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Preoperative plasma cell-free DNA chromosomal instability predicts microvascular invasion in hepatocellular carcinoma: a prospective study

BMC Cancer volume 25, Article number: 867 (2025) Cite this article

Abstract

Background

Microvascular invasion (MVI) has been recognized as a risk factor for early recurrence after hepatectomy in patients with hepatocellular carcinoma (HCC). This study aimed to estimate the performance of an ultrasensitive chromosomal aneuploidy detector (UCAD) model for preoperative MVI prediction in operable HCC patients based on plasma cell-free DNA (cfDNA).

Methods

A prospective study included HCC patients who underwent surgery in 2021. Preoperative peripheral plasma samples of eligible patients were collected to extract cfDNA, which was then subject to next generation sequencing. Low-coverage whole-genome sequencing data were analyzed for chromosomal instability using different parameters, including Z-score, chromosomal instability score (CIN score), tumor fraction (TFx) and a UCAD model (UCAD = CIN score + TFx + Z-score of all chromosomes). Receiver operating characteristic (ROC) curve was used to evaluate the performance of these parameters in preoperative MVI prediction.

Results

Finally, a total of 74 patients with HCC who undergone hepatectomy were prospectively enrolled. Chromosomal instability was evaluated by copy number alterations and oncogenes MCL1 (located at 1q), MYC (located at 8q), TERT (located at 5p), EGFR (located at 7p), and VEGFA (located at 6p) were identified in plasma cfDNA. The UCAD model was a superior parameter in predicting preoperative MVI, with an area under curve (AUC) value 0.749 with a sensitivity of 0.938 specificity of 0.466. Univariate analysis revealed that tumor size (≥ 5 cm) and UCAD (> 0.199) significantly increased the risk of MVI, which were further demonstrated by multivariate analysis, with odd ratio of 1.338 (95%CI, 1.060–1.689) and 2.028 (95%CI, 1.053–3.908) (P < 0.05).

Conclusions

Our cfDNA-based UCAD model has shown a promising performance for preoperative MVI prediction in operable HCC patients.

Trial registration

This study was registered at https://clinicaltrials.gov/ on 16 May 2022, retrospectively registered, Registration number: NCT05371873.

Peer Review reports

Introduction

Primary liver cancer is the fifth most common type of cancer and the second most common cancer-related mortality across the world [1, 2]. Hepatocellular carcinoma (HCC) is the predominant pathological type of primary liver cancer, accounting for approximately 90% of cases, thus being a huge health issue and socioeconomic burden [2]. Liver resection remains the first-line potential treatment to obtain long-term survival for patients with HCC, especially for those with early-stage disease. However, the five-year recurrence rate after hepatectomy is up to 50-70%, which may be due to preoperative microscopic foci or multicenter occurrence [2].

Recently, microvascular invasion (MVI) has been recognized as a risk factor for early tumor recurrence after hepatectomy in HCC patients and preoperative MVI prediction will guide the choice of surgery and prognosis management strategies in operable HCC patients [3,4,5,6,7]. Currently, confirmation of MVI still relies on pathological examination of surgical tissue [8, 9]. Thus, other non-invasive approaches that can predict MVI presence are urgently needed. Actually, many non-invasive indicators have been explored to investigate their performance in preoperative MVI prediction, such as clinicopathological characteristics, radiographic images, and laboratory test indicators (e.g. alpha fetoprotein [AFP]) [10,11,12,13,14,15,16,17,18]. However, no satisfactory preoperative MVI predictor has been widely recognized and recommended so far.

Genomic instability is a hallmark of human cancers, including gene mutation and copy number variation [19]. Previous studies have shown that more than 90% of HCC have genomic alterations at the early stages of tumor progression [20]. Genes frequently mutated in HCC patients include TERT, TP53, CDKN2A, CTNNB1, AXIN1, and ARID1A [21, 22]. Loss of chromosomes 1p, 4q, 8p, 9p, 9q, 10q,13p, 16p and 16q, and gain of 1q, 5p, 6p, 7p, 7q, 8q, 13q and 17p were frequently detected in HCC patients [23,24,25,26]. However, tumor heterogeneity is a thorny issue for the genomics research in HCC using tumor tissue samples [27].

Cell-free DNA (cfDNA) has provided a new type of biological analyte for liquid biopsy, because it may contain tumor-derived DNA, namely, circulating tumor DNA (ctDNA), which currently can be detected by polymerase chain reaction or next generation sequencing techniques [28,29,30,31]. Previous studies have reported that plasma cfDNA can serve as an alternative to tumor tissue for studying the genetic alterations of operable patients with HCC [32, 33]. In addition, several studies have shown that cfDNA has high sensitivity and specificity in the diagnosis and prognosis prediction of urothelial carcinoma [34], breast cancer [35] and liver cancer [36]. In liver cancer, several studies have reported the association between cfDNA variant allele frequency and MVI status [37,38,39]; however, proofs are still not sufficient.

In this study, we attempted to analyze chromosomal instability based on cfDNA-derived low-coverage whole-genome sequencing data, and evaluate the performance of preoperative MVI prediction using different parameters, including an ultrasensitive chromosomal aneuploidy detector (UCAD) model, with the goal to provide a new cfDNA-based approach for preoperative MVI prediction in operable HCC patients.

Materials and methods

Study design and population enrollment

This was a prospective study conducted at the First Affiliated Hospital of Zhejiang University during 2021. Patients were included in this study if they met the following key criteria: (1) 18–80 years old; (2) pathologically diagnosed with HCC; (3) intending to receive liver resection. Exclusion criteria were as follows: (1) concurrent serious complications, including other malignancy, unstable coronary heart disease or congestive heart failure (grade 3–4), chronic kidney disease (stage 4–5), cirrhosis (Child-Pugh grade C), immunodeficiency; (2) pregnancy. This study was conducted in accordance with Declarations of Helsinki and was approved by the Ethics Committee of the First Affiliated Hospital (approval number: IIT20220097B). All participants provided written informed consent. This study was registered at clinicaltrials.gov with number of NCT05371873.

Collection of blood samples and clinicopathological data

A total of 8–10 mL peripheral blood was collected from each patient prior to liver resection, and centrifuged to yield plasma.

The demographics of the participants were collected, as well as the laboratory test indicators, such as AFP, alanine aminotransferase (ALT), aspartate aminotransferase (AST), Hepatitis B Virus (HBV). The pathological characteristics of each participant was also determined by postoperative pathological examination, including tumor size, tumor count, tumor encapsulation, tumor differentiation and MVI. HCC staging was determined according to Barcelona Clinic Liver Cancer (BCLC) staging system, which divides HCC patients into very early stage (BCLC stage 0), early stage (BCLC stage A), intermediate stage (BCLC stage B), advanced stage (BCLC stage C) and end-stage stage (BCLC stage D) [40]. MVI is defined as the nesting mass of cancer cells lining the vascular cavities of endothelial cells or portal and hepatic venous systems, which is graded as M0 (no MVI), M1 (invaded vessels were no more than 5 and located at the peritumoral region adjacent to the tumor surface within 1 cm), and M2 (invaded vessels of more than 5 or at more than 1 cm away from the tumor surface) as described previously [8]. A small number of cancer cells or small clusters of cancer cells, not covered by endothelium but floating freely within isolated blood vessels, were not considered MVI.

Next generation sequencing and chromosomal instability evaluation

Plasma cfDNA was isolated using the QIAseq cfDNA Extraction Kit (Qiangen). Then 10 ng cfDNA was used to prepare the sequencing libraries with the NEBnext Ultra II FS DNA Library Prep Kit following the manufacturer’s instructions. DNA fragments were ligated with 8 bp-barcoded sequencing adaptors and amplified by PCR. Purified sequencing libraries were massively sequenced on Illumina HiSeq X10 platform as previously described [20, 41].

For each sample, at least 10 M paired reads (about 4 G data) were collected and filtered. The reads were mapped to human reference genome hg19. Then, average genomic coverage was calculated using samtools mpileup for each 200 k bin [42], and Z-scores which refer to a score that indicates how many standard deviations a value is above or below the mean for each bin was then normalized using the formula below:

$$\:{coverage}_{normalized}=\frac{{coverege}_{raw}-mean\left({coverage}_{controls,\:raw}\right)}{stdev\left({coverage}_{controls,\:raw}\right)}$$

R package DNACopy (Version 3.4.3) based on circular binary segmentation (CBS) algorithm was used to identify significant genomic breakpoints and genomic segments with copy number variation [43]. A P value of < 0.05 was considered as statistically significant binary segmentation. Absolute segment value was used for further analysis. Chromosomal instability score (CIN score) was calculated using the formula below,

$$\:CI{N}_{Score}=\sum\:_{seg\in\:all\:segment}{{L}_{seg}\times\:V}_{seg}$$

The cfDNA fraction derived from tumor cells (TFx) was evaluated by ichorCNA software based on copy number alterations detected by NGS as previously described [41].

Lastly, chromosomal instability was evaluated by a proprietary bioinformatics model UCAD, which has integrated chromosome Z-scores, CIN score, and TFx (formula is that: UCAD = CIN score + TFx + Z-score of all chromosomes).

Statistically analysis

Continuous variables were expressed as mean and standard deviation, median and interquartile ranges; categorical variables were compared by chi-square test. Logistic regression analysis was used to investigate the risk factors of MVI using hazard ratios or odds ratios with 95% confidence intervals, as appropriate. Missed data was removed from the analyses. Receiver operating characteristic (ROC) curve was used to evaluate the performance of preoperative MVI prediction of the UCAD model, and the optimal cutoff value was identified by Youden’s index. Correlation coefficient was calculated by Pearson correlation analysis. Statistical analyses were performed using R software.

Results

Patient characteristics

Among 102 patients recruited in this study, 24 patients were excluded due to deficiency of preoperative plasma samples, or failure of quality control; one patient with history of other malignancy; and 3 patients due to tumor tissue necrosis. Finally, a total of 74 participants were eligible for this study. As shown in Table 1, 71.6% of the participants were ≥ 55 years old, 79.73% were male, 64.86% were detected with pre-operative AFP less than 20 ng/mL, 32.43% were HBV-positive, and 79.73% were negative for MVI. Most of the participants were at early-stage HCC (BCLC stage 0-A, 86.5%). Forty-two patients were detected with tumor size of less than 3 cm and 19 patients had tumors measuring ≥ 5 cm. Sixty-two patients were observed to have only one single tumor. Tumor encapsulation was presented in 44 patients. Only 15 of the 74 patients had positive MVI and 16 cases had hepatic trans-arterial chemoembolization (TACE) treatment before liver surgery.

Table 1 Patients’ demographic and clinical characteristics
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Performance of Z-score, CIN score, TFx and UCAD model for MVI prediction in HCC

The analysis of chromosome copy number gains of well-studied oncogenes MCL1 (located at 1q), MYC (located at 8q), TERT (located at 5p), EGFR (located at 7p), and VEGFA (located at 6p) were identified in plasma cfDNA.

ROC curve for MVI prediction in HCC was plotted using Z-score, CIN score, TFx and UCAD model individually (Fig. 1). The AUC obtained by using Z-score of each chromosome arm loss or gain ranged from 0.31 to 0.70 (Table 2). Among them, chr1q, chr5p, chr8q, and chr16p had the largest AUC values of 0.70, 0.68, 0.66 and 0.66, respectively (P ≤ 0.05), showing a high diagnostic potential for MVI prediction. Next, we used Z-score of all chromosome to predict MVI, and the AUC value was 0.637 (95% CI, 0.503–0.77), with a sensitivity of 68.8% and a specificity of 58.6% at the optimal cutoff value of 2.26 (Table 3), showing a better performance than any single chromosome alteration. CIN score had an AUC of 0.702 (95% CI, 0.612–0.79) with a sensitivity of 93.8% and a specificity of 46.6% at the optimal cutoff value of 693.19, which showed a promising potential in MVI prediction. The AUC value of TFx was 0.64 (95% CI, 0.503–0.777) with 62.5% sensitivity and 65.6% specificity, respectively. Among these parameters, UCAD model had the largest AUC value of 0.749 (95% CI, 0.635–0.863) (Table 3; Fig. 1), achieving a sensitivity of 0.938 and a specificity of 0.466 at the optimal cutoff value of 0.199. However, AFP only demonstrated an AUC value of 0.565 (95% CI, 0.438–0.691). It was worth noting that the AUC value of UCAD was significantly higher than that of AFP (P<0.05).

Fig. 1
figure 1

ROC curve of all detectable chromosome instability and combined index. ROC, receiver operating characteristic

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Table 2 The AUC values, 95% CI and P-values of each chromosome arm losses and gains for MVI prediction in terms of Z-scores
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Table 3 Performance of Z-score of all chromosomes, CIN scores, TFx, UCAD and AFP for MVI prediction in HCC patients
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Performance of the UCAD model in predicting MVI grades in different subgroups of HCC patients

The performance for MVI prediction between UCAD model and AFP was compared in different subgroups according to the clinical and pathological characteristics of patients. For patients with tumor size less than 3 cm (N = 42), patients who had received TACE treatment before surgery (N = 15), patients with HCC recurrence (N = 18), and patients at late BCLC stage (B or C stage) (N = 10), the UCAD model had a larger AUC value than that of the AFP in each subgroup, although with no significant difference (Fig. 2). For HCC patients with tumor count ≥ 2 (N = 12), UCAD model showed a significantly better predictive performance of MVI than AFP (P = 0.01) (Fig. 2).

Fig. 2
figure 2

ROC curve of MVI prediction with the UCAD and AFP models in different subgroups of HCC. (a) ROC of MVI prediction with the UCAD and AFP models in whole group patients (N = 74). (b) ROC of MVI prediction with the UCAD and AFP models in the subgroup of HCC patients with tumor size less than 3 cm (N = 42). (c) ROC of MVI prediction with the UCAD and AFP models for HCC patients with TACE treatment before liver surgery (N = 15). (d) ROC of MVI prediction with the UCAD and AFP models for HCC patients with recurrence (N = 18). (e) ROC of MVI prediction with the UCAD and AFP models for HCC patients at BCLC stage B and C (N = 10). (f) ROC of MVI prediction with the UCAD and AFP models in HCC patients with total tumor count ≥ 2 (N = 12). AFP, alpha fetoprotein; MVI, microvascular invasion. HCC, hepatocellular carcinoma; MVI, microvascular invasion; ROC, receiver operating characteristic; TACE, trans-arterial chemoembolization

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Factors associated with MVI in HCC

We also performed univariate and multivariate logistic regression to evaluate factors associated with MVI in HCC. Univariate analysis revealed that tumor size (≥ 5 cm) and UCAD (> 0.199) significantly increased the risk of MVI, which were further demonstrated by multivariate analysis, with odd ratio of 1.338 (95%CI, 1.06–1.689) and 2.028 (95%CI, 1.053–3.908) (P < 0.05) (Table 4).

Table 4 Univariate and multivariate Stepwise logistic regression analysis of risk factors for MVI in patients with HCC
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According to the logistic regression results, we could predict the probability of MVI for each subject with the following formula: Logit(P)= -5.06 + 0.49 * Zscore + 0.18 * TFx + 2.11 * CINscore + 1.58 * AFP– 0. * BCLC + 1.56 * TumorCount + 1.65 * TumorSize. The formula of predicting probability for each subject was P = eLogit(P)/(1 + eLogit(P)). The AUC value of the logistic regression model was 0.838 (95% CI: 0.727–0.949), with optimal sensitivity and specificity of 75% and 83% (Fig. 3).

Fig. 3
figure 3

ROC curve of MVI prediction on the basis of the logistic regression model for HCC patients. HCC, hepatocellular carcinoma; MVI, microvascular invasion; ROC, receiver operating characteristic

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Discussion

Previous studies have reported that cfDNA is a potential biomarker for MVI prediction and may predict tumor recurrence after hepatectomy in HCC patients [37, 38]. But proofs are still not sufficient. In this study, we analyzed chromosomal instability based on cfDNA-derived low-coverage whole-genome sequencing data, and evaluated the performance of preoperative MVI prediction using different parameters, including Z score, CIN score, TFx and a proprietary UCAD model which integrated the parameters above. And the results showed that our proprietary cfDNA-based UCAD model outperformed well for preoperative MVI prediction in operable HCC patients.

Compared to tumor sampling, cfDNA is less invasive. In addition, cfDNA has overcome the disadvantages of tumor tissue samples, namely, heterogeneity and subclonal evolution when used for genomics research. In this study, we used low-coverage whole-genome sequencing of cfDNA to evaluate chromosomal instability. And we identified substantial genomic copy number alterations, including MCL1 (located at 1q), MYC (located at 8q), TERT (located at 5p), EGFR (located at 7p), and VEGFA (located at 6p), which have been reported previously [23,24,25].

Previous studies have proved the association between ctDNA variant allele fraction (VAF) and MVI in HCC [37, 38], and two of them have reported that ctDNA VAF can predict preoperative MVI status [37, 38]. Wang et al. divided 73 participants into training cohort (N = 49) and validation cohort (N = 24) and found ctDNA VAF was the only independent risk factor for MVI prediction; ROC analysis using the training cohort obtained an AUC of 0.92, with a sensitivity of 89.7% and a specificity of 80.0% when the cut-off value of ctDNA VAF was set at 0.83% [37, 38]. Using a smaller size of patients (N = 41), Xin et al. found that ctDNA maximal VAF could predict the presence of MVI with an AUC of 0.85, a sensitivity of 64.71% and a specificity of 100% when ctDNA VAF of 0.018 was set as the cutoff value [37, 38]. However, both studies employed DNA targeted sequencing with deep sequencing depth. Unlike targeted panels, low-coverage whole-genome sequencing we used here captures CIN across all chromosomes, including unexpected alterations beyond known HCC-associated genes. While our AUC (0.749) is lower than deep sequencing, low-coverage whole-genome sequencing requires < 10% of the sequencing depth, making it feasible for clinical screening and more cost-effective. Based on the sequencing data, we compared the performance of different parameters in preoperative MVI prediction, including Z score, CIN score, TFx and a UCAD model, which integrated the parameters above. Among them, UCAD model showed the best performance in preoperative MVI prediction for the entire patient population, with an AUC value of 0.749, a sensitivity of 0.938 and specificity of 0.466, followed by CIN score (AUC of 0.702), while Z score and TFx had similar performance (AUC of approx. 6.4), which was significantly better than AFP (AUC of 0.565), a frequently-used biomarker for HCC diagnosis. We further investigated the performance of UCAD model in patient subgroups stratified by clinical and pathological characteristics, and found that UCAD remained superior to AFP in each subgroup, especially in those with multiple tumor (tumor count ≥ 2) with significant difference. Next, both univariate and multivariate logistic regression analysis further confirmed UCAD model as an independent risk factor associated with MVI. Compared to previous approaches employing ctDNA VAF [37, 38], our low-coverage whole-genome sequencing-based UCAD model has an outstanding sensitivity but a lower specificity. What’s more, we established a formula by taking into account the multiple parameters aforementioned, as well as the clinical and pathological characteristics, which can predict MVI status with a larger AUC value of 0.838 (95% CI: 0.727–0.949), and higher sensitivity (75%) and specificity (83%), thus deserving further follow-up and validation.

Finally, although our low-coverage whole-genome sequencing-based UCAD model has provided a promising approach for preoperative MVI prediction, there are some limitations that can’t be neglected. First, the small sample size, especially the low MVI-positive patient proportion, may to some extent affect the solidness of our findings. Secondly, the study was a single center research and the model should be validated in more centers or with more data. Thirdly, the specificity of the UCAD model is not ideal, which should be explored and improved later. Thus, a study enrolling more patients and more centers should be conducted in the future to validate and enhance our findings here.

Conclusions

In conclusion, our low-coverage whole-genome sequencing-based UCAD model has provided a promising novel approach for preoperative MVI prediction in operable HCC patients.

Data availability

The raw sequence data reported in this paper have been deposited in the Genome Sequence Archive (Genomics, Proteomics & Bioinformatics 2021) in National Genomics Data Center (Nucleic Acids Res 2024), China National Center for Bioinformation/ Beijing Institute of Genomics, Chinese Academy of Sciences (GSA-Human: HRA009795) that are publicly accessible at https://ngdc.cncb.ac.cn/gsa-human.

Abbreviations

AFP:

alpha fetoprotein

ALT:

alanine aminotransferase

AST:

aspartate aminotransferase

AUC:

area under curve

BCLC:

Barcelona Clinic Liver Cancer

CBS:

circular binary segmentation

cfDNA:

cell-free DNA

CIN score:

chromosomal instability score

ctDNA:

circulating tumor DNA

HBV:

Hepatitis B Virus

HCC:

hepatocellular carcinoma

MVI:

microvascular invasion

ROC:

receiver operating characteristic

TACE:

transcatheter artery chemoembolization

TFx:

tumor fraction

UCAD:

ultrasensitive chromosomal aneuploidy detector

VAF:

variant allele fraction

References

  1. EASL Clinical Practice Guidelines. Management of hepatocellular carcinoma. J Hepatol. 2018;69(1):182–236. https://doiorg.publicaciones.saludcastillayleon.es/10.1016/j.jhep.2018.03.019.

    Article  Google Scholar 

  2. Heimbach JK, Kulik LM, Finn RS, Sirlin CB, Abecassis MM, Roberts LR, et al. AASLD guidelines for the treatment of hepatocellular carcinoma. Hepatology. 2018;67(1):358–80. https://doiorg.publicaciones.saludcastillayleon.es/10.1002/hep.29086.

    Article  PubMed  Google Scholar 

  3. Roayaie S, Blume IN, Thung SN, Guido M, Fiel MI, Hiotis S, et al. A system of classifying microvascular invasion to predict outcome after resection in patients with hepatocellular carcinoma. Gastroenterology. 2009;137(3):850–5. https://doiorg.publicaciones.saludcastillayleon.es/10.1053/j.gastro.2009.06.003.

    Article  PubMed  Google Scholar 

  4. Imamura H, Matsuyama Y, Tanaka E, Ohkubo T, Hasegawa K, Miyagawa S, et al. Risk factors contributing to early and late phase intrahepatic recurrence of hepatocellular carcinoma after hepatectomy. J Hepatol. 2003;38(2):200–7. https://doiorg.publicaciones.saludcastillayleon.es/10.1016/s0168-8278(02)00360-4.

    Article  PubMed  Google Scholar 

  5. Moris D, Tsilimigras DI, Kostakis ID, Ntanasis-Stathopoulos I, Shah KN, Felekouras E, et al. Anatomic versus non-anatomic resection for hepatocellular carcinoma: A systematic review and meta-analysis. Eur J Surg Oncol. 2018;44(7):927–38. https://doiorg.publicaciones.saludcastillayleon.es/10.1016/j.ejso.2018.04.018.

    Article  PubMed  Google Scholar 

  6. Zhou Y, Xu D, Wu L, Li B. Meta-analysis of anatomic resection versus nonanatomic resection for hepatocellular carcinoma. Langenbecks Arch Surg. 2011;396(7):1109–17. https://doiorg.publicaciones.saludcastillayleon.es/10.1007/s00423-011-0784-9.

    Article  PubMed  Google Scholar 

  7. Kim H, Park MS, Park YN, Kim H, Kim KS, Choi JS, et al. Preoperative radiologic and postoperative pathologic risk factors for early intra-hepatic recurrence in hepatocellular carcinoma patients who underwent curative resection. Yonsei Med J. 2009;50(6):789–95. https://doiorg.publicaciones.saludcastillayleon.es/10.3349/ymj.2009.50.6.789.

    Article  PubMed  PubMed Central  Google Scholar 

  8. [Evidence-based. Practice guidelines for standardized pathological diagnosis of primary liver cancer in China: 2015]. Zhonghua Gan Zang Bing Za Zhi. 2015;23(5):321–7.

    Google Scholar 

  9. Cong WM, Bu H, Chen J, Dong H, Zhu YY, Feng LH, et al. Practice guidelines for the pathological diagnosis of primary liver cancer: 2015 update. World J Gastroenterol. 2016;22(42):9279–87. https://doiorg.publicaciones.saludcastillayleon.es/10.3748/wjg.v22.i42.9279.

    Article  PubMed  PubMed Central  Google Scholar 

  10. Lee S, Kang TW, Song KD, Lee MW, Rhim H, Lim HK, et al. Effect of microvascular invasion risk on early recurrence of hepatocellular carcinoma after surgery and radiofrequency ablation. Ann Surg. 2021;273(3):564–71. https://doiorg.publicaciones.saludcastillayleon.es/10.1097/sla.0000000000003268.

    Article  PubMed  Google Scholar 

  11. Ryu T, Takami Y, Wada Y, Tateishi M, Hara T, Yoshitomi M, et al. A clinical scoring system for predicting microvascular invasion in patients with hepatocellular carcinoma within the Milan criteria. J Gastrointest Surg. 2019;23(4):779–87. https://doiorg.publicaciones.saludcastillayleon.es/10.1007/s11605-019-04134-y.

    Article  PubMed  Google Scholar 

  12. Hong SB, Choi SH, Kim SY, Shim JH, Lee SS, Byun JH, et al. MRI features for predicting microvascular invasion of hepatocellular carcinoma: A systematic review and Meta-Analysis. Liver Cancer. 2021;10(2):94–106. https://doiorg.publicaciones.saludcastillayleon.es/10.1159/000513704.

    Article  PubMed  PubMed Central  Google Scholar 

  13. Ji GW, Zhu FP, Xu Q, Wang K, Wu MY, Tang WW, et al. Radiomic features at Contrast-enhanced CT predict recurrence in early stage hepatocellular carcinoma: A Multi-Institutional study. Radiology. 2020;294(3):568–79. https://doiorg.publicaciones.saludcastillayleon.es/10.1148/radiol.2020191470.

    Article  PubMed  Google Scholar 

  14. Lei Z, Li J, Wu D, Xia Y, Wang Q, Si A, et al. Nomogram for preoperative Estimation of microvascular invasion risk in hepatitis B Virus-Related hepatocellular carcinoma within the Milan criteria. JAMA Surg. 2016;151(4):356–63. https://doiorg.publicaciones.saludcastillayleon.es/10.1001/jamasurg.2015.4257.

    Article  PubMed  Google Scholar 

  15. Wang W, Guo Y, Zhong J, Wang Q, Wang X, Wei H, et al. The clinical significance of microvascular invasion in the surgical planning and postoperative sequential treatment in hepatocellular carcinoma. Sci Rep. 2021;11(1):2415. https://doiorg.publicaciones.saludcastillayleon.es/10.1038/s41598-021-82058-x.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  16. Poté N, Cauchy F, Albuquerque M, Voitot H, Belghiti J, Castera L, et al. Performance of PIVKA-II for early hepatocellular carcinoma diagnosis and prediction of microvascular invasion. J Hepatol. 2015;62(4):848–54. https://doiorg.publicaciones.saludcastillayleon.es/10.1016/j.jhep.2014.11.005.

    Article  CAS  PubMed  Google Scholar 

  17. Suh SW, Lee KW, Lee JM, You T, Choi Y, Kim H, et al. Prediction of aggressiveness in early-stage hepatocellular carcinoma for selection of surgical resection. J Hepatol. 2014;60(6):1219–24. https://doiorg.publicaciones.saludcastillayleon.es/10.1016/j.jhep.2014.01.027.

    Article  PubMed  Google Scholar 

  18. Lee JC, Hung HC, Wang YC, Cheng CH, Wu TH, Lee CF, et al. Risk score model for microvascular invasion in hepatocellular carcinoma: the role of tumor burden and Alpha-Fetoprotein. Cancers (Basel). 2021;13(17). https://doiorg.publicaciones.saludcastillayleon.es/10.3390/cancers13174403.

  19. Laurent-Puig P, Legoix P, Bluteau O, Belghiti J, Franco D, Binot F, et al. Genetic alterations associated with hepatocellular carcinomas define distinct pathways of hepatocarcinogenesis. Gastroenterology. 2001;120(7):1763–73. https://doiorg.publicaciones.saludcastillayleon.es/10.1053/gast.2001.24798.

    Article  CAS  PubMed  Google Scholar 

  20. Jiang P, Chan CW, Chan KC, Cheng SH, Wong J, Wong VW, et al. Lengthening and shortening of plasma DNA in hepatocellular carcinoma patients. Proc Natl Acad Sci U S A. 2015;112(11):E1317–25. https://doiorg.publicaciones.saludcastillayleon.es/10.1073/pnas.1500076112.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  21. Nault JC, Martin Y, Caruso S, Hirsch TZ, Bayard Q, Calderaro J, et al. Clinical impact of genomic diversity from early to advanced hepatocellular carcinoma. Hepatology. 2020;71(1):164–82. https://doiorg.publicaciones.saludcastillayleon.es/10.1002/hep.30811.

    Article  CAS  PubMed  Google Scholar 

  22. Wang S, Shi H, Liu T, Li M, Zhou S, Qiu X, et al. Mutation profile and its correlation with clinicopathology in Chinese hepatocellular carcinoma patients. Hepatobiliary Surg Nutr. 2021;10(2):172–9. https://doiorg.publicaciones.saludcastillayleon.es/10.21037/hbsn.2019.09.17.

    Article  PubMed  PubMed Central  Google Scholar 

  23. Wang K, Lim HY, Shi S, Lee J, Deng S, Xie T, et al. Genomic landscape of copy number aberrations enables the identification of oncogenic drivers in hepatocellular carcinoma. Hepatology. 2013;58(2):706–17. https://doiorg.publicaciones.saludcastillayleon.es/10.1002/hep.26402.

    Article  CAS  PubMed  Google Scholar 

  24. Plentz RR, Schlegelberger B, Flemming P, Gebel M, Kreipe H, Manns MP, et al. Telomere shortening correlates with increasing aneuploidy of chromosome 8 in human hepatocellular carcinoma. Hepatology. 2005;42(3):522–6. https://doiorg.publicaciones.saludcastillayleon.es/10.1002/hep.20847.

    Article  CAS  PubMed  Google Scholar 

  25. Kusano N, Shiraishi K, Kubo K, Oga A, Okita K, Sasaki K. Genetic aberrations detected by comparative genomic hybridization in hepatocellular carcinomas: their relationship to clinicopathological features. Hepatology. 1999;29(6):1858–62. https://doiorg.publicaciones.saludcastillayleon.es/10.1002/hep.510290636.

    Article  CAS  PubMed  Google Scholar 

  26. Ningarhari M, Caruso S, Hirsch TZ, Bayard Q, Franconi A, Védie AL, et al. Telomere length is key to hepatocellular carcinoma diversity and telomerase addiction is an actionable therapeutic target. J Hepatol. 2021;74(5):1155–66. https://doiorg.publicaciones.saludcastillayleon.es/10.1016/j.jhep.2020.11.052.

    Article  CAS  PubMed  Google Scholar 

  27. Lu LC, Hsu CH, Hsu C, Cheng AL. Tumor heterogeneity in hepatocellular carcinoma: facing the challenges. Liver Cancer. 2016;5(2):128–38. https://doiorg.publicaciones.saludcastillayleon.es/10.1159/000367754.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  28. Wan JCM, Massie C, Garcia-Corbacho J, Mouliere F, Brenton JD, Caldas C, et al. Liquid biopsies come of age: towards implementation of Circulating tumour DNA. Nat Rev Cancer. 2017;17(4):223–38. https://doiorg.publicaciones.saludcastillayleon.es/10.1038/nrc.2017.7.

    Article  CAS  PubMed  Google Scholar 

  29. Li Y, Zheng Y, Wu L, Li J, Ji J, Yu Q, et al. Current status of ctdna in precision oncology for hepatocellular carcinoma. J Exp Clin Cancer Res. 2021;40(1):140. https://doiorg.publicaciones.saludcastillayleon.es/10.1186/s13046-021-01940-8.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  30. Ye Q, Ling S, Zheng S, Xu X. Liquid biopsy in hepatocellular carcinoma: Circulating tumor cells and Circulating tumor DNA. Mol Cancer. 2019;18(1):114. https://doiorg.publicaciones.saludcastillayleon.es/10.1186/s12943-019-1043-x.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  31. Li J, Han X, Yu X, Xu Z, Yang G, Liu B, et al. Clinical applications of liquid biopsy as prognostic and predictive biomarkers in hepatocellular carcinoma: Circulating tumor cells and Circulating tumor DNA. J Exp Clin Cancer Res. 2018;37(1):213. https://doiorg.publicaciones.saludcastillayleon.es/10.1186/s13046-018-0893-1.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  32. Ng CKY, Di Costanzo GG, Tosti N, Paradiso V, Coto-Llerena M, Roscigno G, et al. Genetic profiling using plasma-derived cell-free DNA in therapy-naïve hepatocellular carcinoma patients: a pilot study. Ann Oncol. 2018;29(5):1286–91. https://doiorg.publicaciones.saludcastillayleon.es/10.1093/annonc/mdy083.

    Article  CAS  PubMed  Google Scholar 

  33. Cai Z, Chen G, Zeng Y, Dong X, Li Z, Huang Y, et al. Comprehensive liquid profiling of Circulating tumor DNA and protein biomarkers in Long-Term Follow-Up patients with hepatocellular carcinoma. Clin Cancer Res. 2019;25(17):5284–94. https://doiorg.publicaciones.saludcastillayleon.es/10.1158/1078-0432.Ccr-18-3477.

    Article  CAS  PubMed  Google Scholar 

  34. Zeng S, Ying Y, Xing N, Wang B, Qian Z, Zhou Z, et al. Noninvasive detection of urothelial carcinoma by Cost-effective Low-coverage Whole-genome sequencing from Urine-Exfoliated cell DNA. Clin Cancer Res. 2020;26(21):5646–54. https://doiorg.publicaciones.saludcastillayleon.es/10.1158/1078-0432.Ccr-20-0401.

    Article  CAS  PubMed  Google Scholar 

  35. Mo H, Wang X, Ma F, Qian Z, Sun X, Yi Z, et al. Genome-wide chromosomal instability by cell-free DNA sequencing predicts survival in patients with metastatic breast cancer. Breast. 2020;53:111–8. https://doiorg.publicaciones.saludcastillayleon.es/10.1016/j.breast.2020.07.004.

    Article  PubMed  PubMed Central  Google Scholar 

  36. Xu H, Zhu X, Xu Z, Hu Y, Bo S, Xing T, et al. Non-invasive analysis of genomic copy number variation in patients with hepatocellular carcinoma by next generation DNA sequencing. J Cancer. 2015;6(3):247–53. https://doiorg.publicaciones.saludcastillayleon.es/10.7150/jca.10747.

    Article  PubMed  PubMed Central  Google Scholar 

  37. Wang D, Xu Y, Goldstein JB, Ye K, Hu X, Xiao L, et al. Preoperative evaluation of microvascular invasion with Circulating tumour DNA in operable hepatocellular carcinoma. Liver Int. 2020;40(8):1997–2007. https://doiorg.publicaciones.saludcastillayleon.es/10.1111/liv.14463.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  38. Xin Z, Li J, Zhang H, Zhou Y, Song J, Chen P, et al. Cancer genomic alterations can be potential biomarkers predicting microvascular invasion and early recurrence of hepatocellular carcinoma. Front Oncol. 2022;12:783109. https://doiorg.publicaciones.saludcastillayleon.es/10.3389/fonc.2022.783109.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  39. Wang J, Huang A, Wang YP, Yin Y, Fu PY, Zhang X, et al. Circulating tumor DNA correlates with microvascular invasion and predicts tumor recurrence of hepatocellular carcinoma. Ann Transl Med. 2020;8(5):237. https://doiorg.publicaciones.saludcastillayleon.es/10.21037/atm.2019.12.154.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  40. Forner A, Reig ME, de Lope CR, Bruix J. Current strategy for staging and treatment: the BCLC update and future prospects. Semin Liver Dis. 2010;30(1):61–74. https://doiorg.publicaciones.saludcastillayleon.es/10.1055/s-0030-1247133.

    Article  CAS  PubMed  Google Scholar 

  41. Adalsteinsson VA, Ha G, Freeman SS, Choudhury AD, Stover DG, Parsons HA, et al. Scalable whole-exome sequencing of cell-free DNA reveals high concordance with metastatic tumors. Nat Commun. 2017;8(1):1324. https://doiorg.publicaciones.saludcastillayleon.es/10.1038/s41467-017-00965-y.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  42. Li H, Handsaker B, Wysoker A, Fennell T, Ruan J, Homer N, et al. The sequence alignment/map format and samtools. Bioinformatics. 2009;25(16):2078–9. https://doiorg.publicaciones.saludcastillayleon.es/10.1093/bioinformatics/btp352.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  43. Seshan VE, Olshen A. DNAcopy: DNA copy number data analysis. 2013.

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Acknowledgements

Thanks to Lijuan Zhai for the technical support.

Funding

This work was supported by Ministry of Science and Technology of the People’s Republic of China, 13th Five-Year National Science and Technology Major Project (No. 2018ZX10715-013-003), Ministry of Science and Technology of the People’s Republic of China, 13th Five-Year National Key Research and Development Program of China (No. 2019YFC0840600 and No. 2019YFC0840609), Zhejiang Natural Science Foundation (No. LQ21H030003), Innovative Research Groups of National Natural Science Foundation of China (No. 81721091), and Research Unit Project of Chinese Academy of Medical Sciences (No. 2019-I2M-5-030).

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Author notes

  1. Zheyue Shu and Ting Ye contributed equally to this work.

Authors and Affiliations

  1. Division of Hepatobiliary and Pancreatic Surgery, Department of Surgery, The First Affiliated Hospital, Zhejiang University School of Medicine, No. 79 Qingchun Road, Hangzhou, 310003, China

    Zheyue Shu, Jingcheng Wang, Min Zhang & Shusen Zheng

  2. NHC Key Laboratory of Combined Multi-organ Transplantation, Hangzhou, 310003, China

    Zheyue Shu, Jingcheng Wang, Min Zhang & Shusen Zheng

  3. Key Laboratory of the Diagnosis and Treatment of Organ Transplantation, Research Unit of Collaborative Diagnosis and Treatment for Hepatobiliary and Pancreatic Cancer, Chinese Academy of Medical Sciences, Hangzhou, 310003, China

    Jingcheng Wang, Min Zhang & Shusen Zheng

  4. Zhejiang University School of Medicine, Hangzhou, 310058, China

    Zheyue Shu, Ting Ye, Menghan Su & Qi Xia

  5. State Key Laboratory for Diagnosis and Treatment of Infectious Diseases, National Clinical Research Center for Infectious Diseases, National Medical Center for Infectious Diseases, Collaborative Innovation Center for Diagnosis and Treatment of Infectious Diseases, The First Affiliated Hospital, Zhejiang University School of Medicine, No. 79 Qingchun Road, Hangzhou, 310003, China

    Wei Wu & Qi Xia

  6. The First People’s Hospital of Chenzhou, Chenzhou, 424512, China

    Ziliang Qian & Haifen Huang

  7. Key Laboratory for Biomedical Engineering of Ministry of Education, Zhejiang University, Hangzhou, 310003, China

    Qi Xia

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  1. Zheyue Shu
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Contributions

Conceptualization and Methodology: Qi Xia, Shusen Zheng, Zheyue Shu and Ting Ye; Investigation: Wei Wu, Menghan Su, Jingcheng Wang and Min Zhang; Formal Analysis: Zheyue Shu, Ting Ye, Ziliang Qian and Haifen Huang; Writing– Original Draft: Zheyue Shu and Ting Ye; Supervision: Shusen Zheng and Qi Xia. Writing– Review & Editing: all authors. All authors read and approved the final manuscript.

Corresponding authors

Correspondence to Shusen Zheng or Qi Xia.

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Ethics approval and consent to participate

All research was conducted in accordance with both the Declarations of Helsinki and Istanbul. All research was approved by the Clinical Research Ethics Committee of the First Affiliated Hospital, College of Medicine, Zhejiang (Approval No.: IIT20220097B). This study was registered at https://clinicaltrials.gov/ on 16 May 2022, retrospectively registered, Registration number: NCT05371873. Informed consent was obtained from all individual participants included in the study.

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Shu, Z., Ye, T., Wu, W. et al. Preoperative plasma cell-free DNA chromosomal instability predicts microvascular invasion in hepatocellular carcinoma: a prospective study. BMC Cancer 25, 867 (2025). https://doiorg.publicaciones.saludcastillayleon.es/10.1186/s12885-025-14268-9

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BMC Cancer

ISSN: 1471-2407

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