Systematic profiling of alternative splicing signature reveals prognostic predictor for prostate cancer

Zhao, Jing; Chang, Luchen; Gu, Xiaoyu; Liu, Jia; Sun, Bei; Wei, Xi

doi:10.1111/cas.14525

Cited by 19 publications

(14 citation statements)

References 46 publications

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“…To build the prognostic model, univariate Cox regression analysis, LASSO regression, and multivariate Cox regression analysis were applied using R software (v3.6.1) based on the methodology of previously published studies [ 15 – 17 ]. Firstly, univariate analysis was performed to screen the significant DE immune genes based on the detected immune genes using the “survival” package.…”

Section: Methodsmentioning

confidence: 99%

Construction and Validation of a 7-Immune Gene Model for Prognostic Assessment of Esophageal Carcinoma

2020

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Background We constructed a predictive risk model of esophageal carcinoma (EC) for prognostic prediction. Material/Methods Immune genes and the expression data were downloaded from the ImmPort database and The Cancer Genome Atlas database. Univariate analysis, Lasso regression, and multivariate analysis were applied to screen the ultimately included prognostic immune genes for the model based on the training cohort. Survival analysis and receiver operating characteristic (ROC) curve were applied to evaluate the model. The model was further validated in the testing and entire cohorts, and the clinical utility of the model and its ability to assess the subtypes of EC were evaluated in the entire cohort. Results We detected 297 differentially expressed immune genes, including 241 upregulated genes and 56 downregulated genes in EC patients. Based on these genes, we developed a 7-immune gene model of EC, including HSPA6, S100A12, NOS2, DKK1, OSM, AR, and OXTR. The area under the curve (AUC) of the model at 1 year was 0.825. Similarly, the AUC values for the validating cohorts were 0.813 and 0.816, respectively. Pathological stage and risk score of the model were independent prognostic factors. This model was effective for both subtypes of EC. Conclusions We constructed a 7-gene model consisting of HSPA6, S100A12, NOS2, DKK1, OSM, AR, and OXTR. This risk model could be used for prognostic prediction of EC.

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Section: Methodsmentioning

confidence: 99%

Construction and Validation of a 7-Immune Gene Model for Prognostic Assessment of Esophageal Carcinoma

2020

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“…Taken together, ALDH1L1 is involved in the controlled mediation of apoptosis in prostate cancer cells. Alternative mRNA splicing of ALDH1L1 seem to be of clinical relevance as two ALDH1L1 variants were found to be expressed higher in either cancer or normal tissues [ 100 ]. While plenty of single nucleotide polymorphisms were found for ALDH1L1, none of them were significantly associated with an elevated risk of PCa [ 101 ].…”

Section: Aldh1l1mentioning

confidence: 99%

The Multifaceted Role of Aldehyde Dehydrogenases in Prostate Cancer Stem Cells

Püschel

Dubrovska

Gorodetska

2021

Cancers

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Cancer stem cells (CSCs) are the only tumor cells possessing self-renewal and differentiation properties, making them an engine of tumor progression and a source of tumor regrowth after treatment. Conventional therapies eliminate most non-CSCs, while CSCs often remain radiation and drug resistant, leading to tumor relapse and metastases. Thus, targeting CSCs might be a powerful tool to overcome tumor resistance and increase the efficiency of current cancer treatment strategies. The identification and isolation of the CSC population based on its high aldehyde dehydrogenase activity (ALDH) is widely accepted for prostate cancer (PCa) and many other solid tumors. In PCa, several ALDH genes contribute to the ALDH activity, which can be measured in the enzymatic assay by converting 4, 4-difluoro-4-bora-3a, 4a-diaza-s-indacene (BODIPY) aminoacetaldehyde (BAAA) into the fluorescent product BODIPY-aminoacetate (BAA). Although each ALDH isoform plays an individual role in PCa biology, their mutual functional interplay also contributes to PCa progression. Thus, ALDH proteins are markers and functional regulators of CSC properties, representing an attractive target for cancer treatment. In this review, we discuss the current state of research regarding the role of individual ALDH isoforms in PCa development and progression, their possible therapeutic targeting, and provide an outlook for the future advances in this field.

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“…BRCA1/2 germline mutations are most commonly seen in breast and ovarian cancer patients who benefit from treatment with PARP inhibitors (PARPis) or platinum compounds, but BRCA1-D11q splice variants lacking the majority of exon 11 contribute to therapeutic resistance (18,19). We found that prognostic models constructed from AS events data had good efficiency for evaluating the survival time of adrenocortical carcinoma, cervical cancer and prostate cancer patients (20)(21)(22).…”

Section: Introductionmentioning

confidence: 96%

Comprehensive Analysis of Prognostic Alternative Splicing Signature Reveals Recurrence Predictor for Papillary Thyroid Cancer

et al. 2021

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BackgroundAlternative splicing (AS) plays a key role in the diversity of proteins and is closely associated with tumorigenicity. The aim of this study was to systemically analyze RNA alternative splicing (AS) and identify its prognostic value for papillary thyroid cancer (PTC).MethodsAS percent-splice-in (PSI) data of 430 patients with PTC were downloaded from the TCGA SpliceSeq database. We successfully identified recurrence-free survival (RFS)-associated AS events through univariate Cox regression, LASSO regression and multivariate regression and then constructed different types of prognostic prediction models. Gene function enrichment analysis revealed the relevant signaling pathways involved in RFS-related AS events. Simultaneously, a regulatory network diagram of AS and splicing factors (SFs) was established.ResultsWe identified 1397 RFS-related AS events which could be used as the potential prognostic biomarkers for PTC. Based on these RFS-related AS events, we constructed a ten-AS event prognostic prediction signature that could distinguish high-and low-risk patients and was highly capable of predicting PTC patient prognosis. ROC curve analysis revealed the excellent predictive ability of the ten-AS events model, with an area under the curve (AUC) value of 0.889; the highest prediction intensity for one-year RFS was 0.923, indicating that the model could be used as a prognostic biomarker for PTC. In addition, the nomogram constructed by the risk score of the ten-AS model also showed high predictive efficiency for the prognosis of PTC patients. Finally, the constructed SF-AS network diagram revealed the regulatory role of SFs in PTC.ConclusionThrough the limited analysis, AS events could be regarded as reliable prognostic biomarkers for PTC. The splicing correlation network also provided new insight into the potential molecular mechanisms of PTC.

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Systematic profiling of alternative splicing signature reveals prognostic predictor for prostate cancer

Cited by 19 publications

References 46 publications

Construction and Validation of a 7-Immune Gene Model for Prognostic Assessment of Esophageal Carcinoma

Construction and Validation of a 7-Immune Gene Model for Prognostic Assessment of Esophageal Carcinoma

The Multifaceted Role of Aldehyde Dehydrogenases in Prostate Cancer Stem Cells

Comprehensive Analysis of Prognostic Alternative Splicing Signature Reveals Recurrence Predictor for Papillary Thyroid Cancer

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