Dataset Information

Stromal Score-Based Gene Signature: A Prognostic Prediction Model for Colon Cancer.

ABSTRACT:

Background

Growing evidence has revealed the crucial roles of stromal cells in the microenvironment of various malignant tumors. However, efficient prognostic signatures based on stromal characteristics in colon cancer have not been well-established yet. The present study aimed to construct a stromal score-based multigene prognostic prediction model for colon cancer.

Methods

Stromal scores were calculated based on the expression profiles of a colon cancer cohort from TCGA database applying the ESTIMATE algorithm. Linear models were used to identify differentially expressed genes between low-score and high-score groups by limma R package. Univariate, LASSO, and multivariate Cox regression models were used successively to select the prognostic gene signature. Two independent datasets from GEO were used as external validation cohorts.

Results

Low stromal score was demonstrated to be a favorable factor to the overall survival of colon cancer patients in TCGA cohort (p = 0.0046). Three hundred and seven stromal score-related differentially expressed genes were identified. Through univariate, LASSO, and multivariate Cox regression analyses, a gene signature consisting of LEP, NOG, and SYT3 was recognized to build a prognostic prediction model. Based on the predictive values estimated by the established integrated model, patients were divided into two groups with significantly different overall survival outcomes (p < 0.0001). Time-dependent Receiver operating characteristic curve analyses suggested the satisfactory predictive efficacy for the 5-year overall survival of the model (AUC value = 0.733). A nomogram with great predictive performance combining the multigene prediction model and clinicopathological factors was developed. The established model was validated in an external cohort (AUC value = 0.728). In another independent cohort, the model was verified to be of significant prognostic value for different subgroups, which was demonstrated to be especially accurate for young patients (AUC value = 0.763).

Conclusion

The well-established model based on stromal score-related gene signature might serve as a promising tool for the prognostic prediction of colon cancer.

SUBMITTER: Jia J

PROVIDER: S-EPMC8150004 | biostudies-literature |

REPOSITORIES: biostudies-literature

ACCESS DATA

Similar Datasets

Project description:The aim of the present study was to examine the molecular factors associated with the prognosis of colon cancer. Gene expression datasets were downloaded from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus databases to screen differentially expressed genes (DEGs) between colon cancer samples and normal samples. Survival?related genes were selected from the DEGs using the Cox regression method. A co?expression network of survival?related genes was then constructed, and functional clusters were extracted from this network. The significantly enriched functions and pathways of the genes in the network were identified. Using Bayesian discriminant analysis, a prognostic prediction system was established to distinguish the positive from negative prognostic samples. The discrimination efficacy of the system was validated in the GSE17538 dataset using Kaplan?Meier survival analysis. A total of 636 and 1,892 DEGs between the colon cancer samples and normal samples were screened from the TCGA and GSE44861 dataset, respectively. There were 155 survival?related genes selected. The co?expression network of survival?related genes included 138 genes, 534 lines (connections) and five functional clusters, including the signaling pathway, cellular response to cAMP, and immune system process functional clusters. The molecular function, cellular components and biological processes were the significantly enriched functions. The peroxisome proliferator?activated receptor signaling pathway, Wnt signaling pathway, B cell receptor signaling pathway, and cytokine?cytokine receptor interactions were the significant pathways. A prognostic prediction system based on a 65?gene signature was established using this co?expression network. Its discriminatory effect was validated in the TCGA dataset (P=3.56e?12) and the GSE17538 dataset (P=1.67e?6). The 65?gene signature included kallikrein?related peptidase 6 (KLK6), collagen type XI ?1 (COL11A1), cartilage oligomeric matrix protein, wingless?type MMTV integration site family member 2 (WNT2) and keratin 6B. In conclusion, a 65?gene signature was screened in the present study, which showed a prognostic prediction effect in colon adenocarcinoma. KLK6, COL11A1, and WNT2 may be suitable prognostic predictors for colon adenocarcinoma.

Project description:ObjectiveTo screen key autophagy genes in colon cancer and construct an autophagy gene model to predict the prognosis of patients with colon cancer.MethodsThe colon cancer data from the TCGA were downloaded as the training set, data chip of GSE17536 as the validation set. The differential genes of the training set were obtained and were analyzed for enrichment and protein network. Acquire autophagy genes from Human Autophagy Database www.autophagy.lu/project.html. Autophagy genes in differentially expressed genes were extracted using R-packages limma. Using LASSO/Cox regression analysis combined with clinical information to construct the autophagy gene risk scoring model and divide the samples into high and low risk groups according to the risk value. The Nomogram assessment model was used to predict patient outcomes. CIBERSORT was used to calculate the infiltration of immune cells in the samples and study the relationship between high and low risk groups and immune checkpoints.ResultsNine hundred seventy-six differentially expressed genes were screened from training set, including five hundred sixty-eight up-regulated genes and four hundred eight down regulated genes. These differentially expressed genes were mainly involved: the regulation of membrane potential, neuroactive ligand-receptor interaction. We identified eight autophagy genes CTSD, ULK3, CDKN2A, NRG1, ATG4B, ULK1, DAPK1, and SERPINA1 as key prognostic genes and constructed the model after extracting the differential autophagy genes in the training set. Survival analysis showed significant differences in sample survival time after grouping according to the model. Nomogram assessment showed that the model had high reliability for predicting the survival of patients with colon cancer in the 1, 3, 5 years. In the high-risk group, the infiltration degrees of nine types of immune cells are different and the samples can be well distinguished according to these nine types of immune cells. Immunological checkpoint correlation results showed that the expression levels of CTLA4, IDO1, LAG3, PDL1, and TIGIT increased in high-risk groups.ConclusionThe prognosis prediction model based on autophagy gene has a good evaluation effect on the prognosis of colon cancer patients. Eight key autophagy genes can be used as prognostic markers for colon cancer.

Project description:BackgroundColon cancer is one of the deadliest tumors worldwide. Stromal cells and immune cells play important roles in cancer biology and microenvironment across different types of cancer. This study aimed to identify the prognostic value of stromal/immune cell-associated genes for colon cancer in The Cancer Genome Atlas (TCGA) database using bioinformatic technology.MethodsThe gene expression data and corresponding clinical information of colon cancer were downloaded from TCGA database. Stromal and immune scores were estimated based on the ESTIMATE algorithm. Sanger software was used to identify the differentially expressed genes (DEGs) and prognostic DEGs based on stromal and immune scores. External validation of prognostic biomarkers was conducted in Gene Expression Omnibus (GEO) database. Gene ontology (GO) analysis, pathway enrichment analysis, and gene set enrichment analysis (GSEA) were used for functional analysis. STRING and Cytoscape were used to assess the protein-protein interaction (PPI) network and screen hub genes. Quantitative real-time PCR (qRT-PCR) was used to validate the expression of hub genes in clinical tissues. Synaptosomal-associated protein 25 (SNAP25) was selected for analyzing its correlations with tumor-immune system in the TISIDB database.ResultsWorse overall survivals of colon cancer patients were found in high stromal score group (2963 vs. 1930 days, log-rank test P = 0.038) and high immune score group (2894 vs. 2230 days, log-rank test P = 0.076). 563 up-regulated and 9 down-regulated genes were identified as stromal-immune score-related DEGs. 70 up-regulated DEGs associated with poor outcomes were identified by COX proportional hazard regression model, and 15 hub genes were selected later. Then, we verified aquaporin 4 (AQP4) and SNAP25 as prognostic biomarkers in GEO database. qRT-PCR results revealed that AQP4 and SNAP25 were significantly elevated in colon cancer tissues compared with adjacent normal tissues (P = 0.003, 0.001). GSEA and TISIDB suggested that SNAP25 involved in cancer-related signaling pathway, immunity and metabolism progresses.ConclusionSNAP25 is a microenvironment-related and immune-related gene that can predict poor outcomes in colon cancer.