Identification of the mitophagy-related diagnostic biomarkers in hepatocellular carcinoma based on machine learning algorithm and construction of prognostic model

Machine Learning-Based Pathomics Model Predicts Angiopoietin-2 Expression and Prognosis in Hepatocellular Carcinoma

Angiopoietin-2 (ANGPT2) shows promise as prognostic marker and therapeutic target in hepatocellular carcinoma (HCC). However, assessing ANGPT2 expression and prognostic potential using histopathology images viewed with naked eye is challenging. Herein, machine learning was employed to develop a pathomics model for analyzing histopathology images to predict ANGPT2 status. HCC cases obtained from The Cancer Genome Atlas (TCGA-HCC; n = 267) were randomly assigned to the training or testing set, and cases from a single center were employed as a validation set (n = 91). In the TCGA-HCC cohort, the group with high ANGPT2 expression had a significantly lower overall survival compared with the group with low ANGPT2. Histopathologic features in the training set were extracted, screened, and incorporated into a gradient-boosting machine model that generated a pathomics score, which successfully predicted ANGPT2 expression in the three data sets and showed remarkable risk stratification for overall survival in both the TCGA-HCC (P < 0.0001) and single-center cohorts (P = 0.001). Multivariate analysis suggested that the pathomics score could serve as a predictor of prognosis (P < 0.001). Bioinformatics analysis illustrated a distinction in tumor growth and development related gene-enriched pathways, vascular endothelial growth factor-related gene expression, and immune cell infiltration between high and low pathomics scores. This study indicates that the use of histopathology image features can enhance the prediction of molecular status and prognosis in HCC. The integration of image features with machine learning may improve prognosis prediction in HCC.

Machine-Learning-Based Identification of Key Feature RNA-Signature Linked to Diagnosis of Hepatocellular Carcinoma

Background

Hepatocellular carcinoma (HCC) is the third prime cause of malignancy-related mortality worldwide. Early and accurate identification of HCC is crucial for good prognosis, efficacy of therapy, and survival rates of the patients. We aimed to develop a machine-learning model incorporating differentially expressed RNA signatures with laboratory parameters to construct an RNA signature-based diagnostic model for HCC.

Methods

We have used five classifiers (KNN, RF, SVM, LGBM, and DNNs) to predict the liver disease (HCC). The classifiers were trained on 187 samples and then tested on 80 samples. The model included 22 features (age, sex, smoking, cirrhosis, non-cirrhosis, albumin, ALT, AST bilirubin (total and direct), INR, AFP, HBV Ag, HCV Abs, RQmiR-1298, RQmiR-1262, RQmiR-106b-3p, RQmRNARAB11A, and RQSTAT1, RQmRNAATG12, RQLnc-WRAP53, RQLncRNA- RP11-513I15.6).

Results

LGBM achieved the highest accuracy of 98.75% in predicting HCC among all models surpassing Random Forest (96.25%), DNN (91.25%), SVC (88.75%), and KNN (87.50%).

Conclusion

Our machine-learning model incorporating the expression data of RAB11A/STAT1/ATG12/miR-1262/miR-1298/miR-106b-3p/lncRNA-RP11-513I15.6/lncRNA-WRAP53 signature and clinical data represents a potential novel diagnostic model for HCC.

Identification and functional analysis of lactic acid metabolism-related differentially expressed genes in hepatocellular carcinoma

Background: Hepatocellular carcinoma (HCC) is a malignant tumor with high morbidity and mortality rate that seriously threatens human health. We aimed to investigate the expression, prognostic value, and immune cell infiltration of lactic acid metabolism-related genes (LAMRGs) in HCC using bioinformatics. Methods: The HCC database (The Cancer Genome Atlas-Liver Hepatocellular Carcinoma) was downloaded from the Cancer Genome Atlas (TCGA). Differentially expressed genes (DEGs) between normal and tumor groups were identified. The LAMRGs were obtained from literature and GeneCards and MSigDB databases. Lactic acid metabolism-related differentially expressed genes (LAMRDEGs) in HCC were screened from the DEGs and LAMRGs. Functional enrichment analyses of the screened LAMRDEGs were further conducted using Gene Ontology (GO) analysis, Kyoto Encyclopedia of Genes and Genomes (KEGG) analysis, and Gene Set Enrichment Analysis (GSEA). The genes were used in multivariate Cox regression and least absolute shrinkage and selection operator (LASSO) analyses to construct a prognostic model. Then, a protein-protein interaction network was constructed using STRING and CTD databases. Furthermore, the CIBERSORTx online database was used to assess the relationship between immune cell infiltration and hub genes. Results: Twenty-eight lactic acid metabolism-related differentially expressed genes (LAMRDEGs) were identified. The GO and KEGG analyses showed that the LAMRDEGs were related to the prognosis of HCC. The GSEA indicated that the LAMRDEGs were significantly enriched in tumor related pathways. In the multivariate Cox regression analysis, 14 key genes (E2F1, SERPINE1, GYS2, SPP1, PCK1, CCNB1, CYP2C9, IGFBP3, KDM8, RCAN1, ALPL, FBP1, NQO1, and LCAT) were found to be independent prognostic factors of HCC. Finally, the LASSO and Cox regression analyses showed that six key genes (SERPINE1, SPP1, CCNB1, CYP2C9, NQO1, and LCAT) were associated with HCC prognosis. Moreover, the correlation analyses revealed that the expression of the six key genes were associated with immune infiltrates of HCC. Conclusion: The LAMRDEGs can predict the prognosis and may be associated with immune cells infiltration in patients with HCC. These genes might be the promising biomarkers for the prognosis and treatment of HCC.

Molecular mechanisms of TACE refractoriness: Directions for improvement of the TACE procedure

Transcatheter arterial chemoembolisation (TACE) is the standard of care for intermediate-stage hepatocellular carcinoma and selected patients with advanced hepatocellular carcinoma. However, TACE does not achieve a satisfactory objective response rate, and the concept of TACE refractoriness has been proposed to identify patients who do not fully benefit from TACE. Moreover, repeated TACE is necessary to obtain an optimal and sustained anti-tumour response, which may damage the patient's liver function. Therefore, studies have recently been performed to improve the effectiveness of TACE. In this review, we summarise the detailed molecular mechanisms associated with TACE responsiveness and relapse after this treatment to provide more effective targets for adjuvant therapy while helping to improve TACE regimens.