Discriminant analysis followed by unsupervised cluster analysis including exosomal cystatins predict presence of chronic rhinosinusitis, phenotype, and disease severity

2019 ◽  
Vol 9 (9) ◽  
pp. 1069-1076 ◽  
Author(s):  
Michelle M. Miyake ◽  
Alan D. Workman ◽  
Angela L. Nocera ◽  
Dawei Wu ◽  
Sarina K. Mueller ◽  
...  
Keyword(s):  
Cluster Analysis ◽  
Discriminant Analysis ◽  
Chronic Rhinosinusitis ◽  
Disease Severity ◽  
Unsupervised Cluster ◽  
Unsupervised Cluster Analysis

scholarly journals Identification of Tumor Microenvironment-Related Alternative Splicing Events to Predict the Prognosis of Endometrial Cancer

2021 ◽  
Vol 11 ◽  
Author(s):  
Xuan Liu ◽  
Chuan Liu ◽  
Jie Liu ◽  
Ying Song ◽  
Shanshan Wang ◽  
...  
Keyword(s):  
Cluster Analysis ◽  
Endometrial Cancer ◽  
Alternative Splicing ◽  
Tumor Microenvironment ◽  
Risk Score ◽  
Risk Group ◽  
Gene Signature ◽  
Unsupervised Cluster ◽  
Score Model ◽  
Unsupervised Cluster Analysis

BackgroundEndometrial cancer (EC) is one of the most common female malignant tumors. The immunity is believed to be associated with EC patients’ survival, and growing studies have shown that aberrant alternative splicing (AS) might contribute to the progression of cancers.MethodsWe downloaded the clinical information and mRNA expression profiles of 542 tumor tissues and 23 normal tissues from The Cancer Genome Atlas (TCGA) database. ESTIMATE algorithm was carried out on each EC sample, and the OS-related different expressed AS (DEAS) events were identified by comparing the high and low stromal/immune scores groups. Next, we constructed a risk score model to predict the prognosis of EC patients. Finally, we used unsupervised cluster analysis to compare the relationship between prognosis and tumor immune microenvironment.ResultsThe prognostic risk score model was constructed based on 16 OS-related DEAS events finally identified, and then we found that compared with high-risk group the OS in the low-risk group was notably better. Furthermore, according to the results of unsupervised cluster analysis, we found that the better the prognosis, the higher the patient’s ESTIMATE score and the higher the infiltration of immune cells.ConclusionsWe used bioinformatics to construct a gene signature to predict the prognosis of patients with EC. The gene signature was combined with tumor microenvironment (TME) and AS events, which allowed a deeper understanding of the immune status of EC patients, and also provided new insights for clinical patients with EC.

Investigation of the Molecular Action of Bortezomib Treatment In Multiple Myeloma

Blood ◽  
2010 ◽  
Vol 116 (21) ◽  
pp. 5038-5038
Author(s):  
Erica Ballabio ◽  
Xiao-He Chen ◽  
Daniela Tramonti ◽  
James S. Wainscoat ◽  
Jacqueline Boultwood ◽  
...  
Keyword(s):  
Gene Expression ◽  
Multiple Myeloma ◽  
Cluster Analysis ◽  
Cell Lines ◽  
Cell Lymphoma ◽  
26S Proteasome ◽  
Whole Genome ◽  
Unsupervised Cluster ◽  
Rpmi 8226 ◽  
Unsupervised Cluster Analysis

Abstract Abstract 5038 Bortezomib (Velcade®) is a 26S proteasome inhibitor currently used for the treatment of relapsed multiple myeloma (MM) and more recently mantle cell lymphoma patients. Clinical trials are under way to evaluate its efficacy in other hematological malignancies diseases including diffuse large B cell lymphoma (DLBCL). Although it was widely accepted that bortezomib acts as a NF-κB inhibitor, this view has recently been challenged by work by Hideshima et al. (Blood 2009) who found that treated myeloma cells actually displayed down-regulation of NF-κB inhibitors (IκBα), increased activity of pro-NF-κB molecules (RIP2 and IKKβ) as well as increasing levels of NF-κB DNA binding. Additionally, although bortezomib has a potent anti-tumor activity, not all MM patients are responsive. The identification and understanding of the molecular mechanisms leading to bortezomib-resistance will help with the development of new approaches and will improve the treatment of these patients. In order to investigate the direct effect of bortezomib on lymphomas, we treated different cell lines with this drug and levels of cell proliferation and apoptosis were measured. Bortezomib was shown to significantly inhibit proliferation and promote apoptosis in MM cell lines (RPMI-8226, NCI-H929, JJN-3 and Thiel) and DLBCL cell lines (SU-DHL-4 and SU-DHL-10, HLY-1, HLB-1 and RIVA). Since MM cell lines were shown to be the most sensitive to the bortezomib, we selected them to carry out further studies. We next employed whole genome expression arrays (Affymetrix U113plus2.0) and microRNA arrays to identify molecules associated with treatment in these cell lines. Using unsupervised cluster analysis, we observed that the cells treated with bortezomib have a distinct microRNA and gene expression profile from their counterpart controls. Additionally, we used a shRNA whole-genome library to carry out a loss-of-function screen for cells resistant to bortezomib-induced apoptosis. The RPMI-8226 cell line was transduced with the shRNA library. One half of the cells was then treated with bortezomib. Relative gene expression levels were measured by hybridizing samples to custom-made arrays. Unsupervised cluster analysis identified a number of shRNAs which might be involved in mechanisms of resistance to bortezomib. Further functional studies will be necessary to investigate the role of these microRNAs in the mechanism of action of bortezomib. Disclosures: No relevant conflicts of interest to declare.

scholarly journals Genome-Wide Analyses of Prognostic and Therapeutic Alternative Splicing Signatures in Bladder Urothelial Carcinoma

2021 ◽  
Vol 11 ◽  
Author(s):  
Zhongru Fan ◽  
Zhe Zhang ◽  
Chiyuan Piao ◽  
Zhuona Liu ◽  
Zeshu Wang ◽  
...  
Keyword(s):  
Cluster Analysis ◽  
Alternative Splicing ◽  
Urothelial Carcinoma ◽  
Prognostic Value ◽  
Gap Analysis ◽  
Unsupervised Cluster ◽  
Chemotherapy Drugs ◽  
Genome Wide ◽  
Bladder Urothelial Carcinoma ◽  
Unsupervised Cluster Analysis

BackgroundAlternative splicing (AS) is an indispensable post-transcriptional modification applied during the maturation of mRNA, and AS defects have been associated with many cancers. This study was designed to thoroughly analyze AS events in bladder urothelial carcinoma (BLCA) at the genome-wide level.MethodsWe adopted a gap analysis to screen for significant differential AS events (DASEs) associated with BLCA. DASEs with prognostic value for OS and the disease-free interval (DFI) were identified by Cox analysis. In addition, a differential AS network and AS clusters were identified using unsupervised cluster analysis. We examined differences in the sensitivity to chemotherapy and immunotherapy between BLCA patients with high and low overall survival (OS) risk.ResultsAn extensive number of DASEs (296) were found to be clinically relevant in BLCA. A prognosis model was established based prognostic value of OS and DFI. CUGBP elav-like family member 2 (CELF2) was identified as a hub splicing factor for AS networks. We also identified AS clusters associated with OS using unsupervised cluster analysis, and we predicted that the effects of cisplatin and gemcitabine chemotherapy would be different between high- and low-risk groups based on OS prognosis.ConclusionWe completed a comprehensive analysis of AS events in BLCA at the genome-wide level. The present findings revealed that DASEs and splicing factors tended to impact BLCA patient survival and sensitivity to chemotherapy drugs, which may provide novel prospects for BLCA therapies.

scholarly journals Bleeding Patterns in Type I VWD in Effect of VWF Levels: An Individual Participant Data Meta-Analysis of Three Cohorts

Blood ◽  
2018 ◽  
Vol 132 (Supplement 1) ◽  
pp. 1180-1180
Author(s):  
Alberto Tosetto ◽  
Pamela A Christopherson ◽  
Jeroen C.J. Eikenboom ◽  
Julie Grabell ◽  
Paula D. James ◽  
...  
Keyword(s):  
Cluster Analysis ◽  
Blood Group ◽  
Research Funding ◽  
Family Members ◽  
Type I ◽  
Unsupervised Cluster ◽  
Pooled Data ◽  
Index Cases ◽  
Unsupervised Cluster Analysis

Abstract Type I von Willebrand disease (VWD) is the most frequent bleeding disorder, with a prevalence of 10-50 cases per 10,000 persons. VWD usually shows an autosomal dominant inheritance pattern, at least in families having VWF:Ag levels below 30 IU/dL. There is considerable uncertainty whether patients having only mildly reduced VWF levels (in the range 30-50 IU/dL) should be diagnosed as having VWD or if they should be considered as a separate clinical entity, broadly referred to as "reduced VWF patients." We hypothesize that these patients may have a clinically distinct bleeding pattern, in terms of presenting symptoms at diagnosis, bleeding severity, and possibly even clinical. We pooled data from three cohorts of patients. The MCMDM-1 VWD Study is a multicenter survey on type 1 VWD that recruited 154 type 1 VWD families from nine European countries. The Kingston cohort recruited consecutive patients with type 1 VWD diagnosed in Kingston, Canada and includes patients screened because of bleeding symptoms or family history of VWD over a 10 year period. The Zimmerman Program for the Molecular and Clinical Biology of VWD is a multicenter study that enrolled VWD patients mostly from US clinical centres, primarily carrying a diagnosis of type 1 VWD (315 families) but also including type 2 and type 3 subjects. For the present study, only patients with a confirmed diagnosis of type 1 VWD were retained. From the three cohorts, demographic, laboratory and clinical data were abstracted, including severity of bleeding symptoms classified according to the MCMDM-1 study criteria. Only data from index cases and affected family members were retained for the present analysis. Bleeding symptoms receiving a score >=2 (and therefore requiring some type of medical intervention) were classified as "clinically relevant". VWF:Ag and VWF:RCo were measured centrally by the reference core lab of each of the three studies, against the WHO International Standard. For this purpose of the present study, Type 1 VWD is defined as VWF:Ag levels (or VWF:RCo for the Zimmerman Program) below 30 IU/dL; affected family members and index cases with VWF:Ag levels equal or above 30 IU/dL were classified as "low-VWF" patients. Pooled data from the three studies resulted in 1411 patients. At multivariate analysis, blood group O females with lower bleeding scores were the characteristics associated with the "low-VWF" patients; interestingly, the number of clinically relevant bleeding symptoms was associated with the phenotype independently from the total bleeding score. For all considered bleeding symptoms, VWD patients had a higher prevalence of relevant bleeding, with the notable exception of menorrhagia. Particularly in index cases, menorrhagia but also post-surgical bleeding was increased in patients having the "low-VWF" phenotype, although only for menorrhagia the difference was statistically significant (p=0.022). Unsupervised cluster analysis of symptom distribution disclosed three subgroups of patients. The first two were composed by males (first group) or females (second group), both having few or none bleeding symptoms. The third identified by unsupervised cluster analysis group was composed of highly symptomatic patients, predominantly women. In these patients, mucous ("wet") bleeding (epistaxis and menorrhagia) was common together with post-surgical or extraction bleeding. This pattern was not correlated with mean VWF:Ag level or "low-VWF" or Type 1 VWD phenotype. We conclude that "low-VWF" patients are more frequently blood group O, older females, with lower average bleeding scores and number of symptoms and possibly selected because of menorrhagia. A group of "severe bleeders" may be identified (n=270, 19%), having a similar distribution of "low VWF" and "VWD" phenotypes. Disclosures Tosetto: Stago, Novo-Nordisk, BMS: Speakers Bureau; Werfen: Other: Member of Advisory Board, Speakers Bureau. Eikenboom:CSL: Research Funding. James:CSL Behring: Research Funding; Shire: Research Funding; Bayer: Research Funding. Montgomery:BCW: Patents & Royalties: GPIbM assay patent to the BloodCenter of Wisconsin.

scholarly journals Expert artificial intelligence-based natural language processing characterises childhood asthma

2020 ◽  
Vol 7 (1) ◽  
pp. e000524 ◽  
Author(s):  
Hee Yun Seol ◽  
Mary C Rolfes ◽  
Wi Chung ◽  
Sunghwan Sohn ◽  
Euijung Ryu ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Cluster Analysis ◽  
Natural Language Processing ◽  
Natural Language ◽  
Random Sample ◽  
Language Processing ◽  
Childhood Asthma ◽  
Unsupervised Cluster ◽  
Study Results ◽  
Unsupervised Cluster Analysis

IntroductionThe lack of effective, consistent, reproducible and efficient asthma ascertainment methods results in inconsistent asthma cohorts and study results for clinical trials or other studies. We aimed to assess whether application of expert artificial intelligence (AI)-based natural language processing (NLP) algorithms for two existing asthma criteria to electronic health records of a paediatric population systematically identifies childhood asthma and its subgroups with distinctive characteristics.MethodsUsing the 1997–2007 Olmsted County Birth Cohort, we applied validated NLP algorithms for Predetermined Asthma Criteria (NLP-PAC) as well as Asthma Predictive Index (NLP-API). We categorised subjects into four groups (both criteria positive (NLP-PAC+/NLP-API+); PAC positive only (NLP-PAC+ only); API positive only (NLP-API+ only); and both criteria negative (NLP-PAC−/NLP-API−)) and characterised them. Results were replicated in unsupervised cluster analysis for asthmatics and a random sample of 300 children using laboratory and pulmonary function tests (PFTs).ResultsOf the 8196 subjects (51% male, 80% white), we identified 1614 (20%), NLP-PAC+/NLP-API+; 954 (12%), NLP-PAC+ only; 105 (1%), NLP-API+ only; and 5523 (67%), NLP-PAC−/NLP-API−. Asthmatic children classified as NLP-PAC+/NLP-API+ showed earlier onset asthma, more Th2-high profile, poorer lung function, higher asthma exacerbation and higher risk of asthma-associated comorbidities compared with other groups. These results were consistent with those based on unsupervised cluster analysis and lab and PFT data of a random sample of study subjects.ConclusionExpert AI-based NLP algorithms for two asthma criteria systematically identify childhood asthma with distinctive characteristics. This approach may improve precision, reproducibility, consistency and efficiency of large-scale clinical studies for asthma and enable population management.

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