Response In Hepatocellular Carcinoma Patients Research Articles

Myeloid response evaluated by noninvasive CT imaging predicts post-surgical survival and immune checkpoint therapy benefits in patients with hepatocellular carcinoma

BackgroundThe potential of preoperative CT in the assessment of myeloid immune response and its application in predicting prognosis and immune-checkpoint therapy outcomes in hepatocellular carcinoma (HCC) has not been explored.MethodsA total of 165 patients with pathological slides and multi-phase CT images were included to develop a radiomics signature for predicting the imaging-based myeloid response score (iMRS). Overall survival (OS) and recurrence-free survival (RFS) were assessed according to the iMRS risk group and validated in a surgical resection cohort (n = 98). The complementary advantage of iMRS incorporating significant clinicopathologic factors was investigated by the Cox proportional hazards analysis. Additionally, the iMRS in inferring the benefits of immune checkpoint therapy was explored in an immunotherapy cohort (n = 36).ResultsWe showed that AUCs of the optimal radiomics signature for iMRS were 0.941 [95% confidence interval (CI), 0.909–0.973] and 0.833 (0.798–0.868) in the training and test cohorts, respectively. High iMRS was associated with poor RFS and OS. The prognostic performance of the Clinical-iMRS nomogram was better than that of a single parameter (p < 0.05), with a 1-, 3-, and 5-year C-index for RFS of 0.729, 0.709, and 0.713 in the training, test, and surgical resection cohorts, respectively. A high iMRS score predicted a higher proportion of objective response (vs. progressive disease or stable disease; odds ratio, 2.311; 95% CI, 1.144–4.672; p = 0.020; AUC, 0.718) in patients treated with anti-PD-1 and PD-L1.ConclusionsiMRS may provide a promising method for predicting local myeloid immune responses in HCC patients, inferring postsurgical prognosis, and evaluating benefits of immune checkpoint therapy.

A Transcriptomic Biomarker for Predicting the Response to TACE Correlates with the Tumor Microenvironment and Radiomics Features in Hepatocellular Carcinoma.

The response to transarterial chemoembolization (TACE) varies among individuals with hepatocellular carcinoma (HCC). This study aimed to identify a biomarker for predicting TACE response in HCC patients and to investigate its correlations with the tumor microenvironment and pre-TACE radiomics features. GSE104580 data were obtained from the Gene Expression Omnibus (GEO) database. Differentially expressed gene analysis and machine learning algorithms were used to identify genes for constructing the TACE failure signature (TFS). TFS scores were then calculated for HCC patients in The Cancer Genome Atlas (TCGA) cohort. After obtaining images from The Cancer Imaging Archive (TCIA), tumor labeling and radiomics feature extraction, the Rad-score model was generated. Correlation analysis was performed between the TFS score and the Rad-score. CIBERSORT, ssGSEA and TME analysis were performed to explore differences in the immune landscape among distinct risk groups. The immunotherapy response was compared between different groups. ADH1C, CXCL11, EMCN, SPARCL1 and LIN28B were selected and incorporated into the TFS, which demonstrated satisfactory performance in predicting TACE response. Patients in the high TFS score group had poorer overall survival (OS) than those in the low TFS score group. The Rad-score model was constructed using six radiomics features, and the Rad-score was significantly correlated with hub gene expression and the TFS score. The high-TFS group was also characterized by an immunosuppressive tumor microenvironment and exhibited unfavorable responses to immunotherapy with PD-1 and CTLA-4 checkpoint inhibitors. This study established a transcriptomic biomarker for predicting the efficacy of TACE that correlates with radiomics features on pretreatment imaging, tumor immune microenvironment characteristics, and the efficacy of immunotherapy and targeted therapy in HCC patients.

Decoding the Prognostic Significance and Therapeutic Implications of Inflammation-Based Scores in Hepatocellular Carcinoma: A Comprehensive Review.

Hepatocellular carcinoma (HCC) is the most common primary liver cancer, posing a significant global health challenge with an increasing incidence. In recent years, multiple staging systems and scores have been proposed, emphasising the necessity for the development of precise prognostic tools. The well-documented etiological relationship between chronic inflammation and carcinogenesis has prompted researchers to explore novel prognostic markers associated with the inflammatory status of HCC patients. This review summarises the current data about inflammation-based scores in the context of HCC. We discuss established scores like the Glasgow Prognostic Score (GPS), modified GPS (mGPS) and the neutrophil-to-lymphocyte ratio (NLR) and others not as extensively studied, examining their utility in predicting survival outcomes and treatment response in HCC patients. Furthermore, we explore emerging scores, including the prognostic nutritional index (PNI) and other lymphocyte-based scores, assessing their potential in refining risk stratification and guiding therapeutic decisions in the era of precision medicine. As research progresses and these scores undergo further refinement and integration into the evolving landscape of HCC management, they carry significant potential for improving patient outcomes.

Post chemoembolization of hepatocellular carcinoma: a role of computed tomography and MRI in prediction of treatment response

Background Hepatocellular carcinoma (HCC) is the most prevalent and lethal form of primary liver cancer on a global scale. Aim To assess the tumor response to transarterial chemoembolization (TACE) by comparing computed tomography (CT) and magnetic resonance imaging (MRI) findings in patients with HCC. Patients and methods This prospective cohort research included 30 patients, aged from 40 to 77 years, with HCC, had TACE, good renal and liver functions, and were divided into two groups: the responders’ group and the nonresponders’ group. Patients had pre- and post-TACE examinations. Results There was a significant difference regarding the lesion criteria such as multiplicity, size, margins, involvement of both hepatic lobes, and pattern of arterial phase enhancement in both triphasic CT and dynamic MRI. Agreement between triphasic CT results and MRI results was in 26 patients. Disagreement as: two patients were categorized as complete response by triphasic CT versus partial response (PR) by MRI, one was categorized as PR by triphasic CT versus stable disease by MRI, and one was categorized as PR by triphasic CT versus PD by MRI. MRI was considered as the gold standard. Regarding the sensitivity, specificity, accuracy, positive predictive value and negative predictive value of Triphasic CT in the studied patients, it was 89, 91, 90, 80, and 95%, respectively. Conclusion Triphasic CT is considered as the most used imaging techniques for predicting and evaluating the therapeutic response in HCC patients after TACE as referenced to dynamic MRI which is considered as the best imaging modality with high diagnostic standard in assessment of HCC before and after TACE.

The Apparent Diffusion Coefficient of the Paraspinal and Psoas Muscles Are of Prognostic Relevance in Patients With Hepatocellular Carcinoma Undergoing Transarterial Radioembolization.

Transarterial radioembolization (TARE) is a treatment option for early or intermediate stage hepatocellular carcinoma (HCC). Sarcopenia is defined as loss of muscle strength and quality which can be estimated by imaging modalities and has been associated with prognosis and treatment response in HCC patients. Apparent diffusion coefficient (ADC) values derived from diffusion-weighted imaging (DWI) can reflect the tissue composition and might be better to determine muscle changes of sarcopenia than the standard method of computed tomography (CT). The present study sought to elucidate ADC values of the abdominal wall muscles as a prognostic factor in patients undergoing TARE. A retrospective analysis was performed between 2016 and 2020. Overall, 52 patients, 9 women (17.3%) and 43 men (82.7%), with a mean age of 69±8.5 years were included into the analysis. In every case, the first pre-interventional magnetic resonance imaging (MRI) including DWI was used to measure the ADC values of paraspinal and psoas muscle. The 12-month survival after TARE was used as the primary study outcome. Overall, 40 patients (76.9%) of the patient cohort died within the 12-month observation period. Mean overall survival was 10.9 months after TARE for all patients. Mean ADC values for all muscles were 1.31±0.13×10-3mm2/s. The ADC values of the paraspinal muscles were statistically significantly higher compared to the ADC values of the psoas muscles (p=0.0031). A positive correlation was identified between mean ADC and the thrombocyte count (r=0.37, p=0.005) and serum bilirubin (r=-0.30, p=0.03). In the multivariate Cox regression analysis, the mean ADC values of all muscles were associated with the survival after 12 months (HR=0.98, 95% CI=0.97-0.99, p=0.04). ADC values of the abdominal wall muscles could be used as a prognostic biomarker in patients with HCC undergoing TARE. These preliminary results should be confirmed by further studies using external validation cohorts and other treatment modalities.

Use of dose-volume histograms for metabolic response prediction in hepatocellular carcinoma patients undergoing transarterial radioembolization with Y-90 resin microspheres.

Voxel-based dosimetry offers improved outcomes in the treatment of hepatocellular carcinoma (HCC) with transarterial radioembolization (TARE) using glass microspheres. However, the adaptation of voxel-based dosimetry to resin-based microspheres has been poorly studied, and the prognostic relevance of heterogeneous dose distribution remains unclear. This study aims to explore the use of dose-volume histograms for resin microspheres and to determine thresholds for objective metabolic response in HCC patients treated with resin-based TARE. We retrospectively reviewed HCC patients who underwent TARE with Y-90-loaded resin microspheres in our institution between January 2021 and December 2022. Voxel-based dosimetry was performed on post-treatment Y-90 PET/CT images to extract parameters including mean dose absorbed by the tumor (mTD), the percentage of the targeted tumor volume (pTV), and the minimum doses absorbed by consecutive percentages within the tumor volume (D10, D25, D50, D75, D90). Assessment of metabolic response was done according to PERCIST criteria with F-18 FDG PET/CT imaging at 8-12weeks after the treatment. This study included 35 lesions targeted with 22 TARE sessions in 19 patients (15 males, 4 females, mean age 60 ± 13years). Objective metabolic response was achieved in 43% of the lesions (n = 15). Responsive lesions had significantly higher mTD, pTV, and D25-D90 values (all p < 0.05). Optimal cut-off values for mTD, pTV, and D50 were 94.6Gy (sensitivity 73%, specificity 70%, AUC 0.72), 94% (sensitivity 73%, specificity 55%, AUC 0.64), and 91Gy (sensitivity 80%, specificity 80%, AUC 0.80), respectively. Parameters derived from dose-volume histograms could offer valuable insights for predicting objective metabolic response in HCC patients treated with resin-based TARE. If verified with larger prospective cohorts, these parameters could enhance the precision of dose distribution and potentially optimize treatment outcomes.

Identification of fatty acids synthesis and metabolism-related gene signature and prediction of prognostic model in hepatocellular carcinoma.

Fatty acids synthesis and metabolism (FASM)-driven lipid mobilization is essential for energy production during nutrient shortages. However, the molecular characteristics, physiological function and clinical prognosis value of FASM-associated gene signatures in hepatocellular carcinoma (HCC) remain elusive. The Gene Expression Omnibus database (GEO), the Cancer Genome Atlas (TCGA), and International Cancer Genome Consortium (ICGC) database were utilized to acquire transcriptome data and clinical information of HCC patients. The ConsensusClusterPlus was employed for unsupervised clustering. Subsequently, immune cell infiltration, stemness index and therapeutic response among distinct clusters were decoded. The tumor immune dysfunction and exclusion (TIDE) algorithm was utilized to anticipate the response of patients towards immunotherapy, and the genomics of drug sensitivity in cancer (GDSC) tool was employed to predict their response to antineoplastic medications. Least absolute shrinkage and selection operator (LASSO) regression analysis and protein-protein interaction (PPI) network were employed to construct prognostic model and identity hub gene. Single cell RNA sequencing (scRNA-seq) and CellChat were used to analyze cellular interactions. The hub gene of FASM effect on promoting tumor progression was confirmed through a series of functional experiments. Twenty-six FASM-related genes showed differential expression in HCC. Based on these FASM-related differential genes, two molecular subtypes were established, including Cluster1 and Cluster2 subtype. Compared with cluster2, Cluster1 subtype exhibited a worse prognosis, higher risk, higher immunosuppressive cells infiltrations, higher immune escape, higher cancer stemness and enhanced treatment-resistant. PPI network identified Acetyl-CoA carboxylase1 (ACACA) as central gene of FASM and predicted a poor prognosis. A strong interaction between cancer stem cells (CSCs) with high expression of ACACA and macrophages through CD74 molecule (CD74) and integrin subunit beta 1 (ITGB1) signaling was identified. Finally, increased ACACA expression was observed in HCC cells and patients, whereas depleted ACACA inhibited the stemness straits and drug resistance of HCC cells. This study provides a resource for understanding FASM heterogeneity in HCC. Evaluating the FASM patterns can help predict the prognosis and provide new insights into treatment response in HCC patients.

Construction of an Immunogenic Cell Death-Related Gene Signature and Genetic Subtypes for Predicting Prognosis, Immune Microenvironments, and Drug Sensitivity in Hepatocellular Carcinoma.

Immunogenic cell death (ICD) is a type of regulated cell death that modifies the immune response by releasing DAMPs or danger signals. Herein, we aimed to develop an ICD-related predictive model for patients with hepatocellular carcinoma (HCC) and investigate its applicability for predicting prognostic outcomes and immunotherapeutic responses. Differentially expressed genes of ICD were identified in the HCC and normal liver samples. A prognostic risk model and a nomogram containing clinicopathological features were created. To validate the effectiveness of the model, an external dataset was used. Clinical characteristics, prognosis, tumor mutation burden, immune microenvironments, biological function and chemotherapeutic drug sensitivity were evaluated for different genetic subtypes and risk groups. A total of 35 ICD-related genes (ICDRGs) were identified between HCC and normal samples, 11 of which were significantly associated with overall survival (OS) in HCC patients. Four different genetic subtypes were formed and eight ICDRGs were selected to develop a risk prognostic model. The risk scores were shown to be an independent prognostic factor for HCC and positively correlated with pathological severity. Patients in the high-risk group had a higher frequency of TP53 mutations, increased expression of immune checkpoints and human leukocyte antigen genes. The inhibitory concentrations of chemotherapeutic drugs differed in different populations. In this study, we developed an ICDRG risk model and demonstrated its applicability in predicting survival outcomes, immune and chemotherapeutic responses in HCC patients. ICDRGs are expected to be used as novel biomarkers in the medical decision-making of HCC.

Abstract 2799: Intestinal Akkermansia muciniphila complements the therapeutic efficacy of PD1 therapy by reshaping the immunosuppressive microenvironment in nonalcoholic fatty liver disease (NAFLD)-induced hepatocellular carcinoma

Abstract Objectives: Nonalcoholic fatty liver disease (NAFLD)-induced hepatocellular carcinoma (HCC) is an emerging malignancy in the developed world. However, immune checkpoint inhibitors (ICIs) are ineffective for patients with this disease. Advances in research have shown that gut-liver communication contributes to NAFLD progression. Therefore, identifying a key gut microbiota for predicting ICIs response and an effective combinatorial strategy is urgently needed. Design: 16S rRNA sequencing was employed to identify key gut microbiota crucial in developing the mouse NAFLD-induced HCC model. The in vivo roles of Akkermansia muciniphila (Akk) were evaluated by immunofluorescence staining, qPCR, mass spectrometry analysis, ELISA assay, and single-cell RNA sequencing (scRNA-seq) coupled with immune profiling analysis. The clinical and therapeutic value of Akk alone in combination with PD1 treatment was investigated. Results: Akk was decreased from healthy to HCC tissues during HCC tumor development, and daily administration of Akk not only ameliorates liver steatosis and cholesterol biosynthesis but could effectively attenuate the development of NAFLD-induced HCC. Akk repairs the intestinal lining, with a concurrent decrease in the serum concentration of lipopolysaccharide (LPS) and bile acid metabolites. Using scRNA-seq coupled with immune profiling analyses, we found that Akk suppressed the populations of immunosuppressive cells, including monocytic myeloid-derived suppressor cells (m-MDSCs) and M2 macrophages, by suppressing the differentiation of these cells from monocytes, which leads to T cell proliferation and activation. Consistent with this finding, Akk administration, combined with PD1 treatment, exerted a maximal growth-suppressive effect, accompanied by increased infiltration and activation of T cells. Clinically, the fecal level of Akk in HCC patients was decreased in NAFLD-induced HCC patients and serves as a potential biomarker for predicting PD1 response in HCC patients. Conclusions: Akk regulates the immune tumor environment by regulating two key immune suppressive cells via regulation of the LPS flux to develop NALFD-induced HCC. This finding provides a solid therapeutic basis for a rational combination with PD1 for the treatment of this deadly disease. Interestingly, Akk may serve as a predictive biomarker for PD1 response in HCC patients. Citation Format: Xueqian Wu, Fan Ying, Katherine Po Sin Chung, Carmen Oi Ning Leung, Terence Kin Wah Lee. Intestinal Akkermansia muciniphila complements the therapeutic efficacy of PD1 therapy by reshaping the immunosuppressive microenvironment in nonalcoholic fatty liver disease (NAFLD)-induced hepatocellular carcinoma [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 1 (Regular Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(6_Suppl):Abstract nr 2799.

A Novel Cholesterol Metabolism-Related lncRNA Signature Predicts the Prognosis of Patients with Hepatocellular Carcinoma and Their Response to Immunotherapy.

The survival rate of hepatocellular carcinoma (HCC) is low and the prognosis is poor. Metabolic reprogramming is still an emerging hallmark of cancer, and reprogramming of cholesterol metabolism plays a crucial action in tumor pathogenesis. Increasing evidence suggests that cholesterol metabolism affects the cell proliferation, invasion, migration, and resistance to chemotherapy of HCC. To date, no long noncoding RNA (lncRNA) signature associated with cholesterol metabolism has been developed to predict the outcome of patients with HCC. The RNA-seq data as well as the prognostic and clinical data were obtained from The Cancer Genome Atlas (TCGA) database. We conducted univariate and multivariate analyses to assess cholesterol metabolism-related lncRNAs correlated with the prognosis of patients with HCC in order to construct a prognostic signature. Functional differences between low- and high-risk groups were investigated using genomic enrichment analysis (GSEA). Kaplan-Meier (KM) curves were applied to explore the overall survival (OS) of the low- and high-risk groups. Single-sample genomic enrichment analysis (ssGSEA) was applied to investigate the association between this predictive signature and immune function. We subsequently examined how this signature relates to treatment response in HCC patients. A prognostic signature comprising six lncRNAs related to cholesterol metabolism was constructed (AC124798.1, AL031985.3, AC103760.1, NRAV, WAC-AS1 and AC022613.1). We found that low-risk groups showed a better prognosis than high-risk groups. In HCC patients, the cholesterol metabolism-related lncRNA signature may be served as an independent prognostic factor. Cholesterol metabolism-related lncRNA signature had higher diagnostic efficiency compared to clinicopathologic variables. After stratifying patients according to different clinicopathological variables, patients with low-risk had a longer OS compared with high-risk patients. The ssGSEA demonstrated that this signature was closely related to the immune status of HCC patients. GSEA analysis demonstrated that immune- and tumor-related pathways were predominantly enriched in the high-risk group. High-risk patients were more responsive to immune checkpoint inhibitors (ICIs) and conventional chemotherapeutic agents. This cholesterol metabolism-related lncRNA signature can predict the prognosis of HCC patients and guide the clinical management of HCC patients, including immunotherapy.

Identification of VRK1 as a Novel Potential Biomarker for Prognosis and Immunotherapy in Hepatocellular Carcinoma.

Research has indicated that VRK1 is essential for the tumor cell cycle. However, its prognostic and immunotherapeutic predictive significance has not been documented in hepatocellular carcinoma (HCC). The TCGA, ICGC, and GSE14520 datasets were used to investigate VRK1 expression and its predictive significance of survival outcomes. The qRT-PCR and immunohistochemistry (IHC) were used to confirm the findings. The immunotherapeutic response of VRK1 was anticipated by the IMvigor210 cohort. Lastly, the association between immune infiltration, m6A modification, and functional enrichment of differentially expressed genes (DEGs) was investigated in connection to VRK1 expression. VRK1 expression was markedly elevated on both the mRNA and protein levels in HCC. In HCC patients, a high expression of VRK1 was linked to a poor prognosis. Furthermore, there was a substantial positive correlation seen between increased VRK1 expression and the response rate to anti-PD-L1 immunotherapy. Relationships between VRK1 and m6A-related genes as well as different immune cells were shown by correlation studies. Lastly, enrichment analysis revealed a tight relationship between VRK1 and important biological functions, including DNA replication, cell cycle control, and fatty acid metabolism. Our research reveals the potential of VRK1 as a novel biomarker for prognosis and immunotherapy response in HCC patients.

Comprehensive characterization of Fidgetin on tumor immune microenvironment evaluation and immunotherapy in human hepatocellular carcinoma.

Most cancers have a downregulation of Fidgetin (FIGN), which has been linked to tumor growth. However, there aren't many papers that mention FIGN's connection to hepatocellular carcinoma (HCC). Here, FIGN expression in HCC tissues was markedly reduced as compared to nearby normal liver tissues. According to univariate and multivariate Cox regression, it served as an independent predictor of survival outcomes. Patients with high levels of FIGN expression had a worse outcome. FIGN was shown to be engaged in immune-related pathways and to have a positive correlation with immunological score and immune cells according to KEGG pathway analysis. In HCC patients, FIGN was substantially linked with immunological checkpoints and the hot tumor state. Additionally, immunotherapy and chemotherapy showed a significant therapeutic response in HCC patients with low FIGN expression. This research revealed that FIGN expression was tightly related to hepatoma immunity and might be employed as a biomarker to predict patient prognosis and guide medication.

Noncoding RNAs and programmed cell death in hepatocellular carcinoma: Significant role of epigenetic modifications in prognosis, chemoresistance, and tumor recurrence rate.

Hepatocellular carcinoma (HCC) is the most common type of liver cancer with a high death rate in the world. The molecular mechanisms related to the pathogenesis of HCC have not been precisely defined so far. Hence, this review aimed to address the potential cross-talk between noncoding RNAs (ncRNAs) and programmed cell death in HCC. All related papers in the English language up to June 2023 were collected and screened. The searched keywords in scientific databases, including Scopus, PubMed, and Google Scholar, were HCC, ncRNAs, Epigenetic, Programmed cell death, Autophagy, Apoptosis, Ferroptosis, Chemoresistance, Tumor recurrence, Prognosis, and Prediction. According to the reports, ncRNAs, comprising long ncRNAs, microRNAs, circular RNAs, and small nucleolar RNAs can affect cell proliferation, migration, invasion, and metastasis, as well as cell death-related processes, such as autophagy, ferroptosis, necroptosis, and apoptosis in HCC by regulating cancer-associated genes and signaling pathways, for example, phosphoinositide 3-kinase/Akt, extracellular signal-regulated kinase/MAPK, and Wnt/β-catenin signaling pathways. It seems that ncRNAs, as epigenetic regulators, can be utilized as biomarkers in diagnosis, prognosis, survival and recurrence rates prediction, chemoresistance, and evaluation of therapeutic response in HCC patients. However, more scientific evidence is suggested to be accomplished to confirm these results.

Chemokine CCL21 determines immunotherapy response in hepatocellular carcinoma by affecting neutrophil polarization

BackgroundThe efficacy of immune checkpoint inhibitors (ICIs) in hepatocellular carcinoma (HCC) is poor and great heterogeneity among individuals. Chemokines are highly correlated with tumor immune response. Here, we aimed to identify an effective chemokine for predicting the efficacy of immunotherapy in HCC.MethodsChemokine C‐C motif ligand 21 (CCL21) was screened by transcriptomic analysis in tumor tissues from HCC patients with different responses to ICIs. The least absolute shrinkage and selection operator (LASSO) regression analysis was conducted to construct a predictive nomogram. Neutrophils in vitro and HCC subcutaneous tumor model in vivo were applied to explore the role of CCL21 on the tumor microenvironment (TME) of HCC.ResultsTranscriptome analysis showed that CCL21 level was much higher in HCC patients with response to immunotherapy. The predictive nomogram was constructed and validated as a classifier. CCL21 could inhibit N2 neutrophil polarization by suppressing the activation of nuclear factor kappa B (NF-κB) pathway. In addition, CCL21 enhanced the therapeutic efficacy of ICIs.ConclusionCCL21 may serve as a predictive biomarker for immunotherapy response in HCC patients. High levels of CCL21 in TME inhibit immunosuppressive polarization of neutrophils. CCL21 in combination with ICIs may offer a novel therapeutic strategy for HCC.

Genetic Biomarkers of Sorafenib Response in Patients with Hepatocellular Carcinoma.

The identification of biomarkers for predicting inter-individual sorafenib response variability could allow hepatocellular carcinoma (HCC) patient stratification. SNPs in angiogenesis- and drug absorption, distribution, metabolism, and excretion (ADME)-related genes were evaluated to identify new potential predictive biomarkers of sorafenib response in HCC patients. Five known SNPs in angiogenesis-related genes, including VEGF-A, VEGF-C, HIF-1a, ANGPT2, and NOS3, were investigated in 34 HCC patients (9 sorafenib responders and 25 non-responders). A subgroup of 23 patients was genotyped for SNPs in ADME genes. A machine learning classifier method was used to discover classification rules for our dataset. We found that only the VEGF-A (rs2010963) C allele and CC genotype were significantly associated with sorafenib response. ADME-related gene analysis identified 10 polymorphic variants in ADH1A (rs6811453), ADH6 (rs10008281), SULT1A2/CCDC101 (rs11401), CYP26A1 (rs7905939), DPYD (rs2297595 and rs1801265), FMO2 (rs2020863), and SLC22A14 (rs149738, rs171248, and rs183574) significantly associated with sorafenib response. We have identified a genetic signature of predictive response that could permit non-responder/responder patient stratification. Angiogenesis- and ADME-related genes correlation was confirmed by cumulative genetic risk score and network and pathway enrichment analysis. Our findings provide a proof of concept that needs further validation in follow-up studies for HCC patient stratification for sorafenib prescription.

Defining a radiomics feature selection method for predicting response to transarterial chemoembolization in hepatocellular carcinoma patients

AimTo assess the utility of different radiomics feature selection methods in predicting transarterial chemoembolization (TACE) response in hepatocellular carcinoma (HCC) patients. Materials and methodsThis study employed a dataset of 136 paired MR T1-weighted contrast-enhanced abdominal images with liver tumor masks before and after TACE. TACE response for each image pair was classified by European Association for the Study of the Liver (EASL) and modified Response Evaluation Criteria in Solid Tumors (mRECIST) guidelines. 100D feature vectors were generated for the paired tumor areas. Eighteen existing feature selection methods were employed to select the top-k features to train and test a non-linear support vector machine (SVM) with a Gaussian kernel. Five-cross validation was performed to identify the highest performing feature selection methods. ResultsFor all benchmarks, a L0-based method selecting the top-5 or top-10 features achieved the highest performance. For images classified with EASL criteria that were analyzed with the L0-based method, the accuracy (ACC), area under curve (AUC), and balanced F score (F1-score) were 0.75 ​± ​0.06, 0.75 ​± ​0.09, and 0.80 ​± ​0.05, respectively. For images classified with mRECIST criteria that were analyzed with the L0-based method, the ACC, AUC, and F1-score were 0.75 ​± ​0.07, 0.71 ​± ​0.16, and 0.82 ​± ​0.04, respectively. ConclusionA L0-based method that selected the top-5/10 most important features predicted TACE response in HCC patients with the highest accuracy under both EASL and mRECIST criteria. This proof-of-concept investigation represents a step forward in the development of a reliable clinical decision-making tool for management of intermediate HCC patients undergoing TACE.

Deciphering the immunological and prognostic features of hepatocellular carcinoma through ADP-ribosylation–related genes analysis and identify potential therapeutic target ARFIP2

BackgroundHepatocellular carcinoma is one of the most common malignancies, and its prognosis and treatment outcome cannot be accurately predicted. ADP-ribosylation (ADPR) is a post-translationa modification of proteins involved in protein trafficking and immune response. Therefore, it is necessary to explore the ADPR-related genes associated with the prognosis and therapeutic efficacy of hepatocellular carcinoma treatments. MethodsWe downloaded the data of hepatocellular carcinoma samples to identify ADPR-related genes as prognostic markers, and established a novel ADPR-related index (ADPRI) based on univariate and multivariate COX regression analyses. Patients' prognosis, clinical features, somatic variant, tumor immune microenvironment, chemotherapeutic response and immunotherapeutic response were systematically analyzed. Finally, the role of ARFIP2 in hepatocellular carcinoma cells was preliminarily explored in vitro. ResultsThe ADPRI consisting of four ADPR related genes (ARL8B, ARFIP2, PARP12, ADPRHL1) was established to be a reliable predictor of survival in patients with hepatocellular carcinoma and was validated using external datasets. Compared with the low ADPRI group, the high ADPRI group presented higher levels of mutation frequency, immune infiltration and patients in high ADPRI group benefit more from immune checkpoint inhibitor treatment. In addition, we predicted some natural small molecule drugs as potential therapeutic targets for hepatocellular carcinoma. Finally, Knockdown of ARFIP2 inhibits the proliferation and migration of hepatocellular carcinoma cells by inducing the G1/S phase cell cycle arrest in HCC cells. ConclusionsThe ADPRI can be used to accurately predict the prognosis and immunotherapeutic response of hepatocellular carcinoma patients and providing valuable insights for future precision treatment of patients with hepatocellular carcinoma.

Des-gamma-carboxy prothrombin (DCP) as a biomarker for treatment response in hepatocellular carcinoma.

547 Background: Causes of variable disease response to first-line treatment with atezolizumab/bevacizumab (atezo/bev) in hepatocellular carcinoma (HCC) patients remain unclear. Identifying tumor markers that predict treatment efficacy may help raise clinical suspicion for early disease progression. The role of des-gamma-carboxy prothrombin (DCP) is not well established in Hepatitis B Virus (HBV) non-endemic areas. This study aimed to investigate DCP as a predictive biomarker of atezo/bev treatment response in HCC patients. Methods: This single-center retrospective analysis evaluated DCP and radiographic response in HCC patients who received atezo/bev in the first-line setting. DCP was measured at the time of best response evaluation and compared to day one of atezo/bev initiation. Elevated DCP was defined by &gt;=7.5 ng/mL. DCP response was defined as a decrease of &gt;=50% at response assessment compared with baseline. Treatment response was evaluated based on imaging modalities including computed tomography and/or magnetic resonance imaging. Objective response rate (ORR) was defined as the percentage of patients with complete response (CR) and partial response (PR). Disease control rate (DCR) was defined as the percentage of patients with CR, PR, and stable disease (SD). No Response (NR) was defined as the percentage of patients with SD and PD. Comparisons of treatment effects were performed using two-tailed Fisher Exact Probability Test. Results: A total of 53 HCC patients treated with atezo/bev as first-line therapy were included. The best treatment responses according to radiographic evaluation were as follows: CR 3.8%, PR 22.6%, SD 52.8%, and PD 20.8%, respectively. Patients with DCP response had ORR of 69% (odds ratio, 15.75; 95% CI, 3.50-70.9; P=.0002) and DCR of 100% ( P=.047). Patients with &gt;=50% increase in DCP at response evaluation compared to day one of treatment had ORR of 4.5% and NR of 95% ( P=.0035). On day one of treatment, 70% of patients had an elevated DCP. There was no difference between elevated DCP vs non-elevated DCP on day one of treatment and ORR. Conclusions: This study found that DCP has utility in predicting response to atezo/bev for HCC patients in HBV non-endemic areas. Further studies are needed to confirm findings and evaluate the prognostic implications of DCP in HBV non-endemic areas.

Predicting treatment responses using magnetic resonance imaging-based radiomics in hepatocellular carcinoma patients undergoing transarterial radioembolization.

This study evaluates the efficacy of magnetic resonance imaging-based radiomics in predicting treatment responses in hepatocellular carcinoma patients undergoing transarterial radioembolization. Pre-treatment magnetic resonance imaging scans from 65 hepatocellular carcinoma patients were analyzed. Radiomic features were extracted from axial T1-weighted and T2-weighted sequences using a standardized workflow involving image preprocessing, segmentation, and feature extraction. Multivariate logistic regression models combining radiomic and clinical features were developed to predict treatment outcomes. The performance of the models was evaluated using the area under the curve metric. The study included 65 patients with a median age of 64 years; 44.6% showed a complete response, while 55.4% showed a non-complete response. The median radiomics score in the T1-weighted portal phase was -0.49 for non-complete responders and -0.07 for complete responders (p<0.001). In the T2-weighted sequence, the median radiomics score was -0.76 for non-complete responders and 1.1 for complete responders (p<0.001). Tumor size ≥5 cm was a significant predictor of non-complete response in univariate analysis (p=0.027) but not in multivariate analysis after adding radiomics scores. The area under the curve for the radiomics signature in predicting non-complete response was 0.754 for T1-weighted and 0.850 for T2-weighted sequences. Magnetic resonance imaging-based radiomics enhances the prediction of treatment responses in hepatocellular carcinoma patients undergoing transarterial radioembolization. Integrating radiomic features with clinical parameters significantly improves predictive accuracy.

The identification of N6-methyladenosine-related miRNAs predictive of hepatocellular carcinoma prognosis and immunotherapy efficacy.

Hepatocellular carcinoma (HCC) has a high degree of malignancy and poor prognosis. N6-methyladenosine (m6A) modifications and microRNAs (miRNAs) play pivotal roles in tumorigenesis and development. However, the role of m6A-related miRNAs in HCC has not been clarified yet. This study aimed to identify the role of m6A-miRNAs in HCC prognosis through bioinformatics analysis. The clinicopathological information and RNA sequencing data of 369 HCC tumor tissues and 49 tumor-adjacent tissues were downloaded from the TCGA database. A total of 23 m6A regulators were extracted to evaluated the m6A-related miRNAs using Pearson's correlation analysis. Then, we selected prognosis-related m6A-miRNAs using a univariate Cox regression model and used the consensus cluster analysis to explore the characteristics of the m6A-miRNAs. The coefficient of the least absolute shrinkage and selection operator (LASSO) Cox regression was applied to construct a prognostic risk score model. The receiver operated characteristic (ROC) analysis was applied to evaluate the prognostic value of the signature. The biological functions of targeted genes were predicted by Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analyses. Then, to validate the potential predictive value for prognosis, the miRNA expression profiles from the GSE76903 and GSE6857 were used. Single sample Gene Set Enrichment Analysis (ssGSEA) and Estimation of Stromal and Immune cells in Malignant Tumor tissues using Expression data (ESTIMATE) were applied to assess the immune microenvironment of HCC. Additionally, a meta-analysis was used to verify the prognostic value of the m6A-microRNAs. RT-PCR was applied to validated the expression of miRNAs in HCC tissues. Cell viability, transwell assay and RNA m6A dot blot assays of HCC cells was applied to access the function of miR-17-5p. The expression of 48 m6A-related miRNAs was identified and 17 prognostic m6A-miRNAs was discovered. The expression profile of those 17 miRNAs was divided into three clusters, and these clusters were associated with the tumor microenvironment (TME) and prognosis. The nine m6A-related miRNA signature was associated with the prognosis of HCC, the AUC of the ROC was 0.771(TCGA dataset), 0.788(GSE76903) and 0.646(GSE6857). The TME and the expression of immune checkpoint molecules were associated with the risk score. The meta-analysis also validated the prognostic value of the m6A-related miRNAs (miR182-5p (HR:1.58, 95%CI:1.04-2.40) and miR-17-5p (HR:1.58, 95%CI: 1.04-2.40)). The expression of miR-17-5p was upregulated in HCC tissues and miR-17-5p showed an oncogenic role in HCC cells. The clinical innovation is the use of m6A-miRNAs as biomarkers for predicting prognosis regarding immunotherapy response in HCC patients.