Nomograms incorporating hsa_circ_0029325 highly expressed in exosomes of hepatocellular carcinoma predict the postoperative outcomes

Abstract

Background: Liquid biopsies, for example, exosomal circular RNA (circRNA) can be used to assess potential predictive markers for hepatocellular carcinoma (HCC) in patients after curative resection. This study aimed to search for effective prognostic biomarkers for HCC in patients after surgical resection based on exosomal circRNA expression profiles. We developed two nomograms incorporating circRNAs to predict the postoperative recurrence-free survival (RFS) and overall survival (OS) of HCC patients.

Method: Plasma exosomes isolated from HCC patients and healthy individuals were used for circRNA microarray analysis to explore differentially expressed circRNAs. Pearson correlation analysis was used to evaluate the correlation between circRNAs and clinicopathological features. Cox regression analysis was used to explore the correlation between circRNA and postoperative survival time as well as recurrence time. A nomogram based on circRNA and clinicopathological characteristics was established and further evaluated to predict prognosis and recurrence.

Result: Among 60 significantly upregulated circRNAs and 25 downregulated circRNAs, hsa_circ_0029325 was selected to verify its power for predicting HCC outcomes. The high expression level of exosomal hsa_circ_0029325 was significantly correlated with OS (P = 0.001, HR = 2.04, 95% CI 1.41-3.32) and RFS (P = 0.009, HR = 1.62, 95% CI 1.14-2.30). Among 273 HCC patients, multivariate regression analysis showed that hsa_circ_0029325 (HR = 1.96, 95% CI 1.21-3.18), tumor size (HR = 2.11, 95% CI 1.33-3.32), clinical staging (HR = 2.31, 95% CI 1.54-3.48), and tumor thrombus (HR = 1.74, 95% CI 1.12-2.7) were independent risk factors for poor prognosis in HCC patients after radical resection. These independent predictors of prognosis were incorporated into the two nomograms. The AUCs under the 1-year, 3-year, and 5-year survival and recurrence curves of the OS and RFS nomograms were 0.755, 0.749, and 0.742 and 0.702, 0.685, and 0.642, respectively. The C-index, calibration curves, and clinical decision curves showed that the two prediction models had good predictive performance. These results were verified in the validation cohort with 90 HCC patients.

Conclusion: Our study established two reliable nomograms for predicting recurrence and prognosis in HCC patients. We also show that it is feasible to screen potential predictive markers for HCC after curative resection through exosomal circRNA expression profile analysis.

Keywords: Circular RNA; Exosomes; Hepatocellular carcinoma; Nomogram; Recurrence.

Fig. 1

The flow chat of the study

Fig. 2

Differential exosomal circRNA in tumor vs. normal plasma. a and b A western blot for expression of typical exosomal biomarkers (Hsp70, TSG101, CD9 and calnexin). The exosomes were positive with biomarkers staining. c and d Exsomes in tissue and plasma were observed under electron microscopy. e and f The purified particles in tissue and plasma were analyzed by nanoparticle tracking analysis (NTA). g Heat map for the differentially expressed circRNA in the serum of hepatocellular carcinoma patients and healthy controls. h Schematic diagram of the structure of has_circ_0029325. Black arrow indicates the splicing junction sites

Fig. 3

Verification of has_circ_0029325 in cancer tissue exosomes and in HCC patients. a The expression levels of has_circ_0029325 in HCC tissue-derived exosomes and pair paracarcinoma tissues-derived exsomes detected by qRT‐PCR method. b HCC tissue microarrays were used for profiling has_circ_0029325 expression by in situ hybridization. c and d Kaplan–Meier curves were used to show the overall survival and recurrence of HCC patients according to the expression of has_circ_0029325. e ROC curves were established to evaluate the predictive performance of has_circ_0029325 for overall survival and recurrence

Fig. 4

Construction and validation of the nomograms. a and b The overall survival nomogram and recurrence nomogram were established to predict the risk of HCC patients after curative resection. c and d The concordance index for the survival nomogram and recurrent nomogram indicated that the two models fits well. e and f The 1-,3-,5-year ROC curves of the survival model and recurrent model based risk features in training cohort. g and h The 1-,3-,5-year ROC curves of the survival model and recurrent model based risk features in validation cohort

Fig. 5

Validation of the nomograms. A–c The 1-,3-,5-year calibration curves for the overall survival nomogram in training cohort. D–f The 1-, 3-,5-year calibration curves for the recurrence nomogram in training cohort. g–i The 1-,3-,5-year clinical decision curves for the overall survival nomogram in training cohort. J–l The 1-,3-,5-year clinical decision curves for the recurrence nomogram in training cohort

Fig. 6

Risk score models development and validation. a Risk score distribution of HCC patients undergoing surgery. The heat map shows the relationship between predictive factors and overall survival(green = low risk, red = high risk). b Risk score distribution of HCC patients undergoing surgery. The heat map shows the relationship between predictive factors and recurrence(green = low risk, red = high risk). c and d Kaplan–Meier curves were used to show the overall survival and recurrence of high-risk(yellow) and low-risk(green) HCC patients in training cohort. e and f Kaplan–Meier curves were used to show the overall survival and recurrence of high-risk(yellow) and low-risk(green) HCC patients in validation cohort