Background. Matrix metalloproteinase‐9 (MMP‐9) can degrade the extracellular matrix and participate in tumor progression. The relationship between MMP‐9 and immune cells has been reported in various malignant tumors. However, there is a lack of comprehensive pan‐cancer studies on the relationship between MMP‐9 and cancer prognosis and immune infiltration. Method. We used data from TCGA and GTEx databases to comprehensively analyze the differential expression of MMP‐9 in normal and cancerous tissues. Survival analysis was performed to understand the prognostic role of MMP‐9 in different tumors. We then analyzed the expression of MMP‐9 across different tumors and at different clinical stages. Based on the results, we assessed the correlation between MMP‐9 expression and immune‐associated genes and immunocytes. Finally, we calculated the tumor mutation burden (TMB) of 33 cancer types and analyzed the correlation between MMP‐9 and TMB, DNA microsatellite instability, and DNA repair genes. Results. MMP‐9 significantly affected the prognosis and metastasis of various cancers. It was associated based on overall survival, disease‐specific survival in five tumors, progression‐free interval in seven tumors, and clinical stage in eight tumors, as well as with prognosis and metastasis in adrenocortical carcinoma and kidney renal clear cell carcinoma. It was also coexpressed with immune‐related genes and DNA repair genes. The expression of MMP‐9 was positively correlated with the markers of T cells, tumor‐associated macrophages, Th1 cells, and T cell exhaustion. Furthermore, MMP‐9 expression was highly correlated with macrophage M0 in 28 tumors. In addition, its expression was associated with TMB in eight cancer types and DNA microsatellite instability in six cancer types. Conclusion. MMP‐9 is related to immune infiltration in pan‐cancer and can be used as a biomarker related to cancer prognosis and metastasis. Our findings provide prognostic molecular markers and new ideas for immunotherapy.

Although gastric cancer is a malignancy with high morbidity and mortality in China, the survival rate of patients with early gastric cancer (EGC) is high after surgical resection. To strengthen diagnosing and screening is the key to improve the survival and life quality of patients with EGC. This study applied data mining methods to improve screening for the risk of EGC on the basis of noninvasive factors, and displayed important influence factors for the risk of EGC. The dataset was derived from a project of the First Hospital Affiliated Guangdong Pharmaceutical University. A series of questionnaire surveys, serological examinations and endoscopy plus pathology biopsy were conducted in 618 patients with gastric diseases. Their risk of EGC was categorized into low and high risk of EGC by the results of endoscopy plus pathology biopsy. The synthetic minority oversampling technique (SMOTE) was used to solve imbalance categories of the risk of EGC. Four classification models of the risk of EGC was established, including logistic regression (LR) and three data mining algorithms. The three data mining models had higher accuracy than the LR model. Gain curves of the three data mining models were convexes more closer to ideal curves by contrast with that of the LR model. AUC of the three data mining models were larger than that of the LR model as well. The three data mining models predicted the risk of EGC more effectively in comparison with the LR model. Moreover, this study found 16 important influence factors for the risk of EGC, such as occupations, helicobacter pylori infection, drinking hot water and so on. The three data mining models have optimal predictive behaviors over the LR model, therefore can effectively evaluate the risk of EGC and assist clinicians in improving the diagnosis and screening of EGC. Sixteen important influence factors for the risk of EGC were illustrated, which may helpfully assess gastric carcinogenesis, and remind to early prevention and early detection of gastric cancer. This study may also be conducive to clinical researchers in selecting and conducting the optimal predictive models.

Biomedical texts provide important data for investigating drug-drug interactions (DDIs) in the field of pharmacovigilance. Although researchers have attempted to investigate DDIs from biomedical texts and predict unknown DDIs, the lack of accurate manual annotations significantly hinders the performance of machine learning algorithms. In this study, a new DDI prediction framework, Subgraph Enhance model, was developed for DDI (SubGE-DDI) to improve the performance of machine learning algorithms. This model uses drug pairs knowledge subgraph information to achieve large-scale plain text prediction without many annotations. This model treats DDI prediction as a multi-class classification problem and predicts the specific DDI type for each drug pair (e.g. Mechanism, Effect, Advise, Interact and Negative). The drug pairs knowledge subgraph was derived from a huge drug knowledge graph containing various public datasets, such as DrugBank, TwoSIDES, OffSIDES, DrugCentral, EntrezeGene, SMPDB (The Small Molecule Pathway Database), CTD (The Comparative Toxicogenomics Database) and SIDER. The SubGE-DDI was evaluated from the public dataset (SemEval-2013 Task 9 dataset) and then compared with other state-of-the-art baselines. SubGE-DDI achieves 83.91% micro F1 score and 84.75% macro F1 score in the test dataset, outperforming the other state-of-the-art baselines. These findings show that the proposed drug pairs knowledge subgraph-assisted model can effectively improve the prediction performance of DDIs from biomedical texts.

Background. Parkinson’s disease (PD) is a common neurodegenerative disease in middle‐aged and elderly people. Liuwei Dihuang (LWDH) pills have a good effect on PD, but its mechanism remains unclear. Network pharmacology is the result of integrating basic theories and research methods of medicine, biology, computer science, bioinformatics, and other disciplines, which can systematically and comprehensively reflect the mechanism of drug intervention in disease networks. Methods. The main components and targets of herbs in LWDH pills were obtained through Traditional Chinese Medicine Systems Pharmacology Database and Analysis Platform (TCMSP). Its active components were screened based on absorption, distribution, metabolism, and excretion (ADME); the PD‐related targets were obtained from the Genecards, OMIM, TTD, and DRUGBANK databases. We used R to take the intersection of LWDH‐ and PD‐related targets and Cytoscape software to construct the drug‐component‐target network. Moreover, STRING and Cytoscape software was used to analyze protein–protein interactions (PPI), construct a PPI network, and explore potential protein functional modules in the network. The Metascape platform was used to perform KEGG pathway and GO function enrichment analyses. Finally, molecular docking was performed to verify whether the compound and target have good binding activity. Results. After screening and deduplication, 210 effective active ingredients, 204 drug targets, 4333 disease targets, and 162 drug‐disease targets were obtained. We consequently constructed a drug‐component‐targets network and a PPI‐drug‐disease‐targets network. The results showed that the hub components of LWDH pills were quercetin, stigmasterol, kaempferol, and beta‐sitosterol; the hub targets were AKT1, VEGFA, and IL6. GO and KEGG enrichment analyses showed that these targets are involved in neuronal death, G protein‐coupled amine receptor activity, reactive oxygen species metabolic processes, membrane rafts, MAPK signaling pathways, cellular senescence, and other biological processes. Molecular docking showed that the hub components were in good agreement with the hub targets. Conclusion. LWDH pills have implications for the treatment of PD since they contain several active components, target multiple ligands, and activate various pathways. The hub components possibly include quercetin, stigmasterol, kaempferol, and beta‐sitosterol and act through pairing with hub targets, such as AKT1, VEGFA, and IL6, to regulate neuronal death, G protein‐coupled amine receptor activity, reactive oxygen species metabolic process, membrane raft, MAPK signaling pathway, and cellular senescence for the treatment of PD.

Node localization, which is formulated as an unconstrained NP-hard optimization problem, is considered as one of the most significant issues of wireless sensor networks (WSNs). Recently, many swarm intelligent algorithms (SIAs) were applied to solve this problem. This study aimed to determine node location with high precision by SIA and presented a new localization algorithm named LMQPDV-hop. In LMQPDV-hop, an improved DV-Hop was employed as an underground mechanism to gather the estimation distance, in which the average hop distance was modified by a defined weight to reduce the distance errors among nodes. Furthermore, an efficient quantum-behaved particle swarm optimization algorithm (QPSO), named LMQPSO, was developed to find the best coordinates of unknown nodes. In LMQPSO, the memetic algorithm (MA) and Lévy flight were introduced into QPSO to enhance the global searching ability and a new fast local search rule was designed to speed up the convergence. Extensive simulations were conducted on different WSN deployment scenarios to evaluate the performance of the new algorithm and the results show that the new algorithm can effectively improve position precision.