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Necroptosis, as a form of programmed cell death, is involved in many physiological and pathological processes. However, its role in cancer progression and therapeutic response remains controversial. Colon cancer is one of the leading causes of cancer death and patients’ response to immune checkpoint blockade vary to a large degree. In this study, we investigated necroptosis related genes (NRGs) alterations in colon cancer by bioinformatics analysis. Colon cancer patients were classified into two subtypes with distinct clinical and molecular features based on NRGs. After finding differentially expressed genes and lasso regression, a prognostic model based on four necroptosis signature genes was constructed. The necroptosis signature was also a good predictor in the field of chemotherapy and immunotherapy in colon cancer. Altogether, this study illustrates the relationship between necroptosis and colon cancer, and establishes a novel scoring method to predict prognosis and therapeutic response in colon cancer patients.

Nowadays stochastic computer simulations with both numeral and distribution inputs are widely used to mimic complex systems which contain a great deal of uncertainty. This paper studies the design and analysis issues of such computer experiments. First, we provide preliminary results concerning the Wasserstein distance in probability measure spaces. To handle the product space of the Euclidean space and the probability measure space, we prove that, through the mapping from a point in the Euclidean space to the mass probability measure at this point, the Euclidean space can be isomorphic to the subset of the probability measure space, which consists of all the mass measures, with respect to the Wasserstein distance. Therefore, the product space can be viewed as a product probability measure space. We derive formulas of the Wasserstein distance between two components of this product probability measure space. Second, we use the above results to construct Wasserstein distance-based space-filling criteria in the product space of the Euclidean space and the probability measure space. A class of optimal Latin hypercube-type designs in this product space are proposed. Third, we present a Wasserstein distance-based Gaussian process model to analyze data from computer experiments with both numeral and distribution inputs. Numerical examples and real applications to a metro simulation are presented to show the effectiveness of our methods., Comment: 24 pages, 6 figures

In this paper we propose a linear variable screening method for computer experiments when the number of input variables is larger than the number of runs. This method uses a linear model to model the nonlinear data, and screens the important variables by existing screening methods for linear models. When the underlying simulator is nearly sparse, we prove that the linear screening method is asymptotically valid under mild conditions. To improve the screening accuracy, we also provide a two-stage procedure that uses different basis functions in the linear model. The proposed methods are very simple and easy to implement. Numerical results indicate that our methods outperform existing model-free screening methods.