Dynamic network biomarker indicates pulmonary metastasis at the tipping point of hepatocellular carcinoma

CSP I-plus modified rEndostatin inhibits hepatocellular carcinoma metastasis via down-regulation of VEGFA and integrinβ1

BACKGROUND

In our previous study, N end of the Circumsporozoite protein (CSP I-plus) modified recombinant human Endostatin (rEndostatin, endostar) (rES-CSP) was constructed, which had antiangiogenic capability and bound to hepatocellular carcinoma in vivo and in vitro. In this study, the inhibition of rES-CSP on hepatocellular carcinoma metastasis was verified in vivo and in vitro, and its possible mechanism was explored.

METHODS

Firstly, the impact of rES-CSP on the migration, adhesion of hepatoma cell HCCLM3 was identified by wound healing, transwell, and on metastasis of orthotopic xenograft model was identified in nude mouse. Then the expression of metastasis-associated molecules (MMP2, E-cadherin, integrinβ1) and angiogenesis-related factors (VEGFA) in vitro and in vivo were detected by real-time PCR, western blotting, immunohistochemistry.

RESULTS

Finally, we found that rES-CSP could inhibit the migration and invasion of HCCLM3, and decrease tumor metastasis and growth in nude mouse orthotopic xenograft models. The tumor inhibiting rates of rES-CSP and Endostar were 42.46 ± 5.39% and 11.1 ± 1.88%. The lung metastasis rates of the control, Endostar and rES-CSP were 71, 50, and 42.8%, respectively. Compared with Endostar, rES-CSP significantly down-regulated the expression of VEGFA and integrinβ1. Heparin, a competitive inhibitor of CSP I-plus, which can be bind to the highly-sulfated heparan sulfate proteoglycans (HSPGs) over-expressed in liver and hepatocellular carcinoma, alleviated the down-regulation of VEGFA and integrinβ1.

CONCLUSIONS

These indicate that rES-CSP may play a role in inhibiting tumor growth and metastasis by down-regulating the angiogenic factor VEGF and the metastasis-related molecules or by interfering with HSPGs-mediated tumor metastasis.

Medical care of rare and undiagnosed diseases: Prospects and challenges

Rare and undiagnosed diseases tend to be diverse, misdiagnosed, and difficult to diagnose. In some cases, the disease is progressive and life-threatening. Yet, to date, an estimated 95% of rare diseases have no approved therapy. Therefore, rare and undiagnosed diseases are considered the ultimate challenges for understanding human diseases. Here, we review the research progress, research frontiers, and important scientific issues related to rare and undiagnosed diseases. We mainly focus on five topics: (1) the identification and functional analysis of disease-causing genes; (2) the construction of cells, organoids, and animal models for mechanism validation; (3) subtyping and diagnosis; (4) treatment and drug screening based on causative genes and mutations; and (5) new technologies and methods for studying rare and undiagnosed diseases. In this review, we briefly update and discuss the pathogenic mechanisms and precision medicine for rare and undiagnosed diseases.

Impact of preanalytical freezing delay time on the stability of metabolites in oral squamous cell carcinoma tissue samples

INTRODUCTION

Metabolite stability is critical for tissue metabolomics. However, changes in metabolites in tissues over time from the operating room to the laboratory remain underexplored.

OBJECTIVES

In this study, we evaluated the effect of postoperative freezing delay time on the stability of metabolites in normal and oral squamous cell carcinoma (OSCC) tissues.

METHODS

Tumor and paired normal tissues from five OSCC patients were collected after surgical resection, and samples was sequentially quenched in liquid nitrogen at 30, 40, 50, 60, 70, 80, 90 and 120 min (80 samples). Untargeted metabolic analysis by liquid chromatography-mass spectrometry/mass spectrometry in positive and negative ion modes was used to identify metabolic changes associated with delayed freezing time. The trends of metabolite changes at 30-120 and 30-60 min of delayed freezing were analyzed.

RESULTS

190 metabolites in 36 chemical classes were detected. After delayed freezing for 120 min, approximately 20% of the metabolites changed significantly in normal and tumor tissues, and differences in the metabolites were found in normal and tumor tissues. After a delay of 60 min, 29 metabolites had changed significantly in normal tissues, and 84 metabolites had changed significantly in tumor tissues. In addition, we constructed three tissue freezing schemes based on the observed variation trends in the metabolites.

CONCLUSION

Delayed freezing of tissue samples has a certain impact on the stability of metabolites. For metabolites with significant changes, we suggest that the freezing time of tissues be reasonably selected according to the freezing schemes and the actual clinical situation.

APEX1 promotes the oncogenicity of hepatocellular carcinoma via regulation of MAP2K6

Objective: Apurinic/apyrimidinic endonuclease 1 (APEX1), a key enzyme responsible for DNA base excision repair, has been linked to development and progression of cancers. In this work, we aimed to explore the role of APEX1 in hepatocellular carcinoma (HCC) and elucidate its molecular mechanism.

Methods: The expression of APEX1 in HCC tissues and matched adjacent normal tissues (n = 80 cases) was evaluated by immunohistochemistry. Web-based tools UALCAN and the Kaplan-Meier plotter were used to analyze the Cancer Genome Atlas database to compare expression of APEX1 mRNA to 5-year overall survival. APEX1 was stably silenced in two HCC cell lines, Hep 3B and Bel-7402, with shRNA technology. An in vivo tumorigenesis model was established by subcutaneously injecting sh-APEX1-transfected Bel-7402 cells into mice, and tumor growth was determined. We performed high-throughput transcriptome sequencing in sh-APEX1-treated HCC cells to identify the key KEGG signaling pathways induced by silencing of APEX1.

Results: APEX1 was significantly upregulated and predicted poor clinical overall survival in HCC patients. Silencing APEX1 inhibited the proliferation of HCC cells in vivo and in vitro, and it repressed invasion and migration and increased apoptosis and the percentage of cells in G1. Differentially expressed genes upon APEX1 silencing included genes involved in TNF signaling. A positive correlation between the expression of APEX1 and MAP2K6 was noted, and overexpressing MAP2K6 overcame cancer-related phenotypes associated with APEX1 silencing.

Conclusion: APEX1 enhances the malignant properties of HCC via MAP2K6. APEX1 may represent a valuable prognostic biomarker and therapeutic target in HCC.

Detecting critical transition signals from single-cell transcriptomes to infer lineage-determining transcription factors

Analyzing single-cell transcriptomes promises to decipher the plasticity, heterogeneity, and rapid switches in developmental cellular state transitions. Such analyses require the identification of gene markers for semi-stable transition states. However, there are nontrivial challenges such as unexplainable stochasticity, variable population sizes, and alternative trajectory constructions. By advancing current tipping-point theory-based models with feature selection, network decomposition, accurate estimation of correlations, and optimization, we developed BioTIP to overcome these challenges. BioTIP identifies a small group of genes, called critical transition signal (CTS), to characterize regulated stochasticity during semi-stable transitions. Although methods rooted in different theories converged at the same transition events in two benchmark datasets, BioTIP is unique in inferring lineage-determining transcription factors governing critical transition. Applying BioTIP to mouse gastrulation data, we identify multiple CTSs from one dataset and validated their significance in another independent dataset. We detect the established regulator Etv2 whose expression change drives the haemato-endothelial bifurcation, and its targets together in CTS across three datasets. After comparing to three current methods using six datasets, we show that BioTIP is accurate, user-friendly, independent of pseudo-temporal trajectory, and captures significantly interconnected and reproducible CTSs. We expect BioTIP to provide great insight into dynamic regulations of lineage-determining factors.

scMTD: a statistical multidimensional imputation method for single-cell RNA-seq data leveraging transcriptome dynamic information

Background

Single-cell RNA sequencing (scRNA-seq) provides a powerful tool to capture transcriptomes at single-cell resolution. However, dropout events distort the gene expression levels and underlying biological signals, misleading the downstream analysis of scRNA-seq data.

Results

We develop a statistical model-based multidimensional imputation algorithm, scMTD, that identifies local cell neighbors and specific gene co-expression networks based on the pseudo-time of cells, leveraging information on cell-level, gene-level, and transcriptome dynamic to recover scRNA-seq data. Compared with the state-of-the-art imputation methods through several real-data-based analytical experiments, scMTD effectively recovers biological signals of transcriptomes and consistently outperforms the other algorithms in improving FISH validation, trajectory inference, differential expression analysis, clustering analysis, and identification of cell types.

Conclusions

scMTD maintains the gene expression characteristics, enhances the clustering of cell subpopulations, assists the study of gene expression dynamics, contributes to the discovery of rare cell types, and applies to both UMI-based and non-UMI-based data. Overall, scMTD’s reliability, applicability, and scalability make it a promising imputation approach for scRNA-seq data.

Supplementary Information

The online version contains supplementary material available at 10.1186/s13578-022-00886-4.

Buffering variability in cell regulation motifs close to criticality

Bistable biological regulatory systems need to cope with stochastic noise to fine tune their function close to bifurcation points. Here, we study stability properties of this regime in generic systems to demonstrate that cooperative interactions buffer system variability, hampering noise-induced regime shifts. Our analysis also shows that, in the considered cooperativity range, impending regime shifts can be generically detected by statistical early warning signals from distributional data. Our generic framework, based on minimal models, can be used to extract robustness and variability properties of more complex models and empirical data close to criticality.

The fundamental benefits of multiplexity in ecological networks

A tipping point presents perhaps the single most significant threat to an ecological system as it can lead to abrupt species extinction on a massive scale. Climate changes leading to the species decay parameter drifts can drive various ecological systems towards a tipping point. We investigate the tipping-point dynamics in multi-layer ecological networks supported by mutualism. We unveil a natural mechanism by which the occurrence of tipping points can be delayed by multiplexity that broadly describes the diversity of the species abundances, the complexity of the interspecific relationships, and the topology of linkages in ecological networks. For a double-layer system of pollinators and plants, coupling between the network layers occurs when there is dispersal of pollinator species. Multiplexity emerges as the dispersing species establish their presence in the destination layer and have a simultaneous presence in both. We demonstrate that the new mutualistic links induced by the dispersing species with the residence species have fundamental benefits to the well-being of the ecosystem in delaying the tipping point and facilitating species recovery. Articulating and implementing control mechanisms to induce multiplexity can thus help sustain certain types of ecosystems that are in danger of extinction as the result of environmental changes.

Single-cell entropy network detects the activity of immune cells based on ribosomal protein genes

We developed a new computational method, Single-Cell Entropy Network (SCEN) to analyze single-cell RNA-seq data, which used the information of gene-gene associations to discover new heterogeneity of immune cells as well as identify existing cell types. Based on SCEN, we defined association-entropy (AE) for each cell and each gene through single-cell gene co-expression networks to measure the strength of association between each gene and all other genes at a single-cell resolution. Analyses of public datasets indicated that the AE of ribosomal protein genes (RP genes) varied greatly even in the same cell type of immune cells and the average AE of RP genes of immune cells in each person was significantly associated with the healthy/disease state of this person. Based on existing research and theory, we inferred that the AE of RP genes represented the heterogeneity of ribosomes and reflected the activity of immune cells. We believe SCEN can provide more biological insights into the heterogeneity and diversity of immune cells, especially the change of immune cells in the diseases.

A Dual Ranking Algorithm Based on the Multiplex Network for Heterogeneous Complex Disease Analysis

Identifying biomarkers of heterogeneous complex diseases has always been one of the focuses in medical research. In previous studies, the powerful network propagation methods have been applied to finding marker genes related to specific diseases, but existing methods are mostly based on a single network, which may be greatly affected by the incompleteness of the network and the ignorance of a large amount of information about physical and functional interactions between biological components. Other methods that directly integrate multiple types of interactions into an aggregate network have the risks that different types of data may conflict with each other and the characteristics and topologies of each individual network are lost. Meanwhile, biomarkers used in clinical trials should have the characteristics of small quantity and strong discriminate ability. In this study, we developed a multiplex network-based dual ranking framework (DualRank) for heterogeneous complex disease analysis. We applied the proposed method to heterogeneous complex diseases for diagnosis, prognosis, and classification. The results showed that DualRank outperformed competing methods and could identify biomarkers with the small quantity, great prediction performance (average AUC = 0.818) and biological interpretability.

Energy landscape decomposition for cell differentiation with proliferation effect

Complex interactions between genes determine the development and differentiation of cells. We establish a landscape theory for cell differentiation with proliferation effect, in which the developmental process is modeled as a stochastic dynamical system with a birth-death term. We find that two different energy landscapes, denoted U and V, collectively contribute to the establishment of non-equilibrium steady differentiation. The potential U is known as the energy landscape leading to the steady distribution, whose metastable states stand for cell types, while V indicates the differentiation direction from pluripotent to differentiated cells. This interpretation of cell differentiation is different from the previous landscape theory without the proliferation effect. We propose feasible numerical methods and a mean-field approximation for constructing landscapes U and V. Successful applications to typical biological models demonstrate the energy landscape decomposition's validity and reveal biological insights into the considered processes.

Intrinsic entropy model for feature selection of scRNA-seq data

Recent advances of single-cell RNA sequencing (scRNA-seq) technologies have led to extensive study of cellular heterogeneity and cell-to-cell variation. However, the high frequency of dropout events and noise in scRNA-seq data confounds the accuracy of the downstream analysis, i.e. clustering analysis, whose accuracy depends heavily on the selected feature genes. Here, by deriving an entropy decomposition formula, we propose a feature selection method, i.e. an intrinsic entropy (IE) model, to identify the informative genes for accurately clustering analysis. Specifically, by eliminating the 'noisy' fluctuation or extrinsic entropy (EE), we extract the IE of each gene from the total entropy (TE), i.e. TE = IE + EE. We show that the IE of each gene actually reflects the regulatory fluctuation of this gene in a cellular process, and thus high-IE genes provide rich information on cell type or state analysis. To validate the performance of the high-IE genes, we conduct computational analysis on both simulated datasets and real single-cell datasets by comparing with other representative methods. The results show that our IE model is not only broadly applicable and robust for different clustering and classification methods, but also sensitive for novel cell types. Our results also demonstrate that the intrinsic entropy/fluctuation of a gene serves as information rather than noise in contrast to its total entropy/fluctuation.

Determination of WWOX Function in Modulating Cellular Pathways Activated by AP-2α and AP-2γ Transcription Factors in Bladder Cancer

Following the invention of high-throughput sequencing, cancer research focused on investigating disease-related alterations, often inadvertently omitting tumor heterogeneity. This research was intended to limit the impact of heterogeneity on conclusions related to WWOX/AP-2α/AP-2γ in bladder cancer which differently influenced carcinogenesis. The study examined the signaling pathways regulated by WWOX-dependent AP-2 targets in cell lines as biological replicates using high-throughput sequencing. RT-112, HT-1376 and CAL-29 cell lines were subjected to two stable lentiviral transductions. Following CAGE-seq and differential expression analysis, the most important genes were identified and functionally annotated. Western blot was performed to validate the selected observations. The role of genes in biological processes was assessed and networks were visualized. Ultimately, principal component analysis was performed. The studied genes were found to be implicated in MAPK, Wnt, Ras, PI3K-Akt or Rap1 signaling. Data from pathways were collected, explaining the differences/similarities between phenotypes. FGFR3, STAT6, EFNA1, GSK3B, PIK3CB and SOS1 were successfully validated at the protein level. Afterwards, a definitive network was built using 173 genes. Principal component analysis revealed that the various expression of these genes explains the phenotypes. In conclusion, the current study certified that the signaling pathways regulated by WWOX and AP-2α have more in common than that regulated by AP-2γ. This is because WWOX acts as an EMT inhibitor, AP-2γ as an EMT enhancer while AP-2α as a MET inducer. Therefore, the relevance of AP-2γ in targeted therapy is now more evident. Some of the differently regulated genes can find application in bladder cancer treatment.

Extracellular vesicle IL-32 promotes the M2 macrophage polarization and metastasis of esophageal squamous cell carcinoma via FAK/STAT3 pathway

Background

Metastasis is the leading cause of mortality in human cancers, including esophageal squamous cell carcinoma (ESCC). As a pro-inflammatory cytokine, IL-32 was reported to be a poor prognostic factor in many cancers. However, the role of IL-32 in ESCC metastasis remains unknown.

Methods

ESCC cells with ectopic expression or knockdown of IL-32 were established and their effects on cell motility were detected. Ultracentrifugation, Transmission electron microscopy and Western blot were used to verify the existence of extracellular vesicle IL-32 (EV-IL-32). Coculture assay, immunofluorescence, flow cytometry, and in vivo lung metastasis model were performed to identify how EV-IL-32 regulated the crosstalk between ESCC cells and macrophages.

Results

Here, we found that IL-32 was overexpressed and positively correlated to lymph node metastasis of ESCC. IL-32 was significantly higher in the tumor nest compared with the non-cancerous tissue. We found that IL-32β was the main isoform and loaded in EV derived from ESCC cells. The shuttling of EV-IL-32 derived from ESCC cells into macrophages could promote the polarization of M2 macrophages via FAK-STAT3 pathway. IL-32 overexpression facilitated lung metastasis and was positively correlated with the proportion of M2 macrophages in tumor microenvironment.

Conclusions

Taken together, our results indicated that EV-IL-32 derived from ESCC cell line could be internalized by macrophages and lead to M2 macrophage polarization via FAK-STAT3 pathway, thus promoting the metastasis of ESCC. These findings indicated that IL-32 could serve as a potential therapeutic target in patients with ESCC.

Supplementary information

The online version contains supplementary material available at 10.1186/s13046-022-02348-8.

EGFR/MET promotes hepatocellular carcinoma metastasis by stabilizing tumor cells and resisting to RTKs inhibitors in circulating tumor microemboli

The receptor tyrosine kinases (RTKs) family is well-recognized as vital targets for the treatment of hepatocarcinoma cancer (HCC) clinically, whereas the survival benefit of target therapy sorafenib is not satisfactory for liver cancer patients due to metastasis. EGFR and MET are two molecules of the RTK family that were related to the survival time of liver cancer patients and resistance to targeted therapy in clinical reports. However, the mechanism and clinical therapeutic value of EGFR/MET in HCC metastasis are still not completely clarified. The study confirmed that EGFR/MET was highly expressed in HCC cells and tissues and the phosphorylation was stable after metastasis. The expression of EGFR/MET was up-regulated in circulating tumor microemboli (CTM) to accelerate IL-8 production and resistance to the lethal effect of leukocytes. Meanwhile, highly expressed EGFR/MET effectively regulated the Ras/MAPK pathway and stabilized suspended HCC cells by facilitating proliferation and inhibiting apoptosis. Moreover, EGFR/MET promoted phosphorylation of hetero-RTKs, which was dependent on high-energy phosphoric acid compounds rather than their direct interactions. In conclusion, highly expressed EGFR/MET could be used in CTM identification and suitable for preventing metastasis of HCC in clinical practice.

Determination of tipping point in course of PM2.5 organic extracts-induced malignant transformation by dynamic network biomarkers

The dynamic network biomarkers (DNBs) are designed to identify the tipping point and specific molecules in initiation of PM_2.5-induced lung cancers. To discover early-warning signals, we analyzed time-series gene expression datasets over a course of PM_2.5 organic extraction-induced human bronchial epithelial (HBE) cell transformation (0^th~16^th week). A composition index of DNB (CI_DNB) was calculated to determine correlations and fluctuations in molecule clusters at each timepoint. We identified a group of genes with the highest CI_DNB at the 10^th week, implicating a tipping point and corresponding DNBs. Functional experiments revealed that manipulating respective DNB genes at the tipping point led to remarkable changes in malignant phenotypes, including four promoters (GAB2, NCF1, MMP25, LAPTM5) and three suppressors (BATF2, DOK3, DAP3). Notably, co-altered expression of seven core DNB genes resulted in an enhanced activity of malignant transformation compared to effects of single-gene manipulation. Perturbation of pathways (EMT, HMGB1, STAT3, NF-κB, PTEN) appeared in HBE cells at the tipping point. The core DNB genes were involved in regulating lung cancer cell growth and associated with poor survival, indicating their synergistic effects in initiation and development of lung cancers. These findings provided novel insights into the mechanism of dynamic networks attributable to PM_2.5-induced cell transformation.

Biomedical data, computational methods and tools for evaluating disease-disease associations

In recent decades, exploring potential relationships between diseases has been an active research field. With the rapid accumulation of disease-related biomedical data, a lot of computational methods and tools/platforms have been developed to reveal intrinsic relationship between diseases, which can provide useful insights to the study of complex diseases, e.g. understanding molecular mechanisms of diseases and discovering new treatment of diseases. Human complex diseases involve both external phenotypic abnormalities and complex internal molecular mechanisms in organisms. Computational methods with different types of biomedical data from phenotype to genotype can evaluate disease-disease associations at different levels, providing a comprehensive perspective for understanding diseases. In this review, available biomedical data and databases for evaluating disease-disease associations are first summarized. Then, existing computational methods for disease-disease associations are reviewed and classified into five groups in terms of the usages of biomedical data, including disease semantic-based, phenotype-based, function-based, representation learning-based and text mining-based methods. Further, we summarize software tools/platforms for computation and analysis of disease-disease associations. Finally, we give a discussion and summary on the research of disease-disease associations. This review provides a systematic overview for current disease association research, which could promote the development and applications of computational methods and tools/platforms for disease-disease associations.

Landscape dynamic network biomarker analysis reveals the tipping point of transcriptome reprogramming to prevent skin photodamage

Skin, as the outmost layer of human body, is frequently exposed to environmental stressors including pollutants and ultraviolet (UV), which could lead to skin disorders. Generally, skin response process to ultraviolet B (UVB) irradiation is a nonlinear dynamic process, with unknown underlying molecular mechanism of critical transition. Here, the landscape dynamic network biomarker (l-DNB) analysis of time series transcriptome data on 3D skin model was conducted to reveal the complicated process of skin response to UV irradiation at both molecular and network levels. The advanced l-DNB analysis approach showed that: (i) there was a tipping point before critical transition state during pigmentation process, validated by 3D skin model; (ii) 13 core DNB genes were identified to detect the tipping point as a network biomarker, supported by computational assessment; (iii) core DNB genes such as COL7A1 and CTNNB1 can effectively predict skin lightening, validated by independent human skin data. Overall, this study provides new insights for skin response to repetitive UVB irradiation, including dynamic pathway pattern, biphasic response, and DNBs for skin lightening change, and enables us to further understand the skin resilience process after external stress.

Dynamical network biomarkers: Theory and applications

This paper reviews theory of DNB (Dynamical Network Biomarkers) and its applications including both modern medicine and traditional medicine. We show that omics data such as gene/protein expression profiles can be effectively used to detect pre-disease states before critical transitions from healthy states to disease states by using the DNB theory. The DNB theory with big biological data is expected to lead to ultra-early precision and preventive medicine.

Data analysis methods for defining biomarkers from omics data

Omics mainly includes genomics, epigenomics, transcriptomics, proteomics and metabolomics. The rapid development of omics technology has opened up new ways to study disease diagnosis and prognosis and to define prospective information of complex diseases. Since omics data are usually large and complex, the method used to analyze the data and to define important information is crucial in omics study. In this review, we focus on advances in biomarker discovery methods based on omics data in the last decade, and categorize them as individual feature analysis, combinatorial feature analysis and network analysis. We also discuss the challenges and perspectives in this field.

Exploring cell-specific miRNA regulation with single-cell miRNA-mRNA co-sequencing data

Background

Existing computational methods for studying miRNA regulation are mostly based on bulk miRNA and mRNA expression data. However, bulk data only allows the analysis of miRNA regulation regarding a group of cells, rather than the miRNA regulation unique to individual cells. Recent advance in single-cell miRNA-mRNA co-sequencing technology has opened a way for investigating miRNA regulation at single-cell level. However, as currently single-cell miRNA-mRNA co-sequencing data is just emerging and only available at small-scale, there is a strong need of novel methods to exploit existing single-cell data for the study of cell-specific miRNA regulation.

Results

In this work, we propose a new method, CSmiR (Cell-Specific miRNA regulation) to combine single-cell miRNA-mRNA co-sequencing data and putative miRNA-mRNA binding information to identify miRNA regulatory networks at the resolution of individual cells. We apply CSmiR to the miRNA-mRNA co-sequencing data in 19 K562 single-cells to identify cell-specific miRNA-mRNA regulatory networks for understanding miRNA regulation in each K562 single-cell. By analyzing the obtained cell-specific miRNA-mRNA regulatory networks, we observe that the miRNA regulation in each K562 single-cell is unique. Moreover, we conduct detailed analysis on the cell-specific miRNA regulation associated with the miR-17/92 family as a case study. The comparison results indicate that CSmiR is effective in predicting cell-specific miRNA targets. Finally, through exploring cell–cell similarity matrix characterized by cell-specific miRNA regulation, CSmiR provides a novel strategy for clustering single-cells and helps to understand cell–cell crosstalk.

Conclusions

To the best of our knowledge, CSmiR is the first method to explore miRNA regulation at a single-cell resolution level, and we believe that it can be a useful method to enhance the understanding of cell-specific miRNA regulation.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12859-021-04498-6.

Dynamic Host Immune and Transcriptomic Responses to Respiratory Syncytial Virus Infection in a Vaccination-Challenge Mouse Model

Respiratory syncytial virus (RSV) is the major cause of lower respiratory tract infections in children. Inactivated RSV vaccine was developed in the late 1960's, but the vaccine-enhanced disease (VED) occurred to vaccinated infants upon subsequent natural RSV infection. The excessive inflammatory immunopathology in the lungs might be involved in the VED, but the underlying mechanisms remain not fully understood. In this study, we utilized UV-inactivated RSV in the prime/boost approach followed by RSV challenge in BALB/c mice to mimic RSV VED. The dynamic virus load, cytokines, histology and transcriptome profiles in lung tissues of mice were investigated from day 1 to day 6 post-infection. Compared to PBS-treated mice, UV-RSV vaccination leads to a Th2 type inflammatory response characterized by enhanced histopathology, reduced Treg cells and increased IL4+CD4 T cells in the lung. Enhanced production of several Th2 type cytokines (IL-4, IL-5, IL-10) and TGF-β,  reduction of IL-6 and IL-17 were observed in UV-RSV vaccinated mice. A total of 5582 differentially expressed (DE) genes between PBS-treated or vaccinated mice and naïve mice were identified by RNA-Seq. Eleven conserved high-influential modules (HMs) were recognized, majorly grouped into regulatory networks related to cell cycle and cell metabolism, signal transduction, immune and inflammatory responses. At an early time post-infection, the vaccinated mice showed obvious decreased expression patterns of DE genes in 11 HMs compared to PBS-treated mice. The extracellular matrix (HM5) and immune responses (HM8) revealed tremendous differences in expression and regulation characteristics of transcripts between PBS-treated and vaccinated mice at both early and late time points. The highly connected genes in HM5 and HM8 networks were further validated by RT-qPCR. These findings reveal the relationship between RSV VED and immune responses, which could benefit the development of novel RSV vaccines.

In Silico Approaches In Carcinogenicity Hazard Assessment: Current Status and Future Needs

Historically, identifying carcinogens has relied primarily on tumor studies in rodents, which require enormous resources in both money and time. In silico models have been developed for predicting rodent carcinogens but have not yet found general regulatory acceptance, in part due to the lack of a generally accepted protocol for performing such an assessment as well as limitations in predictive performance and scope. There remains a need for additional, improved in silico carcinogenicity models, especially ones that are more human-relevant, for use in research and regulatory decision-making. As part of an international effort to develop in silico toxicological protocols, a consortium of toxicologists, computational scientists, and regulatory scientists across several industries and governmental agencies evaluated the extent to which in silico models exist for each of the recently defined 10 key characteristics (KCs) of carcinogens. This position paper summarizes the current status of in silico tools for the assessment of each KC and identifies the data gaps that need to be addressed before a comprehensive in silico carcinogenicity protocol can be developed for regulatory use.

Single-cell RNA-sequencing atlas reveals an MDK-dependent immunosuppressive environment in ErbB pathway-mutated gallbladder cancer

BACKGROUND & AIMS

Our previous genomic whole-exome sequencing (WES) data identified the key ErbB pathway mutations that play an essential role in regulating the malignancy of gallbladder cancer (GBC). Herein, we tested the hypothesis that individual cellular components of the tumor microenvironment (TME) in GBC function differentially to participate in ErbB pathway mutation-dependent tumor progression.

METHODS

We engaged single-cell RNA-sequencing to reveal transcriptomic heterogeneity and intercellular crosstalk from 13 human GBCs and adjacent normal tissues. In addition, we performed WES analysis to reveal the genomic variations related to tumor malignancy. A variety of bulk RNA-sequencing, immunohistochemical staining, immunofluorescence staining and functional experiments were employed to study the difference between tissues with or without ErbB pathway mutations.

RESULTS

We identified 16 cell types from a total of 114,927 cells, in which epithelial cells, M2 macrophages, and regulatory T cells were predominant in tumors with ErbB pathway mutations. Furthermore, epithelial cell subtype 1, 2 and 3 were mainly found in adenocarcinoma and subtype 4 was present in adenosquamous carcinoma. The tumors with ErbB pathway mutations harbored larger populations of epithelial cell subtype 1 and 2, and expressed higher levels of secreted midkine (MDK) than tumors without ErbB pathway mutations. Increased MDK resulted in an interaction with its receptor LRP1, which is expressed by tumor-infiltrating macrophages, and promoted immunosuppressive macrophage differentiation. Moreover, the crosstalk between macrophage-secreted CXCL10 and its receptor CXCR3 on regulatory T cells was induced in GBC with ErbB pathway mutations. Elevated MDK was correlated with poor overall survival in patients with GBC.

CONCLUSIONS

This study has provided valuable insights into transcriptomic heterogeneity and the global cellular network in the TME, which coordinately functions to promote the progression of GBC with ErbB pathway mutations; thus, unveiling novel cellular and molecular targets for cancer therapy.

LAY SUMMARY

We employed single-cell RNA-sequencing and functional assays to uncover the transcriptomic heterogeneity and intercellular crosstalk present in gallbladder cancer. We found that ErbB pathway mutations reduced anti-cancer immunity and led to cancer development. ErbB pathway mutations resulted in immunosuppressive macrophage differentiation and regulatory T cell activation, explaining the reduced anti-cancer immunity and worse overall survival observed in patients with these mutations.

Genome-wide dynamic network analysis reveals the potential genes for MeJA-induced growth-to-defense transition

BACKGROUND

Methyl jasmonate (MeJA), which has been identified as a lipid-derived stress hormone, mediates plant resistance to biotic/abiotic stress. Understanding MeJA-induced plant defense provides insight into how they responding to environmental stimuli.

RESULT

In this work, the dynamic network analysis method was used to quantitatively identify the tipping point of growth-to-defense transition and detect the associated genes. As a result, 146 genes were detected as dynamic network biomarker (DNB) members and the critical defense transition was identified based on dense time-series RNA-seq data of MeJA-treated Arabidopsis thaliana. The GO functional analysis showed that these DNB genes were significantly enriched in defense terms. The network analysis between DNB genes and differentially expressed genes showed that the hub genes including SYP121, SYP122, WRKY33 and MPK11 play a vital role in plant growth-to-defense transition.

CONCLUSIONS

Based on the dynamic network analysis of MeJA-induced plant resistance, we provide an important guideline for understanding the growth-to-defense transition of plants' response to environment stimuli. This study also provides a database with the key genes of plant defense induced by MeJA.

Tumor Mutation Burden-Associated LINC00638/miR-4732-3p/ULBP1 Axis Promotes Immune Escape via PD-L1 in Hepatocellular Carcinoma

Tumor mutation burden (TMB) is associated with immune infiltration, while its underlying mechanism in hepatocellular carcinoma (HCC) remains unclear. A long noncoding RNA (lncRNA)-related competitive endogenous RNA (ceRNA) network can regulate various tumor behaviors, and research about its correlation with TMB and immune infiltration is warranted. Data were downloaded from TCGA and ArrayExpress databases. Cox analysis and machine learning algorithms were employed to establish a lncRNA-based prognostic model for HCC. We then developed a nomogram model to predict overall survival and odds of death for HCC patients. The association of this prognostic model with TMB and immune infiltration was also analyzed. In addition, a ceRNA network was constructed by using DIANA-LncBasev2 and the starBase database and verified by luciferase reporter and colocalization analysis. Multiplex immunofluorescence was applied to determine the correlation between ULBP1 and PD-L1. An eight-lncRNA (SLC25A30-AS1, HPN-AS1, LINC00607, USP2-AS1, HCG20, LINC00638, MKLN1-AS and LINC00652) prognostic score model was constructed for HCC, which was highly associated with TMB and immune infiltration. Next, we constructed a ceRNA network, LINC00638/miR-4732-3p/ULBP1, that may be responsible for NK cell infiltration in HCC with high TMB. However, patients with high ULBP1 possessed a poorer prognosis. Using multiplex immunofluorescence, we found a significant correlation between ULBP1 and PD-L1 in HCC, and patients with high ULBP1 and PD-L1 had the worst prognosis. In brief, the eight-lncRNA model is a reliable tool to predict the prognosis of HCC patients. The LINC00638/miR-4732-3p/ULBP1 axis may regulate immune escape via PD-L1 in HCC with high TMB.

Single-cell transcriptomics reveal DHX9 in mature B cell as a dynamic network biomarker before lymph node metastasis in CRC

Increasing evidence indicates that mature B cells in the adjacent tumor tissue, both as an intermediate state, are vital in advanced colorectal cancer (CRC), which is associated with a low survival rate. Developing predictive biomarkers that detect the tipping point of mature B cells before lymph node metastasis in CRC is critical to prevent irreversible deterioration. We analyzed B cells in the adjacent tissues of CRC samples from different stages using the dynamic network biomarker (DNB) method. Single-cell profiling of 725 CRC-derived B cells revealed the emergence of a mature B cell subtype. Using the DNB method, we identified stage II as a critical period before lymph node metastasis and that reversed difference genes triggered by DNBs were enriched in the Janus kinase (JAK)-signal transducer and activator of transcription (STAT) pathway involving B cell immune capability. DHX9 (DEAH-box helicase 9) was a specific para-cancerous tissue DNB key gene. The dynamic expression levels of DHX9 and its proximate network genes involved in B cell-related pathways were reversed at the network level from stage I to III. In summary, DHX9 in mature B cells of CRC-adjacent tissues may serve as a predictable biomarker and a potential immune target in CRC progression.

RNA-seq profiling reveals PBMC RNA as a potential biomarker for hepatocellular carcinoma

Hepatocellular carcinoma (HCC) is one of the most common malignant tumors and has extremely high morbidity and mortality. Although many existing studies have focused on the identification of biomarkers, little information has been uncovered regarding the PBMC RNA profile of HCC. We attempted to create a profile throughout using expression of peripheral blood mononuclear cell (PBMC) RNA using RNA-seq technology and compared the transcriptome between HCC patients and healthy controls. Seventeen patients and 17 matched healthy controls were included in this study, and PBMC RNA was sequenced from all samples. Sequencing data were analyzed using bioinformatics tools, and quantitative reverse transcription PCR (qRT-PCR) was used for selected validation of DEGs. A total of 1,578 dysregulated genes were found in the PBMC samples, including 1,334 upregulated genes and 244 downregulated genes. GO enrichment and KEGG studies revealed that HCC is closely linked to differentially expressed genes (DEGs) implicated in the immune response. Expression of 6 selected genes (SELENBP1, SLC4A1, SLC26A8, HSPA8P4, CALM1, and RPL7p24) was confirmed by qRT-PCR, and higher sensitivity and specificity were obtained by ROC analysis of the 6 genes. CALM1 was found to gradually decrease as tumors enlarged. Nearly the opposite expression modes were obtained when compared to tumor sequencing data. Immune cell populations exhibited significant differences between HCC and controls. These findings suggest a potential biomarker for the diagnosis of HCC. This study provides new perspectives for liver cancer development and possible future successful clinical diagnosis.

Sulfarotene, a synthetic retinoid, overcomes stemness and sorafenib resistance of hepatocellular carcinoma via suppressing SOS2-RAS pathway

BACKGROUND

Recurrent hepatocellular carcinoma (HCC) shows strong resistance to sorafenib, and the tumor-repopulating cells (TRCs) with cancer stem cell-like properties are considered a driver for its high recurrent rate and drug resistance.

METHODS

Suppression of TRCs may thus be an effective therapeutic strategy for treating this fatal disease. We evaluated the pharmacology and mechanism of sulfarotene, a new type of synthetic retinoid, on the cancer stem cell-like properties of HCC TRCs, and assessed its preclinical efficacy in models of HCC patient-derived xenografts (PDXs).

RESULTS

Sulfarotene selectively inhibited the growth of HCC TRCs in vitro and significantly deterred TRC-mediated tumor formation and lung metastasis in vivo without apparent toxicity, with an IC₅₀ superior to that of acyclic retinoid and sorafenib, to which the recurrent HCC exhibits significant resistance at advanced stage. Sulfarotene promoted the expression and activation of RARα, which down-regulated SOS2, a key signal mediator associated with RAS activation and signal transduction involved in multiple downstream pathways. Moreover, sulfarotene selectively inhibited tumorigenesis of HCC PDXs with high expression for SOS2.

CONCLUSIONS

Our study identified sulfarotene as a selective inhibitor for the TRCs of HCC, which targets a novel RARα-SOS2-RAS signal nexus, shedding light on a new, promising strategy of target therapy for advanced liver cancer.

Diagnosis of Wilson Disease and Its Phenotypes by Using Artificial Intelligence

WD is caused by ATP7B variants disrupting copper efflux resulting in excessive copper accumulation mainly in liver and brain. The diagnosis of WD is challenged by its variable clinical course, onset, morbidity, and ATP7B variant type. Currently it is diagnosed by a combination of clinical symptoms/signs, aberrant copper metabolism parameters (e.g., low ceruloplasmin serum levels and high urinary and hepatic copper concentrations), and genetic evidence of ATP7B mutations when available. As early diagnosis and treatment are key to favorable outcomes, it is critical to identify subjects before the onset of overtly detrimental clinical manifestations. To this end, we sought to improve WD diagnosis using artificial neural network algorithms (part of artificial intelligence) by integrating available clinical and molecular parameters. Surprisingly, WD diagnosis was based on plasma levels of glutamate, asparagine, taurine, and Fischer's ratio. As these amino acids are linked to the urea-Krebs' cycles, our study not only underscores the central role of hepatic mitochondria in WD pathology but also that most WD patients have underlying hepatic dysfunction. Our study provides novel evidence that artificial intelligence utilized for integrated analysis for WD may result in earlier diagnosis and mechanistically relevant treatments for patients with WD.

Potential transmission chains of variant B.1.1.7 and co-mutations of SARS-CoV-2

The presence of SARS-CoV-2 mutants, including the emerging variant B.1.1.7, has raised great concerns in terms of pathogenesis, transmission, and immune escape. Characterizing SARS-CoV-2 mutations, evolution, and effects on infectivity and pathogenicity is crucial to the design of antibody therapies and surveillance strategies. Here, we analyzed 454,443 SARS-CoV-2 spike genes/proteins and 14,427 whole-genome sequences. We demonstrated that the early variant B.1.1.7 may not have evolved spontaneously in the United Kingdom or within human populations. Our extensive analyses suggested that Canidae, Mustelidae or Felidae, especially the Canidae family (for example, dog) could be a possible host of the direct progenitor of variant B.1.1.7. An alternative hypothesis is that the variant was simply yet to be sampled. Notably, the SARS-CoV-2 whole-genome represents a large number of potential co-mutations. In addition, we used an experimental SARS-CoV-2 reporter replicon system to introduce the dominant co-mutations NSP12_c14408t, 5'UTR_c241t, and NSP3_c3037t into the viral genome, and to monitor the effect of the mutations on viral replication. Our experimental results demonstrated that the co-mutations significantly attenuated the viral replication. The study provides valuable clues for discovering the transmission chains of variant B.1.1.7 and understanding the evolutionary process of SARS-CoV-2.

A tipping point in cancer-immune dynamics leads to divergent immunotherapy responses and hampers biomarker discovery

BACKGROUND

Predicting treatment response or survival of cancer patients remains challenging in immuno-oncology. Efforts to overcome these challenges focus, among others, on the discovery of new biomarkers. Despite advances in cellular and molecular approaches, only a limited number of candidate biomarkers eventually enter clinical practice.

METHODS

A computational modeling approach based on ordinary differential equations was used to simulate the fundamental mechanisms that dictate tumor-immune dynamics and to investigate its implications on responses to immune checkpoint inhibition (ICI) and patient survival. Using in silico biomarker discovery trials, we revealed fundamental principles that explain the diverging success rates of biomarker discovery programs.

RESULTS

Our model shows that a tipping point-a sharp state transition between immune control and immune evasion-induces a strongly non-linear relationship between patient survival and both immunological and tumor-related parameters. In patients close to the tipping point, ICI therapy may lead to long-lasting survival benefits, whereas patients far from the tipping point may fail to benefit from these potent treatments.

CONCLUSION

These findings have two important implications for clinical oncology. First, the apparent conundrum that ICI induces substantial benefits in some patients yet completely fails in others could be, to a large extent, explained by the presence of a tipping point. Second, predictive biomarkers for immunotherapy should ideally combine both immunological and tumor-related markers, as a patient's distance from the tipping point can typically not be reliably determined from solely one of these. The notion of a tipping point in cancer-immune dynamics helps to devise more accurate strategies to select appropriate treatments for patients with cancer.

Site-specific glycoproteomic analysis revealing increased core-fucosylation on FOLR1 enhances folate uptake capacity of HCC cells to promote EMT

Rationale: Epithelial-mesenchymal transition (EMT) has been recognized as an important step toward high invasion and metastasis of many cancers including hepatocellular carcinoma (HCC), while the mechanism for EMT promotion is still ambiguous.

Methods: The dynamic alterations of site-specific glycosylation during HGF/TGF-β1-induced EMT process of three HCC cell lines were systematically investigated using precision glycoproteomic methods. The possible roles of EMT-related glycoproteins and site-specific glycans were further confirmed by various molecular biological approaches.

Results: Using mass spectrometry-based glycoproteomic methods, we totally identified 2306 unique intact glycopeptides from SMMC-7721 and HepG2 cell lines, and found that core-fucosylated glycans were accounted for the largest proportion of complex N-glycans. Through quantification analysis of intact glycopeptides, we found that the majority of core-fucosylated intact glycopeptides from folate receptor α (FOLR1) were up-regulated in the three HGF-treated cell lines. Similarly, core-fucosylation of FOLR1 were up-regulated in SMMC-7721 and Hep3B cells with TGF-β1 treatment. Using molecular approaches, we further demonstrated that FUT8 was a driver for HGF/TGF-β1-induced EMT. The silencing of FUT8 reduced core-fucosylation and partially blocked the progress of HGF-induced EMT. Finally, we confirmed that the level of core-fucosylation on FOLR1 especially at the glycosite Asn-201 positively regulated the cellular uptake capacity of folates, and enhanced uptake of folates could promote the EMT of HCC cells.

Conclusions: Based on the results, we proposed a potential pathway for HGF or TGF-β1-induced EMT of HCC cells: HGF or TGF-β1 treatment of HCC cells can increase the expression of glycosyltransferase FUT8 to up-regulate the core-fucosylation of N-glycans on glycoproteins including the FOLR1; core-fucosylation on FOLR1 can then enhance the folate uptake capacity to finally promote the EMT progress of HCC cells.

Resilience function uncovers the critical transitions in cancer initiation

Considerable evidence suggests that during the progression of cancer initiation, the state transition from wellness to disease is not necessarily smooth but manifests switch-like nonlinear behaviors, preventing the cancer prediction and early interventional therapy for patients. Understanding the mechanism of such wellness-to-disease transitions is a fundamental and challenging task. Despite the advances in flux theory of nonequilibrium dynamics and 'critical slowing down'-based system resilience theory, a system-level approach still lacks to fully describe this state transition. Here, we present a novel framework (called bioRFR) to quantify such wellness-to-disease transition during cancer initiation through uncovering the biological system's resilience function from gene expression data. We used bioRFR to reconstruct the biologically and dynamically significant resilience functions for cancer initiation processes (e.g. BRCA, LUSC and LUAD). The resilience functions display the similar resilience pattern with hysteresis feature but different numbers of tipping points, which implies that once the cell become cancerous, it is very difficult or even impossible to reverse to the normal state. More importantly, bioRFR can measure the severe degree of cancer patients and identify the personalized key genes that are associated with the individual system's state transition from normal to tumor in resilience perspective, indicating that bioRFR can contribute to personalized medicine and targeted cancer therapy.

Screening of gene markers related to the prognosis of metastatic skin cutaneous melanoma based on Logit regression and survival analysis

BACKGROUND

Bioinformatics was used to analyze the skin cutaneous melanoma (SKCM) gene expression profile to provide a theoretical basis for further studying the mechanism underlying metastatic SKCM and the clinical prognosis.

METHODS

We downloaded the gene expression profiles of 358 metastatic and 102 primary (nonmetastatic) CM samples from The Cancer Genome Atlas (TCGA) database as a training dataset and the GSE65904 dataset from the National Center for Biotechnology Information database as a validation dataset. Differentially expressed genes (DEGs) were screened using the limma package of R3.4.1, and prognosis-related feature DEGs were screened using Logit regression (LR) and survival analyses. We also used the STRING online database, Cytoscape software, and Database for Annotation, Visualization and Integrated Discovery software for protein-protein interaction network, Gene Ontology, and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analyses based on the screened DEGs.

RESULTS

Of the 876 DEGs selected, 11 (ZNF750, NLRP6, TGM3, KRTDAP, CAMSAP3, KRT6C, CALML5, SPRR2E, CD3G, RTP5, and FAM83C) were screened using LR analysis. The survival prognosis of nonmetastatic group was better compared to the metastatic group between the TCGA training and validation datasets. The 11 DEGs were involved in 9 KEGG signaling pathways, and of these 11 DEGs, CALML5 was a feature DEG involved in the melanogenesis pathway, 12 targets of which were collected.

CONCLUSION

The feature DEGs screened, such as CALML5, are related to the prognosis of metastatic CM according to LR. Our results provide new ideas for exploring the molecular mechanism underlying CM metastasis and finding new diagnostic prognostic markers.

c-CSN: Single-cell RNA Sequencing Data Analysis by Conditional Cell-specific Network

The rapid advancement of single-cell technologies has shed new light on the complex mechanisms of cellular heterogeneity. However, compared to bulk RNA sequencing (RNA-seq), single-cell RNA-seq (scRNA-seq) suffers from higher noise and lower coverage, which brings new computational difficulties. Based on statistical independence, cell-specific network (CSN) is able to quantify the overall associations between genes for each cell, yet suffering from a problem of overestimation related to indirect effects. To overcome this problem, we propose the c-CSN method, which can construct the conditional cell-specific network (CCSN) for each cell. c-CSN method can measure the direct associations between genes by eliminating the indirect associations. c-CSN can be used for cell clustering and dimension reduction on a network basis of single cells. Intuitively, each CCSN can be viewed as the transformation from less “reliable” gene expression to more “reliable” gene–gene associations in a cell. Based on CCSN, we further design network flow entropy (NFE) to estimate the differentiation potency of a single cell. A number of scRNA-seq datasets were used to demonstrate the advantages of our approach. 1) One direct association network is generated for one cell. 2) Most existing scRNA-seq methods designed for gene expression matrices are also applicable to c-CSN-transformed degree matrices. 3) CCSN-based NFE helps resolving the direction of differentiation trajectories by quantifying the potency of each cell. c-CSN is publicly available at https://github.com/LinLi-0909/c-CSN.

Circulating Tumor Cells Undergoing the Epithelial-Mesenchymal Transition: Influence on Prognosis in Cytokeratin 19-Positive Hepatocellular Carcinoma

Purpose

The purpose of this study was to elucidate the relationship between cytokeratin 19 (CK19) expression and levels of circulating tumor cells (CTCs) in preoperative peripheral blood of patients with hepatocellular carcinoma (HCC), and the potential influence of that relationship on prognosis.

Patients and Methods

CanPatrol™ CTC-enrichment technique and in situ hybridization (ISH) were used to enrich and classify CTCs undergoing the epithelial–mesenchymal transition (EMT) from blood samples of 105 HCC patients. CK19 immunohistochemistry staining was performed on HCC tissues and compared with demographic and clinical data.

Results

In total, 27 of 105 (25.7%) HCC patients were CK19-positive. CK19-positive patients had significantly lower median tumor-free survival (TFS) than CK19-negative patients (5 vs 10 months, P = 0.047). In total, 98 (93.3%) patients showed pre-surgery peripheral blood CTCs (range: 0–76, median: 6), and 57 of 105 (54.3%) patients displayed CTC counts ≥6. Furthermore, CK19-positive patients with CTC count ≥6 showed significantly higher percentage than CK19-negative ones (77.8% vs 46.2%, P = 0.004). CK19-positive patients showed a significantly higher proportion of mesenchymal CTCs among CTCs undergoing EMT than CK19-negative patients (mean rank: 62.28 vs 49.79, P = 0.046). We also found that CK19-positive patients with high CTC count showed significantly shorter median tumor-free survival than CK19-negative patients with low CTC count (5 vs 16 months, P = 0.039).

Conclusion

High CTC count and high percentage of mesenchymal CTCs are closely related to the expression of CK19, which is associated with poor prognosis in HCC patients.

Dynamic prediction for clinically relevant pancreatic fistula: a novel prediction model for laparoscopic pancreaticoduodenectomy

Background

With the recent emerge of dynamic prediction model on the use of diabetes, cardiovascular diseases and renal failure, and its advantage of providing timely predicted results according to the fluctuation of the condition of the patients, we aim to develop a dynamic prediction model with its corresponding risk assessment chart for clinically relevant postoperative pancreatic fistula after laparoscopic pancreaticoduodenectomy by combining baseline factors and postoperative time-relevant drainage fluid amylase level and C-reactive protein-to-albumin ratio.

Methods

We collected data of 251 patients undergoing LPD at West China Hospital of Sichuan University from January 2016 to April 2019. We extracted preoperative and intraoperative baseline factors and time-window of postoperative drainage fluid amylase and C-reactive protein-to-albumin ratio relevant to clinically relevant pancreatic fistula by performing univariate and multivariate analyses, developing a time-relevant logistic model with the evaluation of its discrimination ability. We also established a risk assessment chart in each time-point.

Results

The proportion of the patients who developed clinically relevant postoperative pancreatic fistula after laparoscopic pancreaticoduodenectomy was 7.6% (19/251); preoperative albumin and creatine levels, as well as drainage fluid amylase and C-reactive protein-to-albumin ratio on postoperative days 2, 3, and 5, were the independent risk factors for clinically relevant postoperative pancreatic fistula. The cut-off points of the prediction value of each time-relevant logistic model were 14.0% (sensitivity: 81.9%, specificity: 86.5%), 8.3% (sensitivity: 85.7%, specificity: 79.1%), and 7.4% (sensitivity: 76.9%, specificity: 85.9%) on postoperative days 2, 3, and 5, respectively, the area under the receiver operating characteristic curve was 0.866 (95% CI 0.737–0.996), 0.896 (95% CI 0.814–0.978), and 0.888 (95% CI 0.806–0.971), respectively.

Conclusions

The dynamic prediction model for clinically relevant postoperative pancreatic fistula has a good to very good discriminative ability and predictive accuracy. Patients whose predictive values were above 14.0%, 8.3%, and 7.5% on postoperative days 2, 3, and 5 would be very likely to develop clinically relevant postoperative pancreatic fistula after laparoscopic pancreaticoduodenectomy.

Criticality in the Healthy Brain

The excellence of the brain is its robustness under various types of noise and its flexibility under various environments. However, how the brain works is still a mystery. The critical brain hypothesis proposes a possible mechanism and states that criticality plays an important role in the healthy brain. Herein, using an electroencephalography dataset obtained from patients with psychotic disorders (PDs), ultra-high risk (UHR) individuals and healthy controls (HCs), and its dynamical network analysis, we show that the brain of HCs remains around a critical state, whereas that of patients with PD falls into more stable states. Meanwhile, the brain of UHR individuals is similar to that of PD in terms of entropy but is analogous to that of HCs in causality patterns. These results not only provide evidence for the criticality of the normal brain but also highlight the practicability of using an analytic biophysical tool to study the dynamical properties of mental diseases.

Tgfb3 and Mmp13 regulated the initiation of liver fibrosis progression as dynamic network biomarkers

Liver fibrogenesis is a complex scar-forming process in the liver. We suggested that the liver first responded to chronic injuries with gradual changes, then reached the critical state and ultimately resulted in cirrhosis rapidly. This study aimed to identify the tipping point and key molecules driving liver fibrosis progression. Mice model of liver fibrosis was induced by thioacetamide (TAA), and liver tissues were collected at different time-points post-TAA administration. By dynamic network biomarker (DNB) analysis on the time series of liver transcriptomes, the week 9 post-TAA treatment (pathologically relevant to bridging fibrosis) was identified as the tipping point just before the significant fibrosis transition, with 153 DNB genes as key driving factors. The DNB genes were functionally enriched in fibrosis-associated pathways, in particular, in the top-ranked DNB genes, Tgfb3 negatively regulated Mmp13 in the interaction path and they formed a bistable switching system from a dynamical perspective. In the in vitro study, Tgfb3 promoted fibrogenic genes and down-regulate Mmp13 gene transcription in an immortalized mouse HSC line JS1 and a human HSC line LX-2. The presence of a tipping point during liver fibrogenesis driven by DNB genes marks not only the initiation of significant fibrogenesis but also the repression of the scar resolution.

Integrative Analysis of Metallothioneins Identifies MT1H as Candidate Prognostic Biomarker in Hepatocellular Carcinoma

Background: Metallothioneins (MTs) play crucial roles in the modulation of zinc/copper homeostasis, regulation of neoplastic growth and proliferation, and protection against apoptosis. The present study attempted to visualize the prognostic landscape of MT functional isoforms and identify potential prognostic biomarkers in hepatocellular carcinoma (HCC). Methods: The transcriptional expression, comprehensive prognostic performances, and gene-gene interaction network of MT isoforms in HCC were evaluated via Oncomine, GEPIA, Kaplan-Meier plotter, and GeneMANIA databases. Characterized by good prognostic value in three external cohorts, MT1H was specifically selected as a potential prognostic biomarker in HCC with various clinicopathological features. Functional and pathway enrichment analyses of MT1H status were performed using cBioPortal, the Database for Annotation, Visualization, and Integrated Discovery (DAVID), and ssGSVA method. Results: MT1E/1F/1G/1H/1M/1X/2A was greatly downregulated in HCC. Prognostic analyses elucidated the essential correlations between MT1A/1B/1H/1X/2A/4 attenuation and poor overall survival, between MT1B/1H/4 downregulation and worse relapse-free survival, and between MT1A/1B/1E/1H/1M/2A/4 downregulation and diminished progression-free survival in HCC. Taken together, these results indicated the powerful prognostic value of MT1H among MTs in HCC. In-depth analyses suggested that MT1H may be more applicable to alcohol-derived HCC and involved in the downregulation of the inflammatory pathway, Jak-STAT pathway, TNF pathway, and Wnt signaling pathway. Conclusion: MT-specific isoforms displayed aberrant expression and varying prognostic value in HCC. MT1H repression in HCC was multi-dimensionally detrimental to patient outcomes. Therefore, MT1H was possibly associated with carcinogenesis and exploited as a novel prognostic biomarker and candidate therapeutic target for HCC.

Kinase-substrate Edge Biomarkers Provide a More Accurate Prognostic Prediction in ER-negative Breast Cancer

The estrogen receptor (ER)-negative breast cancer subtype is aggressive with few treatment options available. To identify specific prognostic factors for ER-negative breast cancer, this study included 705,729 and 1034 breast invasive cancer patients from the Surveillance, Epidemiology, and End Results (SEER) and The Cancer Genome Atlas (TCGA) databases, respectively. To identify key differential kinase-substrate node and edge biomarkers between ER-negative and ER-positive breast cancer patients, we adopted a network-based method using correlation coefficients between molecular pairs in the kinase regulatory network. Integrated analysis of the clinical and molecular data revealed the significant prognostic power of kinase-substrate node and edge features for both subtypes of breast cancer. Two promising kinase-substrate edge features, CSNK1A1-NFATC3 and SRC-OCLN, were identified for more accurate prognostic prediction in ER-negative breast cancer patients.

Autoreservoir computing for multistep ahead prediction based on the spatiotemporal information transformation

We develop an auto-reservoir computing framework, Auto-Reservoir Neural Network (ARNN), to efficiently and accurately make multi-step-ahead predictions based on a short-term high-dimensional time series. Different from traditional reservoir computing whose reservoir is an external dynamical system irrelevant to the target system, ARNN directly transforms the observed high-dimensional dynamics as its reservoir, which maps the high-dimensional/spatial data to the future temporal values of a target variable based on our spatiotemporal information (STI) transformation. Thus, the multi-step prediction of the target variable is achieved in an accurate and computationally efficient manner. ARNN is successfully applied to both representative models and real-world datasets, all of which show satisfactory performance in the multi-step-ahead prediction, even when the data are perturbed by noise and when the system is time-varying. Actually, such ARNN transformation equivalently expands the sample size and thus has great potential in practical applications in artificial intelligence and machine learning.

Expression and clinical significance of LAG-3, FGL1, PD-L1 and CD8+T cells in hepatocellular carcinoma using multiplex quantitative analysis

BACKGROUND

Fibrinogen-like protein 1 (FGL1)-Lymphocyte activating gene 3 (LAG-3) pathway is a promising immunotherapeutic target and has synergistic effect with programmed death 1 (PD-1)/programmed death ligand 1 (PD-L1). However, the prognostic significance of FGL1-LAG-3 pathway and the correlation with PD-L1 in hepatocellular carcinoma (HCC) remain unknown.

METHODS

The levels of LAG-3, FGL1, PD-L1 and cytotoxic T (CD8⁺T) cells in 143 HCC patients were assessed by multiplex immunofluorescence. Associations between the marker's expression and clinical significances were studied.

RESULTS

We found FGL1 and LAG-3 densities were elevated while PD-L1 and CD8 were decreased in HCC tissues compared to adjacent normal liver tissues. High levels of FGL1 were strongly associated with high densities of LAG-3⁺cells but not PD-L1. CD8⁺ T cells densities had positive correlation with PD-L1 levels and negative association with FGL1 expression. Elevated densities of LAG-3⁺cells and low levels of CD8⁺ T cells were correlated with poor disease outcome. Moreover, LAG-3⁺cells deteriorated patient stratification based on the abundance of CD8⁺ T cells. Patients with positive PD-L1 expression on tumor cells (PD-L1 TC⁺) tended to have an improved survival than that with negative PD-L1 expression on tumor cells (PD-L1 TC^-). Furthermore, PD-L1 TC^- in combination with high densities of LAG-3⁺cells showed the worst prognosis, and PD-L1 TC⁺ patients with low densities of LAG-3⁺cells had the best prognosis.

CONCLUSIONS

LAG-3, FGL1, PD-L1 and CD8 have distinct tissue distribution and relationships with each other. High levels of LAG-3⁺cells and CD8⁺ T cells represent unfavorable and favorable prognostic biomarkers for HCC respectively.