Therapeutic Response Of Hepatocellular Carcinoma Research Articles

Identification of a Macrophage marker gene signature to evaluate immune infiltration and therapeutic response in hepatocellular carcinoma

BackgroundOnly a minority of hepatocellular carcinoma (HCC) patients can benefit from systemic regimens. Macrophages, which abundantly infiltrate in HCC, could mediate tumour microenvironment remodelling and immune escape, proving to be powerful weapons in combating HCC. Thus, a deeper understanding of macrophages is necessary for improving existing antitumour treatments. MethodsWith a series of bioinformatic approaches, we comprehensively explored the role of macrophage-related genes in human HCCs from multiple single-cell and bulk RNA sequencing datasets. Unsupervised clustering was performed to cluster the macrophage marker genes (MMGs). GSVA and functional enrichment analysis were used to elucidate the functional differences among the MMG-associated clusters. Subsequently, a component analysis algorithm was used to construct a Macrosig score, and the prognosis, biological characteristics, mutation profile, TME cell infiltration status and drug response of patients with different Macrosig scores were further analysed. ResultsWe identified 13 MMGs in 574 HCC samples, based on which three MMG-associated clusters were defined. Overall survival time, clinicopathological features and immune infiltration scores differed among the different clusters. On this basis, 12 hub genes were identified among these clusters; subsequently, a scoring system was constructed to determine the Macrosig score. Importantly, patients with low-Macrosig scores, characterized by increased immune infiltration, increased mutation frequency and increased immune checkpoint expression, including CTLA-4, LAG3, PDCD1 and TIGIT, exhibited enhanced efficacy of immunotherapy when validated in an external database. Moreover, a low-Macrosig score indicates increased sensitivity to AZD.2281, A.443654, ABT.263, ABT.888, AG.014699 and ATRA, while a high Macrosig score indicates increased sensitivity to AZD6482, AKT inhibitor VIII, AS601245, AZ628, AZD.0530 and AZD6244. ConclusionsA novel scoring system was constructed to guide more effective prognostic evaluation and tailoring therapeutic regimens for HCC patients.

Comprehensive assessment of regulatory T-cells-related scoring system for predicting the prognosis, immune microenvironment and therapeutic response in hepatocellular carcinoma.

Regulatory T cells (Tregs) play important roles in tumor immunosuppression and immune escape. The aim of the present study was to construct a novel Tregs-associated biomarker for the prediction of tumour immune microenvironment (TIME), clinical outcomes, and individualised treatment in hepatocellular carcinoma (HCC). Single-cell sequencing data were obtained from the three independent cohorts. Cox and LASSO regression were utilised to develop the Tregs Related Scoring System (TRSSys). GSE140520, ICGC-LIRI and CHCC cohorts were used for the validation of TRSSys. Kaplan-Meier, ROC, and Cox regression were utilised for the evaluation of TRSSys. The ESTIMATE, TIMER 2.0, and ssGSEA algorithm were utilised to determine the value of TRSSys in predicting the TIME. GSVA, GO, KEGG, and TMB analyses were used for mechanistic exploration. Finally, the value of TRSSys in predicting drug sensitivity was evaluated based on the oncoPredict algorithm. Comprehensive validation showed that TRSSys had good prognostic predictive efficacy and applicability. Additionally, ssGSEA, TIMER and ESTIMATE algorithm suggested that TRSSys could help to distinguish different TIME subtypes and determine the beneficiary population of immunotherapy. Finally, the oncoPredict algorithm suggests that TRSSys provides a basis for individualised treatment. TRSSys constructed in the current study is a novel HCC prognostic prediction biomarker with good predictive efficacy and stability. Additionally, risk stratification based on TRSSys can help to identify the TIME landscape subtypes and provide a basis for individualized treatment options.

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.

Molecular subtypes of disulfidptosis-regulated genes and prognosis models for predicting prognosis, tumor microenvironment infiltration, and therapeutic response in hepatocellular carcinoma

Disulfidptosis, a recently identified mode of cellular demise marked by excess SLC7A11-reliant cystine, has been proved to affect the development and resilience of tumor cells through the production of glutathione from cystine. Glutathione synthesis plays a crucial role in chemotherapy resistance and the survival of liver cancer cells. Thus, understanding the relationship between disulfidptosis and hepatocellular carcinoma (HCC) is imperative. A molecular typing approach was employed to classify patients with HCC into two distinct subtypes, namely disulfidptosis and disulfide-homeostasis, based on the expression of genes associated with disulfidptosis. Patients with disulfidptosis exhibited a longer survival time, improved immune status, and heightened sensitivity to conventional chemotherapeutic drugs and immunotherapy. Patients with disulfide-homeostasis demonstrated an immunosuppressive microenvironment, drug resistance, and unfavorable prognosis. A prognostic model was constructed utilizing the significant prognostic variables of the disulfidptosis-regulated genes. A real-world cohort was subjected to multiplex immunofluorescence to validate the clinical outcomes and immune context. Ultimately, our study delved into the prognostic relevance of disulfidptosis in HCC and provides insights into potential avenues for future research.

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.

A signature based on neutrophil extracellular trap-related genes for the assessment of prognosis, immunoinfiltration, mutation and therapeutic response in hepatocellular carcinoma.

Liver cancer is a highly lethal and aggressive form of cancer that poses a significant threat to patient survival. Within this category, liver hepatocellular carcinoma (LIHC) represents the most common subtype of liver cancer. Despite decades of research and treatment, the overall survival rate for LIHC has not significantly improved. Improved models are necessary to differentiate high-risk cases and predict possible treatment options for LIHC patients. Recent studies have identified a set of genes associated with neutrophil extracellular traps (NETs) that may contribute to tumor growth and metastasis; however, their prognostic value in LIHC has yet to be established. This study aims to construct a prognostic signature based on a set of NET-related genes (NRGs) for patients diagnosed with LIHC. The transcriptomic data and clinical information concerning LIHC patients were procured from the Cancer Genome Atlas (TCGA) and International Cancer Genome Consortium LIHC (ICLIHC) databases, respectively. To determine the NRG subtypes, the k-means algorithm was employed, along with consensus clustering. The aforementioned analysis aided the construction of a prognostic signature utilizing the last absolute shrinkage and selection operator Cox analysis. To validate the prognostic model, an external dataset, receiver operating characteristic curve, and principal component analysis were utilized. Moreover, the immune microenvironment and the proportion of immune cells between high- and low-risk cases were scrutinized by ESTIMATE and CIBERSORT algorithms. Finally, gene set enrichment analysis was executed to investigate the potential mechanism of NRGs in the pathogenesis and prognosis of LIHC. Two molecular subtypes of LIHC were identified based on the expression patterns of differentially expressed NRGs (DE-NRGs). The two subtypes demonstrated significant differences in survival rates and immune cell expression levels. The study results demonstrated the role of NRGs in antigen presentation, which led to the promotion of tumor immune escape. A risk model was developed and validated with strong overall survival prediction ability. The model, comprising 34 NRGs, showed a strong ability to predict prognosis. We built a dependable prognostic signature based on NRGs for LIHC. We identified that NRGs could have a significant interaction in LIHC's immune microenvironment and therapeutic response. This finding offers insight into the molecular mechanisms and targeted therapy for LIHC.

Role of volumetric computed tomography in the evaluation of the therapeutic response of hepatocellular carcinoma treated by trans-arterial chemoembolization and radio frequency ablation.

Role of volumetric computed tomography in the evaluation of the therapeutic response of hepatocellular carcinoma treated by trans-arterial chemoembolization and radio frequency ablation.

Pan-Cancer Profiling and Digital Pathology Analysis Reveal Negative Prognostic Biomarker ZPR1 Associated with Immune Infiltration and Treatment Response in Hepatocellular Carcinoma.

ZPR1 is a zinc finger-containing protein that plays a crucial role in neurodegenerative diseases, lipid metabolism disorders, and non-alcoholic fatty liver disease. However, the expression pattern, prognostic value, and treatment response of ZPR1 in pan-cancer and hepatocellular carcinoma (HCC) remain unclear. Pan-cancer expression profiles and relevant clinical data were acquired from UCSC Xena platform. Pan-cancer expression, epigenetic profile, and clinical correlation analysis for ZPR1 were performed. We next explored the prognostic significance and potential biological functions of ZPR1 in HCC. Furthermore, the relationship between ZPR1 and immune infiltration and treatment response was investigated. Finally, quantitative immunohistochemistry (IHC) analysis was applied to assess the correlation of ZPR1 expression and immune microenvironment in HCC tissues using Qupath software. ZPR1 was differentially expressed in most tumor types and significantly up-regulated in HCC. ZPR1 showed hypo-methylated status in most tumors. Pan-cancer correlation analysis indicated that ZPR1 was closely associated with clinicopathological factors and TMB, MSI, and stemness index in HCC. High ZPR1 expression could be an independent risk factor for adverse prognosis in HCC. ZPR1 correlated with immune cell infiltration and therapeutic response. Finally, IHC results suggested that ZPR1 correlated with CD4, CD56, CD68, and PD-L1 expression and is a promising pathological diagnostic marker in HCC. Immune infiltrate-associated ZPR1 could be considered a novel negative prognostic biomarker for therapeutic response in HCC.

Abstract No. 515 Prediction of Therapeutic Response of Hepatocellular Carcinoma to CalliSpheres Drug-Eluting Bead Transcatheter Arterial Chemoembolization Based on Pretherapeutic Dynamic CT and Textural Findings

Abstract No. 515 Prediction of Therapeutic Response of Hepatocellular Carcinoma to CalliSpheres Drug-Eluting Bead Transcatheter Arterial Chemoembolization Based on Pretherapeutic Dynamic CT and Textural Findings

A novel transcription factor-based signature to predict prognosis and therapeutic response of hepatocellular carcinoma.

Background: Hepatocellular carcinoma (HCC) is one of the most common aggressive malignancies with increasing incidence worldwide. The oncogenic roles of transcription factors (TFs) were increasingly recognized in various cancers. This study aimed to develop a predicting signature based on TFs for the prognosis and treatment of HCC. Methods: Differentially expressed TFs were screened from data in the TCGA-LIHC and ICGC-LIRI-JP cohorts. Univariate and multivariate Cox regression analyses were applied to establish a TF-based prognostic signature. The receiver operating characteristic (ROC) curve was used to assess the predictive efficacy of the signature. Subsequently, correlations of the risk model with clinical features and treatment response in HCC were also analyzed. The TF target genes underwent Gene Ontology (GO) function and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analyses, followed by protein-protein-interaction (PPI) analysis. Results: A total of 25 differentially expressed TFs were screened, 16 of which were related to the prognosis of HCC in the TCGA-LIHC cohort. A 2-TF risk signature, comprising high mobility group AT-hook protein 1 (HMGA1) and MAF BZIP transcription factor G (MAFG), was constructed and validated to negatively related to the overall survival (OS) of HCC. The ROC curve showed good predictive efficiencies of the risk score regarding 1-year, 2-year and 3-year OS (mostly AUC >0.60). Additionally, the risk score independently predicted OS for HCC patients both in the training cohort of TCGA-LIHC dataset (HR = 2.498, p = 0.007) and in the testing cohort of ICGC-LIRI-JP dataset (HR = 5.411, p < 0.001). The risk score was also positively correlated to progressive characteristics regarding tumor grade, TNM stage and tumor invasion. Patients with a high-risk score were more resistant to transarterial chemoembolization (TACE) treatment and agents of lapatinib and erlotinib, but sensitive to chemotherapeutics. Further enrichment and PPI analyses demonstrated that the 2-TF signature distinguished tumors into 2 clusters with proliferative and metabolic features, with the hub genes belonging to the former cluster. Conclusion: Our study identified a 2-TF prognostic signature that indicated tumor heterogeneity with different clinical features and treatment preference, which help optimal therapeutic strategy and improved survival for HCC patients.

Characterization of cellular senescence patterns predicts the prognosis and therapeutic response of hepatocellular carcinoma.

Background: Hepatocellular carcinoma (HCC) is a prevalent malignancy with a high mortality rate. Cellular senescence, an irreversible state of cell cycle arrest, plays a paradoxical role in cancer progression. Here, we aimed to identify Hepatocellular carcinoma subtypes by cellular senescence-related genes (CSGs) and to construct a cellular senescence-related gene subtype predictor as well as a novel prognostic scoring system, which was expected to predict clinical outcomes and therapeutic response of Hepatocellular carcinoma. Methods: RNA-seq data and clinical information of Hepatocellular carcinoma patients were derived from The Cancer Genome Atlas (TCGA) and International Cancer Genome Consortium (ICGC). The "multi-split" selection was used to screen the robust prognostic cellular senescence-related genes. Unsupervised clustering was performed to identify CSGs-related subtypes and a discriminant model was obtained through multiple statistical approaches. A CSGs-based prognostic model-CSGscore, was constructed by LASSO-Cox regression and stepwise regression. Immunophenoscore (IPS) and Tumor Immune Dysfunction and Exclusion (TIDE) were utilized to evaluate the immunotherapy response. Tumor stemness indices mRNAsi and mDNAsi were used to analyze the relationship between CSGscore and stemness. Results: 238 robust prognostic differentially expressed cellular senescence-related genes (DECSGs) were used to categorize all 336 hepatocellular carcinoma patients of the TCGA-LIHC cohort into two groups with different survival. Two hub genes, TOP2A and KIF11 were confirmed as key indicators and were used to form a precise and concise cellular senescence-related gene subtype predictor. Five genes (PSRC1, SOCS2, TMEM45A, CCT5, and STC2) were selected from the TCGA training dataset to construct the prognostic CSGscore signature, which could precisely predict the prognosis of hepatocellular carcinoma patients both in the training and validation datasets. Multivariate analysis verified it as an independent prognostic factor. Besides, CSGscore was also a valuable predictor of therapeutic responses in hepatocellular carcinoma. More downstream analysis revealed the signature genes were significantly associated with stemness and tumor progression. Conclusion: Two subtypes with divergent outcomes were identified by prognostic cellular senescence-related genes and based on that, a subtype indicator was established. Moreover, a prognostic CSGscore system was constructed to predict the survival outcomes and sensitivity of therapeutic responses in hepatocellular carcinoma, providing novel insight into hepatocellular carcinoma biomarkers investigation and design of tailored treatments depending on the molecular characteristics of individual patients.

A hypoxia-related prognostic model predicts overall survival and treatment response in hepatocellular carcinoma.

Hypoxia and hypoxia-related genes regulate tumor initiation and progression. However, the exact roles that hypoxia plays in hepatocellular carcinoma (HCC) remain unclear. In the present study, we calculated the hypoxia score of each sample in the GSE14520 training set by single-sample gene set enrichment analysis (ssGSEA). Then, weighted gene coexpression network analysis (WGCNA) was utilized to identify gene modules most correlated with hypoxia. Least absolute shrinkage and selection operator (LASSO) Cox regression analysis was utilized to further compress the candidate genes. We constructed the hypoxia-related prognostic risk score (HPRS) model based on the genes' corresponding Cox regression coefficients. Univariate and multivariate Cox analyses of the hypoxia score and clinicopathological characteristics showed that the hypoxia score and stage were the main risk factors affecting the overall survival of patients. Based on WGCNA, we identified 41 key hypoxia-related gene modules and screened out nine core genes to construct the HPRS model. Importantly, high-HPRS patients have a worse prognosis, while low-HPRS patients have a better prognosis. Further research showed that various immune cells, such as CD8 T cells, cytotoxic cells, and DCs, were significantly enriched in the low-HPRS group compared with the high-HPRS group. Notably, patients in the low-HPRS group were less likely to benefit from immunotherapy and chemotherapy than those in the high-HPRS group. In summary, we identified and validated a hypoxia-derived gene model that could serve as a potential biomarker to predict prognosis and therapeutic response in HCC.

Construction and validation of an angiogenesis-related scoring model to predict prognosis, tumor immune microenvironment and therapeutic response in hepatocellular carcinoma.

Hepatocellular carcinoma (HCC) is one of the most common malignant tumors in the world with high morbidity and mortality. Identifying an effective marker for predicting the prognosis and therapeutic response is extremely meaningful. Angiogenesis-related genes (ARGs) play important roles in the tumor progression and immune-suppressive microenvironment formation. The differential expressed ARGs associated with the prognosis of HCC were identified in the TCGA dataset. Univariate Cox and least absolute shrinkage selection operator (LASSO) regression were applied to construct a ARGs Scoring model. The prognostic value of the ARGs Scoring model was assessed by Cox regression, Kaplan-Meier (KM) and ROC curve analyses. Then the model was further validated in an external dataset, ICGC dataset. The patients were split into two groups based on the ARGs Score and the clinical features were compared. TIMER, CIBERSORT and xCell algorithms were utilized to analyze the correlation between the ARGs Score and tumor immune microenvironment (TIME). Furthermore, we analyzed the efficacy of the model in predicting the therapeutic response for immunotherapy, targeted therapy and TACE treatment in different cohorts. A total of 97 differential expressed ARGs were identified relating to the prognosis of HCC patients from the TCGA dataset. Then the ARGs Scoring model based on a 9-gene signature was constructed using the Cox and LASSO regression analyses. Higher ARGs Score had a poor clinical outcome and was considered to be an independent prognostic predictor for HCC in the multivariate Cox analysis. The ARGs Score was related to the enrichment of various immune cells, such as CD4+ T cells, Treg, macrophage, neutrophil and dendritic cells, exhibiting a more immunosuppressive phenotype. Higher ARGs Score was correlated with higher expression of immune checkpoint genes and poor response to immunotherapy. Furthermore, higher ARGs Score indicated poor therapeutic response in the sorafenib and TACE treatment cohorts, individually. The ARGs Scoring model exhibited robust predictive value for the prognosis and TIME for HCC patients. Higher ARGs Score indicated poor therapeutic response of the immunotherapy, sorafenib and TACE treatment. The ARGs Scoring model could be used as a biomarker to help physicians to develop more individualized treatment for HCC patients.

Role of ADC Mapping in the Assessment of Therapeutic Response of Hepatocellular Carcinoma in Patients with Post-trans-catheter Arterial Chemoembolization

Role of ADC Mapping in the Assessment of Therapeutic Response of Hepatocellular Carcinoma in Patients with Post-trans-catheter Arterial Chemoembolization

Lenvatinib Synergistically Promotes Radiation Therapy in Hepatocellular Carcinoma by Inhibiting Src/STAT3/NF-κB-Mediated Epithelial–Mesenchymal Transition and Metastasis

This study suggested that lenvatinib may incapacitate hepatocellular carcinoma (HCC) to radiation treatment by abrogating radiation-induced Src/signal transducer and the activator of transcription 3 signaling (STAT3)/nuclear factor-κB (NF-κB) to escalate radiation-induced extrinsic and intrinsic apoptosis. These findings uncover the role of targeting Src and its arbitrating epithelial-mesenchymal transition (EMT), which could increase the anti-HCC efficacy of radiation therapy (RT). Lenvatinib and sorafenib are multikinase inhibitors used to treat HCC. Lenvatinib is noninferior to sorafenib in the therapeutic response in HCC. However, whether lenvatinib intensifies the anti-HCC efficacy of RT is ambiguous. Several oncogenic kinases and transcription factors, such as Src, STAT3, and NF-κB, enhance the radiosensitivity of cancers. Therefore, we aimed to investigate the roles of the Src/STAT3/NF-κB axis in HCC after RT treatment and assessed whether targeting Src by lenvatinib may enhance the effectiveness of RT. Hep3B, Huh7, HepG2, and SK-Hep1 HCC cells and 2 types of animal models were used to identify the efficacy of RT combined with lenvatinib. Cellular toxicity, apoptosis, DNA damage, EMT/metastasis regulation, and treatment efficacy were validated by colony formation, flow cytometry, Western blotting, and in vivo experiments, respectively. Knockdown of Src by siRNA was also used to validate the role of Src in RT treatment. Silencing Src reduced STAT3/NF-κB signaling and sensitized HCC to radiation. Lenvatinib reversed radiation-elicited Src/STAT3/NF-κB signaling while enhancing the anti-HCC efficacy of radiation. Both lenvatinib and siSrc promoted the radiation effect of cell proliferation on suppression, inhibition of the invasion ability, and induction of apoptosis in HCC. Lenvatinib also alleviated radiation-triggered oncogenic and EMT-related protein expression. Our findings uncovered the role of the Src/STAT3/NF-κB regulatory axis in response to radiation-induced toxicity and confirmed Src as the key regulatory molecule for radiosensitization of HCC evoked by lenvatinib.

Systematic analyses to explore immune gene sets-based signature in hepatocellular carcinoma, in which IGF2BP3 contributes to tumor progression

Tumor immune microenvironment (TIME) is of critical importance for the development and therapeutic response of hepatocellular carcinoma (HCC). However, limited studies have investigated immune-related indicators for clinical supervision and decision. The current study aimed to develop an improved prognostic signature based on TIME. HCC patients from TCGA and ICGC database were classified into three subtypes (Immunity High, Immunity Medium and Immunity Low) according to ssGSEA scores of 29 immune gene sets. Differentially expressed immune-related genes (DE IRGs) between Immune High and Low groups were screened with an adjusted P < 0.05. Weighted gene co-expression network analysis (WGCNA) was used to establish gene co-expression modules of differentially expressed genes (DEGs) between tumor and normal tissues. 45 survival-related immune genes (SRIGs) were identified at points of intersection between hub genes and DE IRGs. By performing Cox regression and LASSO analysis, 3 of the 45 SRIGs were screened to establish a prognostic model. Patients with high risk scores exhibited worse survival outcome and poorer response to chemotherapy. Potential mechanisms of chemotherapy resistance also have been discussed. More significantly, high -risk patients showed increased immune cell infiltration and checkpoints, which suggested a benefit of immunotherapy. In addition, knockdown of IGF2BP3 was determined to significantly inhibit cell proliferation and migration in HCC. Our immune-related model may be an effective tool for precise diagnosis and treatment of HCC. It may help to select patients suitable for chemotherapy, and immunotherapy.

A Predictive Model for Prognosis and Therapeutic Response in Hepatocellular Carcinoma Based on a Panel of Three MED8-Related Immunomodulators.

The current tumor-node-metastasis (TNM) system is limited in predicting the survival and guiding the treatment of hepatocellular carcinoma (HCC) patients since the TNM system only focuses on the anatomical factors, regardless of the intratumoral molecule heterogeneity. Besides, the landscape of intratumoral immune genes has emerged as a prognostic indicator. The mediator complex subunit 8 (MED8) is a major polymerase regulator and has been described as an oncogene in renal cell carcinoma, but its pathophysiological significance of HCC and its contribution to the prognosis of HCC remain unclear. Here, we aimed to discuss the expression profile and clinical correlation of MED8 in HCC and construct a predictive model based on MED8-related immunomodulators as a supplement to the TNM system. According to our analyses, MED8 was overexpressed in HCC tissues and increased expression of MED8 was an indicator of poor outcome in HCC. The knockdown of MED8 weakened the proliferation, colony forming, and migration of HepG2 and Huh7 cells. Subsequently, a predictive model was identified based on a panel of three MED8-related immunomodulators using The Cancer Genome Atlas (TCGA) database and further validated in International Cancer Genome Consortium (ICGC) database. The combination of the predictive model and the TNM system could improve the performance in predicting the survival of HCC patients. High-risk patients had poor overall survival in TCGA and ICGC databases, as well as in subgroup analysis with early clinicopathology classification. It was also found that high-risk patients had a higher probability of recurrence in TCGA cohort. Furthermore, low-risk score indicated a better response to immunotherapy and drug therapy. This predictive model can be served as a supplement to the TNM system and may have implications in prognosis stratification and therapeutic guidance for HCC.

Exhaled volatile organic compounds for diagnosis of hepatocellular carcinoma

Volatile organic compounds (VOCs) profile for diagnosis and monitoring therapeutic response of hepatocellular carcinoma (HCC) has not been well studied. We determined VOCs profile in exhaled breath of 97 HCC patients and 111 controls using gas chromatography–mass spectrometry and Support Vector Machine algorithm. The combination of acetone, 1,4-pentadiene, methylene chloride, benzene, phenol and allyl methyl sulfide provided the highest accuracy of 79.6%, with 76.5% sensitivity and 82.7% specificity in the training set; and 55.4% accuracy, 44.0% sensitivity, and 75.0% specificity in the test set. This combination was correlated with the HCC stages demonstrating by the increased distance from the classification boundary when the stage advanced. For early HCC detection, d-limonene provided a 62.8% sensitivity, 51.8% specificity and 54.9% accuracy. The levels of acetone, butane and dimethyl sulfide were significantly altered after treatment. Patients with complete response had a greater decreased acetone level than those with remaining tumor post-treatment (73.38 ± 56.76 vs. 17.11 ± 58.86 (× 106 AU, p = 0.006). Using a cutoff of 35.9 × 106 AU, the reduction in acetone level predicted treatment response with 77.3% sensitivity, 83.3% specificity, 79.4%, accuracy, and AUC of 0.784. This study demonstrates the feasibility of exhaled VOCs as a non-invasive tool for diagnosis, monitoring of HCC progression and treatment response.

Immune-related lincRNA pairs predict prognosis and therapeutic response in hepatocellular carcinoma

Growing evidence has demonstrated the functional relevance of long intergenic noncoding RNAs (lincRNAs) to tumorigenesis and immune response. However, immune-related lincRNAs and their value in predicting the clinical outcomes of patients with liver cancer remain largely unexplored. Herein, we utilized the strategy of iterative gene pairing to construct a tumor-specific immune-related lincRNA pairs signature (IRLPS), which did not require specific expression levels, as an indicator of patient outcomes. The 18-IRLPS we developed was associated with overall survival, tumor progression, and recurrence in liver cancer patients. Multivariate analysis revealed that the risk model was an independent predictive factor. A high IRLPS risk was correlated suppressive immune microenvironment, and IRLPS-high patients might benefit more from CD276 blockade or TMIGD2 agonist. Patients in the high-risk group were associated with elevated tumor mutation, increased sensitivity to dopamine receptor antagonists, cisplatin, doxorubicin, and mitomycin but more resistance to vinblastine. Mechanistically, IRLPS high scores might lead to poor prognosis by promoting cell proliferation and metabolic reprogramming. The prognostic significance of the 18-IRLPS was confirmed in independent cancer datasets. These findings highlighted the robust predictive performances of the 18-IRLPS for prognosis and personalized treatment.

Fasting improves therapeutic response in hepatocellular carcinoma through p53-dependent metabolic synergism.

Cancer cells voraciously consume nutrients to support their growth, exposing metabolic vulnerabilities that can be therapeutically exploited. Here, we show in hepatocellular carcinoma (HCC) cells, xenografts, and patient-derived organoids that fasting improves sorafenib efficacy and acts synergistically to sensitize sorafenib-resistant HCC. Mechanistically, sorafenib acts noncanonically as an inhibitor of mitochondrial respiration, causing resistant cells to depend on glycolysis for survival. Fasting, through reduction in glucose and impeded AKT/mTOR signaling, prevents this Warburg shift. Regulating glucose transporter and proapoptotic protein expression, p53 is necessary and sufficient for the sorafenib-sensitizing effect of fasting. p53 is also crucial for fasting-mediated improvement of sorafenib efficacy in an orthotopic HCC mouse model. Together, our data suggest fasting and sorafenib as rational combination therapy for HCC with intact p53 signaling. As HCC therapy is currently severely limited by resistance, these results should instigate clinical studies aimed at improving therapy response in advanced-stage HCC.