Evidence map›Paper›PMID 40074769›Full record

ArticleScientific reports2025

Integrated analysis and experimental validation reveal the prognostic and immunological features associated with coagulation in hepatocellular carcinoma.

Guangzhen Qu, Kun Liu, Weiyu Xu, Dongming Li

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Article in Scientific reports, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

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2citing papers in PubMed
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3 · Its place in the literature

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2 citing papers in PubMed.

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4 · The record

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5 · Who and what money

Authors and funding

4 authors.

Guangzhen QuDepartment of Interventional Radiology, Beijing Chao-Yang Hospital Affiliated with Capital Medical University, Beijing, 100020, China.
Kun LiuDepartment of General Surgery, Beijing Friendship Hospital, Capital Medical University, Beijing, 100050, China.
Weiyu XuDepartment of General Surgery, Beijing Friendship Hospital, Capital Medical University, Beijing, 100050, China.
Dongming LiDepartment of Gastric and Colorectal Surgery, General Surgery Center, The First Hospital of Jilin University, Changchun, 130021, China. doctorlee2020@163.com.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Coagulation is intensively related to various tumors, which affects their progression and prognosis. However, research on the impact of coagulation-associated genes (CAGs) on hepatocellular carcinoma (HCC) occurrence, prognosis, and immune microenvironment is limited. Consequently, our research aims to uncover how CAGs affect the prognosis and immune microenvironments of HCC. We integrated gene expression data and clinical information from three datasets (GSE14520, GSE76427, and TCGA-LIHC). 281 CAGs were obtained from the coagulation-related pathway (hsa04610). We obtained three CAG patterns through a consensus clustering algorithm. Afterward, differential analyses of prognosis, biological processes, immune infiltration, and functional and pathway enrichment were conducted on the three CAG patterns. We intersected CAGs with differentially expressed genes in GSE76427 and then conducted Cox regression analysis to obtain the prognostic genes in HCC. Glycerol-3-phosphate dehydrogenase 2 (GPD2) was selected for further analyses. TCGA-LIHC samples with different GPD2 expression levels were analyzed for prognosis, DNA methylation, immune infiltration, and drug sensitivity. The expression level of GPD2 was verified through quantitative real-time PCR (qPCR) and immunohistochemistry. The wound-healing and Transwell assays were used to analyze the tumor cell migration and the Matrigel invasion and apoptosis assays were performed to determine cell invasion and apoptosis. Three CAG patterns were obtained through an unsupervised consensus clustering algorithm. CAGclusterA held the best prognosis compared to the other two clusters. The CAGclusterC was characterized by poor prognosis and abundant immune cell infiltration. The TCGA-LIHC dataset, as an internal validation, also yielded similar subtype classifications. Afterward, we identified the GPD2 gene, which significantly affected the prognosis of HCC and was positively correlated with the tumor progression. The upregulation of GPD2 expression was closely related to tumorigenic signatures and immune escape. The qPCR confirmed the upregulation of GPD2 expression in HCC tumor cell lines, compared to normal liver cell lines. Immunohistochemical staining confirmed the high expression of GPD2 in HCC tumor tissues compared to normal tissues. Regulating the expression level of GPD2 can inhibit the proliferation, migration, invasion, and induce apoptosis of HCC cells. Our study comprehensively elucidated the coagulation characteristics in HCC and identified a promising oncogenic gene GPD2. Exploring targeted strategies based on coagulation-related characteristics and biomarkers may shed light on HCC treatment.

Indexed as

Blood CoagulationCarcinoma, HepatocellularLiver NeoplasmsApoptosisBiomarkers, TumorCell Line, TumorCell MovementCluster AnalysisDatabases, GeneticDNA MethylationGene Expression ProfilingGene Expression Regulation, NeoplasticHep G2 CellsHumansPrognosisProportional Hazards ModelsBiomarkers, TumorCoagulation-associated genesConsensus clustering algorithmGPD2Hepatocellular carcinomaImmune infiltration

Identifiers

PMID40074769
PMCPMC11904193

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