Development and validation a radiomics nomogram for predicting thymidylate synthase status in hepatocellular carcinoma based on Gd-DTPA contrast enhanced MRI.

OBJECTIVES: The purpose of this study was to develop and validate a radiomics nomogram for predicting thymidylate synthase (TYMS) status in hepatocellular carcinoma (HCC) by using Gd-DTPA contrast enhanced MRI. METHODS: We retrospectively enrolled 147 consecutive patients with surgically confirmed HCC and randomly allocated to training and validation set (7:3). The TYMS status was immunohistochemical determined and classified into low TYMS (positive cells ≤ 25%) and high TYMS (positive cells > 25%) groups. Radiomics features were extracted from the arterial phases and portal venous phase of Gd-DTPA contrast enhanced MRI. Least absolute shrinkage and selection operator (LASSO) were applied for generating the Rad score. Clinical data and MRI findings were assessed to build a clinical model. Rad score combined with clinical features was used to construct radiomics nomogram. RESULTS: A total of 2260 features were extracted and reduced to 7 features as the most important discriminators to build the Rad score. InAFP was identified as the only independent clinical factors for TYMS status. The radiomics nomogram showed good discrimination in training (AUC, 0.759; 95% CI 0.665-0.838) and validation set (AUC, 0.739; 95% CI 0.585-0.860), and showed better discrimination capability (P < 0.05) compared with clinical model in training (AUC, 0.656; 95% CI 0.555-0.746) and validation set (AUC, 0.622; 95% CI 0.463-0.764). CONCLUSIONS: The radiomics nomogram shows favorable predictive efficacy for TYMS status in HCC, which might be helpful for the personalized treatment of HCC.

Development of preoperative and postoperative machine learning models to predict the recurrence of huge hepatocellular carcinoma following surgical resection.

Resection has been commonly utilized for treating huge hepatocellular carcinoma (HCC) with a diameter of ≥10 cm; however, a high rate of mortality is reported due to recurrence. The present study was designed to predict the recurrence following resection based on preoperative and postoperative machine learning models. In total, 1,082 patients with HCC who underwent liver resection in the Eastern Hepatobiliary Surgery Hospital cohort between January 2008 and December 2016 were divided into a training cohort and an internal validation cohort. In addition, 164 patients from Mengchao Hepatobiliary Hospital cohort between January 2014 and December 2016 served as an external validation cohort. The demographic information, and serological, MRI, and pathological data were obtained from each patient prior to and following surgery, followed by evaluating the model performance using the concordance index, time-dependent receiver operating characteristic curves, prediction error cures, and a calibration curve. A preoperative random survival forest (RSF) model and a postoperative RSF model were constructed based on the training set, which outperformed the conventional models, such as the Barcelona Clinic Liver Cancer (BCLC), the 8th edition of the American Joint Committee on Cancer (AJCC 8th) staging systems, and the Chinese stage systems. In addition, the preoperative and postoperative RSF models could also re-stratify patients with BCLC stage A/B/C or AJCC 8th stage IB/II/IIIA/IIIB or Chinese stage IB/IIA/IIB/IIIA into low-risk, intermediate-risk, and high-risk groups in the training and the two validation cohorts. The preoperative and postoperative RSF models were effective for predicting recurrence in patients with huge HCC following hepatectomy.

A novel preoperative inflammation score system established for postoperative prognosis predicting of intrahepatic cholangiocarcinoma.

BACKGROUND: Inflammation is implicated in tumorigenesis and has been reported as an important prognostic factor in cancers. In this study, we aimed to develop and validate a novel inflammation score (IFS) system based on 12 inflammatory markers and explore its impact on intrahepatic cholangiocarcinoma (ICC) survival after hepatectomy. METHODS: Clinical data of 446 ICC patients undergoing surgical treatment were collected from the Primary Liver Cancer Big Data, and then served as a training cohort to establish the IFS. Furthermore, an internal validation cohort including 175 patients was used as internal validation cohort of the IFS. A survival tree analysis was used to divide ICC patients into three groups (low-, median-, and high- IFS-score groups) according to different IFS values. Kaplan-Meier (KM) curves were used to compare the overall survival (OS) and recurrence-free survival (RFS) rates among three different groups. Cox regression analyses were applied to explore the independent risk factors influencing OS and RFS. RESULTS: In the training cohort, 149 patients were in the low-IFS-score group, 187 in the median-IFS-score group, and 110 in the high-IFS-score group. KM curves showed that the high-IFS-score group had worse OS and RFS rates than those of the low- and median-IFS-score groups (P < 0.001) in both the training and validation cohorts. Moreover, multivariable Cox analyses identified high IFS as an independent risk factor for OS and RFS in the training cohort. The area under the curve values for OS prediction of IFS were 0.703 and 0.664 in the training and validation cohorts, respectively, which were higher than those of the American Joint Committee on Cancer (AJCC) 7th edition TNM stage, AJCC 8th edition TNM stage, and the Child-Pugh score. CONCLUSION: Our results revealed the IFS was an independent risk factor for OS and RFS in patients with ICC after hepatectomy and could serve as an effective prognostic prediction system in daily clinical practice.

Pyroptosis related genes signature predicts prognosis and immune infiltration of tumor microenvironment in hepatocellular carcinoma.

Little is known on the relationship between the expression of pyroptosis related genes (PRGs) and prognosis of hepatocellular carcinoma (HCC). In this study, a specific PRGs prognostic model was developed with an aim to improve therapeutic efficiency among HCC patients. In total, 42 PRGs that were differentially expressed between HCC tissues and adjacent tissues and we exhibited the mutation frequency, classification, the location of copy number variation (CNV) alteration and the CNV variation frequency of PRGs. Two clusters were distinguished by the consensus clustering analysis based on the 42 differentially expressed genes (DEGs). There were significant differences in clinical features including T stage, grade, gender, and stage among different clusters. Kaplan-Meier curve analysis showed that cluster 1 had a better prognosis than cluster 2. The prognostic value of PRGs for survival was evaluated to construct a multigene signature using The Cancer Genome Atlas (TCGA) cohort. Based on the univariate analysis and multivariate analysis, a 10-gene signature was built and all HCC patients in the TCGA cohort were divided into low-risk group and high-risk group. HCC patients in the high-risk group showed significantly lower survival possibilities than those in the low-risk group (P < 0.001). Utilizing the median risk score from the TCGA cohort, HCC patients from International Cancer Genome Consortium (ICGC)-LIRI-JP cohort and Gene Expression Omnibus (GEO) cohort (GSE14520) were divided into two risk subgroups. The result showed that overall survival (OS) time was decreased in the high-risk group. Combined with the clinical characteristics, the risk score was an independent factor for predicting the OS of HCC patients. Then, ROC curve and survival analysis were performed to evaluate the prognostic prediction value of the model. Finally, we constructed a PRGs clinical characteristics nomogram to further predict HCC patient survival probability. There were significant differences in immune cell infiltration, GSEA enrichment pathway, IC50 of chemotherapeutics, PRGs mutation frequency between high-risk group and low-risk group. This work suggests PRGs signature played a crucial role in predicting the prognosis, infiltration of cancer microenvironment, and sensitivity of chemotherapeutic agents.

Development and validation of nomogram to predict very early recurrence of combined hepatocellular-cholangiocarcinoma after hepatic resection: a multi-institutional study.

BACKGROUND AND OBJECTIVES: Combined hepatocellular cholangiocarcinoma (cHCC) has a high incidence of early recurrence. The objective of this study is to construct a model predicting very early recurrence (VER) (i.e., recurrence within 6 months after surgery) of cHCC. METHODS: One hundred thirty-one consecutive patients from Eastern Hepatobiliary Surgery Hospital served as a development cohort to construct a nomogram predicting VER by using multi-variable logistic regression analysis. The model was internally and externally validated in a validation cohort of 90 patients from Mengchao Hepatobiliary Hospital using the C concordance statistic, calibration analysis, and decision curve analysis (DCA). RESULTS: The VER nomogram contains microvascular invasion (MiVI), macrovascular invasion (MaVI), and CA19-9 > 25 mAU/mL. The model shows good discrimination with C-indexes of 0.77 (95% CI: 0.69-0.85) and 0.76 (95% CI: 0.66-0.86) in the development cohort and validation cohort respectively. Decision curve analysis demonstrated that the model is clinically useful and the calibration of our model was favorable. Our model stratified patients into two different risk groups, which exhibited significantly different VER. CONCLUSIONS: Our model demonstrated favorable performance in predicting VER in cHCC patients.

Small Molecule-Induced Differentiation As a Potential Therapy for Liver Cancer.

Despite the efficacy demonstrated by immunotherapy recently, liver cancer still remains one of the deadliest cancers, mainly due to heterogeneity of this disease. Continuous exploration of new therapeutics is therefore necessary. Chemical-induced cell differentiation can serve as a promising approach, with its ability to consistently remodel gene expression profile and alter cell fate. Inspired by advances in stem cell and reprogramming field, here it is reported that a small molecule cocktail (SMC) consisted of: SB431542 (TGFß inhibitor), CHIR99021 (GSK3ß inhibitor), BIX01294 (H3K9 methyltransferase/G9a inhibitor), and all-trans retinoic acid (ATRA), can induce differentiation of liver cancer cells including cell lines, primary cancer cells, cancer stem cells, and drug resistant cells. Treated cells lose malignant characteristics and regain hepatocyte phenotype instead. When applied in vivo, SMC induces wide range of tissue necrosis or fibrosis within the tumors, while remaining tissues begin to express hepatic nuclear factor 4α (HNF4α), the hepatic nuclear marker. SMC also leads to tumor abrogation in orthotopic xenograft models and life span extension of animals. The powerful differentiation induction of SMC is exerted through modulation of Akt/mTOR/HIF1α signaling and metabolic reprogramming, as well as suppressing Snail and enhancing HNF4α expression. Together, these results highlight that chemical-induced differentiation has the potential to effectively treat liver cancer disregard of heterogeneity.

Prognostic and Predictive Value of Transcription Factors Panel for Digestive System Carcinoma.

PURPOSE: Digestive system carcinoma is one of the most devastating diseases worldwide. Lack of valid clinicopathological parameters as prognostic factors needs more accurate and effective biomarkers for high-confidence prognosis that guide decision-making for optimal treatment of digestive system carcinoma. The aim of the present study was to establish a novel model to improve prognosis prediction of digestive system carcinoma, with a particular interest in transcription factors (TFs). MATERIALS AND METHODS: A TF-related prognosis model of digestive system carcinoma with data from TCGA database successively were processed by univariate and multivariate Cox regression analyses. Then, for evaluating the prognostic prediction value of the model, ROC curve and survival analysis were performed by external data from GEO database. Furthermore, we verified the expression of TFs expression by qPCR in digestive system carcinoma tissue. Finally, we constructed a TF clinical characteristics nomogram to furtherly predict digestive system carcinoma patient survival probability with TCGA database. RESULTS: By Cox regression analysis, a panel of 17 TFs (NFIC, YBX2, ZBTB47, ZNF367, CREB3L3, HEYL, FOXD1, TIGD1, SNAI1, HSF4, CENPA, ETS2, FOXM1, ETV4, MYBL2, FOXQ1, ZNF589) was identified to present with powerful predictive performance for overall survival of digestive system carcinoma patients based on TCGA database. A nomogram that integrates TFs was established, allowing efficient prediction of survival probabilities and displaying higher clinical utility. CONCLUSION: The 17-TF panel is an independent prognostic factor for digestive system carcinoma, and 17 TFs based nomogram might provide implication an effective approach for digestive system carcinoma patient management and treatment.

An MR-based radiomics model for differentiation between hepatocellular carcinoma and focal nodular hyperplasia in non-cirrhotic liver.

PURPOSE: We aimed to develop and validate a radiomics model for differentiating hepatocellular carcinoma (HCC) from focal nodular hyperplasia (FNH) in non-cirrhotic livers using Gd-DTPA contrast-enhanced magnetic resonance imaging (MRI). METHODS: We retrospectively enrolled 149 HCC and 75 FNH patients treated between May 2015 and May 2019 at our center. Patients were randomly allocated to a training (n=156) and validation set (n=68). In total, 2260 radiomics features were extracted from the arterial phase and portal venous phase of Gd-DTPA contrast-enhanced MRI. Using Max-Relevance and Min-Redundancy, random forest, least absolute shrinkage, and selection operator algorithm for dimensionality reduction, multivariable logistic regression was used to build the radiomics model. A clinical model and combined model were also established. The diagnostic performance of the models was compared. RESULTS: Eight radiomics features were chosen for the radiomics model, and four clinical factors (age, sex, HbsAg, and enhancement pattern) were chosen for the clinical model. A combined model was built using the factors from the previous models. The classification accuracy of the combined model differentiated HCC from FNH in both the training and validation sets (0.956 and 0.941, respectively). The area under the receiver operating characteristic curve of the combined model was significantly better than that of the clinical model for both the training (0.984 vs. 0.937, p=0.002) and validation (0.972 vs. 0.903, p=0.032) sets. CONCLUSIONS: The combined model provided a non-invasive quantitative method for differentiating HCC from FNH in non-cirrhotic liver with high accuracy. Our model may assist clinicians in the clinical decision-making process.

Inhibition of GSK-3ß activity suppresses HCC malignant phenotype by inhibiting glycolysis via activating AMPK/mTOR signaling.

Glycogen synthase kinase-3 beta (GSK-3ß) has been shown to play a critical role in the development of many cancers, but its role in hepatocellular carcinoma (HCC) remains unclear. Deregulating cellular energetics is a signature hallmark of cancer, therefore modulating cancer metabolism has become an attractive anti-cancer approach in recent years. As a key enzyme in glucose metabolism, understanding the role of GSK-3ß in cancer metabolic process may facilitate the development of effective therapeutic approach for HCC. In this study, we showed that inhibition of GSK-3ß led to diminished viability, metastasis and tumorigenicity in HCC cells. Suppression of GSK-3ß activity also reduced glucose consumption, lactate production and adenosine triphosphate (ATP) levels in HCC cells. The decreased extracellular acidification rate (ECAR) and down-regulated key enzymes on the glycolysis pathway by GSK3ß inhibition demonstrated that GSK-3ß was involved in glycolysis process of HCC. Mechanistically, the metabolic change and anti-cancer effect by GSK-3ß inhibition was achieved mainly through activation of adenosine 5'-monophosphate (AMP)-activated protein kinase (AMPK)/mammalian target of rapamycin (mTOR) signaling, which negatively affected glycolysis and cell proliferation. The results from primary HCC cells and from in vivo nude mice model confirmed our observations. Our study results indicated that GSK-3ß may become a promising therapeutic target for HCC.