Stratification Of Hepatocellular Carcinoma Patients Research Articles

Recent Advances in Liver Transplantation for Hepatocellular Carcinoma.

Liver transplantation for hepatocellular carcinoma (HCC) remains an evolving field. Major challenges HCC transplant patients face today include liver organ donor shortages and the need for both better pre-transplant bridging/downstaging therapies and post-transplant HCC recurrence treatment options. The advent of immunotherapy and the demonstrated efficacy of immune checkpoint inhibitors in multiple solid tumors including advanced/unresectable HCC hold promise in expanding both the neoadjuvant and adjuvant HCC transplant treatment regimen, though caution is needed with these immune modulating agents leading up to and following transplant. New options for pre-transplant HCC management will expand access to this curative option as well as ensure patients have adequate control of their HCC prior to transplant to maximize the utility of a liver donor. Machine perfusion has been an active area of investigation in recent years and could expand the organ donor pool, helping address current liver donor shortages. Finally, additional HCC biomarkers such as AFP-L3 and DCP have shown promise in improving risk stratification of HCC patients. Together, these three recent advancements will likely alter HCC transplant guidelines in the coming years.

Genomic Biomarkers to Predict Response to Atezolizumab Plus Bevacizumab Immunotherapy in Hepatocellular Carcinoma: Insights from the IMbrave150 Trial.

Combination immunotherapy, exemplified by atezolizumab plus bevacizumab, has become the standard of care for inoperable hepatocellular carcinoma (HCC). However, the lack of predictive biomarkers and limited understanding of response mechanisms remain a challenge. Using data from the IMbrave150plus cohort, we applied an immune signature score (ISS) predictor to stratify HCC patients treated with atezolizumab plus bevacizumab or with sorafenib alone into potential high and low response groups. By applying multiple statistical approaches including a Bayesian covariate prediction algorithm, we refined the signature to 10 key genes (ISS10) for clinical use while maintaining similar predictive power to the full model. We further validated ISS10 in an independent HCC cohort treated with nivolumab plus ipilimumab. The study identified a significant association between the ISS and treatment response. Among patients classified as high responders, those treated with the atezolizumab plus bevacizumab combination exhibited improved overall and progression-free survival as well as better objective response rate compared to those treated with sorafenib. We also observed a significant correlation between ISS10 and response to nivolumab plus ipilimumab treatment. Analysis of immune cell subpopulations revealed distinct characteristics associated with ISS subtypes. In particular, the ISS10 high subtype displayed a more favorable immune environment with higher proportions of anti-tumor macrophages and activated T-cells, potentially explaining its better response. Our study suggests that ISS and ISS10 are promising predictive biomarkers for enhanced therapeutic outcomes in HCC patients undergoing combination immunotherapy. These markers are crucial for refining patient stratification and personalized treatment approaches to advance the effectiveness of standard-of-care regimens.

Clinical impact and potential utility of non-enhanced computed tomography performed immediately after transarterial chemoembolization for hepatocellular carcinoma.

Intratumoral lipiodol deposition following transarterial chemoembolization (TACE) is associated with the prognosis of hepatocellular carcinoma (HCC) patients. However, there is insufficient evidence regarding the actual clinical significance of the imaging tests conducted to evaluate the lipiodol uptake after TACE. This study evaluates the clinical impact and potential utility of performing immediate post-TACE non-enhanced computed tomography (NECT) on the treatment of HCC. This retrospective study at a tertiary referral center included patients undergoing their first session of conventional TACE for initial treatment of HCC from November 2021 to December 2022 with available immediate post-TACE NECT. Patients were categorized based on lipiodol uptake into Cohorts A (incomplete uptake with additional treatment before the first follow-up 1 month after TACE), B incomplete uptake without additional treatment before first follow-up), and C (complete uptake). Survival curves for the time to progression (TTP) were estimated using the Kaplan-Meier method and were compared by using the log-rank test. Out of 189 patients, 58 (29.6%) showed incomplete lipiodol uptake; 2 in Cohort A and 56 in Cohort B. Cohort C included 131 patients (69.3%). Cohort B had the highest rate of residual viable tumor (48.2%) 1 month after TACE, compared to the other cohorts (0% in Cohort A and 32.1% in Cohort C). The median TTP of Cohort B was 7.9 months [95% confidence interval (CI): 4.6-15.7 months], significantly shorter than the 15.4 months (95% CI: 10.9-20.9 months) for Cohort C (P=0.03). During follow-up, no progression occurred in Cohort A. Assessment of lipiodol uptake by performing immediate post-TACE NECT can stratify HCC patients and facilitate early prediction of therapeutic response. Identifying suboptimal lipiodol uptake immediately after TACE can aid future treatment adjustments and potentially improving oncologic outcomes.

Integrated Bioinformatic Analysis Reveals the Oncogenic, Survival, and Prognostic Characteristics of TPX2 in Hepatocellular Carcinoma.

Targeting protein for Xenopus kinesin-like protein 2 (TPX2), a well-known mitotic protein, has been linked to carcinogenesis in several cancers. This study investigated the role of TPX2 in hepatocellular carcinoma (HCC) from various aspects using bioinformatic analyses. TPX2 expression and its prognostic value in pan-cancers were analyzed using SangerBox. TPX2 expression and its association with prognosis, immune infiltration, tumor mutations, and signaling pathways in HCC were analyzed using UALCAN, BoxKaplan-Meier Plotter, GEPIA, Human Protein Atlas, TIMER 2.0, and SangerBox. Genes co-expressed with TPX2 in HCC were analyzed using the HCCDB database, followed by functional enrichment using SangerBox. Clinical predictive models were established based on TPX2 and its co-expressed genes using the ACLBI database. TPX2 expression significantly increased in pan-cancers and was associated with survival in nearly half of the cancer types. High TPX2 expression has been linked to poor survival outcomes in patients with HCC. TPX2 expression was positively correlated with abundant infiltration of immune cells (including B cells, CD4 + /CD8 + T cells, macrophages, neutrophils, and dendritic cells), TP53 mutation, and carcinogenesis-related pathways, such as the PI3K/AKT/mTOR pathway, cellular response to hypoxia, and tumor proliferation signature. Nineteen genes were found to be co-expressed with TPX2 in HCC, and these genes showed close positive correlations and were mainly implicated in cell cycle-related functions. A prognostic model established using TPX2 and its expressed genes could stratify HCC patients into high- and low-risk groups, with a significantly shorter survival time in high-risk groups. The prognostic model performed well in predicting 1-, 3-, and 5-year survival of patients with HCC, with areas under the curve of 0.801, 0.725, and 0.711, respectively. TPX2 functions as an oncogene in HCC, and its high expression is detrimental to the survival of patients with HCC. Thus, TPX2 may be a prognostic biomarker and potential therapeutic target for HCC.

PARD3 drives tumorigenesis through activating Sonic Hedgehog signalling in tumour-initiating cells in liver cancer

BackgroundPar-3 Family Cell Polarity Regulator (PARD3) is a cellular protein essential for asymmetric cell division and polarized growth. This study aimed to study the role of PARD3 in hepatic tumorigenesis.MethodsThe essential role of PARD3 in mediating hepatic tumorigenesis was assessed in diet-induced spontaneous liver tumour and syngeneic tumour models. The mechanism of PARD3 was delineated by bulk and single-cell RNA sequencing. The clinical significance of PARD3 was identified by tissue array analysis.ResultsPARD3 was overexpressed in tumour tissues and PARD3 overexpression was positively correlated with high tumour stage as well as the poor prognosis in patients. In models of spontaneous liver cancer induced by choline-deficient, amino acid-defined (CDAA) and methionine-choline-deficient (MCD) diets, upregulation of PARD3 was induced specifically at the tumorigenesis stage rather than other early stages of liver disease progression. Site-directed knockout of PARD3 using an adeno-associated virus 8 (AAV8)-delivered CRISPR/Cas9 single-guide RNA (sgRNA) plasmid blocked hepatic tumorigenesis, while PARD3 overexpression accelerated liver tumour progression. In particular, single-cell sequencing analysis suggested that PARD3 was enriched in primitive tumour cells and its overexpression enhanced tumour-initiating cell (TICs). Overexpression of PARD3 maintained the self-renewal ability of the CD133+ TIC population within hepatocellular carcinoma (HCC) cells and promoted the in vitro and in vivo tumorigenicity of CD133+ TICs. Transcriptome analysis revealed that Sonic Hedgehog (SHH) signalling was activated in PARD3-overexpressing CD133+ TICs. Mechanistically, PARD3 interacted with aPKC to further activate SHH signalling and downstream stemness-related genes. Suppression of SHH signalling and aPKC expression attenuated the in vitro and in vivo tumorigenicity of PARD3-overexpressing CD133+ TICs. Tissue array analysis revealed that PARD3 expression was positively associated with the phosphorylation of aPKC, SOX2 and Gli1 and that the combination of these markers could be used to stratify HCC patients into two clusters with different clinicopathological characteristics and overall survival prognoses. The natural compound berberine was selected as a potent suppressor of PARD3 expression and could be used as a preventive agent for liver cancer that completely blocks diet-induced hepatic tumorigenesis in a PARD3-dependent manner.ConclusionThis study revealed PARD3 as a potential preventive target of liver tumorigenesis via TIC regulation.

Identification of prognostic biomarkers for hepatocellular carcinoma with vascular invasion.

Vascular invasion (VI) profoundly impacts the prognosis of hepatocellular carcinoma (HCC), yet the underlying biomarkers and mechanisms remain elusive. This study aimed to identify prognostic biomarkers for HCC patients with VI. Transcriptome data from primary HCC tissues and HCC tissues with VI were obtained through the Genome Expression Omnibus database. Differentially expressed genes (DEGs) in the two types of tissues were analyzed using functional enrichment analysis to evaluate their biological functions. We examined the correlation between DEGs and prognosis by combining HCC transcriptome data and clinical information from The Cancer Genome Atlas database. Univariate and multivariate Cox regression analyses, along with the least absolute shrinkage and selection operator (LASSO) method were utilized to develop a prognostic model. The effectiveness of the model was assessed through time-dependent receiver operating characteristic (ROC) curve, calibration diagram, and decision curve analysis. In the GSE20017 and GSE5093 datasets, a total of 83 DEGs were identified. Gene Ontology analysis indicated that these DEGs were predominantly associated with xenobiotic stimulus, collagen-containing extracellular matrix, and oxygen binding. Additionally, Kyoto Encyclopedia of Genes and Genomes analysis revealed that the DEGs were primarily involved in immune defense and cellular signal transduction. Cox and LASSO regression further identified 7 genes (HSPA8, ABCF2, EAF1, MARCO, EPS8L3, PLA3G1B, C6), which were used to construct a predictive model in the training cohort. We used X-tile software to calculate the optimal cut-off value to stratify HCC patients into low-risk and high-risk groups. Notably, the high-risk group exhibited poorer prognosis than the low-risk group (P < 0.001). The model demonstrated area under the ROC curve (AUC) values of 0.815, 0.730, and 0.710 at 1-year, 3-year, and 5-year intervals in the training cohort, respectively. In the validation cohort, the corresponding AUC values were 0.701, 0.571, and 0.575, respectively. The C-index of the calibration curve for the training and validation cohorts were 0.716 and 0.665. Decision curve analysis revealed the model's efficacy in guiding clinical decision-making. The study indicates that 7 genes may be potential prognostic biomarkers and treatment targets for HCC patients with VI.

Copper metabolism-related risk score identifies hepatocellular carcinoma subtypes and SLC27A5 as a potential regulator of cuproptosis.

Dysregulated copper metabolism has been noticed in many types of cancer including hepatocellular carcinoma (HCC); however, a comprehensive understanding about this dysregulation still remains unclear in HCC. A set of bioinformatic tools was integrated to analyze the expression and prognostic significance of copper metabolism-related genes. A related risk score, termed as CMscore, was developed via univariate Cox regression, least absolute shrinkage and selection operator (LASSO) Cox regression and multivariate Cox regression. Pathway enrichment analyses and tumor immune cell infiltration were further investigated in CMscore stratified HCC patients. Weighted correlation network analysis (WGCNA) was used to identify potential regulator of cuproptosis. Copper metabolism was dysregulated in HCC. HCC patients in the high-CMscore group showed a significantly lower overall survival (OS) and enriched in most cancer-related pathways. Besides, HCC patients with high CMscore had higher expression of pro-tumor immune infiltrates and immune checkpoints. Moreover, cancer patients with high CMscore from two large cohorts exhibited significantly prolonged survival time after immunotherapy. WGCNA and subsequently correlation analysis revealed that SLC27A5 might be a potential regulator of cuproptosis in HCC. In vitro experiments revealed that SLC27A5 inhibited cell proliferation and migration of HCC cells and could upregulate FDX1, the key regulator of cuproptosis. The CMscore is helpful in clustering HCC patients with distinct prognosis, gene mutation signatures, and sensitivity to immunotherapy. SLC27A5 might serve as a potential target in the induction of cuproptosis in HCC.

A multitask deep learning radiomics model for predicting the macrotrabecular-massive subtype and prognosis of hepatocellular carcinoma after hepatic arterial infusion chemotherapy

BackgroundThe macrotrabecular-massive (MTM) is a special subtype of hepatocellular carcinoma (HCC), which has commonly a dismal prognosis. This study aimed to develop a multitask deep learning radiomics (MDLR) model for predicting MTM and HCC patients’ prognosis after hepatic arterial infusion chemotherapy (HAIC).MethodsFrom June 2018 to March 2020, 158 eligible patients with HCC who underwent surgery were retrospectively enrolled in MTM related cohorts, and 752 HCC patients who underwent HAIC were included in HAIC related cohorts during the same period. DLR features were extracted from dual-phase (arterial phase and venous phase) contrast-enhanced computed tomography (CECT) of the entire liver region. Then, an MDLR model was used for the simultaneous prediction of the MTM subtype and patient prognosis after HAIC. The MDLR model for prognostic risk stratification incorporated DLR signatures, clinical variables and MTM subtype.FindingsThe predictive performance of the DLR model for the MTM subtype was 0.968 in the training cohort [TC], 0.912 in the internal test cohort [ITC] and 0.773 in the external test cohort [ETC], respectively. Multivariable analysis identified portal vein tumor thrombus (PVTT) (p = 0.012), HAIC response (p < 0.001), HAIC sessions (p < 0.001) and MTM subtype (p < 0.001) as indicators of poor prognosis. After incorporating DLR signatures, the MDLR model yielded the best performance among all models (AUC, 0.855 in the TC, 0.805 in the ITC and 0.792 in the ETC). With these variables, the MDLR model provided two risk strata for overall survival (OS) in the TC: low risk (5-year OS, 44.9%) and high risk (5-year OS, 4.9%).InterpretationA tool based on MDLR was developed to consider that the MTM is an important prognosis factor for HCC patients. MDLR showed outstanding performance for the prognostic risk stratification of HCC patients who underwent HAIC and may help physicians with therapeutic decision making and surveillance strategy selection in clinical practice.

Identification of tumor antigens and anoikis-based molecular subtypes in the hepatocellular carcinoma immune microenvironment: implications for mRNA vaccine development and precision treatment

Hepatocellular carcinoma (HCC) represents a formidable malignancy with a high lethality. Nonetheless, the development of vaccine and the establishment of prognostic models for precise and personalized treatment of HCC still encounter big challenges. Thus, the aim of this study was to develop HCC vaccines and explore anoikis-based prognostic models based on RNA sequencing data in GEO datasets (GSE10143, GSE76427) and the TCGA-LIHC cohort. Potential HCC antigens were identified using GEPIA2, cBioPortal, and TIMER2. Anoikis-related subtypes and gene clusters were defined by consensus clustering of 566 liver cancer samples based on 28 anoikis regulators, and we further analyzed their relationship with the immune microenvironment of HCC. A predictive model based on anoikis-related long noncoding RNAs (lncRNAs) was developed to accurately predict HCC prognosis. Seven overexpressed genes associated with HCC prognosis and tumor-infiltrating antigen-presenting cells were identified as potential tumor antigens for the development of HCC mRNA vaccines. Two subtypes based on anoikis-related genes (ARGs) and two gene clusters with different characteristics were identified and validated in defined cohorts. The tumor immune microenvironment between the two subtypes showed different cell infiltration and molecular characteristics. Furthermore, a prognostic score based on seven lncRNAs identified by LASSO regression was constructed, with the low-risk group having favorable prognosis, a “hot” immune microenvironment, and better response to immunotherapy. CCNB1, CDK1, DNASE1L3, KPNA2, PRC1, PTTG, and UBE2S were first identified as promising tumor antigens for mRNA vaccine development in HCC. Besides, we innovatively propose anoikis-based molecular subtypes, which not only enable personalized prognostic stratification of HCC patients but also provide a blueprint for identifying optimal candidates for tumor vaccines, enhancing immunotherapeutic strategies.

Machine learning-based integration develops a neutrophil-derived signature for improving outcomes in hepatocellular carcinoma.

The heterogeneity of tumor immune microenvironments is a major factor in poor prognosis among hepatocellular carcinoma (HCC) patients. Neutrophils have been identified as playing a critical role in the immune microenvironment of HCC based on recent single-cell studies. However, there is still a need to stratify HCC patients based on neutrophil heterogeneity. Therefore, developing an approach that efficiently describes "neutrophil characteristics" in HCC patients is crucial to guide clinical decision-making. We stratified two cohorts of HCC patients into molecular subtypes associated with neutrophils using bulk-sequencing and single-cell sequencing data. Additionally, we constructed a new risk model by integrating machine learning analysis from 101 prediction models. We compared the biological and molecular features among patient subgroups to assess the model's effectiveness. Furthermore, an essential gene identified in this study was validated through molecular biology experiments. We stratified patients with HCC into subtypes that exhibited significant differences in prognosis, clinical pathological characteristics, inflammation-related pathways, levels of immune infiltration, and expression levels of immune genes. Furthermore, A risk model called the "neutrophil-derived signature" (NDS) was constructed using machine learning, consisting of 10 essential genes. The NDS's RiskScore demonstrated superior accuracy to clinical variables and correlated with higher malignancy degrees. RiskScore was an independent prognostic factor for overall survival and showed predictive value for HCC patient prognosis. Additionally, we observed associations between RiskScore and the efficacy of immune therapy and chemotherapy drugs. Our study highlights the critical role of neutrophils in the tumor microenvironment of HCC. The developed NDS is a powerful tool for assessing the risk and clinical treatment of HCC. Furthermore, we identified and analyzed the feasibility of the critical gene RTN3 in NDS as a molecular marker for HCC.

Development and validation of a diagnostic and prognostic model for lung metastasis of hepatocellular carcinoma: a study based on the SEER database.

Lung metastasis (LM) is a common occurrence in patients with hepatocellular carcinoma (HCC), and it is associated with a poorer prognosis compared to HCC patients without LM. This study aimed to identify predictors and prognostic factors for LM in HCC patients as well as develop diagnostic and prognostic nomograms specifically tailored for LM in HCC patients. A retrospective analysis was conducted on HCC patients from the Surveillance, Epidemiology, and End Results (SEER) database, covering the period from 2010 to 2015. The study employed multivariate logistic regression analysis to identify risk factors associated with LM in HCC patients. Additionally, multivariate Cox proportional hazards regression analysis was utilized to investigate prognostic factors for HCC patients with LM. Subsequently, two nomograms were developed to predict the risk and prognosis of LM in HCC patients. The performance of the nomograms was evaluated through calibration curves, receiver operating characteristic (ROC) curves, and decision curve analysis (DCA). This retrospective study included a total of 5,934 patients diagnosed with HCC, out of which 174 patients were diagnosed with LM. Through multivariate logistic regression analysis, several independent risk factors for LM in HCC patients were identified, including tumor grade, tumor size, American Joint Committee for Cancer (AJCC) T stage, and AJCC N stage. Furthermore, multivariate Cox analysis revealed that tumor grade, delayed treatment, surgery, and radiation were independent prognostic factors for HCC patients with LM. To assess the predictive power of the developed nomograms, calibration curves, receiver operating characteristic (ROC) analysis, and decision curve analysis (DCA) were employed. The findings demonstrated that the nomograms exhibited satisfactory performance in both the training and validation sets. Additionally, the prognostic nomogram effectively stratified HCC patients with LM into low- and high-risk groups for mortality. These two nomograms optimally predicted the risk and prognosis of LM in HCC patients. Both nomograms have satisfactory performance. This would help clinicians to make accurate clinical decisions.

Identification of Pyroptosis Gene Signature Related Molecular Pattern, Clinical Implication, and Tumor Immunity in Hepatocellular Carcinoma`.

Pyroptosis is a novel form of programmed cell death in cancers, which regulates tumor cell invasion, proliferation, and metastasis, thereby affecting the prognosis of cancer patients. However, the role of Pyroptosis-Related Genes (PGs) in Hepatocellular Carcinoma (HCC) remains unclear. Somatic mutation, copy number variation, and expression of 41 PGs were assessed in HCC and normal liver from the TCGA dataset. The Least Absolute Shrinkage and Selection Operator (LASSO) was used to construct the prognostic model. K-M curves, ROC curves, nomograph, and univariate and multivariate Cox regression were conducted to evaluate the predictive value of PGs. Immune infiltration was analyzed by CIBERSOFT and ssGSEA algorithm. The expression of prognostic PGs was validated by qPCR. Significant mutation and copy number variation of PGs were found in HCC. These genes were involved in an inflammatory response. In addition, 9 out of 41 PGs were differentially expressed in HCC and found to correlate significantly with patient survival. Then, these signature genes were selected to build a prognosis model and were utilized to stratify HCC patients into high and low PGs-score groups. It showed that the high-PGs group had a worse prognosis. Univariate and multivariate Cox regression verified that PGs-score was an independent risk factor for HCC. By ROC curves and nomogram, we showed that PGs-score effectively predicted the 1-, 3-, and 5-year survival of HCC patients and correlated with AFP level and disease stage. Immune infiltration analysis further showed that tumor immunity correlated with the PGs-score, and the expression of immune checkpoint molecule was significantly enhanced in the high PGs group. The PGs-score was also validated in the external validation cohort (ICGC). Finally, the expression of 9 signature genes was validated in normal liver and HCC cell lines. This study elucidated the aberrant regulation of PGs in HCC, and those pyroptosisrelated genes may be applied as a prognostic factor of HCC.

Screening prognostic markers for hepatocellular carcinoma based on pyroptosis-related lncRNA pairs

BackgroundPyroptosis is closely related to cancer prognosis. In this study, we tried to construct an individualized prognostic risk model for hepatocellular carcinoma (HCC) based on within-sample relative expression orderings (REOs) of pyroptosis-related lncRNAs (PRlncRNAs).MethodsRNA-seq data of 343 HCC samples derived from The Cancer Genome Atlas (TCGA) database were analyzed. PRlncRNAs were detected based on differentially expressed lncRNAs between sample groups clustered by 40 reported pyroptosis-related genes (PRGs). Univariate Cox regression was used to screen out prognosis-related PRlncRNA pairs. Then, based on REOs of prognosis-related PRlncRNA pairs, a risk model for HCC was constructed by combining LASSO and stepwise multivariate Cox regression analysis. Finally, a prognosis-related competing endogenous RNA (ceRNA) network was built based on information about lncRNA–miRNA–mRNA interactions derived from the miRNet and TargetScan databases.ResultsHierarchical clustering of HCC patients according to the 40 PRGs identified two groups with a significant survival difference (Kaplan–Meier log-rank, p = 0.026). Between the two groups, 104 differentially expressed lncRNAs were identified (|log2(FC)|> 1 and FDR < 5%). Among them, 83 PRlncRNA pairs showed significant associations between their REOs within HCC samples and overall survival (Univariate Cox regression, p < 0.005). An optimal 11-PRlncRNA-pair prognostic risk model was constructed for HCC. The areas under the curves (AUCs) of time-dependent receiver operating characteristic (ROC) curves of the risk model for 1-, 3-, and 5-year survival were 0.737, 0.705, and 0.797 in the validation set, respectively. Gene Set Enrichment Analysis showed that inflammation-related interleukin signaling pathways were upregulated in the predicted high-risk group (p < 0.05). Tumor immune infiltration analysis revealed a higher abundance of regulatory T cells (Tregs) and M2 macrophages and a lower abundance of CD8 + T cells in the high-risk group, indicating that excessive pyroptosis might occur in high-risk patients. Finally, eleven lncRNA–miRNA–mRNA regulatory axes associated with pyroptosis were established.ConclusionOur risk model allowed us to determine the robustness of the REO-based PRlncRNA prognostic biomarkers in the stratification of HCC patients at high and low risk. The model is also helpful for understanding the molecular mechanisms between pyroptosis and HCC prognosis. High-risk patients may have excessive pyroptosis and thus be less sensitive to immune therapy.

Identification of Atezolizumab Plus Bevacizumab Prognostic Index via Recursive Partitioning Analysis in HCC: The ABE Index.

The purpose of this study was to ascertain a novel prognostic index via recursive partitioning analysis (RPA) in hepatocellular carcinoma (HCC) patients being treated with the combination of atezolizumab plus bevacizumab (ABE) in first-line setting. A total of 784 patients with HCC were included in the analysis. RPA identified three groups of patients: high-risk [Child-Pugh B (CP-B) patients; CP-A and Albumin-Bilirubin (ALBI)-2 patients; CP-A and ALBI-1 patients with macrovascular invasion (MVI), and alpha-fetoprotein (α-FP) ≥400 ng/ml]; intermediate-risk [CP-A and ALBI-1 patients with aspartate aminotransferase (AST) normal value (NV), and αFP ≥400 ng/ml, but without MVI; CP-A and ALBI-1 patients with AST increased value (IV), and neutrophil-lymphocyte ratio (NLR) ≥3, but without MVI]; low-risk (CP-A and ALBI-1 patients with AST NV, and αFP <400 ng/ml, but without MVI; CP-A and ALBI-1 patients with AST IV, and NLR <3, but without MVI; CP-A and ALBI-1 patients with MVI, and αFP <400 ng/ml). Overall survival was 7.0 months in high-risk patients (20.8%), 14.2 months in intermediate-risk patients (19.1%), and 22.5 months in low-risk patients (60.1%). The ABE index allows for easy stratification of HCC patients treated with the combination of ABE in first-line setting.

Tumor Mutational Burden for Predicting Prognosis and Therapy Outcome of Hepatocellular Carcinoma.

Hepatocellular carcinoma (HCC), the primary hepatic malignancy, represents the second-highest cause of cancer-related death worldwide. Many efforts have been devoted to finding novel biomarkers for predicting both patients' survival and the outcome of pharmacological treatments, with a particular focus on immunotherapy. In this regard, recent studies have focused on unravelling the role of tumor mutational burden (TMB), i.e., the total number of mutations per coding area of a tumor genome, to ascertain whether it can be considered a reliable biomarker to be used either for the stratification of HCC patients in subgroups with different responsiveness to immunotherapy, or for the prediction of disease progression, particularly in relation to the different HCC etiologies. In this review, we summarize the recent advances on the study of TMB and TMB-related biomarkers in the HCC landscape, focusing on their feasibility as guides for therapy decisions and/or predictors of clinical outcome.

Cholesterol and C-reactive protein prognostic score predicted prognosis of immune checkpoint inhibitors based interventional therapies for intermediate-to-advanced hepatocellular carcinoma patients

Serum cholesterol (CHO) and C-reactive protein (CRP) have been successfully used as prognostic predictors for several malignancies, respectively. However, the clinicopathological significance of CHO and CRP levels in hepatocellular carcinoma (HCC) patients treated with ICIs-based hepatic artery infusion chemotherapy (HAIC) remains unclear. Serum CHO and CRP levels were measured for a total of 152 HCC patients that had been treated with ICIs-based HAIC from February 2019 to April 2020. Efficacy was evaluated according to tumor response and survival. The median OS was not reached in the CHO-low subgroup and 17.7months in the CHO-high subgroup (P=0.015). The median OS was not reached in the CRP-low subgroup and 20.0months in the CRP-high subgroup (P=0.010). Univariate and multivariate Cox regression analysis demonstrated that both serum CHO and CRP levels were independent risk factors for the OS of HCC patients treated with ICIs-based HAIC (P<0.05). Moreover, Cox regression analysis after Propensity Score Matching showed the similar results. CHO and CRP prognostic score (CCPS) combining CHO and CRP levels could significantly stratify HCC patients receiving ICIs-based HAIC into low-, intermediate-, and high-risk subgroups (P<0.001). Patients in the risk subgroups reported similar disease control rates (P=0.121) and significantly different overall response rates (low- vs intermediate- vs high-risk groups: 70.6% vs 46.6% vs 44.1%, respectively, P=0.038) according to modified Response Evaluation Criteria in Solid Tumors (mRECIST). The results of this study support the association between CCPS high risk with the response and OS for HCC patients receiving ICIs-based HAIC.

A novel cuproptosis-related prognostic gene signature and validation of differential expression in hepatocellular carcinoma.

Background: Cuproptosis is a newly discovered form of programmed cell death, which is characterized by accumulation of intra-cellular copper ion leading to the aggregation of lipoproteins and destabilization of Fe-S cluster proteins in mitochondrial metabolism, thereby affecting the prognosis of patients with cancer. However, the role of cuproptosis-related genes (CRGs) in hepatocellular carcinoma (HCC) remains elusive. Methods: Mutation signature, copy number variation and the expression of 10 CRGs were assessed in HCC from TCGA-LIHC dataset. ICGC-LIRI-JP dataset was used as further validation cohort. The least absolute shrinkage and selection operator (LASSO) was used to construct the prognostic model. Kaplan Meier curves, time-ROC curves, nomogram, univariate and multivariate Cox regression were utilized to evaluate the predictive efficacy of CRGs-score. Immune infiltration was analyzed by CIBERSOFT, ssGSEA algorithm, and TIMER database. The expression of prognostic CRGs was validated by qPCR both in-vitro and in-vivo. Drug sensitivity analysis was performed by pRRophetic. Results: All of the CRGs were differentially expressed in HCC and 5 out of them (CDKN2A, DLAT, GLS, LIPT1, MTF1) correlated with patient survival. These signature genes were selected by LASSO analysis to establish a prognosis model to stratify HCC patients into high and low CRGs-score subgroups. High CRGs-score was associated with a worse prognosis. Subsequently, univariate and multivariate Cox regression verified that CRGs-score was an independent cancer risk factor that correlated with clinical factors including stage and grade. Nomogram integrating the CRGs-score and clinical risk factors performed well to predict patient survival. Immune infiltration analysis further revealed that the expression of immune checkpoint genes was significantly enhanced in high CRGs-score group, especially PD-1 and PD-L1. An independent validation cohort (ICGC) confirmed that CRGs-score as a stable and universally applicable indicator in predicting HCC patient survival. Concordantly, the expression of five confirmed signature genes were also differentially expressed in human HCC cell lines and mouse HCC model. In addition, we also analyzed the sensitivity of 10 clinical targeted therapies between high and low CRGs-score groups. Conclusion: This study elucidated the role of dysregulated CRGs in HCC cohort, with validation with in-vitro and in-vivo models. The CRGs-score might be applied as a novel prognostic factor in HCC.

Primary Tumor Radiomic Model for Identifying Extrahepatic Metastasis of Hepatocellular Carcinoma Based on Contrast Enhanced Computed Tomography.

This study aimed to identify radiomic features of primary tumor and develop a model for indicating extrahepatic metastasis of hepatocellular carcinoma (HCC). Contrast-enhanced computed tomographic (CT) images of 177 HCC cases, including 26 metastatic (MET) and 151 non-metastatic (non-MET), were retrospectively collected and analyzed. For each case, 851 radiomic features, which quantify shape, intensity, texture, and heterogeneity within the segmented volume of the largest HCC tumor in arterial phase, were extracted using Pyradiomics. The dataset was randomly split into training and test sets. Synthetic Minority Oversampling Technique (SMOTE) was performed to augment the training set to 145 MET and 145 non-MET cases. The test set consists of six MET and six non-MET cases. The external validation set is comprised of 20 MET and 25 non-MET cases collected from an independent clinical unit. Logistic regression and support vector machine (SVM) models were identified based on the features selected using the stepwise forward method while the deep convolution neural network, visual geometry group 16 (VGG16), was trained using CT images directly. Grey-level size zone matrix (GLSZM) features constitute four of eight selected predictors of metastasis due to their perceptiveness to the tumor heterogeneity. The radiomic logistic regression model yielded an area under receiver operating characteristic curve (AUROC) of 0.944 on the test set and an AUROC of 0.744 on the external validation set. Logistic regression revealed no significant difference with SVM in the performance and outperformed VGG16 significantly. As extrahepatic metastasis workups, such as chest CT and bone scintigraphy, are standard but exhaustive, radiomic model facilitates a cost-effective method for stratifying HCC patients into eligibility groups of these workups.

An Integrated Liver Function, Systemic Inflammation, and Tumor Characteristic Score Predicts Prognosis in Hepatocellular Carcinoma After Curative Resection.

Several scoring systems are currently used to predict prognosis of hepatocellular carcinoma (HCC), but none of them integrates liver function, systemic inflammation, and tumor characteristics in a unified model. The current study aimed to develop and validate a novel prognostic score that integrates liver function, systemic inflammation, and tumor characteristics in a unified model to predict the prognosis of HCC after curative resection. Patients with HCC who underwent curative liver resection were included in a training set (n = 1027). Multivariate Cox regression was performed to determine the risk factors for a poor prognosis. A prognostic score was developed by assigning points for risk factors in proportion to beta coefficients in a Cox multivariable model. Predictive performance and distinction ability of the prognostic score were further evaluated in two independent validation cohorts treated with either curative resection (n = 281) or transarterial chemoembolization (TACE) (n = 404) and compared with 16 other models. The prognostic predictive system, named the function-inflammation-burden-alpha-fetoprotein (FIBA) score, was derived by assigning points for six independent predictors including albumin, total bilirubin, lymphocyte count, diameter of the largest tumor, number of tumors, and alpha-fetoprotein (AFP). The FIBA score showed an outperformed predictive value compared with other systems in both training and validation cohorts by giving the highest C-index, likelihood ratio chi-square values, and Wald test values as well as the lowest Akaike information criterion. The FIBA score can be used to stratify HCC patients treated with curative resection. Meanwhile, the FIBA score performs well against other prognostic scoring systems and is potentially broadly applicable to a TACE-treated patient cohort.

Metabolism-Related Gene Pairs to Predict the Clinical Outcome and Molecular Characteristics of Early Hepatocellular Carcinoma.

Simple SummaryAfter surgery, about 60–70% of early hepatocellular carcinoma patients suffer from relapse within 5 years, hindering long-term survival. Clinical and pathologic features cannot provide an accurate evaluation. We aimed to construct a stratification model from the metabolic aspect to predict the clinical outcome and reveal the molecular characteristics of different prognostic subgroups. An individualized metabolic signature of 10 gene pairs was developed from 250 early HCCs and validated in 311 samples from different datasets. The signature stratified early HCC cases one-by-one into two risk groups with different survival rates. The molecular characteristics of the two risk groups were analyzed by multi-omics data. The relationships with proliferation, immunity, and drug benefits were summarized. The signature was further validated in 47 institutional transcriptomic HCC samples and 101 public proteomic samples.Recurrence is the main factor affecting the prognosis of early hepatocellular carcinoma (HCC), which is not accurately evaluated by clinical indicators. The metabolic heterogeneity of HCC hints at the possibility of constructing a stratification model to predict the clinical outcome. On the basis of the relative expression orderings of 2939 metabolism-related genes, an individualized signature with 10 metabolism-related gene pairs (10-GPS) was developed from 250 early HCC samples in the discovery datasets, which stratified HCC patients into the high- and low-risk subgroups with significantly different survival rates. The 10-GPS was validated in 311 public transcriptomic samples from two independent validation datasets. A nomogram that included the 10-GPS, age, gender, and stage was constructed for eventual clinical evaluation. The low-risk group was characterized by lower proliferation, higher metabolism, increased activated immune microenvironment, and lower TIDE scores, suggesting a better response to immunotherapy. The high-risk group displayed hypomethylation, higher copy number alterations, mutations, and more overexpression of immune-checkpoint genes, which might jointly lead to poor outcomes. The prognostic accuracy of the 10-GPS was further validated in 47 institutional transcriptomic samples and 101 public proteomic samples. In conclusion, the 10-GPS is a robust predictor of the clinical outcome for early HCC patients and could help evaluate prognosis and characterize molecular heterogeneity.