Predictor For Hepatocellular Carcinoma Patients Research Articles

METTL5 enhances the mRNA stability of TPRKB through m6A modification to facilitate the aggressive phenotypes of hepatocellular carcinoma cell

N6-methyladenosine (m6A) modification plays an important role in RNA molecular functions, therefore affecting the initiation and development of hepatocellular carcinoma (HCC). Herein, multiple datasets were applied to conduct a comprehensive analysis of DEGs within HCC and the analysis revealed significant dysregulation of numerous genes. Functional and signaling pathway enrichment analyses were performed. Further, TP53RK binding protein (TPRKB) emerged as a significant factor, exhibiting high expression level within HCC tissue samples and cells which could predict HCC patients’ poor OS. Knockdown investigations of TPRKB in vitro demonstrated the effect of TPRKB knockdown on attenuating the aggressiveness of HCC cells by suppressing the viability, colony formation, invasive ability, and migratory ability, inducing cell cycle arrest, and facilitating the apoptosis of HCC cells. Investigations in vivo revealed that TPRKB knockdown significantly suppressed tumor growth in mice model. Additionally, the study identified methyltransferase 5, N6-adenosine (METTL5) as a potential regulator of TPRKB expression via m6A modification, positively regulating TPRKB expression by enhancing TPRKB mRNA stability. The dynamic effects of METTL5 and TPRKB upon the phenotypes of HCC cells further confirmed that TPRKB overexpression partially abolished the anti-cancer effects of METTL5 knockdown upon the aggressiveness of HCC cells. Conclusively, our findings uncover that TPRKB, significantly overexpressed in HCC, exerts a critical effect on promoting tumor aggressiveness, and its expression shows to be positively regulated by METTL5 via m6A methylation. These insights deepen the understanding of HCC pathogenesis and open new avenues for targeted therapies, highlighting that METTL5-TPRKB axis is an underlying new therapeutic target in HCC management.

T cell-related ubiquitination genes as prognostic indicators in hepatocellular carcinoma.

T lymphocytes, integral to the adaptive immune system, wield pivotal influence in bolstering anti-tumor responses, and are strictly regulated by ubiquitination modification. The objective of this investigation was to devise a novel prognostic and immunotherapeutic efficacy predictor for hepatocellular carcinoma patients utilizing T cell-related ubiquitination genes (TCRUG). The single-cell RNA sequencing (scRNA-seq) data and bulk RNA data of HCC patients are derived from the GEO database and TCGA database. Based on the processing of scRNA-seq, T cell marker genes are obtained and TCRUG is obtained. Further combined with WGCNA, differential analysis, univariate Cox regression analysis, LASSO analysis, and multivariate Cox regression analysis to filter and screen TCRUG. Finally construct a riskscore for predicting the prognosis of HCC patients, the predictive effect of which is validated in the GEO dataset. In addition, we also studied the correlation between riskscore and immunotherapy efficacy. Finally, the oncogenic role of UBE2E1 in HCC was explored through various in vitro experiments. Based on patients' scRNA-seq data, we finally obtained 3050 T cell marker genes. Combined with bulk RNA data and clinical data from the TCGA database, we constructed a riskscore that accurately predicts the prognosis of HCC patients. This riskscore is an independent prognostic factor for HCC and is used to construct a convenient column chart. In addition, we found that the high-risk group is more suitable for immunotherapy. Finally, the proliferation, migration, and invasion abilities of HCC cells significantly decreased after UBE2E1 expression reduction. This study developed a riskscore based on TCRUG that can accurately and stably predict the prognosis of HCC patients. This riskscore is also effective in predicting the immune therapy response of HCC patients.

Lysosome-Related Genes and RNF19B as Prognostic Markers for Survival and Immunotherapy Efficacy in Hepatocellular Carcinoma.

Hepatocellular carcinoma (HCC) poses a considerable worldwide health concern due to its associated high risk of death. The heterogeneity of HCC poses challenges in developing practical risk stratification tools and identifying prognostic markers for personalized targeted treatments. Recently, lysosomes were shown to be crucial contributors to numerous cellular activities, including tumor initiation and immune response regulation. We aimed to construct a reliable prognostic signature based on lysosome-related genes and determine its association with the immune microenvironment. We comprehensively analyzed lysosome-related genes in HCC to investigate their influence on patient survival and the tumor immune microenvironment. A prognostic signature comprising 14 genes associated with lysosomes was created to estimate the survival outcomes of individuals with HCC. In addition, we verified the prognostic importance of Ring Finger Protein 19B (RNF19B) in patients with HCC through multiplex immunohistochemistry analysis. Our constructed lysosome-related prediction model could significantly discriminate between HCC patients with good and poor survival outcomes ( P < 0.05). We also found that elevated RNF19B expression was linked to unfavorable prognostic outcomes and showed a connection with specific clinicopathological characteristics. Moreover, it was observed that RNF19B could facilitate the transformation of macrophages into M2-polarized macrophages and showed a significant positive correlation with PD-1 and CTLA-4. In summary, our study proposes that the expression of lysosome-related genes is associated with the immune microenvironment, serving as a predictor for HCC patient survival. Meanwhile, RNF19B was identified as a novel prognostic marker for predicting overall survival and immunotherapy effects in patients with HCC.

Histidine-rich glycoprotein in metabolic dysfunction-associated steatohepatitis-related disease progression and liver carcinogenesis.

Metabolic dysfunction-associated steatotic liver disease (MASLD), previously non-alcoholic fatty liver disease (NAFLD), is a leading cause of chronic liver disease worldwide. In 20%-30% of MASLD patients, the disease progresses to metabolic dysfunction-associated steatohepatitis (MASH, previously NASH) which can lead to fibrosis/cirrhosis, liver failure as well as hepatocellular carcinoma (HCC). Here we investigated the role of histidine-rich glycoprotein (HRG), a plasma protein produced by hepatocytes, in MASLD/MASH progression and HCC development. The role of HRG was investigated by morphological, cellular, and molecular biology approaches in (a) HRG knock-out mice (HRG-/- mice) fed on a CDAA dietary protocol or a MASH related diethyl-nitrosamine/CDAA protocol of hepatocarcinogenesis, (b) THP1 monocytic cells treated with purified HRG, and (c) well-characterized cohorts of MASLD patients with or without HCC. In non-neoplastic settings, murine and clinical data indicate that HRG increases significantly in parallel with disease progression. In particular, in MASLD/MASH patients, higher levels of HRG plasma levels were detected in subjects with extensive fibrosis/cirrhosis. When submitted to the pro-carcinogenic protocol, HRG-/- mice showed a significant decrease in the volume and number of HCC nodules in relation to decreased infiltration of macrophages producing pro-inflammatory mediators, including IL-1β, IL-6, IL-12, IL-10, and VEGF as well as impaired angiogenesis. The histopathological analysis (H-score) of MASH-related HCC indicate that the higher HRG positivity in peritumoral tissue significantly correlates with a lower overall patient survival and an increased recurrence. Moreover, a significant increase in HRG plasma levels was detected in cirrhotic (F4) patients and in patients carrying HCC vs. F0/F1 patients. Murine and clinical data indicate that HRG plays a significant role in MASLD/MASH progression to HCC by supporting a specific population of tumor-associated macrophages with pro-inflammatory response and pro-angiogenetic capabilities which critically support cancer cell survival. Furthermore, our data suggest HRG as a possible prognostic predictor in HCC patients with MASLD/MASH-related HCCs.

Development of a novel lncRNA-derived immune gene score using machine learning-based ensembles for predicting the survival of HCC

BackgroundLong noncoding RNAs (lncRNAs) are implicated in the tumor immunology of hepatocellular carcinoma (HCC). MethodsHCC mRNA and lncRNA expression profiles were used to extract immune-related genes with the ImmPort database, and immune-related lncRNAs with the ImmLnc algorithm. The MOVICS package was used to cluster immune-related mRNA, immune-related lncRNA, gene mutation and methylation data on HCC from the TCGA. GEO and ICGC datasets were used to validate the model. Data from single-cell sequencing was used to determine the expression of genes from the model in various immune cell types. ResultsWith this model, the area under the curve (AUC) for 1-, 3- and 5-year survival of HCC patients was 0.862, 0.869 and 0.912, respectively. Single-cell sequencing showed EREG was significantly expressed in a variety of immune cell types. Knockdown of the EREG target gene resulted in significant anti-apoptosis, pro-proliferation and pro-migration effects in HepG2 and HUH7 cells. Moreover, serum and liver tissue EREG levels in HCC patients were significantly higher than those of healthy control patients. ConclusionWe built a prognostic model with good accuracy for predicting HCC patient survival. EREG is a potential immunotherapeutic target and a promising prognostic biomarker.

Assessment of the Clinical Value of Platelet-to-Lymphocyte Ratio in Patients with Hepatocellular Carcinoma.

Hepatocellular carcinoma (HCC) is associated with higher mortality as a result of poor prognosis and unavailability of effective treatment options. This study retrospectively analyzed the clinical value of platelet-to-lymphocyte ratio (PLR) to aid in differentiating early hepatocellular carcinoma from liver cirrhosis patients. Three hundred and nine (309) patients including 155 patients with hepatocellular carcinoma (HCC) and 154 patients with liver cirrhosis were enrolled in this study. General clinical characteristics and blood parameters of each patient were collected, calculated, and retrospectively analyzed. Mann-Whitney U test was calculated to compare the two groups. Receiver operating characteristics (ROC) curve was performed to investigate the diagnostic potential of PLR in the prediction of HCC at a cut-off with high accuracy (area under the curve [AUC]) > 0.80. Hemoglobin (HB) concentration, red blood cell (RBC) count, neutrophil (NEU) count, platelet count, platelet-to-lymphocyte ratio (PLR), and neutrophil-to-lymphocyte ratio (NLR) were significantly higher in the HCC patients than in the liver cirrhosis patients (p < 0.05). ROC curve analysis showed that the AUC, optimal cut-off value, sensitivity, and specificity of PLR to predict HCC patients were 0.912, 98.7, 81.2%, and 80.6% respectively. The results suggest that PLR is a potential biomarker that can be used to predict early HCC.

Comprehensive analysis of cuproptosis-related long non-coding RNAs in prognosis, immune microenvironment infiltration and chemotherapy response of hepatocellular carcinoma.

The objective of this study is to explore the relationship between cuproptosis-related long noncoding RNAs (lncRNAs) in hepatocellular carcinoma (HCC). RNA-seq data, including lncRNAs and related clinical information of HCC patients, were downloaded from The Cancer Genome Atlas database. A signature composed 3 cuproptosis-related lncRNAs was constructed by LASSO analysis, and HCC patients were classified into high- and low-risk groups. Patients in the high-risk group had a poorer prognosis compared with the low-risk group. Univariate Cox and multivariate Cox regression analyses confirmed that the signature model was an independent risk factor compared to other clinical biomarkers. Furthermore, gene set enrichment analysis indicated that metabolism-related pathways were enriched in low-risk group, including drug metabolism, and fatty acid metabolism. Further research demonstrated that there were markedly differences in drug response between the high- and low-risk group. Immune related analysis showed that the most type of immune cells and immunological function in the high-risk group were different with the risk-group. Finally, TP53 mutation rate and the tumor mutational burden in the high-risk group were higher compared with the low-risk group. In conclusion, we constructed a prognostic signature based on the expression of cuproptosis-related lncRNAs to predict HCC patients' prognosis, drug response and immune microenvironment, and further research will be conducted to uncover the mechanisms.

Prediction of preoperative microvascular invasion by dynamic radiomic analysis based on contrast-enhanced computed tomography.

Microvascular invasion (MVI) is a common complication of hepatocellular carcinoma (HCC) surgery, which is an important predictor of reduced surgical prognosis. This study aimed to develop a fully automated diagnostic model to predict pre-surgical MVI based on four-phase dynamic CT images. A total of 140 patients with HCC from two centers were retrospectively included (training set, n = 98; testing set, n = 42). All CT phases were aligned to the portal venous phase, and were then used to train a deep-learning model for liver tumor segmentation. Radiomics features were extracted from the tumor areas of original CT phases and pairwise subtraction images, as well as peritumoral features. Lastly, linear discriminant analysis (LDA) models were trained based on clinical features, radiomics features, and hybrid features, respectively. Models were evaluated by area under curve (AUC), accuracy, sensitivity, specificity, positive and negative predictive values (PPV and NPV). Overall, 86 and 54 patients with MVI- (age, 55.92 ± 9.62 years; 68 men) and MVI+ (age, 53.59 ± 11.47 years; 43 men) were included. Average dice coefficients of liver tumor segmentation were 0.89 and 0.82 in training and testing sets, respectively. The model based on radiomics (AUC = 0.865, 95% CI: 0.725-0.951) showed slightly better performance than that based on clinical features (AUC = 0.841, 95% CI: 0.696-0.936). The classification model based on hybrid features achieved better performance in both training (AUC = 0.955, 95% CI: 0.893-0.987) and testing sets (AUC = 0.913, 95% CI: 0.785-0.978), compared with models based on clinical and radiomics features (p-value < 0.05). Moreover, the hybrid model also provided the best accuracy (0.857), sensitivity (0.875), and NPV (0.917). The classification model based on multimodal intra- and peri-tumoral radiomics features can well predict HCC patients with MVI.

Hepatocellular carcinoma immune prognosis score predicts the clinical outcomes of hepatocellular carcinoma patients receiving immune checkpoint inhibitors

ObjectiveThe predictive biomarkers of immune checkpoint inhibitors (ICIs) in hepatocellular carcinoma (HCC) still need to be further explored. This study aims to establish a new immune prognosis biomarker to predict the clinical outcomes of hepatocellular carcinoma patients receiving immune checkpoint inhibitors.MethodsThe subjects of this study were 151 HCC patients receiving ICIs at Harbin Medical University Cancer Hospital from January 2018 to December 2021. This study collected a wide range of blood parameters from patients before treatment and used Cox’s regression analysis to identify independent prognostic factors in blood parameters, as well as their β coefficient. The hepatocellular carcinoma immune prognosis score (HCIPS) was established through Lasso regression analysis and COX multivariate analysis. The cut-off value of HCIPS was calculated from the receiver operating characteristic (ROC) curve. Finally, the prognostic value of HCIPS was validated through survival analysis, stratified analyses, and nomograms.ResultsHCIPS was composed of albumin (ALB) and thrombin time (TT), with a cut-off value of 0.64. There were 56 patients with HCIPS < 0.64 and 95 patients with HCIPS ≥ 0.64, patients with low HCIPS were significantly related to shorter progression-free survival (PFS) (13.10 months vs. 1.63 months, P < 0.001) and overall survival (OS) (14.83 months vs. 25.43 months, P < 0.001). HCIPS has also been found to be an independent prognostic factor in this study. In addition, the stratified analysis found a significant correlation between low HCIPS and shorter OS in patients with tumor size ≥ 5 cm (P of interaction = 0.032). The C-index and 95% CI of the nomograms for PFS and OS were 0.730 (0.680–0.779) and 0.758 (0.711–0.804), respectively.ConclusionsAs a new score established based on HCC patients receiving ICIs, HCIPS was significantly correlated with clinical outcomes in patients with ICIs and might serve as a new biomarker to predict HCC patients who cloud benefit from ICIs.

Beta2-Microglobulin as Predictive Biomarkers in the Prognosis of Hepatocellular Carcinoma and Development of a New Nomogram.

Accurate prognosis is crucial for improving hepatocellular carcinoma (HCC) patients, clinical management, and outcomes post-liver resection. However, the lack of reliable prognostic indicators poses a significant challenge. This study aimed to develop a user-friendly nomogram to predict HCC patients' post-resection prognosis. We retrospectively analyzed the data from 1091 HCC patients, randomly split into training (n=767) and validation (n=324) cohorts. Receiver operating characteristic (ROC) curves determined the optimal cut-off value for alpha1-microglobulin (α1MG) and Beta2-microglobulin (β2MG). Kaplan-Meier analysis assessed microglobulin's impact on survival, followed by Cox regression to identify prognostic factors and construct a nomogram. The predictive accuracy and discriminative ability of the nomogram were measured by the concordance index (C-index), calibration curves, area under the ROC curve (AUC), and decision curve analysis (DCA), and were compared with the BCLC staging system, Edmondson grade, or BCLC stage plus Edmondson grade. Patients with high β2MG (≥2.395mg/L) had worse overall survival (OS). The nomogram integrated β2MG, BCLC stage, Edmondson grade, microvascular invasion (MVI), and serum carbohydrate antigen 199 (CA199) levels. C-index for training and validation cohorts (0.712 and 0.709) outperformed the BCLC stage (0.660 and 0.657), Edmondson grade (0.579 and 0.564), and the combination of BCLC stage with Edmondson grade (0.681 and 0.668), improving prognosis prediction. Calibration curves demonstrated good agreement between predicted and observed survival. AUC values exceeded 0.700 over time, highlighting the nomogram's discriminative ability. DCA revealed superior overall net income compared to other systems, emphasizing its clinical utility. Our β2MG-based nomogram accurately predicts HCC patients' post-resection prognosis, aiding intervention and follow-up planning. Significantly, our nomogram surpasses existing prognostic indicators, including BCLC stage, Edmondson grade, and the combination of BCLC stage with Edmondson grade, by demonstrating superior predictive performance.

Robust analysis of a novel PANoptosis-related prognostic gene signature model for hepatocellular carcinoma immune infiltration and therapeutic response

PANoptosis, an interplay between pyroptosis, apoptosis, and necroptosis, is deeply involved in cancer development and immunity. However, the influence of PANoptosis in hepatocellular carcinoma (HCC) remains to be further investigated. The differentially expressed PANoptosis-related genes (PANRGs) was screened in The Cancer Genome Atlas (TCGA) database. Accordingly, mutation, bioinformatics, and consensus clustering analyses were performed. Then, a prognostic risk model was developed by least absolute shrinkage and selection operator (LASSO) Cox regression. Furthermore, the prognostic value, immunity correlation and therapeutic response prediction ability of risk model were explored. A total of 18 PANRGs were differently expressed in the TCGA-HCC cohort and were mainly involved in cancer- and cell death-related signal pathways. Using unsupervised clustering method, we identified two PANRGs-mediated clustering patterns. The remarkable differences between the two clusters on overall survival (OS) and clinical features were demonstrated respectively. Based on the five-gene prognostic risk model, the calculated PANRG-scores were used to categorize the subgroups as high- and low-risk. Notably, the high-risk subgroup had a dismal prognosis and exhibited much lower immune infiltration levels of mast cells, nature killer cells and pDCs, but higher levels of aDCs, iDCs and Treg cells than those in the low-risk subgroup. Furthermore, we constructed a reliable nomogram combining clinical traits and PANRG-score to predict the OS of HCC patients. The significantly negative correlation between PANoptosis and tumor mutation burden (TMB), ferroptosis were revealed. In drug sensitivity analysis, the high-risk subgroup had a considerably lower TIDE score, suggesting a preferable response to immunotherapy, and may be more sensitive to Tipifarnib, Imatinib, Doxorubicin, and Gemcitabine. The upregulated mRNA expressions of FADD were validated in 16 paired HCC tissues of Guangxi cohort. Based on PANoptosis-related genes, an integrated risk signature was constructed to provide a roadmap for patient stratification and predict HCC patient's prognosis. The patients with the higher PANRG-score may carry a dismal survival and relatively low immune infiltration, but a potential better immunotherapy response. Therefore, future HCC therapy perspectives should emphasize the setting of PANoptosis to achieve a personalized, practicable and effective therapeutic regimen.

Combining bulk and single-cell RNA-sequencing data to develop an NK cell-related prognostic signature for hepatocellular carcinoma based on an integrated machine learning framework

BackgroundThe application of molecular targeting therapy and immunotherapy has notably prolonged the survival of patients with hepatocellular carcinoma (HCC). However, multidrug resistance and high molecular heterogeneity of HCC still prevent the further improvement of clinical benefits. Dysfunction of tumor-infiltrating natural killer (NK) cells was strongly related to HCC progression and survival benefits of HCC patients. Hence, an NK cell-related prognostic signature was built up to predict HCC patients’ prognosis and immunotherapeutic response.MethodsNK cell markers were selected from scRNA-Seq data obtained from GSE162616 data set. A consensus machine learning framework including a total of 77 algorithms was developed to establish the gene signature in TCGA–LIHC data set, GSE14520 data set, GSE76427 data set and ICGC–LIRI–JP data set. Moreover, the predictive efficacy on ICI response was externally validated by GSE91061 data set and PRJEB23709 data set.ResultsWith the highest C-index among 77 algorithms, a 11-gene signature was established by the combination of LASSO and CoxBoost algorithm, which classified patients into high- and low-risk group. The prognostic signature displayed a good predictive performance for overall survival rate, moderate to high predictive accuracy and was an independent risk factor for HCC patients’ prognosis in TCGA, GEO and ICGC cohorts. Compared with high-risk group, low-risk patients showed higher IPS–PD1 blocker, IPS–CTLA4 blocker, common immune checkpoints expression but lower TIDE score, which indicated low-risk patients might be prone to benefiting from ICI treatment. Moreover, a real-world cohort, PRJEB23709, also revealed better immunotherapeutic response in low-risk group.ConclusionsOverall, the present study developed a gene signature based on NK cell-related genes, which offered a novel platform for prognosis and immunotherapeutic response evaluation of HCC patients.

Phosphotriesterase-related protein as a novel prognostic predictor for hepatocellular carcinoma patients.

Hepatocellular carcinoma (HCC) is the sixth incidence of cancer and the third leading cause of cancer mortality in the world. Facing the ever-increasing population of HCC patients, there is still an urgent need to find good diagnostic and prognostic markers to explore new therapeutic targets. Phosphotriesterase-related (PTER) protein, an expressed protein in the liver and injured or ploycystic kidneys, was reported to be correlated with serum aspartate aminotransferase (AST) and alanine transaminase (ALT). Our study aimed to investigate the expression of PTER protein in HCC patients and the association between PTER protein expression with clinicopathological features of HCC. Western blot analysis and immunohistochemistry (IHC) were performed in paired para-tumor and liver tumor tissues and HCC tissue microarray (TMA) to detect PTER protein expression. Correlation between PTER protein and prognostic factors were analyzed through univariate and multivariate analysis. We identified that PTER protein was significantly up-regulated in HCC tumors. Our data revealed that high PTER protein expression was associated with aggressive clinicopathological features of HCC, such as advanced tumor staging, vascular invasion, recurrence, and shorter overall survival (OS) and disease-free survival (DFS) time. Besides, in multivariate analyses, PTER protein was an independent predictor for OS (P=0.004) and DFS (P=0.013) for HCC patients. Meanwhile, the prognosis of patients with high PTER protein is much worse than those with low PTER protein expression. PTER protein expression is raised in HCC tissues and may be a potential prognostic predictor for HCC patients.

Development and validation of a novel prognostic signature based on m6A/m5C/m1A-related genes in hepatocellular carcinoma

BackgroundRNA methylation modification plays an important role in cancers. This study sought to examine the association between m6A/m5C/m1A-related genes and hepatocellular carcinoma (HCC).MethodsGene expression and clinical data of HCC patients were obtained from the TCGA database. Unsupervised consensus clustering was performed according to the expression of m6A/m5C/m1A-related genes in HCC. The relationships among prognosis, clinicopathological features and molecular subtypes were analyzed. Least absolute shrinkage and selection operator (LASSO) regression analysis was used to establish the m6A/m5C/m1A-related gene prognostic signature. Furthermore, the prognostic signature was validated based on the ICGC dataset. RT‒qPCR was used to detect the expression of the model genes in HCC. Clinicopathological features, functional enrichment, gene mutations, immune cell infiltration, and immunotherapy response in different risk groups were analyzed. A nomogram based on risk score and stage was constructed to predict HCC patient prognosis.ResultsTwo m6A/m5C/m1A-related molecular subtypes were identified in HCC, and the prognosis of cluster C1 was worse than that of cluster C2 (p < 0.001). Highly expressed genes in cluster C1 are significantly correlated with G3-4, T3-4, stage III-IV (p < 0.05). An m6A/m5C/m1A-related prognostic signature was established and validated. The RT‒qPCR results showed that the risk signature genes were significantly upregulated in liver cancer tissue (p < 0.05). The prognosis of HCC patients in the high-risk group was worse than that of those in the low-risk group (p < 0.05). Multivariate Cox analysis indicated that the risk score was an independent factor predicting prognosis in HCC patients. ssGSEA revealed that the risk score correlated with the tumor immune microenvironment in HCC. Gene mutation analysis showed that the tumor mutation burden of patients in the high-risk group was much higher (p < 0.05), and the prognosis of HCC patients with high risk scores and high mutation burden was the worst (p = 0.007). A nomogram combining risk scores with clinicopathological features showed performed well in predicting HCC prognosis.ConclusionsThe m6A/m5C/m1A-related genes could predict the prognosis and tumor microenvironment features of HCC and can be important biomarkers relevant to the immunotherapy response.

Identification of a brand intratumor microbiome signature for predicting prognosis of hepatocellular carcinoma.

Given that prognosis of hepatocellular carcinoma (HCC) differs dramatically, it is imperative to uncover effective and available prognostic biomarker(s). The intratumor microbiome plays a significant role in the response to tumor microenvironment, we aimed to identify an intratumor microbiome signature for predicting the prognosis of HCC patients accurately and investigate its possible mechanisms subsequently. The TCGA HCC microbiome data (TCGA-LIHC-microbiome) was downloaded from cBioPortal. To create an intratumor microbiome-related prognostic signature, univariate and multivariate Cox regression analyses were used to quantify the association of microbial abundance and patients' overall survival (OS), as well as their diseases specific survival (DSS). The performance of the scoring model was evaluated by the area under the ROC curve (AUC). Based on the microbiome-related signature, clinical factors, and multi-omics molecular subtypes on the basis of "icluster" algorithm, nomograms were established to predict OS and DSS. Patients were further clustered into three subtypes based on their microbiome-related characteristics by consensus clustering. Moreover, deconvolution algorithm, weighted correlation network analysis (WGCNA) and gene set variation analysis (GSVA) were used to investigate the potential mechanisms. In TCGA LIHC microbiome data, the abundances of 166 genera among the total 1406 genera were considerably associated with HCC patients' OS. From that filtered dataset we identified a 27-microbe prognostic signature and developed a microbiome-related score (MRS) model. Compared with those in the relatively low-risk group, patients in higher-risk group own a much worse OS (P < 0.0001). Besides, the time-dependent ROC curves with MRS showed excellent predictive efficacy both in OS and DSS. Moreover, MRS is an independent prognostic factor for OS and DSS over clinical factors and multi-omics-based molecular subtypes. The integration of MRS into nomograms significantly improved the efficacy of prognosis prediction (1-year AUC:0.849, 3-year AUC: 0.825, 5-year AUC: 0.822). The analysis of microbiome-based subtypes on their immune characteristics and specific gene modules inferred that the intratumor microbiome may affect the HCC patients' prognosis via modulating the cancer stemness and immune response. MRS, a 27 intratumor microbiome-related prognostic model, was successfully established to predict HCC patients overall survive independently. And the possible underlying mechanisms were also investigated to provide a potential intervention strategy.

Bioinformatics prediction and experimental verification identify cuproptosis-related lncRNA as prognosis biomarkers of hepatocellular carcinoma

Cuproptosis is a form of cell death caused by intracellular copper excess, which plays an important regulatory role in the development and progression of cancers, including hepatocellular carcinoma (HCC), a prevalent malignancy with high morbidity and mortality.This study aimed to create a cuproptosis associated long non-coding RNAs (CAlncRNAs)signature to predict HCC patient survival and immunotherapy response. Firstly, we identified 509 CAlncRNAs using Pearson correlation analysis in The Cancer Genome Atlas (TCGA) datasets, before the three CAlncRNAs (MKLN1-AS, FOXD2-AS1, LINC02870) with the most prognostic value were further screened. Then, we constructed a prognostic risk model for HCCwas using univariate and LASSO Cox regression analyses. Multivariate Cox regression analyses illustrated that this model was an independent prognostic factor for overall survival (OS) prediction, outperforming traditional clinicopathological factors. And the risk score not only could be prognostic factors independent of other factors but also suited for patients with diverse ages, stages, and grades. The 1-, 3-, and 5- years areas under the curves (AUC) values of the model were 0.759, 0.668 and 0.674 respectively. Pathway analyses showed that the high-risk groupenriched in immune-related pathways. Importantly, patients with higher risk scores exhibited higher mutation frequency, higher TMB scores, and lower TIDE scores. Besides, we screened for two chemical drugs (A-443654 and Pyrimethamine) with the greatest value for high-risk HCC patients. Finally, the abnormal high expression of the three CAlncRNAs were confirmed in HCC tissues and cells by Real Time Quantitative PCR (RT-qPCR). And proliferative, migratory and invasion abilities of HCC cell were restrained via silencing CAlncRNAs expression in vitro.In summary, we built a CAlncRNAs-based risk score model, which can be a candidate for HCC patients prognostic prediction and offer some useful information for immunotherapies.

Combination of neutrophil/lymphocyte ratio and albumin/bilirubin grade as a prognostic predictor for hepatocellular carcinoma patients undergoing curative hepatectomy

Background and aimPrognosis determination is essential for hepatocellular carcinoma (HCC) patient management and treatment planning. The current study aimed to evaluate the prognosis performance of NLR, ALBI, and the combination of NLR-ALBI in determining the overall survival (OS) of HCC patients under curative hepatectomy.Methods144 primary HCC patients with curative hepatectomy were recruited in the retrospective study. The clinicopathologic characteristics and OS were compared between stratified groups. The predictive performance of NLR, ALBI, and the combination of NLR-ALBI was explored by the area under the receiver operating characteristic curve (AUC). Univariate and multivariate analyses were used to determine the risk factors of OS.ResultsAUC determined NLR cutoff > 2.60 for predicting prognosis. The univariate analysis indicated pathological differentiation, tumor size, AFP, TNM stage, NLR score, and ALBI grade were significant indicators of OS. However, only TMN grade, AFP, NLR score, and NLR-ALBI score were identified as independent predictors of OS in the multivariable analysis. The AUC of NLR, ALBI and the combination of NLR-ALBI was 0.618(95%CI 0.56–0.710), 0.533 (95%CI 0.437–0.629), 0.679 (95%CI 0.592–0.767) respectively. Patients with higher NLR-ALBI scores presented worse outcomes than those with lower NLR-ALBI scores.ConclusionNLR is an independent prognostic factor of HCC and a reliable biomarker in predicting the OS of HCC patients. The combination of NLR-ALBI showed a better prognostic performance than using NLR or ALBI alone, implicating the effectiveness and feasibility of combining multiple risk factors for postoperative prognosis assessment.

A macropinocytosis-related gene signature predicts the prognosis and immune microenvironment in hepatocellular carcinoma.

Available treatments for hepatocellular carcinoma (HCC), a common human malignancy with a low survival rate, remain unsatisfactory. Macropinocytosis (MPC), a type of endocytosis that involves the non-specific uptake of dissolved molecules, has been shown to contribute to HCC pathology; however, its biological mechanism remains unknown. The current study identified 27 macropinocytosis-related genes (MRGs) from 71 candidate genes using bioinformatics. The R software was used to create a prognostic signature model by filtering standardized mRNA expression data from HCC patients and using various methods to verify the reliability of the model and indicate immune activity. The prognostic signature was constructed using seven MPC-related differentially expressed genes, GSK3B, AXIN1, RAC1, KEAP1, EHD1, GRB2, and SNX5, through LASSO Cox regression. The risk score was acquired from the expression of these genes and their corresponding coefficients. HCC patients in the discovery and validation cohorts were stratified, and the survival of low-risk score patients was improved in both cohorts. Time-dependent ROC analysis indicated that the model's prediction reliability was the highest in the short term. Subsequent immunologic analysis, including KEGG, located the immune action pathway of the differentially expressed genes in the direction of the cancer pathway, etc. Immune infiltration and immune checkpoint tests provided valuable guidance for future follow-up experiments. A risk model with MRGs was constructed to effectively predict HCC patient prognoses and suggest changes in the immune microenvironment during the disease process. The findings should benefit the development of a prognostic stratification and treatment strategy for HCC.

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.

Crosstalk between 5-methylcytosine and N6-methyladenosine machinery defines disease progression, therapeutic response and pharmacogenomic landscape in hepatocellular carcinoma

BackgroundAccumulated evidence highlights the significance of the crosstalk between epigenetic and epitranscriptomic mechanisms, notably 5-methylcytosine (5mC) and N6-methyladenosine (m6A). Herein, we conducted a widespread analysis regarding the crosstalk between 5mC and m6A regulators in hepatocellular carcinoma (HCC).MethodsPan-cancer genomic analysis of the crosstalk between 5mC and m6A regulators was presented at transcriptomic, genomic, epigenetic, and other multi-omics levels. Hub 5mC and m6A regulators were summarized to define an epigenetic and epitranscriptomic module eigengene (EME), which reflected both the pre- and post-transcriptional modifications.Results5mC and m6A regulators interacted with one another at the multi-omic levels across pan-cancer, including HCC. The EME scoring system enabled to greatly optimize risk stratification and accurately predict HCC patients’ clinical outcomes and progression. Additionally, the EME accurately predicted the responses to mainstream therapies (TACE and sorafenib) and immunotherapy as well as hyper-progression. In vitro, 5mC and m6A regulators cooperatively weakened apoptosis and facilitated proliferation, DNA damage repair, G2/M arrest, migration, invasion and epithelial-to-mesenchymal transition (EMT) in HCC cells. The EME scoring system was remarkably linked to potential extrinsic and intrinsic immune escape mechanisms, and the high EME might contribute to a reduced copy number gain/loss frequency. Finally, we determined potential therapeutic compounds and druggable targets (TUBB1 and P2RY4) for HCC patients with high EME.ConclusionsOur findings suggest that HCC may result from a unique synergistic combination of 5mC-epigenetic mechanism mixed with m6A-epitranscriptomic mechanism, and their crosstalk defines therapeutic response and pharmacogenomic landscape.