Skin Cancer Subtypes Research Articles

In vivo CRISPRi screen identified lncRNA portfolio crucial for cutaneous squamous cell carcinoma tumor growth.

Cutaneous squamous cell carcinoma (cSCC) accounts for 20% of all skin cancer deaths globally, making it the second-highest subtype of skin cancer. The prevalence of cSCC in humans, as well as the poor capacity for an efficient prognosis, highlights the need to uncover alternative actors and mechanisms at the foundation of skin cancer development. Significant advances have been made to better understand some key factors in cSCC progression. However, little is known about the role of noncoding RNAs, particularly of a specific category called long noncoding RNA (lncRNA). By performing pseudobulk analysis of single-cell sequencing data from normal and cSCC human skin tissues, we determined a global portfolio of lncRNAs specifically expressed in keratinocyte subpopulations. Integration of CRISPR interference screens in vitro and the xenograft model identified several lncRNAs impacting the growth of cSCC cancer lines both in vitro and in vivo. Among these, we further validated LINC00704 and LINC01116 as proliferation-regulating lncRNAs in cSCC lines and potential biomarkers of cSCC progression. Taken together, our study provides a comprehensive signature of lncRNAs with roles in regulating cSCC progression.

Leveraging AI and patient metadata to develop a novel risk score for skin cancer detection

Melanoma of the skin is the 17th most common cancer worldwide. Early detection of suspicious skin lesions (melanoma) can increase 5-year survival rates by 20%. The 7-point checklist (7PCL) has been extensively used to suggest urgent referrals for patients with a possible melanoma. However, the 7PCL method only considers seven meta-features to calculate a risk score and is only relevant for patients with suspected melanoma. There are limited studies on the extensive use of patient metadata for the detection of all skin cancer subtypes. This study investigates artificial intelligence (AI) models that utilise patient metadata consisting of 23 attributes for suspicious skin lesion detection. We have identified a new set of most important risk factors, namely “C4C risk factors”, which is not just for melanoma, but for all types of skin cancer. The performance of the C4C risk factors for suspicious skin lesion detection is compared to that of the 7PCL and the Williams risk factors that predict the lifetime risk of melanoma. Our proposed AI framework ensembles five machine learning models and identifies seven new skin cancer risk factors: lesion pink, lesion size, lesion colour, lesion inflamed, lesion shape, lesion age, and natural hair colour, which achieved a sensitivity of 80.46±2.50%\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$80.46\\pm 2.50\\%$$\\end{document} and a specificity of 62.09±1.90%\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$62.09\\pm 1.90\\%$$\\end{document} in detecting suspicious skin lesions when evaluated using the metadata of 53,601 skin lesions collected from different skin cancer diagnostic clinics across the UK, significantly outperforming the 7PCL-based method (sensitivity 68.09±2.10%\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$68.09\\pm 2.10\\%$$\\end{document}, specificity 61.07±0.90%\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$61.07\\pm 0.90\\%$$\\end{document}) and the Williams risk factors (sensitivity 66.32±1.90%\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$66.32\\pm 1.90\\%$$\\end{document}, specificity 61.71±0.6%\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$61.71\\pm 0.6\\%$$\\end{document}). Furthermore, through weighting the seven new risk factors we came up with a new risk score, namely “C4C risk score”, which alone achieved a sensitivity of 76.09±1.20%\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$76.09\\pm 1.20\\%$$\\end{document} and a specificity of 61.71±0.50%\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$61.71\\pm 0.50\\%$$\\end{document}, significantly outperforming the 7PCL-based risk score (sensitivity 73.91±1.10%\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$73.91\\pm 1.10\\%$$\\end{document}, specificity 49.49±0.50%\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$49.49\\pm 0.50\\%$$\\end{document}) and the Williams risk score (sensitivity 60.68±1.30%\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$60.68\\pm 1.30\\%$$\\end{document}, specificity 60.87±0.80%\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$60.87\\pm 0.80\\%$$\\end{document}). Finally, fusing the C4C risk factors with the 7PCL and Williams risk factors achieved the best performance, with a sensitivity of 85.24±2.20%\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$85.24\\pm 2.20\\%$$\\end{document} and a specificity of 61.12±0.90%\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$61.12\\pm 0.90\\%$$\\end{document}. We believe that fusing these newly found risk factors and new risk score with image data will further boost the AI model performance for suspicious skin lesion detection. Hence, the new set of skin cancer risk factors has the potential to be used to modify current skin cancer referral guidelines for all skin cancer subtypes, including melanoma.

Transcriptome-Wide Association Study Reveals New Molecular Interactions Associated with Melanoma Pathogenesis.

A transcriptome-wide association study (TWAS) was conducted on genome-wide association study (GWAS) summary statistics of malignant melanoma of skin (UK Biobank dataset) and The Cancer Genome Atlas-Skin Cutaneous Melanoma (TCGA-SKCM) gene expression weights to identify melanoma susceptibility genes. The GWAS included 2465 cases and 449,799 controls, while the gene expression testing was conducted on 103 cases. Afterward, a gene enrichment analysis was applied to identify significant TWAS associations. The melanoma's gene-microRNA (miRNA) regulatory network was constructed from the TWAS genes and their corresponding miRNAs. At last, a disease enrichment analysis was conducted on the corresponding miRNAs. The TWAS detected 27 genes associated with melanoma with p-values less than 0.05 (the top three genes are LOC389458 (RBAK), C16orf73 (MEIOB), and EIF3CL). After the joint/conditional test, one gene (AMIGO1) was dropped, resulting in 26 significant genes. The Gene Ontology (GO) biological process associated the extended gene set (76 genes) with protein K11-linked ubiquitination and regulation of cell cycle phase transition. K11-linked ubiquitin chains regulate cell division. Interestingly, the extended gene set was related to different skin cancer subtypes. Moreover, the enriched pathways were nsp1 from SARS-CoV-2 that inhibit translation initiation in the host cell, cell cycle, translation factors, and DNA repair pathways full network. The gene-miRNA regulatory network identified 10 hotspot genes with the top three: TP53, BRCA1, and MDM2; and four hotspot miRNAs: mir-16, mir-15a, mir-125b, and mir-146a. Melanoma was among the top ten diseases associated with the corresponding (106) miRNAs. Our results shed light on melanoma pathogenesis and biologically significant molecular interactions.

Advances in Chitosan-based Drug Delivery Systems in Melanoma: A Narrative Review.

Melanoma accounts for the minority of skin cancer cases. However, it has the highest mortality rate among the subtypes of skin cancer. At the early stages of the disease, patients show a good prognosis after the surgery, but developing metastases leads to a remarkable drop in patients' 5-year survival rate. Despite the advances made in the therapeutic approaches to this disease, melanoma treatment is still facing several obstacles. Systemic toxicity, water insolubility, instability, lack of proper biodistribution, inadequate cellular penetration, and rapid clearance are some of the challenges that should be addressed in the field of melanoma treatment. While various delivery systems have been developed to circumvent these challenges, chitosan-based delivery platforms have indicated significant success. Chitosan that is produced by the deacetylation of chitin can be formulated into different materials (e.g., nanoparticle, film, and hydrogel) due to its characteristics. Both in vitro and in vivo studies have reported that chitosan-based materials can be used in drug delivery systems while offering a solution for the common problems in this area, such as enhancing biodistribution and skin penetration as well as the sustained release of the drugs. Herein, we reviewed the studies concerning the role of chitosan as a drug delivery system in melanoma and discussed how these drug systems are used for delivering chemotherapeutic drugs (e.g., doxorubicin and paclitaxel), genes (e.g., TRAIL), and RNAs (e.g., miRNA199a and STAT3 siRNA) successfully. Furthermore, we take a look into the role of chitosan-based nanoparticles in neutron capture therapy.

Photodynamic therapy in the treatment of non-melanoma skin cancer: A literature review

Objective: The following study aims to analyze the mechanism of action, efficacy, safety and recent trends in the use of photodynamic therapy (PDT) as a therapeutic approach in the treatment of non-melanoma skin cancer. In addition, it seeks to provide a comprehensive overview of the current state of knowledge on the subject, highlighting its use and availability in the Brazilian scenario. Methods: The research was carried out using a literature review based on data published in Periódicos CAPES, Biblioteca Virtual de Saúde (BVS) and UpToDate. Results: PDT is a technique that has already been consolidated globally and has several advantages in the treatment of non-melanoma skin cancer, as well as actinic keratoses, even in comprehensive cases. PDT is considered a non-invasive, fast, safe method, with few side effects, providing rapid recovery and being well tolerated by patients. It has a significant cure rate and excellent aesthetic results, differing from other treatments often cited for their disadvantages, such as undesirable scars. Conclusion: It is crucial to further explore the benefits of PDT, considering its application in different subtypes of non-melanoma skin cancer and optimizing protocols to expand its clinical application. As a promising innovation, the technology has the prospect of contributing to improving the quality of life of patients affected by this specific type of skin cancer.

Vislocas: Vision transformers for identifying protein subcellular mis-localization signatures of different cancer subtypes from immunohistochemistry images

Proteins must be sorted to specific subcellular compartments to perform their functions. Abnormal protein subcellular localizations are related to many diseases. Although many efforts have been made in predicting protein subcellular localization from various static information, including sequences, structures and interactions, such static information cannot predict protein mis-localization events in diseases. On the contrary, the IHC (immunohistochemistry) images, which have been widely applied in clinical diagnosis, contains information that can be used to find protein mis-localization events in disease states. In this study, we create the Vislocas method, which is capable of finding mis-localized proteins from IHC images as markers of cancer subtypes. By combining CNNs and vision transformer encoders, Vislocas can automatically extract image features at both global and local level. Vislocas can be trained with full-sized IHC images from scratch. It is the first attempt to create an end-to-end IHC image-based protein subcellular location predictor. Vislocas achieved comparable or better performances than state-of-the-art methods. We applied Vislocas to find significant protein mis-localization events in different subtypes of glioma, melanoma and skin cancer. The mis-localized proteins, which were found purely from IHC images by Vislocas, are in consistency with clinical or experimental results in literatures. All codes of Vislocas have been deposited in a Github repository (https://github.com/JingwenWen99/Vislocas). All datasets of Vislocas have been deposited in Zenodo (https://zenodo.org/records/10632698).

MICIL: Multiple-Instance Class-Incremental Learning for skin cancer whole slide images

Artificial intelligence (AI) agents encounter the problem of catastrophic forgetting when they are trained in sequentially with new data batches. This issue poses a barrier to the implementation of AI-based models in tasks that involve ongoing evolution, such as cancer prediction. Moreover, whole slide images (WSI) play a crucial role in cancer management, and their automated analysis has become increasingly popular in assisting pathologists during the diagnosis process. Incremental learning (IL) techniques aim to develop algorithms capable of retaining previously acquired information while also acquiring new insights to predict future data. Deep IL techniques need to address the challenges posed by the gigapixel scale of WSIs, which often necessitates the use of multiple instance learning (MIL) frameworks. In this paper, we introduce an IL algorithm tailored for analyzing WSIs within a MIL paradigm. The proposed Multiple Instance Class-Incremental Learning (MICIL) algorithm combines MIL with class-IL for the first time, allowing for the incremental prediction of multiple skin cancer subtypes from WSIs within a class-IL scenario. Our framework incorporates knowledge distillation and data rehearsal, along with a novel embedding-level distillation, aiming to preserve the latent space at the aggregated WSI level. Results demonstrate the algorithm’s effectiveness in addressing the challenge of balancing IL-specific metrics, such as intransigence and forgetting, and solving the plasticity-stability dilemma.

Developing an efficient method for melanoma detection using CNN techniques

BackgroundMore and more genetic and metabolic abnormalities are now known to cause cancer, which is typically deadly. Any bodily part may become infected by cancerous cells, which can be fatal. Skin cancer is one of the most prevalent types of cancer, and its prevalence is rising across the globe. Squamous and basal cell carcinomas, as well as melanoma, which is clinically aggressive and causes the majority of deaths, are the primary subtypes of skin cancer. Screening for skin cancer is therefore essential.MethodsThe best way to quickly and precisely detect skin cancer is by using deep learning techniques. In this research deep learning techniques like MobileNetv2 and Dense net will be used for detecting or identifying two main kinds of tumors malignant and benign. For this research HAM10000 dataset is considered. This dataset consists of 10,000 skin lesion images and the disease comprises nonmelanocytic and melanocytic tumors. These two techniques can be used for detecting the malignant and benign. All these methods are compared and then a result can be inferred from their performance.ResultsAfter the model evaluation, the accuracy for the MobileNetV2 was 85% and customized CNN was 95%. A web application has been developed with the Python framework that provides a graphical user interface with the best-trained model. The graphical user interface allows the user to enter the patient details and upload the lesion image. The image will be classified with the appropriate trained model which can predict whether the uploaded image is cancerous or non-cancerous. This web application also displays the percentage of cancer affected.ConclusionAs per the comparisons between the two techniques customized CNN gives higher accuracy for the detection of melanoma.

Management of Cutaneous Squamous Cell Carcinoma of the Scalp: The Role of Imaging and Therapeutic Approaches

Cutaneous squamous cell carcinoma (cSCC) is the second most common subtype of skin cancer. The scalp is one of the most frequently affected locations and is associated with a higher rate of complications, compared to other locations. In addition, it has a characteristic thickness and anatomical structure that may influence both growth pattern and treatment of primary cSCC; while clinical peripheral margins may be easily achieved during the surgery, vertical excision of the tumor is limited by the skull. Despite having a unique anatomy, current guidelines do not contemplate specific recommendations for scalp cSCC, which leads to inconsistent decision-making in multidisciplinary committees when discussing tumors with high risk factors or with close margins. This article provides specific recommendations for the management of patients with scalp cSCC, based on current evidence, as well as those aspects in which evidence is lacking, pointing out possible future lines of research. Topics addressed include epidemiology, clinical presentation and diagnosis, imaging techniques, surgical and radiation treatments, systemic therapy for advanced cases, and follow-up. The primary focus of this review is on management of primary cSCC of the scalp with localized disease, although where relevant, some points about recurrent cSCCs or advanced disease cases are also discussed.

The m-TORC1 inhibitor Sirolimus increases the effectiveness of Photodynamic therapy in the treatment of cutaneous Squamous Cell Carcinoma, impairing NRF2 antioxidant signaling.

Squamous Cell Carcinoma (SCC) is a subtype of Non-Melanoma Skin Cancer, the most common group of malignancies worldwide. Photodynamic therapy (PDT) is a non-invasive treatment approved for specific subtypes of SCC. Some malignancies resist PDT, forming more aggressive tumors and multiple relapses. Thus, new approaches aimed at optimizing the response to PDT are needed. The mTORC1 inhibitor rapamycin, also known as Sirolimus (SRL), interferes with protein synthesis and cell metabolism. The use of SRL as an immunosuppressant is associated to lower rates of SCC in kidney-transplanted patients, which are frequently affected by this pathology. We have evaluated SRL pre-treatment efficacy to enhance the damage induced by PDT with Methyl 5-aminolevulinate in two different cutaneous SCC established cell lines (SCC13 and A431) in vitro and therapy sensitization in PDT-resistant cell lines. We tested for the first time the SRL + PDT combination in a SKH-1 mouse model of photocarcinogenesis, diminishing the frequency of lesions and restraining tumor growth. Molecular studies revealed that protoporphyrin IX and reactive oxygen species production induced by PDT were promoted by SRL pre-treatment. Lastly, SRL modifies the expression and intracellular location of NRF2, interfering with the downstream antioxidant response modulated by NQO1 and HO-1. In conclusion, we propose SRL as a potential adjuvant to enhance PDT efficacy for SCC treatment.

Association between statins exposure and risk of skin cancer: an updated meta-analysis.

This study aimed to investigate the relationship between statin (lipophilic statin and hydrophilic statin) exposure and the risk of skin cancer. The incidence of skin cancer under statin exposure was used as the primary outcome, and the relevant studies were screened from Web of Science, PubMed, Cochrane Library, and EBSCO electronic database until September 2022. Ten observational studies and two randomized controlled trials (RCTs) were included. The statistical results indicated that in lipophilic statins, the exposed group had a higher risk of skin cancer than the non-exposed group (OR: 1.09, P = 0.003). However, compared with the non-exposed group, there was no significant difference between hydrophilic statins exposure and the incidence of skin cancer (OR: 1.02, P = 0.341). Further subgroup analysis of the subtypes of statins revealed that compared with the non-exposed group, exposure to lovastatin (OR: 1.18, P = 0.048) or simvastatin (OR: 1.11, P < 0.001) was a risk factor for skin cancer. Besides, subgroup analysis based on the subtypes of skin cancer demonstrated that the risks of melanoma (OR: 1.13, P = 0.009), basal cell carcinoma (BCC) (OR: 1.05, P = 0.036), and squamous cell carcinoma (SCC) (OR: 1.13, P = 0.026) under lipophilic statin exposure were significantly higher than those in the non-exposed group. On the contrary, compared with the non-exposed group, the risk of BCC was significantly reduced under the exposure of hydrophilic statins (OR: 0.93, P = 0.031). This study showed that the relationship between statin exposure and skin cancer risk was affected by the subtypes of statins and skin cancer subtypes.

Risk of Melanoma and Nonmelanoma Skin Cancer in People with Vitiligo: United Kingdom Population−Based Cohort Study

Although genetic studies have found an inverse relationship between vitiligo and skin cancer, epidemiological evidence is conflicting. We investigated the risk of skin cancer in adults with vitiligo using United Kingdom electronic primary care records from the Optimum Patient Care Research Database 2010-2020. Vitiligo cases were age, sex, and general practitioner practice matched to population controls without vitiligo. Incidence of melanoma, nonmelanoma skin cancers (squamous cell carcinoma and basal cell carcinoma), and actinic keratoses was compared between vitiligo cases and controls using Cox regression. A total of 15,156 vitiligo cases were matched to 60,615 controls. Vitiligo was associated with a 38% reduced risk of new-onset skin cancer (adjusted hazard ratio [aHR]= 0.62, 95% confidence interval [CI]= 0.52-0.75, P < 0.001) and skin cancer subtypes; melanoma (aHR= 0.39, 95% CI= 0.23-0.65, P < 0.001), squamous cell carcinoma (aHR= 0.67, 95% CI= 0.49-0.90, P < 0.01), basal cell carcinoma (aHR= 0.65, 95% CI= 0.51-0.83, P < 0.001). There was no significant association for actinic keratosis (aHR= 0.88, 95% CI= 0.77-1.01). People with vitiligo have a markedly reduced incidence of melanoma and nonmelanoma skin cancer. Given concerns that some treatments, such as phototherapy, may increase skin cancer risk, this finding provides reassurance to people with vitiligo and clinicians managing the condition.

Detection of Merkel cell polyomavirus in multiple primary oral squamous cell carcinomas

Oral microbiome studies have mainly focussed on bacteria, with the relationship between viruses and oral cancers remaining poorly understood. Oral cancers can develop even in the absence of any history of daily smoking or drinking. Oral cancer patients frequently have multiple primary cancers in the oral cavity and other organs, such as the upper gastrointestinal tract. Merkel cell polyomavirus (MCPyV) is a novel oncovirus identified from a subtype of skin cancer in 2008. In this study, we investigated the potential involvement of MCPyV in the pathogenesis of oral squamous cell carcinoma (OSCC). Participants comprised 115 Japanese patients with OSCC (single primary: 109 tumours in 109 patients; multiple primaries: 16 tumours in 6 patients) treated in our department between 2014 and 2017. DNA was extracted from formalin-fixed paraffin-embedded specimens of primary lesions. MCPyV DNA copy counts were analysed by quantitative real-time polymerase chain reaction. Twenty-four of the 115 patients (20.9%) were positive for MCPyV DNA. No association was found between presence or absence of MCPyV DNA and clinical characteristics other than number of primary lesions. The MCPyV DNA-positive rate was significantly higher for multiple primary OSCCs (62.5%, 10/16 tumours) than for single primary OSCCs (16.5%, 18/109 tumours; P < 0.001). Furthermore, MCPyV DNA load was significantly higher for patients with multiple primaries (P < 0.05). MCPyV was observed more frequently and DNA load was significantly higher with multiple primary OSCCs than with single primary OSCC. MCPyV may play some role as an oncovirus for multiple primary OSCCs.

Computer-Aided Detection of Skin Cancer Detection from Lesion Images via Deep-Learning Techniques

More and more genetic and metabolic abnormalities are now known to cause cancer, which is typically fatal. Any particular body part may become infected by cancerous cells, which can be fatal. One of the most prevalent types of cancer is skin cancer, which is spreading worldwide.The primary subtypes of skin cancer are squamous and basal cell carcinomas, as well as melanoma, which is clinically aggressive and accounts for the majority of fatalities. Screening for skin cancer is so crucial.Deep Learning is one of the best options to quickly and precisely diagnose skin cancer (DL).This study used the Convolution Neural Network (CNN) deep learning technique to distinguish between the two primary types of cancers, malignant and benign, using the ISIC2018 dataset. The 3533 skin lesions in this dataset range from benign to malignant, and nonmelanocytic to melanocytic malignancies. The images were initially enhanced and edited using ESRGAN. The preprocessing stage involved resizing, normalising, and augmenting the images. By combining the results of numerous repetitions, the CNN approach might be used to categorise images of skin lesions. Several transfer learning models, such as Resnet50, InceptionV3, and Inception Resnet, were then used for fine-tuning. The uniqueness and contribution of this study are the preprocessing stages using ESRGAN and the testing of various models (including the intended CNN, Resnet50, InceptionV3, and Inception Resnet). Results from the model we developed matched those from the pretrained model exactly. The efficiency of the suggested strategy was proved by simulations using the ISIC 2018 skin lesion dataset. In terms of accuracy, the CNN model performed better than the Resnet50 (83.7%), InceptionV3 (85.8%), and Inception Resnet (84%) models.

Gallic acid has an inhibitory effect on skin squamous cell carcinoma and acts on the heat shock protein HSP90AB1

Differences in the features of aggressiveness of non-melanoma skin cancer (NMSC) subtypes, between basal cell carcinoma (BCC) and squamous cell carcinoma (SCC) are relevant characteristics. Comparing the characteristics between NMSC subtypes might help identify molecules associated with cancer metastasis and invasion. Considering these facts, the current study aimed to identify a molecular target for inhibiting skin cancer metastasis and invasion.Proteomic analysis suggested that heat shock protein 90 kDa, alpha, class B member 1 (HSP90AB1), pentaxin (PTX3), caspase-14 (CASP14), S100, actin-1, and profilin were the primary targets related to metastasis and invasion. However, after a differential expression comparison between BCC and SCC, HSP90AB1 was identified as the best target to repress metastasis and invasion. Based on molecular docking results, gallic acid (GA) was selected to inhibit HSP90AB1. A specific Hsp90ab1 siRNA targeting was designed and compared to GA. Interestingly, GA was more efficient in silencing HSP90AB1 than siRNAhsp90ab1. Hence, our data suggest that HSP90AB1 is a crucial biomarker for identifying invasion and metastasis and that its inhibition may be a viable strategy for treating skin cancer.

Detection of Skin Cancer Based on Skin Lesion Images Using Deep Learning

An increasing number of genetic and metabolic anomalies have been determined to lead to cancer, generally fatal. Cancerous cells may spread to any body part, where they can be life-threatening. Skin cancer is one of the most common types of cancer, and its frequency is increasing worldwide. The main subtypes of skin cancer are squamous and basal cell carcinomas, and melanoma, which is clinically aggressive and responsible for most deaths. Therefore, skin cancer screening is necessary. One of the best methods to accurately and swiftly identify skin cancer is using deep learning (DL). In this research, the deep learning method convolution neural network (CNN) was used to detect the two primary types of tumors, malignant and benign, using the ISIC2018 dataset. This dataset comprises 3533 skin lesions, including benign, malignant, nonmelanocytic, and melanocytic tumors. Using ESRGAN, the photos were first retouched and improved. The photos were augmented, normalized, and resized during the preprocessing step. Skin lesion photos could be classified using a CNN method based on an aggregate of results obtained after many repetitions. Then, multiple transfer learning models, such as Resnet50, InceptionV3, and Inception Resnet, were used for fine-tuning. In addition to experimenting with several models (the designed CNN, Resnet50, InceptionV3, and Inception Resnet), this study’s innovation and contribution are the use of ESRGAN as a preprocessing step. Our designed model showed results comparable to the pretrained model. Simulations using the ISIC 2018 skin lesion dataset showed that the suggested strategy was successful. An 83.2% accuracy rate was achieved by the CNN, in comparison to the Resnet50 (83.7%), InceptionV3 (85.8%), and Inception Resnet (84%) models.

Beyond Immunotherapy: Seizing the Momentum of Oncolytic Viruses in the Ideal Platform of Skin Cancers.

Simple SummaryOncolytic viruses (OVs) are the most innovative and promising class of intratumoral immunotherapies. The broad immunogenic landscape of skin cancer, accessible to intralesional infusion and available for direct response assessment, seems to be an ideal platform to expand the role of OVs. The established efficacy of immune checkpoint inhibitors (ICIs) in this field and their hypothetical synergy with OVs have generated expectations for their combined use beyond the current immunotherapy achievements. Despite the recent negative phase III results of the MASTERKEY-265 trial for the combination of T-VEC plus pembrolizumab, such projects, including different ICIs and various natural or genetically modified OVs, continue to attract considerable interest, with numerous clinical trials underway for all the subtypes of skin cancer. To date, the majority of studies confirm the safety of tested OVs in patients with advanced skin cancers but cannot clearly prove whether these viral agents add any therapeutic benefit in the standard ICI-based approach. The aim of this overview is to present the main findings related to the examined OV-containing regimens at pre-clinical and clinical levels, and to discuss the previous failures as well as the future perspectives of oncolytic virotherapy.Despite the durable remissions induced by ICIs and targeted therapies in advanced melanoma and non-melanoma skin cancers, both subtypes usually relapse. Many systematic therapies have been tested to increase efficacy and delay relapse in ICIs, but their success has been limited. Due the feasibility of this approach, skin cancers have become the ideal platform for intralesional infusions of many novel agents, including oncolytic viruses (OVs). Talimogene laherparepvec (T-VEC) was the first FDA-approved OV for the treatment of unresectable melanoma and this virus opened up further potential for the use of this class of agents, especially in combination with ICIs, in order to achieve deeper and longer immune-mediated responses. However, the recently announced phase III MASTERKEY-265 trial was not able to confirm that the addition of T-VEC to pembrolizumab treatment improves progression-free or overall survival over the use of pembrolizumab alone. Despite these results, numerous studies are currently active, evaluating T-VEC and several other OVs as monotherapies or in regimens with ICIs in different subtypes of skin cancer. This overview provides a comprehensive update on the evolution status of all available OVs in melanoma and non-melanoma skin cancers and summarizes the more interesting preclinical findings, the latest clinical evidence, and the future insights in relation to the expected selective incorporation of some of these OVs into oncological practice.

Polypoid Melanoma: Diagnostic Hardships Concerning A Rare Melanoma Variant

Skin cancer is the most common cancer in the United States, with a disproportionate amount of cases diagnosed as basal and squamous cell carcinoma. While melanoma accounts for only a small fraction of all skin cancers, it has greater potential for invasion and metastasis if not identified and treated early. Within this higher mortality subtype of skin cancer, there is a rare variant called polypoid melanoma (PM) that compounds the diagnostic hardships of distinguishing melanomas from more benign lesions. This aggressive melanoma has a wide variety of clinical presentations that can range from an amelanotic, sessile papule to a pedunculated, melanotic nodule. Its growth pattern also contributes to the lesion’s ambiguity, generally undergoing a slow development period that can spontaneously transition into rapid growth. This can present challenges for all potential parties involved, including the patient, primary physicians, and dermatologists alike. Such characteristics can act as barriers in clinically determining the urgency of a biopsy, thereby affecting time to diagnosis and prognosis of their patients. We report one such case of protracted diagnosis of polypoid amelanotic melanoma due to a combination of aesthetic ambiguity and lack of patient proactivity.

Eccrine Porocarcinoma of the Face is a Great Imitator With Aggressive Behavior.

IntroductionEccrine porocarcinoma (EPC) is a rare subtype of non-melanoma skin cancer developing in the intraepithelial portion of eccrine sweat glands. It is branded with a highly metastatic potential and increased rate of local recurrence after treatment. EPC showcased a trend of developing on the extremities, with presentation on the face sparse.ObjectivesAim of the study was to evaluate the frequency, clinical features, and course of this malignancy presented on the face.MethodsA retrospective review of the skin cancers excised between January 2010 and June 2021 was conducted in the plastic surgery department of a tertiary hospital. Patients were included in the study if EPC on the face was histologically confirmed. A prospectively maintained clinic database and the pathological reports were used to collect data.Results4 EPC cases on the face out of 3984 confirmed skin cancers were identified. None of the cases was suspected clinically, but the diagnosis was established following the histopathologic examination. An aggressive postoperative behavior was confirmed in 2 cases.ConclusionsThe variance in the clinical presentation and the non-specific characteristics are perplexing clinical diagnosis, with the histopathologic examination representing the current standard for confirmation. Early diagnosis and adequate surgical resection are recommended as treatment cornerstones. Clinical awareness ought to be raised and a definitive treatment protocol be established for optimized results.

Skin cancer risk among testicular germ-cell cancer survivors: a systematic review and meta-analysis.

Testicular germ-cell tumours (TGCT) are the most common cancer among young adult men. Previous studies suggested TGCT survivors have an increased risk for skin cancer. The goal of this study was to systematically review the literature and evidence regarding skin cancer risk among TGCT survivors compared with the general population. PubMed, EMBASE, Web of Science, Cochrane Databases and reference lists were included in the search. A systematic review of all comparative studies with more than 10 TGCT survivors reporting on skin cancer incidence was performed. A meta-analysis of the Standardized Incidence Rate (SIR) was calculated by pooling study-specific log-transformed estimates using the random-effects model. Risk of bias was assessed using the Newcastle-Ottawa Quality Assessment Scale. Nineteen studies that reported on 147 935 TGCT survivors were included. Pooled SIR for skin cancer and for melanoma incidence among TGCT survivors were 1.93 (95% CI 1.62-2.29, P < 0.0001) and 1.81 (95% CI 1.57-2.08, P < 0.0001), respectively. In conclusion, compared to the general population, TGCT survivors have an increased risk for developing skin cancer and melanoma. Additional long-term studies that include TGCT survivors, additional risk factors and all subtypes of skin cancer are required.