Transcriptional signatures underlying dynamic phenotypic switching and novel disease biomarkers in a linear cellular model of melanoma progression

LncRNAs in melanoma phenotypic plasticity: emerging targets for promising therapies

Long non-coding RNAs (lncRNAs) have received growing attention due to their diverse regulatory roles in cancer, including in melanoma, an aggressive type of skin cancer. The plasticity and phenotypic adaptability of melanoma cells are crucial factors contributing to therapeutic resistance. The identification of molecules playing key roles in melanoma cell plasticity could unravel novel and more effective therapeutic targets. This review presents current concepts of melanoma cell plasticity, illustrating its fluidity and dismissing the outdated notion of epithelial-mesenchymal-like transition as a simplistic binary process. Emphasis is placed on the pivotal role of lncRNAs in orchestrating cell plasticity, employing various mechanisms recently elucidated and unveiling their potential as promising targets for novel therapeutic strategies. Insights into the molecular mechanisms coordinated by lncRNAs in melanoma pave the way for the development of RNA-based therapies, holding great promise for enhancing treatment outcomes and offering a glimpse into a more effective approach to melanoma treatment.

Survival benefit of sentinel lymph node biopsy in Asian melanoma patients

Sentinel lymph node biopsy (SLNB) provides important prognostic information for early-stage melanomas. However, statistics regarding the survival comparison between SLNB and nodal observation in Asia, where acral lentiginous melanoma (ALM) predominates, are limited. This study aimed to identify if SLNB offered survival benefits over nodal observation in early-stage melanomas in Taiwan. The retrospective study included 227 patients who met the SLNB criteria according to the National Comprehensive Cancer Network guidelines and were treated at National Taiwan University Hospital from June 1997 to June 2021. Survival analysis was performed using Kaplan-Meier curves and Cox proportional hazards regression models. Of the study population, ALM accounted for 73.1%; 161 patients (70.9%) underwent SLNB and 66 patients (29.1%) were under nodal observation. Multivariate analysis showed significantly improved melanoma-specific survival (hazard ratio [HR], 0.6; p = .02) in the SLNB group. Among those who underwent completion lymph node dissection (CLND), the non-sentinel node positivity rate was 44.4%. Immediate CLND resulted in significantly longer melanoma-specific survival and distant-metastasis-free survival (DMFS) compared to nodal observation. (HR, 0.2; p = .01 for melanoma-specific survival. HR, 0.3; p = .046 for DMFS). In conclusion, SLNB may provide survival benefits of cutaneous melanoma over nodal observation in the Taiwanese population.

Genome-wide promoter methylation profiling in a cellular model of melanoma progression reveals markers of malignancy and metastasis that predict melanoma survival

The epigenetic changes associated with melanoma progression to advanced and metastatic stages are still poorly understood. To shed light on the CpG methylation dynamics during melanoma development, we analyzed the methylome profiles of a four-stage cell line model of melanoma progression: non-tumorigenic melanocytes (melan-a), premalignant melanocytes (4C), non-metastatic melanoma cells (4C11−), and metastatic melanoma cells (4C11+). We identified 540 hypo- and 37 hypermethylated gene promoters that together characterized a malignancy signature, and 646 hypo- and 520 hypermethylated promoters that distinguished a metastasis signature. Differentially methylated genes from these signatures were correlated with overall survival using TCGA-SKCM methylation data. Moreover, multivariate Cox analyses with LASSO regularization identified panels of 33 and 31 CpGs, respectively, from the malignancy and metastasis signatures that predicted poor survival. We found a concordant relationship between DNA methylation and transcriptional levels for genes from the malignancy (Pyroxd2 and Ptgfrn) and metastasis (Arnt2, Igfbp4 and Ptprf) signatures, which were both also correlated with melanoma prognosis. Altogether, this study reveals novel CpGs methylation markers associated with malignancy and metastasis that collectively could improve the survival prediction of melanoma patients.

Genes regulated by DNA methylation are involved in distinct phenotypes during melanoma progression and are prognostic factors for patients

In addition to mutations, epigenetic alterations are important contributors to malignant transformation and tumor progression. The aim of this work was to identify epigenetic events in which promoter or gene body DNA methylation induces gene expression changes that drive melanocyte malignant transformation and metastasis. We previously developed a linear mouse model of melanoma progression consisting of spontaneously immortalized melanocytes, premalignant melanocytes, a nonmetastatic tumorigenic, and a metastatic cell line. Here, through the integrative analysis of methylome and transcriptome data, we identified the relationship between promoter and/or gene body DNA methylation alterations and gene expression in early, intermediate, and late stages of melanoma progression. We identified adenylate cyclase type 3 (Adcy3) and inositol polyphosphate 4-phosphatase type II (Inpp4b), which affect tumor growth and metastatic potential, respectively. Importantly, the gene expression and DNA methylation profiles found in this murine model of melanoma progression were correlated with available clinical data from large population-based primary melanoma cohorts, revealing potential prognostic markers.

Comprehensive Gene Expression Analysis to Identify Differences and Similarities between Sex- and Stage-Stratified Melanoma Samples

Overall lower incidence and better prognosis are observed in female melanoma patients compared to males. As sex and stage differences in the context of melanoma gene expression are understudied, we aim to highlight them through statistical analysis of melanoma gene expression datasets. Data from seven online datasets, including normal skin, commonly acquired nevi, and melanomas, were collected and analyzed. Sex/stage-related differences were assessed using statistical analyses on survival, gene expression, and its variability. Significantly better overall survival in females was observed in stage I, II but not in stage III. Gene expression variability was significantly different between stages and sexes. Specifically, we observed a significantly lower variability in genes expressed in normal skin and nevi in females compared to males, as well as in female stage I, II melanomas. However, in stage III, variability was lower in males. Similarly, class comparison showed that the gene expression differences between sexes are most notable in non-melanoma followed by early-stage-melanoma samples. Sexual dimorphism is an important aspect to consider for a holistic understanding of early-stage melanomas, not only from the tumor characteristics but also from the gene expression points of view.

Revisiting miRNA Association with Melanoma Recurrence and Metastasis from a Machine Learning Point of View

The diagnostic and prognostic value of miRNAs in cutaneous melanoma (CM) has been broadly studied and supported by advanced bioinformatics tools. From early studies using miRNA arrays with several limitations, to the recent NGS-derived miRNA expression profiles, an accurate diagnostic panel of a comprehensive pre-specified set of miRNAs that could aid timely identification of specific cancer stages is still elusive, mainly because of the heterogeneity of the approaches and the samples. Herein, we summarize the existing studies that report several miRNAs as important diagnostic and prognostic biomarkers in CM. Using publicly available NGS data, we analyzed the correlation of specific miRNA expression profiles with the expression signatures of known gene targets. Combining network analytics with machine learning, we developed specific non-linear classification models that could successfully predict CM recurrence and metastasis, based on two newly identified miRNA signatures. Subsequent unbiased analyses and independent test sets (i.e., a dataset not used for training, as a validation cohort) using our prediction models resulted in 73.85% and 82.09% accuracy in predicting CM recurrence and metastasis, respectively. Overall, our approach combines detailed analysis of miRNA profiles with heuristic optimization and machine learning, which facilitates dimensionality reduction and optimization of the prediction models. Our approach provides an improved prediction strategy that could serve as an auxiliary tool towards precision treatment.