A prognostic four-gene signature and a therapeutic strategy for hepatocellular carcinoma: Construction and analysis of a circRNA-mediated competing endogenous RNA network

Integrating molecular, biochemical, and immunohistochemical features as predictors of hepatocellular carcinoma drug response using machine-learning algorithms

Introduction

Liver cancer, particularly Hepatocellular carcinoma (HCC), remains a significant global health concern due to its high prevalence and heterogeneous nature. Despite the existence of approved drugs for HCC treatment, the scarcity of predictive biomarkers limits their effective utilization. Integrating diverse data types to revolutionize drug response prediction, ultimately enabling personalized HCC management.

Method

In this study, we developed multiple supervised machine learning models to predict treatment response. These models utilized classifiers such as logistic regression (LR), k-nearest neighbors (kNN), neural networks (NN), support vector machines (SVM), and random forests (RF) using a comprehensive set of molecular, biochemical, and immunohistochemical features as targets of three drugs: Pantoprazole, Cyanidin 3-glycoside (Cyan), and Hesperidin. A set of performance metrics for the complete and reduced models were reported including accuracy, precision, recall (sensitivity), specificity, and the Matthews Correlation Coefficient (MCC).

Results and Discussion

Notably, (NN) achieved the best prediction accuracy where the combined model using molecular and biochemical features exhibited exceptional predictive power, achieving solid accuracy of 0.9693 ∓ 0.0105 and average area under the ROC curve (AUC) of 0.94 ∓ 0.06 coming from three cross-validation iterations. Also, found seven molecular features, seven biochemical features, and one immunohistochemistry feature as promising biomarkers of treatment response. This comprehensive method has the potential to significantly advance personalized HCC therapy by allowing for more precise drug response estimation and assisting in the identification of effective treatment strategies.

Pharmacokinetics of aspirin: evaluating shortcomings in the literature

INTRODUCTION

Aspirin is known for its therapeutic benefits in preventing strokes and relieving pain. However, it is toxic to some individuals, and the biological mechanisms causing toxicity are unknown. Limited literature is available on the role of glycine conjugation as the principal pathway in aspirin detoxification. Previous studies have quantified this two-step enzyme reaction as a singular enzymatic process. Consequently, the individual contributions of these enzymes to the kinetics remain unclear.

AREAS COVERED

This review summarized the available information on the pharmacokinetics and detoxification of aspirin by the glycine conjugation pathway. Literature searches were conducted using Google Scholar and the academic journal databases accessible through the North-West University Library. Furthermore, the factors affecting interindividual variation in aspirin metabolism and what is known regarding aspirin toxicity were discussed.

EXPERT OPINION

The greatest drawback in understanding the pharmacokinetics of aspirin is the limited information available on the substrate preference of the xenobiotic ligase (ACSM) responsible for activating salicylate to salicyl-CoA. Furthermore, previous pharmacokinetic studies did not consider the contribution of other substrates from the diet or genetic variants, to the detoxification rate of glycine conjugation. Impaired glycine conjugation might contribute to adverse health effects seen in Reye's syndrome and cancer.

Establishing an oxidative stress mitochondria-related prognostic model in hepatocellular carcinoma based on multi-omics characteristics and machine learning computational framework

Hepatocellular carcinoma (HCC) has high incidence and mortality rates worldwide. Damaged mitochondria are characterized by the overproduction of reactive oxygen species (ROS), which can promote cancer development. The prognostic value of the interplay between mitochondrial function and oxidative stress in HCC requires further investigation. Gene expression data of HCC samples were collected from The Cancer Genome Atlas (TCGA), Gene Expression Omnibus (GEO) and International Cancer Genome Consortium (ICGC). We screened prognostic oxidative stress mitochondria-related (OSMT) genes at the bulk transcriptome level. Based on multiple machine learning algorithms, we constructed a consensus oxidative stress mitochondria-related signature (OSMTS), which contained 26 genes. In addition, we identified six of these genes as having a suitable prognostic value for OSMTS to reduce the difficulty of clinical application. Univariate and multivariate analyses verified the OSMTS as an independent prognostic factor for overall survival (OS) in HCC patients. The OSMTS-related nomogram demonstrated to be a powerful tool for the clinical diagnosis of HCC. We observed differences in biological function and immune cell infiltration in the tumor microenvironment between the high- and low-risk groups. The highest expression of the OSMTS was detected in hepatocytes at the single-cell transcriptome level. Hepatocytes in the high- and low-risk groups differed significantly in terms of biological function and intercellular communication. Moreover, at the spatial transcriptome level, high expression of OSMTS was mainly in regions enriched in hepatocytes and B cells. Potential drugs targeting specific risk subgroups were identified. Our study revealed that the OSMTS can serve as a promising tool for prognosis prediction and precise intervention in HCC patients.

Long Non-coding RNA 02298 Promotes the Malignancy of HCC by Targeting the miR-28-5p/CCDC6 Pathway

Hepatocellular carcinoma (HCC) is a malignancy characterized by a high fatality rate. Increasing evidence indicating that long non-coding RNAs (lncRNAs) play a regulatory role in hepatocellular carcinoma (HCC). Among them, the correlation between LINC02298 and HCC remains unknown. The expression and subcellular localization of LINC02298 in HCC tissues and cell lines were detected by real-time quantitative polymerase chain reaction (RT-qPCR). Furthermore, the correlation between the expression of LINC02298 and clinicopathological features of HCC patients was analyzed. The regulatory effects of LINC02298 in HCC were investigated using colony formation, cell count Kit-8(CCK8), Transwell, EDU, cell cycle and apoptosis analysis. In addition, the expression of EMT-related proteins were detected by western blotting. Dual-luciferase reporter, RT-qPCR and rescue assays were employed to validate the involvement of the LINC02298/miR-28-5p/CCDC6 axis in the progression of HCC. The up-regulation of LINC02298 was observed in hepatocellular carcinoma (HCC) tissues and cells, and it was found to be correlated with a negative prognosis in patients with HCC. Overexpression of LINC02298 enhanced the proliferation, migration, invasion, and induction of Epithelial-Mesenchymal Transition (EMT) while suppressing apoptosis in HCC cells. LINC02298 bind to miR-28-5p to regulate the expression of CCDC6. Inhibition of miR-28-5p saved the inhibitory effect of shLINC02298, and knockdown of CCDC6 also saved the inhibitory effect of miR-28-5p on HCC in vitro and in vivo. LINC02298 regulates the expression of CCDC6 by sponging of miR-28-5p, thereby facilitating the the malignancy and EMT of HCC.

Multidimensional computational study to understand non-coding RNA interactions in breast cancer metastasis

Metastasis is a major breast cancer hallmark due to which tumor cells tend to relocate to regional or distant organs from their organ of origin. This study is aimed to decipher the interaction among 113 differentially expressed genes, interacting non-coding RNAs and drugs (614 miRNAs, 220 lncRNAs and 3241 interacting drugs) associated with metastasis in breast cancer. For an extensive understanding of genetic interactions in the diseased state, a backbone gene co-expression network was constructed. Further, the mRNA-miRNA-lncRNA-drug interaction network was constructed to identify the top hub RNAs, significant cliques and topological parameters associated with differentially expressed genes. Then, the mRNAs from the top two subnetworks constructed are considered for transcription factor (TF) analysis. 39 interacting miRNAs and 1641 corresponding TFs for the eight mRNAs from the subnetworks are also utilized to construct an mRNA-miRNA-TF interaction network. TF analysis revealed two TFs (EST1 and SP1) from the cliques to be significant. TCGA expression analysis of miRNAs and lncRNAs as well as subclass-based and promoter methylation-based expression, oncoprint and survival analysis of the mRNAs are also done. Finally, functional enrichment of mRNAs is also performed. Significant cliques identified in the study can be utilized for identification of newer therapeutic interventions for breast cancer. This work will also help to gain a deeper insight into the complicated molecular intricacies to reveal the potential biomarkers involved with breast cancer progression in future.