Identification of biomarkers for hepatocellular carcinoma based on single cell sequencing and machine learning algorithms.

ABSTRACT: Hepatocellular carcinoma (HCC) remains one of the most lethal cancers around the world. Precision oncology will be crucial for further improving the prognosis of HCC patients. Compared with traditional bulk RNA-seq, single-cell RNA sequencing (scRNA-seq) enables the transcriptomes of a great deal of individual cells assayed in an unbiased manner, showing the potential to deeply reveal tumor heterogeneity. In this study, based on the scRNA-seq results of primary neoplastic cells and paired normal liver cells from eight HCC patients, a new strategy of machine learning algorithms was applied to screen core biomarkers that distinguished HCC tumor tissues from the adjacent normal liver. Expression profiles of HCC cells and normal liver cells were first analyzed by maximum relevance minimum redu

SUBMITTER: Li W 

PROVIDER: S-EPMC9638064 | biostudies-literature | 2022

REPOSITORIES: biostudies-literature