ArticleFrontiers in oncology2023
Combined bulk RNA and single-cell RNA analyses reveal TXNL4A as a new biomarker for hepatocellular carcinoma.
Article in Frontiers in oncology, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.
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3 citing papers in PubMed, 3 citations in OpenAlex.
- Splicing as an integrative layer linking cell cycle regulation, epigenetic dynamics, and environmental responses in plants.Frontiers in plant science · 2026Review
- Integrative single-cell and bulk RNA-seq analysis identifies lactylation-related signature in osteosarcoma.Functional & integrative genomics · 2025Article
- Integration of single-cell sequencing and drug sensitivity profiling reveals an 11-gene prognostic model for liver cancer.Human genomics · 2024Article
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Authors and funding
6 authors at 1 institution in 1 country.
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No grant is acknowledged in the PubMed record.
Abstract
Introduction: Hepatocellular carcinoma (HCC) has a high mortality rate worldwide. The dysregulation of RNA splicing is a major event leading to the occurrence, progression, and drug resistance of cancer. Therefore, it is important to identify new biomarkers of HCC from the RNA splicing pathway. Methods: We performed the differential expression and prognostic analyses of RNA splicing-related genes (RRGs) using The Cancer Genome Atlas-liver hepatocellular carcinoma (LIHC). The International Cancer Genome Consortium (ICGC)-LIHC dataset was used to construct and validate prognostic models, and the PubMed database was used to explore genes in the models to identify new markers. The screened genes were subjected to genomic analyses, including differential, prognostic, enrichment, and immunocorrelation analyses. Single-cell RNA (scRNA) data were used to further validate the immunogenetic relationship. Results: Of 215 RRGs, we identified 75 differentially expressed prognosis-related genes, and a prognostic model incorporating thioredoxin like 4A (TXNL4A) was identified using least absolute shrinkage and selection operator regression analysis. ICGC-LIHC was used as a validation dataset to confirm the validity of the model. PubMed failed to retrieve HCC-related studies on TXNL4A. TXNL4A was highly expressed in most tumors and was associated with HCC survival. Chi-squared analyses indicated that TXNL4A expression positively correlated positively with the clinical features of HCC. Multivariate analyses revealed that high TXNL4A expression was an independent risk factor for HCC. Immunocorrelation and scRNA data analyses indicated that TXNL4A was correlated with CD8 T cell infiltration in HCC. Conclusion: Therefore, we identified a prognostic and immune-related marker for HCC from the RNA splicing pathway.
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