Bioinformatic analysis reveals hub genes and pathways that promote melanoma metastasis

Integrating single-cell and bulk RNA sequencing to predict prognosis and immunotherapy response in prostate cancer

Prostate cancer (PCa) stands as a prominent contributor to morbidity and mortality among males on a global scale. Cancer-associated fibroblasts (CAFs) are considered to be closely connected to tumour growth, invasion, and metastasis. We explored the role and characteristics of CAFs in PCa through bioinformatics analysis and built a CAFs-based risk model to predict prognostic treatment and treatment response in PCa patients. First, we downloaded the scRNA-seq data for PCa from the GEO. We extracted bulk RNA-seq data for PCa from the TCGA and GEO and adopted "ComBat" to remove batch effects. Then, we created a Seurat object for the scRNA-seq data using the package "Seurat" in R and identified CAF clusters based on the CAF-related genes (CAFRGs). Based on CAFRGs, a prognostic model was constructed by univariate Cox, LASSO, and multivariate Cox analyses. And the model was validated internally and externally by Kaplan-Meier analysis, respectively. We further performed GO and KEGG analyses of DEGs between risk groups. Besides, we investigated differences in somatic mutations between different risk groups. We explored differences in the immune microenvironment landscape and ICG expression levels in the different groups. Finally, we predicted the response to immunotherapy and the sensitivity of antitumour drugs between the different groups. We screened 4 CAF clusters and identified 463 CAFRGs in PCa scRNA-seq. We constructed a model containing 10 prognostic CAFRGs by univariate Cox, LASSO, and multivariate Cox analysis. Somatic mutation analysis revealed that TTN and TP53 were significantly more mutated in the high-risk group. Finally, we screened 31 chemotherapeutic drugs and targeted therapeutic drugs for PCa. In conclusion, we identified four clusters based on CAFs and constructed a new CAFs-based prognostic signature that could predict PCa patient prognosis and response to immunotherapy and might suggest meaningful clinical options for the treatment of PCa.

The Genomic Landscape of Melanoma and Its Therapeutic Implications

Melanoma is one of the most aggressive malignancies of the skin. The genetic composition of melanoma is complex and varies among different subtypes. With the aid of recent technologies such as next generation sequencing and single-cell sequencing, our understanding of the genomic landscape of melanoma and its tumor microenvironment has become increasingly clear. These advances may provide explanation to the heterogenic treatment outcomes of melanoma patients under current therapeutic guidelines and provide further insights to the development of potential new therapeutic targets. Here, we provide a comprehensive review on the genetics related to melanoma tumorigenesis, metastasis, and prognosis. We also review the genetics affecting the melanoma tumor microenvironment and its relation to tumor progression and treatment.

Development and Validation of a Combined Ferroptosis and Immune Prognostic Model for Melanoma

Background. Melanoma development and progression are significantly influenced by ferroptosis and the immune microenvironment. However, there are no reliable biomarkers for melanoma prognosis prediction based on ferroptosis and immunological response. Methods. Ferroptosis-related genes (FRGs) were retrieved from the FerrDb website. Immune-related genes (IRGs) were collected in the ImmPort dataset. The TCGA (The Cancer Genome Atlas) and GSE65904 datasets both contained prognostic FRGs and IRGs. The model was created using multivariate Cox regression, the least absolute shrinkage and selection operator (LASSO) Cox regression analysis, and the analysis and comparison between the expression patterns of ferroptosis and immune cell infiltration were done. Last but not least, research was conducted to assess the expression and involvement of the genes in the comprehensive index of ferroptosis and immune (CIFI). Results. Two prognostic ferroptosis- and immune-related markers (PDGFRB and FOXM1) were utilized to develop a CIFI. In various datasets and patient subgroups, CIFI exhibits consistent predictive performance. The fact that CIFI is an independent prognostic factor for melanoma patients was revealed. Patients in the CIFI-high group further exhibited immune-suppressive characteristics and had elevated ferroptosis gene expression levels. The results of in vitro research point to the possibility that the PDGFRB and FOXM1 genes function as oncogenes in melanoma. Conclusion. In this study, a novel prognostic classifier for melanoma patients was developed and validated using ferroptosis and immune expression profiles.

A Necroptosis-Related Gene Signature to Predict the Prognosis of Skin Cutaneous Melanoma

The prognosis of skin cutaneous melanoma (SKCM) remains poor, and patients with SKCM show a poor response to immunotherapy. Thus, we aimed to identify necroptosis-related biomarkers, which can help predict the prognosis of SKCM and improve the effectiveness of precision medicine. Data of SKCM were obtained from The Cancer Genome Atlas (TCGA) and GEO databases. TCGA samples were classified into two clusters by consensus clustering of necroptosis-related genes. Univariate Cox and least absolute shrinkage and selection operator regression analyses led to the identification of 11 genes, which were used to construct a prognostic model. GSE65904 was used as the test set. Principal component, t-distributed stochastic neighbor embedding, and Kaplan–Meier survival analyses indicated that samples in the train and test sets could be divided into two groups, with the high-risk group showing a worse prognosis. Univariate and multivariate Cox regression analyses were performed, and a nomogram, calibration curve, and time-dependent receiver operating characteristic curve were constructed to verify the efficacy of our model. The 1-, 3-, and 5-year areas under the receiver operating characteristic curves for the train set were 0.702, 0.663, and 0.701 and for the test set were 0.613, 0.627, and 0.637, respectively. Moreover, we performed Gene Ontology and Kyoto Encyclopedia of Genes and Genomes pathway enrichment analyses between the high- and low-risk groups. Single sample gene set enrichment analysis, immune cell infiltration analysis, tumor microenvironment scores, immune checkpoint analysis, and half-maximal inhibitory concentration prediction indicated that the high-risk group showed weaker antitumor immunity; further, the response to immune checkpoint inhibitors was worse, and the high-risk group was sensitive to fewer antitumor drugs. Tumor mutational burden analysis, Kaplan–Meier survival analysis, and correlation analysis between risk score and RNA stemness score revealed that the high-risk group with low tumor mutational burden and high RNA stemness score was potentially associated with poor prognosis. To conclude, our model, which was based on 11 necroptosis-related genes, could predict the prognosis of SKCM; in addition, it has guiding significance for the selection of clinical treatment and provides new research directions to enhance necroptosis against SKCM.

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.

The KMT1A/TIMP3/PI3K/AKT circuit regulates tumor growth in cervical cancer

The epigenetic mechanism of tissue inhibitor of metalloproteinase 3 (TIMP3), a well-known tumor suppressor, in cervical cancer (CC) is still unclear. Integrated GEO database, protein interaction network, and a pan-cancer analysis revealed a KMT1A/TIMP3 axis in CC. KMT1A was highly expressed, and TIMP3 was poorly expressed in CC tissues and cells. KMT1A inhibited the activity of TIMP3. Silencing of KMT1A hampered the proliferation, migration, invasion, tumorigenesis and metastases of CC cells in vivo, and increased the apoptosis of cells. TIMP3 downregulation promoted the malignant phenotype and in vivo tumorigenesis and metastasis of CC cells. KMT1A downregulation impaired PI3K/AKT pathway in cells, while TIMP3 silencing promoted PI3K/AKT pathway activity. We propose a novel perspective that KMT1A involves in the growth and metastases via the TIMP3/PI3K/AKT axis in CC. In summary, our study identified a vital role played by KMT1A in the development of CC and the epigenetic mechanism, indicating that targeting KMT1A-related pathways could be conducive to the therapies for CC.

Genomic and Transcriptomic Underpinnings of Melanoma Genesis, Progression, and Metastasis

Melanoma is a deadly skin cancer with rapidly increasing incidence worldwide. The discovery of the genetic drivers of melanomagenesis in the last decade has led the World Health Organization to reclassify melanoma subtypes by their molecular pathways rather than traditional clinical and histopathologic features. Despite this significant advance, the genomic and transcriptomic drivers of metastatic progression are less well characterized. This review describes the known molecular pathways of cutaneous and uveal melanoma progression, highlights recently identified pathways and mediators of metastasis, and touches on the influence of the tumor microenvironment on metastatic progression and treatment resistance. While targeted therapies and immune checkpoint blockade have significantly aided in the treatment of advanced disease, acquired drug resistance remains an unfortunately common problem, and there is still a great need to identify potential prognostic markers and novel therapeutic targets to aid in such cases.

Silencing FYVE, RhoGEF, and PH domain containing 1 (FGD1) suppresses melanoma progression by inhibiting PI3K/AKT signaling pathway

Cutaneous melanoma is the leading cause of death among skin cancers despite the availability of diverse treatments. FGD1 plays an important role in multiple cancers, but how it works in cutaneous melanoma has not been illustrated. Thus, this study was intended to investigate the roles of FGD1 and its underlying mechanisms in cutaneous melanoma. Bioinformatics tools and quantitative real-time polymerase chain reaction (qRT-PCR) were used to analyze the expression of FGD1 in cutaneous melanoma. After the knockdown of FGD1 in melanoma cells, the proliferation, migration, and invasion of cells were analyzed by cell counting kit-8 (CCK8) assay, colony formation assays and transwell assays. Western blot was used to check the expression of key factors in PI3K/AKT pathway. In addition, nude mice models were used to study the role of FGD1 in melanoma development and metastasis in vivo. The data demonstrated that FGD1 was up-regulated and predicted a poor clinical outcome for cutaneous melanoma patients. Knockdown of FGD1 inhibited melanoma cell proliferation, migration, and invasion. The expressions of p-PI3K and p-AKT were significantly decreased, while the expressions of PI3K and AKT showed no marked difference in the knockdown group. Meanwhile, knockdown of FGD1 suppressed the development of melanoma in vivo. This study suggested that knockdown of FGD1 could block melanoma formation and proliferation by inhibiting PI3K/AKT signaling pathway. FGD1 might be a promising therapeutic target for melanoma.

Identification of Genes Potentially Associated with Melanoma Tumorigenesis Through Co-Expression Network Analysis

Background

Melanoma is one of the most malignant and aggressive skin tumors, and its incidence is increasing worldwide. However, few studies have investigated the process of tumorigenesis from normal skin to melanoma.

Methods

Several bioinformatics analyses, including GEO databases, Oncomine database, TCGA database, STRING, MCODE and cytoHubba plug-ins, GEPIA, TIMER and TRRUST and DGIdb, were performed to disclose the hub genes and immunology implicated in primary melanoma tumorigenesis. Finally, immunohistochemistry (IHC) and quantitative real-time PCR (qPCR) were used to validate the results of bioinformatics analysis in vitro.

Results

A total of 295 overlapping DEGs (ODEGs) (157 upregulated and 138 downregulated) and 9 hub genes were identified between primary melanoma and normal skin tissues. Functional analysis of these 9 hub genes indicated that the genes were primarily enriched in cell chemotaxis, the chemokine-mediated signaling pathway, the extracellular region, the extracellular space, chemokine activity and CXCR3 chemokine receptor binding. KEGG pathway enrichment showed that these genes were primarily involved in the chemokine signaling pathway, cytokine–cytokine receptor interaction, the toll-like receptor signaling pathway, the cytosolic DNA-sensing pathway and the TNF signaling pathway. Upregulated CCL5, CCL4, CXCL9 and CXCL10 demonstrated good overall survival (OS), and most of them have a higher expression in stage 0 and 1 of melanoma. Moreover, immune infiltration analysis showed that the above hub genes showed a strong positive correlation between their expression and infiltration of the six immune cell subsets. Transcription factor regulation network suggested that RELA and NFKB1 are the transcription factors of CCL4, CCL5, CXCL10 and CXCL2, while IRF7, IRF3 and IRF1 are the transcription factors of CCL5 and CXCL10. Drug–gene interaction analysis identified 46 drug–gene interactions. In vitro data demonstrated that the level of CCL4, CCL5, CXCL9 and CXCL10 is higher in melanoma than that in normal skin tissues, either at tissue or cell lines level.

Conclusion

In summary, we identified 4 key chemokine members related to tumorigenesis and progression in primary melanoma, and these results may help to elucidate melanoma tumorigenesis and facilitate its treatment.

Chemokine Pathways in Cutaneous Melanoma: Their Modulation by Cancer and Exploitation by the Clinician

The incidence of cutaneous malignant melanoma is rising globally and is projected to continue to rise. Advances in immunotherapy over the last decade have demonstrated that manipulation of the immune cell compartment of tumours is a valuable weapon in the arsenal against cancer; however, limitations to treatment still exist. Cutaneous melanoma lesions feature a dense cell infiltrate, coordinated by chemokines, which control the positioning of all immune cells. Melanomas are able to use chemokine pathways to preferentially recruit cells, which aid their growth, survival, invasion and metastasis, and which enhance their ability to evade anticancer immune responses. Aside from this, chemokine signalling can directly influence angiogenesis, invasion, lymph node, and distal metastases, including epithelial to mesenchymal transition-like processes and transendothelial migration. Understanding the interplay of chemokines, cancer cells, and immune cells may uncover future avenues for melanoma therapy, namely: identifying biomarkers for patient stratification, augmenting the effect of current and emerging therapies, and designing specific treatments to target chemokine pathways, with the aim to reduce melanoma pathogenicity, metastatic potential, and enhance immune cell-mediated cancer killing. The chemokine network may provide selective and specific targets that, if included in current therapeutic regimens, harbour potential to improve outcomes for patients.