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Background Cancer cells primarily utilize aerobic glycolysis for energy production, a phenomenon known as the Warburg effect. Increased aerobic glycolysis supports cancer cell survival and rapid proliferation and predicts a poor prognosis in cancer patients. Methods Molecular profiles from The Cancer Genome Atlas (TCGA) cohort were used to analyze the prognostic value of glycolysis gene signature in human cancers. Gain- and loss-of-function studies were performed to key drivers implicated in hepatocellular carcinoma (HCC) glycolysis. The molecular mechanisms underlying Osteopontin (OPN)-mediated glycolysis were investigated by real-time qPCR, western blotting, immunohistochemistry, luciferase reporter assay, and xenograft and diethyl-nitrosamine (DEN)-induced HCC mouse models. Results Increased glycolysis predicts adverse clinical outcome in many types of human cancers, especially HCC. Then, we identified a handful of differentially expressed genes related to HCC glycolysis. Gain- and loss-of-function studies showed that OPN promotes, while SPP2, LECT2, SLC10A1, CYP3A4, HSD17B13, and IYD inhibit HCC cell glycolysis as revealed by glucose utilization, lactate production, and extracellular acidification ratio. These glycolysis-related genes exhibited significant tumor-promoting or tumor suppressive effect on HCC cells and these effects were glycolysis-dependent. Mechanistically, OPN enhanced HCC glycolysis by activating the αvβ3-NF-κB signaling. Genetic or pharmacological blockade of OPN-αvβ3 axis suppressed HCC glycolysis in xenograft tumor model and hepatocarcinogenesis induced by DEN. Conclusions Our findings reveal crucial determinants for controlling the Warburg metabolism in HCC cells and provide a new insight into the oncogenic roles of OPN in HCC.

In view of the unsatisfactory treatment outcome of liver cancer under current treatment, where the mortality rate is high and the survival rate is poor, in this study we aimed to use RNA sequencing data to explore potential molecular markers that can be more effective in predicting diagnosis and prognosis of hepatocellular carcinoma. RNA sequencing data and corresponding clinical information were obtained from multiple databases. After matching with the apoptotic genes from the Deathbase database, 14 differentially expressed human apoptosis genes were obtained. Using univariate and multivariate Cox regression analyses, two apoptosis genes (BAK1 and CSE1L) were determined to be closely associated with overall survival (OS) in HCC patients. And subsequently experiments also validated that knockdown of BAK1 and CSE1L significantly inhibited cell proliferation and promoted apoptosis in the HCC. Then the two genes were used to construct a prognostic signature and diagnostic models. The high-risk group showed lower OS time compared to low-risk group in the TCGA cohort (P < 0.001, HR = 2.11), GSE14520 cohort (P = 0.003, HR = 1.85), and ICGC cohort (P < 0.001, HR = 4). And the advanced HCC patients showed higher risk score and worse prognosis compared to early-stage HCC patients. Moreover, the prognostic signature was validated to be an independent prognostic factor. The diagnostic models accurately predicted HCC from normal tissues and dysplastic nodules in the training and validation cohort. These results indicated that the two apoptosis-related signature effectively predicted diagnosis and prognosis of HCC and may serve as a potential biomarker and therapeutic target for HCC.

Objectives To develop and validate a pre-transcatheter arterial chemoembolization (TACE) MRI-based radiomics model for predicting tumor response in intermediate-advanced hepatocellular carcinoma (HCC) patients. Materials Ninety-nine intermediate-advanced HCC patients (69 for training, 30 for validation) treated with TACE were enrolled. MRI examinations were performed before TACE, and the efficacy was evaluated according to the mRECIST criterion 3 months after TACE. A total of 396 radiomics features were extracted from T2-weighted pre-TACE images, and least absolute shrinkage and selection operator (LASSO) regression was applied to feature selection and model construction. The performance of the model was evaluated by receiver operating characteristic (ROC) curves, calibration curves, and decision curves. Results The AFP value, Child-Pugh score, and BCLC stage showed a significant difference between the TACE response (TR) and non-TACE response (nTR) patients. Six radiomics features were selected by LASSO and the radiomics score (Rad-score) was calculated as the sum of each feature multiplied by the non-zero coefficient from LASSO. The AUCs of the ROC curve based on Rad-score were 0.812 and 0.866 in the training and validation cohorts, respectively. To improve the diagnostic efficiency, the Rad-score was further integrated with the above clinical indicators to form a novel predictive nomogram. Results suggested that the AUC increased to 0.861 and 0.884 in the training and validation cohorts, respectively. Decision curve analysis showed that the radiomics nomogram was clinically useful. Conclusion The radiomics and clinical indicator-based predictive nomogram can well predict TR in intermediate-advanced HCC and can further be applied for auxiliary diagnosis of clinical prognosis. Key Points • The therapeutic outcome of TACE varies greatly even for patients with the same clinicopathologic features. • Radiomics showed excellent performance in predicting the TACE response. • Decision curves demonstrated that the novel predictive model based on the radiomics signature and clinical indicators has great clinical utility.

Hepatocellular carcinoma (HCC) is the sixth most common cancer and the third leading cause of cancer-related deaths globally. Despite advances in diagnosis and treatment, the incidence and mortality of HCC continue to rise. Piperlongumine (PL), an alkaloid isolated from the fruit of the long pepper, is known to selectively kill tumor tissues while sparing their normal counterparts. However, the killing effects of PL on HCC and the underlying mechanism of PL are not clear. We report that PL may interact with thioredoxin reductase 1 (TrxR1), an important selenocysteine (Sec)-containing antioxidant enzyme, and induce reactive oxygen species (ROS)-mediated apoptosis in HCC cells. Our results suggest that PL induces a lethal endoplasmic reticulum (ER) stress response in HCC cells by targeting TrxR1 and increasing intracellular ROS levels. Notably, PL treatment reduces TrxR1 activity and tumor cell burden in vivo. Additionally, TrxR1 is significantly upregulated in existing HCC databases and available HCC clinical specimens. Taken together, these results suggest PL as a novel anticancer candidate for the treatment of HCC. More importantly, this study reveals that TrxR1 might be an effective target in treating HCC.

Objectives: To investigate potential diagnostic model for predicting benign or malignant status of subcentimeter pulmonary ground-glass nodules (SPGGNs) (≤1 cm) based on CT texture analysis. Methods: A total of 89 SPGGNs from 89 patients were included; 51 patients were diagnosed with adenocarcinoma, and 38 were diagnosed with inflamed or infected benign SPGGNs. Analysis Kit software was used to manually delineate the volume of interest of lesions and extract a total of 396 quantitative texture parameters. The statistical analysis was performed using R software. The SPGGNs were randomly divided into a training set (n = 59) and a validation set (n = 30). All pre-normalized (Z-score) feature values were subjected to dimension reduction using the LASSO algorithm,and the most useful features in the training set were selected. The selected imaging features were then combined into a Rad-score, which was further assessed by ROC curve analysis in the training and validation sets. Results: Four characteristic parameters (ClusterShade_AllDirection_offset4_SD, ShortRunEmphasis_angle45_offset1, Maximum3DDiameter, SurfaceVolumeRatio) were further selected by LASSO (p < 0.05). As a cluster of imaging biomarkers, the above four parameters were used to form the Rad-score. The AUC for differentiating between benign and malignant SPGGNs in the training set was 0.792 (95% CI: 0.671, 0.913), and the sensitivity and specificity were 86.10 and 65.20%, respectively. The AUC in the validation set was 72.9% (95% CI: 0.545, 0.913), and the sensitivity and specificity were 86.70 and 60%, respectively. Conclusion: The present diagnostic model based on the cluster of imaging biomarkers can preferably distinguish benign and malignant SPGGNs (≤1 cm). Advances in knowledge: Texture analysis based on CT images provide a new and credible technique for accurate identification of subcentimeter pulmonary ground-glass nodules.

Alternative splicing within a gene can create different versions of an mRNA, called isoforms. CFIm, composed of a small subunit CFIm25 and two large subunits CFIm68 and CFIm59 (also known as CPSF7), has been proposed as an enhancer-dependent activator of mRNA 3' processing. In this study, we investigated the role of CPSF7 in hepatocellular carcinoma. Experimental evidence suggests that the expression level of CPSF7 is higher in liver cancer cells and tissues than in non-tumor hepatic cells and tissues. Furthermore, knockdown of CPSF7 effectively suppressed cell proliferation, migration and colony formation in liver cancer cells by inhibiting PTEN/AKT signaling. CPSF7 promoted WWP2-FL due to the presence of PTEN ubiquitination sites in this longer transcript. Taken together, we identified that CPSF7 regulates liver cancer growth by targeting WWP2-FL that in turn regulates AKT activation in a PTEN-dependent manner.