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pmid:33672425

Long Non-Coding RNA Landscape in Prostate Cancer Molecular Subtypes: A Feature Selection Approach.

Simona De Summa · Antonio Palazzo · Mariapia Caputo · Rosa Maria Iacobazzi · Brunella Pilato · Letizia Porcelli · Stefania Tommasi · Angelo Virgilio Paradiso · Amalia Azzariti

International journal of molecular sciences · 2021 doi:10.3390/ijms22042227

Paper

complete

Experiment

partial

Data

partial

Analysis

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Execution

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pmid:33672425
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2026-11-01T15:41:27Z
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2026-11-01T21:24:47Z
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paper object · 10 fields
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title
Long Non-Coding RNA Landscape in Prostate Cancer Molecular Subtypes: A Feature Selection Approach.
doi
10.3390/ijms22042227
pmid
33672425
authors array · 9 items
[0]
Simona De Summa
[1]
Antonio Palazzo
[2]
Mariapia Caputo
[3]
Rosa Maria Iacobazzi
[4]
Brunella Pilato
[5]
Letizia Porcelli
[6]
Stefania Tommasi
[7]
Angelo Virgilio Paradiso
[8]
Amalia Azzariti
journal
International journal of molecular sciences
year
2021
abstract
Prostate cancer is one of the most common malignancies in men. It is characterized by a high molecular genomic heterogeneity and, thus, molecular subtypes, that, to date, have not been used in clinical practice. In the present paper, we aimed to better stratify prostate cancer patients through the selection of robust long non-coding RNAs. To fulfill the purpose of the study, a bioinformatic approach focused on feature selection applied to a TCGA dataset was used. In such a way, LINC00668 and long non-coding(lnc)-SAYSD1-1, able to discriminate ERG/not-ERG subtypes, were demonstrated to be positive prognostic biomarkers in ERG-positive patients. Furthermore, we performed a comparison between mutated prostate cancer, identified as "classified", and a group of patients with no peculiar genomic alteration, named "not-classified". Moreover, LINC00920 lncRNA overexpression has been linked to a better outcome of the hormone regimen. Through the feature selection approach, it was found that the overexpression of lnc-ZMAT3-3 is related to low-grade patients, and three lncRNAs: lnc-SNX10-87, lnc-AP1S2-2, and ADPGK-AS1 showed, through a co-expression analysis, significant correlation values with potentially druggable pathways. In conclusion, the data mining of publicly available data and robust bioinformatic analyses are able to explore the unknown biology of malignancies.
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To gain accuracy metrics, a Bootstrap resampling strategy was applied to the dataset (number of iterations = 100).
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sections[14] "4.2.2. Feature Selection and Evaluation of Classification Performances" paragraph 2
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To gain accuracy metrics, a Bootstrap resampling strategy was applied to the dataset (number of iterations = 100).
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Data are available in GDC data portal (https://portal.gdc.cancer.gov/) (accessed on 23 February 2021).
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sections[23] "Data Availability Statement" paragraph 2
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Data are available in GDC data portal (https://portal.gdc.cancer.gov/) (accessed on 23 February 2021).
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Distribution of the molecular subtypes in the TCGA-PRAD cohort.
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2.1. lncRNA Discriminating ERG-Positive Subtype: Feature Selection
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Distribution of the molecular subtypes in the TCGA-PRAD cohort.
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Co-expressed long non-coding RNAs(lncRNAs) and coding genes.
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2.2. lncRNA-mRNA Coexpression Analysis of lncRNA Discriminating ERG-Positive Subtype
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Co-expressed long non-coding RNAs(lncRNAs) and coding genes.
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Functional enrichment of the first subcluster of the protein–protein interaction (PPI) network.
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Functional enrichment of the first subcluster of the protein–protein interaction (PPI) network.
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Functional enrichment of the second subcluster of the PPI network.
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2.3. PPI Network and Identification of Highly Connected Subnetwork in ERG-Positive Subtype
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Functional enrichment of the third subcluster of the PPI network.
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Co-expressed lncRNAs and genes.
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{"sci_version": "1.2", "schema": "https://corpus.bntrg.co.uk/sci/1.0/sci.schema.json", "id": "pmid:33672425", "kind": "science-record", "revision": 4, "content_hash": "sha256:418d51a0a9db41e9eb53120fda0d0bac5a739dbd090165871e3e8143c47e8dea", "created_at": "2026-11-01T15:41:27Z", "updated_at": "2026-11-01T21:24:47Z", "generator": {"name": "corpus-assay-engine", "run_id": "reading-sci-2026-11-01"}, "paper": {"state": "complete", "title": "Long Non-Coding RNA Landscape in Prostate Cancer Molecular Subtypes: A Feature Selection Approach.", "doi": "10.3390/ijms22042227", "pmid": "33672425", "authors": ["Simona De Summa", "Antonio Palazzo", "Mariapia Caputo", "Rosa Maria Iacobazzi", "Brunella Pilato", "Letizia Porcelli", "Stefania Tommasi", "Angelo Virgilio Paradiso", "Amalia Azzariti"], "journal": "International journal of molecular sciences", "year": 2021, "abstract": "Prostate cancer is one of the most common malignancies in men. It is characterized by a high molecular genomic heterogeneity and, thus, molecular subtypes, that, to date, have not been used in clinical practice. In the present paper, we aimed to better stratify prostate cancer patients through the selection of robust long non-coding RNAs. To fulfill the purpose of the study, a bioinformatic approach focused on feature selection applied to a TCGA dataset was used. In such a way, LINC00668 and long non-coding(lnc)-SAYSD1-1, able to discriminate ERG/not-ERG subtypes, were demonstrated to be positive prognostic biomarkers in ERG-positive patients. Furthermore, we performed a comparison between mutated prostate cancer, identified as \"classified\", and a group of patients with no peculiar genomic alteration, named \"not-classified\". Moreover, LINC00920 lncRNA overexpression has been linked to a better outcome of the hormone regimen. Through the feature selection approach, it was found that the overexpression of lnc-ZMAT3-3 is related to low-grade patients, and three lncRNAs: lnc-SNX10-87, lnc-AP1S2-2, and ADPGK-AS1 showed, through a co-expression analysis, significant correlation values with potentially druggable pathways. In conclusion, the data mining of publicly available data and robust bioinformatic analyses are able to explore the unknown biology of malignancies.", "claims": [{"id": "claim-method", "statement": "To gain accuracy metrics, a Bootstrap resampling strategy was applied to the dataset (number of iterations = 100).", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:33672425", "locator": "sections[14] \"4.2.2. 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