Path Planning Research of a UAV Base Station Searching for Disaster Victims' Location Information Based on Deep Reinforcement Learning.

Aiming at the path planning problem of unmanned aerial vehicle (UAV) base stations when performing search tasks, this paper proposes a Double DQN-state splitting Q network (DDQN-SSQN) algorithm that combines state splitting and optimal state to complete the optimal path planning of UAV based on the Deep Reinforcement Learning DDQN algorithm. The method stores multidimensional state information in categories and uses targeted training to obtain optimal path information. The method also references the received signal strength indicator (RSSI) to influence the reward received by the agent, and in this way reduces the decision difficulty of the UAV. In order to simulate the scenarios of UAVs in real work, this paper uses the Open AI Gym simulation platform to construct a mission system model. The simulation results show that the proposed scheme can plan the optimal path faster than other traditional algorithmic schemes and has a greater advantage in the stability and convergence speed of the algorithm.

Optimization of whole-cell vaccines with CpG/αOX40/cGAMP to strengthen the anti-tumor response of CD4+ T cells in melanomas.

METHODS: In this study, we developed a strategy for the prevention and therapy of melanoma using a whole-cell vaccine combined with a CpG/αOX40/cGAMP triple adjuvant. The CpG/αOX40/cGAMP triple adjuvant was used to co-culture melanoma cells in vitro to induce immunogenic death of tumor cells. The mixture of inactivated tumor cells and the triple drug was an optimized tumor whole-cell vaccine, which was injected subcutaneously into mice for tumor prevention and therapy. Furthermore, we analyzed the changes of immune cells in spleen and tumor by flow cytometry and immunohistochemistry, and detected the changes of cytokines after vaccine application by cytometric bead array to explore the specific mechanism of vaccine. RESULTS: In vaccine prevention and therapy experiments, it was observed that the tumor growth was significantly inhibited in the whole-cell vaccine group, and the survival time of mice was significantly prolonged. Flow cytometry results showed that the proportion of CD4+ T cells and CD8+ T cells in tumor of mice in vaccine group was higher than that in control group, especially the CD4+ T cells. CONCLUSION: The optimized vaccine has the unique ability to amplify tumor-specific CD4+ T cells, which improves antitumor sensitivity, and has a significant effect on the prevention and therapy of melanoma mice.

Robust immune response stimulated by in situ injection of CpG/αOX40/cGAMP in αPD-1-resistant malignancy.

Recently, the emergence of immunotherapy has revolutionized traditional tumour treatment. However, effective treatments for patients exhibiting αPD-1 resistance are still lacking. In our study, a combination of cytosine-phosphate-guanine oligodeoxynucleotides (CpG-ODNs), anti-OX40 and cyclic guanosine monophosphate-adenosine monophosphate (cGAMP) injection in situ systematically generated a robust antitumour immune response in TC1 and B16 cells, which are αPD-1-resistant malignancies. More precisely, this method activates both adaptive and innate immunity. Additionally, in situ vaccination with CpG/αOX40/cGAMP fully activates the production of cytokines. However, the combination of αPD-1 does not improve the efficacy of triple therapy, prompting further questions. Collectively, the combination of CpG/αOX40/cGAMP causes the regression of various αPD-1-resistant tumours through the full mobilization of innate and adaptive immunity. In addition, we explored the therapeutic effect of triple therapy on the αPD-1-sensitive cell line CT26. The results showed that triple therapy could significantly enhance the therapeutic effect of αPD-1, and some mice even achieved complete tumour regression after the combined application of αPD-1 and triple treatment.

Construction and validation of a novel aging-related gene signature and prognostic nomogram for predicting the overall survival in ovarian cancer.

BACKGROUND: Ovarian cancer (OC) is the most lethal gynecological malignancy. The objective of this study was to establish and validate an individual aging-related gene signature and a clinical nomogram that can powerfully predict independently the overall survival rate of patients with ovarian cancer. METHODS: Data on transcriptomic profile and relevant clinical information were retrieved from The Cancer Genome Atlas (TCGA) database as a training group, and the same data from three public Gene Expression Omnibus (GEO) databases as validation groups. Univariate Cox regression analysis, lasso regression analysis, and multiple multivariate Cox analysis were analyzed sequentially to select the genes to be included in the aging-associated signature. A risk scoring model was established and verified, the predictive value of the model was evaluated, and a clinical nomogram was established. RESULTS: We found eight genes that were most relevant to prognosis and constructed an eight-mRNA signature. Based on the model, each OC patient's risk score was able to be calculated and patients were split into groups of low and high risks with a distinct outcome. Survival analysis confirmed that the outcome of patients in the high-risk group was dramatically shorter than that of those in the low-risk group, and the eight-mRNA signature can be considered as a powerful and independent predictor that could predict the outcome of OC patient. Additionally, the risk score and age can be used to construct a clinical nomogram as a simpler tool for predicting prognosis. We also explored the association between the risk score and immunity and drug sensitivity. CONCLUSION: This study suggested that the aging-related gene signature could be used as an intervention point and latent prognostic predictor in OC, which may provide new perceptions for postoperative treatment strategies.

Anti-tumour effect of in situ vaccines combined with VEGFR inhibitors in the treatment of metastatic cervical cancer.

Cervical cancer is the fourth most common malignant tumor in the world, for advanced cervical cancer, more than 30% of patients continue to have tumor and relapse or metastasis after the traditional treatment (concurrent chemoradiotherapy), and the response rate of immune checkpoint inhibitor (PD-1) is less 15%, so additional approaches are required. In situ vaccine is a very promising immunotherapy strategy. In the preclinical study, the combination of CPG and anti-Ox40 antibody can completely resolve injection site tumours and distant tumours and leads to the recovery of most mice with lymphoma. However, our early exploration process found that the effect of CpG + OX40 in the treatment of advanced cervical cancer is not ideal. Hence, we explored the anti-tumor effect of CpG + OX40 combined with anti-angiogenic therapy for the first time. The results showed that the combination significantly inhibited the proliferation of primary and secondary tumor volume and prolonged the survival time of mice, compared with the control group, CD3+, CD4 + and CD8 + T cells in the combined group showed an increasing trend. In addition, in terms of metabolism, the anti-vascular effect of anlotinib can significantly reduce the blood supply and metabolic level of tumor, the expression of Ki67 and CD31 in the control group was significantly higher than that in each administration group. In conclusion, our preclinical research results showed that the combination of in situ vaccine and anti-angiogenic therapy has a good anti-tumor effect, and may potentially offer an effective treatment option for patients with advanced cervical cancer.

Development of an Interferon Gamma Response-Related Signature for Prediction of Survival in Clear Cell Renal Cell Carcinoma.

BACKGROUND: Interferon plays a crucial role in the pathogenesis and progression of tumors. Clear cell renal cell carcinoma (ccRCC) represents a prevalent malignant urinary system tumor. An effective predictive model is required to evaluate the prognosis of patients to optimize treatment. MATERIALS AND METHODS: RNA-sequencing data and clinicopathological data from TCGA were involved in this retrospective study. The IFN-γ response genes with significantly different gene expression were screened out. Univariate Cox regression, LASSO regression and multivariate Cox regression were used to establish a new prognostic scoring model for the training group. Survival curves and ROC curves were drawn, and nomogram was constructed. At the same time, we conducted subgroup analysis and experimental verification using our own samples. Finally, we evaluated the relatedness between the prognostic signature and immune infiltration landscapes. In addition, the sensitivity of different risk groups to six drugs and immune checkpoint inhibitors was calculated. RESULTS: The IFN-γ response-related signature included 7 genes: C1S, IFI44, ST3GAL5, NUP93, TDRD7, DDX60, and ST8SIA4. The survival curves of the training and testing groups showed the model's effectiveness (P = 4.372e-11 and P = 1.08e-08, respectively), the ROC curves showed that the signature was stable, and subgroup analyses showed the wide applicability of the model (P<0.001). Multivariate Cox regression analysis showed that the risk model was an independent prognostic factor of ccRCC. A high-risk score may represent an immunosuppressive microenvironment, while the high-risk group exhibited poor sensitivity to drugs. CONCLUSION: Our findings strongly indicate that the IFN-γ response-related signature can be used as an effective prognostic indicator of ccRCC.

Construction and Verification of a Hypoxia-Related 4-lncRNA Model for Prediction of Breast Cancer.

INTRODUCTION: Breast cancer is the most common form of cancer worldwide and a serious threat to women. Hypoxia is thought to be associated with poor prognosis of patients with cancer. Long non-coding RNAs are differentially expressed during tumorigenesis and can serve as unambiguous molecular biomarkers for the prognosis of breast cancer. METHODS: Here, we accessed the data from The Cancer Genome Atlas for model construction and performed Gene Ontology and Kyoto Encyclopedia of Genes and Genomes analyses to identify biological functions. Four prognostic hypoxia-related lncRNAs identified by univariate, LASSO, and multivariate Cox regression analyses were used to develop a prognostic risk-related signature. Kaplan-Meier and receiver operating characteristic curve analyses were performed, and independent prognostic factor analysis and correlation analysis with clinical characteristics were utilized to evaluate the specificity and sensitivity of the signature. Survival analysis and receiver operating characteristic curve analyses of the validation cohort were operated to corroborate the robustness of the model. RESULTS: Our results demonstrate the development of a reliable prognostic gene signature comprising four long non-coding RNAs (AL031316.1, AC004585.1, LINC01235, and ACTA2-AS1). The signature displayed irreplaceable prognostic power for overall survival in patients with breast cancer in both the training and validation cohorts. Furthermore, immune cell infiltration analysis revealed that B cells, CD4 T cells, CD8 T cells, neutrophils, and dendritic cells were significantly different between the high-risk and low-risk groups. The high-risk and low-risk groups could be precisely distinguished using the risk signature to predict patient outcomes. DISCUSSION: In summary, our study proves that hypoxia-related long non-coding RNAs serve as accurate indicators of poor prognosis and short overall survival, and are likely to act as potential targets for future cancer therapy.

Identification of EMT-Related Gene Signatures to Predict the Prognosis of Patients With Endometrial Cancer.

BACKGROUND: Endometrial cancer (EC) is one of the most common gynecological cancers. Epithelial-mesenchymal transition (EMT) is believed to be significantly associated with the malignant progression of tumors. However, there is no relevant study on the relationship between EMT-related gene (ERG) signatures and the prognosis of EC patients. METHODS: We extracted the mRNA expression profiles of 543 tumor and 23 normal tissues from The Cancer Genome Atlas database. Then, we selected differentially expressed ERGs (DEERGs) among these mRNAs. Next, univariate and multivariate Cox regression analyses were performed to select the ERGs with predictive ability for the prognosis of EC patients. In addition, risk score models were constructed based on the selected genes to predict patients' overall survival (OS), progression-free survival (PFS), and disease-free survival (DFS). Finally, nomograms were constructed to estimate the OS and PFS of EC patients, and pan-cancer analysis was performed to further analyze the functions of a certain gene. RESULTS: Six OS-, ten PFS-, and five DFS-related ERGs were obtained. By constructing the prognostic risk score model, we found that the OS, PFS, and DFS of the high-risk group were notably poorer. Last, we found that AQP5 appeared in all three gene signatures, and through pan-cancer analysis, it was also found to play an important role in immunity in lower grade glioma (LGG), which may contribute to the poor prognosis of LGG patients. CONCLUSIONS: We constructed ERG signatures to predict the prognosis of EC patients using bioinformatics methods. Our findings provide a thorough understanding of the effect of EMT in patients with EC and provide new targets and ideas for individualized treatment, which has important clinical significance.

Construction and Validation of a Novel Glycometabolism-Related Gene Signature Predicting Survival in Patients With Ovarian Cancer.

Among all fatal gynecological malignant tumors, ovarian cancer has the highest mortality rate. The purpose of this study was to develop a stable and personalized glycometabolism-related prognostic signature to predict the overall survival of ovarian cancer patients. The gene expression profiles and clinical information of ovarian cancer patients were derived from four public GEO datasets, which were divided into training and testing cohorts. Glycometabolism-related genes significantly associated with prognosis were selected. A risk score model was established and validated to evaluate its predictive value. We found 5 genes significantly related to prognosis and established a five-mRNA signature. The five-mRNA signature significantly divided patients into a low-risk group and a high-risk group in the training set and validation set. Survival analysis showed that high risk scores obtained by the model were significantly correlated with adverse survival outcomes and could be regarded as an independent predictor for patients with ovarian cancer. In addition, the five-mRNA signature can predict the overall survival of ovarian cancer patients in different subgroups. In summary, we successfully constructed a model that can predict the prognosis of patients with ovarian cancer, which provides new insights into postoperative treatment strategies, promotes individualized therapy, and provides potential new targets for immunotherapy.

Hsa_circ_0048179 attenuates free fatty acid-induced steatosis via hsa_circ_0048179/miR-188-3p/GPX4 signaling.

Although circular RNAs (circRNAs) are known to play key roles in non-alcoholic fatty liver disease, much about their targets and mechanisms remains unknown. We therefore investigated the actions and mechanisms of hsa_circ_0048179 in an in vitro model of NAFLD. HepG2 cells were exposed to oleate/palmitate (2:1 ratio) for 24 h to induce intracellular lipid accumulation. Using CCK-8 assays, flow cytometry, fluorescence microscopy, western blotting, RT-qPCR, and Oil red O staining, we found that oleate/palmitate treatment reduced cell viability while increasing apoptosis and lipid accumulation in HepG2 cells. Levels of the antioxidant enzyme GPX4 were decreased in oleate/palmitate-treated HepG2 cells, and there were corresponding increases in reactive oxygen species and damage to mitochondrial cristae. Levels of hsa_circ_0048179 expression were also suppressed by oleate/palmitate treatment, and GPX4 levels were markedly increased in HepG2 cells following transfection with hsa_circ_0048179. Analysis of its mechanism revealed that hsa_circ_0048179 upregulated GPX4 levels by acting as a competitive "sponge" of miR-188-3p and that hsa_circ_0048179 attenuated oleate/palmitate-induced lipid accumulation in HepG2 cells by sponging miR-188-3p. Collectively, our findings suggest that hsa_circ_0048179 may play a key role in the pathogenesis of steatosis and may thus be a useful target for drug development.

HPV16 E6 oncoprotein-induced upregulation of lncRNA GABPB1-AS1 facilitates cervical cancer progression by regulating miR-519e-5p/Notch2 axis.

Human papillomaviruses 16 (HPV16) is the primary causative agent of cervical cancer (CC). E6 oncoprotein plays a crucial role in cervical carcinogenesis and commonly cause the dysregulation of the long noncoding RNAs (lncRNAs) expression. However, the biological function of lncRNAs in HPV16-related CC remains largely unexplored. In the present study HPV16 E6-induced differential expression of lncRNAs, miRNA, and mRNA were identified using microarray-based analysis and verified in tumor r cell lines and tumor tissues, and the function of lncRNA in CC was investigated in vitro and in vivo. We found that an lncRNA, named GABPB1-AS1, was significantly upregulated in HPV16-positive CC tissues and cell lines. GABPB1-AS1 expression in HPV16-positive CC tissues was positively associated with tumor size, lymph node metastasis, and FIGO stage. High expression of GABPB1-AS1 was correlated with a poor prognosis for HPV16-positive CC patients. Functionally, E6-induced GABPB1-AS1 overexpression facilitated CC cells proliferation and invasion in vitro and in vivo. Mechanistically, GABPB1-AS1 acted as a competing endogenous RNA (ceRNA) by sponging miR-519e-5p, resulting in the de-repression of its target gene Notch2 which is well known as an oncogene. Therefore, GABPB1-AS1 functioned as a tumor activator in CC pathogenesis by binding to miR-519e-5p and destroying its tumor suppressive function. Collectively, current results demonstrate that GABPB1-AS1 is associated with CC progression, and may be a promising biomarker or target for the clinical management of CC.

Comprehensive analysis and establishment of a prediction model of alternative splicing events reveal the prognostic predictor and immune microenvironment signatures in triple negative breast cancer.

BACKGROUND: Triple-negative breast cancer (TNBC) is widely concerning because of high malignancy and poor prognosis. There is increasing evidence that alternative splicing (AS) plays an important role in the development of cancer and the formation of the tumour microenvironment. However, comprehensive analysis of AS signalling in TNBC is still lacking and urgently needed. METHODS: Transcriptome and clinical data of 169 TNBC tissues and 15 normal tissues were obtained and integrated from the cancer genome atlas (TCGA), and an overview of AS events was downloaded from the SpliceSeq database. Then, differential comparative analysis was performed to obtain cancer-associated AS events (CAAS). Metascape was used to perform parent gene enrichment analysis based on CAAS. Unsupervised cluster analysis was performed to analyse the characteristics of immune infiltration in the microenvironment. A splicing network was established based on the correlation between CAAS events and splicing factors (SFs). We then constructed prediction models and assessed the accuracy of these models by receiver operating characteristic (ROC) curve and Kaplan-Meier survival analyses. Furthermore, a nomogram was adopted to predict the individualized survival rate of TNBC patients. RESULTS: We identified 1194 cancer-associated AS events (CAAS) and evaluated the enrichment of 981 parent genes. The top 20 parent genes with significant differences were mostly related to cell adhesion, cell component connection and other pathways. Furthermore, immune-related pathways were also enriched. Unsupervised clustering analysis revealed the heterogeneity of the immune microenvironment in TNBC. The splicing network also suggested an obvious correlation between SFs expression and CAAS events in TNBC patients. Univariate and multivariate Cox regression analyses showed that the survival-related AS events were detected, including some significant participants in the carcinogenic process. A nomogram incorporating risk, AJCC and radiotherapy showed good calibration and moderate discrimination. CONCLUSION: Our study revealed AS events related to tumorigenesis and the immune microenvironment, elaborated the potential correlation between SFs and CAAS, established a prognostic model based on survival-related AS events, and created a nomogram to better predict the individual survival rate of TNBC patients, which improved our understanding of the relationship between AS events and TNBC.