Subtype Of Hepatocellular Carcinoma Research Articles

A novel classification of HCC basing on fatty-acid-associated lncRNA

Aberrant long noncoding RNA (lncRNA) expression and fatty acid signaling dysfunction both contribute to hepatocellular carcinoma (HCC) occurrence and development. However, the relationship and interaction mechanism between lncRNAs and fatty acid signaling in HCC remain unclear. Data regarding RNA expression and clinical outcomes for patients with HCC were obtained from The Cancer Genome Atlas (TCGA), HCCDB, and the Gene Expression Omnibus (GEO) databases. Hallmark pathways were identified using the single-sample gene set enrichment analysis (ssGSEA) method. ConsensusClusterPlus was used to establish a consistency matrix for classifying samples into three subtypes. A risk signature was established, and predictive values for key lncRNAs related to prognosis were evaluated using Kaplan–Meier analysis and receiver operating characteristic curves. The ESTIMATE algorithm, MCP-Counter, and ssGSEA were used to evaluate the characteristics of the tumor immune microenvironment. The CTRP2.0 and PRISM were used to analyze drug sensitivity in HCC subtypes. We discovered seven fatty-acid-associated lncRNAs with predictive prognostic capabilities, including TRAF3IP2-AS1, SNHG10, AL157392.2, LINC02641, AL357079.1, AC046134.2, and A1BG-AS. Three subtypes were obtained, which presented with differences in prognosis, clinical information, mutation features, pathway traits, immune characteristics, and drug sensitivity. The seven key lncRNAs identified in this study might serve as promising biomarkers for predicting prognosis in patients with HCC, and the three HCC subtypes classified according to lncRNA expression profiles could improve HCC classification.

Update in Pathology of Practically Encountered Lesions and Diseases in the Liver

This issue of the American Journal of Surgical Pathology, Reviews and Reports is devoted to recent advances and update in the pathology of the liver. The articles provide insights into the current nomenclature, classification, diagnosis, differential diagnoses, clinical and prognostic aspects of various liver diseases, and lesions that are informative to pathologists in their daily practice. Hepatocellular adenomas (HCAs) is generally a benign liver neoplasm that typically arises in the noncirrhotic liver; however, malignant transformation does occur, and hence, subtyping is critical. Drs Jung and Liu present a case of HCA that was initially diagnosed as inflammatory HCA on a biopsy and subsequently was confirmed to be β-catenin–activated inflammatory HCA on the resection specimen. They then comprehensively review recent updates on molecular classification and the key histologic features of each subtype and discuss common diagnostic pitfalls. Over the past decades, hepatocellular carcinoma (HCC) and intrahepatic cholangiocarcinoma (iCCA) have been recognized to be heterogeneous in morphology, molecular features, and biological behavior. Recent World Health Organization classification describes various histomorphological subtypes of HCC and iCCA, some with characteristic molecular profiles, clinical correlates, and prognostic implications. In their review, Drs Hwang and Kim nicely discuss recently described subtypes of HCC and iCCA: macrotrabecular HCC, steatohepatitic HCC, scirrhous HCC, and small duct iCCA. Congestive hepatopathy shows a unique pattern of fibrosis, that is, pericentral fibrosis at early stage, which may progress to bridging fibrosis and cirrhosis. Making a diagnosis of congestive hepatopathy and staging its fibrosis are important tasks for pathologists, as patients may undergo liver biopsies before cardiac transplantation to assess the degree of liver fibrosis, which may impact clinical decision-making. Besides patients' own cardiac dysfunction, congestive hepatopathy can also be seen after a Fontan procedure. Drs Gosse and Bosch describe a case of congestive hepatopathy due to altered circulation with a Fontan procedure. They then discuss the fibrosis scoring systems recently described for Fontan-associated liver disease and congestive heart disease–associated hepatopathy. Combined hepatocellular-cholangiocarcinoma is a rare primary liver tumor with neoplastic components of both hepatocytic and cholangiocytic differentiation within the same tumor. There is recent consensus paper and World Health Organization classification of this unique tumor. Drs Kondo, Akiba, and Yano herein report a case of this tumor and thoughtfully review the terminology, classification, and clinical and pathological characteristics of combined hepatocellular-cholangiocarcinoma and its differential diagnosis. Hemophagocytic lymphohistiocytosis is a rare disease with a high mortality. Liver involvement is common, with most patients demonstrating acute hepatitis. Therefore, liver biopsies are frequently performed when this entity is suspected. Dr Xiong and colleagues summarize the clinical features of hemophagocytic lymphohistiocytosis, emphasizing its hepatic manifestations. They describe the highly variable histologic patterns and address difficulties encountered by pathologists. We are extremely grateful to all the contributors for sharing their expertise and knowledge in forming this issue. We hope that the information provides highlights and elucidates the practically encountered lesions in the liver. Guest Editors

Histomorphological Subtypes of Hepatocellular Carcinoma and Intrahepatic Cholangiocarcinoma: Review and Update

AbstractPrimary liver cancers comprise a heterogeneous group of neoplasms, with the 2 main entities being hepatocellular carcinoma (HCC) and intrahepatic cholangiocarcinoma (iCCA). Over the past decades, HCC and iCCA have been recognized to be heterogeneous in morphology, molecular features, and biological behavior, and the most recent World Health Organization classification of digestive system tumors describes various histomorphological subtypes of HCC and iCCA, some of which also have characteristic molecular features, clinical correlates, and prognostic implications. In this review, we discuss 4 recently described subtypes of HCC and iCCA—macrotrabecular HCC, steatohepatitic HCC, scirrhous HCC, and small duct iCCA—based on a series of cases.

A Mitophagy-Related Gene Signature for Subtype Identification and Prognosis Prediction of Hepatocellular Carcinoma.

Globally, hepatocellular carcinoma (HCC) is the sixth most common cancer. In this study, the correlation between mitophagy and HCC prognosis was evaluated using data from The Cancer Genome Atlas (TCGA). Clinical and transcriptomic data of HCC patients were downloaded from TCGA dataset, and mitophagy-related gene (MRG) datasets were obtained from the Molecular Signature Database. Then, a consensus clustering analysis was performed to classify the patients into two clusters. Furthermore, tumor prognosis, clinicopathological features, functional analysis, immune infiltration, immune checkpoint (IC)-related gene expression level, tumor stem cells, ferroptosis status, and N6-methyladenosine analysis were compared between the two clusters. Finally, a mitophagy-related signature was developed. Two clusters (C1 and C2) were identified using the consensus clustering analysis based on the MRG signature. Patients with the C1 subtype exhibited upregulated pathways with better liver function, downregulated cancer-related pathways, lower cancer stem cell scores, lower Tumor Immune Dysfunction and Exclusion scores (TIDE), different ferroptosis status, and better prognosis compared with the patients with the C2 subtype. The C2 subtype was characterized by the increased grade of HCC, as well as the increased number of immune-related pathways and m6A-related genes. Higher immune scores were also observed for the C2 subtype. A signature containing four MRGs (PGAM5, SQSTM1, ATG9A, and GABARAPL1) which can accurately predict the prognosis of HCC patients was then identified. This four-gene signature exhibited a predictive effect in five other cancer types, namely glioma, uveal melanoma, acute myeloid leukemia, adrenocortical carcinoma, and mesothelioma. The mitophagy-associated subtypes of HCC were closely related to the immune microenvironment, immune checkpoint-related gene expression, cancer stem cells, ferroptosis status, m6A, prognosis, and HCC progression. The established MRG signature could predict prognosis in patients with HCC.

Characterization of immune features and immunotherapy response in subtypes of hepatocellular carcinoma based on mitophagy.

Mitophagy is suggested to be involved in tumor initiation and development; however, mitophagy heterogeneity in hepatocellular carcinoma (HCC) and its association with immune status and prognosis remain unclear. Differentially expressed genes (DEGs) were identified using expression profiles acquired from The Cancer Genome Atlas (TCGA). Mitophagy-related subtypes were identified using the ConsensusClusterPlus software. The differences in prognosis, clinical characteristics, and immune status, including immune cell infiltration, immune function, immune-checkpoint gene expression, and response to immunotherapy, were compared between subtypes. A mitophagy-related gene signature was constructed by applying least absolute shrinkage and selection operator regression to the TCGA cohort. The International Cancer Genome Consortium cohort and the cohort from Peking Union Medical College Hospital were utilized for validation. Carbonyl cyanide m-chlorophenylhydrazone was used to induce mitophagy in HCC cell lines to obtain our own mitophagy signature. Real-time polymerase chain reaction was used for the experimental validation of the expression of model genes. Two mitophagy-related subtypes with distinct prognoses, clinical characteristics, immune states, and biological function patterns were identified based on the mitophagy-related DEGs. The subtype that showed higher mitophagy-related DEG expression had worse survival outcomes, suppressed immune function, higher immune-checkpoint gene expression, and a better response to immunotherapy, indicating that this subpopulation in HCC may benefit from immune-checkpoint blockade therapy and other immunotherapies. A risk model consisting of nine mitophagy-related genes was constructed and its performance was confirmed in two validation cohorts. The risk score was an independent risk factor even when age, sex, and tumor stage were considered. Our study identified two distinct mitophagy subtypes and built a mitophagy signature, uncovering mitophagy heterogeneity in HCC and its association with immune status and prognosis. These findings shed light on the treatment of HCC, especially with immunotherapy.

Generative adversarial networks (GAN)-based data augmentation of rare liver cancers: The SFR 2021 Artificial Intelligence Data Challenge

PurposeThe 2021 edition of the Artificial Intelligence Data Challenge was organized by the French Society of Radiology together with the Centre National d’Études Spatiales and CentraleSupélec with the aim to implement generative adversarial networks (GANs) techniques to provide 1000 magnetic resonance imaging (MRI) cases of macrotrabecular-massive (MTM) hepatocellular carcinoma (HCC), a rare and aggressive subtype of HCC, generated from a limited number of real cases from multiple French centers. Materials and methodsA dedicated platform was used by the seven inclusion centers to securely upload their anonymized MRI examinations including all three cross-sectional images (one late arterial and one portal-venous phase T1-weighted images and one fat-saturated T2-weighted image) in compliance with general data protection regulation. The quality of the database was checked by experts and manual delineation of the lesions was performed by the expert radiologists involved in each center. Multidisciplinary teams competed between October 11th, 2021 and February 13th, 2022. ResultsA total of 91 MTM-HCC datasets of three images each were collected from seven French academic centers. Six teams with a total of 28 individuals participated in this challenge. Each participating team was asked to generate one thousand 3-image cases. The qualitative evaluation was performed by three radiologists using the Likert scale on ten randomly selected cases generated by each participant. A quantitative evaluation was also performed using two metrics, the Frechet inception distance and a leave-one-out accuracy of a 1-Nearest Neighbor algorithm. ConclusionThis data challenge demonstrates the ability of GANs techniques to generate a large number of images from a small sample of imaging examinations of a rare malignant tumor.

Integrating machine learning to construct aberrant alternative splicing event related classifiers to predict prognosis and immunotherapy response in patients with hepatocellular carcinoma.

Introduction: In hepatocellular carcinoma (HCC), alternative splicing (AS) is related to tumor invasion and progression. Methods: We used HCC data from a public database to identify AS subtypes by unsupervised clustering. Through feature analysis of different splicing subtypes and acquisition of the differential alternative splicing events (DASEs) combined with enrichment analysis, the differences in several subtypes were explored, cell function studies have also demonstrated that it plays an important role in HCC. Results: Finally, in keeping with the differences between these subtypes, DASEs identified survival-related AS times, and were used to construct risk proportional regression models. AS was found to be useful for the classification of HCC subtypes, which changed the activity of tumor-related pathways through differential splicing effects, affected the tumor microenvironment, and participated in immune reprogramming. Conclusion: In this study, we described the clinical and molecular characteristics providing a new approach for the personalized treatment of HCC patients.

Relationships of Cuproptosis-Related Genes With Clinical Outcomes and the Tumour Immune Microenvironment in Hepatocellular Carcinoma.

Background: Cuproptosis is a recently identified form of regulated cell death that plays a critical role in the onset and progression of various cancers. However, the effects of cuproptosis-related genes (CRGs) on hepatocellular carcinoma (HCC) are poorly understood. This study aimed to identify the cuproptosis subtypes and established a novel prognostic signature of HCC. Methods: We collected gene expression data and clinical outcomes from the TCGA, ICGC, and GEO datasets, analysed and identified 16 CRGs and the different subtypes of cuproptosis related to overall survival (OS), and further examined the differences in prognosis and immune infiltration among the subtypes. Subtypes-related differentially expressed genes (DEGs) were employed to build a prognostic signature. The relationship of the signature with the immune landscape as well as the sensitivity to different therapies was explored. Moreover, a nomogram was constructed to predict the outcome based on different clinicopathological characteristics. Results: Three cuproptosis subtypes were identified on the basis of 16 CRGs, and subtype B had an advanced clinical stage and worse OS. The immune response and function in subtype B were significantly suppressed, which may be an important reason for its poor prognosis. Based on the DEGs among the three subtypes, a prognostic model of five CRGs was constructed in the training set, and its predictive ability was validated in two external validation sets. HCC patients were classified into high and low-risk subgroups according to the risk score, and found that patients in the low-risk group showed significantly higher survival possibilities than those in the high-risk group (p < 0.001). The independent predictive performance of the risk score was assessed and verified by multivariate Cox regression analysis (p < 0.001). We further created an accurate nomogram to improve the clinical applicability of the risk score, showing good predictive ability and calibration. Low- and high-risk patients exhibit distinct immune cell infiltration and immune checkpoint changes. By further analyzing the risk score, patients in the high-risk group were found to be resistant to immunotherapy and a variety of chemotherapy drugs. Conclusion: Our study identified three cuproptosis subtypes and established a novel prognostic model that provides new insights into HCC subtype prognostic assessment and guides more effective treatment regimens.

Metabolomics-based classification reveals subtypes of hepatocellular carcinoma.

Hepatocellular carcinoma (HCC) is the second leading cause of cancer-related death, and the prognosis varies due to its high heterogeneity, systematic evaluation of HCC is mainly based on genomic and transcriptomic features, metabolomics-based classification has yet to be reported. Here we performed RNA-seq on 50 paired samples and metabolomics analysis on 72 paired samples of both normal and tumor tissues from HCC patients. Through unsupervised hierarchical cluster analysis with train and test data sets, metabolic and gene expression signatures were identified. We found that most fluxes related to glutamate are attenuated, except for the glutamate-proline pathway. Three subgroups were identified with distinct survival, clinical observations, and metabolic/gene signatures. S1 is characterized by a relatively poor prognosis, a low concentration of the degradation products of phosphatidylcholine and phosphatidylethanolamine, an enrichment of specific genes related to focal adhesion, and an upregulation of genes on chromosome 6q27. Beyond commonly downregulated metabolites, S2 tumors are largely characterized by few alterations in metabolites and genes, as well as low incidence of mutations/loss of heterozygosity, the metabolite signature of this group consists of hexoses and their phosphates, and the prognosis is the best, with a 5-year survival rate of greater than 80%. S3 is characterized by the worst survival (an approximately 20% 5-year survival rate), unsaturated fatty acid metabolites, an upregulation of specific genes involved in metastasis, and an upregulation of genes on chromosome 1q21. The metabolite-based classifications are more stable and reproducible, with each subgroup characterized by a distinct molecular signature and disease prognosis.

Role of 5-methylcytosine in determining the prognosis, tumor microenvironment, and applicability of precision medicine in patients with hepatocellular carcinoma.

Background: 5-methylcytosine has a profound impact on the development and progression of hepatocellular carcinoma. The aim of this study was to investigate the usefulness of 5-methylcytosine in determining the prognosis, tumor microenvironment, and applicability of precision medicine in hepatocellular carcinoma. Methods: We collected data of seven hepatocellular carcinoma cohorts (The Cancer Genome Atlas, International Cancer Genome Consortium, GSE14520, GSE6764, GSE9843, GSE63898, GSE76427). An unsupervised clustering method was used to identify novel subtypes of hepatocellular carcinoma based on the expression 5-methylcytosine gene signatures. The 5-methylcytosine score was determined using the least absolute shrinkage and selection operator method based on the differential expression of genes in the identified subtypes. Subsequently, we investigated the association between 5-methylcytosine-based clusters (according to the 5-methylcytosine score) and clinical outcomes, immunophenotypes, classical molecular subtypes, and therapeutic opportunities in hepatocellular carcinoma. Finally, we examined the sensitivity of patients with high 5-methylcytosine score to drugs. Results: We identified two hepatocellular carcinoma-specific, 5-methylcytosine-based subtypes (clusters 1 and 2). Cluster 1 exhibited significantly higher 5-methylcytosine scores versus cluster 2. The 5-methylcytosine-based subtypes accurately predicted classical molecular subtypes, immunophenotypes, prognosis, and therapeutic opportunities for patients with hepatocellular carcinoma. Cluster 1 (high 5-methylcytosine score) was characterized by lower anticancer immunity and worse prognosis versus cluster 2 (low 5-methylcytosine score). Moreover, cluster 1 (high 5-methylcytosine score) exhibited low sensitivity to cancer immunotherapy, but high sensitivity to radiotherapy and targeted therapy with lenvatinib. Conclusion: The novel 5-methylcytosine-based subtypes (according to the 5-methylcytosine score) may reflect the prognosis, tumor microenvironment, and applicability of precision medicine in patients with hepatocellular carcinoma.

Regulatory T-cells-related signature for identifying a prognostic subtype of hepatocellular carcinoma with an exhausted tumor microenvironment.

Regulatory T-Cells (Tregs) are important in the progression of hepatocellular cancer (HCC). The goal of this work was to look into Tregs-related genes and develop a Tregs-related prognostic model. We used the weighted gene co-expression network analysis (WGCNA) to look for Tregs-related genes in the TCGA, ICGC, and GSE14520 cohorts and then used the non-negative matrix factorization (NMF) algorithm to find Tregs-related subpopulations. The LASSO-Cox regression approach was used to determine Tregs-related genes, which were then condensed into a risk score. A total of 153 overlapping genes among the three cohorts were considered Tregs-related genes. Based on these genes, two Tregs-associated clusters that varied in both prognostic and biological characteristics were identified. When compared with Cluster 1, Cluster 2 was a TME-exhausted HCC subpopulation with substantial immune cell infiltration but a poor prognosis. Five Tregs-related genes including HMOX1, MMP9, CTSC, SDC3, and TNFRSF11B were finally used to construct a prognostic model, which could accurately predict the prognosis of HCC patients in the three datasets. Patients in the high-risk scores group with bad survival outcomes were replete with immune/inflammatory responses, but exhausted T cells and elevated PD-1 and PD-L1 expression. The results of qRT-PCR and immunohistochemical staining (IHC) analysis in clinical tissue samples confirmed the above findings. Moreover, the signature also accurately predicted anti-PD-L1 antibody responses in the IMvigor210 dataset. Finally, HMOX1, MMP9, and TNFRSF11B were expressed differently in Hep3B and Huh7 cells after being treated with a PD1/PD-L1 inhibitor. In conclusion, our study uncovered a Tregs-related prognostic model that could identify TME- exhausted subpopulations and revealed that PD1/PD-L1 inhibitors could alter the expression levels of HMOX1, MMP9, and TNFRSF11B in Hep3B and Huh7 cells, which might help us better understand Tregs infiltration and develop personalized immunotherapy treatments for HCC patients.

Multi-omics data integration for hepatocellular carcinoma subtyping with multi-kernel learning.

Hepatocellular carcinoma (HCC) is a leading malignant liver tumor with high mortality and morbidity. Patients at the same stage can be defined as different molecular subtypes associated with specific genomic disorders and clinical features. Thus, identifying subtypes is essential to realize efficient treatment and improve survival outcomes of HCC patients. Here, we applied a regularized multiple kernel learning with locality preserving projections method to integrate mRNA, miRNA and DNA methylation data of HCC patients to identify subtypes. We identified two HCC subtypes significantly correlated with the overall survival. The patient 3-years mortality rates in the high-risk and low-risk group was 51.0% and 23.5%, respectively. The high-risk group HCC patients were 3.37 times higher in death risk compared to the low-risk group after adjusting for clinically relevant covariates. A total of 196 differentially expressed mRNAs, 2,151 differentially methylated genes and 58 differentially expressed miRNAs were identified between the two subtypes. Additionally, pathway activity analysis showed that the activities of six pathways between the two subtypes were significantly different. Immune cell infiltration analysis revealed that the abundance of nine immune cells differed significantly between the two subtypes. We further applied the weighted gene co-expression network analysis to identify gene modules that may affect patients prognosis. Among the identified modules, the key module genes significantly associated with prognosis were found to be involved in multiple biological processes and pathways, revealing the mechanism underlying the progression of HCC. Hub gene analysis showed that the expression levels of CDK1, CDCA8, TACC3, and NCAPG were significantly associated with HCC prognosis. Our findings may bring novel insights into the subtypes of HCC and promote the realization of precision medicine.

Multi-omics subtyping of hepatocellular carcinoma patients using a Bayesian network mixture model.

Comprehensive molecular characterization of cancer subtypes is essential for predicting clinical outcomes and searching for personalized treatments. We present bnClustOmics, a statistical model and computational tool for multi-omics unsupervised clustering, which serves a dual purpose: Clustering patient samples based on a Bayesian network mixture model and learning the networks of omics variables representing these clusters. The discovered networks encode interactions among all omics variables and provide a molecular characterization of each patient subgroup. We conducted simulation studies that demonstrated the advantages of our approach compared to other clustering methods in the case where the generative model is a mixture of Bayesian networks. We applied bnClustOmics to a hepatocellular carcinoma (HCC) dataset comprising genome (mutation and copy number), transcriptome, proteome, and phosphoproteome data. We identified three main HCC subtypes together with molecular characteristics, some of which are associated with survival even when adjusting for the clinical stage. Cluster-specific networks shed light on the links between genotypes and molecular phenotypes of samples within their respective clusters and suggest targets for personalized treatments.

Development and experimental verification of a prognosis model for cuproptosis-related subtypes in HCC.

Cuproptosis is a recently discovered mechanism of programmed cell death caused by intracellular aggregation of mitochondrial lipoylated proteins and destabilization of iron-sulfur proteins triggered by copper. Hepatocellular carcinoma (HCC) is a common malignant tumor with a poor prognosis. We aimed to predict the survival of patients with HCC using the cuproptosis-related gene (CRG) expression. We analyzed the expression, methylation, and mutation status of CRGs in 538 HCC patients and correlated the date with clinical prognosis. HCC patients were divided into two clusters based on their CRG expression. The relationship between CRGs, risk genes, and the immune microenvironment was analyzed using the CIBERSORT algorithm and the single-cell data analysis method. A cuproptosis risk model was constructed according to the five risk genes using the LASSO COX method. To facilitate the clinical applicability of the proposed risk model, we constructed a nomogram and conducted an antineoplastic drug sensitivity analysis. Our results suggest that the expression levels of CRGs in HCC are regulated by methylation. The prognoses were significantly different between the patients of the two clusters. The prognostic risk score positively correlated with memory T cell activation and negatively correlated with natural killer (NK) and regulatory T cell activation. Our findings indicate the involvement of CRG regulation in HCC and provide new insights into prognosis assessment. Drug sensitivity analysis predicted drug candidates for the treatment of patients with different HCC subtypes.

Identification of novel lactate metabolism signatures and molecular subtypes for prognosis in hepatocellular carcinoma.

Background: Evidence has shown that lactate, an immune signaling molecule, is associated with hepatocellular carcinoma (HCC) progression and immune suppression. Therefore, identifying lactate metabolism-related molecules is a promising therapeutic strategy to inhibit the development of HCC and overcome chemotherapy resistance. Long noncoding RNAs (lncRNAs) are related to tumorigenesis and metastasis. Hence, verifying the molecular subtypes of lncRNAs related to lactate metabolism will play a critical role in managing HCC. Methods: Based on HCC data in The Cancer Genome Atlas (TCGA), lactate metabolic pathway-related genes were enriched by gene collection and enrichment analysis (GSEA). Lactate metabolism-related lncRNAs (LM_lncRNAs) were identified by correlation analysis, HCC molecular subtypes were determined using nonnegative matrix factorization (NMF) clustering, and the response of the three subtypes to chemotherapeutics was further evaluated using the Genomic Tumor Sensitive Cell Line (GDSC) dataset. LM_lncRNAs were examined via Lasso-Cox regression analysis to determine prognosis for patients. A Nomagram plot was used to predict patient survival time. Results: Three molecular subtypes of HCC were identified. The survival rate of patients with C1 subtype was higher than that of those with C2 and C3. Additionally, patients with C3 subtype have higher levels of immune cell infiltration and high expression of genes related to immune checkpoints. The GDSC results indicated that patients with C3 subtypes were more sensitive to chemotherapy drugs such as sorafenib and sunitinib. The prognostic risk assessment model consisted of six risk factors (AC034229.4, AC131009.1, MYOSLID, AC008667.1, AC012073.1, AC068025.1) and two protective factors (LINC00402 and AC103858.1). Based on Kaplan-Meier analysis, low-risk HCC patients had a high survival rate, and the receiver operating characteristic curve (ROC), calibration curve, and C-index confirmed good prediction ability. Conclusion: In this study, the molecular subtyping method and prediction model of lactate metabolism-related lncRNAs (LM_lncRNAs) were constructed for the prognosis of HCC patients. This work demonstrated the potential targets of LM_lncRNAs and provided a novel perspective and therapeutic paradigm for future clinical translation.

Discovery and characterization of tumor antigens in hepatocellular carcinoma for mRNA vaccine development.

BackgroundmRNA vaccines are emerging as new targets for cancer immunotherapy. However, the potential tumor antigens for mRNA vaccine design in hepatocellular carcinoma (HCC) remain to be elucidated.MethodsGenetic and RNA-Seq data were obtained from TCGA and ICGC. Tumor-specific antigens (TSAs) were identified by differential expression, mutation status, HLA binding, antigen-presenting cell (APC) correlation, immune checkpoint (ICP) relevance and prognosis. Consensus clustering was used for patient classification. The molecular and immune status of TSAs and clustered patients, including prognostic ability, tumor microenvironment, tumor-related signature and tumor immune dysfunction and exclusion (TIDE), were further characterized.ResultsFive dysregulated and mutated TSAs were identified in HCC (TSA5): FXYD6, JAM2, GALNT16, C7, and CCDC146. Seven immune gene modules and five immune subtypes (IS1–IS5) of HCC were identified. The immune subtypes and TSA5-related modules showed distinct molecular, cellular and clinical characteristics. According to our study, IS1 patients may be suitable for vaccination.

Hallmark-guided subtypes of hepatocellular carcinoma for the identification of immune-related gene classifiers in the prediction of prognosis, treatment efficacy, and drug candidates.

Hepatocellular carcinoma (HCC), accounting for ~90% of all primary liver cancer, is a prevalent malignancy worldwide. The intratumor heterogeneity of its causative etiology, histology, molecular landscape, and immune phenotype makes it difficult to precisely recognize individuals with high mortality risk or tumor-intrinsic treatment resistance, especially immunotherapy. Herein, we comprehensively evaluated the activities of cancer hallmark gene sets and their correlations with the prognosis of HCC patients using gene set variation analysis (GSVA) and identified two HCC subtypes with distinct prognostic outcomes. Based on these subtypes, seven immune-related genes (TMPRSS6, SPP1, S100A9, EPO, BIRC5, PLXNA1, and CDK4) were used to construct a novel prognostic gene signature [hallmark-guided subtypes-based immunologic signature (HGSIS)] via multiple statistical approaches. The HGSIS-integrated nomogram suggested an enhanced predictive performance. Interestingly, oncogenic hallmark pathways were significantly enriched in the high-risk group and positively associated with the risk score. Distinct mutational landscapes and immune profiles were observed between different risk groups. Moreover, immunophenoscore (IPS) and tumor immune dysfunction and exclusion (TIDE) analysis showed different sensitivities of HGSIS risk groups for immune therapy efficacy, and the pRRophetic algorithm indicated distinguishable responses for targeted/chemotherapies in different groups. KIF2C was picked out as the key target concerning HGSIS, and the top 10 small molecules were predicted to bind to the active site of KIF2C via molecular docking, which might be further used for candidate drug discovery of HCC. Taken together, our study offers novel insights for clinically significant subtype recognition, and the proposed signature may be a helpful guide for clinicians to improve the treatment regimens.

M6A-related lncRNAs predict clinical outcome and regulate the tumor immune microenvironment in hepatocellular carcinoma

LncRNA N6-methylandenosine (m6A) modification has been shown to be associated with the constitution of the tumor microenvironment (TME) and tumorigenesis. It’s essential to understand the mechanisms of lncRNA m6A modification in hepatocellular carcinoma (HCC) and identify relative prognostic predictors to guide therapy and explore potential therapeutic targets. Pearson correlation analysis was performed to identify m6A-related lncRNAs in 374 patients with HCC. Unsupervised cluster analysis of the potential m6A-related lncRNA-based HCC subtypes was conducted, followed by the concurrent analysis of their relationship with TME characteristics, immune checkpoints, immune features, and prognosis through single sample gene set enrichment analysis and ESTIMATE algorithm. Cox regression analyses were performed to screen prognostic m6A-related lncRNA, construct an m6A-related lncRNA signature (m6A-RLRS), and establish an integrated nomogram for the prognosis of patients with HCC. We identified 61 m6A-related lncRNAs and two HCC subtypes defined by consensus cluster of m6A-related lncRNAs with distinct clinical features. Progression-free survival (PFS), three TME-related scores, 15 immune-associated gene sets, and two immune checkpoints expression were found to be significantly different among the two subtypes. Twenty-five prognostic m6A-related lncRNAs were determined, four of which were included to establish an m6A-RLRS with favorable discrimination, and the signature was validated in the validation set and an independent FAHWMU cohort (n = 60). Furthermore, a novel nomogram combining signature and clinical predictors was generated with a C-index of 0.703, and an original ceRNA regulatory network consisting of 9 lncRNAs, 28 miRNAs, and 75 target mRNAs was constructed. Finally, the differential expression of four m6A-related lncRNA was verified by qRT-PCR. In conclusion, m6A-related lncRNA prognostic signature and molecular subtype contributes to accurately predict the prognosis of HCC and provide potential novel therapeutic targets.

Nomogram for the Preoperative Prediction of the Macrotrabecular-Massive Subtype of Hepatocellular Carcinoma

BackgroundThe macrotrabecular-massive subtype of hepatocellular carcinoma (MTM-HCC) is an aggressive histological type and results in poor prognosis. We developed a nomogram model based on laboratory results to predict the presence of MTM-HCC.MethodsA total of 357 HCC patients who underwent radical surgery between January 2015 and December 2020 at Ningbo Medical Center Lihuili Hospital were grouped according to histological type. After propensity score matching (PSM), 267 patients were divided into MTM-HCC (n = 76) and non-MTM-HCC (n = 191) groups. A LASSO regression analysis model was used to select predictive factors. Finally, a nomogram for predicting the presence of MTM-HCC was established. Decision curve analysis (DCA) was conducted to determine the clinical usefulness of the nomogram model by quantifying the net benefits along with the increase in threshold probabilities.ResultsThe 1-, 3-, and 5-year disease-free survival (DFS) and overall survival (OS) rates for MTM-HCC were 60.0%, 36.0%, 32.4% and 92.1%, 68.7%, 52.2%, respectively. Survival analysis indicated that the probabilities of achieving DFS and OS were significantly worse in the MTM-HCC group than in the non-MTM-HCC group (P < 0.05). The nomogram model that included AST levels, PT and AFP levels achieved a better C-index of 0.723 (95% CI: 0.659–0.787). DCA revealed that the nomogram model could lead to net benefits and exhibited a wider range of threshold probabilities in the prediction of MTM-HCC.ConclusionThe nomogram model included AST, PT and AFP could achieve an optimal performance in the preoperative prediction of MTM-HCC.

Identification of Ferroptosis-related molecular model and immune subtypes of hepatocellular carcinoma for individual therapy.

Excessive iron accumulation and lipid peroxidation are primary characteristics of ferroptosis in hepatocellular carcinoma (HCC). Ferroptosis inducer combined with immunotherapy has become a new hope for HCC patients. Therefore, the construction and validation of subtype-specific sensitivity to ferroptosis inducer will be helpful for hierarchical management and precise individual therapy. RNA-seq transcriptome and clinical data of HCC patients were extracted from International Cancer Genome Consortium (ICGC) dataset and The Cancer Genome Atlas (TCGA) dataset. Consistency matrix and data clustering of the both cohorts were constructed by 'ConsensusClusterPlus' package. Single-sample gene set enrichment analysis (ssGSEA) analysis was performed to evaluate immune infiltration. Cox analysis was utilized to construct a ferroptosis phenotype-related prognostic model (FRPM) in HCC. The predictive efficiency of the constructed FRPM was evaluated through Kaplan Meier (K-M) survival analyses and Receiver Operating Characteristic (ROC) curves. The expression levels of candidate genes were detected and validated by Real-Time PCR between liver cancer tissues and adjacent non-tumor liver tissues. 45 differentially expressed ferroptosis-related genes (FRGs) were identified between HCC tissues and non-tumor liver tissues. Furthermore, four ferroptosis-associated clusters (FACs) of HCC were established via consensus clustering. Subsequently, we established a FRPM, consisting of four prognostic genes (SLC7A11, SLC1A5, GCLM and SAT1), to evaluate the survival of HCC patients, based on which, patients were divided into high-risk group and low-risk group. The high-risk group exhibited worse survival compared to low-risk group (p< 0.0001 both in TCGA and ICGC cohorts). Patients belong to different FACs or different risk scores showed distinct clinical characteristics. Moreover, in the validation experiment, the transcriptional expression levels of the four prognostic genes were consistent with the results drew from datasets. We revealed a novel FRGs signature, which may provide the molecular characteristic data for effectively prognostic evaluation and potential personalized therapy for HCC patients.