Genomic alterations in hepatocellular carcinoma patients undergoing liver transplantation predict recurrence and prognosis

doi:10.21203/rs.3.rs-3928472/v1

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Genomic alterations in hepatocellular carcinoma patients undergoing liver transplantation predict recurrence and prognosis

https://doi.org/10.21203/rs.3.rs-3928472/v1

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Liver transplantation (LT) stands as a pivotal treatment for hepatocellular carcinoma (HCC), outperforming comprehensive treatments in long-term efficacy. However, the 5-year post-LT survival rate hovers between 60% and 70%, largely due to recurrent HCC, spotlighting the critical need for biomarkers that can predict recurrence and prognosis following LT. Our study embarked on this challenge by retrospectively analyzing data from 37 HCC patients who underwent LT from January 2019 to January 2021. Employing whole exome sequencing on tissue and control blood samples, we segregated these patients into recurrence (n = 14) and non-recurrence groups (n = 23), based on a one-year postoperative threshold. Our analysis unveiled a distinctive genomic mutation spectrum in these patients, highlighting five predominantly mutated genes: BCLAF1, MUC4, TP53, FMN2, and TTC7A. Notably, clinical features between the two groups showed no significant divergence. However, the recurrence group demonstrated markedly inferior overall survival (OS) compared to their counterparts (p < 0.0001). Multivariate regression pinpointed 304 genes as independent predictors for recurrence-free survival (RFS) and 482 genes for OS (p < 0.05). Additionally, our research led to the development of a novel 13-gene model, which markedly influences both RFS and OS. Patients classified within the high-risk category of this model experienced significantly poorer outcomes. This study is a trailblazer in linking genomic alterations with the recurrence and survival rates of HCC patients post-LT, introducing a 13-gene model that offers substantial predictive and prognostic utility.

hepatocellular carcinoma

liver transplantation

genomic characteristics

recurrence

prognosis

Hepatocellular carcinoma (HCC) is one of the most common malignant tumors and the third leading cause of cancer death worldwide[1]. There are many treatments for HCC in contemporary medicine, such as partial hepatectomy, trans-arterial chemoembolization, radiotherapy, targeted therapy and immunotherapy. Liver transplantation (LT) is recognized as one of the most effective treatment option for patients with HCC conforming to criteria in the world, which could remove tumors completely[2]. The criteria for selecting LT candidates for HCC are of paramount importance. The Milan criteria proposed in 1996 is of great significance in the history of LT for HCC. However, the stringent nature of these criteria has unfortunately caused many to miss the opportunity for a transplant. The extended Milan criteria, including Hangzhou criteria and UCSF (University of California, San Francisco) criteria, have also achieved similar survival results as the Milan criteria in practical clinical application[3]. Some studies have suggested that more extended criteria beyond the Milan criteria could be applied to LT[4–6]. However, HCC recurrence after LT is relatively common, and the 10-year overall survival and recurrence rate after LT can reach 43.3% and 41.1% in those whose disease was not downstaged. Treatment of recurrence remains challenging and tumor recurrence after LT is still an important cause of death after liver transplantation[7].

Given this, it becomes crucial to investigate the predictive factors and underlying mechanisms of HCC recurrence post-LT, aiming to enhance patient prognosis. Alpha-fetoprotein (AFP) is the first serological test for diagnosis and follow-up of HCC patients and can be used to assess the severity of tumor burden and response to different treatments[8]. Microvascular invasion (MVI) is increasingly recognized as a significant risk factor for recurrence and metastasis of HCC after liver resection or transplantation. With the development of molecular biology and omics, the study of HCC recurrence has also been deepened to the molecular level[9]. To better explore the oncological features of hepatocellular carcinoma, it is essential to portray the genomic landscape. Whole-exome sequencing (WES) shed light on the gene mutation and mechanisms for the tumorigenesis and progression of HCC[10]. Shi et al. identified a list of genes as markers and constructed a predictive model of HCC early recurrence after resection using WES[11].

Notably, although many studies have delved into the molecular aspects of HCC, few studies have shown the correlation between gene exonic variants and recurrent tumor recurrence after liver transplantation for HCC, which aroused our interest. Recurrence after LT of HCC presents large individual variability, and it has become a valuable clinical study to screen out more accurately the high-risk groups for early recurrence. Herein, we examined clinical factors, gene mutations, and their correlations in HCC LT patients to discern molecules linked to their prognosis.

Patient information and sample collection

This retrospective study included 37 HCC patients diagnosed at our Hospital between January 2019 and July 2021. The inclusion criteria are implemented in accordance with Hangzhou standards[12]. Patients with incompletely available follow-up data as well as lack of essential data for analysis were excluded. 37 pairs of tumor samples and control blood samples underwent WES. It was approved by the Ethics Committee of the Affiliated Hospital of Qingdao University (QYFY WZLL 27714), and written informed consent was waived for this retrospective analysis.

WES sequencing

Genomic DNA (gDNA) was extracted from formalin-fixed paraffin-embedded (FFPE) tissues using a Genomic DNA Tissue Extraction Kit (Concert®). Leukocyte genomic DNA was extracted using a magnetic universal genomic DNA kit (TIANGEN®). NGS testing for the whole-exome Hyb Panel was performed and followed the manufacturer’s instructions. Briefly, 200ng of gDNA was sheared to 200 ~ 300 bp in conjunction with bisulfite restriction assay. Indexed paired-end adapters for the Illumina platform were synthesized by Integrated DNA Technologies (IDT). Sheared DNA was end-repaired, A-tailed, and adapter ligated using reagents from the KAPA Hyper DNA Library Preparation Kit (Roche Diagnostics). Unligated adapters were removed by the size selection function of Agencourt AMPure XP beads (Beckman Coulter), and the ligation products were PCR amplified to form a pre-library for hybridization. Prepared DNA libraries were sequenced on the Illumina NovaSeq6000 platform (Illumina, San Diego, CA) and 150-bp paired-end reads were generated. The sequencing principle is sequencing while synthesis, and the average sequencing depth of tissue and control blood is 500X and 100X respectively.

Data Filtering and Variant Calling

Sequencing reads were parsed using fastp (V.2.20.0) for adapter trimming and removal of low-quality bases[13]. The obtained clean paired-end reads were aligned to the hg19 human genome reference using Burrows-Wheeler Aligner version 0.7.17 (http://bio-bwa.sourceforge.net/index.shtml). Duplicate reads were removed from the aligned and sequenced binary alignment map files (Broad Institute, Cambridge, MA) using Picard (https://broadinstitute.github.io/picard/). GATK4 was used for base quality score recalibration and insertion or deletion realignment algorithms according to the GATK4 best practice recommended workflow. MuTect2 (v 4.2.3) from GATK (Broad Institute) was used to detect SNVs and minor indels[14]. CNVs and fusions were identified using CNVkit (dx1.1). Subsequently, the Variant Calling Format (VCF) is annotated with ANNOVAR[15]. Tumor mutational burden (TMB) is defined as the total number of nonsynonymous somatic mutations. Additional analysis of mutation filters and checks is implemented via custom scripts.

Statistical analysis

All tests were performed with the R environment version 4.0.2 (R core team, Vienna, Austria, https://www.r-project.org) or GraphPad Prism 6.0 (GraphPad Software, San Diego, CA). Comparisons between groups were based on Student’s t test. The nonparametric Wilcoxon rank-sum test was applied for TMB. If not noted otherwise, the tests applied were two-sided. The Kaplan-Meier curve analysis recurrence-free survival (RFS) and overall survival (OS) was compared using the log-rank test. All reported P values were two-tailed, and P < 0.05 was considered statistically significant.

Clinical characteristics analysis of recurrence group and non-recurrence group

In this retrospective study, 37 patients underwent LT were enrolled. The one-year recurrence rate of patients is 37.8%. The patients were divided into two groups, a recurrence group (n = 14) and a non-recurrence group (n = 23), according to whether there was recurrence within one year. Compared with the non-recurrence group, the OS of patients in the recurrence group was significantly worse (p < 0.0001, Fig. 1A). The clinical features of these patients are summarized in Table 1. No significant differences in clinical features were observed between the two groups.

Table 1

Statistical table of basic condition of patients (N = 37)
Characteristic	Number of cases (%)	Recurrence (n)	Non-Recurrence (n)	P value
Age (Years)				0.8285
< 60	27	11	16
≥ 60	10	3	7
Preoperative AFP (ng/ml)				1
< 400	34	13	21
≥ 400	3	1	2
Descending treatment before LT operation				1
Yes	14	5	9
No	23	9	14
Number of tumors				0.4612
< 3	30	10	20
≥ 3	7	4	3
Tumor differentiation				0.1675
High	5	0	5
Mediam	32	14	18
Maximum tumor diameter (cm)
≤ 5	24
> 5	13
Cumulative tumor diameter (cm)				0.2616
≤ 5	17	7	17
> 5	20	7	6
MVI				0.011
Yes	13	9	4
No	24	5	19

Overview of molecular characteristics between the two groups

37 pairs of tumor samples and control blood samples were comprehensively characterized via WES. There were 9,724 mutated genes, and the top three mutation types were missense mutation, splice site and nonsense mutation, respectively. The mutation landscape is shown in Fig. 1B, and top five mutated genes were BCLAF1, MUC4, TP53, FMN2 and TTC7A with mutation prevalence rates of 73%, 57%, 49%, 49% and 46% in our cohort, respectively. The results are basically consistent with previous research reports[16, 17]. Moreover, we identified 32 differential genes (p < 0.05, Supplementary table 1) between two groups, of which 25 genes were only detected in the recurrence group (Fig. 1C). TP53 (recurrence group vs. non-recurrence group: 75% VS. 36%) and TTN (recurrence group vs. non-recurrence group: 67% VS. 28%) had the highest mutation frequencies in 32 differential genes. Then, we compared TMB and tumor neoantigen burden (TNB) between the two groups and found that both of two did not differ significantly between the two groups (Fig. 1D, E). Further, patients were divided into two groups according to the median TMB (mTMB = 3.01), namely TMB-High group (TMB ≥ 3.01) and TMB-Low group (TMB < 3.01). The analysis results showed that TMB was not associated with RFS and OS (Fig. 1F, G).

Univariate and multivariate cox regression analysis on relapse-free survival of clinical factors and molecular mutation characteristics

Firstly, in order to understand the impact of clinical factors on patients' RFS, we conducted univariate and multivariate cox regression analysis. The clinical factors including gender, age, alpha-fetoprotein (AFP) categories, tuomr size, tumordifferentiation, tumor number, microvascular invasion (MVI) and treatment before LT. The results of univariate cox regression analysis showed that both high AFP and positive MVI were significantly associated with poor RFS (p < 0.05, Table 2, Fig. 2A, B). However, multivariate cox regression analysis results showed that none of the 8 clinical factors was an independent prognostic factor for RFS.

Table 2

Univariate regression analysis and multivariate regression analysis of 8 clinical factors on RFS.
Variables	Univariable analysis			Mutivariable analysis
Variables	HR	CI95%	P.value	HR	CI95%	P.value
Gender (female vs male)	0	0-Inf	0.132
Age (year) (< 65 vs ≥ 65)	0.62	0.18–2.21	0.46
AFP categories (< 25 ng/ml vs ≥ 25 ng/ml)	0.30	0.1–0.94	0.029	0.42	0.12–1.44	0.17
Tumor size (cm) (< 8 cm vs ≥ 8 cm)	2.54	0.71–9.06	0.137
Tumor differentiation (low vs high)	0	0-Inf	0.077
Tumor number (single vs multiple)	0.58	0.2–1.7	0.314
Microvascular invasion (negative vs positive)	3.57	1.26–10.13	0.011	2.64	0.86–8.03	0.08
Treatment.BeforeLT(Yes vs No)	0.92	0.31–2.69	0.877

Table 3

Univariate regression analysis and multivariate regression analysis of 8 clinical factors on OS.
Variables	Univariable analysis			Mutivariable analysis
Variables	HR	CI95%	P.value	HR	CI95%	P.value
Gender (female vs male)	0	0-Inf	0.247
Age (year) (< 65 vs ≥ 65)	0.53	0.12–2.44	0.407
AFP categories (< 25 ng/ml vs ≥ 25 ng/ml)	0.77	0.25–2.45	0.663	1.19	0.34–4.13	0.78
Tumor size (cm) (< 8 cm vs ≥ 8 cm)	1.34	0.29–6.23	0.711
Tumor differentiation (low vs high)	0	0-Inf	0.117
Tumor number (single vs multiple)	1.43	0.31–6.61	0.649
Microvascular invasion (negative vs positive)	3.09	0.98–9.75	0.043	3.29	0.95–11.38	0.06
Treatment.BeforeLT(Yes vs No)	1.32	0.42–4.17	0.637

In terms of molecular mutation characteristics, the results of univariate cox regression analysis showed that 304 genes were significantly associated with RFS (p < 0.05), and among them, 301 gene mutants including TTN, NDUFS7, FOXO3, etc (Fig. 2C-E) were significantly associated with poor RFS (Supplementary Table 2). In order to eliminate multiple confounding factors and find indicators that can independently predict recurrence, we separately included age, gender, AFP, MVI and 304 genes significantly associated with RFS into multivariable cox regression analysis and found that 205 gene mutants were significantly associated with poorer RFS (p < 0.05, Supplementary Table 3). Examples listing three genes (TTN, NDUFS7, FOXO3) are shown in Fig. 2G, H.

Univariate and multivariate cox regression analysis on overall survival of clinical factors and molecular mutation characteristics

Similarly, we used the same analysis method to explore the indicators related to OS. In terms of 8 clinical factors, results of univariate cox regression analysis showed that only MVI were significantly associated with poor OS (p < 0.05, Table 4, Fig. 3A). However, multivariate cox regression analysis results showed that none of the 8 clinical factors was an independent prognostic factor for OS. The results of gene mutation showed that 811 genes were detected significantly associated with OS (p < 0.05), and among them, 810 gene mutants including TTN, NDUFS7, FOXO3, etc. (Fig. 3B-D) were significantly associated with poor OS (p < 0.05, Supplementary Table 4). Then multivariate cox regression models were performed and there were still 482 gene mutations were independent risk factors for OS (p < 0.05, Supplementary Table 5). Examples listing the above three genes (TTN, NDUFS7, FOXO3) are shown in Fig. 3E-G.

Table 4

gene list of 13-gene-based model
Gene	Mutation situation			Univariate analysis of RFS			Univariate analysis of OS			Multivariate analysis of RFS			Multivariate analysis of OS
Gene	N1	N2	P.value	HR	CI95.	P.value	HR	CI95.	P.value	HR	CI95.	P.value	HR	CI95.	P.value
BOD1L1	3	0	0.03	7.12	1.95–26.01	0.00	3.58	0.95–13.55	0.05	6.18	1.559–24.53	0.01	5.66	1.303–24.599	0.02
C14orf159	3	0	0.03	8.11	2.25–29.18	0.00	3.67	0.95–14.21	0.04	33.64	5.163–219.24	0.00	11.12	1.724–71.669	0.01
CSPG4	3	0	0.03	9.92	2.68–36.62	0.00	6.80	1.88–24.61	0.00	17.26	3.435–86.697	0.00	7.05	1.702–29.233	0.01
EML6	3	0	0.03	8.48	2.35–30.53	0.00	4.58	1.18–17.83	0.02	19.95	4.158–95.719	0.00	9.66	1.767–52.806	0.01
KCNB2	3	0	0.03	5.98	1.51–23.68	0.00	6.58	1.68–25.77	0.00	6.62	1.21-36.154	0.03	5.01	1.005–24.935	0.05
OR4D10	3	0	0.03	7.92	1.99–31.47	0.00	6.75	1.74–26.25	0.00	23.46	4.071-135.201	0.00	13.26	2.332–75.356	0.00
PACS2	3	0	0.03	6.28	1.59–24.81	0.00	6.16	1.58–24.02	0.00	7.87	1.657–37.381	0.01	9.70	1.932–48.649	0.01
SPRED3	3	0	0.03	7.39	1.87–29.24	0.00	7.23	1.85–28.19	0.00	12.44	2.178–71.041	0.01	5.45	1.086–27.322	0.04
TAF1	3	0	0.03	7.53	1.89–30.04	0.00	6.45	1.66–25.12	0.00	18.23	3.397–97.846	0.00	13.34	2.513–70.78	0.00
VRK3	3	0	0.03	3.57	1.11–11.47	0.02	9.10	2.59–31.94	0.00	5.49	1.139–26.45	0.03	11.56	1.914–69.764	0.01
FOXO3	4	1	0.03	3.59	1.08–11.9	0.03	3.64	0.93–14.21	0.05	3.81	1.128–12.86	0.03	4.36	1.049–18.081	0.04
NDUFS7	4	1	0.03	3.75	1.17–12.03	0.02	7.67	2.12–27.71	0.00	4.51	1.075–18.878	0.04	7.11	1.53-32.996	0.01
TTN	8	7	0.04	4.84	1.36–17.21	0.01	11.82	1.52–91.81	0.00	6.21	1.455–26.505	0.01	25.34	2.257-284.425	0.01
N1: recurrence group, n = 12; N2 :non-recurrence group,n = 25.

Establish of the 13-gene-based relapse predicting and prognostic model

To search for molecular signatures that predict recurrence in liver transplanted HCC patients, we performed a genetic analysis. Among the 32 differential genes between the recurrence group and the non-recurrence group, the genes with high mutation frequency in the recurrence group were intersected with the RFS and OS independent predictive factors after multivariate cox regression analysis, and 13 genes were obtained (Table 4). Among them, 11 gene mutations were detected only in the relapse group. The samples with at least one mutation in these 13 genes were defined as high-risk group, and all 13 wild-type genes were defined as low-risk group[18]. The results showed that compared with the low-risk group, the RFS (p = 0.0042) and OS (p = 0.0074) of the high-risk group were significantly poor (Fig. 4A, B).

Taking into account the covariates that may affect RFS or OS, we performed multivariate analysis, and the results showed that the13-gene-based model had a significant impact on both RFS and OS, and the high-risk group tended to have worse RFS (p = 0.014) and OS (p = 0.01) than the low-risk group (Fig. 4C, D). The ROC curve of the performed 13-gene-based model to illustrate the accuracy (area under the curve, AUC: 0.73) in Fig. 4E. The above results indicate that the 13-gene-based model can not only predict the recurrence of HCC patients who undergo LT, but also serve as a prognostic indicator for the OS of such patients. It is worth noting that this result needs to be verified in future prospective trials with larger sample sizes.

To our knowledge, this is the first study to explore the relationship between genomic alterations and post-transplant recurrence and survival in HCC patients undergo LT. The one-year recurrence rate in our cohort was broadly consistent with previous studies[19]. Moreover, compared with the non-recurrence group, the OS of patients in the recurrence group was significantly poor in our cohort (p < 0.0001). Moreover, compared with the non-recurrence group, the OS of patients in the recurrence group was significantly poor in our cohort (p < 0.0001). This finding reinforces the necessity for more effective predictive biomarkers in this particular patient group.

Our study further expands the existing oncoprint by revealing the mutational landscape of these patients. We identified some of the top mutated genes, including BCLAF1, MUC4, TP53, FMN2, TIC7A, MUC16, TTN, RBPJ, CHEK2 and PCLO in this patient cohort, primarily consisting of Missense_Mutation and Multi_Hit. Studies showed that above genes participated in the development and progression of the HCC. For example, BCLAF1 was found to promote HCC angiogenesis by regulating HIF-1α transcription[20]. MUC4 gene mutations were evident in HCC patients with poorer responses to PD-1 antibodies combined with mTKIs[11]. TP53 mutation is the most common mutation in HCC, and affects its progression and prognosis[21]. We also identified 32 differential genes between the recurrence group and the non-recurrence group, including ADGRG6, TP53, TTN and so on. TMB is reported to be related to the efficacy of immunotherapy in HCC[22, 23]. While we observed a trend towards higher TMB and TNB in recurrence group, these differences were not statistically significant.

Among clinical variables, both serum AFP levels and MVI showed a significant correlation with RFS, and MVI showed a significant correlation with OS. While these are widely recognized clinical risk factors, the reliance solely on these parameters for risk stratification has inherent limitations, including a lag in real-time predictability[24, 25]. The multivariable cox regression analysis showed that 304 and 482 genes were identified as independent prognostic factors for RFS and for OS, respectively. The large number of prognostic genes can be attributed to the extensive genomic panel that we utilized for this study. One limitation, however, is that the biological functions, with the attempts at pathway enrichment analysis, of many of these genes remain poorly understood.

Our study also established a novel 13-gene-based model, including BOD1L1, C14orf159, CSPG4, EML6, KCNB2, OR4D10, PACS2, SPRED3, TAF1, VRK3, FOXO3, NDUFS7 and TTN, as an independent prognostic factor for both RFS and OS. Moreover, these 13 genes are also differential genes between the recurrence group and the non-recurrence group. Some genes in this signature have been previously reported, affirming the reliability of our findings. TAF1 is regarded as a driver gene of HCC that may play a key role in the development and progression of HCC[26, 27]. A meta-analysis included five studies involving 1059 HCC cases and found a significant correlation between high FOXO3 levels and the development of HCC and a shorter overall survival period[28]. Wang R et al. analyzed the HCC cohort of TCGA (The Cancer Genome Atlas) project and found a close relationship between TTN mutations and immune infiltration. Survival analysis showed that TTN mutations predicted poor prognosis[29]. The 13-gene-model can divide patients into high-risk and low-risk groups, and survival analysis results showed that compared with the low-risk group, the RFS (p = 0.0042) and OS (p = 0.0074) of the high-risk group were significantly poor. Moreover, the results of multivariate analysis showed that 13-gene-model had a significant independent impact on both RFS and OS. To a certain extent, it shows that 13-gene-model can not only predict recurrence in HCC patients undergoing LT, but also serve as a prognostic indicator for OS in such patients. The potential clinical application of this 13-gene signature warrants further exploration.

The primary limitation of this study is its relatively small sample size. As a logical next step, future research should encompass prospective clinical trials and multi-omics analyses to validate and expand upon our findings.

In this study, we revealed the genomic features related to recurrence and survival after liver transplantation in HCC patients based on WES, and established a novel 13-genetic signature prognostic model. It provides evidence for the recurrence and prognosis of such patients, and also provides a certain reference for research design.

Data availability statement

All of the data and materials are available from the corresponding author upon reasonable ask.

Acknowledgements

The authors thank Dr. Ruixia Li (Harbin Medical University), Mr. Dongsheng Chen, Ms. Qin Shuai, Ms. Feiyan Cui, Ms. Bin Wang and Ms. Wanqiu Jia from Simceredx for the kindly assistance.

Funding

This work was supported by the Science Foundation of the Fujian Province (Grant No.2021J01779) and Joint Funds for the Innovation of Science and Technology, Fujian Province (Grant No.2020Y9089).

Competing interests

The authors declare that they have no competing interests.

Data availability statement

All of the data and materials are available from the corresponding author upon reasonable ask.

Sung H, Ferlay J, Siegel RL, Laversanne M, Soerjomataram I, Jemal A, Bray F. Global Cancer Statistics 2020: GLOBOCAN Estimates of Incidence and Mortality Worldwide for 36 Cancers in 185 Countries. Cancer J Clin. 2021;71(3):209–49.
Mehta N, Bhangui P, Yao FY, Mazzaferro V, Toso C, Akamatsu N, Durand F, Ijzermans J, Polak W, Zheng S et al. Liver Transplantation for Hepatocellular Carcinoma. Working Group Report from the ILTS Transplant Oncology Consensus Conference. Transplantation 2020, 104(6):1136–1142.
Mazzaferro V, Bhoori S, Sposito C, Bongini M, Langer M, Miceli R, Mariani L. Milan criteria in liver transplantation for hepatocellular carcinoma: an evidence-based analysis of 15 years of experience. Liver transplantation: official publication Am Association Study Liver Dis Int Liver Transplantation Soc. 2011;17(Suppl 2):44–57.
Kudo M, Kawamura Y, Hasegawa K, Tateishi R, Kariyama K, Shiina S, Toyoda H, Imai Y, Hiraoka A, Ikeda M, et al. Management of Hepatocellular Carcinoma in Japan: JSH Consensus Statements and Recommendations 2021 Update. Liver cancer. 2021;10(3):181–223.
Korean Liver Cancer A, National Cancer Center K. 2022 KLCA-NCC Korea practice guidelines for the management of hepatocellular carcinoma. Clin Mol Hepatol. 2022;28(4):583–705.
Xie D, Shi J, Zhou J, Fan J, Gao Q. Clinical practice guidelines and real-life practice in hepatocellular carcinoma: A Chinese perspective. Clin Mol Hepatol. 2023;29(2):206–16.
Anisetti B, Ahmed AK, Coston T, Gardner L, Majeed U, Reynolds J, Babiker H. Delayed brain metastasis in recurrent hepatocellular carcinoma following liver transplantation: a case report highlighting the predictive value of microvascular invasion. Clinical journal of gastroenterology 2023.
Terentiev AA, Moldogazieva NT. Alpha-fetoprotein: a renaissance. Tumour biology: J Int Soc Oncodevelopmental Biology Med. 2013;34(4):2075–91.
Wang H, Yang C, Li D, Wang R, Li Y, Lv L. Bioinformatics analysis and experimental validation of a novel autophagy-related signature relevant to immune infiltration for recurrence prediction after curative hepatectomy. Aging. 2023;15(7):2610–30.
Huang M, He M, Guo Y, Li H, Shen S, Xie Y, Li X, Xiao H, Fang L, Li D, et al. The Influence of Immune Heterogeneity on the Effectiveness of Immune Checkpoint Inhibitors in Multifocal Hepatocellular Carcinomas. Clin cancer research: official J Am Association Cancer Res. 2020;26(18):4947–57.
Shi H, Zhang W, Hu B, Wang Y, Zhang Z, Sun Y, Mao G, Li C, Lu S. Whole-exome sequencing identifies a set of genes as markers of hepatocellular carcinoma early recurrence. Hep Intl. 2023;17(2):393–405.
Zheng SS, Xu X, Wu J, Chen J, Wang WL, Zhang M, Liang TB, Wu LM. Liver transplantation for hepatocellular carcinoma: Hangzhou experiences. Transplantation. 2008;85(12):1726–32.
Chen S, Zhou Y, Chen Y, Gu J. fastp: an ultra-fast all-in-one FASTQ preprocessor. Bioinformatics. 2018;34(17):i884–90.
Cibulskis K, Lawrence MS, Carter SL, Sivachenko A, Jaffe D, Sougnez C, Gabriel S, Meyerson M, Lander ES, Getz G. Sensitive detection of somatic point mutations in impure and heterogeneous cancer samples. Nat Biotechnol. 2013;31(3):213–9.
Wang K, Li M, Hakonarson H. ANNOVAR: functional annotation of genetic variants from high-throughput sequencing data. Nucleic Acids Res. 2010;38(16):e164.
Li W, Wu H, Xu X, Zhang Y. Comprehensive analysis of genomic and immunological profiles in Chinese and Western hepatocellular carcinoma populations. Aging. 2021;13(8):11564–94.
Huang K, Wu Y, Fan W, Zhao Y, Xue M, Liu H, Tang Y, Li J. Identification of BRD7 by whole-exome sequencing as a predictor for intermediate-stage hepatocellular carcinoma in patients undergoing TACE. J Cancer Res Clin Oncol. 2023;149(13):11247–61.
Chen X, Wang D, Liu J, Qiu J, Zhou J, Ying J, Shi Y, Wang Z, Lou H, Cui J et al. Genomic alterations in biliary tract cancer predict prognosis and immunotherapy outcomes. J Immunother Cancer 2021, 9(11).
!!!. INVALID CITATION !!!.
Wen Y, Zhou X, Lu M, He M, Tian Y, Liu L, Wang M, Tan W, Deng Y, Yang X, et al. Bclaf1 promotes angiogenesis by regulating HIF-1alpha transcription in hepatocellular carcinoma. Oncogene. 2019;38(11):1845–59.
Long J, Wang A, Bai Y, Lin J, Yang X, Wang D, Yang X, Jiang Y, Zhao H. Development and validation of a TP53-associated immune prognostic model for hepatocellular carcinoma. EBioMedicine. 2019;42:363–74.
Tang B, Zhu J, Zhao Z, Lu C, Liu S, Fang S, Zheng L, Zhang N, Chen M, Xu M, et al. Diagnosis and prognosis models for hepatocellular carcinoma patient's management based on tumor mutation burden. J Adv Res. 2021;33:153–65.
Gabbia D, De Martin S. Tumor Mutational Burden for Predicting Prognosis and Therapy Outcome of Hepatocellular Carcinoma. Int J Mol Sci 2023, 24(4).
Luo P, Wu S, Yu Y, Ming X, Li S, Zuo X, Tu J. Current Status and Perspective Biomarkers in AFP Negative HCC: Towards Screening for and Diagnosing Hepatocellular Carcinoma at an Earlier Stage. Pathol Oncol research: POR. 2020;26(2):599–603.
Colli A, Fraquelli M, Casazza G, Massironi S, Colucci A, Conte D, Duca P. Accuracy of ultrasonography, spiral CT, magnetic resonance, and alpha-fetoprotein in diagnosing hepatocellular carcinoma: a systematic review. Am J Gastroenterol. 2006;101(3):513–23.
Cai C, Xie X, Zhou J, Fang X, Wang F, Wang M. Identification of TAF1, SAT1, and ARHGEF9 as DNA methylation biomarkers for hepatocellular carcinoma. J Cell Physiol. 2020;235(1):611–8.
Jhunjhunwala S, Jiang Z, Stawiski EW, Gnad F, Liu J, Mayba O, Du P, Diao J, Johnson S, Wong KF, et al. Diverse modes of genomic alteration in hepatocellular carcinoma. Genome Biol. 2014;15(8):436.
Fondevila F, Fernandez-Palanca P, Mendez-Blanco C, Payo-Serafin T, Lozano E, Marin JJG, Gonzalez-Gallego J, Mauriz JL. Association of FOXO3 Expression with Tumor Pathogenesis, Prognosis and Clinicopathological Features in Hepatocellular Carcinoma: A Systematic Review with Meta-Analysis. Cancers 2021, 13(21).
Wang R, Hu X, Liu X, Bai L, Gu J, Li Q. Construction of liver hepatocellular carcinoma-specific lncRNA-miRNA-mRNA network based on bioinformatics analysis. PLoS ONE. 2021;16(4):e0249881.

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Genomic alterations in hepatocellular carcinoma patients undergoing liver transplantation predict recurrence and prognosis

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Abstract

Figures

Introduction

Methods

Patient information and sample collection

WES sequencing

Data Filtering and Variant Calling

Statistical analysis

Results

Clinical characteristics analysis of recurrence group and non-recurrence group

Overview of molecular characteristics between the two groups

Establish of the 13-gene-based relapse predicting and prognostic model

Discussion

Conclusion

Data availability statement

Declarations

References

Supplementary Files

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