Uveal melanoma (UM) is the most common primary intraocular malignancy in adults.,Many previous studies have demonstrated that the infiltrating of immune and stromal cells in the tumor microenvironment contributes significantly to prognosis.,Dataset TCGA-UVM, download from TCGA portal, was taken as the training cohort, and GSE22138, obtained from GEO database, was set as the validation cohort.,ESTIMATE algorithm was applied to find intersection differentially expressed genes (DEGs) among tumor microenvironment.,Kaplan-Meier analysis and univariate Cox regression model were performed on intersection DEGs to initial screen for potential prognostic genes.,Then these genes entered into the validation cohort for validation using the same methods as that in the training cohort.,Moreover, we conducted correlation analyses between the genes obtained in the validation cohort and the status of chromosome 3, chromosome 8q, and tumor metastasis to get prognosis genes.,At last, the immune infiltration analysis was performed between the prognostic genes and 6 main kinds of tumor-infiltrating immune cells (TICs) for understanding the role of the genes in the tumor microenvironment.,959 intersection DEGs were found in the UM microenvironment.,Kaplan-Meier and Cox analysis was then performed in the training and validation cohorts on these DEGs, and 52 genes were identified with potential prognostic value.,After comparing the 52 genes to chromosome 3, chromosome 8q, and metastasis, we obtained 21 genes as the prognostic genes.,The immune infiltration analysis showed that Neutrophil had the potential prognostic ability, and almost every prognostic gene we had identified was correlated with abundances of Neutrophil and CD8+ T Cell.,Identifying 21 prognosis genes (SERPINB9, EDNRB, RAPGEF3, HFE, RNF43, ZNF415, IL12RB2, MTUS1, NEDD9, ZNF667, AZGP1, WARS, GEM, RAB31, CALHM2, CA12, MYEOV, CELF2, SLCO5A1, ISM1, and PAPSS2) could accurately identify patients' prognosis and had close interactions with Neutrophil in the tumor environment, which may provide UM patients with personalized prognosis prediction and new treatment insights.
Uveal melanoma (UM) is the most common primary intraocular malignancy in adults.,Monosomy 3 and BAP1 mutation are strong prognostic factors predicting metastatic risk in UM.,Nuclear BAP1 (nBAP1) expression is a close immunohistochemical surrogate for both genetic alterations.,Not all laboratories perform routine BAP1 immunohistochemistry or genetic testing, and rely mainly on clinical information and anatomic/morphologic analyses for UM prognostication.,The purpose of our study was to pilot deep learning (DL) techniques to predict nBAP1 expression on whole slide images (WSIs) of hematoxylin and eosin (H&E) stained UM sections.,One hundred forty H&E-stained UMs were scanned at 40 × magnification, using commercially available WSI image scanners.,The training cohort comprised 66 BAP1+ and 74 BAP1− UM, with known chromosome 3 status and clinical outcomes.,Nonoverlapping areas of three different dimensions (512 × 512, 1024 × 1024, and 2048 × 2048 pixels) for comparison were extracted from tumor regions in each WSI, and were resized to 256 × 256 pixels.,Deep convolutional neural networks (Resnet18 pre-trained on Imagenet) and auto-encoder-decoders (U-Net) were trained to predict nBAP1 expression of these patches.,Trained models were tested on the patches cropped from a test cohort of WSIs of 16 BAP1+ and 28 BAP1− UM cases.,The trained model with best performance achieved area under the curve values of 0.90 for patches and 0.93 for slides on the test set.,Our results show the effectiveness of DL for predicting nBAP1 expression in UM on the basis of H&E sections only.,Our pilot demonstrates a high capacity of artificial intelligence-related techniques for automated prediction on the basis of histomorphology, and may be translatable into routine histology laboratories.