Significance In Hepatocellular Carcinoma Research Articles

Development of a promising PPAR signaling pathway-related prognostic prediction model for hepatocellular carcinoma

The peroxisome proliferator-activated receptor (PPAR) signaling pathway plays a crucial role in systemic cell metabolism, energy homeostasis and immune response inhibition. However, its significance in hepatocellular carcinoma (HCC) has not been well documented. In our study, based on the RNA sequencing data of HCC, consensus clustering analyses were performed to identify PPAR signaling pathway-related molecular subtypes, each of which displaying varying survival probabilities and immune infiltration status. Following, a prognostic prediction model of HCC was developed by using the random survival forest method and Cox regression analysis. Significant difference in survival outcome, immune landscape, drug sensitivity and pathological features were observed between patients with different prognosis. Additionally, decision tree and nomogram models were adopted to optimize the prognostic prediction model. Furthermore, the robustness of the model was verified through single-cell RNA-sequencing data. Collectively, this study systematically elucidated that the PPAR signaling pathway-related prognostic model has good predictive efficacy for patients with HCC. These findings provide valuable insights for further research on personalized treatment approaches for HCC.

Characterization of m6A RNA methylation mediated immune heterogeneity and functional validation in hepatocellular carcinoma.

N6 -methyladenosine (m6 A) mediates RNA modification in various biological processes. It plays a key role in hepatocellular carcinoma (HCC) through regulating methyltransferase. The present study aims to analyze the correlation between the m6 A and the immune status of HCC, and to construct an m6 A-related prognostic signature for HCC. HCC subtypes with different m6 A modification activities were identified based on the m6 A-related genes. Lasso Cox regression was applied to construct an m6 A-related prognostic model for HCC. Then, the prognostic potential of the constructed signature was evaluated and validated in the external validation dataset. Small interfering RNAs were designed to knockdown FBXO5. CCK-8 assay, Edu staining, wound healing assay, and Transwell cell invasion assay were used to detect cell proliferation, migration, and invasion ability. Two m6 A-related HCC subtypes were identified. The m6 A modification active group showed an immune suppressive microenvironment compared to the m6 A modification inactive group. The differentially expressed genes (DEGs) between the HCC subtypes were screened. Enrichment analysis was performed using the DEGs. Subsequently, an m6 A-related prognostic model was established. The prognostic model performed well in both training and validation datasets. Moreover, knockdown of FBXO5, one of the genes in the prognostic model, inhibited the proliferation, migration, and invasion of HepG2 cells. The heterogeneity of m6 A RNA methylation is associated with immune status in HCC. The constructed m6 A-related gene-based signature can predict the prognosis of HCC patients. The genes in the prognostic model also have therapeutic potential for HCC.

Repression of YEATS2 induces cellular senescence in hepatocellular carcinoma and inhibits tumor growth

ABSTRACT Hepatocellular carcinoma (HCC) stands as the third leading cause of cancer-related fatalities globally. In this study, we observed a significant increase in the expression level of the YEATS2 gene in HCC patients, and it is negatively correlated with the patients’ survival rate. While we have previously identified the association between YEATS2 and the survival of pancreatic cancer cells, the regulatory mechanisms and significance in HCC are still to be fully elucidated. Our study shows that knockdown (KD) of YEATS2 expression leads to DNA damage, which in turn results in an upregulation of γ-H2A.X expression and activation of the canonical senescence-related pathway p53/p21Cip1. Moreover, our transcriptomic analysis reveals that YEATS2 KD cells can enhance the expression of p21Cip1 via the c-Myc/miR-93-5p pathway, consequently fostering the senescence of HCC cells. The initiation of cellular senescence through dual-channel activation suggests that YEATS2 plays a pivotal regulatory role in the process of cell proliferation. Ultimately, our in vivo research utilizing a nude mouse tumor model revealed a notable decrease in both tumor volume and weight after the suppression of YEATS2 expression. This phenomenon is likely attributable to the attenuation of proliferative cell activity, coupled with a concurrent augmentation in the population of natural killer (NK) cells. In summary, our research results have supplemented the understanding of the regulatory mechanisms of HCC cell proliferation and indicated that targeting YEATS2 may potentially inhibit liver tumor growth.

Deciphering m6A signatures in hepatocellular carcinoma: Single-cell insights, immune landscape, and the protective role of IGFBP3.

RNA m6 methyladenosine (m6A) modifications impact tumor biology and immune processes, particularly in hepatocellular malignant tumors. Using a consensus clustering algorithm on 371 hepatocellular carcinoma (HCC) samples, we identified three m6A-modified subtypes and correlated them with positive tumor microenvironment (TME) markers for distinct immune phenotypes. Stratifying patients based on m6A scores revealed a low presentation group with better immune penetration, lower tumor mutation load, and increased expression of immune checkpoint markers like CTLA-4 and PD-1, suggesting enhanced responsiveness to immunization therapy. A machine-learning model of 23 m6A genes was constructed. Single-cell analysis revealed a surprising enrichment of IGFBP3 in astrocytes, prompting the exploration of associated signaling pathways. Experimental verification shows that IGFBP3 is significantly enhanced in normal tissues, while immunohistochemical analysis shows that its expression is lower in tumor tissues, indicating its protective effect in HCC and a good prognosis. Importantly, high IGFBP3 expression is associated with better outcomes in patients receiving immunotherapy. Moreover, cytotoxic T lymphocyte (CTL) experiments have confirmed that high expression of IGFBP3 is associated with stronger T cell-killing ability. In summary, the comprehensive evaluation of m6A modification, immune characteristics, and single-cell analysis in this study not only revealed the TME of HCC but also made significant contributions to the progress of personalized HCC immunotherapy targeting IGFBP3. This study provides a solid theoretical foundation for clinical translation and emphasizes its potential impact on developing effective treatment strategies.

Identification of a DNA damage repair-related LncRNA signature for predicting the prognosis and immunotherapy response of hepatocellular carcinoma

BackgroundDNA damage repair (DDR) may affect tumorigenesis and therapeutic response in hepatocellular carcinoma (HCC). Long noncoding RNAs (LncRNAs) can regulate DDR and play a vital role in maintaining genomic stability in cancers. Here, we identified a DDR-related prognostic signature in HCC and explored its potential clinical value.MethodsData of HCC samples were obtained from the Cancer Genome Atlas (TCGA), and a list of DDR-related genes was extracted from the Molecular Signatures database (MSigDB). A DDR-related lncRNAs signature associated to overall survival (OS) was constructed using the least absolute shrinkage and selection operator-cox regression, and was further validated by the Kaplan-Meier curve and receiver operating characteristic curve. A nomogram integrating other clinical risk factors was established. Moreover, the relationships between the signature with somatic mutation, immune landscape and drug sensitivity were explored.ResultsThe prognostic model of 5 DDR-related lncRNAs was constructed and classified patients into two risk groups at median cut-off. The low-risk group had a better OS, and the signature was an independent prognostic indicator in HCC. A nomogram of the signature combined with TNM stage was constructed. TP53 gene was more frequently mutated in the high-risk group. Marked differences in immune cells were observed, such as CD4 + T cells, NK cells and macrophages, between the two groups. Moreover, an increase in the expression of immune checkpoint molecules was found in the high-risk group. The low-risk group presented with a significantly higher response to sorafenib or cisplatin. Finally, potential value of this signature was validated in real-world HCC patients.ConclusionOur findings provided a promising insight into DDR-related lncRNAs in HCC and a personalized prediction tool for prognosis and therapeutic response.

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.

Disulfidptosis and ferroptosis related genes predict prognosis and personalize treatment for hepatocellular carcinoma.

Understanding the interplay between disulfidptosis, ferroptosis, and hepatocellular carcinoma (HCC) could provide valuable insights into the pathogenesis of HCC and potentially identify novel therapeutic targets for the treatment of this deadly disease. This study aimed to identify a prognostic signature for HCC by examining the differential expression of genes related to disulfidptosis and ferroptosis (DRG-FRG), and to assess its clinical applicability. By integrating 23 disulfidptosis and 259 ferroptosis related genes with HCC messenger RNA (mRNA) expression data from The Cancer Genome Atlas (TCGA), differentially expressed DRG-FRG genes were identified. From these, 11 DRG-FRG genes were selected to construct a risk signature model using least absolute shrinkage and selection operator regression analyses. The prognostic performance of this model was evaluated by Kaplan-Meier survival analysis and time-dependent receiver operating characteristic (ROC) analysis. Subsequently, a nomogram was built by combining the signature with clinical variables. To further delve into the underlying mechanisms, we performed bioinformatics analysis using a variety of databases. A prognostic signature based on 11 DRG-FRG genes effectively categorized HCC patients into high- and low-risk groups, showing a significant survival difference. Even after considering clinical variables, this signature remained an independent prognostic factor. Furthermore, the signature played a role in various critical biological processes and pathways that drive HCC progression. Potential therapeutic benefits could be derived from small molecule drugs targeting NQO1 and SLC7A11. Interestingly, the high-risk group exhibited resistance to several chemotherapeutic drugs, yet showed sensitivity to others when contrasted with the low-risk group. Lastly, the DRG-FRG genes signature had a strong correlation with the tumor immune microenvironment, marked by an elevated expression of immune checkpoint molecules in the high-risk group. The signature based on 11 DRG-FRG genes stands out as a promising prognostic biomarker for HCC. Beyond its predictive value, it sheds light on the intricate crosstalk between DRG-FRG genes and HCC. Importantly, these findings could pave the way for enhanced prognostic prediction, informed treatment decisions, and the advancement of immunotherapy for HCC patients.

Urinary microbiome-based metagenomic signature for the noninvasive diagnosis of hepatocellular carcinoma

BackgroundGut microbial dysbiosis is implicated in chronic liver disease and hepatocellular carcinoma (HCC), but the role of microbiomes from various body sites remains unexplored. We assessed disease-specific alterations in the urinary microbiome in HCC patients, investigating their potential as diagnostic biomarkers.MethodsWe performed cross-sectional analyses of urine samples from 471 HCC patients and 397 healthy controls and validated the results in an independent cohort of 164 HCC patients and 164 healthy controls. Urinary microbiomes were analyzed by 16S rRNA gene sequencing. A microbial marker-based model distinguishing HCC from controls was built based on logistic regression, and its performance was tested.ResultsMicrobial diversity was significantly reduced in the HCC patients compared with the controls. There were significant differences in the abundances of various bacteria correlated with HCC, thus defining a urinary microbiome-derived signature of HCC. We developed nine HCC-associated genera-based models with robust diagnostic accuracy (area under the curve [AUC], 0.89; balanced accuracy, 81.2%). In the validation, this model detected HCC with an AUC of 0.94 and an accuracy of 88.4%.ConclusionsThe urinary microbiome might be a potential biomarker for the detection of HCC. Further clinical testing and validation of these results are needed in prospective studies.

Unraveling the underlying pathogenic factors driving nonalcoholic steatohepatitis and hepatocellular carcinoma: an in-depth analysis of prognostically relevant gene signatures in hepatocellular carcinoma

BackgroundNonalcoholic steatohepatitis (NASH) is a progressive manifestation of nonalcoholic fatty liver disease (NAFLD) that can lead to fibrosis, cirrhosis, and hepatocellular carcinoma (HCC). Despite the growing knowledge of NASH and HCC, the association between the two conditions remains to be fully explored. Bioinformatics has emerged as a valuable approach for identifying disease-specific feature genes, enabling advancements in disease prediction, prevention, and personalized treatment strategies.Materials and methodsIn this study, we utilized CellChat, copy number karyotyping of aneuploid tumors (CopyKAT), consensus Non-negative Matrix factorization (cNMF), Gene set enrichment analysis (GSEA), Gene set variation analysis (GSVA), Monocle, spatial co-localization, single sample gene set enrichment analysis (ssGSEA), Slingshot, and the Scissor algorithm to analyze the cellular and immune landscape of NASH and HCC. Through the Scissor algorithm, we identified three cell types correlating with disease phenotypic features and subsequently developed a novel clinical prediction model using univariate, LASSO, and multifactor Cox regression.ResultsOur results revealed that macrophages are a significant pathological factor in the development of NASH and HCC and that the macrophage migration inhibitory factor (MIF) signaling pathway plays a crucial role in cellular crosstalk at the molecular level. We deduced three prognostic genes (YBX1, MED8, and KPNA2), demonstrating a strong diagnostic capability in both NASH and HCC.ConclusionThese findings shed light on the pathological mechanisms shared between NASH and HCC, providing valuable insights for the development of novel clinical strategies.

Identification and validation of methylation-CpG prognostic signature for prognosis of hepatocellular carcinoma.

Epigenetic biomarkers help predict the prognosis of cancer patients and evaluating the clinical outcome of immunization therapy. In this study, we present a personalized gene methylation-CpG signature to enhance the accuracy of survival prediction for individuals with hepatocellular carcinoma (HCC). Utilizing RNA sequencing and methylation datasets from GEO as well as TCGA, we conducted single sample GSEA (ssGSEA), WGCNA, as well as Cox regression. Through these analyses, we identified 175 oxidative stress and immune-related genes along with 4 CpG loci that are associated with the prognosis of HCC. Subsequently, we constructed a prognostic signature for HCC utilizing these 4 CpG sites, referred to as the HCC Prognostic Signature of Methylation-CpG sites (HPSM). Further investigation revealed an enrichment of immune-related signal pathways in the HPSM-low group, which demonstrated a positive correlation with better survival among HCC patients. Moreover, the methylation of the CpG sites in HPSM was found to be closely linked to drug sensitivity. In vitro experiments tentatively confirmed that promoter methylation regulated the expression of BMPER, one of the CpG sites within HPSM. The expression of BMPER was significantly correlated with cell death in the oxidative stress pathway, and overexpression of BMPER effectively inhibited HCC cell proliferation. Consequently, our findings suggest that HPSM is an independent predictive factor and holds promise for accurately predicting the prognosis of HCC patients.

A disulfidptosis-related lncRNAs signature in hepatocellular carcinoma: prognostic prediction, tumor immune microenvironment and drug susceptibility

Disulfidptosis, a novel type of programmed cell death, has attracted researchers’ attention worldwide. However, the role of disulfidptosis-related lncRNAs (DRLs) in liver hepatocellular carcinoma (LIHC) not yet been studied. We aimed to establish and validate a prognostic signature of DRLs and analyze tumor microenvironment (TME) and drug susceptibility in LIHC patients. RNA sequencing data, mutation data, and clinical data were obtained from the Cancer Genome Atlas Database (TCGA). Lasso algorithm and cox regression analysis were performed to identify a prognostic DRLs signature. Kaplan–Meier curves, principal component analysis (PCA), nomogram and calibration curve, function enrichment, TME, immune dysfunction and exclusion (TIDE), tumor mutation burden (TMB), and drug sensitivity analyses were analyzed. External datasets were used to validate the predictive value of DRLs. qRT-PCR was also used to validate the differential expression of the target lncRNAs in tissue samples and cell lines. We established a prognostic signature for the DRLs (MKLN1-AS and TMCC1-AS1) in LIHC. The signature could divide the LIHC patients into low- and high-risk groups, with the high-risk subgroup associated with a worse prognosis. We observed discrepancies in tumor-infiltrating immune cells, immune function, function enrichment, and TIDE between two risk groups. LIHC patients in the high-risk group were more sensitive to several chemotherapeutic drugs. External datasets, clinical tissue, and cell lines confirmed the expression of MKLN1-AS and TMCC1-AS1 were upregulated in LIHC and associated with a worse prognosis. The novel signature based on the two DRLs provide new insight into LIHC prognostic prediction, TME, and potential therapeutic strategies.

Tumor Promoting Effect of Long Non-Coding RNA RP11-556E13.1 and its Clinical Significance in Hepatocellular Carcinoma.

The aim of this study was to reveal the function of the long non-coding RNA (lncRNA) RP11-556E13.1 (RP11) and its clinical significance in hepatocellular carcinoma (HCC). LncRNA and mRNA expression profiling was performed using lncRNA and mRNA microarrays in HCC and adjacent tissues. Human tissue samples were analyzed by semiquantitative real-time polymerase chain reaction (sqRT-PCR) to evaluate the expression of RP11. Smart silencer RNA (siRNA) was used to knockdown the expression of RP11 in HCC cells. The function of RP11 was determined by some cell function experiments in HCC cells. Western blotting (WB) was performed to detect proteins that were presumably associated with these function changes. An Affymetrix Human HTA2.0 microarray was used to detect the underlying mechanism of RP11 in HCC. lncRNA RP11 was the most significantly upregulated lncRNA in HCC tissues compared with the adjacent tissues (p < 0.05, fold change = 20.24). The expression of RP11 was significantly higher in HCC tissues compared to adjacent tissues in 112 tissue pairs (p < 0.05). The higher the expression of RP11 in HCC tissues, the bigger the tumor size, the poorer the histological differentiation, and the lower the overall survival rate of the patients (all p < 0.05). After the knockdown of RP11, HCC cells displayed inhibited proliferation, increased apoptosis rate, and G1/S arrest. Moreover, the expression of cleaved PARP1 and cleaved caspase-3 was increased. GO enrichment and KEGG pathway enrichment analysis showed some important pathways that might be related to the knockdown of RP11 in HCC cells. lncRNA RP11 is an HCC-promoting gene and a potential prognostic predictor of HCC.

Conduction and validation of a novel mitotic spindle assembly related signature in hepatocellular carcinoma: prognostic prediction, tumor immune microenvironment and drug susceptibility.

Introduction: We have developed a risk-scoring model using gene expression levels related to mitotic spindle assembly (MSA) to predict the prognosis of liver cancer. Methods and results: Initially, we identified 470 genes related to MSA from public databases. Subsequently, through analysis of sequencing data from liver cancer patient samples in online databases, we identified 7 genes suitable for constructing the risk-scoring model. We validated the predictive accuracy and clinical utility of the model. Through drug sensitivity analysis, we identified SAC3D1 as a gene sensitive to the most common anti-tumor drugs among these 7 genes. We propose SAC3D1 as a significant target for future clinical treatment. Furthermore, we conducted in vivo and in vitro experiments to validate the relevance of SAC3D1 to MSA and found its significant impact on the PI3K/Akt signaling pathway and spindle function. Conclusion: Our research introduces a novel risk-scoring model that accurately predicts liver cancer prognosis. Additionally, our findings suggest SAC3D1 as a promising therapeutic target for hepatocellular carcinoma, potentially revealing new mechanisms underlying liver cancer development.

Comprehensive pan-cancer investigation of carnosine dipeptidase 1 and its prospective prognostic significance in hepatocellular carcinoma.

Carnosine dipeptidase 1 (CNDP1), an enzyme integral to the hydrolysis of dipeptides containing histidine, plays an indispensable role in myriad physiological processes, including hydrolysis of proteins, maturation of specific biochemical functionalities within proteins, tissue regeneration, and regulation of cell cycle. However, the implications of CNDP1 in oncogenesis and its prognostic value are not yet fully elucidated. Initially, we procured the GSE40367 dataset from the Gene Expression Omnibus and established a protein-protein interaction network. Thereafter, we conducted functional and pathway enrichment analyses utilizing GO, KEGG, and GSEA. Moreover, we undertook an association analysis concerning the expression of CNDP1 with immune infiltration, along with survival analysis across various cancers and specifically in hepatocellular carcinoma (HCC). Our study uncovered a total of 2,248 differentially expressed genes, with a down-regulation of CNDP1 in HCC and other cancers. Our explorations into the relationship between CNDP1 and immune infiltration disclosed a negative correlation between CNDP1 expression and the presence of immune cells in HCC. Survival analyses revealed that diminished expression of CNDP1 correlates with an adverse prognosis in HCC and several other types of cancer. These observations intimate that CNDP1 holds promise as a novel prognostic biomarker for both pan-cancer and HCC.

Construction and validation of a novel lysosomal signature for hepatocellular carcinoma prognosis, diagnosis, and therapeutic decision-making

Lysosomes is a well-recognized oncogenic driver and chemoresistance across variable cancer types, and has been associated with tumor invasiveness, metastasis, and poor prognosis. However, the significance of lysosomes in hepatocellular carcinoma (HCC) is not well understood. Lysosomes-related genes (LRGs) were downloaded from Genome Enrichment Analysis (GSEA) databases. Lysosome-related risk score (LRRS), including eight LRGs, was constructed via expression difference analysis (DEGs), univariate and LASSO-penalized Cox regression algorithm based on the TCGA cohort, while the ICGC cohort was obtained for signature validation. Based on GSE149614 Single-cell RNA sequencing data, model gene expression and liver tumor niche were further analyzed. Moreover, the functional enrichments, tumor microenvironment (TME), and genomic variation landscape between LRRSlow/LRRShigh subgroup were systematically investigated. A total of 15 Lysosomes-related differentially expressed genes (DELRGs) in HCC were detected, and then 10 prognosis DELRGs were screened out. Finally, the 8 optimal DELRGs (CLN3, GBA, CTSA, BSG, APLN, SORT1, ANXA2, and LAPTM4B) were selected to construct the LRRS prognosis signature of HCC. LRRS was considered as an independent prognostic factor and was associated with advanced clinicopathological features. LRRS also proved to be a potential marker for HCC diagnosis, especially for early-stage HCC. Then, a nomogram integrating the LRRS and clinical parameters was set up displaying great prognostic predictive performance. Moreover, patients with high LRRS showed higher tumor stemness, higher heterogeneity, and higher genomic alteration status than those in the low LRRS group and enriched in metabolism-related pathways, suggesting its underlying role in the progression and development of liver cancer. Meanwhile, the LRRS can affect the proportion of immunosuppressive cell infiltration, making it a vital immunosuppressive factor in the tumor microenvironment. Additionally, HCC patients with low LRRS were more sensitive to immunotherapy, while patients in the high LRRS group responded better to chemotherapy. Upon single-cell RNA sequencing, CLN3, GBA, and LAPTM4B were found to be specially expressed in hepatocytes, where they promoted cell progression. Finally, RT-qPCR and external datasets confirmed the mRNA expression levels of model genes. This study provided a direct links between LRRS signature and clinical characteristics, tumor microenvironment, and clinical drug-response, highlighting the critical role of lysosome in the development and treatment resistance of liver cancer, providing valuable insights into the prognosis prediction and treatment response of HCC, thereby providing valuable insights into prognostic prediction, early diagnosis, and therapeutic response of HCC.

Expression of small nucleolar RNA SNORA51 and its clinical significance in hepatocellular carcinoma.

Small nucleolar RNA H/ACA Box 51 (SNORA51) is involved in progression of multiple cancers. However, its role in hepatocellular carcinoma (HCC) is still unclear. The aim of the present study was to analyze the expression of SNORA51 in HCC and its clinical significance. A total of 136 patients with HCC who underwent surgery from January 1, 2016 to December 31, 2018 were included. The expression of SNORA51 in cancer tissues and adjacent tissues was compared using reverse transcription-quantitative PCR and bioinformatics methods. Methylation of the SNORA51 promoter in cancer and adjacent tissues was compared using bioinformatics. The relationship between SNORA51 expression levels and clinicopathological characteristics of patients with HCC, in addition to prognosis, was analyzed. The expression of SNORA51 in HCC was significantly higher compared with that in adjacent tissues (P<0.05). starBase demonstrated that higher expression levels of SNORA51 were associated with a significantly worse prognosis of patients with HCC compared with those who had lower expression levels of SNORA51 (P<0.05). Bioinformatics analysis using The University of Alabama at Birmingham Cancer Data Analysis Portal demonstrated that methylation of the SNORA51 promoter region in HCC was significantly decreased compared with adjacent tissues (P<0.05). A high expression of SNORA51 was significantly associated with portal vein tumor thrombus, vascular invasion and TNM stage (P<0.05). The median survival time of patients with high SNORA51 expression was significantly lower compared with those who had low SNORA51 expression (P<0.05). Both uni- and multivariate Cox regression analysis demonstrated that SNORA51 expression was an independent risk factor that significantly worsened the prognosis of patients with HCC (P<0.05). The overexpression of SNORA51 in patients with HCC was significantly associated with a poor prognosis and may be related to the reduced methylation of the SNORA51 promoter region. Therefore, SNORA51 may be a promising biomarker for prediction of the prognosis of patients with HCC and may be a therapeutic target for the treatment of HCC in future.

Predictive Roles of ADAM17 in Patient Survival and Immune Cell Infiltration in Hepatocellular Carcinoma

Hepatocellular carcinoma (HCC) is the deadliest malignant tumour worldwide. The metalloproteinase ADAM17 is associated with tumour formation and development; however, its significance in HCC is unclear. This study aimed to investigate the role of ADAM17 in HCC and the correlation between its expression and immune cell infiltration. ADAM17 expression was analysed in pan-cancer and HCC tissues using The Cancer Genome Atlas and Genotype-Tissue Expression datasets. Kaplan–Meier survival analysis displayed a negative association between ADAM17 expression and the overall survival of patients with HCC. High ADAM17 expression was linked to poor tumour/node (T/N) stage and alpha fetoprotein (AFP) levels. Gene Set Enrichment Analysis, Gene Ontology, and Kyoto Encyclopaedia of Genes and Genomes analyses revealed the enrichment of several pathways, including epithelial–mesenchymal transition, inflammatory response, Hedgehog, and KRAS signalling, in patients with upregulated ADAM17. ADAM17 was shown to be positively correlated with immune cell infiltration and immune checkpoint expression via the Tumour Immune Estimation Resource (TIMER) database and immunohistochemistry analyses. Protein–protein interaction (PPI) network analysis revealed that ADAM17 plays a core role in cancer development and immune evasion. In vitro and in vivo experiments demonstrated that ADAM17 influences HCC growth and metastasis. In conclusion, ADAM17 is upregulated in most cancers, particularly HCC, and is critical in the development and immune evasion of HCC.

Ubiquitin-specific protease 22 promotes tumorigenesis and progression by an FKBP12/mTORC1/autophagy positive feedback loop in hepatocellular carcinoma.

Ubiquitin-specific protease 22 (USP22) has been identified as a potential marker for cancer stem cells in hepatocellular carcinoma (HCC). It can promote HCC stemness, which is considered a driver of tumorigenesis. Here, we sought to determine the role of USP22 in tumorigenesis, elucidate its underlying mechanism, and explore its therapeutic significance in HCC. As a result, we found that tissue-specific Usp22 overexpression accelerated tumorigenesis, whereas Usp22 ablation decelerated it in a c-Myc/NRasGV12-induced HCC mouse model and that the mammalian target of rapamycin complex 1 (mTORC1) pathway was activated downstream. USP22 overexpression resulted in increased tumorigenic properties that were reversed by rapamycin in vitro and in vivo. In addition, USP22 activated mTORC1 by deubiquitinating FK506-binding protein 12 (FKBP12) and activated mTORC1, in turn, further stabilizing USP22 by inhibiting autophagic degradation. Clinically, HCC patients with high USP22 expression tend to benefit from mTOR inhibitors after liver transplantation (LT). Our results revealed that USP22 promoted tumorigenesis and progression via an FKBP12/mTORC1/autophagy positive feedback loop in HCC. Clinically, USP22 may be an effective biomarker for selecting eligible recipients with HCC for anti-mTOR-based therapy after LT.

NPC1 promotes autophagy with tumor promotion and acts as a prognostic model for hepatocellular carcinoma

Backgroundmore and more studies have indicated that autophagy plays a crucial role in hepatocellular carcinoma (HCC) in recent years. Hence, our study aimed to establish a prognostic signature for HCC based on autophagy-related genes (ARGs) in order to predict the prognosis of HCC. MethodsAll original gene-expression data and clinical information were downloaded from The Cancer Genome Atlas (TCGA), International Cancer Genome Consortium (ICGC) and Gene Expression Omnibus (GEO). ARGs were obtained from the Human Autophagy Database (HADb). Univariate Cox regression analysis, Least absolute shrinkage and selection operator (LASSO) and Principal Component Analysis (PCA) analysis were performed to identify and validate the validity and reliability of our eight-gene signature, Gene Set Enrichment Analysis (GSEA) was used to perform enrichment analysis by comparing high-risk and low-risk groups in KEGG (Kyoto Encyclopedia of Genes and Genomes) and GO (Gene Ontology) gene sets. Finally, we verified the gene (NPC1) by functional experiments in vitro and in vivo. Results8 ARGs were identified for establishing an eight-gene signature. Then, we validated our eight-gene signature in training, internal, external, and entire testing cohorts. Besides, we also explored the relationships between the eight-gene signature and immune infiltration or immune checkpoints. We also identified NPC1 was closely related to Activated CD4 T cell and Type I IFN Response, and higher expressed level of HCC patients was more sensitive to CTLA4 and TNFRSF9 immune checkpoint inhibitors. NPC1 is highly expressed in HCC cells and tumor tissues, which promotes the proliferation, migration, and invasion of tumor cells by activating autophagy.. Conclusion8 ARGs were used to establish a gene signature to predict the prognosis of HCC. we inferred that NPC1 can promote late autophagy, it could be a future novel therapeutic target of HCC.

Development of the RF-GSEA Method for Identifying Disulfidptosis-Related Genes and Application in Hepatocellular Carcinoma.

Disulfidptosis is a newly discovered cellular programmed cell death mode. Presently, a considerable number of genes related to disulfidptosis remain undiscovered, and its significance in hepatocellular carcinoma remains unrevealed. We have developed a powerful analytical method called RF-GSEA for identifying potential genes associated with disulfidptosis. This method draws inspiration from gene regulation networks and graph theory, and it is implemented through a combination of random forest regression model and Gene Set Enrichment Analysis. Subsequently, to validate the practical application value of this method, we applied it to hepatocellular carcinoma. Based on the RF-GSEA method, we developed a disulfidptosis-related signature. Lastly, we looked into how the disulfidptosis-related signature is connected to HCC prognosis, the tumor microenvironment, the effectiveness of immunotherapy, and the sensitivity of chemotherapy drugs. The RF-GSEA method identified a total of 220 disulfidptosis-related genes, from which 7 were selected to construct the disulfidptosis-related signature. The high-disulfidptosis-related score group had a worse prognosis compared to the low-disulfidptosis-related score group and showed lower infiltration levels of immune-promoting cells. The high-disulfidptosis-related score group had a higher likelihood of benefiting from immunotherapy compared to the low-disulfidptosis-related score group. The RF-GSEA method is a powerful tool for identifying disulfidptosis-related genes. The disulfidptosis-related signature effectively predicts HCC prognosis, immunotherapy response, and drug sensitivity.