Metabolomic biomarkers for the diagnosis and post-transplant outcomes of AFP negative hepatocellular carcinoma

Unveiling Endogenous Serum Peptides as Potential Biomarkers for Hepatocellular Carcinoma in Patients with Liver Cirrhosis

Hepatocellular carcinoma (HCC) is a leading cause of cancer-related deaths worldwide, mainly associated with liver cirrhosis. Current diagnostic methods for HCC have limited sensitivity and specificity, highlighting the need for improved early detection and intervention. In this study, we used a comprehensive approach involving endogenous peptidome along with bioinformatics analysis to identify and evaluate potential biomarkers for HCC. Serum samples from 40 subjects, comprising 20 HCC cases and 20 patients with liver cirrhosis (CIRR), were analyzed. Among 2568 endogenous peptides, 67 showed significant differential expression between the HCC vs CIRR. Further analysis revealed three endogenous peptides (VMHEALHNHYTQKSLSLSPG, NRFTQKSLSLSPG, and SARQSTLDKEL) that showed far better performance compared to AFP in terms of area under the receiver operating characteristic curve (AUC), showcasing their potential as biomarkers for HCC. Additionally, endogenous peptide IAVEWESNGQPENNYKT that belongs to the precursor protein Immunoglobulin heavy constant gamma 4 was detected in 100% of the HCC group and completely absent in the CIRR group, suggesting a promising diagnostic biomarker. Gene ontology and pathway analysis revealed the potential involvement of these dysregulated peptides in HCC. These findings provide valuable insights into the molecular basis of HCC and may contribute to the development of improved diagnostic methods and therapeutic targets for HCC.

Clinical application value of long non-coding RNAs signatures of genomic instability in predicting prognosis of hepatocellular carcinoma

Hepatocellular carcinoma (HCC) presents challenges due to its high recurrence and metastasis rates and poor prognosis. While current clinical diagnostic and prognostic indicators exist, their accuracy remains imperfect due to their biological complexity. Therefore, there is a quest to identify improved biomarkers for HCC diagnosis and prognosis. By combining long non-coding RNA (lncRNA) expression and somatic mutations, Duan et al identified five representative lncRNAs from 88 lncRNAs related to genomic instability (GI), forming a GI-derived lncRNA signature (LncSig). This signature outperforms previously reported LncSig and TP53 mutations in predicting HCC prognosis. In this editorial, we comprehensively evaluate the clinical application value of such prognostic evaluation model based on sequencing technology in terms of cost, time, and practicability. Additionally, we provide an overview of various prognostic models for HCC, aiding in a comprehensive understanding of research progress in prognostic evaluation methods.

Biomarkers of chronic kidney disease in older individuals: navigating complexity in diagnosis

Chronic kidney disease (CKD) in older individuals is a matter of growing concern in the field of public health across the globe. Indeed, prevalence of kidney function impairment increases with advancing age and is often exacerbated by age-induced modifications of kidney function, presence of chronic diseases such as diabetes, hypertension, and cardiovascular disorders, and increased burden related to frailty, cognitive impairment and sarcopenia. Accurate assessment of CKD in older individuals is crucial for timely intervention and management and relies heavily on biomarkers for disease diagnosis and monitoring. However, the interpretation of these biomarkers in older patients may be complex due to interplays between CKD, aging, chronic diseases and geriatric syndromes. Biomarkers such as serum creatinine, estimated glomerular filtration rate (eGFR), and albuminuria can be significantly altered by systemic inflammation, metabolic changes, and medication use commonly seen in this population. To overcome the limitations of traditional biomarkers, several innovative proteins have been investigated as potential, in this review we aimed at consolidating the existing data concerning the geriatric aspects of CKD, describing the challenges and considerations in using traditional and innovative biomarkers to assess CKD in older patients, highlighting the need for integration of the clinical context to improve biomarkers' accuracy.

Metabolomic biomarkers in liquid biopsy: accurate cancer diagnosis and prognosis monitoring

Liquid biopsy, a novel detection method, has recently become an active research area in clinical cancer owing to its unique advantages. Studies on circulating free DNA, circulating tumor cells, and exosomes obtained by liquid biopsy have shown great advances and they have entered clinical practice as new cancer biomarkers. The metabolism of the body is dynamic as cancer originates and progresses. Metabolic abnormalities caused by cancer can be detected in the blood, sputum, urine, and other biological fluids via systemic or local circulation. A considerable number of recent studies have focused on the roles of metabolic molecules in cancer. The purpose of this review is to provide an overview of metabolic markers from various biological fluids in the latest clinical studies, which may contribute to cancer screening and diagnosis, differentiation of cancer typing, grading and staging, and prediction of therapeutic response and prognosis.

Biomarker Discovery for Hepatocellular Carcinoma in Patients with Liver Cirrhosis Using Untargeted Metabolomics and Lipidomics Studies

Hepatocellular carcinoma (HCC), the most prevalent form of liver cancer, is the third leading cause of mortality globally. Patients with HCC have a poor prognosis due to the fact that the emergence of symptoms typically occurs at a late stage of the disease. In addition, conventional biomarkers perform suboptimally when identifying HCC in its early stages, heightening the need for the identification of new and more effective biomarkers. Using metabolomics and lipidomics approaches, this study aims to identify serum biomarkers for identification of HCC in patients with liver cirrhosis (LC). Serum samples from 20 HCC cases and 20 patients with LC were analyzed using ultra-high-performance liquid chromatography-Q Exactive mass spectrometry (UHPLC-Q-Exactive-MS). Metabolites and lipids that are significantly altered between HCC cases and patients with LC were identified. These include organic acids, amino acids, TCA cycle intermediates, fatty acids, bile acids, glycerophospholipids, sphingolipids, and glycerolipids. The most significant variability was observed in the concentrations of bile acids, fatty acids, and glycerophospholipids. In the context of HCC cases, there was a notable increase in the levels of phosphatidylethanolamine and triglycerides, but the levels of fatty acids and phosphatidylcholine exhibited a substantial decrease. In addition, it was observed that all of the identified metabolites exhibited a superior area under the receiver operating characteristic (ROC) curve in comparison to alpha-fetoprotein (AFP). The pathway analysis of these metabolites revealed fatty acid, lipid, and energy metabolism as the most impacted pathways. Putative biomarkers identified in this study will be validated in future studies via targeted quantification.

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.