Sensitivity For Hepatocellular Carcinoma Research Articles

Construction of an Immunogenic Cell Death-Related Gene Signature and Genetic Subtypes for Predicting Prognosis, Immune Microenvironments, and Drug Sensitivity in Hepatocellular Carcinoma.

Immunogenic cell death (ICD) is a type of regulated cell death that modifies the immune response by releasing DAMPs or danger signals. Herein, we aimed to develop an ICD-related predictive model for patients with hepatocellular carcinoma (HCC) and investigate its applicability for predicting prognostic outcomes and immunotherapeutic responses. Differentially expressed genes of ICD were identified in the HCC and normal liver samples. A prognostic risk model and a nomogram containing clinicopathological features were created. To validate the effectiveness of the model, an external dataset was used. Clinical characteristics, prognosis, tumor mutation burden, immune microenvironments, biological function and chemotherapeutic drug sensitivity were evaluated for different genetic subtypes and risk groups. A total of 35 ICD-related genes (ICDRGs) were identified between HCC and normal samples, 11 of which were significantly associated with overall survival (OS) in HCC patients. Four different genetic subtypes were formed and eight ICDRGs were selected to develop a risk prognostic model. The risk scores were shown to be an independent prognostic factor for HCC and positively correlated with pathological severity. Patients in the high-risk group had a higher frequency of TP53 mutations, increased expression of immune checkpoints and human leukocyte antigen genes. The inhibitory concentrations of chemotherapeutic drugs differed in different populations. In this study, we developed an ICDRG risk model and demonstrated its applicability in predicting survival outcomes, immune and chemotherapeutic responses in HCC patients. ICDRGs are expected to be used as novel biomarkers in the medical decision-making of HCC.

A novel mitochondrial unfolded protein response-related risk signature to predict prognosis, immunotherapy and sorafenib sensitivity in hepatocellular carcinoma.

Hepatocellular carcinoma (HCC) is a common cause of cancer-associated death worldwide. The mitochondrial unfolded protein response (UPRmt) not only maintains mitochondrial integrity but also regulates cancer progression and drug resistance. However, no study has used the UPRmt to construct a prognostic signature for HCC. This work aimed to establish a novel signature for predicting patient prognosis, immune cell infiltration, immunotherapy, and chemotherapy response based on UPRmt-related genes (MRGs). Transcriptional profiles and clinical information were obtained from the TCGA and ICGC databases. Cox regression and LASSO regression analyses were applied to select prognostic genes and develop a risk model. The TIMER algorithm was used to investigate immunocytic infiltration in the high- and low-risk subgroups. Here, two distinct clusters were identified with different prognoses, immune cell infiltration statuses, drug sensitivities, and response to immunotherapy. A risk score consisting of seven MRGs (HSPD1, LONP1, SSBP1, MRPS5, YME1L1, HDAC1 and HDAC2) was developed to accurately and independently predict the prognosis of HCC patients. Additionally, the expression of core MRGs was confirmed by immunohistochemistry (IHC) staining, single-cell RNA sequencing, and spatial transcriptome analyses. Notably, the expression of prognostic MRGs was significantly correlated with sorafenib sensitivity in HCC and markedly downregulated in sorafenib-treated HepG2 and Huh7 cells. Furthermore, the knockdown of LONP1 decreased the proliferation, invasion, and migration of HepG2 cells, suggesting that upregulated LONP1 expression contributed to the malignant behaviors of HCC cells. To our knowledge, this is the first study to investigate the consensus clustering algorithm, prognostic potential, immune microenvironment infiltration and drug sensitivity based on the expression of MRGs in HCC. In summary, the UPRmt-related classification and prognostic signature could assist in determining the prognosis and personalized therapy of HCC patients from the perspectives of predictive, preventative and personalizedmedicine.

Machine learning-based disulfidptosis-related lncRNA signature predicts prognosis, immune infiltration and drug sensitivity in hepatocellular carcinoma.

Disulfidptosis a new cell death mode, which can cause the death of Hepatocellular Carcinoma (HCC) cells. However, the significance of disulfidptosis-related Long non-coding RNAs (DRLs) in the prognosis and immunotherapy of HCC remains unclear. Based on The Cancer Genome Atlas (TCGA) database, we used Least Absolute Shrinkage and Selection Operator (LASSO) and Cox regression model to construct DRL Prognostic Signature (DRLPS)-based risk scores and performed Gene Expression Omnibus outside validation. Survival analysis was performed and a nomogram was constructed. Moreover, we performed functional enrichment annotation, immune infiltration and drug sensitivity analyses. Five DRLs (AL590705.3, AC072054.1, AC069307.1, AC107959.3 and ZNF232-AS1) were identified to construct prognostic signature. DRLPS-based risk scores exhibited better predictive efficacy of survival than conventional clinical features. The nomogram showed high congruence between the predicted survival and observed survival. Gene set were mainly enriched in cell proliferation, differentiation and growth function related pathways. Immune cell infiltration in the low-risk group was significantly higher than that in the high-risk group. Additionally, the high-risk group exhibited higher sensitivity to Afatinib, Fulvestrant, Gefitinib, Osimertinib, Sapitinib, and Taselisib. In conclusion, our study highlighted the potential utility of the constructed DRLPS in the prognosis prediction of HCC patients, which demonstrated promising clinical application value.

Signature construction and molecular subtype identification based on liver-specific genes for prediction of prognosis, immune activity, and anti-cancer drug sensitivity in hepatocellular carcinoma

BackgroundLiver specific genes (LSGs) are crucial for hepatocyte differentiation and maintaining normal liver function. A deep understanding of LSGs and their heterogeneity in hepatocellular carcinoma (HCC) is necessary to provide clues for HCC diagnosis, prognosis, and treatment.MethodsThe bulk and single-cell RNA-seq data of HCC were downloaded from TCGA, ICGC, and GEO databases. Through unsupervised cluster analysis, LSGs-based HCC subtypes were identified in TCGA-HCC samples. The prognostic effects of the subtypes were investigated with survival analyses. With GSVA and Wilcoxon test, the LSGs score, stemness score, aging score, immune score and stromal score of the samples were estimated and compared. The HCC subtype-specific genes were identified. The subtypes and their differences were validated in ICGC-HCC samples. LASSO regression analysis was used for key gene selection and risk model construction for HCC overall survival. The model performance was estimated and validated. The key genes were validated for their heterogeneities in HCC cell lines with quantitative real-time PCR and at single-cell level. Their dysregulations were investigated at protein level. Their correlations with HCC response to anti-cancer drugs were estimated in HCC cell lines.ResultsWe identified three LSGs-based HCC subtypes with different prognosis, tumor stemness, and aging level. The C1 subtype with low LSGs score and high immune score presented a poor survival, while the C2 subtype with high LSGs score and immune score indicated an enduring survival. Although no significant survival difference between C2 and C3 HCCs was shown, the C2 HCCs presented higher immune score and stroma score. The HCC subtypes and their differences were confirmed in ICGC-HCC dataset. A five-gene prognostic signature for HCC survival was constructed. Its good performance was shown in both the training and validation datasets. The five genes presented significant heterogeneities in different HCC cell lines and hepatocyte subclusters. Their dysregulations were confirmed at protein level. Furthermore, their significant associations with HCC sensitivities to anti-cancer drugs were shown.ConclusionsLSGs-based HCC subtype classification and the five-gene risk model might provide useful clues not only for HCC stratification and risk prediction, but also for the development of more personalized therapies for effective HCC treatment.

Prediction of CAF-related genes in immunotherapy and drug sensitivity in hepatocellular carcinoma: a multi-database analysis.

This study aims to identify the cancer-associated fibroblasts (CAF)-related genes that can affect immunotherapy and drug sensitivity in hepatocellular carcinoma (HCC). Expression data and survival data associated with HCC were obtained in The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) databases. Weighted correlation network analysis (WGCNA) analysis was performed to obtain CAF-related genes. Least Absolute Shrinkage and Selection Operator (LASSO) regression was used for regression analysis and risk models. Subsequently, Gene Set Enrichment Analysis (GSEA) analysis, Gene Set Enrichment Analysis (ssGSEA) analysis, Tumor Immune Dysfunction and Exclusion (TIDE) analysis and drug sensitivity analysis were performed on the risk models. Survival analysis of CAF scores showed that the survival rate was lower in samples with high CAF scores than those with low scores. However, this difference was not significant, suggesting CAF may not directly influence the prognosis of HCC patients. Further screening of CAF-related genes yielded 33 CAF-related genes. Seven risk models constructed based on CDR2L, SPRED1, PFKP, ENG, KLF2, FSCN1 and VCAN, showed significant differences in immunotherapy and partial drug sensitivity in HCC. Seven CAF-related genes may have important roles in immunotherapy, drug sensitivity and prognostic survival in HCC patients.

Comprehensive Bioinformatic Analysis Reveals an Autophagy-related Gene Signature for Predicting Outcome, Immune Status, and Drug Sensitivity in Hepatocellular Carcinoma.

Hepatocellular carcinoma (HCC) is one of the most common malignancies in the world, but molecular complexity and tumor heterogeneity make predictive models for HCC prognosis ineffective. Many recent studies have suggested that autophagy is important in tumor progression. Using autophagy-related genes (ARGs), we attempted to create a novel signature for individual prognosis prediction in patients with HCC. Differentially expressed ARGs (DE-ARGs) in HCC and normal samples were screened using TCGA datasets. Univariate Cox and multivariate Cox regression analyses were performed to determine ARGs related to survival in HCC. An autophagy-based signature was constructed using LASSO, and its correlation with the prognosis and the immune infiltration of HCC patients was explored. In this study, we screened 32 survival-related DE-ARGs by analyzing TCGA datasets. Functional enrichment indicated that the 32 DE-ARGs may play important functional and regulatory roles in cellular autophagy, the regulation of multiple signaling pathways, as well as in the context of cancer and neurological diseases. Based on PPI Network, we identified several hub genes. LASSO Cox regression analysis selected five genes (CASP8, FOXO1, PRKCD, SPHK1, and SQSTM1) for a novel prognostic model. AUCs of 0.752, 0.686, and 0.665 in the TCGA whole set indicated that the model accurately predicted 1-, 3-, and 5-year overall survival, respectively. Cox regression analysis showed that the five-gene signature is an independent and robust predictor in patients with HCC. The high-risk group demonstrated higher levels of immune cell infiltration and exhibited a strong correlation with the immune microenvironment and tumor stem cells. In addition, we further identified PRKCD and SQSTM1 as critical regulators involved in HCC progression. The expression levels of PRKCD and SQSTM1 genes play a crucial role in chemotherapy drug sensitivity and resistance. We introduce here a novel ARG-based predictive feature for HCC patients. Effective use of this signature will aid in determining a patient's prognosis and may lead to novel approaches to clinical decision-making and therapy.

Disulfidptosis-related lncRNA signatures assess immune microenvironment and drug sensitivity in hepatocellular carcinoma

Hepatocellular carcinoma (HCC) is associated with a high mortality rate, where resistance to immunotherapy and chemotherapy plays a crucial role. A newly identified form of cell death called disulfidptosis shows promise, but its biological mechanism in HCC remains uncertain. In this study, a prognostic model was developed for Disulfidptosis-related long non-coding RNAs (DRLs) from 370 HCC patients sourced from TCGA-LIHC, utilizing five key features: AC026356.1, AC073254.1, PXN-AS1 expression, AC026412.3, and AC099066.2. High-risk HCC patients had lower survival, CD4+ T cell infiltration, and elevated immune checkpoint gene expression. Furthermore, based on the features of DRLs, HCC was classified into three subtypes. Notably, patients belonging to different subtypes demonstrated varying overall survival rates, immune cell infiltration patterns, and sensitivity to immune therapy. Moreover, the novel DRL AC026412.3 (HR = 40.207) emerged as the most significant prognostic factor, exhibiting high expression across all HCC cells. Elevated expression of AC026412.3 promoted HCC cell proliferation and induced resistance to gefitinib. In conclusion, we have discovered five DRLs and constructed a prognostic risk model. Our findings validate the correlation between DRL-related prognostic models, tumor subtypes, and the HCC immune microenvironment along with its implications for immunotherapy. Moreover, further investigation into the molecular mechanisms of key biomarkers like AC026412.3 in the future will contribute significantly to advancing our comprehension of HCC's pathogenesis and drug resistance mechanisms.

Comprehensive analysis of diverse programmed cell death patterns in the prognosis, tumor microenvironment and drug sensitivity in hepatocellular carcinoma.

Treatment failure in patients with liver hepatocellular carcinoma (LIHC) is primarily caused by tumor progression and therapy resistance. Tumor immunity plays a crucial role in regulating the homeostasis of cells through the process of programmed cell death (PCD). However, the expression profile and clinical significance of PCD-related genes in LIHC require further investigation. In this study, we analyzed twelve commonly observed PCD patterns to construct a prognostic model. We collected RNA-seq data, genomics, and clinical information from TCGA-LIHC and GSE14520 cohorts to validate the prognostic gene signature. We discovered 75 PCD-related differentially expressed genes (DEGs) with prognostic significance in LIHC. Using these genes, we constructed a PCD-related score (PCDscore) with an 11-gene signature through LASSO COX regression analysis. Validation in the GSE14520 cohort demonstrated that LIHC patients with high PCDscore had poorer prognoses. Unsupervised clustering based on the 11 model genes revealed 3 molecular subtypes of LIHC with distinct prognoses. By incorporating PCDscore with clinical features, we constructed a highly predictive nomogram. Additionally, PCDscore was correlated with immune checkpoint genes and immune cell infiltration. LIHC patients with high PCDscore exhibited sensitivity to common chemotherapy drugs (such as cisplatin and docetaxel). To summarize, our study developed a novel PCDscore model that comprehensively analyzed different cell death modes, providing an accurate prediction of clinical prognosis and drug sensitivity for LIHC patients.

Targeting DDX11 promotes PARP inhibitor sensitivity in hepatocellular carcinoma by attenuating BRCA2-RAD51 mediated homologous recombination.

Homologous recombination (HR) is a major DNA double-strand break (DSB) repair pathway of clinical interest because of treatment with poly(ADP-ribose) polymerase inhibitors (PARPi). Cooperation between RAD51 and BRCA2 is pivotal for DNA DSB repair, and its dysfunction induces HR deficiency and sensitizes cancer cells to PARPi. The depletion of the DEAD-box protein DDX11 was found to suppress HR in hepatocellular carcinoma (HCC) cells. The HR ability of HCC cells is not always dependent on the DDX11 level because of natural DDX11 mutations. In Huh7 cells, natural DDX11 mutations were detected, increasing the susceptibility of Huh7 cells to olaparib in vitro and in vivo. The HR deficiency of Huh7 cells was restored when CRISPR/Cas9-mediated knock-in genomic editing was used to revert the DDX11 Q238H mutation to wild type. The DDX11 Q238H mutation impeded the phosphorylation of DDX11 by ATM at serine 237, preventing the recruitment of RAD51 to damaged DNA sites by disrupting the interaction between RAD51 and BRCA2. Clinically, a high level of DDX11 correlated with advanced clinical characteristics and a poor prognosis and served as an independent risk factor for overall and disease-free survival in patients with HCC. We propose that HCC with a high level of wild-type DDX11 tends to be more resistant to PARPi because of enhanced recombination repair, and the key mutation of DDX11 (Q238H) is potentially exploitable.

Overexpression of TMEM79 combined with SMG5 is related to prognosis, tumor immune infiltration and drug sensitivity in hepatocellular carcinoma

BackgroundHepatocellular carcinoma (HCC) is a primary liver malignancy that is now relatively common worldwide. TMEM79 has been reported to play diagnostic and prognostic markers in a variety of cancers and was found to be closely associated with immune infiltration. SMG5 is associated with immune cell infiltration in HCC. Multiple nonsense-mediated mRNA processes require the involvement of SMG5. TMEM79 and SMG5 complexes may be prognostic markers for prostate cancer. However, the relationship between TMEM79 expression in HCC and prognosis, its role and mechanism of action, and its relationship with SMG5 have not been studied. This article focuses on not only the prognostic role of TMEM79 and its biological significance, including immuno-infiltration, tumor mutations and drug sensitivity, but also the interaction with SMG5 in HCC.MethodsDifferential expression analysis and the multiCox proportional hazards regression analyses of TMEM79 and SMG5 were performed by multiple databases. Then, use IHC to verify our results. Subsequently, we used R software to analyze the clinical phenotype of both: analysis of clinicopathological features, enrichment analysis, analysis of immune infiltration, analysis of immune checkpoints, analysis of drug sensitivity, and immunotherapy.ResultsBoth the database studies and the results of our research group showed that TMEM79 and SMG5 were differentially expressed in HCC and normal tissues. Validation of immunohistochemistry showed that differential expression of TMEM79 and SMG5, which influenced the prognosis of patients with HCC, could be an independent prognostic factor. Results of the TCGA database study showed that TMEM79 and SMG5 were correlated with immune infiltration, immune checkpoints, drug sensitivity, and immunotherapy. We typed TMEM79-related molecules in HCC according to R software. Two types of TMEM79 correlated with clinical features, survival of patients with HCC, and immune infiltration.ConclusionTMEM79 are highly expressed in HCC and play an important role in the prognosis of patients with HCC. TMEM79 and SMG5 are positively correlated and may both associated with immune infiltration, and closely linked to immune checkpoints, drug sensitivity, and immunotherapy in HCC.

Bioinformatics identification of a T-cell-related signature for predicting prognosis and drug sensitivity in hepatocellular carcinoma.

Hepatocellular carcinoma (HCC) is a fatal disease with poor clinical outcomes. T cells play a vital role in the crosstalk between the tumour microenvironment and HCC. Single-cell RNA sequencing data were downloaded from the GSE149614 dataset. The T-cell-related prognostic signature (TRPS) was developed with the integrative procedure including 10 machine learning algorithms. The TRPS was established using 7 T-cell-related markers in the Cancer Genome Atlas cohort with 1-, 2- and 3-year area under curve values of 0.820, 0.725 and 0.678, respectively. TRPS acted as an independent risk factor for HCC patients. HCC patients with a high TRPS-based risk score had a higher Tumour Immune Dysfunction and Exclusion score, lower PD1 and CTLA4 immunophenoscore and lower level of immunoactivated cells, including CD8+ T cells and NK cells. The response rate was significantly higher in patients with low-risk scores in immunotherapy cohorts, including IMigor210 and GSE91061. The TRPS-based nomogram had a relatively good predictive value in evaluating the mortality risk at 1, 3 and 5years in HCC. Overall, this study develops a TRPS by integrated bioinformatics analysis. This TRPS acted as an independent risk factor for the OS rate of HCC patients. It can screen for HCC patients who might benefit from immunotherapy, chemotherapy and targeted therapy.

Three E2F target-related genes signature for predicting prognosis, immune features, and drug sensitivity in hepatocellular carcinoma.

Background: Hepatocellular carcinoma (HCC) is extremely malignant and difficult to treat. The adenoviral early region 2 binding factors (E2Fs) target pathway is thought to have a major role in tumor growth. This study aimed to identify a predictive E2F target signature and facilitate individualized treatment for HCC patients. Methods: We constructed an E2F target-related gene profile using univariate COX and LASSO regression models and proved its predictive efficacy in external cohorts. Furthermore, we characterized the role of the E2F target pathway in pathway enrichment, immune cell infiltration, and drug sensitivity of HCC. Results: Lasso Cox regression created an E2F target-related gene signature of GHR, TRIP13, and CDCA8. HCC patients with high risk were correlated with shorter survival time, immune evasion, tumor stem cell characteristics and high sensitivity to Tipifarnib and Camptothecin drugs. Conclusion: Hepatocellular carcinoma prognosis was predicted by an E2F target signature. This finding establishes the theoretical usefulness of the E2F target route in customized identification and treatment for future research.

Cuproptosis-related signature for clinical prognosis and immunotherapy sensitivity in hepatocellular carcinoma.

Copper homeostasis imbalance has been implicated in tumor progression, aggressiveness, and treatment response. However, the precise roles of cuproptosis-related genes (CRGs) in hepatocellular carcinoma (HCC) remain poorly understood. In this study, we employed a consensus clustering algorithm to identify distinct molecular subtypes. We then performed Kaplan-Meier analysis and univariate Cox regression analysis to identify prognostic differentially expressed genes. The expression of these genes was subsequently validated using qPCR on fresh-frozen tissues obtained from HCC patients. Moreover, leveraging the TCGA-HCC cohort, we constructed a CRGs-related risk prediction model using the LASSO and multivariate Cox regression analysis. By analyzing the data, we successfully established a CRGs risk prognostic model for HCC patients, comprising five differential genes (CAD, SGCB, TXNRD1, KDR, and MTND4P20). Cox regression analysis revealed that the CRGs risk score could serve as an independent prognostic factor for overall survival (hazard ratio [HR] = 1.308, 95% confidence interval [CI] = 1.200 - 1.426, P < 0.001). The area under the curve (AUC) values of the CRGs-score for predicting 1-year, 3-year, and 5-year survival rates were 0.785, 0.724, and 0.723, respectively. Notably, the expression levels of immune checkpoints (including PD-1, PD-L1, and CTLA4) significantly differed between the low- and high-risk score groups. Furthermore, the low-risk score group displayed increased sensitivity to sorafenib, cisplatin, cyclopamine, nilotinib, salubrinal, and gemcitabine, whereas the high-risk score group exhibited heightened sensitivity to lapatinib, erlotinib, and gefitinib. Our findings highlight the potential of the CRGs risk score as an independent and promising biomarker for clinical prognosis and immunotherapy sensitivity in HCC patients.

SAT-287 - Potential biomarkers predicting ferroptosis sensitivity in hepatocellular carcinoma

SAT-287 - Potential biomarkers predicting ferroptosis sensitivity in hepatocellular carcinoma

A cuproptosis random forest cox score model-based evaluation of prognosis, mutation characterization, immune infiltration, and drug sensitivity in hepatocellular carcinoma.

Hepatocellular carcinoma is the third most deadly malignant tumor in the world with a poor prognosis. Although immunotherapy represents a promising therapeutic approach for HCC, the overall response rate of HCC patients to immunotherapy is less than 30%. Therefore, it is of great significance to explore prognostic factors and investigate the associated tumor immune microenvironment features. By analyzing RNA-seq data of the TCGA-LIHC cohort, the set of cuproptosis related genes was extracted via correlation analysis as a generalization feature. Then, a random forest cox prognostic model was constructed and the cuproptosis random forest cox score was built by random forest feature filtering and univariate multivariate cox regression analysis. Subsequently, the prognosis prediction of CRFCS was evaluated via analyzing data of independent cohorts from GEO and ICGC by using KM and ROC methods. Moreover, mutation characterization, immune cell infiltration, immune evasion, and drug sensitivity of CRFCS in HCC were assessed. A cuproptosis random forest cox score was built based on a generalization feature of four cuproptosis related genes. Patients in the high CRFCS group exhibited a lower overall survival. Univariate multivariate Cox regression analysis validated CRFCS as an independent prognostic indicator. ROC analysis revealed that CRFCS was a good predictor of HCC (AUC =0.82). Mutation analysis manifested that microsatellite instability (MSI) was significantly increased in the high CRFCS group. Meanwhile, tumor microenvironment analysis showed that the high CRFCS group displayed much more immune cell infiltration compared with the low CRFCS group. The immune escape assessment analysis demonstrated that the high CRFCS group displayed a decreased TIDE score indicating a lower immune escape probability in the high CRFCS group compared with the low CRFCS group. Interestingly, immune checkpoints were highly expressed in the high CRFCS group. Drug sensitivity analysis revealed that HCC patients from the high CRFCS group had a lower IC50 of sorafenib than that from the low CRFCS group. In this study, we constructed a cuproptosis random forest cox score (CRFCS) model. CRFCS was revealed to be a potential independent prognostic indicator of HCC and high CRFCS samples showed a poor prognosis. Interestingly, CRFCS were correlated with TME characteristics as well as clinical treatment efficacy. Importantly, compared with the low CRFCS group, the high CRFCS group may benefit from immunotherapy and sorafenib treatment.

HDAC5-mediated exosomal Maspin and miR-151a-3p as biomarkers for enhancing radiation treatment sensitivity in hepatocellular carcinoma

BackgroundTumor-derived exosomes are critical elements of the cell–cell communication response to various stimuli. This study aims to reveal that the histone deacetylase 5 (HDAC5) and p53 interaction upon radiation in hepatocellular carcinoma intricately regulates the secretion and composition of exosomes.MethodsWe observed that HDAC5 and p53 expression were significantly increased by 2 Gy and 4 Gy radiation exposure in HCC. Normal- and radiation-derived exosomes released by HepG2 were purified to investigate the exosomal components.ResultsWe found that in the radiation-derived exosome, exosomal Maspin was notably increased. Maspin is known as an anti-angiogenic gene. The expression of Maspin was regulated at the cellular level by HDAC5, and it was elaborately regulated and released in the exosome. Radiation-derived exosome treatment caused significant inhibition of angiogenesis in HUVECs and mouse aortic tissues. Meanwhile, we confirmed that miR-151a-3p was significantly reduced in the radiation-derived exosome through exosomal miRNA sequencing, and three HCC-specific exosomal miRNAs were also decreased. In particular, miR-151a-3p induced an anti-apoptotic response by inhibiting p53, and it was shown to induce EMT and promote tumor growth by regulating p53-related tumor progression genes. In the HCC xenograft model, radiation-induced exosome injection significantly reduced angiogenesis and tumor size.ConclusionsOur present findings demonstrated HDAC5 is a vital gene of the p53-mediated release of exosomes resulting in tumor suppression through anti-cancer exosomal components in response to radiation. Finally, we highlight the important role of exosomal Maspin and mi-151a-3p as a biomarker in enhancing radiation treatment sensitivity.Graphical Therapeutic potential of HDAC5 through p53-mediated exosome modulation in radiation treatment of hepatocellular carcinoma

Ferroptosis and cuproptosis prognostic signature for prediction of prognosis, immunotherapy and drug sensitivity in hepatocellular carcinoma: development and validation based on TCGA and ICGC databases.

Hepatocellular carcinoma (HCC) is a common malignancy. Ferroptosis and cuproptosis promote HCC spread and proliferation. While fewer studies have combined ferroptosis and cuproptosis to construct prognostic signature of HCC. This work attempts to establish a novel scoring system for predicting HCC prognosis, immunotherapy, and medication sensitivity based on ferroptosis-related genes (FRGs) and cuproptosis-related genes (CRGs). FerrDb and previous literature were used to identify FRGs. CRGs came from original research. The Cancer Genome Atlas (TCGA) and International Cancer Genome Consortium (ICGC) databases included the HCC transcriptional profile and clinical information [survival time, survival status, age, gender, Tumor Node Metastasis (TNM) stage, etc.]. Correlation, Cox, and least absolute shrinkage and selection operator (LASSO) regression analyses were used to narrow down prognostic genes and develop an HCC risk model. Using "caret", R separated TCGA-HCC samples into a training risk set and an internal test risk set. As external validation, we used ICGC samples. We employed Kaplan-Meier analysis and receiver operating characteristic (ROC) curve to evaluate the model's clinical efficacy. CIBERSORT and TIMER measured immunocytic infiltration in high- and low-risk populations. TXNRD1 [hazard ratio (HR) =1.477, P<0.001], FTL (HR =1.373, P=0.001), GPX4 (HR =1.650, P=0.004), PRDX1 (HR =1.576, P=0.002), VDAC2 (HR =1.728, P=0.008), OTUB1 (HR =1.826, P=0.002), NRAS (HR =1.596, P=0.005), SLC38A1 (HR =1.290, P=0.002), and SLC1A5 (HR =1.306, P<0.001) were distinguished to build predictive model. In both the model cohort (P<0.001) and the validation cohort (P<0.05), low-risk patients had superior overall survival (OS). The areas under the curve (AUCs) of the ROC curves in the training cohort (1-, 3-, and 5-year AUCs: 0.751, 0.727, and 0.743), internal validation cohort (1-, 3-, and 5-year AUCs: 0.826, 0.624, and 0.589), and ICGC cohort (1-, 3-, and 5-year AUCs: 0.699, 0.702, and 0.568) were calculated. Infiltration of immune cells and immunological checkpoints were also connected with our signature. Treatments with BI.2536, Epothilone.B, Gemcitabine, Mitomycin.C, Obatoclax. Mesylate, and Sunitinib may profit high-risk patients. We analyzed FRGs and CRGs profiles in HCC and established a unique risk model for treatment and prognosis. Our data highlight FRGs and CRGs in clinical practice and suggest ferroptosis and cuproptosis may be therapeutic targets for HCC patients. To validate the model's clinical efficacy, more HCC cases and prospective clinical assessments are needed.

Integrated systemic analysis of FAM72A to identify its clinical relevance, biological function, and relationship to drug sensitivity in hepatocellular carcinoma.

The family with sequence similarity 72 member A (FAM72A) protein has been identified as an effector of multiple pathological processes in many cancers. The value of FAM72A in HCC remains largely unknown. Data from TCGA-LIHC, ICGC-LIRI-JP, IMvigor210, cBioPortal, GeneMANIA, and TIMER were processed and visualized to explore the association between FAM72A and the prognosis, stemness phenotype, mutational burden, immune cell infiltration, and drug sensitivity in HCC patients. Potential pathways were also revealed. Furthermore, we experimentally verified the results in vivo and in vitro using immunohistochemistry, western blotting, and CCK-8 assays. First, FAM72A mRNA expression was significantly upregulated in HCC. High FAM72A expression was independently associated with a poor prognosis. Experimental validation confirmed that FAM72A was remarkably overexpressed in HCC patients and mice. Moreover, FAM72A knockdown suppressed HCC cell proliferation. In addition, the frequency of TP53 mutations was significantly higher in the high FAM72A expression group. Subsequently, the enrichment analysis revealed that FAM72A was closely related to immune processes and mTOR pathways. Silencing FAM72A increased the expression levels of mTOR in HCC cell lines. The FAM72A-mTOR pathway was strongly associated with a poor prognosis for patients with HCC. Patients with high FAM72A expression levels might be more resistant to sorafenib. Furthermore, the expression of FAM72A and mTOR was significantly associated with the abundance of some tumor-infiltrating immune cells, especially CD4+ T cells. Finally, patients with high levels of FAM72A and mTOR were more sensitive to immunotherapy. FAM72A, a member of the FAM72 family, might be a prognostic and immunotherapeutic target for HCC patients.

LPCAT1 acts as an independent prognostic biomarker correlated with immune infiltration in hepatocellular carcinoma

BackgroundLysophosphatidylcholine acyltransferase 1 (LPCAT1) is overexpressed in multiple human tumors. However, the role of LPCAT1 in hepatocellular carcinoma (HCC) has not been understood. We aim to explore the relationships between LPCAT1 expression and prognosis, clinicopathological features, tumor microenvironment (TME), immune cell infiltration, immune checkpoint gene expression, and related signaling pathways in HCC. Furthermore, we also explored the relationship between LPCAT1 expression and drug sensitivity to HCC treatment.MethodsThe expression profiles were acquired from the Cancer Genome Atlas (TCGA) and the Human Protein Atlas (THPA). Immune status and infiltration in cancer tissues were explored using the single sample gene set enrichment analysis (ssGSEA) and CIBERSORT algorithm.ResultsLPCAT1 was overexpressed in HCC, and its expression was related to poor prognosis, LPCAT1 was an independent prognostic biomarker in HCC. Expression of LPCAT1 increased statistically with the increase of clinical stage and grade of HCC patients. GO and KEGG network analysis revealed that LPCAT1 positively associated molecules were mostly enriched in functions related to cell adhesion. The TME score of high-LPCAT1 group was significantly higher than that of low-LPCAT1 group. Immune infiltrating cells positively correlated with LPCAT1 expression were Macrophages M0, B cells memory, Dendritic cells activated, T cells regulatory and T cells gamma delta in HCC. We found a positive correlation between LPCAT1 and most immune checkpoint gene expression. The IC50 of 5-Fluorouracil, Gemcitabine, Mitomycin C, Sorafenib and Cabozantinib in patients with high-LPCAT1 expression was lower than that in patients with low-LPCAT1 expression. Our findings provide a wealth of information for further understanding of the biological functions and signaling pathways of LPCAT1 in HCC.ConclusionsLPCAT1 is an independent prognostic biomarker and associated with tumor microenvironment, immune cell infiltration, immune checkpoint expression and drug sensitivity in hepatocellular carcinoma.

ORM 1 as a biomarker of increased vascular invasion and decreased sorafenib sensitivity in hepatocellular carcinoma.

This study aimed to clarify the role of Orosomucoid 1 (ORM1) in the development and therapy resistance in hepatocellular carcinoma (HCC). The mRNA expression level of ORM1 was analyzed via integrative analysis of Gene Express Omnibus (GEO) and The Cancer Genome Atlas (TCGA) datasets. The protein expression level of ORM1 in our cohort was determined using immunohistochemistry. Correlation analysis was used to investigate the relationship between ORM1 expression and clinical parameters. The Cell Counting Kit-8 assay was used to clarify the role of ORM1 in HCC malignant behaviors, including cell growth and sorafenib sensitivity, in vitro. The results indicated that ORM1 was significantly downregulated in the hepatic cancer cells compared to that in the non-cancerous cells. However, it was upregulated in microvascular invasion samples, especially in the cancer embolus compared to that in the surrounding tumor cells. Though Kaplan-Meier analysis did not show an association of ORM1 expression with the overall survival rates of HCC patients, univariate analysis indicated that ORM1 expression was highly correlated with tumor grade and stage. An in vitro assay also revealed that downregulation of ORM1 led to the suppression of tumor growth and enhancement of sorafenib sensitivity without epithelial-to-mesenchymal transition (EMT) alteration, which was consistent with our bioinformatic analysis. Hence, ORM1 played a key role in HCC tumorigenesis and may serve as a potential target for the development of therapeutics against HCC in the future.