Preoperative immune landscape predisposes adverse outcomes in hepatocellular carcinoma patients with liver transplantation

Mitotic catastrophe heterogeneity: implications for prognosis and immunotherapy in hepatocellular carcinoma

Background and aims

The mitotic catastrophe (MC) pathway plays an important role in hepatocellular carcinoma (HCC) progression and tumor microenvironment (TME) regulation. However, the mechanisms linking MC heterogeneity to immune evasion and treatment response remain unclear.

Methods

Based on 94 previously published highly correlated genes for MC, HCC patients' data from the Cancer Genome Atlas (TCGA) and changes in immune signatures and prognostic stratification were studied. Time and spatial-specific differences for MCGs were assessed by single-cell RNA sequencing and spatial transcriptome (ST) analysis. Multiple external databases (GEO, ICGC) were employed to construct an MC-related riskscore model.

Results

Identification of two MC-related subtypes in HCC patients from TCGA, with clear differences in immune signatures and prognostic risk stratification. Spatial mapping further associates low MC tumor regions with significant immune escape-related signaling. Nomogram combining MC riskscore and traditional indicators was validated great effect for early prediction of HCC patient outcomes.

Conclusion

MC heterogeneity enables immune escape and therapy resistance in HCC. The MC gene signature serves as a reliable prognostic indicator for liver cancer. By revealing clear immune and spatial heterogeneity of HCC, our integrated approach provides contextual therapeutic strategies for optimal clinical decision-making.

Comprehensive profiling of enhancer RNA in stage II/III colorectal cancer defines two prognostic subtypes with implications for immunotherapy

BACKGROUND

Recently, enhancer RNAs (eRNAs) have garnered attention as pivotal biomarkers for the onset and progression of cancer. However, the landscape of eRNAs and the implications of eRNA-based molecular subtypes in stage II/III colorectal cancer (CRC) remain largely unexplored.

METHODS

Comprehensive profiling of eRNAs was conducted on a public stage II/III CRC cohort with total RNA-seq data. We used unsupervised clustering of prognostic eRNAs to establish an eRNA-based subtyping system. Further evaluations included molecular characteristics, immune infiltration, clinical outcomes, and drug responses. Finally, we validated the eRNA-based subtyping system in The Cancer Genome Atlas (TCGA) CRC cohort.

RESULTS

We identified a total of 6453 expressed eRNAs, among which 237 were prognostic. A global upregulation of eRNAs was observed in microsatellite-stable (MSS) CRCs when compared to microsatellite instability-high (MSI-H) CRCs. Through consensus clustering, two novel molecular subtypes, termed Cluster 1(C1) and Cluster 2(C2), were further identified. C1, associated with the activation of epithelial-mesenchymal transition (EMT), hypoxia, and KRAS signaling pathways, showed poorer prognosis. C2, correlated with the canonical CRC subtype, exhibited superior survival outcomes. In addition, C1 showed enrichment with immune infiltration and more sensitivity to immune checkpoint inhibitors.

CONCLUSION

Our study unravels the molecular heterogeneity of stage II/III CRC at the eRNA level and highlights the potential applications of the novel eRNA-based subtyping system in predicting prognosis and guiding immunotherapy.

Metformin reduces hepatocarcinogenesis by inducing downregulation of Cyp26a1 and CD8+ T cells

BACKGROUND

Hepatocellular carcinoma (HCC) is a highly heterogeneous cancer with major challenges in both prevention and therapy. Metformin, adenosine monophosphate-activated protein kinase (AMPK) activator, has been suggested to reduce the incidence of HCC when used for patients with diabetes in preclinical and clinical studies. However, the possible effects of metformin and their mechanisms of action in non-diabetic HCC have not been adequately investigated.

METHODS

Fah-/-  mice were used to construct a liver-injury-induced non-diabetic HCC model for exploring hepatocarcinogenesis and therapeutic potential of metformin. Changes in relevant tumour and biochemical indicators were measured. Bulk and single-cell RNA-sequencing analyses were performed to validate the crucial role of proinflammatory/pro-tumour CD8+ T cells. In vitro and in vivo experiments were performed to confirm Cyp26a1-related antitumour mechanisms of metformin.

RESULTS

RNA-sequencing analysis showed that chronic liver injury led to significant changes in AMPK-, glucose- and retinol metabolism-related pathways in Fah-/- mice. Metformin prevented the formation of non-diabetic HCC in Fah-/- mice with chronic liver injury. Cyp26a1 ddexpression in hepatocytes was significantly suppressed after metformin treatment. Moreover, downregulation of Cyp26a1 occurred in conjunction with increased levels of all-trans-retinoic acid (atRA), which is involved in the activation of metformin-suppressed hepatocarcinogenesis in Fah-/- mice. In contrast, both CD8+  T-cell infiltration and proinflammatory/pro-tumour cytokines in the liver were significantly upregulated in Fah-/- mice during chronic liver injury, which was notably reversed by either metformin or atRA treatment. Regarding mechanisms, metformin regulated the decrease in Cyp26a1 enzyme expression and increased atRA expression via the AMPK/STAT3/Gadd45β/JNK/c-Jun pathway.

CONCLUSIONS

Metformin inhibits non-diabetic HCC by upregulating atRA levels and downregulating CD8+ T cells. This is the first reporting that the traditional drug metformin regulates the metabolite atRA via the Cyp26a1-involved pathway. The present study provides a potential application of metformin and atRA in non-diabetic HCC.

KDM6A promotes hepatocellular carcinoma progression and dictates lenvatinib efficacy by upregulating FGFR4 expression

BACKGROUND

Hepatocellular carcinoma (HCC) is one of the major causes of death from cancer and has a very poor prognosis with few effective therapeutic options. Despite the approval of lenvatinib for the treatment of patients suffering from advanced HCC, only a small number of patients can benefit from this targeted therapy.

METHODS

Diethylnitrosamine (DEN)-CCL4 mouse liver tumour and the xenograft tumour models were used to evaluate the function of KDM6A in HCC progression. The xenograft tumour model and HCC cell lines were used to evaluate the role of KDM6A in HCC drug sensitivity to lenvatinib. RNA-seq and ChIP assays were conducted for mechanical investigation.

RESULTS

We revealed that KDM6A exhibited a significant upregulation in HCC tissues and was associated with an unfavourable prognosis. We further demonstrated that KDM6A knockdown remarkably suppressed HCC cell proliferation and migration in vitro. Moreover, hepatic Kdm6a loss also inhibited liver tumourigenesis in a mouse liver tumour model. Mechanistically, KDM6A loss downregulated the FGFR4 expression to suppress the PI3K-AKT-mTOR signalling pathway, leading to a glucose and lipid metabolism re-programming in HCC. KDM6A and FGFR4 levels were positively correlated in HCC specimens and mouse liver tumour tissues. Notably, KDM6A knockdown significantly inhibited the efficacy of lenvatinib therapy in HCC cells in vitro and in vivo.

CONCLUSIONS

Our findings revealed that KDM6A promoted HCC progression by activating FGFR4 expression and may be an essential molecule for influencing the efficacy of lenvatinib in HCC therapy.

Five Critical Gene-Based Biomarkers With Optimal Performance for Hepatocellular Carcinoma

Hepatocellular carcinoma (HCC) is one of the most fatal cancers in the world. There is an urgent need to understand the molecular background of HCC to facilitate the identification of biomarkers and discover effective therapeutic targets. Published transcriptomic studies have reported a large number of genes that are individually significant for HCC. However, reliable biomarkers remain to be determined. In this study, built on max-linear competing risk factor models, we developed a machine learning analytical framework to analyze transcriptomic data to identify the most miniature set of differentially expressed genes (DEGs). By analyzing 9 public whole-transcriptome datasets (containing 1184 HCC samples and 672 nontumor controls), we identified 5 critical differentially expressed genes (DEGs) (ie, CCDC107, CXCL12, GIGYF1, GMNN, and IFFO1) between HCC and control samples. The classifiers built on these 5 DEGs reached nearly perfect performance in identification of HCC. The performance of the 5 DEGs was further validated in a US Caucasian cohort that we collected (containing 17 HCC with paired nontumor tissue). The conceptual advance of our work lies in modeling gene-gene interactions and correcting batch effect in the analytic framework. The classifiers built on the 5 DEGs demonstrated clear signature patterns for HCC. The results are interpretable, robust, and reproducible across diverse cohorts/populations with various disease etiologies, indicating the 5 DEGs are intrinsic variables that can describe the overall features of HCC at the genomic level. The analytical framework applied in this study may pave a new way for improving transcriptome profiling analysis of human cancers.

GepLiver: an integrative liver expression atlas spanning developmental stages and liver disease phases

Chronic liver diseases usually developed through stepwise pathological transitions under the persistent risk factors. The molecular changes during liver transitions are pivotal to improve liver diagnostics and therapeutics yet still remain elusive. Cumulative large-scale liver transcriptomic studies have been revealing molecular landscape of various liver conditions at bulk and single-cell resolution, however, neither single experiment nor databases enabled thorough investigations of transcriptomic dynamics along the progression of liver diseases. Here we establish GepLiver, a longitudinal and multidimensional liver expression atlas integrating expression profiles of 2469 human bulk tissues, 492 mouse samples, 409,775 single cells from 347 human samples and 27 liver cell lines spanning 16 liver phenotypes with uniformed processing and annotating methods. Using GepLiver, we have demonstrated dynamic changes of gene expression, cell abundance and crosstalk harboring meaningful biological associations. GepLiver can be applied to explore the evolving expression patterns and transcriptomic features for genes and cell types respectively among liver phenotypes, assisting the investigation of liver transcriptomic dynamics and informing biomarkers and targets for liver diseases.

Effects of donor source on transcriptomic profiles of human kidney tissue

Normal human tissue is a critical reference control in biomedical research. However, the type of tissue donor can significantly affect the underlying biology of the samples. We investigated the impact of tissue donor source type by performing transcriptomic analysis on healthy kidney tissue from three donor source types: cadavers, organ donors, and normal-adjacent tissue from surgical resections of clear cell renal cell carcinomas, and we compared the gene expression profiles to those of clear cell renal cell carcinoma samples. Comparisons among the normal samples revealed general similarity, with notable differences in gene expression pathways involving immune system and inflammatory processes, response to extracellular stimuli, ion transport, and metabolism. When compared to tumors, the transcriptomic profiles of the normal adjacent tissue were highly similar to the profiles from cadaveric and organ donor tissue samples, arguing against the presence of a field cancerization effect in clear cell renal cell carcinoma. We conclude that all three normal source types are suitable for reference kidney control samples, but important differences must be noted for particular research areas and tissue banking strategies.

Comprehensive characterization of enhancer RNA in hepatocellular carcinoma reveals three immune subtypes with implications for immunotherapy

Hepatocellular carcinoma (HCC) is highly heterogeneous. Molecular subtyping for guiding immunotherapy is warranted. Previous studies have indicated that enhancer RNAs (eRNAs) are involved in tumor heterogeneity and immune infiltration. However, the eRNA landscape and its correlation with immune infiltration in HCC remain unknown. Here we first revealed the genome-wide eRNA landscape in two HCC cohorts. Then we divided individuals with HCC into three immune-related clusters (C1, C2, and C3) based on eRNA expression profiles. The prognosis, biological properties, immune infiltration, clinical features, genomic features, and drug response were analyzed. C1 was enriched in immune infiltration and potentially sensitive to immune checkpoint inhibitors (ICIs). C2 displayed features of immune depletion, high proliferation activity, malignant clinical features, and the worst prognosis. C2 may benefit from targeted therapy. C3 presented moderate immune infiltration, metabolism-related signatures, and the best prognosis. Transarterial chemoembolization (TACE) may be effective for C3. Finally, we constructed a 51-eRNA classifier for subtype prediction and validated its efficacy in The Cancer Genome Atlas (TCGA) cohort and Sun Yat-sen University Cancer Center (SYSUCC) cohort. Our results provide a novel method for immune classification of HCC, shed new light on tumor heterogeneity, and may aid in HCC immunotherapy.

Integrating the Epigenome and Transcriptome of Hepatocellular Carcinoma to Identify Systematic Enhancer Aberrations and Establish an Aberrant Enhancer-Related Prognostic Signature

Recently, emerging evidence has indicated that aberrant enhancers, especially super-enhancers, play pivotal roles in the transcriptional reprogramming of multiple cancers, including hepatocellular carcinoma (HCC). In this study, we performed integrative analyses of ChIP-seq, RNA-seq, and whole-genome bisulfite sequencing (WGBS) data to identify intergenic differentially expressed enhancers (DEEs) and genic differentially methylated enhancers (DMEs), along with their associated differentially expressed genes (DEE/DME-DEGs), both of which were also identified in independent cohorts and further confirmed by HiC data. Functional enrichment and prognostic model construction were conducted to explore the functions and clinical significance of the identified enhancer aberrations. We identified a total of 2,051 aberrant enhancer-associated DEGs (AE-DEGs), which were highly concurrent in multiple HCC datasets. The enrichment results indicated the significant overrepresentations of crucial biological processes and pathways implicated in cancer among these AE-DEGs. A six AE-DEG-based prognostic signature, whose ability to predict the overall survival of HCC was superior to that of both clinical phenotypes and previously published similar prognostic signatures, was established and validated in TCGA-LIHC and ICGC-LIRI cohorts, respectively. In summary, our integrative analysis depicted a landscape of aberrant enhancers and associated transcriptional dysregulation in HCC and established an aberrant enhancer-derived prognostic signature with excellent predictive accuracy, which might be beneficial for the future development of epigenetic therapy for HCC.

Single-Cell Transcriptomics Reveals the Expression of Aging- and Senescence-Associated Genes in Distinct Cancer Cell Populations

The human aging process is associated with molecular changes and cellular degeneration, resulting in a significant increase in cancer incidence with age. Despite their potential correlation, the relationship between cancer- and ageing-related transcriptional changes is largely unknown. In this study, we aimed to analyze aging-associated transcriptional patterns in publicly available bulk mRNA-seq and single-cell RNA-seq (scRNA-seq) datasets for chronic myelogenous leukemia (CML), colorectal cancer (CRC), hepatocellular carcinoma (HCC), lung cancer (LC), and pancreatic ductal adenocarcinoma (PDAC). Indeed, we detected that various aging/senescence-induced genes (ASIGs) were upregulated in malignant diseases compared to healthy control samples. To elucidate the importance of ASIGs during cell development, pseudotime analyses were performed, which revealed a late enrichment of distinct cancer-specific ASIG signatures. Notably, we were able to demonstrate that all cancer entities analyzed in this study comprised cell populations expressing ASIGs. While only minor correlations were detected between ASIGs and transcriptome-wide changes in PDAC, a high proportion of ASIGs was induced in CML, CRC, HCC, and LC samples. These unique cellular subpopulations could serve as a basis for future studies on the role of aging and senescence in human malignancies.

Preoperative immune landscape predisposes adverse outcomes in hepatocellular carcinoma patients with liver transplantation

Immune class in hepatocellular carcinoma (HCC) has been shown to possess immunogenic power; however, how preestablished immune landscapes in premalignant and early HCC stages impact the clinical outcomes of HCC patients remains unexplored. We sequenced bulk transcriptomes for 62 malignant tumor samples from a Korean HCC cohort in which 38 patients underwent total hepatectomy, as well as for 15 normal and 47 adjacent nontumor samples. Using in silico deconvolution of expression mixtures, 22 immune cell fractions for each sample were inferred, and validated with immune cell counting by immunohistochemistry. Cell type-specific immune signatures dynamically shifted from premalignant stages to the late HCC stage. Total hepatectomy patients displayed elevated immune infiltration and prolonged disease-free survival compared to the partial hepatectomy patients. However, patients who exhibited an infiltration of regulatory T cells (Tregs) during the pretransplantation period displayed a high risk of tumor relapse with suppressed immune responses, and pretreatment was a potential driver of Treg infiltration in the total hepatectomy group. Treg infiltration appeared to be independent of molecular classifications based on transcriptomic data. Our study provides not only comprehensive immune signatures in adjacent nontumor lesions and early malignant HCC stages but also clinical guidance for HCC patients who will undergo liver transplantation.