Systemic Therapy for Hepatocellular Carcinoma: Advances and Hopes

HCC-Check: A Novel Diagnostic Tool for Early Detection of Hepatocellular Carcinoma Based on Cytokeratin-1 and Epithelial Membrane Antigen: A Cross-Sectional Study

Background: Hepatocellular carcinoma is frequently diagnosed in advanced stages, leading to a poorer prognosis. Therefore, early diagnosis and identification of biomarkers may significantly improve outcomes. Methods: This cross-sectional study enrolled 486 participants distributed among 3 groups: F1 to F3 = 184, F4 = 183, and hepatocellular carcinoma = 119. Liver fibrosis staging was performed using FibroScan, while imaging features were used for hepatocellular carcinoma detection. Epithelial membrane antigen and cytokeratin-1 levels in serum were quantified through Western blot and ELISA, respectively. Results: Patients diagnosed with hepatocellular carcinoma exhibited significantly elevated levels of epithelial membrane antigen and cytokeratin-1 compared to non-hepatocellular carcinoma patients, with a highly significant statistical difference (P < .0001). Epithelial membrane antigen demonstrated diagnostic performance with an area under the curve of 0.75, a sensitivity of 69.0%, and a specificity of 68.5%. Cytokeratin-1 for the identification of hepatocellular carcinoma showed a sensitivity of 79.0% and a specificity of 81.4%, resulting in an area under the curve of 0.87. The developed HCC-Check, which incorporates epithelial membrane antigen, cytokeratin-1, albumin, and alpha-fetoprotein, displayed a higher area under the curve of 0.95 to identify hepatocellular carcinoma, with a sensitivity of 89.8% and a specificity of 83.9%. Notably, HCC-Check values exceeding 2.57 substantially increased the likelihood of hepatocellular carcinoma, with an estimated odds ratio of 50.65, indicating a higher susceptibility to hepatocellular carcinoma development than those with lower values. The HCC-Check diagnostic test exhibited high precision in identifying patients with hepatocellular carcinoma, particularly those with small tumor sizes (<5 cm) and a single nodule, as reflected in area under the curve values of 0.92 and 0.85, respectively. HCC-Check was then applied to the validation study to test its accuracy and reproducibility, showing superior area under the curves for identifying different stages of hepatocellular carcinoma. These outcomes underscore the effectiveness of the test in the early detection of hepatocellular carcinoma. Conclusion: The HCC-Check test presents a highly accurate diagnostic method for detecting hepatocellular carcinoma in its early stages.

HDLBP-stabilized lncFAL inhibits ferroptosis vulnerability by diminishing Trim69-dependent FSP1 degradation in hepatocellular carcinoma

Recent studies have suggested that exploring the potential mechanisms regulating ferroptosis vulnerability may contribute to improving the systemic therapeutic efficacy in HCC. High-density lipoprotein-binding protein (HDLBP), the largest RNA-binding protein, is an important transporter that protects cells from overaccumulation of cholesterol, but few studies have elucidated the role of HDLBP in the regulation of ferroptosis vulnerability in HCC. Our study suggests that HDLBP was markedly elevated in HCC compared with noncancerous liver tissues and that this elevation inhibited the ferroptosis vulnerability of HCC. Further experiments revealed that HDLBP bound to and stabilized the long noncoding RNA lncFAL (ferroptosis-associated lncRNA), which is derived from the plexin B2 gene. Moreover, our study suggests that the splicing of lncFAL was increased by YTH N6-methyladenosine (m6A) RNA-binding protein 2 (YTHDF2) in a m6A-dependent manner. Although HDLBP or lncFAL could not regulate the GPX4 antioxidant signalling pathway, lncFAL reduced ferroptosis vulnerability by directly binding to ferroptosis suppressor protein 1 (FSP1) and competitively abolishing Trim69-dependent FSP1 polyubiquitination degradation. More importantly, FSP1 inhibition promoted the antitumour activity of ferroptosis inducers both in vitro and in vivo. Collectively, our results provide a clinically promising demonstration that HDLBP stabilizes lncFAL, which mediates a FSP1-dependent anti-ferroptosis mechanism in HCC. These results support the enormous potential of disrupting FSP1 as a promising therapeutic approach for HCC patients with high HDLBP or lncFAL expression.

A novel medication decision gene signature predicts response to individualized therapy and prognosis outcomes in hepatocellular carcinoma patients

Molecular targeted therapy has shown potential in hepatocellular carcinoma (HCC) patients, and immunotherapy applications are developing rapidly. However, clinical guidance for making individualized therapy decisions for HCC patients remains lacking. MDH (Medication Decision in HCC) gene signatures comprising 70 genes were screened using transcriptomic data from multikinase inhibitor (TKI)-resistant HCC cells and HCC patient-derived xenograft model (PDX) models. Four MDH subtypes with distinct biological and clinical characteristics were defined by unsupervised cluster analysis of HCC data from The Cancer Genome Atlas (TCGA) database. To facilitate individualized and reasonable clinical guidance for each HCC patient, we constructed the MDH score. Comprehensive analysis suggested high MDH scores were associated with TKI resistance, a high proportion of stromal cell infiltration and poor survival outcomes. We recommend concomitant stromal activity intervention and immunotherapy for this type of HCC. Moreover, low MDH scores indicate TKI sensitivity, and a combination of targeted and immunotherapy is recommended. The nomogram constructed by iteration least absolute shrinkage and selection operator (LASSO) Cox regression analysis successfully predicted 3- or 5-year survival outcomes and mortality risks of HCC patients. In conclusion, TKI resistance model-based MDH gene signatures provide novel insight into potential mechanisms of drug resistance and heterogeneity in HCC. Integrative analysis plus a simplified decision model may aid personalized treatment and prognostic assessment among HCC patients.

Online Diagnosis and Classification of CT Images Collected by Internet of Things Using Deep Learning

Deep learning technology has recently played an important role in image, language processing, and feature extraction. In the past disease diagnosis, most medical staff fixed the images together for observation and then combined with their own work experience to judge. The diagnosis results are subjective, time-consuming, and inefficient. In order to improve the efficiency of diagnosis, this paper applies the deep learning algorithm to the online diagnosis and classification of CT images. Based on this, in this paper, the deep learning algorithm is applied to CT image online diagnosis and classification. Based on a brief analysis of the current situation of CT image classification, this paper proposes to use the Internet of things technology to collect CT image information and establishes the Internet of things to collect the CT image model. In view of image classification and diagnosis, the convolution neural network algorithm in the deep learning algorithm is proposed to diagnose and classify CT images, and several factors affecting the accuracy of classification are proposed, including the convolution number and network layer number. Using the CT image of the hospital brain for simulation analysis, the simulation results confirm the effectiveness of the deep learning algorithm. With the increase of convolution and network layer and the decrease of compensation, the accuracy of image classification will decline. Using the maximum pool method, reducing the step size can improve the classification effect. Using relu function as the activation function can improve the classification accuracy. In the process of large data set processing, appropriately adding a network layer can improve classification accuracy. In the diagnosis and analysis of brain CT images, the overall classification accuracy is close to 70%, and in the diagnosis of tumor diseases, the accuracy is higher, up to 80%.

A SNARE Protein Identification Method Based on iLearnPlus to Efficiently Solve the Data Imbalance Problem

Machine learning has been widely used to solve complex problems in engineering applications and scientific fields, and many machine learning-based methods have achieved good results in different fields. SNAREs are key elements of membrane fusion and required for the fusion process of stable intermediates. They are also associated with the formation of some psychiatric disorders. This study processes the original sequence data with the synthetic minority oversampling technique (SMOTE) to solve the problem of data imbalance and produces the most suitable machine learning model with the iLearnPlus platform for the identification of SNARE proteins. Ultimately, a sensitivity of 66.67%, specificity of 93.63%, accuracy of 91.33%, and MCC of 0.528 were obtained in the cross-validation dataset, and a sensitivity of 66.67%, specificity of 93.63%, accuracy of 91.33%, and MCC of 0.528 were obtained in the independent dataset (the adaptive skip dipeptide composition descriptor was used for feature extraction, and LightGBM with proper parameters was used as the classifier). These results demonstrate that this combination can perform well in the classification of SNARE proteins and is superior to other methods.

Identify DNA-Binding Proteins Through the Extreme Gradient Boosting Algorithm

The exploration of DNA-binding proteins (DBPs) is an important aspect of studying biological life activities. Research on life activities requires the support of scientific research results on DBPs. The decline in many life activities is closely related to DBPs. Generally, the detection method for identifying DBPs is achieved through biochemical experiments. This method is inefficient and requires considerable manpower, material resources and time. At present, several computational approaches have been developed to detect DBPs, among which machine learning (ML) algorithm-based computational techniques have shown excellent performance. In our experiments, our method uses fewer features and simpler recognition methods than other methods and simultaneously obtains satisfactory results. First, we use six feature extraction methods to extract sequence features from the same group of DBPs. Then, this feature information is spliced together, and the data are standardized. Finally, the extreme gradient boosting (XGBoost) model is used to construct an effective predictive model. Compared with other excellent methods, our proposed method has achieved better results. The accuracy achieved by our method is 78.26% for PDB2272 and 85.48% for PDB186. The accuracy of the experimental results achieved by our strategy is similar to that of previous detection methods.

AOPM: Application of Antioxidant Protein Classification Model in Predicting the Composition of Antioxidant Drugs

Antioxidant proteins can not only balance the oxidative stress in the body, but are also an important component of antioxidant drugs. Accurate identification of antioxidant proteins is essential to help humans fight diseases and develop new drugs. In this paper, we developed a friendly method AOPM to identify antioxidant proteins. 188D and the Composition of k-spaced Amino Acid Pairs were adopted as the feature extraction method. In addition, the Max-Relevance-Max-Distance algorithm (MRMD) and random forest were the feature selection and classifier, respectively. We used 5-folds cross-validation and independent test dataset to evaluate our model. On the test dataset, AOPM presented a higher performance compared with the state-of-the-art methods. The sensitivity, specificity, accuracy, Matthew’s Correlation Coefficient and an Area Under the Curve reached 87.3, 94.2, 92.0%, 0.815 and 0.972, respectively. In addition, AOPM still has excellent performance in predicting the catalytic enzymes of antioxidant drugs. This work proved the feasibility of virtual drug screening based on sequence information and provided new ideas and solutions for drug development.

Roles of M6A Regulators in Hepatocellular Carcinoma: Promotion or Suppression

Hepatocellular carcinoma (HCC) is the sixth globally diagnosed cancer with a poor prognosis. Although the pathological factors of hepatocellular carcinoma are well elucidated, the underlying molecular mechanisms remain unclear. N6-methyladenosine (m6A) is an adenosine methylation occurring at the N6 site, which is the most prevalent modification of eukaryotic mRNA. Recent studies have shown that m6A can regulate gene expression, thus modulating the processes of cell self-renewal, differentiation, and apoptosis. The methyls in m6A are installed by methyltransferases ("writers"), removed by demethylases ("erasers") and recognized by m6A-binding proteins ("readers"). In this review, we discuss the roles of above regulators in the progression and prognosis of HCC, and summarize the clinical association between m6A modification and hepatocellular carcinoma, so as to provide more valuable information for clinical treatment.