Prognostic Significance In Hepatocellular Carcinoma Research Articles

The m6A methyltransferase METTL5 promotes neutrophil extracellular trap network release to regulate hepatocellular carcinoma progression.

Hepatocellular carcinoma (HCC) is one of the most common malignant tumors worldwide, it has a poor prognosis due to its highly invasive and metastatic nature. Consequently, identifying effective prognostic markers and potential therapeutic targets has been extensively investigated. METTL5, an 18S rRNA methyltransferase, is abnormally high in HCC. But its biological function and prognostic significance in HCC remain largely unelucidated. This study aimed to investigate the role of METTL5 in HCC progression, and elucidate its possible molecular mechanisms in HCC via transcriptome sequencing, providing new insights for identifying new HCC prognostic markers and therapeutic targets. The METTL5 expression in HCC and paracancerous tissues was analyzed using HCC immunohistochemical microarrays and bioinformatic retrieval methods to correlate METTL5 with clinicopathological features and survival prognosis. We constructed a METTL5 knockdown hepatocellular carcinoma cell line model and an animal model to determine the effect of METTL5 on hepatocellular carcinoma progression. Subsequently, RNA sequencing was performed to analyze the molecular mechanism of METTL5 in HCC based on the sequencing results, and relevant experiments were performed to verify it. We found that METTL5 expression was elevated in hepatocellular carcinoma tissues and correlated with poor patient prognosis, and in the analysis of clinicopathological features showed a correlation with TNM staging. In hepatocellular carcinoma cell lines with knockdown of METTL5, the malignant biological behavior was significantly reduced both invitro and invivo. Based on the sequencing results as well as the results of GO functional enrichment analysis and KEGG pathway enrichment analysis, we found that METTL5 could promote the generation and release of neutrophil extracellular capture network (NETs) and might further accelerate the progression of HCC. The m6A methyltransferase METTL5 is overexpressed in hepatocellular carcinoma (HCC) and correlates with poor prognosis. METTL5 accelerates malignant progression of HCC by promoting generation and release of the neutrophil extracellular traps (NETs) network, providing new insights for clinical biomarkers and immunotherapeutic targets in HCC prognosis.

Combined OLA1 and CLEC3B Gene Is a Prognostic Signature for Hepatocellular Carcinoma and Impact Tumor Progression.

Hepatocellular carcinoma (HCC), partly because of its complexity and high heterogeneity, has a poor prognosis and an extremely high mortality rate. In this study, mRNA sequencing expression profiles and relevant clinical data of HCC patients were gathered from different public databases. Kaplan-Meier survival curves as well as ROC curves validated that OLA1|CLEC3B was an independent predictor with better predictive capability of HCC prognosis compared to OLA1 and CLEC3B separately. Further, the cell transfection experiment verified that knockdown of OLA1 inhibited cell proliferation, facilitated apoptosis, and improved sensitivity of HCC cells to gemcitabine. In this study, the prognostic model of HCC composed of OLA1/CLEC3B genes was constructed and verified, and the prediction ability was favorable. A higher level of OLA1 along with a lower level of CEC3B is a sign of poor prognosis in HCC. We revealed a novel gene pair OLA1|CLEC3B overexpressed in HCC patients, which may serve as a promising independent predictor of HCC survival and an approach for innovative diagnostic and therapeutic strategies.

Development of a Combined Oxidative Stress and Endoplasmic Reticulum Stress-Related Prognostic Signature for Hepatocellular Carcinoma.

Oxidative stress and endoplasmic reticulum stress are important components of the cellular stress process, which plays a critical role in tumor initiation and progression. First, the correlation between oxidative stress and endoplasmic reticulum stress was detected in 68 human hepatocellular carcinoma (HCC) tissue microarray samples by immunohistochemistry. Differentially expressed oxidative stress- and endoplasmic reticulum stressrelated genes (OESGs) then were screened in HCC. Next, an OESGs prognostic signature was constructed for HCC in the training cohort (TCGA-LIHC from The Cancer Genome Atlas), by least absolute shrinkage and selection operator Cox and stepwise Cox regression analyses, and was verified in the external cohort (GSE14520 from the Gene Expression Omnibus). The MCP counter was employed to evaluate immune cell infiltration. The C-index was used to evaluate the predictive power of prognostic signature. Finally, a prognostic nomogram model was constructed to predict the survival probability of patients with HCC based on the results of Cox regression analysis. We demonstrated a positive correlation between oxidative stress and ER stress in human HCC samples. We then identified five OESGs as a prognostic signature consisting of IL18RAP, ECT2, PPARGC1A, STC2, and NQO1 for HCC. Related risk scores correlated with tumor stage, grade, and response to transcatheter arterial chemoembolization therapy, and the higher risk score group had less T cells, CD8+ T cells, cytotoxic lymphocytes and natural killer cell infiltration. The C-index of our OESGs prognostic signature was superior to four previously published signatures. Furthermore, we developed a nomogram based on the OESGs prognostic signature and clinical parameters for patients with HCC that is an effective quantitative analysis tool to predict patient survival. The OESGs signature showed excellent performance in predicting survival and therapeutic responses for patients with HCC.

Prognosis Prediction of CRAFITY Score in HCC Undergoing TACE Combined with PD-(L)1 Inhibitors and Molecular Targeted Therapy.

The CRAFITY (C-reactive protein and alpha-fetoprotein in immunotherapy) score has demonstrated prognostic significance in hepatocellular carcinoma (HCC) patients undergoing immunotherapy. The study aimed to validate accuracy of CRAFITY score on predicting prognosis for patients with HCC treated with transarterial chemoembolization (TACE) combined with PD-(L)1 inhibitors and molecular targeted therapy. Eighty-five HCC patients who underwent TACE in combination with molecular targeted therapy (MTT) and PD-(L)1 Inhibitors were consecutively enrolled from November 2019 to November 2022. Patients were divided into CRAFITY 0 score (n=32), CRAFITY 1 score (n=31), and CRAFITY 2 score (n=22), respectively. The primary outcomes were overall survival (OS) and progression-free survival (PFS), and the secondary outcomes included tumor response rate and treatment-related adverse events (TRAEs). Factors affecting survival were identified via Cox regression analysis. The median overall survival (OS) for HCC patients with CRAFITY scores of 0, 1, and 2 was 33.4 months (95% confidence interval [CI]: 27.1-39.7), 34.5 months (95% CI: 23.1-45.9), and 24.2 months (95% CI: 13.9-39.3), respectively, there were statistical differences among the three groups (p<0.05). The progression-free survival (PFS) was 14.1 months (95% CI: 10.0-18.2), 14.1 months (95% CI: 9.0-19.2), and 9.3 months (95% CI: 7.2-11.4) for patients with CRAFITY scores of 1, 2, and 3, respectively, with a significant difference between the three groups (p<0.05). In patients with CRAFITY scores of 1, 2, and 3, the disease control rates (DCR) were 94%, 84%, and 73%, respectively (p < 0.05), while the overall response rates (ORR) were 78.1%, 67.7%, and 59.1%, respectively (p = 0.318). A higher CRAFITY score showed a correlation with an increased frequency of fatigue and grade 3 fever (p<0.05). Moreover, CRAFITY 2 score was an independent risk factor for both OS (HR = 2.610(1.281-4.564), p = 0.014) and PFS (HR = 2.419(1.281-4.564), p = 0.006). The CRAFITY score may provide an efficient predictive capacity for prognosis in HCC patients undergoing TACE combined with PD-(L)1 inhibitors and molecular targeted therapy.

PPIH gene regulation system and its prognostic significance in hepatocellular carcinoma: a comprehensive analysis.

Peptidyl-prolyl isomerase H (PPIH) is a member of the cyclophilin protein family, which functions as a molecular chaperone and is involved in the splicing of pre-mRNA. According to reports, the malignant progression of HCC related to hepatitis B virus (HBV) is tightly associated with RNA-binding proteins. Nevertheless, there is no research on PPIH expression or its function in the occurrence and progression of HCC. We are the first to reveal that the mRNA and protein levels of Ppih are substantially overexpressed in HCC, as the outcomes show. A significant correlation existed between enriched expression of Ppih within HCC and more advanced, poorly differentiated, and TP53-mutated tumors. These findings, which suggest that Ppih may serve as a predictive biomarker for people with HCC, serve as a starting point for further investigation into the function of Ppih in the progression of carcinogenesis. Accordingly, we utilized clinical samples and bioinformatics analysis to assess Ppih's mRNA, protein expression, and gene regulatory system in HCC. Additionally, Wilcoxon signed-rank testing and logistic regression were utilized to inspect the association between clinicopathological factors and Ppih. Clinical pathological traits linked to overall survival (OS) among HCC patients were examined via TCGA data via Cox regression and the Kaplan-Meier approach. Additionally, via TCGA data collection, gene set enrichment assessment was also conducted.

Retracted: Development and Validation of a Novel Mitophagy-Related Gene Prognostic Signature for Hepatocellular Carcinoma Based on Immunoscore Classification of Tumor.

[This retracts the article DOI: 10.1155/2021/5070099.].

Combining bulk and single-cell RNA-sequencing data to develop an NK cell-related prognostic signature for hepatocellular carcinoma based on an integrated machine learning framework

BackgroundThe application of molecular targeting therapy and immunotherapy has notably prolonged the survival of patients with hepatocellular carcinoma (HCC). However, multidrug resistance and high molecular heterogeneity of HCC still prevent the further improvement of clinical benefits. Dysfunction of tumor-infiltrating natural killer (NK) cells was strongly related to HCC progression and survival benefits of HCC patients. Hence, an NK cell-related prognostic signature was built up to predict HCC patients’ prognosis and immunotherapeutic response.MethodsNK cell markers were selected from scRNA-Seq data obtained from GSE162616 data set. A consensus machine learning framework including a total of 77 algorithms was developed to establish the gene signature in TCGA–LIHC data set, GSE14520 data set, GSE76427 data set and ICGC–LIRI–JP data set. Moreover, the predictive efficacy on ICI response was externally validated by GSE91061 data set and PRJEB23709 data set.ResultsWith the highest C-index among 77 algorithms, a 11-gene signature was established by the combination of LASSO and CoxBoost algorithm, which classified patients into high- and low-risk group. The prognostic signature displayed a good predictive performance for overall survival rate, moderate to high predictive accuracy and was an independent risk factor for HCC patients’ prognosis in TCGA, GEO and ICGC cohorts. Compared with high-risk group, low-risk patients showed higher IPS–PD1 blocker, IPS–CTLA4 blocker, common immune checkpoints expression but lower TIDE score, which indicated low-risk patients might be prone to benefiting from ICI treatment. Moreover, a real-world cohort, PRJEB23709, also revealed better immunotherapeutic response in low-risk group.ConclusionsOverall, the present study developed a gene signature based on NK cell-related genes, which offered a novel platform for prognosis and immunotherapeutic response evaluation of HCC patients.

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.

Development and validation of a selenium metabolism regulators associated prognostic model for hepatocellular carcinoma

BackgroundSelenium metabolism has been implicated in human health. This study aimed to identify a selenium metabolism regulator-based prognostic signature for hepatocellular carcinoma (HCC) and validate the role of INMT in HCC.MethodsTranscriptome sequencing data and clinical information related to selenium metabolism regulators in TCGA liver cancer dataset were analysed. Next, a selenium metabolism model was constructed by multiple machine learning algorithms, including univariate, least absolute shrinkage and selection operator, and multivariate Cox regression analyses. Then, the potential of this model for predicting the immune landscape of different risk groups was evaluated. Finally, INMT expression was examined in different datasets. After knockdown of INMT, cell proliferation and colony formation assays were conducted.ResultsA selenium metabolism model containing INMT and SEPSECS was established and shown to be an independent predictor of prognosis. The survival time of low-risk patients was significantly longer than that of high-risk patients. These two groups had different immune environments. In different datasets, including TCGA, GEO, and our PUMCH dataset, INMT was significantly downregulated in HCC tissues. Moreover, knockdown of INMT significantly promoted HCC cell proliferation.ConclusionsThe current study established a risk signature of selenium metabolism regulators for predicting the prognosis of HCC patients. INMT was identified as a biomarker for poor prognosis of HCC.

Establishment and validation of a ferroptosis-related prognostic signature for hepatocellular carcinoma.

Hepatocellular carcinoma (HCC) is the most common type of primary liver cancer with high heterogeneity. The prognosis of HCC is quite poor and the prognostic prediction also has challenges. Ferroptosis is recently recognized as a kind of iron-dependent cell death, which is involved in tumor progression. However, further study is needed to validate the influence of drivers of ferroptosis (DOFs) on the prognosis of HCC. The FerrDb database and the Cancer Genome Atlas (TCGA) database were applied to retrieve DOFs and information of HCC patients respectively. HCC patients were randomly divided into training and testing cohorts with a 7:3 ratio. Univariate Cox regression, LASSO and multivariate Cox regression analyses were carried out to identify the optimal prognosis model and calculate the risk score. Then, univariate and multivariate Cox regression analyses were performed to assess the independence of the signature. At last, gene functional, tumor mutation and immune-related analyses were conducted to explore the underlying mechanism. Internal and external databases were used to confirm the results. Finally, the tumor tissue and normal tissue from HCC patients were applied to validate the gene expression in the model. Five genes were identified to develop as a prognostic signature in the training cohort relying on the comprehensive analysis. Univariate and multivariate Cox regression analyses confirmed that the risk score was able to be an independent factor for the prognosis of HCC patients. Low-risk patients showed better overall survival than high-risk patients. Receiver operating characteristic (ROC) curve analysis confirmed the signature's predictive capacity. Furthermore, internal and external cohorts were consistent with our results. There was a higher proportion of nTreg cell, Th1 cell, macrophage, exhausted cell and CD8+T cell in the high-risk group. The Tumor Immune Dysfunction and Exclusion (TIDE) score suggested that high-risk patients could respond better to immunotherapy. Besides, the experimental results showed that some genes were differentially expressed between tumor and normal tissues. In summary, the five ferroptosis gene signature showed potential in prognosis of patients with HCC and could also be regarded as a value biomarker for immunotherapy response in these patients.

Prognostic 7-SLC-Gene Signature Identified via Weighted Gene Co-Expression Network Analysis for Patients with Hepatocellular Carcinoma.

Solute carrier (SLC) proteins play an important role in tumor metabolism. But SLC-associated genes' prognostic significance in hepatocellular carcinoma (HCC) remained elusive. We identified SLC-related factors and developed an SLC-related classifier to predict and improve HCC prognosis and treatment. From the TCGA database, corresponding clinical data and mRNA expression profiles of 371 HCC patients were acquired, and those of 231 tumor samples were derived from the ICGC database. Genes associated with clinical features were filtered using weighted gene correlation network analysis (WGCNA). Next, univariate LASSO Cox regression studies developed SLC risk profiles, with the ICGC cohort data being used in validation. Univariate Cox regression analysis revealed that 31 SLC genes (P < 0.05) were related to HCC prognosis. 7 (SLC22A25, SLC2A2, SLC41A3, SLC44A1, SLC48A1, SLC4A2, and SLC9A3R1) of these genes were applied in developing a SLC gene prognosis model. Samples were classified into the low-andhigh-risk groups by the prognostic signature, with those in the high-risk group showing a significantly worse prognosis (P < 0.001 in the TCGA cohort and P=0.0068 in the ICGC cohort). ROC analysis validated the signature's prediction power. In addition, functional analyses showed enrichment of immune-related pathways and different immune status between the two risk groups. The 7-SLC-gene prognostic signature established in this study helped predict the prognosis, and was also correlated with the tumor immune status and infiltration of different immune cells in the tumor microenvironment. The current findings may provide important clinical indications for proposing a novel combination therapy consists of targeted anti-SLC therapy and immunotherapy for HCC patients.

Potential Common Molecular Mechanisms Between Periodontitis and Hepatocellular Carcinoma: A Bioinformatic Analysis and Validation.

Hepatocellular carcinoma (HCC) is the most common primary liver cancer and has a poor prognosis. Periodontitis, or tooth loss, is considered to be related to hepatocarcinogenesis and its poor prognosis. This study aimed to explore potential associations and cross-talk mechanisms between periodontitis and HCC. Periodontitis and HCC microarray datasets were acquired from the Gene Expression Omnibus (GEO) database and were analyzed to obtain differentially expressed (DE) lncRNAs, miRNAs and mRNAs. Functional enrichment analysis was used to detect the functions of these mRNAs. Then, a ceRNA network of periodontitis-related HCC was constructed. Least absolute shrinkage and selection operator (LASSO) regression, random forest algorithm, and support vector machine-recursive feature elimination (SVM-RFE) were performed to explore the diagnostic significance of mRNAs in periodontitis-related HCC. Cox regression analyses were conducted to screen mRNAs with prognostic significance in HCC. Quantitative real-time PCR (qRT-PCR) and immunohistochemistry (IHC) were conducted to validate the expression of these mRNAs in HCC tissues. A ceRNA network was constructed. Functional enrichment analysis indicated that the network is associated with immune and inflammatory responses, the cell cycle and liver metabolic function. LASSO, random forest algorithm and SVM-RFE showed the diagnostic significance of DE mRNAs in HCC. Cox regression analyses revealed that MSH2, GRAMD1C and CTHRC1 have prognostic significance for HCC, and qRT-PCR and IHC validated this finding. Periodontitis may affect the occurrence of HCC by changing the immune and inflammatory response, the cell cycle and liver metabolic function. MSH2, GRAMD1C and CTHRC1 are potential prognostic biomarkers for HCC.

Constructing and validating of m7G-related genes prognostic signature for hepatocellular carcinoma and immune infiltration: potential biomarkers for predicting the overall survival

To investigate the prognostic significance of N7-methylguanosine (m7G) regulators and immune infiltration in liver hepatocellular carcinoma (LIHC). The research measured predictive m7G genes in LIHC samples from The Cancer Genome Atlas (TCGA) and International Cancer Genome Consortium (ICGC) datasets. Data on the stemness index based on mRNA expression (mRNAsi), gene mutations, and corresponding clinical characteristics were obtained from TCGA and ICGC. Lasso regression was used to construct the prediction model to assess the m7G prognostic signals in LIHC. Based on these genes, Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) analyses were performed to identify key biological functions and pathways. The correlation between m7G RNA methylation regulators and the prognosis and immune infiltration of LIHC was evaluated. There were 21 m7G-related differentially expressed genes (DEGs) in LIHC and healthy tissues, and LIHC patients could be divided into two categories by consensus clustering of these DEGs. A five-gene predictive approach was employed using least absolute shrinkage and selection operator (LASSO) Cox regression analysis. Patients in the low-risk group showed a significantly higher survival rate compared with those in the high-risk group (P=0.001). Validations using the ICGC database. Also, univariate and multivariate Cox regression analyses suggested that the risk score produced by the predictive model is an independent predictor for LIHC [hazard ratio (HR): 1.848, 95% confidence interval (CI): 1.286-2.656; HR: 2.597, 95% CI: 1.358-4.965]. The ROC curves of the ICGC cohort revealed that the five-gene prediction model performed well [area under the curve (AUC) =0.642 at 1 year, AUC =0.686 at 2 years, and AUC =0.667 at 3 years]. Immuno-oncology scoring revealed that in the high-risk group, among 16 immune cells, the expressions of neutrophils and natural killer (NK) cells were low and that of regulatory T-cells (Tregs) was high. LIHC occurrence and progression are linked to m7G-related genes. Corresponding prognostic models help forecast the prognosis of LIHC patients. m7G-related genes and associated immune cell infiltration in the TME may serve as potential therapeutic targets in LIHC, which requires further trials. In addition, the m7G-related gene signature offers a viable alternative to predict LIHC, and these m7G-related genes show a prospective research area for LIHC targeted treatment in the future.

PP202 Stem cell-like subtype from developmental hierarchies of fetal liver shows clinical relevance with prognostic significance in hepatocellular carcinoma

PP202 Stem cell-like subtype from developmental hierarchies of fetal liver shows clinical relevance with prognostic significance in hepatocellular carcinoma

Identification and analysis of C17orf53 as a prognostic signature for hepatocellular carcinoma

C17orf53 is a novel gene for DNA synthesis and homologous recombination. However, the exact role of C17orf53 in hepatocellular carcinoma (HCC) remains unclear. In this study, we analyzed it using a set of public datasets. UALCAN, Human Protein Atlas (HPA), Kaplan‒Meier Plotter, Tumor Immune Estimation Resource (TIMER), cBioPortal, GEPIA, GeneMANIA, and LinkedOmics were used. Functional analysis was conducted in SK-Hep-1 cells by using small interfering RNA (siRNA). C17orf53 was highly expressed and predicted unfavorable survival in HCC patients. Moreover, it showed positive correlations with the abundance of B cells, macrophages and dendritic cells. In addition, we identified 126 genes that were positively correlated with C17orf53 and its coeffector minichromosome maintenance 8 (MCM8). These genes were mainly enriched in the cell cycle, DNA replication and Fanconi anemia pathways. Knockdown of C17orf53 significantly inhibited the proliferation of SK-Hep-1 cells and decreased the expression of MCM8, cyclin D1 and proliferating cell nuclear antigen (PCNA). Overall, C17orf53 is a novel prognostic signature for HCC.

Machine learning-based transcriptome analysis of lipid metabolism biomarkers for the survival prediction in hepatocellular carcinoma.

Hepatocellular carcinoma (HCC) is the most common primary malignancy of the liver with a very high fatality rate. Our goal in this study is to find a reliable lipid metabolism-related signature associated with prognostic significance for HCC. In this study, HCC lipid metabolism-related molecular subtype analysis was conducted based on the 243 lipid metabolism genes collected from the Molecular Signatures Database. Several significant disparities in prognosis, clinicopathological characteristics, and immune and ferroptosis-related status were found across the three subtypes, especially between C1 and C3 subgroups. Differential expression analysis yielded 57 differentially expressed genes (DEGs) between C1 and C3 subtypes. GO and KEGG analysis was employed for functional annotation. Three of 21 prognostic DEGs (CXCL8, SLC10A1, and ADH4) were finally selected through machine-learning-based discovery and validation strategy. The risk score = (0.103) × expression value of CXCL8 + (−0.0333) × expression value of SLC10A1 + (−0.0812) × expression value of ADH4. We used these three to construct a HCC prognostic risk model, which stratified the patients of the validation cohort into two risk subtypes with significantly different overall survival. Our work provides possible significance of the lipid metabolism-associated model in stratifying patient prognosis and its feasibility to guide therapeutic selection.

Abstract IA12: Prognostic and predictive biomarkers for cabozantinib in HCC

Abstract In recent years, the therapeutic landscape for patients with advanced/unresectable hepatocellular carcinoma (HCC) has become more complex, novel immunotherapy combinations have demonstrated their activity and represent a new standard of care, such as atezolizumab plus bevacizumab, or will soon be included in the treatment algorithm, such as durvalumab plus tremelimumab. For other combinations such as cabozantinib plus atezolizumab, a progression-free survival (PFS) benefit not associated with an improvement in overall survival (OS) was shown. However, tyrosine kinase inhibitors, including cabozantinib, still remain important therapeutic options in the management of patients with advanced HCC. In this field there is a lack of prognostic and predictive biomarkers and alpha-fetoprotein (AFP) remains the most widely used prognostic biomarker in clinical practice and the only predictive biomarker available only for ramucirumab. Cabozantinib inhibits receptor tyrosine kinases implicated in HCC progression, tumor immunosuppression, and resistance to antiangiogenic therapy, including VEGF receptors 1–3, MET, and the TAM family kinases TYRO3, AXL, and MER. Cabozantinib significantly improved OS and PFS compared to placebo in patients with previously treated advanced HCC in the phase 3 CELESTIAL trial. In the presented exploratory analyses, outcomes were evaluated according to baseline plasma biomarker levels and changes during treatment at week 4 or 8. Biomarkers chosen for the study included AFP, cabozantinib targets and their ligands, and other plasma proteins with prognostic significance in HCC. OS and PFS were analyzed by baseline levels as dichotomized or continuous variables and by on-treatment changes at week 4 or 8 as continuous variables; multivariable analyses adjusted for clinical covariates were also performed. Finally, outcomes were also evaluated based on clinical factors as ALBI grade and adverse events. Several biomarkers or clinical factors at baseline or during treatment were identified as prognostic and associated with different OS and/or PFS. Low baseline levels of AFP, MET, HGF, GAS6, IL-8, ANG2, high levels of IGF-1, and ALBI grade 1 were identified as potential favorable prognostic biomarkers/factors in previously treated advanced HCC. However, hazard ratios for OS and PFS favored cabozantinib over placebo at low and high baseline levels for all biomarkers, and no baseline biomarkers predicted treatment benefit. Cabozantinib induced pharmacodynamic changes in several biomarkers, but these changes were not associated with OS or PFS, except for AFP response which correlated with improved OS and PFS. The occurrence of some adverse events during treatment has been associated with longer OS and PFS with cabozantinib. Future studies should explore both circulating and tumor biomarkers related to angiogenic signaling, inflammation, and immune-cell function, in order to identify prognostic and predictive biomarkers to guide clinical decision-making and improve outcomes for patients with advanced HCC. Citation Format: Lorenza Rimassa. Prognostic and predictive biomarkers for cabozantinib in HCC [abstract]. In: Proceedings of the AACR Special Conference: Advances in the Pathogenesis and Molecular Therapies of Liver Cancer; 2022 May 5-8; Boston, MA. Philadelphia (PA): AACR; Clin Cancer Res 2022;28(17_Suppl):Abstract nr IA12.

Prognostic significance and immune characteristics of CMTM4 in hepatocellular carcinoma

BackgroundPrevious study has shown that chemokine-like factor (CKLF)-like MARVEL transmembrane domain-containing family member 4 (CMTM4) can bind and maintain programmed cell death ligand 1 (PD-L1) expression to promote tumor progression by alleviating the suppression of tumor-specific T cell activity, suggesting its potential role in tumor immunotherapy. However, the role of CMTM4 in tumor immunity has not been well clarified, especially in hepatocellular carcinoma (HCC).MethodsThe protein expression of CMTM4/PD-L1/CD4/CD8 was detected by immunohistochemistry (IHC) detection in 90 cases of HCC tissues. The mRNA expression profiles and related prognosis data were obtained from The Cancer Genome Atlas-Liver Hepatocellular Carcinoma (TCGA-LIHC). Two immune therapy cohorts were from Imvigor210 and GSE176307.ResultsThough the single protein expression of CMTM4, PD-L1, CD4 or CD8 in HCC tissues by IHC detection didn’t show a significant relationship with the prognosis of HCC patients, we found that high co-expression of CMTM4/PD-L1/CD4 showed a good prognosis of HCC patients. Further Timer 2.0 analysis identified that HCC patients with high expression of CMTM4/PD-L1 and high infiltration of CD4+ T cells had a better overall survival than those with low infiltration of CD4+ T cells. Moreover, a series of bioinformatics analyses revealed that CMTM4-related genes posed important effects on prognosis and immunity in HCC patients, and CMTM4 had a positive correlation with infiltration of CD4+ and CD8+ T cells in HCC. At last, we used two immunotherapy cohorts to verify that the combination of CMTM4 with PD-L1 could improve the prognosis of tumor patients underwent immunotherapy.ConclusionsCMTM4 and PD-L1 co-expression with T cell infiltration shows prognostic significance in HCC, suggesting combined effect from multiple proteins should be considered in HCC treatment.

Prognostic signature for hepatocellular carcinoma based on 4 pyroptosis-related genes

BackgroundHepatocellular carcinoma (HCC) is a cancer with a poor prognosis. Many recent studies have suggested that pyroptosis is important in tumour progression. However, the role of pyroptosis-related genes (PRGs) in HCC remains unclear.Materials and methodsWe identified differentially expressed PRGs in tumours versus normal tissues. Through univariate, LASSO, and multivariate Cox regression analyses, a prognostic PRG signature was established. The signature effectiveness was evaluated by time-dependent receiver operating characteristic (t-ROC) curve and Kaplan–Meier (KM) survival analysis. The signature was validated in the ICGC (LIRI-JP) cohort. In addition, single-sample gene enrichment analysis (ssGSEA) showed the infiltration of major immune cell types and the activity of common immune pathways in different subgroups.ResultsTwenty-nine pyroptosis-related DEGs from The Cancer Genome Atlas-Liver Hepatocellular Carcinoma (TCGA-LIHC) dataset were detected, and four genes (CTSV, CXCL8, MKI67 and PRF1) among them were selected to construct a prognostic signature. Then, the patients were divided into high- and low-risk groups. The pyroptosis-related signature was significantly associated with overall survival (OS). In addition, the patients in the high-risk group had lower levels of immune infiltration.ConclusionThe prognostic signature for HCC based on 4 pyroptosis-related genes has reliable prognostic and predictive value for HCC patients.