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pmid:34680461

Role of Lamin A/C as Candidate Biomarker of Aggressiveness and Tumorigenicity in Glioblastoma Multiforme.

Giuliana Gatti · Laura Vilardo · Carla Musa · Chiara Di Pietro · Fabrizio Bonaventura · Ferdinando Scavizzi · Alessio Torcinaro · Barbara Bucci · Raffaele Saporito · Ivan Arisi · Francesca De Santa · Marcello Raspa · Loredana Guglielmi · Igea D'Agnano

Biomedicines · 2021 doi:10.3390/biomedicines9101343

Paper

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Experiment

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Data

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Role of Lamin A/C as Candidate Biomarker of Aggressiveness and Tumorigenicity in Glioblastoma Multiforme.
doi
10.3390/biomedicines9101343
pmid
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Giuliana Gatti
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Laura Vilardo
[2]
Carla Musa
[3]
Chiara Di Pietro
[4]
Fabrizio Bonaventura
[5]
Ferdinando Scavizzi
[6]
Alessio Torcinaro
[7]
Barbara Bucci
[8]
Raffaele Saporito
[9]
Ivan Arisi
[10]
Francesca De Santa
[11]
Marcello Raspa
[12]
Loredana Guglielmi
[13]
Igea D'Agnano
journal
Biomedicines
year
2021
abstract
Nuclear lamina components have long been regarded as scaffolding proteins, forming a dense fibrillar structure necessary for the maintenance of the nucleus shape in all the animal kingdom. More recently, mutations, aberrant localisation and deregulation of these proteins have been linked to several diseases, including cancer. Using publicly available data we found that the increased expression levels of the nuclear protein Lamin A/C correlate with a reduced overall survival in The Cancer Genome Atlas Research Network (TCGA) patients affected by glioblastoma multiforme (GBM). We show that the expression of the LMNA gene is linked to the enrichment of cancer-related pathways, particularly pathways related to cell adhesion and cell migration. Mimicking the modulation of LMNA in a GBM preclinical cancer model, we confirmed both in vitro and in vivo that the increased expression of LMNA is associated with an increased aggressiveness and tumorigenicity. In addition, delving into the possible mechanism behind LMNA-induced GBM aggressiveness and tumorigenicity, we found that the mTORC2 component, Rictor, plays a central role in mediating these effects.
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Correlation analysis, survival analysis, and Kaplan-Meier curve plots (survival time vs survival probability) were obtained using R-Bioconductor [17,18,19] and the “survival” package [20]. To associate the survival time to the expression level of selected genes, the subjects were divided in two subpopulations based on a specific quartile of expression values and two survival Kaplan-Meier curves were plotted for each of the two subpopulations: if a statistically significant difference was found between the curves, the gene was considered associated to the survival probability. Kaplan-Meier curves were compared using the Log-rank test.
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Correlation analysis, survival analysis, and Kaplan-Meier curve plots (survival time vs survival probability) were obtained using R-Bioconductor [17,18,19] and the “survival” package [20]. To associate the survival time to the expression level of selected genes, the subjects were divided in two subpopulations based on a specific quartile of expression values and two survival Kaplan-Meier curves were plotted for each of the two subpopulations: if a statistically significant difference was found between the curves, the gene was considered associated to the survival probability. Kaplan-Meier curves were compared using the Log-rank test.
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Using publicly available data we found that the increased expression levels of the nuclear protein Lamin A/C correlate with a reduced overall survival in The Cancer Genome Atlas Research Network (TCGA) patients affected by glioblastoma multiforme (GBM).
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Using publicly available data we found that the increased expression levels of the nuclear protein Lamin A/C correlate with a reduced overall survival in The Cancer Genome Atlas Research Network (TCGA) patients affected by glioblastoma multiforme (GBM).
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Correlation analysis, survival analysis, and Kaplan-Meier curve plots (survival time vs survival probability) were obtained using R-Bioconductor [17,18,19] and the “survival” package [20]. To associate the survival time to the expression level of selected genes, the subjects were divided in two subpopulations based on a specific quartile of expression values and two survival Kaplan-Meier curves were plotted for each of the two subpopulations: if a statistically significant difference was found between the curves, the gene was considered associated to the survival probability. Kaplan-Meier curves were compared using the Log-rank test.
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