GLIPR1 and SPARC expression profile reveals a signature associated with prostate Cancer Brain metastasis

Barros, Eliane Gouvêa de Oliveira; Branco, Luíza Castello; Costa, Nathalia Meireles Da; Nicolau-Neto, Pedro; Palmero, Celia Yelimar; Pontes, Bruno; Amaral, Rackele Ferreira do; Alves-Leon, Soniza Vieira; Souza, Jorge Marcondes de; Romão, Luciana; Fernandes, Priscila Valverde; Martins, Ivanir; Takiya, Christina Maeda; Pinto, Luís Felipe Ribeiro; Palumbo, Antônio; Nasciutti, Luiz Eurico

doi:10.1016/j.mce.2021.111230

Cited by 4 publications

(4 citation statements)

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“…Their involvement in cancer development was already established. 30 , 31 As can be appreciated in Figure 3 , their average abundance in EPS-urine was very low. Their detection was facilitated by the extended dynamic range provided by the DIA scanning mode.…”

Section: Resultsmentioning

confidence: 93%

“…Though the starting number of quantified proteins was very high (1670), the four-feature signature included just two proteins. Their involvement in cancer development was already established. , As can be appreciated in Figure , their average abundance in EPS-urine was very low. Their detection was facilitated by the extended dynamic range provided by the DIA scanning mode.…”

Section: Resultsmentioning

confidence: 94%

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Data-Independent Acquisition Mass Spectrometry of EPS-Urine Coupled to Machine Learning: A Predictive Model for Prostate Cancer

et al. 2023

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Prostate cancer (PCa) is annually the most frequently diagnosed cancer in the male population. To date, the diagnostic path for PCa detection includes the dosage of serum prostate-specific antigen (PSA) and the digital rectal exam (DRE). However, PSA-based screening has insufficient specificity and sensitivity; besides, it cannot discriminate between the aggressive and indolent types of PCa. For this reason, the improvement of new clinical approaches and the discovery of new biomarkers are necessary. In this work, expressed prostatic secretion (EPS)-urine samples from PCa patients and benign prostatic hyperplasia (BPH) patients were analyzed with the aim of detecting differentially expressed proteins between the two analyzed groups. To map the urinary proteome, EPS-urine samples were analyzed by data-independent acquisition (DIA), a high-sensitivity method particularly suitable for detecting proteins at low abundance. Overall, in our analysis, 2615 proteins were identified in 133 EPS-urine specimens obtaining the highest proteomic coverage for this type of sample; of these 2615 proteins, 1670 were consistently identified across the entire data set. The matrix containing the quantified proteins in each patient was integrated with clinical parameters such as the PSA level and gland size, and the complete matrix was analyzed by machine learning algorithms (by exploiting 90% of samples for training/testing using a 10-fold cross-validation approach, and 10% of samples for validation). The best predictive model was based on the following components: semaphorin-7A (sema7A), secreted protein acidic and rich in cysteine (SPARC), FT ratio, and prostate gland size. The classifier could predict disease conditions (BPH, PCa) correctly in 83% of samples in the validation set. Data are available via ProteomeXchange with the identifier PXD035942.

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Section: Resultsmentioning

confidence: 93%

Section: Resultsmentioning

confidence: 94%

Data-Independent Acquisition Mass Spectrometry of EPS-Urine Coupled to Machine Learning: A Predictive Model for Prostate Cancer

et al. 2023

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“…In the KO‐TRAMP and A537T‐TRAMP mice, elevated miRNAs resulted in reduction of prognostic markers of prostate cancer and metastasis, such as cysteine dioxygenase 1 ( Cdo1 ; Meller et al , 2016 ) and tensin 1 ( Tns1 ; Z. Zhu et al , 2020 ). The synergistic action of reduced miRNAs and increase in their target mRNAs, such as Unc5c (Latil et al , 2003 ), Thsd4 (Wu et al , 2020 ), Sparc (de Oliveira‐Barros et al , 2021 ), Runx1t1 (Kan et al , 2010 ) and Pcdh17 (Costa et al , 2011 ), known to promote prostate cancer, may contribute further to the prostate lesions and tumours in the A537T‐TRAMP mice (Fig 6E ). In the KO‐TRAMP mice, Vcan (with a role in cell adhesion and tumour progression; Ricciardelli et al , 1998 ) and Myb proto‐oncogene (Li et al , 2014 ) levels, that are markers of prostate metastasis (Edwards, 2012 ), were increased, which correlated with reduced miR‐669c‐5p, and reduction in miR‐676‐5p correlated with increased expression of insulin‐like growth factor‐1 ( Igf1 ) that promotes cell growth and malignancy (Perry et al , 2017 ; Fig 6F ).…”

Section: Resultsmentioning

confidence: 99%

Multi‐omic profiling reveals an RNA processing rheostat that predisposes to prostate cancer

et al. 2023

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Prostate cancer is the most commonly diagnosed malignancy and the third leading cause of cancer deaths. GWAS have identified variants associated with prostate cancer susceptibility; however, mechanistic and functional validation of these mutations is lacking. We used CRISPR‐Cas9 genome editing to introduce a missense variant identified in the ELAC2 gene, which encodes a dually localised nuclear and mitochondrial RNA processing enzyme, into the mouse Elac2 gene as well as to generate a prostate‐specific knockout of Elac2. These mutations caused enlargement and inflammation of the prostate and nodule formation. The Elac2 variant or knockout mice on the background of the transgenic adenocarcinoma of the mouse prostate (TRAMP) model show that Elac2 mutation with a secondary genetic insult exacerbated the onset and progression of prostate cancer. Multiomic profiling revealed defects in energy metabolism that activated proinflammatory and tumorigenic pathways as a consequence of impaired noncoding RNA processing and reduced protein synthesis. Our physiologically relevant models show that the ELAC2 variant is a predisposing factor for prostate cancer and identify changes that underlie the pathogenesis of this cancer.

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“…In previous studies, it was reported that the genes belonging to our 17-gene expression signature were associated with metastasis. The expression of SPARC and DCN is significantly higher in prostate cancer cell lines that actively invade the astrocyte monolayer [74]. In craniopharyngioma, a higher tumor stroma expression of SPARC leads to higher brain infiltration [75].…”

Section: Discussionmentioning

confidence: 99%

Tumor Nonimmune-Microenvironment-Related Gene Expression Signature Predicts Brain Metastasis in Lung Adenocarcinoma Patients after Surgery: A Machine Learning Approach Using Gene Expression Profiling

Haam

Han

Lee

et al. 2021

Cancers

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Using a machine learning approach with a gene expression profile, we discovered a tumor nonimmune-microenvironment-related gene expression signature, including extracellular matrix (ECM) remodeling, epithelial–mesenchymal transition (EMT), and angiogenesis, that could predict brain metastasis (BM) after the surgical resection of 64 lung adenocarcinomas (LUAD). Gene expression profiling identified a tumor nonimmune-microenvironment-related 17-gene expression signature that significantly correlated with BM. Of the 17 genes, 11 were ECM-remodeling-related genes. The 17-gene expression signature showed high BM predictive power in four machine learning classifiers (areas under the receiver operating characteristic curve = 0.845 for naïve Bayes, 0.849 for support vector machine, 0.858 for random forest, and 0.839 for neural network). Subgroup analysis revealed that the BM predictive power of the 17-gene signature was higher in the early-stage LUAD than in the late-stage LUAD. Pathway enrichment analysis showed that the upregulated differentially expressed genes were mainly enriched in the ECM–receptor interaction pathway. The immunohistochemical expression of the top three genes of the 17-gene expression signature yielded similar results to NanoString tests. The tumor nonimmune-microenvironment-related gene expression signatures found in this study are important biological markers that can predict BM and provide patient-specific treatment options.

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GLIPR1 and SPARC expression profile reveals a signature associated with prostate Cancer Brain metastasis

Cited by 4 publications

References 81 publications

Data-Independent Acquisition Mass Spectrometry of EPS-Urine Coupled to Machine Learning: A Predictive Model for Prostate Cancer

Data-Independent Acquisition Mass Spectrometry of EPS-Urine Coupled to Machine Learning: A Predictive Model for Prostate Cancer

Multi‐omic profiling reveals an RNA processing rheostat that predisposes to prostate cancer

Tumor Nonimmune-Microenvironment-Related Gene Expression Signature Predicts Brain Metastasis in Lung Adenocarcinoma Patients after Surgery: A Machine Learning Approach Using Gene Expression Profiling

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