RKIP Regulates Differentiation-Related Features in Melanocytic Cells

Penas, Cristina; Apraiz, Aintzane; Muñoa, Iraia; Arroyo-Berdugo, Yoana; Rasero, Javier; Ezkurra, Pilar Ariadna; Velasco, Verónica; Subirán, Nerea; Bosserhoff, Anja‐Katrin; Alonso, Santos; Asumendi, Aintzane; Boyano, María Dolores

doi:10.3390/cancers12061451

Cited by 8 publications

(12 citation statements)

References 68 publications

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“…To this end, we interrogated public databases for correlations between the expression of MGRN1 and relevant clinical parameters and we also used a collection of melanoma specimens previously obtained at the Biocruces Bizkaia Health Research Institute (BBHRI). This sample cohort has been described and successfully used in previous studies [ 28 , 33 ], and the relevant clinical information on the patients is available. Using the data compiled in the GEPIA database ( ), we compared the relative expression of the MGRN1 gene in different types of tumors from human patients and paired normal tissues (TCGA and GTEx cohorts), as well as in melanoma compared with normal skin.…”

Section: Resultsmentioning

confidence: 99%

MGRN1 as a Phenotypic Determinant of Human Melanoma Cells and a Potential Biomarker

Abrisqueta

Cerdido

Sánchez-Beltrán

et al. 2022

Life

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Mahogunin Ring Finger 1 (MGRN1), a ubiquitin ligase expressed in melanocytes, interacts with the α melanocyte-stimulating hormone receptor, a well-known melanoma susceptibility gene. Previous studies showed that MGRN1 modulates the phenotype of mouse melanocytes and melanoma cells, with effects on pigmentation, shape, and motility. Moreover, MGRN1 knockdown augmented the burden of DNA breaks in mouse cells, indicating that loss of MGRN1 promoted genomic instability. However, data concerning the roles of MGRN1 in human melanoma cells remain scarce. We analyzed MGRN1 knockdown in human melanoma cells. Transient MGRN1 depletion with siRNA or permanent knockdown in human melanoma cells by CRISPR/Cas9 caused an apparently MITF-independent switch to a more dendritic phenotype. Lack of MGRN1 also increased the fraction of human cells in the S phase of the cell cycle and the burden of DNA breaks but did not significantly impair proliferation. Moreover, in silico analysis of publicly available melanoma datasets and estimation of MGRN1 in a cohort of clinical specimens provided preliminary evidence that MGRN1 expression is higher in human melanomas than in normal skin or nevi and pointed to an inverse correlation of MGRN1 expression in human melanoma with patient survival, thus suggesting potential use of MGRN1 as a melanoma biomarker.

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Section: Resultsmentioning

confidence: 99%

MGRN1 as a Phenotypic Determinant of Human Melanoma Cells and a Potential Biomarker

Abrisqueta

Cerdido

Sánchez-Beltrán

et al. 2022

Life

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“…Primary lesions of early-stage malignant melanoma show an overall decrease of RKIP when compared with benign lesions (i.e., nevi) [ 68 ]. As RKIP downregulation alters cellular processes closely related to the neoplastic transformation such as proliferation and migration it might be a useful marker for early diagnosis rather than prognosis.…”

Section: Rkip and Cancermentioning

confidence: 99%

“…Furthermore, post-translational regulation of RKIP in melanoma involves mir-21, one of the targets of NANOG and EMT. Some evidence suggests a direct correlation between mir-21 and RKIP levels as overexpression of RKIP in primary melanoma cultures is associated with a significant reduction of mir-21 and at the same time with lower cell mobility [ 68 ].…”

Section: Rkip and Cancermentioning

confidence: 99%

Understanding Mechanisms of RKIP Regulation to Improve the Development of New Diagnostic Tools

Papale

Netti

Stallone

et al. 2022

Cancers

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One of the most dangerous aspects of cancer cell biology is their ability to grow, spread and form metastases in the main vital organs. The identification of dysregulated markers that drive intracellular signalling involved in the malignant transformation of neoplastic cells and the understanding of the mechanisms that regulate these processes is undoubtedly a key objective for the development of new and more targeted therapies. RAF-kinase inhibitor protein (RKIP) is an endogenous tumour suppressor protein that affects tumour cell survival, proliferation, and metastasis. RKIP might serve as an early tumour biomarker since it exhibits significantly different expression levels in various cancer histologies and it is often lost during metastatic progression. In this review, we discuss the specific impact of transcriptional, post-transcriptional and post-translational regulation of expression and activation/inhibition of RKIP and focus on those tumours for which experimental data on all these factors are available. In this way, we could select how these processes cooperate with RKIP expression in (1) Lung cancer; (2) Colon cancer, (3) Breast cancer; (4) myeloid neoplasm and Multiple Myeloma, (5) Melanoma and (6) clear cell Renal Cell Carcinoma. Furthermore, since RKIP seems to be a key marker of the development of several tumours and it may be assessed easily in various biological fluids, here we discuss the potential role of RKIP dosing in more accessible biological matrices other than tissues. Moreover, this objective may intercept the still unmet need to identify new and more accurate markers for the early diagnosis and prognosis of many tumours.

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“…In this context, for many years we have focused on classifying early-stage melanoma patients on the basis of serological and novel histopathological biomarkers that can be assessed at the time of melanoma diagnosis and initial surgery, thereby identifying profiles that are most likely to develop to more advanced stages and enabling more effective treatments for metastatic disease to be implemented [ 9 , 10 , 11 ]. Thus, in independent studies we previously studied, on the one hand, cytokines in the serum of patients [ 9 ] and, on the other hand, histopathological biomarkers in melanoma biopsies [ 9 , 10 ]. We found that cytokines such as IL-4, IL-6, and GM-CSF are significantly related to a bad prognosis of patients [ 9 ].…”

Section: Introductionmentioning

confidence: 99%

Melanoma Clinical Decision Support System: An Artificial Intelligence-Based Tool to Diagnose and Predict Disease Outcome in Early-Stage Melanoma Patients

Díaz-Ramón

Gardeazábal

Izu

et al. 2023

Cancers

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This study set out to assess the performance of an artificial intelligence (AI) algorithm based on clinical data and dermatoscopic imaging for the early diagnosis of melanoma, and its capacity to define the metastatic progression of melanoma through serological and histopathological biomarkers, enabling dermatologists to make more informed decisions about patient management. Integrated analysis of demographic data, images of the skin lesions, and serum and histopathological markers were analyzed in a group of 196 patients with melanoma. The interleukins (ILs) IL-4, IL-6, IL-10, and IL-17A as well as IFNγ (interferon), GM-CSF (granulocyte and macrophage colony-stimulating factor), TGFβ (transforming growth factor), and the protein DCD (dermcidin) were quantified in the serum of melanoma patients at the time of diagnosis, and the expression of the RKIP, PIRIN, BCL2, BCL3, MITF, and ANXA5 proteins was detected by immunohistochemistry (IHC) in melanoma biopsies. An AI algorithm was used to improve the early diagnosis of melanoma and to predict the risk of metastasis and of disease-free survival. Two models were obtained to predict metastasis (including “all patients” or only patients “at early stages of melanoma”), and a series of attributes were seen to predict the progression of metastasis: Breslow thickness, infiltrating BCL-2 expressing lymphocytes, and IL-4 and IL-6 serum levels. Importantly, a decrease in serum GM-CSF seems to be a marker of poor prognosis in patients with early-stage melanomas.

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RKIP Regulates Differentiation-Related Features in Melanocytic Cells

Cited by 8 publications

References 68 publications

MGRN1 as a Phenotypic Determinant of Human Melanoma Cells and a Potential Biomarker

MGRN1 as a Phenotypic Determinant of Human Melanoma Cells and a Potential Biomarker

Understanding Mechanisms of RKIP Regulation to Improve the Development of New Diagnostic Tools

Melanoma Clinical Decision Support System: An Artificial Intelligence-Based Tool to Diagnose and Predict Disease Outcome in Early-Stage Melanoma Patients

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