Purpose Recurrence and metastasis are the most common causes of high mortality rates in patients with serous ovarian cancer (SOC). Non-structural maintenance of chromosomes (non-SMC) condensin I complex subunit H (NCAPH) is a newly identified essential oncoprotein whose function in SOC pathogenesis has not been reported yet. Angiogenic factor with G patch and FHA domains 1 (AGGF1) is an effective promoter of angiogenesis in humans, leading to cancer cell infiltration and progression. Forkhead box C2 (FOXC2) plays a pivotal role in epithelial-to-mesenchymal transition (EMT). The present study analyzed the correlations among the expressions of these three proteins and their relationships with the clinicopathological characteristics and survival of patients with SOC. Patients and Methods The expressions of NCAPH, AGGF1, and FOXC2 were detected by the immunohistochemical examination of 153 SOC tissue samples and 30 serous ovarian cystadenoma tissue samples. Clinicopathologic and follow-up data of the patients were collected. Results The expressions of NCAPH, AGGF1, and FOXC2 were remarkably higher in the SOC tissue samples than in the serous ovarian cystadenoma tissue samples. The protein expressions were positively correlated with the histological tumor grade, the International Federation of Gynecology and Obstetrics (FIGO) stage, lymph node metastasis, and intraperitoneal implantation, but were negatively correlated with the overall survival (OS). Moreover, multivariate analysis showed that the NCAPH, AGGF1, and FOXC2 expressions, FIGO stage, and histological tumor grade were independent adverse prognostic factors for OS in patients with SOC. Conclusion The results of this study show that the expressions of NCAPH, AGGF1, and FOXC2 are promising biomarkers and possible therapeutic targets in patients with SOC.

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As the size of the radar hardware platform becomes smaller and smaller, the cost becomes lower and lower. The application of indoor radar-based human motion recognition has become a reality, which can be realized in a low-cost device with simple architecture. Compared with narrow-band radar (such as continuous wave radar, etc.), the human motion echo signal of the carrier-free ultra-wideband (UWB) radar contains more abundant characteristic information of human motion, which is helpful for identifying different types of human motion. In this paper, a novel feature extraction method by two-dimensional variational mode decomposition (2D-VMD) algorithm is proposed. And it is used for extracting the primary features of human motion. The 2D-VMD algorithm is an adaptive non-recursive multiscale decomposition method for nonlinear and nonstationary signals. Firstly, the original 2D radar echo signals are decomposed by the 2D-VMD algorithm to capture several 2D intrinsic mode function (BIMFs) which represent different groups of central frequency components of a certain type of human motion. Secondly, original echo signals are reconstructed according to the several BIMFs, which not only have a certain inhibitory effect on the clutter in the echo signal, but can also further demonstrate that the BIMFs obtained by the 2D-VMD algorithm can represent the original 2D echo signal well. Finally, based on the measured ten different types of UWB radar human motion 2D echo analysis signals, the characteristics of these different types of human motion are extracted and the original echo signal are reconstructed. Then, the three indicators of the PCC, UQI, and PSNR between the original echo signals and extraction/reconstruction 2D signals are analyzed, which illustrate the effectiveness of 2D-VMD algorithm to extract feature of human motion 2D echo signals of the carrier-free UWB radar. Experimental results show that BIMFs by 2D-VMD algorithm can well represent the echo signal characteristics of this type of human motion, which is a very effective tool for human motion radar echo signal feature extraction.

Long non-coding RNAs (lncRNAs) are key regulators of a range of human diseases, including various cancers, with multiple previous studies having explored lncRNA dysregulation in the context of gastric cancer (GC). The present study sought to expand upon these previous results by downloading lncRNA, mRNA, and microRNA (miRNA) expression profiles derived from 180 GC tissues and 24 normal control tissues within the Cancer Genome Atlas (TCGA) database. These datasets were then interrogated to identify GC-related differentially expressed (DE) RNAs (|fold change| ! 2, FDR< 0.01), leading to the identification of 1946 DE lncRNAs, 123 DE miRNAs, and 3159 DE mRNAs. These results were then used to generate a putative GC-related competitive endogenous RNA (ceRNA) network composed of 131 lncRNAs, 9 miRNAs, and 78 mRNAs. Subsequent survival analyses based upon this network revealed 17 of these lncRNAs to be significantly associated with GC patient survival (P < 0.05). Further multivariable Cox regression and lasso analyses allowed for the construction of an 8-lncRNA risk score that was able to effectively predict GC patient survival with good discriminative ability. The Kaplan-Meier Plotter database further confirmed that network hub genes that were related to these 8 lncRNAs were associated with GC patient prognosis (P < 0.05). As the ceRNA network in the present study was constructed with a focus on both disease stage and differential gene expression, it represents a key resource that will offer valuable insights into the mechanistic roles of ceRNA pathways in GC development and progression.