	US 12,469,607 B2
	Method for constructing prognostic model of hepatoma and application thereof
Fubing Wang, Wuhan (CN); Fei Long, Wuhan (CN); and Wei Zhong, Wuhan (CN)
Assigned to WUHAN UNIVERSITY, Wuhan (CN)
Filed by WUHAN UNIVERSITY, Wuhan (CN)
Filed on Jan. 23, 2025, as Appl. No. 19/035,563.
Claims priority of application No. 202410345247.0 (CN), filed on Mar. 25, 2024.
Prior Publication US 2025/0299833 A1, Sep. 25, 2025
Int. Cl. G16H 50/50 (2018.01); G16B 5/20 (2019.01); G16B 20/00 (2019.01); G16B 40/20 (2019.01); G16H 20/10 (2018.01); G16H 50/20 (2018.01)

CPC G16H 50/50 (2018.01) [G16B 5/20 (2019.02); G16B 20/00 (2019.02); G16B 40/20 (2019.02); G16H 20/10 (2018.01); G16H 50/20 (2018.01)]

6 Claims

1. A method for constructing a prognostic model of hepatoma, the method comprising the following steps:

(1) obtaining and identifying fibroblasts with high FAP expression, specifically as follows:

(1.1) performing a subgroup classification of hepatoma single-cell data in a collected and integrated discovery cohort using R package seurat to extract fibroblast subgroups with high COL1A1 expression; and

(1.2) further subdividing the fibroblast subgroups with high COL1A1 expression obtained in the Step (1.1) to identify fibroblasts with high FAP expression;

(2) obtaining and identifying tumor-associated macrophages (TAMs), specifically as follows;

(2.1) performing a subgroup classification of hepatoma single-cell data in a collected and integrated discovery cohort using R package seurat to extract macrophage subgroups with high CD68 expression;

(2.2) further subdividing the extracted macrophage subgroups with high CD68 expression;

(2.3) performing an OR analysis to assess the enrichment preference of different cell types in different samples and screening for cell types highly enriched in hepatoma samples;

(2.4) reconstructing a macrophage differentiation process using an RNA rate analysis in another collected and integrated single-cell validation cohort, and identifying the type of macrophages that terminally differentiate with tumor development as TAMs; the macrophage type being high Disabled-2 (DAB2) expression or high Secreted Phosphoprotein 1 (SPP1) expression; and

(3) co-localizing the fibroblasts with high FAP expression obtained in the Step (1) and the TAMs obtained in the Step (2);

(4) communicating and analysing the fibroblasts with high FAP expression after the localization in the Step (3) with TAMs to obtain CCC ligand-receptor genes, specifically as follows;

(4.1) identifying the CCC ligand-receptors between TAMs and fibroblasts with high FAP expression using R package NicheNet, and identifying the target genes of fibroblasts with high FAP expression affected by TAMs;

(4.2) analysing, the function of target genes by g: Profiler to understand the main functional regulation of TAMs on fibroblasts with high FAP expression;

(4.3) scoring a tissue sequencing sample based on the activity of CCC ligand-receptors using the ssGSEA algorithm, and the scoring result being LRscore;

(4.4) identifying a cutoff value of optimal survival probability grouping of samples by R package survminer, and testing the predictive effect of LRscore on an overall survival probability of patients by Kaplan-Meier curves, wherein if log-rank p<0.05, the test standards are satisfied;

(4.5) on the basis of the Step (4.4), testing the predictive effect of LRscore on immunotherapy response in patients with hepatoma by box plots, wherein if wilcox.testp<0.05, the test standards are satisfied; and

(4.6) obtaining CCC ligand-receptor genes based on the result of the Step (4.5);

(5) screening the CCC ligand-receptor genes obtained in the Step (4) based on machine learning to obtain key CCC ligand-receptor genes, the key CCC ligand-receptor genes are CD320, GPC1, ITGA5 and ENG;

and;

(6) constructing a prognostic model of hepatoma according to the key CCC ligand-receptor genes obtained in the Step (5);

(6.1) based on the modeling genes determined in the Step (5), constructing a multivariate Cox model in the TCGA and GEO hepatoma cohorts, calculating model scores, and the model score being calculated according to the following equation:

Coxmodel score=Σ_iExpression (mRNA)_i*Coefficent (mRNA)_i

where i is the key gene screened;

(6.2) using KM curves to evaluate the survival prediction performance of the model constructed in the Step (6.1);

(6.3) predicting patients' response to immunotherapy based on Coxmodel score, wherein non-responders exhibit higher Coxmodel scores than responders, and patients are classified into a non-responder group and a responder group according to the Coxmodel scores, and the method further comprises:

administering a first treatment regimen comprising an immunotherapy to a patient classified into the responder group.