Identification of Keratinocyte Differentiation-Involved Genes for Metastatic Melanoma by Gene Expression Profiles

Envoplakin Inhibits Macrophage Polarization by Altering the Inflammatory Tumor Microenvironment of Melanoma Through the RAS / ERK Signaling Pathway

Purpose

Tumor growth induces the tumor margin to become a transition zone rich in immune cells. EVPL is a potential prognostic biomarker for melanoma. Melanoma is difficult to cure because of its high metastasis, so it is urgent to find effective genes to inhibit tumor progression and regulate tumor microenvironment.

Methods

Firstly, differentially expressed genes (DEGs) among normal skin, nevus and melanoma samples in GSE3189 were screened. Bioinformatics was used to further explore the hub genes and enriched pathways closely related to the inflammatory response of DEGs in melanoma. We selected EVPL, which is associated with the Ras/Raf signaling pathway, for in vitro study. CCK-8, colony formation, wound healing, Transwell and flow cytometry assays were respectively used to evaluate the proliferation, migration, invasion, and apoptosis of cancer cells. Enzyme-linked immunosorbent assay was conducted for the monitoring of changes in the tumor microenvironment. To evaluate the effect of EVPL on macrophage recruitment, we established a co-culture system in a Transwell chamber. The polarization of macrophages was examined after treatment of cells with RAS/ERK signaling inhibitors SCH772984 and sh-EVPL. Additionally, changes in the expression of pathway proteins were measured by Western blot.

Results

Among the screened hub genes, EVPL was associated with the Ras/Raf pathway, a key signaling pathway in melanoma, and may be involved in regulating the inflammatory microenvironment of melanoma. Oe-EVPL was proved to suppress melanoma cell malignant progression. By inhibiting EVPL expression, the inhibitory effects on melanoma progression induced by the addition of SCH772984 were reversed. Furthermore, EVPL was found to inhibit the expression of chemokines, the recruitment of macrophages, and the polarization of macrophages through the Ras/Raf/ERK signaling pathway.

Conclusion

EVPL can inhibit the progression of melanoma through the RAS/ERK signaling pathway, change the inflammatory tumor microenvironment of melanoma, and inhibit the recruitment of macrophages.

A pan-cancer analysis of the oncogenic and immunological roles of transglutaminase 1 (TGM1) in human cancer

BACKGROUND

There is currently a limited number of studies on transglutaminase type 1 (TGM1) in tumors. The objective of this study is to perform a comprehensive analysis across various types of cancer to determine the prognostic significance of TGM1 in tumors and investigate its role in the immune environment.

METHOD

Pan-cancer and mutational data were retrieved from the TCGA database and analyzed using R (version 3.6.4) and its associated software package. The expression difference and prognosis of TGM1 were examined, along with its correlation with tumor heterogeneity, stemness, mutation landscape, and RNA modification. Additionally, the relationship between TGM1 expression and tumor immunity was investigated using the TIMER method.

RESULTS

TGM1 is expressed differently in various tumors and normal samples and is associated with the overall survival and progression-free time of KIRC, ACC, SKCM, LIHC, and STES. In LICH, we found a negative correlation between TGM1 expression and 6 indicators of tumor stemness. The mutation frequencies of BLCA, LIHC, and KIRC were 1.7%, 0.3%, and 0.3% respectively. In BLCA and BRCA, there was a significant correlation between TGM1 expression and the infiltration of CD4 + T cells, CD8 + T cells, neutrophils, and dendritic cells.

CONCLUSION

TGM1 has the potential to serve as both a prognostic marker and a drug target.

Has-miR-300-GADD45B promotes melanoma growth via cell cycle

Response to oncogenic factors like UV, GADD45 family in skin participates in scavenging ROS, DNA repair and cell cycle control. Because of this, the previous study of the chronic UVB injury model has found that hsa-miR-300 can conduct intercellular transport by exosomes and target regulation of GADD45B. Whether the hsa-miR-300-GADD45B still regulates tumor development by cell cycle pathway is unclear. Through transcriptomic analysis of primary (n=39) and metastatic (n=102) melanoma, it was confirmed that in metastatic samples, some of the 97 down-regulated genes participate in maintaining skin homeostasis while 42 up-regulated genes were enriched in cancer-related functions. Furthermore, CDKN1A, CDKN2A, CXCR4 and RAD51 in the melanoma pathway, were also differentially expressed between normal skin and melanoma. CDKN1A and CDKN2A were also found to be involved in TP53-dependent cell cycle regulation. In conclusion, it was speculated that CDKN1A, CDKN2A, TP53, GADD45B and hsa-miR-300 may have regulatory relationships. It was demonstrated that there is a bidirectional regulation between hsa-miR-300 and TP53. In addition, miR-300 can regulate CDKN1A by GADD45B/TP53 and promote melanoma growth by accelerating the cell cycle transition from G1/S to G2 phase.

Construction and evaluation of a prognostic risk model of tumor metastasis-related genes in patients with non-small cell lung cancer

Background

Lung cancer is a high-incidence cancer, and it is also the most common cause of cancer death worldwide. 80–85% of lung cancer cases can be classified as non-small cell lung cancer (NSCLC).

Methods

NSCLC transcriptome data and clinical information were downloaded from the TCGA database and GEO database. Firstly, we analyzed and identified the differentially expressed genes (DEGs) between non-metastasis group and metastasis group of NSCLC in the TCGA database, Gene Ontology (GO), Kyoto Encyclopedia of Genes and Genomes (KEGG) were consulted to explore the functions of the DEGs. Thereafter, univariate Cox regression and LASSO Cox regression algorithms were applied to identify prognostic metastasis-related signature, followed by the construction of the risk score model and nomogram for predicting the survival of NSCLC patients. GSEA analyzed that differentially expressed gene-related signaling pathways in the high-risk group and the low-risk group. The survival of NSCLC patients was analyzed by the Kaplan–Meier method. ROC curve was plotted to evaluate the accuracy of the model. Finally, the GEO database was further applied to verify the metastasis‑related prognostic signature.

Results

In total, 2058 DEGs were identified. GO functions and KEGG pathways analysis results showed that the DEGs mainly concentrated in epidermis development, skin development, and the pathway of Neuro active ligand -receptor interaction in cancer. A six-gene metastasis-related risk signature including C1QL2, FLNC, LUZP2, PRSS3, SPIC, and GRAMD1B was constructed to predict the overall survival of NSCLC patients. The reliability of the gene signature was verified in GSE13213. The NSCLC patients were grouped into low-risk and high-risk groups based on the median value of risk scores. And low-risk patients had lower risk scores and longer survival time. Univariate and multivariate Cox regression verified that this signature was an independent risk factor for NSCLC.

Conclusion

Our study identified 6 metastasis biomarkers in the NSCLC. The biomarkers may contribute to individual risk estimation, survival prognosis.