Identification and validation of an individualized autophagy-clinical prognostic index in gastric cancer patients

Abstract

Background: The purpose of this study is to perform bioinformatics analysis of autophagy-related genes in gastric cancer, and to construct a multi-gene joint signature for predicting the prognosis of gastric cancer.

Methods: GO and KEGG analysis were applied for differentially expressed autophagy-related genes in gastric cancer, and PPI network was constructed in Cytoscape software. In order to optimize the prognosis evaluation system of gastric cancer, we established a prognosis model integrating autophagy-related genes. We used single factor Cox proportional risk regression analysis to screen genes related to prognosis from 204 autophagy-related genes in The Atlas Cancer Genome (TCGA) gastric cancer cohort. Then, the generated genes were applied to the Least Absolute Shrinkage and Selection Operator (LASSO). Finally, the selected genes were further included in the multivariate Cox proportional hazard regression analysis to establish the prognosis model. According to the median risk score, patients were divided into high-risk group and low-risk group, and survival analysis was conducted to evaluate the prognostic value of risk score. Finally, by combining clinic-pathological features and prognostic gene signatures, a nomogram was established to predict individual survival probability.

Results: GO analysis showed that the 28 differently expressed autophagy-related genes was enriched in cell growth, neuron death, and regulation of cell growth. KEGG analysis showed that the 28 differently expressed autophagy-related genes were related to platinum drug resistance, apoptosis and p53 signaling pathway. The risk score was constructed based on 4 genes (GRID2, ATG4D,GABARAPL2, CXCR4), and gastric cancer patients were significantly divided into high-risk and low-risk groups according to overall survival. In multivariate Cox regression analysis, risk score was still an independent prognostic factor (HR = 1.922, 95% CI = 1.573-2.349, P < 0.001). Cumulative curve showed that the survival time of patients with low-risk score was significantly longer than that of patients with high-risk score (P < 0.001). The external data GSE62254 proved that nomograph had a great ability to evaluate the prognosis of individual gastric cancer patients.

Conclusions: This study provides a potential prognostic marker for predicting the prognosis of GC patients and the molecular biology of GC autophagy.

Keywords: Autophagy; Bioinformatics; Gastric cancer; Prognosis.

Fig. 1

Heatmap of the expression levels of 28 DE-ATGs in TCGA-STAD. N normal; T Tumor; Red upregulation; Green downregulation. The value of expression intensity are based on the gene expression level analysis by R software.

Fig. 2

GO, KEGG enrichment analysis and PPI network. a GO analysis of 28 differentially expressed autophagy-related genes. “BP” stands for “biological process”, “CC” stands for “cellular component” and “MF” stands for “molecular function”. b KEGG analysis of 28 differentially expressed autophagy-related genes. c PPI network diagram of 28 differentially expressed autophagy-related genes

Fig. 3

Regression analysis to select autophagy genes related to prognosis of gastric cancer. a Forest map of autophagy genes related to STAD survival, analyzed by univariate Cox regression. b Boxplot of autophagy genes associated with STAD survival, analyzed by LASSO regression.”N” stands for “normal” and “T” stands for “Tumor”. c LASSO coefficient spectrum of 10 genes in STAD. Generate a coefficient distribution map for a logarithmic (λ) sequence. d Selecting the best parameters for STAD in the LASSO model (λ)

Fig. 4

Characteristics of prognostic gene signatures. a Distribution of risk score and patient survival time, and status of STAD. The black dotted line is the optimal cut-off value for dividing patients into low-risk and high-risk groups. b Heat map of autophagy-related gene expression profiles in the prognostic signature of STAD

Fig. 5

Autophagy-related gene signatures are significantly associated with gastric cancer survival. a Univariate Cox regression analysis. Forest plot of associations between risk factors and the survival of STAD. b Multiple Cox regression analysis. The autophagy-associated gene signature is an independent predictor of TCGA-STAD. c Kaplan–Meier analysis of TCGA gastric cancer patients was stratified by median risk. High risk scores are associated with general poor survival of TCGA-STAD. d Multi-index ROC curve of risk score and other indicators

Fig. 6

The nomogram can predict the prognosis probability in STAD. a A nomogram of the STAD cohort (training set) used to predict the OS. (B-C) Calibration maps used to predict the 3-year (b) and 5-year survival (c) in the training set. Calibration plots for 3-year (d) and 5-year survival (e) in the GSE62254 gastric cancer cohort (test group). The x-axis and y-axis represent the predicted and actual survival rates of the nomogram, respectively. The solid line represents the predicted nomogram, and the vertical line represents the 95% confidence interval