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Abstract The objective of this work was to investigate the sorption behavior of gases, namely CO2, CH4, and N2, by molecules of coal sampled from Linglu mine under different water inclusion rates. To this end, the adsorption, diffusion, adsorption heat, and potential energy distribution characteristics of the gases in the coal pores at different water inclusion rates were analyzed using molecular dynamics and grand canonical ensemble Monte Carlo methods. The results showed that the adsorption relationship of the coal molecules on CO2, CH4, and N2 exhibited a downtrend followed by an uptrend when the water content was increased from 0 to 3.6%. The adsorption amount of CO2 was approximately twice as much as those of CH4 and N2, indicating that the competitive adsorption advantage of CO2 compared with those of CH4 and N2 was unaffected by the water content. The trend in the average heat of adsorption was generally consistent with the trend in the density of coal molecules under different moisture contents. Under the same conditions, the diffusion coefficient within a coal molecule was negatively related to the water content in the system. The layer spacing of the water molecules (2.875 Å) was greater than the liquid–water layer spacing, indicating the formation of a water molecule layer at this point, which inhibited gas adsorption. This study lays a theoretical foundation for further investigating the microscopic mechanism of coal–water interaction.

Abstract Chimeric Antigen Receptor (CAR) T‐cell therapy is a promising cancer treatment method. However, its application in bladder cancer (BC) remains limited, partially because of the absence of appropriate target molecules. Sialylated cancer‐derived IgG (SIA‐CIgG) is highly expressed in BC and is closely associated with malignant biological behavior. However, its potential as a target for CAR‐T cell therapy to treat BC is yet to be established. Here, it is found that SIA‐CIgG is highly expressed in most BC samples but displayed limited expression in normal tissues. CAR‐T cells specifically targeting SIA‐CIgG can effectively lyse BC cells and the cytotoxicity depends on SIA‐CIgG expression. Furthermore, SIA‐CIgG CAR‐T cells demonstrate milder tumor cell lysis and enhanced persistence compared with human epidermal growth factor receptor 2 (HER2) CAR‐T cells, which have undergone extensive clinical trials. After repeated tumor antigen challenges, SIA‐CIgG CAR‐T cells display substantial alterations in both the transcriptome and chromatin accessibility. When combining SIA‐CIgG CAR‐T cell therapy with FDA‐approved drugs to treat BC, the histone deacetylase inhibitor (HDACi), vorinostat, is found to enhance the ablility of CAR‐T cells for tumor cell lysis. Therefore, the combination of SIA‐CIgG CAR‐T cells and vorinostat is promising for BC treatment.

Abstract This study aimed to investigate the adsorption properties of CO2, CH4, and N2 on anthracite. A molecular structural model of anthracite (C208H162O12N4) was established. Simulations were performed for the adsorption properties of single-component and multi-component gases at various temperatures, pressures, and gas ratios. The grand canonical ensemble Monte Carlo approach based on molecular mechanics and dynamics theories was used to perform the simulations. The results showed that the isotherms for the adsorption of single-component CO2, CH4, and N2 followed the Langmuir formula, and the CO2 adsorption isotherm growth gradient was negatively correlated with pressure but positively correlated with temperature. When the CO2 injection in the gas mixture was increased from 1 to 3% for the multi-component gas adsorption, the proportion of CO2 adsorption rose from 1/3 to 2/3, indicating that CO2 has a competing-adsorption advantage. The CO2 adsorption decreased faster with increasing temperature, indicating that the sensitivity of CO2 to temperature is stronger than that of CH4 and N2. The adsorbent potential energies of CO2, CH4, and N2 diminished with rising temperature in the following order: CO2

Abstract. Bladder cancer (BC) is the most common malignant tumor of the genitourinary system. The age of individuals diagnosed with BC tends to decrease in recent years. A variety of standard therapeutic options are available for the clinical management of BC, but limitations exist. It is difficult to surgically eliminate small lesions, while radiation and chemotherapy damage normal tissues, leading to severe side effects. Therefore, new approaches are required to improve the efficacy and specificity of BC treatment. Synthetic biology is a field emerging in the last decade that refers to biological elements, devices, and materials that are artificially synthesized according to users’ needs. In this review, we discuss how to utilize genetic elements to regulate BC-related gene expression periodically and quantitatively to inhibit the initiation and progression of BC. In addition, the design and construction of gene circuits to distinguish cancer cells from normal cells to kill the former but spare the latter are elaborated. Then, we introduce the development of genetically modified T cells for targeted attacks on BC. Finally, synthetic nanomaterials specializing in detecting and killing BC cells are detailed. This review aims to describe the innovative details of the clinical diagnosis and treatment of BC from the perspective of synthetic biology.

PurposeWe aimed to verify the prognostic value of the glycocalyx as a marker of blood–brain barrier damage in patients with acute ischemic stroke undergoing endovascular therapy.MethodsWe recruited patients with large vessel occlusion who were undergoing recanalization and tested their glycocalyx at multiple time points. On the basis of the 90-day follow-up data, the patients were divided into a survivor group and a nonsurvivor group. In addition, neurological function was tracked, and patients were divided into a neurological deterioration group and a group without neurological deterioration. Associations between outcomes and dynamic changes in the glycocalyx were determined using a linear mixed model, and significant factors were used as covariates.ResultsNonsurvivors and patients with neurological deterioration had significantly higher syndecan-1 concentrations than survivors and patients without neurological deterioration, and syndecan-1 tended to decline after endovascular therapy (p < 0.05). The increased level of syndecan-1 at 36 h after endovascular treatment was positively correlated with the National Institute of Health Stroke Scale score for neurological deterioration (r = 0.702, p = 0.005). However, there was no significant difference in the level of hyaluronic acid or heparan sulfate in the plasma of patients with different clinical outcomes.ConclusionPre-reperfusion syndecan-1 levels in patients with large vessel occlusion stroke are associated with 90-day mortality and the re-degradation of syndecan-1 is positively associated with neurological deterioration.

The cellular composition of recurrent tumors can provide insight into resistance to therapy and inform on second line therapies. Here, using a genetically modified mouse, the authors perform barcoding experiments of the primary tumors to allow them to study the clonal dynamics of tumor recurrence.

Abstract Background Bladder cancer is the tenth most common cancer globally, but existing biomarkers and prognostic models are limited. Method In this study, we used four bladder cancer cohorts from The Cancer Genome Atlas and Gene Expression Omnibus databases to perform univariate Cox regression analysis to identify common prognostic genes. We used the least absolute shrinkage and selection operator regression to construct a prognostic Cox model. Kaplan–Meier analysis, receiver operating characteristic curve, and univariate/multivariate Cox analysis were used to evaluate the prognostic model. Finally, a co-expression network, CIBERSORT, and ESTIMATE algorithm were used to explore the mechanism related to the model. Results A total of 11 genes were identified from the four cohorts to construct the prognostic model, including eight risk genes (SERPINE2, PRR11, DSEL, DNM1, COMP, ELOVL4, RTKN, and MAPK12) and three protective genes (FABP6, C16orf74, and TNK1). The 11-genes model could stratify the risk of patients in all five cohorts, and the prognosis was worse in the group with a high-risk score. The area under the curve values of the five cohorts in the first year are all greater than 0.65. Furthermore, this model’s predictive ability is stronger than that of age, gender, grade, and T stage. Through the weighted co-expression network analysis, the gene module related to the model was found, and the key genes in this module were mainly enriched in the tumor microenvironment. B cell memory showed low infiltration in high-risk patients. Furthermore, in the case of low B cell memory infiltration and high-risk score, the prognosis of the patients was the worst. Conclusion The proposed 11-genes model is a promising biomarker for estimating overall survival in bladder cancer. This model can be used to stratify the risk of bladder cancer patients, which is beneficial to the realization of individualized treatment.

To identify key biomarkers in gemcitabine (GEM)-resistant bladder cancer (BCa) and investigate their associations with tumor-infiltrating immune cells in a tumor immune microenvironment, we performed the present study on the basis of large-scale sequencing data. Expression profiles from the Gene Expression Omnibus GSE77883 dataset and The Cancer Genome Atlas BLCA dataset were analyzed. Both BCa development and GEM-resistance were identified to be immune-related through evaluating tumor-infiltrating immune cells. Eighty-two DEGs were obtained to be related to GEM-resistance. Functional enrichment analysis demonstrated they were related to regulation of immune cells proliferation. Protein–protein interaction network selected six key genes (CAV1, COL6A2, FABP4, FBLN1, PCOLCE, and CSPG4). Immunohistochemistry confirmed the down-regulation of the six key genes in BCa. Survival analyses revealed the six key genes were significantly associated with BCa overall survival. Correlation analyses revealed the six key genes had high infiltration of most immune cells. Gene set enrichment analysis further detected the key genes might regulate GEM-resistance through immune response and drug metabolism of cytochrome P450. Next, microRNA-gene regulatory network identified three key microRNAs (hsa-miR-124-3p, hsa-miR-26b-5p, and hsa-miR-192-5p) involved in GEM-resistant BCa. Connectivity Map analysis identified histone deacetylase inhibitors might circumvent GEM-resistance. In conclusion, CAV1, COL6A2, FABP4, FBLN1, PCOLCE, and CSPG4 were identified to be critical biomarkers through regulating the immune cell infiltration in an immune microenvironment of GEM-resistance and could act as promising treatment targets for GEM-resistant muscle-invasive BCa.

BackgroundBladder cancer lacks useful and robust prognostic markers to stratify patients at risk. Our study is to identify a robust prognostic marker for bladder cancer.MethodsThe transcriptome and clinical data of bladder cancer were downloaded from multiple databases. We searched for genes with robust prognosis by Kaplan-Meier analysis of the whole genome. CIBERSORT and TIMER algorithm was used to calculate the degree of immune cell infiltration.ResultsWe identified OLFML2B as a robust prognostic marker for bladder cancer in five cohorts. Kaplan-Meier analysis showed that patients with a high level of OLFML2B expression had a poor prognosis. The expression of OLFML2B increased with the increase of stage and grade. We found that patients with high expression of OLFML2B still had a poor prognosis in two small bladder cancer cohorts. OLFML2B also has the prognostic ability in ten other tumors, and the prognosis is poor in high expression. The correlation analysis between OLFML2B and immune cells showed that it was positively correlated with the degree of macrophage infiltration and highly co-expressed with tumor-associated macrophage markers. Finally, the Wound-healing assay and Colony formation assay results showed that the migration and proliferation ability of bladder cancer cell lines decreased after the knockdown of OLFML2B.ConclusionsIn summary, OLFML2B is a robust risk prognostic marker, and it can help patients with bladder cancer improve individualized treatment.

Kidney renal papillary cell carcinoma (KIRP), the second most common subtype of renal cell carcinoma, still lacks effective treatment regimens for individualized immunotherapy because of the heterogeneity of its elusive immune microenvironment. Therefore, we aimed to comprehensively evaluate the immune microenvironment of KIRP by using the computational biology strategy to analyze the expression profile data of 289 KIRP patients obtained from The Cancer Genome Atlas database. Based on multidimensional, multi-omics bioinformatics analysis, we found that the tumor of patients with KIRP exhibited “hot” tumor characteristics but the CD8+ T cells in the tumor tissues did not limit tumor progression. Thus, patients with KIRP may realize higher clinical benefits by receiving treatment that can reverse CD8+ T-cell exhaustion. Among them, C1 and C3 immune subtypes could realize the best efficacy of reversing CD8+ T-cell exhaustion. Moreover, CCL5 and FASLG expression may be related to the formation of the immunosuppressive microenvironment in the tumors of patients with KIRP. In conclusion, the immune microenvironment landscape presented in this study provides a novel insight for further experimental and clinical exploration of tailored immunotherapy for patients with KIRP.

As the transmembrane receptor of Netrin-1, the tumor suppressor gene Unc-5 Netrin Receptor C (UNC5C) can trigger apoptosis. Although its tumor suppressor effects have been demonstrated in solid tumors such as colon cancer, there is still a lack of systematic research on its expression regulation mechanism. To address this need, we analyzed datasets from The Cancer Genome Atlas (TCGA) database, including multi-omics data for 32 types of cancers and 10,967 cases. Analysis of these data revealed a trend of significantly decreased UNC5C expression in 16 types of solid tumors. Additionally, low UNC5C expression is related to poor prognosis of five types of tumors and restoring the expression of UNC5C can effectively inhibit the proliferation potential of renal cancer cells. Promoter DNA methylation, chromatin remodeling-mediated epigenetic regulation, transcriptional inhibition, RNA-binding protein and miRNA-mediated post-transcriptional inhibition, genetic changes caused by deep deletion and truncated mutations, and ubiquitinating enzyme-mediated protein degradation can synergistically cause the down-regulation of UNC5C expression in solid tumors. This study is the first to analyze the comprehensive molecular mechanism of down-regulation of the tumor suppressor gene UNC5C from multiple dimensions using pan-cancer data. Our results suggest that analyses of gene expression regulation relying on computational biological methods may help guide the targeted therapy of tumor suppressor gene reactivation.

Abstract Background Parametric feature selection methods for machine learning and association studies based on genetic data are not robust with respect to outliers or influential observations. While rank-based, distribution-free statistics offer a robust alternative to parametric methods, their practical utility can be limited, as they demand significant computational resources when analyzing high-dimensional data. For genetic studies that seek to identify variants, the hypothesis is constrained, since it is typically assumed that the effect of the genotype on the phenotype is monotone (e.g., an additive genetic effect). Similarly, predictors for machine learning applications may have natural ordering constraints. Cross-validation for feature selection in these high-dimensional contexts necessitates highly efficient computational algorithms for the robust evaluation of many features. Results We have developed an R extension package, fastJT, for conducting genome-wide association studies and feature selection for machine learning using the Jonckheere-Terpstra statistic for constrained hypotheses. The kernel of the package features an efficient algorithm for calculating the statistics, replacing the pairwise comparison and counting processes with a data sorting and searching procedure, reducing computational complexity from O(n 2) to O(n log(n)). The computational efficiency is demonstrated through extensive benchmarking, and example applications to real data are presented. Conclusions fastJT is an open-source R extension package, applying the Jonckheere-Terpstra statistic for robust feature selection for machine learning and association studies. The package implements an efficient algorithm which leverages internal information among the samples to avoid unnecessary computations, and incorporates shared-memory parallel programming to further boost performance on multi-core machines.

Abstract Background Time- and dose-to-event phenotypes used in basic science and translational studies are commonly measured imprecisely or incompletely due to limitations of the experimental design or data collection schema. For example, drug-induced toxicities are not reported by the actual time or dose triggering the event, but rather are inferred from the cycle or dose to which the event is attributed. This exemplifies a prevalent type of imprecise measurement called grouped failure time, where times or doses are restricted to discrete increments. Failure to appropriately account for the grouped nature of the data, when present, may lead to biased analyses. Results We present groupedSurv, an R package which implements a statistically rigorous and computationally efficient approach for conducting genome-wide analyses based on grouped failure time phenotypes. Our approach accommodates adjustments for baseline covariates, and analysis at the variant or gene level. We illustrate the statistical properties of the approach and computational performance of the package by simulation. We present the results of a reanalysis of a published genome-wide study to identify common germline variants associated with the risk of taxane-induced peripheral neuropathy in breast cancer patients. Conclusions groupedSurv enables fast and rigorous genome-wide analysis on the basis of grouped failure time phenotypes at the variant, gene or pathway level. The package is freely available under a public license through the Comprehensive R Archive Network.

PurposeTherapeutic targets of tumor-associated macrophages have been discovered and used clinically as immunotherapy. M2 macrophages are tumor-associated macrophages that promote cancer progression. This article explores the related factors and the effects of type M2 macrophages.MethodWe obtained bladder cancer (BC) sequencing data from TCGA and GSE31189. We used the CIBERSORT algorithm calculate M2 macrophage proportions among 22 type immune cells. The Estimate package was used to measure BC purity. M2 macrophage-related genes were selected using WGCNA. Receiver operating characteristic curves and Kaplan–Meier analyses were performed to determine the risk score, conducted for M2 macrophage-related factors. The Pearson test was used to determine the correlation among M2 macrophage-related genes, clinical phenotype, immune phenotype and tumor mutation burden (TMB). The TIMER database was used to calculate correlations among M2 macrophages and other cancers.ResultsExpression of four M2 macrophages co-expressed genes (CD163, CD209, CSF1, MMD) positively correlated with infiltration of M2 macrophages, which were enriched in the negative regulation of immune system process and the positive regulation of tumor necrosis factor production. M2 macrophage-related factors are robust biomarkers for predicting the BC and immune phenotypes. The Cox regression model built on these four co-expression factors showed a close correlation with outcome (AUC = 0.64). The four co-expression factors negatively correlated outcome and TMB.ConclusionFour co-expressed genes promote high levels of infiltration of type M2 macrophages in the negative regulation of immune system processes and the positive regulation of tumor necrosis factor production processes. These co-expressed genes and the biological process they involve might suggest new strategies for regulation of chemotaxis in M2 macrophages.

BackgroundBladder cancer is a common malignant tumor characterized by high mortality and high management costs; however, it lacks useful molecular prognostic markers. Tribbles pseudokinase 3 (TRIB3) is a pseudokinase that participates in cell tumor progression and metabolism and whose function in bladder cancer is not precisely known.Main MethodsWe downloaded transcriptome data and clinical data of bladder cancer from associated databases and extracted the expression matrix of TRIB3 for multiple bioinformatics analysis. RT-PCR detected the expression of TRIB3 in bladder cancer cells. After knockdown of TRIB3 with siRNA, we investigated TRIB3 function using CCK8, Cell Cycle and Transwell assays.Key FindingsKaplan–Meier analysis of TRIB3 in the four cohorts showed that high expression of TRIB3 correlated with poor outcome. Expression of TRIB3 positively correlated with stage and grade and down-regulation of TRIB3 expression significantly inhibited proliferation, migration and cell cycle of bladder cancer cells.SignificanceTRIB3 is a potential prognostic marker and therapeutic target. It can be used to individualize the treatment of bladder cancer.

BackgroundHigh-grade serous ovarian cancer (HGSOC) is a common cause of death from gynecological cancer, with an overall survival rate that has not significantly improved in decades. Reliable bio-markers are needed to identify high-risk HGSOC to assist in the selection and development of treatment options.MethodThe study included ten HGSOC cohorts, which were merged into four separate cohorts including a total of 1,526 samples. We used the relative expression of immune genes to construct the gene-pair matrix, and the least absolute shrinkage and selection operator regression was performed to build the prognosis model using the training set. The prognosis of the model was verified in the training set (363 cases) and three validation sets (of 251, 354, and 558 cases). Finally, the differences in immune cell infiltration and gene enrichment pathways between high and low score groups were identified.ResultsA prognosis model of HGSOC overall survival rate was constructed in the training set, and included data for 35 immune gene-related gene pairs and the regression coefficients. The risk stratification of HGSOC patients was successfully performed using the training set, with a p-value of Kaplan-Meier of < 0.001. A score from this model is an independent prognostic factor of HGSOC, and prognosis was evaluated in different clinical subgroups. This model was also successful for the other three validation sets, and the results of Kaplan-Meier analysis were statistically significant (p < 0.05). The model can also predict patient progression-free survival with HGSOC to reflect tumor growth status. There was a lower infiltration level of M1 macrophages in the high-risk group compared to that in the low-risk group (p < 0.001). Finally, the immune-related pathways were enriched in the low-risk group.ConclusionThe prognostic model based on immune-related gene pairs developed is a potential prognostic marker for high-grade serous ovarian cancer treated with platinum. The model has robust prognostic ability and wide applicability. More prospective studies will be needed to assess the practical application of this model for precision therapy.

Purpose: In the tumor microenvironment, the functional differences among various tumor-associated macrophages (TAM) are not completely clear. Tumor-associated macrophages are thought to promote the progression of cancer. This article focuses on exploring M2 macrophage-related factors and behaviors of renal clear cell carcinoma.Method: We obtained renal clear cell carcinoma data from TCGA-KIRC-FPKM, GSE8050, GSE12606, GSE14762, and GSE3689. We used the “Cibersort” algorithm to calculate type M2 macrophage proportions among 22 types of immune cells. M2 macrophage-related co-expression module genes were selected using weighted gene co-expression network analysis (WGCNA). A renal clear cell carcinoma prognosis risk score was built based on M2 macrophage-related factors. The ROC curve and Kaplan–Meier analysis were performed to evacuate the risk score in various subgroups. The Pearson test was used to calculate correlations among M2 macrophage-related genes, clinical phenotype, immune phenotype, and tumor mutation burden (TMB). We measured differences in co-expression of genes at the protein level in clear renal cell carcinoma tissues.Results: There were six M2 macrophage co-expressed genes (F13A1, FUCA1, SDCBP, VSIG4, HLA-E, TAP2) related to infiltration of M2 macrophages; these were enriched in neutrophil activation and involved in immune responses, antigen processing, and presentation of exogenous peptide antigen via MHC class I. M2-related factor frequencies were robust biomarkers for predicting the renal clear cell carcinoma patient clinical phenotype and immune microenvironment. The Cox regression model, built based on M2 macrophage-related factors, showed a close prognostic correlation (AUC = 0.78). The M2 macrophage-related prognosis model also performed well in various subgroups. Using western blotting, we found that VSIG4 protein expression levels were higher in clear renal cell carcinoma tissues than in normal tissues.Conclusion: These co-expressed genes were most related to the M2 macrophage phenotype. They correlated with the immune microenvironment and predicted outcomes of renal clear cell carcinoma. These co-expressed genes and the biological processes associated with them might provide the basis for new strategies to intervene via chemotaxis of M2 macrophages.

PurposeTo identify immune-related co-expressed genes that promote CD8+ T cell infiltration in bladder cancer, and to explore the interactions among relevant genes in the tumor microenvironment.MethodWe obtained bladder cancer gene matrix and clinical information data from TCGA, GSE32894 and GSE48075. The “estimate” package was used to calculate tumor purity and immune score. The CIBERSORT algorithm was used to assess CD8+ T cell proportions. Weighted gene co-expression network analysis was used to identify the co-expression modules with CD8+ T cell proportions and bladder tumor purity. Subsequently, we performed correlation analysis among angiogenesis factors, angiogenesis inhibitors, immune inflammatory responses, and CD8+ T cell related genes in tumor microenvironment.ResultsA CD8+ T cell related co-expression network was identified. Eight co-expressed genes (PSMB8, PSMB9, PSMB10, PSME2, TAP1, IRF1, FBOX6, ETV7) were identified as CD8+ T cell-related genes that promoted infiltration of CD8+ T cells, and were enriched in the MHC class I tumor antigen presentation process. The proteins level encoded by these genes (PSMB10, PSMB9, PSMB8, TAP1, IRF1, and FBXO6) were lower in the high clinical grade patients, which suggested the clinical phenotype correlation both in mRNA and protein levels. These factors negatively correlated with angiogenesis factors and positively correlated with angiogenesis inhibitors. PD-1 and PD-L1 positively correlated with these genes which suggested PD-1 expression level positively correlated with the biological process composed by these co-expression genes. In the high expression group of these genes, inflammation and immune response were more intense, and the tumor purity was lower, suggesting that these genes were immune protective factors that improved the prognosis in patients with bladder cancer.ConclusionThese co-expressed genes promote high levels of infiltration of CD8+ T cells in an immunoproteasome process involved in MHC class I molecules. The mechanism might provide new pathways for treatment of patients who are insensitive to PD-1 immunotherapy due to low degrees of CD8+ T cell infiltration.

Background Kidney renal clear cell carcinoma (KIRC) affects the genitourinary system. Although treatment of KIRC in early stages can be highly successful, this type of cancer is difficult to detect until later stages of disease that are less easily treatable. Previous studies have focused on tumor cells, but have ignored the contributions of the tumor microenvironment. Methods We analyzed KIRC gene expression data from The Cancer Genome Atlas with the ESTIMATE algorithm to identify differentially expressed genes. Through protein–protein interaction network analysis, we identified clusters and picked genes from the clusters for further analysis. Differential expression, Kaplan–Meier, and univariate Cox analyses were used to select prognostic biomarkers. Gene Set Enrichment Analysis (GSEA) and Tumor Immune Estimation Resource (TIMER) analysis were used to explore the immune characteristics of these genes as biomarkers. Results Through the ESTIMATE algorithm and other system biology tools, TNFSF13B was identified as a prognostic biomarker. TNFSF13B is highly expressed in tumors, and high expression of TNFSF13B leads to poor prognosis. Further GSEA and TIMER analysis revealed that the expression of TNFSF13B was related to the immune signaling pathway and lymphocyte infiltration. Our findings strongly suggest that TNFSF13B may be a potential biomarker or target related to the tumor microenvironment for KIRC.

Abstract The morbidity and mortality of prostate carcinoma has increased in recent years and has become the second most common ale malignant carcinoma worldwide. The interaction mechanisms between different genes and signaling pathways, however, are still unclear. Methods Variation analysis of GSE38241, GSE69223, GSE46602 and GSE104749 were realized by GEO2R in Gene Expression Omnibus database. Function enrichment was analyzed by DAVID.6.8. Furthermore, the PPI network and the significant module were analyzed by Cytoscape, STRING and MCODE.GO. Pathway analysis showed that the 20 candidate genes were closely related to mitosis, cell division, cell cycle phases and the p53 signaling pathway. A total of six independent prognostic factors were identified in GSE21032 and TCGA PRAD. Oncomine database and The Human Protein Atlas were applied to explicit that six core genes were over expression in prostate cancer compared to normal prostate tissue in the process of transcriptional and translational. Finally, gene set enrichment were performed to identified the related pathway of core genes involved in prostate cancer. Result Hierarchical clustering analysis revealed that these 20 core genes were mostly related to carcinogenesis and development. CKS2, TK1, MKI67, TOP2A, CCNB1 and RRM2 directly related to the recurrence and prognosis of prostate cancer. This result was verified by TCGA database and GSE21032. Conclusion These core genes play a crucial role in tumor carcinogenesis, development, recurrence, metastasis and progression. Identifying these genes could help us to understand the molecular mechanisms and provide potential biomarkers for the diagnosis and treatment of prostate cancer.

Aim. In this paper, we aimed to develop and validate a risk prediction method using independent prognosis genes selected robustly in prostate cancer. Method. We considered 723 samples obtained from TCGA (the Cancer Genome Atlas), GSE46602, and GSE21032. Prostate cancer prognosis-related genes with P

The mechanical behaviors of Martian regolith-structure interfaces are of great significance for the design of rover, development of excavation tools, and construction of infrastructure in Mars exploration. This paper presents an experimental investigation on the properties of a Martian regolith simulant (JEZ-1) through one-dimensional oedometer test, direct shear test, and interface direct shear tests between JEZ-1 and steel plates with different roughness. Oedometer result reveals that the compression and swelling indexes of the JEZ-1 are quite low, thus it is a less compressible and lower swelling soil. The cohesion and adhesion of JEZ-1 are lower than 5 kPa. The values of the internal friction angle range from 39.7° to 40.6°, and the interface friction angles are 16.7° to 36.2° for the smooth and rough interface. Furthermore, the direct shear and interface direct shear results indicate that the interface friction angles are lower than the internal friction angles of JEZ-1 and increase close to the internal friction angles with increasing interface roughness.

Hippocampal neuron death is a key factor in vascular dementia (VD) induced by chronic cerebral hypoperfusion (CCH). Dl-3-n-butylphthalide (NBP) is a multiple-effects drug. Therefore, the potential molecular mechanisms underlying CCH and its feasible treatment should be investigated. This study had two main purposes: first, to identify a potential biomarker in a rat model of CCH induced VD using antibody microarrays; and second, to explore the neuroprotective role of NBP at targeting the potential biomarker. Glial cell line-derived neurotrophic factor (GDNF)/GDNF family receptor alpha-1 (GFRα1)/receptor tyrosine kinase (Ret) signaling is altered in the hippocampus of CCH rats; however, NBP treatment improved cognitive function, protected against hippocampal neuron apoptosis via regulation of GDNF/GFRα1/Ret, and activated the phosphorylation AKT (p-AKT) and ERK1/2 (p-ERK1/2) signaling. We also found that 1 h oxygen-glucose deprivation (OGD) followed by 48 h reperfusion (R) in cultured hippocampal neurons led to downregulation of GDNF/GFRα1/Ret. NBP upregulated the signaling and increased neuronal survival. Ret inhibitor (NVP-AST487) inhibits Ret and downstream effectors, including p-AKT and p-ERK1/2. Additionally, both GDNF and GFRα1 expression are markedly inhibited in hippocampal neurons by coincubation with NVP-AST487, particularly under conditions of OGD/R. GDNF/GFRα1/Ret signaling and neuronal viability can be maintained by NBP, which activates p-AKT and p-ERK1/2, increases expression of Bcl-2, and decreases expression of Bax and cleaved caspase-3. The current study showed that GDNF/GFRα1/Ret signaling plays an essential role in the CCH induced VD. NBP was protective against hippocampal neuron apoptosis, and this was associated with regulation of GDNF/GFRα1/Ret and AKT/ERK1/2 signaling pathways, thus reducing cognitive impairment.

Evidence has long suggested that epidermal growth factor receptor (EGFR) may play a prominent role in triple-negative breast cancer (TNBC) pathogenesis, but clinical trials of EGFR inhibitors have yielded disappointing results. Using a candidate drug screen, we identified that inhibition of cyclin-dependent kinases 12 and 13 (CDK12/13) dramatically sensitizes diverse models of TNBC to EGFR blockade. This combination therapy drives cell death through the 4E-BP1-dependent suppression of the translation and translation-linked turnover of driver oncoproteins, including MYC. A genome-wide CRISPR/Cas9 screen identified the CCR4-NOT complex as a major determinant of sensitivity to the combination therapy whose loss renders 4E-BP1 unresponsive to drug-induced dephosphorylation, thereby rescuing MYC translational suppression and promoting MYC stability. The central roles of CCR4-NOT and 4E-BP1 in response to the combination therapy were further underscored by the observation of CNOT1 loss and rescue of 4E-BP1 phosphorylation in TNBC cells that naturally evolved therapy resistance. Thus, pharmacological inhibition of CDK12/13 reveals a long-proposed EGFR dependence in TNBC that functions through the cooperative regulation of translation-coupled oncoprotein stability. [ABSTRACT FROM AUTHOR]