Molecular Subtypes of Oral Squamous Cell Carcinoma Based on Immunosuppression Genes Using a Deep Learning Approach

Background: The mechanisms through which immunosuppressed patients bear increased risk and worse survival in oral squamous cell carcinoma (OSCC) are unclear. Here, we used deep learning to investigate the genetic mechanisms underlying immunosuppression in the survival of OSCC patients, especially from the aspect of various survival-related subtypes. Materials and methods: OSCC samples data were obtained from The Cancer Genome Atlas (TCGA), International Cancer Genome Consortium (ICGC), and OSCCrelated genetic datasets with survival data in the National Center for Biotechnology Information (NCBI). Immunosuppression genes (ISGs) were obtained from the HisgAtlas and DisGeNET databases. Survival analyses were performed to identify the ISGs with significant prognostic values in OSCC. A deep learning (DL)-based model was established for robustly differentiating the survival subpopulations of OSCC samples. In order to understand the characteristics of the different survival-risk subtypes of OSCC samples, differential expression analysis and functional enrichment analysis were performed. Results: A total of 317 OSCC samples were divided into one inferring cohort (TCGA) and four confirmation cohorts (ICGC set, GSE41613, GSE42743, and GSE75538). Eleven ISGs (i.e., BGLAP, CALCA, CTLA4, CXCL8, FGFR3, HPRT1, IL22, ORMDL3, TLR3, SPHK1, and INHBB) showed prognostic value in OSCC. The DL-based model provided two optimal subgroups of TCGA-OSCC samples with significant differences (p = 4.91E-22) and good model fitness [concordance index (C-index) = 0.77]. The DL model was validated by using four external confirmation cohorts: ICGC cohort (n = 40, C-index = 0.39), GSE41613 dataset (n = 97, C-index = 0.86), GSE42743 dataset (n = 71, C-index = 0.87), and GSE75538 dataset (n = 14, C-index = 0.48). Importantly, subtype Sub1 demonstrated a lower probability of survival and thus a more aggressive nature compared with subtype Sub2. ISGs in subtype Sub1 were enriched in the tumorinfiltrating immune cells-related pathways and cancer progression-related pathways, while those in subtype Sub2 were enriched in the metabolism-related pathways. Conclusion: The two survival subtypes of OSCC identified by deep learning can benefit clinical practitioners to divide immunocompromised patients with oral cancer into two subpopulations and give them target drugs and thus might be helpful for improving the survival of these patients and providing novel therapeutic strategies in the precision medicine area

Adaptive Angular-sector Segmentation Radar Target Recognition based on Grey System

The aspect sensitivity of high-resolution range profile (HRRP) leads to the anomalous change of the HRRP statistical characteristic, which is one of inextricable problems on the target recognition based on HRRP. Aiming at the HRRP statistical characteristic, an adaptive angular-sector segmentation method is proposed through based on the grey relational mode. Comparing to the equal interval angular-sector segmentation method, the new method improves the recognition performance. And these simulation results of five kinds of aircraft targets HRRPs prove the feasibility and validity

SPAG5 Expression Predicts Poor Prognosis and is Associated With Adverse Immune Infiltration in Lung Adenocarcinomas

Background: Sperm-associated antigen 5 (SPAG5) has been identified as a novel driver oncogene involved in multiple cancers; however, its role in lung adenocarcinoma (LUAD) needs further investigation. Our study aims to elucidate the potential significance of SPAG5 in LUAD prognosis and its implications for the efficacy of immunotherapy. Methods: In this study, we used bioinformatics analysis and tissue microarray (TMA) staining to examine the potential role of SPAG5 in LUAD survival and response to immunotherapy. We used the Oncomine, TIMER2.0, Gene Expression Profiling Interactive Analysis (GEPIA), Sangerbox, PredicScan, and Kaplan-Meier Plotter databases to examine the expression and prognostic role of SPAG5 in the LUAD of The Cancer Genome Atlas (TCGA), Gene Expression Omnibus (GEO), and other databases. We also used Cancer Single-cell State Atlas (CancerSEA) and Tumor Immune Estimation Resource (TIMER2.0) to analyze the association of SPAG5 with malignant phenotype and tumor immune microenvironment. Furthermore, Immune Cell Abundance Identifier (ImmuCellAI) analysis of TCGA sequencing data was used to predict the role of SPAG5 in determining the response to immune checkpoint blockade (ICB) treatment in LUAD. Co-expression analysis of programmed death-ligand 1 (PD-L1) and SPAG5 was performed using LUAD TMA immunohistochemistry (IHC) analysis. Results: Our findings indicate that SPAG5 is overexpressed in LUAD and is positively correlated with advanced clinical stage, poor overall survival, relapse-free survival, and progression-free survival outcomes. SPAG5 may be involved in regulating the cell cycle, proliferation, invasion, DNA damage and repair, and tumor immunosuppression. Furthermore, TMA IHC analysis showed a positive correlation between PD-L1 expression in LUAD and SPAG5 which suggests that SPAG5 may serve as a potential predictor of response to ICB therapy in LUAD. Conclusions: Our results highlight the role of SAPG5 in promoting a tumor malignancy phenotype and immunosuppression in LUAD and suggest that SPAG5 may serve as a potential response marker for ICB therapy

Fault-tolerant scheduling for real-time tasks on multiple earth-observation satellites

Fault-tolerance plays an important role in improving the reliability of multiple earth-observing satellites, especially in emergent scenarios such as obtaining photographs on battlefields or earthquake areas. Fault tolerance can be implemented through scheduling approaches. Unfortunately, little attention has been paid to fault-tolerant scheduling on satellites. To address this issue, we propose a novel dynamic fault-tolerant scheduling model for real-time tasks running on multiple observation satellites. In this model, the primary-backup policy is employed to tolerate one satellite’s permanent failure at one time instant. In the light of the fault-tolerant model, we develop a novel fault-tolerant satellite scheduling algorithm named FTSS. To improve the resource utilization, we apply the overlapping technology that includes primary-backup copy overlapping (i.e., PB overlapping) and backup-backup copy overlapping (i.e., BB overlapping). According to the satellites characterized with time windows for observations, we extensively analyze the overlapping mechanism on satellites. We integrate the overlapping mechanism with FTSS, which employs the task merging strategies including primary-backup copy merging (i.e., PB merging), backup-backup copy merging (i.e., BB merging) and primary-primary copy merging (i.e., PP merging). These merging strategies are used to decrease the number of tasks required to be executed, thereby enhancing system schedulability. To demonstrate the superiority of our FTSS, we conduct extensive experiments using the real-world satellite parameters supplied from the satellite tool kit or STK; we compare FTSS with the three baseline algorithms, namely, NMFTSS, NOFTSS, and NMNOFTSS. The experimental results indicate that FTSS efficiently improves the scheduling quality of others and is suitable for fault-tolerant satellite scheduling.status: publishe

Long noncoding RNA KCNMA1-AS1 promotes osteogenic differentiation of human bone marrow mesenchymal stem cells by activating the SMAD9 signaling pathway

Abstract The human bone marrow mesenchymal stem cells (hBMSCs) undergo intense osteogenic differentiation, a crucial bone formation mechanism. Evidence from prior studies suggested an association between long noncoding RNAs (lncRNAs) and the osteogenic differentiation of hBMSCs. However, precise roles and molecular mechanisms are still largely unknown. In this work, we report for the first time that lncRNA KCNMA1 antisense RNA 1 (KCNMA1-AS1) plays a vital role in regulating hBMSCs’ osteogenic differentiation. Here, it was observed that the KCNMA1-AS1 expression levels were significantly upregulated during osteogenic differentiation. In addition, KCNMA1-AS1 overexpression enhanced in vitro osteogenic differentiation of hBMSCs and in vivo bone formation, whereas knockdown of KCNMA1-AS1 resulted in the opposite result. Additionally, the interaction between KCNMA1-AS1 and mothers against decapentaplegic homolog 9 (SMAD9) was confirmed by an RNA pull-down experiment, mass spectrometry, and RIP assay. This interaction regulated the activation of the SMAD9 signaling pathway. Moreover, rescue assays demonstrated that the inhibitor of the SMAD9 signaling pathway reversed the stimulative effects on osteogenic differentiation of hBMSCs by KCNMA1-AS1 overexpression. Altogether, our results stipulate that KCNMA1-AS1 promotes osteogenic differentiation of hBMSCs via activating the SMAD9 signaling pathway and can serve as a biomarker and therapeutic target in treating bone defects

sj-jpg-9-onc-10.1177_11795549231199915 – Supplemental material for SPAG5 Expression Predicts Poor Prognosis and is Associated With Adverse Immune Infiltration in Lung Adenocarcinomas

Supplemental material, sj-jpg-9-onc-10.1177_11795549231199915 for SPAG5 Expression Predicts Poor Prognosis and is Associated With Adverse Immune Infiltration in Lung Adenocarcinomas by Gang Xiao, Xie Xu, Zhibo Chen, Jie Zeng and Jianjiang Xie in Clinical Medicine Insights: Oncology</p