Xin Lai

Tampere University, Faculty of Medicine and Health Technology (MET), Faculty Member

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The main objective of my research is to develop a Systems Biology approach that is applicable to basic and clinical medicines as well as basic researches of biology. Such an approach integrates experiments in interactive cycles with bioinformatics and mathematical modelling provides a promising way to investigate biomedical problems at the system-level. Keywords: Mathematical Modeling, Omics Data Analysis, Network Biology, MicroRNA, Cancer.

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Papers by Xin Lai

Sepsis and Autoimmune Disease: Pathology, Systems Medicine, and Artificial Intelligence

Pathologic conditions in which the body׳s own response to an insult is the most damaging aspect o... more Pathologic conditions in which the body׳s own response to an insult is the most damaging aspect of the disease, such as in auto-immunity and sepsis, have seen researchers and clinicians struggle to find therapeutic targets for decades. Large clinical cohorts and advanced molecular biology are key elements in the search for new medications. Methodology with increasing resolution is constantly being developed to identify new subsets of cells embedded in the disease framework and to map their complex gene expression profile, secretome and epigenome. Big data that is thus being generated require advanced computational methods in order to distil knowledge from them, and in order to use them as the basis for modeling approaches. Models need to integrate the spatial, temporal and organizational domains in order to reflect biology with a certain degree of fidelity. The ultimate goal of the combined efforts of clinical studies, molecular biology with cutting-edge technology and comprehensive...

Editorial: Macrophage immunity and metabolism in cancer: Novel diagnostic and therapeutic strategies

Frontiers in Immunology, Dec 16, 2022

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A network medicine approach for identifying diagnostic and prognostic biomarkers and exploring drug repurposing in human cancer

Computational and Structural Biotechnology Journal

Cancer is a heterogeneous disease mainly driven by abnormal gene perturbations in regulatory netw... more Cancer is a heterogeneous disease mainly driven by abnormal gene perturbations in regulatory networks. Therefore, it is appealing to identify the common and specific perturbed genes from multiple cancer networks. We developed an integrative network medicine approach to identify novel biomarkers and investigate drug repurposing across cancer types. We used a network-based method to prioritize genes in cancer-specific networks reconstructed using human transcriptome and interactome data. The prioritized genes show extensive perturbation and strong regulatory interaction with other highly perturbed genes, suggesting their vital contribution to tumorigenesis and tumor progression, and are therefore regarded as cancer genes. The cancer genes detected show remarkable performances in discriminating tumors from normal tissues and predicting survival times of cancer patients. Finally, we developed a network proximity approach to systematically screen drugs and identified dozens of candidates with repurposable potential in several cancer types. Taken together, we demonstrated the power of the network medicine approach to identify novel biomarkers and repurposable drugs in multiple cancer types. We have also made the data and code freely accessible to ensure reproducibility and reusability of the developed computational workflow.

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Artificial intelligence in cancer target identification and drug discovery

Signal Transduction and Targeted Therapy

Artificial intelligence is an advanced method to identify novel anticancer targets and discover n... more 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 lead...

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Multi-Level Computational Modeling of Anti-Cancer Dendritic Cell Vaccination Utilized to Select Molecular Targets for Therapy Optimization

Frontiers in Cell and Developmental Biology, 2022

Dendritic cells (DCs) can be used for therapeutic vaccination against cancer. The success of this... more Dendritic cells (DCs) can be used for therapeutic vaccination against cancer. The success of this therapy depends on efficient tumor-antigen presentation to cytotoxic T lymphocytes (CTLs) and the induction of durable CTL responses by the DCs. Therefore, simulation of such a biological system by computational modeling is appealing because it can improve our understanding of the molecular mechanisms underlying CTL induction by DCs and help identify new strategies to improve therapeutic DC vaccination for cancer. Here, we developed a multi-level model accounting for the life cycle of DCs during anti-cancer immunotherapy. Specifically, the model is composed of three parts representing different stages of DC immunotherapy – the spreading and bio-distribution of intravenously injected DCs in human organs, the biochemical reactions regulating the DCs’ maturation and activation, and DC-mediated activation of CTLs. We calibrated the model using quantitative experimental data that account for...

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Mathematical Modelling in Biomedicine: A Primer for the Curious and the Skeptic

International Journal of Molecular Sciences, 2021

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A disease network‐based deep learning approach for characterizing melanoma

International Journal of Cancer, 2021

Multiple types of genomic variations are present in cutaneous melanoma and some of the genomic fe... more Multiple types of genomic variations are present in cutaneous melanoma and some of the genomic features may have an impact on the prognosis of the disease. The access to genomics data via public repositories such as The Cancer Genome Atlas (TCGA) allows for a better understanding of melanoma at the molecular level, therefore making characterization of substantial heterogeneity in melanoma patients possible. Here, we proposed an approach that integrates genomics data, a disease network, and a deep learning model to classify melanoma patients for prognosis, assess the impact of genomic features on the classification and provide interpretation to the impactful features. We integrated genomics data into a melanoma network and applied an autoencoder model to identify subgroups in TCGA melanoma patients. The model utilizes communities identified in the network to effectively reduce the dimensionality of genomics data into a patient score profile. Based on the score profile, we identified three patient subtypes that show different survival times. Furthermore, we quantified and ranked the impact of genomic features on the patient score profile using a machine‐learning technique. Follow‐up analysis of the top‐ranking features provided us with the biological interpretation of them at both pathway and molecular levels, such as their mutation and interactome profiles in melanoma and their involvement in pathways associated with signaling transduction, immune system and cell cycle. Taken together, we demonstrated the ability of the approach to identify disease subgroups using a deep learning model that captures the most relevant information of genomics data in the melanoma network.

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RNA Sequencing of Collecting Duct Renal Cell Carcinoma Suggests an Interaction between miRNA and Target Genes and a Predominance of Deregulated Solute Carrier Genes

Cancers, 2019

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Bacterial Adherence and Dwelling Probability: Two Drivers of Early Alveolar Infection by Streptococcus pneumoniae Identified in Multi-Level Mathematical Modeling

Frontiers in Cellular and Infection Microbiology, 2018

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Systems biology-based investigation of cooperating microRNAs as monotherapy or adjuvant therapy in cancer

Nucleic Acids Research, 2019

MicroRNAs (miRNAs) are short, noncoding RNAs that regulate gene expression by suppressing mRNA tr... more MicroRNAs (miRNAs) are short, noncoding RNAs that regulate gene expression by suppressing mRNA translation and reducing mRNA stability. A miRNA can potentially bind many mRNAs, thereby affecting the expression of oncogenes and tumor suppressor genes as well as the activity of whole pathways. The promise of miRNA therapeutics in cancer is to harness this evolutionarily conserved mechanism for the coordinated regulation of gene expression, and thus restoring a normal cell phenotype. However, the promiscuous binding of miRNAs can provoke unwanted off-target effects, which are usually caused by high-dose single-miRNA treatments. Thus, it is desirable to develop miRNA therapeutics with increased specificity and efficacy. To achieve that, we propose the concept of miRNA cooperativity in order to exert synergistic repression on target genes, thus lowering the required total amount of miRNAs. We first review miRNA therapies in clinical application. Next, we summarize the knowledge on the mo...

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Serum levels of miR-320 family members are associated with clinical parameters and diagnosis in prostate cancer patients

Oncotarget, 2017

We studied the association of the serum levels of the microRNA family members miR-320a/-b/-c with... more We studied the association of the serum levels of the microRNA family members miR-320a/-b/-c with clinico-pathological data to assess their applicability as diagnostic biomarker in prostate cancer (PCa) patients. The levels of miR-320a/b/-c in 3 groups were evaluated by qRT-PCR (145 patients with PCa, 31 patients with benign prostatic hyperplasia (BPH) and 19 healthy controls). The levels of the three family members of miR-320 were directly correlated within each group (P < 0.001), but they differed significantly among the three groups (P < 0.001). The serum levels of the miR-320 family members were significantly increased in older patients compared to younger patients (≤ 66 years vs. > 66 years, P ≤ 0.001). In addition, the levels of all three miR-320 family members were significantly different in patients with low tumor stage compared with those with high tumor stage (miR-320a: P = 0.034; miR-320b: P = 0.006; miR-320c: P = 0.007) and in patients with low serum PSA compared with those with high serum PSA (≤ 4 ng vs. > 4 ng; miR-320a: P = 0.003; miR-320b: P = 0.003; miR-320c: P = 0.006). The levels of these miRNAs were inversely correlated with serum PSA levels. Detection in the serum samples of PCa patients with or without PSA relapse revealed higher levels of miR-320a/-b/-c in the group without PSA relapse before/after radical prostatectomy than in that with PCa relapse. In summary, the differences among the PCa/BPH/healthy control groups with respect to miR-320a/-b/-c levels in conjunction with higher levels in patients without a PSA relapse than in those with a relapse suggest the diagnostic potential of these miRNA-320 family members in PCa patients.

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Innate extracellular vesicles from melanoma patients suppress β-catenin in tumor cells by miRNA-34a

Life Science Alliance, 2019

Upon tumor development, new extracellular vesicles appear in circulation. Our knowledge of their ... more Upon tumor development, new extracellular vesicles appear in circulation. Our knowledge of their relative abundance, function, and overall impact on cancer development is still preliminary. Here, we demonstrate that plasma extracellular vesicles (pEVs) of non-tumor origin are persistently increased in untreated and post-excision melanoma patients, exhibiting strong suppressive effects on the proliferation of tumor cells. Plasma vesicle numbers, miRNAs, and protein levels were elevated two- to tenfold and detected many years after tumor resection. The vesicles revealed individual and clinical stage-specific miRNA profiles as well as active ADAM10. However, whereas pEV from patients preventing tumor relapse down-regulated β-catenin and blocked tumor cell proliferation in an miR-34a–dependent manner, pEV from metastatic patients lost this ability and stimulated β-catenin–mediated transcription. Cancer-induced pEV may constitute an innate immune mechanism suppressing tumor cell activity...

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A gene regulatory architecture that controls region‐independent dynamics of oligodendrocyte differentiation

Glia, 2019

Oligodendrocytes (OLs) facilitate information processing in the vertebrate central nervous system... more Oligodendrocytes (OLs) facilitate information processing in the vertebrate central nervous system via axonal ensheathment. The structure and dynamics of the regulatory network that mediates oligodendrogenesis are poorly understood. We employed bioinformatics and meta-analysis of highthroughput datasets to reconstruct a regulatory network underpinning OL differentiation. From this network, we identified families of feedforward loops comprising the transcription factors (TFs) Olig2, Sox10, and Tcf7l2 and their targets. Among the targets, we found eight other TFs related to OL differentiation, suggesting a hierarchical architecture in which some TFs (Olig2, Sox10, and Tcf7l2) regulate via feedforward loops the expression of others (Sox2, Sox6, Sox11, Nkx2-2, Nkx6-2, Hes5, Myt1, and Myrf). Model simulations with a kinetic model reproduced the mechanisms of OL differentiation only when in the model, Sox10-mediated repression of Tcf7l2 by miR-338/miR-155 was introduced, a prediction confirmed in genetic functional experiments. Additional model simulations suggested that OLs from dorsal regions emerge through BMP/Sox9 signaling. K E Y W O R D S network biology, ODE mathematical model, Oligodendrocytes, TF-miRNA circuits, TFregulated feedforward loop 1 | INTRODUCTION Rapid information processing in the vertebrate central nervous system (CNS) is dependent on saltatory conduction as a consequence of axonal ensheathment by myelin-producing glial cells (Nave & Werner, 2014). Developmental disturbances in myelin production (i.e., dysmyelination) or impairment of myelin sheaths (i.e., demyelination) are typical hallmarks of neurodegenerative diseases in humans including leukodystrophies and Multiple Sclerosis. Oligodendrocytes (OLs) are the myelin-forming glial cells of the CNS that originate from embryonic neuroepithelial stem cells located at the ventricular zone of the developing spinal cord. OLs are generated from OL precursor cells (OPCs) that are derived from these germinal zones, undergoing a number of developmental stages followed by terminal differentiation and initiation of myelin production (Rowitch, 2004). Myelination is a complex process and requires appropriate coordination and organization of downstream gene regulatory and metabolic processes. For example, genes encoding for myelin proteins are already expressed in the progenitor stages during development, followed by their protein synthesis during OL maturation, and the Abbreviations: FPKM, fragment per kilobase of exon per million fragments mapped;

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Exosomal lncRNAs and cancer: connecting the missing links

Bioinformatics, 2018

Motivation: Extracellular vesicles (EVs), including exosomes and microvesicles, are potent and cl... more Motivation: Extracellular vesicles (EVs), including exosomes and microvesicles, are potent and clinically valuable tools for early diagnosis, prognosis and potentially the targeted treatment of cancer. The content of EVs is closely related to the type and status of the EV-secreting cell. Circulating exosomes are a source of stable RNAs including mRNAs, microRNAs and long noncoding RNAs (lncRNAs). Results: This review outlines the links between EVs, lncRNAs and cancer. We highlight communication networks involving the tumor microenvironment, the immune system and metastasis. We show examples supporting the value of exosomal lncRNAs as cancer biomarkers and therapeutic targets. We demonstrate how a system biology approach can be used to model cell-cell communication via exosomal lncRNAs and to simulate effects of therapeutic interventions. In addition, we introduce algorithms and bioinformatics resources for the discovery of tumor-specific lncRNAs and tools that are applied to determine exosome content and lncRNA function. Finally, this review provides a comprehensive collection and guide to databases for exosomal lncRNAs.

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MiR-205-5p and miR-342-3p cooperate in the repression of the E2F1 transcription factor in the context of anticancer chemotherapy resistance

Theranostics, 2018

High rates of lethal outcome in tumour metastasis are associated with the acquisition of invasive... more High rates of lethal outcome in tumour metastasis are associated with the acquisition of invasiveness and chemoresistance. Several clinical studies indicate that E2F1 overexpression across high-grade tumours culminates in unfavourable prognosis and chemoresistance in patients. Thus, fine-tuning the expression of E2F1 could be a promising approach for treating patients showing chemoresistance. Methods: We integrated bioinformatics, structural and kinetic modelling, and experiments to study cooperative regulation of E2F1 by microRNA (miRNA) pairs in the context of anticancer chemotherapy resistance. Results: We showed that an enhanced E2F1 repression efficiency can be achieved in chemoresistant tumour cells through two cooperating miRNAs. Sequence and structural information were used to identify potential miRNA pairs that can form tertiary structures with E2F1 mRNA. We then employed molecular dynamics simulations to show that among the identified triplexes, miR-205-5p and miR-342-3p can form the most stable triplex with E2F1 mRNA. A mathematical model simulating the E2F1 regulation by the cooperative miRNAs predicted enhanced E2F1 repression, a feature that was verified by in vitro experiments. Finally, we integrated this cooperative miRNA regulation into a more comprehensive network to account for E2F1-related chemoresistance in tumour cells. The network model simulations and experimental data indicate the ability of enhanced expression of both miR-205-5p and miR-342-3p to decrease tumour chemoresistance by cooperatively repressing E2F1. Conclusions: Our results suggest that pairs of cooperating miRNAs could be used as potential RNA therapeutics to reduce E2F1-related chemoresistance.

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Corrigendum: Multiplicity of Mathematical Modeling Strategies to Search for Molecular and Cellular Insights into Bacteria Lung Infection

Frontiers in physiology, 2017

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THP-1-derived macrophages render lung epithelial cells hypo-responsive to Legionella pneumophila – a systems biology study

Scientific Reports, 2017

Immune response in the lung has to protect the huge alveolar surface against pathogens while secu... more Immune response in the lung has to protect the huge alveolar surface against pathogens while securing the delicate lung structure. Macrophages and alveolar epithelial cells constitute the first line of defense and together orchestrate the initial steps of host defense. In this study, we analysed the influence of macrophages on type II alveolar epithelial cells during Legionella pneumophila-infection by a systems biology approach combining experimental work and mathematical modelling. We found that L. pneumophila-infected THP-1-derived macrophages provoke a pro-inflammatory activation of neighboring lung epithelial cells, but in addition render them hypo-responsive to direct infection with the same pathogen. We generated a kinetic mathematical model of macrophage activation and identified a paracrine mechanism of macrophage-secreted IL-1β inducing a prolonged IRAK-1 degradation in lung epithelial cells. This intercellular crosstalk may help to avoid an overwhelming inflammatory response by preventing excessive local secretion of pro-inflammatory cytokines and thereby negatively regulating the recruitment of immune cells to the site of infection. This suggests an important but ambivalent immunomodulatory role of macrophages in lung infection. Pneumonia has the highest mortality rate of all infectious diseases while exhibiting almost no improvements for the last decades 1-3. Among young children, pneumonia still causes more deaths than malaria, AIDS, and measles together 4,5. Disease outcome critically depends on alveolar macrophages and pulmonary epithelial cells as the first line of defense 6,7. Upon pathogen recognition, macrophage-derived cytokines such as TNF-α, IL-1α and IL-1β trigger the response to local infection by activating lung epithelial cells 8-10. As a complementary line of defense, alveolar macrophages decelerate growth of internalized pathogens by apoptosis 11,12. However, pro-inflammatory mechanisms have to be carefully regulated, as organ function has to be maintained as well. Signalling cascades including interleukin-1 receptor-associated kinases (IRAK) and the transcription factor NF-κB are critical for balancing the initial innate immune response 13. Accordingly, interruption of NF-κB signalling compromises host defense whereas enhanced NF-κB activity can lead to lung injury 10,14,15 .

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Multiplicity of Mathematical Modeling Strategies to Search for Molecular and Cellular Insights into Bacteria Lung Infection

Frontiers in Physiology, 2017

Even today two bacterial lung infections, namely pneumonia and tuberculosis, are among the 10 mos... more Even today two bacterial lung infections, namely pneumonia and tuberculosis, are among the 10 most frequent causes of death worldwide. These infections still lack effective treatments in many developing countries and in immunocompromised populations like infants, elderly people and transplanted patients. The interaction between bacteria and the host is a complex system of interlinked intercellular and the intracellular processes, enriched in regulatory structures like positive and negative feedback loops. Severe pathological condition can emerge when the immune system of the host fails to neutralize the infection. This failure can result in systemic spreading of pathogens or overwhelming immune response followed by a systemic inflammatory response. Mathematical modeling is a promising tool to dissect the complexity underlying pathogenesis of bacterial lung infection at the molecular, cellular and tissue levels, and also at the interfaces among levels. In this article, we introduce mathematical and computational modeling frameworks that can be used for investigating molecular and cellular mechanisms underlying bacterial lung infection. Then, we compile and discuss published results on the modeling of regulatory pathways and cell populations relevant for lung infection and inflammation. Finally, we discuss how to make use of this multiplicity of modeling approaches to open new avenues in the search of the molecular and cellular mechanisms underlying bacterial infection in the lung.

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MicroRNA Clusters

Encyclopedia of Systems Biology, 2013

Time delay and protein modulation analysis in a model of RNA silencing

Abstract RNA silencing is a recently discovered mechanism fo r p sttranscriptional regulation of ... more Abstract RNA silencing is a recently discovered mechanism fo r p sttranscriptional regulation of gene expression . Precisely, in RNA interference, RNAi, endogenous ex pr ssed or exogenously promoted small RNAs promote and modulate the degradation of complementa ry messenger RNA involved in the synthesis of targeted proteins. In this paper we investigated th e role of time delay and protein regulation in the posttranslational protein regulation through RNA in terference. Towards this end, we used and modified a simple model accounting for RNAi and used qualitati ve bifurcation analysis, sensitivity analysis and predictive simulations to analyze it. Our results s uggest that some processes in the system, like Dice rmediated FDSRNA mRNA degradation or non specific mRNA degradation, play an important role in the modulation of RNA silencing, whereas silencing seem s virtually independent of modulation in other processes.

Sepsis and Autoimmune Disease: Pathology, Systems Medicine, and Artificial Intelligence

Editorial: Macrophage immunity and metabolism in cancer: Novel diagnostic and therapeutic strategies

Frontiers in Immunology, Dec 16, 2022

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A network medicine approach for identifying diagnostic and prognostic biomarkers and exploring drug repurposing in human cancer

Computational and Structural Biotechnology Journal

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Artificial intelligence in cancer target identification and drug discovery

Signal Transduction and Targeted Therapy

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Multi-Level Computational Modeling of Anti-Cancer Dendritic Cell Vaccination Utilized to Select Molecular Targets for Therapy Optimization

Frontiers in Cell and Developmental Biology, 2022

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Mathematical Modelling in Biomedicine: A Primer for the Curious and the Skeptic

International Journal of Molecular Sciences, 2021

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A disease network‐based deep learning approach for characterizing melanoma

International Journal of Cancer, 2021

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RNA Sequencing of Collecting Duct Renal Cell Carcinoma Suggests an Interaction between miRNA and Target Genes and a Predominance of Deregulated Solute Carrier Genes

Cancers, 2019

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Bacterial Adherence and Dwelling Probability: Two Drivers of Early Alveolar Infection by Streptococcus pneumoniae Identified in Multi-Level Mathematical Modeling

Frontiers in Cellular and Infection Microbiology, 2018

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Systems biology-based investigation of cooperating microRNAs as monotherapy or adjuvant therapy in cancer

Nucleic Acids Research, 2019

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Serum levels of miR-320 family members are associated with clinical parameters and diagnosis in prostate cancer patients

Oncotarget, 2017

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Innate extracellular vesicles from melanoma patients suppress β-catenin in tumor cells by miRNA-34a

Life Science Alliance, 2019

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A gene regulatory architecture that controls region‐independent dynamics of oligodendrocyte differentiation

Glia, 2019

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Exosomal lncRNAs and cancer: connecting the missing links

Bioinformatics, 2018

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MiR-205-5p and miR-342-3p cooperate in the repression of the E2F1 transcription factor in the context of anticancer chemotherapy resistance

Theranostics, 2018

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Corrigendum: Multiplicity of Mathematical Modeling Strategies to Search for Molecular and Cellular Insights into Bacteria Lung Infection

Frontiers in physiology, 2017

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THP-1-derived macrophages render lung epithelial cells hypo-responsive to Legionella pneumophila – a systems biology study

Scientific Reports, 2017

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Multiplicity of Mathematical Modeling Strategies to Search for Molecular and Cellular Insights into Bacteria Lung Infection

Frontiers in Physiology, 2017

Download

MicroRNA Clusters

Encyclopedia of Systems Biology, 2013

Time delay and protein modulation analysis in a model of RNA silencing

Sepsis and Autoimmune Disease: Pathology, Systems Medicine, and Artificial Intelligence

Pathologic conditions in which the body's own response to an insult is the most damaging aspect o... more Pathologic conditions in which the body's own response to an insult is the most damaging aspect of the disease, such as in auto-immunity and sepsis, have seen researchers and clinicians struggle to find therapeutic targets for decades. Large clinical cohorts and advanced molecular biology are key elements in the search for new medications. Methodology with increasing resolution is constantly being developed to identify new subsets of cells embedded in the disease framework and to map their complex gene expression profile, secretome and epigenome. Big data that is thus being generated require advanced computational methods in order to distil knowledge from them, and in order to use them as the basis for modeling approaches. Models need to integrate the spatial, temporal and organizational domains in order to reflect biology with a certain degree of fidelity. The ultimate goal of the combined efforts of clinical studies, molecular biology with cutting-edge technology and comprehensive mathematical modeling is the bench-to-bedside translation of knowledge to reduce the socio-economic burden of disease.

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The Role of MicroRNAs in Cancer Biology and Therapy from a Systems Biology Perspective

Since the discovery of microRNAs (miRNAs) in Caenorhabditis elegans, our understanding of their c... more Since the discovery of microRNAs (miRNAs) in Caenorhabditis elegans, our understanding of their cellular function has progressed continuously. Today, we have a good understanding of miRNA-mediated gene regulation, miRNA-mediated cross talk between genes including competing endogenous RNAs, and miRNAmediated signaling transduction both in normal human physiology and in diseases. Besides, these noncoding RNAs have shown their value for clinical applications, especially in an oncological context. They can be used as reliable biomarkers for cancer diagnosis and prognosis and attract increasing attention as potential therapeutic targets. Many achievements made in the miRNA field are based on joint efforts from computational and molecular biologists. Systems biology approaches, which integrate computational and experimental methods, have played a fundamental role in uncovering the cellular functions of miRNAs. In this chapter, we review and discuss the role of miRNAs in oncology from a system biology perspective. We first describe biological facts about miRNA genetics and function. Next, we discuss the role of miRNAs in cancer progression and review the application of miRNAs in cancer diagnostics and therapy. Finally, we elaborate on the role that miRNAs play in cancer gene regulatory networks. Taken together, we emphasize the importance of systems biology approaches in our continued efforts to study miRNA cancer regulation.

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Artificial intelligence in cancer target identification and drug discovery

by Xin Lai and Le Zhang

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Melanoma 2.0. Skin cancer as a paradigm for emerging diagnostic technologies, computational modelling and artificial intelligence

2022

is a physicist and a professor of systems tumour immunology. Xin Lai is a computer scientist and ... more is a physicist and a professor of systems tumour immunology. Xin Lai is a computer scientist and has expertise in gene network-driven patient classification and therapy assessment. Andreas Baur is a dermatologist and a translational researcher who investigates the role of extracellular vesicles in cancer and immunity. Michael Erdmann is a dermatologists with expertise in skin cancer, dermatohistopathology and total body photography as a diagnostics tool in skin cancer. Shailendra Gupta is a computer scientist and has expertise in computer-driven drug repurposing. Cristiano Guttà is an experimentalist who works in computer model-based cancer patient classification. Lucie Heinzerling is a professor of onco-dermatology and translational research. Markus Heppt is a dermatologist with focus on skin cancer and his research interest is drug discovery in melanoma.

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A network medicine approach for identifying diagnostic and prognostic biomarkers and exploring drug repurposing in human cancer

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A disease network-based deep learning approach for characterizing melanoma

Multiple types of genomic variations are present in cutaneous melanoma and some of the genomic fe... more Multiple types of genomic variations are present in cutaneous melanoma and some of the genomic features may have an impact on the prognosis of the disease. The access to genomics data via public repositories such as The Cancer Genome Atlas (TCGA) allows for a better understanding of melanoma at the molecular level, therefore making characterization of substantial heterogeneity in melanoma patients possible. Here, we proposed an approach that integrates genomics data, a disease network, and a deep learning model to classify melanoma patients for prognosis, assess the impact of genomic features on the classification and provide interpretation to the impactful features. We integrated genomics data into a melanoma network and applied an autoencoder model to identify subgroups in TCGA melanoma patients. The model utilizes communities identified in the network to effectively reduce the dimensionality of genomics data into a patient score profile. Based on the score profile, we identified three patient subtypes that show different survival times. Furthermore, we quantified and ranked the impact of genomic features on the patient score profile using a machine-learning technique. Follow-up analysis of the top-ranking features provided us with the biological interpretation of them at both pathway and molecular levels, such as their mutation and interactome profiles in melanoma and their involvement in pathways associated with signaling transduction, immune system and cell cycle. Taken together, we demonstrated the ability of the approach to identify disease subgroups using a deep learning model that captures the most relevant information of genomics data in the melanoma network.

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Mathematical {Modelling} in {Biomedicine}: {A} {Primer} for the {Curious} and the {Skeptic

In most disciplines of natural sciences and engineering, mathematical and computational modelling... more In most disciplines of natural sciences and engineering, mathematical and computational modelling are mainstay methods which are usefulness beyond doubt. These disciplines would not have reached today's level of sophistication without an intensive use of mathematical and computational models together with quantitative data. This approach has not been followed in much of molecular biology and biomedicine, however, where qualitative descriptions are accepted as a satisfactory replacement for mathematical rigor and the use of computational models is seen by many as a fringe practice rather than as a powerful scientific method. This position disregards mathematical thinking as having contributed key discoveries in biology for more than a century, e.g., in the connection between genes, inheritance, and evolution or in the mechanisms of enzymatic catalysis. Here, we discuss the role of computational modelling in the arsenal of modern scientific methods in biomedicine. We list frequent misconceptions about mathematical modelling found among biomedical experimentalists and suggest some good practices that can help bridge the cognitive gap between modelers and experimental researchers in biomedicine. This manuscript was written with two readers in mind. Firstly, it is intended for mathematical modelers with a background in physics, mathematics, or engineering who want to jump into biomedicine. We provide them with ideas to motivate the use of mathematical modelling when discussing with experimental partners. Secondly, this is a text for biomedical researchers intrigued with utilizing mathematical modelling to investigate the pathophysiology of human diseases to improve their diagnostics and treatment.

Systems biology-based investigation of cooperating {microRNAs} as monotherapy or adjuvant therapy in cancer

MiR}-205-5p and {miR}-342-3p cooperate in the repression of the {E2F1} transcription factor in the context of anticancer chemotherapy resistance

THP}-1-derived macrophages render lung epithelial cells hypo-responsive to {Legionella} pneumophila - a systems biology study

A gene regulatory architecture that controls region-independent dynamics of oligodendrocyte differentiation

RNA} {Sequencing} of {Collecting} {Duct} {Renal} {Cell} {Carcinoma} {Suggests} an {Interaction} between {miRNA} and {Target} {Genes} and a {Predominance} of {Deregulated} {Solute} {Carrier} {Genes

Collecting duct carcinoma (CDC) is a rare renal cell carcinoma subtype with a very poor prognosis... more Collecting duct carcinoma (CDC) is a rare renal cell carcinoma subtype with a very poor prognosis. There have been only a few studies on gene expression analysis in CDCs. We compared the gene expression profiles of two CDC cases with those of eight normal tissues of renal cell carcinoma patients. At a threshold of {\textbar}log2fold-change{\textbar} \≥ 1, 3349 genes were upregulated and 1947 genes were downregulated in CDCs compared to the normal samples. Pathway analysis of the deregulated genes revealed that cancer pathways and cell cycle pathways were most prominent in CDCs. The most upregulated gene was keratin 17, and the most downregulated gene was cubilin. Among the most downregulated genes were four solute carrier genes (SLC3A1, SLC9A3, SLC26A7, and SLC47A1). The strongest negative correlations between miRNAs and mRNAs were found between the downregulated miR-374b-5p and its upregulated target genes HIST1H3B, HK2, and SLC7A11 and between upregulated miR-26b-5p and its downregulated target genes PPARGC1A, ALDH6A1, and MARC2. An upregulation of HK2 and a downregulation of PPARGC1A, ALDH6A1, and MARC2 were observed at the protein level. Survival analysis of the cancer genome atlas (TCGA) dataset showed for the first time that low gene expression of MARC2, cubilin, and SLC47A1 and high gene expression of KRT17 are associated with poor overall survival in clear cell renal cell carcinoma patients. Altogether, we identified dysregulated protein-coding genes, potential miRNA-target interactions, and prognostic markers that could be associated with CDC.

Bacterial {Adherence} and {Dwelling} {Probability}: {Two} {Drivers} of {Early} {Alveolar} {Infection} by {Streptococcus} pneumoniae {Identified} in {Multi}-{Level} {Mathematical} {Modeling

Pneumococcal infection is the most frequent cause of pneumonia, and one of the most prevalent dis... more Pneumococcal infection is the most frequent cause of pneumonia, and one of the most prevalent diseases worldwide. The population groups at high risk of death from bacterial pneumonia are infants, elderly and immunosuppressed people. These groups are more vulnerable because they have immature or impaired immune systems, the efficacy of their response to vaccines is lower, and antibiotic treatment often does not take place until the inflammatory response triggered is already overwhelming. The immune response to bacterial lung infections involves dynamic interactions between several types of cells whose activation is driven by intracellular molecular networks. A feasible approach to the integration of knowledge and data linking tissue, cellular and intracellular events and the construction of hypotheses in this area is the use of mathematical modelling. For this paper, we used a multi-level computational model to analyse the role of cellular and molecular interactions during the first 10 hours after alveolar invasion of Streptococcus pneumoniae bacteria. By ‘multi-level’ we mean that we simulated the interplay between different temporal and spatial scales in a single computational model. In this instance, we included the intracellular scale of processes driving lung epithelial cell activation together with the scale of cell-to-cell interactions at the alveolar tissue. In our analysis, we combined systematic model simulations with logistic regression analysis and decision trees to find genotypic-phenotypic signatures that explain differences in bacteria strain infectivity. According to our simulations, pneumococci benefit from a high dwelling probability and a high proliferation rate during the first stages of infection. In addition to this, the model predicts that during the very early phases of infection the bacterial capsule could be an impediment to the establishment of the alveolar infection because it impairs bacterial colonisation.

THP}-1-derived macrophages render lung epithelial cells hypo-responsive to {Legionella} pneumophila – a systems biology study

Exosomal {lncRNAs} and cancer: connecting the missing links

Motivation: Extracellular vesicles (EVs), including exosomes and microvesicles, are potent and cl... more Motivation: Extracellular vesicles (EVs), including exosomes and microvesicles, are potent and clinically valuable tools for early diagnosis, prognosis and potentially the targeted treatment of cancer. The content of EVs is closely related to the type and status of the EV-secreting cell. Circulating exosomes are a source of stable RNAs including mRNAs, microRNAs and long non-coding RNAs (lncRNAs). Results: This review outlines the links between EVs, lncRNAs and cancer. We highlight communication networks involving the tumor microenvironment, the immune system and metastasis. We show examples supporting the value of exosomal lncRNAs as cancer biomarkers and therapeutic targets. We demonstrate how a system biology approach can be used to model cell-cell communication via exosomal lncRNAs and to simulate effects of therapeutic interventions. In addition, we introduce algorithms and bioinformatics resources for the discovery of tumor-specific lncRNAs and tools that are applied to determine exosome content and lncRNA function. Finally, this review provides a comprehensive collection and guide to databases for exosomal lncRNAs. Supplementary information: Supplementary data are available at Bioinformatics online.

Serum levels of {miR}-320 family members are associated with clinical parameters and diagnosis in prostate cancer patients

We studied the association of the serum levels of the microRNA family members miR-320a/-b/-c with... more We studied the association of the serum levels of the microRNA family members miR-320a/-b/-c with clinico-pathological data to assess their applicability as diagnostic biomarker in prostate cancer (PCa) patients. The levels of miR-320a/-b/-c in 3 groups were evaluated by qRT-PCR (145 patients with PCa, 31 patients with benign prostatic hyperplasia (BPH) and 19 healthy controls). The levels of the three family members of miR-320 were directly correlated within each group (P {\textless} 0.001), but they differed significantly among the three groups (P {\textless} 0.001). The serum levels of the miR-320 family members were significantly increased in older patients compared to younger patients (≤ 66 years vs. {\textgreater} 66 years, P ≤ 0.001). In addition, the levels of all three miR-320 family members were significantly different in patients with low tumor stage compared with those with high tumor stage (miR-320a: P = 0.034; miR-320b: P = 0.006; miR-320c: P = 0.007) and in patients with low serum PSA compared with those with high serum PSA (≤ 4 ng vs. {\textgreater} 4 ng; miR-320a: P = 0.003; miR-320b: P = 0.003; miR-320c: P = 0.006). The levels of these miRNAs were inversely correlated with serum PSA levels. Detection in the serum samples of PCa patients with or without PSA relapse revealed higher levels of miR-320a/-b/-c in the group without PSA relapse before/after radical prostatectomy than in that with PCa relapse. In summary, the differences among the PCa/BPH/healthy control groups with respect to miR-320a/-b/-c levels in conjunction with higher levels in patients without a PSA relapse than in those with a relapse suggest the diagnostic potential of these miRNA-320 family members in PCa patients.

Time delay and protein modulation analysis in a model of {RNA} silencing

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Multiplicity of {Mathematical} {Modeling} {Strategies} to {Search} for {Molecular} and {Cellular} {Insights} into {Bacteria} {Lung} {Infection

Innate extracellular vesicles from melanoma patients suppress β-catenin in tumor cells by {miRNA}-34a

Computational Biology of Non-Coding RNA: Methods and Protocols

This volume details a collection of state-of-art methods including identification of novel ncRNAs... more This volume details a collection of state-of-art methods including identification of novel ncRNAs and their targets, functional annotation and disease association in different biological contexts. Chapters guide readers through an overview of disease-specific ncRNAs, computational methods and workflows for ncRNA discovery, annotation based on high-throughput sequencing data, bioinformatics tools and databases for ncRNA analyses, network-based methods, and kinetic modelling of ncRNA-mediated gene regulation. Written in the highly successful Methods in Molecular Biology series format, chapters include introductions to their respective topics, lists of the necessary materials and reagents, step-by-step, readily reproducible laboratory protocols, and tips on troubleshooting and avoiding known pitfalls. Authoritative and cutting-edge, Computational Biology of Non-Coding RNA: Methods and Protocols aims to provide a state-of-the-art collection of computational methods and approaches that will be of value to researchers interested in ncRNA field.

MicroRNA} {Clusters

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