Benign Nevi Research Articles

The Utilization of PRAME in the Diagnosis, Prognosis, and Treatment of Melanoma.

Melanoma, a deadly form of skin cancer, has seen improved survival rates due to advances in diagnosis and treatment, yet the need for further improvement remains critical. Tumor-associated antigens, such as PRAME (Preferentially Expressed Antigen in Melanoma), offer promising avenues for enhanced diagnostic precision, prognostic assessment, and targeted immunotherapy. PRAME, a cancer testis antigen, is selectively expressed in various cancers, including melanoma, and plays a key role in promoting tumorigenesis through inhibition of retinoic acid signaling, epithelial-to-mesenchymal transition, and immune evasion. This review explores the diagnostic utility of PRAME in distinguishing melanoma from benign nevi, its prognostic value in aggressive melanoma subtypes, and its potential as a therapeutic target in cancer vaccines and adoptive T-cell therapies. While PRAME-targeted therapies face challenges such as tumor heterogeneity and immune suppression, ongoing research aims to overcome these barriers, offering hope for more effective melanoma treatments.

The Impact of Melanoma Imaging Biomarker Cues on Detection Sensitivity and Specificity in Melanoma versus Clinically Atypical Nevi.

Incorporation of dermoscopy and artificial intelligence (AI) is improving healthcare professionals' ability to diagnose melanoma earlier, but these algorithms often suffer from a "black box" issue, where decision-making processes are not transparent, limiting their utility for training healthcare providers. To address this, an automated approach for generating melanoma imaging biomarker cues (IBCs), which mimics the screening cues used by expert dermoscopists, was developed. This study created a one-minute learning environment where dermatologists adopted a sensory cue integration algorithm to combine a single IBC with a risk score built on many IBCs, then immediately tested their performance in differentiating melanoma from benign nevi. Ten participants evaluated 78 dermoscopic images, comprised of 39 melanomas and 39 nevi, first without IBCs and then with IBCs. Participants classified each image as melanoma or nevus in both experimental conditions, enabling direct comparative analysis through paired data. With IBCs, average sensitivity improved significantly from 73.69% to 81.57% (p = 0.0051), and the average specificity improved from 60.50% to 67.25% (p = 0.059) for the diagnosis of melanoma. The index of discriminability (d') increased significantly by 0.47 (p = 0.002). Therefore, the incorporation of IBCs can significantly improve physicians' sensitivity in melanoma diagnosis. While more research is needed to validate this approach across other healthcare providers, its use may positively impact melanoma screening practices.

Malignant blue melanoma

Malignant blue melanomas (MBMs) arise from blue nevi and all related intradermal melanocytic proliferations. They harbor specific, mutually exclusive mutations in the G-coupled protein pathway, mainly involving GNAQ or GNA11. Other rare genetic drivers include CYSLTR2 or PCLB4 mutations. PKC and GRM1-gene fusions have been recently added to this list. MBMs have a predilection for the scalp area, presenting as rapidly growing nodules within a pre-existing lesion. Histopathologically, these tumors are located in the dermis and subcutaneous fat and consist of large nodules or expanding dense sheets. Tumor necrosis is commonly seen. Large spindle-shaped and epithelioid melanocytes with high-grade cytological atypia and frequent mitotic figures are at higher magnification. A benign blue nevus or intermediate-grade blue melanocytoma is frequently found on the side of the central mass. Loss of nuclear BAP1 immunoreactivity is a poor prognostic factor.

Utility of Keystone Flap in Armory of Dermatosurgeons

Abstract Purpose and Background: To create awareness among dermatosurgeons about the versality of keystone flaps in re-surfacing post-excisional small, medium and large skin defects. Aims and Objectives: Single-staged tension free re-surfacing of various sized skin defects using keystone flap and to have least donor site morbidity, primary healing and maximum functional outcome. Material and Methods: This retrospective study was conducted, between October 2021 – January 2023 in Department of Plastic Surgery, PGIMS Rohtak, on 15 males and 1 female aged between 18 - 65 years. Sixteen Keystone flaps (type IIA n=13, Omega cum Sydney melanoma unit modification n=3) were utilized for resurfacing skin defects arising after excision of basal cell carcinoma (n=3) and benign hairy nevus (n=2) on cheek, basal cell carcinoma forehead (n=1), electric burns on deltoid (n=2), giant neurofibroma back (n=1), tattoo forearm (n=3), chronic non-specific granuloma thumb (n=2) and pilonidal sinus disease (n=2). Skin with maximum expansibility was chosen as donor site with utmost care of facial lines and its aesthetic subunits. Flaps were dissected all around preserving maximum number of hand-held doppler-identified perforators in pedicular area. Sutures were removed on 10th to 15th post-operative day. Follow-ups ranged from 2 to 15 months. Results: None had wound dehiscence or loss of flap. Conclusion: Keystone flaps have defect-adaptive design, simple bio-geometry, technical simplicity, decreased downtime, cost effectivity and efficiency and can be designed to resurface post-excisional skin defect from head to toe with acceptable results, therefore should strongly be considered by dermatosurgeons while excising skin lesions.

New Aspects Regarding the Fluorescence Spectra of Melanin and Neuromelanin in Pigmented Human Tissue Concerning Hypoxia.

Melanin is a crucial pigment in melanomagenesis. Its fluorescence in human tissue is exceedingly weak but can be detected through advanced laser spectroscopy techniques. The spectral profile of melanin fluorescence distinctively varies among melanocytes, nevomelanocytes, and melanoma cells, with melanoma cells exhibiting a notably "red" fluorescence spectrum. This characteristic enables the diagnosis of melanoma both in vivo and in histological samples. Neuromelanin, a brain pigment akin to melanin, shares similar fluorescence properties. Its fluorescence can also be quantified with high spectral resolution using the same laser spectroscopic methods. Documented fluorescence spectra of neuromelanin in histological samples from the substantia nigra substantiate these findings. Our research reveals that the spectral behavior of neuromelanin fluorescence mirrors that of melanin in melanomas. This indicates that the typical red fluorescence is likely influenced by the microenvironment around (neuro)melanin, rather than by direct pigment interactions. Our ongoing studies aim to further explore this distinctive "red" fluorescence. We have observed this red fluorescence spectrum in post-mortem measurements of melanin in benign nevus. The characteristic red spectrum is also evident here (unlike the benign nevus in vivo), suggesting that hypoxia may contribute to this phenomenon. Given the central role of hypoxia in both melanoma development and treatment, as well as in fundamental Parkinson's disease mechanisms, this study discusses strategies aimed at reinforcing the hypothesis that red fluorescence from (neuro)melanin serves as an indicator of hypoxia.

In vivo Raman spectroscopic and fluorescence study of suspected melanocytic lesions and surrounding healthy skin.

Cutaneous melanoma is the most lethal skin cancer and noninvasively distinguishing it from benign tumor is a major challenge. Raman spectroscopic measurements were conducted on 65 suspected melanocytic lesions and surrounding healthy skin from 47 patients. Compared to the spectra of healthy skin, spectra of melanocytic lesions exhibited lower intensities in carotenoid bands and higher intensities in lipid and melanin bands, suggesting similar variations in the content of these components. Distinct variations were observed among the autofluorescence intensities of healthy skin, benign nevi and malignant melanoma. By incorporating autofluorescence information, the classification accuracy of the support vector machine for spectra of healthy skin, nevi, and melanoma reached 90.2%, surpassing the 87.9% accuracy achieved without autofluorescence, with this difference being statistically significant. These findings indicate the diagnostic value of autofluorescence intensity, which reflect differences in fluorophore content, chemical composition, and structure among healthy skin, nevi, and melanoma.

Using Deep Learning to Distinguish Highly Malignant Uveal Melanoma from Benign Choroidal Nevi.

Background: This study aimed to evaluate the potential of human-machine interaction (HMI) in a deep learning software for discerning the malignancy of choroidal melanocytic lesions based on fundus photographs. Methods: The study enrolled individuals diagnosed with a choroidal melanocytic lesion at a tertiary clinic between 2011 and 2023, resulting in a cohort of 762 eligible cases. A deep learning-based assistant integrated into the software underwent training using a dataset comprising 762 color fundus photographs (CFPs) of choroidal lesions captured by various fundus cameras. The dataset was categorized into benign nevi, untreated choroidal melanomas, and irradiated choroidal melanomas. The reference standard for evaluation was established by retinal specialists using multimodal imaging. Trinary and binary models were trained, and their classification performance was evaluated on a test set consisting of 100 independent images. The discriminative performance of deep learning models was evaluated based on accuracy, recall, and specificity. Results: The final accuracy rates on the independent test set for multi-class and binary (benign vs. malignant) classification were 84.8% and 90.9%, respectively. Recall and specificity ranged from 0.85 to 0.90 and 0.91 to 0.92, respectively. The mean area under the curve (AUC) values were 0.96 and 0.99, respectively. Optimal discriminative performance was observed in binary classification with the incorporation of a single imaging modality, achieving an accuracy of 95.8%. Conclusions: The deep learning models demonstrated commendable performance in distinguishing the malignancy of choroidal lesions. The software exhibits promise for resource-efficient and cost-effective pre-stratification.

Assessing the Impact of Simulated Color Vision Deficiency on Ophthalmologists' Ability to Differentiate between Choroidal Melanoma and Choroidal Nevus.

Background: Color vision deficiency (CVD) is an often-overlooked issue within the medical community, and its consequences remain insufficiently explored. We aim to evaluate how CVD affects diagnostic accuracy and distinguish between malignant choroidal melanoma and benign choroidal nevus among ophthalmologists. Methods: In this cross-sectional study, we engaged ophthalmologists through a web-based survey distributed via the professional ophthalmology society's social media channels. The survey encompassed a series of three fundus images representing normal fundus, choroidal nevus, and choroidal melanoma. Each image underwent simulation for the three primary types of CVD-protanopia, deuteranopia, and tritanopia-alongside a non-simulated version. Results: The study included 41 participants, averaging 40 years of age (±9.2), comprising 28 (68%) men and 13 (32%) women. Significantly lower rates of identifying orange pigments were observed in simulated protanopia images compared to non-simulated ones (p = 0.038). In simulated deutranopia images, the recognition of melanotic lesions was notably reduced compared to non-simulated images (p = 0.048). No such limitation was observed for tritanopia. However, participants retained their ability to identify subretinal fluid and estimate tumor thickness in simulated and non-simulated images. Concerning simulated images of choroidal nevi, participants misdiagnosed nevi as choroidal melanoma in 37% of cases in simulated protanopia nevi images and 41% in simulated deutranopia nevi images. This resulted in unnecessary referrals of benign lesions as malignant, emphasizing the potential for mistaken diagnoses. Nevertheless, almost all simulated images of malignant melanoma were correctly referred for specialized oncological treatment. Conclusions: The simulated CVD conditions of protanopia and deuteranopia affected the accuracy of identifying the melanotic nature of the choroidal tumor and the presence of orange pigments. This limitation led to challenges in correctly diagnosing choroidal melanoma and choroidal nevus, resulting in extra referrals for nevus cases. However, participants were safe and could still determine the possible risk of eyes with choroidal melanoma, so most referred melanoma cases to specialized oncologists as needed.

Optimized attention-induced multihead convolutional neural network with efficientnetv2-fostered melanoma classification using dermoscopic images.

Melanoma is an uncommon and dangerous type of skin cancer. Dermoscopic imaging aids skilled dermatologists in detection, yet the nuances between melanoma and non-melanoma conditions complicate diagnosis. Early identification of melanoma is vital for successful treatment, but manual diagnosis is time-consuming and requires a dermatologist with training. To overcome this issue, this article proposes an Optimized Attention-Induced Multihead Convolutional Neural Network with EfficientNetV2-fostered melanoma classification using dermoscopic images (AIMCNN-ENetV2-MC). The input pictures are extracted from the dermoscopic images dataset. Adaptive Distorted Gaussian Matched Filter (ADGMF) is used to remove the noise and maximize the superiority of skin dermoscopic images. These pre-processed images are fed to AIMCNN. The AIMCNN-ENetV2 classifies acral melanoma and benign nevus. Boosted Chimp Optimization Algorithm (BCOA) optimizes the AIMCNN-ENetV2 classifier for accurate classification. The proposed AIMCNN-ENetV2-MC is implemented using Python. The proposed approach attains an outstanding overall accuracy of 98.75%, less computation time of 98 s compared with the existing models.

Pioneering Dermatological Disease Detection with CNNs

Abstract: Skin cancer poses a significant public health concern globally, with its incidence steadily rising. Early and accurate detection plays a pivotal role in improving patient outcomes and survival rates. With leveraging advancements in deep learning, this project explores the efficacy of Convolutional Neural Networks (CNNs) in automating the classification and detection of skin cancer from dermatoscopic images. The methodology employed in this project involves the development and optimization of a CNN architecture tailored for skin cancer analysis. The proposed model harnesses the hierarchical feature learning capabilities of CNN to automatically extract intricate patterns and discriminative features from dermatoscopic images, enabling accurate differentiation among various skin lesion types, including melanoma, basal cell carcinoma, and benign nevi. In addition to classification, the developed CNN architecture integrates a lesion detection mechanism. This auxiliary functionality enables the identification and delineation of potentially cancerous regions within skin lesions. The proposed model’s detection capabilities are evaluated through bounding box prediction and segmentation masks, exhibit notable performance in localizing suspicious areas, aiding dermatologists in targeted examinations. With the help of this project, we can prevent the cause of damage to skin at an early stage.

Molecular Markers in Melanoma Progression: A Study on the Expression of miRNA Gene Subtypes in Tumoral vs. Benign Nevi.

This study investigates the differential expression of miRNA gene subtypes in tumoral versus benign nevi in individuals with melanoma, aiming to identify clinically significant correlations that could serve as reliable markers for assessing tumor stage and progression. Conducted between 2019 and 2022, this descriptive, quantitative observational research analyzed 90 formalin-fixed paraffin-embedded (FFPE) samples from the Pius Brinzeu County Emergency Clinical Hospital, Timisoara, including 45 samples of advanced-stage melanoma and 45 samples of pigmented nevi. miRNA purification and analysis were performed using the miRNeasy Kit and the Human Cancer PathwayFinder miScript miRNA PCR Array, with statistical analysis (including logistic regression) to determine associations with cancer staging, such as high Breslow index risk, number of mitoses, and vascular invasion. After the analysis and comparison of 180 miRNA gene subtypes, we selected 10 of the most upregulated and 10 most downregulated genes. The results revealed that hsa-miR-133b, hsa-miR-335-5p, hsa-miR-200a-3p, and hsa-miR-885-5p were significantly upregulated in melanoma samples, with fold changes ranging from 1.09 to 1.12. Conversely, hsa-miR-451a and hsa-miR-29b-3p showed notable downregulation in melanoma, with fold changes of 0.90 and 0.92, respectively. Additionally, logistic regression analysis identified hsa-miR-29b-3p (OR = 2.51) and hsa-miR-200a-3p (OR = 2.10) as significantly associated with an increased risk of a high Breslow index, while hsa-miR-127-3p and hsa-miR-451a were associated with a reduced risk. Conclusively, this study underscores the significant alterations in miRNA expression in melanoma compared to benign nevi and highlights the potential of specific miRNAs as biomarkers for melanoma progression. The identification of miRNAs with significant associations to melanoma characteristics suggests their utility in developing non-invasive, cost-effective diagnostic tools and in guiding therapeutic decisions, potentially improving patient outcomes in melanoma management.

Enhancing skin lesion segmentation with a fusion of convolutional neural networks and transformer models

Accurate segmentation is crucial in diagnosing and analyzing skin lesions. However, automatic segmentation of skin lesions is extremely challenging because of their variable sizes, uneven color distributions, irregular shapes, hair occlusions, and blurred boundaries. Owing to the limited range of convolutional networks receptive fields, shallow convolution cannot extract the global features of images and thus has limited segmentation performance. Because medical image datasets are small in scale, the use of excessively deep networks could cause overfitting and increase computational complexity. Although transformer networks can focus on extracting global information, they cannot extract sufficient local information and accurately segment detailed lesion features. In this study, we designed a dual-branch encoder that combines a convolution neural network (CNN) and a transformer. The CNN branch of the encoder comprises four layers, which learn the local features of images through layer-wise downsampling. The transformer branch also comprises four layers, enabling the learning of global image information through attention mechanisms. The feature fusion module in the network integrates local features and global information, emphasizes important channel features through the channel attention mechanism, and filters irrelevant feature expressions. The information exchange between the decoder and encoder is finally achieved through skip connections to supplement the information lost during the sampling process, thereby enhancing segmentation accuracy. The data used in this paper are from four public datasets, including images of melanoma, basal cell tumor, fibroma, and benign nevus. Because of the limited size of the image data, we enhanced them using methods such as random horizontal flipping, random vertical flipping, random brightness enhancement, random contrast enhancement, and rotation. The segmentation accuracy is evaluated through intersection over union and duration, integrity, commitment, and effort indicators, reaching 87.7 % and 93.21 %, 82.05 % and 89.19 %, 86.81 % and 92.72 %, and 92.79 % and 96.21 %, respectively, on the ISIC 2016, ISIC 2017, ISIC 2018, and PH2 datasets, respectively (code: https://github.com/hyjane/CCT-Net).

LncRNA LINC00847 Accelerates Melanoma Progression by Regulating MiR-133a-3p/TGFBR1 Axis.

Growing evidence has suggested that lncRNAs play a regulatory role in tumorigenesis. Dysregulation of a newly identified lncRNA (LINC00847) has been involved in several tumors. Nevertheless, the expression and roles of lncRNAs in skin melanoma remain unclear. Therefore, we attempted to investigate the expressions and roles of lncRNAs in this study. Expression levels of LINC00847 were quantified in tissue samples from the TCGA database and clinically recruited participants. LINC00847 was inhibited in cells by transfecting with si-LINC00847 or si-NC. Expressions of LINC00847 and miR-133a-3p were determined using RT-qPCR, and the TGFBR1 level was determined using Western blotting. Targeting sites of LINC00847 with miR-133a-3p and miR-133a-3p with TGFBR1 were predicted by bioinformatic tools and proved by dual-luciferase reporter system and RNA immunoprecipitation. Cell proliferation, invasion, and migration abilities were assessed using CCK8, cell colony formation, cell wound scratch, and transwell assay, respectively. In both TCGA and clinical cohorts, the expression of LINC00847 was abnormally upregulated in skin melanoma tissues than that of benign nevus. Besides, LINC00847 expression increased more markedly in A375 and SK-MEL-28 cells than in normal epidermal melanocytes (HEMa-LP cells). LINC00847 knockdown remarkably restrained skin melanoma cell proliferation, metastasis, and wound healing rate. Furthermore, miR-133a-3p/TGFBR1 was the downstream target for LINC00847. LINC00847 negatively regulated miR-133a-3p expression in skin melanoma cells. Both miR-133a-3p inhibitors and TGFBR1 vector transfection reversed the effect of LINC00847 silence in skin melanoma cells. LINC00847 was highly expressed in skin melanoma, and its overexpression accelerated the malignant tumor behavior of skin melanoma cells. The miR-133a-3p /TGFBR1 axis was involved in the roles of LINC00847 in skin melanoma.

Dermatological Malignancy Detection Using CNN

Abstract: Skin cancer is a prevalent and potentially life-threatening disease that continues to pose a significant public health concern. Starting stage detection Plays a crucial part in enhancing treatment efficacy. Furthermore, the advancement of deep learning has enabled the creation of automated frameworks for detecting maligant cancer. This proposed system utilizes a varied dataset encompassing various types of skin cancer, such as malignant melanomas and benign nevi, to educate a learning model. Utilizing CNN, pertinent features are automatically extracted from skin cancer images, facilitating precise classification. Assessment of the suggested system showcases encouraging outcomes concerning sensitivity, specificity, and overall precision. Comparative assessments against conventional techniques underscore the superior efficacy and effectiveness of the devised model in identifying potential skin cancer instances. Moreover, the system's interpretive capacity is heightened through the integration integration of attention mechanisms offering insights into the areas of focus within the skin cancer images

LPCAT1 promotes melanoma cell proliferation via Akt signaling.

Melanoma is the most lethal type of skin cancer with an increasing cutaneous cancer‑related mortality rate worldwide. Despite therapeutic advances in targeted therapy and immunotherapy, the overall survival of patients with melanoma remains unsatisfactory. Thus, a further understanding of the pathogenesis of melanoma may aid towards the development of therapeutic strategies. Lysophosphatidylcholine acyltransferase 1 (LPCAT1) is a key enzyme that converts lysophosphatidylcholine into phosphatidylcholine in lipid remodeling. In the present study, LPCAT1 was found to play a pro‑proliferative role in melanoma. Firstly, the expression of LPCAT1 was found to be upregulated in tissues from patients with melanoma compared with that in benign nevi. Subsequently, LPCAT1 knockdown was performed, utilizing short hairpin RNA, which induced melanoma cell cycle arrest at the G1/S transition and promoted cell death. Moreover, LPCAT1 facilitated melanoma cell growth in an Akt‑dependent manner. In summary, the results of the present study indicate that targeting LPCAT1 may impede cell proliferation by inhibiting Akt signaling, thus providing a promising therapeutic strategy for melanoma in clinical practice.

Diagnostic and therapeutic considerations in patients with bilateral diffuse uveal melanocytic proliferation.

This review aims to summarize the current knowledge concerning the clinical features, diagnostic work-up, and therapeutic approach of bilateral diffuse uveal melanocytic proliferation (BDUMP). A meticulous literature search was performed in the PubMed database. A supplementary search was made in Google Scholar to complete the collected items. Our search strategy utilized the following keywords: "bilateral diffuse uveal melanocytic proliferation", "BDUMP", and "Paraneoplastic Syndrome". Articles were considered based on their relevance, with the search spanning publications up to 2023. Studies were excluded if they did not contribute pertinent information or lacked methodological rigor. A critical appraisal of included studies was conducted, assessing study design, sample size, methodology, and potential bias, ensuring a thorough and transparent review process. BDUMP is a rare and potentially sight-threatening condition characterized by the bilateral proliferation of melanocytes within the uvea. BDUMP is typically observed in middle-aged or elderly individuals and is often associated with an underlying malignancy, most commonly of gastrointestinal origin. BDUMP is frequently misdiagnosed as a benign nevus or choroidal metastasis, leading to delayed diagnosis and treatment. The ophthalmic symptoms and signs typically precede the diagnosis of a systemic malignancy, emphasizing the crucial role of ophthalmologists in the recognition of BDUMP. Several diagnostic modalities can aid in the diagnosis of BDUMP, including ophthalmic examination, imaging studies such as optical coherence tomography, fluorescein angiography, and indocyanine green angiography, and biopsy of the uveal tissue. Treatment of BDUMP is directed towards the underlying malignancy and may include chemotherapy, radiotherapy, or surgical resection. Additionally, strict monitoring with regular follow-ups may contribute to the detection of new lesions and the reduction in the size of existing ones. BDUMP can be considered a potential biomarker in the management of malignancies, especially when the primary underlying tumor has not been detected. Further research is needed to better understand the pathogenesis of BDUMP and its association with malignancy.

Evaluation of the accuracy of clinical diagnosis of conjunctival neoplasms : Comparison with histological findings after excision

Neoplasms of the conjunctiva include many different entities with abroad variety of clinical presentations. This can make aprecise clinical diagnosis difficult. R0 resection is the gold standard treatment for most malignant conjunctival neoplasms, but not every benign lesions must treated by excision. In clinical practice it is important to make an accurate clinical diagnosis to enable the best possible management of conjunctival neoplasms. The aim of this study was to determine the accuracy of clinical diagnosis of neoplasms of the conjunctiva. Within aretrospective design, the data from all patients with excision of aconjunctival lesion between 2011 and 2020 in the Department of Ophthalmology of the UKSH Campus Kiel were extracted and analyzed. The specificity, sensitivity, and positive and negative predictive value for the preoperative clinical rating of dignity and diagnosis were evaluated based on the histological diagnostic findings. Of 220 included cases, 75% were benign and 25% malignant. The most frequent neoplasm of the conjunctiva was benign conjunctival nevus. The sensitivity for clinical prediction of abenign lesion was 0.86 (95% confidence interval [CI] 0.59-0.92), the specificity 0.95 (CI 0.85-0.99), and the positive predictive value 0.98 (CI 0.94-1.0). The sensitivity for clinical prediction of malign dignity was 0.95 (CI 0.85-0.99), the specificity 0.88 (CI 0.83-0.93), and the positive predictive value 0.73 (CI 0.61-0.83). The derived values for clinical diagnosis of conjunctival neoplasms can be rated as good. However, in clinical practice, untypical lesions can be hard to diagnose correctly, and the clinical diagnosis should be carefully reviewed; if in doubt, excision should be preferred.

Assessment of RAS-RAF-MAPK Pathway Mutation Status in Healthy Skin, Benign Nevi, and Cutaneous Melanomas: Pilot Study Using Droplet Digital PCR.

In the present study, we employed the ddPCR and IHC techniques to assess the prevalence and roles of RAS and RAF mutations in a small batch of melanoma (n = 22), benign moles (n = 15), and normal skin samples (n = 15). Mutational screening revealed the coexistence of BRAF and NRAS mutations in melanomas and nevi and the occurrence of NRAS G12/G13 variants in healthy skin. All investigated nevi had driver mutations in the BRAF or NRAS genes and elevated p16 protein expression, indicating cell cycle arrest despite an increased mutational burden. BRAF V600 mutations were identified in 54% of melanomas, and NRAS G12/G13 mutations in 50%. The BRAF mutations were associated with the Breslow index (BI) (p = 0.029) and TIL infiltration (p = 0.027), whereas the NRAS mutations correlated with the BI (p = 0.01) and the mitotic index (p = 0.04). Here, we demonstrate that the "young" ddPCR technology is as effective as a CE-IVD marked real-time PCR method for detecting BRAF V600 hotspot mutations in tumor biopsies and recommend it for extended use in clinical settings. Moreover, ddPCR was able to detect low-frequency hotspot mutations, such as NRAS G12/G13, in our tissue specimens, which makes it a promising tool for investigating the mutational landscape of sun-damaged skin, benign nevi, and melanomas in more extensive clinical studies.

Review: Are moles senescent?

Melanocytic nevi (skin moles) have been regarded as a valuable example of cell senescence occurring invivo. However, a study of induced nevi in a mouse model reported that the nevi were arrested by cell interactions rather than a cell-autonomous process like senescence, and that size distributions of cell nests within nevi could not be accounted for by a stochastic model of oncogene-induced senescence. Moreover, others reported that some molecular markers used to identify cell senescence in human nevi are also found in melanoma cells-not senescent. It has thus been questioned whether nevi really are senescent, with potential implications for melanoma diagnosis and therapy. Here I review these areas, along with the genetic, biological, and molecular evidence supporting senescence in nevi. In conclusion, there is strong evidence that cells of acquired human benign (banal) nevi are very largely senescent, though some must contain a minor non-senescent cell subpopulation. There is also persuasive evidence that this senescence is primarily induced by dysfunctional telomeres rather than directly oncogene-induced.

Kissing Nevus of Eyelid: A Rare Case Report

Introduction : Kissing nevus, also known as kissing mole, is a rare dermatological condition in the world and characterized by the presence of congenital nevi on both the upper and lower eyelids in symmetrical fashion. Additionally, this report highlights the treatment options available for patients with kissing nevus and discusses the importance of regular monitoring.
 Case Illustration : An 11-year-old female patient came with a complaint of a mole on the upper and lower eyelids since birth. The mole was felt to be getting bigger and starting to bother her. The patient feels insecure with the presence of this mole. The patient underwent nevus excision followed by retroauricular graft placement on the superior and inferior eyelidss. Then a biopsy showed benign nevus. After follow-up, it was found that the graft had grown and there was no necrotic tissue.
 Discussion : Kissing nevus is a rare skin condition that affects babies and young children. It is characterized by two identical birthmarks that appear on either side of the body, such as on the eyelids. Surgery involves removing the birthmarks surgically. Our patient underwent wide excision and palpebral reconstruction.
 Conclusion : Kissing nevuse is a unique skin condition marked by the presence of congenital nevi on both the upper and lower eyelids in a symmetrical pattern. It is worth noting that while treatment may be effective, there is a risk of scarring, infection, and other side effects associated with these procedures.