Comprehensive strategies for management of postoperative hyper-progression recurrence (HPR) of hepatocellular carcinoma: a 12-year large sample multi-center study

Comprehensive strategies for management of postoperative hyper-progression recurrence (HPR) of hepatocellular carcinoma: a 12-year large sample multi-center study

PDF

Lunan Qi¹^,²^,³, Jingxuan Xu¹^,³, Yuanyuan Chen³^,⁴, Zhan Lu¹^,³^,⁵, Min Zhou³^,⁶, Yingwu Huang⁷, Yongchi Ling⁸, Hai Huang⁹, Yuchong Peng¹⁰, Tao Peng²^,³^,¹¹, Bangde Xiang¹^,²^,³, Liang Ma¹^,²^,³

Cancer Biology & Medicine | 2025, 22(8) : 964 - 987

Less

Cancer Biology & Medicine | 2025, 22(8): 964-987

• Original Article •

Comprehensive strategies for management of postoperative hyper-progression recurrence (HPR) of hepatocellular carcinoma: a 12-year large sample multi-center study

Full

Affiliations

¹Department of Hepatobiliary Surgery, Guangxi Medical University Cancer Hospital, Nanning 530021, China

²Guangxi Liver Cancer Diagnosis and Treatment Engineering and Technology Research Center, Nanning 530021, China

³Key Laboratory of Early Prevention and Treatment for Regional High Frequency Tumor, Ministry of Education, Nanning 530021, China

⁴Department of Ultrasound, First Affiliated Hospital of Guangxi Medical University, Nanning 530021, China

⁵Department of Hepatobiliary Surgery, Guangxi International Zhuang Medical Hospital, Nanning 530021, China

⁶Department of General Surgery, Liuzhou Workers’ Hospital, Liuzhou 545005, China

⁷Department of Hepatobiliary Surgery, Chongzuo People’s Hospital, Chongzuo 532200, China

⁸Department of Surgery, Fusui County People’s Hospital of Guangxi, Fusui 543200, China

⁹Department of Hepatobiliary Surgery, Wuming Hospital of Guangxi Medical University, Wuming 530100, China

¹⁰Department of General Surgery, Chongqing Hospital of Traditional Chinese Medicine, Chongqing 400021, China

¹¹Department of Hepatobiliary Surgery, First Affiliated Hospital of Guangxi Medical University, Nanning 530021, China

About Author:

Author contributions

Conceived and designed the analysis: Lunan Qi, Liang Ma.

Collected the data: Lunan Qi, Zhan Lu, Min Zhou, Yingwu Huang, Yongchi Ling, Hai Huang, Yuchong Peng, Tao Peng, Liang Ma.

Contributed data or analysis tools: Lunan Qi, Jingxuan Xu, Yuanyuan Chen.

Performed the analysis: Jingxuan Xu, Yuanyuan Chen.

Wrote the paper: Lunan Qi, Jingxuan Xu.

Published: 2025-08-15 doi: 10.20892/j.issn.2095-3941.2024.0514

Outline

Abstract

Less

Objective:

Hyper-progression recurrence (HPR) after hepatectomy is a specific recurrence pattern associated with extremely poor prognosis in patients with hepatocellular carcinoma (HCC). This study was aimed at investigating the probable risk factors and establishing comprehensive models for formulating clinical strategies.

Methods:

Overall, 16,158 patients with HCC from 8 hospitals were screened, among whom 3,125 patients who underwent R0 resection were included, and divided into development (n = 2,113) and validation (n = 1,012) cohorts. A comprehensive study of HPR predictive models and biological features was conducted.

Results:

Among the 3,125 enrolled patients, 506 (16.19%) developed HPR. The influence of HPR on extremely poor prognosis was reflected by recurrence features, adverse effects on systemic and liver function, and limited therapeutic options. Nine variables closely associated with HPR were identified, and incorporated into nomogram and conditional inference tree models, which successfully achieved pre- and post-operative HPR risk stratification and facilitated clinical decision-making. Multi-dimensional verification also confirmed the predictive accuracy of model combinations and their reliability in clinical applications. Furthermore, biological analyses revealed that HCCs with HPR exhibited hyperactive biological processes, inactive metabolism, and immune exhaustion features, together with high MYCN/HMGA2 co-expression, thereby enhancing understanding of the molecular events leading to HPR and providing valuable knowledge for HPR management.

Conclusions:

HPR after hepatectomy is associated with extremely poor prognosis and requires substantial attention. We constructed comprehensive predictive models and propose a clinical strategy for guiding HPR prevention and management.

Key words

Hepatocellular carcinoma / hyper-progression recurrence / predictive models / biological feature / clinical strategy

Cite this Article

Lunan Qi, Jingxuan Xu, Yuanyuan Chen, Zhan Lu, Min Zhou, Yingwu Huang, Yongchi Ling, Hai Huang, Yuchong Peng, Tao Peng, Bangde Xiang, Liang Ma. Comprehensive strategies for management of postoperative hyper-progression recurrence (HPR) of hepatocellular carcinoma: a 12-year large sample multi-center study[J]. Cancer Biology & Medicine, 2025 , 22 (8) : 964 -987 . DOI: 10.20892/j.issn.2095-3941.2024.0514

Full Text

Less

Introduction

Less

Hepatocellular carcinoma (HCC) is the third leading cause of cancer-related death worldwide¹. With improvements in surgical techniques and perioperative management of patients, HCC can be resected safely. However, the high incidence of post-surgical recurrence severely compromises long-term survival^2,3. Therefore, the effective control of post-surgical recurrence to increase the overall efficacy of HCC has become an important area of focus in the era of precision medicine.

Meaningful survival can be achieved with appropriate prevention and treatment of recurrence². Various therapeutics have been used to manage recurrence, including adjuvant therapeutics for recurrence prevention and anti-recurrence therapeutics^4–12. However, varied outcomes have been observed in patients undergoing active anti-recurrence treatment, primarily because of the multiplicity of recurrence patterns. In our previous study, we proposed a novel four-type classification for recurrent HCC with potential value for predicting survival and guiding treatment strategies¹³. Notably, type IV recurrence, also called hyper-progression recurrence (HPR), involves the sudden simultaneous occurrence of multiple intrahepatic recurrence nodules (>5 recurrence lesions) in the early postoperative period¹³. Patients experiencing HPR have extremely poor prognosis, owing to rapid deterioration. Furthermore, HPR may occur in patients with different HCC stages, including early stage. Therefore, improving understanding of the implications of HPR and developing prevention strategies are crucial. However, our previous research has not extensively explored HPR risk factors and preventive strategies. In addition, to our knowledge, no prior report has focused on HPR.

Given that HPR can occur in patients with various HCC stages, the commonly used staging systems have limited value in HPR prediction. Therefore, we conducted a large sample study of multicenter cohorts to further investigate HPR risk factors. Subsequently, we established a nomogram and conditional inference tree (CTREE) models based on the identified risk factors to comprehensively assess HPR risk. Furthermore, we used a “model layer pass” approach and combined the biological features of HPR to develop a clinical strategy to guide HPR prevention and management.

Materials and methods

Less

Patients and follow-up

From January 2010 to March 2021, 16,158 patients with HCC at 8 hospitals (Guangxi Medical University Cancer Hospital, First Affiliated Hospital of Guangxi Medical University, Liuzhou Workers’ Hospital, Chongzuo People’s Hospital, Chongqing Hospital of Traditional Chinese Medicine, Wuming Hospital of Guangxi Medical University, Fusui County People’s Hospital, and Guangxi International Zhuang Medical Hospital) were screened. Finally, 3,125 patients who underwent R0 resection (according to the intraoperative criteria in the Guidelines for the Diagnosis and Treatment of Primary Liver Cancer¹⁴) with a mean age of 45 years (range: 14–83 years) were enrolled. The inclusion criteria were as follows: 1) treatment with R0 resection; 2) definitive pathological diagnosis of HCC according to World Health Organization criteria; 3) no prior anticancer treatment; and 4) tumor stage determined according to the Chinese Liver Cancer staging system¹⁴ and tumor differentiation defined according to the Edmondson grading system. The baseline data and follow-up of each center are shown in Table S1.

HPR was defined as the first sudden simultaneous appearance of numerous intrahepatic nodules (>5) without (type A) or with (type B) macrovascular invasion or extra-hepatic metastasis in the early postoperative period (within 2 years)¹³. According to the above definition, all patients included in this study were required to have either definite recurrence or regular follow-up over 2 years, to better identify patients with HPR. Therefore, patients with less than 2 years of follow-up and no definite recurrence at their last assessment were excluded. The end-point of the follow-up was January 31, 2023. Cohort information and the study design are shown in the supplementary information and Figure 1A.

Statistical analysis

All statistical analyses were performed in IBM SPSS statistical software version 22 for Windows and R version 3.6.1 (IBM, Armonk, NY, USA). The clinicopathological characteristics of the development and validation cohorts were evaluated with χ² test or Fisher’s exact test. In addition, overall survival (OS) and recurrence-free survival (RFS) were estimated with the Kaplan-Meier method and compared between groups with the log-rank test. Univariate and multivariate logistic regression analyses were conducted to identify independent predictors based on variables in the development cohort. In the univariate logistic regression analysis, potential predictors (P < 0.05) were further included in subsequent multivariate regression analysis (using the forward likelihood ratio method, with P < 0.05 as the entry and removal criteria).

Nomogram models were established according to the results of multivariable logistic regression analysis. The “rms” package in R was used to develop the nomograms. In the nomogram, “points” represents the score of each variable under different values, whereas “total points” represent the sum of the individual term points for all variables included in the model. Concordance statistics was used to measure the discriminative ability of the nomogram, and a calibration plot was drawn to reflect the agreement between the observed outcomes and predicted probabilities. Each patient’s score on the nomogram was computed. Receiver operating characteristic (ROC) curve analysis was applied to further evaluate the predictive performance of the nomogram score and identify the optimal cut-off based on the maximal Youden index. The clinical utility of the nomogram was determined through decision curve analysis (DCA) after calculation of the net benefits at each risk threshold probability. The clinical impact curve of the nomogram was also plotted to demonstrate the significance of the nomogram.

A CTREE model was used for HPR risk stratification. The CTREE determines the optimal split of a parent node (a region in the feature space) with multiple testing procedures at each stage. According to the Gini criteria, patients were partitioned into 2 child nodes from the entire feature space, and risk tree growth and pruning continued until the terminal nodes had no subsequent splits attaining statistical significance. The probability of HPR was calculated for each terminal node and accordingly yielded a risk stratification model. All statistical assessments were two-tailed, and the threshold for statistical significance was set at P <0.05.

Results

Less

Characteristics and prognosis of HPR

In this multi-center study, HPR was observed in 16.19% (506/3,125) of patients: 233 and 273 with types A and B, respectively (Figure 1B). Analysis of the time to recurrence (TTR) of HPR confirmed that all HPR occurred in the early postoperative period (within 2 years), and the high incidence period was 2–4 months after hepatectomy (Figure 1C). The log-rank test indicated that patients with HPR (either type A or B) after hepatectomy had significantly lower RFS and OS than those without HPR (all P <0.05) (Figure 1D).

During the study, 2,165 of the 3,125 patients experienced recurrence. The effects on systemic and liver function during recurrence and treatment after recurrence were analyzed in 2,165 patients with definite recurrence. Higher proportions of patients with poor Eastern Cooperative Oncology Group (ECOG) scores (>2) and poor liver function (stage B or C) were observed in patients with HPR during recurrence than those without HPR (Figure 1E). In addition, more than half the patients without HPR (57.3%) underwent curative treatment, including re-resection, liver transplantation, local ablation, or combined potentially curative treatment. In contrast, all patients with HPR received palliative treatments [e.g., transcatheter arterial chemoembolization (TACE)/hepatic artery infusion chemotherapy (HAIC), radiotherapy, or systemic treatment] or supportive care (e.g., Chinese medicine, treatment of liver function, symptomatic treatment, and/or nutritional support) alone (Figure 1F).

Risk factors for HPR identification

Subsequently, we explored the risk factors for HPR. First, the 3,125 patients were randomly divided into development (n = 2,113) and validation (n = 1,012) cohorts in an approximately 2:1 ratio. The patients’ clinicopathological features were similar between cohorts (Table S2). In the development cohort, both univariate and multivariate analysis revealed that HPR was associated with young age (≤45 years), high alpha-fetoprotein (AFP) level (≥400 ng/mL), tumor size >10 cm, >3 tumor nodes, portal vein/hepatic vein tumor thrombosis (PVTT/HVTT), microvascular invasion (MVI), high Ki67 index (≥30%), incomplete tumor capsule, and postoperative complications (all P <0.05) (Table 1). Therefore, these 9 variables (age, AFP level, tumor size, tumor nodes, PVTT/HVTT, MVI, Ki67 index, tumor capsule, and postoperative complications) were identified as independent predictors of HPR. The absolute value of the coefficient between each variable did not exceed 0.42 (Figure 2A). Multicollinearity analysis revealed that each variable’s variance inflation factor did not exceed 2, thus indicating no collinearity among these 9 predictors (Figure 2B).

Preoperative predictive nomogram establishment

Notably, 6 of the 9 predictors above (age, AFP level, tumor size, node numbers, tumor capsule, and PVTT/HVTT status) were available preoperatively. Therefore, to better predict HPR before the operation, we established a preoperative nomogram based on these 6 predictors in the development cohort (Figure 2C). Bootstrap validation revealed minimal evidence of model overfitting, given that the C-index of this nomogram was 0.940 (Figure 2D). Similar results were obtained for the validation cohort (Figure S1A). In addition, ROC curve analysis revealed that the area under the curve values of the preoperative nomogram was 0.940 [95% confidence interval (CI): 92.8%–95.3%] and 0.929 (95% CI: 91.0%–94.8%) in the development and validation cohort, respectively (Figure 2E; Figure S1B). According to DCA and clinical impact curves, the preoperative nomogram had good predictive efficiency and remarkable predictive power in both cohorts, and a high-risk threshold of 0–0.95 was most beneficial in predicting HPR (Figure 2F; Figure S1C, D).

Combination of the preoperative nomogram and CTREE model for preoperative HPR risk assessment

According to the preoperative nomogram, when the cutoff value of predicted HPR incidence was set at 5%, the corresponding total nomogram score was 134, and the negative predictive value (98.98% and 98.13% in the development and validation cohorts, respectively) and sensitivity (95.82% and 94.65% in the development and validation cohorts, respectively) were extremely high at this score (Figure 3A). When the cutoff value of predicted HPR incidence was set at 95%, the corresponding total nomogram score was 420, and the positive predictive value (97.50% and 100% in the development and validation cohorts, respectively) and specificity (99.99% and 100% in the development and validation cohorts, respectively) were extremely high at this score (Figure 3A). Furthermore, similar very low (or very high) predicted incidence and actual incidence corresponded to these 2 score points in both cohorts. Therefore, the preoperative nomogram indicated that patients with total scores of <134 or >420 had very low risk or very high risk of HPR, respectively.

However, when the patient score was 134–420, prediction errors were observed. Table S3 shows similar scores between patients 1 and 2; 3 and 4; 5 and 6; 7 and 8; and 9 and 10. However, their actual outcomes differed. Therefore, patients with scores of 134–420 were considered to be at potential risk of HPR, and HPR risk stratification could not be performed on the basis of the nomogram score alone. Consequently, we constructed a CTREE model to further aid in risk stratification and clinical decision-making for the 1,027 patients with scores of 134–420 (Figure 3B). Overall, 12 terminal nodes representing different risk hierarchies were identified with the CTREE model. Patients in nodes 1, 2, 3, and 6 were considered to have high HPR risk (> 60%), whereas those in nodes 4, 7, 8, and 9 were considered to have moderate risk (20%–60%), and those in nodes and 5, 10, 11, and 12 were considered to have low risk (< 20%) (Figure 3B). As presented in Table S3, 4 patients (in nodes 3, 5, 7, and 9) among the 5 patients with HPR were ultimately assigned to the high-risk group, thus reflecting the rationality of the CTREE model.

From the preoperative nomogram combined with the CTREE model, 424 patients (299 and 125 in the development and validation cohorts, respectively) were classified as having high to very high HPR risk. The actual HPR incidence in these patients was 73.6% (220/299) and 76.0% (95/125) in the development and validation cohorts, respectively. Survival analysis indicated that patients with high to very high HPR risk had significantly shorter RFS and OS than those with very low to moderate risk in both cohorts (Figure 3C, D). In addition, we retrospectively collected a control cohort of patients (from Guangxi Medical University Cancer Hospital, First Affiliated Hospital of Guangxi Medical University, and Wuming Hospital of Guangxi Medical University) with high to very high HPR risk, who underwent only palliative treatment (e.g., TACE/HIAC, radiotherapy, or systemic treatment). Details of the cohort are shown in Table S4. The surgical group displayed a significantly shorter OS than the palliative treatment group (Figure 3E). Therefore, for patients with preoperatively predicted high to very high HPR risk, surgery can be considered not to provide benefits.

Postoperative predictive nomogram model establishment and values

Except for the 424 patients with high to very high HPR risk, the remaining 2,701 patients had very low to moderate HPR risk. For such patients, surgical recommendations should be specific to the situation. If all patients with very low to moderate HPR risk were to undergo surgery, HPR would still occur at some rate. Consequently, we constructed a postoperative nomogram for HPR prediction based on all 9 predictors (Figure 4A). The C-index of the postoperative nomogram was 0.944 and 0.942 in the development and validation cohorts, respectively (Figure 4B; Figure S2A). Multi-dimensional verification, including ROC analysis, DCA, and clinical impact curves, also confirmed the predictive accuracy and reliability of the postoperative nomogram in both cohorts (Figure 4C, D; Figure S2B–D).

Furthermore, according to maximization of the Youden index, the best cutoff score was 205.6, and the sensitivity and specificity were 87.3% and 89.0%, respectively, in the development cohort and 75.1% and 89.8%, respectively, in the validation cohort. Therefore, with a threshold score of 205.6 post-surgery, patients with high scores (>205.6) had significantly higher HPR rates, and lower RFS and OS, than those with low scores (≤205.6) (Figure 4E, F). Similar results were obtained in the validation cohort based on the same cutoff score in the development cohort (Figure S3A–C).

In addition, postoperative adjuvant (PA)-TACE/HAIC is a common adjuvant therapy for recurrence prevention of HCC after hepatectomy. Notably, among patients with high scores (>205.6), those who received PA-TACE/HAIC in the early period after surgery displayed lower HPR rates, and significantly longer RFS and OS, than those without PA-TACE/HAIC, in both the development (Figure S4A–C) and validation cohorts (Figure S4D–F). These findings indicated that early post-operative adjuvant therapies decrease the incidence of HPR and improve prognosis.

Biological function profiles of HCCs with HPR

To better explore the molecular characteristics underlying HPR, we performed whole-transcriptome expression profiling based on RNA sequencing for 119 HCCs (in 31 patients with HPR and 88 patients without HPR, respectively). First, gene set enrichment analysis (GSEA) demonstrated that many cancer-related process clusters were enriched in patients with HPR, including cancer development-related, cellular process-related, metabolism-related, and immune-related clusters (Figure 5A; Table S5). With the non-HPR group as a reference, we identified 4,986 differentially expressed genes (DEGs) (log₂FC >0, P <0.05) (Figure 5B). Weighted gene co-expression network analysis (WGCNA) was used to construct the co-expression network for these 4,986 DEGs, with a soft threshold power (β) value of 5 and a scale-free network (Figure S5A, B). The co-expression modules were generated with dynamic tree cutting, and 23 modules were determined (Figure 5C). Module cluster analysis revealed that several modules had similar expression profiles. For instance, in modules ME7–13, the DEGs clustered in cellular processes and cancer-related pathways, whereas in modules ME16–19 and ME14–15, most DEGs were enriched in metabolism-related pathways and immune-related pathways, respectively (Figure 5D). Module trait relationships revealed that modules ME9 and ME11 were positively correlated with HPR, as well as several malignancy-related clinicopathologic factors (e.g., high AFP level, large tumor size, high Ki67, and PVTT/HVTT), whereas modules ME15 and ME19 were negatively correlated with HPR (Figure 5D). GO and KEGG pathway analyses of DEGs in these modules are illustrated in Figure 5E. The hub genes of these modules, according to cytoHubba analysis, are illustrated in Figure 5F. In addition, with ssGSEA based on immune-related gene sets from the Broad Institute gene-set enrichment database, HCCs with HPR were characterized primarily by immunodeficient or immunosuppressive signatures (Figure S6A). Details of each immune infiltrate status “enrichment score” are illustrated in Figure S6B. The findings were consistent with the GSEA and WGCNA results described above.

The MYCN/HMGA2 co-expression phenotype enables identification of HCC and its malignant biological characteristics

Two hub genes in modules ME9 and ME11, MYCN and HMGA2, interacted with many DEGs in these 2 modules (Figure 6A). Heatmaps of DEGs revealed that most of these DEGs were up-regulated in patients with rather than without HPR (Figure 6A). GO and KEGG pathway analyses of these DEGs revealed that they were enriched primarily in cellular processes and embryonic stem cell development-related processes, and participated in multiple proto-oncogene-related and EMT-related pathways (e.g., PI3K-AKT, P53, Hedgehog, Wnt, and Hippo signaling pathways) (Figure 6B). In addition, high frequency co-expression of MYCN/HMGA2 was found in patients with HPR, according to RNA-sequencing data of 119 HCCs (Figure 6C). Immunofluorescence analysis of MYCN/HMGA2 co-expression was also performed in HCC tissues to provide validation (Figure 6D). Log-rank tests among 119 patients indicated that patients with MYCN/HMGA2-double high expression (both log FPMK+1 of MYCN and HMGA2 > median) had the lowest RFS and OS among sub-types, particularly with respect to MYCN/HMGA2-double negative patients (Figure 6E).

To better display the molecular characteristics of MYCN/HMGA2 co-expression cell subsets in HCC, we performed single-cell RNA sequencing analysis on 27,999 single cells derived from 4 HCCs (MYCN/HMGA2-double high expression HCC; only MYCN high expression HCC; only HMGA2 high expression HCC; and MYCN/HMGA2-double negative HCC) and 2 adjacent tissues. Nine distinct cell types, including epithelial, endothelial, immune (T cell, B cell, NK cell, etc.), and stromal cells, were assigned known marker genes (Figure 6F i), and the proportion of each cell type markedly varied by sample (Figure 6F ii). On the basis of copy number variation analysis, 8 main malignant sub-clusters were identified in the epithelial cells (Figure 6F iii). MYCN expression was concentrated in sub-clusters 1, 3, and 5; whereas HMGA2 expression was concentrated in sub-clusters 0, 1, 3, and 6. Sub-clusters 1 and 3 were the MYCN/HMGA2 co-expression sub-clusters (Figure 6F iv). Notably, several mesenchymal markers (CDH, VIM, and IGF2BP1) and stem cell markers (EpCAM, C-MYC, and CD44) had high expression in these 2 sub-clusters (1 and 3), thus indicating the EMT characteristics and CSC phenotype of the MYCN/HMGA2 co-expression sub-clusters (Figure S7). Sub-cluster 3 was also characterized by high expression of proliferation-associated genes (CCNB1, CCNB2, and MKI67) (Figure S7). In addition, gene set variation analysis revealed the signature heterogeneity among 8 sub-clusters (Figure 6F v). The 2 MYCN/HMGA2 co-expression sub-clusters shared common activated signatures, such as Wnt and TGF-β signals, alongside unique signatures such as E2E target, MYC target, and cell cycle (G2M) checkpoint signals in only sub-cluster 3 (Figure 6F v).

Treatment patterns and efficacy analysis in patients after HPR

As described previously, among the 506 patients with HPR, 271 (53.6%) received palliative treatment, and 235 (46.4%) received only supportive care. Post-recurrence survival (PFS) analysis was conducted to assess the efficacy of various treatment modalities after HPR. Patients who received palliative treatments had a longer median PFS (6 months) than patients who received only supportive care (3 months) (P <0.001) (Figure S8A). Among palliative treatment patterns, the combination therapy modalities achieved the longest PFS in both HPR type A and type B (P <0.001) (Figure S8B, C). In addition, for HPR type A, locoregional therapies achieved better PFS than systemic therapies; for HPR type B, systemic therapies performed slightly better than locoregional therapies (Figure S8B, C).

In addition, MYCN and HMGA2 mRNA levels were analyzed with qRT-PCR in 159 patients with HPR. According to the expression level (−ΔΔCT) in tumor tissues, 159 patients were further subdivided into a MYCN/HMGA2-double high expression group (both mRNA level ≥ mean level, n = 54), a MYCN or HMGA2 high expression group (n = 52), and a MYCN/HMGA2-double low expression group (both mRNA level < mean level, n = 53). PFS was significantly longer in patients with MYCN/HMGA2-double low expression who received palliative treatments than in patients receiving only supportive care (P = 0.028), although this finding was not statistically significant in the MYCN/HMGA2 double-high-expression group (P = 0.250) and the MYCN or HMGA2 high expression group (P = 0.077) (Figure S8D–F). Therefore, high expression of MYCN/HMGA2 might compromise therapeutic efficacy in patients with HPR.

Summary of clinical strategies for HPR prevention and management

On the basis of our results, we tentatively proposed a comprehensive clinical strategy to guide HPR management from pre-operative decision-making to treatment after HPR (Figure 7). Specifically, the pre-operative nomogram model may be used to identify patients not suitable for surgery (patients with very high HPR risk) and those suitable for surgery (patients with very low HPR risk). Patients with a preoperative nomogram score of 134–420 were considered to have potential HPR risk and were further assessed with the CTREE model, which classified them into high-, moderate-, or low- HPR risk categories to optimize surgical decision-making. The postoperative nomogram model could then be used to further assess HPR risk for postoperative patients, to guide early postoperative adjuvant therapies for recurrence prevention. Furthermore, the biological insights into HPR advance knowledge of the molecular factors associated with HPR and may guide treatment choice after HPR.

Discussion

Less

We previously identified a highly specific pattern of early recurrence called HPR¹³. This study further evaluated the characteristics of HPR and its effects on survival in a sufficiently large sample of multicenter data, to capture a consistent and clinically significant fraction of patients. Although the incidence of HPR in the present study was only 16.19%, its influence on patients’ poor prognosis was highly significant. Notably, under standardized periodic follow-up after surgery, all HPR cases occur in very early postoperative stages, which is one of the major reasons for extremely poor prognosis. However, unlike most common pattern of early recurrences, HPR exhibits a key feature of sudden simultaneous occurrence of a high recurrence-nodule burden. We observed relatively high ECOG scores (>2) and poor liver function in patients with HPR during recurrence, thus suggesting that the high recurrence-nodule burden adversely influences patients’ systemic and liver function, thereby leading to poor OS. Additionally, the limited treatment options for HPR might also contribute to the extremely poor prognosis of HPR.

In this study, we identified 9 clinicopathological factors associated with HPR, which provide an enhanced explanation of HPR occurrence in 3 dimensions. Five factors (large tumor, multi-tumor nodes, incomplete tumor capsule, MVI, and PVTT/HVTT) have been recognized as tumor-related factors reflecting primary tumor invasion features^15–18. These factors constitute the first-dimensional element of HPR and indicate that wide intrahepatic/extra-hepatic dissemination has occurred preoperatively. Second, high AFP level and Ki67 index may reflect strong primary tumor embryonic-like plasticity and strong proliferation potential^19–21. These factors may confer a rapidly adaptive and robust proliferative function on metastatic cells, thereby forming a second dimension potentially explaining the “latency-free” nature of HPR. Third, we noted that post-operative complications were the only surgery-related factors for HPR. Postoperative complications can alter the immune microenvironment, thus enabling immune escape and rapid growth of metastatic tumors. Importantly, post-operative complications were the only controllable factor among the 9 identified risk factors. Therefore, greater attention should be paid to improving perioperative management to decrease HPR risk by controlling the incidence of postoperative complications.

Younger patients with HCC tend to have poorer prognosis than older patients^22,23, in agreement with our findings indicating younger age (≤45 years) as a risk factor. The exact reasons for this phenomenon remain unclear but might be partly attributable to younger patients presenting with more advanced stages of HCC at diagnosis, because of limited early screening and follow-up^24,25. Therefore, regular screening for liver tumors in young adults at high risk of developing HCC, such as those with hepatitis B or C virus (HBV/HCV) infection, is imperative. However, younger patients might also have better liver functional reserves and stronger tolerance than older individuals. Therefore, whenever feasible, resection surgery remains recommended for young patients, particularly those with early-stage HCC with favorable systemic conditions.

A nomogram integrating diverse clinicopathologic factors and gene expression data can generate the individual probability of tumor recurrence or survival in patients with HCC^26–29. In contrast to previous studies, this study focused on HPR and used an innovative design in which HPR risk factors were identified, and a “model layer pass” method was used to predict and stratify for HPR risk. Specifically, we first established a pre-operative nomogram for HPR prediction based on the 6 variables available pre-operatively. Because of the large sample size of this study, the pre-operative nomogram demonstrated satisfactory predictive power. However, we also considered the limitations of the nomogram, in that it could only weigh the score of each risk factor for each patient but did not account for the effects of different combinations of risk factor stratification on outcome events. Therefore, we further established CTREE model, which offers straightforward visualization of the HPR risk stratification of patients with different combinations of risk factors and provides a decision-making procedure to assign patients. PVTT/HVTT status was identified as the first discriminator among all examined variables, thus suggesting PVTT/HVTT had more pronounced effects than other variables on HPR risk. Notably, with the CTREE model, PVTT/HVTT positive patients were successfully assigned to sub-groups according to high (nodes 1, 2, and 3), moderate (node 4), or low (node 5) HPR risk, thereby providing a basis for deciding on the appropriateness of surgery in patients with advanced stage HCC. Furthermore, the actual HPR incidence in patients with high HPR risk predicted according to the combination of the preoperative nomogram and CTREE model was >70% in both cohorts, thereby reflecting the high efficiency of this preoperative model combination. Survival analysis also confirmed the reliability of the preoperative model combinations in clinical applications.

For patients with high to very high HPR risk, surgery did not result in better OS than other palliative treatments yet exposed patients to surgical trauma. Therefore, concluding that surgery does not benefit patients with high to very high risk of HPR is reasonable, and less invasive and traumatic palliative therapies might be preferred. Recently, encouraging progress has been made in conversion surgery following the combination of multiple preoperative locoregional and systemic therapies for advanced HCC^30–32. Consequently, the conversion therapy strategy might be a potential option for patients with high risk of HPR; however, further validation is necessary in future prospective research. Very low to low-risk patients may achieve the most surgical benefits. However, surgery should be cautiously recommended for moderate-risk patients after multiple considerations (e.g., surgical risk and patient tolerance). Despite hierarchical decision-making through preoperative model combinations, some patients will still experience postoperative HPR, possibly because of pathological risk factors (e.g., MVI and Ki67) that cannot be included in the preoperative model combinations. Consequently, we established a postoperative nomogram for postoperative HPR prediction, which also showed excellent performance. This nomogram enabled further stratification of postoperative patients and may facilitate decision-making regarding early postoperative adjuvant therapy.

Overall, our “model layer pass” method has potential value in clinical decision-making before and after surgery. The preoperative model combinations may enable accurate identification of patients truly suitable for hepatectomy, thereby increasing surgical effectiveness; whereas the post-operative prediction model may guide the design of postoperative protocols (e.g., postoperative monitoring and early adjuvant therapy). Notably, the 9 variables incorporated into our models are frequently available clinical indicators, thus supporting the feasibility of easy and broad application of our “model layer pass” in clinical practice.

Genomic analysis of primary tumors in patients with HPR may enhance knowledge of the molecular events relevant to HPR. For instance, in this study, HCCs with HPR were characterized by frequent activation of cellular processes and numerous enriched DEGs involved in many cancer invasion/proliferation signaling pathways, including the Wnt, PI3K-AKT, TGF-β, MYC, and Hedgehog signaling pathways. The hyperactivity of biological processes and the wide activation of multiple cancer proliferation/metastasis-related pathways may partly explain the “latency-free” and “hyper-progression” nature of HPR. Additionally, module trait relationships revealed that modules (ME9 and ME11) containing many cancer invasion/proliferation DEGs were significantly positively correlated with HPR as well as several malignant-related clinicopathologic factors (e.g., high AFP level, large tumor size, high Ki67, and PVTT/HVTT). These findings are consistent with our multivariate analysis results, which indicated that patients with HPR presented 9 related clinicopathologic factors, thus reflecting the rationality of using the 9 factors in our model combinations above.

The MYCN/HMGA2 co-expression pattern was highly frequent in patients with HPR. MYCN encodes an important transcription factor that mediates epigenetic reprogramming and drives lineage plasticity in many cancers^33,34. HMGA2 encodes a non-histone architectural transcription factor that modulates the transcription of genes by binding AT-rich sequences, thereby altering chromatin structure (chromatin remodeling) and influencing biological processes³⁵. Herein, we observed that MYCN and HMGA2 interacted with numerous cellular process and embryonic stem cell development-related DEGs. Moreover, scRNA-seq analysis revealed the high malignancy potential, including the EMT characteristics, high proliferation, and CSC phenotype, of MYCN/HMGA2 co-expression cell sub-clusters in HCC. These findings suggested the importance of the MYCN/HMGA2 co-expression pattern in the development of HPR, and suggested its close association with hyperactive biological processes and widely activated cancer-related pathways in HPR, although the specific mechanism remains unclear.

Compared with supportive care alone, palliative treatment prolonged PFS in patients with HPR. Notably, combination therapy modalities achieved the longest PFS in both HPR type A and type B. Therefore, combination therapy modalities might be preferred for patients with HPR, regardless of type. Nevertheless, the efficacy of traditional treatments for HPR is limited, possibly because of the high frequency co-expression pattern of MYCN/HMGA2. However, preclinical studies have shown that HMGA2 inhibitors inhibit the growth of cancer cells and prevent tumor transformation³⁶. Additionally, MYCN positive HCC is highly sensitive to trans-retinoic acid, because of the high expression of stem markers such as EPCAM³⁷. We suggest that the detection of MYCN/HMGA2 might become a routine procedure in HCC profiling, and targeting of MYCN/HMGA2 might provide a promising approach for HPR treatment.

This study has several limitations that should be acknowledged. The primary limitation was the difference in sample sizes between center 1 and other centers, which might have introduced potential bias in the analysis. Furthermore, the study population was recruited exclusively from China and had predominant representation of HBV-related HCC cases, thus limiting the generalizability of our findings to other HCC etiologies. To address these limitations, future research should incorporate 1) prospective collection of balanced sample sizes across multiple centers to enhance statistical power and representation and 2) inclusion of an external validation cohort comprising non-HBV-related HCC (e.g., HCV-related HCC), to verify the robustness of our findings across diverse etiological backgrounds. Additionally, further dimensional molecular research (e.g., proteomics and metabolomics, scATAC-seq, and spatial multi-omics analysis) of HPR should be conducted to provide more precise information regarding the personalized treatment for HPR.

Conclusions

Less

In conclusion, HPR after hepatectomy has extremely poor prognosis, mainly because of its short TTR, large tumor burden, adverse effects on systemic and liver function, and limited therapeutic options. Multivariate analyses revealed 9 clinicopathological factors associated with HPR. Subsequently, comprehensive models for HPR prediction and risk stratification were established, which displayed excellent predictive performance and applicability to formulating pre- and postoperative strategies. Multi-dimensional verification also confirmed the predictive accuracy of the model combinations and their reliability in clinical applications. Biological analyses revealed that HCCs with HPR exhibited hyperactive biological processes, inactive metabolism, and immune exhaustion features, together with high frequency of MYCN/HMGA2 co-expression, thus enhancing understanding of the molecular events leading to HPR and providing valuable knowledge for guiding HPR therapy. Overall, in this study, we explored the clinicopathological factors and biological features associated with HPR, and propose a comprehensive clinical strategy for guiding HPR prevention and management.

Funding

Less

This work was supported by the National Natural Science Foundation of China (Grant Nos. NSFC 82273405, and 81972306). This work was also supported partly by the Guangxi Nature Sciences grants (Grant No. 2018GXNSFAA138028) and the Guangxi Medical University Training Program for Distinguished Young Scholars.

References

Less

Bray F,Laversanne M,Sung H,Ferlay J,Siegel RL,Soerjomataram I, et al.Global cancer statistics 2022: GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 countries.CA Cancer J Clin. 2024; 74: 229–63.

Tabrizian P,Jibara G,Shrager B,Schwartz M,Roayaie S.Recurrence of hepatocellular cancer after resection: patterns, treatments, and prognosis.Ann Surg. 2015; 261: 947–55.

Llovet JM,Kelley RK,Villanueva A,Singal AG,Pikarsky E,Roayaie S, et al.Hepatocellular carcinoma.Nat Rev Dis Primers. 2021; 7: 6.

Li SH,Mei J,Cheng Y,Li Q,Wang QX,Fang CK, et al.Postoperative adjuvant hepatic arterial infusion chemotherapy with FOLFOX in hepatocellular carcinoma with microvascular invasion: a multicenter, phase III, randomized study.J Clin Oncol. 2023; 41: 1898–908.

Wang Z,Ren Z,Chen Y,Hu J,Yang G,Yu L, et al.Adjuvant transarterial chemoembolization for HBV-related hepatocellular carcinoma after resection: a randomized controlled study.Clin Cancer Res. 2018; 24: 2074–81.

Wang WH,Wang Z,Wu JX,Zhang T,Rong WQ,Wang LM, et al.Survival benefit with IMRT following narrow-margin hepatectomy in patients with hepatocellular carcinoma close to major vessels.Liver Int. 2015; 35: 2603–10.

Lee JH,Lee JH,Lim YS,Yeon JE,Song TJ,Yu SJ, et al.Adjuvant immunotherapy with autologous cytokine-induced killer cells for hepatocellular carcinoma.Gastroenterology. 2015; 148: 1383–91.

Xia Y,Li J,Liu G,Wang K,Qian G,Lu Z, et al.Long-term effects of repeat hepatectomy vs percutaneous radiofrequency ablation among patients with recurrent hepatocellular carcinoma: a randomized clinical trial.JAMA Oncol. 2020; 6: 255–63.

Peng Z,Chen S,Wei M,Lin M,Jiang C,Mei J, et al.Advanced recurrent hepatocellular carcinoma: treatment with sorafenib alone or in combination with transarterial chemoembolization and radiofrequency ablation.Radiology. 2018; 287: 705–14.

10.

Ali R,Riaz A,Gabr A,Abouchaleh N,Mora R,Al Asadi A, et al.Clinical outcomes of Y90 radioembolization for recurrent hepatocellular carcinoma following curative resection.Eur J Nucl Med Mol Imaging. 2017; 44: 2195–202.

11.

Lou J,Li Y,Liang K,Guo Y,Song C,Chen L, et al.Hypofractionated radiotherapy as a salvage treatment for recurrent hepatocellular carcinoma with inferior vena cava/right atrium tumor thrombus: a multi-center analysis.BMC Cancer. 2019; 19: 668.

12.

Zhuang L,Mou HB,Yu LF,Zhu HK,Yang Z,Liao Q, et al.Immune checkpoint inhibitor for hepatocellular carcinoma recurrence after liver transplantation.Hepatobiliary Pancreat Dis Int. 2020; 19: 91–3.

13.

Qi L-N,Ma L,Wu F-X,Chen Y-Y,Xu J-X,Peng Y-C, et al.Clinical implications and biological features of a novel postoperative recurrent HCC classification: a multi-centre study.Liver Int. 2022; 42: 2283–98.

14.

Zhou J,Sun H-C,Wang Z,Cong W-M,Zeng M-S,Zhou W-P, et al.Guidelines for the diagnosis and treatment of primary liver cancer (2022 edition).Liver Cancer. 2023; 12: 405–44.

15.

Lu XY,Xi T,Lau WY,Dong H,Xian ZH,Yu H, et al.Pathobiological features of small hepatocellular carcinoma: correlation between tumor size and biological behavior.J Cancer Res Clin Oncol. 2011; 137: 567–75.

16.

Liang BY,Gu J,Xiong M,Zhang EL,Zhang ZY,Chen XP, et al.Tumor size may influence the prognosis of solitary hepatocellular carcinoma patients with cirrhosis and without macrovascular invasion after hepatectomy.Sci Rep. 2021; 11: 16343.

17.

Calderaro J,Petitprez F,Becht E,Laurent A,Hirsch TZ,Rousseau B, et al.Intra-tumoral tertiary lymphoid structures are associated with a low risk of early recurrence of hepatocellular carcinoma.J Hepatol. 2019; 70: 58–65.

18.

Erstad DJ,Tanabe KK.Prognostic and therapeutic implications of microvascular invasion in hepatocellular carcinoma.Ann Surg Oncol. 2019; 26: 1474–93.

19.

Ma WJ,Wang HY,Teng LS.Correlation analysis of preoperative serum alpha-fetoprotein (AFP) level and prognosis of hepatocellular carcinoma (HCC) after hepatectomy.World J Surg Oncol. 2013; 11: 212.

20.

Scholzen T,Gerdes J.The Ki-67 protein: from the known and the unknown.J Cell Physiol. 2000; 182: 311–22.

21.

Mrouj K,Andrés-Sánchez N,Dubra G,Singh P,Sobecki M,Chahar D, et al.Ki-67 regulates global gene expression and promotes sequential stages of carcinogenesis.Proc Natl Acad Sci USA. 2021; 118: e2026507118.

22.

Diao YK,Liu JW,Wu H,Wang MD,Fan XP,Chen TH, et al.Long-term oncologic outcomes of liver resection for hepatocellular carcinoma in adolescents and young adults: a multicenter study from a hepatitis B virus-endemic area.Am J Surg. 2021; 222: 751–8.

23.

Xu XS,Chen W,Miao RC,Zhou YY,Wang ZX,Zhang LQ, et al.Survival analysis of hepatocellular carcinoma: a comparison between young patients and aged patients.Chin Med J (Engl). 2015; 128: 1793–800.

24.

Kim JH,Choi MS,Lee H,Kim DY,Lee JH,Koh KC, et al.Clinical features and prognosis of hepatocellular carcinoma in young patients from a hepatitis B-endemic area.J Gastroenterol Hepatol. 2006; 21: 588–94.

25.

Aramaki M,Kawano K,Sasaki A,Ohno T,Tahara K,Kai S, et al.Hepatocellular carcinoma in young adults.Hepatogastroenterology. 2005; 52: 1795–7.

26.

Zhang ZJ,Wei BJ,Liu ZK,Xuan ZF,Zhou L,Zheng SS.Nomogram for prediction of hepatocellular carcinoma recurrence after liver resection.Hepatobiliary Pancreat Dis Int. 2025; 24: 269–76.

27.

Fang Q,Yang R,Chen D,Fei R,Chen P,Deng K, et al.A novel nomogram to predict prolonged survival after hepatectomy in repeat recurrent hepatocellular carcinoma.Front Oncol. 2021; 11: 646638.

28.

Liu WR,Tian MX,Tang Z,Fang Y,Zhou YF,Song SS, et al.Nine-factor-based immunohistochemistry classifier predicts recurrence for early-stage hepatocellular carcinoma after curative resection.Br J Cancer. 2020; 123: 92–100.

29.

Xu W,Li R,Liu F.Novel prognostic nomograms for predicting early and late recurrence of hepatocellular carcinoma after curative hepatectomy.Cancer Manag Res. 2020; 12: 1693–712.

30.

Kaseb AO,Hasanov E,Cao HST,Xiao L,Vauthey JN,Lee SS, et al.Perioperative nivolumab monotherapy versus nivolumab plus ipilimumab in resectable hepatocellular carcinoma: a randomised, open-label, phase 2 trial.Lancet Gastroenterol Hepatol. 2022; 7: 208–18.

31.

Chiang CL,Chiu KWH,Chan KSK,Lee FAS,Li JCB,Wan CWS, et al.Sequential transarterial chemoembolisation and stereotactic body radiotherapy followed by immunotherapy as conversion therapy for patients with locally advanced, unresectable hepatocellular carcinoma (START-FIT): a single-arm, phase 2 trial.Lancet Gastroenterol Hepatol. 2023; 8: 169–78.

32.

Arita J,Ichida A,Nagata R,Mihara Y,Kawaguchi Y,Ishizawa T, et al.Conversion surgery after preoperative therapy for advanced hepatocellular carcinoma in the era of molecular targeted therapy and immune checkpoint inhibitors.J Hepatobiliary Pancreat Sci. 2022; 29: 732–40.

33.

Dardenne E,Beltran H,Benelli M,Gayvert K,Berger A,Puca L, et al.N-Myc induces an EZH2-mediated transcriptional program driving neuroendocrine prostate cancer.Cancer Cell. 2016; 30: 563–77.

34.

Berger A,Brady NJ,Bareja R,Robinson B,Conteduca V,Augello MA, et al.N-Myc-mediated epigenetic reprogramming drives lineage plasticity in advanced prostate cancer.J Clin Invest. 2019; 129: 3924–40.

35.

Zhou X,Benson KF,Ashar HR,Chada K.Mutation responsible for the mouse pygmy phenotype in the developmentally regulated factor HMGI-C.Nature. 1995; 376: 771–4.

36.

Pentimalli F,Dentice M,Fedele M,Pierantoni GM,Cito L,Pallante P, et al.Suppression of HMGA2 protein synthesis could be a tool for the therapy of well differentiated liposarcomas overexpressing HMGA2.Cancer Res. 2003; 63: 7423–7.

37.

Qin XY,Suzuki H,Honda M,Okada H,Kaneko S,Inoue I, et al.Prevention of hepatocellular carcinoma by targeting MYCN-positive liver cancer stem cells with acyclic retinoid.Proc Natl Acad Sci USA. 2017; 115: 4969–74.

38.

Vallet-Pichard A,Mallet V,Nalpas B,Verkarre V,Nalpas A,Dhalluin-Venier V, et al.FIB-4: an inexpensive and accurate marker of fibrosis in HCV infection. comparison with liver biopsy and fibrotest.Hepatology. 2007; 46: 32–6.

39.

Sterling RK,Lissen E,Clumeck N,Sola R,Correa MC,Montaner J, et al.Development of a simple noninvasive index to predict significant fibrosis in patients with HIV/HCV coinfection.Hepatology. 2006; 43: 1317–25.

40.

Clavien PA,Barkun J,de Oliveira ML,Vauthey JN,Dindo D,Schulick RD, et al.The Clavien-Dindo classification of surgical complications: five-year experience.Ann Surg. 2009; 250: 187–96.

41.

Shi J,Lai EC,Li N,Guo WX,Xue J,Lau WY, et al.A new classification for hepatocellular carcinoma with portal vein tumor thrombus.J Hepatobiliary Pancreat Sci. 2011; 18: 74–80.

Appendix

Less

Year 2025 volume 22 Issue 8

PDF

Cite this Article

BibTeX

Article Info

doi: 10.20892/j.issn.2095-3941.2024.0514

Receive Date：2024-11-14
Online Date：2026-11-07
Published：2025-08-15

Article Data

Affiliations

History

Received：2024-11-14
Accepted：2025-04-15

Funding

This work was supported by the National Natural Science Foundation of China (Grant Nos. NSFC 82273405, and 81972306). This work was also supported partly by the Guangxi Nature Sciences grants (Grant No. 2018GXNSFAA138028) and the Guangxi Medical University Training Program for Distinguished Young Scholars.

Affiliations