Identification of autophagy-related risk signatures for the prognosis, diagnosis, and targeted therapy in cervical cancer

Abstract

Background: To rummage autophagy-related prognostic, diagnostic, and therapeutic biomarkers in cervical cancer (CC).

Methods: The RNA-sequence and clinical information were from the TCGA and GTEx databases. We operated Cox regression to determine signatures related to overall survival (OS) and recurrence-free survival (RFS) respectively. The diagnostic and therapeutic effectiveness of prognostic biomarkers were further explored.

Results: We identified nine (VAMP7, MTMR14, ATG4D, KLHL24, TP73, NAMPT, CD46, HGS, ATG4C) and three risk signatures (SERPINA1, HSPB8, SUPT20H) with prognostic values for OS and RFS respectively. Six risk signatures (ATG4C, ATG4D, CD46, TP73, SERPINA1, HSPB8) were selected for qPCR. We screened five prognostic signatures(ATG4C, CD46, HSPB8, MTMR14, NAMPT) with diagnostic function through the GEO database. Correlation between our models and treatment targets certificated the prognostic score provided a reference for precision medicine.

Conclusions: We constructed OS and RFS prognostic models in CC. Autophagy-related risk signatures might serve as diagnostic and therapeutic biomarkers.

Keywords: Autophagy; Biomarkers; Cervical cancer; mRNA; qPCR.

Fig. 1

The flow diagram of the integral analysis

Fig. 2

Identification and functional annotation of differently expressed autophagy-related genes (DEARGs) in CC patients. a Volcano plot for DEARGs in the tumor and normal samples, in the volcano map, we used the ordinate “6” as the boundary for gene annotation. b Top 8 GO analysis of 53 DEARGs from the three aspects of BP, CC and MF. c Top 10 significant KEGG signal pathways. d PPI network of 53 DEARGs

Fig. 3

Construction of OS and RFS risk prognostic models. a Univariate Cox regression analysis of the risk signatures for OS. b Univariate Cox regression analysis of the risk signatures for RFS. c, d Lasso Cox regression model for OS. e, f Lasso Cox regression model for RFS

Fig. 4

Characteristics of prognostic gene signatures. a, c K–M curves for OS (a) and RFS (c) in the high- and low-risk groups when stratified by the autophagy-related signatures. b, d ROC curve of risk score at 1,3,5 years for OS (b) and RFS (d) respectively. e, f Distribution of OS-related risk score and RFS-related risk score, the black dotted line is the optimal cut-off value for dividing patients into low- and high-risk groups. g, h Distribution of patient survival time, and status. i, j Heatmap of autophagy-related gene expression profiles in the prognostic signature of CC

Fig. 5

Independent prognostic analysis and nomogram diagram. a Univariate Cox regression analysis. Forest plot of associations between risk factors and the survival for OS. b Multiple Cox regression analysis. The autophagy-associated gene signature is an independent predictor of CC patients for OS. c Univariate Cox regression analysis. Forest plot of associations between risk factors and the survival for RFS. d Multiple Cox regression analysis. The autophagy-associated gene signature is an independent predictor of CC patients for RFS. e A nomogram of the CC cohort (training set) used to predict the OS. f A nomogram of the CC cohort (training set) used to predict the RFS

Fig. 6

Discrimination of diagnostic value and therapeutic target sensitivity based on risk signature. a–f AUC of risk signature with diagnostic value (ATG4C, ATG4D, CD46, HSPB8, MTMR14, SUPT20H). g–k Correlation of the OS-related risk score and expression level of targets of precise treatment in CC. l–n Correlation of the RFS-related risk score and expression level of targets of precise treatment in CC

Fig. 7

RNA expression of ATG4C, ATG4D, CD46, TP73, SERPINA1 and HSPB8 in CC and normal samples. a–f qPCR determined the RNA expression of ATG4C, ATG4D, CD46, TP73, SERPINA1 and HSPB8 in CC and normal samples. Quantitative normalization of the gene was performed in each sample using GAPDH expression as an internal control