Identification and validation of prognostic signature for breast cancer based on genes potentially involved in autophagy.
ABSTRACT: We aimed to identify prognostic signature based on autophagy-related genes (ARGs) for breast cancer patients. The datasets of breast cancer were downloaded from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO). Least absolute shrinkage and selection operator (LASSO) Cox regression was conducted to construct multiple-ARG risk signature. In total, 32 ARGs were identified as differentially expressed between tumors and adjacent normal tissues based on TCGA. Six ARGs (IFNG, TP63, PPP1R15A, PTK6, EIF4EBP1 and NKX2-3) with non-zero coefficient were selected from the 32 ARGs using LASSO regression. The 6-ARG signature divided patients into high-and low-risk group. Survival analysis indicated that low-risk group had longer survival time than high-risk group. We further validated the 6-ARG signature using dataset from GEO and found similar results. We analyzed the associations between ARGs and breast cancer survival in TCGA and nine GEO datasets, and obtained 170 ARGs with significant associations. EIF4EBP1, FOS and FAS were the top three ARGs with highest numbers of significant associations. EIF4EBP1 may be a key ARG which had a higher expression level in patients with more malignant molecular subtypes and higher grade breast cancer. In conclusion, our 6-ARG signature was of significance in predicting of overall survival of patients with breast cancer. EIF4EBP1 may be a key ARG associated with breast cancer survival.
Project description:<h4>Background</h4>Kidney renal clear cell carcinoma (KIRC) is a fatal malignancy of the urinary system. Autophagy is implicated in KIRC occurrence and development. Here, we evaluated the prognostic value of autophagy-related genes (ARGs) in kidney renal clear cell carcinoma.<h4>Materials and methods</h4>We analyzed RNA sequencing and clinical KIRC patient data obtained from TCGA and ICGC to develop an ARG prognostic signature. Differentially expressed ARGs were further evaluated by functional assessment and bioinformatic analysis. Next, ARG score was determined in 215 KIRC patients using univariable Cox and LASSO regression analyses. An ARG nomogram was built based on multivariable Cox analysis. The prognosis nomogram model based on the ARG signatures and clinicopathological information was evaluated for discrimination, calibration, and clinical usefulness.<h4>Results</h4>A total of 47 differentially expressed ARGs were identified. Of these, 8 candidates that significantly correlated with KIRC overall survival were subjected to LASSO analysis and an ARG score built. Functional enrichment and bioinformatic analysis were used to reveal the differentially expressed ARGs in cancer-related biological processes and pathways. Multivariate Cox analysis was used to integrate the ARG nomogram with the ARG signature and clinicopathological information. The nomogram exhibited proper calibration and discrimination (C-index?=?0.75, AUC?=?>0.7). Decision curve analysis also showed that the nomogram was clinically useful.<h4>Conclusions</h4>KIRC patients and doctors could benefit from ARG nomogram use in clinical practice.
Project description:OBJECTIVE:This study aimed to identify the novel prognostic gene signature based on autophagy-associated genes (ARGs) in hepatocellular carcinoma (HCC). METHODS:The RNA sequencing data and clinical information of HCC and normal tissues were obtained from The Cancer Genome Atlas (TCGA) database. The differentially expressed ARGs were screened by the Wilcoxon signed-rank test. Cox regression analysis and Lasso regression analysis were performed to screen the ARGs and establish the prognostic prediction model. Kaplan-Meier and receiver operating characteristic (ROC) curves were both used to evaluate the accuracy of the model. GSE14520 dataset (testing cohort) was used to validate the prognostic risk model in TCGA. A clinical nomogram was established to predict the survival rate of HCC patients. RESULTS:Totally 27 differentially expressed ARGs were identified. Three OS-related ARGs (SQSTM1, HSPB8, and BIRC5) were identified via the Cox regression and Lasso regression analyses. Based on these three ARGs, a prognostic prediction model was constructed. HCC patients with high risk score present poorer prognosis than those with low risk score both in TCGA cohort (P=4.478e-04) and testing cohort (P=1.274e-03). Moreover, the risk score curve shows a well feasibility in predicting the patients' survival both in TCGA and GEO cohort with the area under the ROC curve (AUC) of 0.756 and 0.672, respectively. Besides, the calibration curves and C-index indicated that the clinical nomogram performs well to predict survival rate in HCC patients. CONCLUSIONS:The survival model based on the ARGs may be a promising tool to predict the prognosis in HCC patients.
Project description:Accumulating evidence revealed that autophagy played vital roles in breast cancer (BC) progression. Thus, the aim of this study was to investigate the prognostic value of autophagy-related genes (ARGs) and develop a ARG-based model to evaluate 5-year overall survival (OS) in BC patients. We acquired ARG expression profiling in a large BC cohort (N = 1007) from The Cancer Genome Atlas (TCGA) database. The correlation between ARGs and OS was confirmed by the LASSO and Cox regression analyses. A predictive model was established based on independent prognostic variables. Thus, time-dependent receiver operating curve (ROC), calibration plot, decision curve and subgroup analysis were conducted to determine the predictive performance of ARG-based model. Four ARGs (ATG4A, IFNG, NRG1 and SERPINA1) were identified using the LASSO and multivariate Cox regression analyses. A ARG-based model was constructed based on the four ARGs and two clinicopathological risk factors (age and TNM stage), dividing patients into high-risk and low-risk groups. The 5-year OS of patients in the low-risk group was higher than that in the high-risk group (P < 0.0001). Time-dependent ROC at 5 years indicated that the four ARG-based tool had better prognostic accuracy than TNM stage in the training cohort (AUC: 0.731 vs 0.640, P < 0.01) and validation cohort (AUC: 0.804 vs 0.671, P < 0.01). The mutation frequencies of the four ARGs (ATG4A, IFNG, NRG1 and SERPINA1) were 0.9%, 2.8%, 8% and 1.3%, respectively. We built and verified a novel four ARG-based nomogram, a credible approach to predict 5-year OS in BC, which can assist oncologists in determining effective therapeutic strategies.
Project description:To identify a glycolysis-related gene signature for the evaluation of prognosis in patients with breast cancer, we analyzed the data of a training set from TCGA database and four validation cohorts from the GEO and ICGC databases which included 1,632 patients with breast cancer. We conducted GSEA, univariate Cox regression, LASSO, and multiple Cox regression analysis. Finally, an 11<i>-</i>gene signature related to glycolysis for predicting survival in patients with breast cancer was developed. And Kaplan-Meier analysis and ROC analyses suggested that the signature showed a good prognostic ability for BC in the TCGA, ICGC, and GEO datasets. The analyses of univariate Cox regression and multivariate Cox regression revealed that it's an important prognostic factor independent of multiple clinical features. Moreover, a prognostic nomogram, combining the gene signature and clinical characteristics of patients, was constructed. These findings provide insights into the identification of breast cancer patients with a poor prognosis.
Project description:Emerging evidence suggests that the dysregulation of autophagy-related genes (ARGs) is coupled with the carcinogenesis and progression of breast cancer (BRCA). We constructed three subtype-specific risk models using differentially expressed ARGs. In Luminal, Her-2, and Basal-like BRCA, four- (BIRC5, PARP1, ATG9B, and TP63), three- (ITPR1, CCL2, and GAPDH), and five-gene (PRKN, FOS, BAX, IFNG, and EIF4EBP1) risk models were identified, which all have a receiver operating characteristic > 0.65 in the training and testing dataset. Multivariable Cox analysis showed that those risk models can accurately and independently predict the overall survival of BRCA patients. Comprehensive analysis showed that the 12 identified ARGs were correlated with the overall survival of BRCA patients; six of the ARGs (PARP1, TP63, CCL2, GAPDH, FOS, and EIF4EBP1) were differentially expressed between BRCA and normal breast tissue at the protein level. In addition, the 12 identified ARGs were highly interconnected and displayed high frequency of copy number variation in BRCA samples. Gene set enrichment analysis suggested that the deactivation of the immune system was the important driving force for the progression of Basal-like BRCA. This study demonstrated that the 12 ARG signatures were potential multi-dimensional biomarkers for the diagnosis, prognosis, and treatment of BRCA.
Project description:Increasing evidence indicates that angiogenesis is crucial in the development and progression of gastric cancer (GC). This study aimed to develop a prognostic relevant angiogenesis-related gene (ARG) signature and a nomogram. The expression profile of the 36 ARGs and clinical information of 372 GC patients were extracted from The Cancer Genome Atlas (TCGA). Consensus clustering was applied to divide patients into clusters 1 and 2. Least absolute shrinkage and selection operator (LASSO) Cox regression analyses were used to identify the survival related ARGs and establish prognostic gene signatures, respectively. The Asian Cancer Research Group (ACRG) (<i>n</i> = 300) was used for external validation. Risk score of ARG signatures was calculated, and a prognostic nomogram was developed. Gene set enrichment analysis of the ARG model risk score was performed. Cluster 2 patients had more advanced clinical stage and shorter survival rates. ARG signatures carried prognostic relevance in both cohorts. Moreover, ARG-risk score was proved as an independent prognostic factor. The predictive value of the nomogram incorporating the risk score and clinicopathological features was superior to tumor, lymph node, metastasis (TNM) staging. The high-risk score group was associated with several cancer and metastasis-related pathways. The present study suggests that ARG-based nomogram could serve as effective prognostic biomarkers and allow a more precise risk stratification.
Project description:Glioma is one of the leading causes of death from cancer, and autophagy-related genes (ARGs) play an important role in glioma occurrence, progression, and treatment. In this study, the gene expression profiles and clinical data of glioma patients were obtained from The Cancer Genome Atlas (TCGA) and the Chinese Glioma Genome Atlas (CGGA), respectively. ARGs were obtained from the Human Autophagy Database. We analyzed the expression of the ARGs in glioma and found that 73 ARGs were differentially expressed in tumor and normal tissues. Univariate Cox regression analysis was used to identify prognostic differentially expressed ARGs (PDEARGs). Least absolute shrinkage and selection operator (LASSO) and multivariate Cox regression analyses were performed on the PDEARGs to determine the risk genes; and BRIC5, NFE2L2, GABARAP, IKBKE, BID, MAPK3, FKBP1B, MAPK8IP1, PRKCQ, CX3CL1, NPC1, HSP90AB1, DAPK2, SUPT20H, and PTEN were selected to establish a prognostic risk score model for TCGA and CGGA cohorts. This model accurately stratified patients with different survival outcomes, and the autophagy-related signature was also appraised as being an independent prognostic factor. We also constructed a prognostic nomogram using risk score, age, gender, WHO grade, isocitrate dehydrogenase (IDH) mutation status, and 1p/19q co-deletion status; and the calibration plots showed excellent prognostic performance. Finally, Pearson correlation analysis suggested that the ARG signature also played an essential role in the tumor immune microenvironment. In summary, we constructed and verified a novel autophagy-related signature that was tightly associated with the tumor immune microenvironment and could serve as an independent prognostic biomarker in gliomas.
Project description:<b>Background:</b> Autophagy plays an important role in the development of cancer. However, the prognostic value of autophagy-related genes (ARGs) in cervical cancer (CC) is unclear. The purpose of this study is to construct a survival model for predicting the prognosis of CC patients based on ARG signature. <b>Methods:</b> ARGs were obtained from the Human Autophagy Database and Molecular Signatures Database. The expression profiles of ARGs and clinical data were downloaded from the TCGA database. Differential expression analysis of CC tissues and normal tissues was performed using R software to screen out ARGs with an aberrant expression. Univariate Cox, Lasso, and multivariate Cox regression analyses were used to construct a prognostic model which was validated by using the test set and the entire set. We also performed an independent prognostic analysis of risk score and some clinicopathological factors of CC. Finally, a clinical practical nomogram was established to predict individual survival probability. <b>Results:</b> Compared with normal tissues, there were 63 ARGs with an aberrant expression in CC tissues. A risk model based on 3 ARGs was finally obtained by Lasso and Cox regression analysis. Patients with high risk had significantly shorter overall survival (OS) than low-risk patients in both train set and validation set. The ROC curve validated its good performance in survival prediction, suggesting that this model has a certain extent sensitivity and specificity. Multivariate Cox analysis showed that the risk score was an independent prognostic factor. Finally, we mapped a nomogram to predict 1-, 3-, and 5-year survival for CC patients. The calibration curves indicated that the model was reliable. <b>Conclusion:</b> A risk prediction model based on CHMP4C, FOXO1, and RRAGB was successfully constructed, which could effectively predict the prognosis of CC patients. This model can provide a reference for CC patients to make precise treatment strategy.
Project description:Background:Although increased early detection, diagnosis and treatment have improved the outcome of breast cancer patients, prognosis estimation still poses challenges due to the disease heterogeneity. Accumulating data indicated an evident correlation between tumor immune microenvironment and clinical outcomes. Objective:To construct an immune-related signature that can estimate disease prognosis and patient survival in breast cancer. Methods:Gene expression profiles and clinical data of breast cancer patients were collected from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) databases, which were further divided into a training set (n = 499), a testing set (n = 234) and a Meta-validation set (n = 519). In the training set, immune-related genes were recognized using combination of gene expression data and ESTIMATE algorithm-derived immune scores. An immune-related prognostic signature was generated with LASSO Cox regression analysis. The prognostic value of the signature was validated in the testing set and the Meta-validation set. Results:A total of 991 immune-related genes were identified. Twelve genes with non-zero coefficients in LASSO analysis were used to construct an immune-related prognostic signature. The 12-gene signature significantly stratified patients into high and low immune risk groups in terms of overall survival independent of clinical and pathologic factors. The signature also significantly stratified overall survival in clinical defined groups, including stage I/II disease. Several biological processes, such as immune response, were enriched among genes in the immune-related signature. The percentage of M2 macrophage infiltration was significantly different between low and high immune risk groups. Time-dependent ROC curves indicated good performance of our signature in predicting the 1-, 3- and 5-year overall survival for patients from the full TCGA cohort. Furthermore, the composite signature derived by integrating immune-related signature with clinical factors, provided a more accurate estimation of survival relative to molecular signature alone. Conclusion:We developed a 12-gene prognostic signature, providing novel insights into the identification of breast cancer with a high risk of death and assessment of the possibility of immunotherapy incorporation in personalized breast cancer management.
Project description:Background:Autophagy, a highly conserved self-digesting process, has been deeply involved in the development and progression of oral squamous cell carcinoma (OSCC). However, the prognostic value of autophagy-related genes (ARGs) for OSCC still remains unclear. Our study set out to develop a multigene expression signature based on ARGs for individualized prognosis assessment in OSCC patients. Methods:Based on The Cancer Genome Atlas (TCGA) database, we identified prognosis-related ARGs through univariate COX regression analysis. Then we performed the least absolute shrinkage and selection operator (LASSO) regression analysis to identify an optimal autophagy-related multigene signature with the subsequent validation in testing set, GSE41613 and GSE42743 datasets. Results:We identified 36 prognosis-related ARGs for OSCC. Subsequently, the multigene signature based on 13 prognostic ARGs was constructed and successfully divided OSCC patients into low and high-risk groups with significantly different overall survival in TCGA training set (p < 0.0001). The autophagy signature remained as an independent prognostic factor for OSCC in univariate and multivariate Cox regression analyses. The area under the curve (AUC) values of the receiver operating characteristic (ROC) curves for 1, 3, and 5-year survival were 0.758, 0.810, 0.798, respectively. Then the gene signature was validated in TCGA testing set, GSE41613 and GSE42743 datasets. Moreover, Gene Ontology (GO), Kyoto Encyclopedia of Genes and Genomes (KEGG) analysis, and single-sample gene set enrichment analysis (ssGSEA) revealed the underlying biological characteristics and signaling pathways associated with this signature in OSCC. Finally, we constructed a nomogram by combining the gene signature with multiple clinical parameters (age, gender, TNM-stage, tobacco, and alcohol history). The concordance index (C-index) and calibration plots demonstrated favorable predictive performance of our nomogram. Conclusion:In summary, we identified and verified a 13-ARGs prognostic signature and nomogram, which provide individualized prognosis evaluation and show insight for potential therapeutic targets for OSCC.