Predicting Hepatocellular Carcinoma Prognosis Research Articles

TRIP13 overexpression in hepatocellular carcinoma: implications for poor prognosis and immune cell infiltration

PurposeThe overexpression of TRIP13 has been observed in many types of cancer and has been identified as an oncogene. However, its role in hepatocellular carcinoma (HCC) has not been extensively studied. This study aimed to investigate the expression of TRIP13 in HCC and its impact on immune cell infiltration and prognosis.MethodsWe analyzed TCGA and GSE62232 datasets to assess TRIP13 expression in HCC. Kaplan–Meier and subgroup analysis were performed to examine the correlation between TRIP13 expression and HCC. Univariate and Cox regression analysis were conducted to determine the predictive value of TRIP13 in assessing patient outcomes. A nomogram was developed using TRIP13 mRNA expression to predict HCC prognosis. TRIP13 expression was validated using immunohistochemistry in our patient cohort. Survival and subgroup analyses were conducted to investigate the role of TRIP13 in HCC prognosis.ResultsThe results indicated that TRIP13 upregulation in HCC was a strong independent predictor of poor outcome, as determined by Kaplan–Meier and Cox regression analyses. A high AUC value of 0.982 from ROC curves suggested that TRIP13 upregulation could serve as a reliable diagnostic indicator for HCC. The immunohistochemical validation of TRIP13 expression in the patient cohort confirmed its prognostic significance, and high TRIP13 expression was found to be associated with increased infiltration of Th2 cells and decreased infiltration of neutrophils, Th17 cells, and dendritic cells.ConclusionThese findings suggest that TRIP13 could be a potential prognostic biomarker for HCC.

Development of a novel tumor microenvironment-related radiogenomics model for prognosis prediction in hepatocellular carcinoma.

The tumour microenvironment (TME) has occupied a potent position in the tumorigenesis and tumor progression of hepatocellular carcinoma (HCC). Radiogenomics is an emerging field that integrates imaging and genetic information, thus offering a novel class of non-invasive biomarkers with diagnostic, prognostic, and treatment response. However, optimal evaluation methodologies for radiogenomics in patients with HCC have not been well established. Therefore, this study aims to develop a radiogenomics models, associating contrast-enhanced computed tomography (CECT) based radiomics features and transcriptomics data with TME, to increase predictive precision for overall survival (OS) in patients with HCC. Transcriptome profiles of 365 patients with HCC from The Cancer Genome Atlas (TCGA)-HCC cohort were used to obtain TME-related genes by differential expression analysis. TME-related radiomics features of 53 patients with HCC from The Cancer Imaging Archive (TCIA)-HCC cohort matched with the TCGA-HCC cohort were screened via correlation analysis. Furthermore, a radiogenomics score-based prognostic model was constructed using the least absolute shrinkage and selection operator (LASSO) Cox regression analysis in the TCIA-HCC cohort. Finally, the ability to predict prognosis and the value of the model in identifying the abundance of immune cell infiltration were investigated. A radiogenomics prognostic model was developed, which incorporated 1 radiomics feature [original_gray-level co-occurrence matrix (glcm)_inverse difference normalized (Idn)] and 3 genes [spen paralogue and orthologue C‑terminal domain containing 1 (SPOCD1); killer cell lectin like receptor B1 (KLRB1); G protein-coupled receptor 182 (GPR182)]. The model performed satisfactorily in the training and test sets [1-year, 2-year, 3-year area under the curve (AUC) of 0.81, 0.85 and 0.87 in the training set, respectively; and 0.73, 0.83, and 0.84 in the test set, respectively]. Moreover, the model showed that higher radiogenomics scores were associated with worse OS and lower levels of immune infiltration. The novel CECT-based radiogenomics model may provide valuable insights for prognostic stratification and TME assessment of patients with HCC.

A novel signature of the ligand and receptor genes associated with disulfidoptosis for prediction of prognosis, immunologic therapy responses in hepatocellular carcinoma

BackgroudWe aimed to explore the prognostic features of ligand and receptor genes associated with disulfidoptosis in hepatocellular carcinoma (HCC) and establish a risk signature utilizing these genes to predict the prognosis of HCC patients. MethodsWe used scRNA-seq data from GSE166635 to differentiate malignant cells from normal cells using “copykat”.The study thoroughly examined the disparities in disulfidoptosis scores and the associated gene expressions between malignant and normal cells.We identified key ligand and receptor genes that are specific to HCC cells.Subsequently, Correlation analysis was conducted to ascertain the ligand and receptor genes associated with disulfidoptosis.We performed univariate Cox regression analysis to identify prognostic ligand and receptor genes associated with disulfidoptosis.We employed LASSO to construct a risk signature using prognostic ligand and receptor genes associated with disulfidoptosis.Lastly, we developed a nomogram model that integrates the risk signature with clinicopathological characteristics. ResultsMalignant cells displayed a marked increase in disulfidoptosis scores and the expression of associated genes compared to normal cells.We identified 110 receptor and ligand genes significantly associated with disulfidoptosis, and narrowed them down to create a risk signature comprising eight genes.Multivariate analysis confirmed the risk signature as an independent prognostic factor for HCC and validated its predictive value for immunotherapy outcomes.A novel nomogram was developed, incorporating stage information and the risk signature derived from disulfidoptosis-related receptor and ligand genes, demonstrating excellent predictive accuracy and reliability in HCC prognosis prediction. ConclusionRisk signatures based on disulfidoptosis-associated ligand and receptor genes can effectively predict HCC prognosis and may inform immunotherapy strategies.

Identification of tumor antigens and anoikis-based molecular subtypes in the hepatocellular carcinoma immune microenvironment: implications for mRNA vaccine development and precision treatment

Hepatocellular carcinoma (HCC) represents a formidable malignancy with a high lethality. Nonetheless, the development of vaccine and the establishment of prognostic models for precise and personalized treatment of HCC still encounter big challenges. Thus, the aim of this study was to develop HCC vaccines and explore anoikis-based prognostic models based on RNA sequencing data in GEO datasets (GSE10143, GSE76427) and the TCGA-LIHC cohort. Potential HCC antigens were identified using GEPIA2, cBioPortal, and TIMER2. Anoikis-related subtypes and gene clusters were defined by consensus clustering of 566 liver cancer samples based on 28 anoikis regulators, and we further analyzed their relationship with the immune microenvironment of HCC. A predictive model based on anoikis-related long noncoding RNAs (lncRNAs) was developed to accurately predict HCC prognosis. Seven overexpressed genes associated with HCC prognosis and tumor-infiltrating antigen-presenting cells were identified as potential tumor antigens for the development of HCC mRNA vaccines. Two subtypes based on anoikis-related genes (ARGs) and two gene clusters with different characteristics were identified and validated in defined cohorts. The tumor immune microenvironment between the two subtypes showed different cell infiltration and molecular characteristics. Furthermore, a prognostic score based on seven lncRNAs identified by LASSO regression was constructed, with the low-risk group having favorable prognosis, a “hot” immune microenvironment, and better response to immunotherapy. CCNB1, CDK1, DNASE1L3, KPNA2, PRC1, PTTG, and UBE2S were first identified as promising tumor antigens for mRNA vaccine development in HCC. Besides, we innovatively propose anoikis-based molecular subtypes, which not only enable personalized prognostic stratification of HCC patients but also provide a blueprint for identifying optimal candidates for tumor vaccines, enhancing immunotherapeutic strategies.

Thymidine kinase 1 appears to be a marker for the prognosis of hepatocellular carcinoma based on a large-scale, multicenter study.

The aim of this study was to investigate the role of thymidine kinase 1 (TK1) levels in hepatocellular carcinoma (HCC) prognosis and to develop a nomogram for predicting HCC prognosis. In this study, 1066 HCC patients were enrolled between August 2018 and April 2022. TK1 levels were measured within one week before enrollment, and the relationship with HCC prognosis was evaluated. Next, all patients were randomly assigned to the training set (70%, n=746) and the validation set (30%, n=320). We used multivariate Cox analysis to find independent prognostic factors in the training set to construct a nomogram. The predictive power of the nomogram was assessed using receiver operating characteristic (ROC) curves, calibration curves, and decision curve analysis (DCA). The optimal critical value of TK1 was determined as 2.35 U/L using X-tile software. Before and after propensity score matching (PSM), the median overall survival (mOS) of the low-TK1 group (<2.35U/L) remained significantly longer than that of the high-TK1 group (≥ 2.35U/L) (48.1 vs 16.5 months, p < 0.001; 75.7 vs 19.8 months, p = 0.001). Moreover, multivariate Cox analysis showed that the low TK1 level was an independent positive prognostic indicator. Additionally, the area under the ROC curve for predicting the 1-year, 2-year, and 3-year survival rates was 0.770, 0.758, and 0.805, respectively. TK1 could serve as a prognostic marker for HCC. In addition, the nomogram showed good predictive capability for HCC prognosis.

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.

Novel risk model based on angiogenesis-related lncRNAs for prognosis prediction of hepatocellular carcinoma

Hepatocellular carcinoma (HCC) is a major cause of cancer-related death due to early metastasis or recurrence. Tumor angiogenesis plays an essential role in the tumorigenesis of HCC. Accumulated studies have validated the crucial role of lncRNAs in tumor angiogenesis. Here, we established an angiogenesis-related multi-lncRNAs risk model based on the machine learning for HCC prognosis prediction. Firstly, a total of 348 differential expression angiogenesis-related lncRNAs were identified by correlation analysis. Then, 20 of these lncRNAs were selected through univariate cox analysis and used for in-depth study of machine learning. After 1,000 random sampling cycles calculating by random forest algorithm, four lncRNAs were found to be highly associated with HCC prognosis, namely LUCAT1, AC010761.1, AC006504.7 and MIR210HG. Subsequently, the results from both the training and validation sets revealed that the four lncRNAs-based risk model was suitable for predicting HCC recurrence. Moreover, the infiltration of macrophages and CD8 T cells were shown to be closely associated with risk score and promotion of immune escape. The reliability of this model was validated by exploring the biological functions of lncRNA MIR210HG in HCC cells. The results showed that MIR210HG silence inhibited HCC growth and migration through upregulating PFKFB4 and SPAG4. Taken together, this angiogenesis-related risk model could serve as a reliable and promising tool to predict the prognosis of HCC.

Identification and validation of a m7G-related lncRNA signature for predicting the prognosis and therapy response in hepatocellular carcinoma.

N7-methylguanosine (m7G) is one of the most common RNA posttranscriptional modifications; however, its potential role in hepatocellular carcinoma (HCC) remains unknown. We developed a prediction signature based on m7G-related long noncoding RNAs (lncRNAs) to predict HCC prognosis and provide a reference for immunotherapy and chemotherapy. RNA-seq data from The Cancer Genome Atlas (TCGA) database and relevant clinical data were used. Univariate and multivariate Cox regression analyses were conducted to identify m7G-related lncRNAs with prognostic value to build a predictive signature. We evaluated the prognostic value and clinical relevance of this signature and explored the correlation between the predictive signature and the chemotherapy treatment response of HCC. Moreover, an in vitro study to validate the function of CASC19 was performed. Six m7G-related lncRNAs were identified to create a signature. This signature was considered an independent risk factor for the prognosis of patients with HCC. TIDE analyses showed that the high-risk group might be more sensitive to immunotherapy. ssGSEA indicated that the predictive signature was strongly related to the immune activities of HCC. HCC in high-risk patients was more sensitive to the common chemotherapy drugs bleomycin, doxorubicin, gemcitabine, and lenalidomide. In vitro knockdown of CASC19 inhibited the proliferation, migration and invasion of HCC cells. We established a 6 m7G-related lncRNA signature that may assist in predicting the prognosis and response to chemotherapy and immunotherapy of HCC.

Establishment and Validation of a Four-stress Granule-related Gene Signature in Hepatocellular Carcinoma.

Stress granules (SGs) as membrane-less cytoplasmic foci formed in response to unfavorable external stimuli could promote cancer cells to adapt to hostile environments. Hepatocellular carcinoma (HCC) is prone to be highly aggressive once diagnosed, which markedly reduces patient survival time. Therefore, it is crucial to develop valid diagnostic markers to prognosticate HCC patient prognosis, which promotes individualized precision therapeutics in HCC. Considering the pro-tumorigenic activity of SGs, it is of great potential value to construct a prognostic tool for HCC based on the expression profiles of SG-related genes (SGGs). Bioinformatic analysis was employed to establish an SGG-based prognostic signature. Western blotting and real-time polymerase chain reaction assays were used to assess the expression patterns of the related SGGs. Loss-of-function experiments were performed to analyze the effect of the SGGs on SG formation and cell survival. A four-SGG signature (KPNA2, MEX3A, WDR62, and SFN) targeting HCC was established and validated to exhibit a robust performance in predicting HCC prognosis. Consistently, all four genes were further found to be highly expressed in human HCC tissues. More important, we demonstrated that individually knocking down the four SGGs significantly reduced HCC cell proliferation and metastasis by compromising the SG formation process. We developed an SGG-based predictive signature that can be used as an independent prognostic tool for HCC. The strong predictive power of this signature was further elucidated by the carcinogenic activity of KPNA2, MEX3A, WDR62, and SFN in HCC cells by regulating SG formation.

Combining WGCNA and machine learning to construct immune-related EMT patterns to predict HCC prognosis and immune microenvironment.

Hepatocellular carcinoma (HCC) is a malignancy with a very high mortality rate. Because of its high heterogeneity, there is an urgent need to find biomarkers that accurately predict prognosis. Epithelial-mesenchymal transition (EMT) is closely associated with frequent recurrence and high mortality of HCC. Therefore, it is necessary to comprehensively analyze the prognostic value and immunological properties of EMT gene in HCC. In our study, we performed bioinformatics analysis of the TCGA and ICGC liver cancer cohorts and identified the module genes of immune-associated EMTs (iEMT) by Weighted Gene Co-Expression Network Analysis (WGCNA). Further we used machine learning (support vector machines-recursive feature elimination and Lasso) to identify three central iEMT genes (ARMC9, ADAM15 and STC2) and construct iEMT_score. Subsequently, in the training and validation cohorts, it was demonstrated that the overall survival (OS) of patients in the high iEMT_score group was worse than that of patients in the low iEMT_score group. Based on this, we have constructed a nomogram that is easy for clinicians to use. In addition, our study explored differences in pathway enrichment, immunological properties, and sensitivity to common chemotherapy and targeted drugs in different subgroups of iEMT_score. Finally, we showed through in vitro experiments that knockdown of ARMC9 could significantly inhibit the proliferation, migration and invasion of HCC cells BEL7402. Taken together, our findings suggest that iEMT_score is an excellent biomarker for predicting prognosis and provide some new insights for personalized treatment of HCC patients.

Prognostic model for hepatocellular carcinoma based on anoikis-related genes: immune landscape analysis and prediction of drug sensitivity.

Hepatocellular carcinoma (HCC) represents a complex ailment characterized by an unfavorable prognosis in advanced stages. The involvement of immune cells in HCC progression is of significant importance. Moreover, metastasis poses a substantial impediment to enhanced prognostication for HCC patients, with anoikis playing an indispensable role in facilitating the distant metastasis of tumor cells. Nevertheless, limited investigations have been conducted regarding the utilization of anoikis factors for predicting HCC prognosis and assessing immune infiltration. This present study aims to identify hepatocellular carcinoma-associated anoikis-related genes (ANRGs), establish a robust prognostic model for HCC, and delineate distinct immune characteristics based on the anoikis signature. Cell migration and cytotoxicity experiments were performed to validate the accuracy of the ANRGs model. Consensus clustering based on ANRGs was employed in this investigation to categorize HCC samples obtained from both TCGA and Gene Expression Omnibus (GEO) cohorts. To assess the differentially expressed genes, Cox regression analysis was conducted, and subsequently, prognostic gene signatures were constructed using LASSO-Cox methodology. External validation was performed at the International Cancer Genome Conference. The tumor microenvironment (TME) was characterized utilizing ESTIMATE and CIBERSORT algorithms, while machine learning techniques facilitated the identification of potential target drugs. The wound healing assay and CCK-8 assay were employed to evaluate the migratory capacity and drug sensitivity of HCC cell lines, respectively. Utilizing the TCGA-LIHC dataset, we devised a nomogram integrating a ten-gene signature with diverse clinicopathological features. Furthermore, the discriminative potential and clinical utility of the ten-gene signature and nomogram were substantiated through ROC analysis and DCA. Subsequently, we devised a prognostic framework leveraging gene expression data from distinct risk cohorts to predict the drug responsiveness of HCC subtypes. In this study, we have established a promising HCC prognostic ANRGs model, which can serve as a valuable tool for clinicians in selecting targeted therapeutic drugs, thereby improving overall patient survival rates. Additionally, this model has also revealed a strong connection between anoikis and immune cells, providing a potential avenue for elucidating the mechanisms underlying immune cell infiltration regulated by anoikis.

A novel prognostic signature for hepatocellular carcinoma based on SUMOylation-related genes

SUMOylation (SUMO modification) has been confirmed to play an essential role in the progression of various malignancies. As the value of SUMOylation-related genes (SRGs) in prognosis prediction of hepatocellular carcinoma (HCC) has not been explored, we aim to construct an HCC SRGs signature. RNA sequencing was utilized to identify differentially expressed SRGs. The 87 identified genes were used in Univariate Cox regression analysis and the Least Absolute Shrinkage and Selection Operator (LASSO) analysis to build a signature. The accuracy of the model was validated by the ICGC and GEO datasets. The GSEA revealed that the risk score was associated with common cancer-related pathways. The ssGSEA showed that NK cells in the high-risk group were significantly reduced. The sensitivities of anti-cancer drugs confirmed the sensitivity of the high-risk group to sorafenib was lower. Further, our cohort showed that risk scores were correlated with advanced grade and vascular invasion (VI). Finally, the results of H&E staining and immunohistochemistry of Ki67 showed that higher-risk patients are more malignant.

Integrative analysis of lactylation-related genes and establishment of a novel prognostic signature for hepatocellular carcinoma.

Lactylation has been found to involve in regulating many types of biological process in cancers. However, research on lactylation-related genes in predicting the prognosis of hepatocellular carcinoma (HCC) remains limited. The differential expression of lactylation-related genes (EP300 and HDAC1-3) in pan-cancer were examined in public databases. HCC patient tissues were obtained for mRNA expression and lactylation level detection by RT-qPCR and western blotting. Transwell migration assay, CCK-8 assay, EDU staining assay and RNA-seq were performed to verify the potential function and mechanisms in HCC cell lines after lactylation inhibitor apicidin treatment. lmmuCellAI, quantiSeq, xCell, TIMER and CIBERSOR were used to analyze the correlation between transcription levels of lactylation-related genes and immune cell infiltration in HCC. Risk model of lactylation-related genes was constructed by LASSO regression analysis, and prediction effect of the model was evaluated. The mRNA levels of lactylation-related genes and lactylation levels were higher in HCC tissues than normal samples. The lactylation levels, cell migration, and proliferation ability of HCC cell lines were suppressed after apicidin treatment. The dysregulation of EP300 and HDAC1-3 was associated with proportion of immune cell infiltration, especially B cell. Upregulation of HDAC1 and HDAC2 was closely associated with poorer prognosis. Finally, a novel risk model, based on HDAC1 and HDAC2, was developed for prognosis prediction in HCC. HDAC1 and HDAC2 are expected to become new biomarkers for HCC. Risk scoring model based on HDAC1 and HDAC2 can be used to predict the prognosis of HCC patients.

NK cell marker gene-based model shows good predictive ability in prognosis and response to immunotherapies in hepatocellular carcinoma

Hepatocellular carcinoma (HCC) is the fourth leading cause of malignancy worldwide, and its progression is influenced by the immune microenvironment. Natural killer (NK) cells are essential in the anti-tumor response and have been linked to immunotherapies for cancers. Therefore, it is important to unify and validate the role of NK cell-related gene signatures in HCC. In this study, we used RNA-seq analysis on HCC samples from public databases. We applied the ConsensusClusterPlus tool to construct the consensus matrix and cluster the samples based on their NK cell-related expression profile data. We employed the least absolute shrinkage and selection operator regression analysis to identify the hub genes. Additionally, we utilized the CIBERSORT and ESTIMATE web-based methods to perform immune-related evaluations. Our results showed that the NK cell-related gene-based classification divided HCC patients into three clusters. The C3 cluster was activated in immune activation signaling pathways and showed better prognosis and good clinical features. In contrast, the C1 cluster was remarkably enriched in cell cycle pathways. The stromal score, immune score, and ESTIMATE score in C3 were much higher than those in C2 and C1. Furthermore, we identified six hub genes: CDC20, HMOX1, S100A9, CFHR3, PCN1, and GZMA. The NK cell-related genes-based risk score subgroups demonstrated that a higher risk score subgroup showed poorer prognosis. In summary, our findings suggest that NK cell-related genes play an essential role in HCC prognosis prediction and have therapeutic potential in promoting NK cell antitumor immunity. The six identified hub genes may serve as useful biomarkers for novel therapeutic targets.

High Expression of Ten Eleven Translocation 1 Is Associated with Poor Prognosis in Hepatocellular Carcinoma.

DNA methylation patterns have been found to be distinct between tumor and normal patients. However, the effect of DNA demethylation enzymes, ten eleven translocation (TET) proteins, has not been comprehensively characterized in liver cancer. In this research, we sought to unravel the linkage of TET proteins with prognosis, immune characteristics and biological pathways in hepatocellular carcinoma (HCC). Four independent datasets with gene expression data and clinical data of HCC samples were downloaded from public databases. CIBERSORT, single sample Gene Set Enrichment Analysis (ssGSEA), MCP-counter, and TIMER were implemented to evaluate immune cell infiltration. limma was employed to screen differentially expressed genes (DEGs) between two groups. The demethylation-related risk model was established by using univariate Cox regression analysis, the least absolute shrinkage and selection operator (LASSO), and stepwise Akaike information criterion (stepAIC). TET1 was significantly higher expressed in tumor samples than that in normal samples. HCC patients with advanced stages (III+IV) and grades (G3+G4) had higher TET1 expression compared to early stages (I+II) and grades (G1+G2). HCC samples with high TET1 expression had worse prognosis than that with low expression. High and low TET1 expression groups had distinct immune cell infiltration and response to immunotherapy and chemotherapy. We identified 90 DEGs related to DNA demethylation in high vs. low TET1 expression groups. Furthermore, we established a risk model based on 90 DEGs containing seven key prognostic genes (SERPINH1, CDC20, HACD2, SPHK1, UGT2B15, SLC1A5, and CYP2C9) with effectiveness and robustness in predicting HCC prognosis. Our study suggested TET1 as a potential indicator in HCC progression. TET1 was closely involved in immune infiltration and activation of oncogenic pathways. The DNA demethylation-related risk model was potential to be applied for predicting HCC prognosis in clinics.

RAD54L promotes progression of hepatocellular carcinoma via the homologous recombination repair pathway.

Hepatocellular carcinoma (HCC) is a malignant tumor with high incidence worldwide. The underlying mechanisms remain poorly understood. The DNA metabolic process of homologous recombination repair (HRR) has been linked to a high probability of tumorigenesis and drug resistance. This study aimed to determine the role of HRR in HCC and identify critical HRR-related genes that affect tumorigenesis and prognosis. A total of 613 tumor and 252 para-carcinoma tissue samples were collected from The Cancer Genome Atlas (TCGA) and the International Cancer Genome Consortium (ICGC) to obtain differentially expressed genes (DEGs). HRR-related genes were assessed using gene enrichment and pathway analyses. Survival analysis was performed using the Kaplan-Meier method in the Gene Expression Profiling Interactive Analysis portal. The levels of RAD54L in the HRR pathway were detected by RT-qPCR and western blotting in para-carcinoma and HCC tissues and in L02 normal human liver cells and Huh7 HCC cells. Immunohistochemistry (IHC) was performed on the clinical specimens to determine the connection between gene expression and clinical features. Bioinformatics analysis revealed that the HRR pathway was enriched in HCC tissues. Upregulation of HRR pathway DEGs in HCC tissues was positively correlated with tumor pathological staging and negatively associated with patient overall survival. RAD54B, RAD54L, and EME1 genes in the HRR pathway were screened as markers for predicting HCC prognosis. RT-qPCR identified RAD54L as the most significantly expressed of the three genes. Western blotting and IHC quantitative analyses further demonstrated that RAD54L protein levels were higher in HCC tissues. IHC analysis of 39 pairs of HCC and para-carcinoma tissue samples also revealed an association between RAD54L and Edmondson-Steiner grade and the proliferation-related gene Ki67. The collective findings positively correlate RAD54L in the HRR signaling pathway with HCC staging and implicate RAD54L as a marker to predict HCC progression.

Cellular senescence-related gene signature as a valuable predictor of prognosis in hepatocellular carcinoma.

Hepatocellular carcinoma (HCC) is a lethal tumor. Its prognosis prediction remains a challenge. Meanwhile, cellular senescence, one of the hallmarks of cancer, and its related prognostic genes signature can provide critical information for clinical decision-making. Using bulk RNA sequencing and microarray data of HCC samples, we established a senescence score model via multi-machine learning algorithms to predict the prognosis of HCC. Single-cell and pseudo-time trajectory analyses were used to explore the hub genes of the senescence score model in HCC sample differentiation. A machine learning model based on cellular senescence gene expression profiles was identified in predicting HCC prognosis. The feasibility and accuracy of the senescence score model were confirmed in external validation and comparison with other models. Moreover, we analyzed the immune response, immune checkpoints, and sensitivity to immunotherapy drugs of HCC patients in different prognostic risk groups. Pseudo-time analyses identified four hub genes in HCC progression, including CDCA8, CENPA, SPC25, and TTK, and indicated related cellular senescence. This study identified a prognostic model of HCC by cellular senescence-related gene expression and insight into novel potential targeted therapies.

A novel NHEJ gene signature based model for risk stratification and prognosis prediction in hepatocellular carcinoma

BackgroundNon-homologous DNA end joining (NHEJ) is the predominant DNA double-strand break (DSB) repair pathway in human. However, the relationship between NHEJ pathway and hepatocellular carcinoma (HCC) is unclear. We aimed to explore the potential prognostic role of NHEJ genes and to develop an NHEJ-based prognosis signature for HCC.MethodsTwo cohorts from public database were incorporated into this study. The Kaplan–Meier curve, the Least absolute shrinkage and selection operator (LASSO) regression analysis, and Cox analyses were implemented to determine the prognostic genes. A NHEJ-related risk model was created and verified by independent cohorts. We derived enriched pathways between the high- and low-risk groups using Gene Set Enrichment Analysis (GSEA). CIBERSORT and microenvironment cell populations-counter algorithm were used to perform immune infiltration analysis. XRCC6 is a core NHEJ gene and immunohistochemistry (IHC) was further performed to elucidate the prognostic impact. In vitro proliferation assays were conducted to investigate the specific effect of XRCC6.ResultsA novel NHEJ-related risk model was developed based on 6 NHEJ genes and patients were divided into distinct risk groups according to the risk score. The high-risk group had a poorer survival than those in the low-risk group (P < 0.001). Meanwhile, an obvious discrepancy in the landscape of the immune microenvironment also indicated that distinct immune status might be a potential determinant affecting prognosis as well as immunotherapy reactiveness. High XRCC6 expression level associates with poor outcome in HCC. Moreover, XRCC6 could promote HCC cell proliferation in vitro.ConclusionsIn brief, this work reveals a novel NHEJ-related risk signature for prognostic evaluation of HCC patients, which may be a potential biomarker of HCC immunotherapy.

A pyroptosis-related gene signature for prognosis prediction in hepatocellular carcinoma.

Hepatocellular carcinoma (HCC) is one of the most invasive cancers with a low 5-year survival rate. Pyroptosis, a specialized form of cell death, has shown its association with cancer progression. However, its role in the prognosis of HCC has not been fully understood. In our study, clinical information and mRNA expression for 1076 patients with HCC were obtained from the five public cohorts. Pyroptotic clusters were generated by unsupervised clustering based on 40 pyroptosis-related genes (PRGs) in the TCGA and ICGC cohort. A pyroptosis-related signature was constructed using least absolute shrinkage and selection operator (LASSO) regression according to differentially expressed genes (DEGs) of pyroptotic clusters. The signature was then tested in the validation cohorts (GES10142 and GSE14520) and subsequently validated in the CPTAC cohort (n=159) at both mRNA and protein levels. Response to sorafenib was explored in GSE109211. Three clusters were identified based on the 40 PRGs in the TCGA cohort. A total of 24 genes were selected based on DEGs of the above three pyroptotic clusters to construct the pyroptotic risk score. Patients with the high-risk score showed shorter overall survival (OS) compared to those with the low-risk score in the training set (P<0.001; HR, 3.06; 95% CI, 2.22-4.24) and the test set (P=0.008; HR, 1.61; 95% CI, 1.13-2.28). The predictive ability of the risk score was further confirmed in the CPTAC cohort at both mRNAs (P<0.001; HR, 2.99; 95% CI, 1.67-5.36) and protein levels (P<0.001; HR, 2.97; 95% CI 1.66-5.31). The expression of the model genes was correlated with immune cell infiltration, angiogenesis-related genes, and sensitivity to antiangiogenic therapy (P<0.05). In conclusion, we established a prognostic signature of 24 genes based on pyroptosis clusters for HCC patients, providing insight into the risk stratification of HCC.

Prognostic signatures of sphingolipids: Understanding the immune landscape and predictive role in immunotherapy response and outcomes of hepatocellular carcinoma.

Hepatocellular carcinoma (HCC) is a complex disease with a poor outlook for patients in advanced stages. Immune cells play an important role in the progression of HCC. The metabolism of sphingolipids functions in both tumor growth and immune infiltration. However, little research has focused on using sphingolipid factors to predict HCC prognosis. This study aimed to identify the key sphingolipids genes (SPGs) in HCC and develop a reliable prognostic model based on these genes. The TCGA, GEO, and ICGC datasets were grouped using SPGs obtained from the InnateDB portal. A prognostic gene signature was created by applying LASSO-Cox analysis and evaluating it with Cox regression. The validity of the signature was verified using ICGC and GEO datasets. The tumor microenvironment (TME) was examined using ESTIMATE and CIBERSORT, and potential therapeutic targets were identified through machine learning. Single-cell sequencing was used to examine the distribution of signature genes in cells within the TME. Cell viability and migration were tested to confirm the role of the key SPGs. We identified 28 SPGs that have an impact on survival. Using clinicopathological features and 6 genes, we developed a nomogram for HCC. The high- and low-risk groups were found to have distinct immune characteristics and response to drugs. Unlike CD8 T cells, M0 and M2 macrophages were found to be highly infiltrated in the TME of the high-risk subgroup. High levels of SPGs were found to be a good indicator of response to immunotherapy. In cell function experiments, SMPD2 and CSTA were found to enhance survival and migration of Huh7 cells, while silencing these genes increased the sensitivity of Huh7 cells to lapatinib. The study presents a six-gene signature and a nomogram that can aid clinicians in choosing personalized treatments for HCC patients. Furthermore, it uncovers the connection between sphingolipid-related genes and the immune microenvironment, offering a novel approach for immunotherapy. By focusing on crucial sphingolipid genes like SMPD2 and CSTA, the efficacy of anti-tumor therapy can be increased in HCC cells.