%0 Journal Article
%T SIMarker: Cellular similarity detection and its application to diagnosis and prognosis of liver cancer.
%A Tong M
%A Luo S
%A Gu L
%A Wang X
%A Zhang Z
%A Liang C
%A Huang H
%A Lin Y
%A Huang J
%J Comput Biol Med
%V 171
%N 0
%D 2024 Mar 14
%M 38368754
%F 6.698
%R 10.1016/j.compbiomed.2024.108113
%X <B>BACKGROUND: </B>The emergence of single-cell technology offers a unique opportunity to explore cellular similarity and heterogeneity between precancerous diseases and solid tumors. However, there is lacking a systematic study for identifying and characterizing similarities at single-cell resolution.<BR><B>METHODS: </B>We developed SIMarker, a computational framework to detect cellular similarities between precancerous diseases and solid tumors based on gene expression at single-cell resolution. Taking hepatocellular carcinoma (HCC) as a case study, we quantified the cellular and molecular connections between HCC and cirrhosis. Core analysis modules of SIMarker is publicly available at https://github.com/xmuhuanglab/SIMarker ("SIM" means "similarity" and "Marker" means "biomarkers).<BR><B>RESULTS: </B>We found PGA5+ hepatocytes in HCC showed cirrhosis-like characteristics, including similar transcriptional programs and gene regulatory networks. Consequently, the genes constituting the gene expression program of these cirrhosis-like subpopulations were designated as cirrhosis-like signatures (CLS). Strikingly, our utilization of CLS enabled the development of diagnosis and prognosis biomarkers based on within-sample relative expression orderings of gene pairs. These biomarkers achieved high precision and concordance compared with previous studies.<BR><B>CONCLUSIONS: </B>Our work provides a systematic method to investigate the clinical translational significance of cellular similarities between HCC and cirrhosis, which opens avenues for identifying similar paradigms in other categories of cancers and diseases.