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KEAP1 regulates the cytoprotection induced by NRF2 and has been reported to be a candidate tumor suppressor. Recent evidence has shown that mutations in several driver genes cause aberrant DNA methylation patterns, a hallmark of cancer. However, the correlation between KEAP1 mutations and DNA methylation in lung cancer has still not been investigated. In this study, we systematically carried out an integrated multi-omics analysis to explore the correlation between KEAP1 mutations and DNA methylation and its effect on gene expression in lung adenocarcinoma (LUAD). We found that most of the DNA aberrations associated with KEAP1 mutations in LAUD were hypomethylation. Surprisingly, we found several NRF2-regulated genes among the genes that showed differential DNA methylation. Moreover, we identified an 8-gene signature with altered DNA methylation pattern and elevated gene expression levels in LUAD patients with mutated KEAP1, and evaluated the prognostic value of this signature in various clinical datasets. These results establish that KEAP1 mutations are associated with DNA methylation changes capable of shaping regulatory network functions. Combining both epigenomic and transcriptomic changes along with KEAP1 mutations may provide a better understanding of the molecular mechanisms associated with the progression of lung cancer and may help to provide better therapeutic approaches.

The variability of illumination and weather conditions lead to a big challenge for infield image segmentation. Therefore, robust, fast, and automated algorithms are highly required to obtain reliable image segmentation results. This research was aimed to develop efficient unsupervised clustering algorithms for oilseed rape image segmentation in the field. The Naive Bayes rule was first employed to select a supreme color feature from ten color models. An initialization approach based on the genetic algorithm (GA) was then used to define the initial cluster centroids for subsequent Gaussian mixture model (GMM), self-organizing map (SOM), fuzzy c-mean (FCM), and k-means algorithms. The length of the chromosome was determined using cluster validity indices. Finally, the performances of these algorithms were evaluated based on the image segmentation quality and computation time. After testing the proposed method on the image datasets from two fields, the results revealed that the highest segmentation accuracy of 96% was obtained using the optimized SOM and the lowest computation time was obtained using the k-means. The GA-based initialization speeded up the convergence process and ensured consistent labeling between runs. All clustering algorithms were proved to be robust to varying illumination conditions and can process images with a very complex background in an automated fashion.