Analysis of Melanoma Gene Expression Signatures at the Single-Cell Level Uncovers 45-Gene Signature Related to Prognosis

CD24, NFIL3, FN1, and KLRK1 signature predicts melanoma immunotherapy response and survival

Melanoma poses a significant health concern due to its propensity to metastasize and its high mortality rate. Immunotherapy has emerged as a promising treatment strategy for harnessing the patient's immune system to fight tumor cells. However, not all patients respond equally to immunotherapy, highlighting the need for predictive biomarkers to identify potential responders and optimize treatment strategies. Using data from 579 immunology-related genes evaluated by the NanoString nCounter Human Immunology v2 Panel, we integrated transcriptomic data with the clinical characteristics of 35 individuals to develop a predictive signature for immunotherapy response in melanoma patients. Through comprehensive analysis, we identified 18 genes upregulated in non-responder patients and three upregulated in responder patients. In multivariate analysis, CD24, NFIL3, FN1, and KLRK1 were identified as key predictors with significant potential for forecasting treatment outcomes. We then calculated a score incorporating the expression levels of these genes. The score achieved high accuracy in discriminating responders from non-responders, with an area under the curve of 0.935 (p < 0.001). The signature was also significantly associated with progression-free survival, overall survival, and survival following immunotherapy (p < 0.001). The validation of the signature in two independent cohorts confirmed its robustness and applicability, with areas under the curve of 0.758 (p = 0.036) and 0.833 (p = 0.004), respectively. This study represents a significant advance in precision medicine for melanoma. By identifying patients unlikely to benefit from immunotherapy, our approach could help optimize treatment allocation and improve patient outcomes. KEY MESSAGES: Novel 4-gene signature predicts immunotherapy failure in melanoma. High accuracy for personalized treatment decisions. Signature associated with decreased survival for non-responders. Signature validated in independent cohorts, enhancing generalizability. Potential to tailor treatment strategies and avoid unnecessary burden to patients.

Mapping integral cell-type-specific interferon-induced gene regulatory networks (GRNs) involved in systemic lupus erythematosus using systems and computational analysis

Systemic lupus erythematosus (SLE) is a systemic autoimmune disorder characterized by the production of autoantibodies, resulting in inflammation and organ damage. Although extensive research has been conducted on SLE pathogenesis, a comprehensive understanding of its molecular landscape in different cell types has not been achieved. This study uncovers the molecular mechanisms of the disease by thoroughly examining gene regulatory networks within neutrophils, dendritic cells, T cells, and B cells. Firstly, we identified genes and ncRNAs with differential expression in SLE patients compared to controls for different cell types. Furthermore, the derived differentially expressed genes were curated based on immune functions using functional enrichment analysis to create a protein-protein interaction network. Topological network analysis of the formed genes revealed key hub genes associated with each of the cell types. To understand the regulatory mechanism through which these hub genes function in the diseased state, their associations with transcription factors, and non-coding RNAs in different immune cell types were investigated through correlation analysis and regression models. Finally, by integrating these findings, distinct gene regulatory networks were constructed, which provide a novel perspective on the molecular, cellular, and immunological landscapes of SLE. Importantly, we reveal the crucial role of IRF3, IRF7, and STAT1 in neutrophils, dendritic cells, and T cells, where their aberrant upregulation in disease states might enhance the production of type I IFN. Furthermore, we found MYB to be a crucial regulator that might activate T cells toward autoimmune responses in SLE. Similarly, in B-cell lymphocytes, we found FOXO1 to be a key player in autophagy and chemokine regulation. These findings were also validated using single-cell RNASeq analysis using an independent dataset. Genotype variations of these genes were also explored using the GWAS central database to ensure their targetability. These findings indicate that IRF3, IRF7, STAT1, MYB, and FOXO1 are promising targets for therapeutic interventions for SLE.

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.

Malignant Melanoma: An Overview, New Perspectives, and Vitamin D Signaling

Melanoma, originating through malignant transformation of melanin-producing melanocytes, is a formidable malignancy, characterized by local invasiveness, recurrence, early metastasis, resistance to therapy, and a high mortality rate. This review discusses etiologic and risk factors for melanoma, diagnostic and prognostic tools, including recent advances in molecular biology, omics, and bioinformatics, and provides an overview of its therapy. Since the incidence of melanoma is rising and mortality remains unacceptably high, we discuss its inherent properties, including melanogenesis, that make this disease resilient to treatment and propose to use AI to solve the above complex and multidimensional problems. We provide an overview on vitamin D and its anticancerogenic properties, and report recent advances in this field that can provide solutions for the prevention and/or therapy of melanoma. Experimental papers and clinicopathological studies on the role of vitamin D status and signaling pathways initiated by its active metabolites in melanoma prognosis and therapy are reviewed. We conclude that vitamin D signaling, defined by specific nuclear receptors and selective activation by specific vitamin D hydroxyderivatives, can provide a benefit for new or existing therapeutic approaches. We propose to target vitamin D signaling with the use of computational biology and AI tools to provide a solution to the melanoma problem.

A method to improve the prediction performance of cancer-gene association by screening negative training samples through gene network data

This work focuses on data sampling in cancer-gene association prediction. Currently, researchers are using machine learning methods to predict genes that are more likely to produce cancer-causing mutations. To improve the performance of machine learning models, methods have been proposed, one of which is to improve the quality of the training data. Existing methods focus mainly on positive data, i.e. cancer driver genes, for screening selection. This paper proposes a low-cancer-related gene screening method based on gene network and graph theory algorithms to improve the negative samples selection. Genetic data with low cancer correlation is used as negative training samples. After experimental verification, using the negative samples screened by this method to train the cancer gene classification model can improve prediction performance. The biggest advantage of this method is that it can be easily combined with other methods that focus on enhancing the quality of positive training samples. It has been demonstrated that significant improvement is achieved by combining this method with three state-of-the-arts cancer gene prediction methods.

MODILM: towards better complex diseases classification using a novel multi-omics data integration learning model

BACKGROUND

Accurately classifying complex diseases is crucial for diagnosis and personalized treatment. Integrating multi-omics data has been demonstrated to enhance the accuracy of analyzing and classifying complex diseases. This can be attributed to the highly correlated nature of the data with various diseases, as well as the comprehensive and complementary information it provides. However, integrating multi-omics data for complex diseases is challenged by data characteristics such as high imbalance, scale variation, heterogeneity, and noise interference. These challenges further emphasize the importance of developing effective methods for multi-omics data integration.

RESULTS

We proposed a novel multi-omics data learning model called MODILM, which integrates multiple omics data to improve the classification accuracy of complex diseases by obtaining more significant and complementary information from different single-omics data. Our approach includes four key steps: 1) constructing a similarity network for each omics data using the cosine similarity measure, 2) leveraging Graph Attention Networks to learn sample-specific and intra-association features from similarity networks for single-omics data, 3) using Multilayer Perceptron networks to map learned features to a new feature space, thereby strengthening and extracting high-level omics-specific features, and 4) fusing these high-level features using a View Correlation Discovery Network to learn cross-omics features in the label space, which results in unique class-level distinctiveness for complex diseases. To demonstrate the effectiveness of MODILM, we conducted experiments on six benchmark datasets consisting of miRNA expression, mRNA, and DNA methylation data. Our results show that MODILM outperforms state-of-the-art methods, effectively improving the accuracy of complex disease classification.

CONCLUSIONS

Our MODILM provides a more competitive way to extract and integrate important and complementary information from multiple omics data, providing a very promising tool for supporting decision-making for clinical diagnosis.