Developments in predictive biomarkers for hepatocellular carcinoma therapy

Predicting the prognosis of hepatic arterial infusion chemotherapy in hepatocellular carcinoma

Hepatic artery infusion chemotherapy (HAIC) has good clinical efficacy in the treatment of advanced hepatocellular carcinoma (HCC); however, its efficacy varies. This review summarized the ability of various markers to predict the efficacy of HAIC and provided a reference for clinical applications. As of October 25, 2023, 51 articles have been retrieved based on keyword predictions and HAIC. Sixteen eligible articles were selected for inclusion in this study. Comprehensive literature analysis found that methods used to predict the efficacy of HAIC include serological testing, gene testing, and imaging testing. The above indicators and their combined forms showed excellent predictive effects in retrospective studies. This review summarized the strategies currently used to predict the efficacy of HAIC in middle and advanced HCC, analyzed each marker's ability to predict HAIC efficacy, and provided a reference for the clinical application of the prediction system.

Predicting the prognosis of hepatic arterial infusion chemotherapy in hepatocellular carcinoma

Hepatic artery infusion chemotherapy (HAIC) has good clinical efficacy in the treatment of advanced hepatocellular carcinoma (HCC); however, its efficacy varies. This review summarized the ability of various markers to predict the efficacy of HAIC and provided a reference for clinical applications. As of October 25, 2023, 51 articles have been retrieved based on keyword predictions and HAIC. Sixteen eligible articles were selected for inclusion in this study. Comprehensive literature analysis found that methods used to predict the efficacy of HAIC include serological testing, gene testing, and imaging testing. The above indicators and their combined forms showed excellent predictive effects in retrospective studies. This review summarized the strategies currently used to predict the efficacy of HAIC in middle and advanced HCC, analyzed each marker's ability to predict HAIC efficacy, and provided a reference for the clinical application of the prediction system.

Biomarker discovery and application-An opportunity to resolve the challenge of liver cancer diagnosis and treatment

Liver cancer is one of the most common malignancies, with severe morbidity and mortality. While considerable progress has been made in liver cancer treatment, the 5-year overall survival (OS) of patients has not improved significantly. Reasons include the inadequate capability of early screening and diagnosis, a high incidence of recurrence and metastasis, a high degree of tumor heterogeneity, and an immunosuppressive tumor microenvironment. Therefore, the identification and validation of specific and robust liver cancer biomarkers are of major importance for early screening, timely diagnosis, accurate prognosis, and the prevention of tumor progression. In this review, we highlight some of the latest research progress and potential applications of liver cancer biomarkers, describing hotspots and prospective directions in biomarker discovery.

CT Radiomics to Predict Macrotrabecular-Massive Subtype and Immune Status in Hepatocellular Carcinoma

Background Macrotrabecular-massive (MTM) subtype of hepatocellular carcinoma (HCC) is an aggressive variant associated with angiogenesis and immunosuppressive tumor microenvironment, which is expected to be noninvasively identified using radiomics approaches. Purpose To construct a CT radiomics model to predict the MTM subtype and to investigate the underlying immune infiltration patterns. Materials and Methods This study included five retrospective data sets and one prospective data set from three academic medical centers between January 2015 and December 2021. The preoperative liver contrast-enhanced CT studies of 365 adult patients with resected HCC were evaluated. The Third Xiangya Hospital of Central South University provided the training set and internal test set, while Yueyang Central Hospital and Hunan Cancer Hospital provided the external test sets. Radiomic features were extracted and used to develop a radiomics model with machine learning in the training set, and the performance was verified in the two test sets. The outcomes cohort, including 58 adult patients with advanced HCC undergoing transarterial chemoembolization and antiangiogenic therapy, was used to evaluate the predictive value of the radiomics model for progression-free survival (PFS). Bulk RNA sequencing of tumors from 41 patients in The Cancer Genome Atlas (TCGA) and single-cell RNA sequencing from seven prospectively enrolled participants were used to investigate the radiomics-related immune infiltration patterns. Area under the receiver operating characteristics curve of the radiomics model was calculated, and Cox proportional regression was performed to identify predictors of PFS. Results Among 365 patients (mean age, 55 years ± 10 [SD]; 319 men) used for radiomics modeling, 122 (33%) were confirmed to have the MTM subtype. The radiomics model included 11 radiomic features and showed good performance for predicting the MTM subtype, with AUCs of 0.84, 0.80, and 0.74 in the training set, internal test set, and external test set, respectively. A low radiomics model score relative to the median value in the outcomes cohort was independently associated with PFS (hazard ratio, 0.4; 95% CI: 0.2, 0.8; P = .01). The radiomics model was associated with dysregulated humoral immunity involving B-cell infiltration and immunoglobulin synthesis. Conclusion Accurate prediction of the macrotrabecular-massive subtype in patients with hepatocellular carcinoma was achieved using a CT radiomics model, which was also associated with defective humoral immunity. Published under a CC BY 4.0 license. Supplemental material is available for this article. See also the editorial by Yoon and Kim in this issue.

LINC02875 Upregulation Contributed to Poor Prognosis for the Hepatocellular Carcinoma and Progression for the Cancerous Cells

The prognostic implications and physiological effect of LINC02875 are unknown in hepatocellular carcinoma (HCC). We sought to examine the prognostic value of LINC02875 in HCC and assessed its role in HCC cellular function. LINC02875 expression was evaluated by RT-qPCR in HCC specimens and cell lines. LINC02875 expression was subjected to assess the correlation with clinical parameters by Chi-squared test and overall survival by Kaplan - Meier curve and Cox regression analysis. The effects of LINC02875 on the biological characteristics of HCC cells were studied by MTS and Transwell assay. LINC02875 was high-expressed in HCC, and this was associated with unfavorable clinical features and poor prognosis of HCC, especially HBV-related HCC. Knockdown of LINC02875 inhibited the proliferation, migration, and invasion of HCC cells. miR-485-5p was a downstream microRNA of LINC02875. LINC02875 affects the prognosis of HCC patients, especially HBV-related ones. LINC02875 represents a suitable therapeutic target for HCC.

Establishment of a Prognostic Model for Hepatocellular Carcinoma Based on Bioinformatics and the Role of NR6A1 in the Progression of HCC

BACKGROUND AND AIMS

Generally acceptable prognostic models for hepatocellular carcinoma (HCC) are not available. This study aimed to establish a prognostic model for HCC by identifying immune-related differentially expressed genes (IR-DEGs) and to investigate the potential role of NR6A1 in the progression of HCC.

METHODS

Bioinformatics analysis using The Cancer Genome Atlas and ImmPort databases was used to identify IR-DEGs. Lasso Cox regression and multivariate Cox regression analysis were used to establish a prognostic model of HCC. Kaplan-Meier analysis and the receiver operating characteristic (ROC) curves were used to evaluate the performance of the prognostic model, which was further verified in the International Cancer Genome Consortium (ICGC) database. Gene set enrichment analysis was used to explore the potential pathways of NR6A1. Cell counting kit 8, colony formation, wound healing, and Transwell migration assays using Huh7 cells, and tumor formation models in nude mice were conducted.

RESULTS

A prognostic model established based on ten identified IR-DEGs including HSPA4, FABP6, MAPT, NDRG1, APLN, IL17D, LHB, SPP1, GLP1R, and NR6A1, effectively predicted the prognosis of HCC patients, was confirmed by the ROC curves and verified in ICGC database. NR6A1 expression was significantly up-regulated in HCC patients, and NR6A1 was significantly associated with a low survival rate. Gene set enrichment analysis showed the enrichment of cell cycle, mTOR, WNT, and ERBB signaling pathways in patients with high NR6A1 expression. NR6A1 promoted cell proliferation, invasiveness, migration, and malignant tumor formation and growth in vitro and in vivo.

CONCLUSIONS

An effective prognostic model for HCC, based on a novel signature of 10 immune-related genes, was established. NR6A1 was up-regulated in HCC and was associated with a poor prognosis of HCC. NR6A1 promoted cell proliferation, migration, and growth of HCC, most likely through the cell cycle, mTOR, WNT, and ERBB signaling pathways.

Encouraging specific biomarkers-based therapeutic strategies for hepatocellular carcinoma

The prevention, early discovery and effective treatment of patients with hepatocellular carcinoma (HCC) remain a global medical challenge. At present, HCC is still mainly treated by surgery, supplemented by vascular embolization, radio frequency, radiotherapy, chemotherapy and biotherapy. The application of multikinase inhibitor sorafenib, chimeric antigen receptor T cells, or PD-1/PD-L1 inhibitors can prolong the median survival of HCC patients. However, the treatment efficacy is still unsatisfactory due to HCC metastasis and postoperative recurrence. During the process of hepatocyte malignant transformation, HCC tissues can express and secrete many types of specific biomarkers, or oncogenic antigen molecules into blood, for example, alpha-fetoprotein, glypican-3, Wnt3a (one of the key signaling molecules in the Wnt/β-catenin pathway), insulin-like growth factor (IGF)-II or IGF-I receptor, vascular endothelial growth factor, secretory clusterin and so on. In addition, combining immunotherapy with non-coding RNAs might improve anti-cancer efficacy. These biomarkers not only contribute to HCC diagnosis or prognosis, but may also become molecular targets for HCC therapy under developing or clinical trials. This article reviews the progress in emerging biomarkers in basic research or clinical trials for HCC immunotherapy.

Identification, Verification and Pathway Enrichment Analysis of Prognosis-Related Immune Genes in Patients With Hepatocellular Carcinoma

Hepatocellular carcinoma is a common malignant tumor with poor prognosis, poor treatment effect, and lack of effective biomarkers. In this study, bioinformatics analysis of immune-related genes of hepatocellular carcinoma was used to construct a multi-gene combined marker that can predict the prognosis of patients. The RNA expression data of hepatocellular carcinoma were downloaded from The Cancer Genome Atlas (TCGA) database, and immune-related genes were obtained from the IMMPORT database. Differential analysis was performed by Wilcox test to obtain differentially expressed genes. Univariate Cox regression analysis, lasso regression analysis and multivariate Cox regression analysis were performed to establish a prognostic model of immune genes, a total of 5 genes (HDAC1, BIRC5, SPP1, STC2, NR6A1) were identified to construct the models. The expression levels of 5 genes in HCC tissues were significantly different from those in paracancerous tissues. The Kaplan-Meier survival curve showed that the risk score calculated according to the prognostic model was significantly related to the overall survival (OS) of HCC. The receiver operating characteristic (ROC) curve confirmed that the prognostic model had high accuracy. Independent prognostic analysis was performed to prove that the risk value can be used as an independent prognostic factor. Then, the gene expression data of hepatocellular carcinoma in the ICGC database was used as a validation data set for the verification of the above steps. In addition, we used the CIBERSORT software and TIMER database to conduct immune infiltration research, and the results showed that the five genes of the model and the risk score have a certain correlation with the content of immune cells. Moreover, through Gene Set Enrichment Analysis (GSEA) and the construction of protein interaction networks, we found that the p53-mediated signal transduction pathway is a potentially important signal pathway for hepatocellular carcinoma and is positively regulated by certain genes in the prognostic model. In conclusion, this study provides potential targets for predicting the prognosis and treatment of hepatocellular carcinoma patients, and also provides new ideas about the correlation between immune genes and potential pathways of hepatocellular carcinoma.

Prediction of Hepatocellular Carcinoma Response to Transcatheter Arterial Chemoembolization: A Real-World Study Based on Non-Contrast Computed Tomography Radiomics and General Image Features

Objective

To construct a predictive model of short-term response and overall survival for transcatheter arterial chemoembolization (TACE) treatment in hepatocellular carcinoma (HCC) patients based on non-contrast computed tomography (NC-CT) radiomics and clinical features.

Methods

Ninety-four HCC patients who underwent CT scanning 1 week before the first TACE treatment were retrospectively recruited and divided randomly into a training group (n = 47) and a validation group (n = 47). NC-CT radiomics data were extracted using MaZda software, and the compound model was calculated from radiomics and clinical features by logistic regression. The performance of the different models was compared by examining the area under the receiver operating characteristic curve (AUC). The prediction of prognosis was evaluated using survival analysis.

Results

Thirty NC-CT radiomic features were extracted and analyzed. The compound model was formed using four NC-CT run-length matrix (RLM) features and general image features, which included the maximum diameter (cm) of the tumor and the number of tumors (n). The AUCs of the model for TACE response were 0.840 and 0.815, whereas the AUCs of the six-and-twelve grade were 0.754 and 0.750 in the training and validation groups, respectively. HCC patients were divided into two groups using the cutoff value of the model: a group in which the TACE-response led to good survival and a group in which TACE-nonresponse caused poor prognosis.

Conclusion

Radiomic features from NC-CT predicted TACE-response. The compound model generated by NC-CT radiomics and clinical features is effective and directly predicts TACE-response and overall survival. The model may be used repeatedly and is easy to operate.

Intrahepatic recurrence of hepatocellular carcinoma after resection: an update

Hepatocellular carcinoma recurrence occurs in 40-70% of patients after hepatic resection. Despite the high frequency of hepatocellular cancer relapse, there is no established guidance for the management of such cases. The evaluation of prognostic factors that indicate a high risk of recurrence after surgery such as the tumor number and size and the presence of microvascular invasion may guide the therapeutic strategy and point out which patients should be strictly monitored. Additionally, the administration of adjuvant treatment or ab initio liver transplantation in selected patients with high-risk characteristics could have a significant impact on the prevention of relapse and overall survival. Once the recurrence has occurred in the liver remnant, the available therapeutic options include re-resection, salvage liver transplantation and locoregional treatments, although the therapeutic choice is often challenging and should be based on the characteristics of the recurrent tumor, the patient profile and most importantly the timing of relapse. Aggressive combination treatments are often required in challenging cases of early relapse. The results of the above treatment strategies are reviewed and compared to determine the optimal management of patients with recurrent hepatocellular cancer following liver resection.

Sequential Treatment of Sorafenib-Regorafenib Versus Sorafenib-Physician's Choice: A Propensity Score-Matched Analysis

BACKGROUND

Regorafenib has been shown to improve clinical outcomes compared to placebo, becoming a standard second-line therapy for sorafenib-progressed and -tolerated hepatocellular carcinoma (HCC) patients.

OBJECTIVE

We performed a multicentre, retrospective study in Italy and Korea to evaluate the effectiveness of the treatment sequence sorafenib-regorafenib compared with sorafenib and physician's choice in a real-life setting.

PATIENTS AND METHODS

A propensity score model was developed to control the results for baseline variable imbalances between the arm treated with sorafenib and regorafenib (S-R) and the arm treated with sorafenib and physician's choice (S-P). Survival analysis was conducted on the matched population.

RESULTS

After the application of propensity score matching, we analysed 99 patients in the arm treated with S-R and 99 patients in the arm treated with S-P. For the S-R group, the median overall survival was 22.2 months (95% CI 17.1-27.4), compared to 17.9 months (95% CI 15.1-50.0) for the S-P group. The results of the univariate analysis showed a 31% reduction of death risk for patients treated with S-R (p = 0.0382) compared to patients treated with S-P. Interaction tests highlighted the predictive role of alpha-fetoprotein (AFP), neutrophil-to-lymphocyte ratio (NLR), and extrahepatic spread.

CONCLUSION

This study provides additional proof of the superiority of the S-R treatment over the S-P treatment approach in advanced HCC patients from a real-life setting.

Development and validation of a comprehensive radiomics nomogram for prognostic prediction of primary hepatic sarcomatoid carcinoma after surgical resection

Objective: This study aimed to establish and validate a radiomics nomogram comprised of clinical factors and radiomics signatures to predict prognosis of primary hepatic sarcomatoid carcinoma (PHSC) patients after surgical resection. Methods: In this retrospective study, 79 patients with pathological confirmation of PHSC and underwent surgical resection were recruited. A radiomics nomogram was developed by radiomics signatures and independent clinical risk factors selecting from multivariate Cox regression. All patients were stratified as high risk and low risk by nomogram. Model performance and clinical usefulness were assessed by C-index, calibration curve, decision curve analysis (DCA) and survival curve. Results: A total of 79 PHSC were included with 1-year and 3-year overall survival rates of 63.3% and 35.4%, respectively. The least absolute shrinkage and selection operator (LASSO) method selected 3 features. Multivariate Cox analysis found six independent prognostic factors. The radiomics nomogram showed a significant prediction value with overall survival (HR: 7.111, 95%CI: 3.933-12.858, P<0.001). C-index of nomogram was 0.855 and 0.829 in training and validation set, respectively. Decision curve analysis validated the clinical utility of this nomogram. There was a significant difference in the 1-year and 3-year survival rates of stratified high-risk and low-risk patients in the whole cohort (30.6% vs. 90.1% and 5.6% vs. 62.4%, respectively, P < 0.001). Conclusion: This radiomics nomogram serve as a potential tool for predicting prognosis of PHSC after surgical resection, and help to identify high risk patients who may obtain feeble survival benefit from surgical resection.

Radiomics for liver tumours

Current research, especially in oncology, increasingly focuses on the integration of quantitative, multiparametric and functional imaging data. In this fast-growing field of research, radiomics may allow for a more sophisticated analysis of imaging data, far beyond the qualitative evaluation of visible tissue changes. Through use of quantitative imaging data, more tailored and tumour-specific diagnostic work-up and individualized treatment concepts may be applied for oncologic patients in the future. This is of special importance in cross-sectional disciplines such as radiology and radiation oncology, with already high and still further increasing use of imaging data in daily clinical practice. Liver targets are generally treated with stereotactic body radiotherapy (SBRT), allowing for local dose escalation while preserving surrounding normal tissue. With the introduction of online target surveillance with implanted markers, 3D-ultrasound on conventional linacs and hybrid magnetic resonance imaging (MRI)-linear accelerators, individualized adaptive radiotherapy is heading towards realization. The use of big data such as radiomics and the integration of artificial intelligence techniques have the potential to further improve image-based treatment planning and structured follow-up, with outcome/toxicity prediction and immediate detection of (oligo)progression. The scope of current research in this innovative field is to identify and critically discuss possible application forms of radiomics, which is why this review tries to summarize current knowledge about interdisciplinary integration of radiomics in oncologic patients, with a focus on investigations of radiotherapy in patients with liver cancer or oligometastases including multiparametric, quantitative data into (radio)-oncologic workflow from disease diagnosis, treatment planning, delivery and patient follow-up.

The role of PNI to predict survival in advanced hepatocellular carcinoma treated with Sorafenib

BACKGROUND AND AIMS

The present study aims to investigate the role of the prognostic nutritional index (PNI) on survival in patients with advanced hepatocellular carcinoma (HCC) treated with sorafenib.

METHODS

This multicentric study included a training cohort of 194 HCC patients and three external validation cohorts of 129, 76 and 265 HCC patients treated with Sorafenib, respectively. The PNI was calculated as follows: 10 × serum albumin (g/dL) + 0.005 × total lymphocyte count (per mm3). Univariate and multivariate analyses were performed to investigate the association between the covariates and the overall survival (OS).

RESULTS

A PNI cut-off value of 31.3 was established using the ROC analysis. In the training cohort, the median OS was 14.8 months (95% CI 12-76.3) and 6.8 months (95% CI 2.7-24.6) for patients with a high (>31.3) and low (<31.3) PNI, respectively. At both the univariate and the multivariate analysis, low PNI value (p = 0.0004), a 1-unit increase of aspartate aminotransferase (p = 0.0001), and age > 70 years (p< 0.0038) were independent prognostic factors for OS. By performing the same multivariate analysis of the training cohort, the PNI <31.3 versus >31.3 was found to be an independent prognostic factor for predicting OS in all the three validation cohorts.

CONCLUSIONS

PNI represents a prognostic tool in advanced HCC treated with first-line Sorafenib. It is readily available and low-cost, and it could be implemented in clinical practice in patients with HCC.