Construction and verification of a prognostic model combining anoikis and immune prognostic signatures for primary liver cancer

doi:10.12122/j.issn.1673-4254.2025.09.16

Abstract

Abstract:

Objective To establish a prognostic model for primary liver cancer (PLC) using bioinformatics methods. Methods Based on the data from 404 patients in the Cancer Genome Atlas (TCGA) database, we constructed a prognostic model integrating the differentially expressed genes, anoikis, and immune-related genes (DAIs) using univariate Cox regression and the LASSO-Cox approach. The predictive ability of the model was evaluated using Kaplan-Meier method and receiver-operating characteristic curves, and a nomogram was developed to facilitate its clinical applications. Gene set enrichment analysis (GSEA) was performed to explore the associated pathways and relationship between the DAIs and the tumor immune microenvironment, and the half-maximal inhibitory concentration (IC₅₀) of liver cancer drugs was calculated using the "pRRophetic" R package. We also detected the expression of SEMA7A in paired tumor and adjacent tissues from liver cancer patients. Results We constructed and validated a prognostic model based on 7 DAIs (NR4A3, SEMA7A, IL11, AR, BIRC5, EGF, and SPP1), and obtained consistent results in both the TCGA training cohort and GEO validation cohort (GSE14520), where the patients in the low-risk group were characterized by more favorable clinical outcomes and immune status. By integrating this prognostic signature with clinical information, a composite nomogram was generated. Somatic mutation analysis showed that TTN, TP53, and CTNNB1 mutations accounted for the largest proportion of total mutations, and the patients in the low-risk-low-TMB group had higher survival rate. Drug sensitivity analysis revealed differences in sensitivity to chemotherapeutic agents between high- and low-risk groups and between TP53 mutations and non-mutations. In clinical tissue specimens, SEMA7A expression was significantly higher in liver cancer tissues than in the adjacent tissues. Conclusions We established a new prognostic model based on DAIs for predicting clinical outcomes and therapeutic response of patients with primary liver cancer.

Key words: liver cancer, anoikis, immune-related genes, prognosis model, bioinformatics

Ying WANG, Jing LI, Yidi WANG, Mingyu HUA, Weibin HU, Xiaozhi ZHANG. Construction and verification of a prognostic model combining anoikis and immune prognostic signatures for primary liver cancer[J]. Journal of Southern Medical University, 2025, 45(9): 1967-1979.

Figures/Tables 9

Fig.1 Flowchart of this study.

Tab.1 Clinical data of liver cancer patients in TCGA and GEO databases

Clinical	Total (n=576)	TCGA (n=351)	GEO (n=225)
Gender [n (%)]
Female	149 (25.87)	119 (33.90)	30 (13.33)
Male	427 (74.13)	232 (66.10)	195 (86.67)
Age (mean year)	55.78	59.06	50.66
Age (median year)	56 (16-82)	61 (16-82)	50 (21-77)
Stage [n (%)]
I	273 (47.40)	177 (50.43)	96 (42.67)
II	163 (28.30)	85 (24.22)	78 (34.67)
III	7 (1.22)	4 (1.14)	3 (1.33)
IIIA	89 (15.45)	60 (17.09)	29 (12.89)
IIIB	23 (3.99)	8 (2.28)	15 (6.67)
IIIC	13 (2.26)	9 (2.56)	4 (1.78)
IV	2 (0.35)	2 (0.57)	0 (0.00)
IVA	2 (0.35)	2 (0.57)	0 (0.00)
IVB	4 (0.69)	4 (1.14)	0 (0.00)

Fig.3 Identification of the DAIs. A: Forest maps of the predictive power of 20 characteristic genes. B: LASSO regression analysis based on DAIs. C: LASSO coefficient of DAIs gene in PLC. D: LASSO gene coefficient histogram.

Fig.4 Validation of risk signature. A, B: Prediction of time-dependent ROC for 1, 3, and 5-year OS in TCGA cohort and GEO cohort. C, D: Kaplan-Meier survival curves showed that there were differences in OS between high and low TCGA and GEO groups. E, F: Risk scores, survival status and heat maps of 7 DAIs between high and low groups of TCGA (E) and GSE14520 (F). G, H: Multivariate cox regression analysis of TCGA (G) and GEO (H) risk scores and clinical data. I: Nomogram of clinical data and risk groups of TCGA. J: Standard curves showing good nomogram accuracy.

Fig. 6 Immune status and prediction of response to immunotherapy. A-D: Heatmaps and enrichment scores of 22 immune cells in TCGA (A, B) and GEO (C, D) high-risk and low-risk groups. E-L: TIDE score, rejection score, dysfunction score and MSI score between TCGA (E-H) and GEO (I-L) groups. *P<0.05, **P<0.01, ***P<0.001, ****P<0.0001).

Fig.7 Somatic mutations and TMB in risk signature. A, B: The waterfall map shows the top 20 genes with the highest mutation frequency in the high-risk group (A) and low-risk group (B) of TCGA. C, D: The bar chart shows the top 10 high mutation pathways in the high-risk (C) and low-risk (D) TCGA groups. E: Box chart showing TMB score comparison between high- and low-risk groups. F: Kaplan-Meier survival curves show OS differences between groups classified according to TMB and TCGA risk.

Fig. 8 Drug sensitivity analysis. A-J: Comparison of half maximum inhibitory concentrations (IC50) of docetaxel, adriamycin, cisplatin and bleomycin in high and low risk TCGA groups (A-E) and TP53 mutant and non-mutant groups (F-J).

Fig.9 Immunohistochemistry and HPA website for verifying the expression of DAIs in primary liver cancer and adjacent tissues (Original magnification: ×100). A: Representative immunohistochemical images of SEMA7A in PLC tissues and adjacent tissues. B: Representative immunohistochemical images of NR4A3, AR, BIRC5, SPP1 and SEMA7A in PLC tissues and normal tissues on the HPA website.

Tab.2 Expression of SEMA7A in liver cancer tissues and adjacent normal liver tissues (n)

Gene	Expression level	Tissue		χ²	P
Gene	Expression level	Cancer	Normal	χ²	P
SEMA7A	High expression	28	7	7.72	<0.01
SEMA7A	Low expression	6	9	7.72	<0.01

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