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Abstract: Although breast cancer treatment has been developed remarkably in recent years, it remains the primary cause of death among women. Immune checkpoint blockade therapy has significantly altered the way breast cancer is treated, although not all patients benefit from the changes. At present, the most effective mechanism of immune checkpoint blockade application in malignant tumors is not clear and efficacy may be influenced by many factors, including host, tumor, and tumor microenvironment dynamics. Therefore, there is a pressing need for tumor immunomarkers that can be used to screen patients and help determine which of them would benefit from breast cancer immunotherapy. At present, no single tumor marker can predict treatment efficacy with sufficient accuracy. Multiple markers may be combined to more accurately pinpoint patients who will respond favorably to immune checkpoint blockade medication. In this review, we have examined the breast cancer treatments, developments in research on the role of tumor markers in maximizing the clinical efficacy of immune checkpoint inhibitors, prospects for the identification of novel therapeutic targets, and the creation of individualized treatment plans. We also discuss how tumor markers can provide guidance for clinical practice.

Although platinum-based chemotherapy can improve pathologic complete response (pCR) in patients with triple-negative breast cancer (TNBC), the impact on survival of platinum-based neoadjuvant and adjuvant chemotherapy is still controversial. Our meta-analysis aimed at analyzing survival with platinum-based neoadjuvant and adjuvant chemotherapy in patients with TNBC. We searched PubMed, EMBASE, MEDLINE, Cochrane databases, and several major conferences up to January 2021. Fixed and random models were used for our meta-analysis. Disease-free survival (DFS), overall survival (OS), and side effects data were extracted from the included literature in addition to the corresponding pooled hazard ratio (HR) and odds ratio (OR) with 95% confidence intervals (CIs). A total of nine studies involving 3247 patients were included. The pooled analysis suggested that compared with anthracycline- and/or paclitaxel-based chemotherapy, platinum-based chemotherapy could further improve DFS (HR = 0.56, 95% CI 0.45-0.67, p 0.01) and OS (HR = 0.54, 95% CI 0.38-0.70, p 0.01) in patients with TNBC. The subgroup analysis showed that platinum-based chemotherapy could further improve DFS (HR = 0.59, 95% CI 0.43-0.74, p 0.01) and OS (HR = 0.61, 95% CI 0.40-0.83, p 0.01) in neoadjuvant chemotherapy and DFS (HR = 0.53, 95% CI 0.37-0.69, p 0.01) and OS (HR = 0.46, 95% CI 0.23-0.69, p 0.01) in adjuvant chemotherapy compared with anthracycline- and/or paclitaxel-based chemotherapy in patients with TNBC. In addition, compared with anthracycline-based chemotherapy, platinum-based chemotherapy without anthracycline chemotherapy could further improve DFS (HR = 0.53, 95% CI 0.37-0.70, p 0.01) and OS (HR = 0.46, 95%CI 0.19-0.72, p 0.01) in patients with TNBC. Compared with anthracycline- and/or paclitaxel-based chemotherapy, all-grade diarrhea, fatigue, and grade ≥ 3 anemia were higher in platinum-based chemotherapy. In contrast, all-grade anemia, leukopenia, neutropenia, peripheral neuropathy, myalgia/arthralgia, cardiac toxicity were lower in platinum-based chemotherapy; grade ≥ 3 leukopenia, neutropenia and myalgia/arthralgia were also lower. Compared with anthracycline- and/or paclitaxel-based chemotherapy, platinum-based chemotherapy was more associated with improved DFS and OS in TNBC patients. The benefit of survival is consistent with platinum-based neoadjuvant and adjuvant chemotherapy. The side effects of platinum-based chemotherapy are tolerable.

Matrix metalloproteinases (MMPs) are a group of zinc-dependent enzyme families that play an important role in regulating human physiological as well as pathological processes, especially in malignant tumors. Numerous experimental studies have shown that MMPs are not only involved in the occurrence and development of solid tumors but also play a key role in the staging and grading of tumors. The specific processes by which MMPs are involved in tumor cell invasion and metastasis mainly include degradation of the extracellular matrix, regulation of gene polymorphism, promotion of epithelial-mesenchymal transformation, and induction of adhesion molecule expression. The correlated expression of MMPs in different solid tumors provides a basis for tumor markers, tumor prognosis, and drug targets. In this review, the function, classification, and nomenclature of MMPs will be summarized, and the relevant expression of MMPs in solid tumors, as well as the clinical survival rate and general prognosis associated with MMPs, will be elaborated to provide useful information on which to base the search for new targets for tumor therapy.

Risk stratification using prognostic markers facilitates clinical decision-making in treatment of osteosarcoma (OS). In this study, we performed a comprehensive analysis of DNA methylation and transcriptome data from OS patients to establish an optimal methylated lncRNA signature for determining OS patient prognosis. The original OS datasets were downloaded from the the Therapeutically Applicable Research to Generate Effective Treatments (TARGET) database. Univariate, Lasso, and machine learning algorithm-iterative Lasso Cox regression analyses were used to establish a methylated lncRNA signature that significantly correlated with OS patient survival. The validity of this signature was verified by the Kaplan-Meier curves, Receiver Operating Characteristic (ROC) curves. We established a four-methylated lncRNA signature that can predict OS patient survival (verified in independent cohort [GSE39055]). Kaplan-Meier analysis showed that the signature can distinguish between the survival of high- and low-risk patients. ROC analysis corroborated this finding and revealed that the signature had higher prediction accuracy than known biomarkers. Kaplan-Meier analysis of the clinical subgroup showed that the signature's prognostic ability was independent of clinicopathological factors. The four-methylated lncRNA signature is an independent prognostic biomarker of OS.

Immune-related genes (IRGs) are responsible for osteosarcoma (OS) initiation and development. We aimed to develop an optimal IRGs-based signature to assess of OS prognosis. Sample gene expression profiles and clinical information were downloaded from the Therapeutically Applicable Research to Generate Effective Treatments (TARGET) and Genotype-Tissue Expression (GTEx) databases. IRGs were obtained from the ImmPort database. R software was used to screen differentially expressed IRGs (DEIRGs) and functional correlation analysis. DEIRGs were analyzed by univariate Cox regression and iterative LASSO Cox regression analysis to develop an optimal prognostic signature, and the signature was further verified by independent cohort (GSE39055) and clinical correlation analysis. The analyses yielded 604 DEIRGs and 10 hub IRGs. A prognostic signature consisting of 13 IRGs was constructed, which strikingly correlated with OS overall survival and distant metastasis (p

Ewing sarcoma (ES) is one of the most common malignant bone tumors in children and adolescents. The immune microenvironment plays an important role in the development of ES. Here, we developed an optimal signature for determining ES patient prognosis based on immune-related genes (IRGs). We analyzed the ES gene expression profile dataset, GSE17679, from the GEO database and extracted differential expressed IRGs (DEIRGs). Then, we conducted functional correlation and protein–protein interaction (PPI) analyses of the DEIRGs and used the machine learning algorithm-iterative Lasso Cox regression analysis to build an optimal DEIRG signature. In addition, we applied ES samples from the ICGC database to test the optimal gene signature. We performed univariate and multivariate Cox regressions on clinicopathological characteristics and optimal gene signature to evaluate whether signature is an important prognostic factor. Finally, we calculated the infiltration of 24 immune cells in ES using the ssGSEA algorithm, and analyzed the correlation between the DEIRGs in the optimal gene signature and immune cells. A total of 249 DEIRGs were screened and an 11-gene signature with the strongest correlation with patient prognoses was analyzed using a machine learning algorithm. The 11-gene signature also had a high prognostic value in the ES external verification set. Univariate and multivariate Cox regression analyses showed that 11-gene signature is an independent prognostic factor. We found that macrophages and cytotoxic, CD8 T, NK, mast, B, NK CD56bright, TEM, TCM, and Th2 cells were significantly related to patient prognoses; the infiltration of cytotoxic and CD8 T cells in ES was significantly different. By correlating prognostic biomarkers with immune cell infiltration, we found that FABP4 and macrophages, and NDRG1 and Th2 cells had the strongest correlation. Overall, the IRG-related 11-gene signature can be used as a reliable ES prognostic biomarker and can provide guidance for personalized ES therapy.

Background: Nucleolar and spindle-associated protein 1 (NUSAP1) was previously reported to be associated with poor prognosis in multiple cancers. In the present study, we comprehensively investigated the clinicopathological features and potential prognostic value of NUSAP1 in cervical squamous cell carcinoma and endocervical adenocarcinoma (CESC). Methods: The expression profiles of the genes were extracted from Gene Expression Omnibus (GEO), The Cancer Genome Atlas (TCGA), International Cancer Genome Consortium (ICGC), Cancer Cell Line Encyclopedia (CCLE), Gene Expression Profiling Interactive Analysis (GEPIA), and The Human Protein Atlas databases. The association between clinicopathological characteristics and NUSAP1 was analyzed using logistic regression in TCGA patients and receiver operating characteristic (ROC) curve analysis for GSE7803, GSE9750, and GSE63514 datasets. The prognostic value of NUSAP1 in TCGA patients was evaluated using the Kaplan-Meier method and Cox regression. Gene set enrichment analysis (GSEA) was conducted using TCGA dataset. Results: A total of 68 differentially expressed genes (DEGs) were identified in CESC. ROC analysis of NUSAP1 suggested that the area under the ROC curve was 0.968. Kaplan-Meier survival analysis indicated that CESC with low expression of NUSAP1 has a worse prognosis than CESC with high NUSAP1 expression (P = 0.005). The logistic regression revealed that low NUSAP1 expression in CESC was related to advanced tumor stage in TCGA database. Moreover, Cox regression analysis showed that NUSAP1 expression correlated significantly with prognosis in the case of patients in TCGA database. GSEA demonstrated that CESC patients with high expression of NUSAP1 were enriched in the G2M checkpoint, MYC targets, and breast cancer ZNF217. Conclusion: The results suggest that identification of DEGs might enhance our understanding of the causes and molecular mechanisms underlying the development of CESC. Moreover, NUSAP1 may play an important role in CESC progression and prognosis and may serve as a valuable indicator of poor survival in CESC.

Intervertebral disc degeneration (IVDD) is an age-related degenerative disease that is the main cause of low back pain. It seriously affects the quality of life of patients and places a heavy economic burden on families and society. The Wnt pathway plays an important role in the growth, development, and degeneration of intervertebral discs (IVDs). In the embryonic stage, the Wnt pathway participates in the growth and development of IVD by promoting the transformation of progenitor cells into notochord cells and the extension of the notochord. However, the activation of the Wnt pathway after birth promotes IVD cell senescence, apoptosis, and degradation of the extracellular matrix and induces the production of inflammatory factors, thereby accelerating the IVDD process. This article reviews the relationship between the Wnt pathway and IVD, emphasizing its influence on IVD growth, development, and degeneration. Targeting this pathway may become an effective strategy for the treatment of IVDD.

ObjectiveOsteosarcoma (OS), the most common malignant bone tumor, is prone to early metastasis and recurrence, resulting in poor prognosis. The purpose of this study was to investigate gene expression in OS samples, and to study the relationship between tumor infiltrating immune cells (TIICS) in the tumor microenvironment and the occurrence, development, and prognosis of OS. MethodsIntegrated analysis was performed on the gene expression profile of OS samples in the Therapeutically Applicable Research to Generate Effective Treatments (TARGET) database, and normal muscle tissue samples in the Genotype-Tissue Expression (GTEx) database. Screening of differentially expressed genes in OS samples and healthy muscle tissue samples was performed using R software. Functional annotation and pathway enrichment analyses were performed, a protein-protein interaction network was constructed, and the Hub genes were screened. The infiltration differences of 22 TIICs in OS samples and normal muscle tissue samples were analyzed using CIBERSORT. ROC curve, and OS survival analyses were performed on the Hub genes and 22 TIICs. The correlation between Hub genes with prognostic value and the 22 TIICs was discussed. ResultsTen Hub genes were screened. OS and normal muscle tissue samples were compared. Significant differences were observed in the infiltration of follicular helper T cells, resting NK cells, plasma cells, activated CD4 memory T cells, gamma delta T cells, monocytes, M0 macrophages and resting dendritic cells (PTNNI2, TTN, ACTN3, TNNC2, MYL3, and survival of OS patients. Correlation analysis revealed close association between ACTN3, TNNI2, TNNC2, MYL3, TTN, and the infiltration of M0 macrophages and plasma cells. ConclusionOur findings provide a theoretical basis for further study of the molecular mechanisms underlying OS development and occurrence. It serves as a reference for the development of new diagnostics, identification of prognostic biomarkers, and targeted drug therapy for OS.

Background: Osteosarcoma (OS) is a bone malignant tumor that occurs in children and adolescents. Due to a lack of reliable prognostic biomarkers, the prognosis of OS patients is often uncertain. This study aimed to construct an autophagy-related gene signature to predict the prognosis of OS patients.Methods: The gene expression profile data of OS and normal muscle tissue samples were downloaded separately from the Therapeutically Applied Research To Generate Effective Treatments (TARGET) and Genotype-Tissue Expression (GTEx) databases . The differentially expressed autophagy-related genes (DEARGs) in OS and normal muscle tissue samples were screened using R software, before being subjected to Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analysis. A protein-protein interaction (PPI) network was constructed and hub autophagy-related genes were screened. Finally, the screened autophagy-related genes were subjected to univariate Cox regression, Lasso Cox regression, survival analysis, and clinical correlation analysis.Results: A total of 120 DEARGs and 10 hub autophagy-related genes were obtained. A prognostic autophagy-related gene signature consisting of 9 genes ( BNIP3 , MYC , BAG1 , CALCOCO2 , ATF4 , AMBRA1 , EGFR , MAPK1 , and PEX ) was constructed. This signature was significantly correlated to the prognosis ( P

Intervertebral disc degeneration (IDD) is one of the main causes of low back pain, which seriously reduces the quality of life of patients and places a heavy economic burden on their families. Cellular senescence is considered to be an important factor leading to IDD, and inflammatory response, oxidative stress, and mitochondrial dysfunction are closely related to intervertebral disc (IVD) senescence. Therefore, inhibition of the inflammatory response and oxidative stress, along with maintaining mitochondrial function, may be useful in treating IDD. The sirtuins are a family of evolutionarily conserved nicotinamide adenine dinucleotide (NAD+)-dependent histone deacetylases, which are the major molecules mediating life extension or delay of aging-related diseases. The sirtuin protein family consist of seven members (SIRT1 – 7), which are mainly involved in various aging-related diseases by regulating inflammation, oxidative stress, and mitochondrial function. Among them, SIRT1, SIRT2, SIRT3, and SIRT6 are closely related to IDD. In addition, some activators of sirtuin proteins, such as resveratrol, melatonin, magnolol, 1,4-dihydropyridine (DHP), SRT1720, and nicotinamide mononucleotide (NMN), have been evaluated in preclinical studies for their effects in preventing IDD. This review described the biological functions of sirtuins and the important roles of SIRT1, SIRT2, SIRT3, and SIRT6 in IDD by regulating oxidative stress, inflammatory response, and mitochondrial function. In addition, we introduce the status of some sirtuin activators in IDD preclinical studies. This review will provide a background for further clarification of the molecular mechanism underlying IDD and the development of potential therapeutic drugs.

This study aimed to find potential diagnostic markers for osteoarthritis (OA) and analyze the role of immune cells infiltration in this pathology. We used OA datasets from the Gene Expression Omnibus database. First, R software was used to identify differentially expressed genes (DEGs) and perform functional correlation analysis. Then least absolute shrinkage and selection operator (LASSO) logistic regression and support vector machine-recursive feature elimination algorithms were used to screen and verify the diagnostic markers of OA. Finally, CIBERSORT was used to evaluate the infiltration of immune cells in OA tissues, and the correlation between diagnostic markers and infiltrating immune cells was analyzed. A total of 458 DEGs were screened in this study. GRB10 and E2F3 (AUC = 0.962) were identified as diagnostic markers of OA. Immune cell infiltration analysis found that resting mast cells, T regulatory cells, CD4 memory resting T cells, activated NK cells, and eosinophils may be involved in the OA process. In addition, GRB10 was correlated with NK resting cells, naive CD4 + T cells, and M1 macrophages, while E2F3 was correlated with resting mast cells. In conclusion, GRB10 and E2F3 can be used as diagnostic markers of osteoarthritis, and immune cell infiltration plays an important role in the occurrence and progression of OA.

Idiopathic pulmonary fibrosis (IPF), characterized by irreversible scarring and progressive destruction of the lung tissue, is one of the most common types of idiopathic interstitial pneumonia worldwide. However, there are no reliable candidates for curative therapies. Hence, elucidation of the mechanisms of IPF genesis and exploration of potential biomarkers and prognostic indicators are essential for accurate diagnosis and treatment of IPF. Recently, efficient microarray and bioinformatics analyses have promoted an understanding of the molecular mechanisms of disease occurrence and development, which is necessary to explore genetic alternations and identify potential diagnostic biomarkers. However, high false-positive rates results have been observed based on single microarray datasets. In the current study, we performed a comprehensive analysis of the differential expression, biological functions, and interactions of IPF-related genes. Three publicly available microarray datasets including 54 IPF samples and 34 normal samples were integrated by performing gene set enrichment analysis and analyzing differentially expressed genes (DEGs). Our results identified 350 DEGs genetically associated with IPF. Gene ontology analyses revealed that the changes in the modules were mostly enriched in the positive regulation of smooth muscle cell proliferation, positive regulation of inflammatory responses, and the extracellular space. Kyoto encyclopedia of genes and genomes enrichment analysis of DEGs revealed that IPF involves the TNF signaling pathway, NOD-like receptor signaling pathway, and PPAR signaling pathway. To identify key genes related to IPF in the protein-protein interaction network, 20 hub genes were screened out with highest scores. Our results provided a framework for developing new pathological molecular networks related to specific diseases in silico.

Breast cancer is the most common malignancy threatening women's health worldwide. The GTPase IMAP family genes are proteins belonging to the immune-associated nucleotide subfamily of the GTP-binding superfamily and nucleotide-binding proteins. However, little is known about the role of different GTPase IMAP family genes in breast cancer.We obtained differential genes from the GEPIA and UALCAN databases and then used the Kaplan-Meier plotter, The Human Protein Atlas, NetworkAnalyst, STRING, and TIMER to analyze the prognostic value, protein expression, and immune cell infiltration of the GTPase IMAP family in patients with breast cancer.Among the GIMAP family genes, the expression levels of GIMAP1, GIMAP5, GIMAP6, GIMAP7, and GIMAP8 were significantly low in breast tumor tissues. In the overall population, patients with high expression of all genes of the GIMAP family had a significantly higher overall survival (OS), with the most significant increase correlated with the GIMAP2 gene (hazard ratio [HR] = 0.45, 95% confidence interval [CI], 0.34-0.59, P = 3.1e-09). However, patients with high expression of the GIMAP family genes in triple-negative breast cancer compared to those with low expression had a significant OS benefit, with the most pronounced benefit correlated with the GIMAP2 gene (HR = 0.37, 95% CI, 0.23-0.59, P = 1.4e-05). GIMAP7 and GIMAP8 were significantly upregulated in breast tumor tissues. The expression of genes in different GIMAP families was positively correlated with the infiltration and expression of six immune cell types (B cells, CD4+ T cells, CD8+ T cells, macrophages, neutrophils, and dendritic cells).This study may provide novel insights into the selection of GIMAP family prognostic biomarkers for breast cancer.

The work presented in this paper focuses on the estimation of the heat transfer coefficients by measured surface temperatures which contains large disturbances. In previous works on the calculation of heat transfer coefficients from the measured surface temperatures, the impact of large disturbance on the accuracy of the estimation of heat transfer coefficient was not considered. To solve this problem, we introduce an integrated approach which contains Gaussian Kernel (GK) function and the Particle Swarm Optimization (PSO) algorithm. Moreover, we use the real industrial data of the SAE 1800 slab from Baosteel Corporation to show the validity of this new approach. The simulation experiment results show that our GK-PSO method can reduce the influence of large disturbances effectively. Finally, we use the corrected heat transfer coefficients to improve the accuracy of the heat transfer model. The model can be used to predict the shell thickness of slabs, the predicted results are also validated by the actual measured data.

Pancreatic cancer (PC) is one of the most malignant tumors. Despite considerable progress in the treatment of PC, the prognosis of patients with PC is poor. The aim of this study was to identify potential biomarkers for the diagnosis and prognosis of PC. First, the original data of three independent mRNA expression datasets were downloaded from the Gene Expression Omnibus and The Cancer Genome Atlas databases and screened for differentially expressed genes (DEGs) using the R software. Subsequently, Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes pathway enrichment analyses of the DEGs were performed, and a protein-protein interaction (PPI) network was constructed to screen for hub genes. The hub genes were analyzed for genetic variations, as well as for survival, prognostic, and diagnostic value, using the cBioPortal and Gene Expression Profiling Interactive Analysis (GEPIA) databases and the pROC package. After screening for potential biomarkers, the mRNA and protein levels of the biomarkers were verified at the tissue and cellular levels using the Cancer Cell Line Encyclopedia, GEPIA, and the Human Protein Atlas. As a result, a total of 248 DEGs were identified. The GO terms enriched in DEGs were related to the separation of mitotic sister chromatids and the binding of the spindle to the extracellular matrix. The enriched pathways were associated with focal adhesion, ECM-receptor interaction, and phosphatidylinositol 3-kinase (PI3K)/AKT signaling. The top 20 genes were selected from the PPI network as hub genes, and based on the analysis of multiple databases, MCM2 and NUSAP1 were identified as potential biomarkers for the diagnosis and prognosis of PC. In conclusion, our results show that MCM2 and NUSAP1 can be used as potential biomarkers for the diagnosis and prognosis of PC. The study also provides new insights into the underlying molecular mechanisms of PC.

Background: Neuropathic pain is a serious clinical problem to be solved. Protein kinase A (PKA) is widely distributed in the central nervous system and participates in various signal transduction pathways to regulate cell proliferation and apoptosis. However, it is unclear whether PKA is involved in neuropathic pain. This study aimed to investigate PKA expression in neuropathic pain and its possible mechanisms of involvement. Methods: The mRNA expression dataset of neuropathic pain (GSE24982) was downloaded from the Gene Expression Omnibus database, and differentially expressed genes (DEGs) were screened using the R software. DEGs were subjected to Gene Ontology term and Kyoto Encyclopedia of Genes and Genomes pathway enrichment analyses. A protein-protein interaction network was constructed using the STRING database, and the Cytoscape plug-in cytoHubba was used to screen for hub genes. A spared nerve injury (SNI) rat model was established and the paw withdrawal threshold was determined using von Frey filaments. Western blotting and immunofluorescence were used to detect the expression and cellular localization of key proteins in the spinal cord, respectively. Western blotting, ELISA, and TUNEL assays were used to detect cell signal transduction, inflammation, and apoptosis, respectively. Results: Among 449 DEGs and 20 hub genes, PKA was identified as a key gene involved in neuropathic pain. After SNI, mechanical allodynia occurred, PKA expression in the spinal cord increased, the p38MAPK pathway was activated, and spinal cord inflammation and apoptosis occurred in rats. Immunofluorescence staining showed that PKA colocalized with neurons, astrocytes, and microglia, and TUNEL with GFAP, Iba-1, Neun double labeling showed that apoptotic cells were mainly neurons. Intrathecal injection of a PKA inhibitor not only relieved mechanical hyperalgesia, inflammatory reaction, and apoptosis in SNI rats, but also inhibited p38MAPK pathway activation. Intrathecal injection of a p38MAPK inhibitor attenuated mechanical hyperalgesia, inflammatory reaction, and apoptosis, but did not affect PKA expression. Conclusions: PKA is involved in neuropathic pain by activating the p38MAPK pathway to mediate spinal cord cell apoptosis. This study provides novel insights that my aid in the elucidation of the pathogenesis of neuropathic pain.

The purpose of this study was to identify potential molecular markers of lung squamous cell carcinoma (LUSC). Three datasets containing LUSC mRNA sequencing data were downloaded from the Gene Expression Omnibus, The Cancer Genome Atlas and the Gene Expression Profiling Interactive Analysis databases. These datasets were used to identify significantly differentially expressed genes (DEGs) in LUSC. A protein-protein interaction network of the DEGs was constructed followed by Gene Ontology, Kyoto Encyclopedia of Genes and Genomes and overall survival analyses of the DEGs. A total of 37 DEGs between LUSC and normal tissues were identified, including 26 downregulated genes and 11 upregulated genes. Biological Process enrichment analysis revealed that the DEGs were mainly enriched in 'cell adhesion', 'cell-matrix adhesion', 'anatomical structure morphogenesis', 'ECM-receptor interaction' and 'focal adhesion'. Overall survival analysis demonstrated that transcription factor 21, α-2-macroglobulin, acyl-CoA synthetase long chain family member 5, integrin subunit β8, meiotic nuclear divisions 1 and secretoglobin family 1A member 1 were significantly associated with the occurrence and development of lung cancer, and these genes were selected as hub genes. The results obtained in the present study may aid the elucidation of the molecular mechanisms involved in the development of LUSC and may provide potential targets for LUSC treatment.

Ewing's sarcoma (ES) is a common malignant bone tumor in children and adolescents. Although great efforts have been made to understand the pathogenesis and development of ES, the underlying molecular mechanism remains unclear. The present study aimed to identify new key genes as potential biomarkers for the diagnosis, targeted therapy or prognosis of ES. mRNA expression profile chip data sets GSE17674, GSE17679 and GSE45544 were downloaded from the Gene Expression Omnibus database. Differentially expressed genes (DEGs) were screened using the R software limma package, and functional and pathway enrichment analyses were performed using the enrichplot package and GSEA software. The NetworkAnalyst online tool, as well as Cytoscape and its plug-ins cytoHubba and NetworkAnalyzer, were used to construct a protein-protein interaction network (PPI) and conduct module analysis to screen key (hub) genes. LABSO COX regression and overall survival (OS) analysis of the Hub genes were performed. A total of 211 DEGs were obtained by integrating and analyzing the three data sets. The functions and pathways of the DEGs were mainly associated with the regulation of small-molecule metabolic processes, cofactor-binding, amino acid, proteasome and ribosome biosynthesis in eukaryotes, as well as the Rac1, cell cycle and P53 signaling pathways. A total of one important module and 20 hub genes were screened from the PPI network using the Maximum Correlation Criteria algorithm of cytoHubba. LASSO COX regression results revealed that titin (TTN), fast skeletal muscle troponin T, skeletal muscle actin α-actin, nebulin, troponin C type 2 (fast), myosin light-chain 3 (MYL3), slow skeletal muscle troponin T (TNNT1), myosin-binding protein C1 slow-type, tropomyosin 3 and myosin heavy-chain 7 were associated with prognosis in patients with ES. The Kaplan-Meier curves demonstrated that high mRNA expression levels of TNNT1 (P

Our work is devoted to an inverse problem for three-dimensional parabolic partial differential equations. When the surface temperature data are given, the problem of reconstructing the heat flux and the source term is investigated. There are two main contributions of this paper. First, an adjoint problem approach is used for analysis of the Frechet gradient of the cost functional. Second, an improved conjugate gradient method is proposed to solve this problem. Based on Lipschitz continuity of the gradient, the convergence analysis of the conjugate gradient algorithm is studied. Copyright © 2016 John Wiley & Sons, Ltd.

Glutamate serves as a major excitatory neurotransmitter in the mammalian central nervous system and is stored in synaptic cleft by an uptake system that is dependent on the high-affinity glutamate transporters (ETTAs), which locate in the plasma membrane of glial cells and neurons. ETTAs can rapidly terminate the action of glutamate and maintain its normal physiological functions. If the content or function of glutamate transporters is abnormal, it can result in many physiological dysfunctions. Studies have demonstrated that high-affinity glutamate transporters play an important role in the development of chronic pain, which might be a new therapeutic target for the pain.谷氨酸是哺乳动物中枢神经系统中最主要的兴奋性神经递质，位于突触间隙中的谷氨酸有赖于位于胶质细胞和神经元细胞膜上的高亲和力谷氨酸转运体给予及时清除，维持其正常的生理功能。谷氨酸转运体的量或功能异常，会引起多种生理功能紊乱。研究表明高亲和力谷氨酸转运体在慢性疼痛产生过程中发挥重要作用，可能成为疼痛治疗的新靶点。.

Traditional preferred path mining algorithms cannot accurately identify the level of user interest in a web page, and they are rather complex. In order to solve these problems, the paper proposed an improved calculation method which evaluated the level of user interest in a web page and a mining algorithm of preferred path in the cloud computing environment. Firstly, the paper used Preference Degree to evaluate the user interest in a web page, and then designed a model based on RBF neural network to predict preference degree. This model adequately considered the some key factors such as visit quantity, access time, page bytes, and the nonlinearity relationships among these factors. Secondly, based on preference degree predicting model, the paper presented a new mining algorithm. The algorithm used locations of non-preferred URLs to get the preferred path. The experimental results show that the preference degree prediction model can identify user interest more accurately and comprehensively. In addition, the newly proposed mining algorithm of preferred paths has a lower time complexity and saves more time.