Weighted correlation network and differential expression analyses identify candidate genes associated with BRAF gene in melanoma

Dermokine mutations contribute to epithelial-mesenchymal transition and advanced melanoma through ERK/MAPK pathways

To discover vulnerabilities associated with dermokine (DMKN) as a new trigger of the epithelial-mesenchymal transition (EMT) -driven melanoma, we undertook a genome-wide genetic screening using transgenic. Here, we showed that DMKN expression could be constitutively increased in human malignant melanoma (MM) and that this correlates with poor overall survival in melanoma patients, especially in BRAF-mutated MM samples. Furthermore, in vitro, knockdown of DMKN inhibited the cell proliferation, migration, invasion, and apoptosis of MM cancer cells by the activation of ERK/MAPK signaling pathways and regulator of STAT3 in downstream molecular. By interrogating the in vitro melanoma dataset and characterization of advanced melanoma samples, we found that DMKN downregulated the EMT-like transcriptional program by disrupting EMT cortical actin, increasing the expression of epithelial markers, and decreasing the expression of mesenchymal markers. In addition, whole exome sequencing was presented with p.E69D and p.V91A DMKN mutations as a novel somatic loss of function mutations in those patients. Moreover, our purposeful proof-of-principle modeled the interaction of ERK with p.E69D and p.V91A DMKN mutations in the ERK-MAPK kinas signaling that may be naturally associated with triggering the EMT during melanomagenesis. Altogether, these findings provide preclinical evidence for the role of DMKN in shaping the EMT-like melanoma phenotype and introduced DMKN as a new exceptional responder for personalized MM therapy.

TFEB inhibition induces melanoma shut-down by blocking the cell cycle and rewiring metabolism

Melanomas are characterised by accelerated cell proliferation and metabolic reprogramming resulting from the contemporary dysregulation of the MAPK pathway, glycolysis and the tricarboxylic acid (TCA) cycle. Here, we suggest that the oncogenic transcription factor EB (TFEB), a key regulator of lysosomal biogenesis and function, controls melanoma tumour growth through a transcriptional programme targeting ERK1/2 activity and glucose, glutamine and cholesterol metabolism. Mechanistically, TFEB binds and negatively regulates the promoter of DUSP-1, which dephosphorylates ERK1/2. In melanoma cells, TFEB silencing correlates with ERK1/2 dephosphorylation at the activation-related p-Thr185 and p-Tyr187 residues. The decreased ERK1/2 activity synergises with TFEB control of CDK4 expression, resulting in cell proliferation blockade. Simultaneously, TFEB rewires metabolism, influencing glycolysis, glucose and glutamine uptake, and cholesterol synthesis. In TFEB-silenced melanoma cells, cholesterol synthesis is impaired, and the uptake of glucose and glutamine is inhibited, leading to a reduction in glycolysis, glutaminolysis and oxidative phosphorylation. Moreover, the reduction in TFEB level induces reverses TCA cycle, leading to fatty acid production. A syngeneic BRAFV600E melanoma model recapitulated the in vitro study results, showing that TFEB silencing sustains the reduction in tumour growth, increase in DUSP-1 level and inhibition of ERK1/2 action, suggesting a pivotal role for TFEB in maintaining proliferative melanoma cell behaviour and the operational metabolic pathways necessary for meeting the high energy demands of melanoma cells.

Comparative mRNA/micro-RNA co-expression network drives melanomagenesis by promoting epithelial-mesenchymal transition and vasculogenic mimicry signaling

This study aimed to identify a novel disease-associated differentially co-expressed mRNA-microRNA (miRNA) that is associated with vasculogenic mimicry (VM) and epithelial-to-mesenchymal transition (EMT) network at different stages of melanoma. By applying weighted gene co-expression network analysis, we constructed a VM+EMT biological network with the available microarray dataset downloaded from a public database. Quantitative real-time PCR, immunohistochemical staining, and CD31-periodic acid solution dual staining were performed to confirm the expression of genes associated with EMT and VM formation in subjects with malignant melanoma (n = 18) and primary melanoma (n = 13) and in healthy subjects (n = 10). Our findings suggested that phosphatidylserine-specific phospholipase A1-alpha (PLA1A) and dermokine (DMKN) genes function as oncogenes that trigger VM and EMT processes during melanomagenesis on interaction with miR-370, miR-563, and miR-770-5p. PLA1A and DMKN genes can be considered potential VM+EMT network-based diagnostic biomarkers for distinguishing between melanoma patients. We postulate that a network with altered PLA1A/miR-563 and DMNK/miR-770-5p/miR-370 may contribute to melanomagenesis by triggering the EMT signaling pathway and VM formation. This study provides a potentially valuable approach for the early diagnosis and prognosis of melanoma progression.

Identification and Validation of Immune- and Stemness-Related Prognostic Signature of Melanoma

Purpose: Our aim was to construct a signature that accurately predicted the prognostic and immune response of melanoma. Methods: First, the weighted co-expression network analysis (WGCNA) algorithm was used to identify the hub genes related to clinical phenotypes of melanoma in the cancer genome atlas (TCGA) database. Nest, the least absolute shrinkage and selection operator (LASSO) analysis was used to dimensionality reduction of these hub genes and constructed a prognostic signature to predict the prognosis and immunosuppressive response of melanoma. Result: Through in-depth analysis, we constructed a 5-mRNA prognostic signature and verified its prognostic value in internal (TCGA-SKCM, n = 452) and external independent datasets (GSE53118, n = 79). Based on this signature, the tumor immune microenvironment (TME) of melanoma was characterized, and the result was found that patients in the high-risk group had lower CD8 T cell infiltration and immune checkpoint expression (PD-1, PD-L1, CTLA4), as well as higher M0/M2 macrophage infiltration. Our results also found the risk score based on a 5-mRNA signature was significantly associated with tumor mutational burden (TMB) and tumor stem cell markers (CD20, CD38, ABCB5, CD44, etc.). Lastly, we built a nomogram for clinician prediction for the prognosis of patients with melanoma. Conclusion: Our findings indicated that the 5-mRNA signature has an important predictive value for the overall survival of melanoma. By analyzing the tumor immune microenvironment and tumor stem cell marker between different groups, a new method is provided for the stratified diagnosis and treatment of melanoma.

High heterogeneity undermines generalization of differential expression results in RNA-Seq analysis

BACKGROUND

RNA sequencing (RNA-Seq) has been widely applied in oncology for monitoring transcriptome changes. However, the emerging problem that high variation of gene expression levels caused by tumor heterogeneity may affect the reproducibility of differential expression (DE) results has rarely been studied. Here, we investigated the reproducibility of DE results for any given number of biological replicates between 3 and 24 and explored why a great many differentially expressed genes (DEGs) were not reproducible.

RESULTS

Our findings demonstrate that poor reproducibility of DE results exists not only for small sample sizes, but also for relatively large sample sizes. Quite a few of the DEGs detected are specific to the samples in use, rather than genuinely differentially expressed under different conditions. Poor reproducibility of DE results is mainly caused by high variation of gene expression levels for the same gene in different samples. Even though biological variation may account for much of the high variation of gene expression levels, the effect of outlier count data also needs to be treated seriously, as outlier data severely interfere with DE analysis.

CONCLUSIONS

High heterogeneity exists not only in tumor tissue samples of each cancer type studied, but also in normal samples. High heterogeneity leads to poor reproducibility of DEGs, undermining generalization of differential expression results. Therefore, it is necessary to use large sample sizes (at least 10 if possible) in RNA-Seq experimental designs to reduce the impact of biological variability and DE results should be interpreted cautiously unless soundly validated.

Powerful quantifiers for cancer transcriptomics

Every day, investigators find a new link between a form of cancer and a particular alteration in the sequence or/and expression level of a key gene, awarding this gene the title of “biomarker”. The clinician may choose from numerous available panels to assess the type of cancer based on the mutation or expression regulation (“transcriptomic signature”) of “driver” genes. However, cancer is not a “one-gene show” and, together with the alleged biomarker, hundreds other genes are found as mutated or/and regulated in cancer samples. Regardless of the platform, a well-designed transcriptomic study produces three independent features for each gene: Average expression level, expression variability and coordination with expression of each other gene. While the average expression level is used in all studies to identify what genes were up-/down-regulated or turn on/off, the other two features are unfairly ignored. We use all three features to quantify the transcriptomic change during the progression of the disease and recovery in response to a treatment. Data from our published microarray experiments on cancer nodules and surrounding normal tissue from surgically removed tumors prove that the transcriptomic topologies are not only different in histopathologically distinct regions of a tumor but also dynamic and unique for each human being. We show also that the most influential genes in cancer nodules [the Gene Master Regulators (GMRs)] are significantly less influential in the normal tissue. As such, “smart” manipulation of the cancer GMRs expression may selectively kill cancer cells with little consequences on the normal ones. Therefore, we strongly recommend a really personalized approach of cancer medicine and present the experimental procedure and the mathematical algorithm to identify the most legitimate targets (GMRs) for gene therapy.

Construction and validation of an eight-gene signature with great prognostic value in bladder cancer

Bladder cancer (BC) is one of the most common malignancies in urinary system with a common malignancy in urinary system with a high mortality and recurrence rate, so we attempt to construct a gene signature to predict the prognosis of BCs. We initially established a co-expression network by performing WGCNA analysis and further identified magenta module as key module (P = 8e-05, R2 = 0.4). Subsequently, we screened 12 genes associated with survival from the key module, which were selected to construct an eight-gene signature by establishing a LASSO Cox model. Moreover, we reckoned the risk score (RS) of each sample, through which we could divide samples into two groups (the high-risk and low-risk groups) and verify the signature, in the training set and 3 validation sets (internal test set, GSE13507and E-MTAB-4321). This signature could distinguish between the high- and low- risk patients well (survival analysis: P = 0.015; AUC: 0.61 at 1 year, 0.61 at 3 years and 0.61 at 5 years). In the validation sets, this signature also showed good performance, which was consistent with the training test. Furthermore, we plotted a nomogram to predict the possibility of the overall survival (OS) and three calibration curves to predict the effectiveness of the nomogram, which suggested good value and clinical utility of the nomogram. In conclusion, we established an eight-gene signature, which was probably effective in the prediction of prognosis of patients with BC.