A novel network-based method identifies a cuproplasia-related pan-cancer gene signature to predict patient outcome.

Copper is a vital micronutrient involved in many biological processes and is an essential component of tumour cell growth and migration. Copper influences tumour growth through a process called cuproplasia, defined as abnormal copper-dependent cell-growth and proliferation. Copper-chelation therapy targeting this process has demonstrated efficacy in several clinical trials against cancer. While the molecular pathways associated with cuproplasia are partially known, genetic heterogeneity across different cancer types has limited the understanding of how cuproplasia impacts patient survival. Utilising RNA-sequencing data from The Cancer Genome Atlas (TCGA) and the Genotype-Tissue Expression (GTEx) datasets, we generated gene regulatory networks to identify the critical cuproplasia-related genes across 23 different cancer types. From this, we identified a novel 8-gene cuproplasia-related gene signature associated with pan-cancer survival, and a 6-gene prognostic risk score model in low grade glioma. These findings highlight the use of gene regulatory networks to identify cuproplasia-related gene signatures that could be used to generate risk score models. This can potentially identify patients who could benefit from copper-chelation therapy and identifies novel targeted therapeutic strategies.

Palliative procedures for malignant gastric outlet obstruction: a network meta-analysis.

BACKGROUND: The optimal treatment for malignant gastric outlet obstruction (GOO) remains uncertain. This systematic review aimed to comprehensively investigate the efficacy and safety of four palliative treatments for malignant GOO: gastrojejunostomy, endoscopic ultrasound-guided gastroenterostomy (EUS-GE), stomach-partitioning gastrojejunostomy (PGJ), and endoscopic stenting. METHODS: We searched PubMed, Embase, Cochrane Library, Scopus, and Web of Science databases, ClinicalTrials.gov, and the World Health Organization International Clinical Trials Registry Platform for randomized controlled trials (RCTs) and cohort studies comparing the four treatments for malignant GOO. We included studies that reported at least one of the following clinical outcomes: clinical success, 30-day mortality, reintervention rate, or length of hospital stay. Evidence from RCTs and non-RCTs was naïve combined to perform network meta-analysis through the frequentist approach using an inverse variance model. Treatments were ranked by P score. RESULTS: This network meta-analysis included 3617 patients from 4 RCTs, 4 prospective cohort studies, and 32 retrospective cohort studies. PGJ was the optimal approach in terms of clinical success and reintervention (P scores: 0.95 and 0.90, respectively). EUS-GE had the highest probability of being the optimal treatment in terms of 30-day mortality and complications (P scores: 0.82 and 0.99, respectively). Cluster ranking to combine the P scores for 30-day mortality and reintervention indicated the benefits of PGJ and EUS-GE (cophenetic correlation coefficient: 0.94; PGJ and EUS-GE were in the same cluster). CONCLUSION: PGJ and EUS-GE are recommended for malignant GOO. PGJ could be the alternative choice in centers with limited resources or in patients who are unsuitable for EUS-GE.

The winning methods for predicting cellular position in the DREAM single-cell transcriptomics challenge.

MOTIVATION: Predicting cell locations is important since with the understanding of cell locations, we may estimate the function of cells and their integration with the spatial environment. Thus, the DREAM challenge on single-cell transcriptomics required participants to predict the locations of single cells in the Drosophila embryo using single-cell transcriptomic data. RESULTS: We have developed over 50 pipelines by combining different ways of preprocessing the RNA-seq data, selecting the genes, predicting the cell locations and validating predicted cell locations, resulting in the winning methods which were ranked second in sub-challenge 1, first in sub-challenge 2 and third in sub-challenge 3. In this paper, we present an R package, SCTCwhatateam, which includes all the methods we developed and the Shiny web application to facilitate the research on single-cell spatial reconstruction. All the data and the example use cases are available in the Supplementary data.

A new look at the structure of the neutral Au18 cluster: hollow versus filled golden cage.

The geometry of the neutral Au18 gold cluster was probed by a combination of quantum chemical calculations and far-infrared multiple photon dissociation (FIR-MPD) spectroscopy of a Kr messenger complex. Two low-lying isomers are identified to potentially contribute to the experimental IR spectrum, both being derived from a star-like Au17 structure upon capping with one extra Au atom either inside (18_1) or outside (18_5) the star. In particular, the present detection of structure 18_1 by DFT computations where a golden cage encapsulates an endohedral Au atom, is intriguing as a stable core-shell isomer has, to our knowledge, never been found before for such small neutral gold clusters. DFT and local coupled-cluster (DLPNO and PNO-CCSD(T)) computations indicate that both Au18 isomers are close to each other, within ∼3 kcal mol-1, on the energy scale. Although the exact energy ordering is again method-dependent and remains, at present, inconclusive, the most striking spectral signatures of both isomers are related to vibrational modes localized at atoms capping the inner pentaprism sub-structure that result in prominent peaks centered at ∼80 cm-1, close to the most prominent experimental feature found at 78 cm-1. The calculated IR spectra of both core-shell and hollow isomers are very similar to each other and both agree comparably well with the experimental FIR-MPD spectra of the Au18Kr1,2 complexes.

The Impacts of Collaborative Writing on Individual Writing Skills.

The current study was to see if collaborative writing activities would impact each student's writing quality after getting engaged in an academic writing course for argumentative essays. The study enrolled 62 third-year English majors at the Faculty of Foreign Languages, Van Lang University, Ho Chi Minh City, Vietnam, 35 in the experimental group and 27 in the control group. The ages of the students ranged from 19 to 21. All teaching/learning activities, such as the selection of topics, brainstorming, or peer/lecturer feedback, applied to train the students of the two groups were similar except for the essay-composing stage. While the control-group students composed an essay individually, the experimental-group students conducted it collaboratively. That is, the group members composed an essay together. Data from pre-and post-tests of students' writing were analyzed to compare the students' writing quality. The study discovered that jointly authored papers performed much better than those written alone and that collaborative writing activity significantly affected each student's writing quality.

NIBNA: a network-based node importance approach for identifying breast cancer drivers.

MOTIVATION: Identifying meaningful cancer driver genes in a cohort of tumors is a challenging task in cancer genomics. Although existing studies have identified known cancer drivers, most of them focus on detecting coding drivers with mutations. It is acknowledged that non-coding drivers can regulate driver mutations to promote cancer growth. In this work, we propose a novel node importance-based network analysis (NIBNA) framework to detect coding and non-coding cancer drivers. We hypothesize that cancer drivers are crucial to the formation of community structures in cancer network, and removing them from the network greatly perturbs the network structure thereby critically affecting the functioning of the network. NIBNA detects cancer drivers using a three-step process: first, a condition-specific network is built by incorporating gene expression data and gene networks; second, the community structures in the network are estimated; and third, a centrality-based metric is applied to compute node importance. RESULTS: We apply NIBNA to the BRCA dataset, and it outperforms existing state-of-art methods in detecting coding cancer drivers. NIBNA also predicts 265 miRNA drivers, and majority of these drivers have been validated in literature. Further we apply NIBNA to detect cancer subtype-specific drivers, and several predicted drivers have been validated to be associated with cancer subtypes. Lastly, we evaluate NIBNA's performance in detecting epithelial-mesenchymal transition drivers, and we confirmed 8 coding and 13 miRNA drivers in the list of known genes. AVAILABILITY AND IMPLEMENTATION: The source code can be accessed at https://github.com/mandarsc/NIBNA. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

A pseudotemporal causality approach to identifying miRNA-mRNA interactions during biological processes.

MOTIVATION: microRNAs (miRNAs) are important gene regulators and they are involved in many biological processes, including cancer progression. Therefore, correctly identifying miRNA-mRNA interactions is a crucial task. To this end, a huge number of computational methods has been developed, but they mainly use the data at one snapshot and ignore the dynamics of a biological process. The recent development of single cell data and the booming of the exploration of cell trajectories using 'pseudotime' concept have inspired us to develop a pseudotime-based method to infer the miRNA-mRNA relationships characterizing a biological process by taking into account the temporal aspect of the process. RESULTS: We have developed a novel approach, called pseudotime causality, to find the causal relationships between miRNAs and mRNAs during a biological process. We have applied the proposed method to both single cell and bulk sequencing datasets for Epithelia to Mesenchymal Transition, a key process in cancer metastasis. The evaluation results show that our method significantly outperforms existing methods in finding miRNA-mRNA interactions in both single cell and bulk data. The results suggest that utilizing the pseudotemporal information from the data helps reveal the gene regulation in a biological process much better than using the static information. AVAILABILITY AND IMPLEMENTATION: R scripts and datasets can be found at https://github.com/AndresMCB/PTC. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

pDriver: a novel method for unravelling personalized coding and miRNA cancer drivers.

MOTIVATION: Unravelling cancer driver genes is important in cancer research. Although computational methods have been developed to identify cancer drivers, most of them detect cancer drivers at population level. However, two patients who have the same cancer type and receive the same treatment may have different outcomes because each patient has a different genome and their disease might be driven by different driver genes. Therefore new methods are being developed for discovering cancer drivers at individual level, but existing personalized methods only focus on coding drivers while microRNAs (miRNAs) have been shown to drive cancer progression as well. Thus, novel methods are required to discover both coding and miRNA cancer drivers at individual level. RESULTS: We propose the novel method, pDriver, to discover personalized cancer drivers. pDriver includes two stages: (i) constructing gene networks for each cancer patient and (ii) discovering cancer drivers for each patient based on the constructed gene networks. To demonstrate the effectiveness of pDriver, we have applied it to five TCGA cancer datasets and compared it with the state-of-the-art methods. The result indicates that pDriver is more effective than other methods. Furthermore, pDriver can also detect miRNA cancer drivers and most of them have been confirmed to be associated with cancer by literature. We further analyze the predicted personalized drivers for breast cancer patients and the result shows that they are significantly enriched in many GO processes and KEGG pathways involved in breast cancer. AVAILABILITY AND IMPLEMENTATION: pDriver is available at https://github.com/pvvhoang/pDriver. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Another look at energetically quasi-degenerate structures of the gold cluster Au27 q with q = 1, 0, -1.

Quantum chemical computations were used to reinvestigate the geometries, spectroscopic, and energetic properties of the gold clusters Au27 q in three charge states (q = 1, 0, -1). Density functional theory (DFT) and the domain-based local pair natural orbital modification of the coupled-cluster theory DLPNO-CCSD(T) calculations revealed that, at variance with earlier reports in the literature, while the anion Au27 - tends to exist in a tube-like form, both the lowest-energy Au27 and Au27 + isomers exhibit a pyramidal shape. However, several isomers were found to lie very close in energy, thus rendering a structural transition and their coexistence easy to occur. More specifically, the equilibrium geometry of the neutral Au27 is a core-shell pyramid-like structure with one gold atom located inside. We also identified a novel ground state for the anion Au27 - and located for the first time the global minimum of the cation Au27 + . The vertical detachment energies of the neutral and anionic states were also computed and used to assign the available experimental photoelectron spectra. Although many Au27 isomers were predicted to be energetically quasi-degenerate, the corresponding distinctive vibrational signatures can be used as fingerprints for the identification of cluster geometrical features.

DriverGroup: a novel method for identifying driver gene groups.

MOTIVATION: Identifying cancer driver genes is a key task in cancer informatics. Most existing methods are focused on individual cancer drivers which regulate biological processes leading to cancer. However, the effect of a single gene may not be sufficient to drive cancer progression. Here, we hypothesize that there are driver gene groups that work in concert to regulate cancer, and we develop a novel computational method to detect those driver gene groups. RESULTS: We develop a novel method named DriverGroup to detect driver gene groups by using gene expression and gene interaction data. The proposed method has three stages: (i) constructing the gene network, (ii) discovering critical nodes of the constructed network and (iii) identifying driver gene groups based on the discovered critical nodes. Before evaluating the performance of DriverGroup in detecting cancer driver groups, we firstly assess its performance in detecting the influence of gene groups, a key step of DriverGroup. The application of DriverGroup to DREAM4 data demonstrates that it is more effective than other methods in detecting the regulation of gene groups. We then apply DriverGroup to the BRCA dataset to identify driver groups for breast cancer. The identified driver groups are promising as several group members are confirmed to be related to cancer in literature. We further use the predicted driver groups in survival analysis and the results show that the survival curves of patient subpopulations classified using the predicted driver groups are significantly differentiated, indicating the usefulness of DriverGroup. AVAILABILITY AND IMPLEMENTATION: DriverGroup is available at https://github.com/pvvhoang/DriverGroup. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Comment on 'Structural characterization, reactivity and vibrational properties of silver clusters: a new global minimum for Ag16' by P. L. Rodríguez-Kessler, A. R. Rodríguez-Domínguez, D. MacLeod Carey and A. Muñoz-Castro, Phys. Chem. Chem. Phys., 2020, 22, 27255, DOI: D0CP04018E.

A recent paper by Rodríguez-Kessler et al., Phys. Chem. Chem. Phys., 2020, 22, 27255-27262, reported not only results of quantum chemical computations (using the PW91 density functional) on Ag16 clusters as emphasized in the article's title, but also on the Ag15 size. These authors confirmed previous results obtained by McKee and Samokhvalov (J. Phys. Chem. A, 2017, 121, 5018-5028 using the M06 density functional) that the most stable isomer of Ag15 is a C2v structure. We wish to point out that two low symmetry isomers of Ag15 that have a similar energy content are even lower in energy than their reported C2v global minimum. The relative energies between low-lying Ag15 isomers were again found to be method-dependent, and within the expected accuracy of DFT and CCSD(T) methods they could be considered as energetically degenerate, and likely coexist in a molecular beam. The new lower-energy Ag15 isomers appear to fit more consistently within the structural evolution of small silver clusters.

The lowest-energy structure of the gold cluster Au10: planar vs. nonplanar?

The onset of the transition from 2D to 3D structures in pure gold clusters remains a matter of continuing debate. In this theoretical study we revisit several planar and non-planar structural motifs of the size Au10 with the aim to clarify this issue. Computations using a long-range corrected exchange-correlation functional LC-BLYP, coupled-cluster theories CCSD(T) and PNO-LCCSD(T)-F12 reveal that, at variance with previous reports on the preference of a planar elongated hexagon, both planar and nonplanar isomers of the neutral Au10 are energetically degenerated up to 300 K. Its 3D equilibrium geometry is a core-shell structure which can be built up from a trigonal prism by capping four extra Au atoms outside. A comparison to the available experimental vibrational spectra allows us to argue that both lowest-lying 2D and 3D isomers of Au10 likely coexist in the molecular beam during measurement of its FIR spectra. This result also suggests that the 2D-3D transition of neutral gold clusters occurs at the size Au10.

Association of serum adiponectin and leptin levels with renal function in kidney transplant recipients with or without new-onset diabetes after transplantation.

PURPOSE: To evaluate serum adiponectin and leptin concentration in new-onset diabetes after transplantation (NODAT) and non-NODAT patients and association with renal function in kidney transplant recipients (KTRs). PATIENTS AND METHODS: A study of 314 consecutive adults KTRs divided into four groups: 236 individuals without NODAT who had renal insufficiency (RI; n = 56) or normal renal function (n = 180) and 78 patients with NODAT who had RI (n = 17) or normal renal function (n = 61). NODAT was diagnosed based on venous fasting blood glucose or HbA1c with the criteria of the American Diabetes Association. Renal insufficiency was defined according to KDOQI 2002 guidelines. RESULTS: In the NODAT group, the median level of serum adiponectin was lower than that of non-NODAT one (30 µg/ml vs 37.15 µg/ml, p < 0.001); in contrast, the median leptin concentration was higher (4.27 ng/ml vs 4.05 ng/ml, p = 0.024). In the RI group, both median serum adiponectin and leptin levels were higher than those of non-RI one (Adiponectin: 40.01 µg/ml vs 33.7 µg/ml; Leptin: 4.51 ng/ml vs 3.91 ng/ml, p < 0.001 both). We found that BMI was related to both adiponectin and leptin levels in both NODAT, non-NODAT, and all subject groups, based on univariate and multivariate linear regression analysis. CONCLUSION: New-onset diabetes after transplantation, BMI, and renal insufficiency were affected to the serum level of adiponectin and leptin in KTRs.

Adsorption/Desorption Behaviors and SERS Chemical Enhancement of 6-Mercaptopurine on a Nanostructured Gold Surface: The Au20 Cluster Model.

Computational approaches are employed to elucidate the binding mechanism and the SERS phenomenon of 6-mercaptopurine (6MP) adsorbed on the tetrahedral Au20 cluster as a simple model for a nanostructured gold surface. Computations are carried out in both vacuum and aqueous environments using a continuum model. In the gaseous phase and neutral conditions, interaction of 6MP with the gold cluster is mostly dominated by a covalent Au-S bond and partially stabilized by the Au⋅⋅⋅H-N coupling. However, in acidic solution, the nonconventional Au⋅⋅⋅H-S hydrogen-bond becomes the most favorable binding mode. The 6MP affinity for gold clusters decreases in the order of vacuum > neutral solution > acidic medium. During the adsorption, the energy gap of Au20 substantially declines, leading to an increase in its electrical conductivity, which can be converted to an electrical noise. Moreover, such interaction is likely a reversible process and triggered by either the low pH in sick tissues or the presence of cysteine residues in protein matrices. While N-H bending and stretching vibrations play major roles in the SERS phenomenon of 6MP on gold surfaces in neutral solution, the strongest enhancement in acidic environment is mostly due to an Au⋅⋅⋅H-S coupling, rather than an aromatic ring-gold surface π overlap as previously proposed.

Correction to: Identifying miRNA synergism using multiple-intervention causal inference.

After publication of this supplement article [1], it was brought to our attention that the Fig. 3 was incorrect. The correct Fig. 3 is as below.

Elucidating the binding mechanism of thione-containing mercaptopurine and thioguanine drugs to small gold clusters.

Density functional theory methods were employed to clarify the adsorption/desorption behaviors of the thione-containing mercaptopurine and thioguanine drugs on the gold surface using both small Au6 and Au8 clusters as model reactants. Structural features, thermodynamic parameters, bonding characteristics, and electronic properties of the resulting complexes were investigated using the Perdew-Burke-Ernzerhof (PBE) and LC-BLYP functionals along with correlation-consistent basis sets, namely cc-pVDZ-PP for gold and cc-pVTZ for non-metals. Computed results show that the drug molecules tend to anchor on the gold cluster at the S atom with binding energies around -34 to -40 kcal/mol (in vacuum) and - 28 to -32 kcal/mol (in aqueous solution). As compared to Au8 , Au6 undergoes a shorter recovery time and a larger change of energy gap that could be converted to an electrical signal for selective detection of the drugs. Furthermore, interactions between the drugs and gold clusters are reversible processes and a drug release mechanism was also proposed. Accordingly, the drugs are able to separate from the gold surface due to either a slight change of pH in tumor cells or the presence of cysteine residues in protein matrices.

CBNA: A control theory based method for identifying coding and non-coding cancer drivers.

A key task in cancer genomics research is to identify cancer driver genes. As these genes initialise and progress cancer, understanding them is critical in designing effective cancer interventions. Although there are several methods developed to discover cancer drivers, most of them only identify coding drivers. However, non-coding RNAs can regulate driver mutations to develop cancer. Hence, novel methods are required to reveal both coding and non-coding cancer drivers. In this paper, we develop a novel framework named Controllability based Biological Network Analysis (CBNA) to uncover coding and non-coding cancer drivers (i.e. miRNA cancer drivers). CBNA integrates different genomic data types, including gene expression, gene network, mutation data, and contains a two-stage process: (1) Building a network for a condition (e.g. cancer condition) and (2) Identifying drivers. The application of CBNA to the BRCA dataset demonstrates that it is more effective than the existing methods in detecting coding cancer drivers. In addition, CBNA also predicts 17 miRNA drivers for breast cancer. Some of these predicted miRNA drivers have been validated by literature and the rest can be good candidates for wet-lab validation. We further use CBNA to detect subtype-specific cancer drivers and several predicted drivers have been confirmed to be related to breast cancer subtypes. Another application of CBNA is to discover epithelial-mesenchymal transition (EMT) drivers. Of the predicted EMT drivers, 7 coding and 6 miRNA drivers are in the known EMT gene lists.

Structural Evolution and Stability Trend of Small-Sized Gold Clusters Aun (n = 20-30).

Structural evolution and stability pattern of pure neutral gold clusters Aun in the small size range of n = 20-30 are examined using density functional theory (DFT) calculations. The equilibrium geometries are either confirmed or determined, and some new ground state structures are identified. The most stable configurations of Au21-Au23 sizes are formed by adding extra gold atoms to the highly stable pyramidal structure of Au20, while flat-cage shapes are the best candidates for the global minima of both Au24 and Au25. For larger sizes of n = 26-30, pyramidal motifs tend to dominate the lower-lying population rather than tubular conformations as previously reported. The energy gaps, excitation energies, and exciton binding energies are also computed to test out the performance of the computational methods employed. Accordingly, a density functional with long-range exchange effects is highly recommended to quantitatively investigate both the ground and excited states of pure gold clusters.

Identifying miRNA synergism using multiple-intervention causal inference.

BACKGROUND: Studying multiple microRNAs (miRNAs) synergism in gene regulation could help to understand the regulatory mechanisms of complicated human diseases caused by miRNAs. Several existing methods have been presented to infer miRNA synergism. Most of the current methods assume that miRNAs with shared targets at the sequence level are working synergistically. However, it is unclear if miRNAs with shared targets are working in concert to regulate the targets or they individually regulate the targets at different time points or different biological processes. A standard method to test the synergistic activities is to knock-down multiple miRNAs at the same time and measure the changes in the target genes. However, this approach may not be practical as we would have too many sets of miRNAs to test. RESULTS: n this paper, we present a novel framework called miRsyn for inferring miRNA synergism by using a causal inference method that mimics the multiple-intervention experiments, e.g. knocking-down multiple miRNAs, with observational data. Our results show that several miRNA-miRNA pairs that have shared targets at the sequence level are not working synergistically at the expression level. Moreover, the identified miRNA synergistic network is small-world and biologically meaningful, and a number of miRNA synergistic modules are significantly enriched in breast cancer. Our further analyses also reveal that most of synergistic miRNA-miRNA pairs show the same expression patterns. The comparison results indicate that the proposed multiple-intervention causal inference method performs better than the single-intervention causal inference method in identifying miRNA synergistic network. CONCLUSIONS: Taken together, the results imply that miRsyn is a promising framework for identifying miRNA synergism, and it could enhance the understanding of miRNA synergism in breast cancer.

Identifying miRNA-mRNA regulatory relationships in breast cancer with invariant causal prediction.

BACKGROUND: microRNAs (miRNAs) regulate gene expression at the post-transcriptional level and they play an important role in various biological processes in the human body. Therefore, identifying their regulation mechanisms is essential for the diagnostics and therapeutics for a wide range of diseases. There have been a large number of researches which use gene expression profiles to resolve this problem. However, the current methods have their own limitations. Some of them only identify the correlation of miRNA and mRNA expression levels instead of the causal or regulatory relationships while others infer the causality but with a high computational complexity. To overcome these issues, in this study, we propose a method to identify miRNA-mRNA regulatory relationships in breast cancer using the invariant causal prediction. The key idea of invariant causal prediction is that the cause miRNAs of their target mRNAs are the ones which have persistent causal relationships with the target mRNAs across different environments. RESULTS: In this research, we aim to find miRNA targets which are consistent across different breast cancer subtypes. Thus, first of all, we apply the Pam50 method to categorize BRCA samples into different "environment" groups based on different cancer subtypes. Then we use the invariant causal prediction method to find miRNA-mRNA regulatory relationships across subtypes. We validate the results with the miRNA-transfected experimental data and the results show that our method outperforms the state-of-the-art methods. In addition, we also integrate this new method with the Pearson correlation analysis method and Lasso in an ensemble method to take the advantages of these methods. We then validate the results of the ensemble method with the experimentally confirmed data and the ensemble method shows the best performance, even comparing to the proposed causal method. CONCLUSIONS: This research found miRNA targets which are consistent across different breast cancer subtypes. Further functional enrichment analysis shows that miRNAs involved in the regulatory relationships predicated by the proposed methods tend to synergistically regulate target genes, indicating the usefulness of these methods, and the identified miRNA targets could be used in the design of wet-lab experiments to discover the causes of breast cancer.