Search

Abnormal activation of protein kinases and phosphatases is implicated in various tumorigenesis, including hepatocellular carcinoma (HCC). Advanced HCC patients are treated with systemic therapy, including tyrosine kinase inhibitors, which extend overall survival. Investigation of the underlying mechanism of protein kinase signaling will help to improve the efficacy of HCC therapy. Combining single-cell RNA sequencing data and TCGA RNA-seq data, we profiled the protein kinases, phosphatases, and other phosphorylation-related genes (PRGs) of HCC patients in this study. We found nine protein kinases and PRGs with high expression levels that were mainly detected in HCC cancer stem cells, including POLR2G, PPP2R1A, POLR2L, PRC1, ITBG1BP1, MARCKSL1, EZH2, DTYMK, and AURKA. Survival analysis with the TCGA dataset showed that these genes were associated with poor prognosis of HCC patients. Further correlation analysis showed that these genes were involved in cell cycle-related pathways that may contribute to the development of HCC. Among them, AURKA and EZH2 were identified as two hub genes by Ingenuity Pathway Analysis. Treatment with an AURKA inhibitor (alisertib) and an EZH2 inhibitor (gambogenic) inhibited HCC cell proliferation, migration, and invasion. We also found that both AURKA and EZH2 were highly expressed in TP53-mutant HCC samples. Our comprehensive analysis of PRGs contributes to illustrating the mechanisms underlying HCC progression and identifying potential therapeutic targets for future clinical trials.

Gastric cancer (GC) is a malignant disease of the digestive tract and a life-threatening disease worldwide. Ferroptosis, an iron-dependent cell death caused by lipid peroxidation, is reported to be highly correlated with gastric tumorigenesis and immune cell activity. However, the underlying relationship between ferroptosis and the tumor microenvironment in GC and potential intervention strategies have not been unveiled. In this study, we profiled the transcriptome and prognosis data of ferroptosis-related genes (FRGs) in GC samples of the TCGA-STAD dataset. The infiltrating immune cells in GC were estimated using the CIBERSORT and XCELL algorithms. We found that the high expression of the hub FRGs (MYB, PSAT1, TP53, and LONP1) was positively correlated with poor overall survival in GC patients. The results were validated in an external GC cohort (GSE62254). Further immune cell infiltration analysis revealed that CD4+ T cells were the major infiltrated cells in the tumor microenvironment of GC. Moreover, the hub FRGs were significantly positively correlated with activated CD4+ T cell infiltration, especially Th cells. The gene features in the high-FRG score group were enriched in cell division, DNA repair, protein folding, T cell receptor, Wnt and NIK/NF-kappaB signaling pathways, indicating that the hub FRGs may mediate CD4+ T cell activation by these pathways. In addition, an upstream transcriptional regulation network of the hub FRGs by lncRNAs was also developed. Three lncRNAs (A2M-AS1, C2orf27A, and ZNF667-AS1) were identified to be related to the expression of the hub FRGs. Collectively, these results showed that lncRNA A2M-AS1, C2orf27A, and ZNF667-AS1 may target the hub FRGs and impair CD4+ T cell activation, which finally leads to poor prognosis of GC. Effective interventions for the above lncRNAs and the hub FRGs can help promote CD4+ T cell activation in GC patients and improve the efficacy of immunotherapy. These findings provide a novel idea of GC immunotherapy and hold promise for future clinical application.

ABSTRACTDiabetes mellitus (DM) is one of the most prevalent metabolic disorders that poses a global threat to human health. It can lead to complications in multiple organs and tissues, owing to its wide-ranging impact on the human body. Diabetic cardiomyopathy (DCM) is a specific cardiac manifestation of DM, which is characterized by heart failure in the absence of coronary heart disease, hypertension and valvular heart disease. Given that oxidative stress is a key factor in the pathogenesis of DCM, intervening to mitigate oxidative stress may serve as a therapeutic strategy for managing DCM. Naringenin is a natural product with anti-oxidative stress properties that can suppress oxidative damage by regulating various oxidative stress signaling pathways. In this review, we address the relationship between oxidative stress and its primary signaling pathways implicated in DCM, and explores the therapeutic potential of naringenin in DCM.

Objective: To identify circadian clock (CC)-related key genes with clinical significance, providing potential biomarkers and novel insights into the CC of ovarian cancer (OC).Methods: Based on the RNA-seq profiles of OC patients in The Cancer Genome Atlas (TCGA), we explored the dysregulation and prognostic power of 12 reported CC-related genes (CCGs), which were used to generate a circadian clock index (CCI). Weighted gene co-expression network analysis (WGCNA) and protein-protein interaction (PPI) network were used to identify potential hub genes. Downstream analyses including differential and survival validations were comprehensively investigated.Results: Most CCGs are abnormally expressed and significantly associated with the overall survival (OS) of OC. OC patients with a high CCI had lower OS rates. While CCI was positively related to core CCGs such as ARNTL, it also showed significant associations with immune biomarkers including CD8+ T cell infiltration, the expression of PDL1 and CTLA4, and the expression of interleukins (IL-16, NLRP3, IL-1β, and IL-33) and steroid hormones-related genes. WGCNA screened the green gene module to be mostly correlated with CCI and CCI group, which was utilized to construct a PPI network to pick out 15 hub genes (RNF169, EDC4, CHCHD1, MRPL51, UQCC2, USP34, POM121, RPL37, SNRPC, LAMTOR5, MRPL52, LAMTOR4, NDUFB1, NDUFC1, POLR3K) related to CC. Most of them can exert prognostic values for OS of OC, and all of them were significantly associated with immune cell infiltration. Additionally, upstream regulators including transcription factors and miRNAs of key genes were predicted.Conclusion: Collectively, 15 crucial CC genes showing indicative values for prognosis and immune microenvironment of OC were comprehensively identified. These findings provided insight into the further exploration of the molecular mechanisms of OC.

Background: Hepatocellular carcinoma (HCC) is a prevalent malignancy with a high mortality rate. Cellular senescence, an irreversible state of cell cycle arrest, plays a paradoxical role in cancer progression. Here, we aimed to identify Hepatocellular carcinoma subtypes by cellular senescence-related genes (CSGs) and to construct a cellular senescence-related gene subtype predictor as well as a novel prognostic scoring system, which was expected to predict clinical outcomes and therapeutic response of Hepatocellular carcinoma.Methods: RNA-seq data and clinical information of Hepatocellular carcinoma patients were derived from The Cancer Genome Atlas (TCGA) and International Cancer Genome Consortium (ICGC). The “multi-split” selection was used to screen the robust prognostic cellular senescence-related genes. Unsupervised clustering was performed to identify CSGs-related subtypes and a discriminant model was obtained through multiple statistical approaches. A CSGs-based prognostic model-CSGscore, was constructed by LASSO-Cox regression and stepwise regression. Immunophenoscore (IPS) and Tumor Immune Dysfunction and Exclusion (TIDE) were utilized to evaluate the immunotherapy response. Tumor stemness indices mRNAsi and mDNAsi were used to analyze the relationship between CSGscore and stemness.Results: 238 robust prognostic differentially expressed cellular senescence-related genes (DECSGs) were used to categorize all 336 hepatocellular carcinoma patients of the TCGA-LIHC cohort into two groups with different survival. Two hub genes, TOP2A and KIF11 were confirmed as key indicators and were used to form a precise and concise cellular senescence-related gene subtype predictor. Five genes (PSRC1, SOCS2, TMEM45A, CCT5, and STC2) were selected from the TCGA training dataset to construct the prognostic CSGscore signature, which could precisely predict the prognosis of hepatocellular carcinoma patients both in the training and validation datasets. Multivariate analysis verified it as an independent prognostic factor. Besides, CSGscore was also a valuable predictor of therapeutic responses in hepatocellular carcinoma. More downstream analysis revealed the signature genes were significantly associated with stemness and tumor progression.Conclusion: Two subtypes with divergent outcomes were identified by prognostic cellular senescence-related genes and based on that, a subtype indicator was established. Moreover, a prognostic CSGscore system was constructed to predict the survival outcomes and sensitivity of therapeutic responses in hepatocellular carcinoma, providing novel insight into hepatocellular carcinoma biomarkers investigation and design of tailored treatments depending on the molecular characteristics of individual patients.

Human body detection is very important in the research of automotive safety technology. The extraction and analysis of human micro-motion based on frequency-modulated continuous wave (FMCW) radar is gradually receiving attention. Aimed at the modulation effect of human micro-motion on FMCW radar, a human running model is proposed to study human radar characteristics. According to the scattering characteristics of rigid bodies, the analytical expression of human running radar echoes is established. By using time–frequency analysis, the micro-Doppler features in the radar echoes are extracted during the running period. Under running conditions, the micro-Doppler characteristics of key components are studied. This model is applied to the real FMCW radar verification platform, and the runners are measured at a distance of 10 m. The fit rate of all parts of the human body can reach above 90%. The overall fit rate of the human model can reach up to 90.6%. The model proposed is a realistic and simple human kinematic model. This model, which can realize the real simulation of a running human body and provide strong support for human target radar echo analysis, can fill the deficiency of FMCW radar technology in the complex motion model.

Roof weighting failures of pre-driven recovery rooms may cause huge economic losses and even casualties. The excessive dynamic load from the sliding of the broken main roof rock block causes the recovery room roof and inner supports to lose their support capacity during the last weighting. Discrete element software UDEC was used to investigate the surrounding rock deformation of the pre-driven recovery room with the main roof break at different positions. The results show that when the recovery room’s length from the main roof rock layer’s fracture region is short, the immediate roof is subjected to high deviatoric stress and the occurrence of horizontal stress concentration due to the deflection of the fractured rocks, and eventually the roof supports fail owing to the development of macroscopic shear crack zones. In this research, the hydraulic fracturing technique was applied to pretreat the main roof (fine-grained sandstone) of the 31108 panel at Huoluowan Coal Mine. Field observations suggest that the strength and duration of the periodic weighting after hydraulic fracturing treatment are both significantly reduced, effectively maintaining the bearing layer performance of the roof of the recovery room and coal pillar walls. The proposed fracturing design offers an effective method of ground control to the coal mines facing roof weighting failures of recovery rooms.

Background: Endometrial cancer (EC) is one of the most lethal gynecological cancers around the world. The aim of this study is to identify the potential immune microenvironment-related biomarkers associated with the prognosis for EC.Methods: RNA-seq data and clinical information of EC patients were derived from The Cancer Genome Atlas (TCGA). The immune score of each EC sample was obtained by ESTIMATE algorithm. Weighted gene co-expression network analysis (WGCNA) was used to identify the interesting module and potential key genes concerning the immune score. The expression patterns of the key genes were then verified via the GEPIA database. Finally, CIBERSORT was applied to evaluate the relative abundances of 22 immune cell types in EC.Results: Immune scores were significantly associated with tumor grade and histology of EC, and high immune scores may exert a protective influence on the survival outcome for EC. WGCNA indicated that the black module was significantly correlated with the immune score. Function analysis revealed it mainly involved in those terms related to immune regulation and inflammatory response. Moreover, 11 key genes (APOL3, C10orf54, CLEC2B, GIMAP1, GIMAP4, GIMAP6, GIMAP7, GIMAP8, GYPC, IFFO1, TAGAP) were identified from the black module, validated by the GEPIA database, and revealed strong correlations with infiltration levels of multiple immune cell types, as was the prognosis of EC.Conclusion: In this study, 11 key genes showed abnormal expressions and strong correlations with immune infiltration in EC, most of which were significantly associated with the prognosis of EC. These findings made them promising therapeutic targets for the treatment of EC.

In this paper, we propose a novel single image enhancement technique for defogging by using dark channel prior. The traditional dark channel prior methods for defogging have problems of high time complexity, edge effect, and failure of dark channel prior. To overcome the problems of high time complexity and edge effect, firstly, a four-point weighting algorithm is proposed to estimate the atmospheric light value accurately, and the dark channel prior is used to estimate the rough transmittance. Then, the gray-scale image of the input image is used to refine the transmittance. After that, an atmospheric scattering model is designed to restore the fog-free image. To solve the problem that the dark channel prior can not process the high brightness area, a combination of edge detection and maximum inter-class variance is used to segment the sky area and non-sky area. Finally, the improved defogging method is used for processing the non-sky area, and the enhancement algorithm via sequential decomposition is used for handling the sky area. Extensive experiments show that the improved algorithm can not only reduce the time complexity, but also effectively improve the edge effect. At the same time, it can also solve the problem of failure of dark channel prior.

With the development of 3D scanning technology, a huge volume of point cloud data has been collected at a lower cost. The huge data set is the main burden during the data processing of point clouds, so point cloud simplification is critical. The main aim of point cloud simplification is to reduce data volume while preserving the data features. Therefore, this paper provides a new method for point cloud simplification, named FPPS (feature-preserved point cloud simplification). In FPPS, point cloud simplification entropy is defined, which quantifies features hidden in point clouds. According to simplification entropy, the key points including the majority of the geometric features are selected. Then, based on the natural quadric shape, we introduce a point cloud matching model (PCMM), by which the simplification rules are set. Additionally, the similarity between PCMM and the neighbors of the key points is measured by the shape operator. This represents the criteria for the adaptive simplification parameters in FPPS. Finally, the experiment verifies the feasibility of FPPS and compares FPPS with other four-point cloud simplification algorithms. The results show that FPPS is superior to other simplification algorithms. In addition, FPPS can partially recognize noise.