The Use of Machine Learning to Create a Risk Score to Predict Survival in Patients with Hepatocellular Carcinoma: A TCGA Cohort Analysis

Factors related to tumor response rate from TCGA three omics data-variants, expression, methylation

The Cancer Genome Atlas (TCGA) and its patient-derived multi-omics datasets have been the backbone of cancer research, and with novel approaches, it continues to shed new insight into the disease. In this study, we delved into a method of multi-omics integration of patient datasets and the association of biological pathways related to the disease. First, across thirty-three types of cancer present in TCGA, we merged genomic mutations and drug response datasets and filtered for the viable variant-drug response combinations available in TCGA, containing more than three samples for each drug response label with RNA sequencing (RNA-seq) and genomic methylation data available for each patient. We identified two distinct combinations in TCGA, one being pancreatic adenocarcinoma patients with/without rs121913529 variant in KRAS gene treated with gemcitabine, and the other low-grade glioma with/without rs121913500 variant in IDH1 gene administered with temozolomide. In these two groups, different patterns of gene expression were observed in the pathways often associated with cancer progression, such as mTOR and PDGF between the patients with complete response and progressive disease. Our result will provide yet another example of the relevance of these biological pathways in cancer drug response and a way for multi-omics integration in cancer datasets.

The Role of Artificial Intelligence in the Detection and Implementation of Biomarkers for Hepatocellular Carcinoma: Outlook and Opportunities

Liver cancer is a leading cause of cancer-related death worldwide, and its early detection and treatment are crucial for improving morbidity and mortality. Biomarkers have the potential to facilitate the early diagnosis and management of liver cancer, but identifying and implementing effective biomarkers remains a major challenge. In recent years, artificial intelligence has emerged as a promising tool in the cancer sphere, and recent literature suggests that it is very promising in facilitating biomarker use in liver cancer. This review provides an overview of the status of AI-based biomarker research in liver cancer, with a focus on the detection and implementation of biomarkers for risk prediction, diagnosis, staging, prognostication, prediction of treatment response, and recurrence of liver cancers.

MARCKS is a New Prognostic Biomarker in Hepatocellular Carcinoma

Background

Hepatocellular carcinoma (HCC) is one of the most common type of cancers, but there is still a lack of known biomarkers for the effective diagnosis or prognosis of HCC. Myristoylated alanine-rich C-kinase substrate (MARCKS) is a substrate of protein kinase C, which was located in the cell plasma membrane. The purpose of our study was to evaluate the role of MARCKS in HCC.

Methods

The role of MARCKS in HCC was explored by bioinformatics and experiment.

Results

We demonstrated that MARCKS expression was significantly elevated in HCC datasets of TCGA. MARCKS was up-regulated in tumor sample in HCC. Functional enrichment indicated that MARCKS-related differentially expressed genes (DEGs) were mainly enriched in cell junction tissue, response to growth factors and cell population proliferation. Tumor and ECM-receptor interactions related pathways were enriched by the KEGG. MARCKS expression in HCC patients was higher in females, younger individuals, and those at worse clinical stages. Cox regression analysis showed that MARCKS expression was a risk factor for overall survival and disease-specific survival of patients.

Conclusion

MARCKS was up-regulated in HCC, may play a crucial role in HCCs, and has prognostic value for clinical outcomes.

Potential Predictive Value of miR-125b-5p, miR-155-5p and Their Target Genes in the Course of COVID-19

Purpose

This study aimed to provide new biomarkers for predicting the disease course of COVID-19 by analyzing the dynamic changes of microRNA (miRNA) and its target gene expression in the serum of COVID-19 patients at different stages.

Methods

Serum samples were collected from all COVID-19 patients at three time points: the acute stage, the turn-negative stage, and the recovery stage. The expression level of miRNA and the target mRNA was measured by Quantitative Real-Time Polymerase Chain Reaction (RT-qPCR). The classification tree model was established to predict the disease course, and the prediction efficiency of independent variables in the model was analyzed using the receiver operating characteristic (ROC) curve.

Results

The expression of miR-125b-5p and miR-155-5p was significantly up-regulated in the acute stage and gradually decreased in the turn-negative and recovery stages. The expression of the target genes CDH5, STAT3, and TRIM32 gradually down-regulated in the acute, turn-negative, and recovery stages. MiR-125b-5p, miR-155-5p, STAT3, and TRIM32 constituted a classification tree model with 100% accuracy of prediction and AUC >0.7 for identification and prediction in all stages.

Conclusion

MiR-125b-5p, miR-155-5p, STAT3, and TRIM32 could be useful biomarkers to predict the time nodes of the acute, turn-negative, and recovery stages of COVID-19.