CDKN2A-mediated molecular subtypes characterize the hallmarks of tumor microenvironment and guide precision medicine in triple-negative breast cancer.

Currently, breast cancer (BRCA) has become the most common cancer in the world, whose pathological mechanism is complex. Among its subtypes, triple-negative breast cancer (TNBC) has the worst prognosis. With the increasing number of diagnosed TNBC patients, the urgent need of novel biomarkers is also rising. Cyclin-dependent kinase inhibitor 2A (CDKN2A) has recently emerged as a key regulator associated with ferroptosis and cuproptosis (FAC) and has exhibited a significant effect on BRCA, but its detailed mechanism remains elusive. Herein, we conducted the first converge comprehensive landscape analysis of FAC-related gene CDKN2A in BRCA and disclosed its prognostic value in BRCA. Then, an unsupervised cluster analysis based on CDKN2A-correlated genes unveiled three subtypes, namely cold-immune subtype, IFN-γ activated subtype and FTL-dominant subtype. Subsequent analyses depicting hallmarks of tumor microenvironment (TME) among three subtypes suggested strong association between TNBC and CDKN2A. Given the fact that the most clinically heterogeneous TNBC always displayed the most severe outcomes and lacked relevant drug targets, we further explored the potential of immunotherapy for TNBC by interfering CDKN2A and constructed the CDKN2A-derived prognostic model for TNBC patients by Lasso-Cox. The 21-gene-based prognostic model showed high accuracy and was verified in external independent validation cohort. Moreover, we proposed three drugs for TNBC patients based on our model via targeting epidermal growth factor receptor. In summary, our study indicated the potential of CDKN2A as a pioneering prognostic predictor for TNBC and provided a rationale of immunotherapy for TNBC, and offered fresh perspectives and orientations for cancer treatment via inducing ferroptosis and cuproptosis to develop novel anti-cancer treatment strategies.

Integrative omics analysis identifies biomarkers of idiopathic pulmonary fibrosis.

Idiopathic pulmonary fibrosis (IPF) is an interstitial lung disease characterized by chronic progressive pulmonary fibrosis and a poor prognosis. Genetic studies, including transcriptomic and proteomics, have provided new insight into revealing mechanisms of IPF. Herein we provided a novel strategy to identify biomarkers by integrative analysis of transcriptomic and proteomic profiles of IPF patients. We examined the landscape of IPF patients' gene expression in the transcription and translation phases and investigated the expression and functions of two new potential biomarkers. Differentially expressed (DE) mRNAs were mainly enriched in pathways associated with immune system activities and inflammatory responses, while DE proteins are related to extracellular matrix production and wound repair. The upregulated genes in both phases are associated with wound repair and cell differentiation, while the downregulated genes in both phases are associated with reduced immune activities and the damage of the alveolar tissues. On this basis, we identified thirteen potential marker genes. Among them, we validated the expression changes of butyrophilin-like 9 (BTNL9) and plasmolipin (PLLP) and investigated their functional pathways in the IPF mechanism. Both genes are downregulated in the tissues of IPF patients and Bleomycin-induced mice, and co-expression analysis indicates that they have a protective effect by inhibiting extracellular matrix production and promoting wound repair in alveolar epithelial cells.

Soluble form of suppression of tumorigenicity-2 predicts clinical stability of inpatients with community-acquired pneumonia.

The soluble form of the suppression of tumorigenicity-2 (sST2) is a biomarker for risk classification and prognosis of heart failure, and its production and secretion in the alveolar epithelium are significantly correlated with the inflammation-inducing in pulmonary diseases. However, the predictive value of sST2 in pulmonary disease had not been widely studied. This study investigated the potential value in prognosis and risk classification of sST2 in patients with community-acquired pneumonia. Clinical data of ninety-three CAP inpatients were retrieved and their sST2 and other clinical indices were studied. Cox regression models were constructed to probe the sST2's predictive value for patients' restoring clinical stability and its additive effect on pneumonia severity index and CURB-65 scores. Patients who did not reach clinical stability within the defined time (30 days from hospitalization) have had significantly higher levels of sST2 at admission (P < 0.05). In univariate and multivariate Cox regression analysis, a high sST2 level (≥72.8 ng/mL) was an independent reverse predictor of clinical stability (P < 0.05). The Cox regression model combined with sST2 and CURB-65 (AUC: 0.96) provided a more accurate risk classification than CURB-65 (AUC:0.89) alone (NRI: 1.18, IDI: 0.16, P < 0.05). The Cox regression model combined with sST2 and pneumonia severity index (AUC: 0.96) also provided a more accurate risk classification than pneumonia severity index (AUC:0.93) alone (NRI: 0.06; IDI: 0.06, P < 0.05). sST2 at admission can be used as an independent early prognostic indicator for CAP patients. Moreover, it can improve the predictive power of CURB-65 and pneumonia severity index score.

Gene fusion of IL7 involved in the regulation of idiopathic pulmonary fibrosis.

BACKGROUND: Idiopathic pulmonary fibrosis (IPF) is a rare form of immune-mediated interstitial lung disease characterized by progressive pulmonary fibrosis and scarring. The pathogenesis of IPF is still unclear. Gene fusion events exist universally during transcription and show alternated patterns in a variety of lung diseases. Therefore, the comprehension of the function of gene fusion in IPF might shed light on IPF pathogenesis research and facilitate treatment development. METHODS: In this study, we included 91 transcriptome datasets from the National Center for Biotechnology Information (NCBI), including 52 IPF patients and 39 healthy controls. We detected fusion events in these datasets and probed gene fusion-associated differential gene expression and functional pathways. To obtain robust results, we corrected the batch bias across different projects. RESULTS: We identified 1550 gene fusion events in all transcriptomes and studied the possible impacts of IL7 = AC083837.1 gene fusion. The two genes locate adjacently in chromosome 8 and share the same promoters. Their fusion is associated with differential expression of 282 genes enriched in six Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways and 35 functional gene sets. Gene ontology (GO) enrichment analysis shows that IL7 = AC083837.1 gene fusion is associated with the enrichment of 187 gene sets. The co-expression network of interleukin-7 (IL7) indicates that decreased IL7 expression is associated with many pathways that regulate IPF progress. CONCLUSION: Based on the results, we conclude that IL7 = AC083837.1 gene fusion might exacerbate fibrosis in IPF via enhancing activities of natural killer cell-mediated cytotoxicity, skin cell apoptosis, and vessel angiogenesis, the interaction of which contributes to the development of fibrosis and the deterioration of respiratory function of IPF patients. Our work unveils the possible roles of gene fusion in regulating IPF and demonstrates that gene fusion investigation is a valid approach in probing immunologic mechanisms and searching potential therapeutic targets for treating IPF.The reviews of this paper are available via the supplemental material section.

Issues of Z-factor and an approach to avoid them for quality control in high-throughput screening studies.

MOTIVATION: High-throughput screening (HTS) is a vital automation technology in biomedical research in both industry and academia. The well-known Z-factor has been widely used as a gatekeeper to assure assay quality in an HTS study. However, many researchers and users may not have realized that Z-factor has major issues. RESULTS: In this article, the following four major issues are explored and demonstrated so that researchers may use the Z-factor appropriately. First, the Z-factor violates the Pythagorean theorem of statistics. Second, there is no adjustment of sampling error in the application of the Z-factor for quality control (QC) in HTS studies. Third, the expectation of the sample-based Z-factor does not exist. Fourth, the thresholds in the Z-factor-based criterion lack a theoretical basis. Here, an approach to avoid these issues was proposed and new QC criteria under homoscedasticity were constructed so that researchers can choose a statistically grounded criterion for QC in the HTS studies. We implemented this approach in an R package and demonstrated its utility in multiple CRISPR/CAS9 or siRNA HTS studies. AVAILABILITY AND IMPLEMENTATION: The R package qcSSMDhomo is freely available from GitHub: https://github.com/Karena6688/qcSSMDhomo. The file qcSSMDhomo_1.0.0.tar.gz (for Windows) containing qcSSMDhomo is also available at Bioinformatics online. qcSSMDhomo is distributed under the GNU General Public License. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Co-expression network analysis of lncRNAs and mRNAs in rat liver tissue reveals the complex interactions in response to pathogenic cytotoxicity.

Liver is one of the most vital organs to maintain homeostasis because of its peculiar detoxification functionalities to detoxify chemicals and metabolize drugs and toxins. Due to its crucial functions, the liver is also prone to various diseases, i.e., hepatitis, cirrhosis and hepatoma, etc. Additionally, long non-coding RNAs (lncRNAs) has emerged as key regulators which are found to play important roles in transcription, splicing, translation, replication, chromatin shaping and post translational modification of proteins in living cells. However, the underlying mechanisms of biological processes mediated by lncRNA remain unclear. Here, with the aim of disclosing potential lncRNAs implicated in the biological processes in liver in response to cytotoxicity, we performed a co-expression network analysis based on the transcriptome data of the damaged liver tissue of Rattus norvegicus induced by three cytotoxic compounds (carbon tetrachloride, chloroform and thioacetamide). Our analysis unveils that many biological processes and pathways were collectively affected by the three cytotoxic compounds, including drug metabolism, oxidation-reduction process, oxidative stress, glucuronidation, liver development and flavonoid biosynthetic process, etc. Also, our network analysis has identified several highly conserved lncRNA-mRNA interactions participating in those correlated processes and pathways, implying their potential roles in response to the induced cytotoxicity in liver. Our study provides new insights into lncRNA-mRNA regulatory mechanisms in response to pathogenic cytotoxic damaging in liver and facilitates the development of lncRNA-oriented therapies for hepatic diseases in the future.

Transcriptome analysis reveals lncRNA-mediated complex regulatory network response to DNA damage in the liver tissue of Rattus norvegicus.

DNA is prone to damages, which would result in genetic disorders and enhance risk of tumorigenesis. Hence, understanding the molecular mechanisms of DNA damage and repair will provide deep insights into tumorigenesis, carcinogenesis as well as the corresponding treatments. Aiming at investigating potential long noncoding RNAs (lncRNAs) response against DNA damage, we performed a comprehensive transcriptomic analysis based on RNA sequencing data of the liver tissue from Rattus norvegicus, in which DNA damage was induced using aflatoxin B1, ifosfamide and N-nitrosodimethylamine. Through our analyses, numerous novel lncRNAs are identified for the first time, and differential network analysis discloses lncRNA-mediated regulatory networks related to DNA-damage response. The result shows that these DNA-damage-inducing chemicals might disrupt many lncRNA-mediated interactions involved in diverse biological processes and pathways, for example, immune function and cell adhesion. In contrast, the host might also activate a few RNA interactions in response to DNA damage, involving response to drug and regulation of cell cycle.

Effects of Intranasal Cellulose Powder on Asthma Control in Children With Mild-to-Moderate Perennial Allergic Rhinitis: A Randomized, Placebo-Controlled Trial.

BACKGROUND: Perennial allergic rhinitis (PAR) often coexists in asthmatic patients. Intranasal cellulose powder (ICP) was reportedly effective in ameliorating PAR. OBJECTIVE: We investigated whether ICP is equally effective compared with intranasal corticosteroids in improving asthma control as well as nasal symptoms among children with PAR and allergic asthma (AA). METHODS: Between July 2015 and September 2016, we did a single-center, randomized, placebo-controlled trial. Asthmatic children aged 6 to 11 years with mild-to-moderate PAR were randomly assigned to formoterol/budesonide inhalation (4·5 µg/80 µg, twice daily) plus intranasal budesonide 64 µg twice daily (group A), ICP 250 µg thrice daily (group B), or intranasal placebo 250 µg thrice daily (group C) for 8 weeks. The primary outcome was change in asthma control test for children (C-ACT) score from baseline to week 8 posttreatment. Changes in spirometry, peak expiratory flow (PEF), fractional exhaled nitric oxide (FeNO), and visual analog scale (VAS) for nasal and ocular symptoms were detected as secondary outcomes. RESULTS: We included 121 patients (38 in group A, 41 in group B, and 42 in group C) in full-analysis set. C-ACT score was markedly higher at week 8 compared with baseline (mean difference: 5.11, 6.05, and 4.85 points in groups A, B, and C, respectively; P < .05). There were interactions between baseline and treatment in C-ACT scores ( P < .05). Group B demonstrated greater improvement in C-ACT score than group C among children with baseline C-ACT score of 6 to 18. 95% confidence intervals of group A at baseline overlapped with those of groups B and C. The treatment achieved reduced VAS symptoms in groups A and B but not in group C. Incidence of adverse events was comparable. No serious adverse event was reported. CONCLUSIONS: ICP could be recommended for children with PAR and AA who have poorer asthma control.

Re-analysis of the coral Acropora digitifera transcriptome reveals a complex lncRNAs-mRNAs interaction network implicated in Symbiodinium infection.

BACKGROUND: Being critically important to the ecosystem, the stability of coral reefs is directly related to the marine and surrounding terrestrial environments. However, coral reefs are now undergoing massive and accelerating devastation due to bleaching. The fact that the breakdown of symbiosis between coral and the dinoflagellate, zooxanthellae, has been well elucidated as the main cause of bleaching, implying the establishment of symbiosis with zooxanthellae plays a crucial role in maintaining coral survival. However, the relevant molecular and cellular mechanisms have not been well studied yet. In this study, based on the deep RNA-sequencing data derived from Mohamed, A. R. et al., an integrated transcriptome analysis was performed to deeply investigate global transcriptome changes of the coral Acropora digitifera in response to infection by dinoflagellate of the genus Symbiodinium. RESULTS: The results revealed that compared to RefTranscriptome_v1.0 (A. digitifera transcriptome assembly v1.0), numerous novel transcripts and isoforms were identified, the Symbiodinium-infected coral larvae at 4 h generated the highest proportion of specific isoforms. Alternative splicing analysis showed that intron retention predominated in all alternative transcripts among six statuses. Additionally, 8117 lncRNAs were predicted via a stringent stepwise filtering pipeline. A complex lncRNAs-mRNAs network including 815 lncRNAs and 6395 mRNAs were established, in which 21 lncRNAs were differentially expressed at 4 h post infection. Functional clustering analysis for those differentially lncRNAs-coexpressed mRNAs demonstrated that several biological processes and pathways related to protein kinase activity, metabolic pathways, mitochondrion, ribosome, etc. were enriched. CONCLUSIONS: Our study not only refined A. digitifera transcriptome via identification of novel transcripts and isoforms, but also predicted a high-confidence dataset of lncRNAs. Functional study based on the construction of lncRNAs-mRNAs co-expression network has disclosed a complex lncRNA-mediated regulation in response to Symbiodinium infection exhibited in A. digitifera. Once validated, these lncRNAs could be good potential targets for treatment and prevention of bleaching in coral.

A comprehensive comparison and overview of R packages for calculating sample entropy.

Sample entropy is a powerful tool for analyzing the complexity and irregularity of physiology signals which may be associated with human health. Nevertheless, the sophistication of its calculation hinders its universal application. As of today, the R language provides multiple open-source packages for calculating sample entropy. All of which, however, are designed for different scenarios. Therefore, when searching for a proper package, the investigators would be confused on the parameter setting and selection of algorithms. To ease their selection, we have explored the functions of five existing R packages for calculating sample entropy and have compared their computing capability in several dimensions. We used four published datasets on respiratory and heart rate to study their input parameters, types of entropy, and program running time. In summary, NonlinearTseries and CGManalyzer can provide the analysis of sample entropy with different embedding dimensions and similarity thresholds. CGManalyzer is a good choice for calculating multiscale sample entropy of physiological signal because it not only shows sample entropy of all scales simultaneously but also provides various visualization plots. MSMVSampEn is the only package that can calculate multivariate multiscale entropies. In terms of computing time, NonlinearTseries, CGManalyzer, and MSMVSampEn run significantly faster than the other two packages. Moreover, we identify the issues in MVMSampEn package. This article provides guidelines for researchers to find a suitable R package for their analysis and applications using sample entropy.

Entropy Change of Biological Dynamics in Asthmatic Patients and Its Diagnostic Value in Individualized Treatment: A Systematic Review.

Asthma is a chronic respiratory disease featured with unpredictable flare-ups, for which continuous lung function monitoring is the key for symptoms control. To find new indices to individually classify severity and predict disease prognosis, continuous physiological data collected from monitoring devices is being studied from different perspectives. Entropy, as an analysis method for quantifying the inner irregularity of data, has been widely applied in physiological signals. However, based on our knowledge, there is no such study to summarize the complexity differences of various physiological signals in asthmatic patients. Therefore, we organized a systematic review to summarize the complexity differences of important signals in patients with asthma. We searched several medical databases and systematically reviewed existing asthma clinical trials in which entropy changes in physiological signals were studied. As a conclusion, we find that, for airflow, heart rate variability, center of pressure and respiratory impedance, their entropy values decrease significantly in asthma patients compared to those of healthy people, while, for respiratory sound and airway resistance, their entropy values increase along with the progression of asthma. Entropy of some signals, such as respiratory inter-breath interval, shows strong potential as novel indices of asthma severity. These results will give valuable guidance for the utilization of entropy in physiological signals. Furthermore, these results should promote the development of management and diagnosis of asthma using continuous monitoring data in the future.

Entropy change of biological dynamics in COPD.

In this century, the rapid development of large data storage technologies, mobile network technology, and portable medical devices makes it possible to measure, record, store, and track analysis of large amount of data in human physiological signals. Entropy is a key metric for quantifying the irregularity contained in physiological signals. In this review, we focus on how entropy changes in various physiological signals in COPD. Our review concludes that the entropy change relies on the types of physiological signals under investigation. For major physiological signals related to respiratory diseases, such as airflow, heart rate variability, and gait variability, the entropy of a patient with COPD is lower than that of a healthy person. However, in case of hormone secretion and respiratory sound, the entropy of a patient is higher than that of a healthy person. For mechanomyogram signal, the entropy increases with the increased severity of COPD. This result should give valuable guidance for the use of entropy for physiological signals measured by wearable medical device as well as for further research on entropy in COPD.

Effect and mechanism of AR-6 in experimental rheumatoid arthritis.

The root of Clematis chinensis Osbeck has been used widely in rheumatoid arthritis in Chinese traditional medicine and AR-6 is a triterpene saponin isolated from it. In this present study, we investigated in vivo effects of oral AR-6 in chronic rat adjuvant-induced arthritis (AA) and in vitro effect in macrophage and synoviocytes cells. Arthritic scores and serum inflammatory mediators were evaluated 19 days after AA induction by endermic injection of Freund's complete adjuvant in Sprague-Dawley(S-D) rats. Oral administration of AR-6 to arthritic rats resulted in a clear decrease of clinical signs compared to untreated controls. The synoviocyte and macrophage response ex vivo were then analyzed. Anti-arthritic effects of AR-6 correlated with significant decrease of NO and TNF-alpha produced by peritoneal macrophages, ex vivo and in vitro. AR-6 also significant decreased the proliferation of synoviocyte. These data indicate that AR-6 is a potential anti-inflammatory therapeutic and preventive agent.

[Development of a freezing drier for lyophilization of biomaterials].

To observe and assess the performance and effect of our self-made FD-1 freezing drier on biomaterials. R502 compressor and R502 refrigerating agent were adopted. In the experiment, FD-1 lyophilized collagen sponge, strain and defibrinogenase. The evaporating-condenser temperature reached -45 degrees C and the small icebox temperature reached -30 degrees C under the loading or free-loading circumstances in the lyophilizing box. The lyophilized collagen sponge had many pores in the structure, and the strain and the defibrinogenase were lyophilized and maintained satisfactorily. This freezing drier is suitable for lyophilizing some biomaterial samples in small or medium batches.