Somatic mutational landscapes of adherens junctions and their functional consequences in cutaneous melanoma development

Korla, Praveen Kumar; Chen, Chih-Chieh; Gracilla, Daniel Esguerra; Lai, Ming‐Tsung; Chen, Chih-Mei; Chen, Huan Yuan; Hwang, Tritium; Chen, Shih Yin; Sheu, Jim Jinn‐Chyuan

doi:10.7150/thno.46705

Cited by 6 publications

(4 citation statements)

References 75 publications

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“…Alterations in such regulation lead to melanoma development. Changes in cell-cell communications by somatic mutations in AJ cadherins function as one of mechanisms to trigger melanoma development [22]. We believe the adherens junctions and the wnt signalling pathway in part contribute to the prognostic importance of 23-gene score in melanoma.…”

Section: Discussionmentioning

confidence: 93%

A Novel 23-Gene Expression Signature is Predictor of Inferior Prognosis in Melanoma

Zhang

et al. 2021

Preprint

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Background: Melanoma is a serious form of skin cancer that begins in melanocytes. Metastasis, somatic mutations and gene expression profiles are important prognostic factors for melanoma patients. However, accurate prediction of patient prognosis remains an unsolved problem for the disease. This study was to develop a novel gene profile to accurately classify melanoma patients into subgroups with different survival probabilities. Methods: Survival-related genes were determined by Kaplan–Meier survival analysis and multivariate analysis using the expression and clinical data of 467 melanoma patients from The Cancer Genome Atlas (TCGA) database and validated in an independent Gene Expression Omnibus (GEO) dataset. Feature selection was performed by the Least Absolute Shrinkage and Selection Operator (LASSO) method. A prognostic 23-gene score was established and compared with two known gene-expression risk scores. The stratification of melanoma patients was performed by unsupervised hierarchical clustering of 23 gene expression levels to identify clusters of melanoma patients with different survival probabilities. Results: The LASSO model comprising 23 genes was considered as the optimal model. The 23-gene score was associated with increased mortality in melanoma patients regardless of clinicopathological characteristics. Hierarchical clustering analysis of the 23 genes revealed three subgroups of melanoma patients. The cluster3 melanoma tumours were associated with higher 23-gene score and poorer overall survival than cluster1 and cluster2 tumours. The 23-gene score had higher area under curve (0.76) than the 8-gene risk score and IRGs score (0.58 and 0.59) in the prediction of overall survival of melanoma patients. Conclusions: The 23-gene score is superior to the two established prognostic gene signatures in the prediction of prognosis of melanoma patients.

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Section: Discussionmentioning

confidence: 93%

A Novel 23-Gene Expression Signature is Predictor of Inferior Prognosis in Melanoma

Zhang

et al. 2021

Preprint

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“…As a result, we found many melanoma progression–related KEGG pathways, e.g. melanogenesis [ 43 , 44 ], MAPK signaling pathways [ 45 , 46 ] and adherens junction [ 47 , 48 ] ( Figure 5D and Supplementary Figure 26 ). Notably, the HIF-1 signaling pathway was again exclusively identified by SCARP ( Figure 5D , Supplementary Figures 28 – 30 and Supplementary Table 5 ) [ 43 , 49 ].…”

Section: Resultsmentioning

confidence: 99%

Incorporating network diffusion and peak location information for better single-cell ATAC-seq data analysis

Yu,

Leng,

Hou

et al. 2024

Briefings in Bioinformatics

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Single-cell assay for transposase-accessible chromatin using sequencing (scATAC-seq) data provided new insights into the understanding of epigenetic heterogeneity and transcriptional regulation. With the increasing abundance of dataset resources, there is an urgent need to extract more useful information through high-quality data analysis methods specifically designed for scATAC-seq. However, analyzing scATAC-seq data poses challenges due to its near binarization, high sparsity and ultra-high dimensionality properties. Here, we proposed a novel network diffusion–based computational method to comprehensively analyze scATAC-seq data, named Single-Cell ATAC-seq Analysis via Network Refinement with Peaks Location Information (SCARP). SCARP formulates the Network Refinement diffusion method under the graph theory framework to aggregate information from different network orders, effectively compensating for missing signals in the scATAC-seq data. By incorporating distance information between adjacent peaks on the genome, SCARP also contributes to depicting the co-accessibility of peaks. These two innovations empower SCARP to obtain lower-dimensional representations for both cells and peaks more effectively. We have demonstrated through sufficient experiments that SCARP facilitated superior analyses of scATAC-seq data. Specifically, SCARP exhibited outstanding cell clustering performance, enabling better elucidation of cell heterogeneity and the discovery of new biologically significant cell subpopulations. Additionally, SCARP was also instrumental in portraying co-accessibility relationships of accessible regions and providing new insight into transcriptional regulation. Consequently, SCARP identified genes that were involved in key Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways related to diseases and predicted reliable cis-regulatory interactions. To sum up, our studies suggested that SCARP is a promising tool to comprehensively analyze the scATAC-seq data.

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“…We further conducted the functional enrichment analysis using the genes in SCARP-derived GRN. As a result, we found many melanoma progression-related KEGG pathways, e.g., melanogenesis [41][42], MAPK signaling pathways [43][44], and Adherens junction [45][46] (Fig. 4d and Supplementary Fig.…”

Section: Resultsmentioning

confidence: 99%

Single-Cell ATAC-seq analysis via Network Refinement with peaks location information

Sun

Hou

et al. 2022

Preprint

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Single-cell ATAC-seq (scATAC-seq) data provided new insights into the elaboration of cellular heterogeneity and transcriptional regulation. However, scATAC-seq data posed challenges for data analysis because of its near binarization, high sparsity, and ultra-high dimensionality properties. Here we proposed a novel network diffusion-based method to comprehensively analyze scATAC-seq data, named Single-Cell ATAC-seq Analysis via Network Refinement with Peaks Location Information (SCARP). By modeling the prior probability of co-accessibility between adjacent peaks as a decreasing function of genomic distance, SCARP is the first scATAC-seq analysis method that utilizes the genomic information of peaks, which contributed to characterizing co-accessibility of peaks. SCARP used network to model the accessible relationships between cells and peaks, aggregated information with the diffusion method, and then performed dimensionality reduction to obtain low-dimensional cell embeddings as well as peak embeddings. We have demonstrated through sufficient experiments that SCARP facilitated superior analysis of scATAC-seq data. Specifically, SCARP exhibited outstanding cell clustering performance to better elucidate cell heterogeneity, and can be used to reveal new biologically significant cell subpopulations. SCARP was also instrumental in portraying co-accessibility relationships of accessible regions and providing new insight into transcriptional regulation, and those SCARP-derived genes were involved in some key KEGG pathways related to diseases. To sum up, our studies suggested that SCARP is a promising tool to comprehensively analyze the scATAC-seq data from a new perspective.

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Somatic mutational landscapes of adherens junctions and their functional consequences in cutaneous melanoma development

Cited by 6 publications

References 75 publications

A Novel 23-Gene Expression Signature is Predictor of Inferior Prognosis in Melanoma

A Novel 23-Gene Expression Signature is Predictor of Inferior Prognosis in Melanoma

Incorporating network diffusion and peak location information for better single-cell ATAC-seq data analysis

Single-Cell ATAC-seq analysis via Network Refinement with peaks location information

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