Dataset Information

Development and validation of prognostic model based on the analysis of autophagy-related genes in colon cancer.

ABSTRACT:

Background

Autophagy, a process of self-digestion, is closely related to multiple biological processes of colon cancer. This study aimed to construct and evaluate prognostic signature of autophagy-related genes (ARGs) to predict overall survival (OS) in colon cancer patients.

Materials and methods

First, a total of 234 ARGs were downloaded via The Cancer Genome Atlas (TCGA) database. Based on the TCGA dataset, differentially expressed ARGs were identified in colon cancer. The univariate and multivariate Cox regression analysis was performed to screen prognostic ARGs to construct the prognostic model. The feasibility of the prognostic model was evaluated using receiver operating characteristic curves and Kaplan-Meier curves. A prognostic model integrating the gene signature with clinical parameters was established with a nomogram.

Results

We developed an autophagy risk signature based on the 6 ARGs (ULK3, ATG101, MAP1LC3C, TSC1, DAPK1, and SERPINA1). The risk score was positively correlated with poor outcome and could independently predict prognosis. Furthermore, the autophagy-related signature could effectively reflect the levels of immune cell type fractions and indicate an immunosuppressive microenvironment.

Conclusion

We innovatively identified and validated 6 autophagy-related gene signature that can independently predict prognosis and reflect overall immune response intensity in the colon cancer microenvironment.

SUBMITTER: Wang Y

PROVIDER: S-EPMC8351728 | biostudies-literature |

REPOSITORIES: biostudies-literature

ACCESS DATA

Similar Datasets

Project description:Currently, immunotherapy has entered the clinical diagnosis and treatment guidelines for colon cancer, but existing immunotherapy markers cannot predict the effectiveness of immunotherapy well. This study utilized the TCGA-COAD queue to perform differential gene analysis on high and low-mutation burden samples, and screen differentially expressed genes (DEGs). To explore new molecular markers or predictive models of immunotherapy by using DEGs for NMF classification and prognostic model construction. Through systematic bioinformatics analysis, the TCGA-COAD cohort was successfully divided into high mutation burden subtypes and low mutation burden subtypes by NMF typing using DEGs. The proportion of MSI-H between high mutation burden subtypes was significantly higher than that of low mutation burden subtypes, but there was no significant difference in immunotherapy efficacy between the two subtypes. Drug sensitivity analysis showed significant differences in drug sensitivity between the two subtypes. Subsequently, we constructed a prognostic model using DEGs, which can effectively predict patient survival and immunotherapy outcomes. The prognosis and immunotherapy outcomes of the low-risk group were significantly better than those of the high-risk group. The external dataset validation of the constructed prognostic model using the GSE39582 dataset from the GEO database yielded consistent results. At the same time, we also analyzed the TMB and MSI situation between the high and low-risk groups, and the results showed that there was no significant difference in TMB between the high and low-risk groups, but the proportion of MSI-H in the high-risk group was significantly higher than that in the low-risk group. Finally, we conclude that TMB is not a suitable molecular marker for predicting the efficacy of immunotherapy in colon cancer. The newly constructed prognostic model can effectively differentiate the prognosis of colon cancer patients and predict their immunotherapy efficacy.

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:BackgroundAccumulating evidence implies that autophagy plays a critical role in breast cancer development and progression. It is crucial to screen out autophagy-related encoding genes (ARGs) with prognostic value in breast cancer and reveal their biological properties in the aggressiveness of breast cancer.MethodsUnivariate and multivariate Cox proportional hazards analyses were used to identify a prognostic risk model of ARGs from The Cancer Genome Atlas (TCGA). Kaplan-Meier analysis, univariate and multivariate Cox regression analyses and receiver operating characteristic (ROC) curve analysis were performed to validate the risk model. Western blot and immunohistochemistry (IHC) were conducted to assess the expression of VPS35 (one of ARGs in risk model). CCK8, Colony formation assay, Transwell migration/invasion assays and autophagy flux assay were used to confirm biological function of VPS35 in breast cancer.ResultsIn this study, the prognostic risk model consisting of six ARGs (VPS35, TRIM21, PRKAB2, RUFY4, MAP1LC3A and LARP1) in breast cancer were identified. The risk model was further verified as a novel independent prognostic factor for breast cancer patients. We also clarified that vacuolar protein sorting-associated protein 35 (VPS35), one of ARGs in the risk model, was upregulated in breast cancer samples and cell lines. VPS35 overexpression was correlated with more aggressive phenotype of breast cancer and indicated worse prognosis in both progression-free survival and overall survival analyses. Meanwhile, VPS35 knockdown inhibited breast cancer cell proliferation, migration and invasion, suggesting that VPS35 promoted the progression of breast cancer. VPS35 silence also influenced autophagy process, indicating that VPS35 was essential for autophagy completion.ConclusionTaken together, the six ARGs risk model has a remarkably prognostic value for breast cancer. Among them, VPS35 might exert as a significant oncogenic and prognostic factor for breast cancer and could be a promising autophagy-related therapeutic target in clinical practice.

Project description:BackgroundAutophagy plays an important role in triple-negative breast cancer (TNBC). However, the prognostic value of autophagy-related genes (ARGs) in TNBC remains unknown. In this study, we established a survival model to evaluate the prognosis of TNBC patients using ARGs signature.MethodsA total of 222 autophagy-related genes were downloaded from The Human Autophagy Database. The RNA-sequencing data and corresponding clinical data of TNBC were obtained from The Cancer Genome Atlas (TCGA) database. Differentially expressed autophagy-related genes (DE-ARGs) between normal samples and TNBC samples were determined by the DESeq2 package. Then, univariate Cox, least absolute shrinkage and selection operator (LASSO), and multivariate Cox regression analyses were performed. According to the LASSO regression results based on univariate Cox, we identified a prognostic signature for overall survival (OS), which was further validated by using the Gene Expression Omnibus (GEO) cohort. We also found an independent prognostic marker that can predict the clinicopathological features of TNBC. Furthermore, a nomogram was drawn to predict the survival probability of TNBC patients, which could help in clinical decision for TNBC treatment. Finally, we validated the requirement of an ARG in our model for TNBC cell survival and metastasis.ResultsThere are 43 DE-ARGs identified between normal and tumor samples. A risk model for OS using CDKN1A, CTSD, CTSL, EIF4EBP1, TMEM74, and VAMP3 was established based on univariate Cox regression and LASSO regression analysis. Overall survival of TNBC patients was significantly shorter in the high-risk group than in the low-risk group for both the training and validation cohorts. Using the Kaplan-Meier curves and receiver operating characteristic (ROC) curves, we demonstrated the accuracy of the prognostic model. Multivariate Cox regression analysis was used to verify risk score as an independent predictor. Subsequently, a nomogram was proposed to predict 1-, 3-, and 5-year survival for TNBC patients. The calibration curves showed great accuracy of the model for survival prediction. Finally, we found that depletion of EIF4EBP1, one of the ARGs in our model, significantly reduced cell proliferation and metastasis of TNBC cells.ConclusionBased on six ARGs (CDKN1A, CTSD, CTSL, EIF4EBP1, TMEM74, and VAMP3), we developed a risk prediction model that can help clinical doctors effectively predict the survival status of TNBC patients. Our data suggested that EIF4EBP1 might promote the proliferation and migration in TNBC cell lines. These findings provided a novel insight into the vital role of the autophagy-related genes in TNBC and may provide new therapeutic targets for TNBC.

Project description:BackgroundThough immunotherapy has become one of the standard therapies for colon cancer, the overall effective rate of immunotherapy is very low. Constructing an immune-related genes prognostic index (IRGPI) model may help to predict the response to immunotherapy and clinical outcomes.MethodsDifferentially expressed immune-related genes (DEIRGs) between normal tissues and colon cancer tissues were identified and used to construct the co-expression network. Genes in the module with the most significant differences were further analyzed. Independent prognostic immune-related genes (IRGs) were identified by univariate and multivariate cox regression analysis. Independent prognostic IRGs were used to construct the IRGPI model using the multivariate cox proportional hazards regression model, and the IRGPI model was validated by independent dataset. ROC curves were plotted and AUCs were calculated to estimate the predictive power of the IRGPI model to prognosis. Gene set enrichment analysis (GSEA) was performed to screen the enriched KEGG pathways in the high-risk and low-risk phenotype. Correlations between IRGPI and clinical characteristic, immune checkpoint expression, TMB, immune cell infiltration, immune function, immune dysfunction, immune exclusion, immune subtype were analyzed.ResultsTotally 680 DEIRGs were identified. Three independent IRGs,NR5A2, PPARGC1A and LGALS4, were independently related to survival. NR5A2, PPARGC1A and LGALS4 were used to establish the IRGPI model. Survival analysis showed that patients with high-risk showed worse survival than patients in the low-risk group. The AUC of the IRGPI model for 1-year, 3-year and 5-year were 0.584, 0.608 and 0.697, respectively. Univariate analysis and multivariate cox regression analysis indicated that IRGPI were independent prognostic factors for survival. Stratified survival analysis showed that patients with IRGPI low-risk and low TMB had the best survival, which suggested that combination of TMB and IRGPI can better predict clinical outcome. Immune cell infiltration, immune function, immune checkpoint expression and immune exclusion were different between IRGPI high-risk and low-risk patients.ConclusionAn immune-related genes prognostic index (IRGPI) was constructed and validated in the current study and the IRGPI maybe a potential biomarker for evaluating response to immunotherapy and clinical outcome for colon cancer patients.