Identification of a gene signature for discriminating metastatic from primary melanoma using a molecular interaction network approach

GLI Transcriptional Targets S100A7 and KRT16 Show Upregulated Expression Patterns in Epidermis Overlying the Tumor Mass in Melanoma Samples

Although not completely understood, the role of the Hedgehog-GLI (HH-GLI) signaling pathway in melanoma and epithelial skin tumors has been reported before. In this study, we confirmed in various melanoma cell line models that keratin 16 (KRT16) and S100 Calcium-Binding Protein A7 (S100A7) are transcriptional targets of GLI Family Zinc Finger (GLI) proteins. Besides their important role in protecting and maintaining the epidermal barrier, keratins are somehow tightly connected with the S100 family of proteins. We found that stronger expression of KRT16 indeed corresponds to stronger expression of S100A7 in our clinical melanoma samples. We also report a trend regarding staining of GLI1, which corresponds to stronger staining of GLI3, KRT16, and S100A7 proteins. The most interesting of our findings is that all the proteins are detected specifically in the epidermis overlying the tumor, but rarely in the tumor itself. The examined proteins were also not detected in the healthy epidermis at the edges of the sample, suggesting that the staining is specific to the epidermis overlaying the tumor mass. Of all proteins, only S100A7 demonstrated a statistically significant trend regarding tumor staging and staining intensity. Results from our clinical samples prove that immune infiltration is an important feature of melanoma. Pigmentophages and tumor-infiltrating lymphocytes (TIL) demonstrate a significant association with tumor stage, while mononuclear cells are equally present in all stages. For S100A7, we found an association between the number of TILs and staining intensity. Considering these new findings presented in our study, we suggest a more detailed examination of the possible role of the S100A7 protein as a biomarker in melanoma.

Mutation p.Arg127Pro in the 1A Domain of KRT16 Causes Pachyonychia Congenita in Chinese Patient: A Case Report of PC Associated with Acral Melanoma

Pachyonychia congenita (PC) is a group of rare hereditary disorders, characterised by hypertrophic nails and palmoplantar keratoderma (PPK), particularly localised to the pressure areas of the feet. At a molecular level, it is caused by mutations in genes encoding KRT6A, KRT6B, KRT6C, KRT16, or KRT17. To identify the underlying gene mutation in a Chinese family with PC presenting with disabling palmoplantar keratoderma and subsequent associated acral melanoma. Genomic DNA was extracted from peripheral blood samples of three available individuals in the Chinese family, which included the patient and his two unaffected sisters. The index patient presented with severe palmoplantar keratoderma as well as a newly diagnosed acral malignant melanoma (MM). Whole-exome sequencing (WES) was carried out with amplification of exon 1 of KRT16 by polymerase chain reaction (PCR). PCR products were then sequenced to identify potential mutations. We identified the proline substitution mutation p.Arg127Pro (c.380G>C) in our patient's 1A domain of KRT16. The same mutation was not found in his sisters or unrelated healthy controls. The mutation (p.Arg127Pro (c.380G>C)) in KRT16 has been reported in Dutch patients with PC. However, it is the first such report of a patient with a PC of Chinese origin. In addition, the acral MM occurred under the background of genetic PPK caused by KRT16 mutation in this patient.

The transmembrane proteins (TMEM) and their role in cell proliferation, migration, invasion, and epithelial-mesenchymal transition in cancer

Transmembrane proteins (TMEM) are located in the different biological membranes of the cell and have at least one passage through these cellular compartments. TMEM proteins carry out a wide variety of functions necessary to maintain cell homeostasis TMEM165 participates in glycosylation protein, TMEM88 in the development of cardiomyocytes, TMEM45A in epidermal keratinization, and TMEM74 regulating autophagy. However, for many TMEM proteins, their physiological function remains unknown. The role of these proteins is being recently investigated in cancer since transcriptomic and proteomic studies have revealed that exits differential expression of TMEM proteins in different neoplasms concerning cancer-free tissues. Among the cellular processes in which TMEM proteins have been involved in cancer are the promotion or suppression of cell proliferation, epithelial-mesenchymal transition, invasion, migration, intravasation/extravasation, metastasis, modulation of the immune response, and response to antineoplastic drugs. Inclusive data suggests that the participation of TMEM proteins in these cellular events could be carried out through involvement in different cell signaling pathways. However, the exact mechanisms not clear. This review shows a description of the involvement of TMEM proteins that promote or decrease cell proliferation, migration, and invasion in cancer cells, describes those TMEM proteins for which both a tumor suppressor and a tumor promoter role have been identified, depending on the type of cancer in which the protein is expressed. As well as some TMEM proteins involved in chemoresistance. A better characterization of these proteins is required to improve the understanding of the tumors in which their expression and function are altered; in addition to improving the understanding of the role of these proteins in cancer will show those TMEM proteins be potential candidates as biomarkers of response to chemotherapy or prognostic biomarkers or as potential therapeutic targets in cancer.

Identification of Breast Cancer Metastasis Markers from Gene Expression Profiles Using Machine Learning Approaches

Cancer metastasis accounts for approximately 90% of cancer deaths, and elucidating markers in metastasis is the first step in its prevention. To characterize metastasis marker genes (MGs) of breast cancer, XGBoost models that classify metastasis status were trained with gene expression profiles from TCGA. Then, a metastasis score (MS) was assigned to each gene by calculating the inner product between the feature importance and the AUC performance of the models. As a result, 54, 202, and 357 genes with the highest MS were characterized as MGs by empirical p-value cutoffs of 0.001, 0.005, and 0.01, respectively. The three sets of MGs were compared with those from existing metastasis marker databases, which provided significant results in most comparisons (p-value < 0.05). They were also significantly enriched in biological processes associated with breast cancer metastasis. The three MGs, SPPL2C, KRT23, and RGS7, showed highly significant results (p-value < 0.01) in the survival analysis. The MGs that could not be identified by statistical analysis (e.g., GOLM1, ELAVL1, UBP1, and AZGP1), as well as the MGs with the highest MS (e.g., ZNF676, FAM163B, LDOC2, IRF1, and STK40), were verified via the literature. Additionally, we checked how close the MGs were to each other in the protein-protein interaction networks. We expect that the characterized markers will help understand and prevent breast cancer metastasis.

Systems-level profiling of early peripheral host-response landscape variations across COVID-19 severity states in an Indian cohort

Host immune response to COVID-19 plays a significant role in regulating disease severity. Although big data analysis has provided significant insights into the host biology of COVID-19 across the world, very few such studies have been performed in the Indian population. This study utilizes a transcriptome-integrated network analysis approach to compare the immune responses between asymptomatic or mild and moderate-severe COVID-19 patients in an Indian cohort. An immune suppression phenotype is observed in the early stages of moderate-severe COVID-19 manifestation. A number of pathways are identified that play crucial roles in the host control of the disease such as the type I interferon response and classical complement pathway which show different activity levels across the severity spectrum. This study also identifies two transcription factors, IRF7 and ESR1, to be important in regulating the severity of COVID-19. Overall this study provides a deep understanding of the peripheral immune landscape in the COVID-19 severity spectrum in the Indian genetic background and opens up future research avenues to compare immune responses across global populations.

RNA-seq and ChIP-seq Identification of Unique and Overlapping Targets of GLI Transcription Factors in Melanoma Cell Lines

BACKGROUND

Despite significant progress in therapy, melanoma still has a rising incidence worldwide, and novel treatment strategies are needed. Recently, researchers have recognized the involvement of the Hedgehog-GLI (HH-GLI) signaling pathway in melanoma and its consistent crosstalk with the MAPK pathway. In order to further investigate the link between the two pathways and to find new target genes that could be considered for combination therapy, we set out to find transcriptional targets of all three GLI proteins in melanoma.

METHODS

We performed RNA sequencing on three melanoma cell lines (CHL-1, A375, and MEL224) with overexpressed GLI1, GLI2, and GLI3 and combined them with the results of ChIP-sequencing on endogenous GLI1, GLI2, and GLI3 proteins. After combining these results, 21 targets were selected for validation by qPCR.

RESULTS

RNA-seq revealed a total of 808 differentially expressed genes (DEGs) for GLI1, 941 DEGs for GLI2, and 58 DEGs for GLI3. ChIP-seq identified 527 genes that contained GLI1 binding sites in their promoters, 1103 for GLI2 and 553 for GLI3. A total of 15 of these targets were validated in the tested cell lines, 6 of which were detected by both RNA-seq and ChIP-seq.

CONCLUSIONS

Our study provides insight into the unique and overlapping transcriptional output of the GLI proteins in melanoma. We suggest that our findings could provide new potential targets to consider while designing melanoma-targeted therapy.

Bioinformatic analysis identifies epidermal development genes that contribute to melanoma progression

Several diagnostic and prognostic markers for melanoma have been identified in last few years. However, their actual contribution to melanoma progression have not been investigated in detail. This study was aimed to identify genes, biological processes, and signaling pathways implicated in melanoma progression by applying bioinformatics analysis. We identified nine differentially expressed genes (DEGs) (IL36RN, KRT6A, KRT6B, KRT16, S100A7, SPRR1A, SPRR1B, SPRR2B, and KLK7) that were upregulated in primary melanoma compared with metastatic melanoma in all five datasets analyzed. All these genes except IL36RN, both form a protein-protein interaction network and have cellular functions associated with constitutive processes of keratinocytes. Thus, they were generically termed Epidermal Development and Cornification (EDC) genes. The differential expression of these genes in primary and metastatic melanoma was confirmed in the TCGA-SKCM cohort. High expression of the EDC genes correlated with reduced tumor thickness in primary melanoma and shorter survival in metastatic melanoma. Analysis of DEGs from primary melanoma patients displaying high or low expression of all eight EDC revealed that the upregulated genes are enriched in biological process related to cell migration, extracellular matrix organization, invasion, and Epithelial-Mesenchymal Transition. Further analysis of enriched curated oncogenic genesets together with RPPA data of phosphorylated proteins revealed the activation of MEK, ATF2, and EGFR pathways in tumors displaying high expression of EDC genes. Thus, EDC genes may contribute to melanoma progression by promoting the activation of MEK, ATF2, and EGFR pathways together with biological processes associated with tumor aggressiveness.

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

Since the current melanoma clinicopathological staging system remains restricted to predicting survival outcomes, establishing precise prognostic targets is needed. Here, we used gene expression signature (GES) classification and Cox regression analyses to biologically characterize melanoma cells at the single-cell level and construct a prognosis-related gene signature for melanoma. By analyzing publicly available scRNA-seq data, we identified six distinct GESs (named: “Anti-apoptosis”, “Immune cell interactions”, “Melanogenesis”, “Ribosomal biogenesis”, “Extracellular structure organization”, and “Epithelial-Mesenchymal Transition (EMT)”). We verified these GESs in the bulk RNA-seq data of patients with skin cutaneous melanoma (SKCM) from The Cancer Genome Atlas (TCGA). Four GESs (“Immune cell interactions”, “Melanogenesis”, “Ribosomal biogenesis”, and “Extracellular structure organization”) were significantly correlated with prognosis (p = 1.08 × 10−5, p = 0.042, p = 0.001, and p = 0.031, respectively). We identified a prognostic signature of melanoma composed of 45 genes (MPS_45). MPS_45 was validated in TCGA-SKCM (HR = 1.82, p = 9.08 × 10−6) and three other melanoma datasets (GSE65904: HR = 1.73, p = 0.006; GSE19234: HR = 3.83, p = 0.002; and GSE53118: HR = 1.85, p = 0.037). MPS_45 was independently associated with survival (p = 0.002) and was proved to have a high potential for predicting prognosis in melanoma patients.

Development and Validation of Machine Learning Models for Predicting Occult Nodal Metastasis in Early-Stage Oral Cavity Squamous Cell Carcinoma

IMPORTANCE

Given that early-stage oral cavity squamous cell carcinoma (OCSCC) has a high propensity for subclinical nodal metastasis, elective neck dissection has become standard practice for many patients with clinically negative nodes. Unfortunately, for most patients without regional metastasis, this risk-averse treatment paradigm results in unnecessary morbidity.

OBJECTIVES

To develop and validate predictive models of occult nodal metastasis from clinicopathological variables that were available after surgical extirpation of the primary tumor and to compare predictive performance against depth of invasion (DOI), the currently accepted standard.

DESIGN, SETTING, AND PARTICIPANTS

This diagnostic modeling study collected clinicopathological variables retrospectively from 7 tertiary care academic medical centers across the US. Participants included adult patients with early-stage OCSCC without nodal involvement who underwent primary surgical extirpation with or without upfront elective neck dissection. These patients were initially evaluated between January 1, 2000, and December 31, 2019.

EXPOSURES

Largest tumor dimension, tumor thickness, DOI, margin status, lymphovascular invasion, perineural invasion, muscle invasion, submucosal invasion, dysplasia, histological grade, anatomical subsite, age, sex, smoking history, race and ethnicity, and body mass index (calculated as weight in kilograms divided by height in meters squared).

MAIN OUTCOMES AND MEASURES

Occult nodal metastasis identified either at the time of elective neck dissection or regional recurrence within 2 years of initial surgery.

RESULTS

Of the 634 included patients (mean [SD] age, 61.2 [13.6] years; 344 men [54.3%]), 114 (18.0%) had occult nodal metastasis. Patients with occult nodal metastasis had a higher frequency of lymphovascular invasion (26.3% vs 8.1%; P < .001), perineural invasion (40.4% vs 18.5%; P < .001), and margin involvement by invasive tumor (12.3% vs 6.3%; P = .046) compared with those without pathological lymph node metastasis. In addition, patients with vs those without occult nodal metastasis had a higher frequency of poorly differentiated primary tumor (20.2% vs 6.2%; P < .001) and greater DOI (7.0 vs 5.4 mm; P < .001). A predictive model that was built with XGBoost architecture outperformed the commonly used DOI threshold of 4 mm, achieving an area under the curve of 0.84 (95% CI, 0.80-0.88) vs 0.62 (95% CI, 0.57-0.67) with DOI. This model had a sensitivity of 91.7%, specificity of 72.6%, positive predictive value of 39.3%, and negative predictive value of 97.8%.

CONCLUSIONS AND RELEVANCE

Results of this study showed that machine learning models that were developed from multi-institutional clinicopathological data have the potential to not only reduce the number of pathologically node-negative neck dissections but also accurately identify patients with early OCSCC who are at highest risk for nodal metastases.

A new blood-based RNA signature (R9), for monitoring effectiveness of tuberculosis treatment in a South Indian longitudinal cohort

Tuberculosis (TB) treatment involves a multidrug regimen for six months, and until two months, it is unclear if treatment is effective. This delay can lead to the evolution of drug resistance, lung damage, disease spread, and transmission. We identify a blood-based 9-gene signature using a computational pipeline that constructs and interrogates a genome-wide transcriptome-integrated protein-interaction network. The identified signature is able to determine treatment response at week 1-2 in three independent public datasets. Signature-based R9-score correctly detected treatment response at individual timepoints (204 samples) from a newly developed South Indian longitudinal cohort involving 32 patients with pulmonary TB. These results are consistent with conventional clinical metrics and can discriminate good from poor treatment responders at week 2 (AUC 0.93(0.81-1.00)). In this work, we provide proof of concept that the R9-score can determine treatment effectiveness, making a case for designing a larger clinical study.

Revisiting miRNA Association with Melanoma Recurrence and Metastasis from a Machine Learning Point of View

The diagnostic and prognostic value of miRNAs in cutaneous melanoma (CM) has been broadly studied and supported by advanced bioinformatics tools. From early studies using miRNA arrays with several limitations, to the recent NGS-derived miRNA expression profiles, an accurate diagnostic panel of a comprehensive pre-specified set of miRNAs that could aid timely identification of specific cancer stages is still elusive, mainly because of the heterogeneity of the approaches and the samples. Herein, we summarize the existing studies that report several miRNAs as important diagnostic and prognostic biomarkers in CM. Using publicly available NGS data, we analyzed the correlation of specific miRNA expression profiles with the expression signatures of known gene targets. Combining network analytics with machine learning, we developed specific non-linear classification models that could successfully predict CM recurrence and metastasis, based on two newly identified miRNA signatures. Subsequent unbiased analyses and independent test sets (i.e., a dataset not used for training, as a validation cohort) using our prediction models resulted in 73.85% and 82.09% accuracy in predicting CM recurrence and metastasis, respectively. Overall, our approach combines detailed analysis of miRNA profiles with heuristic optimization and machine learning, which facilitates dimensionality reduction and optimization of the prediction models. Our approach provides an improved prediction strategy that could serve as an auxiliary tool towards precision treatment.

DPF3, A Putative Candidate Gene For Melanoma Etiopathogenesis in Gray Horses

Melanoma prevalence in gray horses reaches up to 50% and more. Several studies have documented a genetic melanoma predisposition which is referred to the 4.6 kb duplication in intron 6 of STX17 and its surrounding haplotype. However, the genetic background and mechanisms responsible for differences in etiopathogenesis of equine dermal melanomatosis still remain unknown. In the current study, we performed a genome wide association analysis in 141 Lipizzan horses and subsequently identified one candidate gene on chromosome 24 putatively involved in melanoma pathogenesis in gray horses. The associated SNP was located in the intronic region of DPF3, a gene which is involved in humans in cell growth, proliferation, apoptosis and motility of cancer cells. The replication study in 1210 horses from seven breeds demonstrated, that the G/G genotype of the DPF3 associated SNP exhibits putative melanoma suppression effects. As a conclusion DPF3 represents a candidate gene, which might play an essential role for gray horses coping with high genetic melanoma related tumor load.

Machine learning and deep learning methods that use omics data for metastasis prediction

Knowing metastasis is the primary cause of cancer-related deaths, incentivized research directed towards unraveling the complex cellular processes that drive the metastasis. Advancement in technology and specifically the advent of high-throughput sequencing provides knowledge of such processes. This knowledge led to the development of therapeutic and clinical applications, and is now being used to predict the onset of metastasis to improve diagnostics and disease therapies. In this regard, predicting metastasis onset has also been explored using artificial intelligence approaches that are machine learning, and more recently, deep learning-based. This review summarizes the different machine learning and deep learning-based metastasis prediction methods developed to date. We also detail the different types of molecular data used to build the models and the critical signatures derived from the different methods. We further highlight the challenges associated with using machine learning and deep learning methods, and provide suggestions to improve the predictive performance of such methods.

MetaCancer: A deep learning-based pan-cancer metastasis prediction model developed using multi-omics data

Predicting metastasis in the early stages means that clinicians have more time to adjust a treatment regimen to target the primary and metastasized cancer. In this regard, several computational approaches are being developed to identify metastasis early. However, most of the approaches focus on changes on one genomic level only, and they are not being developed from a pan-cancer perspective. Thus, we here present a deep learning (DL)-based model, MetaCancer, that differentiates pan-cancer metastasis status based on three heterogeneous data layers. In particular, we built the DL-based model using 400 patients' data that includes RNA sequencing (RNA-Seq), microRNA sequencing (microRNA-Seq), and DNA methylation data from The Cancer Genome Atlas (TCGA). We quantitatively assess the proposed convolutional variational autoencoder (CVAE) and alternative feature extraction methods. We further show that integrating mRNA, microRNA, and DNA methylation data as features improves our model's performance compared to when we used mRNA data only. In addition, we show that the mRNA-related features make a more significant contribution when attempting to distinguish the primary tumors from metastatic ones computationally. Lastly, we show that our DL model significantly outperformed a machine learning (ML) ensemble method based on various metrics.

PathExt: a general framework for path-based mining of omics-integrated biological networks

MOTIVATION

Transcriptomes are routinely used to prioritize genes underlying specific phenotypes. Current approaches largely focus on differentially expressed genes (DEGs), despite the recognition that phenotypes emerge via a network of interactions between genes and proteins, many of which may not be differentially expressed. Furthermore, many practical applications lack sufficient samples or an appropriate control to robustly identify statistically significant DEGs.

RESULTS

We provide a computational tool-PathExt, which, in contrast to differential genes, identifies differentially active paths when a control is available, and most active paths otherwise, in an omics-integrated biological network. The sub-network comprising such paths, referred to as the TopNet, captures the most relevant genes and processes underlying the specific biological context. The TopNet forms a well-connected graph, reflecting the tight orchestration in biological systems. Two key advantages of PathExt are (i) it can extract characteristic genes and pathways even when only a single sample is available, and (ii) it can be used to study a system even in the absence of an appropriate control. We demonstrate the utility of PathExt via two diverse sets of case studies, to characterize (i) Mycobacterium tuberculosis response upon exposure to 18 antibacterial drugs where only one transcriptomic sample is available for each exposure; and (ii) tissue-relevant genes and processes using transcriptomic data for 39 human tissues. Overall, PathExt is a general tool for prioritizing context-relevant genes in any omics-integrated biological network for any condition(s) of interest, even with a single sample or in the absence of appropriate controls.

AVAILABILITYAND IMPLEMENTATION

The source code for PathExt is available at https://github.com/NarmadaSambaturu/PathExt.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

VB10, a new blood biomarker for differential diagnosis and recovery monitoring of acute viral and bacterial infections

BACKGROUND

Precise differential diagnosis between acute viral and bacterial infections is important to enable appropriate therapy, avoid unnecessary antibiotic prescriptions and optimize the use of hospital resources. A systems view of host response to infections provides opportunities for discovering sensitive and robust molecular diagnostics.

METHODS

We combine blood transcriptomes from six independent datasets (n = 756) with a knowledge-based human protein-protein interaction network, identifies subnetworks capturing host response to each infection class, and derives common response cores separately for viral and bacterial infections. We subject the subnetworks to a series of computational filters to identify a parsimonious gene panel and a standalone diagnostic score that can be applied to individual samples. We rigorously validate the panel and the diagnostic score in a wide range of publicly available datasets and in a newly developed Bangalore-Viral Bacterial (BL-VB) cohort.

FINDING

We discover a 10-gene blood-based biomarker panel (Panel-VB) that demonstrates high predictive performance to distinguish viral from bacterial infections, with a weighted mean AUROC of 0.97 (95% CI: 0.96-0.99) in eleven independent datasets (n = 898). We devise a new stand-alone patient-wise score (VB10) based on the panel, which shows high diagnostic accuracy with a weighted mean AUROC of 0.94 (95% CI 0.91-0.98) in 2996 patient samples from 56 public datasets from 19 different countries. Further, we evaluate VB10 in a newly generated South Indian (BL-VB, n = 56) cohort and find 97% accuracy in the confirmed cases of viral and bacterial infections. We find that VB10 is (a) capable of accurately identifying the infection class in culture-negative indeterminate cases, (b) reflects recovery status, and (c) is applicable across different age groups, covering a wide spectrum of acute bacterial and viral infections, including uncharacterized pathogens. We tested our VB10 score on publicly available COVID-19 data and find that our score detected viral infection in patient samples.

INTERPRETATION

Our results point to the promise of VB10 as a diagnostic test for precise diagnosis of acute infections and monitoring recovery status. We expect that it will provide clinical decision support for antibiotic prescriptions and thereby aid in antibiotic stewardship efforts.

FUNDING

Grand Challenges India, Biotechnology Industry Research Assistance Council (BIRAC), Department of Biotechnology, Govt. of India.

Transcriptional signatures underlying dynamic phenotypic switching and novel disease biomarkers in a linear cellular model of melanoma progression

Despite advances in therapeutics, the progression of melanoma to metastasis still confers a poor outcome to patients. Nevertheless, there is a scarcity of biological models to understand cellular and molecular changes taking place along disease progression. Here, we characterized the transcriptome profiles of a multi-stage murine model of melanoma progression comprising a nontumorigenic melanocyte lineage (melan-a), premalignant melanocytes (4C), nonmetastatic (4C11-) and metastasis-prone (4C11+) melanoma cells. Clustering analyses have grouped the 4 cell lines according to their differentiated (melan-a and 4C11+) or undifferentiated/"mesenchymal-like" (4C and 4C11-) morphologies, suggesting dynamic gene expression patterns associated with the transition between these phenotypes. The cell plasticity observed in the murine melanoma progression model was corroborated by molecular markers described during stepwise human melanoma differentiation, as the differentiated cell lines in our model exhibit upregulation of transitory and melanocytic markers, whereas "mesenchymal-like" cells show increased expression of undifferentiated and neural crest-like markers. Sets of differentially expressed genes (DEGs) were detected at each transition step of tumor progression, and transcriptional signatures related to malignancy, metastasis and epithelial-to-mesenchymal transition were identified. Finally, DEGs were mapped to their human orthologs and evaluated in uni- and multivariate survival analyses using gene expression and clinical data of 703 drug-naïve primary melanoma patients, revealing several independent candidate prognostic markers. Altogether, these results provide novel insights into the molecular mechanisms underlying the phenotypic switch taking place during melanoma progression, reveal potential drug targets and prognostic biomarkers, and corroborate the translational relevance of this unique sequential model of melanoma progression.

Transcript levels of keratin 1/5/6/14/15/16/17 as potential prognostic indicators in melanoma patients

Keratins (KRTs), the intermediate filament-forming proteins of epithelial cells, are extensively used as diagnostic biomarkers in cancers and associated with tumorigenesis and metastasis in multiple cancers. However, the diverse expression patterns and prognostic values of KRTs in melanoma have yet to be elucidated. In the current study, we examined the transcriptional and clinical data of KRTs in patients with melanoma from GEO, TCGA, ONCOMINE, GEPIA, cBioPortal, TIMER and TISIDB databases. We found that the mRNA levels of KRT1/2/5/6/8/10/14/15/16/17 were significantly differential expressed between primary melanoma and metastatic melanoma. The expression levels of KRT1/2/5/6/10/14/15/16/17 were correlated with advanced tumor stage. Survival analysis revealed that the high transcription levels of KRT1/5/6/14/15/16/17 were associated with low overall survival in melanoma patients. GSEA analysis indicated that the most involved hallmarks pathways were P53 pathway, KRAS signaling, estrogen response early and estrogen response late. Furthermore, we found some correlations among the expression of KRTs and the infiltration of immune cells. Our study may provide novel insights for the selection of prognostic biomarkers for melanoma.

Identification, Validation, and Functional Annotations of Genome-Wide Profile Variation between Melanocytic Nevus and Malignant Melanoma

Cutaneous melanoma (CM) is known as an aggressive malignant cancer; some of which are directly derived from melanocytic nevi, which have been attracting growing attention from the last decades. This study focused on comprehensive identification, validation, and functional annotations of prognostic differentially expressed genes (DEGs) between melanocytic nevus and malignant melanoma in genome-wide profiles. DEGs were obtained using three chip datasets from GEO database to identify after standardization annotation. A total of 73 DEGs were identified as possible candidate prognostic biomarkers between melanocytic nevus and malignant melanoma. In addition, survival curves indicated that six hub genes, including FABP5, IVL, KRT6A, KRT15, KRT16, and TIMP2, were significant prognostic signatures for CM and of significant value to predict transformation from nevi to melanoma. Furthermore, immunohistochemistry staining was performed to validate differential expression levels and prognostic implications of six hub genes between CM tissue and nevus tissues from the First Affiliated Hospital of Soochow University cohort. It suggested that significantly elevated FABP5, IVL, KRT6A, KRT15, KRT16, and TIMP2 proteins expressed in the CM than in the nevus tissues. Functional enrichment and significant pathways of the six significant hub genes indicated that the mostly involved hallmarks include the P53 pathway, K-ras signaling, estrogen response late, and estrogen response early. In summary, this study identified significant DEGs participating in the process of malignant transformation from nevus to melanoma tissues based on comprehensive genomic profiles. Transcription profiles of FABP5, IVL, KRT6A, KRT15, KRT16, and TIMP2 provided clues of prognostic implications, which might help us evaluate malignant potential of nevus and underlying carcinogenesis progress from melanocytic nevus to melanoma.

Gene co-expression and histone modification signatures are associated with melanoma progression, epithelial-to-mesenchymal transition, and metastasis

BACKGROUND

We have previously developed a murine cellular system that models the transformation from melanocytes to metastatic melanoma cells. This model was established by cycles of anchorage impediment of melanocytes and consists of four cell lines: differentiated melanocytes (melan-a), pre-malignant melanocytes (4C), malignant (4C11-), and metastasis-prone (4C11+) melanoma cells. Here, we searched for transcriptional and epigenetic signatures associated with melanoma progression and metastasis by performing a gene co-expression analysis of transcriptome data and a mass-spectrometry-based profiling of histone modifications in this model.

RESULTS

Eighteen modules of co-expressed genes were identified, and some of them were associated with melanoma progression, epithelial-to-mesenchymal transition (EMT), and metastasis. The genes in these modules participate in biological processes like focal adhesion, cell migration, extracellular matrix organization, endocytosis, cell cycle, DNA repair, protein ubiquitination, and autophagy. Modules and hub signatures related to EMT and metastasis (turquoise, green yellow, and yellow) were significantly enriched in genes associated to patient survival in two independent melanoma cohorts (TCGA and Leeds), suggesting they could be sources of novel prognostic biomarkers. Clusters of histone modifications were also linked to melanoma progression, EMT, and metastasis. Reduced levels of H4K5ac and H4K8ac marks were seen in the pre-malignant and tumorigenic cell lines, whereas the methylation patterns of H3K4, H3K56, and H4K20 were related to EMT. Moreover, the metastatic 4C11+ cell line showed higher H3K9me2 and H3K36me3 methylation, lower H3K18me1, H3K23me1, H3K79me2, and H3K36me2 marks and, in agreement, downregulation of the H3K36me2 methyltransferase Nsd1.

CONCLUSIONS

We uncovered transcriptional and histone modification signatures that may be molecular events driving melanoma progression and metastasis, which can aid in the identification of novel prognostic genes and drug targets for treating the disease.

Cytotoxic Effects of Plant Sap-Derived Extracellular Vesicles on Various Tumor Cell Types

Edible plants have been widely used in traditional therapeutics because of the biological activities of their natural ingredients, including anticancer, antioxidant, and anti-inflammatory properties. Plant sap contains such medicinal substances and their secondary metabolites provide unique chemical structures that contribute to their therapeutic efficacy. Plant extracts are known to contain a variety of extracellular vesicles (EVs) but the effects of such EVs on various cancers have not been investigated. Here, we extracted EVs from four plants-Dendropanax morbifera, Pinus densiflora, Thuja occidentalis, and Chamaecyparis obtusa-that are known to have cytotoxic effects. We evaluated the cytotoxic effects of these EVs by assessing their ability to selectively reduce the viability of various tumor cell types compared with normal cells and low metastatic cells. EVs from D. morbifera and P. densiflora sap showed strong cytotoxic effects on tumor cells, whereas those from T. occidentalis and C. obtusa had no significant effect on any tumor cell types. We also identified synergistic effect of EVs from D. morbifera and P. densiflora saps on breast and skin tumor cells and established optimized treatment concentrations. Our findings suggest these EVs from plant sap as new candidates for cancer treatment.

Better prognostic determination and feature characterization of cutaneous melanoma through integrative genomic analysis

Melanoma is the most dangerous type of skin cancer and has highly heterogeneous features. Despite progress in melanoma classification, interpatient heterogeneity remains difficult to predict, especially in terms of long-term survival. Here, based on mRNA-seq, miRNA-seq and DNA methylation data from 447 cutaneous melanoma patients in the Cancer Genome Atlas, we performed integrative and single-dataset clustering analyses. A novel group of patients was identified, including 301 with better, 55 with poorer and 91 with intermediate prognoses. Immune genes were upregulated in the better prognostic group, and higher immune scores (representing a greater extent of immune cell infiltration into tumor tissues) were associated with better prognoses. Higher expression of 115 genes was determined to predict better outcomes. The better prognostic group also exhibited DNA hypomethylation, and immune pathways were enriched among the hypomethylated genes. Using exome-seq data from the same patients, we observed that the better prognostic group harbored the highest number of mutations. The mutational signature in the better prognostic group was associated with ultraviolet light exposure. These integrated investigations have potential therapeutic significance, as they clarify the molecular heterogeneity of cutaneous melanoma and enhance its classification.

Genome-wide analysis of canine oral malignant melanoma metastasis-associated gene expression

Oral malignant melanoma (OMM) is the most common canine melanocytic neoplasm. Overlap between the somatic mutation profiles of canine OMM and human mucosal melanomas suggest a shared UV-independent molecular aetiology. In common with human mucosal melanomas, most canine OMM metastasise. There is no reliable means of predicting canine OMM metastasis, and systemic therapies for metastatic disease are largely palliative. Herein, we employed exon microarrays for comparative expression profiling of FFPE biopsies of 18 primary canine OMM that metastasised and 10 primary OMM that did not metastasise. Genes displaying metastasis-associated expression may be targets for anti-metastasis treatments, and biomarkers of OMM metastasis. Reduced expression of CXCL12 in the metastasising OMMs implies that the CXCR4/CXCL12 axis may be involved in OMM metastasis. Increased expression of APOBEC3A in the metastasising OMMs may indicate APOBEC3A-induced double-strand DNA breaks and pro-metastatic hypermutation. DNA double strand breakage triggers the DNA damage response network and two Fanconi anaemia DNA repair pathway members showed elevated expression in the metastasising OMMs. Cross-validation was employed to test a Linear Discriminant Analysis classifier based upon the RT-qPCR-measured expression levels of CXCL12, APOBEC3A and RPL29. Classification accuracies of 94% (metastasising OMMs) and 86% (non-metastasising OMMs) were estimated.

High-resolution population structure and runs of homozygosity reveal the genetic architecture of complex traits in the Lipizzan horse

BACKGROUND

The sample ascertainment bias due to complex population structures remains a major challenge in genome-wide investigations of complex traits. In this study we derived the high-resolution population structure and levels of autozygosity of 377 Lipizzan horses originating from five different European stud farms utilizing the SNP genotype information of the high density 700 k Affymetrix Axiom™ Equine genotyping array. Scanning the genome for overlapping runs of homozygosity (ROH) shared by more than 50% of horses, we identified homozygous regions (ROH islands) in order to investigate the gene content of those candidate regions by gene ontology and enrichment analyses.

RESULTS

The high-resolution population network approach revealed well-defined substructures according to the origin of the horses (Austria, Slovakia, Croatia and Hungary). The highest mean genome coverage of ROH (SROH) was identified in the Austrian (SROH = 342.9), followed by Croatian (SROH = 214.7), Slovakian (SROH = 205.1) and Hungarian (SROH = 171.5) subpopulations. ROH island analysis revealed five common islands on ECA11 and ECA14, hereby confirming a closer genetic relationship between the Hungarian and Croatian as well as between the Austrian and Slovakian samples. Private islands were detected for the Hungarian and the Austrian Lipizzan subpopulations. All subpopulations shared a homozygous region on ECA11, nearly identical in position and length containing among other genes the homeobox-B cluster, which was also significantly (p < 0.001) highlighted by enrichment analysis. Gene ontology terms were mostly related to biological processes involved in embryonic morphogenesis and anterior/posterior specification. Around the STX17 gene (causative for greying), we identified a ROH island harbouring the genes NR4A3, STX17, ERP44 and INVS. Within further islands on ECA14, ECA16 and ECA20 we detected the genes SPRY4, NDFIP1, IMPDH2, HSP90AB1, whereas SPRY4 and HSP90AB1 are involved in melanoma metastasis and survival rate of melanoma patients in humans.

CONCLUSIONS

We demonstrated that the assessment of high-resolution population structures within one single breed supports the downstream genetic analyses (e.g. the identification of ROH islands). By means of ROH island analyses, we identified the genes SPRY4, NDFIP1, IMPDH2, HSP90AB1, which might play an important role for further studies on equine melanoma. Furthermore, our results highlighted the impact of the homeobox-A and B cluster involved in morphogenesis of Lipizzan horses.

Gene-Specific Intron Retention Serves as Molecular Signature that Distinguishes Melanoma from Non-Melanoma Cancer Cells in Greek Patients

Background: Skin cancer represents the most common human malignancy, and it includes BCC, SCC, and melanoma. Since melanoma is one of the most aggressive types of cancer, we have herein attempted to develop a gene-specific intron retention signature that can distinguish BCC and SCC from melanoma biopsy tumors. Methods: Intron retention events were examined through RT-sqPCR protocols, using total RNA preparations derived from BCC, SCC, and melanoma Greek biopsy specimens. Intron-hosted miRNA species and their target transcripts were predicted via the miRbase and miRDB bioinformatics platforms, respectively. Ιntronic ORFs were recognized through the ORF Finder application. Generation and visualization of protein interactomes were achieved by the IntAct and Cytoscape softwares, while tertiary protein structures were produced by using the I-TASSER online server. Results: c-MYC and Sestrin-1 genes proved to undergo intron retention specifically in melanoma. Interaction maps of proteins encoded by genes being potentially targeted by retained intron-accommodated miRNAs were generated and SRPX2 was additionally delivered to our melanoma-specific signature. Novel ORFs were identified in MCT4 and Sestrin-1 introns, with potentially critical roles in melanoma development. Conclusions: The property of c-MYC, Sestrin-1, and SRPX2 genes to retain specific introns could be clinically used to molecularly differentiate non-melanoma from melanoma tumors.

Immunomagnetic-Enriched Subpopulations of Melanoma Circulating Tumour Cells (CTCs) Exhibit Distinct Transcriptome Profiles

Cutaneous melanoma circulating tumour cells (CTCs) are phenotypically and molecularly heterogeneous. We profiled the gene expression of CTC subpopulations immunomagnetic-captured by targeting either the melanoma-associated marker, MCSP, or the melanoma-initiating marker, ABCB5. Firstly, the expression of a subset of melanoma genes was investigated by RT-PCR in MCSP-enriched and ABCB5-enriched CTCs isolated from a total of 59 blood draws from 39 melanoma cases. Of these, 6 MCSP- and 6 ABCB5-enriched CTC fractions were further analysed using a genome-wide gene expression microarray. The transcriptional programs of both CTC subtypes included cell survival maintenance, cell proliferation, and migration pathways. ABCB5-enriched CTCs were specifically characterised by up-regulation of genes involved in epithelial to mesenchymal transition (EMT), suggesting an invasive phenotype. These findings underscore the presence of at least two distinct melanoma CTC subpopulations with distinct transcriptional programs, which may have distinct roles in disease progression and response to therapy.

Identification of the potential prognostic genes of human melanoma

The melanoma is one of the most dangerous forms of skin diseases. It may spread to other parts of the body and cause serious illness and death. Early detection and diagnosis are crucial. However, the systemic expression analysis for the different staging of melanoma is still lacking to date. In this study, we analyzed the gene expression profiles of the different staging of melanoma by the differential expression analysis and random forest analysis. First, the results of the principal component analysis showed that the clustering of primary tumor samples, normal samples, and pigment nevus samples got closer, while the clustering of tumor metastatic samples and normal samples was far away. Moreover, the gene expression of tumor metastasis stage and the initial stage had obvious differences. Almost 426 genes identified had differential expression. The functional enrichment of differentially expressed genes was associated with the epidermal cell differentiation, epidermis development, and the keratinocyte differentiation. Taken together, our findings identified the differentially expressed signatures between primary melanoma and metastatic melanoma. Our results would provide the potential mechanisms of melanoma.

Genetics of metastasis: melanoma and other cancers

Melanoma is a malignant neoplasm of melanocytes that accounts for the majority of skin cancer deaths despite comprising less than 5% of all cutaneous malignancies. Its incidence has increased faster than that of any other cancer over the past half-century and the annual costs of treatment in the United States alone have risen rapidly. Although the majority of primary melanomas are cured with local excision, metastatic melanoma historically carries a grim prognosis, with a median survival of 9 months and a long-term survival rate of 10%. Given the urgent need to develop treatment strategies for metastatic melanoma and the explosion of genetic technologies over the past 20 years, there has been extensive research into the genetic alterations that cause melanocytes to become malignant. More recently, efforts have focused on the genetic changes that drive melanoma metastasis. This review aims to summarize the current knowledge of the genetics of primary cutaneous and ocular melanoma, the genetic changes associated with metastasis in melanoma and other cancer types, and non-genetic factors that may contribute to metastasis.