An Observational Study on the Molecular Profiling of Primary Melanomas Reveals a Progression Dependence on Mitochondrial Activation

TMEM160 Promotes Tumor Growth in Lung Adenocarcinoma and Cervical Adenocarcinoma Cell Lines

The Chromosome-Centric Human Proteome Project (C-HPP) is an international initiative. It aims to create a protein list expressed in human cells by each chromosomal and mitochondrial DNA to enhance our understanding of disease mechanisms, akin to the gene list generated by the Human Genome Project. Transmembrane protein 160 (TMEM160) is a member of the transmembrane proteins (TMEM) family. TMEM proteins have been implicated in cancer-related processes, including cell proliferation, migration, epithelial-mesenchymal transition, metastasis, and resistance to chemotherapy and radiotherapy. This study aimed to investigate the role of TMEM160 in non-small cell lung cancer and cervical cancer using cell lines, clinical samples, and xenograft studies. Our findings demonstrated that TMEM160 knockdown decreased the proliferation of lung and cervical cancer cell lines. We observed that TMEM160 is localized in the nucleus and cytoplasm and dynamic localization during mitosis of cancer cells and discovered a novel interaction between TMEM160 and nuclear proteins such as NUP50. Furthermore, the TMEM160 interactome was enriched in processes associated with apical junctions, xenobiotic metabolism, glycolysis, epithelial-mesenchymal transition, reactive oxygen species, UV response DNA, the P53 pathway, and the mitotic spindle. This study provides an initial understanding of the function of TMEM160 in lung and cervical cancer progression and clarifies the need to continue investigating the participation of TMEM160 in these cancers.

Identification of genetic fingerprint of type I interferon therapy in visceral metastases of melanoma

Malignant melanoma is a difficult-to-treat skin cancer with increasing incidence worldwide. Although type-I interferon (IFN) is no longer part of guidelines, several melanoma patients are treated with type-I interferon (IFN) at some point of the disease, potentially affecting its genetic progression. We run genome-wide copy number variation (CNV) analysis on previously type-I IFN-treated (n = 17) and control (n = 11) visceral metastases of melanoma patients. Results were completed with data from the TCGA and MM500 databases. We identified metastasis- and brain metastasis-specific gene signatures mostly affected by CN gains. Some cases were genetically resistant to IFN showing characteristic gene alterations (e.g. ABCA4 or ZEB2 gain and alterations of DNA repair genes). Analysis of a previously identified type-I IFN resistance gene set indicates that only a proportion of these genes was exclusive for the IFN-treated metastases reflecting a possible selective genomic pressure of endogenous IFNs during progression. Our data suggest that previous type-I IFN treatment and/or endogenous IFN production by immune response affect genomic progression of melanoma which may have clinical relevance, potentially influence immune checkpoint regulation in the tumor microenvironment.

Differentiating between early melanomas and melanocytic nevi: A state-of-the-art review

Clinicians and dermatologists are challenged by accurate diagnosis of melanocytic lesions, due to melanoma's resemblance to benign skin conditions. Several methodologies have been proposed to diagnose melanoma, and to differentiate between a cancerous and a benign skin condition. First, the ABCD rule and Menzies method use skin lesion characteristics to interpret the condition. The 7-point checklist, 3-point checklist, and CASH algorithm are score-based methods. Each of these methods attributes a score point to the features found on the skin lesion. Furthermore, reflectance confocal microscopy (RCM), an integrated clinical and dermoscopic risk scoring system (iDscore), and a deep convoluted neural network (DCNN) also aids in diagnosis. RCM optically sections live tissues to reveal morphological and cellular structures. The skin lesion's clinical parameters determine iDscore's score point system. The DCNN model is based on a detailed learning algorithm. Therefore, we discuss the conventional and new methodologies for the identification of skin diseases. Moreover, our review attempts to provide clinicians with a comprehensible summary of the wide range of techniques that can help differentiate between early melanomas and melanocytic nevi.

Metastatic Progression of Human Melanoma

This Topical Collection, comprising 13 papers (10 original articles and 3 reviews), addresses various aspects of the field of melanoma progression: genomic and proteomic approaches, experimental studies, the questions of sentinel lymph node dissection, and metastasis formation of uveal and conjunctival melanomas is also discussed [...].