Identification of heterogeneity and prognostic key genes associated with uveal melanoma using single-cell RNA-sequencing technology

Identification of feature genes and molecular mechanisms involved in cell communication in uveal melanoma through analysis of single‑cell sequencing data

Uveal melanoma (UM) is a highly metastatic cancer with resistance to immunotherapy. The present study aimed to identify novel feature genes and molecular mechanisms in UM through analysis of single-cell sequencing data. For this purpose, data were downloaded from The Cancer Genome Atlas and National Center for Biotechnology Information Gene Expression Omnibus public databases. The statistical analysis function of the CellPhoneDB software package was used to analyze the ligand-receptor relationships of the feature genes. The Metascape database was used to perform the functional annotation of notable gene sets. The randomForestSRC package and random survival forest algorithm were applied to screen feature genes. The CIBERSORT algorithm was used to analyze the RNA-sequencing data and infer the relative proportions of the 22 immune-infiltrating cell types. In vitro, small interfering RNAs were used to knockdown the expression of target genes in C918 cells. The migration capability and viability of these cells were then assessed by gap closure and Cell Counting Kit-8 assays. In total, 13 single-cell sample subtypes were clustered by t-distributed Stochastic Neighbor Embedding and annotated by the R package, SingleR, into 7 cell categories: Tissue stem cells, epithelial cells, fibroblasts, macrophages, natural killer cells, neurons and endothelial cells. The interactions in NK cells|Endothelial cells, Neurons|Endothelial cells, CD74_APP, and SPP1_PTGER4 were more significant than those in the other subsets. T-Box transcription factor 2, tropomyosin 4, plexin D1 (PLXND1), G protein subunit α I2 (GNAI2) and SEC14-like lipid binding 1 were identified as the feature genes in UM. These marker genes were found to be significantly enriched in pathways such as vasculature development, focal adhesion and cell adhesion molecule binding. Significant correlations were observed between key genes and immune cells as well as immune factors. Relationships were also observed between the expression levels of the key genes and multiple disease-related genes. Knockdown of PLXND1 and GNAI2 expression led to significantly lower viability and gap closure rates of C918 cells. Therefore, the results of the present study uncovered cell communication between endothelial cells and other cell types, identified innovative key genes and provided potential targets of gene therapy in UM.

Epidemiological and transcriptome data identify shared gene signatures and immune cell infiltration in type 2 diabetes and non-small cell lung cancer

BACKGROUND

Numerous previous studies have reported an association between type 2 diabetes mellitus (T2DM) and lung cancer risk, but the underlying mechanism of the interaction remains unclear. This study aimed to investigate the shared genetic features and immune infiltration processes between lung cancer and T2DM.

METHODS

Epidemiological data from the National Health and Nutrition Examination Survey (NHANES) 2000-2018 was used to explore the relationship between lung cancer and diabetes systematically. In addition, we also used bioinformatics methods to analyze the transcriptome data from the Gene Expression Omnibus (GEO) to explore the potential functional mechanisms from the perspective of genes and immune infiltration.

RESULTS

Logistic regression analysis showed that prediabetes (OR = 3.289,95%CI 1.231, 8.788, p = 0.01760, model 3)and type 2 diabetes (OR = 3.032 95%CI,1.015, 9.054, p = 0.04689) were significantly associated with an increased risk of lung cancer after adjusting for multiple covariates. Data from NHANES showed an inverted U-shaped relationship between fasting blood glucose and glycosylated haemoglobin and the risk of lung cancer (P for non-linear < 0.001). Transcriptome data showed that we screened 57 co-DEGs, of which 25 were up-regulated co-DEGs and 32 were down-regulated. Ten core DEGs were identified by bioinformatics analysis, which were SMC6, CDC27, CDC7, RACGAP1, SMC4, NCF4, NCF1, NCF2, SELPLG and CFP. Correlation analysis showed that some core DEGs were significantly associated with simultaneous dysregulation of immune cells.

CONCLUSION

The identified core genes of NSCLC and T2DM are associated with dysregulated immune cells, which provides a potential research avenue for diagnosing and treating lung cancer combined with diabetes.

Single-cell sequencing in primary intraocular tumors: understanding heterogeneity, the microenvironment, and drug resistance

Retinoblastoma (RB) and uveal melanoma (UM) are the most common primary intraocular tumors in children and adults, respectively. Despite continued increases in the likelihood of salvaging the eyeball due to advancements in local tumor control, prognosis remains poor once metastasis has occurred. Traditional sequencing technology obtains averaged information from pooled clusters of diverse cells. In contrast, single-cell sequencing (SCS) allows for investigations of tumor biology at the resolution of the individual cell, providing insights into tumor heterogeneity, microenvironmental properties, and cellular genomic mutations. SCS is a powerful tool that can help identify new biomarkers for diagnosis and targeted therapy, which may in turn greatly improve tumor management. In this review, we focus on the application of SCS for evaluating heterogeneity, microenvironmental characteristics, and drug resistance in patients with RB and UM.

Hepatic passaging of NRAS-mutant melanoma influences adhesive properties and metastatic pattern

BACKGROUND

Liver metastasis is a poor prognostic factor for treatment of advanced cutaneous melanoma with either immunotherapy or targeted therapies. In this study we focused on NRAS mutated melanoma, a cohort with high unmet clinical need.

METHODS

WT31 melanoma was repeatedly passaged over the liver after intravenous injections five times generating the subline WT31_P5IV. The colonization of target organs, morphology, vascularization and the gene expression profiles of metastases were analyzed.

RESULTS

After intravenous injection lung metastasis was significantly decreased and a trend towards increased liver metastasis was detected for WT31_P5IV as compared to parental WT31. Besides, the ratio of lung to liver metastases was significantly smaller. Histology of lung metastases revealed reduced proliferation of WT31_P5IV in relation to WT31 while both size and necrotic areas were unaltered. Liver metastases of both sublines showed no differences in vascularization, proliferation or necrosis. To identify tumor-intrinsic factors that altered the metastatic pattern of WT31_P5IV RNA sequencing was performed and revealed a differential regulation of pathways involved in cell adhesion. Ex vivo fluorescence imaging confirmed that initial tumor cell retention in the lungs was significantly reduced in WT31_P5IV in comparison to WT31.

CONCLUSION

This study demonstrates that tumor-intrinsic properties influencing the metastatic pattern of NRAS mutated melanoma are strongly affected by hepatic passaging and the hematogenous route tumor cells take. It has implications for the clinical setting as such effects might also occur during metastatic spread or disease progression in melanoma patients.

SELPLG Expression Was Potentially Correlated With Metastasis and Prognosis of Osteosarcoma

Background: Osteosarcoma (OS) is the most prevalent malignant primary bone tumor in children. Selectin P ligand gene (SELPLG) has been studied in several cancers. Our research aimed to explore the role of SELPLG in OS.

Methods: All OS patient data was obtained from TARGET and GEO databases. Differential expression analyses were conducted in limma package of R. Functional analyses included GO and KEGG enrichment analyses. Immune cell infiltration analysis was done in CIBERSORT software. The overall survival was calculated using survival and survminer package of R.

Results: Significantly lower SELPLG expression was observed in metastatic OS samples compared with non-metastatic OS samples, both in TARGET and in GSE21257. Low SELPLG expression was an independent undesirable prognostic factor for OS patients, in both TARGET and GEO datasets. Totally 62 differentially expressed gene (DEG) overlaps were found between high SELPLG vs. low SELPLG and non-metastatic vs. metastatic OS samples, affecting metastases and thereby influencing the prognosis, which were significantly enriched in 40 GO and six KEGG terms. Five types of immune cells were significantly differentially infiltrated between high and low SELPLG expression OS patients.

Conclusion: SELPLG is closely correlated with metastases and prognosis of OS patients. The OS patients with low SELPLG expression have relatively poorer prognosis and SELPLG is a potential prognostic biomarker for OS.