Innovative Insights into Liver Cancer : Multi-Omics Reveals Critical Subtypes and Hub Genes
Cheng, Jin Yuan ; Liu, Zi ; Liu, Xin ; Kabir, Muhammad LU
and Qiu, Wang Ren
(2025)
In Current Bioinformatics
21(10).
- Abstract
Introduction/Objective: Hepatocellular carcinoma (HCC) is a highly heterogeneous malignant tumor, characterized by elevated mortality rates and poor diagnostic outcomes. Accurate identification of cancer subtypes is crucial for guiding personalized treatment and improving patient prognosis. Methods: A method for precisely identifying HCC subtypes by integrating multi-omics data was presented. This approach combines the GRACES dimensionality reduction technique with the hMKL subtype identification model to analyze data from 266 HCC patients. Results: We identified two subtypes more accurately, both significantly associated with overall survival. Their respective three-year mortality rates were 55.9% and 27.9%. Additionally, we observed... (More)
Introduction/Objective: Hepatocellular carcinoma (HCC) is a highly heterogeneous malignant tumor, characterized by elevated mortality rates and poor diagnostic outcomes. Accurate identification of cancer subtypes is crucial for guiding personalized treatment and improving patient prognosis. Methods: A method for precisely identifying HCC subtypes by integrating multi-omics data was presented. This approach combines the GRACES dimensionality reduction technique with the hMKL subtype identification model to analyze data from 266 HCC patients. Results: We identified two subtypes more accurately, both significantly associated with overall survival. Their respective three-year mortality rates were 55.9% and 27.9%. Additionally, we observed significant differences in the activity of five pathways between these two subtypes, along with notable variations in the abundance and status of seven types of immune cells. Through further determination of the PPI network and centrality indicators, 13 up-regulated hub genes and 14 down-regulated hub genes were identified. Discussion: Based on the above results, we compared and discussed the hub genes with the textual data, examined differences in gene upregulation and downregulation, and evaluated findings from other bioinformatics analyses to identify potential biomarkers. Conclusion: Limited research on ENPP3 and C3 in HCC suggests their potential as biomarkers. Additionally, low expression levels of PIK3R1, KDR, and CYP3A5, along with high expression levels of EGLN3 and EPO, may indicate a higher risk of liver cancer in patients. Single-gene survival analysis highlighted the significant impact of highly expressed genes on HCC prognosis, with PKM, RRM2, and EPO playing crucial roles in the risk scores.
(Less)
- author
- Cheng, Jin Yuan
; Liu, Zi
; Liu, Xin
; Kabir, Muhammad
LU
and Qiu, Wang Ren
- organization
- publishing date
- 2025-04-24
- type
- Contribution to journal
- publication status
- published
- subject
- keywords
- Feature selection, hepatocellular carcinoma, immunocell infiltration analysis, multi-core learning, multi-omics data, subtype recognition
- in
- Current Bioinformatics
- volume
- 21
- issue
- 10
- publisher
- Bentham Science Publishers
- external identifiers
-
- scopus:105007915385
- ISSN
- 1574-8936
- DOI
- 10.2174/0115748936365348250331112230
- language
- English
- LU publication?
- yes
- additional info
- Publisher Copyright: © 2025 Bentham Science Publishers.
- id
- ba42ef35-b0ab-40c4-8fb6-6c4bc3bec1d0
- date added to LUP
- 2026-01-19 14:31:19
- date last changed
- 2026-01-19 14:45:30
@article{ba42ef35-b0ab-40c4-8fb6-6c4bc3bec1d0,
abstract = {{<p>Introduction/Objective: Hepatocellular carcinoma (HCC) is a highly heterogeneous malignant tumor, characterized by elevated mortality rates and poor diagnostic outcomes. Accurate identification of cancer subtypes is crucial for guiding personalized treatment and improving patient prognosis. Methods: A method for precisely identifying HCC subtypes by integrating multi-omics data was presented. This approach combines the GRACES dimensionality reduction technique with the hMKL subtype identification model to analyze data from 266 HCC patients. Results: We identified two subtypes more accurately, both significantly associated with overall survival. Their respective three-year mortality rates were 55.9% and 27.9%. Additionally, we observed significant differences in the activity of five pathways between these two subtypes, along with notable variations in the abundance and status of seven types of immune cells. Through further determination of the PPI network and centrality indicators, 13 up-regulated hub genes and 14 down-regulated hub genes were identified. Discussion: Based on the above results, we compared and discussed the hub genes with the textual data, examined differences in gene upregulation and downregulation, and evaluated findings from other bioinformatics analyses to identify potential biomarkers. Conclusion: Limited research on ENPP3 and C3 in HCC suggests their potential as biomarkers. Additionally, low expression levels of PIK3R1, KDR, and CYP3A5, along with high expression levels of EGLN3 and EPO, may indicate a higher risk of liver cancer in patients. Single-gene survival analysis highlighted the significant impact of highly expressed genes on HCC prognosis, with PKM, RRM2, and EPO playing crucial roles in the risk scores.</p>}},
author = {{Cheng, Jin Yuan and Liu, Zi and Liu, Xin and Kabir, Muhammad and Qiu, Wang Ren}},
issn = {{1574-8936}},
keywords = {{Feature selection; hepatocellular carcinoma; immunocell infiltration analysis; multi-core learning; multi-omics data; subtype recognition}},
language = {{eng}},
month = {{04}},
number = {{10}},
publisher = {{Bentham Science Publishers}},
series = {{Current Bioinformatics}},
title = {{Innovative Insights into Liver Cancer : Multi-Omics Reveals Critical Subtypes and Hub Genes}},
url = {{http://dx.doi.org/10.2174/0115748936365348250331112230}},
doi = {{10.2174/0115748936365348250331112230}},
volume = {{21}},
year = {{2025}},
}