Consensus clustering with missing labels (ccml): a consensus clustering tool for multi-omics integrative prediction in cohorts with unequal sample coverage.

Multi-omics data integration is a complex and challenging task in biomedical research. Consensus clustering, also known as meta-clustering or cluster ensembles, has become an increasingly popular downstream tool for phenotyping and endotyping using multiple omics and clinical data. However, current consensus clustering methods typically rely on ensembling clustering outputs with similar sample coverages (mathematical replicates), which may not reflect real-world data with varying sample coverages (biological replicates). To address this issue, we propose a new consensus clustering with missing labels (ccml) strategy termed ccml, an R protocol for two-step consensus clustering that can handle unequal missing labels (i.e. multiple predictive labels with different sample coverages). Initially, the regular consensus weights are adjusted (normalized) by sample coverage, then a regular consensus clustering is performed to predict the optimal final cluster. We applied the ccml method to predict molecularly distinct groups based on 9-omics integration in the Karolinska COSMIC cohort, which investigates chronic obstructive pulmonary disease, and 24-omics handprint integrative subgrouping of adult asthma patients of the U-BIOPRED cohort. We propose ccml as a downstream toolkit for multi-omics integration analysis algorithms such as Similarity Network Fusion and robust clustering of clinical data to overcome the limitations posed by missing data, which is inevitable in human cohorts consisting of multiple data modalities. The ccml tool is available in the R language (https://CRAN.R-project.org/package=ccml, https://github.com/pulmonomics-lab/ccml, or https://github.com/ZhoulabCPH/ccml).

Diagnostic Prediction of portal vein thrombosis in chronic cirrhosis patients using data-driven precision medicine model.

BACKGROUND: Portal vein thrombosis (PVT) is a significant issue in cirrhotic patients, necessitating early detection. This study aims to develop a data-driven predictive model for PVT diagnosis in chronic hepatitis liver cirrhosis patients. METHODS: We employed data from a total of 816 chronic cirrhosis patients with PVT, divided into the Lanzhou cohort (n = 468) for training and the Jilin cohort (n = 348) for validation. This dataset encompassed a wide range of variables, including general characteristics, blood parameters, ultrasonography findings and cirrhosis grading. To build our predictive model, we employed a sophisticated stacking approach, which included Support Vector Machine (SVM), Naïve Bayes and Quadratic Discriminant Analysis (QDA). RESULTS: In the Lanzhou cohort, SVM and Naïve Bayes classifiers effectively classified PVT cases from non-PVT cases, among the top features of which seven were shared: Portal Velocity (PV), Prothrombin Time (PT), Portal Vein Diameter (PVD), Prothrombin Time Activity (PTA), Activated Partial Thromboplastin Time (APTT), age and Child-Pugh score (CPS). The QDA model, trained based on the seven shared features on the Lanzhou cohort and validated on the Jilin cohort, demonstrated significant differentiation between PVT and non-PVT cases (AUROC = 0.73 and AUROC = 0.86, respectively). Subsequently, comparative analysis showed that our QDA model outperformed several other machine learning methods. CONCLUSION: Our study presents a comprehensive data-driven model for PVT diagnosis in cirrhotic patients, enhancing clinical decision-making. The SVM-Naïve Bayes-QDA model offers a precise approach to managing PVT in this population.

Radiomultiomics: quantitative CT clusters of severe asthma associated with multi-omics.

RATIONALE: Lung quantitative computed tomographic (qCT) severe asthma clusters have been reported, but their replication and underlying disease mechanisms are unknown. We identified and replicated qCT clusters of severe asthma in two independent asthma cohorts and determined their association with molecular pathways. METHODS: We used consensus clustering on qCT measurements of airway and lung CT scans, performed in 105 severe asthmatic adults from the U-BIOPRED cohort. The same qCT measurements were used to replicate qCT clusters in a subsample of the ATLANTIS asthma cohort (n=97). We performed integrated enrichment analysis using blood, sputum, bronchial biopsies, bronchial brushings and nasal brushings transcriptomics and blood and sputum proteomics to characterize radiomultiomic-associated clusters (RACs). RESULTS: qCT clusters and clinical features in U-BIOPRED were replicated in the matched ATLANTIS cohort. In the U-BIOPRED cohort, RAC1 (n=30) was predominantly female with elevated BMI, mild airflow limitation, normal qCT parameters and upregulation of the complement pathway. RAC2 (n=34) subjects had a lower degree of airflow limitation, airway wall thickness and dilatation, with upregulation of proliferative pathways, including neurotrophic receptor tyrosine kinase 2/tyrosine kinase receptor B (NTRK2/TRKB), and down-regulation of semaphorin pathways. RAC3 (n=41) showed increased lung attenuation area and air trapping, severe airflow limitation, hyperinflation, and upregulation of cytokine signaling and signaling by interleukin pathways, and matrix metallopeptidase 1, 2 and 9. CONCLUSIONS: U-BIOPRED severe asthma qCT clusters were replicated in a matched independent asthmatic cohort and associated with specific molecular pathways. Radiomultiomics might represent anovel strategy to identify new molecular pathways in asthma pathobiology.

Multiomics integration-based molecular characterizations of COVID-19.

The coronavirus disease 2019 (COVID-19) pandemic, caused by the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), rapidly became a global health challenge, leading to unprecedented social and economic consequences. The mechanisms behind the pathogenesis of SARS-CoV-2 are both unique and complex. Omics-scale studies are emerging rapidly and offer a tremendous potential to unravel the puzzle of SARS-CoV-2 pathobiology, as well as moving forward with diagnostics, potential drug targets, risk stratification, therapeutic responses, vaccine development and therapeutic innovation. This review summarizes various aspects of understanding multiomics integration-based molecular characterizations of COVID-19, which to date include the integration of transcriptomics, proteomics, genomics, lipidomics, immunomics and metabolomics to explore virus targets and developing suitable therapeutic solutions through systems biology tools. Furthermore, this review also covers an abridgment of omics investigations related to disease pathogenesis and virulence, the role of host genetic variation and a broad array of immune and inflammatory phenotypes contributing to understanding COVID-19 traits. Insights into this review, which combines existing strategies and multiomics integration profiling, may help further advance our knowledge of COVID-19.

CircCPSF6 promotes hepatocellular carcinoma cancer progression by regulating MAP4K4 through sponging miR-145-5p.

BACKGROUND: Aberrant expression of circRNAs is involved in the progression of hepatocellular carcinoma (HCC). This study aimed at screening the pro-tumorigenic circular RNAs (circRNAs) in HCC and the mechanisms of circCPSF6 expression influencing HCC characteristics. METHOD: circCPSF6 was identified in HCC tissues using high-throughput sequencing data, and its expression was verified in both HCC tissues and cell lines using quantitative real-time PCR (qRT-PCR). CCK-8 and Transwell assays were used to evaluate the effects of circCPSF6 on HCC proliferation and migration. A xenograft mouse model was used to investigate the effects of circCPSF6 on HCC progression in vivo, and the significance of circCPSF6 in HCC was verified both in vivo and in vitro. circCPSF6-associated miRNAs and mRNAs were identified using bioinformatic analyses. Luciferase reporter, RNA pull-down, Fluorescence in situ hybridization, and RNA immunoprecipitation assays were performed to elucidate the circCPSF6 regulatory axis in HCC. RESULT: CircCPSF6 expression was increased in HCC cell lines and tissues, and the expression of its parental mRNA was positively correlated with tumor severity and negatively correlated with survival. Mechanistic analyses of HCC cell lines showed that tumorigenesis was inhibited by circCPSF6 knockdown and promoted by its overexpression. Functional analyses revealed that circCPSF6 mediated HCC development by sponging miR-145-5p as a competing endogenous RNA. Furthermore, this sponging upregulated the miR-145-5p target gene MAP4K4, a classical pro-tumorigenic gene. CONCLUSION: Our findings reveal a regulatory network that includes the circCPSF6-miR-145-5p-MAP4K4 axis. Elements of this axis are potential HCC biomarkers, as well as targets for HCC treatment.

Mucins 3A and 3B Are Expressed in the Epithelium of Human Large Airway.

Aberrant mucus secretion is a hallmark of chronic obstructive pulmonary disease (COPD). Expression of the membrane-tethered mucins 3A and 3B (MUC3A, MUC3B) in human lung is largely unknown. In this observational cross-sectional study, we recruited subjects 45-65 years old from the general population of Stockholm, Sweden, during the years 2007-2011. Bronchial mucosal biopsies, bronchial brushings, and bronchoalveolar lavage fluid (BALF) were retrieved from COPD patients (n = 38), healthy never-smokers (n = 40), and smokers with normal lung function (n = 40). Protein expression of MUC3A and MUC3B in bronchial mucosal biopsies was assessed by immunohistochemical staining. In a subgroup of subjects (n = 28), MUC3A and MUC3B mRNAs were quantified in bronchial brushings using microarray. Non-parametric tests were used to perform correlation and group comparison analyses. A value of p < 0.05 was considered statistically significant. MUC3A and MUC3B immunohistochemical expression was localized to ciliated cells. MUC3B was also expressed in basal cells. MUC3A and MUC3B immunohistochemical expression was equal in all study groups but subjects with emphysema had higher MUC3A expression, compared to those without emphysema. Smokers had higher mRNA levels of MUC3A and MUC3B than non-smokers. MUC3A and MUC3B mRNA were higher in male subjects and correlated negatively with expiratory air flows. MUC3B mRNA correlated positively with total cell concentration and macrophage percentage, and negatively with CD4/CD8 T cell ratio in BALF. We concluded that MUC3A and MUC3B in large airways may be a marker of disease or may play a role in the pathophysiology of airway obstruction.

Baseline characteristics and survival of patients of idiopathic pulmonary fibrosis: a longitudinal analysis of the Swedish IPF Registry.

BACKGROUND: Observational data under real-life conditions in idiopathic pulmonary fibrosis (IPF) is scarce. We explored anti-fibrotic treatment, disease severity and phenotypes in patients with IPF from the Swedish IPF Registry (SIPFR). METHODS: Patients enrolled between September 2014 and April 2020 and followed ≥ 6 months were investigated. Demographics, comorbidities, lung function, composite variables, six-minute walking test (6MWT), quality of life, and anti-fibrotic therapy were evaluated. Agreements between classification of mild physiological impairment (defined as gender-age-physiology (GAP) stage 1) with physiological and composite measures of severity was assessed using kappa values and their impact on mortality with hazard ratios. The factor analysis and the two-step cluster analysis were used to identify phenotypes. Univariate and multivariable survival analyses were performed between variables or groups. RESULTS: Among 662 patients with baseline data (median age 72.7 years, 74.0% males), 480 had a follow up ≥ 6 months with a 5 year survival rate of 48%. Lung function, 6MWT, age, and BMI were predictors of survival. Patients who received anti-fibrotic treatment ≥ 6 months had better survival compared to untreated patients [p = 0.007, HR (95% CI): 1.797 (1.173-2.753)] after adjustment of age, gender, BMI, smoking status, forced vital capacity (FVC) and diffusion capacity of carbon monoxide (DLCO). Patients with mild physiological impairment (GAP stage 1, composite physiological index (CPI) ≤ 45, DLCO ≥ 55%, FVC ≥ 75%, and total lung capacity (TLC) ≥ 65%, respectively) had better survival, after adjustment for age, gender, BMI and smoking status and treatment. Patients in cluster 1 had the worst survival and consisted mainly of male patients with moderate-severe disease and an increased prevalence of heart diseases at baseline; Cluster 2 was characterized by mild disease with more than 50% females and few comorbidities, and had the best survival; Cluster 3 were younger, with moderate-severe disease and had few comorbidities. CONCLUSION: Disease severity, phenotypes, and anti-fibrotic treatment are closely associated with the outcome in IPF, with treated patients surviving longer. Phenotypes may contribute to predicting outcomes of patients with IPF and suggest the patients' need for special management, whereas single or composite variables have some limitations as disease predictors.

Impact on physical and mental health among medical personnel in Wuhan during COVID-19 outbreak: a cluster analysis.

Background: Increased stress among medical personnel had been reported in previous virus outbreaks. The novel coronavirus disease (COVID-19) emerged in December 2019, caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). No qualitative assessment has yet described the physical and mental health conditions of frontline medical personnel in the COVID-19 outbreaks. Methods: Here, 251 frontline medical personnel involved in COVID-19 missions completed electronic questionnaires, consisting of 31 categorical variables related to their physical and mental health status, medical history and environmental conditions. We constructed a correlation amongst these variables through pairwise Kendall rank correlation coefficient test. Then, clusters of highly correlated variables were identified using the leading eigenvector. Finally, we used the network and clusters to clarify the correlations amongst variables. Results: This qualitative study identified the six clusters. Cluster 1 was characterized by skin allergy. Cluster 2 was predominantly associated with anxiety. Cluster 3 consisted mostly of respiratory symptoms. The participants in cluster 4 had medical history. Cluster 5 and cluster 6 were characterized by disinfection and demography, respectively. Finally, we revealed three major findings. First, more than 80% of medical personnel worry about COVID-19-related infection and experience newly appearing anxiety (56.2%), airway or heart symptoms (34.3%) and skin allergies (20.3%). Second, COVID-19-related worry significantly associates with all variables in the anxiety and respiratory symptom clusters. Third, new-onset skin allergies did not associate with either disinfection or anxiety, but did associate with a previous history of allergies. Conclusions: COVID-19-related worry leads to physical and mental health problems amongst medical personnel. Effective responses and interventions could relieve a series of new-onset physical and mental health problems.

Smoking-associated increase in mucins 1 and 4 in human airways.

RATIONALE: Smoking-related chronic obstructive pulmonary disease (COPD) is associated with dysregulated production of mucus. Mucins (MUC) are important both for mucus secretion and epithelial defense. We have examined the distribution of MUC1 and MUC4 in the airway epithelial cells of never-smokers and smokers with and without COPD. METHODS: Mucosal biopsies and bronchial wash samples were obtained by bronchoscopy from age- and sex-matched COPD-patients (n = 38; GOLD I-II/A-B), healthy never-smokers (n = 40) and current smokers with normal lung function (n = 40) from the Karolinska COSMIC cohort (NCT02627872). Cell-specific expressions of MUC1, MUC4 and regulating factors, i.e., epithelial growth factor receptor (EGFR) 1 and 2, were analyzed by immunohistochemistry. Soluble MUC1 was measured by quantitative immunodetection on slot blot. RESULTS: The levels of cell-bound MUC1 expression in basal cells and in soluble MUC1 in bronchial wash were increased in smokers, regardless of airway obstruction. Patients with chronic bronchitis had higher MUC1 expression. The expression of MUC4 in cells with goblet cell phenotype was increased in smokers. The expression of EGFR2, but not that of EGFR1, was higher in never-smokers than in smokers. CONCLUSIONS: Smoking history and the presence of chronic bronchitis, regardless of airway obstruction, affect both cellular and soluble MUC1 in human airways. Therefore, MUC1 may be a novel marker for smoking- associated airway disease.

The unsynchronized changes of CT image and nucleic acid detection in COVID-19: reports the two cases from Gansu, China.

The novel coronavirus disease (COVID-19) outbreak started in December 2019 in Wuhan, China, caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). The CT image is used to assess the disease progress, whereas the continued two times of negative results from SARS-CoV-2 nucleic acid detection had been considered as a criterion for ending antiviral treatment. We compared the two COVID-19 cases with similar backgrounds and CT image repeated intervals under treatment. Our report highlighted the unsynchronized expression in the changes of CT image and nucleic acid detection in COVID-19, and lasting positive nucleic acid test result in patients recovered from pneumonia. It may be contributed to recognize the disease and improve prevention.

Integration of multi-omics datasets enables molecular classification of COPD.

Chronic obstructive pulmonary disease (COPD) is an umbrella diagnosis caused by a multitude of underlying mechanisms, and molecular sub-phenotyping is needed to develop molecular diagnostic/prognostic tools and efficacious treatments.The objective of these studies was to investigate whether multi-omics integration improves the accuracy of molecular classification of COPD in small cohorts.Nine omics data blocks (comprising mRNA, micro RNA, proteomes and metabolomes) collected from several anatomical locations from 52 female subjects were integrated by similarity network fusion (SNF). Multi-omics integration significantly improved the accuracy of group classification of COPD patients from healthy never-smokers and from smokers with normal spirometry, reducing required group sizes from n=30 to n=6 at 95% power. Seven different combinations of four to seven omics platforms achieved >95% accuracy.For the first time, a quantitative relationship between multi-omics data integration and accuracy of data-driven classification power has been demonstrated across nine omics data blocks. Integrating five to seven omics data blocks enabled 100% correct classification of COPD diagnosis with groups as small as n=6 individuals, despite strong confounding effects of current smoking. These results can serve as guidelines for the design of future systems-based multi-omics investigations, with indications that integrating five to six data blocks from several molecular levels and anatomical locations suffices to facilitate unsupervised molecular classification in small cohorts.

CORACLE (COVID-19 liteRAture CompiLEr): A platform for efficient tracking and extraction of SARS-CoV-2 and COVID-19 literature, with examples from post-COVID with respiratory involvement.

Background: During the COVID-19 pandemic a need to process large volumes of publications emerged. As the pandemic is winding down, the clinicians encountered a novel syndrome - Post-acute Sequelae of COVID-19 (PASC) - that affects over 10 % of those who contract SARS-CoV-2 and presents a significant challenge in the medical field. The continuous influx of publications underscores a need for efficient tools for navigating the literature. Objectives: We aimed to develop an application which will allow monitoring and categorizing COVID-19-related literature through building publication networks and medical subject headings (MeSH) maps to identify key publications and networks. Methods: We introduce CORACLE (COVID-19 liteRAture CompiLEr), an innovative web application designed to analyse COVID-19-related scientific articles and to identify research trends. CORACLE features three primary interfaces: The "Search" interface, which displays research trends and citation links; the "Citation Map" interface, allowing users to create tailored citation networks from PubMed Identifiers (PMIDs) to uncover common references among selected articles; and the "MeSH" interface, highlighting current MeSH trends and their associations. Results: CORACLE leverages PubMed data to categorize literature on COVID-19 and PASC, aiding in the identification of relevant research publication hubs. Using lung function in PASC patients as a search example, we demonstrate how to identify and visualize the interactions between the relevant publications. Conclusion: CORACLE is an effective tool for the extraction and analysis of literature. Its functionalities, including the MeSH trends and customizable citation mapping, facilitate the discovery of emerging trends in COVID-19 and PASC research.

Endotypes of severe neutrophilic and eosinophilic asthma from multi-omics integration of U-BIOPRED sputum samples.

BACKGROUND: Clustering approaches using single omics platforms are increasingly used to characterise molecular phenotypes of eosinophilic and neutrophilic asthma. Effective integration of multi-omics platforms should lead towards greater refinement of asthma endotypes across molecular dimensions and indicate key targets for intervention or biomarker development. OBJECTIVES: To determine whether multi-omics integration of sputum leads to improved granularity of the molecular classification of severe asthma. METHODS: We analyzed six -omics data blocks-microarray transcriptomics, gene set variation analysis of microarray transcriptomics, SomaSCAN proteomics assay, shotgun proteomics, 16S microbiome sequencing, and shotgun metagenomic sequencing-from induced sputum samples of 57 severe asthma patients, 15 mild-moderate asthma patients, and 13 healthy volunteers in the U-BIOPRED European cohort. We used Monti consensus clustering algorithm for aggregation of clustering results and Similarity Network Fusion to integrate the 6 multi-omics datasets of the 72 asthmatics. RESULTS: Five stable omics-associated clusters were identified (OACs). OAC1 had the best lung function with the least number of severe asthmatics with sputum paucigranulocytic inflammation. OAC5 also had fewer severe asthma patients but the highest incidence of atopy and allergic rhinitis, with paucigranulocytic inflammation. OAC3 comprised only severe asthmatics with the highest sputum eosinophilia. OAC2 had the highest sputum neutrophilia followed by OAC4 with both clusters consisting of mostly severe asthma but with more ex/current smokers in OAC4. Compared to OAC4, there was higher incidence of nasal polyps, allergic rhinitis, and eczema in OAC2. OAC2 had microbial dysbiosis with abundant Moraxella catarrhalis and Haemophilus influenzae. OAC4 was associated with pathways linked to IL-22 cytokine activation, with the prediction of therapeutic response to anti-IL22 antibody therapy. CONCLUSION: Multi-omics analysis of sputum in asthma has defined with greater granularity the asthma endotypes linked to neutrophilic and eosinophilic inflammation. Modelling diverse types of high-dimensional interactions will contribute to a more comprehensive understanding of complex endotypes. KEY POINTS: Unsupervised clustering on sputum multi-omics of asthma subjects identified 3 out of 5 clusters with predominantly severe asthma. One severe asthma cluster was linked to type 2 inflammation and sputum eosinophilia while the other 2 clusters to sputum neutrophilia. One severe neutrophilic asthma cluster was linked to Moraxella catarrhalis and to a lesser extent Haemophilus influenzae while the second cluster to activation of IL-22.

MiRNA-miRNA synergistic network: construction via co-regulating functional modules and disease miRNA topological features.

Synergistic regulations among multiple microRNAs (miRNAs) are important to understand the mechanisms of complex post-transcriptional regulations in humans. Complex diseases are affected by several miRNAs rather than a single miRNA. So, it is a challenge to identify miRNA synergism and thereby further determine miRNA functions at a system-wide level and investigate disease miRNA features in the miRNA-miRNA synergistic network from a new view. Here, we constructed a miRNA-miRNA functional synergistic network (MFSN) via co-regulating functional modules that have three features: common targets of corresponding miRNA pairs, enriched in the same gene ontology category and close proximity in the protein interaction network. Predicted miRNA synergism is validated by significantly high co-expression of functional modules and significantly negative regulation to functional modules. We found that the MFSN exhibits a scale free, small world and modular architecture. Furthermore, the topological features of disease miRNAs in the MFSN are distinct from non-disease miRNAs. They have more synergism, indicating their higher complexity of functions and are the global central cores of the MFSN. In addition, miRNAs associated with the same disease are close to each other. The structure of the MFSN and the features of disease miRNAs are validated to be robust using different miRNA target data sets.

[Experimental study of VX2 rabbits metastatic and inflammatory adenopathy by using dynamic contrast-enhanced MR imaging].

OBJECTIVE: To study the dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) features of VX2 rabbits carcinoma metastatic and inflammatory lymph nodes. METHODS: Twenty healthy adult New-Zealand white rabbits were divided into metastatic lymph node group and inflammatory lymph node group randomly. There were ten rabbits in each group. All 20 rabbits underwent DCE-MRI. Peak enhancement, time to peak, maximum slope and signal intensity versus time curves were generated from each node individually. RESULTS: DCE-MRI were obtained for nine cases of metastatic lymph nodes and nine cases of inflammatory lymph nodes. The signal intensity versus time curves of metastatic lymph nodes were appeared as rapid ascending and plateauing, and peak enhancement, time to peak, maximum slope of metastatic lymph nodes group were 284% ± 125%, (118 ± 47) s and (6.5 ± 2.7)%/s, respectively. The signal intensity versus time curves of inflammatory lymph nodes were appeared as insisting ascending, and peak enhancement, time to peak, maximum slope of inflammatory lymph nodes group were 199% ± 109%, (143 ± 40) s and (3.6 ± 1.5)%/s, respectively. There was significantly higher peak enhancement, shorter time to peak and higher maximum slope in the metastatic lymph nodes group compared with the inflammatory lymph nodes group (P < 0.05). CONCLUSIONS: DCE-MRI can accurately reflect the hemodynamic characteristics of VX2 rabbits neoplasm metastatic lymph nodes and inflammatory lymph nodes, and can differentiate VX2 rabbits neoplasm metastatic lymph nodes from inflammatory lymph nodes.

miRNA-mRNA-protein dysregulated network in COPD in women.

Rationale: Chronic obstructive pulmonary disease (COPD) is a complex disease caused by a multitude of underlying mechanisms, and molecular mechanistic modeling of COPD, especially at a multi-molecular level, is needed to facilitate the development of molecular diagnostic and prognostic tools and efficacious treatments. Objectives: To investigate the miRNA-mRNA-protein dysregulated network to facilitate prediction of biomarkers and disease subnetwork in COPD in women. Measurements and Results: Three omics data blocks (mRNA, miRNA, and protein) collected from BAL cells from female current-smoker COPD patients, smokers with normal lung function, and healthy never-smokers were integrated with miRNA-mRNA-protein regulatory networks to construct a COPD-specific dysregulated network. Furthermore, downstream network topology, literature annotation, and functional enrichment analysis identified both known and novel disease-related biomarkers and pathways. Both abnormal regulations in miRNA-induced mRNA transcription and protein translation repression play roles in COPD. Finally, the let-7-AIFM1-FKBP1A pathway is highlighted in COPD pathology. Conclusion: For the first time, a comprehensive miRNA-mRNA-protein dysregulated network of primary immune cells from the lung related to COPD in females was constructed to elucidate specific biomarkers and disease pathways. The multi-omics network provides a new molecular insight from a multi-molecular aspect and highlights dysregulated interactions. The highlighted let-7-AIFM1-FKBP1A pathway also indicates new hypotheses of COPD pathology.

Pulmonary mucosa-associated lymphoid tissue lymphoma: computed tomography and ¹8F fluorodeoxyglucose-positron emission tomography/computed tomography imaging findings and follow-up.

PURPOSE: The aim of the study was to present the computed tomography (CT) and fluorine 18 (F) fluorodeoxyglucose-positron emission tomography (FDG-PET)/CT imaging findings of pulmonary mucosa-associated lymphoid tissue (MALT) lymphoma and evaluate their roles in the follow-up of this tumor. METHODS: Computed tomography and FDG-PET/CT imaging findings of 18 cases of pathologically proven pulmonary MALT lymphoma were reviewed retrospectively. RESULTS: Multiple and solitary lesions were detected in 15 and 3 patients, respectively. Of those patients with multiple pulmonary lesions, 12 were bilateral, and 3 were unilateral. A total of 51 pulmonary lesions were identified in 18 patients, which included lesions with consolidation (31/51), mass and nodule (12/51), and ground-glass attenuation (8/51). F fluorodeoxyglucose-PET/CT imaging (n = 8) revealed increased FDG uptake in all lesions in 8 cases. At follow-up, 3 patients experienced complete remission, 10 had partial remission, and 2 remained stable. CONCLUSIONS: Computed tomography and FDG-PET/CT images of the pulmonary MALT lymphoma usually reveal multiple, bilateral consolidations, masses, or nodules with air bronchogram and increased FDG uptake. Computed tomography and FDG-PET/CT imaging play important roles in the diagnosis and follow-up of patients with pulmonary MALT lymphoma.

MicroRNA miR-24-3p reduces DNA damage responses, apoptosis, and susceptibility to chronic obstructive pulmonary disease.

The pathogenesis of chronic obstructive pulmonary disease (COPD) involves aberrant responses to cellular stress caused by chronic cigarette smoke (CS) exposure. However, not all smokers develop COPD and the critical mechanisms that regulate cellular stress responses to increase COPD susceptibility are not understood. Because microRNAs are well-known regulators of cellular stress responses, we evaluated microRNA expression arrays performed on distal parenchymal lung tissue samples from 172 subjects with and without COPD. We identified miR-24-3p as the microRNA that best correlated with radiographic emphysema and validated this finding in multiple cohorts. In a CS exposure mouse model, inhibition of miR-24-3p increased susceptibility to apoptosis, including alveolar type II epithelial cell apoptosis, and emphysema severity. In lung epithelial cells, miR-24-3p suppressed apoptosis through the BH3-only protein BIM and suppressed homology-directed DNA repair and the DNA repair protein BRCA1. Finally, we found BIM and BRCA1 were increased in COPD lung tissue, and BIM and BRCA1 expression inversely correlated with miR-24-3p. We concluded that miR-24-3p, a regulator of the cellular response to DNA damage, is decreased in COPD, and decreased miR-24-3p increases susceptibility to emphysema through increased BIM and apoptosis.

[Comparative analysis of TACE alone or plus RFA in the treatment of 167 cases of intermediate and advanced staged primary hepatocellular carcinoma].

OBJECTIVE: To evaluate the clinical efficacy and survival rate of transarterial chemoembolization (TACE) alone or plus radiofrequency ablation (RFA) in patients with intermediate or advanced stage primary hepatocellular carcinoma (HCC). METHODS: In this retrospective study, 467 cases received RFA or TACE plus RFA. Among them, 167 cases with strict clinical procedure (TACE alone or plus RFA) and complete follow-up data were included. Eighty-seven cases received TACE and 80 cases had TACE plus RFA between January 2000 and December 2006. Hierarchical analyses were performed using log-rank tests and survival curve was estimated by Kaplan-Meier method. RESULTS: A total of 167 patients received TACE alone or plus RFA for a follow-up period of 1 to 89 months. In the TACE alone group, the time-to-progression (TTP) was an average of 3.6 months. The median survival was 13 months, one-year survival rate 52.9%, three-year survival rate 11.5% and five-year survival rate 4.6%. In the TACE plus RFA group, the TTP time was an average of 10.8 months. The median survival time was 30 months, one-year survival rate 85.0%, three-year survival rate 45.0% and five-year survival rate 11.3%. In the TACE alone group, the median survival of intermediate stage HCC was 14 months, one-year survival rate 62.2%, three-year survival rate 13.3% and five-year survival rate 4.4%; In the TACE plus RFA group, the median survival of intermediate stage HCC was 14 months, one-year survival rate 90.1%, three-year survival rate 52.9% and five-year survival rate 13.7%. All differences of two groups has statistical significance (P < 0.05). In intermediate stage HCC, the median survival of TACE alone group was 14 months, one-year survival rate 62.2%, three-year survival rate 13.3%, five-year survival rate 4.4% versus 32 months, 90.1%, 52.9%, 13.7% in the TACE plus RFA group respectively. For the advanced stage HCC, the median survival time was 12 months, one-year survival rate 35%, three-year survival rate 7.1% and five-year survival rate 0 in the TACE alone group versus 28 months, 62.1%, 24.1% and 6.9% in the TACE plus RFA group (P = 0.00). There was significantly statistic difference between both groups in intermediate and advanced staging HCC. Among them, 60/485 (12.4%) patients required a therapy of post-TACE hepatic dysfunctions versus 13/168 (7.7%) in the TACE plus RFA group (P = 0.004, ANOVA method). CONCLUSION: The regimen of TACE plus RFA has the advantages of tumor control, liver function protection and survival extending in the treatment of HCC than TACE alone in intermediate or advanced stage HCC.

[Significance and prognostic factors in 84 patients with colorectal liver metastases treated with radiofrequency ablation].

OBJECTIVE: To investigate the prognostic factors and significance of patients with colorectal liver metastases (CLM) treated with radiofrequency ablation (RFA). METHODS: A retrospective study was conducted for the clinic outcomes, follow-up data and survival status in 84 patients with CLM undergoing RFA between January 2000 and December 2008. Univariate and multivariate analyses were performed by log-rank test and Cox's proportional hazard model respectively. RESULTS: A total of 265 lesions in 84 patients received RFA with a follow-up of 1-10 years. The median survival was 29 months, 1-year survival rate 98%, 3-year survival rate 27% and 5-year survival rate 7%. For those lesion < or = 4 cm and lesion number < 3, the median survival time was 30 months, 1-year survival rate 100%, 3-year survival rate 31% and 5-year survival rate 16%. For those with lesions > 4 cm or lesion number > 3, the median survival time was 28 months, 1-year survival rate 96%, 3-year survival rate 21% and 5-year survival rate 0. For those receiving RFA combined with chemotherapy, the median survival time was 32 months, three-year survival rate 29% and five-year survival rate 8%. For those on molecular-target therapy, the median survival time was 41 months, 3-year survival rate 60% and 5-year survival rate 20%. The multivariate statistical analysis showed that the influences of lesion number and size (P = 0.004), chemotherapeutic agents and timing (P = 0.004) and extra-liver metastases (P = 0.097) had statistic significance to the survival rate. CONCLUSIONS: RFA has a favorable outcome in the treatment of CLM patients. The prognostic factors of overall survival are correlated with the lesion size and presence or absence of extra-hepatic metastasis. It may effectively improve the patient prognosis by RFA in combination with chemotherapy and especially molecular-target therapy.