Heterogeneity Of Hepatocellular Carcinoma Research Articles

Machine learning and multi-omics data reveal driver gene-based molecular subtypes in hepatocellular carcinoma for precision treatment.

The heterogeneity of Hepatocellular Carcinoma (HCC) poses a barrier to effective treatment. Stratifying highly heterogeneous HCC into molecular subtypes with similar features is crucial for personalized anti-tumor therapies. Although driver genes play pivotal roles in cancer progression, their potential in HCC subtyping has been largely overlooked. This study aims to utilize driver genes to construct HCC subtype models and unravel their molecular mechanisms. Utilizing a novel computational framework, we expanded the initially identified 96 driver genes to 1192 based on mutational aspects and an additional 233 considering driver dysregulation. These genes were subsequently employed as stratification markers for further analyses. A novel multi-omics subtype classification algorithm was developed, leveraging mutation and expression data of the identified stratification genes. This algorithm successfully categorized HCC into two distinct subtypes, CLASS A and CLASS B, demonstrating significant differences in survival outcomes. Integrating multi-omics and single-cell data unveiled substantial distinctions between these subtypes regarding transcriptomics, mutations, copy number variations, and epigenomics. Moreover, our prognostic model exhibited excellent predictive performance in training and external validation cohorts. Finally, a 10-gene classification model for these subtypes identified TTK as a promising therapeutic target with robust classification capabilities. This comprehensive study provides a novel perspective on HCC stratification, offering crucial insights for a deeper understanding of its pathogenesis and the development of promising treatment strategies.

Integrated Exploration of Epigenetic Dysregulation Reveals a Stemness/EMT Subtype and MMP12 Linked to the Progression and Prognosis in Hepatocellular Carcinoma.

Epigenetic dysregulation drives aberrant transcriptional programs playing a critical role in hepatocellular carcinoma (HCC), which may provide novel insights into the heterogeneity of HCC. This study performed an integrated exploration on the epigenetic dysregulation of miRNA and methylation. We discovered and validated three patterns endowed with gene-related transcriptional traits and clinical outcomes. Specially, a stemness/epithelial-mesenchymal transition (EMT) subtype was featured by immune exhaustion and the worst prognosis. Besides, MMP12, a characteristic gene, was highly expressed in the stemness/EMT subtype, which was verified as a pivotal regulator linked to the unfavorable prognosis and further proven to promote tumor proliferation, invasion, and metastasis in vitro experiments. Proteomic analysis by mass spectrometry sequencing also indicated that the overexpression of MMP12 was significantly associated with cell proliferation and adhesion. Taken together, this study unveils innovative insights into epigenetic dysregulation and identifies a stemness/EMT subtype-specific gene, MMP12, correlated with the progression and prognosis of HCC.

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

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

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.

Heterogeneity of hepatocellular carcinoma: from mechanisms to clinical implications.

Hepatocellular Carcinoma (HCC) is one of the most common types of primary liver cancer. Current treatment options have limited efficacy against this malignancy, primarily owing to difficulties in early detection and the inherent resistance to existing drugs. Tumor heterogeneity is a pivotal factor contributing significantly to treatment resistance and recurrent manifestations of HCC. Intratumoral heterogeneity is an important aspect of the spectrum of complex tumor heterogeneity and contributes to late diagnosis and treatment failure. Therefore, it is crucial to thoroughly understand the molecular mechanisms of how tumor heterogeneity develops. This review aims to summarize the possible molecular dimensions of tumor heterogeneity with an emphasis on intratumoral heterogeneity, evaluate its profound impact on the diagnosis and therapeutic strategies for HCC, and explore the suitability of appropriate pre-clinical models that can be used to best study tumor heterogeneity; thus, opening new avenues for cancer treatment.

Characterizing Metabolic Heterogeneity of Hepatocellular Carcinoma with Hyperpolarized 13C Pyruvate MRI and Mass Spectrometry.

Purpose To characterize the metabolomic profiles of two hepatocellular carcinoma (HCC) rat models, track evolution of these profiles to a stimulated tumor state, and assess their effect on lactate flux with hyperpolarized (HP) carbon 13 (13C) MRI. Materials and Methods Forty-three female adult Fischer rats were implanted with N1S1 or McA-RH7777 HCC tumors. In vivo lactate-to-pyruvate ratio (LPR) was measured with HP 13C MRI at 9.4 T. Ex vivo mass spectrometry was used to measure intratumoral metabolites, and Ki67 labeling was used to quantify proliferation. Tumors were first compared with three normal liver controls. The tumors were then compared with stimulated variants via off-target hepatic thermal ablation treatment. All comparisons were made using the Mann-Whitney test. Results HP 13C pyruvate MRI showed greater LPR in N1S1 tumors compared with normal liver (mean [SD], 0.564 ± 0.194 vs 0.311 ± 0.057; P < .001 [n = 9]), but not for McA-RH7777 (P = .44 [n = 8]). Mass spectrometry confirmed that the glycolysis pathway was increased in N1S1 tumors and decreased in McA-RH7777 tumors. The pentose phosphate pathway was also decreased only in McA-RH7777 tumors. Increased proliferation in stimulated N1S1 tumors corresponded to a net increase in LPR (six stimulated vs six nonstimulated, 0.269 ± 0.148 vs 0.027 ± 0.08; P = .009), but not in McA-RH7777 (eight stimulated vs six nonstimulated, P = .13), despite increased proliferation and metastases. Mass spectrometry demonstrated relatively increased lactate production with stimulation in N1S1 tumors only. Conclusion Two HCC subtypes showed divergent glycolytic dependency at baseline and during transformation to a high proliferation state. This metabolic heterogeneity in HCC should be considered with use of HP 13C MRI for diagnosis and tracking. Keywords: Molecular Imaging-Probe Development, Liver, Abdomen/GI, Oncology, Hepatocellular Carcinoma © RSNA, 2024 See also commentary by Ohliger in this issue.

Cuproptosis-related molecular classification and gene signature of hepatocellular carcinoma and experimental verification.

Hepatocellular carcinoma (HCC) is a highly heterogeneous malignancy with poor overall prognosis. Cuproptosis, a recently proposed mode of copper-dependent cell death, plays a critical role in the malignant progression of various tumors; however, the expression and prognostic value of cuproptosis-related regulatory genes in HCC remain unclear. Genomic, genetic, and expression profiles of ten key cuproptosis-related regulatory genes were analyzed using The Cancer Genome Atlas Liver Hepatocellular Carcinoma (TCGA-LIHC) dataset and protein expression data from the Human Protein Atlas (HPA) database. Unsupervised clustering of HCC patients based on these ten key cuproptosis-related regulatory genes was used to identify different HCC subtypes and analyze the differences in clinical and immune characteristics among subtypes. Subsequently, univariate Cox and least absolute shrinkage and selection operator (LASSO) Cox analyses were used to establish a cuproptosis-related prognostic signature, and the accuracy of prognostic signature prediction was internally validated by Kaplan-Meier survival analysis and time-dependent receiver operating characteristic curve in TCGA training and testing cohorts. The prognostic signature was externally validated using TCGA-LIHC entire cohort and International Cancer Genome Consortium Liver Cancer (ICGC-LIRI) cohorts. Finally, the expression landscape of cuproptosis-related regulatory genes in prognostic signature was explored by quantitative real-time polymerase chain reaction (qRT-PCR), western blotting and immunohistochemistry (IHC) experiments. Ten cuproptosis-related genes were differentially expressed in normal and HCC tissues. Unsupervised clustering identified two subtypes and HCC patients with these two subtypes had different clinical prognoses and immune characteristics, as well as different degrees of response to immunotherapy. Lipoyltransferase 1 (LIPT1), dihydrolipoamide s-acetyltransferase (DLAT), and cyclin dependent kinase inhibitor 2A (CDKN2A) were selected to construct a prognostic signature, which significantly distinguished HCC patients with different survival periods in the TCGA training and testing cohorts and was well validated in both the TCGA-LIHC entire cohort and ICGC-LIRI cohort. The risk score of the prognostic signature was confirmed to be an independent prognostic factor, and nomograms were generated to effectively predict the probability of HCC patient survival. The qRT-PCR, western blotting and IHC results also revealed a significant imbalance in the expression of these cuproptosis-related genes in HCC. The classification and prognostic signature based on cuproptosis-related regulatory genes helps to explain the heterogeneity of HCC, which may contribute to the individualized treatment of patients with the disease.

Investigating autophagy and intricate cellular mechanisms in hepatocellular carcinoma: Emphasis on cell death mechanism crosstalk

Hepatocellular carcinoma (HCC) stands as a formidable global health challenge due to its prevalence, marked by high mortality and morbidity rates. This cancer type exhibits a multifaceted etiology, prominently linked to viral infections, non-alcoholic fatty liver disease, and genomic mutations. The inherent heterogeneity of HCC, coupled with its proclivity for developing drug resistance, presents formidable obstacles to effective therapeutic interventions. Autophagy, a fundamental catabolic process, plays a pivotal role in maintaining cellular homeostasis, responding to stressors such as nutrient deprivation. In the context of HCC, tumor cells exploit autophagy, either augmenting or impeding its activity, thereby influencing tumorigenesis. This comprehensive review underscores the dualistic role of autophagy in HCC, acting as both a pro-survival and pro-death mechanism, impacting the trajectory of tumorigenesis. The anti-carcinogenic potential of autophagy is evident in its ability to enhance apoptosis and ferroptosis in HCC cells. Pertinently, dysregulated autophagy fosters drug resistance in the carcinogenic context. Both genomic and epigenetic factors can regulate autophagy in HCC progression. Recognizing the paramount importance of autophagy in HCC progression, this review introduces pharmacological compounds capable of modulating autophagy—either inducing or inhibiting it, as promising avenues in HCC therapy.

Signature construction and molecular subtype identification based on liver-specific genes for prediction of prognosis, immune activity, and anti-cancer drug sensitivity in hepatocellular carcinoma

BackgroundLiver specific genes (LSGs) are crucial for hepatocyte differentiation and maintaining normal liver function. A deep understanding of LSGs and their heterogeneity in hepatocellular carcinoma (HCC) is necessary to provide clues for HCC diagnosis, prognosis, and treatment.MethodsThe bulk and single-cell RNA-seq data of HCC were downloaded from TCGA, ICGC, and GEO databases. Through unsupervised cluster analysis, LSGs-based HCC subtypes were identified in TCGA-HCC samples. The prognostic effects of the subtypes were investigated with survival analyses. With GSVA and Wilcoxon test, the LSGs score, stemness score, aging score, immune score and stromal score of the samples were estimated and compared. The HCC subtype-specific genes were identified. The subtypes and their differences were validated in ICGC-HCC samples. LASSO regression analysis was used for key gene selection and risk model construction for HCC overall survival. The model performance was estimated and validated. The key genes were validated for their heterogeneities in HCC cell lines with quantitative real-time PCR and at single-cell level. Their dysregulations were investigated at protein level. Their correlations with HCC response to anti-cancer drugs were estimated in HCC cell lines.ResultsWe identified three LSGs-based HCC subtypes with different prognosis, tumor stemness, and aging level. The C1 subtype with low LSGs score and high immune score presented a poor survival, while the C2 subtype with high LSGs score and immune score indicated an enduring survival. Although no significant survival difference between C2 and C3 HCCs was shown, the C2 HCCs presented higher immune score and stroma score. The HCC subtypes and their differences were confirmed in ICGC-HCC dataset. A five-gene prognostic signature for HCC survival was constructed. Its good performance was shown in both the training and validation datasets. The five genes presented significant heterogeneities in different HCC cell lines and hepatocyte subclusters. Their dysregulations were confirmed at protein level. Furthermore, their significant associations with HCC sensitivities to anti-cancer drugs were shown.ConclusionsLSGs-based HCC subtype classification and the five-gene risk model might provide useful clues not only for HCC stratification and risk prediction, but also for the development of more personalized therapies for effective HCC treatment.

TPA: Two-stage progressive attention segmentation framework for hepatocellular carcinoma on multi-modality MRI.

Dynamic contrast enhanced magnetic resonance imaging (DCE-MRI) plays a crucial role in the diagnosis and measurement of hepatocellular carcinoma (HCC). The multi-modality information contained in the multi-phase images of DCE-MRI is important for improving segmentation. However, this remains a challenging task due to the heterogeneity of HCC, which may cause one HCC lesion to have varied imaging appearance in each phase of DCE-MRI. In particular, some phases exhibit inconsistent sizes and boundaries will result in a lack of correlation between modalities, and it may pose inaccurate segmentation results. We aim to design a multi-modality segmentation model that can learn meaningful inter-phase correlation for achieving HCC segmentation. In this study, we propose a two-stage progressive attention segmentation framework (TPA) for HCC based on the transformer and the decision-making process of radiologists. Specifically, the first stage aims to fuse features from multi-phase images to identify HCC and provide localization region. In the second stage, a multi-modality attention transformer module (MAT) is designed to focus on the features that can represent the actual size. We conduct training, validation, and test in a single-center dataset (386 cases), followed by external test on a batch of multi-center datasets (83 cases). Furthermore, we analyze a subgroup of data with weak inter-phase correlation in the test set. The proposed model achieves Dice coefficient of 0.822 and 0.772 in the internal and external test sets, respectively, and 0.829, 0.791 in the subgroup. The experimental results demonstrate that our model outperforms state-of-the-art models, particularly within subgroup. The proposed TPA provides best segmentation results, and utilizing clinical prior knowledge for network design is practical and feasible.

Bulk and single-cell transcriptome reveal the immuno-prognostic subtypes and tumour microenvironment heterogeneity in HCC.

Accumulating evidences suggest tumour microenvironment (TME) profoundly influence clinical outcome in hepatocellular carcinoma (HCC). Existing immune subtypes are susceptible to batch effects, and integrative analysis of bulk and single-cell transcriptome is helpful to recognize immune subtypes and TME in HCC. Based on the relative expression ordering (REO) of 1259 immune-related genes, an immuno-prognostic signature was developed and validated in 907 HCC samples from five bulk transcriptomic cohorts, including 72 in-house samples. The machine learning models based on subtype-specific gene pairs with stable REOs were constructed to jointly predict immuno-prognostic subtypes in single-cell RNA-seq data and validated in another single-cell data. Then, cancer characteristics, immune landscape, underlying mechanism and therapeutic benefits between subtypes were analysed. An immune-related signature with 29 gene pairs stratified HCC samples individually into two risk subgroups (C1 and C2), which was an independent prognostic factor for overall survival. The machine learning models verified the immune subtypes from five bulk cohorts to two single-cell transcriptomic data. Integrative analysis revealed that C1 had poorer outcomes, higher CNV burden and malignant scores, higher sensitivity to sorafenib, and exhibited an immunosuppressive phenotype with more regulators, e.g., myeloid-derived suppressor cells (MDSCs), Mø_SPP1, while C2 was characterized with better outcomes, higher metabolism, more benefit from immunotherapy, and displayed active immune with more effectors, e.g., tumour infiltrating lymphocyte and dendritic cell. Moreover, both two single-cell data revealed the crosstalk of SPP1-related L-R pairs between cancer and immune cells, especially SPP1-CD44, might lead to immunosuppression in C1. The REO-based immuno-prognostic subtypes were conducive to individualized prognosis prediction and treatment options for HCC. This study paved the way for understanding TME heterogeneity between immuno-prognostic subtypes of HCC.

Multifunctional metal-coordinated Co-assembled carrier-free nanoplatform based on dual-drugs for ferroptosis-mediated cocktail therapy of hepatocellular carcinoma growth and metastasis

The heterogeneity of hepatocellular carcinoma (HCC) and the complexity of the tumor microenvironment (TME) pose challenges to efficient drug delivery and the antitumor efficacy of combined or synergistic therapies. Herein, a metal-coordinated carrier-free nanodrug (named as USFe3+ LA NPs) was developed for ferroptosis-mediated multimodal synergistic anti-HCC. Natural product ursolic acid (UA) was incorporated to enhance the sensitivity of tumor cells to sorafenib (SRF). Surface decoration of cell penetration peptide and epithelial cell adhesion molecule aptamer facilitated the uptake of USFe3+ LA NPs by HepG2 cells. Meanwhile, Fe3+ ions could react with intracellular hydrogen peroxide, generating toxic hydroxyl radical (·OH) for chemodynamical therapy (CDT) and amplified ferroptosis by cystine/glutamate antiporter system (System Xc−), which promoted the consumption of glutathione (GSH) and inhibited the expression of glutathione peroxidase 4 (GPX4). Notably, these all-in-one nanodrugs could inhibit tumor metastasis and induced immunogenic cell death (ICD). Last but not least, the nanodrugs demonstrated favorable biocompatibility, augmenting the immune response against the programmed death-ligand 1 (PD-L1) by increasing cytotoxic T cell infiltration. In vivo studies revealed significant suppression of tumor growth and distant metastasis. Overall, our work introduced a novel strategy for applications of metal-coordinated co-assembled carrier-free nano-delivery system in HCC combination therapy, especially in the realms of cancer metastasis prevention and immunotherapy.

DNA damage repair-related gene signature for identifying the immune status and predicting the prognosis of hepatocellular carcinoma

The heterogeneity of hepatocellular carcinoma (HCC) poses a challenge for accurate prognosis prediction. DNA damage repair genes (DDRGs) have an impact on a wide range of malignancies. However, the relevance of these genes in HCC prognosis has received little attention. In this study, we aimed to develop a prognostic signature to identify novel therapy options for HCC. We acquired mRNA expression profiles and clinical data for HCC patients from The Cancer Genome Atlas (TCGA) database. A polygenic prognostic model for HCC was constructed using selection operator Cox analysis and least absolute shrinkage. The model was validated using International Cancer Genome Consortium (ICGC) data. Overall survival (OS) between the high-risk and low-risk groups was compared using Kaplan‒Meier analysis. Independent predictors of OS were identified through both univariate and multivariate Cox analyses. To determine immune cell infiltration scores and activity in immune-related pathways, a single-sample gene set enrichment analysis was performed. The protein and mRNA expression levels of the prognostic genes between HCC and normal liver tissues were also examined by immunohistochemistry (IHC), immunofluorescence (IF) and quantitative real-time PCR (qRT-PCR). A novel ten-gene signature (CHD1L, HDAC1, KPNA2, MUTYH, PPP2R5B, NEIL3, POLR2L, RAD54B, RUVBL1 and SPP1) was established for HCC prognosis prediction. Patients in the high-risk group had worse OS than those in the low-risk group. Receiver operating characteristic curve analysis confirmed the predictive ability of this prognostic gene signature. Multivariate Cox analysis showed that the risk score was an independent predictor of OS. Functional analysis revealed a strong association with cell cycle and antigen binding pathways, and the risk score was highly correlated with tumor grade, tumor stage, and types of immune infiltrate. High expression levels of the prognostic genes were significantly correlated with increased sensitivity of cancer cells to antitumor drugs. IHC, IF and qRT-PCR all indicated that the prognostic genes were highly expressed in HCC relative to normal liver tissue, consistent with the results of bioinformatics analysis. Ten DDRGs were utilized to create a new signature for identifying the immunological state of HCC and predicting prognosis. In addition, blocking these genes could represent a promising treatment.

Single-cell RNA sequencing revealed potential targets for immunotherapy studies in hepatocellular carcinoma.

Hepatocellular carcinoma (HCC) is a solid tumor prone to chemotherapy resistance, and combined immunotherapy is expected to bring a breakthrough in HCC treatment. However, the tumor and tumor microenvironment (TME) of HCC is highly complex and heterogeneous, and there are still many unknowns regarding tumor cell stemness and metabolic reprogramming in HCC. In this study, we combined single-cell RNA sequencing data from 27 HCC tumor tissues and 4 adjacent non-tumor tissues, and bulk RNA sequencing data from 374 of the Cancer Genome Atlas (TCGA)-liver hepatocellular carcinoma (LIHC) samples to construct a global single-cell landscape atlas of HCC. We analyzed the enrichment of signaling pathways of different cells in HCC, and identified the developmental trajectories of cell subpopulations in the TME using pseudotime analysis. Subsequently, we performed transcription factors regulating different subpopulations and gene regulatory network analysis, respectively. In addition, we estimated the stemness index of tumor cells and analyzed the intercellular communication between tumors and key TME cell clusters. We identified novel HCC cell clusters that specifically express HP (HCC_HP), which may lead to higher tumor differentiation and tumor heterogeneity. In addition, we found that the HP gene expression-positive neutrophil cluster (Neu_AIF1) had extensive and strong intercellular communication with HCC cells, tumor endothelial cells (TEC) and cancer-associated fibroblasts (CAF), suggesting that clearance of this new cluster may inhibit HCC progression. Furthermore, ErbB signaling pathway and GnRH signaling pathway were found to be upregulated in almost all HCC tumor-associated stromal cells and immune cells, except NKT cells. Moreover, the high intercellular communication between HCC and HSPA1-positive TME cells suggests that the immune microenvironment may be reprogrammed. In summary, our present study depicted the single-cell landscape heterogeneity of human HCC, identified new cell clusters in tumor cells and neutrophils with potential implications for immunotherapy research, discovered complex intercellular communication between tumor cells and TME cells.

Identification of a prognostic evaluator from glutamine metabolic heterogeneity studies within and between tissues in hepatocellular carcinoma

Background: The liver is the major metabolic organ of the human body, and abnormal metabolism is the main factor influencing hepatocellular carcinoma (HCC). This study was designed to determine the effect of glutamine metabolism on HCC heterogeneity and to develop a prognostic evaluator based on the heterogeneity study of glutamine metabolism within HCC tumors and between tissues.Methods: Single-cell transcriptome data were extracted from the GSE149614 dataset and processed using the Seurat package in R for quality control of these data. HCC subtypes in the Cancer Genome Atlas and the GSE14520 dataset were identified via consensus clustering based on glutamine family amino acid metabolism (GFAAM) process genes. The machine learning algorithms gradient boosting machine, support vector machine, random forest, eXtreme gradient boosting, decision trees, and least absolute shrinkage and selection operator were utilized to develop the prognosis model of differentially expressed genes among the molecular gene subtypes.Results: The samples in the GSE149614 dataset included 10 cell types, and there was no significant difference in the GFAAM pathway. HCC was classified into three molecular subtypes according to GFAAM process genes, showing molecular heterogeneity in prognosis, clinicopathological features, and immune cell infiltration. C1 showed the worst survival rate and the highest immune score and immune cell infiltration. A six-gene model for prognostic and immunotherapy responses was constructed among subtypes, and the calculated high-risk score was significantly correlated with poor prognosis, high immune abundance, and a low response rate of immunotherapy in HCC.Conclusion: Our discovery of GFAAM-associated marker genes may help to further decipher the role in HCC occurrence and progression. In particular, this six-gene prognostic model may serve as a predictor of treatment and prognosis in HCC patients.

Single-Cell RNA Sequencing: A Deep Dive into The Cellular Landscape and Interactions in Hepatocellular Carcinoma

Hepatocellular carcinoma (HCC) is a leading cause of cancer-related deaths worldwide, with its progression highly influenced by the cellular interplay within the tumor microenvironment that is underexplored. Aiming to bridge this gap, our study utilizes single-cell RNA sequencing (scRNA-seq) to examine the cellular heterogeneity of HCC and investigate the roles of distinct cell populations. scRNA-seq was performed on eight DEN mice HCC samples, followed by bioinformatic analysis with Seurat package. Nine distinct cell populations were identified, with three unique macrophage populations suggestive of their role as tumor-associated macrophages (TAMs). The detected endothelial cells and pericytes hint at ongoing neoangiogenesis, with implications that endothelial cells might function as tumor-associated endothelial cells (TECs) and pericytes as carcinoma-associated fibroblasts (CAFs). Our findings provide insights into the potential roles of various cell populations in the HCC tumor microenvironment, which paves the way for developing novel therapies. These postulations, while offering a deeper understanding of HCC's cellular landscape, necessitate experimental validation for confirmation.

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.

Bio-diagnostic performances of microRNAs set related to DNA damage response pathway among hepatitis C virus-associated hepatocellular carcinoma patients

BackgroundUp to date, a well-defined microRNAs (miRNAs) profile involved in hepatocellular carcinoma (HCC) pathogenesis remains indecisive. Thus, employing miRNAs for HCC diagnosis is demanded for early therapeutic interventions. We aimed to evaluate the usage of miRNAs set related to the SuperPath: miRNAs involved in DNA damage response pathway as effective biomarkers for HCV-related HCC diagnosis. ResultsThe study enrolled 97 patients with HCV-related HCC, 84 with hepatitis C virus (HCV), 97 with liver cirrhosis (LC), and 84 healthy individuals. Serum miRNA-23a, miRNA-203, miRNA-100-5p, and miRNA-16 were quantified using qRT-PCR experiments, AFP and routine LFTs were estimated via standard techniques. Pathway enrichment analysis along with the construction of miRNAs regulatory network were performed. With respect to healthy individuals, miRNA-203, miRNA-100-5p, and miRNA-16 were significantly downregulated in HCC, HCV, and LC groups, while miRNA-23a showed significant upregulation (p < 0.001). miRNAs exhibited significant correlations with AFP, ALT, AST, and albumin. Also, elevated levels of miRNA-23a were recognized in patients with multiple focal lesions and/or lesion size > 5 cm. Additionally, the diagnostic performance of miRNA-23a expression level at a selected cut-off value of 3.99 overtakes AFP, while expressions of miR-203, miRNA-100-5p, and miRNA-16 represent poor diagnostic outcomes. ConclusionsKeeping in mind the individual variability and high level of heterogeneity in HCC, our data revealed the diagnostic value of miRNA-23a expression in HCV-related HCC patients. Further extra in silico HCC-specific microRNAs sets are demanded in diagnosis.

Identification of disulfidptosis-related genes with immune infiltration in hepatocellular carcinoma

Hepatocellular carcinoma (HCC) is a common malignant primary tumor that is usually diagnosed at an advanced stage; thus, there is an urgent need for efficient and sensitive novel diagnostic markers to determine the prognosis and halt disease progression in patients with HCC. Disulfidptosis is a recently discovered form of programmed cell death, essentially an abnormal accumulation of intracellular bisulfides. Therefore, our study aimed to investigate the role of disulfidptosis-related genes (DRGs) in the pathogenesis of HCC. Based on public databases, our work demonstrates the relationship between DRG and expression, immunity, mutation/drug sensitivity, and functional enrichment in HCC. We also revealed the significant heterogeneity of HCC in different DRGs sub-clusters and in differentially expressed genes (DEGs), respectively. Subsequently, the most relevant candidate gene, SLC7A11, was screened by machine learning to further validate the significance of SLC7A11 in the clinical features, prognosis, nomogram pattern, and immune infiltration of HCC. Our study, which elucidates the potential mechanisms of DRGs and HCC, reveals that SLC7A11 can serve as a novel prognostic biomarker and provides opportunities and challenges for individualized cancer immunotherapy strategies.

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

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