Identification of prognostic splicing factors and exploration of their potential regulatory mechanisms in pancreatic adenocarcinoma

Rong, Minhua; Zhu, Zhan-Hui; Guan, Ying; Li, Meiwei; Zheng, Jiashuo; Huang, Yue-qi; Wei, Dan‐Ming; Li, Yingmei; Wu, Ximei; Bu, Hui-ping; Peng, H.; Wei, Xiaolin; Li, Guosheng; Li, Mingxuan; Chen, Ming‐Hui; Huang, Su-Hua

doi:10.7717/peerj.8380

Cited by 6 publications

(7 citation statements)

References 36 publications

(22 reference statements)

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“…Stimulation or inhibition of TNF superfamily signaling pathways may influence tumor progression [ 31 ]. We compared our model with the model established by Rong et al [ 32 ]. In terms of sample size, our study included more samples ( n = 178).…”

Section: Discussionmentioning

confidence: 99%

Molecular Analysis of Prognosis and Immune Pathways of Pancreatic Cancer Based on TNF Family Members

Zeng

Tang

et al. 2021

Journal of Oncology

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Background. Tumor necrosis factor (TNF) family members play a vital role in anticancer therapy. This study aimed to screen the critical markers for the prognostic analysis of pancreatic adenocarcinoma (PAAD) by analyzing the clustering patterns of TNF family members in PAAD. Methods. In this study, the NMF clustering method was adopted to cluster samples from The Cancer Genome Atlas (TCGA) to acquire the clustering pattern of the TNF family in PAAD. Differential gene analysis was performed according to TNF family gene clusters. The support vector machine (SVM) method was conducted for further gene screening, and the risk score model of the screened genes was constructed by Lasso. The single sample gene set enrichment analysis (ssGSEA) method was adopted for immunoenrichment analysis and tumor immune cycle analysis. Genes associated with risk scores were analyzed by Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analysis. Results. We clustered PAAD into two groups based on TNF family genes. Nineteen TNF family genes were significantly associated with the clinical characteristics of PAAD patients. The risk score formula was composed of RHOD, UBE2C, KLHDC7b, MSLN, ADAM8, NME3, GNG2, and MCOLN3. GSE57495 and GSE62452 datasets verified that patients in the high-risk group had a worse prognosis than those in the low-risk group. The risk score-related genes analyzed by GO and KEGG were mainly involved in the modulation of chemical synaptic transmission and synaptic vesicle cycle pathway. There were significant differences in the expression of 15 immune cells between the high-risk group and the low-risk group. The risk score was positively correlated with HCK, interferon, MHC-I, and STAT1. The expression of genes relevant to chemokine, immunostimulator, MHC, and receptor was strongly associated with the risk score. Conclusion. The risk score model based on the TNF family can predict the prognosis and immune status of PAAD patients. Further research is needed to verify the clinical prognostic value of risk scores.

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Section: Discussionmentioning

confidence: 99%

Molecular Analysis of Prognosis and Immune Pathways of Pancreatic Cancer Based on TNF Family Members

Zeng

Tang

et al. 2021

Journal of Oncology

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“…ASEs with PSI values >75% were selected in this study. Missing values were supplied using "impute" package in R (36)(37)(38). ASEs with PSI average < 0.05 or standard deviation < 0.01 were removed.…”

Section: Data Collectionmentioning

confidence: 99%

Aberrant RNA Splicing Events Driven by Mutations of RNA-Binding Proteins as Indicators for Skin Cutaneous Melanoma Prognosis

Mei¹,

Song

Zhang

et al. 2020

Front. Oncol.

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The worldwide incidence of skin cutaneous melanoma (SKCM) is increasing at a more rapid rate than other tumors. Aberrant alternative splicing (AS) is found to be common in cancer; however, how this process contributes to cancer prognosis still remains largely unknown. Mutations in RNA-binding proteins (RBPs) may trigger great changes in the splicing process. In this study, we comprehensively analyzed DNA and RNA sequencing data and clinical information of SKCM patients, together with widespread changes in splicing patterns induced by RBP mutations. We screened mRNA expression-related and prognosis-related mutations in RBPs and investigated the potential affections of RBP mutations on splicing patterns. Mutations in 853 RBPs were demonstrated to be correlated with splicing aberrations (p < 0.01). Functional enrichment analysis revealed that these alternative splicing events (ASEs) may participate in tumor progress by regulating the modification process, cell-cycle checkpoint, metabolic pathways, MAPK signaling, PI3K-Akt signaling, and other important pathways in cancer. We also constructed a prediction model based on overall survival-related AS events (OS-ASEs) affected by RBP mutations, which exhibited a good predict efficiency with the area under the curve of 0.989. Our work highlights the importance of RBP mutations in splicing alterations and provides effective biomarkers for prediction of prognosis of SKCM.

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“…The AS events of these samples were obtained from the TCGA SpliceSeq database (http://bioinformatics.mdanderson.org/ TCGASpliceSeq), which identifies AS events and describes their genome profiles using the RNA-seq data of the TCGA samples (Ryan et al, 2016). Particularly, we downloaded the AS isoform average percent spliced-in (PSI) values of the LGG and GBM samples, respectively, from TCGA SpliceSeq database with the common parameter settings (i.e., the percentage of samples with PSI value >75%, minimum PSI range >0 and minimum PSI standard deviation >0.1) according to the previous studies (Yang et al, 2019;Rong et al, 2020;Wei et al, 2021). Based on the classification criteria of TCGA SpliceSeq, we classified the types of AS events into Alternate Acceptors (AA), Alternate Donors (AD), Exon Skip (ES), Retained Intron (RI), Alternate Promoters (AP), Alternate Terminators (AT) and Mutually Exclusive Exons (ME).…”

Section: Data Collection and Processingmentioning

confidence: 99%

Genome-Wide Analysis for the Regulation of Gene Alternative Splicing by DNA Methylation Level in Glioma and its Prognostic Implications

Yang

Wang

et al. 2022

Front. Genet.

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Glioma is a primary high malignant intracranial tumor with poorly understood molecular mechanisms. Previous studies found that both DNA methylation modification and gene alternative splicing (AS) play a key role in tumorigenesis of glioma, and there is an obvious regulatory relationship between them. However, to date, no comprehensive study has been performed to analyze the influence of DNA methylation level on gene AS in glioma on a genome-wide scale. Here, we performed this study by integrating DNA methylation, gene expression, AS, disease risk methylation at position, and clinical data from 537 low-grade glioma (LGG) and glioblastoma (GBM) individuals. We first conducted a differential analysis of AS events and DNA methylation positions between LGG and GBM subjects, respectively. Then, we evaluated the influence of differential methylation positions on differential AS events. Further, Fisher’s exact test was used to verify our findings and identify potential key genes in glioma. Finally, we performed a series of analyses to investigate influence of these genes on the clinical prognosis of glioma. In total, we identified 130 glioma-related genes whose AS significantly affected by DNA methylation level. Eleven of them play an important role in glioma prognosis. In short, these results will help to better understand the pathogenesis of glioma.

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Identification of prognostic splicing factors and exploration of their potential regulatory mechanisms in pancreatic adenocarcinoma

Cited by 6 publications

References 36 publications

Molecular Analysis of Prognosis and Immune Pathways of Pancreatic Cancer Based on TNF Family Members

Molecular Analysis of Prognosis and Immune Pathways of Pancreatic Cancer Based on TNF Family Members

Aberrant RNA Splicing Events Driven by Mutations of RNA-Binding Proteins as Indicators for Skin Cutaneous Melanoma Prognosis

Genome-Wide Analysis for the Regulation of Gene Alternative Splicing by DNA Methylation Level in Glioma and its Prognostic Implications

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