SFPQ Promotes Hepatocellular Carcinoma Progression by Affecting GTF2H3 Splicing Variants

Background: Hepatocellular carcinoma (HCC), the predominant type of liver cancer, exhibits a high mortality rate due to unclear molecular mechanisms and limited biomarkers. Accumulating evidence indicates that splicing factors (SFs) can promote tumor cell proliferation, invasion, metastasis, and drug resistance by altering the splicing patterns of target genes. However, the regulatory mechanisms underlying SF-mediated alternative splicing (AS) are highly intricate, and the specific functions and pathways of SFs in HCC initiation and progression remain to be further elucidated. Methods: First, Cox regression and LASSO regression analyses of splicing factors (SFs) were performed using the TCGA database, followed by validation using the ICGC dataset. The prognostic value of SFPQ was assessed with the Kaplan–Meier plotter. Next, the potential association of SFPQ with the immune microenvironment of HCC was explored using TIMER and CIBERSORT. In addition, we screened AS events associated with SFPQ expression in TCGA-LIHC, conducted GO/KEGG pathway enrichment analyses, and investigated the correlation between GTF2H3 percent spliced in index (PSI) values and clinicopathological features in HCC patients. Finally, the effect of targeting the exon 10- or exon 11-containing variants of GTF2H3 on HCC was examined at the cellular level. Results: LASSO regression analysis identified five key SFs significantly associated with HCC prognosis, including SFPQ, HTRA2, DAZAP1, PCBP2, and YBX1. Among these, SFPQ was determined as the most critical SF influencing HCC survival, and its overexpression was positively correlated with HCC progression. GO and KEGG analyses revealed that the pathways potentially associated with SFPQ include metabolic pathways, oxidative phosphorylation, mRNA surveillance pathway, and the PPAR signaling pathway. Furthermore, SFPQ expression was associated with the exclusion of exons 10 and 11 of the GTF2H3 gene. Computational analysis also suggested a potential link between SFPQ and immune cell infiltration. Targeting the exon 11-containing variant of GTF2H3 significantly reduced cell viability and migration of HCC cells. Conclusion: SFPQ expression is elevated in HCC and correlates with tumor aggressiveness and poor patient prognosis. Our findings suggest that SFPQ may influence HCC progression through its association with the GTF2H3 exon 10/11 ME event and the immune microenvironment. However, further experimental validation is required to confirm these mechanisms. Our findings suggest that SFPQ may serve as a promising biomarker and therapeutic candidate for HCC.

Authors

Institutions

Publication Details

Journal
Cancers
Published
2026-10-09
DOI
https://doi.org/10.3390/cancers18203258
Primary Topic
RNA Research and Splicing
Type
article
Field-Weighted Citation Impact
0.00
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

SFPQ Promotes Hepatocellular Carcinoma Progression by Affecting GTF2H3 Splicing Variants

Zhou zhihang, Siyuan Chen, Jian Gao, Xueying Chen et al.
Cancers
RNA Research and Splicing
article

SFPQ Promotes Hepatocellular Carcinoma Progression by Affecting GTF2H3 Splicing Variants

Zhou zhihang, Siyuan Chen, Jian Gao, Xueying Chen, Qin Tang
article en

Abstract

Background: Hepatocellular carcinoma (HCC), the predominant type of liver cancer, exhibits a high mortality rate due to unclear molecular mechanisms and limited biomarkers. Accumulating evidence indicates that splicing factors (SFs) can promote tumor cell proliferation, invasion, metastasis, and drug resistance by altering the splicing patterns of target genes. However, the regulatory mechanisms underlying SF-mediated alternative splicing (AS) are highly intricate, and the specific functions and pathways of SFs in HCC initiation and progression remain to be further elucidated. Methods: First, Cox regression and LASSO regression analyses of splicing factors (SFs) were performed using the TCGA database, followed by validation using the ICGC dataset. The prognostic value of SFPQ was assessed with the Kaplan–Meier plotter. Next, the potential association of SFPQ with the immune microenvironment of HCC was explored using TIMER and CIBERSORT. In addition, we screened AS events associated with SFPQ expression in TCGA-LIHC, conducted GO/KEGG pathway enrichment analyses, and investigated the correlation between GTF2H3 percent spliced in index (PSI) values and clinicopathological features in HCC patients. Finally, the effect of targeting the exon 10- or exon 11-containing variants of GTF2H3 on HCC was examined at the cellular level. Results: LASSO regression analysis identified five key SFs significantly associated with HCC prognosis, including SFPQ, HTRA2, DAZAP1, PCBP2, and YBX1. Among these, SFPQ was determined as the most critical SF influencing HCC survival, and its overexpression was positively correlated with HCC progression. GO and KEGG analyses revealed that the pathways potentially associated with SFPQ include metabolic pathways, oxidative phosphorylation, mRNA surveillance pathway, and the PPAR signaling pathway. Furthermore, SFPQ expression was associated with the exclusion of exons 10 and 11 of the GTF2H3 gene. Computational analysis also suggested a potential link between SFPQ and immune cell infiltration. Targeting the exon 11-containing variant of GTF2H3 significantly reduced cell viability and migration of HCC cells. Conclusion: SFPQ expression is elevated in HCC and correlates with tumor aggressiveness and poor patient prognosis. Our findings suggest that SFPQ may influence HCC progression through its association with the GTF2H3 exon 10/11 ME event and the immune microenvironment. However, further experimental validation is required to confirm these mechanisms. Our findings suggest that SFPQ may serve as a promising biomarker and therapeutic candidate for HCC.

CancersVol. 18(20)
Second Affiliated Hospital of Chongqing Medical University (CN)
Openalex Percentile: Top 23%
RNA Research and Splicing
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.