Abstract
AbstractIntroductionHepatocellular carcinoma (HCC), the most common form of liver cancer, is a global health concern and a leading cause of cancer-related deaths. HCC accounts for a significant portion of liver cancers and has low survival rates of 5% to 30%, especially for HCC patients with a survival rate of 15%. Early detection is challenging due to the absence of symptoms in the early stages. The complexity and molecular diversity of HCC contribute to its poor prognosis. Understanding its molecular subtypes and mechanisms is crucial for improved management.MethodsThe study utilized publicly available data to investigate the potential diagnostic and prognostic biomarkers for hepatocellular carcinoma (HCC) based on their transcript per million (TPM) expression levels. A dataset of 407 HCC patient profiles was analyzed for survival trends and gene expression patterns.ResultsThrough a comprehensive approach, over 900 potential prognostic candidates were identified. Further analysis narrowed down 647 prognostic and diagnostic candidate biomarkers. The study also explored the role of the CmPn signaling network in HCC, reaffirming that its components could act as prognostic markers. Additionally, the study-utilized machine learning to discover 102 transcription factors (TFs) associated with HCC, from candidate biomarkers.ConclusionsThe findings provide insights into the molecular basis of HCC and offer potential avenues for improved diagnosis, treatment, and patient outcomes. The expanded prognostic biomarker pool aids in pinpointing HCC-specific grading and staging biomarkers, facilitating targeted therapies for improved patient outcomes and survival rates
Publisher
Cold Spring Harbor Laboratory