Influence of transcriptional variants on metastasis

Poloni, Joice de Faria; Bonatto, Diego

doi:10.1080/15476286.2018.1493328

Cited by 8 publications

(9 citation statements)

References 186 publications

(430 reference statements)

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“…Different splice variants are associated with primary tumors and metastases when compared to normal tissue. Classical examples are E-cadherin, CD44, TP53, and EGFR [50,51]. However, there is little overlap between individual splice variants and different cancer types [16].…”

Section: Discussionmentioning

confidence: 99%

“…Alternative splicing may affect tumor progression towards metastasis [50]. Among top differentially expressed gene variants in metastatic lesions were genes coding for CEA Cell Adhesion Molecule 1 (CEACAM1), tropomyosin 1 (TPM1), and several chemokines and chemokine receptors such as C-X-C Motif Chemokine Ligand 12 (CXCL12) and C-X-C Motif Chemokine Receptor 3 (CXCR3).…”

Section: Discussionmentioning

confidence: 99%

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A Transcriptome-Wide Isoform Landscape of Melanocytic Nevi and Primary Melanomas Identifies Gene Isoforms Associated with Malignancy

Hakobyan

Loeffler‐Wirth

Arakelyan

et al. 2021

IJMS

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Genetic splice variants have become of central interest in recent years, as they play an important role in different cancers. Little is known about splice variants in melanoma. Here, we analyzed a genome-wide transcriptomic dataset of benign melanocytic nevi and primary melanomas (n = 80) for the expression of specific splice variants. Using kallisto, a map for differentially expressed splice variants in melanoma vs. benign melanocytic nevi was generated. Among the top genes with differentially expressed splice variants were Ras-related in brain 6B (RAB6B), a member of the RAS family of GTPases, Macrophage Scavenger Receptor 1 (MSR1), Collagen Type XI Alpha 2 Chain (COLL11A2), and LY6/PLAUR Domain Containing 1 (LYPD1). The Gene Ontology terms of differentially expressed splice variants showed no enrichment for functional gene sets of melanoma vs. nevus lesions, but between type 1 (pigmentation type) and type 2 (immune response type) melanocytic lesions. A number of genes such as Checkpoint Kinase 1 (CHEK1) showed an association of mutational patterns and occurrence of splice variants in melanoma. Moreover, mutations in genes of the splicing machinery were common in both benign nevi and melanomas, suggesting a common mechanism starting early in melanoma development. Mutations in some of these genes of the splicing machinery, such as Serine and Arginine Rich Splicing Factor A3 and B3 (SF3A3, SF3B3), were significantly enriched in melanomas as compared to benign nevi. Taken together, a map of splice variants in melanoma is presented that shows a multitude of differentially expressed splice genes between benign nevi and primary melanomas. The underlying mechanisms may involve mutations in genes of the splicing machinery.

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Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

A Transcriptome-Wide Isoform Landscape of Melanocytic Nevi and Primary Melanomas Identifies Gene Isoforms Associated with Malignancy

Hakobyan

Loeffler‐Wirth

Arakelyan

et al. 2021

IJMS

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“…The way of expression has been suggested as a tumor progression marker (Bagnato & Rosanò, 2019). The experimental studies reported that the 2 variably expressed splicing isoforms of hMENA are present among the isoforms in breast cancer, lung cancer, and pancreatic cancer (De Faria Poloni & Bonatto, 2018). These are hMENA11a and hMENAΔv6.…”

Section: Specific Role Of β‐Arrestins Isoforms In Cancer Regulationmentioning

confidence: 99%

Deciphering the signaling mechanisms of β‐arrestin1 and β‐arrestin2 in regulation of cancer cell cycle and metastasis

Aamna

Dan

Sahu

et al. 2022

Journal Cellular Physiology

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β‐Arrestins are ubiquitously expressed intracellular proteins with many functions which interact directly and indirectly with a wide number of cellular partners and mediate downstream signaling. Originally, β‐arrestins were identified for their contribution to GPCR desensitization to agonist‐mediated activation, followed by receptor endocytosis and ubiquitylation. However, current investigations have now recognized that in addition to GPCR arresting (hence the name arrestin). β‐Arrestins are adaptor proteins that control the recruitment, activation, and scaffolding of numerous cytoplasmic signaling complexes and assist in G‐protein receptor signaling, thus bringing them into close proximity. They have participated in various cellular processes such as cell proliferation, migration, apoptosis, and transcription via canonical and noncanonical pathways. Despite their significant recognition in several physiological processes, these activities are also involved in the onset and progression of various cancers. This review delivers a concise overview of the role of β‐arrestins with a primary emphasis on the signaling processes which underlie the mechanism of β‐arrestins in the onset of cancer. Understanding these processes has important implications for understanding the therapeutic intervention and treatment of cancer in the future.

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“…We successfully classified 40% of basal-like breast cancer patients (75/188) from the Cancer Genome Atlas (TCGA) [35] as basal Blike based on a unique 25 spliced gene signature characteristic of cells undergoing EMT. In this signature, we found well-known markers of malignancy, such as ENAH EMT splice variant that promotes lung metastasis [36] or CSF1 variant which promotes macrophage infiltration and distal metastasis [37], together with new promising splicing candidates of tumour progression and invasiveness (PLOD2, CTNND1, SPAG9). Finally, expression of this basal B signature was sufficient to identify triple negative breast cancer tumours with poor survival, highlighting the prognostic value of the newly identified splicing biomarkers to subclassify one of the most heterogenous and difficult to treat type of breast cancer.…”

Section: Introductionmentioning

confidence: 99%

A cell-to-patient machine learning transfer approach uncovers novel basal-like breast cancer prognostic markers amongst alternative splice variants

et al. 2021

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Background Breast cancer is amongst the 10 first causes of death in women worldwide. Around 20% of patients are misdiagnosed leading to early metastasis, resistance to treatment and relapse. Many clinical and gene expression profiles have been successfully used to classify breast tumours into 5 major types with different prognosis and sensitivity to specific treatments. Unfortunately, these profiles have failed to subclassify breast tumours into more subtypes to improve diagnostics and survival rate. Alternative splicing is emerging as a new source of highly specific biomarkers to classify tumours in different grades. Taking advantage of extensive public transcriptomics datasets in breast cancer cell lines (CCLE) and breast cancer tumours (TCGA), we have addressed the capacity of alternative splice variants to subclassify highly aggressive breast cancers. Results Transcriptomics analysis of alternative splicing events between luminal, basal A and basal B breast cancer cell lines identified a unique splicing signature for a subtype of tumours, the basal B, whose classification is not in use in the clinic yet. Basal B cell lines, in contrast with luminal and basal A, are highly metastatic and express epithelial-to-mesenchymal (EMT) markers, which are hallmarks of cell invasion and resistance to drugs. By developing a semi-supervised machine learning approach, we transferred the molecular knowledge gained from these cell lines into patients to subclassify basal-like triple negative tumours into basal A- and basal B-like categories. Changes in splicing of 25 alternative exons, intimately related to EMT and cell invasion such as ENAH, CD44 and CTNND1, were sufficient to identify the basal-like patients with the worst prognosis. Moreover, patients expressing this basal B-specific splicing signature also expressed newly identified biomarkers of metastasis-initiating cells, like CD36, supporting a more invasive phenotype for this basal B-like breast cancer subtype. Conclusions Using a novel machine learning approach, we have identified an EMT-related splicing signature capable of subclassifying the most aggressive type of breast cancer, which are basal-like triple negative tumours. This proof-of-concept demonstrates that the biological knowledge acquired from cell lines can be transferred to patients data for further clinical investigation. More studies, particularly in 3D culture and organoids, will increase the accuracy of this transfer of knowledge, which will open new perspectives into the development of novel therapeutic strategies and the further identification of specific biomarkers for drug resistance and cancer relapse.

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Influence of transcriptional variants on metastasis

Cited by 8 publications

References 186 publications

A Transcriptome-Wide Isoform Landscape of Melanocytic Nevi and Primary Melanomas Identifies Gene Isoforms Associated with Malignancy

A Transcriptome-Wide Isoform Landscape of Melanocytic Nevi and Primary Melanomas Identifies Gene Isoforms Associated with Malignancy

Deciphering the signaling mechanisms of β‐arrestin1 and β‐arrestin2 in regulation of cancer cell cycle and metastasis

A cell-to-patient machine learning transfer approach uncovers novel basal-like breast cancer prognostic markers amongst alternative splice variants

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