Malignant Skin Cancer Research Articles

Squamous Cell Carcinoma: From Diagnosis to Effective Treatment-A Comprehensive Guide

Squamous cell carcinoma (SCC) is the second most common malignant skin cancer, and its occurrence is on the rise globally. The main risk factors are cumulative sun exposure, immunosuppression, and inflammation. Early diagnosis and treatment are crucial to prevent metastasis and improve patient outcomes. Advanced diagnostic techniques, such as dermoscopy and reflectance confocal microscopy (RCM), have improved the accuracy of SCC diagnosis. Dermoscopy allows for better identification and targeted biopsy of suspicious areas, while RCM provides noninvasive and precise monitoring of treatment progress. The treatment of SCC has also advanced significantly in recent years. For patients with severe actinic damage and multiple in situ/low-risk SCC, cancer treatment has shown promising results. This approach involves the use of topical agents to target the tumor and the surrounding areas where cancer may develop. Additionally, novel therapies, such as epidermal growth factor receptor inhibitors and immune checkpoint inhibitors have been developed for locally advanced and metastatic SCC. In conclusion, the incidence of SCC has been increasing globally, and early diagnosis and treatment are crucial to prevent metastasis and improve patient outcomes. Advanced diagnostic techn

Wireless Light‐Emitting Diode‐Driven Functional Microneedle Devices for Skin Cancer Therapy

AbstractPhotodynamic therapy, a noninvasive cancer treatment strategy, is a promising remedy for malignant skin cancers. However, treatment of skin cancer with this method requires sufficient photosensitizer molecules to permeate into cancer cells before illumination for effective activation to induce potent reactive oxygen species for eliminating cancer cells. However, transdermal drug delivery using conventional photosensitizers faces major challenges due to skin barriers, diminishing the effectiveness of drug penetration and therapeutic efficacies. To overcome these limitations, biocompatible, physiologically dissolvable, and optically activatable functional microneedle devices are applied for effective percutaneous penetration of drug molecules into solid tumors in a murine model. The proposed wireless light‐emitting diode light‐driven functional microneedle device that effectively induces apoptosis of cancer cells and disruption of the tumor area and can enhance in vitro, ex vivo, and in vivo drug‐delivery effectiveness for treating skin cancer. The design and strategy of the present functional microneedle devices can help shed light on future advanced cancer therapy.

Merkel Cell Carcinoma Case Reports With Merkel Cell Polyomavirus Test and Review of the Literature.

Merkel cell carcinoma (MCC) is known as a rare and highly malignant neuroendocrine skin cancer and often occurs in the sun-exposed parts of the elderly individuals. In this article, we reported 2 cases of MCC and reviewed relative literature. Case 1 was a 91-year-old woman who presented with a half-year history of a brown nodule on the left temple. The histopathological and immunohistochemistry examination diagnosis was MCC with negative staining of Merkel cell polyomavirus large T antigen (CM2B4). Case 2 was a 76-year-old man with a nodule on his right buttock that gradually increased from approximately 3 mm to 1.5 cm in diameter in 1 month without pain. The biopsy diagnosis was MCC with positive staining of CM2B4. Previous studies have found that the genetic mutation and prognosis of polyomavirus-associated MCC (MCCP) and nonviral MCC (MCCN) are significantly different. Large T antigen plays a crucial role in Merkel cell polyomavirus (MCPyV) oncogenesis. Testing for the MCPyV at the onset of MCC is recommended, which is helpful in predicting the prognosis of patients.

Microneedle System Coated with Hydrogels Containing Protoporphyrin IX for Potential Application in Pharmaceutical Technology.

The article aims to outline the potential of treating malignant skin cancer with microneedles covered with polymer layers containing a photosensitizer-protoporphyrin IX disodium salt (PPIX). The usefulness of stereolithography (SLA), which is a form of 3D-printing technology, for the preparation of a microneedle system with protoporphyrin IX was demonstrated. The SLA method allowed for pyramid-shaped microneedles to be printed that were covered with three different 0.1% PPIX hydrogels based on sodium alginate, xanthan, and poloxamer. Rheological tests and microscopic analysis of the hydrogels were performed. Microneedles coated with two layers of poloxamer-based hydrogel containing 0.1% PPIX were subjected to release tests in Franz diffusion cells. The release profile of PPIX initially increased and then remained relatively constant. The amount of substance released after a four-hour test in three Franz cells was 0.2569 ± 0.0683 mg/cm2. Moreover, the acute toxicity of this type of microneedle was assessed using the Microtox system. The obtained results show the usefulness of further development studies on microneedles as carriers of photosensitizing agents.

Case Report: Gastric Metastasis revealing a Disseminated Skin Melanoma: A Case Report and Literature Review

Abstract Background Melanoma, an aggressive malignant skin cancer, has the ability to spread both locoregionally and to distant sites. The risk of metastasis is correlated to invasion depth and the presence of ulceration. Although gastrointestinal (GI) metastases are uncommon, gastric involvement is particularly rare. Case presentation We report a case of a 62-year-old male who presented with abdominal pain, dyspepsia, anorexia, and weight loss. On physical examination abdominal masses and hepatomegaly were detected. Radiological imaging showed widespread masses in the abdominal and thoracic regions. Upper GI endoscopy identified an umbilicated protruded lesion with central dark pigmentation at the antro-fundic junction. Histopathological examination and immunohistochemical staining were consistent with melanoma. A subsequent rigorous skin examination uncovered a primary malignant skin melanoma. Due to worsening general condition, the patient received palliative hospice care. Conclusion This report highlights the critical need for vigilant skin examination when encountering gastric lesions with dark pigmentation, which led to the identification of initially undetected cutaneous melanoma.

Topical Delivery of Dual Loaded Nano-Transfersomes Mediated Chemo-Photodynamic Therapy against Melanoma via Inducing Cell Cycle Arrest and Apoptosis.

Melanoma is a malignant skin cancer associated with high mortality rates and drug resistance, posing a significant threat to human health. The combination of chemotherapy and photodynamic therapy (PDT) represents a promising strategy to enhance antitumor efficacy through synergistic anti-cancer effects. Topical delivery of chemotherapeutic drugs and photosensitizers (PS) offers a non-invasive and safe way to treat melanoma. However, the effectiveness of these treatments is often hindered by challenges such as limited skin permeability and instability of the PS. In this study, transfersomes (TFS) were designed to facilitate transdermal delivery of the chemotherapeutic drug 5-Fluorouracil (5-FU) and the PS Imperatorin (IMP) for combined chemo-photodynamic therapy for melanoma. The cytotoxic and phototoxic effects of TFS-mediated PDT (TFS-UVA) were investigated in A375 cells and nude mice. The study also demonstrated that TFS-UVA generated intracellular ROS, induced G2/ M phase cell cycle arrest, and promoted cell apoptosis. In conclusion, this study indicated that 5-FU/ IMP-TFS serves as an effective transdermal therapeutic strategy for chemo-PDT in treating melanoma.

Deep learning approach for skin melanoma and benign classification using empirical wavelet decomposition.

Melanoma is a malignant skin cancer that causes high mortality. Early detection of melanoma can save patients' lives. The features of the skin lesion images can be extracted using computer techniques to differentiate early between melanoma and benign skin lesions. A new model of empirical wavelet decomposition (EWD) based on tan hyperbolic modulated filter banks (THMFBs) (EWD-THMFBs) was used to obtain the features of skin lesion images by MATLAB software. The EWD-THMFBs model was compared with the empirical short-time Fourier decomposition method based on THMFBs (ESTFD-THMFBs) and the empirical Fourier decomposition method based on THMFBs (EFD-THMFBs). The accuracy rates obtained for EWD-THMFBs, ESTFD-THMFBs, and EFD-THMFBs models were 100%, 98.89%, and 83.33%, respectively. The area under the curve (AUC) was 1, 0.97, and 0.91, respectively. The EWD-THMFBs model performed best in extracting features from skin lesion images. This model can be programmed on a mobile to detect skin lesions in rural areas by a nurse before consulting a dermatologist.

The Role of Caspases in Melanoma Pathogenesis.

Melanoma (malignant melanoma, MM) is an aggressive malignant skin cancer with an increasing incidence rate. The complete pathogenesis of MM in not clear. Due to DNA damage, mutations, dysregulation of growth factors, inactivation of tumor suppressor genes, and activation of oncogenes, excessive uncontrolled growth of abnormal melanocytes occurs in melanomas. Caspases are a group of proteolytic enzymes that participate in several processes important in regulating mechanisms at the cellular level. They play a role in cell homeostasis and programmed cell death (apoptosis) and in the regulation of non-apoptotic cell death processes. Dysregulation of caspase activation plays a role in the etiology of cancers, including melanoma. Caspases can initiate and execute apoptosis and are involved in regulating cell death and controlling tumor growth. These enzymes also inhibit tumor growth by cleaving and inactivating proteins that are involved in cell proliferation and angiogenesis. Moreover, caspases are involved in the activation of immune processes through the processing and presentation of tumor antigens, which facilitates recognition of the tumor by the immune system. The role of caspases in melanoma is complex, and they may inhibit melanoma growth and progression. This work aims to review the current knowledge of the role of individual caspases in melanoma pathogenesis.

Melanoma image synthesis: a review using generative adversarial networks

Melanoma is a highly malignant skin cancer that may be fatal if not promptly detected and treated. The limited availability of high-quality melanoma images, which are needed for training machine learning models, is one of the obstacles to detecting melanoma. Generative adversarial networks (GANs) have grown in popularity as a strong technique for image synthesis. This research is also targeted at the sustainable development goal (SDG) for health care. In this study, we survey existing GAN-based melanoma image synthesis methods. In this work, we briefly introduce GANs and how they may be used for generating synthetic images. Ensuring healthy lifestyles and promoting well-being for everyone, regardless of age, is the main aim. A comparative study is carried out on how GANs are used in current research to generate melanoma images and how they improve the classification performance of neural networks. Various public and proprietary datasets for training GANs in melanoma image synthesis are also discussed. Lastly, we assess the examined studies' performance using measures like the Frechet Inception distance (FID), Inception score, structural similarity ındex (SSIM), and various classification performance metrics. We compare the evaluated findings and suggest further GAN-based melanoma image-creation research.

A non-invasive approach to skin cancer diagnosis via graphene electrical tattoos and electrical impedance tomography

Objective. Making up one of the largest shares of diagnosed cancers worldwide, skin cancer is also one of the most treatable. However, this is contingent upon early diagnosis and correct skin cancer-type differentiation. Currently, methods for early detection that are accurate, rapid, and non-invasive are limited. However, literature demonstrating the impedance differences between benign and malignant skin cancers, as well as between different types of skin cancer, show that methods based on impedance differentiation may be promising. Approach. In this work, we propose a novel approach to rapid and non-invasive skin cancer diagnosis that leverages the technologies of difference-based electrical impedance tomography (EIT) and graphene electronic tattoos (GETs). Main results. We demonstrate the feasibility of this first-of-its-kind system using both computational numerical and experimental skin phantom models. We considered variations in skin cancer lesion impedance, size, shape, and position relative to the electrodes and evaluated the impact of using individual and multi-electrode GET (mGET) arrays. The results demonstrate that this approach has the potential to differentiate based on lesion impedance, size, and position, but additional techniques are needed to determine shape. Significance. In this way, the system proposed in this work, which combines both EIT and GET technology, exhibits potential as an entirely non-invasive and rapid approach to skin cancer diagnosis.

Aspects of Lighting and Color in Classifying Malignant Skin Cancer with Deep Learning

Malignant skin cancers are common in emerging countries, with excessive sun exposure and genetic predispositions being the main causes. Variations in lighting and color, resulting from the diversity of devices and lighting conditions during image capture, pose a challenge for automated diagnosis through digital images. Deep learning techniques emerge as promising solutions to improve the accuracy of identifying malignant skin lesions. This work aims to investigate the impact of lighting and color correction methods on automated skin cancer diagnosis using deep learning architectures, focusing on the relevance of these characteristics for accuracy in identifying malignant skin cancer. The developed methodology includes steps for hair removal, lighting, and color correction, defining the region of interest, and classification using deep neural network architectures. We employed deep learning techniques such as LCDPNet, LLNeRF, and DSN for lighting and color correction, which still need to be tested in this context. The results emphasize the importance of image preprocessing, especially in lighting and color adjustments, where the best results show an accuracy increase of between 3% and 4%. We observed that different deep neural network architectures react variably to lighting and color corrections. Some architectures are more sensitive to variations in these characteristics, while others are more robust. Advanced lighting and color correction can thus significantly improve the accuracy of malignant skin cancer diagnosis.

Estimating population-level 25-hydroxyvitamin D concentrations in Australia and New Zealand using the sun exposure (SUNEX) microsimulation model

Exposure to ultraviolet (UV) radiation from the sun has both harms and benefits for human health. The best-known benefit of sun exposure is the generation of vitamin D within the skin and the best-known harm is malignant skin cancer. Australia and New Zealand have very high ambient UV radiation, resulting in high rates of skin cancer incidence and mortality, yet there is an appreciable prevalence of vitamin D deficiency (defined as blood 25-hydroxyvitamin D (25(OH)D) < 50 nmol/L) in both populations. The purpose of this study was to create a microsimulation model to replicate population 25(OH)D concentrations of people living in Australia and New Zealand, thus enabling the effect of different population-wide interventions to be estimated. We used large population datasets containing data on sun behaviours and socio-demographic variables, and environmental data on UV radiation, ozone, and solar zenith angle. Latitude, weather and time of day were accounted for. We simulated the conversion of daily UV radiation to a standard vitamin D dose (SDD) (100 J/m2 vitamin D-weighted UV) and monthly accumulation of SDD to 25(OH)D concentration. The model was calibrated to match the seasonal prevalence of vitamin D deficiency. This report describes the Sun Exposure (SUNEX) microsimulation model, its development, data inputs and calibration against population prevalence of vitamin D deficiency.

Analyzing Single Nucleotide Polymorphisms and Their Chromosomal Distribution to Unravel the Genetic Landscape of Melanoma

Melanoma, a malignant skin cancer, continues to pose significant challenges in early detection, optimal treatment strategies, and understanding its etiology. This study aimed to elucidate the complex genetic landscape of melanoma by analyzing 2,641 Single Nucleotide Polymorphisms (SNPs) across 18 genomic variant categories within a melanoma cohort. The analysis revealed diverse genetic variations, including Missense and Frameshift Variants, with a significant number of unknown variants that require further exploration. A significant association (p &lt; 0.05) was found between the identified variants and the melanoma cohort. The study's findings underscore the heterogeneous nature of melanoma and emphasize the potential need for personalized therapeutic approaches. This comprehensive study contributes valuable insights into melanoma's genetic landscape and holds promise for future interventions and personalized prevention and treatment strategies.

A precise model for skin cancer diagnosis using hybrid U-Net and improved MobileNet-V3 with hyperparameters optimization

Skin cancer is a frequently occurring and possibly deadly disease that necessitates prompt and precise diagnosis in order to ensure efficacious treatment. This paper introduces an innovative approach for accurately identifying skin cancer by utilizing Convolution Neural Network architecture and optimizing hyperparameters. The proposed approach aims to increase the precision and efficacy of skin cancer recognition and consequently enhance patients' experiences. This investigation aims to tackle various significant challenges in skin cancer recognition, encompassing feature extraction, model architecture design, and optimizing hyperparameters. The proposed model utilizes advanced deep-learning methodologies to extract complex features and patterns from skin cancer images. We enhance the learning procedure of deep learning by integrating Standard U-Net and Improved MobileNet-V3 with optimization techniques, allowing the model to differentiate malignant and benign skin cancers. Also substituted the crossed-entropy loss function of the Mobilenet-v3 mathematical framework with a bias loss function to enhance the accuracy. The model's squeeze and excitation component was replaced with the practical channel attention component to achieve parameter reduction. Integrating cross-layer connections among Mobile modules has been proposed to leverage synthetic features effectively. The dilated convolutions were incorporated into the model to enhance the receptive field. The optimization of hyperparameters is of utmost importance in improving the efficiency of deep learning models. To fine-tune the model's hyperparameter, we employ sophisticated optimization methods such as the Bayesian optimization method using pre-trained CNN architecture MobileNet-V3. The proposed model is compared with existing models, i.e., MobileNet, VGG-16, MobileNet-V2, Resnet-152v2 and VGG-19 on the “HAM-10000 Melanoma Skin Cancer dataset". The empirical findings illustrate that the proposed optimized hybrid MobileNet-V3 model outperforms existing skin cancer detection and segmentation techniques based on high precision of 97.84%, sensitivity of 96.35%, accuracy of 98.86% and specificity of 97.32%. The enhanced performance of this research resulted in timelier and more precise diagnoses, potentially contributing to life-saving outcomes and mitigating healthcare expenditures.

A Novel Approach Using Transfer Learning Architectural Models Based Deep Learning Techniques for Identification and Classification of Malignant Skin Cancer

A Novel Approach Using Transfer Learning Architectural Models Based Deep Learning Techniques for Identification and Classification of Malignant Skin Cancer

The promotive role of lncRNA MIR205HG in proliferation, invasion, and migration of melanoma cells via the JMJD2C/ALKBH5 axis.

Melanoma is a highly malignant skin cancer. This study aimed to investigate the role of long non-coding RNA MIR205 host gene (lncRNA MIR205HG) in proliferation, invasion, and migration of melanoma cells via jumonji domain containing 2C (JMJD2C) and ALKB homolog 5 (ALKBH5). Real-time quantitative polymerase chain reaction or Western blot assay showed that MIR205HG, JMJD2C, and ALKBH5 were increased in melanoma cell lines. Cell counting kit-8, colony formation, and Transwell assays showed that silencing MIR205HG inhibited proliferation, invasion, and migration of melanoma cells. RNA immunoprecipitation, actinomycin D treatment, and chromatin immunoprecipitation showed that MIR205HG may bind to human antigen R (HuR, ELAVL1) and stabilized JMJD2C expression, and JMJD2C may increase the enrichment of H3K9me3 in the ALKBH5 promotor region to promote ALKBH5 transcription. The tumor xenograft assay based on subcutaneous injection of sh-MIR205HG-treated melanoma cells showed that silencing MIR205HG suppressed tumor growth and reduced Ki67 positive rate by inactivating the JMJD2C/ALKBH5 axis. Generally, MIR205HG facilitated proliferation, invasion, and migration of melanoma cells through HuR-mediated stabilization of JMJD2C and increasing ALKBH5 transcription by erasing H3K9me3.

The combination of single-cell and RNA sequencing analysis decodes the melanoma tumor microenvironment and identifies novel T cell-associated signature genes.

Skin cutaneous melanoma (SKCM), a malignant melanocyte-derived skin cancer, potentially leads to fatal outcomes without effective treatment. The variability in immunotherapy responses among melanoma patients is significantly influenced by the intricate immune microenvironment, particularly due to the status of tumor T cells, encompassing their activity, exhaustion levels, and antigen recognition capabilities. This study utilized single-cell RNA sequencing (scRNA-seq) to analyze 34 melanoma samples from two public datasets (GSE215120 and GSE115978). Herein, we extracted 706 marker genes associated with immune checkpoint (ICP) therapy from these T cells, 509 markers of T cells from 11 melanoma tissues, and eventually identified 33 candidate genes. These genes underwent LASSO and COX regression analyses to identify the signature genes. Of the initial 33 candidate genes, we successfully isolated six distinct T cell-associated immunotherapy-related genes (IRTGs). Additionally, the computation of each patient risk score proved beneficial in evaluating the immune cell infiltration level and functions as an independent prognostic factor for melanoma patient survival. The risk score results revealed promising predictive outcomes in determining the response of melanoma patients to immunotherapy. Notably, our study is the first to reveal the potential correlation between signature gene PEB4B and the immune microenvironment in melaoma, which was explored with multiple immunofluorescence (IF) and Immune Infiltration Assessment. In a conclusion, our findings demonstrate the potential utility of a risk score dependent on signature genes as a predictive tool for assessing the prognosis and response to immunotherapeutic interventions in melanoma patients.

Advancing immunotherapy for melanoma: the critical role of single-cell analysis in identifying predictive biomarkers.

Melanoma, a malignant skin cancer arising from melanocytes, exhibits rapid metastasis and a high mortality rate, especially in advanced stages. Current treatment modalities, including surgery, radiation, and immunotherapy, offer limited success, with immunotherapy using immune checkpoint inhibitors (ICIs) being the most promising. However, the high mortality rate underscores the urgent need for robust, non-invasive biomarkers to predict patient response to adjuvant therapies. The immune microenvironment of melanoma comprises various immune cells, which influence tumor growth and immune response. Melanoma cells employ multiple mechanisms for immune escape, including defects in immune recognition and epithelial-mesenchymal transition (EMT), which collectively impact treatment efficacy. Single-cell analysis technologies, such as single-cell RNA sequencing (scRNA-seq), have revolutionized the understanding of tumor heterogeneity and immune microenvironment dynamics. These technologies facilitate the identification of rare cell populations, co-expression patterns, and regulatory networks, offering deep insights into tumor progression, immune response, and therapy resistance. In the realm of biomarker discovery for melanoma, single-cell analysis has demonstrated significant potential. It aids in uncovering cellular composition, gene profiles, and novel markers, thus advancing diagnosis, treatment, and prognosis. Additionally, tumor-associated antibodies and specific genetic and cellular markers identified through single-cell analysis hold promise as predictive biomarkers. Despite these advancements, challenges such as RNA-protein expression discrepancies and tumor heterogeneity persist, necessitating further research. Nonetheless, single-cell analysis remains a powerful tool in elucidating the mechanisms underlying therapy response and resistance, ultimately contributing to the development of personalized melanoma therapies and improved patient outcomes.

A model for skin cancer using combination of ensemble learning and deep learning.

Skin cancer has a significant impact on the lives of many individuals annually and is recognized as the most prevalent type of cancer. In the United States, an estimated annual incidence of approximately 3.5 million people receiving a diagnosis of skin cancer underscores its widespread prevalence. Furthermore, the prognosis for individuals afflicted with advancing stages of skin cancer experiences a substantial decline in survival rates. This paper is dedicated to aiding healthcare experts in distinguishing between benign and malignant skin cancer cases by employing a range of machine learning and deep learning techniques and different feature extractors and feature selectors to enhance the evaluation metrics. In this paper, different transfer learning models are employed as feature extractors, and to enhance the evaluation metrics, a feature selection layer is designed, which includes diverse techniques such as Univariate, Mutual Information, ANOVA, PCA, XGB, Lasso, Random Forest, and Variance. Among transfer models, DenseNet-201 was selected as the primary feature extractor to identify features from data. Subsequently, the Lasso method was applied for feature selection, utilizing diverse machine learning approaches such as MLP, XGB, RF, and NB. To optimize accuracy and precision, ensemble methods were employed to identify and enhance the best-performing models. The study provides accuracy and sensitivity rates of 87.72% and 92.15%, respectively.

IDO2-siRNA Carried by Salmonella Combined with Nifuroxazide Attenuates Melanoma Growth.

Melanoma, a highly malignant skin cancer, is a hot topic in oncology treatment research. Nowadays, tumor immunotherapy, especially immunotherapy combined with other therapies, has attracted more and more attention. Indoleamine 2,3-dioxygenase 2 (IDO2), a ratelimiting enzyme of the tryptophan metabolism pathway in the urine of dogs with immunosuppression, is highly expressed in melanoma tissue. Additionally, IDO2 significantly inhibits the anti-tumor immunity of the body and has become a novel target of melanoma treatment. Nifuroxazide, as an intestinal antibacterial agent, was found to be able to inhibit Stat3 expression and exert an anti-tumor effect. Therefore, the present study aimed to examine the therapeutic effect of a self-designed IDO2-small interfering RNA (siRNA) delivered by attenuated Salmonella combined with nifuroxazide on melanoma- bearing mice, as well as determine its underlying mechanism. The effect of nifuroxazide on melanoma was detected by flow cytometry, CCK-8 and colony- forming ability assays, respectively, in vitro. The plasmid of siRNA-IDO2 was constructed, and the mice-bearing melanoma model was established. After the treatment, the tumor growth and survival rate were monitored, and the morphological changes of tumor tissue were detected by HE staining. The expression of related proteins was detected by Western blotting, and the expression of CD4 and CD8 positive T cells in tumor tissue was detected by IHC and IF, and the proportion of CD4 and CD8 positive T cells in spleen was detected by flow cytometry. The results demonstrated that the combination therapy effectively inhibited the phosphorylation of Stat3 and the expression level of IDO2 in melanoma cells, which effectively inhibited tumor growth and prolonged the survival time of tumor-bearing mice. The mechanistic study revealed that, compared with control groups and monotherapy groups, the combination treatment group reduced the atypia of tumor cells, increased the apoptotic rate, enhanced the infiltration of T lymphocytes in tumor tissue and increased the CD4+ and CD8+ T lymphocytes in the spleen, suggesting that the mechanism may be associated with the inhibition of tumor cell proliferation, the increase of apoptosis and the enhancement of the cellular immunity. In conclusion, IDO2-siRNA combined with nifuroxazide therapy could serve a significant role in the treatment of melanoma-bearing mice, enhance the tumor immunity and provide an experimental basis for identifying a novel combination method for the treatment of melanoma clinically.