Sex-biased Regulation of Extracellular Matrix Genes in COPD.

Compared to men, women often develop COPD at an earlier age with worse respiratory symptoms despite lower smoking exposure. However, most preventive, and therapeutic strategies ignore biological sex differences in COPD. Our goal was to better understand sex-specific gene regulatory processes in lung tissue and the molecular basis for sex differences in COPD onset and severity. We analyzed lung tissue gene expression and DNA methylation data from 747 individuals in the Lung Tissue Research Consortium (LTRC), and 85 individuals in an independent dataset. We identified sex differences in COPD-associated gene regulation using gene regulatory networks. We used linear regression to test for sex-biased associations of methylation with lung function, emphysema, smoking, and age. Analyzing gene regulatory networks in the control group, we identified that genes involved in the extracellular matrix (ECM) have higher transcriptional factor targeting in females than in males. However, this pattern is reversed in COPD, with males showing stronger regulatory targeting of ECM-related genes than females. Smoking exposure, age, lung function, and emphysema were all associated with sex-specific differential methylation of ECM-related genes. We identified sex-based gene regulatory patterns of ECM-related genes associated with lung function and emphysema. Multiple factors including epigenetics, smoking, aging, and cell heterogeneity influence sex-specific gene regulation in COPD. Our findings underscore the importance of considering sex as a key factor in disease susceptibility and severity.

A Low Expression of NRF2 Enhances Oxidative Stress and Autophagy in Myofibroblasts, Promoting Progression of Chronic Obstructive Pulmonary Disease.

BACKGROUND: The inflammation phenotypes are often closely related to oxidative stress and autophagy pathway activation, which could be developed as a treatment target. AIMS: The aim of this study was to explore the underlying mechanism of inflammation in chronic obstructive pulmonary disease (COPD). METHODS: The lung tissue single-cell RNA-seq (scRNA-seq) dataset of GSE171541 was downloaded from the Gene Expression Omnibus (GEO) database. The marker genes were obtained from the CellMarker database. "Seurat" and "harmony" R packages were used for single-cell profiling analysis. Then, the "AUCell" R package was employed to calculate the reactive oxygen species (ROS) and autophagy pathway scores. Gene regulation network analysis was performed by applying the "SCENIC" package, followed by conducting correlation analysis with Spearman's rank correlation method. The cigarettes were used to develop a traumatic model in mice, and the expression of relevant genes was measured by qRT-PCR. RESULTS: The scRNA-seq analysis classified 12 cell subgroups in which the contractility of myofibroblasts was closely associated with the progression of COPD. Further analysis showed that ROS and autophagy pathways were significantly activated in myofibroblasts and that the nuclear factor erythroid 2-related factor 2 (NRF2) and its mediated oxidative stress pathway were inhibited in myofibroblasts. In addition, the downregulated NRF2 gene was negatively correlated with the expression of autophagy and ROS activation. In the traumatic mice model, NRF2 was downregulated in COPD mice but further elevated in the COPD+NRF2 mice group. Interestingly, the mRNA levels of Kelchlike ECH-associated protein 1 (Keap1), NADPH oxidase (NOX), and Cathepsin B (CTSB) were upregulated in COPD group in comparison to the control group but they were downregulated by NRF2. These results suggested that low-expressed NFR2 promoted autophagy and ROS pathway activation in myofibroblasts for COPD progression. CONCLUSION: We identified a cell myofibroblast cluster closely associated with COPD progression using the scRNA-seq analysis. The downregulated NFR2, as a key risk factor, mediated myofibroblast death by activating the oxidative stress and autophagy pathway for COPD progression.

Gene regulatory networks reveal sex difference in lung adenocarcinoma.

BACKGROUND: Lung adenocarcinoma (LUAD) has been observed to have significant sex differences in incidence, prognosis, and response to therapy. However, the molecular mechanisms responsible for these disparities have not been investigated extensively. METHODS: Sample-specific gene regulatory network methods were used to analyze RNA sequencing data from non-cancerous human lung samples from The Genotype Tissue Expression Project (GTEx) and lung adenocarcinoma primary tumor samples from The Cancer Genome Atlas (TCGA); results were validated on independent data. RESULTS: We found that genes associated with key biological pathways including cell proliferation, immune response and drug metabolism are differentially regulated between males and females in both healthy lung tissue and tumor, and that these regulatory differences are further perturbed by tobacco smoking. We also discovered significant sex bias in transcription factor targeting patterns of clinically actionable oncogenes and tumor suppressor genes, including AKT2 and KRAS. Using differentially regulated genes between healthy and tumor samples in conjunction with a drug repurposing tool, we identified several small-molecule drugs that might have sex-biased efficacy as cancer therapeutics and further validated this observation using an independent cell line database. CONCLUSIONS: These findings underscore the importance of including sex as a biological variable and considering gene regulatory processes in developing strategies for disease prevention and management.

Lung adenocarcinoma (LUAD) is a disease that affects males and females differently. Biological sex not only influences chances of developing the disease, but also how the disease progresses and how effective various therapies may be. We analyzed sex-specific gene regulatory networks consisting of transcription factors and the genes they regulate in both healthy lung tissue and in LUAD and identified sex-biased differences. We found that genes associated with cell proliferation, immune response, and drug metabolism are differentially targeted by transcription factors between males and females. We also found that several genes that are drug targets in LUAD, are also regulated differently between males and females. Importantly, these differences are also influenced by an individual's smoking history. Extending our analysis using a drug repurposing tool, we found candidate drugs with evidence that they might work better for one sex or the other. These results demonstrate that considering the differences in gene regulation between males and females will be essential if we are to develop precision medicine strategies for preventing and treating LUAD.

Optimal Inference of Hidden Markov Models Through Expert-Acquired Data.

This paper focuses on inferring a general class of hidden Markov models (HMMs) using data acquired from experts. Expert-acquired data contain decisions/actions made by humans/users for various objectives, such as navigation data reflecting drivers' behavior, cybersecurity data carrying defender decisions, and biological data containing the biologist's actions (e.g., interventions and experiments). Conventional inference methods rely on temporal changes in data without accounting for expert knowledge. This paper incorporates expert knowledge into the inference of HMMs by modeling expert behavior as an imperfect reinforcement learning agent. The proposed method optimally quantifies experts' perceptions about the system model, which, alongside the temporal changes in data, contributes to the inference process. The proposed inference method is derived through a combination of dynamic programming and optimal recursive Bayesian estimation. The applicability of this method is demonstrated to a wide range of inference criteria, such as maximum likelihood and maximum a posteriori. The performance of the proposed method is investigated through a comprehensive numerical experiment using a benchmark problem and biological networks.

Multiome Perturb-seq unlocks scalable discovery of integrated perturbation effects on the transcriptome and epigenome.

Single-cell CRISPR screens link genetic perturbations to transcriptional states, but high-throughput methods connecting these induced changes to their regulatory foundations are limited. Here we introduce Multiome Perturb-seq, extending single-cell CRISPR screens to simultaneously measure perturbation-induced changes in gene expression and chromatin accessibility. We apply Multiome Perturb-seq in a CRISPRi screen of 13 chromatin remodelers in human RPE-1 cells, achieving efficient assignment of sgRNA identities to single nuclei via an improved method for capturing barcode transcripts from nuclear RNA. We organize expression and accessibility measurements into coherent programs describing the integrated effects of perturbations on cell state, finding that ARID1A and SUZ12 knockdowns induce programs enriched for developmental features. Pseudotime analysis of perturbations connects accessibility changes to changes in gene expression, highlighting the value of multimodal profiling. Overall, our method provides a scalable and simply implemented system to dissect the regulatory logic underpinning cell state.

Dynamics and bifurcations in genetic circuits with fibration symmetries.

Circuit building blocks of gene regulatory networks (GRN) have been identified through the fibration symmetries of the underlying biological graph. Here, we analyse analytically six of these circuits that occur as functional and synchronous building blocks in these networks. Of these, the lock-on, toggle switch, Smolen oscillator, feed-forward fibre and Fibonacci fibre circuits occur in living organisms, notably Escherichia coli; the sixth, the repressilator, is a synthetic GRN. We consider synchronous steady states determined by a fibration symmetry (or balanced colouring) and determine analytic conditions for local bifurcation from such states, which can in principle be either steady-state or Hopf bifurcations. We identify conditions that characterize the first bifurcation, the only one that can be stable near the bifurcation point. We model the state of each gene in terms of two variables: mRNA and protein concentration. We consider all possible 'admissible' models-those compatible with the network structure-and then specialize these general results to simple models based on Hill functions and linear degradation. The results systematically classify using graph symmetries the complexity and dynamics of these circuits, which are relevant to understand the functionality of natural and synthetic cells.

DeepGRNCS: deep learning-based framework for jointly inferring gene regulatory networks across cell subpopulations.

Inferring gene regulatory networks (GRNs) allows us to obtain a deeper understanding of cellular function and disease pathogenesis. Recent advances in single-cell RNA sequencing (scRNA-seq) technology have improved the accuracy of GRN inference. However, many methods for inferring individual GRNs from scRNA-seq data are limited because they overlook intercellular heterogeneity and similarities between different cell subpopulations, which are often present in the data. Here, we propose a deep learning-based framework, DeepGRNCS, for jointly inferring GRNs across cell subpopulations. We follow the commonly accepted hypothesis that the expression of a target gene can be predicted based on the expression of transcription factors (TFs) due to underlying regulatory relationships. We initially processed scRNA-seq data by discretizing data scattering using the equal-width method. Then, we trained deep learning models to predict target gene expression from TFs. By individually removing each TF from the expression matrix, we used pre-trained deep model predictions to infer regulatory relationships between TFs and genes, thereby constructing the GRN. Our method outperforms existing GRN inference methods for various simulated and real scRNA-seq datasets. Finally, we applied DeepGRNCS to non-small cell lung cancer scRNA-seq data to identify key genes in each cell subpopulation and analyzed their biological relevance. In conclusion, DeepGRNCS effectively predicts cell subpopulation-specific GRNs. The source code is available at https://github.com/Nastume777/DeepGRNCS.

Integrative multi-omics increase resolution of the sea urchin posterior gut gene regulatory network at single cell level.

Drafting gene regulatory networks (GRNs) requires embryological knowledge pertaining to the cell type families, information on the regulatory genes, causal data from gene knockdown experiments and validations of the identified interactions by cis-regulatory analysis. We use multi-omics involving next-generation sequencing (-seq) to obtain the necessary information for drafting Strongylocentrotus purpuratus posterior gut GRN. Here we present an update to the GRN using i) a single cell RNA-seq derived cell atlas highlighting the 2 day post fertilization (dpf) sea urchin gastrula cell type families, as well as the genes expressed at single cell level, ii) a set of putative cis-regulatory modules and transcription factor (TF) binding sites obtained from chromatin accessibility ATAC-seq data, and iii) interactions directionality obtained from differential bulk RNA-seq following knockdown of the TF Sp-Pdx1, a key regulator of gut patterning in sea urchins. Combining these datasets, we draft the GRN for the hindgut Sp-Pdx1 positive cells in the 2 dpf gastrula embryo. Overall, our data suggests the complex connectivity of the posterior gut GRN and increases the resolution of gene regulatory cascades operating within it.

TFTF: An R-Based Integrative Tool for Decoding Human Transcription Factor-Target Interactions.

Transcription factors (TFs) are crucial in modulating gene expression and sculpting cellular and organismal phenotypes. The identification of TF-target gene interactions is pivotal for comprehending molecular pathways and disease etiologies but has been hindered by the demanding nature of traditional experimental approaches. This paper introduces a novel web application and package utilizing the R program, which predicts TF-target gene relationships and vice versa. Our application integrates the predictive power of various bioinformatic tools, leveraging their combined strengths to provide robust predictions. It merges databases for enhanced precision, incorporates gene expression correlation for accuracy, and employs pan-tissue correlation analysis for context-specific insights. The application also enables the integration of user data with established resources to analyze TF-target gene networks. Despite its current limitation to human data, it provides a platform to explore gene regulatory mechanisms comprehensively. This integrated, systematic approach offers researchers an invaluable tool for dissecting the complexities of gene regulation, with the potential for future expansions to include a broader range of species.

Inferring Interaction Networks from Transcriptomic Data: Methods and Applications.

Transcriptomic data is a treasure trove in modern molecular biology, as it offers a comprehensive viewpoint into the intricate nuances of gene expression dynamics underlying biological systems. This genetic information must be utilized to infer biomolecular interaction networks that can provide insights into the complex regulatory mechanisms underpinning the dynamic cellular processes. Gene regulatory networks and protein-protein interaction networks are two major classes of such networks. This chapter thoroughly investigates the wide range of methodologies used for distilling insightful revelations from transcriptomic data that include association-based methods (based on correlation among expression vectors), probabilistic models (using Bayesian and Gaussian models), and interologous methods. We reviewed different approaches for evaluating the significance of interactions based on the network topology and biological functions of the interacting molecules and discuss various strategies for the identification of functional modules. The chapter concludes with highlighting network-based techniques of prioritizing key genes, outlining the centrality-based, diffusion- based, and subgraph-based methods. The chapter provides a meticulous framework for investigating transcriptomic data to uncover assembly of complex molecular networks for their adaptable analyses across a broad spectrum of biological domains.

Peripheral whole blood microRNA expression in relation to vascular function: a population-based study.

BACKGROUND: As key regulators of gene expression, microRNAs affect many cardiovascular mechanisms and have been associated with several cardiovascular diseases. In this study, we aimed to investigate the relation of whole blood microRNAs with several quantitative measurements of vascular function, and explore their biological role through an integrative microRNA-gene expression analysis. METHODS: Peripheral whole blood microRNA expression was assessed through RNA-Seq in 2606 participants (45.8% men, mean age: 53.93, age range: 30 to 95 years) from the Rhineland Study, an ongoing population-based cohort study in Bonn, Germany. Weighted gene co-expression network analysis was used to cluster microRNAs with highly correlated expression levels into 14 modules. Through linear regression models, we investigated the association between each module's expression and quantitative markers of vascular health, including pulse wave velocity, total arterial compliance index, cardiac index, stroke index, systemic vascular resistance index, reactive skin hyperemia and white matter hyperintensity burden. For each module associated with at least one trait, one or more hub-microRNAs driving the association were defined. Hub-microRNAs were further characterized through mapping to putative target genes followed by gene ontology pathway analysis. RESULTS: Four modules, represented by hub-microRNAs miR-320 family, miR-378 family, miR-3605-3p, miR-6747-3p, miR-6786-3p, and miR-330-5p, were associated with total arterial compliance index. Importantly, the miR-320 family module was also associated with white matter hyperintensity burden, an effect partially mediated through arterial compliance. Furthermore, hub-microRNA miR-192-5p was related to cardiac index. Functional analysis corroborated the relevance of the identified microRNAs for vascular function by revealing, among others, enrichment for pathways involved in blood vessel morphogenesis and development, angiogenesis, telomere organization and maintenance, and insulin secretion. CONCLUSIONS: We identified several microRNAs robustly associated with cardiovascular function, especially arterial compliance and cardiac output. Moreover, our results highlight miR-320 as a regulator of cerebrovascular damage, partly through modulation of vascular function. As many of these microRNAs were involved in biological processes related to vasculature development and aging, our results contribute to the understanding of vascular physiology and provide putative targets for cardiovascular disease prevention.

DeepIMAGER: Deeply Analyzing Gene Regulatory Networks from scRNA-seq Data.

Understanding the dynamics of gene regulatory networks (GRNs) across diverse cell types poses a challenge yet holds immense value in unraveling the molecular mechanisms governing cellular processes. Current computational methods, which rely solely on expression changes from bulk RNA-seq and/or scRNA-seq data, often result in high rates of false positives and low precision. Here, we introduce an advanced computational tool, DeepIMAGER, for inferring cell-specific GRNs through deep learning and data integration. DeepIMAGER employs a supervised approach that transforms the co-expression patterns of gene pairs into image-like representations and leverages transcription factor (TF) binding information for model training. It is trained using comprehensive datasets that encompass scRNA-seq profiles and ChIP-seq data, capturing TF-gene pair information across various cell types. Comprehensive validations on six cell lines show DeepIMAGER exhibits superior performance in ten popular GRN inference tools and has remarkable robustness against dropout-zero events. DeepIMAGER was applied to scRNA-seq datasets of multiple myeloma (MM) and detected potential GRNs for TFs of RORC, MITF, and FOXD2 in MM dendritic cells. This technical innovation, combined with its capability to accurately decode GRNs from scRNA-seq, establishes DeepIMAGER as a valuable tool for unraveling complex regulatory networks in various cell types.

Developing the 'omic toolkit of comparative physiologists.

Typical 'omic analyses reduce complex biological systems to simple lists of supposedly independent variables, failing to account for changes in the wider transcriptional landscape. In this commentary, we discuss the utility of network approaches for incorporating this wider context into the study of physiological phenomena. We highlight opportunities to build on traditional network tools by utilising cutting-edge techniques to account for higher order interactions (i.e. beyond pairwise associations) within datasets, allowing for more accurate models of complex 'omic systems. Finally, we show examples of previous works utilising network approaches to gain additional insight into their organisms of interest. As 'omics grow in both their popularity and breadth of application, so does the requirement for flexible analytical tools capable of interpreting and synthesising complex datasets.

Integrated transcriptomic and proteomic analysis of exogenous abscisic acid regulation on tuberous root development in Pseudostellaria heterophylla.

Abscisic acid (ABA) significantly regulates plant growth and development, promoting tuberous root formation in various plants. However, the molecular mechanisms of ABA in the tuberous root development of Pseudostellaria heterophylla are not yet fully understood. This study utilized Illumina sequencing and de novo assembly strategies to obtain a reference transcriptome associated with ABA treatment. Subsequently, integrated transcriptomic and proteomic analyses were used to determine gene expression profiles in P. heterophylla tuberous roots. ABA treatment significantly increases the diameter and shortens the length of tuberous roots. Clustering analysis identified 2,256 differentially expressed genes and 679 differentially abundant proteins regulated by ABA. Gene co-expression and protein interaction networks revealed ABA positively induced 30 vital regulators. Furthermore, we identified and assigned putative functions to transcription factors (PhMYB10, PhbZIP2, PhbZIP, PhSBP) that mediate ABA signaling involved in the regulation of tuberous root development, including those related to cell wall metabolism, cell division, starch synthesis, hormone metabolism. Our findings provide valuable insights into the complex signaling networks of tuberous root development modulated by ABA. It provided potential targets for genetic manipulation to improve the yield and quality of P. heterophylla, which could significantly impact its cultivation and medicinal value.

Early B-Cell Factor 1: An Archetype for a Lineage-Restricted Transcription Factor Linking Development to Disease.

The development of highly specialized blood cells from hematopoietic stem cells (HSCs) in the bone marrow (BM) is dependent upon a stringently orchestrated network of stage- and lineage-restricted transcription factors (TFs). Thus, the same stem cell can give rise to various types of differentiated blood cells. One of the key regulators of B-lymphocyte development is early B-cell factor 1 (EBF1). This TF belongs to a small, but evolutionary conserved, family of proteins that harbor a Zn-coordinating motif and an IPT/TIG (immunoglobulin-like, plexins, transcription factors/transcription factor immunoglobulin) domain, creating a unique DNA-binding domain (DBD). EBF proteins play critical roles in diverse developmental processes, including body segmentation in the Drosophila melanogaster embryo, and retina formation in mice. While several EBF family members are expressed in neuronal cells, adipocytes, and BM stroma cells, only B-lymphoid cells express EBF1. In the absence of EBF1, hematopoietic progenitor cells (HPCs) fail to activate the B-lineage program. This has been attributed to the ability of EBF1 to act as a pioneering factor with the ability to remodel chromatin, thereby creating a B-lymphoid-specific epigenetic landscape. Conditional inactivation of the Ebf1 gene in B-lineage cells has revealed additional functions of this protein in relation to the control of proliferation and apoptosis. This may explain why EBF1 is frequently targeted by mutations in human leukemia cases. This chapter provides an overview of the biochemical and functional properties of the EBF family proteins, with a focus on the roles of EBF1 in normal and malignant B-lymphocyte development.

Nonlinear causal discovery with confounders.

This article introduces a causal discovery method to learn nonlinear relationships in a directed acyclic graph with correlated Gaussian errors due to confounding. First, we derive model identifiability under the sublinear growth assumption. Then, we propose a novel method, named the Deconfounded Functional Structure Estimation (DeFuSE), consisting of a deconfounding adjustment to remove the confounding effects and a sequential procedure to estimate the causal order of variables. We implement DeFuSE via feedforward neural networks for scalable computation. Moreover, we establish the consistency of DeFuSE under an assumption called the strong causal minimality. In simulations, DeFuSE compares favorably against state-of-the-art competitors that ignore confounding or nonlinearity. Finally, we demonstrate the utility and effectiveness of the proposed approach with an application to gene regulatory network analysis. The Python implementation is available at https://github.com/chunlinli/defuse.

Integrated analysis of single-cell RNA sequencing and bulk RNA data reveals gene regulatory networks and targets in dilated cardiomyopathy.

Dilated cardiomyopathy (DCM) is a common cause of heart failure, thromboembolism, arrhythmias, and sudden cardiac death. The quality of life and long-term survival rates of patients with dilated DCM have greatly improved in recent decades. Nevertheless, the clinical prognosis for DCM patients remains unfavorable. The primary driving factors underlying the pathogenesis of DCM remain incompletely understood. The present study aimed to identify driving factors underlying the pathogenesis of DCM from the perspective of gene regulatory networks. Single-cell RNA sequencing data and bulk RNA data were obtained from the Gene Expression Omnibus (GEO) database. Differential gene analysis, single-cell genomics analysis, and functional enrichment analysis were conducted using R software. The construction of Gene Regulatory Networks was performed using Python. We used the pySCENIC method to analyze the single-cell data and identified 401 regulons. Through variance decomposition, we selected 19 regulons that showed significant responsiveness to DCM. Next, we employed the ssGSEA method to assess regulons in two bulk RNA datasets. Significant statistical differences were observed in 9 and 13 regulons in each dataset. By intersecting these differentiated regulons and identifying shared targets that appeared at least twice, we successfully pinpointed three differentially expressed targets across both datasets. In this study, we assessed and identified 19 gene regulatory networks that were responsive to the disease. Furthermore, we validated these networks using two bulk RNA datasets of DCM. The elucidation of dysregulated regulons and targets (CDKN1A, SAT1, ZFP36) enhances the molecular understanding of DCM, aiding in the development of tailored therapies for patients.

Contrasting philosophical and scientific views in the long history of studying the generation of form in development.

Focusing on the opposing ways of thinking of philosophers and scientists to explain the generation of form in biological development, I show that today's controversies over explanations of early development bear fundamental similarities to the dichotomy of preformation theory versus epigenesis in Greek antiquity. They are related to the acceptance or rejection of the idea of a physical form of what today would be called information for the generating of the embryo as a necessary pre-requisite for specific development and heredity. As a recent example, I scrutinize the dichotomy of genomic causality versus self-organization in 20th and 21st century theories of the generation of form. On the one hand, the generation of patterns and form, as well as the constant outcome in development, are proposed to be causally related to something that is "preformed" in the germ cells, the nucleus of germ cells, or the genome. On the other hand, it is proposed that there is no pre-existing form or information, and development is seen as a process where genuinely new characters emerge from formless matter, either by immaterial "forces of life," or by physical-chemical processes of self-organization. I also argue that these different ways of thinking and the research practices associated with them are not equivalent, and maintain that it is impossible to explain the generation of form and constant outcome of development without the assumption of the transmission of pre-existing information in the form of DNA sequences in the genome. Only in this framework of "preformed" information can "epigenesis" in the form of physical and chemical processes of self-organization play an important role.

The prognostic impact of pathogenic stromal cell-associated genes in lung adenocarcinoma.

BACKGROUND: Lung adenocarcinoma (LUAD) stands as the most prevalent subtype among lung cancers. Interactions between stromal and cancer cells influence tumor growth, invasion, and metastasis. However, the regulatory mechanisms of stromal cells in the lung adenocarcinoma tumor microenvironment remain unclear. This study seeks to elucidate the regulatory connections among critical pathogenic genes and their associated expression variations within distinct stromal cell subtypes. METHOD: Analysis and investigation were conducted on a total of 114,019 single-cell RNA data and 346 The Cancer Genome Atlas (TCGA) LUAD-related samples using bioinformatics and statistical algorithms. Differential gene expression analysis was performed for tumor samples and controls, followed by Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis. Differential genes between stromal cells and other cell clusters were identified and intersected with the differential genes from TCGA. We employed a combination of LASSO regression and multivariable Cox regression to identify the ultimate set of pathogenic gene. Survival models were trained to predict the relationship between patient survival and these pathogenic genes. Analysis of transcription factor (TF) cell specificity and pseudotime trajectories within stromal cell subpopulations revealed that vascular endothelial cells (ECs) and matrix cancer-associated fibroblasts (CAFs) are key in regulation of the prognosis-associated genes CAV2, COL1A1, TIMP1, ETS2, AKAP12, ID1 and COL1A2. RESULTS: Seven pathogenic genes associated with LUAD in stromal cells were identified and used to develop a survival model. High expression of these genes is linked to a greater risk of poor survival. Stromal cells were categorized into eight subtypes and one unannotated cluster. Mesothelial cells, vascular endothelial cells (ECs), and matrix cancer-associated fibroblasts (CAFs) showed cell-specific regulation of the pathogenic genes. CONCLUSIONS: The seven disease-causing genes in vascular ECs and matrix CAFs can be used to detect the survival status of LUAD patients, providing new directions for future targeted drug design.

Computational single-cell methods for predicting cancer risk.

Despite recent biotechnological breakthroughs, cancer risk prediction remains a formidable computational and experimental challenge. Addressing it is critical in order to improve prevention, early detection and survival rates. Here, I briefly summarize some key emerging theoretical and computational challenges as well as recent computational advances that promise to help realize the goals of cancer-risk prediction. The focus is on computational strategies based on single-cell data, in particular on bottom-up network modeling approaches that aim to estimate cancer stemness and dedifferentiation at single-cell resolution from a systems-biological perspective. I will describe two promising methods, a tissue and cell-lineage independent one based on the concept of diffusion network entropy, and a tissue and cell-lineage specific one that uses transcription factor regulons. Application of these tools to single-cell and single-nucleus RNA-seq data from stages prior to invasive cancer reveal that they can successfully delineate the heterogeneous inter-cellular cancer-risk landscape, identifying those cells that are more likely to turn cancerous. Bottom-up systems biological modeling of single-cell omic data is a novel computational analysis paradigm that promises to facilitate the development of preventive, early detection and cancer-risk prediction strategies.