Prognostic genes of melanoma identified by weighted gene co-expression network analysis and drug repositioning using a network-based method

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.

Pivotal factors associated with the immunosuppressive tumor microenvironment and melanoma metastasis

BACKGROUND

Considering melanoma is the deadliest malignancy among dermatoma and presently lacks effective therapies, there is an urgent need to investigate the potential mechanisms underlying melanoma metastasis and determine prospective therapeutic targets for precise treatment of melanoma.

METHOD

Hub genes in melanoma metastasis were identified by analyzing RNA-seq data (mRNA, miRNA, and lncRNA) obtained from TCGA database. Then the identified hub genes were validated in human tissues with qRT-PCR, followed by survival analysis. Competing endogenous RNAs of the hub genes were defined to clarify potential molecular mechanism of melanoma progression. Then central gene-related signaling pathways were analyzed, followed by immune cell abundance analysis in tumor microenvironment with CYTERSORTx.

RESULT

A tetrad of IL2RA, IL2RG, IFNG, and IL7R genes were determined as hub genes and verified by qRT-PCR, which were significantly associated with unfavorable prognosis in melanoma. LINC02446, LINC01857, and LINC02384 may act as competing endogenous lncRNAs of IL2RA and IL7R through absorbing their shared miR.891a.5p and miR.203b.3p. JAK-STAT signaling pathway identified as the most relevant pathway in melanoma metastasis, as well as a wealthy of genes including TNFRSF 13B, TNFRSF17, TNFRSF9, TNFRSF8, TNFRSF13C, TNFRSF11B, LAG3, NRP1, ENTPD1, NT5E, CCL21, and CCR7, may induce tumor autoimmune suppression through enhancing regulatory T-cell abundance and performance in the tumor microenvironment. And regulatory T-cell proportion was indeed critically elevated in metastatic melanoma relative to primary melanoma, as well as in highly expressed IL2RA, IL2RG, IL7R, and IFNG group than their respective counterparts.

CONCLUSION

Elevated IL2RA, IL2RG, IL7R, and IFNG expression may play a central role in promoting melanoma metastasis through up regulation of intratumoral regulatory T-cell proportion mainly by activation of JAK-STAT signaling pathway. LINC02446, LINC01857, and LINC02384 may stimulate melanoma progression by reducing tumor-protecting miR.891a.5p and miR.203b.3p. A number of identified molecules including TNFRSF13B, LAG3, NRP1, ENTPD1, NT5E, CCL21, and CCR7 can serve as future therapeutic targets in melanoma treatment.

Identification of lncRNA-mRNA Regulatory Module to Explore the Pathogenesis and Prognosis of Melanoma

Skin cutaneous melanoma (SKCM) is an aggressive form of skin cancer that results in high mortality rate worldwide. It is vital to discover effective prognostic biomarkers and therapeutic targets for the treatment of melanoma. Long non-coding RNA (lncRNA) has been verified to play an essential role in the regulation of gene expression in diseases and tumors. Therefore, it is significant to explore the function of lncRNAs in the development and progression of SKCM. In this paper, a set of differentially expressed lncRNAs (DElncRNAs) and mRNAs (DEmRNAs) were first screened out using 471 cutaneous melanoma samples and 813 normal skin samples. Gene Ontology and KEGG pathway enrichment analysis were performed to obtain the significant function annotations and pathways of DEmRNAs. We also ran survival analysis on both DElncRNAs and DEmRNAs to identify prognostic-related lncRNAs and mRNAs. Next, a set of hub genes derived from protein-protein interaction (PPI) network analysis and lncRNA target genes screened from starbase-ENCORI database were integrated to construct a lncRNA-mRNA regulatory module, which includes 6 lncRNAs 4 target mRNAs. We further checked the capacity of these lncRNA and mRNA in the diagnosis of melanoma, and found that single lncRNA can effectively distinguish tumor and normal tissue. Moreover, we ran CMap analysis to select a list of small molecule drugs for SKCM, such as EGFR inhibitor AG-490, growth factor receptor inhibitor GW-441756 and apoptosis stimulant betulinic-acid, which have shown therapeutic effect in the treatment of melanoma.

Cellular and Molecular Aspects of Anti-Melanoma Effect of Minocycline-A Study of Cytotoxicity and Apoptosis on Human Melanotic Melanoma Cells

Minocycline is a tetracycline compound with pleiotropic pharmacological properties. In addition to its antibacterial action, it shows many non-antimicrobial effects, including an anti-cancer activity. The anti-cancer action was confirmed in studies on ovarian carcinoma cells, hepatocellular carcinoma cells, glioma cells, or acute myeloid leukemia cells. Malignant melanoma remains a serious medical problem despite the extensive knowledge of the disease. The low effectiveness of the standard treatment, as well as the resistance to therapy, result in high mortality rates. This work aimed to investigate the potential and mechanisms of anti-melanoma action of minocycline. Human skin melanotic melanoma cell line COLO 829 was used in the study. The obtained results showed that minocycline decreased cell viability and inhibited the growth of melanoma cells, proportional to the drug concentration as well as to the time of incubation. The EC50 values were calculated to be 78.6 µM, 31.7 µM, and 13.9 µM for 24 h, 48 h, and 72 h, respectively. It was observed that treated cells had a disturbed cell cycle and significantly changed morphology. Moreover, minocycline caused a decrease in mitochondrial membrane potential and an increase in cells with a low level of reduced thiols. Finally, it was found that the anti-melanoma effect of minocycline was related to the induction of apoptosis. The drug activated caspases 8, 9, and 3/7 as well as increased the number of annexin V-positive cells. The presented results show that minocycline possesses anti-melanoma potential.

Proteogenomic analysis of melanoma brain metastases from distinct anatomical sites identifies pathways of metastatic progression

Melanoma brain metastases (MBM) portend a grim prognosis and can occur in up to 40% of melanoma patients. Genomic characterization of brain metastases has been previously carried out to identify potential mutational drivers. However, to date a comprehensive multi-omics approach has yet to be used to analyze brain metastases. In this case report, we present an unbiased proteogenomics analyses of a patient's primary skin cancer and three brain metastases from distinct anatomic locations. We performed molecular profiling comprised of a targeted DNA panel and full transcriptome as well as proteomics using mass spectrometry. Phylogeny demonstrated that all MBMs shared a SMARCA4 mutation and deletion of 12q. Proteogenomics identified multiple pathways upregulated in the MBMs compared to the primary tumor. The protein, PIK3CG, was present in many of these pathways and had increased gene expression in metastatic melanoma tissue from the cancer genome atlas data. Proteomics demonstrated PIK3CG levels were significantly increased in all 3 MBMs and this finding was further validated by immunohistochemistry. In summary, this case report highlights the potential role of proteogenomics in identifying pathways involved in metastatic tumor progression. Furthermore, our multi-omics approach can be considered to aid in precision oncology efforts and provide avenues for therapeutic innovation.