Germ cell-specific gene 2 accelerates cell cycle in epithelial ovarian cancer by inhibiting GSK3α-p27 cascade.

Epithelial ovarian cancer (EOC) is one of the most common malignant gynecological tumors with rapid growth potential and poor prognosis, however, the molecular mechanism underlying its outgrowth remained elusive. Germ cell-specific gene 2 (GSG2) was previously reported to be highly expressed in ovarian cancer and was essential for the growth of EOC. In this study, GSG2-knockdown cells and GSG2-overexpress cells were established through lentivirus-mediated transfection with Human ovarian cancer cells HO8910 and SKOV3. Knockdown of GSG2 inhibited cell proliferation and induced G2/M phase arrest in EOC. Interestingly, the expression of p27, a well-known regulator of the cell cycle showed a most significant increase after GSG2 knockdown. Further phosphorylation-protein array demonstrated the phosphorylation of GSK3αSer21 decreased in GSG2-knockdown cells to the most extent. Notably, inhibiting GSK3α activity effectively rescued GSG2 knockdown's suppression on cell cycle as well as p27 expression in EOC. Our study substantiates that GSG2 is able to phosphorylate GSK3α at Ser21 and then leads to the reduction of p27 expression, resulting in cell cycle acceleration and cell proliferation promotion. Thus, GSG2 may have the potential to become a promising target in EOC.

A novel CSN5/CRT O-GlcNAc/ER stress regulatory axis in platinum resistance of epithelial ovarian cancer.

Background: High levels of COP9 signalosome subunit 5 (CSN5) in epithelial ovarian cancer (EOC) are associated with poor prognosis and are implicated in mediating platinum resistance in EOC cells. The underlying mechanisms, however, remained undefined. This study aimed to elucidate the molecular process and identify potential therapeutic targets. Methods: RNA-sequencing was used to investigate differentially expressed genes between platinum-resistant EOC cells with CSN5 knockdown and controls. O-GlcNAc proteomics were employed to identify critical modulators downstream of CSN5. The omics findings were confirmed through qRT-PCR and immunoblotting. In vitro and in vivo experiments assessed the sensitivity of resistant EOCs to platinum. Results: We demonstrated an involvement of aberrant O-GlcNAc and endoplasmic reticulum (ER) stress disequilibrium in CSN5-mediated platinum resistance of EOC. Genetic or pharmacologic inhibition of CSN5 led to tumor regression and surmounted the intrinsic EOC resistance to platinum both in vitro and in vivo. Integration of RNA-sequencing and O-GlcNAc proteomics pinpointed calreticulin (CRT) as a potential target of aberrant O-GlcNAc modification. CSN5 upregulated O-GlcNAc-CRT at T346 to inhibit ER stress-induced cell death. Blocking T346 O-GlcNAc-CRT through CSN5 deficiency or T346A mutation resulted in Ca2+ disturbances, followed by ER stress overactivation, mitochondrial dysfunction, and ultimately cell apoptosis. Conclusion: This study reveals that CSN5-mediated aberrant O-GlcNAc-CRT acts as a crucial ER stress checkpoint, governing cell fate response to stress, and emphasizes an unrecognized role for the CSN5/CRT O-GlcNAc/ER stress axis in platinum resistance of EOC.

Reinforcement learning-based consensus control for MASs with intermittent constraints.

In this article, an adaptive optimal consensus control problem is studied for multiagent systems in the strict-feedback structure with intermittent constraints (the constraints appear intermittently). More specifically, by designing a novel switch-like function and an improved coordinate transformation, the constrained states are converted into unconstrained states, and the problem of intermittent constraints is resolved without requiring "feasibility conditions". In addition, using the composite learning algorithm and neural networks to construct the identifier, a simplified identifier-actor-critic-based reinforcement learning strategy is proposed to obtain the approximate optimal controller under the framework of backstepping. Meanwhile, with the aid of the nonlinear dynamic surface control technique, the issue of "explosion of complexity" in backstepping is removed, and the requirements for filter parameters are loosened. Based on Lyapunov stability theory, it is demonstrated that all signals in the closed-loop system are bounded. Finally, two simulation examples are used to verify the effectiveness of the proposed method.

IFN-γ in ovarian tumor microenvironment upregulates HLA-E expression and predicts a poor prognosis.

BACKGROUND: Inflammation and immunity are two main characteristics of tumor microenvironment (TME). Interferon-gamma (IFN-γ) is generally considered as a pro-inflammatory cytokine which mediates anti-tumor immune response. Recently, IFN-γ was also reported to play a protumorigenic role. However, the mechanisms of tumor-promoting effect induced by IFN-γ remain unclear. METHODS: The expression of leukocyte antigen-E (HLA-E), IFN-γ, CD3 and CD56 in clinical samples of ovarian cancer was detected by mutiplexed immunohistochemistry. The mechanism to induce HLA-E overexpression by IFN-γ was explored using human ovarian cancer cell lines through western blot and flow cytometry. We further clarify the role of overexpressed-HLA-E on natural killer (NK)-mediated cell lysis. RESULTS: We found that IFN-γ could upregulate HLA-E protein expression through activating of JAK/STAT1 signaling pathway, and increase cell surface HLA-E level through enhancing proteasome activity. We also observed that only high levels of membrane HLA-E expression contributed to the inhibition of NK-mediated cytotoxicity. We showed that progression-free survival (PFS) of ovarian cancer patients was negatively correlated with IFN-γ expression in their tumor tissues, due to more tumor infiltrating NK cells compared with T lymphocytes. CONCLUSIONS: Our study revealed the protumorigenic role of IFN-γ by upregulation of HLA-E expression and rendering tumors less susceptible to immune attack. We also provided a novel insight into the relationship between tumor microenvironment and immune evasion.

Targeting endoplasmic reticulum stress signaling in ovarian cancer therapy.

The endoplasmic reticulum (ER), an organelle present in various eukaryotic cells, is responsible for intracellular protein synthesis, post-translational modification, and folding and transport, as well as the regulation of lipid and steroid metabolism and Ca2+ homeostasis. Hypoxia, nutrient deficiency, and a low pH tumor microenvironment lead to the accumulation of misfolded or unfolded proteins in the ER, thus activating ER stress (ERS) and the unfolded protein response, and resulting in either restoration of cellular homeostasis or cell death. ERS plays a crucial role in cancer oncogenesis, progression, and response to therapies. This article reviews current studies relating ERS to ovarian cancer, the most lethal gynecologic malignancy among women globally, and discusses pharmacological agents and possible targets for therapeutic intervention.

Finite-time cluster synchronization for complex dynamical networks under FDI attack: A periodic control approach.

In this paper, the finite-time cluster synchronization problem is addressed for complex dynamical networks (CDNs) with cluster characteristics under false data injection (FDI) attacks. A type of FDI attack is taken into consideration to reflect the data manipulation that controllers in CDNs may suffer. In order to improve the synchronization effect while reducing the control cost, a new periodic secure control (PSC) strategy is proposed in which the set of pinning nodes changes periodically. The aim of this paper is to derive the gains of the periodic secure controller such that the synchronization error of the CDN remains at a certain threshold in finite time with the presence of external disturbances and false control signals simultaneously. Through considering the periodic characteristics of PSC, a sufficient condition is obtained to guarantee the desired cluster synchronization performance, based on which the gains of the periodic cluster synchronization controllers are acquired by resolving an optimization problem proposed in this paper. A numerical case is carried out to validate the cluster synchronization performance of the PSC strategy under cyber attacks.

Clinical value of serum and tissue AGR2 for diagnosis and prognosis in epithelial ovarian cancer.

Purpose: To clarify the value of AGR2 for diagnosis and prognosis in epithelial ovarian cancer (EOC). Methods: Serum AGR2 from 203 subjects were detected by ELISA, while CA125 and HE4 were determined by enhanced chemiluminescence immunoassay. The diagnostic efficacy was assessed using receiver operating characteristic curves. Tissue microarray was employed to compare tissue AGR2. Results: Combined detection of AGR2, CA125 and HE4 improved the diagnostic specificity in the discrimination of EOC from healthy controls. Serum AGR2 was significantly higher, while CA125 and HE4 were significantly lower in EOC patients post-operatively. Low AGR2 expression may predict poorer prognosis. Conclusion: Incorporation of AGR2 improved the specificity of CA125 and HE4 in EOC diagnosis, and may act as a tumor suppressor whose low expression in EOC patients predicted poorer outcomes.

ASAP2 interrupts c-MET-CIN85 interaction to sustain HGF/c-MET-induced malignant potentials in hepatocellular carcinoma.

BACKGROUND: Sustained activation of hepatocyte growth factor (HGF)/c-MET signaling is a major driver of hepatocellular carcinoma (HCC) progression, but underlying mechanism is unclear. ArfGAP With SH3 Domain, Ankyrin Repeat And PH Domain 2 (ASAP2) can reportedly activate GTPases and promote receptor tyrosine kinase signaling. However, the exact role of ASAP2 in HCC, especially for c-MET activation, also remains elusive. METHODS: ASAP2 expression levels in HCC tissues and cells were quantified using qRT-PCR, western blot (WB) analysis, and immunohistochemistry staining. Cell counting kit-8 (CCK-8) and colony formation assays were performed to evaluate cell proliferation rates. Flow cytometry assays were conducted to assess apoptosis rates. Wound healing and Transwell assays were performed to determine cell migration and invasion capacities. Epithelial-mesenchymal transition (EMT)-related marker expression levels were also examined. Subcutaneous implantation and tail vein injection models were applied for in vivo growth and metastasis evaluations, respectively. Bioinformatics analyses of The Cancer Genome Atlas and STRING datasets were performed to explore ASAP2 downstream signaling. Co-immunoprecipitation and Cycloheximide chasing experiments were performed to assess protein-protein interactions and protein half-life, respectively. RESULTS: ASAP2 had higher expression levels in HCC tissues than in normal liver, and also predicted poor prognosis. Knocking down ASAP2 significantly impaired cell proliferation, migration, and invasion capacities, but promoted apoptosis in HCC cells in vitro. However, overexpression of ASAP2 achieved the opposite effects. In vivo experiments confirmed that ASAP2 could promote HCC cell growth and facilitate lung metastasis. Interestingly, ASAP2 was essential for triggering EMT. Gene Set Enrichment Analysis demonstrated that c-MET signaling was greatly enriched in ASAP2-high HCC cases. Additionally, c-MET signaling activity was significantly decreased following ASAP knockdown, evidenced by reduced c-MET, p-AKT, and p-ERK1/2 protein levels. Importantly, ASAP2 knockdown effectively attenuated HGF/c-MET signaling-induced malignant phenotypes. c-MET and ASAP2 expression levels were positively correlated in our cohort. Mechanistically, ASAP2 can directly bind to CIN85, thereby disrupting its interaction with c-MET, and can thus antagonize CIN85-induced c-MET internalization and lysosome-mediated degradation. Notably, knocking down CIN85 can rescue the observed inhibitory effects caused by ASAP2 knockdown. CONCLUSIONS: This study highlights the importance of ASAP2 in sustaining c-MET signaling, which can facilitate HCC progression.

Bounded synchronization for uncertain master-slave neural networks: An adaptive impulsive control approach.

This paper investigates the bounded synchronization of the discrete-time master-slave neural networks (MSNNs) with uncertainty. To deal with the unknown parameter in the MSNNs, a parameter adaptive law combined with the impulsive mechanism is proposed to improve the estimation efficiency. Meanwhile, the impulsive method also is applied to the controller design for saving the energy. In addition, a novel time-varying Lyapunov functional candidate is employed to depict the impulsive dynamical characteristic of the MSNNs, wherein a convex function related to the impulsive interval is used to obtain a sufficient condition for bounded synchronization of the MSNNs. Based on the above condition, the controller gain is calculated utilizing an unitary matrix. An algorithm is proposed to reduce the boundary of the synchronization error by optimizing its parameters. Finally, a numerical example is provided to illustrate the correctness and the superiority of the developed results.

EGFR is a potential therapeutic target for highly glycosylated and aggressive pancreatic neuroendocrine neoplasms.

pNENs are relative indolent tumors with heterogeneous clinical presentation at diagnosis. It is important to establish aggressive subgroups of pNENs and identify potential therapeutic targets. Patients with pNEN (322 cases) were included to examine the association between glycosylation biomarkers and clinical/pathological traits. The molecular and metabolic features stratified by glycosylation status were assessed by RNA-seq/whole exome sequencing and immunohistochemistry. A considerable proportion of patients had elevated glycosylation biomarkers (carbohydrate antigen [CA] 19-9, 11.9%; CA125, 7.5%; carcinoembryonic antigen [CEA], 12.8%). CA19-9 (hazard ratio [HR] = 2.26, P = .019), CA125 (HR = 3.79, P = .004) and CEA (HR = 3.16, P = .002) were each independent prognostic variables for overall survival. High glycosylation group, defined as pNENs with elevated level of circulating CA19-9, CA125 or CEA, accounted for 23.4% of all pNENs. High glycosylation (HR = 3.14, P = .001) was an independent prognostic variable for overall survival and correlated with G3 grade (P < .001), poor differentiation (P = .001), perineural invasion (P = .004) and distant metastasis (P < .001). Epidermal growth factor receptor (EGFR) was enriched in high glycosylation pNENs using RNA-seq. EGFR was expressed in 21.2% of pNENs using immunohistochemistry and associated with poor overall survival (P = .020). A clinical trial focusing on EGFR expressed pNENs was initiated (NCT05316480). Thus, pNEN with aberrant glycosylation correlates with a dismal outcome and suggests potential therapeutic target of EGFR.

Glutamine is a substrate for glycosylation and CA19-9 biosynthesis through hexosamine biosynthetic pathway in pancreatic cancer.

BACKGROUND: Carbohydrate antigen 19-9 (CA19-9) is the most widely used biomarker for pancreatic cancer. Since CA19-9 closely correlates with patient outcome and tumor stage in pancreatic cancer, the deciphering of CA19-9 biosynthesis provides a potential clue for treatment. METHODS: Concentration of amino acids was detected by ultrahigh-performance liquid chromatography tandem mass spectrometry. Metabolic flux of glutamine was examined by isotope tracing untargeted metabolomics. Label-free quantitative N-glycosylation proteomics was used to examine N-glycosylation alterations. RESULTS: Among all amino acids, glutamine was higher in CA19-9-high pancreatic cancers (> 37 U/mL, 66 cases) than in CA19-9-normal clinical specimens (≤ 37 U/mL, 37 cases). The glutamine concentration in clinical specimens was positively correlated with liver metastasis or lymphovascular invasion. Glutamine blockade using diazooxonorleucine suppressed pancreatic cancer growth and intraperitoneal and lymphatic metastasis. Glutamine promotes O-GlcNAcylation, protein glycosylation, and CA19-9 biosynthesis through the hexosamine biosynthetic pathway. UDP-N-acetylglucosamine (UDP-GlcNAc) levels correlated with the glutamine influx through hexosamine biosynthetic pathway and supported CA19-9 biosynthesis. CONCLUSIONS: Glutamine is a substrate for CA19-9 biosynthesis in pancreatic cancer. Glutamine blockade may be a potential therapeutic strategy for pancreatic cancer.

TPX2 promotes ovarian tumorigenesis by interacting with Lamin A/C and affecting its stability.

OBJECTIVE: Ovarian cancer (OC) is one of the fatal gynecologic malignancies. However, there are no effective prognostic or therapeutic indicators for OC. Herein, we aim to reveal the potential function of targeting protein for Xklp2 (TPX2) in OC progression. METHODS: Immunohistochemical and bioinformatic analyses were used to evaluate the level of TPX2 in OC samples. Effects of TPX2 on cell proliferation, cell apoptosis and ROS production were evaluated in vivo and in vitro. Mass spectrometry, Co-IP and immunofluorescence assays were performed to identify and verify protein-protein interactions. RESULTS: Our data showed that pathological overexpression (OE) of the TPX2 in OC could manifest a poor prognosis. Functional studies demonstrated that TPX2 silencing led to the suppression of cell proliferation in vitro and in vivo through an increase in reactive oxygen species (ROS) level and apoptosis, while TPX2 OE exhibited the opposite effect. Furthermore, by mass spectrometric analysis, we identified a novel interacting partner, Lamin A/C, for TPX2. Mechanistically, TPX2 regulated Lamin A/C's stability by modulating the level of phospho-Lamin A/C (Ser 22). CONCLUSION: Our findings thus suggest that TPX2 may be a promising therapeutic target for OC.

Flow Rate-Independent Multiscale Liquid Biopsy for Precision Oncology.

Immunoaffinity-based liquid biopsies of circulating tumor cells (CTCs) hold great promise for cancer management but typically suffer from low throughput, relative complexity, and postprocessing limitations. Here, we address these issues simultaneously by decoupling and independently optimizing the nano-, micro-, and macro-scales of an enrichment device that is simple to fabricate and operate. Unlike other affinity-based devices, our scalable mesh approach enables optimum capture conditions at any flow rate, as demonstrated with constant capture efficiencies, above 75% between 50 and 200 µL min-1. The device achieved 96% sensitivity and 100% specificity when used to detect CTCs in the blood of 79 cancer patients and 20 healthy controls. We demonstrate its postprocessing capacity with the identification of potential responders to immune checkpoint inhibition (ICI) therapy and the detection of HER2 positive breast cancer. The results compare well with other assays, including clinical standards. This suggests that our approach, which overcomes major limitations associated with affinity-based liquid biopsies, could help improve cancer management.

Higher aorta dose increased neutrophil-to-lymphocyte ratio resulting in poorer outcomes in stage II-III non-small cell lung cancer.

BACKGROUND: This study focused on the relationship between the neutrophil-to-lymphocyte ratio (NLR) and the dose of organs at risk in patients with stage II-III non-small cell lung cancer (NSCLC) receiving intensity-modulated radiotherapy. METHODS: The clinical characteristics and dosimetric parameters of 372 patients were collected retrospectively. A high NLR was defined as that ≥1.525. Survival analysis was conducted using the Kaplan-Meier and Cox regression analysis. Least absolute shrinkage and selection operator (LASSO) analysis was conducted to select appropriate dosimetric parameters. The risk factors of NLR were evaluated using univariate and multivariate logistic regression analyses. RESULTS: Patients with a high NLR had poorer progression-free survival (PFS) (p = 0.011) and overall survival (OS) (p = 0.061). A low NLR (<1.525) predicted better PFS (hazard ratio [HR] 0.676, 95% confidence interval [CI]: 0.508-0.900, p = 0.007) and OS (HR 0.664, 95% CI: 0.490-0.901, p = 0.009). The aorta dose differed between the low and high NLR groups (all <0.1) in the univariate analysis. An aorta V10 was confirmed as a significant risk factor for a high NLR (odds ratio [OR] 1.029, 95% CI: 1.011-1.048, p = 0.002). Receiving chemotherapy before (OR 0.428, 95% CI: 0.225-0.813, p = 0.010) and during (OR 0.491, 95% CI: 0.296-0.815, p = 0.006) radiotherapy were predictive factors of a low NLR. CONCLUSION: The aorta dose was significantly associated with a high NLR. Patients with stage II-III NSCLC with a high NLR had poorer prognosis. Receiving chemotherapy before and/or during radiotherapy predicted a low NLR.

Anti-Synchronization of Discrete-Time Fuzzy Memristive Neural Networks via Impulse Sampled-Data Communication.

This work is concerned with the anti-synchronization (A-S) of drive-response (D-R) memristive neural networks (MNNs) based on fuzzy rules. A novel impulsive sampled-data communication mechanism is proposed by considering information security of the MNNs, in which the random response delay of sensors caused by the impulse signal is also investigated. As the state of MNNs cannot be outputted accurately and transmitted persistently, the state observers of the D-R MNNs are established, which is beneficial to design the A-S controller. By analyzing the stability of the augmented error system (AES) based on the fuzzy-based Lyapunov-Krasovskii functional (FLKF), sufficient conditions of the A-S between D-R MNNs are derived. An illustrative example is given to verify the effectiveness of the proposed A-S strategies.

Deep Reinforcement Learning on Autonomous Driving Policy With Auxiliary Critic Network.

Deep reinforcement learning (DRL) is a machine learning method based on rewards, which can be extended to solve some complex and realistic decision-making problems. Autonomous driving needs to deal with a variety of complex and changeable traffic scenarios, so the application of DRL in autonomous driving presents a broad application prospect. In this article, an end-to-end autonomous driving policy learning method based on DRL is proposed. On the basis of proximal policy optimization (PPO), we combine a curiosity-driven method called recurrent neural network (RNN) to generate an intrinsic reward signal to encounter the agent to explore its environment, which improves the efficiency of exploration. We introduce an auxiliary critic network on the original actor-critic framework and choose the lower estimate which is predicted by the dual critic network when the network update to avoid the overestimation bias. We test our method on the lane- keeping task and overtaking task in the open racing car simulator (TORCS) driving simulator and compare with other DRL methods, experimental results show that our proposed method can improve the training efficiency and control performance in driving tasks.

Guaranteeing Global Stability for Neuro-Adaptive Control of Unknown Pure-Feedback Nonaffine Systems via Barrier Functions.

Most existing approximation-based adaptive control (AAC) approaches for unknown pure-feedback nonaffine systems retain a dilemma that all closed-loop signals are semiglobally uniformly bounded (SGUB) rather than globally uniformly bounded (GUB). To achieve the GUB stability result, this article presents a neuro-adaptive backstepping control approach by blending the mean value theorem (MVT), the barrier Lyapunov functions (BLFs), and the technique of neural approximation. Specifically, we first resort the MVT to acquire the intermediate and actual control inputs from the nonaffine structures directly. Then, neural networks (NNs) are adopted to approximate the unknown nonlinear functions, in which the compact sets for maintaining the approximation capabilities of NNs are predetermined actively through the BLFs. It is shown that, with the developed neuro-adaptive control scheme, global stability of the resulting closed-loop system is ensured. Simulations are conducted to verify and clarify the developed approach.

Distributed Extended State Estimation for Complex Networks With Nonlinear Uncertainty.

This article studies the distributed state estimation issue for complex networks with nonlinear uncertainty. The extended state approach is used to deal with the nonlinear uncertainty. The distributed state predictor is designed based on the extended state system model, and the distributed state estimator is designed by using the measurement of the corresponding node. The prediction error and the estimation error are derived. The prediction error covariance (PEC) is obtained in terms of the recursive Riccati equation, and the upper bound of the PEC is minimized by designing an optimal estimator gain. With the vectorization approach, a sufficient condition concerning stability of the upper bound is developed. Finally, a numerical example is presented to illustrate the effectiveness of the designed extended state estimator.

Adaptive Multigradient Recursive Reinforcement Learning Event-Triggered Tracking Control for Multiagent Systems.

This article proposes a fault-tolerant adaptive multigradient recursive reinforcement learning (RL) event-triggered tracking control scheme for strict-feedback discrete-time multiagent systems. The multigradient recursive RL algorithm is used to avoid the local optimal problem that may exist in the gradient descent scheme. Different from the existing event-triggered control results, a new lemma about the relative threshold event-triggered control strategy is proposed to handle the compensation error, which can improve the utilization of communication resources and weaken the negative impact on tracking accuracy and closed-loop system stability. To overcome the difficulty caused by sensor fault, a distributed control method is introduced by adopting the adaptive compensation technique, which can effectively decrease the number of online estimation parameters. Furthermore, by using the multigradient recursive RL algorithm with less learning parameters, the online estimation time can be effectively reduced. The stability of closed-loop multiagent systems is proved by using the Lyapunov stability theorem, and it is verified that all signals are semiglobally uniformly ultimately bounded. Finally, two simulation examples are given to show the availability of the presented control scheme.