Medicine in Omics最新文献

Hematoxylin and eosin stained slides are routinely used for the diagnosis and grading of endometrioid endometrial carcinoma (EEC). These images present a high degree of cellular heterogeneity, which may contain clinically relevant information such as prognosis and is difficult to be quantified objectively by eyes. Besides traditional microscopic image assessment, a lot of effort has been put in molecular characterization of tumors. How molecular events manifest at tumor tissue level is not well understood. In this paper, we investigated whether quantitative morphological features extracted from histopathological images are associated with patient survival and somatic mutation of genes in EEC using the multi-modality data from The Cancer Genome Atlas. A computational image analysis pipeline was developed to extract image features that characterize the size, shape, staining, and density of cell nuclei. For prognosis prediction, we built a prognostic model based on the image features. In a validation set, the risk score predicted by our model was an independent prognostic factor for overall survival in a multivariate Cox proportional hazards model (hazard ratio with 95% confidence interval: 3.38 [1.55–7.37], p = 2.15e−3). To link tumor tissue morphology with somatic mutation, a two-sided Mann-Whitney U test was used to compare the distribution of each feature between mutated and nonmutated cases for frequently mutated genes. We found that TP53 and TTN were significantly associated with tissue morphological changes. These findings show the promising potential of computational histopathology image analysis in predicting patient survival and exploring genotype-phenotype associations.

Psoriasis classification requires the accurate identification of the lesional types for the early and effective diagnosis and it is worth interesting that the normal and psoriasis cell tissues exhibit different gene expression. Therefore, gene expression data is an effective source for psoriasis classification and there is a challenge regarding the selection of suitable gene signatures for its purpose. In this present study, the gene expression-based microarray data were used and 35 expression features linked with psoriasis were utilized to feed into our machine learning model. Overall, the performance of our model based on 35 mentioned-above features surpassed that of other state-of-the-art classifiers with an average accuracy of 98.3%, recall of 98.6%, and precision of 98% in 5-fold cross-validation tests. We also validate our model on two different sets of psoriasis and the performance results are significant. These results have suggested that our 35 expression signatures have been identified as key features for classifying samples between lesion and non-lesion. More specifically, the expression levels of few genes i.e., FABP5, TGM1, or BCAR3 are discovered as newly potential biomarkers for psoriasis classification and treatment with high confidence. This study, therefore, could shed light on developing the prediction models for psoriasis classification and treatment using gene expression profiles.

Copy number variations (CNVs) play an important role in the genome aberrations and human diseases. Comprehensive discovery of CNVs from whole genome sequencing data remains difficult because of low sensitivity and high false detective rate (FDR). We presented a novel framework which integrated SNV-based recalibration probabilistic model and image classification architecture (ImageCNV) for CNVs discovery. A Naive Bayesian model and a deep neural network InceptionV3 were adopted to infer candidate CNVs, and we utilize the benchmark datasets to evaluate the performance of our framework. ImageCNV yielded comparable sensitivity and lower FDR, complementing other methods based on different signals and providing a new perspective for the detection of CNVs. ImageCNV is freely available at https://github.com/minqing1/ImageCNV.