[Relationship Between Tim-3 and Galectin-9 Expression Levels, Clinical Pathological Characteristics, and Prognosis in Patients After Radical Resection of Colorectal Cancer]. / ç»“ç›´è‚ ç™Œæ ¹æ²»æœ¯åŽæ‚£è€ Tim-3、galectin-9è¡¨è¾¾æ°´å¹³ä¸Žå ¶ä¸´åºŠç— ç†ç‰¹å¾åŠé¢„åŽçš„å ³ç³».

Objective: Some colorectal cancer patients still face high recurrence rates and poor prognoses even after they have undergone the surgical treatment of radical resection. Identifying potential biochemical markers and therapeutic targets for the prognostic evaluation of patients undergoing radical resection of colorectal cancer is crucial for improving their clinical outcomes. Recently, it has been reported that the T cell immunoglobulin and mucin domain protein 3 (Tim-3) and its ligand galactose lectin 9 (galectin-9) play crucial roles in immune dysfunction caused by various tumors, such as colorectal cancer. However, their expressions, biological functions, and prognostic value in colorectal cancer are still unclear. This study aims to investigate the relationship between Tim-3 and galectin-9 expression levels and the clinicopathological characteristics and prognosis of patients undergoing radical resection of colorectal cancer. Methods: A total of 171 patients who underwent radical resection of colorectal cancer at Chengdu Fifth People's Hospital between February 2018 and March 2019 were selected. Immunohistochemistry was performed to assess the expression levels of Tim-3 and galectin-9 in the cancer tissue samples and the paracancerous tissue samples of the patients. The relationship between Tim-3 and galectin-9 expression levels and the baseline clinical parameters of the patients was analyzed accordingly. Kaplan-Meier analysis was performed to assess the association between Tim-3 and galectin-9 expression levels and the relapse-free survival (RFS) and the overall survival (OS) of colorectal cancer patients. Cox regression analysis was conducted to identify factors associated with adverse prognosis in the patients. Results: The immunohistochemical results showed that the high expression levels of Tim-3 and galectin-9 were observed in 70.18% (120/171) and 32.16% (55/171), respectively, of the colorectal cancer tissues, whereas the low expression levels were 29.82% (51/171) and 67.84% (116/171), respectively. Furthermore, the expression score of Tim-3 was significantly higher in colorectal cancer tissues than that in the paracancerous tissues, while the expression score of galectin-9 was lower than that in the paracancerous tissues (P<0.05). Further analysis revealed that the expression of Tim-3 and galectin-9 was associated with the depth of tumor infiltration, vascular infiltration, and clinical staging (P<0.05). During the follow-up period of 14-63 months, 7 out of 171 patients were lost to follow-up. Among the remaining patients, 49 and 112 cases presented abnormally low expression of Tim-3 and galectin-9, respectively, whereas 115 and 52 cases presented high expression of Tim-3 and galectin-9, respectively. Kaplan-Meier survival analysis demonstrated that patients with high Tim-3 expression in colorectal cancer tissues had significantly lower RFS and OS than those with low expression did (RFS: log-rank=22.66, P<0.001; OS: log-rank=19.71, P<0.001). Conversely, patients with low galectin-9 expression had significantly lower RFS and OS than those with high expression did (RFS: log-rank=19.45, P<0.001; OS: log-rank=22.24, P<0.001). Cox multivariate analysis indicated that TNM stage Ⅲ (HR=2.26, 95% CI: 1.20-5.68), high expression of Tim-3 (HR=0.80, 95% CI: 0.33-0.91), and low expression of galectin-9 (HR=1.80, 95% CI: 1.33-4.70) were independent risk factors affecting RFS and OS in patients (P<0.05). Conclusion: Aberrant expression of Tim-3 and galectin-9 is observed in colorectal cancer tissues. High expression of Tim-3 and low expression of galectin-9 are closely associated with adverse clinico-pathological characteristics and prognosis. They are identified as independent influencing factors that may trigger adverse prognostic events in patients. These findings suggest that Tim-3 and galectin-9 have potential as new therapeutic targets and clinical indicators.

Investigating the mechanisms of drug resistance and prognosis in ovarian cancer using single-cell RNA sequencing and bulk RNA sequencing.

Ovarian cancer stands as a prevalent malignancy within the realm of gynecology, and the emergence of resistance to chemotherapeutic agents remains a pivotal impediment to both prognosis and treatment. Through a single-cell level investigation, we scrutinize the drug resistance and mitotic activity of the core tumor cells in ovarian cancer. Our study revisits the interrelationships and temporal trajectories of distinct epithelial cells (EPCs) subpopulations, while identifying genes associated with ovarian cancer prognosis. Notably, our findings establish a strong association between the drug resistance of EPCs and oxidative phosphorylation pathways. Subsequently, through subpopulation and temporal trajectory analysis, we confirm the intermediate position of EPCs subpopulation C0. Furthermore, we delve into the immunological functions and differentially expressed genes associated with the prognosis of C0, shedding light on the potential for constructing novel ovarian cancer prognosis models and identifying new therapeutic targets.

HGSMDA: miRNA-Disease Association Prediction Based on HyperGCN and Sørensen-Dice Loss.

Biological research has demonstrated the significance of identifying miRNA-disease associations in the context of disease prevention, diagnosis, and treatment. However, the utilization of experimental approaches involving biological subjects to infer these associations is both costly and inefficient. Consequently, there is a pressing need to devise novel approaches that offer enhanced accuracy and effectiveness. Presently, the predominant methods employed for predicting disease associations rely on Graph Convolutional Network (GCN) techniques. However, the Graph Convolutional Network algorithm, which is locally aggregated, solely incorporates information from the immediate neighboring nodes of a given node at each layer. Consequently, GCN cannot simultaneously aggregate information from multiple nodes. This constraint significantly impacts the predictive efficacy of the model. To tackle this problem, we propose a novel approach, based on HyperGCN and Sørensen-Dice loss (HGSMDA), for predicting associations between miRNAs and diseases. In the initial phase, we developed multiple networks to represent the similarity between miRNAs and diseases and employed GCNs to extract information from diverse perspectives. Subsequently, we draw into HyperGCN to construct a miRNA-disease heteromorphic hypergraph using hypernodes and train GCN on the graph to aggregate information. Finally, we utilized the Sørensen-Dice loss function to evaluate the degree of similarity between the predicted outcomes and the ground truth values, thereby enabling the prediction of associations between miRNAs and diseases. In order to assess the soundness of our methodology, an extensive series of experiments was conducted employing the Human MicroRNA Disease Database (HMDD v3.2) as the dataset. The experimental outcomes unequivocally indicate that HGSMDA exhibits remarkable efficacy when compared to alternative methodologies. Furthermore, the predictive capacity of HGSMDA was corroborated through a case study focused on colon cancer. These findings strongly imply that HGSMDA represents a dependable and valid framework, thereby offering a novel avenue for investigating the intricate association between miRNAs and diseases.

Integrin ß5 subunit regulates hyperglycemia-induced vascular endothelial cell apoptosis through FoxO1-mediated macroautophagy.

BACKGROUND: Hyperglycemia frequently induces apoptosis in endothelial cells and ultimately contributes to microvascular dysfunction in patients with diabetes mellitus (DM). Previous research reported that the expression of integrins as well as their ligands was elevated in the diseased vessels of DM patients. However, the association between integrins and hyperglycemia-induced cell death is still unclear. This research was designed to investigate the role played by integrin subunit ß5 (ITGB5) in hyperglycemia-induced endothelial cell apoptosis. METHODS: We used leptin receptor knockout (Lepr-KO) ( db / db ) mice as spontaneous diabetes animal model. Selective deletion of ITGB5 in endothelial cell was achieved by injecting vascular targeted adeno-associated virus via tail vein. Besides, we also applied small interfering RNA in vitro to study the mechanism of ITGB5 in regulating high glucose-induced cell apoptosis. RESULTS: ITGB5 and its ligand, fibronectin, were both upregulated after exposure to high glucose in vivo and in vitro . ITGB5 knockdown alleviated hyperglycemia-induced vascular endothelial cell apoptosis and microvascular rarefaction in vivo.In vitro analysis revealed that knockdown of either ITGB5 or fibronectin ameliorated high glucose-induced apoptosis in human umbilical vascular endothelial cells (HUVECs). In addition, knockdown of ITGB5 inhibited fibronectin-induced HUVEC apoptosis, which indicated that the fibronectin-ITGB5 interaction participated in high glucose-induced endothelial cell apoptosis. By using RNA-sequencing technology and bioinformatic analysis, we identified Forkhead Box Protein O1 (FoxO1) as an important downstream target regulated by ITGB5. Moreover, we demonstrated that the excessive macroautophagy induced by high glucose can contribute to HUVEC apoptosis, which was regulated by the ITGB5-FoxO1 axis. CONCLUSION: The study revealed that high glucose-induced endothelial cell apoptosis was positively regulated by ITGB5, which suggested that ITGB5 could potentially be used to predict and treat DM-related vascular complications.

Multi-Kernel Graph Attention Deep Autoencoder for MiRNA-Disease Association Prediction.

Accumulating evidence indicates that microRNAs (miRNAs) can control and coordinate various biological processes. Consequently, abnormal expressions of miRNAs have been linked to various complex diseases. Recognizable proof of miRNA-disease associations (MDAs) will contribute to the diagnosis and treatment of human diseases. Nevertheless, traditional experimental verification of MDAs is laborious and limited to small-scale. Therefore, it is necessary to develop reliable and effective computational methods to predict novel MDAs. In this work, a multi-kernel graph attention deep autoencoder (MGADAE) method is proposed to predict potential MDAs. In detail, MGADAE first employs the multiple kernel learning (MKL) algorithm to construct an integrated miRNA similarity and disease similarity, providing more biological information for further feature learning. Second, MGADAE combines the known MDAs, disease similarity, and miRNA similarity into a heterogeneous network, then learns the representations of miRNAs and diseases through graph convolution operation. After that, an attention mechanism is introduced into MGADAE to integrate the representations from multiple graph convolutional network (GCN) layers. Lastly, the integrated representations of miRNAs and diseases are input into the bilinear decoder to obtain the final predicted association scores. Corresponding experiments prove that the proposed method outperforms existing advanced approaches in MDA prediction. Furthermore, case studies related to two human cancers provide further confirmation of the reliability of MGADAE in practice.

Obesity, birth weight, and lifestyle factors for frailty: a Mendelian randomization study.

Obesity, birth weight and lifestyle factors have been found associated with the risk of frailty in observational studies, but whether these associations are causal is uncertain. We conducted a two-sample Mendelian randomization study to investigate the associations. Genetic instruments associated with the exposures at the genome-wide significance level (p < 5 × 10-8) were selected from corresponding genome-wide association studies (n = 143,677 to 703,901 individuals). Summary-level data for the frailty index were obtained from the UK Biobank (n = 164,610) and Swedish TwinGene (n = 10,616). The ß of the frailty index was 0.15 (p = 3.88 × 10-9) for 1 standard deviation increase in the prevalence of smoking initiation, 0.19 (p = 3.54 × 10-15) for leisure screen time, 0.13 (p = 5.26 × 10-7) for body mass index and 0.13 (p = 1.80 × 10-4) for waist circumference. There was a suggestive association between genetically predicted higher birth weight and moderate-to-vigorous intensity physical activity with the decreased risk of the frailty index. We observed no causal association between genetically predicted age of smoking initiation and alcoholic drinks per week with the frailty index. This study supports the causal roles of smoking initiation, leisure screen time, overall obesity, and abdominal obesity in frailty. The possible association between higher birth weight, proper physical activity and a decreased risk of frailty needs further confirmation.

Establishment of pelvic inflammatory disease model induced by vaginal injection of Ureaplasma urealyticum liquids combined with fatigue and hunger.

Pelvic inflammatory disease (PID) is an inflammation of the upper genital tract. PID is the leading cause of some severe sequelae in the absence of timely and accurate diagnosis and treatment. An appropriate animal model is needed to explore the underlying mechanism of PID sequelae. This study introduced an animal model of PID by vaginal injection of liquid Ureaplasma urealyticum combined with fatigue and hunger (UVF). This study was designed to test the feasibility of a rat model. A rat model was established using UVF irradiation. Levels of some inflammatory cytokines in the serum and the homogenates of the fallopian tubes were measured by ELISA, RT-PCR, and flow cytometry and compared with another rat model of Ureaplasma urealyticum liquids injected into the two uterus horns during laparotomy. Inflammatory alterations and adhesions were observed after hematoxylin and eosin (H&E) staining and detected using the Blauer scoring system. The results showed that the combined UVF and rat model caused apparent obstruction, edema, and adhesion in the fallopian tubes and connective tissues. The rat model showed upregulated CD4, CD8, and CD4/CD8 in peripheral blood mononuclear cells (PBMCs) and significantly increased levels of IL-4, IL-6, IL-10, and IL-17. UVF also enhanced the expression of tumor necrosis factor (TNF)-α, transforming growth factor (TGF)-ß, vascular endothelial growth factor (VEGF) ß, and matrix metalloproteinase (MMP)-2 (P<0.05). The UVF rat model can induce inflammatory alterations in the fallopian tubes and connective tissues, and can be used as a model of PID.

HER2-targeting two-dimensional black phosphorus as a nanoplatform for chemo-photothermal therapy in breast cancer.

Trastuzumab (Tmab) targeted therapy or its combination with chemotherapy is normally insufficient to elicit a comprehensive therapeutic response owing to the inherent or acquired drug resistance and systemic toxicity observed in highly invasive HER2-positive breast cancer. In this study, we propose a novel approach that integrates photothermal therapy (PTT) with targeted therapy and chemotherapy, thereby achieving additive or synergistic therapeutic outcomes. We utilize PEGylated two-dimensional black phosphorus (2D BP) as a nanoplatform and photothermal agent to load chemotherapeutic drug mitoxantrone (MTO) and conjugate with Tmab (BP-PEG-MTO-Tmab). The in vitro and in vivo experiments demonstrated that the HER2-targeting BP-PEG-MTO-Tmab complexes exhibited desirable biocompatibility, safety and enhanced cancer cell uptake efficiency, resulting in increased accumulation and prolonged retention of BP and MTO within tumors. Consequently, the complex improved photothermal and chemotherapy treatment efficacy in HER2-positive cells in vitro and a subcutaneous tumor model in vivo, while minimized harm to normal cells and showed desirable organ compatibility. Collectively, our study provides compelling evidence for the remarkable efficacy of targeted and synergistic chemo-photothermal therapy utilizing all-in-one nanoparticles as a delivery system for BP and chemotherapeutic drug in HER2-positive breast cancer.

BioSTD: A New Tensor Multi-View Framework via Combining Tensor Decomposition and Strong Complementarity Constraint for Analyzing Cancer Omics Data.

Advances in omics technology have enriched the understanding of the biological mechanisms of diseases, which has provided a new approach for cancer research. Multi-omics data contain different levels of cancer information, and comprehensive analysis of them has attracted wide attention. However, limited by the dimensionality of matrix models, traditional methods cannot fully use the key high-dimensional global structure of multi-omics data. Moreover, besides global information, local features within each omics are also critical. It is necessary to consider the potential local information together with the high-dimensional global information, ensuring that the shared and complementary features of the omics data are comprehensively observed. In view of the above, this article proposes a new tensor integrative framework called the strong complementarity tensor decomposition model (BioSTD) for cancer multi-omics data. It is used to identify cancer subtype specific genes and cluster subtype samples. Different from the matrix framework, BioSTD utilizes multi-view tensors to coordinate each omics to maximize high-dimensional spatial relationships, which jointly considers the different characteristics of different omics data. Meanwhile, we propose the concept of strong complementarity constraint applicable to omics data and introduce it into BioSTD. Strong complementarity is used to explore the potential local information, which can enhance the separability of different subtypes, allowing consistency and complementarity in the omics data to be fully represented. Experimental results on real cancer datasets show that our model outperforms other advanced models, which confirms its validity.

Network embedding framework for driver gene discovery by combining functional and structural information.

Comprehensive analysis of multiple data sets can identify potential driver genes for various cancers. In recent years, driver gene discovery based on massive mutation data and gene interaction networks has attracted increasing attention, but there is still a need to explore combining functional and structural information of genes in protein interaction networks to identify driver genes. Therefore, we propose a network embedding framework combining functional and structural information to identify driver genes. Firstly, we combine the mutation data and gene interaction networks to construct mutation integration network using network propagation algorithm. Secondly, the struc2vec model is used for extracting gene features from the mutation integration network, which contains both gene's functional and structural information. Finally, machine learning algorithms are utilized to identify the driver genes. Compared with the previous four excellent methods, our method can find gene pairs that are distant from each other through structural similarities and has better performance in identifying driver genes for 12 cancers in the cancer genome atlas. At the same time, we also conduct a comparative analysis of three gene interaction networks, three gene standard sets, and five machine learning algorithms. Our framework provides a new perspective for feature selection to identify novel driver genes.

Multi-View Enhanced Tensor Nuclear Norm and Local Constraint Model for Cancer Clustering and Feature Gene Selection.

The analysis of cancer data from multi-omics can effectively promote cancer research. The main focus of this article is to cluster cancer samples and identify feature genes to reveal the correlation between cancers and genes, with the primary approach being the analysis of multi-view cancer omics data. Our proposed solution, the Multi-View Enhanced Tensor Nuclear Norm and Local Constraint (MVET-LC) model, aims to utilize the consistency and complementarity of omics data to support biological research. The model is designed to maximize the utilization of multi-view data and incorporates a nuclear norm and local constraint to achieve this goal. The first step involves introducing the concept of enhanced partial sum of tensor nuclear norm, which significantly enhances the flexibility of the tensor nuclear norm. After that, we incorporate total variation regularization into the MVET-LC model to further augment its performance. It enables MVET-LC to make use of the relationship between tensor data structures and sparse data while paying attention to the feature details of the tensor data. To tackle the iterative optimization problem of MVET-LC, the alternating direction method of multipliers is utilized. Through experimental validation, it is demonstrated that our proposed model outperforms other comparison models.

Establishment of a prognostic model for melanoma based on necroptosis-related genes.

In this study, the clinical implications and potential functions of necroptosis-related genes (NRGs) in melanoma were systematically characterized. A novel NRG signature was then constructed to analyze the immune status and prognosis of patients with melanoma. The NRG signatures for melanoma prognosis were searched using the Cancer Genome Atlas (TCGA) dataset and followed by stepwise Cox regression analysis. Patients with melanoma were divided into two groups, and survival analysis, receiver operating characteristic (ROC), and univariate and multivariate analyses were subsequently performed. The correlation of risk score (RS) with tumor immunity and RT-polymerase chain reaction (PCR) was analyzed to further verify the gene signatures. Data on tumor mutational burden (TMB) and chromosomal copy number variation (CNV) were analyzed. Three NRGs were identified as prognostic risk signatures and were significantly related to overall survival (OS) in melanoma. The signatures had better diagnostic accuracy. Furthermore, analysis of mutations in the NRGs and the incidence of chromosomal CNV helped to reveal the relationship between mutations and melanoma occurrence. A nomogram was established based on RSs. The risk characteristics were significantly associated with immunity and high risk is closely correlated with melanoma development. In vitro experiments revealed that necrostatin-1 (Nec-1) promoted cell viability and repressed the expression levels of interleukin (IL)12A and proprotein convertase subtilisin/kexin type (PCSK)1. Additionally, the expression levels of IL12A, CXCL10, and PCSK1 decreased in tumor tissues of melanoma patients. NRGs exert vital roles in immunity and might be applied as a prognostic factor of melanoma.

ETGPDA: identification of piRNA-disease associations based on embedding transformation graph convolutional network.

BACKGROUND: Piwi-interacting RNAs (piRNAs) have been proven to be closely associated with human diseases. The identification of the potential associations between piRNA and disease is of great significance for complex diseases. Traditional "wet experiment" is time-consuming and high-priced, predicting the piRNA-disease associations by computational methods is of great significance. METHODS: In this paper, a method based on the embedding transformation graph convolution network is proposed to predict the piRNA-disease associations, named ETGPDA. Specifically, a heterogeneous network is constructed based on the similarity information of piRNA and disease, as well as the known piRNA-disease associations, which is applied to extract low-dimensional embeddings of piRNA and disease based on graph convolutional network with an attention mechanism. Furthermore, the embedding transformation module is developed for the problem of embedding space inconsistency, which is lightweighter, stronger learning ability and higher accuracy. Finally, the piRNA-disease association score is calculated by the similarity of the piRNA and disease embedding. RESULTS: Evaluated by fivefold cross-validation, the AUC of ETGPDA achieves 0.9603, which is better than the other five selected computational models. The case studies based on Head and neck squamous cell carcinoma and Alzheimer's disease further prove the superior performance of ETGPDA. CONCLUSIONS: Hence, the ETGPDA is an effective method for predicting the hidden piRNA-disease associations.

Development of a Machine-Learning Model for Prediction of Extubation Failure in Patients with Difficult Airways after General Anesthesia of Head, Neck, and Maxillofacial Surgeries.

(1) Background: Extubation failure after general anesthesia is significantly associated with morbidity and mortality. The risk of a difficult airway after the general anesthesia of head, neck, and maxillofacial surgeries is significantly higher than that after general surgery, increasing the incidence of extubation failure. This study aimed to develop a multivariable prediction model based on a supervised machine-learning algorithm to predict extubation failure in adult patients after head, neck, and maxillofacial surgeries. (2) Methods: A single-center retrospective study was conducted in adult patients who underwent head, neck, and maxillofacial general anesthesia between July 2015 and July 2022 at the Shanghai Ninth People's Hospital. The primary outcome was extubation failure after general anesthesia. The dataset was divided into training (70%) and final test sets (30%). A five-fold cross-validation was conducted in the training set to reduce bias caused by the randomly divided dataset. Clinical data related to extubation failure were collected and a stepwise logistic regression was performed to screen out the key features. Six machine-learning methods were introduced for modeling, including random forest (RF), k-nearest neighbor (KNN), logistic regression (LOG), support vector machine (SVM), extreme gradient boosting (XGB), and optical gradient boosting machine (GBM). The best performance model in the first cross-validation dataset was further optimized and the final performance was assessed using the final test set. (3) Results: In total, 89,279 patients over seven years were reviewed. Extubation failure occurred in 77 patients. Next, 186 patients with a successful extubation were screened as the control group according to the surgery type for patients with extubation failure. Based on the stepwise regression, seven variables were screened for subsequent analysis. After training, SVM and LOG models showed better prediction ability. In the k-fold dataset, the area under the curve using SVM and LOG were 0.74 (95% confidence interval, 0.55-0.93) and 0.71 (95% confidence interval, 0.59-0.82), respectively, in the k-fold dataset. (4) Conclusion: Applying our machine-learning model to predict extubation failure after general anesthesia in clinical practice might help to reduce morbidity and mortality of patients with difficult airways after head, neck, and maxillofacial surgeries.

Effects of selenium supplementation on Polycystic Ovarian Syndrome: a systematic review and meta-analysis on randomized clinical trials.

BACKGROUND: This study provides a systematic review and meta-analysis of randomized controlled trials, which examined the effect of the selenium supplementation on polycystic ovary syndrome (PCOS). METHODS: Confirmed studies related to selenium supplementation and PCOS were searched from the databases of EMBASE, PubMed and Web of Science. Data were reported as weighted mean difference (WMD) or standard mean difference (SMD) and associated 95% confidence intervals (CIs). Analysis was performed with Stata version 12.0. RESULTS: A total of 389 cases (selenium group n = 195, control group n = 194) were included in this studies. This meta-analysis showed that selenium supplementation has a positive effect on TAC, and supplementation of selenium does not significantly improve the level of BMI, Weight, LDL, HDL, Triglycerides, Total Testosterone, HOMA-IR, NO, GSH, MDA and FPG. CONCLUSION: Although selenium can improve TAC in PCOS patients, it has no significant effect on BMI, Total Testosterone, et al. In terms of the results of this meta-analysis, it is not recommended for patients with PCOS to use selenium as a regular trace element supplement. Based on the improving effect of selenium on TAC, supplementation of selenium may have a positive effect on improving follicle quality for some PCOS patients who have poor follicle quality caused by oxidative stress or who want to undergo IVF.

DM-MOGA: a multi-objective optimization genetic algorithm for identifying disease modules of non-small cell lung cancer.

BACKGROUND: Constructing molecular interaction networks from microarray data and then identifying disease module biomarkers can provide insight into the underlying pathogenic mechanisms of non-small cell lung cancer. A promising approach for identifying disease modules in the network is community detection. RESULTS: In order to identify disease modules from gene co-expression networks, a community detection method is proposed based on multi-objective optimization genetic algorithm with decomposition. The method is named DM-MOGA and possesses two highlights. First, the boundary correction strategy is designed for the modules obtained in the process of local module detection and pre-simplification. Second, during the evolution, we introduce Davies-Bouldin index and clustering coefficient as fitness functions which are improved and migrated to weighted networks. In order to identify modules that are more relevant to diseases, the above strategies are designed to consider the network topology of genes and the strength of connections with other genes at the same time. Experimental results of different gene expression datasets of non-small cell lung cancer demonstrate that the core modules obtained by DM-MOGA are more effective than those obtained by several other advanced module identification methods. CONCLUSIONS: The proposed method identifies disease-relevant modules by optimizing two novel fitness functions to simultaneously consider the local topology of each gene and its connection strength with other genes. The association of the identified core modules with lung cancer has been confirmed by pathway and gene ontology enrichment analysis.

A network-based method for identifying cancer driver genes based on node control centrality.

Cancer is one of the major contributors to human mortality and has a serious influence on human survival and health. In biomedical research, the identification of cancer driver genes (cancer drivers for short) is an important task; cancer drivers can promote the progression and generation of cancer. To identify cancer drivers, many methods have been developed. These computational models only identify coding cancer drivers; however, non-coding drivers likewise play significant roles in the progression of cancer. Hence, we propose a Network-based Method for identifying cancer Driver Genes based on node Control Centrality (NMDGCC), which can identify coding and non-coding cancer driver genes. The process of NMDGCC for identifying driver genes mainly includes the following two steps. In the first step, we construct a gene interaction network by using mRNAs and miRNAs expression data in the cancer state. In the second step, the control centrality of the node is used to identify cancer drivers in the constructed network. We use the breast cancer dataset from The Cancer Genome Atlas (TCGA) to verify the effectiveness of NMDGCC. Compared with the existing methods of cancer driver genes identification, NMDGCC has a better performance. NMDGCC also identifies 295 miRNAs as non-coding cancer drivers, of which 158 are related to tumorigenesis of BRCA. We also apply NMDGCC to identify driver genes related to the different breast cancer subtypes. The result shows that NMDGCC detects many cancer drivers of specific cancer subtypes.

LDCMFC: Predicting Long Non-Coding RNA and Disease Association Using Collaborative Matrix Factorization Based on Correntropy.

With the development of bioinformatics, the important role played by lncRNAs in various intractable diseases has aroused the interest of many experts. In recent studies, researchers have found that several human diseases are related to lncRANs. Moreover, it is very difficult and expensive to explore the unknown lncRNA-disease associations (LDAs), so only a few associations have been confirmed. It is vital to find a more accurate and effective method to identify potential LDAs. In this study, a method of collaborative matrix factorization based on correntropy (LDCMFC) is proposed for the identification of potential LDAs. To improve the robustness of the algorithm, the traditional minimization of the Euclidean distance is replaced with the maximized correntropy. In addition, the weighted K nearest known neighbor (WKNKN) method is used to rebuild the adjacency matrix. Finally, the performance of LDCMFC is tested by 5-fold cross-validation. Compared with other traditional methods, LDACMFC obtains a higher AUC of 0.8628. In different types of studies of three important cancer cases, most of the potentially relevant lncRNAs derived from the experiments have been validated in the databases. The final result shows that LDCMFC is a feasible method to predict LDAs.

Effect and mechanisms of kaempferol against endometriosis based on network pharmacology and in vitro experiments.

Endometriosis is a common gynecological disease, and its underlying mechanisms remain elusive. Patients are at a higher risk of recurrence after surgery or drug withdrawal. In this study, to identify a potentially effective and safe therapy for endometriosis, we screened potential target genes of kaempferol on endometriosis using network pharmacology and further validation. Network pharmacology showed kaempferol may suppress migratory and invasive properties by modulating the phosphoinositide 3-kinase (PI3K) pathway and its downstream target matrix metalloproteinase (MMP)9. Furthermore, in vitro experiments showed that kaempferol repressed the migration and invasion of endometrial cells, and this effect may be involved in mediating the PI3K-related genes, phosphatase and tensin homolog (PTEN) and MMP9. Network pharmacology and in vitro experiments showed that kaempferol, repressed the implantation of endometrial cells and formation of ectopic lesions by inhibiting migration and invasion and regulating PTEN and MMP9, which may be associated with the PI3K pathway.

NEXGB: A Network Embedding Framework for Anticancer Drug Combination Prediction.

Compared to single-drug therapy, drug combinations have shown great potential in cancer treatment. Most of the current methods employ genomic data and chemical information to construct drug-cancer cell line features, but there is still a need to explore methods to combine topological information in the protein interaction network (PPI). Therefore, we propose a network-embedding-based prediction model, NEXGB, which integrates the corresponding protein modules of drug-cancer cell lines with PPI network information. NEXGB extracts the topological features of each protein node in a PPI network by struc2vec. Then, we combine the topological features with the target protein information of drug-cancer cell lines, to generate drug features and cancer cell line features, and utilize extreme gradient boosting (XGBoost) to predict the synergistic relationship between drug combinations and cancer cell lines. We apply our model on two recently developed datasets, the Oncology-Screen dataset (Oncology-Screen) and the large drug combination dataset (DrugCombDB). The experimental results show that NEXGB outperforms five current methods, and it effectively improves the predictive power in discovering relationships between drug combinations and cancer cell lines. This further demonstrates that the network information is valid for detecting combination therapies for cancer and other complex diseases.