Tumor-adjacent tissue co-expression profile analysis reveals pro-oncogenic ribosomal gene signature for prognosis of resectable hepatocellular carcinoma

Higher inflammatory response in hepatocellular carcinoma is associated with immune cell infiltration and a better outcome

BACKGROUND AND AIMS

Hepatocellular carcinoma (HCC) often develops from chronic liver inflammation. Inflammation within a tumor can either promote cancer progression or activate an immune response against it. This study aims to determine the clinical significance of enhanced inflammation in HCC.

METHODS

Data from 655 HCC patients across four cohorts (TCGA, GSE6764, GSE76427, GSE89377) were examined. Inflammatory response was quantified using a scoring system derived from the gene set variation analysis of the "INFLAMMATORY_RESPONSE" gene set.

RESULTS

A stepwise increase in inflammatory response was noted from normal liver to cirrhosis, with consistently lower levels in HCC across both GSE6764 and GSE89377 cohorts (both p < 0.001). Similar trends were observed in interferon response, pathways such as IL6/JAK/STAT3 and complement signaling, coagulation cascade, and allograft rejection (all p < 0.02). HCCs with high inflammatory response were associated with increased immune cell infiltrations (p < 0.01) and cytolytic activity (p < 0.001). Interestingly, these HCCs had reduced mutation rates, no relationship with cell proliferation, and displayed both immune responses and pro-cancerous signals including epithelial-mesenchymal transition, KRAS, and hypoxia. Further, a high inflammatory score correlated with improved disease-free survival in TCGA (p = 0.034) and overall survival in GSE76427 (p = 0.008).

CONCLUSION

HCC with higher levels of inflammatory response demonstrated increased immune cell infiltration, enhanced immune-related and other pro-cancerous-related signaling, and showed a trend toward a better patient prognosis.

Potential applications of dual haptoglobin expression in the reclassification and treatment of hepatocellular carcinoma

HCC is a malignancy characterized by high incidence and mortality rates. Traditional classifications of HCC primarily rely on tumor morphology, phenotype, and multicellular molecular levels, which may not accurately capture the cellular heterogeneity within the tumor. This study integrates scRNA-seq and bulk RNA-seq to spotlight HP as a critical gene within a subgroup of HCC malignant cells. HP is highly expressed in HCC malignant cells and lowly expressed in T cells. Within malignant cells, elevated HP expression interacts with C3, promoting Th1-type responses via the C3/C3AR1 axis. In T cells, down-regulating HP expression favors the expression of Th1 cell-associated marker genes, potentially enhancing Th1-type responses. Consequently, we developed a "HP-promoted Th1 response reclassification" gene set, correlating higher activity scores with improved survival rates in HCC patients. Additionally, four predictive models for neoadjuvant treatment based on HP and C3 expression were established: 1) Low HP and C3 expression with high Th2 cell infiltration; 2) High HP and low C3 expression with high Th2 cell infiltration; 3) High HP and C3 expression with high Th1 cell infiltration; 4) Low HP and high C3 expression with high Th1 cell infiltration. In conclusion, the HP gene selected from the HCC malignant cell subgroup (Malignant_Sub 6) might serve as a potential ally against the tumor by promoting Th1-type immune responses. The establishment of the "HP-promoted Th1 response reclassification" gene set offers predictive insights for HCC patient survival prognosis and neoadjuvant treatment efficacy, providing directions for clinical treatments.

The Loss of an Orphan Nuclear Receptor NR2E3 Augments Wnt/β-catenin Signaling via Epigenetic Dysregulation that Enhances Sp1-β catenin-p300 Interactions in Hepatocellular Carcinoma

The orphan nuclear receptor NR2E3 (Nuclear receptor subfamily 2 group E, Member 3) is an epigenetic player that modulates chromatin accessibility to activate p53 during liver injury. Nonetheless, a precise tumor suppressive and epigenetic role of NR2E3 in hepatocellular carcinoma (HCC) development remains unclear. HCC patients expressing low NR2E3 exhibit unfavorable clinical outcomes, aligning with heightened activation of the Wnt/β-catenin signaling pathway. The murine HCC models utilizing NR2E3 knockout mice consistently exhibits accelerated liver tumor formation accompanied by enhanced activation of Wnt/β-catenin signaling pathway and inactivation of p53 signaling. At cellular level, the loss of NR2E3 increases the acquisition of aggressive cancer cell phenotype and tumorigenicity and upregulates key genes in the WNT/β-catenin pathway with increased chromatin accessibility. This event is mediated through increased formation of active transcription complex involving Sp1, β-catenin, and p300, a histone acetyltransferase, on the promoters of target genes. These findings demonstrate that the loss of NR2E3 activates Wnt/β-catenin signaling at cellular and organism levels and this dysregulation is associated with aggressive HCC development and poor clinical outcomes. In summary, NR2E3 is a novel tumor suppressor with a significant prognostic value, maintaining epigenetic homeostasis to suppress the Wnt/β-catenin signaling pathway that promotes HCC development.

METTL8 links mt-tRNA m3C modification to the HIF1α/RTK/Akt axis to sustain GBM stemness and tumorigenicity

Epitranscriptomic RNA modifications are crucial for the maintenance of glioma stem cells (GSCs), the most malignant cells in glioblastoma (GBM). 3-methylcytosine (m3C) is a new epitranscriptomic mark on RNAs and METTL8 represents an m3C writer that is dysregulated in cancer. Although METTL8 has an established function in mitochondrial tRNA (mt-tRNA) m3C modification, alternative splicing of METTL8 can also generate isoforms that localize to the nucleolus where they may regulate R-loop formation. The molecular basis for METTL8 dysregulation in GBM, and which METTL8 isoform(s) may influence GBM cell fate and malignancy remain elusive. Here, we investigated the role of METTL8 in regulating GBM stemness and tumorigenicity. In GSC, METTL8 is exclusively localized to the mitochondrial matrix where it installs m3C on mt-tRNAThr/Ser(UCN) for mitochondrial translation and respiration. High expression of METTL8 in GBM is attributed to histone variant H2AZ-mediated chromatin accessibility of HIF1α and portends inferior glioma patient outcome. METTL8 depletion impairs the ability of GSC to self-renew and differentiate, thus retarding tumor growth in an intracranial GBM xenograft model. Interestingly, METTL8 depletion decreases protein levels of HIF1α, which serves as a transcription factor for several receptor tyrosine kinase (RTK) genes, in GSC. Accordingly, METTL8 loss inactivates the RTK/Akt axis leading to heightened sensitivity to Akt inhibitor treatment. These mechanistic findings, along with the intimate link between METTL8 levels and the HIF1α/RTK/Akt axis in glioma patients, guided us to propose a HIF1α/Akt inhibitor combination which potently compromises GSC proliferation/self-renewal in vitro. Thus, METTL8 represents a new GBM dependency that is therapeutically targetable.

Identification of PANoptosis-related subtypes, construction of a prognosis signature, and tumor microenvironment landscape of hepatocellular carcinoma using bioinformatic analysis and experimental verification

Background

Hepatocellular carcinoma (HCC) is one of the most lethal malignancies worldwide. PANoptosis is a recently unveiled programmed cell death pathway, Nonetheless, the precise implications of PANoptosis within the context of HCC remain incompletely elucidated.

Methods

We conducted a comprehensive bioinformatics analysis to evaluate both the expression and mutation patterns of PANoptosis-related genes (PRGs). We categorized HCC into two clusters and identified differentially expressed PANoptosis-related genes (DEPRGs). Next, a PANoptosis risk model was constructed using LASSO and multivariate Cox regression analyses. The relationship between PRGs, risk genes, the risk model, and the immune microenvironment was studies. In addition, drug sensitivity between high- and low-risk groups was examined. The expression profiles of these four risk genes were elucidate by qRT-PCR or immunohistochemical (IHC). Furthermore, the effect of CTSC knock down on HCC cell behavior was verified using in vitro experiments.

Results

We constructed a prognostic signature of four DEPRGs (CTSC, CDCA8, G6PD, and CXCL9). Receiver operating characteristic curve analyses underscored the superior prognostic capacity of this signature in assessing the outcomes of HCC patients. Subsequently, patients were stratified based on their risk scores, which revealed that the low-risk group had better prognosis than those in the high-risk group. High-risk group displayed a lower Stromal Score, Immune Score, ESTIMATE score, and higher cancer stem cell content, tumor mutation burden (TMB) values. Furthermore, a correlation was noted between the risk model and the sensitivity to 56 chemotherapeutic agents, as well as immunotherapy efficacy, in patient with. These findings provide valuable guidance for personalized clinical treatment strategies. The qRT-PCR analysis revealed that upregulated expression of CTSC, CDCA8, and G6PD, whereas downregulated expression of CXCL9 in HCC compared with adjacent tumor tissue and normal liver cell lines. The knockdown of CTSC significantly reduced both HCC cell proliferation and migration.

Conclusion

Our study underscores the promise of PANoptosis-based molecular clustering and prognostic signatures in predicting patient survival and discerning the intricacies of the tumor microenvironment within the context of HCC. These insights hold the potential to advance our comprehension of the therapeutic contribution of PANoptosis plays in HCC and pave the way for generating more efficacious treatment strategies.

The cell death-related genes machine learning model for precise therapy and clinical drug selection in hepatocellular carcinoma

Hepatocellular carcinoma (HCC) is the prevailing subtype of hepatocellular malignancy. While previous investigations have evidenced a robust link with programmed cell death (PCD) and tumorigenesis, a comprehensive inquiry targeting the relationship between multiple PCDs and HCC remains scant. Our aim was to develop a predictive model for different PCD patterns in order to investigate their impact on survival rates, prognosis and drug response rates in HCC patients. We performed functional annotation and pathway analysis on identified PCD-related genes (PCDRGs) using multiple bioinformatics tools. The prognostic value of these PCDRGs was verified through a dataset obtained from GEO. Consensus clustering analysis was utilized to elucidate the correlation between diverse PCD clusters and pertinent clinical characteristics. To comprehensively uncover the distinct PCD regulatory patterns, our analysis integrated gene expression profiling, immune cell infiltration and enrichment analysis. To predict survival differences in HCC patients, we established a PCD model. To enhance the clinical applicability for the model, we developed a highly accurate nomogram. To address the treatment of HCC, we identified several promising chemotherapeutic agents and novel targeted drugs. These drugs may be effective in treating HCC and could improve patient outcomes. To develop a cell death feature for HCC patients, we conducted an analysis of 12 different PCD mechanisms using eligible data obtained from public databases. Through this analysis, we were able to identify 1254 PCDRGs likely to contribute to cell death on HCC. Further analysis of 1254 PCDRGs identified 37 genes with prognostic value in HCC patients. These genes were then categorized into two PCD clusters A and B. The categorization was based on the expression patterns of the genes in the different clusters. Patients in PCD cluster B had better survival probabilities. This suggests that PCD mechanisms, as represented by the genes in cluster B, may have a protective effect against HCC progression. Furthermore, the expression of PCDRGs was significantly higher in PCD cluster A, indicating that this cluster may be more closely associated with PCD mechanisms. Furthermore, our observations indicate that patients exhibiting elevated tumour mutation burden (TMB) are at an augmented risk of mortality, in comparison to those displaying low TMB and low-risk statuses, who are more likely to experience prolonged survival. In addition, we have investigated the potential distinctions in the susceptibility of diverse risk cohorts towards emerging targeted therapies, designed for the treatment of HCC. Moreover, our investigation has shown that AZD2014, SB505124, LJI308 and OSI-207 show a greater efficacy in patients in the low-risk category. Conversely, for the high-risk group patients, PD173074, ZM447439 and CZC24832 exhibit a stronger response. Our findings suggest that the identification of risk groups and personalized treatment selection could lead to better clinical outcomes for patients with HCC. Furthermore, significant heterogeneity in clinical response to ICI therapy was observed among HCC patients with varying PCD expression patterns. This novel discovery underscores the prospective usefulness of these expression patterns as prognostic indicators for HCC patients and may aid in tailoring targeted treatment for those of distinct risk strata. Our investigation introduces a novel prognostic model for HCC that integrates diverse PCD expression patterns. This innovative model provides a novel approach for forecasting prognosis and assessing drug sensitivity in HCC patients, driving a more personalized and efficacious treatment paradigm, elevating clinical outcomes. Nonetheless, additional research endeavours are required to confirm the model's precision and assess its potential to inform clinical decision-making for HCC patients.

Protocol to analyze immune cells in the tumor microenvironment by transcriptome using machine learning

Immunotherapy is a promising strategy to treat cancer. Here, we present a protocol for analyzing the transcriptome-based phenotypic alterations and immune cell infiltration in the tumor microenvironment. We describe steps for integrating single-cell RNA sequencing (scRNA-seq) data, comparing phenotypes and origins of mononuclear phagocytes, inferring the differentiation trajectory and infiltration process, and identifying infiltration-associated genes using machine learning. We then detail procedures for exploring the impact of these genes in prognosis through the integrated microarray and bulk RNA-seq data to obtain potential drug targets. For complete details on the use and execution of this protocol, please refer to Liao et al.1.

THBS1+ myeloid cells expand in SLD hepatocellular carcinoma and contribute to immunosuppression and unfavorable prognosis through TREM1

Hepatocellular carcinoma (HCC) is an inflammation-associated cancer arising from viral or non-viral etiologies including steatotic liver diseases (SLDs). Expansion of immunosuppressive myeloid cells is a hallmark of inflammation and cancer, but their heterogeneity in HCC is not fully resolved and might underlie immunotherapy resistance. Here, we present a high-resolution atlas of innate immune cells from patients with HCC that unravels an SLD-associated contexture characterized by influx of inflammatory and immunosuppressive myeloid cells, including a discrete population of THBS1+ regulatory myeloid (Mreg) cells expressing monocyte- and neutrophil-affiliated genes. THBS1+ Mreg cells expand in SLD-associated HCC, populate fibrotic lesions, and are associated with poor prognosis. THBS1+ Mreg cells are CD163+ but distinguished from macrophages by high expression of triggering receptor expressed on myeloid cells 1 (TREM1), which contributes to their immunosuppressive activity and promotes HCC tumor growth in vivo. Our data support myeloid subset-targeted immunotherapies to treat HCC.

RNA-binding protein RPS7 promotes hepatocellular carcinoma progression via LOXL2-dependent activation of ITGB1/FAK/SRC signaling

BACKGROUND

Metastasis is one of the leading cause contributes to treatment failure and poor prognosis of hepatocellular carcinoma (HCC) patients. The underlying mechanism of HCC metastasis remains to be determined. Although several RNA binding proteins (RBPs) have been found to participate in tumorigenesis and progression of liver cancer, the role of RBPs in HCC patients with extrahepatic metastases is poorly understood.

METHODS

By performing RNA-seq of primary HCC tissues (including HCC with extrahepatic metastasis and those did not develop metastasis), we identified a set of HCC metastasis-associated RBPs candidates. Among which, ribosomal protein S7 (RPS7) was found to be remarkably increased in HCC tissues and be strongly related to HCC poor survival. Overexpression or CRISPR-Cas9-mediated knockout were applied to investigate the role of RPS7 on the metastasis-associated phenotypes of HCC cells. RNA sequencing, RIP, RNA-pull down, dual luciferase reporter assay, nascent RNA capture assay, and RNA decay and so on, were applied to reveal the underlying mechanism of RPS7 induced HCC metastasis.

RESULTS

Gain- and loss- of function analyses revealed that RPS7 promoted HCC cells adhesion, migration and invasion capabilities, as well as lung metastasis. Mechanistically, we uncovered that lysyl oxidase-like 2 (LOXL2) was a critical downstream target of RPS7. RPS7 could stabilize LOXL2 mRNA by binding to AUUUA motifs in the 3155-3375 region of the 3'UTR of LOXL2 mRNA, thus increased LOXL2 expression via elevating LOXL2 mRNA abundance. Further research revealed that LOXL2 could accelerate focal adhesion formation through maintaining the protein stability of ITGB1 and activating ITGB1-mediated FAK/SRC signaling pathway, and thereby contribute to the pro-metastasis effect of RPS7.

CONCLUSIONS

Taken together, our data reveal a novel function of RPS7 in HCC metastasis, also reveal the critical roles of the RPS7/LOXL2/ITGB1 axis in HCC metastasis and shed new light on the exploration of molecular drugs against HCC.

Identification and verification of a prognostic autophagy-related gene signature in hepatocellular carcinoma

This study aimed to investigate the potential of autophagy-related genes (ATGs) as a prognostic signature for HCC and explore their relationships with immune cells and immune checkpoint molecules. A total of 483 samples were collected from the GEO database (n = 115) and The Cancer Genome Atlas (TCGA) database (n = 368). The GEO dataset was used as the training set, while the TCGA dataset was used for validation. The list of ATGs was obtained from the human autophagy database (HADB). Using Cox regression and LASSO regression methods, a prognostic signature based on ATGs was established. The independent use of this prognostic signature was tested through subgroup analysis. Additionally, the predictive value of this signature for immune-related profiles was explored. Following selection through univariate Cox regression analysis and iterative LASSO Cox analysis, a total of 11 ATGs were used in the GEO dataset to establish a prognostic signature that stratified patients into high- and low-risk groups based on survival. The robustness of this prognostic signature was validated using an external dataset. This signature remained a prognostic factor even in subgroups with different clinical features. Analysis of immune profiles revealed that patients in the high-risk group exhibited immunosuppressive states characterized by lower immune scores and ESTIMATE scores, greater tumour purity, and increased expression of immune checkpoint molecules. Furthermore, this signature was found to be correlated with the infiltration of different immune cell subpopulations. The results suggest that the ATG-based signature can be utilized to evaluate the prognosis of HCC patients and predict the immune status within the tumour microenvironment (TME). However, it is important to note that this study represents a preliminary attempt to use ATGs as prognostic indicators for HCC, and further validation is necessary to determine the predictive power of this signature.

Higher Inflammatory Response in Hepatocellular Carcinoma is Associated with Immune Cell Infiltration and a Better Outcome

Background & Aims

Hepatocellular carcinoma (HCC) often develops from chronic liver inflammation. Inflammation within a tumor can either promote cancer progression or activate an immune response against it. This study aims to determine the clinical significance of enhanced inflammation in HCC.

Methods

Data from 655 HCC patients across four cohorts (TCGA, GSE6764, GSE76427, GSE89377) were examined. Inflammatory response was quantified using a scoring system derived from the gene set variation analysis of the "INFLAMMATORY_RESPONSE" gene set.

Results

A stepwise increase in inflammatory response was noted from normal liver to cirrhosis, with consistently lower levels in HCC across both GSE6764 and GSE89377 cohorts (both p<0.001). Similar trends were observed in interferon response, pathways such as IL6/JAK/STAT3 and complement signaling, coagulation cascade, and allograft rejection (all p<0.02). HCCs with high inflammatory response were associated with increased immune cell infiltrations (p<0.01) and cytolytic activity (p<0.001). Interestingly, these HCCs had reduced mutation rates, no relationship with cell proliferation, and displayed both immune responses and pro-cancerous signals including epithelial-mesenchymal transition, KRAS, and hypoxia. Further, a high inflammatory score correlated with improved disease-free survival in TCGA (p=0.034) and overall survival in GSE76427 (p=0.008).

Conclusion

HCC with higher levels of inflammatory response demonstrated increased immune cell infiltration, enhanced immune-related and other pro-cancerous-related signaling, and better patient prognosis.

Single-cell RNA-Seq Elucidates the Crosstalk Between Cancer Stem Cells and the Tumor Microenvironment in Hepatocellular Carcinoma

Background: The challenge of systemic treatment for hepatocellular carcinoma (HCC) stems from the development of drug resistance, primarily driven by the interplay between cancer stem cells (CSCs) and the tumor microenvironment (TME). However, there is a notable dearth of comprehensive research investigating the crosstalk between CSCs and stromal cells or immune cells within the TME of HCC. Methods: We procured single-cell RNA sequencing (scRNA-Seq) data from 16 patients diagnosed with HCC. Employing meticulous data quality control and cell annotation procedures, we delineated distinct CSCs subtypes and performed multi-omics analyses encompassing metabolic activity, cell communication, and cell trajectory. These analyses shed light on the potential molecular mechanisms governing the interaction between CSCs and the TME, while also identifying CSCs' developmental genes. By combining these developmental genes, we employed machine learning algorithms and RT-qPCR to construct and validate a prognostic risk model for HCC. Results: We successfully identified CSCs subtypes residing within malignant cells. Through meticulous enrichment analysis and assessment of metabolic activity, we discovered anomalous metabolic patterns within the CSCs microenvironment, including hypoxia and glucose deprivation. Moreover, CSCs exhibited aberrant activity in signaling pathways associated with lipid metabolism. Furthermore, our investigations into cell communication unveiled that CSCs possess the capacity to modulate stromal cells and immune cells through the secretion of MIF or MDK, consequently exerting regulatory control over the TME. Finally, through cell trajectory analysis, we found developmental genes of CSCs. Leveraging these genes, we successfully developed and validated a prognostic risk model (APCS, ADH4, FTH1, and HSPB1) with machine learning and RT-qPCR. Conclusions: By means of single-cell multi-omics analysis, this study offers valuable insights into the potential molecular mechanisms governing the interaction between CSCs and the TME, elucidating the pivotal role CSCs play within the TME. Additionally, we have successfully established a comprehensive clinical prognostic model through bulk RNA-Seq data.

Integration of single-cell and bulk RNA-sequencing to analyze the heterogeneity of hepatocellular carcinoma and establish a prognostic model

BACKGROUND

The highly heterogeneous nature of hepatocellular carcinoma (HCC) results in different responses and prognoses to the same treatment in patients with similar clinical stages.

AIMS

Thus, it is imperative to investigate the association between HCC tumor heterogeneity and treatment response and prognosis.

METHODS AND RESULTS

At first, we downloaded scRNA-seq, bulk RNA-seq, and clinical data from TCGA and GEO databases. We conducted quality control, normalization using SCTransform, dimensionality reduction using PCA, batch effect removal using Harmony, dimensionality reduction using UMAP, and cell annotation-based marker genes on the scRNA-seq data. We recognized tumor cells, identified tumor-related genes (TRGs), and performed cell communication analysis. Next, we developed a prognostic model using univariable Cox, LASSO, and multivariate Cox analyses. The signature was evaluated using survival analysis, ROC curves, C-index, and nomogram. Last, we studied the predictability of the signature in terms of prognosis and immunotherapeutic response for HCC, assessed a variety of drugs for clinical treatment, and used the qRT-PCR analysis to validate the mRNA expression levels of prognostic TRGs.

CONCLUSION

To conclude, this study expounded upon the influence of tumor cell heterogeneity on the prediction of treatment outcomes and prognosis in HCC. This, in turn, enhances the predictive ability of the TNM staging system and furnishes novel perspectives on the prognostic assessment and therapy of HCC.

Oxeiptosis core genes and their multi-omics analysis in hepatocellular carcinoma

Oxeiptosis is a recently discovered caspase-independent, non-inflammatory programmed cell death modality. Current studies suggest that oxeiptosis has crucial effects on biological processes in a variety of diseases. However, the mechanism of oxeiptosis in hepatocellular carcinoma (HCC) remains unclear and no relevant studies have been published. Therefore, this study is intended to investigate the mechanism and prognostic role of oxeiptosis-related genes in HCC. We explored the mechanisms and molecular phenotypes underlying the role of oxeiptosis in HCC through multi-omics analysis. Firstly, we obtained RNA-sequencing and clinical data from public database and divided the samples into trial and validation cohorts in subsequent analyses. We then screened oxeiptosis core genes (OCGs) and screened prognosis-related genes. Based on different molecular markers, we identified the molecular phenotypes of HCC, and the potential OCGs molecular mechanisms were explored. Subsequently, we construct a prognostic prediction system for HCC. Finally, we analyzed the tumor microenvironment and the immune escape phenomenon. We screened a total of 69 OCGs, most of which were prognostic risk factors for HCC. A majority of OCGs were enriched in cell cycle regulation and mitotic processes, which were related to both tumor cell proliferation and death. We identified 2 different molecular typing options with significant differences in prognosis, function, and signaling pathway enrichment between different molecular subtypes. The prognostic prediction model combined with molecular phenotypes and had a good predictive effect. Finally, we found CD4 + T-cell exhaustion in samples with specific molecular phenotypes. Through multi-omics analysis of OCGs, we not only revealed the possible molecular mechanisms of OCGs in HCC but also provided a prognostic prediction system for clinical application through molecular typing and risk scoring model. Meanwhile, we found immune escape mechanisms in HCC.

Tumor-associated endothelial cell prognostic risk model and tumor immune environment modulation in liver cancer based on single-cell and bulk RNA sequencing: Experimental verification

BACKGROUND

To build a prognostic and immunotherapeutic response prediction model for liver cancer based on marker genes of tumor-associated endothelial cell (TEC).

METHOD

Single cell sequencing data from Gene Expression Omnibus (GEO) liver cancer patients were utilized to identify TEC subpopulations. Models were built from transcriptomic and clinical data of TCGA liver cancer patients. The GSE76427 and ICGC databases were used as independent validation sets. Time-dependent receiver operating characteristic (ROC) curves and Kaplan-Meier curves were used to verify the ability of the model to predict survival. XCELL, TIMER, QUANTISEQ, CIBERSORT, CIBERSORT-ABS, and ssGSEA were applied to evaluate tumor immune cell infiltration. The TIDE score was used to predict the effect of immunotherapy. Immune blockade checkpoint gene, tumor mutational load and GSVA enrichment analyses were further explored. The expression levels of candidate genes were measured and validated by real-time PCR between liver cancer tissues and adjacent nontumor liver tissues.

RESULTS

Eighty-seven genes were identified as marker genes for TECs. IGFBP3, RHOC, S100A16, FSCN1, and CLEC3B were included in the constructed prognostic model. Time-dependent ROC curve values were higher than 0.700 in both the model and validation groups. The low risk group exhibited high immune cell infiltration and function than the higher risk group. The TIDE score indicated that the low-risk group benefited more from immunotherapy than the high-risk group. The risk score and multiple immune blockade checkpoint genes and immune-related pathways were strongly correlated.

CONCLUSION

Novel signatures of TEC marker genes showed a powerful ability to predict prognosis and immunotherapy response in patients with liver cancer.

Identification of subclusters and prognostic genes based on glycolysis/gluconeogenesis in hepatocellular carcinoma

Background

This study aimed to examine glycolysis/gluconeogenesis-related genes in hepatocellular carcinoma (HCC) and evaluate their potential roles in HCC progression and immunotherapy response.

Methods

Data analyzed in this study were collected from GSE14520, GSE76427, GSE174570, The Cancer Genome Atlas (TCGA), PXD006512, and GSE149614 datasets, metabolic pathways were collected from MSigDB database. Differentially expressed genes (DEGs) were identified between HCC and controls. Differentially expressed glycolysis/gluconeogenesis-related genes (candidate genes) were obtained and consensus clustering was performed based on the expression of candidate genes. Bioinformatics analysis was used to evaluate candidate genes and screen prognostic genes. Finally, the key results were tested in HCC patients.

Results

Thirteen differentially expressed glycolysis/gluconeogenesis-related genes were validated in additional datasets. Consensus clustering analysis identified two distinct patient clusters (C1 and C2) with different prognoses and immune microenvironments. Immune score and tumor purity were significantly higher in C1 than in C2, and CD4+ memory activated T cell, Tfh, Tregs, and macrophage M0 were higher infiltrated in HCC and C1 group. The study also identified five intersecting DEGs from candidate genes in TCGA, GSE14520, and GSE141198 as prognostic genes, which had a protective role in HCC patient prognosis. Compared with the control group, the prognostic genes all showed decreased expression in HCC patients in RT-qPCR and Western blot analyses. Flow cytometry verified the abnormal infiltration level of immune cells in HCC patients.

Conclusion

Results showed that glycolysis/gluconeogenesis-related genes were associated with patient prognosis, immune microenvironment, and response to immunotherapy in HCC. It suggests that the model based on five prognostic genes may valuable for predicting the prognosis and immunotherapy response of HCC patients.

Comprehensive analysis and validation of SNX7 as a novel biomarker for the diagnosis, prognosis, and prediction of chemotherapy and immunotherapy response in hepatocellular carcinoma

BACKGROUND

Studies have demonstrated that Sorting nexin 7 (SNX7) functions as an anti-apoptotic protein in liver tissue and plays a crucial role in the survival of hepatocytes during early embryonic development. However, its diagnostic and prognostic value as well as the predictive value of chemotherapy and immunotherapy have not been reported in hepatocellular carcinoma (HCC).

METHODS

SNX7 mRNA expression and its diagnostic efficacy were examined in GEO datasets, and the findings were further confirmed in TCGA, ICGC cohorts, and cell lines. The protein level of SNX7 was determined using CPTAC and HPA databases, and the results were validated through immunohistochemistry (IHC). Survival analyses were performed in TCGA and ICGC cohorts, and the results were subsequently validated via Kaplan-Meier Plotter. The response to chemotherapy and immunotherapy was predicted via GDSC dataset and TIDE algorithm, respectively. R packages were employed to explore the relationship between SNX7 expression and immune infiltration, m6A modification, as well as the functional enrichment of differentially expressed genes (DEGs).

RESULTS

The expression of SNX7 at both mRNA and protein levels was significantly upregulated in HCC tissues. SNX7 exhibited superior diagnostic efficacy compared to AFP alone for HCC detection, and combining it with AFP improved the diagnostic accuracy for HCC. High SNX7 was associated with unfavorable outcomes, including poor overall survival, disease-specific survival, progression-free survival, and advanced pathological stage, in patients with HCC, and SNX7 was identified as an independent risk factor for HCC. Moreover, elevated SNX7 expression was positively correlated with increased sensitivity to various chemotherapy drugs, including sorafenib, while it was associated with resistance to immunotherapy in HCC patients. Correlation analysis revealed a relationship between SNX7 and multiple m6A-related genes and various immune cells. Finally, enrichment analysis demonstrated strong associations of SNX7 with critical biological processes, such as cell cycle regulation, cellular senescence, cell adhesion, DNA replication, and mismatch repair pathway in HCC.

CONCLUSIONS

Our study highlights the association of SNX7 with the immune microenvironment and its potential influence on HCC progression. SNX7 emerges as a promising novel biomarker for the diagnosis, prognosis, and prediction of response to chemotherapy and immunotherapy in patients with HCC.

Molecular subtypes and scoring tools related to Foxo signaling pathway for assessing hepatocellular carcinoma prognosis and treatment responsiveness

Background: Transcription factors in Foxo signaling pathway influence hepatocellular carcinoma metastasis through epithelial mesenchymal transition-related pathways. Prognostic factors in the Foxo signaling pathway are feasible for HCC prognosis and therapeutic management. Methods: Based on the differentially expressed genes and Foxo signaling pathway genes in HCC, the ConsensusClusterPlus package was conducted to identify Foxo signaling pathway-related molecular subtypes in HCC. Based on the DEGs in the FMSs, the optimal prognostic factors in HCC were screened by cox and least absolute shrinkage and selection operator (LASSO) cox analysis to form the Foxo prognosis score (FPS). The prognostic predictive effectiveness of FPS was assessed by Kaplan Meier (K-M) analysis and Receiver Operating Characteristic (ROC) analysis. Additionally, tumor microenvironment (TME) score, tumor mutation burden (TMB) and treatment sensitivity differences in FMSs and FPS groups were also evaluated. Results: There were low, medium and high Foxo signaling pathway activity molecular subtypes in HCC named FMS 1, FMS 2 and FMS 3, respectively. FMS 1 with lowest Foxo signaling pathway activity presented an excellent survival advantage, while FMS 3 with highest Foxo signaling pathway activity exhibited an inhibitory TME status. According to FPS grouping, low FPS exhibited favorable survival, low TMB and anti-tumor activity. Patients in the low FPS group were mostly in the early stage of cancer. Moreover, we found that patients with high and low FPS exhibited different sensitivity to chemotherapy, and patients with low FPS were more sensitive to immunotherapy. Conclusion: We revealed a novel molecular subtype and prognostic tool based on Foxo signaling pathway signature, which could potentially provide a direction for accurate and effective assessment of potential personalized treatment options and prognostic management for HCC patients.

MIF is a critical regulator of mononuclear phagocytic infiltration in hepatocellular carcinoma

Immunotherapy targeting tumor-associated macrophages (TAMs) is a promising approach to treating cancer. However, the limited drug targets and ambiguous mechanisms impede the development of clinical immunotherapy strategies. To elucidate the underlying processes involved in mononuclear phagocyte (MNP) infiltration and phenotypic changes in hepatocellular carcinoma (HCC), we integrated single-cell RNA-sequencing data from 100,030 cells derived from patients with HCC and healthy individuals and compared the phenotypes and origins of the MNPs in the tumor core, tumor periphery, adjacent normal tissue, and healthy liver samples. Using machine learning and multi-omics analyses, we identified 445 infiltration-associated genes and potential drug targets affecting this process. Through in vitro experiments, we found that the expression of macrophage migration inhibitory factor (MIF) is the upstream regulator of secreted phosphoprotein 1 (SPP1) and promote migration in TAMs. Our findings also indicate that MIF promotes tumor metastasis and invasion and is a promising potential target for treating HCC.

Integrated analysis revealing a novel stemness-metabolism-related gene signature for predicting prognosis and immunotherapy response in hepatocellular carcinoma

Hepatocellular carcinoma (HCC) is a malignant lethal tumor and both cancer stem cells (CSCs) and metabolism reprogramming have been proven to play indispensable roles in HCC. This study aimed to reveal the connection between metabolism reprogramming and the stemness characteristics of HCC, established a new gene signature related to stemness and metabolism and utilized it to assess HCC prognosis and immunotherapy response. The clinical information and gene expression profiles (GEPs) of 478 HCC patients came from the Gene Expression Omnibus (GEO) and the Cancer Genome Atlas (TCGA). The one-class logistic regression (OCLR) algorithm was employed to calculate the messenger ribonucleic acid expression-based stemness index (mRNAsi), a new stemness index quantifying stemness features. Differentially expressed analyses were done between high- and low-mRNAsi groups and 74 differentially expressed metabolism-related genes (DEMRGs) were identified with the help of metabolism-related gene sets from Molecular Signatures Database (MSigDB). After integrated analysis, a risk score model based on the three most efficient prognostic DEMRGs, including Recombinant Phosphofructokinase Platelet (PFKP), phosphodiesterase 2A (PDE2A) and UDP-glucuronosyltransferase 1A5 (UGT1A5) was constructed and HCC patients were divided into high-risk and low-risk groups. Significant differences were found in pathway enrichment, immune cell infiltration patterns, and gene alterations between the two groups. High-risk group patients tended to have worse clinical outcomes and were more likely to respond to immunotherapy. A stemness-metabolism-related model composed of gender, age, the risk score model and tumor-node-metastasis (TNM) staging was generated and showed great discrimination and strong ability in predicting HCC prognosis and immunotherapy response.

E2F target score is associated with cell proliferation and survival of patients with hepatocellular carcinoma

BACKGROUND

E2F target genes are essential for the cell cycle. A score that quantifies its activity is expected to reflect the aggressiveness and prognosis of hepatocellular carcinoma.

METHODS

Cohorts of hepatocellular carcinoma patients (total n = 655) from The Cancer Genome Atlas, GSE89377, GSE76427, and GSE6764 were analyzed. The cohorts were divided into high versus low by the median.

RESULTS

All the Hallmark cell proliferation-related gene sets were consistently enriched in hepatocellular carcinoma with high E2F targets score, and E2F score was associated with grade, tumor size, American Joint Committee on Cancer staging, proliferation score, and MKI67 expression, as well as with less abundance of hepatocytes and stromal cells. E2F targets enriched DNA repair, mTORC1 signaling, glycolysis, and unfolded protein response gene sets and were significantly associated with the higher intratumoral genomic heterogeneity, homologous recombination deficiency, and progression of hepatocellular carcinoma. On the other hand, there was no relationship between E2F targets and mutation rates or neoantigens. High E2F hepatocellular carcinoma did not enrich any of the immune-response-related gene sets but was associated with high infiltration of Th1, Th2 cells, and M2 macrophage; however, there was no difference in cytolytic activity. In both early (I and II) and late (III and IV) stages of hepatocellular carcinoma, a high E2F score was associated with worse survival and was an independent prognostic factor for overall and disease-specific survival in patients with hepatocellular carcinoma.

CONCLUSION

The E2F target score, associated with cancer aggressiveness and worse survival, could be used as a prognostic biomarker in patients with hepatocellular carcinoma.

Expression Patterns of HOX Gene Family Defines Tumor Microenvironment and Immunotherapy in Hepatocellular Carcinoma

Hepatocellular carcinoma (HCC) pathophysiology is prevalently related with HOX genes. However, the study on associations of extensive HOX genes with tumor microenvironment and drug sensitivity of HCC remains scarce. The data sets of HCC were downloaded from TCGA, ICGC, and GEO by bioinformatics method and analyzed. Based on a computational frame, HCC samples were divided into a high and a low HOXscore group, and significantly shorter survival time in the high HOXscore was observed relative to low HOXscore group using survival analysis. Gene set enrichment analysis (GSEA) revealed that the high HOXscore group was more likely to be enriched in cancer-specific pathways. Furthermore, the high HOXscore group was involved in the infiltration of inhibitory immune cells. In response to anti-cancer drugs, the high HOXscore group was more sensitive to mitomycin and cisplatin. Importantly, the HOXscore was associated with the therapeutic efficacy of PD-L1 blockade, suggesting that the development of potential drugs targeting these HOX genes to aid the clinical benefits of immunotherapy is needed. In addition, RT-qPCR and immunohistochemistry showed 10 HOX genes mRNA expression was higher in HCC compared to the normal tissues. This study provides a comprehensive analysis of HOX genes family in HCC and revealed the potential function of these HOX genes family in tumor microenvironment (TME) and identified their therapeutic liability in targeted therapy and immunotherapy. Eventually, this work highlights the cross-talk and potential clinical utility of HOX genes family in HCC therapy.

Novel insights into the progression and prognosis of the calpain family members in hepatocellular carcinoma: a comprehensive integrated analysis

Objectives: The goal of our bioinformatics study was to comprehensively analyze the association between the whole calpain family members and the progression and prognosis of hepatocellular carcinoma (HCC). Methods: The data were collected from The Cancer Genome Atlas (TCGA). The landscape of the gene expression, copy number variation (CNV), mutation, and DNA methylation of calpain members were analyzed. Clustering analysis was performed to stratify the calpain-related groups. The least absolute shrinkage and selection operator (LASSO)-based Cox model was used to select hub survival genes. Results: We found 14 out of 16 calpain members expressed differently between tumor and normal tissues of HCC. The clustering analyses revealed high- and low-risk calpain groups which had prognostic difference. We found the high-risk calpain group had higher B cell infiltration and higher expression of immune checkpoint genes HAVCR2, PDCD1, and TIGHT. The CMap analysis found that the histone deacetylase (HDAC) inhibitor trichostatin A and the PI3K-AKT-mTOR pathway inhibitors LY-294002 and wortmannin might have a therapeutic effect on the high-risk calpain group. The DEGs between calpain groups were identified. Subsequent univariate Cox analysis of each DEG and LASSO-based Cox model obtained a calpain-related prognostic signature. The risk score model of this signature showed good ability to predict the overall survival of HCC patients in TCGA datasets and external validation datasets from the Gene Expression Omnibus database and the International Cancer Genome Consortium database. Conclusion: We found that calpain family members were associated with the progression, prognosis, and drug response of HCC. Our results require further studies to confirm.

GBAP1 functions as a tumor promotor in hepatocellular carcinoma via the PI3K/AKT pathway

Hepatocellular carcinoma (HCC) is common worldwide, and novel therapeutic targets and biomarkers are needed to improve outcomes. In this study, bioinformatics analyses combined with in vitro and in vivo assays were used to identify the potential therapeutic targets. Differentially expressed genes (DEG) in HCC were identified by the intersection between The Cancer Genome Atlas and International Cancer Genome Consortium data. The DEGs were evaluated by a gene set enrichment analysis as well as Gene Ontology and Kyoto Encyclopedia of Genes and Genomes analyses. A protein interaction network, univariate Cox regression, and Lasso regression were used to screen out hub genes correlated with survival. Increased expression of the long noncoding RNA GBAP1 in HCC was confirmed in additional datasets and its biological function was evaluated in HCC cell lines and nude mice. Among 121 DEGs, GBAP1 and PRC1 were identified as hub genes with significant prognostic value. Overexpression of GBAP1 in HCC was confirmed in 21 paired clinical tissues and liver cancer or normal cell lines. The inhibition of GBAP1 expression reduced HCC cell proliferation and promoted apoptosis by inactivating the PI3K/AKT pathway in vitro and in vivo. Therefore, GBAP1 has a pro-oncogenic function in HCC and is a candidate prognostic biomarker and therapeutic target.

Identification and characterization of a novel molecular classification based on disulfidptosis-related genes to predict prognosis and immunotherapy efficacy in hepatocellular carcinoma

BACKGROUND

Disulfidptosis has been discovered as a mechanism of cell death mediating by SLC7A11. Nonetheless, little is known about the relationship between disulfidptosis-related genes (DRG) and hepatocellular carcinoma (HCC).

METHODS

7 datasets including 1,302 HCC patients and 62,530 cells were downloaded. We adopted consensus clustering algorithm to construct the consensus matrix and cluster the samples' DRG related expression profile data. Then, weighted gene co-expression network analysis (WGCNA) was conducted to identify hub gene modules associated with the identified clusters and determine the correlation between modules. A DRG.score was constructed based on genes through differential analysis and WGCNA of the 2 clusters.

RESULTS

Univariate and multivariate Cox regression analysis show that SLC7A11 and LRPPRC can be used as an independent factor in HCC. Then, two molecular subgroups with significantly different survival were identified based on 10 DRG. The cluster.A showed a worse prognosis, higher immune infiltration, and higher immune checkpoint expression. Then, by differential analysis and WGCNA of the 2 clusters, we identified 5 hub genes, and constructed a DRG.score. Univariate and multivariate Cox regression analysis show that DRG.score can be used as an independent factor to predict the prognosis in HCC. Furthermore, high DRG.score group had a worse prognosis, and was validated in TCGA-LIHC, LIRI-JP, GSE14520, GSE36376, and GSE76427. Preclinically, patients with higher DRG.score demonstrated significant immunotherapy therapeutic advantages and transcatheter arterial chemoembolization clinical benefits.

CONCLUSIONS

SLC7A11 and LRPPRC play an essential role in HCC prognosis prediction. The DRG.score might become useful biomarkers for novel therapeutic targets.

TWEAK Signaling-Induced ID1 Expression Drives Malignant Transformation of Hepatic Progenitor Cells During Hepatocarcinogenesis

The malignant transformation of hepatic progenitor cells (HPCs) in the inflammatory microenvironment is the root cause of hepatocarcinogenesis. However, the potential molecular mechanisms are still elusive. The HPCs subgroup is identified by single-cell RNA (scRNA) sequencing and the phenotype of HPCs is investigated in the primary HCC model. Bulk RNA sequencing (RNA-seq) and proteomic analyses are also performed on HPC-derived organoids. It is found that tumors are formed from HPCs in peritumor tissue at the 16th week in a HCC model. Furthermore, it is confirmed that the macrophage-derived TWEAK/Fn14 promoted the expression of inhibitor of differentiation-1 (ID1) in HPCs via NF-κB signaling and a high level of ID1 induced aberrant differentiation of HPCs. Mechanistically, ID1 suppressed differentiation and promoted proliferation in HPCs through the inhibition of HNF4α and Rap1GAP transcriptions. Finally, scRNA sequencing of HCC patients and investigation of clinical specimens also verified that the expression of ID1 is correlated with aberrant differentiation of HPCs into cancer stem cells, patients with high levels of ID1 in HPCs showed a poorer prognosis. This study provides important intervention targets and a theoretical basis for the clinical diagnosis and treatment of HCC.

Lactobacillus brevis alleviates the progress of hepatocellular carcinoma and type 2 diabetes in mice model via interplay of gut microflora, bile acid and NOTCH 1 signaling

Type 2 diabetes (T2DM) clinically exhibits a higher incidence of hepatocellular carcinoma (HCC), contributing to a lousy prognosis in patients harboring both diseases. Microflora-based therapy draws attention with low side effects. Accumulating evidence shows that Lactobacillus brevis can improve blood glucose and body weight of the T2DM mice model and reduce several cancer incidences. However, the therapeutic effect of Lactobacillus brevis in affecting the prognosis of T2DM+HCC remains unknown. In this study, we aim to explore this question via an established T2DM+HCC mice model. We observed a significant alleviation after the probiotic intervention. Lactobacillus brevis improves blood glucose and insulin resistance and ameliorates Mechanically. Combined with a multi-omics approach including 16SrDNA, GC-MS, and RNA-seq, we identified distinct intestinal microflora composition and metabolites after Lactobacillus brevis intervention. Furthermore, we found that Lactobacillus brevis delayed disease progression by regulating MMP9 and NOTCH 1 signaling pathways, potentially through gut microflora and BA interaction. This study indicates that Lactobacillus brevis may improve the prognosis of T2DM + HCC, providing novel therapeutic opportunities via targeting intestinal flora for patients with T2DM+HCC.

CD69 serves as a potential diagnostic and prognostic biomarker for hepatocellular carcinoma

The prevalence and mortality of hepatocellular carcinoma (HCC) are still increasing. This study aimed to identify potential therapeutic targets related to patient prognosis. Data were downloaded from TCGA, GSE25097, GSE36376, and GSE76427 datasets. Differential analysis and enrichment analysis were performed in HCC. Cell deaths were evaluated, and least absolute shrinkage and selection operator regression (LASSO) regression was analyzed to screen candidate genes. Additionally, immune cell infiltration in HCC was assessed. We identified 4088 common DEGs with the same direction of differential expression in all four datasets, they were mainly enriched in immunoinflammation and cell cycle pathways. Apoptosis was significantly suppressed in HCC in GSEA and GSVA. After LASSO regression analysis, we screened CD69, CDC25B, MGMT, TOP2A, and TXNIP as candidate genes. Among them, CD69 significantly influenced the overall survival of HCC patients in both TCGA and GSE76427. CD69 may be a protective factor for outcome of HCC patients. In addition, CD69 was positive correlation with T cells and CD3E. CD69, CDC25B, MGMT, TOP2A, and TXNIP were potential diagnostic and prognostic target for HCC, especially CD69.

Development of a TMErisk model based on immune infiltration in tumour microenvironment to predict prognosis of immune checkpoint inhibitor treatment in hepatocellular carcinoma

Immune checkpoint inhibitor (ICI) treatment has created the opportunity of improved outcome for patients with hepatocellular carcinoma (HCC). However, only a minority of HCC patients benefit from ICI treatment owing to poor treatment efficacy and safety concerns. There are few predictive factors that precisely stratify HCC responders to immunotherapy. In this study, we developed a tumour microenvironment risk (TMErisk) model to divide HCC patients into different immune subtypes and evaluated their prognosis. Our results indicated that virally mediated HCC patients who had more common tumour protein P53 (TP53) alterations with lower TMErisk scores were appropriate for ICI treatment. HCC patients with alcoholic hepatitis who more commonly harboured catenin beta 1 (CTNNB1) alterations with higher TMErisk scores could benefit from treatment with multi-tyrosine kinase inhibitors. The developed TMErisk model represents the first attempt to anticipate tumour tolerance of ICIs in the TME through the degree of immune infiltration in HCCs.

Body mass index and serum alpha-fetoprotein level associated with PD1 expression and prognosis in patients with hepatocellular carcinoma

Background

Many factors affect the outcome of treatment with programmed death 1 (PD1) inhibitors for hepatocellular carcinoma (HCC). The objective of this study was to investigate the associations of clinicopathological parameters with PD1 expression and HCC prognosis.

Methods

A total of 372 HCC patients (Western population) from The Cancer Genome Atlas (TCGA), and 115 primary HCC tissues and 52 adjacent tissues (Dataset GSE76427, Eastern population) from Gene Expression Omnibus (GEO) database were enrolled in this study. The primary outcome was 2-year relapse-free survival. Kaplan-Meier survival curves with the log-rank test were used to analyze the differences in prognosis between the two groups. X-tile software was used to confirm the optimal cut-off for clinicopathological parameters while assessing the outcome. Immunofluorescence was performed on HCC tissues to evaluate PD1 expression.

Results

Expression of PD1 was up-regulated in tumor tissue from both TCGA and GSE76427 patients, which positively correlated with body mass index (BMI), serum alpha-fetoprotein (AFP) level, and prognosis. Patients with higher PD1, lower AFP, or lower BMI had longer overall survival than those with lower PD1, higher AFP, or higher BMI, respectively. AFP and PD1 expression were validated in 17 primary HCC patients from the first affiliated hospital, Zhejiang University School of Medicine. Finally, we confirmed longer relapse-free survival with higher PD1 or lower AFP.

Conclusion

The findings indicate that BMI and AFP are associated with PD1 expression and HCC prognosis, offering insight for clinical management and personalized immunotherapy for HCC.

Genome stability‑related lncRNA ZFPM2‑AS1 promotes tumor progression via miR‑3065‑5p/XRCC4 in hepatocellular carcinoma

Long noncoding RNAs (lncRNAs) have a certain link to genomic stability (GS). However, the regulatory relationship of lncRNAs and GS has not been thoroughly investigated in hepatocellular carcinoma (HCC). In the present study, samples were retrieved from The Cancer Genome Atlas with somatic mutations and lncRNA expression data. Cox regression analysis was used to identify independent prognostic factors. The RNA levels were determined by reverse transcription‑quantitative PCR and protein levels were detected by western blot analysis. Cell Counting Kit‑8 and colony‑formation assays were used to assess cell viability. Cell migration was measured by wound‑healing and Transwell assays. Cell apoptosis and cell‑cycle progression were evaluated by flow cytometry. GS was detected by alkaline comet and chromosomal aberration assays. A xenograft model and lung metastasis model were used to assess the role of zinc finger protein, FOG family member 2 antisense 1 (ZFPM2‑AS1) in tumor growth in vivo. The molecular mechanisms underlying the biological functions of ZFPM2‑AS1 were investigated through bioinformatics prediction, RNA pull‑down and luciferase reporter assays. A total of 85 genomic instability‑related lncRNAs were identified and a prognostic model was developed. The prognostic model exhibited good predictive power (area under the receiver operating characteristic curve, 0.786). ZFPM2‑AS1 was significantly upregulated in tumor tissues (P<0.001) and it promoted DNA damage repair (P<0.01) and tumor progression in vitro and in vivo. Luciferase reporter assays demonstrated that miR‑3065‑5p was able to bind directly with ZFPM2‑AS1 and X‑ray repair cross complementing 4 (XRCC4). ZFPM2‑AS1 upregulated XRCC4 expression by acting as a sponge (P<0.001). In the present study, a prognostic model for HCC was developed and validated, and one lncRNA of its components was experimentally investigated. ZFPM2‑AS1 regulates XRCC4 by sponging miR‑3065‑5p to promote GS and HCC progression.

Integrated investigation of the clinical implications and targeted landscape for RNA methylation modifications in hepatocellular carcinoma

BACKGROUND

RNA methylation (RM) is a crucial post-translational modification (PTM) that directs epigenetic regulation. It mostly consists of N¹-methyladenosine (m¹A), 5-methylcytosine (m⁵C), N³-methylcytidine (m³C), N⁶-methyladenosine (m⁶A), and 2'-O-methylation (Nm). The "writers" mainly act as intermediaries between these modifications and associated biological processes. However, little is known about the interactions and potential functions of these RM writers in hepatocellular carcinoma (HCC).

METHODS

The expression properties and genetic alterations of 38 RM writers were assessed in HCC samples from five bioinformatic datasets. Two patterns associated with RM writers were identified using consensus clustering. Then, utilizing differentially expressed genes (DEGs) from different RM subtypes, we built a risk model called RM_Score. Additionally, we investigated the correlation of RM_Score with clinical characteristics, tumor microenvironment (TME) infiltration, molecular subtypes, therapeutic response, immunotherapy effectiveness, and competing endogenous RNA (ceRNA) network.

RESULTS

RM writers were correlated with TME cell infiltration and prognosis. Cluster_1/2 and gene.cluster_A/B were shown to be capable of distinguishing the HCC patients with poor prognosis after consensus and unsupervised clustering of RNA methylation writers. Additionally, we constructed RNA modification pattern-specific risk model and subdivided the cases into RM_Score high and RM_Score low subgroups. In individual cohorts or merged datasets, the high RM_Score was related to a worse overall survival of HCC patients. RM_Score also exhibited correlations with immune and proliferation related pathways. In response to anti-cancer treatments, the RM_Score had a negative correlation (drug sensitive) with drugs that focused on the MAPK/ERK and metabolism signaling, and a positive correlation (drug resistant) with compounds targeting RKT and PI3K/mTOR signaling pathway. Notably, the RM_Score was connected to the therapeutic effectiveness of PD-L1 blockage, implying that RM writers may be the target of immunotherapy to optimize clinical outcomes. Additionally, a ceRNA network was generated including 2 lncRNAs, 4 miRNAs, and 7 mRNAs that was connected to RM writers.

CONCLUSIONS

We thoroughly investigated the potential functions of RNA methylation writers and established an RM_patterns-based risk model for HCC patients. This study emphasized the critical functions of RM modification in TME infiltration, targeted therapy, and immunotherapy, providing potential targets for HCC.

Machine learning integrations for development of a T-cell-tolerance derived signature to improve the clinical outcomes and precision treatment of hepatocellular carcinoma

Hepatocellular carcinoma (HCC) is characterized by high rates of recurrence and metastasis and poor prognosis. A recently discovered concept of T cell tolerance (TCT) has become an entirely new target of cancer immunotherapy. Unfortunately, the effect of TCT on the outcomes of HCC has not been explored. In this study, 7 public datasets and one external clinical cohort, including 1716 HCC patients were explored. Through WGCNA analysis and differential analysis, we explored the key TCT-related modulates. A total of 95 machine learning integrations across all validation cohorts were compared and the optimal method with the highest average C-index value was selected to construct the TCT derived signature (TCTS). In all independent clinical cohorts, TCTS showed accurate prediction of the prognosis, and was significantly correlated with clinical indicators and molecular features. Compared with 77 published gene signatures, the TCTS exhibited superior predictive performance. In the external clinical cohort, a novel nomogram (comprising TNM stage, Hepatitis B, Vascular invasion, Perineural invasion, AFP and TCTS) was constructed to test the clinical performance of TCTS. The results showed that the high TCTS scoring group showed dismal prognosis, improved sensitivity to oxaliplatin and good response to anti-PD-1/PD-L1 immunotherapy. Moreover, the low TCTS score group had few genomic alterations, low immune activation and low PD-1/PD-L1 expression levels. In conclusion, TCTS is an ideal biomarker for predicting the clinical outcomes and improving precision treatment of HCC.

Comprehensive Analysis of the Sorafenib-Associated Druggable Targets on Differential Gene Expression and ceRNA Network in Hepatocellular Carcinoma

Hepatocellular carcinoma (HCC) is the predominant pathological type of liver cancer. Several therapeutic treatments, including sorafenib and regorafenib, have only modestly improved survival in patients with HCC. The aim of this study was to investigate the expression profiles and the regulation of competitive endogenous RNAs (ceRNAs) of the sorafenib-related target genes in HCC. Based on clinical information and expression profiles of HCC clinical samples from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) databases, shared differentially expressed genes (DEGs) were analyzed and identified. Sorafenib-associated DEGs (SADs) were obtained by intersecting the DEGs with the sorafenib target genes from SuperTarget database. The expression patterns of SADs were verified in the Oncomine database. The biological functions of the SADs were annotated by gene set enrichment analysis (GSEA). In addition, a ceRNA network associated with SADs was constructed. Long non-coding RNAs (lncRNAs) in network that were significantly associated with overall survival were identified as prognosis of patients by Cox regression analysis. Finally, the expression levels of prognostic genes in HCC tissues and cell lines were verified using qRT-PCR. Gene expression differential analysis yielded a total of 146 common DEGs were obtained, including 21 upregulated and 125 downregulated DEGs. Among them, ten SADs were detected to be differentially expressed between tumor and normal tissues, including AXL, CYP2C19, CYP2C8, CYP2C9, CYP3A4, FGFR2, GMNN, PDGFRA, and TTK. GSEA analysis grouped them into three categories by function. The first category (CYP2C19, CYP2C8, CYP2C9 and CYP3A4) and second category (GMNN, TTK and EGER2) had the opposite roles in the enriched terms and pathways, while the third class (AXL and PDGFRA) has enrichment terms and pathways that intersect with those of the first and second categories. A ceRNA network associated with SADs was also constructed including 49 lncRNAs, 14 miRNAs, and 8 mRNAs. Three of these lncRNAs, SNHG7, GAS5 and HCP5, were found upregulated in HCC tissues and to be independent predictors in HCC patients. Significant correlations were found in expression between the prognostic lncRNAs and SADs. Ten SADs were systematically identified using expression data from HCC and normal tissues from TCGA and GEO datasets. GSEA analysis provided us with insight into the function of SADs. In the future, we will continue to explore the mechanisms of coordinated regulation of SADs-related prognostic lncRNAs and SADs at the ceRNA axis level and their potential functions in the development of HCC.

DNA methylation-mediated silencing of Neuronatin promotes hepatocellular carcinoma proliferation through the PI3K-Akt signaling pathway

AIMS

To explore the methylation status, function, and underlying mechanism of the imprinted gene Neuronatin (NNAT) in hepatocellular carcinoma (HCC) progression.

MAIN METHODS

Immunohistochemistry (IHC) was performed to evaluate the expression of NNAT in HCC samples. Bisulfite genomic sequencing PCR (BSP) was applied to examine the methylation status of the NNAT promoter. In addition, colony formation, 5-Ethynyl-20-deoxyuridine (EdU) assays and subcutaneous xenograft nude models were used to explore the roles of NNAT in HCC cell proliferation. Furthermore, RNA-seq and phospho-specific protein microarray assays were conducted to illustrate the underlying mechanism by which NNAT regulates HCC progression.

KEY FINDINGS

NNAT was obviously downregulated in HCC tissues, and its expression level was closely associated with tumor growth and patient prognosis. The downregulation of NNAT in HCC was induced by hypermethylation of CpG islands in the promoter region, and hypermethylation was correlated with overall survival of HCC. Moreover, the enforced expression of NNAT significantly inhibited HCC cell proliferation in vitro and in vivo. Transcriptome analysis showed that the alteration of NNAT expression was mainly related to dysregulation of the PI3K-Akt signaling pathway. Finally, phospho-specific antibody microarray detection further revealed that overexpressed NNAT can increase the phosphorylation levels of LKB1, Met, and elF4E and decrease the phosphorylation levels of PTEN, which are all involved in the PI3K-Akt signaling pathway.

SIGNIFICANCE

Our research provides new insights into the epigenetic regulation of imprinted genes in tumorigenesis and implies that the imprinted gene NNAT may act as a prognostic biomarker and tumor suppressor in HCC.

Multi-omics analysis of pyroptosis regulation patterns and characterization of tumor microenvironment in patients with hepatocellular carcinoma

Background

Hepatocellular carcinoma (HCC) is a primary malignant tumor of the liver, and pyroptosis has been identified as a novel cellular program that plays a role in numerous diseases including cancer. However, the functional role of pyroptosis in HCC remains unclear. The purpose of this study is to explore the relationship between the two found hub genes and provide targets for clinical treatment.

Methods

The Cancer Genome Atlas (TCGA) database was used to collect the gene data and clinically-related information of patients with HCC. After the differentially expressed genes (DEGs) were identified, they were intersected with the genes related to pyroptosis, and a risk prediction model was established to predict the overall survival (OS). Subsequently, drug sensitivity analysis, Gene Ontology (GO), Kyoto Encyclopedia of Genes and Genomes (KEGG), Gene Set Enrichment Analysis (GSEA), and Gene Set Variation Analysis (GSVA) was used to analyze the biological characteristics of the DEGs. Different immune cell infiltration and related pathways were analyzed, and hub genes were identified by protein-protein interaction (PPI). Finally, the expression of hub genes was verified by real-time quantitative PCR (qRT-PCR) and immunohistochemistry.

Results

We conducted a comprehensive bioinformatics analysis to investigate the molecular mechanisms of pyroptosis in hepatocellular carcinoma (HCC). A total of 8,958 differentially expressed genes were identified, and 37 differentially expressed genes were associated with pyroptosis through intersection. Moreover, we developed an OS model with excellent predictive ability and discovered the differences in biological function, drug sensitivity, and immune microenvironment between high-risk and low-risk groups. Through enrichment analysis, we found that the differentially expressed genes are related to various biological processes. Then, 10 hub genes were identified from protein-protein interaction networks. Finally, midkine (MDK) was screened from the 10 hub genes and further verified by PCR and immunohistochemistry, which revealed its high expression in HCC.

Conclusion

We have developed a reliable and consistent predictive model based on the identification of potential hub genes, which can be used to accurately forecast the prognosis of patients, thus providing direction for further clinical research and treatment.

PCLassoLog: A protein complex-based, group Lasso-logistic model for cancer classification and risk protein complex discovery

Risk gene identification has attracted much attention in the past two decades. Since most genes need to be translated into proteins and cooperate with other proteins to form protein complexes to carry out cellular functions, which significantly extends the functional diversity of individual proteins, revealing the molecular mechanism of cancer from a comprehensive perspective needs to shift from identifying individual risk genes toward identifying risk protein complexes. Here, we embed protein complexes into the regularized learning framework and propose a protein complex-based, group Lasso-logistic model (PCLassoLog) to discover risk protein complexes. Experiments on deep proteomic data of two cancer types show that PCLassoLog yields superior predictive performance on independent datasets. More importantly, PCLassoLog identifies risk protein complexes that not only contain individual risk proteins but also incorporate close partners that synergize with them. Furthermore, selection probabilities are calculated and two other protein complex-based models are proposed to complement PCLassoLog in identifying reliable risk protein complexes. Based on PCLassoLog, a pan-cancer analysis is performed to identify risk protein complexes in 12 cancer types. Finally, PCLassoLog is used to discover risk protein complexes associated with gene mutation. We implement all protein complex-based models as an R package PCLassoReg, which may serve as an effective tool to discover risk protein complexes in various contexts.

Screening for Lipid-Metabolism-Related Genes and Identifying the Diagnostic Potential of ANGPTL6 for HBV-Related Early-Stage Hepatocellular Carcinoma

Lipid metabolic reprogramming is one of the hallmarks of hepatocarcinogenesis and development. Therefore, lipid-metabolism-related genes may be used as potential biomarkers for hepatocellular carcinoma (HCC). This study aimed to screen for genes with dysregulated expression related to lipid metabolism in HCC and explored the clinical value of these genes. We screened differentially expressed proteins between tumorous and adjacent nontumorous tissues of hepatitis B virus (HBV)-related HCC patients using a Nanoscale Liquid Chromatography–Tandem Mass Spectrometry platform and combined it with transcriptomic data of lipid-metabolism-related genes from the GEO and HPA databases to identify dysregulated genes that may be involved in lipid metabolic processes. The potential clinical values of these genes were explored by bioinformatics online analysis tools (GEPIA, cBioPortal, SurvivalMeth, and TIMER). The expression levels of the secreted protein (angiopoietin-like protein 6, ANGPTL6) in serum were analyzed by ELISA. The ability of serum ANGPTL6 to diagnose early HCC was assessed by ROC curves. The results showed that serum ANGPTL6 could effectively differentiate between HBV-related early HCC patients with normal serum alpha-fetoprotein (AFP) levels and the noncancer group (healthy participants and chronic hepatitis B patients) (AUC = 0.717, 95% CI: from 0.614 to 0.805). Serum ANGPTL6 can be used as a potential second-line biomarker to supplement serum AFP in the early diagnosis of HBV-related HCC.

Impact of WTAP in small HCC and paired adjacent non-neoplastic liver tissue on recurrence: A cohort study with external extension analysis

Background: To evaluate prognostic value of WTAP levels in tumor and paired adjacent non-neoplastic liver tissues (PANLT) for cases of hepatitis B virus (HBV)-positive Asian small hepatocellular carcinoma (sHCC) patients who received curative partial hepatectomy. Method: The investigation with two external cohorts were included. Associations between hazard risk of recurrence and continuous WTAP levels were investigated with restricted cubic spline models. Cox and inverse probability weighting models were established for survival analysis. Based on interaction effects, further stratification analysis was performed. Landmark analysis was employed to analyze cases of late recurrence. Finally, sensitivity analysis was performed to assess unmeasured confounders. Findings: In an investigation cohort of 307 patients, restricted cubic spline models indicated that hazard risk of recurrence increases with elevated WTAP levels for sHCC and PANLT. However, using Cox and inverse probability weighting models, no significant differences were observed in recurrence-free survival (RFS) between groups with different WTAP levels in sHCC. Multivariate analysis showed that patients with high PANLT WTAP levels had significantly worse RFS (HR 1.567, 95% CI 1.065-2.307; p = 0.023). Based on the significant interaction effect between WTAP levels in sHCC and PANLT, stratification analysis revealed that recurrence risk is more pronounced in patients with high WTAP levels in both PANLT and sHCC. Landmark analysis showed that late recurrence was more likely to occur in patients with high PANLT WTAP levels (HR 2.058, 95% CI 1.113-3.805; p = 0.021). Moreover, the detrimental effects of elevated PANLT WTAP levels on RFS were validated with two external cohorts. Sensitivity analysis confirmed the robustness of results. Conclusion: Increased PANLT WTAP expression levels independently predict high recurrence risk in HBV-positive Asian sHCC patients. Both tumor tissues and PANLT need to be considered together in future clinical practice to obtain a more comprehensive and accurate evaluation for recurrence risk.

Expression, Prognostic Value, and Functional Mechanism of Polarity-Related Genes in Hepatocellular Carcinoma

Hepatocellular carcinoma (HCC) is a common malignant tumor with high mortality and poor prognoses around the world. Within-cell polarity is crucial to cell development and function maintenance, and some studies have found that it is closely related to cancer initiation, metastasis, and prognosis. The aim of our research was to find polarity-related biomarkers which improve the treatment and prognosis of HCC. For the knowledge-driven analysis, 189 polarity-related genes (PRGs) were retrieved and curated manually from the molecular signatures database and reviews. Meanwhile, in the data-driven part, genomic datasets and clinical records of HCC was obtained from the cancer genome atlas database. The potential candidates were considered in the respect to differential expression, mutation rate, and prognostic value. Sixty-one PRGs that passed the knowledge and data-driven screening were applied for function analysis and mechanism deduction. Elastic net model combing least absolute shrinkage and selection operator and ridge regression analysis refined the input into a 12-PRG risk model, and its pharmaceutical potency was evaluated. These findings demonstrated that the integration of multi-omics of PRGs can help us in untangling the liver cancer pathogenesis as well as illustrate the underlying mechanisms and therapeutic targets.

Seven Hub Genes Predict the Prognosis of Hepatocellular Carcinoma and the Corresponding Competitive Endogenous RNA Network

Purpose

This study was aimed at identifying hub genes and ceRNA regulatory networks linked to prognosis in hepatocellular carcinoma (HCC) and to identify possible therapeutic targets.

Methods

Differential expression analyses were performed to detect the differentially expressed genes (DEGs) in the four datasets (GSE76427, GSE6764, GSE62232, and TCGA). The intersected DEmRNAs were identified to explore biological significance by enrichment analysis. We built a competitive endogenous RNA (ceRNA) network of lncRNA-miRNA-mRNA. The mRNAs of the ceRNA network were used to perform Cox and Kaplan-Meier analyses to obtain prognosis-related genes, followed by the selection of genes with an area under the curve >0.8 to generate the random survival forest model and obtain feature genes. Furthermore, the feature genes were subjected to least absolute shrinkage and selection operator (LASSO) and univariate Cox analyses were used to identify the hub genes. Finally, the infiltration status of immune cells in the HCC samples was determined.

Results

A total of 1923 intersected DEmRNAs were identified in four datasets and involved in cell cycle and carbon metabolism. ceRNA network was created using 10 lncRNAs, 67 miRNAs, and 1,923 mRNAs. LASSO regression model was performed to identify seven hub genes, SOCS2, MYOM2, FTCD, ADAMTSL2, TMEM106C, LARS, and KPNA2. Among them, TMEM106C, LARS, and KPNA2 had a poor prognosis. KPNA2 was considered a key gene base on LASSO and Cox analyses and involved in the ceRNA network. T helper 2 cells and T helper cells showed a higher degree of infiltration in HCC.

Conclusion

The findings revealed seven hub genes implicated in HCC prognosis and immune infiltration. A corresponding ceRNA network may help reveal their potential regulatory mechanism.

Hepatocellular carcinoma stage: an almost loss of fatty acid metabolism and gain of glucose metabolic pathways dysregulation

Cancer cells rewire the metabolic processes beneficial for cancer cell proliferation, survival, and their progression. In this study, metabolic processes related to glucose, glutamine, and fatty acid metabolism signatures were collected from the molecular signatures database and investigated in the context of energy metabolic pathways through available genome-wide expression profiles of liver cancer cohorts by gene sets-based pathway activation scoring analysis. The outcomes of this study portray that the fatty acid metabolism, transport, and its storage related signatures are highly expressed across early stages of liver tumors and on the contrary, the gene sets related to glucose transport and glucose metabolism are prominently activated in the hepatocellular carcinoma (HCC) stage. Based on the results, these metabolic pathways are clearly dysregulated across specific stages of carcinogenesis. The identified dimorphic metabolic pathway dysregulation patterns are further reconfirmed by examining corresponding metabolic pathway genes expression patterns across various stages encompassing profiles. Recurrence is the primary concern in the carcinogenesis of liver tumors due to liver tissues regeneration. Hence, to further explore these dysregulation effects on recurrent cirrhosis and recurrent HCC sample containing profile GSE20140 was examined and interestingly, this result also reiterated these differential metabolic pathways dysregulation. In addition, a recently established metabolome profile for the massive panel of cancer cell-lines, including liver cancer cell-lines, was used for further exploration. These findings also reassured those differential metabolites abundance of the fatty acid and glucose metabolic pathways enlighten those dimorphic metabolic pathways dysregulation. Moreover, ROC curves of fatty acid metabolic pathway genes such as acetyl-CoA carboxylase (ACACB), acyl-CoA dehydrogenase long chain (ACADL), and acyl-CoA dehydrogenase medium chain (ACADM) as well as glucose metabolic pathway genes such as phosphoglycerate kinase (PGK1), pyruvate dehydrogenase (PDHA1), pyruvate dehydrogenase kinase (PDK1) demonstrated greater sensitivity and specificity in the corresponding stage-specific tumors with significant p-values (p < 0.05). Furthermore, overall survival (OS) and recurrence-free survival (RFS) studies also reconfirmed that the rate-limiting genes expression of fatty acid and glucose metabolic pathways reveal better and poor survival in HCC patient cohorts, respectively. In conclusion, all these results clearly show that metabolic rewiring and the existence of two diverse metabolic pathways dysregulation involving fatty acid and glucose metabolism across the stages of liver tumors have been identified. These findings might be useful for developing therapeutic target treatments in stage-specific tumors.

Molecular subtypes based on Wnt-signaling gene expression predict prognosis and tumor microenvironment in hepatocellular carcinoma

Based on increasing research evidence, hepatocellular carcinoma (HCC) is heterogeneous, and genetic profiling has led to the identification of multiple subtypes of this disease. To advance our knowledge and the ability to use individualized medicine in the treatment of HCC, it is essential to perform a complete and methodical characterization of various molecular subtypes. The canonical Wnt/β-catenin pathway is an evolutionarily conserved complicated signaling mechanism that plays a role in carcinogenesis and progression of HCC. In this study, we acquired RNA sequencing, somatic mutation, and clinical data from 701 patients from The Cancer Genome Atlas and Gene Expression Omnibus databases and stratified patients into two subgroups: WNT-high and WNT-low. In general, the WNT-high subtype is associated with an immunosuppressive microenvironment, poor prognosis, cancer-related pathways, and a low response to immune checkpoint therapy. We also found that WNT3 is negatively linked to CD8⁺ T-cell infiltration using multiple immunofluorescence assays. Finally, we developed a WNT-related prognostic model to predict the survival time of patients with HCC. In summary, we developed a new classification scheme for HCC based on Wnt signaling signatures. This classification produced substantial clinical effects, both in terms of assessing patient prognosis and immunotherapy administered to patients with HCC.

A necroptosis-related prognostic model for predicting prognosis, immune landscape, and drug sensitivity in hepatocellular carcinoma based on single-cell sequencing analysis and weighted co-expression network

Background: Hepatocellular carcinoma (HCC) is a highly lethal cancer and is the second leading cause of cancer-related deaths worldwide. Unlike apoptosis, necroptosis (NCPS) triggers an immune response by releasing damage-related molecular factors. However, the clinical prognostic features of necroptosis-associated genes in HCC are still not fully explored.

Methods: We analyzed the single-cell datasets GSE125449 and GSE151530 from the GEO database and performed weighted co-expression network analysis on the TCGA data to identify the necroptosis genes. A prognostic model was built using COX and Lasso regression. In addition, we performed an analysis of survival, immunity microenvironment, and mutation. Furthermore, the hub genes and pathways associated with HCC were localized within the single-cell atlas.

Results: Patients with HCC in the TCGA and ICGC cohorts were classified using a necroptosis-related model with significant differences in survival times between high- and low-NCPS groups (p &lt; 0.05). High-NCPS patients expressed more immune checkpoint-related genes, suggesting immunotherapy and some chemotherapies might prove beneficial to them. In addition, a single-cell sequencing approach was conducted to investigate the expression of hub genes and associated signaling pathways in different cell types.

Conclusion: Through the analysis of single-cell and bulk multi-omics sequencing data, we constructed a prognostic model related to necroptosis and explored the relationship between high- and low-NCPS groups and immune cell infiltration in HCC. This provides a new reference for further understanding the role of necroptosis in HCC.

Aging-related features predict prognosis and immunotherapy efficacy in hepatocellular carcinoma

The aging microenvironment serves important roles in cancers. However, most studies focus on circumscribed hot spots such as immunity and metabolism. Thus, it is well ignored that the aging microenvironment contributes to the proliferation of tumor. Herein, we established three prognosis-distinctive aging microenvironment subtypes, including AME1, AME2, and AME3, based on aging-related genes and characterized them with “Immune Exclusion,” “Immune Infiltration,” and “Immune Intermediate” features separately. AME2-subtype tumors were characterized by specific activation of immune cells and were most likely to be sensitive to immunotherapy. AME1-subtype tumors were characterized by inhibition of immune cells with high proportion of Catenin Beta 1 (CTNNB1) mutation, which was more likely to be insensitive to immunotherapy. Furthermore, we found that CTNNB1 may inhibit the expression of C-C Motif Chemokine Ligand 19 (CCL19), thus restraining immune cells and attenuating the sensitivity to immunotherapy. Finally, we also established a robust aging prognostic model to predict the prognosis of patients with hepatocellular carcinoma. Overall, this research promotes a comprehensive understanding about the aging microenvironment and immunity in hepatocellular carcinoma and may provide potential therapeutic targets for immunotherapy.

Endometrial Cancer-Adjacent Tissues Express Higher Levels of Cancer-Promoting Genes than the Matched Tumors

Molecular alterations in tumor-adjacent tissues have recently been recognized in some types of cancer. This phenomenon has not been studied in endometrial cancer. We aimed to analyze the expression of genes associated with cancer progression and metabolism in primary endometrial cancer samples and the matched tumor-adjacent tissues and in the samples of endometria from cancer-free patients with uterine leiomyomas. Paired samples of tumor-adjacent tissues and primary tumors from 49 patients with endometrial cancer (EC), samples of endometrium from 25 patients with leiomyomas of the uterus, and 4 endometrial cancer cell lines were examined by the RT-qPCR, for MYC, NR5A2, CXCR2, HMGA2, LIN28A, OCT4A, OCT4B, OCT4B1, TWIST1, STK11, SNAI1, and miR-205-5p expression. The expression levels of MYC, NR5A2, SNAI1, TWIST1, and STK11 were significantly higher in tumor-adjacent tissues than in the matched EC samples, and this difference was not influenced by the content of cancer cells in cancer-adjacent tissues. The expression of MYC, NR5A2, and SNAI1 was also higher in EC-adjacent tissues than in samples from cancer-free patients. In addition, the expression of MYC and CXCR2 in the tumor related to non-endometrioid adenocarcinoma and reduced the risk of recurrence, respectively, and higher NR5A2 expression in tumor-adjacent tissue increased the risk of death. In conclusion, tissues proximal to EC present higher levels of some cancer-promoting genes than the matched tumors. Malignant tumor-adjacent tissues carry a diagnostic potential and emerge as new promising target of anticancer therapy.

Diagnostic and prognostic value of m5C regulatory genes in hepatocellular carcinoma

Background: A high mortality rate makes hepatocellular carcinoma (HCC) one of the most common types of cancer globally. 5-methylcytosine (m5C) is an epigenetic modification that contributes to the prognosis of several cancers, but its relevance to HCC remains unknown. We sought to determine if the m5C-related regulators had any diagnostic or prognostic value in HCC.

Methods: M5C regulatory genes were screened and compared between HCC and normal tissue from The Cancer Genome Atlas (TCGA)and Gene Expression Omnibus (GEO) databases. Least absolute shrinkage and selection operator method (LASSO) and univariate Cox regression analysis of differentially expressed genes were then performed to identify diagnostic markers. A LASSO prognostic model was constructed using M5C regulatory genes with prognostic values screened by TCGA expression data. HCC patients were stratified based on risk score, then clinical characteristics analysis and immune correlation analysis were performed for each subgroup, and the molecular functions of different subgroups were analyzed using both Gene Set Enrichment Analysis (GSEA) and Gene Set Variation Analysis (GSVA). The prognostic model was evaluated using univariate and multivariate Cox analyses as well as a nomogram. Molecular typing was performed according to m5C regulatory genes and immune checkpoint genes expression respectively, and clinical characterization and immune correlation analysis were performed for each subgroup.

Results: M5C regulatory genes are expressed differently in HCC patients with different clinical and pathological characteristics, and mutations in these genes are frequent. Based on five m5C regulators (NOP2, NSUN2, TET1, YBX1, and DNMT3B), we constructed a prognostic model with high predictive ability. The risk score was found to be an independent prognostic indicator. Additionally, risk scores can also be applied in subgroups with different clinical characteristics as prognostic indicators.

Conclusion: The study combined data from TCGA and GEO for the first time to reveal the genetic and prognostic significance of m5C-related regulators in HCC, which provides new directions for identifying predictive biomarkers and developing molecularly targeted therapies for HCC.

Low adipocyte hepatocellular carcinoma is associated with aggressive cancer biology and with worse survival

Hepatocellular carcinoma (HCC) is the second leading cause of cancer-related death worldwide, and non-alcoholic fatty liver disease is strongly associated with its development. To explore the role of adipocytes in HCC, we investigated intratumoral adipocytes, also known as cancer-associated adipocytes (CAA). Based on our prior breast cancer findings, we hypothesized that low intratumoral adipocytes would be associated with aggressive cancer biology, worse tumor microenvironment (TME), and clinical outcomes. The Cancer Genome Atlas (TCGA) was used and validated by the Gene Expression Omnibus (GEO) cohort. xCell algorithm was used to quantify intratumoral adipocytes and top 90% were defined as adipocyte high (AH) and bottom 10% as adipocyte low (AL). We found that AL-HCC was significantly associated with worse disease-free survival (DFS), disease-specific survival (DSS), and overall survival (OS). AL-HCC were higher-grade, had high MKI67 expression, enriched cell proliferation-related gene sets, and had increased altered fraction, aneuploidy, and homologous recombination defects. Also, anti-cancer immune cells, CD8, Th1, and M1 cells, as well as pro-cancer Th2 cells were increased in AL-HCC. Micro-RNAs miR-122 (associated with cholesterol metabolism) and miR-885 (associated with liver pathologies) were significantly increased in the AL TME. In conclusion, we found that AL-HCC has worse patient outcomes and is biologically more aggressive with enhanced cell proliferation. Our findings take initial steps to clarify the role of adipocytes in HCC.

Integrated multi-omics analysis identifies ENY2 as a predictor of recurrence and a regulator of telomere maintenance in hepatocellular carcinoma

Hepatocellular carcinoma (HCC) is the most common type of primary liver cancer and has a high recurrence rate. Accurate prediction of recurrence risk is urgently required for tailoring personalized treatment programs for individual HCC patients in advance. In this study, we analyzed a gene expression dataset from an HCC cohort with 247 samples and identified five genes including ENY2, GPAA1, NDUFA4L2, NEDD9, and NRP1 as the variables for the prediction of HCC recurrence, especially the early recurrence. The Cox model and risks score were validated in two public HCC cohorts (GSE76427 and The Cancer Genome Atlas (TCGA)) and one cohort from Huashan Hospital, which included a total of 641 samples. Moreover, the multivariate Cox regression analysis revealed that the risk score could serve as an independent prognostic factor in the prediction of HCC recurrence. In addition, we found that ENY2, GPAA1, and NDUFA4L2 were significantly upregulated in HCC of the two validation cohorts, and ENY2 had significantly higher expression levels than another four genes in malignant cells, suggesting that ENY2 might play key roles in malignant cells. The cell line analysis revealed that ENY2 could promote cell cycle progression, cell proliferation, migration, and invasion. The functional analysis of the genes correlated with ENY2 revealed that ENY2 might be involved in telomere maintenance, one of the fundamental hallmarks of cancer. In conclusion, our data indicate that ENY2 may regulate the malignant phenotypes of HCC via activating telomere maintenance.

Oncogenic signaling pathway mediated by Notch pathway-related genes induces immunosuppression and immunotherapy resistance in hepatocellular carcinoma

The Notch pathway is a highly conserved signaling pathway involved in the regulation of cell proliferation and differentiation. However, the relationships between Notch pathway-related genes (NPRGs), immunosuppression, and immunotherapy resistance of hepatocellular carcinoma (HCC) remain unclear. Gene expression data and clinical information were extracted from GSE14520, GSE36376, GSE76427, LIRI-JP, TCGA-LIHC, GSE20140, GSE27150, and IMvigor210 datasets. A consensus clustering analysis based on 10 NPRGs was performed to determine the molecular subtypes, and then a notchScore was constructed based on differentially expressed and prognostic genes between molecular subtypes. Two molecular subgroups with significantly distinct survival and immune cell infiltration were identified. Then, a notchScore was constructed to quantify the Notch index of each patient with HCC. Next, we investigated the correlations between the clinical characteristics and the notchScore using logistic regression. Furthermore, multivariate Cox analysis showed that a high notchScore was an independent predictor of poor overall survival (OS) in the TCGA and LIRI-JP datasets and was associated with higher pathological stages. Additionally, a high notchScore was associated with higher immune cells, higher ESTIMATE score, higher immune score, higher stromal score, higher immune checkpoint, and lower tumor purity, which was consistent with the "immunity tidal model theory." Importantly, a high notchScore was sensitive to immunotherapy. Additionally, GSEA indicated that several GO and KEGG items associated with apoptosis, immune-related pathways, and cell cycle signal pathways were significantly enriched in the high notchScore phenotype pathway. Our findings propose that a high notchScore is a prognostic biomarker and correlates with immune infiltration and sensitivity to immunotherapy in HCC.