Identifying FL11 subtype by characterizing tumor immune microenvironment in prostate adenocarcinoma via Chou's 5-steps rule.

Prostate cancer is one of the leading causes of death in men worldwide, revealing a substantial heterogeneity in terms of molecular and clinical behaviors. Tumor infiltrating immune cell is associated with prognosis and response to immunotherapy in several cancer types. However, until now, the immune infiltrate profile of distinct subtypes for prostate cancer remains poorly characterized. In this study, using immune infiltration profiles as well as transcriptomic datasets, we characterized this subtype of prostate tumors. We observed that the FLI1 subtype of prostate tumors was highly enriched in immune system processes, immune related KEGG pathways and biological processes. We also expanded this approach to explore the immune infiltration profile of the high FLI1 expression subtype for skin cutaneous melanoma, similar results were found. Investigation of the association of immune infiltration features with the FLI1 expression demonstrated that many important features were associated with the FLI1 expression.

Beneficial Effects of Green Tea EGCG on Skin Wound Healing: A Comprehensive Review.

Epigallocatechin gallate (EGCG) is associated with various health benefits. In this review, we searched current work about the effects of EGCG and its wound dressings on skin for wound healing. Hydrogels, nanoparticles, micro/nanofiber networks and microneedles are the major types of EGCG-containing wound dressings. The beneficial effects of EGCG and its wound dressings at different stages of skin wound healing (hemostasis, inflammation, proliferation and tissue remodeling) were summarized based on the underlying mechanisms of antioxidant, anti-inflammatory, antimicrobial, angiogenesis and antifibrotic properties. This review expatiates on the rationale of using EGCG to promote skin wound healing and prevent scar formation, which provides a future clinical application direction of EGCG.

Prediction of Non-coding RNAs as Drug Targets.

MiRNA is a class of small non-coding RNA molecule that regulates gene expression at post-transcriptional level. Increasing evidences show aberrant expression of miRNAs in a variety of diseases. Targeting the dysregulated miRNAs with small molecule drugs has become a novel therapeutics for many human diseases, especially cancers. In this chapter, we introduced a series of computational studies for prediction of small molecule and miRNA associations. Based on different hypotheses, such as transcriptional response similarity, functional consistence or network closeness, the small molecule-miRNA networks were constructed and further analyzed. In addition, several resources that collected experimentally validated relationships or computational predicted associations between small molecules and miRNAs were provided. Collectively, these computational frameworks and databases pave a new way for miRNA-targeted therapy and drug repositioning.

Characterize the relationship between essential and TATA-containing genes for S. cerevisiae by network topologies in the perturbation sensitivity network.

Essential genes are those that are indispensable for the survival and propagation of an organism. TATA-containing genes are associated with responses to various stresses and are highly regulated. Although both essential genes and TATA genes are very important in the function of biological systems, their relationship remains unclear because they have typically been researched independently. In this study, to investigate the relationship between essential genes and TATA genes, S. cerevisiae genes were classified as: essential TATA-containing, non-essential TATA-containing, essential non-TATA, and non-essential non-TATA genes. Network-based methods were applied to analyze these four gene categories in the S. cerevisiae perturbation sensitivity (PS) network, which was created from the transcriptional profiling of hundreds of different gene disruptions. All of the topological properties were found to be statistically discriminative among the four gene categories, and the non-essential TATA-containing genes had the most important roles in the yeast PS network.

Comparative analysis of housekeeping and tissue-selective genes in human based on network topologies and biological properties.

Housekeeping genes are genes that are turned on most of the time in almost every tissue to maintain cellular functions. Tissue-selective genes are predominantly expressed in one or a few biologically relevant tissue types. Benefitting from the massive gene expression microarray data obtained over the past decades, the properties of housekeeping and tissue-selective genes can now be investigated on a large-scale manner. In this study, we analyzed the topological properties of housekeeping and tissue-selective genes in the protein-protein interaction (PPI) network. Furthermore, we compared the biological properties and amino acid usage between these two gene groups. The results indicated that there were significant differences in topological properties between housekeeping and tissue-selective genes in the PPI network, and housekeeping genes had higher centrality properties and may play important roles in the complex biological network environment. We also found that there were significant differences in multiple biological properties and many amino acid compositions. The functional genes enrichment and subcellular localizations analysis was also performed to investigate the characterization of housekeeping and tissue-selective genes. The results indicated that the two gene groups showed significant different enrichment in drug targets, disease genes and toxin targets, and located in different subcellular localizations. At last, the discriminations between the properties of two gene groups were measured by the F-score, and expression stage had the most discriminative index in all properties. These findings may elucidate the biological mechanisms for understanding housekeeping and tissue-selective genes and may contribute to better annotate housekeeping and tissue-selective genes in other organisms.

Identifying novel associations between small molecules and miRNAs based on integrated molecular networks.

MOTIVATION: miRNAs play crucial roles in human diseases and newly discovered could be targeted by small molecule (SM) drug compounds. Thus, the identification of small molecule drug compounds (SM) that target dysregulated miRNAs in cancers will provide new insight into cancer biology and accelerate drug discovery for cancer therapy. RESULTS: In this study, we aimed to develop a novel computational method to comprehensively identify associations between SMs and miRNAs. To this end, exploiting multiple molecular interaction databases, we first established an integrated SM-miRNA association network based on 690 561 SM to SM interactions, 291 600 miRNA to miRNA associations, as well as 664 known SM to miRNA targeting pairs. Then, by performing Random Walk with Restart algorithm on the integrated network, we prioritized the miRNAs associated to each of the SMs. By validating our results utilizing an independent dataset we obtained an area under the ROC curve greater than 0.7. Furthermore, comparisons indicated our integrated approach significantly improved the identification performance of those simple modeled methods. This computational framework as well as the prioritized SM-miRNA targeting relationships will promote the further developments of targeted cancer therapies. CONTACT: yxli@sibs.ac.cn, lixia@hrbmu.edu.cn or jiangwei@hrbmu.edu.cn SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Characterization of BioPlex network by topological properties.

Protein-protein interaction (PPI) networks are emerging as valuable prototypes to study important problems in molecular cellular biology and systems biomedicine. An analysis of the topological properties of a PPI network is very helpful for understanding the function and structure of networks. In this study, we analyzed the topological patterns in the BioPlex network containing interactions among 10,961 proteins; most interactions were previously undocumented. The BioPlex network is a comprehensive map of human protein interactions and represents the first phase of a long-term effort to profile the entire human ORFEOME collection. Similar to other biological networks, we observed that the BioPlex network has several topological properties. We also quantified correlations profiles for the BioPlex network and compared them to randomized versions of the same network. We found that for the BioPlex network, edges between proteins with intermediate degrees were strongly suppressed, whereas edges between low-connected proteins were favored. Finally, the degrees of essential genes were compared with the degrees of non-essential genes and randomly selected proteins. There were no significant differences between the groups.

Characterization of TATA-containing genes and TATA-less genes in S. cerevisiae by network topologies and biological properties.

The TATA box is the core sequence of the promoter and the binding site of many transcription factors. Based on the presence or absence of TATA box, genes can be defined as TATA-containing or TATA-less genes. Many important stress-response functions and highly variable expression patterns are found to be correlated with the TATA box. However, until now, the relationships and differences between TATA-containing and TATA-less genes remain unclear. In this study, based on the transcriptional profiling of the Saccharomyces cerevisiae genome, the perturbation sensitivity (PS) network is constructed. The topological and biological properties are used to investigate differences between TATA-containing and TATA-less genes. Significant differences are found in all topological properties and most of the biological properties. Notably, the TF number, determined mathematically by the number of transcription factors regulating a gene, demonstrates the highest discrimination between TATA-containing and TATA-less genes when all properties are estimated by the F-score.

Characterization of essential genes by topological properties in the perturbation sensitivity network.

Genes that are indispensable for survival are called essential genes. In recent years, the analysis of essential genes has become extremely important for understanding the way a cell functions. With the advent of large-scale gene expression profiling technologies, it is now possible to profile transcriptional changes in the entire genome of Saccharomyces cerevisiae. Notwithstanding the accumulation of gene expression profiling in recent years, only a few studies have used these data to construct the network for S. cerevisiae. In this paper, based on the transcriptional profiling of the S. cerevisiae genome in hundreds of different gene disruptions, the perturbation sensitivity (PS) network is constructed. A scale-free topology with node degree following a power-law distribution is shown in the PS network. Twelve topological properties are used to investigate the characteristics of essential and non-essential genes in the PS network. Most of the properties are found to be statistically discriminative between essential and non-essential genes. In addition, the F-score is used to estimate the essentiality of each property, and the core number demonstrates the highest F-score among all properties.

Dissection of human MiRNA regulatory influence to subpathway.

The global insight into the relationships between miRNAs and their regulatory influences remains poorly understood. And most of complex diseases may be attributed to certain local areas of pathway (subpathway) instead of the entire pathway. Here, we reviewed the studies on miRNA regulations to pathways and constructed a bipartite miRNAs and subpathways network for systematic analyzing the miRNA regulatory influences to subpathways. We found that a small fraction of miRNAs were global regulators, environmental information processing pathways were preferentially regulated by miRNAs, and miRNAs had synergistic effect on regulating group of subpathways with similar function. Integrating the disease states of miRNAs, we also found that disease miRNAs regulated more subpathways than nondisease miRNAs, and for all miRNAs, the number of regulated subpathways was not in proportion to the number of the related diseases. Therefore, the study not only provided a global view on the relationships among disease, miRNA and subpathway, but also uncovered the function aspects of miRNA regulations and potential pathogenesis of complex diseases. A web server to query, visualize and download for all the data can be freely accessed at http://bioinfo.hrbmu.edu.cn/miR2Subpath.

Analysis and identification of toxin targets by topological properties in protein-protein interaction network.

Proteins do not exert their function in isolation of one another, but interact together in protein-protein interaction (PPI) networks. Analysis of topological properties of proteins in the PPI network is very helpful to understand the function of proteins. However, until recently, no one has ever undertaken to investigate toxin targets by topological properties. In this study, for the first time, 12 topological properties are used to investigate the characteristics of toxin targets in the PPI network. Most of the topological properties are found to be statistically discriminative between toxin targets and other proteins, and toxin targets tend to play more important roles in the PPI network. In addition, based on the topological properties and the sequence information, support vector machine (SVM) is used to predict toxin targets. The results obtained by the jackknife test and 10-fold cross validation are encouraging, indicating that SVM is a useful tool for predicting toxin targets, or at least can play complementary roles in relevant areas.

Human proteins characterization with subcellular localizations.

Proteins are responsible for performing the vast majority of cellular functions which are critical to a cell's survival. The knowledge of the subcellular localization of proteins can provide valuable information about their molecular functions. Therefore, one of the fundamental goals in cell biology and proteomics is to analyze the subcellular localizations and functions of these proteins. Recent large-scale human genomics and proteomics studies have made it possible to characterize human proteins at a subcellular localization level. In this study, according to the annotation in Swiss-Prot, 8842 human proteins were classified into seven subcellular localizations. Human proteins in the seven subcellular localizations were compared by using topological properties, biological properties, codon usage indices, mRNA expression levels, protein complexity and physicochemical properties. All these properties were found to be significantly different in the seven categories. In addition, based on these properties and pseudo-amino acid compositions, a machine learning classifier was built for the prediction of protein subcellular localization. The study presented here was an attempt to address the aforementioned properties for comparing human proteins of different subcellular localizations. We hope our findings presented in this study may provide important help for the prediction of protein subcellular localization and for understanding the general function of human proteins in cells.

Differential whole-genome doubling based signatures for improvement on clinical outcomes and drug response in patients with breast cancer.

Whole genome doublings (WGD), a hallmark of human cancer, is pervasive in breast cancer patients. However, the molecular mechanism of the complete impact of WGD on survival and treatment response in breast cancer remains unclear. To address this, we performed a comprehensive and systematic analysis of WGD, aiming to identify distinct genetic alterations linked to WGD and highlight its improvement on clinical outcomes and treatment response for breast cancer. A linear regression model along with weighted gene co-expression network analysis (WGCNA) was applied on The Cancer Genome Atlas (TCGA) dataset to identify critical genes related to WGD. Further Cox regression models with random selection were used to optimize the most useful prognostic markers in the TCGA dataset. The clinical implication of the risk model was further assessed through prognostic impact evaluation, tumor stratification, functional analysis, genomic feature difference analysis, drug response analysis, and multiple independent datasets for validation. Our findings revealed a high aneuploidy burden, chromosomal instability (CIN), copy number variation (CNV), and mutation burden in breast tumors exhibiting WGD events. Moreover, 247 key genes associated with WGD were identified from the distinct genomic patterns in the TCGA dataset. A risk model consisting of 22 genes was optimized from the key genes. High-risk breast cancer patients were more prone to WGD and exhibited greater genomic diversity compared to low-risk patients. Some oncogenic signaling pathways were enriched in the high-risk group, while primary immune deficiency pathways were enriched in the low-risk group. We also identified a risk gene, ANLN (anillin), which displayed a strong positive correlation with two crucial WGD genes, KIF18A and CCNE2. Tumors with high expression of ANLN were more prone to WGD events and displayed worse clinical survival outcomes. Furthermore, the expression levels of these risk genes were significantly associated with the sensitivities of BRCA cell lines to multiple drugs, providing valuable insights for targeted therapies. These findings will be helpful for further improvement on clinical outcomes and contribution to drug development in breast cancer.

MultiDS-MDA: Integrating multiple data sources into heterogeneous network for predicting novel metabolite-drug associations.

Metabolic processes in the human body play an important role in maintaining normal life activities, and the abnormal concentration of metabolites is closely related to the occurrence and development of diseases. The use of drugs is considered to have a major impact on metabolism, and drug metabolites can contribute to efficacy, drug toxicity and drug-drug interaction. However, our understanding of metabolite-drug associations is far from complete, and individual data source tends to be incomplete and noisy. Therefore, the integration of various types of data sources for inferring reliable metabolite-drug associations is urgently needed. In this study, we proposed a computational framework, MultiDS-MDA, for identifying metabolite-drug associations by integrating multiple data sources, including chemical structure information of metabolites and drugs, the relationships of metabolite-gene, metabolite-disease, drug-gene and drug-disease, the data of gene ontology (GO) and disease ontology (DO) and known metabolite-drug connections. The performance of MultiDS-MDA was evaluated by 5-fold cross-validation, which achieved an area under the ROC curve (AUROC) of 0.911 and an area under the precision-recall curve (AUPRC) of 0.907. Additionally, MultiDS-MDA showed outstanding performance compared with similar approaches. Case studies for three metabolites (cholesterol, thromboxane B2 and coenzyme Q10) and three drugs (simvastatin, pravastatin and morphine) also demonstrated the reliability and efficiency of MultiDS-MDA, and it is anticipated that MultiDS-MDA will serve as a powerful tool for future exploration of metabolite-drug interactions and contribute to drug development and drug combination.

Lysine-specific histone demethylase 1A (KDM1A/LSD1) inhibition attenuates DNA double-strand break repair and augments the efficacy of temozolomide in glioblastoma.

BACKGROUND: Efficient DNA repair in response to standard chemo and radiation therapies often contributes to glioblastoma (GBM) therapy resistance. Understanding the mechanisms of therapy resistance and identifying the drugs that enhance the therapeutic efficacy of standard therapies may extend the survival of GBM patients. In this study, we investigated the role of KDM1A/LSD1 in DNA double-strand break (DSB) repair and a combination of KDM1A inhibitor and temozolomide (TMZ) in vitro and in vivo using patient-derived glioma stem cells (GSCs). METHODS: Brain bioavailability of the KDM1A inhibitor (NCD38) was established using LS-MS/MS. The effect of a combination of KDM1A knockdown or inhibition with TMZ was studied using cell viability and self-renewal assays. Mechanistic studies were conducted using CUT&Tag-seq, RNA-seq, RT-qPCR, western blot, homologous recombination (HR) and non-homologous end joining (NHEJ) reporter, immunofluorescence, and comet assays. Orthotopic murine models were used to study efficacy in vivo. RESULTS: TCGA analysis showed KDM1A is highly expressed in TMZ-treated GBM patients. Knockdown or knockout or inhibition of KDM1A enhanced TMZ efficacy in reducing the viability and self-renewal of GSCs. Pharmacokinetic studies established that NCD38 readily crosses the blood-brain barrier. CUT&Tag-seq studies showed that KDM1A is enriched at the promoters of DNA repair genes and RNA-seq studies confirmed that KDM1A inhibition reduced their expression. Knockdown or inhibition of KDM1A attenuated HR and NHEJ-mediated DNA repair capacity and enhanced TMZ-mediated DNA damage. A combination of KDM1A knockdown or inhibition and TMZ treatment significantly enhanced the survival of tumor-bearing mice. CONCLUSIONS: Our results provide evidence that KDM1A inhibition sensitizes GBM to TMZ via attenuation of DNA DSB repair pathways.

Predicting human microRNA precursors based on an optimized feature subset generated by GA-SVM.

MicroRNAs (miRNAs) are non-coding RNAs that play important roles in post-transcriptional regulation. Identification of miRNAs is crucial to understanding their biological mechanism. Recently, machine-learning approaches have been employed to predict miRNA precursors (pre-miRNAs). However, features used are divergent and consequently induce different performance. Thus, feature selection is critical for pre-miRNA prediction. We generated an optimized feature subset including 13 features using a hybrid of genetic algorithm and support vector machine (GA-SVM). Based on SVM, the classification performance of the optimized feature subset is much higher than that of the two feature sets used in microPred and miPred by five-fold cross-validation. Finally, we constructed the classifier miR-SF to predict the most recently identified human pre-miRNAs in miRBase (version 16). Compared with microPred and miPred, miR-SF achieved much higher classification performance. Accuracies were 93.97%, 86.21% and 64.66% for miR-SF, microPred and miPred, respectively. Thus, miR-SF is effective for identifying pre-miRNAs.

Deep learning-based transcription factor activity for stratification of breast cancer patients.

Transcription factors directly bind to DNA and regulate the expression of the gene, causing epigenetic modification of the DNA. They often mediate epigenetic parameters of transcriptional and posttranscriptional mechanisms, and their expression activities can be used to characterize genomic aberrations in cancer cell. In this study, the activity profile of transcription factors inferred by VIPER algorithm. The autoencoder model was applied for compressing the transcription factor activity profile for obtaining more useful transformed features for stratifying patients into two different breast cancer subtypes. The deep learning-based subtypes exhibited superior prognostic value and yielded better risk-stratification than the transcription factor activity-based method. Importantly, according to transformed features, a deep neural network was constructed to predict the subtypes, and achieved the accuracy of 94.98% and area under the ROC curve of 0.9663, respectively. The proposed subtypes were found to be significantly associated with immune infiltration, tumor immunogenicity and so on. Furthermore, the ceRNA network was constructed for the breast cancer subtypes. Besides, 11 master regulators were found to be associated with patients in cluster 1. Given the robustness performance of our deep learning model over multiple breast cancer cohorts, we expected this model may be useful in the area of prognosis prediction and lead some possibility for personalized medicine in breast cancer patients.

Integrated multi-cohorts for characterizing the immunogenomic landscape and predicting drug response in triple-negative breast cancer.

Triple-negative breast cancer (TNBC) is the breast cancer subtype with the highest fatality rate, and it seriously threatens women's health. Recent studies found that the level of immune cell infiltration in TNBC was associated with tumor progression and prognosis. However, due to practical constraints, most of these TNBC immune infiltration studies only used a small number of patient samples and a few immune cell types. Therefore, it is necessary to integrate more TNBC patient samples and immune cell types to comprehensively study immune infiltration in TNBC to contribute to the prognosis and treatment of patients. In this study, 12 TNBC datasets were integrated and an extensive collection of 182 gene sets with immune-related signatures were included to comprehensively investigate tumor immune microenvironment of TNBC. A single sample gene set enrichment analysis was performed to calculate the infiltration score of each immune-related signature in each patient, and an immune-related risk scoring model for TNBC was constructed to accurately assess patient prognosis. Significant differences were found in immunogenomic landscape between different immune risk subtypes. In addition, the immunotherapy response and chemotherapy drug sensitivity of patients with different immune risk subtypes were also analyzed. The results showed that there were significant differences in these characteristics. Finally, a prediction model for immune risk subtypes of TNBC patients was constructed to accurately predict patients with unknown subtypes. Based on the aforementioned findings, we believed that the immune-related risk score constructed in this study can assist in providing personalized medicine to TNBC patients.

Prognostic and predictive value of a metabolic risk score model in breast cancer: an immunogenomic landscape analysis.

Breast cancer is a kind of malignant tumor that occurs in breast tissue, which is the most common cancer in women. Cellular metabolism is a critical determinant of the viability and function of cancer cells in tumor microenvironment. In this study, based on the gene expression profile of metabolism-related genes, the prognostic value of 20 metabolic pathways in patients with breast cancer was identified. A universal risk stratification signature that relies on 20 metabolic pathways was established and validated in training cohort, two testing cohorts and The Cancer Genome Atlas pan cancer cohort. Then, the relationship between metabolic risk score subtype, prognosis, immune infiltration level, cancer genotypes and their impact on therapeutic benefit were characterized. Results demonstrated that the patients with the low metabolic risk score subtype displayed good prognosis, high level of immune infiltration and exhibited a favorable response to neoadjuvant chemotherapy and immunotherapy. Taken together, the work presented in this study may deepen the understanding of metabolic hallmarks of breast cancer, and may provide some valuable information for personalized therapies in patients with breast cancer.

Integrated analysis of telomerase enzymatic activity unravels an association with cancer stemness and proliferation.

Active telomerase is essential for stem cells and most cancers to maintain telomeres. The enzymatic activity of telomerase is related but not equivalent to the expression of TERT, the catalytic subunit of the complex. Here we show that telomerase enzymatic activity can be robustly estimated from the expression of a 13-gene signature. We demonstrate the validity of the expression-based approach, named EXTEND, using cell lines, cancer samples, and non-neoplastic samples. When applied to over 9,000 tumors and single cells, we find a strong correlation between telomerase activity and cancer stemness. This correlation is largely driven by a small population of proliferating cancer cells that exhibits both high telomerase activity and cancer stemness. This study establishes a computational framework for quantifying telomerase enzymatic activity and provides new insights into the relationships among telomerase, cancer proliferation, and stemness.