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

Author:

Li Weimin,Liu Jixing,Zhu Wenjuan,Jin Xiaoxin,Yang Zhi,Gao Wenzhe,Sun Jichun,Zhu Hongwei

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 redundancy (mRMR) to get a top 50 signature gene feature. For further analysis, the incremental feature selection (IFS) method and leave-one-out cross validation (LOOCV) were conducted to build an optimal classification model and to extract 21 potentially essential biomarkers for HCC cells. Our results provided new insights into HCC pathogenesis that might be valuable for HCC diagnosis and therapy.

Publisher

Frontiers Media SA

Subject

Genetics (clinical),Genetics,Molecular Medicine

Cited by 1 articles. 订阅此论文施引文献 订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献

1. Identification and Validation of the Most Prevalent Biomarkers in Hepatocellular Carcinoma;2023 26th International Conference on Computer and Information Technology (ICCIT);2023-12-13

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