Predicting Hepatocellular Carcinoma Prognosis Research Articles

Aberrant fragmentomic features of circulating cell-free mitochondrial DNA enable early detection and prognosis prediction of hepatocellular carcinoma.

Early detection and effective prognosis prediction in patients with hepatocellular carcinoma (HCC) provides an avenue for survival improvement, yet more effective approaches are greatly needed. We sought to develop the detection and prognosis models with ultra-sensitivity and low cost based on fragmentomic features of circulating cell free mtDNA (ccf-mtDNA). Capture-based mtDNA sequencing was carried out in plasma cell-free DNA samples from 1168 participants, including 571 patients with HCC, 301 patients with chronic hepatitis B or liver cirrhosis (CHB/LC) and 296 healthy controls (HC). The systematic analysis revealed significantly aberrant fragmentomic features of ccf-mtDNA in HCC group when compared with CHB/LC and HC groups. Moreover, we constructed a random forest algorithm-based HCC detection model by utilizing ccf-mtDNA fragmentomic features. Both internal and two external validation cohorts demonstrated the excellent capacity of our model in distinguishing early HCC patients from HC and high-risk population with CHB/LC, with AUC exceeding 0.983 and 0.981, sensitivity over 89.6% and 89.61%, and specificity over 98.20% and 95.00%, respectively, greatly surpassing the performance of alpha-fetoprotein (AFP) and mtDNA copy number. We also developed a HCC prognosis prediction model by LASSO-Cox regression to select 20 fragmentomic features, which exhibited exceptional ability in predicting 1-year, 2-year and 3-year survival (AUC = 0.8333, 0.8145 and 0.7958 for validation cohort, respectively). We have developed and validated a high-performing and low-cost approach in a large clinical cohort based on aberrant ccf-mtDNA fragmentomic features with promising clinical translational application for the early detection and prognosis prediction of HCC patients.

Integrating anoikis and ErbB signaling insights with machine learning and single-cell analysis for predicting prognosis and immune-targeted therapy outcomes in hepatocellular carcinoma.

Hepatocellular carcinoma (HCC) poses a significant global health challenge due to its poor prognosis and limited therapeutic modalities. Anoikis and ErbB signaling pathways are pivotal in cancer cell proliferation and metastasis, but their relevance in HCC remains insufficiently explored. This study evaluates the prognostic significance of anoikis and ErbB signaling pathways in HCC by utilizing data from The Cancer Genome Atlas (TCGA), the International Cancer Genome Consortium (ICGC), three additional independent validation cohorts, and an in-house cohort. Advanced bioinformatics analyses and 167 machine learning models based on leave-one-out cross-validation (LOOCV) were used to predict HCC prognosis and assess outcomes of immune-targeted therapies. Additionally, key biological processes of the anoikis and ErbB signaling pathways in HCC were further investigated. The single sample Gene Set Enrichment Analysis revealed a strong correlation between upregulated ErbB signaling in high anoikis-expressing tumors and poor clinical outcomes. The development of the Anoikis-ErbB Related Signature (AERS) using the LASSO + RSF model demonstrated robust predictive capabilities, as validated across multiple patient cohorts, and proved effective in predicting responses to immune-targeted therapies. Further investigation highlighted activated NOTCH signaling pathways and decreased macrophage infiltration was associated with resistance to sorafenib and immune checkpoint inhibitors, as evidenced by bulk and single-cell RNA sequencing (scRNA-seq). AERS provides a novel tool for clinical prognosis and paves the way for immune-targeted therapeutic approaches, underscoring the potential of integrated molecular profiling in enhancing treatment strategies for HCC.

Prognostic modeling of hepatocellular carcinoma based on T-cell proliferation regulators: a bioinformatics approach.

The prognostic value and immune significance of T-cell proliferation regulators (TCRs) in hepatocellular carcinoma (HCC) have not been previously reported. This study aimed to develop a new prognostic model based on TCRs in patients with HCC. This study used The Cancer Genome Atlas-Liver Hepatocellular Carcinoma (TCGA-LIHC) and International Cancer Genome Consortium-Liver Cancer-Riken, Japan (ICGC-LIRI-JP) datasets along with TCRs. Differentially expressed TCRs (DE-TCRs) were identified by intersecting TCRs and differentially expressed genes between HCC and non-cancerous samples. Prognostic genes were determined using Cox regression analysis and were used to construct a risk model for HCC. Kaplan-Meier survival analysis was performed to assess the difference in survival between high-risk and low-risk groups. Receiver operating characteristic curve was used to assess the validity of risk model, as well as for testing in the ICGC-LIRI-JP dataset. Additionally, independent prognostic factors were identified using multivariate Cox regression analysis and proportional hazards assumption, and they were used to construct a nomogram model. TCGA-LIHC dataset was subjected to tumor microenvironment analysis, drug sensitivity analysis, gene set variation analysis, and immune correlation analysis. The prognostic genes were analyzed using consensus clustering analysis, mutation analysis, copy number variation analysis, gene set enrichment analysis, and molecular prediction analysis. Among the 18 DE-TCRs, six genes (DCLRE1B, RAN, HOMER1, ADA, CDK1, and IL1RN) could predict the prognosis of HCC. A risk model that can accurately predict HCC prognosis was established based on these genes. An efficient nomogram model was also developed using clinical traits and risk scores. Immune-related analyses revealed that 39 immune checkpoints exhibited differential expression between the high-risk and low-risk groups. The rate of immunotherapy response was low in patients belonging to the high-risk group. Patients with HCC were further divided into cluster 1 and cluster 2 based on prognostic genes. Mutation analysis revealed that HOMER1 and CDK1 harbored missense mutations. DCLRE1B exhibited an increased copy number, whereas RAN exhibited a decreased copy number. The prognostic genes were significantly enriched in tryptophan metabolism pathways. This bioinformatics analysis identified six TCR genes associated with HCC prognosis that can serve as diagnostic markers and therapeutic targets for HCC.

Serum thymosin beta 10 level as a potential prognosis prediction of hepatocellular carcinoma and hepatic diseases

Thymosin Beta 10 (TMSB10) is a thymosin family member that has been identified as being overexpressed in a wide variety of human cancers. This study aimed to determine the expression level of TMSB10 in sera of patients with hepatocellular carcinoma (HCC) and liver cirrhosis. And to reveal the association between TMSB10 and different stages in patients with HCC. We also wanted to know how TMSB10 is predictive in HCC patients and its relation to the Barcelona Clinic's staging system. The study included 41 HCC patients, 15 liver cirrhosis patients, and 15 normal control subjects. The enzyme-linked immunosorbent assay was used to determine serum levels of TMSB10 and alpha-fetoprotein (AFP) in serum of normal control individuals and patients with liver cirrhosis and different stages of HCC, and to evaluate the relationship of AFP with TMSB10. The TMSB10 levels in patients with HCC were statistically different than in the control group and in the different stages of HCC. We found a statistically significant difference in the distribution of TMSB10 between the three study groups (p< 0.001). There was an association between TMSB10 concentration and AFP. The level of TMSB10 in the serum of the HCC subgroups was then analyzed. The TMSB10 level increased with the advance of HCC stages. The TMSB10 level in HCC patients did not correlate with levels of liver function tests including aminotransferase, aspartate aminotransferase, albumin, prothrombin, bilirubin, or alkaline phosphatase. In conclusion, serum TMSB10 levels can be used as a potential prognostic marker for clinical stages of HCC.

Uncovering gene expression signatures and diagnostic – Biomarkers in hepatocellular carcinoma through multinomial logistic regression analysis

Hepatocellular carcinoma (HCC) is one of the leading causes of cancer death worldwide, and classifying the developmental stages of HCC can help with early prognosis and treatment. This study aimed to investigate diagnostic and prognostic molecular signatures underlying the progression of HCC, including tumor initiation and growth, and to classify its developmental stages based on gene expression levels. We integrated data from two cancer systems, including 78 patients with Edmondson-Steiner (ES) grade and 417 patients with TNM stage cancer. Functional profiling was performed using identified signatures. Using a multinomial logistic regression model (MLR), we classified controls, early-stage HCC, and advanced-stage HCC. The model was validated in three independent cohorts comprising 45 patients (neoplastic stage), 394 patients (ES grade), and 466 patients (TNM stage). Multivariate Cox regression was employed for HCC prognosis prediction. We identified 35 genes with gradual upregulation or downregulation in both ES grade and TNM stage patients during HCC progression. These genes are involved in cell division, chromosome segregation, and mitotic cytokinesis, promoting tumor cell proliferation through the mitotic cell cycle. The MLR model accurately differentiated controls, early-stage HCC, and advanced-stage HCC across multiple cancer systems, which was further validated in various independent cohorts. Survival analysis revealed a subset of five genes from TNM stage (HR: 3.27, p < 0.0001) and three genes from ES grade (HR: 7.56, p < 0.0001) that showed significant association with HCC prognosis. The identified molecular signature not only initiates tumorigenesis but also promotes HCC development. It has the potential to improve clinical diagnosis, prognosis, and therapeutic interventions for HCC. This study enhances our understanding of HCC progression and provides valuable insights for precision medicine approaches.

Identifying epithelial-mesenchymal transition-related genes as prognostic biomarkers and therapeutic targets of hepatocellular carcinoma by integrated analysis of single-cell and bulk-RNA sequencing data.

Hepatocellular carcinoma (HCC) remains one of the most lethal cancers globally. Patients with advanced HCC tend to have poor prognoses and shortened survival. Recently, data from bulk RNA sequencing have been employed to discover prognostic markers for various cancers. However, they fall short in precisely identifying core molecular and cellular activities within tumor cells. In our present study, we combined bulk-RNA sequencing (bulk RNA-seq) data with single-cell RNA sequencing (scRNA-seq) to develop a prognostic model for HCC. The goal of our research is to uncover new biomarkers and enhance the accuracy of HCC prognosis prediction. Integrating single-cell sequencing data with transcriptomics were used to identify epithelial-mesenchymal transition (EMT)-related genes (ERGs) implicated in HCC progression and their clinical significance was elucidated. Utilizing marker genes derived from core cells and ERGs, we constructed a prognostic model using univariate Cox analysis, exploring a multitude of algorithmic combinations, and further refining it through multivariate Cox analysis. Additionally, we conducted an in-depth investigation into the disparities in clinicopathological features, immune microenvironment composition, immune checkpoint expression, and chemotherapeutic drug sensitivity profiles between high- and low-risk patient cohorts. We developed a prognostic model predicated on the expression profiles of eight signature genes, namely HSP90AA1, CIRBP, CCR7, S100A9, ADAM17, ENG, PGF, and INPP4B, aiming at predicting overall survival (OS) outcomes. Notably, patients classified with high-risk scores exhibited a propensity towards diminished OS rates, heightened frequencies of stage III-IV disease, increased tumor mutational burden (TMB), augmented immune cell infiltration, and diminished responsiveness to immunotherapeutic interventions. This study presented a novel prognostic model for predicting the survival of HCC patients by integrating scRNA-seq and bulk RNA-seq data. The risk score emerges as a promising independent prognostic factor, showing a correlation with the immune microenvironment and clinicopathological features. It provided new clinical tools for predicting prognosis and aided future research into the pathogenesis of HCC.

Fatty acid metabolism affects hepatocellular carcinoma progression via the PPAR-γ signaling pathway and fatty acid β-oxidation

PurposeThis study aimed to explore novel targets for hepatocellular carcinoma (HCC) treatment by investigating the role of fatty acid metabolism. MethodsRNA-seq and clinical data of HCC were obtained from the Gene Expression Omnibus (GEO) and The Cancer Genome Atlas (TCGA) databases. Bioinformatic analyses were employed to identify differentially expressed genes (DEGs) related to prognosis. A signature was then constructed using the Least Absolute Shrinkage and Selection Operator (LASSO) Cox regression to classify HCC patients from the TCGA database into low-risk and high-risk groups. The predictive performance of the signature was evaluated through principal components analysis (PCA), Kaplan Meier (KM) survival analysis, receiver operating characteristics (ROC) curves, nomogram, genetic mutations, drug sensitivity analysis, immunological correlation analysis, and enrichment analysis. Single-cell maps were constructed to illustrate the distribution of core genes. Immunohistochemistry (IHC), quantitative real-time PCR (qRT-PCR), and western blot were employed to verify the expression of core genes. The function of one core gene was validated through a series of in vitro assays, including cell viability, colony formation, wound healing, trans-well migration, and invasion assays. The results were analyzed in the context of relevant signaling pathways. ResultsBioinformatic analyses identified 15 FAMGs that were related to prognosis. A 4-gene signature was constructed, and patients were divided into high- and low-risk groups according to the signature. The high-risk group exhibited a poorer prognosis compared to the low-risk group in both the training (P < 0.001) and validation (P = 0.020) sets. Furthermore, the risk score was identified as an independent predictor of OS (P < 0.001, HR = 8.005). The incorporation of the risk score and clinicopathologic features into a nomogram enabled the effective prediction of patient prognosis. The model was able to effectively predict the immune microenvironment, drug sensitivity to chemotherapy, and gene mutation for each group. Single-cell maps demonstrated that FAMGs in the model were distributed in tumor cells. Enrichment analyses revealed that the cell cycle, fatty acid β oxidation and PPAR signaling pathways were the most significant pathways. Among the four key prognostically related FAMGs, Spermine Synthase (SMS) was selected and validated as a potential oncogene affecting cell cycle, PPAR-γ signaling pathway and fatty acid β oxidation in HCC. ConclusionsThe risk characteristics based on FAMGs could serve as independent prognostic indicators for predicting HCC prognosis and could also serve as evaluation criteria for gene mutations, immunity, and chemotherapy drug therapy in HCC patients. Meanwhile, targeted fatty acid metabolism could be used to treat HCC through related signaling pathways.

Integrating bulk-RNA and single-cell analysis reveals heterogeneous expression of cuproptosis-related sorafenib-resistant genes in hepatocellular carcinoma

Abstract Objectives Cuproptosis represents the copper-dependent novel cell death pattern. However, the effects of cuproptosis-related sorafenib-resistant genes on prognosis, treatment response, and sorafenib resistance in hepatocellular carcinoma (HCC) patients are still unclear. The present work aims to develop a cuproptosis-related signature for predicting HCC prognosis. Methods Cuproptosis-related sorafenib-resistant differentially expressed genes (CRSRDEGs) were identified by correlation analysis between cuproptosis genes and sorafenib-resistant genes using electronic databases TCGA and GEO. Besides, the cuproptosis-related sorafenib-resistant risk score model (CRSRRSM) was established through LASSO and univariate Cox regression analyses. Later, this model was adopted for analyzing HCC patient prognosis. Certain potential drugs and treatment sensitivity were also analyzed in HCC patients receiving sorafenib or transarterial chemoembolization (TACE) treatment. Results The CRSRRSM achieved excellent efficiency in predicting the prognosis and sorafenib or TACE treatment response of HCC patients. As revealed by somatic mutational analyses, CRSRRSM was associated with tumor mutational burden (TMB), especially for TP53, CSMD3, and OBSCN mutations. According to functional enrichment analysis, CRSRRSM was closely correlated with tumor-related pathways, cuproptosis-related tricarboxylic acid (TCA) cycle, and drug resistance. Notably, potential drugs such as sepantronium bromide, AZD8055, and RO-3306, the promising alternatives for treating HCC patients with sorafenib resistance, were also proposed based on CRSRRSM. Furthermore, single-cell transcriptomic analysis revealed that high-risk malignant cells demonstrated an increased capacity of proliferation and immune evasion. Conclusions A model, designated CRSRRSM, was constructed that can effectively predict the prognosis, sorafenib treatment response, and potential drugs for sorafenib resistance in HCC patients. This model provides potential implications for clinical management of HCC patients with sorafenib resistance.

Enhancing prognostic prediction in hepatocellular carcinoma post-TACE: a machine learning approach integrating radiomics and clinical features.

This study aimed to investigate the use of radiomics features and clinical information by four machine learning algorithms for predicting the prognosis of patients with hepatocellular carcinoma (HCC) who have been treated with transarterial chemoembolization (TACE). A total of 105 patients with HCC treated with TACE from 2002 to 2012 were enrolled retrospectively and randomly divided into two cohorts for training (n = 74) and validation (n = 31) according to a ratio of 7:3. The Spearman rank, random forest, and univariate Cox regression were used to select the optimal radiomics features. Univariate Cox regression was used to select clinical features. Four machine learning algorithms were used to develop the models: random survival forest, eXtreme gradient boosting (XGBoost), gradient boosting, and the Cox proportional hazard regression model. The area under the curve (AUC) and C-index were devoted to assessing the performance of the models in predicting HCC prognosis. A total of 1,834 radiomics features were extracted from the computed tomography images of each patient. The clinical risk factors for HCC prognosis were age at diagnosis, TNM stage, and metastasis, which were analyzed using univariate Cox regression. In various models, the efficacy of the combined models generally surpassed that of the radiomics and clinical models. Among four machine learning algorithms, XGBoost exhibited the best performance in combined models, achieving an AUC of 0.979 in the training set and 0.750 in the testing set, demonstrating its strong prognostic prediction capability. The superior performance of the XGBoost-based combined model underscores its potential as a powerful tool for enhancing the precision of prognostic assessments for patients with HCC.

Proteo-genomic characterization of cirrhosis-associated liver cancers reveals potential subtypes and therapeutic targets.

This study aimed to identify potential subtypes of hepatocellular carcinoma (HCC) associated with cirrhosis and to investigate key markers using bioinformatic analysis of gene expression datasets-0. Three data sets (GSE17548, GSE56140, and GSE87630) were extracted from the Gene Expression Omnibus (GEO) database and normalized using the Limma package in R. Principal component analysis (PCA) and cluster analysis was performed to examine data distribution and identify subtypes. Differential gene expression analysis was performed using the Limma software package. Protein-protein interaction analysis and functional annotation were performed using the STRING database and Cytoscape software. Important signaling pathways and processes were identified using Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) Pathway Analysis. The analysis revealed different subtypes of HCC associated with cirrhosis and identified several key genes, including CCNB2, MCM4, and CDC20, with strong binding power and prognostic value. Functional annotation indicated involvement in cell cycle regulation and metabolic pathways. ROC analysis showed high sensitivity and specificity of these genes in predicting HCC prognosis. These results suggest that CCNB2, MCM4, and CDC20 may serve as potential biomarkers for predicting HCC prognosis in patients with cirrhosis and provide insights into the molecular mechanisms of HCC progression.

A novel risk scoring system predicts overall survival of hepatocellular carcinoma using cox proportional hazards machine learning method

BackgroundRobust and practical prognosis prediction models for hepatocellular carcinoma (HCC) patients play crucial roles in personalized precision medicine. Material and methodsWe recruited two independent HCC cohorts (discovery cohort and validation cohort), totally consisting of 222 HCC patients undergone surgical resection. We quantified the expressions of immune-related proteins (CD8, CD68, CD163, PD-1 and PD-L1) in paired HCC tissues and non-tumor liver tissues from these HCC patients using immunohistochemistry (mIHC) assays. We constructed the HCC prognosis prediction model using five different machine learning methods based on the patients in the discovery cohort, such as Cox proportional hazards (CoxPH). ResultsWe identified 19 features that were associated with overall survival of HCC patients in the discovery cohort (p < 0.1), such as immune-related features CD68+ and CD8+ cell infiltration. We constructed five HCC prognosis prediction models using five different machine learning methods. Among the five different machine learning models, the CoxPH model achieved the best performance (area under the curve [AUC], 0.839; C-index, 0.779). According to the risk score from CoxPH model, we divided HCC patients into high-risk group/low-risk group. In both discovery cohort and validation cohort, the patients in low-risk group showed longer overall survival compared with those in high-risk group (p = 1.8 × 10−7 and 3.4 × 10−5, respectively). Moreover, our novel scoring system efficiently predicted the 6, 12, and 18 months survival rate of HCC patients with AUC >0.75 in both discovery cohort and validation cohort. In addition, we found that the scoring system could also distinguish the patients with high/low risks of relapse in both discovery cohort and validation cohort (p = 0.00015 and 0.00012). ConclusionThe novel CoxPH-based risk scoring model on clinical, laboratory-testing and immune-related features showed high prediction efficiencies for overall survival and recurrence of HCCs undergone surgical resection. Our results may be helpful to optimize clinical follow-up or therapeutic interventions.

Construction of a Prognostic Model for Hepatocellular Carcinoma Based on Macrophage Polarization-Related Genes.

The progression of hepatocellular carcinoma (HCC) is related to macrophage polarization (MP). Our aim was to identify genes associated with MP in HCC patients and develop a prognostic model based on these genes. We successfully developed a prognostic model consisting of six MP-related genes (SCN4A, EBF3, ADGRB2, HOXD9, CLEC1B, and MSC) to calculate the risk score for each patient. Patients were then classified into high- and low-risk groups based on their median risk score. The performance of the MP-related prognostic model was evaluated using Kaplan-Meier and ROC curves, which yielded favorable results. Additionally, the nomogram demonstrated good clinical effectiveness and displayed consistent survival predictions with actual observations. Gene Set Enrichment Analysis (GSEA) revealed enrichment of pathways related to KRAS signaling downregulation, the G2M checkpoint, and E2F targets in the high-risk group. Conversely, pathways associated with fatty acid metabolism, xenobiotic metabolism, bile acid metabolism, and adipogenesis were enriched in the low-risk group. The risk score positively correlated with the number of invasion-related genes. Immune checkpoint expression differed significantly between the two groups. Patients in the high-risk group exhibited increased sensitivity to mitomycin C, cisplatin, gemcitabine, rapamycin, and paclitaxel, while those in the low-risk group showed heightened sensitivity to doxorubicin. These findings suggest that the high-risk group may have more invasive HCC with greater susceptibility to specific drugs. IHC staining revealed higher expression levels of SCN4A in HCC tissues. Furthermore, experiments conducted on HepG2 cells demonstrated that supernatants from cells with reduced SCN4A expression promoted M2 macrophage polarization marker, CD163 in THP-1 cells. Reduced SCN4A expression induced HCC-related genes, while increased SCN4A expression reduced their expression in HepG2 cells. The MP-related prognostic model comprising six MPRGs can effectively predict HCC prognosis, infer invasiveness, and guide drug therapy. SCN4A is identified as a suppressor gene in HCC.

M1A regulator-mediated methylation modification patterns correlated with autophagy to predict the prognosis of hepatocellular carcinoma.

N1-methyladenosine (m1A), among the most common internal modifications on RNAs, has a crucial role to play in cancer development. The purpose of this study were systematically investigate the modification characteristics of m1A in hepatocellular carcinoma (HCC) to unveil its potential as an anticancer target and to develop a model related to m1A modification characteristics with biological functions. This model could predict the prognosis for patients with HCC. An integrated analysis of the TCGA-LIHC database was performed to explore the gene signatures and clinical relevance of 10 m1A regulators. Furthermore, the biological pathways regulated by m1A modification patterns were investigated. The risk model was established using the genes that showed differential expression (DEGs) between various m1A modification patterns and autophagy clusters. These in vitro experiments were subsequently designed to validate the role of m1A in HCC cell growth and autophagy. Immunohistochemistry was employed to assess m1A levels and the expression of DEGs from the risk model in HCC tissues and paracancer tissues using tissue microarray. The risk model, constructed from five DEGs (CDK5R2, TRIM36, DCAF8L, CYP26B, and PAGE1), exhibited significant prognostic value in predicting survival rates among individuals with HCC. Moreover, HCC tissues showed decreased levels of m1A compared to paracancer tissues. Furthermore, the low m1A level group indicated a poorer clinical outcome for patients with HCC. Additionally, m1A modification may positively influence autophagy regulation, thereby inhibiting HCC cells proliferation under nutrient deficiency conditions. The risk model, comprising m1A regulators correlated with autophagy and constructed from five DEGs, could be instrumental in predicting HCC prognosis. The reduced level of m1A may represent a potential target for anti-HCC strategies.

Ferroptosis-related lncRNA NRAV affects the prognosis of hepatocellular carcinoma via the miR-375-3P/SLC7A11 axis

Ferroptosis has important value in cancer treatment. It is significant to explore the new ferroptosis-related lncRNAs prediction model in Hepatocellular carcinoma (HCC) and the potential molecular mechanism of ferroptosis-related lncRNAs. We constructed a prognostic multi-lncRNA signature based on ferroptosis-related differentially expressed lncRNAs in HCC. qRT-PCR was applied to determine the expression of lncRNA in HCC cells. The biological roles of NRAV in vitro and in vivo were determined by performing a series of functional experiments. Furthermore, dual-luciferase reporter and RNA immunoprecipitation (RIP) assays were used to confirm the interaction of NRAV with miR-375-3P. We identified 6 differently expressed lncRNAs associated with the prognosis of HCC. Kaplan–Meier analyses revealed the high-risk lncRNAs signature associated with poor prognosis of HCC. Moreover, the AUC of the lncRNAs signature showed utility in predicting HCC prognosis. Further functional experiments show that the high expression of NRAV can strengthen the viciousness of HCC. Interestingly, we found that NRAV can enhance iron export and ferroptosis resistance. Further study showed that NRAV competitively binds to miR-375-3P and attenuates the inhibitory effect of miR-375-3P on SLC7A11, affecting the prognosis of patients with HCC. In conclusion, We developed a novel ferroptosis-related lncRNAs prognostic model with important predictive value for the prognosis of HCC. NRAV is important in ferroptosis induction through the miR-375-3P/SLC7A11 axis.

A hypoxia–glycolysis–lactate-related gene signature for prognosis prediction in hepatocellular carcinoma

BackgroundLiver cancer ranks sixth in incidence and third in mortality globally and hepatocellular carcinoma (HCC) accounts for 90% of it. Hypoxia, glycolysis, and lactate metabolism have been found to regulate the progression of HCC separately. However, there is a lack of studies linking the above three to predict the prognosis of HCC. The present study aimed to identify a hypoxia–glycolysis–lactate-related gene signature for assessing the prognosis of HCC.MethodsThis study collected 510 hypoxia-glycolysis-lactate genes from Molecular Signatures Database (MSigDB) and then classified HCC patients from TCGA-LIHC by analyzing their hypoxia-glycolysis-lactate genes expression. Differentially expressed genes (DEGs) were screened out to construct a gene signature by LASSO-Cox analysis. Univariate and multivariate regression analyses were used to evaluate the independent prognostic value of the gene signature. Analyses of immune infiltration, somatic cell mutations, and correlation heatmap were conducted by “GSVA” R package. Single-cell analysis conducted by “SingleR”, “celldex”, “Seurat”, and “CellCha” R packages revealed how signature genes participated in hypoxia/glycolysis/lactate metabolism and PPI network identified hub genes.ResultsWe classified HCC patients from TCGA-LIHC into two clusters and screened out DEGs. An 18-genes prognostic signature including CDCA8, CBX2, PDE6A, MED8, DYNC1LI1, PSMD1, EIF5B, GNL2, SEPHS1, CCNJL, SOCS2, LDHA, G6PD, YBX1, RTN3, ADAMTS5, CLEC3B, and UCK2 was built to stratify the risk of HCC. The risk score of the hypoxia-glycolysis-lactate gene signature was further identified as a valuable independent factor for estimating the prognosis of HCC. Then we found that the features of clinical characteristics, immune infiltration, somatic cell mutations, and correlation analysis differed between the high-risk and low-risk groups. Furthermore, single-cell analysis indicated that the signature genes could interact with the ligand-receptors of hepatocytes/fibroblasts/plasma cells to participate in hypoxia/glycolysis/lactate metabolism and PPI network identified potential hub genes in this process: CDCA8, LDHA, YBX1.ConclusionThe hypoxia–glycolysis–lactate-related gene signature we built could provide prognostic value for HCC and suggest several hub genes for future HCC studies.

A Novel Four-Gene Signature Based on Nonsense-Mediated RNA Decay for Predicting Prognosis in Hepatocellular Carcinoma: Bioinformatics Analysis and Functional Validation.

Nonsense-mediated RNA decay (NMD), a surveillance pathway for selective degradation of aberrant mRNAs, is associated with cancer progression. Its potential as a predictor for aggressive hepatocellular carcinoma (HCC) is unclear. Here, we present an innovative NMD risk model for predicting HCC prognosis. The Cancer Genome Atlas (TCGA) data of 374 liver HCC (LIHC) and 50 normal liver samples were extracted. A risk model based on NMD-related genes was developed through least absolute shrinkage and selection operator Cox (LASSO-Cox) regression of the LIHC-TCGA data. Prognostic validation was done using GSE54236, GSE116174, and GSE76427 data. Univariate and multivariate Cox regression analyses were conducted to assess the prognostic value of the model. We also constructed nomograms for survival prediction. Tumor immune infiltration was evaluated using the CIBERSORT algorithm, and the tumor cell phenotype was assessed. Finally, mouse experiments verified UPF3B knockdown effects on HCC tumor characteristics. We developed a risk model based on four NMD-related genes (PABPC1, RPL8, SMG5, and UPF3B) and validated it using GSE54236, GSE116174, and GSE76427 data. The model effectively distinguished high- and low-risk groups corresponding to unfavorable and favorable HCC outcomes. Its prognostic prediction accuracy was confirmed through time-dependent ROC analysis, and clinical-use nomograms with calibration curves were developed. Single-cell RNA sequencing results indicated significantly higher expression of SMG5 and UPF3B in tumor cells. Knockdown of SMG5 and UPF3B inhibited HCC cell proliferation, invasion, and migration, while affecting cell-cycle progression and apoptosis. In vivo, UPF3B knockdown delayed tumor growth and increased immune cell infiltration. Our NMD-related gene-based risk model can help identify therapeutic targets and biomarkers for HCC. Additionally, it assists clinicians in predicting the prognosis of HCC patients.

Applying image features of proximal paracancerous tissues in predicting prognosis of patients with hepatocellular carcinoma

BackgroundMost of the methods using digital pathological image for predicting Hepatocellular carcinoma (HCC) prognosis have not considered paracancerous tissue microenvironment (PTME), which are potentially important for tumour initiation and metastasis. This study aimed to identify roles of image features of PTME in predicting prognosis and tumour recurrence of HCC patients. MethodsWe collected whole slide images (WSIs) of 146 HCC patients from Sun Yat-sen Memorial Hospital (SYSM dataset). For each WSI, five types of regions of interests (ROIs) in PTME and tumours were manually annotated. These ROIs were used to construct a Lasso Cox survival model for predicting the prognosis of HCC patients. To make the model broadly useful, we established a deep learning method to automatically segment WSIs, and further used it to construct a prognosis prediction model. This model was tested by the samples of 225 HCC patients from the Cancer Genome Atlas Liver Hepatocellular Carcinoma (TCGA-LIHC). ResultsIn predicting prognosis of the HCC patients, using the image features of manually annotated ROIs in PTME achieved C-index 0.668 in the SYSM testing dataset, which is higher than the C-index 0.648 reached by the model only using image features of tumours. Integrating ROIs of PTME and tumours achieved C-index 0.693 in the SYSM testing dataset. The model using automatically segmented ROIs of PTME and tumours achieved C-index of 0.665 (95% CI: 0.556–0.774) in the TCGA-LIHC samples, which is better than the widely used methods, WSISA (0.567), DeepGraphSurv (0.593), and SeTranSurv (0.642). Finally, we found the Texture SumAverage Skew HV on immune cell infiltration and Texture related features on desmoplastic reaction are the most important features of PTME in predicting HCC prognosis. We additionally used the model in prediction HCC recurrence for patients from SYSM-training, SYSM-testing, and TCGA-LIHC datasets, indicating the important roles of PTME in the prediction. ConclusionsOur results indicate image features of PTME is critical for improving the prognosis prediction of HCC. Moreover, the image features related with immune cell infiltration and desmoplastic reaction of PTME are the most important factors associated with prognosis of HCC.

Development and Validation of a Novel Nomogram Integrated with Hypoxic and Lactate Metabolic Characteristics for Prognosis Prediction in Hepatocellular Carcinoma

Development and Validation of a Novel Nomogram Integrated with Hypoxic and Lactate Metabolic Characteristics for Prognosis Prediction in Hepatocellular Carcinoma

Disulfidptosis-related genes of prognostic signature and immune infiltration features in hepatocellular carcinoma supported by bulk and single-cell RNA sequencing data.

Disulfidptosis is a new type of cellular death triggered in response to disulfide stress and is strongly linked to the progression of malignancies. Hepatocellular carcinoma (HCC) is a very common malignancy. Some reports have suggested a link between disulfidptosis-related genes (DRGs) and cancer; however, further research needs to be conducted. In this study, HCC data from the Cancer Genome Atlas-Liver Hepatocellular Carcinoma and Gene Expression Omnibus data sets were collected and analyzed. A univariate Cox regression analysis, least absolute shrinkage and selection operator, and multivariate Cox regression analysis were conducted to identify the hub DRGs signature for prognosis. The HCC patients were allocated to high- and low-risk groups based on their disulfidptosis risk scores. The model was validated with a high degree of precision using both internal and external validation data sets. "ESTIMATE" and "CIBERSORT" packages were employed to assess the immunological landscapes and immune cell infiltration. The IMvigor210 cohort was chosen to validate the immunotherapy results. A drug sensitivity analysis was conducted to identify targeted medications. The expression of the hub DRGs in the HCC cells was confirmed using cytological techniques. The bioinformatic analysis revealed that 16 genes showed differential expression. A prognostic model was developed based on four genes: RPN1, SLC2A1, SLC2A4, and SLC7A11. A notable difference in prognosis was observed between the two risk groups. Based on the results of the immune microenvironment, tumor mutation burden, immunotherapy, and drug screening analyses, the DRGs signature can be employed in HCC immunotherapy decision making. Further, the expression levels of the hub DRGs were significantly upregulated in the HCC cells. Our four-DRGs signature could be used to predict HCC prognosis. Further, this study showed that the hub DRGs could serve as biomarkers for immunotherapy prediction and could potentially guide targeted therapies.

The combination of CD8 and TIM3 expression to predict survival outcomes in hepatocellular carcinoma.

543 Background: CD8+ T cells have anti-tumor immunity in a variety of cancers including hepatocellular carcinoma (HCC). Tumor-infiltrating CD8+ T cells express immune checkpoint proteins and focus on a therapeutic targeting. T cell immunoglobulin and mucin domain protein 3 (TIM3) has been reported to be associated with suppression of anti-tumor immunity, poor prognosis, and tumor progression in HCC, resulting in up-regulation on exhausted CD8+ T cells. As the exhausted T cells showed a decrease of effector cytokine production and cytolytic activity, leading to the tumor progression, TIM3 is one of the most important exhaustion markers. In the present study, we investigated the role of CD8 and TIM3 as a prognostic biomarker in HCC. Methods: We analyzed 474 HCC patients with clinical and transcriptomic data from The Cancer Genome Atlas (TCGA: n=359) and GSE 76427 (n=115) to examine associations between CD8, TIM3, and programmed death receptor-1/programmed cell death ligand 1 (PD-1/PD-L1) expression and survival outcomes. Each gene was divided into low- and high-expression groups using the tertiles of gene expression. Results: The patients with high CD8 and low TIM3 expression were associated with better overall survival (OS) (P=0.021 and P=0.025, respectively) in TCGA, while PD-1/PD-L1 were not associated with OS (P=0.306 and P=0.318, respectively). Moreover, the patients with high CD8 and low TIM3 expression were validated in GSE dataset (P=0.014 and P=0.009, respectively). The patients with high CD8 and low TIM3 expression were associated with improved OS compared with the overall HCC patient (P=0.008). Among the patients with high CD8 expression, low TIM3 expression (non-exhausted CD8+ T cells) was associated with better OS (P=0.009), but PD-1/PD-L1 were not associated with OS (P=0.491 and P=0.772, respectively). Finally, multivariate analysis showed that the patients withnon-exhausted CD8+ T cells were significantly superior for the survival outcomes (Hazard ratio=2.80, 95% CI=1.27–6.19, P=0.01). Conclusions: High CD8 and low TIM3 expression is associated with better OS in HCC than PD-1/PD-L1, suggesting that CD8 and TIM3 are useful biomarkers for predicting HCC prognosis. Restoring exhausted T cells via the suppression of TIM3 is a promising strategy for cancer treatment, which become a breakthrough in the cancer immunotherapy.