Immunological Significance of Prognostic DNA Methylation Sites in Hepatocellular Carcinoma

The association between tumour heterogeneity and immune evasion mechanisms in hepatocellular carcinoma and its clinical implications

Hepatocellular carcinoma (HCC) is the third leading cause of cancer-related mortality worldwide. The emergence of combination therapy, atezolizumab (anti-PDL1, immune checkpoint inhibitor) and bevacizumab (anti-VEGF) has revolutionised the management of HCC. Despite this breakthrough, the best overall response rate with first-line systemic therapy is only about 30%, owing to intra-tumoural heterogeneity, complex tumour microenvironment and the lack of predictive biomarkers. Many groups have attempted to classify HCC based on the immune microenvironment and have consistently observed better outcomes in immunologically "hot" HCC. We summarised possible mechanisms of tumour immune evasion based on the latest literature and the rationale for combination/sequential therapy to improve treatment response. Lastly, we proposed future strategies and therapies to overcome HCC immune evasion to further improve treatment outcomes of HCC.

Role of HDAC6-STAT3 in immunomodulatory pathways in Colorectal cancer cells

Colorectal cancer (CRC) is one of the most common malignant neoplasms and the second leading cause of death from tumors worldwide. Therefore, there is a great need to study new therapeutical strategies, such as effective immunotherapies against these malignancies. Unfortunately, many CRC patients do not respond to current standard immunotherapies, making it necessary to search for adjuvant treatments. Histone deacetylase 6 (HDAC6) is involved in several processes, including immune response and tumor progression. Specifically, it has been observed that HDAC6 is required to activate the Signal Transducer and Activator of Transcription 3 (STAT3), a transcription factor involved in immunogenicity, by activating different genes in these pathways, such as PD-L1. Over-expression of immunosuppressive pathways in cancer cells deregulates T-cell activation. Therefore, we focused on the pharmacological inhibition of HDAC6 in CRC cells because of its potential as an adjuvant to avoid immunotolerance in immunotherapy. We investigated whether HDAC6 inhibitors (HDAC6is), such as Nexturastat A (NextA), affected STAT3 activation in CRC cells. First, we found that NextA is less cytotoxic than the non-selective HDACis panobinostat. Then, NextA modified STAT3 and decreased the mRNA and protein expression levels of PD-L1. Importantly, transcriptomic analysis showed that NextA treatment affected the expression of critical genes involved in immunomodulatory pathways in CRC malignancies. These results suggest that treatments with NextA reduce the functionality of STAT3 in CRC cells, impacting the expression of immunomodulatory genes involved in the inflammatory and immune responses. Therefore, targeting HDAC6 may represent an interesting adjuvant strategy in combination with immunotherapy.

Machine learning in the identification of prognostic DNA methylation biomarkers among patients with cancer: A systematic review of epigenome-wide studies

BACKGROUND

DNA methylation biomarkers have great potential in improving prognostic classification systems for patients with cancer. Machine learning (ML)-based analytic techniques might help overcome the challenges of analyzing high-dimensional data in relatively small sample sizes. This systematic review summarizes the current use of ML-based methods in epigenome-wide studies for the identification of DNA methylation signatures associated with cancer prognosis.

METHODS

We searched three electronic databases including PubMed, EMBASE, and Web of Science for articles published until 2 January 2023. ML-based methods and workflows used to identify DNA methylation signatures associated with cancer prognosis were extracted and summarized. Two authors independently assessed the methodological quality of included studies by a seven-item checklist adapted from 'A Tool to Assess Risk of Bias and Applicability of Prediction Model Studies (PROBAST)' and from the 'Reporting Recommendations for Tumor Marker Prognostic Studies (REMARK). Different ML methods and workflows used in included studies were summarized and visualized by a sunburst chart, a bubble chart, and Sankey diagrams, respectively.

RESULTS

Eighty-three studies were included in this review. Three major types of ML-based workflows were identified. 1) unsupervised clustering, 2) supervised feature selection, and 3) deep learning-based feature transformation. For the three workflows, the most frequently used ML techniques were consensus clustering, least absolute shrinkage and selection operator (LASSO), and autoencoder, respectively. The systematic review revealed that the performance of these approaches has not been adequately evaluated yet and that methodological and reporting flaws were common in the identified studies using ML techniques.

CONCLUSIONS

There is great heterogeneity in ML-based methodological strategies used by epigenome-wide studies to identify DNA methylation markers associated with cancer prognosis. In theory, most existing workflows could not handle the high multi-collinearity and potentially non-linearity interactions in epigenome-wide DNA methylation data. Benchmarking studies are needed to compare the relative performance of various approaches for specific cancer types. Adherence to relevant methodological and reporting guidelines are urgently needed.

Tumor Mutational Burden for Predicting Prognosis and Therapy Outcome of Hepatocellular Carcinoma

Hepatocellular carcinoma (HCC), the primary hepatic malignancy, represents the second-highest cause of cancer-related death worldwide. Many efforts have been devoted to finding novel biomarkers for predicting both patients' survival and the outcome of pharmacological treatments, with a particular focus on immunotherapy. In this regard, recent studies have focused on unravelling the role of tumor mutational burden (TMB), i.e., the total number of mutations per coding area of a tumor genome, to ascertain whether it can be considered a reliable biomarker to be used either for the stratification of HCC patients in subgroups with different responsiveness to immunotherapy, or for the prediction of disease progression, particularly in relation to the different HCC etiologies. In this review, we summarize the recent advances on the study of TMB and TMB-related biomarkers in the HCC landscape, focusing on their feasibility as guides for therapy decisions and/or predictors of clinical outcome.

Identification and validation of a novel cuproptosis-related gene signature in multiple myeloma

Background: Cuproptosis is a newly identified unique copper-triggered modality of mitochondrial cell death, distinct from known death mechanisms such as necroptosis, pyroptosis, and ferroptosis. Multiple myeloma (MM) is a hematologic neoplasm characterized by the malignant proliferation of plasma cells. In the development of MM, almost all patients undergo a relatively benign course from monoclonal gammopathy of undetermined significance (MGUS) to smoldering myeloma (SMM), which further progresses to active myeloma. However, the prognostic value of cuproptosis in MM remains unknown. Method: In this study, we systematically investigated the genetic variants, expression patterns, and prognostic value of cuproptosis-related genes (CRGs) in MM. CRG scores derived from the prognostic model were used to perform the risk stratification of MM patients. We then explored their differences in clinical characteristics and immune patterns and assessed their value in prognosis prediction and treatment response. Nomograms were also developed to improve predictive accuracy and clinical applicability. Finally, we collected MM cell lines and patient samples to validate marker gene expression by quantitative real-time PCR (qRT-PCR). Results: The evolution from MGUS and SMM to MM was also accompanied by differences in the CRG expression profile. Then, a well-performing cuproptosis-related risk model was developed to predict prognosis in MM and was validated in two external cohorts. The high-risk group exhibited higher clinical risk indicators. Cox regression analyses showed that the model was an independent prognostic predictor in MM. Patients in the high-risk group had significantly lower survival rates than those in the low-risk group (p < 0.001). Meanwhile, CRG scores were significantly correlated with immune infiltration, stemness index and immunotherapy sensitivity. We further revealed the close association between CRG scores and mitochondrial metabolism. Subsequently, the prediction nomogram showed good predictive power and calibration. Finally, the prognostic CRGs were further validated by qRT-PCR in vitro. Conclusion: CRGs were closely related to the immune pattern and self-renewal biology of cancer cells in MM. This prognostic model provided a new perspective for the risk stratification and treatment response prediction of MM patients.

MJDs family members: Potential prognostic targets and immune-associated biomarkers in hepatocellular carcinoma

Hepatocellular carcinoma (HCC) is one of the most common gastrointestinal malignancies. It is not easy to be diagnosed in the early stage and is prone to relapse, with a very poor prognosis. And immune cell infiltration and tumor microenvironment play important roles in predicting therapeutic response and prognosis of HCC. Machado-Joseph domain-containing proteases (MJDs), as a gene family extensively involved in tumor progression, has pro-cancer and anti-cancer effects. However, the relationship between MJDs family members and immune cell infiltration and tumor microenvironment in HCC remains unclear. Therefore, cBio Cancer Genomics Portal (cBioPortal), The Cancer Genome Atlas (TCGA), UALCAN, Human Protein Atlas (HPA), MethSurv, and Tumor Immune Estimation Resource (TIMER) databases were performed to investigate the mRNA expression, DNA methylation, clinicopathologic features, immune cell infiltration and other related functions of MJDs family members in HCC. The results indicated that the expression of ATXN3, JOSD1, and JOSD2 was dramatically increased in HCC tissues and cell lines, and was correlated with histological grade, specimen type, TP53 mutation, lymph node metastatic, gender, and age of patients with HCC. Meanwhile, these genes also showed clinical value in improving the overall survival (OS), disease-specific survival (DSS), progression free survival (PFS), and relapse-free survival (RFS) in patients with HCC. The prognostic model indicated that the worse survival was associated with overall high expression of MJDs members. Next, the results suggested that promotor methylation levels of the MJDs family were closely related to these family mRNA expression levels, clinicopathologic features, and prognostic values in HCC. Moreover, the MJDs family were significantly correlated with CD4⁺ T cells, CD8⁺ T cells, B cells, neutrophils, macrophages, and DCs. And MJDs family members’ expression were substantially associated with the levels of several lymphocytes, immunomoinhibitors, immunomostimulators, chemokine ligands, and chemokine receptors. In addition, the expression levels of MJDs family were significantly correlated with cancer-related signaling pathways. Taken together, our results indicated that the aberrant expression of MJDs family in HCC played a critical role in clinical feature, prognosis, tumor microenvironment, immune-related molecules, mutation, gene copy number, and promoter methylation level. And MJDs family may be effective immunotherapeutic targets for patients with HCC and have the potential to be prognostic biomarkers.

Novel γδ T cell-based prognostic signature to estimate risk and aid therapy in hepatocellular carcinoma

BACKGROUND

Numerous studies have revealed that gamma delta (γδ) T cell infiltration plays a crucial regulatory role in hepatocellular carcinoma (HCC) development. Nonetheless, a comprehensive analysis of γδ T cell infiltration in prognosis evaluation and therapeutic prediction remains unclear.

METHODS

Multi-omic data on HCC patients were obtained from public databases. The CIBERSORT algorithm was applied to decipher the tumor immune microenvironment (TIME) of HCC. Weighted gene co-expression network analysis (WGCNA) was performed to determine significant modules with γδ T cell-specific genes. Kaplan-Meier survival curves and receiver operating characteristic analyses were used to validate prognostic capability. Additionally, the potential role of RFESD inhibition by si-RFESD in vitro was investigated using EdU and CCK-8 assays.

RESULTS

A total of 16,421 genes from 746 HCC samples (616 cancer and 130 normal) were identified based on three distinct cohorts. Using WGCNA, candidate modules (brown) with 1755 significant corresponding genes were extracted as γδ T cell-specific genes. Next, a novel risk signature consisting of 11 hub genes was constructed using multiple bioinformatic analyses, which presented great prognosis prediction reliability. The risk score exhibited a significant correlation with ICI and chemotherapeutic targets. HCC samples with different risks experienced diverse signalling pathway activities. The possible interaction of risk score with tumor mutation burden (TMB) was further analyzed. Subsequently, the potential functions of the RFESD gene were explored in HCC, and knockdown of RFESD inhibited cell proliferation in HCC cells. Finally, a robust prognostic risk-clinical nomogram was developed and validated to quantify clinical outcomes.

CONCLUSIONS

Collectively, comprehensive analyses focusing on γδ T cell patterns will provide insights into prognosis prediction, the mechanisms of immune infiltration, and advanced therapy strategies in HCC.

A novel DNA methylation-driver gene signature for long-term survival prediction of hepatitis-positive hepatocellular carcinoma patients

BACKGROUND

Abnormal DNA methylation is one of the most general epigenetic modifications in hepatocellular carcinoma (HCC). Recent research showed that DNA methylation was a prognostic indicator of all-cause HCC and nonviral HCC. However, whether DNA methylation-driver genes could be used for predicting survival, the probability of hepatitis-positive HCC remains unclear.

METHODS

In this study, DNA methylation-driver genes (MDGs) were screened by a joint analysis of methylome and transcriptome data of 142 hepatitis-positive HCC patients. Subsequently, a prognostic risk score and nomogram were constructed. Finally, correlation analyses between the risk score and signaling pathways and immunity were conducted by GSVA and CIBERSORT.

RESULTS

Through random forest screening and Cox progression analysis, 10 prognostic methylation-driver genes (AC008271.1, C11orf53, CASP8, F2RL2, GBP5, LUCAT1, RP11-114B7.6, RP11-149I23.3, RP11-383 J24.1, and SLC35G2) were screened out. As a result, a prognostic risk score signature was constructed. The independent value of the risk score for prognosis prediction were addressed in the TCGA-HCC and the China-HCC cohorts. Next, clinicopathological features were analyzed and HBV status and histological grade were screened to construct a nomogram together with the risk score. The prognostic efficiency of the nomogram was validated by the calibration curves and the concordance index (C index: 0.829, 95% confidence interval: 0.794-0.864), while its clinical application ability was confirmed by decision curve analysis (DCA). At last, the relationship between the risk score and signaling pathways, as well as the correlations between immune cells were elucidated preliminary.

CONCLUSIONS

Taken together, our study explored a novel DNA methylation-driver gene risk score signature and an efficient nomogram for long-term survival prediction of hepatitis-positive HCC patients.

N6-Methyladenine-Related Signature for Immune Microenvironment and Response to Immunotherapy in Hepatocellular Carcinoma

Background

The prognostic value of m6A-related genes in hepatocellular carcinoma (HCC) and its correlation with the immune microenvironment still requires further investigation.

Methods

Consensus clustering by m6A related genes was used to classify 374 patients with HCC from The Cancer Genome Atlas (TCGA) database. Then we performed the least absolute shrinkage and selection operator (LASSO) to construct the m6A related genes model. The International Cancer Genome Consortium (ICGC) and Gene Expression Omnibus (GEO) datasets were used to verify and evaluate the model. ESTIMATE, CIBERSORTx, the expression levels of immune checkpoint genes, and TIDE were used to investigate the tumor microenvironment (TME) and the response to immunotherapy. Gene set enrichment analyses (GSEA), tumor-associated macrophages (TAMs), and gene-drug sensitivity were also analyzed.

Results

By expression value and regression coefficient of five m6A related genes, we constructed the risk score of each patient. The patients with a higher risk score had a considerably poorer prognosis in the primary and validated cohort. For further discussing TME and the response to immunotherapy, we divided the entire set into two groups based on the risk score. Our findings implied that the tumor-infiltrating lymphocytes (TILs) were proportional to the risk scores, which seemed to contradict that patients with higher scores had a poor prognosis. Further, we found that the high-risk group had higher expression of PD-L1, CTLA-4, and PDCD1, indicating immune dysfunction, which may be a fundamental reason for poor prognosis. This was further reinforced by the fact that the low-risk group responded better than the high-risk group to monotherapy and combination therapy.

Conclusion

The m6A related risk score is a new independent prognostic factor that correlates with immunotherapy response. It can provide a new therapeutic strategy for improving individual immunotherapy in HCC.

Prognostic Role of ceRNA Network in Immune Infiltration of Hepatocellular Carcinoma

Background: Increasing evidence supports that competing endogenous RNAs (ceRNAs) and tumor immune infiltration act as pivotal players in tumor progression of hepatocellular carcinoma (HCC). Nonetheless, comprehensive analysis focusing on ceRNAs and immune infiltration in HCC is lacking.

Methods: RNA and miRNA sequencing information, corresponding clinical annotation, and mutation data of HCC downloaded from The Cancer Genome Atlas Liver Hepatocellular Carcinoma (TCGA-LIHC) project were employed to identify significant differentially expressed mRNAs (DEMs), miRNAs (DEMis), and lncRNAs (DELs) to establish a ceRNA regulatory network. The Kyoto Encyclopedia of Genes and Genomes (KEGG) and Gene ontology (GO) enrichment pathways were analyzed to functionally annotate these DEMs. A multigene-based risk signature was developed utilizing least absolute shrinkage and selection operator method (LASSO) algorithm. Moreover, survival analysis and receiver operating characteristic (ROC) analysis were applied for prognostic value validation. Seven algorithms (TIMER, XCELL, MCPcounter, QUANTISEQ, CIBERSORT, EPIC, and CIBERSORT-ABS) were utilized to characterize tumor immune microenvironment (TIME). Finally, the mutation data were analyzed by employing “maftools” package.

Results: In total, 136 DELs, 128 DEMis, and 2,028 DEMs were recognized in HCC. A specific lncRNA–miRNA–mRNA network consisting of 3 lncRNAs, 12 miRNAs, and 21 mRNAs was established. A ceRNA-based prognostic signature was established to classify samples into two risk subgroups, which presented excellent prognostic performance. In additional, prognostic risk-clinical nomogram was delineated to assess risk of individual sample quantitatively. Besides, risk score was significantly associated with contexture of TIME and immunotherapeutic targets. Finally, potential interaction between risk score with tumor mutation burden (TMB) was revealed.

Conclusion: In this work, comprehensive analyses of ceRNAs coexpression network will facilitate prognostic prediction, delineate complexity of TIME, and contribute insight into precision therapy for HCC.

Landscape of Prognostic m6A RNA Methylation Regulators in Hepatocellular Carcinoma to Aid Immunotherapy

Background: Hepatocellular carcinoma (HCC) is the sixth most common malignancy with a high mortality worldwide. N6-methyladenosine (m6A) may participate extensively in tumor progression.

Methods: To reveal the landscape of tumor immune microenvironment (TIME), ESTIMATE analysis, ssGSEA algorithm, and the CIBERSORT method were used. Taking advantage of consensus clustering, two different HCC categories were screened. We analyzed the correlation of clustering results with TIME and immunotherapy. Then, we yielded a risk signature by systematical bioinformatics analyses. Immunophenoscore (IPS) was implemented to estimate the immunotherapeutic significance of risk signature.

Results: The m6A-based clusters were significantly correlated with overall survival (OS), immune score, immunological signature, immune infiltrating, and ICB-associated genes. Risk signature possessed robust prognostic validity and significantly correlated with TIME context. IPS was employed as a surrogate of immunotherapeutic outcome, and patients with low-risk scores showed significantly higher immunophenoscores.

Conclusion: Collectively, m6A-based clustering subtype and signature was a robust prognostic indicator and correlated with TIME and immunotherapy, providing novel insight into antitumor management and prognostic prediction in HCC.

Immunological Value of Prognostic Signature Based on Cancer Stem Cell Characteristics in Hepatocellular Carcinoma

Background: Liver cancer stem cells, characterized by self-renewal and initiating cancer cells, were decisive drivers of progression and therapeutic resistance in hepatocellular carcinoma (HCC). However, a comprehensive understanding of HCC stemness has not been identified.

Methods: RNA sequencing information, corresponding clinical annotation, and mutation data of HCC were downloaded from The Cancer Genome Atlas-LIHC project. Two stemness indices, mRNA expression-based stemness index (mRNAsi) and epigenetically regulated-mRNAsi, were used to comprehensively analyze HCC stemness. Estimation of Stromal and Immune Cells in Malignant Tumors using Expression Data and single-sample gene-set enrichment analysis algorithm were performed to characterize the context of tumor immune microenvironment (TIME). Next, differentially expressed gene (DEG) analysis and weighted gene co-expression network analysis (WGCNA) were employed to identify significant mRNAsi-related modules with hub genes. Kyoto Encyclopedia of Genes and Genomes and Gene Ontology enrichment pathways were analyzed to functionally annotate these key genes. The least absolute shrinkage and selection operator (LASSO) Cox regression analysis was performed to establish a prognostic signature. Kaplan–Meier survival curves and receiver operating characteristic (ROC) analysis were applied for prognostic value validation. Seven algorithms (XCELL, TIMER, QUANTISEQ, MCPcounter, EPIC, CIBERSORT, and CIBERSORT-ABS) were utilized to draw the landscape of TIME. Finally, the mutation data were analyzed by employing “maftools” package.

Results: mRNAsi was significantly elevated in HCC samples. mRNAsi escalated as tumor grade increased, with poor prognosis presenting the higher stemness index. The stemness-related (greenyellow) modules with 175 hub genes were screened based on DEGs and WGCNA. A prognostic signature was established using LASSO analysis of prognostic hub genes to classify samples into two risk subgroups, which exhibited good prognostic performance. Additionally, prognostic risk-clinical nomogram was drawn to estimate risk quantitatively. Moreover, risk score was significantly associated with contexture of TIME and immunotherapeutic targets. Finally, potential interaction between risk score with tumor mutation burden (TMB) was elucidated.

Conclusion: This work comprehensively elucidated that stemness characteristics served as a crucial player in clinical outcome, complexity of TIME, and immunotherapeutic prediction from both mRNAsi and mRNA level. Quantitative identification of stemness characteristics in individual tumor will contribute into predicting clinical outcome, mapping landscape of TIME further optimizing precision immunotherapy.

Multi-omics analysis reveals prognostic value of tumor mutation burden in hepatocellular carcinoma

BACKGROUND

Hepatocellular carcinoma (HCC) was the sixth common malignancies characteristic with highly aggressive in the world. It was well established that tumor mutation burden (TMB) act as indicator of immunotherapeutic responsiveness in various tumors. However, the role of TMB in tumor immune microenvironment (TIME) is still obscure.

METHOD

The mutation data was analyzed by employing "maftools" package. Weighted gene co-expression network analysis (WGCNA) was implemented to determine candidate module and significant genes correlated with TMB value. Differential analysis was performed between different level of TMB subgroups employing R package "limma". Gene ontology (GO) enrichment analysis was implemented with "clusterProfiler", "enrichplot" and "ggplot2" packages. Then risk score signature was developed by systematical bioinformatics analyses. K-M survival curves and receiver operating characteristic (ROC) plot were further analyzed for prognostic validity. To depict comprehensive context of TIME, XCELL, TIMER, QUANTISEQ, MCPcounter, EPIC, CIBERSORT, and CIBERSORT-ABS algorithm were employed. Additionally, the potential role of risk score on immune checkpoint blockade (ICB) immunotherapy was further explored. The quantitative real-time polymerase chain reaction was performed to detect expression of HTRA3.

RESULTS

TMB value was positively correlated with older age, male gender and early T status. A total of 75 intersection genes between TMB-related genes and differentially expressed genes (DEGs) were screened and enriched in extracellular matrix-relevant pathways. Risk score based on three hub genes significantly affected overall survival (OS) time, infiltration of immune cells, and ICB-related hub targets. The prognostic performance of risks score was validated in the external testing group. Risk-clinical nomogram was constructed for clinical application. HTRA3 was demonstrated to be a prognostic factor in HCC in further exploration. Finally, mutation of TP53 was correlated with risk score and do not interfere with risk score-based prognostic prediction.

CONCLUSION

Collectively, a comprehensive analysis of TMB might provide novel insights into mutation-driven mechanism of tumorigenesis further contribute to tailored immunotherapy and prognosis prediction of HCC.