A probabilistic computational framework for the prediction of corrosion-induced cracking in large structures.

Corrosion can initiate cracking that leads to structural integrity reduction. Quantitative corrosion assessment is challenging, and the modeling of corrosion-induced crack initiation is essential for model-based corrosion reliability analysis of various structures. This paper proposes a probabilistic computational analysis framework for corrosion-to-crack transitions by integrating a phase-field model with machine learning and uncertainty quantification. An electro-chemo-mechanical phase-field model is modified to predict pitting corrosion evolution, in which stress is properly coupled into the electrode chemical potential. A crack initiation criterion based on morphology is proposed to quantify the pit-to-cracking transition. A spatiotemporal surrogate modeling method is developed to facilitate this, consisting of a Convolution Neural Network (CNN) to map corrosion morphology to latent spaces, and a Gaussian Process regression model with a nonlinear autoregressive exogenous model (NARX) architecture for prediction of corrosion dynamics in the latent space over time. It enables the real-time prediction of corrosion morphology and crack initiation behaviors (whether, when, and where the corrosion damage triggers the crack initiation), and thus makes it possible for probabilistic analysis, with uncertainty quantified. Examples at various stress and corrosion conditions are presented to demonstrate the proposed computational framework.

Correction: Qian et al. Capsaicin Suppresses Cell Proliferation, Induces Cell Cycle Arrest and ROS Production in Bladder Cancer Cells through FOXO3a-Mediated Pathways. Molecules 2016, 21, 1406.

During the course of a review of our publications, an inadvertent error in the article [...].

A novel AIRE mutation leads to autoimmune polyendocrine syndrome type-1.

Autoimmune polyendocrine syndrome type-1 (APS-1) is a rare inherited monogenic autoimmune disease characterized by the presence of at least two of three following major clinical features: chronic mucocutaneous candidiasis, hypoparathyroidism, and adrenal insufficiency. Mutations in autoimmune regulator (AIRE) gene have been found to contribute to APS-1. In the present study, we reported a 36-years-old male APS-1 patient who presented with hypoparathyroidism and Addison's disease. The proband underwent complete clinical examinations and mutation screening was performed by Sanger sequencing on AIRE gene. A novel homozygous mutation in exon 9 of the AIRE gene (c.1024C>T) was identified. Based on sequencing findings, HEK293T cell-based assays were conducted to analyze the subcellular localization and mutant transcript processing. Our results revealed that p.Q342X mutant localized in nuclear speckles and exerted a dominant-negative effect on wildtype AIRE function. We reported the c.1024C>T mutation of AIRE gene for the first time, which enriched the AIRE mutation database and contributed to further understanding of APS-1.

Geranylgeranyl Transferase-I Knockout Inhibits Oxidative Injury of Vascular Smooth Muscle Cells and Attenuates Diabetes-Accelerated Atherosclerosis.

The proliferation of vascular smooth muscle cells (VSMCs) induced by oxidative injury is one of the main features in diabetes-accelerated atherosclerosis. Geranylgeranyl transferase-I (GGTase-I) is an essential enzyme mediating posttranslational modification, especially the geranylgeranylation of small GTPase, Rac1. Our previous studies found that GGTase-I played an important role in diabetes-accelerated atherosclerosis. However, its exact role is largely unclear. In this study, mouse conditional knockout of VSMC GGTase-I (Pggt1b Δ/Δ mice) was generated using the CRISPR/Cas9 system. The mouse model of diabetes-accelerated atherosclerosis was induced by streptozotocin injections and an atherogenic diet. We found that GGTase-I knockout attenuated diabetes-accelerated atherosclerosis in vivo and suppressed high-glucose-induced VSMC proliferation in vitro. Moreover, after a 16-week duration of diabetes, Pggt1b Δ/Δ mice exhibited lower α-smooth muscle actin (α-SMA) and nitrotyrosine level, Rac1 activity, p47phox and NOXO1 expression, and phospho-ERK1/2 and phosphor-JNK content than wild-type mice. Meanwhile, the same changes were found in Pggt1b Δ/Δ VSMCs cultured with high glucose (22.2 mM) in vitro. In conclusion, GGTase-I knockout efficiently blocked diabetes-accelerated atherosclerosis, and this protective effect must be related to the inhibition of VSMC proliferation. The potential mechanisms probably involved interfering Rac1 geranylgeranylation, inhibiting the assembly of NADPH oxidase cytosolic regulatory subunits, reducing oxidative injury, and decreasing ERK1/2 and JNK phosphorylation.

The Quality of Clinical Practice Guidelines and Consensuses on the Management of Primary Aldosteronism: A Critical Appraisal.

Background: Several guidelines and expert consensuses have been developed for management of primary aldosteronism (PA). It is important to understand the detailed recommendations and quality of these guidelines to help physicians make informed and reliable decision. Methods: PubMed, EMBASE, and three websites were searched for practice guidelines or consensuses of PA from inception to January 24, 2019. We summarized the major recommendations on the management of PA from these guidelines and consensuses. The Appraisal of Guidelines for Research and Evaluation II was used to assess quality of the included guidelines and consensuses. Results: We identified three clinical practice guidelines and three consensus statements. Most of the recommendations on the diagnosis and treatment of PA from these guidelines and consensuses were consistent. Some minor conflicts were recorded for patient's screen and confirmation test. All included guideline documents have a good quality (score, >70%) on the scope and purpose (mean score, 81.02%) and clarity of presentation of the recommendations (mean score, 86.88%). However, the reporting for the stakeholder involvement (mean score, 54.32%) and applicability (mean score, 47.92%) were insufficient. There was an insufficient rigorousness in most of the guideline documents (mean score, 45.56%) on the development process. The Endocrine Society practice guideline 2016 ranked highest in quality (score, 81.13%). Conclusions: Existing guideline documents provided valuable recommendations on the management of PA, but further efforts are needed to improve the methodological quality. The Endocrine Society practice guideline 2016 was recommended for use.

PPWD1 is associated with the occurrence of postmenopausal osteoporosis as determined by weighted gene co­expression network analysis.

Postmenopausal osteoporosis (PMO) is the most common type of primary osteoporosis (OP), a systemic skeletal disease. Although many factors have been revealed to contribute to the occurrence of PMO, specific biomarkers for the early diagnosis and therapy of PMO are not available. In the present study, a weighted gene co­expression network analysis (WGCNA) was performed to screen gene modules associated with menopausal status. The turquoise module was verified as the clinically significant module, and 12 genes (NUP133, PSMD12, PPWD1, RBM8A, CRNKL1, PPP2R5C, RBM22, PIK3CB, SKIV2L2, PAPOLA, SRSF1 and COPS2) were identified as 'real' hub genes in both the protein­protein interaction (PPI) network and co­expression network. Furthermore, gene expression analysis by microarray in blood monocytes from pre­ and post­menopausal women revealed an increase in the expression of these hub genes in postmenopausal women. However, only the expression of peptidylprolyl isomerase domain and WD repeat containing 1 (PPWD1) was correlated with bone mineral density (BMD) in postmenopausal women. In the validation set, a similar expression pattern of PPWD1 was revealed. Functional enrichment analysis revealed that the fatty acid metabolism pathway was significantly abundant in the samples that exhibited a higher expression of PPWD1. Collectively, PPWD1 is indicated as a potential diagnostic biomarker for the occurrence of PMO.

PPARα gene is a diagnostic and prognostic biomarker in clear cell renal cell carcinoma by integrated bioinformatics analysis.

Genetic alterations in lipid metabolism genes are correlated with progression and poor prognosis of Clear cell renal cell carcinoma (ccRCC). PPARα play a critical role in lipid metabolism. This study aimed to identify that PPARα is a diagnosis and prognostic biomarker in ccRCC by integrated bioinformatics analysis. UALCAN database was used to explore the differential expression status and prognostic value of PPARα gene in various tumor types, qRT-PCR and immunohistochemical staining experiments were utilized for validation. Next, ccRCC data were obtained from TCGA. Correlation between PPARα expression levels and patients' clinicopathological characteristics was assessed, and the clinically diagnosis and prognostic value of PPARα were explored in ccRCC. According to the gene set enrichment analysis (GSEA) analysis, PPARα gene associated biological pathways were identified. PPARα has prognostic significance only in ccRCC tumors. Expression of PPARα was associated with ccRCC stages. PPARα was significantly down-regulated in ccRCC and associated with survival. Gender, tumor dimension, grade and stage showed a significant relevance with PPARα expression. Lower PPARα expression revealed significantly poorer survival and progression compared with higher PPARα expression. Adjusted by other clinical risk factors, PPARα remained an independent prognostic factor. Moreover, Low PPARα expression was a potential diagnostic biomarker of ccRCC. A nomogram was constructed based on PPARα expression and other clinicopathological risk factors, and it performed well in predict patients survival. GSEA analysis showed that PPARα gene associated biological pathways were enriched in mTOR pathway, AKT pathway, IGF1-mTOR pathway and Wnt signaling pathways. In conclusion, PPARα expression was decreased in ccRCC tumors. Lower expression of PPARα is closely correlated with poorer survival. It can be used as a clinically diagnosis and prognostic biomarker in ccRCC.

Prognostic value of a gene signature in clear cell renal cell carcinoma.

Renal cancer is a common urogenital system malignance. Novel biomarkers could provide more and more critical information on tumor features and patients' prognosis. Here, we performed an integrated analysis on the discovery set and established a three-gene signature to predict the prognosis for clear cell renal cell carcinoma (ccRCC). By constructing a LASSO Cox regression model, a 3-messenger RNA (3-mRNA) signature was identified. Based on the 3-mRNA signature, we divided patients into high- and low-risk groups, and validated this by using three other data sets. In the discovery set, this signature could successfully distinguish between the high- and low-risk patients (hazard ratio (HR), 2.152; 95% confidence interval (CI),1.509-3.069; p < 0.0001). Analysis of internal and two external validation sets yielded consistent results (internal: HR, 2.824; 95% CI, 1.601-4.98; p < 0.001; GSE29609: HR, 3.002; 95% CI, 1.113-8.094; p = 0.031; E-MTAB-3267: HR, 2.357; 95% CI, 1.243-4.468; p = 0.006). Time-dependent receiver operating characteristic (ROC) analysis indicated that the area under the ROC curve at 5 years was 0.66 both in the discovery and internal validation set, while the two external validation sets also suggested good performance of the 3-mRNA signature. Besides that, a nomogram was built and the calibration plots and decision curve analysis indicated the good performance and clinical utility of the nomogram. In conclusion, this 3-mRNA classifier proved to be a useful tool for prognostic evaluation and could facilitate personalized management of ccRCC patients.

Co-expression Network Analysis of Biomarkers for Adrenocortical Carcinoma.

Adrenocortical carcinoma (ACC) is a rare malignancy with a poor prognosis. And currently, there are no specific diagnostic biomarkers for ACC. In our study, we aimed to screen biomarkers for disease diagnosis, progression and prognosis. We firstly used the microarray data from public database Gene Expression Omnibus database to construct a weighted gene co-expression network, and then to identify gene modules associated with clinical features of ACC. Though this algorithm, a significant module with R2 = 0.64 (P = 9 × 10-5) was identified. Co-expression network and protein-protein interaction network were performed for screen the candidate hub genes. Checked by The Cancer Genome Atlas (TCGA) database, another independent dataset GSE19750, and GEPIA database, using one-way ANOVA, Pearson's correlation, survival analysis, diagnostic capacity (ROC curve) and expression level revalidation, a total 12 real hub genes were identified. Gene ontology and KEGG pathway analysis of genes in the significant module revealed that the hub genes are significantly enriched in cell cycle regulation. Moreover, gene set enrichment analysis suggests that the samples with highly expressed hub genes are correlated with cell cycle. Taken together, our integrated analysis has identified 12 hub genes that are associated with the progression and prognosis of ACC; these hub genes might lead to poor outcomes by regulating the cell cycle.

Identification of Biomarkers Associated With Pathological Stage and Prognosis of Clear Cell Renal Cell Carcinoma by Co-expression Network Analysis.

Clear cell renal cell carcinoma (ccRCC) is the most common subtype among renal cancer whose prognostic is affected by the tumor progression associated with complex gene interactions. However, there is currently no available molecular markers associated with ccRCC progression and used or clinical application. In our study, microarray data of 101 ccRCC samples and 95 normal kidney samples were analyzed and 2,425 differentially expressed genes (DEGs) were screened. Weighted gene co-expression network analysis (WGCNA) was then conducted and 11 co-expressed gene modules were identified. Module preservation analysis revealed that two modules (red and black) were found to be most stable. In addition, Pearson's correlation analysis identified the module most relevant to pathological stage(patho-module) (r = 0.44, p = 3e-07). Functional enrichment analysis showed that biological processes of the patho-module focused on cell cycle and cell division related biological process and pathway. In addition, 29 network hub genes highly related to ccRCC progression were identified from the stage module. These 29 hub genes were subsequently validated using 2 other independent datasets including GSE53757 (n = 72) and TCGA (n = 530), and the results indicated that all hub genes were significantly positive correlated with the 4 stages of ccRCC progression. Kaplan-Meier survival curve showed that patients with higher expression of each hub gene had significantly lower overall survival rate and disease-free survival rate, indicating that all hub genes could act as prognosis and recurrence/progression biomarkers of ccRCC. In summary, we identified 29 molecular markers correlated with different pathological stages of ccRCC. They may have important clinical implications for improving risk stratification, therapeutic decision and prognosis prediction in ccRCC patients.

Identification of key genes and pathways in human clear cell renal cell carcinoma (ccRCC) by co-expression analysis.

Human clear cell renal cell carcinoma (ccRCC) is the most common solid lesion within kidney, and its prognostic is influenced by the progression covering a complex network of gene interactions. In our study, we screened differential expressed genes, and constructed protein-protein interaction (PPI) network and a weighted gene co-expression network to identify key genes and pathways associated with the progression of ccRCC (n = 56). Functional and pathway enrichment analysis demonstrated that upregulated differentially expressed genes (DEGs) were significantly enriched in response to wounding, positive regulation of immune system process, leukocyte activation, immune response and cell activation. Downregulated DEGs were significantly enriched in oxidation reduction, monovalent inorganic cation transport, ion transport, excretion and anion transport. In the PPI network, top 10 hub genes were identified (TOP2A, MYC, ALB, CDK1, VEGFA, MMP9, PTPRC, CASR, EGFR and PTGS2). In co-expression network, 6 ccRCC-related modules were identified. They were associated with immune response, metabolic process, cell cycle regulation, angiogenesis and ion transport. In conclusion, our study illustrated the hub genes and pathways involved in the progress of ccRCC, and further molecular biological experiments are needed to confirm the function of the candidate biomarkers in human ccRCC.

A novel correlation between ATP5A1 gene expression and progression of human clear cell renal cell carcinoma identified by co­expression analysis.

Clear cell renal cell carcinoma (ccRCC) is the most common solid lesion within kidneys, and its prognostic is influenced by the progression covering a complex network of gene interactions. In our study, a weighted gene co­expression network was constructed to identify gene modules associated with the progression of ccRCC (n=35). In the significant module (R2 = -0.53), a total of 13 network hub genes were identified, and 2 of them were hub nodes in the protein-protein interaction network as well. In validation, ATP5A1 showed a higher correlation with the disease progression than any other hub gene in the hub module (P=0.001219). In the test set (n=202), ATP5A1 was also highly expressed in normal kidney than ccRCC tissues of each grade (P<0.001). Functional and pathway enrichment analysis demonstrated that ATP5A1 is overrepresented in pathway of oxidative phosphorylation, which associated with tumorigenesis and tumor progression. Gene set enrichment analysis (GSEA) also demonstrated that the gene set of 'oxidative phosphorylation' and metabolic pathways were enriched in ccRCC samples with ATP5A1 highly expressed (P<0.05). In conclusion, based on the co­expression analysis, ATP5A1 was validated to be associated with progression of ccRCC, probably by regulating tumor-related phosphorylation.

TM4SF1 regulates apoptosis, cell cycle and ROS metabolism via the PPARγ-SIRT1 feedback loop in human bladder cancer cells.

Transmembrane-4-L-Six-Family-1 (TM4SF1) is a member of the L6 family and functions as a signal transducer to regulate cell development, growth and motility. Here we show that TM4SF1 is strongly upregulated in human muscle invasive bladder cancer (MIBC) tissues, corroborating the bioinformatical results of transcriptome analysis. Moreover, tissue microarray (TMA) shows significant correlations (p < 0.05) between high expression of TM4SF1 and T stage, TNM stage, lymph node metastasis status and survival rate of MIBC patients, indicating a positive association between TM4SF1 expression and poorer prognosis. Furthermore, in vitro and in vivo studies indicate that the proliferation of human bladder cancer (BCa) cells is significantly suppressed by knockdown of TM4SF1 (p < 0.05). Functionally, the reduction of TM4SF1 could induce cell cycle arrest and apoptosis possibly via the upregulation of reactive oxygen species (ROS) in BCa cells. In addition, these observations could be recovered by treatment with GW9662 (antagonist of PPARγ) and resveratrol (activator of SIRT1). Taken together, our results suggest that high expression of TM4SF1 predicts poor prognosis of MIBC.

Co-expression network analysis identified FCER1G in association with progression and prognosis in human clear cell renal cell carcinoma.

Clear cell renal cell carcinoma (ccRCC) is the most common solid lesion within kidney, and its prognostic is influenced by the progression covering a complex network of gene interactions. In current study, the microarray data GSE66272 containing ccRCC and adjacent normal tissues was analyzed to identify 4042 differentially expressed genes, on which weighted gene co-expression network analysis was performed. Then 12 co-expressed gene modules were identified. The highest association was found between blue module and pathological stage (r = -0.77) by Pearson's correlation analysis. Functional enrichment analysis revealed that biological processes of blue module focused on inflammatory response, immune response, chemotaxis (all p < 1e-10). In the significant module, a total of 38 network hub genes were identified, FCER1G exhibited the highest correlation (r = 0.95) with ccRCC progression. In addition, FCER1G was hub node in the protein-protein interaction network of the genes in blue module as well. Thus, FCER1G was subsequently selected for validation. In the test set GSE53757 and RNA-sequencing data, FCER1G expression was also positively correlated with four stages of ccRCC progression (p < 0.001). Receiver operating characteristic (ROC) curve indicated that FCER1G could distinguish localized (pathological stage I, II) from non-localized (pathological stage III, IV) ccRCC (AUC=0.74, p < 0.001). Besides, FCER1G could be a prognostic gene in clinical practice as well, revealed by survival analysis based on RNA-sequencing data (p < 0.05). In conclusion, using weighted gene co-expression analysis, FCER1G was identified and validated in association with ccRCC progression and prognosis, which might improve the prognosis by influencing immune-related pathways.

Co-expression network analysis identified six hub genes in association with progression and prognosis in human clear cell renal cell carcinoma (ccRCC).

Human clear cell renal cell carcinoma (ccRCC) is one of the most common types of malignant adult kidney tumors. We constructed a weighted gene co-expression network to identify gene modules associated with clinical features of ccRCC (n = 97). Six hub genes (CCNB2, CDC20, CEP55, KIF20A, TOP2A and UBE2C) were identified in both co-expression and protein-protein interaction (PPI) networks, which were highly correlated with pathologic stage. The significance of expression of the hub genes in ccRCC was ranked top 4 among all cancers and correlated with poor prognosis. Functional analysis revealed that the hub genes were significantly enriched in cell cycle regulation and cell division. Gene set enrichment analysis suggested that the samples with highly expressed hub gene were correlated with cell cycle and p53 signaling pathway. Taken together, six hub genes were identified to be associated with progression and prognosis of ccRCC, and they might lead to poor prognosis by regulating p53 signaling pathway.

Knockdown of SIRT1 Suppresses Bladder Cancer Cell Proliferation and Migration and Induces Cell Cycle Arrest and Antioxidant Response through FOXO3a-Mediated Pathways.

Bladder cancer (BCa) is one of the most common tumors, but its underlying mechanism has not been fully clarified. Our transcriptome analysis suggested a close link of Sirtuins, Peroxisome Proliferator-Activated Receptor (PPAR), cell cycle regulation, reactive oxygen species (ROS) metabolism, and Forkhead Box Class O (FOXO) signaling pathway in BCa. SIRT1 is a key member of Sirtuins, playing important roles in aging and energy metabolism, which has been reported to be involved in various metabolic diseases and tumors. We observed that SIRT1 was upregulated in BCa tissues at both mRNA and protein levels. By establishing a SIRT1-knockdown BCa cell model, our results suggested that proliferation and viability were suppressed. Moreover, migration rate was inhibited as well, possibly via reduction of epithelial-mesenchymal transition (EMT). In addition, cell cycle arrest was significantly induced, consisting with strongly decreased proteins involved (CDK2/4/6). Furthermore, ROS production was slightly reduced, accompanied by increasing of antioxidant enzymes and total/acetylated FOXO3a. Consistently with our Path-net analysis, we observed no significant alteration of apoptosis in the SIRT1-knockdown BCa cells. Taken together, our results suggested that SIRT1 deficiency in BCa cells could suppress cell viability by activating antioxidant response and inducing cell cycle arrest possibly via FOXO3a-related pathways.

Identification and bioinformatics analysis of miRNAs associated with human muscle invasive bladder cancer.

Accumulated evidence has indicated that micro (mi)RNAs play vital roles in the occurrence and development of human muscle invasive bladder cancer (MIBC), however, little is known about the miRNAs' regulatory networks. In the present study, the authors aimed to use bioinformatics analysis to identify the key miRNAs and potential target genes, as well as studying the underlying mechanisms for MIBC. They collected several human MIBC tissues to generate a miRNA expression analysis by microarray analysis comparing with normal bladder tissues, identifying 104 differentially expressed miRNAs (102 were downregulated and 2 were upregulated) and predicted 11,884 putative target genes of the dysregulated miRNAs. To understand the function of dysregulated miRNAs in the development of MIBC, networks among miRNAs and genes, gene ontologies and pathways were built. The subsequent bioinformatics analysis indicated that the mitogen­associated protein kinase (MAPK) signaling pathway, apoptosis and pathways in cancer and the cell cycle, were significantly enriched Overall, these results provided comprehensive information on the biological function of dysregulated miRNAs in the development of MIBC. The identification of miRNAs and their putative targets may offer new diagnostic and therapeutic strategies for human muscle invasive bladder cancer.