenvironment for predicting the prognosis of patients with pancreatic cancer.

ABSTRACT:

Background

The aim of the present study was to construct a novel gene signature on the tumor microenvironment (TME) to predict the prognosis of patients with pancreatic ductal adenocarcinoma (PDAC).

Methods

We downloaded gene expression profiles and clinical information of PDAC from The Cancer Genome Atlas (TCGA) datasets, as well as Gene Expression Omnibus (GEO) datasets (GSE78229, GSE62452, and GSE28735). Differentially expressed genes were generated by comparing high versus low score groups of immune/stromal subgroups based on the Estimation of STromal and Immune cells in MAlignant Tumor tissues using Expression data algorithm. Subsequently, a prognostic risk score model was constructed and validated through univariate and multivariate Cox regression analyses. Finally, functional enrichment analysis and protein-protein interactions were performed to predict the functional implication of the prognostic model.

Results

We picked out 1,797 upregulated genes in immune groups and stromal groups. Through further analysis, we constructed a 7-gene signature on the TME. The risk score from the model effectively differentiated patients into high-risk and low-risk groups with different overall survival and was validated by GEO datasets. A functional analysis suggested that 7 selected genes and their co-expressed genes were mainly enriched in immune response, extracellular structure organization, and cell adhesion molecule binding.

Conclusions

Our results showed that the 7-gene model on the TME can be used to assess the prognosis of patients with PDAC.

SUBMITTER: Yang B

PROVIDER: S-EPMC8102236 | biostudies-literature |

REPOSITORIES: biostudies-literature

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Project description:BackgroundAccumulating evidences demonstrated tumor microenvironment (TME) of bladder cancer (BLCA) may play a pivotal role in modulating tumorigenesis, progression, and alteration of biological features. Currently we aimed to establish a prognostic model based on TME-related gene expression for guiding clinical management of BLCA.MethodsWe employed ESTIMATE algorithm to evaluate TME cell infiltration in BLCA. The RNA-Seq data from The Cancer Genome Atlas (TCGA) database was used to screen out differentially expressed genes (DEGs). Underlying relationship between co-expression modules and TME was investigated via Weighted gene co-expression network analysis (WGCNA). COX regression and the least absolute shrinkage and selection operator (LASSO) analysis were applied for screening prognostic hub gene and establishing a risk predictive model. BLCA specimens and adjacent tissues from patients were obtained from patients. Bladder cancer (T24, EJ-m3) and bladder uroepithelial cell line (SVHUC1) were used for genes validation. qRT-PCR was employed to validate genes mRNA level in tissues and cell lines.Results365 BLCA samples and 19 adjacent normal samples were selected for identifying DEGs. 2141 DEGs were identified and used to construct co-expression network. Four modules (magenta, brown, yellow, purple) were regarded as TME regulatory modules through WGCNA and GO analysis. Furthermore, seven hub genes (ACAP1, ADAMTS9, TAP1, IFIT3, FBN1, FSTL1, COL6A2) were screened out to establish a risk predictive model via COX and LASSO regression. Survival analysis and ROC curve analysis indicated our predictive model had good performance on evaluating patients prognosis in different subgroup of BLCA. qRT-PCR result showed upregulation of ACAP1, IFIT3, TAP1 and downregulation of ADAMTS9, COL6A2, FSTL1,FBN1 in BLCA specimens and cell lines.ConclusionsOur study firstly integrated multiple TME-related genes to set up a risk predictive model. This model could accurately predict BLCA progression and prognosis, which offers clinical implication for risk stratification, immunotherapy drug screen and therapeutic decision.