Background: Head and neck squamous cell carcinoma (HNSCC) is a highly aggressive disease with a poor prognosis for advanced tumors. Anoikis play a key role in cancer metastasis, facilitating the detachment and survival of cancer cells from the primary tumor site. However, few studies have focused on the role of anoikis in HNSC, especially on the prognosis.Methods: Anoikis-related genes (ANRGs) integrated from Genecards and Harmonizome portals were used to identify HNSCC subtypes and to construct a prognostic model for HNSCC patients. Also, we explored the immune microenvironment and enrichment pathways between different subtypes. Finally, we provide clinical experts with a novel nomogram based on ANRGs, with DCA curves indicating the potential clinical benefit of the model for clinical strategies.Results: We identified 69 survival-related HNSCC anoikis-related DEGs, from which 7 genes were selected to construct prognostic models. The prognostic risk score was identified as an independent prognostic factor. Functional analysis showed that these high and low risk groups had different immune status and drug sensitivity. Next risk scores were combined with HNSCC clinicopathological features together to construct a nomogram, and DCA analysis showed that the model could benefit patients from clinical treatment strategies.Conclusion: The predictive seven-gene signature and nomogram established in this study can assist clinicians in selecting personalized treatment for patients with HNSCC.
The satellite image downlink scheduling problem (SIDSP) is included in satellite mission planning as an important part. A customer demand is finished only if the corresponding images are eventually downloaded. Due to the growing customer demands and the limited ground resources, SIDSP is an oversubscribed scheduling problem. In this paper, we investigate SIDSP with the case study of China's commercial remote sensing satellite constellation (SuperView-1) and exploit the serial scheduling scheme for solving it. The idea is first determining a permutation of the downlink requests and then producing a schedule from the given ordered requests. A schedule generation algorithm (SGA) is proposed to assign the downlink time window for each scheduled request according to a given request permutation. A hybrid genetic algorithm (HGA) combined with neighborhood search is proposed to optimize the downlink request permutation with the purpose of maximizing the utility function. Experimental results on six groups of instances with different density demonstrate the effectiveness of the proposed approach.
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