Overall Survival Of Hepatocellular Carcinoma Patients Research Articles

Prediction of prognosis of patients with hepatocellular carcinoma based on immune-related score

BackgroundImmune-related scores are currently used for prognostic evaluation and as an immunotherapy reference in various cancers. However, the relationship between immune-related score and hepatocellular carcinoma (HCC) prognosis has not yet been investigated. This study aimed to explore the clinical application value of immune-related score for predicting HCC prognosis-related indicators including disease-free survival (DFS) and overall survival (OS), and to construct a clinical nomogram prediction model related to verification. MethodsThis study included 284 HCC patients who were selected from the Cancer Genome Atlas (TCGA) database and linked to the immune-related score downloaded from the public platform. A Cox proportional hazards regression model was used to estimate the adjusted risk ratio, and a nomogram was constructed based on multivariate analysis results and clinical significance. The model was internally verified by bootstrap. The performance of the prediction model was evaluated using the C-index and calibration curves. ResultsPatients were divided into three subgroups according to the immune-related score level. Compared with patients in the low immune-related score group, the DFS of patients in the medium and high immune-related score groups was significantly prolonged (HR: 0.53, 95% CI: 0.32–0.87; HR: 0.37, 95% CI: 0.21–0.63, respectively). The OS of patients in the medium and high immune-related score groups was also significantly prolonged (HR: 0.43, 95% CI: 0.20–0.95, p = 0.038; HR: 0.29, 95% CI: 0.14–0.58, p < 0.001, respectively). The C-indexes for predicting DFS and OS were 0.687 (95% CI: 0.665–0.700) and 0.743 (95% CI: 0.709–0.776), respectively. The calibration curves of 3-year and 5-year DFS and OS showed that the results predicted by the nomogram were in good agreement with the actual observations. ConclusionsModerate/high-grade immune-related score was significantly associated with better DFS and OS in HCC patients. In addition, a nomogram for prognosis estimation can help clinicians predict the survival status of patients.

N6-Methyladenosine-Modified ATP8B1-AS1 Exerts Oncogenic Roles in Hepatocellular Carcinoma via Epigenetically Activating MYC.

N6-methyladenosine (m6A) modification has shown critical roles in regulating mRNA fate. Non-coding RNAs also have important roles in various diseases, including hepatocellular carcinoma (HCC). However, the potential influences of m6A modification on non-coding RNAs are still unclear. In this study, we identified a novel m6A-modified ATP8B1-AS1 and aimed to investigate the effects of m6A on the expression and role of ATP8B1-AS1 in HCC. qPCR was performed to measure the expression of related genes. The correlation between gene expression and prognosis was analyzed using public database. m6A modification level was measured using MeRIP and single-base elongation- and ligation-based qPCR amplification method. The roles of ATP8B1-AS1 in HCC were investigated using in vitro and in vivo functional assays. The mechanisms underlying the roles of ATP8B1-AS1 were investigated by ChIRP and ChIP assays. ATP8B1-AS1 is highly expressed in HCC tissues and cell lines. High expression of ATP8B1-AS1 is correlated with poor overall survival of HCC patients. ATP8B1-AS1 is m6A modified and the 792 site of ATP8B1-AS1 is identified as an m6A modification site. m6A modification increases the stability of ATP8B1-AS1 transcript. m6A modification level of ATP8B1-AS1 is increased in HCC tissues and cell lines, and correlated with poor overall survival of HCC patients. ATP8B1-AS1 promotes HCC cell proliferation, migration, and invasion, which were abolished by the mutation of m6A-modified 792 site. Mechanistic investigation revealed that m6A-modified ATP8B1-AS1 interacts with and recruits m6A reader YTHDC1 and histone demethylase KDM3B to MYC promoter region, leading to the reduction of H3K9me2 level at MYC promoter region and activation of MYC transcription. Functional rescue assays showed that depletion of MYC largely abolished the oncogenic roles of ATP8B1-AS1. m6A modification level of ATP8B1-AS1 is increased and correlated with poor prognosis in HCC. m6A-modified ATP8B1-AS1 exerts oncogenic roles in HCC via epigenetically activating MYC expression.

Multiparametric MRI-based model for prediction of local progression of hepatocellular carcinoma after thermal ablation.

To develop a deep learning radiomics of multiparametric magnetic resonance imaging (DLRMM)-based model that incorporates preoperative and postoperative signatures for prediction of local tumor progression (LTP) after thermal ablation (TA) in hepatocellular carcinoma (HCC). From May 2017 to October 2021, 417 eligible patients with HCC were retrospectively enrolled from three hospitals (one primary cohort [PC, n = 189] and two external test cohorts [ETCs][n = 135, 93]). DLRMM features were extracted from T1WI + C, T2WI, and DWI using ResNet18 model. An integrative model incorporating the DLRMM signature with clinicopathologic variables were further built to LTP risk stratification. The performance of these models were compared by areas under receiver operating characteristic curve (AUC) using DeLong test. A total of 1668 subsequences and 31,536 multiparametric MRI slice including T1WI, T2WI, and DWI were collected simultaneously. The DLRMM signatures were extracted from tumor and ablation zone, respectively. Ablative margin, multiple tumors, and tumor abutting major vessels were regarded as risk factors for LTP in clinical model. The AUC of DLRMM model were 0.864 in PC, 0.843 in ETC1, and 0.858 in ETC2, which was higher significantly than those in clinical model (p < 0.001). After integrating clinical variable, DLRMM model obtained significant improvement with AUC of 0.870-0.869 in three cohorts (all, p < 0.001), which can provide the risk stratification for overall survival of HCC patients. The DLRMM model is essential to identify LTP risk of HCC patients who underwent TA and may potentially benefit personalized decision-making.

TMEM64 is highly expressed in hepatocellular carcinoma and promotes tumor cell proliferation and invasion

To analyze the expression of TMEM64 in hepatocellular carcinoma (HCC) and investigate the effect of TMEM64 expression level on proliferation and invasion of HCC cells in vitro. We analyzed the expression level of TMEM64 in HCC and adjacent tissues based on data from TCGA and GTEx databases. The prognostic value of TMEM64 for HCC patients was examined using Kaplan-Meier survival analysis and a Cox regression model, and a nomogram was constructed based on TMEM64 expression and clinical characteristics of the patients. Functional enrichment analysis was performed to explore the potential signaling pathways, and immune cell infiltration was assessed using single sample gene set enrichment analysis. We also performed cell experiment to observe the changes in proliferation, migration, and invasion in HCCLM3 cells with TMEM64 knockdown and in Huh7 cells with TMEM64 overexpression using CCK-8, EdU, colony formation, Transwell, and wound healing assays. The expression level of TMEM64 was significantly higher in HCC than in the adjacent tissues (P < 0.05). Kaplan-Meier analysis suggested that a high expression of TMEM64 was associated with poor outcomes of the patients (P < 0.05). Multivariate Cox regression analysis indicated that a high TMEM64 expression was an independent risk factor for overall survival of HCC patients (P < 0.05). TMEM64 expression level was negatively correlated with the levels of immune cell infiltration by NK cells, CD8 + T cells, and plasma pDCs cells (P < 0.05). GO, KEGG, and GSEA enrichment analyses showed that TMEM64 was significantly enriched with tumor invasion and metastasis pathways. The nomogram and calibration curves indicated a moderate prediction reliability of the model. In the cell experiment, TMEM64 knockdown obviously suppressed and TMEM64 overexpression markedly promoted the proliferation, migration, and invasion of HCC cells (P < 0.01). A high TMEM64 expression may serve as an independent risk factor for poor prognosis of HCC and promotes proliferation, migration, and invasion of HCC cells in vitro.

Clinical Significance of Apurinic/Apyrimidinic Endodeoxyribonuclease 1 and MicroRNA Axis in Hepatocellular Carcinoma.

Identification of prognostic factors for hepatocellular carcinoma (HCC) opens new perspectives for therapy. Circulating and cellular onco-miRNAs are noncoding RNAs which can control the expression of genes involved in oncogenesis through post-transcriptional mechanisms. These microRNAs (miRNAs) are considered novel prognostic and predictive factors in HCC. The apurinic/apyrimidinic endodeoxyribonuclease 1 (APE1) contributes to the quality control and processing of specific onco-miRNAs and is a negative prognostic factor in several tumors. The present work aims to: a) define APE1 prognostic value in HCC; b) identify miRNAs regulated by APE1 and their relative target genes and c) study their prognostic value. We used The Cancer Genome Atlas (commonly known as TCGA) data analysis to evaluate the expression of APE1 in HCC. To identify differentially-expressed miRNAs (DEmiRNAs) upon APE1 depletion through specific small interfering RNA, we used NGS and nanostring approaches in the JHH-6 HCC tumor cell line. Bioinformatics analyses were performed to identify signaling pathways involving APE1-regulated miRNAs. Microarray analysis was performed to identify miRNAs correlating with serum APE1 expression. APE1 is considerably overexpressed in HCC tissues compared to normal liver, according to the TCGA-liver HCC (known as LIHC) dataset. Enrichment analyses showed that APE1-regulated miRNAs are implicated in signaling and metabolic pathways linked to cell proliferation, transformation, and angiogenesis, identifying Cyclin Dependent Kinase 6 and Lysosomal Associated Membrane Protein 2 as targets. miR-33a-5p, miR-769, and miR-877 are related to lower overall survival in HCC patients. Through array profiling, we identified eight circulating DE-miRNAs associated with APE1 overexpression. A training phase identified positive association between sAPE1 and miR-3180-3p and miR-769. APE1 regulates specific miRNAs having prognostic value in HCC.

Novel insights into the progression and prognosis of the calpain family members in hepatocellular carcinoma: a comprehensive integrated analysis.

Objectives: The goal of our bioinformatics study was to comprehensively analyze the association between the whole calpain family members and the progression and prognosis of hepatocellular carcinoma (HCC). Methods: The data were collected from The Cancer Genome Atlas (TCGA). The landscape of the gene expression, copy number variation (CNV), mutation, and DNA methylation of calpain members were analyzed. Clustering analysis was performed to stratify the calpain-related groups. The least absolute shrinkage and selection operator (LASSO)-based Cox model was used to select hub survival genes. Results: We found 14 out of 16 calpain members expressed differently between tumor and normal tissues of HCC. The clustering analyses revealed high- and low-risk calpain groups which had prognostic difference. We found the high-risk calpain group had higher B cell infiltration and higher expression of immune checkpoint genes HAVCR2, PDCD1, and TIGHT. The CMap analysis found that the histone deacetylase (HDAC) inhibitor trichostatin A and the PI3K-AKT-mTOR pathway inhibitors LY-294002 and wortmannin might have a therapeutic effect on the high-risk calpain group. The DEGs between calpain groups were identified. Subsequent univariate Cox analysis of each DEG and LASSO-based Cox model obtained a calpain-related prognostic signature. The risk score model of this signature showed good ability to predict the overall survival of HCC patients in TCGA datasets and external validation datasets from the Gene Expression Omnibus database and the International Cancer Genome Consortium database. Conclusion: We found that calpain family members were associated with the progression, prognosis, and drug response of HCC. Our results require further studies to confirm.

Development and Validation of Novel Models Including Tumor Micronecrosis for Predicting the Postoperative Survival of Patients with Hepatocellular Carcinoma.

The heterogeneity of hepatocellular carcinoma (HCC) leads to the unsatisfying predictive performance of current staging systems. HCC patients with pathological tumor micronecrosis have an immunosuppressive microenvironment. We aimed to develop novel prognostic models by integrating micronecrosis to predict the survival of HCC patients after hepatectomy more precisely. We enrolled 765 HCC patients receiving curative hepatic resection. They were randomly divided into a training cohort (n= 536) and a validation cohort (n = 229). We developed two prognostic models for postoperative recurrence-free survival (RFS) and overall survival (OS) based on independent factors identified through multivariate Cox regression analyses. The predictive performance was assessed using the Harrell concordance index (C-index) and the time-dependent area under the receiver operating characteristic curve, compared with six conventional staging systems. The RFS and OS nomograms were developed based on tumor micronecrosis, tumor size, albumin-bilirubin grade, tumor number and prothrombin time. The C-indexes for the RFS nomogram and OS nomogram were respectively 0.66 (95% CI, 0.62-0.69) and 0.74 (95% CI, 0.69-0.79) in the training cohort, which was significantly better than those of the six common staging systems (0.52-0.61 for RFS and 0.53-0.63 for OS). The results were further confirmed in the validation group, with the C-indexes being 0.66 and 0.77 for the RFS and OS nomograms, respectively. The two nomograms could more accurately predict RFS and OS in HCC patients receiving curative hepatic resection, thereby aiding in formulating personalized postoperative follow-up plans.

Integrated analysis of single-cell and bulk RNA-sequencing reveals tumor heterogeneity and a signature based on NK cell marker genes for predicting prognosis in hepatocellular carcinoma.

Background: Natural killer (NK) cells are a type of innate immune cell that recognize and eliminate tumor cells and infected cells, without prior sensitization or activation. Herein, we aimed to construct a predictive model based on NK cell-related genes for hepatocellular carcinoma (HCC) patients and assess the feasibility of utilizing this model for prognosis prediction. Methods: Single-cell RNA-seq data were obtained from the Gene Expression Omnibus (GEO) database to identify marker genes of NK cells. Univariate Cox and lasso regression were performed to further establish a signature in the TCGA dataset. Subsequently, qPCR and immunohistochemistry (IHC) staining were employed to validate the expression levels of prognosis signature genes in HCC. The effectiveness of the model was further validated using two external cohorts from the GEO and ICGC datasets. Clinical characteristics, prognosis, tumor mutation burden, immune microenvironments, and biological function were compared for different genetic subtypes and risk groups. Finally, molecular docking was performed to evaluate the binding affinity between the hub gene and chemotherapeutic drugs. Results: A total of 161 HCC-related NK cell marker genes (NKMGs) were identified, 28 of which were significantly associated with overall survival in HCC patients. Based on differences in gene expression characteristics, HCC patients were classified into three subtypes. Ten prognosis genes (KLRB1, CD7, LDB2, FCER1G, PFN1, FYN, ACTG1, PABPC1, CALM1, and RPS8) were screened to develop a prognosis model. The model not only demonstrated excellent predictive performance on the training dataset, but also were successfully validated on two independent external datasets. The risk scores derived from the model were shown to be an independent prognosis factor for HCC and were correlated with pathological severity. Moreover, qPCR and IHC staining confirmed that the expression of the prognosis genes was generally consistent with the results of the bioinformatic analysis. Finally, molecular docking revealed favorable binding energies between the hub gene ACTG1 and chemotherapeutic drugs. Conclusion: In this study, we developed a model for predicting the prognosis of HCC based on NK cells. The utilization of NKMGs as innovative biomarkers showed promise in the prognosis assessment of HCC.

Inflammation-related nomogram for predicting survival of patients with unresectable hepatocellular carcinoma received conversion therapy

The efficacy of conversion therapy for patients with unresectable hepatocellular carcinoma (HCC) is a common clinical concern.To analyse the prognostic factors of overall survival (OS) in patients with unresectable HCC who received conversion therapy.One hundred and fifty patients who met the inclusion criteria were enrolled and divided into a training cohort (n = 120) and a validation cohort (n = 30). Using the independent risk factors in the training cohort, a nomogram model was constructed to predict OS for patients treated with transarterial chemoembolization following hepatic resection. The nomogram was internally validated with the bootstrapping method. The predictive performance of nomogram was assessed by Harrell's concordance index (C-index), calibration plot and time-dependent receiver operating characteristic curves and compared with six other conventional HCC staging systems.Multivariate Cox analysis identified that albumin, blood urea nitrogen, gamma-glutamyl transpeptidase to platelet ratio, platelet to lymphocyte ratio, macrovascular invasion and tumour number were the six independent prognostic factors correlated with OS in nomogram model. The C-index in the training cohort and validation cohort were 0.752 and 0.807 for predicting OS, which were higher than those of the six conventional HCC staging systems (0.563 to 0.715 for the training cohort and 0.458 to 0.571 for the validation cohort). The calibration plots showed good consistency between the nomogram prediction of OS and the actual observations of OS. Decision curve analyses indicated satisfactory clinical utility. With a total nomogram score of 196, patients were accurately classified into low-risk and high-risk groups. Furthermore, we have deployed the model into online calculators that can be accessed for free at https://ctmodelforunresectablehcc.shinyapps.io/DynNomapp/.The nomogram achieved optimal individualized prognostication of OS in HCC patients who received conversion therapy, which could be a useful clinical tool to help guide postoperative personalized interventions and prognosis judgement.

Development and validation of the OSASH score to predict overall survival of hepatocellular carcinoma after surgical resection: a dual-institutional study

ObjectiveTo develop and validate a risk score based on preoperative clinical-radiological parameters for predicting overall survival (OS) in patients undergoing surgical resection for hepatocellular carcinoma (HCC).MethodsFrom July 2010 to December 2021, consecutive patients with surgically-proven HCC who underwent preoperative contrast-enhanced MRI were retrospectively enrolled. A preoperative OS risk score was constructed in the training cohort using a Cox regression model and validated in a propensity score-matched internal validation cohort and an external validation cohort.ResultsA total of 520 patients were enrolled, among whom 210, 210, and 100 patients were from the training, internal validation, and external validation cohorts, respectively. Independent predictors for OS included incomplete tumor “capsule,” mosaic architecture, tumor multiplicity, and serum alpha-fetoprotein, which were incorporated into the “OSASH score.” The C-index the OSASH score was 0.85, 0.81, and 0.62 in the training, internal, and external validation cohorts, respectively. Using 32 as the cutoff point, the OSASH score stratified patients into prognostically distinct low- and high-risk groups among all study cohorts and six subgroups (all p < 0.05). Furthermore, patients with BCLC stage B-C HCC and OSASH-low risk achieved comparable OS to that of patients with BCLC stage 0-A HCC and OSASH-high risk in the internal validation cohort (5-year OS rates, 74.7 vs. 77.8%; p = 0.964).ConclusionThe OSASH score may help predict OS in HCC patients undergoing hepatectomy and identify potential surgical candidates among those with BCLC stage B-C HCC.Clinical relevance statementBy incorporating three preoperative MRI features and serum AFP, the OSASH score may help predict postsurgical overall survival in patients with hepatocellular carcinoma and identify potential surgical candidates among those with BCLC stage B and C HCC.Key Points• The OSASH score incorporating three MRI features and serum AFP can be used to predict OS in HCC patients who received curative-intent hepatectomy.• The score stratified patients into prognostically distinct low- and high-risk strata in all study cohorts and six subgroups.• Among patients with BCLC stage B and C HCC, the score identified a subgroup of low-risk patients who achieved favorable outcomes after surgery.

Intrahepatic Cholangiocarcinoma and Hepatocellular Carcinoma: Real-life Data on Liver Disease, Treatment and Prognosis.

Hepatocellular carcinoma (HCC) and intrahepatic cholangiocarcinoma (iCCA) have common features and differences. This real-life study investigated their characteristics, treatment modalities, and prognoses. This retrospective comparative study was performed in 1,075 patients seen at one tertiary center between January 2008 and December 2020. Overall survival (OS) was estimated by the Kaplan-Meier method. Subclassification of iCCAs after histological and radiological review, and molecular profiling was performed. HCCs patients were more likely to have early-stage disease than iCCA patients. iCCA patients were more likely to be female, especially those patients without cirrhosis (43% vs. 17%). Cirrhosis was prominent among HCC patients (89% vs. 34%), but no difference in underlying liver disease among cirrhotic patients was found. OS of HCC patients was 18.4 (95% CI: 6.4, 48.3) months, that of iCCA patients was 7.0 (95% CI: 3.4, 20.1) months. OS of Barcelona Clinic Liver Cancer C HCC patients was 7.8 (95% CI: 4.3, 14.2) months, that of advanced/metastatic iCCA patients was 8.5 (95% CI: 5.7, 12.3) months. In patients treated with sorafenib, OS was longer in HCC patients who received subsequent tyrosine kinase inhibitor therapies. No significant OS difference was found between iCCA patients with and without cirrhosis or according to histological subtype. A targetable molecular alteration was detected in 50% of the iCCA patients. In this French series, cirrhosis was common in iCCA, which showed etiological factors comparable to those of HCC, implying a distinct oncogenic pathway. Both entities had a dismal prognosis at advanced stages. However, systemic therapies sequencing in HCC and molecular profiling in iCCA offer new insights.

Antler-derived microRNA PC-5p-1090 inhibits HCC cell proliferation, migration, and invasion by targeting MARCKS, SMARCAD1, and SOX9.

The capability of microRNAs (miRNAs) to regulate gene expression across species has opened new avenues for miRNA-based therapeutics. Here, we investigated the potential of PC-5p-1090 (miR-PC-1090), a miRNA found in deer antlers, to control the malignant phenotypes of hepatocellular carcinoma (HCC) cells. Using Cell Counting Kit-8 and transwell assays, we found that heterologous expression of miR-PC-1090 inhibited HCC cell proliferation, migration, and invasion. Bioinformatics analysis indicated that predicted miR-PC-1090 targets, including MARCKS, SMARCAD1, and SOX9, were significantly elevated in HCC tissues, and their high expressions were associated with poor overall survival of HCC patients. Moreover, mechanistic investigations revealed that miR-PC-1090 promoted the degradation of MARCKS and SMARCAD1 mRNAs and hindered the translation of SOX9 mRNA by recognizing their 3' untranslated regions. Subsequent loss-of-function and rescue experiments confirmed the involvement of MARCKS, SMARCAD1, and SOX9 in miR-PC-1090-suppressed HCC cell proliferation, migration, and invasion. Notably, MARCKS knockdown induced the downregulation of phosphorylated MARCKS and a corresponding upregulation of phosphorylated AKT in HCC. Conversely, miR-PC-1090 repressed MARCKS phosphorylation and effectively circumvented the activation of the PI3K/AKT pathway. Furthermore, miR-PC-1090 regulates the Wnt/β-catenin pathway through SMARCAD1- and SOX9-mediated reduction of β-catenin expression. Overall, our results illustrate the tumor-suppressive activity and molecular mechanism of antler-derived miR-PC-1090 in HCC cells, indicating its potential as a multiple-target agent for HCC treatment.

CD69 serves as a potential diagnostic and prognostic biomarker for hepatocellular carcinoma

The prevalence and mortality of hepatocellular carcinoma (HCC) are still increasing. This study aimed to identify potential therapeutic targets related to patient prognosis. Data were downloaded from TCGA, GSE25097, GSE36376, and GSE76427 datasets. Differential analysis and enrichment analysis were performed in HCC. Cell deaths were evaluated, and least absolute shrinkage and selection operator regression (LASSO) regression was analyzed to screen candidate genes. Additionally, immune cell infiltration in HCC was assessed. We identified 4088 common DEGs with the same direction of differential expression in all four datasets, they were mainly enriched in immunoinflammation and cell cycle pathways. Apoptosis was significantly suppressed in HCC in GSEA and GSVA. After LASSO regression analysis, we screened CD69, CDC25B, MGMT, TOP2A, and TXNIP as candidate genes. Among them, CD69 significantly influenced the overall survival of HCC patients in both TCGA and GSE76427. CD69 may be a protective factor for outcome of HCC patients. In addition, CD69 was positive correlation with T cells and CD3E. CD69, CDC25B, MGMT, TOP2A, and TXNIP were potential diagnostic and prognostic target for HCC, especially CD69.

MiR-17-5p slows progression of hepatocellular carcinoma by downregulating TGFβR2.

Downregulation of miR-17-5p has been reported in several cancers, but whether and how miR-17-5p is downregulated in hepatocellular carcinoma (HCC) is unknown. Here, we examined whether miR-17-5p is downregulated in HCC and whether that affects expression of its target gene encoding transforming growth factor β receptor 2 (TGFβR). We screened for potential microRNAs (miRNAs) involved in HCC by analyzing published transcriptomes from HCC patients. Expression of miR-17-5p was measured in HCC cell lines and in tissues from HCC patients using quantitative real-time PCR. The in vitro effects of miR-17-5p on HCC cells were assessed by EdU proliferation assay, CCK-8 cell proliferation assay, colony-formation assay, transwell migration/invasion assay, wound healing assay, and flow cytometry. Effects of miR-17-5p were evaluated in vivo using mice with subcutaneous tumors. Effects of the miRNA on the epithelial-mesenchymal transition (EMT) were assessed, while its effects on TGFβR2 expression were analyzed using bioinformatics and a dual luciferase reporter assay. Patients with low miR-17-5p expression showed lower rates of overall and recurrence-free survival than patients with high miR-17-5p expression, and multivariate Cox regression identified low miR-17-5p expression as an independent predictor of poor overall survival in HCC patients. In vitro, miR-17-5p significantly inhibited HCC cell proliferation, migration, invasion, and the EMT, while promoting apoptosis. In vivo, it slowed the development of tumors. These protective effects of miR-17-5p were associated with downregulation of TGFβR2. The miRNA miR-17-5p can negatively regulate the expression of TGFβR2 and inhibit the EMT, thereby slowing tumor growth in HCC, suggesting a potential therapeutic approach against HCC.

Construction of endothelial cell-related and autophagy-related prognostic models for hepatocellular carcinoma based on single-cell data.

We aimed to develop an autophagy-related prognostic model with single-cell RNA sequencing (ScRNA-Seq) data for hepatocellular carcinoma (HCC) patients. ScRNA-Seq datasets of HCC patients were analyzed by Seurat. The expression of genes involved in canonical and noncanonical autophagy pathways in scRNA-seq data was also compared. Cox regression was applied to construct an AutRG risk prediction model. Subsequently, we examined the characteristics of AutRG high-risk and low-risk group patients. Six major cell types (hepatocytes, myeloid cells, T/NK cells, B cells, fibroblast cells, and endothelial cells) were identified in the scRNA-Seq dataset. The results showed that most of the canonical and noncanonical autophagy genes were highly expressed in hepatocytes, with the exception of MAP 1LC3B, SQSTM1, MAP 1LC3A, CYBB, and ATG3. Six AutRG risk prediction models originating from different cell types were constructed and compared. The AutRG prognostic signature (GAPDH, HSP90AA1, and TUBA1C) in endothelial cells had the best overall performance for predicting the overall survival of HCC patients, with 1-year, 3-year, and 5-year AUCs equal to 0.758, 0.68, and 0.651 in the training cohort and 0.760, 0.796, and 0.840 in the validation cohort, respectively. The different tumor mutation burden, immune infiltration, and gene set enrichment characteristics of the AutRG high-risk and low-risk group patients were identified. We constructed an endothelial cell-related and autophagy-related prognostic model of HCC patients using the ScRNA-Seq dataset for the first time. This model demonstrated the good calibration ability of HCC patients and provided a new understanding of the evaluation of prognosis.

Outcomes of laparoscopic radiofrequency ablation versus percutaneous radiofrequency ablation for hepatocellular carcinoma.

Few studies have compared the therapeutic outcomes in patients with HCC who underwent laparoscopic radiofrequency ablation (LRFA) versus percutaneous radiofrequency ablation (PRFA) for hepatocellular carcinoma (HCC). Therefore, this study compared the recurrence and survival outcomes of the two RFA methods in patients with HCC. Recurrence and overall survival outcomes were evaluated in 307 patients who underwent LRFA (n = 151) or PRFA (n = 156) as a treatment method for de novo HCC. Inverse probability of treatment weighting (IPTW) analysis was performed to reduce the impact of treatment selection bias. There were no significant differences in major baseline characteristics between the LRFA and PRFA groups. However, the proportion of cirrhotic patients was higher in the LRFA group, whereas the LRFA group had more tumors and a more advanced tumor-node-metastasis stage. Moreover, the mean tumor size was significantly larger in the LRFA group than in the PRFA group. In a multivariate analysis, serum albumin level, more than three tumors, and the RFA method were identified as significant predictors of recurrence-free survival. Moreover, for the overall survival of HCC patients, serum albumin levels, days of hospital stay during RFA, and the RFA method were independent predictors. In the IPTW-adjusted analysis, the LRFA group showed significantly higher recurrence-free survival and overall survival. Our study revealed that compared with PRFA, LRFA was associated with longer recurrence-free survival and favorable overall survival in patients with HCC. Therefore, LRFA should be considered the primary therapy in patients with HCC eligible for RFA.

Construction of a prognostic model for HCC based on ferroptosis-related lncRNAs expression and its potential to predict the response and irAEs of immunotherapy.

Background: Ferroptosis is an iron-dependent programmed cell death process, and studies have confirmed that it plays an important regulatory role in the occurrence and development of various malignancies including hepatocellular carcinoma (HCC). In addition, the role of abnormally expressed long non-coding RNAs (lncRNAs) in regulating and driving the occurrence and development of HCC has attracted more and more attention. However, there is still a lack of research on the role of ferroptosis-related lncRNAs in the prognosis prediction of HCC patients. Method: In this study, we used the Pearson test method to analyze the association between differentially expressed lncRNAs and ferroptosis-related genes in HCC and normal tissues obtained from The Cancer Genome Atlas (TCGA), and found 68 aberrantly expressed and prognosis-related ferroptosis-related lncRNAs. Based on this, we established an HCC prognostic model composed of 12 ferroptosis-related lncRNAs. In addition, HCC patients were divided into a high-risk group and a low-risk group according to the risk score of this 12 ferroptosis-related lncRNAs prognostic model. Gene enrichment analysis indicated that ferroptosis-related lncRNA-based expression signatures may regulate HCC immune microenvironment signaling pathways through ferroptosis, chemical carcinogenesis-reactive oxygen species, and NK cell-mediated cytotoxicity pathways. In addition, immune cell correlation analysis showed that there were significant differences in immune infiltrating cell subtypes, such as Th cells, macrophages, monocytes, and Treg cells between the two groups. In addition, the expression of multiple immune checkpoint molecules was found to be significantly increased in the high-risk group (eg, PD1, CTLA-4, CD86, etc.). Results: Our research provides a new method for predicting prognosis using a ferroptosis-related lncRNA expression signature prognostic model in hepatocellular carcinoma. And it provides new tools for predicting patient response and adverse effects of immunotherapy. Conclusion: In conclusion, ferroptosis-related lncRNA expression signatures can be used to construct a prognostic prediction model to predict the overall survival of HCC patients, and can be used as an independent influencing factor for prognosis. Further analysis showed that ferroptosis-related lncRNAs may affect the efficacy of immunotherapy in patients with HCC by altering the tumor microenvironment, so this model may serve as a new indicator of the response and irAEs of HCC to immunotherapy.

Predicting Overall Survival with Deep Learning from 18F-FDG PET-CT Images in Patients with Hepatocellular Carcinoma before Liver Transplantation

Positron emission tomography and computed tomography with 18F-fluorodeoxyglucose (18F-FDG PET-CT) were used to predict outcomes after liver transplantation in patients with hepatocellular carcinoma (HCC). However, few approaches for prediction based on 18F-FDG PET-CT images that leverage automatic liver segmentation and deep learning were proposed. This study evaluated the performance of deep learning from 18F-FDG PET-CT images to predict overall survival in HCC patients before liver transplantation (LT). We retrospectively included 304 patients with HCC who underwent 18F-FDG PET/CT before LT between January 2010 and December 2016. The hepatic areas of 273 of the patients were segmented by software, while the other 31 were delineated manually. We analyzed the predictive value of the deep learning model from both FDG PET/CT images and CT images alone. The results of the developed prognostic model were obtained by combining FDG PET-CT images and combining FDG CT images (0.807 AUC vs. 0.743 AUC). The model based on FDG PET-CT images achieved somewhat better sensitivity than the model based on CT images alone (0.571 SEN vs. 0.432 SEN). Automatic liver segmentation from 18F-FDG PET-CT images is feasible and can be utilized to train deep-learning models. The proposed predictive tool can effectively determine prognosis (i.e., overall survival) and, thereby, select an optimal candidate of LT for patients with HCC.

Effect of N6-methyladenosine methylation-related gene signature for predicting the prognosis of hepatocellular carcinoma patients.

Hepatocellular carcinoma (HCC) is a common cause of cancer-related death in humans. Increasing evidence indicates that an imbalance in N6-methyladenosine (m6A) methylation is linked to the occurrence and development of cancer. We then developed a prognostic model as an independent risk factor with which predict the prognosis of HCC. We obtained the gene expression and clinical data of HCC patients from the TCGA databases. The prognostic value of m6A methylation-related genes in patients who had HCC were subjected to comprehensive bioinformatics analysis. We use to construct the risk scoring formula. We collected pathological specimens from 68 patients who had HCC, and conducted immunohistochemical staining experiments on the specimens. There was a significant correlation between candidate m6A methylation-related genes (YTHDF2, METTL14 and ZC3H13) overall survival of HCC patients. Among the 68 HCC patient specimens that underwent immunohistochemical staining, all cancer tissues were positive for METTL14, YTHDF2, and ZC3H13 staining in contrast to the adjacent tissues. We conducted a Kaplan-Meier survival analysis. The results showed that patients who had low METTL14 expression had a longer survival time than those of patients who had high METTL14 expression. Also, patients with low YTHDF2 expression had a longer survival time than patients with high YTHDF2 expression. Finally, patients with high ZC3H13 expression lived longer than those with low ZC3H13 expression. This result is consistent with the bioinformatics analysis conclusion above. Generally, the prognostic model that was based on m6A methylation-related genes in this study can effectively predict the prognosis of HCC patients.

Construction of a lipid metabolism-related risk model for hepatocellular carcinoma by single cell and machine learning analysis.

One of the most common cancers is hepatocellular carcinoma (HCC). Numerous studies have shown the relationship between abnormal lipid metabolism-related genes (LMRGs) and malignancies. In most studies, the single LMRG was studied and has limited clinical application value. This study aims to develop a novel LMRG prognostic model for HCC patients and to study its utility for predictive, preventive, and personalized medicine. We used the single-cell RNA sequencing (scRNA-seq) dataset and TCGA dataset of HCC samples and discovered differentially expressed LMRGs between primary and metastatic HCC patients. By using the least absolute selection and shrinkage operator (LASSO) regression machine learning algorithm, we constructed a risk prognosis model with six LMRGs (AKR1C1, CYP27A1, CYP2C9, GLB1, HMGCS2, and PLPP1). The risk prognosis model was further validated in an external cohort of ICGC. We also constructed a nomogram that could accurately predict overall survival in HCC patients based on cancer status and LMRGs. Further investigation of the association between the LMRG model and somatic tumor mutational burden (TMB), tumor immune infiltration, and biological function was performed. We found that the most frequent somatic mutations in the LMRG high-risk group were CTNNB1, TTN, TP53, ALB, MUC16, and PCLO. Moreover, naïve CD8+ T cells, common myeloid progenitors, endothelial cells, granulocyte-monocyte progenitors, hematopoietic stem cells, M2 macrophages, and plasmacytoid dendritic cells were significantly correlated with the LMRG high-risk group. Finally, gene set enrichment analysis showed that RNA degradation, spliceosome, and lysosome pathways were associated with the LMRG high-risk group. For the first time, we used scRNA-seq and bulk RNA-seq to construct an LMRG-related risk score model, which may provide insights into more effective treatment strategies for predictive, preventive, and personalized medicine of HCC patients.