PABPC1 promotes cell proliferation and metastasis in pancreatic adenocarcinoma by regulating COL12A1 expression

Yao, Wei; Yao, Yibin; He, Wenfeng; Zhao, Chengsi; Liu, Di; Wang, Genwang; Wang, Zuozheng

doi:10.1002/iid3.919

Cited by 5 publications

(1 citation statement)

References 45 publications

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“…PABPC1 promotes cell proliferation and metastasis by regulating COL12A1 expression in pancreatic adenocarcinoma. 33 Furthermore, PABPC1 expression is downregulated in several malignant gliomas, and therefore, it may be a predictor of preneural subtypes of gliomas; notably, in patients with glioma, high PABPC1 expression is significantly associated with a favorable prognosis. 34 In contrast, our risk model indicated that increased PABPC1 expression is associated with poor HCC prognosis.…”

Section: Discussionmentioning

confidence: 99%

A Novel Four-Gene Signature Based on Nonsense-Mediated RNA Decay for Predicting Prognosis in Hepatocellular Carcinoma: Bioinformatics Analysis and Functional Validation

Zhao,

Wang,

Zhao

et al. 2024

JHC

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Nonsense-mediated RNA decay (NMD), a surveillance pathway for selective degradation of aberrant mRNAs, is associated with cancer progression. Its potential as a predictor for aggressive hepatocellular carcinoma (HCC) is unclear. Here, we present an innovative NMD risk model for predicting HCC prognosis. Methods: The Cancer Genome Atlas (TCGA) data of 374 liver HCC (LIHC) and 50 normal liver samples were extracted. A risk model based on NMD-related genes was developed through least absolute shrinkage and selection operator Cox (LASSO-Cox) regression of the LIHC-TCGA data. Prognostic validation was done using GSE54236, GSE116174, and GSE76427 data. Univariate and multivariate Cox regression analyses were conducted to assess the prognostic value of the model. We also constructed nomograms for survival prediction. Tumor immune infiltration was evaluated using the CIBERSORT algorithm, and the tumor cell phenotype was assessed. Finally, mouse experiments verified UPF3B knockdown effects on HCC tumor characteristics. Results:We developed a risk model based on four NMD-related genes (PABPC1, RPL8, SMG5, and UPF3B) and validated it using GSE54236, GSE116174, and GSE76427 data. The model effectively distinguished high-and low-risk groups corresponding to unfavorable and favorable HCC outcomes. Its prognostic prediction accuracy was confirmed through time-dependent ROC analysis, and clinical-use nomograms with calibration curves were developed. Single-cell RNA sequencing results indicated significantly higher expression of SMG5 and UPF3B in tumor cells. Knockdown of SMG5 and UPF3B inhibited HCC cell proliferation, invasion, and migration, while affecting cell-cycle progression and apoptosis. In vivo, UPF3B knockdown delayed tumor growth and increased immune cell infiltration. Conclusion: Our NMD-related gene-based risk model can help identify therapeutic targets and biomarkers for HCC. Additionally, it assists clinicians in predicting the prognosis of HCC patients.

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Section: Discussionmentioning

confidence: 99%

A Novel Four-Gene Signature Based on Nonsense-Mediated RNA Decay for Predicting Prognosis in Hepatocellular Carcinoma: Bioinformatics Analysis and Functional Validation

Zhao,

Wang,

Zhao

et al. 2024

JHC

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PABPC1 silencing inhibits pancreatic cancer cell proliferation and EMT, and induces apoptosis via PI3K/AKT pathway

Zhu,

Wang,

Wang

et al. 2024

Cytotechnology

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High-dimensional Biomarker Identification for Scalable and Interpretable Disease Prediction via Machine Learning Models

Dai,

Zou,

Zou

2024

Preprint

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Omics data generated from high-throughput technologies and clinical features jointly impact many complex human diseases. Identifying key biomarkers and clinical risk factors is essential for understanding disease mechanisms and advancing early disease diagnosis and precision medicine. However, the high-dimensionality and intricate associations between disease outcomes and omics profiles present significant analytical challenges. To address these, we propose an ensemble data-driven biomarker identification tool, Hybrid Feature Screening (HFS), to construct a candidate feature set for downstream advanced machine learning models. The pre-screened candidate features from HFS are further refined using a computationally efficient permutation-based feature importance test, forming the comprehensive High-dimensional Feature Importance Test (HiFIT) framework. Through extensive numerical simulations and real-world applications, we demonstrate HiFITs superior performance in both outcome prediction and feature importance identification. An R package implementing HiFIT is available on GitHub.

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PABPC1 promotes cell proliferation and metastasis in pancreatic adenocarcinoma by regulating COL12A1 expression

Cited by 5 publications

References 45 publications

A Novel Four-Gene Signature Based on Nonsense-Mediated RNA Decay for Predicting Prognosis in Hepatocellular Carcinoma: Bioinformatics Analysis and Functional Validation

A Novel Four-Gene Signature Based on Nonsense-Mediated RNA Decay for Predicting Prognosis in Hepatocellular Carcinoma: Bioinformatics Analysis and Functional Validation

PABPC1 silencing inhibits pancreatic cancer cell proliferation and EMT, and induces apoptosis via PI3K/AKT pathway

High-dimensional Biomarker Identification for Scalable and Interpretable Disease Prediction via Machine Learning Models

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