Network integration of multi-tumour omics data suggests novel targeting strategies

The biomedical applications of artificial intelligence: an overview of decades of research

A significant area of computer science called artificial intelligence (AI) is successfully applied to the analysis of intricate biological data and the extraction of substantial associations from datasets for a variety of biomedical uses. AI has attracted significant interest in biomedical research due to its features: (i) better patient care through early diagnosis and detection; (ii) enhanced workflow; (iii) lowering medical errors; (v) lowering medical costs; (vi) reducing morbidity and mortality; (vii) enhancing performance; (viii) enhancing precision; and (ix) time efficiency. Quantitative metrics are crucial for evaluating AI implementations, providing insights, enabling informed decisions, and measuring the impact of AI-driven initiatives, thereby enhancing transparency, accountability, and overall impact. The implementation of AI in biomedical fields faces challenges such as ethical and privacy concerns, lack of awareness, technology unreliability, and professional liability. A brief discussion is given of the AI techniques, which include Virtual screening (VS), DL, ML, Hidden Markov models (HMMs), Neural networks (NNs), Generative models (GMs), Molecular dynamics (MD), and Structure-activity relationship (SAR) models. The study explores the application of AI in biomedical fields, highlighting its enhanced predictive accuracy, treatment efficacy, diagnostic efficiency, faster decision-making, personalised treatment strategies, and precise medical interventions.

Integrating artificial intelligence in drug discovery and early drug development: a transformative approach

Artificial intelligence (AI) can transform drug discovery and early drug development by addressing inefficiencies in traditional methods, which often face high costs, long timelines, and low success rates. In this review we provide an overview of how to integrate AI to the current drug discovery and development process, as it can enhance activities like target identification, drug discovery, and early clinical development. Through multiomics data analysis and network-based approaches, AI can help to identify novel oncogenic vulnerabilities and key therapeutic targets. AI models, such as AlphaFold, predict protein structures with high accuracy, aiding druggability assessments and structure-based drug design. AI also facilitates virtual screening and de novo drug design, creating optimized molecular structures for specific biological properties. In early clinical development, AI supports patient recruitment by analyzing electronic health records and improves trial design through predictive modeling, protocol optimization, and adaptive strategies. Innovations like synthetic control arms and digital twins can reduce logistical and ethical challenges by simulating outcomes using real-world or virtual patient data. Despite these advancements, limitations remain. AI models may be biased if trained on unrepresentative datasets, and reliance on historical or synthetic data can lead to overfitting or lack generalizability. Ethical and regulatory issues, such as data privacy, also challenge the implementation of AI. In conclusion, in this review we provide a comprehensive overview about how to integrate AI into current processes. These efforts, although they will demand collaboration between professionals, and robust data quality, have a transformative potential to accelerate drug development.

Application progress of artificial intelligence in tumor diagnosis and treatment

The rapid advancement of artificial intelligence (AI) has introduced transformative opportunities in oncology, enhancing the precision and efficiency of tumor diagnosis and treatment. This review examines recent advancements in AI applications across tumor imaging diagnostics, pathological analysis, and treatment optimization, with a particular focus on breast cancer, lung cancer, and liver cancer. By synthesizing findings from peer-reviewed studies published over the past decade, this paper analyzes the role of AI in enhancing diagnostic accuracy, streamlining therapeutic decision-making, and personalizing treatment strategies. Additionally, this paper addresses challenges related to AI integration into clinical workflows and regulatory compliance. As AI continues to evolve, its applications in oncology promise further improvements in patient outcomes, though additional research is needed to address its limitations and ensure ethical and effective deployment.

Emerging Signatures of Hematological Malignancies from Gene Expression and Transcription Factor-Gene Regulations

Hematological malignancies are a diverse group of cancers developing in the peripheral blood, the bone marrow or the lymphatic system. Due to their heterogeneity, the identification of novel and advanced molecular signatures is essential for enhancing their characterization and facilitate its translation to new pharmaceutical solutions and eventually to clinical applications. In this study, we collected publicly available microarray data for more than five thousand subjects, across thirteen hematological malignancies. Using PANDA to estimate gene regulatory networks (GRNs), we performed hierarchical clustering and network analysis to explore transcription factor (TF) interactions and their implications on biological pathways. Our findings reveal distinct clustering patterns among leukemias and lymphomas, with notable differences in gene and TF expression profiles. Gene Set Enrichment Analysis (GSEA) identified 57 significantly enriched KEGG pathways, highlighting both common and unique biological processes across HMs. We also identified potential drug targets within these pathways, emphasizing the role of TFs such as CEBPB and NFE2L1 in disease pathophysiology. Our comprehensive analysis enhances the understanding of the molecular landscape of HMs and suggests new avenues for targeted therapeutic strategies. These findings also motivate the adoption of regulatory networks, combined with modern biotechnological possibilities, for insightful pan-cancer exploratory studies.

PyNetCor: a high-performance Python package for large-scale correlation analysis

The development of multi-omics technologies has generated an abundance of biological datasets, providing valuable resources for investigating potential relationships within complex biological systems. However, most correlation analysis tools face computational challenges when dealing with these high-dimensional datasets containing millions of features. Here, we introduce pyNetCor, a fast and scalable tool for constructing correlation networks on large-scale and high-dimensional data. PyNetCor features optimized algorithms for both full correlation coefficient matrix computation and top-k correlation search, outperforming other tools in the field in terms of runtime and memory consumption. It utilizes a linear interpolation strategy to rapidly estimate P-values and achieve false discovery rate control, demonstrating a speedup of over 110 times compared to existing methods. Overall, pyNetCor supports large-scale correlation analysis, a crucial foundational step for various bioinformatics workflows, and can be easily integrated into downstream applications to accelerate the process of extracting biological insights from data.

Using real-world evidence in haematology

Most new drug approvals are based on data from large randomized clinical trials (RCTs). However, there are sometimes contradictory conclusions from seemingly similar trials and generalizability of conclusions from these trials is limited. These considerations explain, in part, the gap between conclusions from data of RCTs and those from registries termed real world data (RWD). Recently, real-world evidence (RWE) from RWD processed by artificial intelligence has received increasing attention. We describe the potential of using RWD in haematology concluding RWE from RWD may complement data from RCTs to support regulatory decisions.

A systematic review of computational approaches to understand cancer biology for informed drug repurposing

Cancer is the second leading cause of death globally, trailing only heart disease. In the United States alone, 1.9 million new cancer cases and 609,360 deaths were recorded for 2022. Unfortunately, the success rate for new cancer drug development remains less than 10%, making the disease particularly challenging. This low success rate is largely attributed to the complex and poorly understood nature of cancer etiology. Therefore, it is critical to find alternative approaches to understanding cancer biology and developing effective treatments. One such approach is drug repurposing, which offers a shorter drug development timeline and lower costs while increasing the likelihood of success. In this review, we provide a comprehensive analysis of computational approaches for understanding cancer biology, including systems biology, multi-omics, and pathway analysis. Additionally, we examine the use of these methods for drug repurposing in cancer, including the databases and tools that are used for cancer research. Finally, we present case studies of drug repurposing, discussing their limitations and offering recommendations for future research in this area.

A systematic evaluation of quenching and extraction procedures for quantitative metabolome profiling of HeLa carcinoma cell under 2D and 3D cell culture conditions

Metabolic reprogramming has been coined as a hallmark of cancer, accompanied by which the alterations in metabolite levels have profound effects on gene expression, cellular differentiation, and the tumor environment. Yet a systematic evaluation of quenching and extraction procedures for quantitative metabolome profiling of tumor cells is currently lacking. To achieve this, this study is aimed at establishing an unbiased and leakage-free metabolome preparation protocol for HeLa carcinoma cell. We evaluated 12 combinations of quenching and extraction methods from three quenchers (liquid nitrogen, -40°C 50% methanol, 0.5°C normal saline) and four extractants (-80°C 80% methanol, 0.5°C methanol/chloroform/water [1:1:1 v/v/v], 0.5°C 50% acetonitrile, 75°C 70% ethanol) for global metabolite profiling of adherent HeLa carcinoma cells. Based on the isotope dilution mass spectrometry (IDMS) method, gas/liquid chromatography in tandem with mass spectrometry was used to quantitatively determine 43 metabolites including sugar phosphates, organic acids, amino acids (AAs), adenosine nucleotides, and coenzymes involved in central carbon metabolism. The results showed that the total amount of the intracellular metabolites in cell extracts obtained using different sample preparation procedures with the IDMS method ranged from 21.51 to 295.33 nmol per million cells. Among 12 combinations, cells that washed twice with phosphate buffered saline (PBS), quenched with liquid nitrogen, and then extracted with 50% acetonitrile were found to be the most optimal method to acquire intracellular metabolites with high efficiency of metabolic arrest and minimal loss during sample preparation. In addition, the same conclusion was drawn as these 12 combinations were applied to obtain quantitative metabolome data from three-dimensional (3D) tumor spheroids. Furthermore, a case study was carried out to evaluate the effect of doxorubicin (DOX) on both adherent cells and 3D tumor spheroids using quantitative metabolite profiling. Pathway enrichment analysis using targeted metabolomics data showed that DOX exposure would significantly affect AA metabolism-related pathways, which might be related to the mitigation of redox stress. Strikingly, our data suggested that compared to two-dimensional (2D) cells the increased intracellular glutamine level in 3D cells benefited replenishing the tricarboxylic acid (TCA) cycle when the glycolysis was limited after dosing with DOX. Taken together, this study provides a well-established quenching and extraction protocol for quantitative metabolome profiling of HeLa carcinoma cell under 2D and 3D cell culture conditions. Based on this, quantitative time-resolved metabolite data can serve to the generation of hypotheses on metabolic reprogramming to reveal its important role in tumor development and treatment.

Patient subgrouping with distinct survival rates via integration of multiomics data on a Grassmann manifold

Background

Patient subgroups are important for easily understanding a disease and for providing precise yet personalized treatment through multiple omics dataset integration. Multiomics datasets are produced daily. Thus, the fusion of heterogeneous big data into intrinsic structures is an urgent problem. Novel mathematical methods are needed to process these data in a straightforward way.

Results

We developed a novel method for subgrouping patients with distinct survival rates via the integration of multiple omics datasets and by using principal component analysis to reduce the high data dimensionality. Then, we constructed similarity graphs for patients, merged the graphs in a subspace, and analyzed them on a Grassmann manifold. The proposed method could identify patient subgroups that had not been reported previously by selecting the most critical information during the merging at each level of the omics dataset. Our method was tested on empirical multiomics datasets from The Cancer Genome Atlas.

Conclusion

Through the integration of microRNA, gene expression, and DNA methylation data, our method accurately identified patient subgroups and achieved superior performance compared with popular methods.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12911-022-01938-y.

WINNER: A network biology tool for biomolecular characterization and prioritization

BACKGROUND AND CONTRIBUTION

In network biology, molecular functions can be characterized by network-based inference, or "guilt-by-associations." PageRank-like tools have been applied in the study of biomolecular interaction networks to obtain further the relative significance of all molecules in the network. However, there is a great deal of inherent noise in widely accessible data sets for gene-to-gene associations or protein-protein interactions. How to develop robust tests to expand, filter, and rank molecular entities in disease-specific networks remains an ad hoc data analysis process.

RESULTS

We describe a new biomolecular characterization and prioritization tool called Weighted In-Network Node Expansion and Ranking (WINNER). It takes the input of any molecular interaction network data and generates an optionally expanded network with all the nodes ranked according to their relevance to one another in the network. To help users assess the robustness of results, WINNER provides two different types of statistics. The first type is a node-expansion p-value, which helps evaluate the statistical significance of adding "non-seed" molecules to the original biomolecular interaction network consisting of "seed" molecules and molecular interactions. The second type is a node-ranking p-value, which helps evaluate the relative statistical significance of the contribution of each node to the overall network architecture. We validated the robustness of WINNER in ranking top molecules by spiking noises in several network permutation experiments. We have found that node degree-preservation randomization of the gene network produced normally distributed ranking scores, which outperform those made with other gene network randomization techniques. Furthermore, we validated that a more significant proportion of the WINNER-ranked genes was associated with disease biology than existing methods such as PageRank. We demonstrated the performance of WINNER with a few case studies, including Alzheimer's disease, breast cancer, myocardial infarctions, and Triple negative breast cancer (TNBC). In all these case studies, the expanded and top-ranked genes identified by WINNER reveal disease biology more significantly than those identified by other gene prioritizing software tools, including Ingenuity Pathway Analysis (IPA) and DiAMOND.

CONCLUSION

WINNER ranking strongly correlates to other ranking methods when the network covers sufficient node and edge information, indicating a high network quality. WINNER users can use this new tool to robustly evaluate a list of candidate genes, proteins, or metabolites produced from high-throughput biology experiments, as long as there is available gene/protein/metabolic network information.

Evaluating a therapeutic window for precision medicine by integrating genomic profiles and p53 network dynamics

The response variation to anti-cancer drugs originates from complex intracellular network dynamics of cancer. Such dynamic networks present challenges to determining optimal drug targets and stratifying cancer patients for precision medicine, although several cancer genome studies provided insights into the molecular characteristics of cancer. Here, we introduce a network dynamics-based approach based on attractor landscape analysis to evaluate the therapeutic window of a drug from cancer signaling networks combined with genomic profiles. This approach allows for effective screening of drug targets to explore potential target combinations for enhancing the therapeutic window of drug responses. We also effectively stratify patients into desired/undesired response groups using critical genomic determinants, which are network-specific origins of variability to drug response, and their dominance relationship. Our methods provide a viable and quantitative framework to connect genotype information to the phenotypes of drug response with regard to network dynamics determining the therapeutic window.

The Wnt pathway can stabilize hybrid phenotypes in the epithelial-mesenchymal transition: A logical modeling approach

The Wnt pathway is important to regulate a variety of biochemical functions and can contribute to cancer development through its influence on the epithelial-mesenchymal transition (EMT). Multiple circuits have been reported to participate in the regulation of the Wnt signaling, however, the way these circuits coordinately regulate this signaling is still unclear. Moreover, the mechanisms responsible for the appearance of hybrid phenotypes (cells presenting both E and M features) are not well determined. The hybrid phenotype can present much higher metastatic potential than the mesenchymal phenotype. In this study, we propose a Boolean model of the Wnt pathway signaling contemplating recent published biochemical information on hepatocarcinoma. The model presents good coherence with experimental data for perturbed and wild-type cases. With the model, we propose two new molecular circuits involving several molecules that can stabilize hybrid states during the EMT. Moreover, we found that the two well studied circuits, AKT1/β-catenin and SNAIL1/miR-34, can cooperate with the predicted ones to favor the stabilization of the hybrid states. These findings highlight some possible unrecognized mechanisms during Wnt signaling and may provide alternative therapeutic strategies to control cancer metastatization.

Artificial intelligence in cancer target identification and drug discovery

Artificial intelligence is an advanced method to identify novel anticancer targets and discover novel drugs from biology networks because the networks can effectively preserve and quantify the interaction between components of cell systems underlying human diseases such as cancer. Here, we review and discuss how to employ artificial intelligence approaches to identify novel anticancer targets and discover drugs. First, we describe the scope of artificial intelligence biology analysis for novel anticancer target investigations. Second, we review and discuss the basic principles and theory of commonly used network-based and machine learning-based artificial intelligence algorithms. Finally, we showcase the applications of artificial intelligence approaches in cancer target identification and drug discovery. Taken together, the artificial intelligence models have provided us with a quantitative framework to study the relationship between network characteristics and cancer, thereby leading to the identification of potential anticancer targets and the discovery of novel drug candidates.

A Boolean Model of the Proliferative Role of the lncRNA XIST in Non-Small Cell Lung Cancer Cells

The long non-coding RNA X inactivate-specific transcript (lncRNA XIST) has been verified as an oncogenic gene in non-small cell lung cancer (NSCLC) whose regulatory role is largely unknown. The important tumor suppressors, microRNAs: miR-449a and miR-16 are regulated by lncRNA XIST in NSCLC, these miRNAs share numerous common targets and experimental evidence suggests that they synergistically regulate the cell-fate regulation of NSCLC. LncRNA XIST is known to sponge miR-449a and miR-34a, however, the regulatory network connecting all these non-coding RNAs is still unknown. Here we propose a Boolean regulatory network for the G1/S cell cycle checkpoint in NSCLC contemplating the involvement of these non-coding RNAs. Model verification was conducted by comparison with experimental knowledge from NSCLC showing good agreement. The results suggest that miR-449a regulates miR-16 and p21 activity by targeting HDAC1, c-Myc, and the lncRNA XIST. Furthermore, our circuit perturbation simulations show that five circuits are involved in cell fate determination between senescence and apoptosis. The model thus allows pinpointing the direct cell fate mechanisms of NSCLC. Therefore, our results support that lncRNA XIST is an attractive target of drug development in tumor growth and aggressive proliferation of NSCLC, and promising results can be achieved through tumor suppressor miRNAs.

Multi-omics data integration approaches for precision oncology

Next-generation sequencing (NGS) has been pivotal to enhance the molecular characterization of human malignancies, allowing multiple omics data types to be available for cancer researchers and practitioners. In this context,...

The future of research in hematology: Integration of conventional studies with real-world data and artificial intelligence

Most national health-care systems approve new drugs based on data of safety and efficacy from large randomized clinical trials (RCTs). Strict selection biases and study-entry criteria of subjects included in RCTs often do not reflect those of the population where a therapy is intended to be used. Compliance to treatment in RCTs also differs considerably from real world settings and the relatively small size of most RCTs make them unlikely to detect rare but important safety signals. These and other considerations may explain the gap between evidence generated in RCTs and translating conclusions to health-care policies in the real world. Real-world evidence (RWE) derived from real-world data (RWD) is receiving increasing attention from scientists, clinicians, and health-care policy decision-makers - especially when it is processed by artificial intelligence (AI). We describe the potential of using RWD and AI in Hematology to support research and health-care decisions.

Metapath-Based Deep Convolutional Neural Network for Predicting miRNA-Target Association on Heterogeneous Network

Predicting the interactions between microRNAs (miRNAs) and target genes is of great significance for understanding the regulatory mechanism of miRNA and treating complex diseases. The emergence of large-scale, heterogeneous biological networks has offered unprecedented opportunities for revealing miRNA-associated target genes. However, there are still some limitations about automatically learn the feature information of the network in the existing methods. Since network representation learning can self-adaptively capture structure information of the network, we propose a framework based on heterogeneous network representation, MDCNN (Metapath-Based Deep Convolutional Neural Network), to predict the associations between miRNAs and target genes. MDCNN samples the paths between the node pairs in the form of meta-path based on the heterogeneous information network (HIN) about miRNAs and target genes. Then the node feature and the path feature which is learned by the Deep Convolutional Neural Network (DCNN) are spliced together as the representation of the miRNA-target gene, to predict the miRNA-target gene interactions. The experiment results indicate that the performance of MDCNN outperforms other methods in multiple validation metrics by fivefold cross validation. We set an ablation study to identify the necessity of miRNA similarity and target gene similarity for improving the prediction ability of MDCNN. The case studies on hsa-miR-26b-5p and CDKN1A further demonstrates that MDCNN can successfully predict potential miRNA-target gene interactions.

Identification of a Prognostic Index Based on a Metabolic-Genomic Landscape Analysis of Hepatocellular Carcinoma (HCC)

Background

Metabolic disorders have attracted increasing attention from scientists who conduct research on various tumours, especially hepatocellular carcinoma (HCC). The purpose of this study was to assess the prognostic significance of metabolism in HCC.

Methods

The expression profiles of metabolism-related genes (MRGs) of 349 surviving HCC patients were extracted from The Cancer Genome Atlas (TCGA) database. Subsequently, a series of biomedical computational algorithms were used to identify a seven-MRG signature as a prognostic model. GSEA indicated the function and pathway enrichment of these MRGs. Then, drug sensitivity analysis was used to identify the hub gene, which was tested using IHC staining.

Results

A total of 420 differential MRGs and 116 differentially expressed transcription factors (TFs) were identified in HCC patients based on data from the TCGA database. The GO and KEGG enrichment analyses indicated that metabolic disturbance might be involved in the development of HCC. LASSO regression analysis was used to construct a seven-MRG signature (DHDH, ENO1, G6PD, LPCAT1, PDE6D, PIGU and PPAT) that could predict the prognosis of HCC patients. GSEA revealed the functional and pathway enrichment of these seven MRGs. Then, drug sensitivity analysis indicated that G6PD might play a key role in the prognosis of HCC by promoting chemoresistance. Finally, we used IHC staining to demonstrate the relationship between G6PD expression levels and clinical parameters in HCC patients.

Conclusion

The results of this study provide a potential method for predicting the prognosis of HCC patients and avenues for further studies of HCC metabolism. Moreover, the function of G6PD may play a key role in the development and progression of HCC.

Integrative Network Fusion: A Multi-Omics Approach in Molecular Profiling

Recent technological advances and international efforts, such as The Cancer Genome Atlas (TCGA), have made available several pan-cancer datasets encompassing multiple omics layers with detailed clinical information in large collection of samples. The need has thus arisen for the development of computational methods aimed at improving cancer subtyping and biomarker identification from multi-modal data. Here we apply the Integrative Network Fusion (INF) pipeline, which combines multiple omics layers exploiting Similarity Network Fusion (SNF) within a machine learning predictive framework. INF includes a feature ranking scheme (rSNF) on SNF-integrated features, used by a classifier over juxtaposed multi-omics features (juXT). In particular, we show instances of INF implementing Random Forest (RF) and linear Support Vector Machine (LSVM) as the classifier, and two baseline RF and LSVM models are also trained on juXT. A compact RF model, called rSNFi, trained on the intersection of top-ranked biomarkers from the two approaches juXT and rSNF is finally derived. All the classifiers are run in a 10x5-fold cross-validation schema to warrant reproducibility, following the guidelines for an unbiased Data Analysis Plan by the US FDA-led initiatives MAQC/SEQC. INF is demonstrated on four classification tasks on three multi-modal TCGA oncogenomics datasets. Gene expression, protein expression and copy number variants are used to predict estrogen receptor status (BRCA-ER, N = 381) and breast invasive carcinoma subtypes (BRCA-subtypes, N = 305), while gene expression, miRNA expression and methylation data is used as predictor layers for acute myeloid leukemia and renal clear cell carcinoma survival (AML-OS, N = 157; KIRC-OS, N = 181). In test, INF achieved similar Matthews Correlation Coefficient (MCC) values and 97% to 83% smaller feature sizes (FS), compared with juXT for BRCA-ER (MCC: 0.83 vs. 0.80; FS: 56 vs. 1801) and BRCA-subtypes (0.84 vs. 0.80; 302 vs. 1801), improving KIRC-OS performance (0.38 vs. 0.31; 111 vs. 2319). INF predictions are generally more accurate in test than one-dimensional omics models, with smaller signatures too, where transcriptomics consistently play the leading role. Overall, the INF framework effectively integrates multiple data levels in oncogenomics classification tasks, improving over the performance of single layers alone and naive juxtaposition, and provides compact signature sizes.

Minichromosome maintenance 6 complex component identified by bioinformatics analysis and experimental validation in esophageal squamous cell carcinoma

Esophageal squamous cell carcinoma (ESCC), the main subtype of esophageal cancer (EC), is a common lethal type of cancer with a high mortality rate. The aim of the present study was to select key relevant genes and identify potential mechanisms involved in the development of ESCC based on bioinformatics analysis. Minichromosome maintenance 6 complex component (MCM6) has been identified to be upregulated in multiple malignancies; however, its contributions to ESCC remain unclear. For the purposes of the present study, four datasets were downloaded from the Gene Expression Omnibus (GSE63941, GSE26886, GSE17351 and GSE77861), and the intersection of the differentially expressed genes was obtained using a Venn diagram. The protein‑protein interaction was then constructed, and the modules were verified by Cytoscape, in which the key genes have a high connectivity degree with other genes. Gene Ontology and Kyoto Encyclopedia of Genes and Genomes pathway were subsequently filtered out to analyze the development of ESCC. MCM6, an upregulated gene, was selected and connected with most of the other genes, for further research validation. The expression levels of MCM6 were then assessed using the Oncomine, GEPIA and UALCAN databases and validated in both ESCC tissues samples and cell lines by immunohistochemistry and RT‑qPCR. Cell counting kit‑8 (CCK‑8), flow cytometry, wound healing and Transwell assays were used to determine the proliferation, apoptosis, cell cycle, migration and invasion of ESCC cells. A total of 24 genes were identified by a series of bioinformatics analyses and the results revealed that the genes were associated with DNA replication and cell cycle. Experimental validation revealed that MCM6 expression was significantly elevated in both ESCC tissues and cell lines. The results were consistent with those of bioinformatics analysis. Furthermore, the knockdown of MCM6 inhibited cell proliferation, migration and invasion and promoted cell apoptosis, and made cells arrested in S stage. In summary, the findings of bioinformatics analysis provided a novel hypothesis for ESCC progression. In particular, the aberrantly elevated expression of MCM6 is a potential biomarker for ESCC diagnosis and treatment.

Patient-specific multi-omics models and the application in personalized combination therapy

The rapid advancement of high-throughput technologies and sharp decrease in cost have opened up the possibility to generate large amount of multi-omics data on an individual basis. The development of high-throughput -omics, including genomics, epigenomics, transcriptomics, proteomics, metabolomics and microbiomics, enables the application of multi-omics technologies in the clinical settings. Combination therapy, defined as disease treatment with two or more drugs to achieve efficacy with lower doses or lower drug toxicity, is the basis for the care of diseases like cancer. Patient-specific multi-omics data integration can help the identification and development of combination therapies. In this review, we provide an overview of different -omics platforms, and discuss the methods for multi-omics, high-throughput, data integration, personalized combination therapy.

Construction, comparison and evolution of networks in life sciences and other disciplines

Network approaches have become pervasive in many research fields. They allow for a more comprehensive understanding of complex relationships between entities as well as their group-level properties and dynamics. Many networks change over time, be it within seconds or millions of years, depending on the nature of the network. Our focus will be on comparative network analyses in life sciences, where deciphering temporal network changes is a core interest of molecular, ecological, neuropsychological and evolutionary biologists. Further, we will take a journey through different disciplines, such as social sciences, finance and computational gastronomy, to present commonalities and differences in how networks change and can be analysed. Finally, we envision how borrowing ideas from these disciplines could enrich the future of life science research.

A U-statistics for integrative analysis of multilayer omics data

MOTIVATION

The emerging multilayer omics data provide unprecedented opportunities for detecting biomarkers that are associated with complex diseases at various molecular levels. However, the high-dimensionality of multiomics data and the complex disease etiologies have brought tremendous analytical challenges.

RESULTS

We developed a U-statistics-based non-parametric framework for the association analysis of multilayer omics data, where consensus and permutation-based weighting schemes are developed to account for various types of disease models. Our proposed method is flexible for analyzing different types of outcomes as it makes no assumptions about their distributions. Moreover, it explicitly accounts for various types of underlying disease models through weighting schemes and thus provides robust performance against them. Through extensive simulations and the application to dataset obtained from the Alzheimer's Disease Neuroimaging Initiatives, we demonstrated that our method outperformed the commonly used kernel regression-based methods.

AVAILABILITY AND IMPLEMENTATION

The R-package is available at https://github.com/YaluWen/Uomic.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

Translating Cancer Molecular Variability into Personalized Information Using Bulk and Single Cell Approaches

Cancer research is striving toward new frontiers of assigning the correct personalized drug(s) to a given patient. However, extensive tumor heterogeneity poses a major obstacle. Tumors of the same type often respond differently to therapy, due to patient-specific molecular aberrations and/or untargeted tumor subpopulations. It is frequently not possible to determine a priori which patients will respond to a certain therapy or how an efficient patient-specific combined therapy should be designed. Large-scale datasets have been growing at an accelerated pace and various technologies and analytical tools for single cell and bulk level analyses are being developed to extract significant individualized signals from such heterogeneous data. However, personalized therapies that dramatically alter the course of the disease remain scarce, and most tumors still respond poorly to medical care. In this review, the basic concepts of bulk and single cell approaches are discussed, as well as their emerging role in individualized designs of drug therapies, including the advantages and limitations of their applications in personalized medicine.

Recent advances in network medicine: From disease mechanisms to new treatment strategies

Conventional reductionist approaches have guided most of our understanding in disease diagnostic and treatment. However, most diseases are not consequence of perturbations in a single protein or metabolite, but rather of the effect that these perturbations have in their cellular context. The emerging field of network medicine offers a set of tools to explore molecular networks and to retrieve insights about mechanisms of different diseases. The study of the protein interactome, the map of physical interactions among human proteins, revealed that disease proteins tend to interact with each other, linking diseases to well-defined interactome neighborhoods. These disease-associated neighborhoods have been defined as disease modules, and they can uncover the biological significance of genes identified by genetic studies, reveal molecular mechanisms that connect different phenotypes, and help identify new pharmacological strategies for disease treatment. Therefore, network medicine offers a framework in which the complexity of different aspects of multiple sclerosis can be explored in an integrative fashion, which can ultimately provide insights about disease mechanisms and treatment.

Single Spheroid Metabolomics: Optimizing Sample Preparation of Three-Dimensional Multicellular Tumor Spheroids

Tumor spheroids are important model systems due to the capability of capturing in vivo tumor complexity. In this work, the experimental design of metabolomics workflows using three-dimensional multicellular tumor spheroid (3D MTS) models is addressed. Non-scaffold based cultures of the HCT116 colon carcinoma cell line delivered highly reproducible MTSs with regard to size and other key parameters (such as protein content and fraction of viable cells) as a prerequisite. Carefully optimizing the multiple steps of sample preparation, the developed procedure enabled us to probe the metabolome of single MTSs (diameter range 790 ± 22 µm) in a highly repeatable manner at a considerable throughput. The final protocol consisted of rapid washing of the spheroids on the cultivation plate, followed by cold methanol extraction. ¹³C enriched internal standards, added upon extraction, were key to obtaining the excellent analytical figures of merit. Targeted metabolomics provided absolute concentrations with average biological repeatabilities of <20% probing MTSs individually. In a proof of principle study, MTSs were exposed to two metal-based anticancer drugs, oxaliplatin and the investigational anticancer drug KP1339 (sodium trans-[tetrachloridobis(1H-indazole)ruthenate(III)]), which exhibit distinctly different modes of action. This difference could be recapitulated in individual metabolic shifts observed from replicate single MTSs. Therefore, biological variation among single spheroids can be assessed using the presented analytical strategy, applicable for in-depth anticancer drug metabolite profiling.

CEPICS: A Comparison and Evaluation Platform for Integration Methods in Cancer Subtyping

Cancer subtypes can improve our understanding of cancer, and suggest more precise treatment for patients. Multi-omics molecular data can characterize cancers at different levels. Up to now, many computational methods that integrate multi-omics data for cancer subtyping have been proposed. However, there are no consistent criteria to evaluate the integration methods due to the lack of gold standards (e.g., the number of subtypes in a specific cancer). Since comprehensive evaluation and comparison between different methods serves as a useful tool or guideline for users to select an optimal method for their own purpose, we develop a scalable platform, CEPICS, for comprehensively evaluating and comparing multi-omics data integration methods in cancer subtyping. Given a user-specified maximum number of subtypes, k-max, CEPICS provides (1) cancer subtyping results using up to five built-in state-of-the-art integration methods under the number of subtypes from two to k-max, (2) a report including the evaluation of each user-selected method and comparisons across them using clustering performance metrics and clinical survival analysis, and (3) an overall analysis of subtyping results by different methods representing a robust cancer subtype prediction for samples. Furthermore, users can upload subtyping results of their own methods to compare with the built-in methods. CEPICS is implemented as an R package and is freely available at https://github.com/GaoLabXDU/CEPICS.

Biomarker Potential of Plasma MicroRNA-150-5p in Prostate Cancer

Background and Objectives: Over decades, prostate cancer (PCa) has become one of the leading causes of cancer mortality in men. Extensive evidence exists that microRNAs (miRNAs or miRs) are key players in PCa and a new class of non-invasive cancer biomarkers. Materials and Methods: We performed miRNA profiling in plasma and tissues of PCa patients and attempted the validation of candidate individual miRs as biomarkers. Results: The comparison of tissue and plasma profiling results revealed five commonly dysregulated miRs, namely, miR-130a-3p, miR-145-5p, miR-148a-3p, miR-150-5p, and miR-365a-3p, of which only three show concordant changes—miR-130a-3p and miR-150-5p were downregulated and miR-148a-3p was upregulated in both tissue and plasma samples, respectively. MiR-150-5p was validated as significantly downregulated in both plasma and tissue cancer samples, with a fold change of −2.697 (p < 0.001), and −1.693 (p = 0.035), respectively. ROC analysis showed an area under the curve (AUC) of 0.817 (95% CI: 0.680–0.995) for plasma samples and 0.809 (95% CI: 0.616–1.001) for tissue samples. Conclusions: We provide data indicating that miR-150-5p plasma variations in PCa patients are associated with concordant changes in prostate cancer tissues; however, given the heterogeneous nature of previous findings of miR-150-5p expression in PCa cells, additional future studies of a larger sample size are warranted in order to confirm the biomarker potential and role of miRNA-150-5p in PCa biology.

Intersectin 1 (ITSN1) identified by comprehensive bioinformatic analysis and experimental validation as a key candidate biological target in breast cancer

Background

As one of the most common cancers, breast carcinoma is the most common disease in women. Intersectin 1 (ITSN1) contributes to the actin cytoskeleton reconstruction in breast cancer.

Purpose

The objective of this study to explore the functions of ITSN1 in breast carcinoma.

Methods

We downloaded microarray datasets GSE8087, GSE50697, and GSE98238 from the Gene Expression Omnibus database. Differentially expressed genes (DEGs) were used to construct a protein–protein interaction (PPI) network using STRING database, and the modules from PPI network were verified by Cytoscape software. Gene ontology terms and Kyoto Encyclopedia of Gene and Genome pathway were used to analyze the biological functions using the DAVID database. ONCOMINE, GEPIA, UALCAN, and Human Protein Atlas databases were used to investigate the characteristics of ITSN1 for the prognosis of breast carcinoma. Cell counting kit-8, flow cytometry, and colony formation assays were used to detect cell viability, cell apoptosis, and cell proliferation. RT-PCR and Western blot assays were used to detect ITSN1, Ki67, and cleaved caspase-3 expressions.

Results

Low expressions of ITSN1 were significantly associated with clinical cancer stages. RT-PCR and Western blot analysis showed low expression of ITSN1 in breast cancer tissues and cell lines. ITSN1 inhibition could promote cell proliferation and inhibit cell apoptosis, while ITSN1 overexpression could inhibit cell proliferation and increase cell apoptosis by regulating the levels of expression of Ki67 and cleaved-caspase-3.

Conclusion

The results indicated that ITSN1 could be a prognostic biomarker for survivals of breast cancer patients.

Systems Biology approaches to cancer: towards new therapeutical strategies and personalized approaches

Network approaches are ubiquitous, from social and ecological systems up to complex biological processes. In our recently published work we used the network framework for a Systems Medicine approach to multiple cancer types, in order to highlight similitudes and differences that can be exploited to extend existing therapeutical strategies. These approaches shed new light to oncological processes, but allow also to pose "old" questions (like the search for novel drug targets) in a "new" way.

RO3280: A Novel PLK1 Inhibitor, Suppressed the Proliferation of MCF-7 Breast Cancer Cells Through the Induction of Cell Cycle Arrest at G2/M Point

BACKGROUND

As a member of serine/threonine-protein kinase, Polo.like kinase 1 (PLK1) plays crucial roles during mitosis and also contributes to DNA damage response and repair. PLK1 is aberrantly expressed in many types of tumor cells and increased levels of PLK1 are closely related to tumorigenesis and poor clinical outcomes. Therefore, PLK1 is accepted as one of the potential targets for the discovery of novel anticancer agents. The objective of this study was to assess the cytotoxic effects of a novel PLK1 inhibitor, RO3280, against MCF-7, human breast cancer cells; HepG2, human hepatocellular carcinoma cells; and PC3, human prostate cancer cells, as well as non-cancerous L929 fibroblast cells.

METHODS

Antiproliferative activity of RO3280 was examined using the XTT assay. Flow cytometry assay was performed to evaluate cell cycle distribution, apoptosis, multicaspase activity, mitochondrial membrane potential, and DNA damage response. Apoptosis with fluorescence imaging studies was also examined.

RESULTS

According to the results of XTT assay, although RO3280 displayed potent cytotoxicity in all treated cancer cells, the most sensitive cell line was identified as MCF-7 cells that were selected for further studies. The compound induced a cell cycle arrest in MCF-7 cells at G2/M phase and significantly induced apoptosis, multicaspase activity, DNA damage response, and decreased mitochondrial membrane potential of MCF-7 cells.

CONCLUSION

Overall, RO3280 induces anticancer effects promoted mainly by DNA damage, cell cycle arrest, and apoptosis in breast cancer cells. Further studies are needed to assess its usability as an anticancer agent with specific cancer types.