We aim to find a biomarker that can effectively predict the prognosis of patients with cutaneous melanoma (CM). The RNA sequencing data of CM was downloaded from The Cancer Genome Atlas (TCGA) database and randomly divided into training group and test group. Survival statistical analysis and machine-learning approaches were performed on the RNA sequencing data of CM to develop a prognostic signature. Using univariable Cox proportional hazards regression, random survival forest algorithm, and receiver operating characteristic (ROC) in the training group, the four-mRNA signature including CD276, UQCRFS1, HAPLN3, and PIP4P1 was screened out. The four-mRNA signature could divide patients into low-risk and high-risk groups with different survival outcomes (log-rank p < 0.001). The predictive efficacy of the four-mRNA signature was confirmed in the test group, the whole TCGA group, and the independent GSE65904 (log-rank p < 0.05). The independence of the four-mRNA signature in prognostic prediction was demonstrated by multivariate Cox analysis. ROC and timeROC analyses showed that the efficiency of the signature in survival prediction was better than other clinical variables such as melanoma Clark level and tumor stage. This study highlights that the four-mRNA model could be used as a prognostic signature for CM patients with potential clinical application value.

Pathological neovascularization in choroid, a leading cause of blindness, is a characteristic of many fundus diseases, such as diabetic retinopathy and age-related macular degeneration. The present study aimed to elucidate the key signaling pathways in choroidal neovascularization (CNV) by analyzing the mRNA profiles of choroid and retina in tree shrews with CNV. We induced choroidal angiogenesis by laser photocoagulation in 15 tree shrews and obtained mRNA profiles of their choroids and retinas by high-throughput transcriptome sequencing. Hierarchical cluster analysis, weighted gene co-expression network analysis (WGCNA), protein-protein interaction (PPI) network analysis, hematoxylin and eosin (HE) staining, CD31 immunohistochemistry (IHC), and reverse transcription quantitative PCR (RT-qPCR) were performed. After laser photocoagulation, we obtained a total of 350 differentially expressed genes (DEGs) in the choroid, including 59 genes in Module-FASN (“ME-FASN”) module and 28 genes in Module-RPL (“ME-RPL”) module. A total of 69 DEGs in retina, including 20 genes in Module-SLC (“ME-SLC”) module. Bioinformatics analysis demonstrated that DEGs in choroid were mainly involved in membrane transport; DEGs in “ME-RPL” were prominent in pathways associated with IgA production, antigen presentation, and cell adhesion molecules (CAMs) signaling. DEGs in “ME-FASN” were involved in fatty acid metabolism and PPAR signaling pathway, while DEGs in “ME-SLC” were involved in GABAergic synapse, neuroactive life receptor interaction, cholinergic synapse, and retrograde endocannabinoid signaling pathway. PPI network analysis demonstrated that the ribosomal protein family genes (RPL31, RPL7, RPL26L1, and RPL19) are key factors of “ME-RPL,” acyl-CoA superfamily genes (ACACA, ACAT1, ACAA2, and ACACB) and FASN are key factors of “ME-FASN” and superfamily of solid carrier genes (SLC17A6, SLC32A1, SLC12A5, and SLC6A1) and complement genes (C4A, C3, and C2) are key factors of “ME-SLC.” In conclusion, the present study discovered the important signal transductions (fatty acid metabolic pathway and CAMs signaling) and genes (ribosomal protein family and the complement system) in tree shrew CNV. We consider that our findings hold implications in unraveling molecular mechanisms that underlie occurrence and development of CNV.

Cancer is one of the most threatening diseases to humans. It can invade multiple significant organs, including lung, liver, stomach, pancreas, and even brain. The identification of cancer biomarkers is one of the most significant components of cancer studies as the foundation of clinical cancer diagnosis and related drug development. During the large-scale screening for cancer prevention and early diagnosis, obtaining cancer-related tissues is impossible. Thus, the identification of cancer-associated circulating biomarkers from liquid biopsy targeting has been proposed and has become the most important direction for research on clinical cancer diagnosis. Here, we analyzed pan-cancer extracellular microRNA profiles by using multiple machine-learning models. The extracellular microRNA profiles on 11 cancer types and non-cancer were first analyzed by Boruta to extract important microRNAs. Selected microRNAs were then evaluated by the Max-Relevance and Min-Redundancy feature selection method, resulting in a feature list, which were fed into the incremental feature selection method to identify candidate circulating extracellular microRNA for cancer recognition and classification. A series of quantitative classification rules was also established for such cancer classification, thereby providing a solid research foundation for further biomarker exploration and functional analyses of tumorigenesis at the level of circulating extracellular microRNA.