scholarly journals A MiRNA-based Signature is Associated With Tumor Mutational Burden in Colon Adenocarcinoma

2020 ◽  
Author(s):  
Weijie xue ◽  
Yixiu Wang ◽  
Zhiqi Gong ◽  
Chenyu Yang ◽  
Yuwei Xie ◽  
...  
Keyword(s):  
Principal Component Analysis ◽  
Differential Expression ◽  
Colon Adenocarcinoma ◽  
Principal Component ◽  
Enrichment Analysis ◽  
Component Analysis ◽  
Immune Checkpoints ◽  
Training Set ◽  
Test Set ◽  
Lasso Method

Abstract Background:Tumor mutation burden (TMB) has become an independent biomarker for predicting the response of Immune checkpoint inhibitors.MiRNA plays an important role in cancer-related immune regulation but the relationship between expression of miRNA and TMB is unclear in colon adenocarcinoma (COAD).Method:The transcriptome profiling data, clinical data, mutation annotation data and miRNA expression profiles for cases with COAD were downloaded from TCGA database, and then COAD samples were randomly divided into training set and test set. The differential expression miRNAs of high and low TMB group in training set was obtained as a signature by the least absolute shrinkage and selection operator (LASSO) logistic regression, and it was verified in test set. UsingLASSO method, principal component analysis (PCA) and ROC to verify the credibility of signature.In addition, the correlation betweenthemiRNA-based signature and immune checkpoints was performed.In the end, enrichment analysis of the miRNAs in signature was performed to explore the biological function.Results:18 differential expression miRNAswere obtainedaccording to LASSO method. According to LASSO method, principal component analysis (PCA) and ROC, we found that the credibility of signature, and the signature can discriminate the high and low TMB level. Furthermore, the results of correlation between the 18-miRNA-based signature and immune checkpoints showed that the miRNA-based model has a strong positive correlation with TMB, weak positive correlation with CTLA4 and CD274 (PD-L1). However, there is no correlation between the model and SNCA (PD-1).Finally, enrichment analysis of the 18miRNAs demonstrated that the 18 miRNAs were involved in process of immunity and cancer pathways.Conclusion:We established a novel miRNA-based signature integrating expression of miRNAs and TMB levels. The 18-miRNA-based signature can effectively predict and discriminate the TMB levels in COAD, and provides apotential guide of ICIs treatment.

Iterative Supervised Principal Component Analysis-Driven Ligand Design for Regioselective Ti-Catalyzed Pyrrole Synthesis

2020 ◽  
Author(s):  
Xin Yi See ◽  
Benjamin Reiner ◽  
Xuelan Wen ◽  
T. Alexander Wheeler ◽  
Channing Klein ◽  
...  
Keyword(s):  
Principal Component Analysis ◽  
De Novo ◽  
Principal Component ◽  
Component Analysis ◽  
Catalyst Design ◽  
Data Driven ◽  
Initial Reaction ◽  
Training Set ◽  
Reaction Conditions ◽  
Component Loadings

<div> <div> <div> <p>Herein, we describe the use of iterative supervised principal component analysis (ISPCA) in de novo catalyst design. The regioselective synthesis of 2,5-dimethyl-1,3,4-triphenyl-1H- pyrrole (C) via Ti- catalyzed formal [2+2+1] cycloaddition of phenyl propyne and azobenzene was targeted as a proof of principle. The initial reaction conditions led to an unselective mixture of all possible pyrrole regioisomers. ISPCA was conducted on a training set of catalysts, and their performance was regressed against the scores from the top three principal components. Component loadings from this PCA space along with k-means clustering were used to inform the design of new test catalysts. The selectivity of a prospective test set was predicted in silico using the ISPCA model, and only optimal candidates were synthesized and tested experimentally. This data-driven predictive-modeling workflow was iterated, and after only three generations the catalytic selectivity was improved from 0.5 (statistical mixture of products) to over 11 (> 90% C) by incorporating 2,6-dimethyl- 4-(pyrrolidin-1-yl)pyridine as a ligand. The successful development of a highly selective catalyst without resorting to long, stochastic screening processes demonstrates the inherent power of ISPCA in de novo catalyst design and should motivate the general use of ISPCA in reaction development. </p> </div> </div> </div>

scholarly journals miRNA-Based Signature Associated With Tumor Mutational Burden in Colon Adenocarcinoma

2021 ◽  
Vol 11 ◽  
Author(s):  
Weijie Xue ◽  
Yixiu Wang ◽  
Yuwei Xie ◽  
Chenyu Yang ◽  
Zhiqi Gong ◽  
...  
Keyword(s):  
Mirna Expression ◽  
Malignant Tumors ◽  
Checkpoint Inhibitors ◽  
Expression Profiles ◽  
Colon Adenocarcinoma ◽  
Principal Component ◽  
Enrichment Analysis ◽  
Immune Checkpoints ◽  
Strong Positive Correlation ◽  
Positive Correlation

Colon adenocarcinoma (COAD) is one of the most common malignant tumors. Tumor mutation burden (TMB) has become an independent biomarker for predicting the response to immune checkpoint inhibitors (ICIs). miRNAs play an important role in cancer-related immune regulation. However, the relationship between miRNA expression and TMB in COAD remains unclear. Therefore, the transcriptome profiling data, clinical data, mutation annotation data, and miRNA expression profiles for cases of COAD were downloaded from the TCGA database. Subsequently, 323 COAD cases were randomly divided into training and test sets. The differential expression of miRNAs in the high and low TMB groups in the training set was obtained as a signature using the least absolute shrinkage and selection operator (LASSO) logistic regression and verified in the test set. Based on the LASSO method, principal component analysis (PCA), and ROC, we found that the signature was credible because it can discriminate between high and low TMB levels. In addition, the correlation between the 18-miRNA-based signature and immune checkpoints was performed, followed by qRT-PCR, to measure the relative expression of 18 miRNAs in COAD patients. The miRNA-based model had a strong positive correlation with TMB and a weak positive correlation with CTLA4 and CD274 (PD-L1). However, no correlation was observed between the model and SNCA (PD-1). Finally, enrichment analysis of the 18 miRNAs was performed to explore their biological functions. The results demonstrated that 18 miRNAs were involved in the process of immunity and cancer pathways. In conclusion, the 18-miRNA-based signature can effectively predict and discriminate between the different TMB levels of COAD and provide a guide for its treatment with ICIs.

Iterative Supervised Principal Component Analysis-Driven Ligand Design for Regioselective Ti-Catalyzed Pyrrole Synthesis

2020 ◽  
Author(s):  
Xin Yi See ◽  
Benjamin Reiner ◽  
Xuelan Wen ◽  
T. Alexander Wheeler ◽  
Channing Klein ◽  
...  
Keyword(s):  
Principal Component Analysis ◽  
De Novo ◽  
Principal Component ◽  
Component Analysis ◽  
Catalyst Design ◽  
Data Driven ◽  
Initial Reaction ◽  
Training Set ◽  
Reaction Conditions ◽  
Component Loadings

<div> <div> <div> <p>Herein, we describe the use of iterative supervised principal component analysis (ISPCA) in de novo catalyst design. The regioselective synthesis of 2,5-dimethyl-1,3,4-triphenyl-1H- pyrrole (C) via Ti- catalyzed formal [2+2+1] cycloaddition of phenyl propyne and azobenzene was targeted as a proof of principle. The initial reaction conditions led to an unselective mixture of all possible pyrrole regioisomers. ISPCA was conducted on a training set of catalysts, and their performance was regressed against the scores from the top three principal components. Component loadings from this PCA space along with k-means clustering were used to inform the design of new test catalysts. The selectivity of a prospective test set was predicted in silico using the ISPCA model, and only optimal candidates were synthesized and tested experimentally. This data-driven predictive-modeling workflow was iterated, and after only three generations the catalytic selectivity was improved from 0.5 (statistical mixture of products) to over 11 (> 90% C) by incorporating 2,6-dimethyl- 4-(pyrrolidin-1-yl)pyridine as a ligand. The successful development of a highly selective catalyst without resorting to long, stochastic screening processes demonstrates the inherent power of ISPCA in de novo catalyst design and should motivate the general use of ISPCA in reaction development. </p> </div> </div> </div>

scholarly journals Prognostic value of tumor immune cell infiltration patterns in colon adenocarcinoma based on systematic bioinformatics analysis

2021 ◽  
Vol 21 (1) ◽  
Author(s):  
Hao Xu ◽  
Qianhui Xu ◽  
Lu Yin
Keyword(s):  
Principal Component Analysis ◽  
Immune Cells ◽  
Prognostic Value ◽  
Immune Cell ◽  
Colon Adenocarcinoma ◽  
Principal Component ◽  
Component Analysis ◽  
Cell Infiltration ◽  
Immune Cell Infiltration ◽  
Consensus Clustering

Abstract Background Although immunotherapy for colon cancer has made promising progress, only a few patients currently benefit from it. A recent study revealed that infiltrating immune cells are highly relevant to tumor prognosis and influence the expression of immune-related genes. However, the characterization of immune cell infiltration (ICI) has not yet been comprehensively analyzed and quantified in colon adenocarcinoma (COAD). Methods The multiomic data of COAD samples were downloaded from TCGA. ESTIMATE algorithm, ssGSEA method and CIBERSORT analysis were conducted to estimate the subpopulations of infiltrating immune cells. COAD subtypes based on ICI pattern were identified by consensus clustering then principal-component analysis was performed to obtain ICI scores to quantify the ICI patterns in individual tumors. Kaplan–Meier analysis was employed to validate prognostic value. Gene set enrichment analysis (GSEA) was applied for functional annotation. Finally, the mutation data was analyzed by employing “maftools” package. Results Three bioinformatics algorithms were used to evaluate the ICI patterns from 538 patients with COAD. Two ICI subtypes were determined using consensus clustering, and the ICI score was constructed by performing principal component analysis. Our findings showed that a higher ICI score often indicated a more advanced tumor and worse prognosis. The high-ICI score subgroup had a higher stromal score and more M0 macrophages but fewer plasma cells and decreased CD8 T cell infiltration. In addition, patients with high ICI scores had significantly higher expression levels of HAVCR2 and PCDC1LG2. Real-time polymerase chain reaction (PCR) was conducted to determine the prognostic significances of ICI-related genes. Conclusions In conclusion, ICI score may be considered as an original and useful indicator for independent prognostic prediction and individual immune-related therapy.

Use of advanced statistical learning methods and principal component analysis in quantitative structure–genotoxicity relationship study of amines

2011 ◽  
Vol 76 (4) ◽  
pp. 243-264 ◽  
Author(s):  
Yueying Ren ◽  
Baowei Zhao ◽  
Xiaojun Yao
Keyword(s):  
Principal Component Analysis ◽  
Statistical Learning ◽  
Linear Models ◽  
Nonlinear Models ◽  
Nonlinear Modeling ◽  
Principal Component ◽  
Component Analysis ◽  
Support Vector ◽  
Learning Methods ◽  
Test Set

The paper highlighted the use of advanced nonlinear modeling and subset selection techniques in the construction of a good, predictive model for genotoxicity study of amines. Essentials accounting for a reliable model were all considered carefully. Chemicals were represented by a large number of CODESSA descriptors. Division of a whole sample into the training set and the test set was performed by principal component analysis (PCA). Six descriptors selected by the best multi-linear regression (BMLR) method in CODESSA program were used as inputs to build nonlinear models, using advanced statistical learning methods such as support vector machine (SVM) and projection pursuit regression (PPR). The models were validated through three ways, i.e. internal cross-validation (CV), a test set and an independent validation set. Analysis shows that nonlinear models produced better results than linear models and PPR model outperforms the rest in the following order: PPR > SVM > linear SVM ≥ BMLR. In addition, the relationships between the descriptors and the mutagenic behavior of compounds are well discussed.

scholarly journals miRNA-Based Feature Classifier Is Associated with Tumor Mutational Burden in Head and Neck Squamous Cell Carcinoma

2020 ◽  
Vol 2020 ◽  
pp. 1-14
Author(s):  
Yu Xia ◽  
Qi Wang ◽  
Xiaolin Huang ◽  
Xinhai Yin ◽  
Jukun Song ◽  
...  
Keyword(s):  
Mirna Expression ◽  
Expression Patterns ◽  
Enrichment Analysis ◽  
Functional Enrichment ◽  
Differentially Expressed ◽  
Immune Checkpoints ◽  
Training Set ◽  
Test Set ◽  
Tumor Mutation Burden ◽  
Mutation Burden

Tumor mutation burden (TMB) is considered to be an independent genetic biomarker that can predict the tumor patient’s response to immune checkpoint inhibitors (ICIs). Meanwhile, microRNA (miRNA) plays a key role in regulating the anticancer immune response. However, the correlation between miRNA expression patterns and TMB is not elucidated in HNSCC. In the HNSCC cohort of the TCGA dataset, miRNAs that were differentially expressed in high TMB and low TMB samples were screened. The least absolute contraction and selection operator (LASSO) method is used to construct a miRNA-based feature classifier to predict the TMB level in the training set. The test set is used to verify the classifier. The correlation between the miRNA-based classifier index and the expression of three immune checkpoints (PD1, PDL1, and CTLA4) was explored. We further perform functional enrichment analysis on the miRNA contained in the miRNA-based feature classifier. Twenty-five differentially expressed miRNAs are used to build miRNA-based feature classifiers to predict TMB levels. The accuracy of the 25-miRNA-based signature classifier is 0.822 in the training set, 0.702 in the test set, and 0.774 in the total set. The miRNA-based feature classifier index showed a low correlation with PD1 and PDL1, but no correlation with CTLA4. The enrichment analysis of these 25 miRNAs shows that they are involved in many immune-related biological processes and cancer-related pathways. The miRNA expression patterns are related to tumor mutation burden, and miRNA-based feature classifiers can be used as biomarkers to predict TMB levels in HNSCC.

scholarly journals Fast diagnosis of men’s fertility using Raman spectroscopy combined with chemometric methods: An experimental study

2021 ◽  
Author(s):  
Roudabeh Sadat Moazeni Pourasil ◽  
Kambiz Gilany
Keyword(s):  
Principal Component Analysis ◽  
Experimental Study ◽  
Raman Spectroscopy ◽  
Seminal Plasma ◽  
Semen Analysis ◽  
Principal Component ◽  
Component Analysis ◽  
Metabolic Fingerprinting ◽  
Training Set ◽  
Infertile Men

Background: Idiopathic infertile men suffer from unexplained male infertility; they are infertile despite having a normal semen analysis, a normal history, and physical examination, and when female infertility factor has been ruled out. Objective: The present study aimed to develop a metabolic fingerprinting methodology using Raman spectroscopy combined with Chemometrics to detect idiopathic infertile men vs. fertile ones by seminal plasma. Materials and Methods: In this experimental study, the seminal plasma of 26 men including 13 fertile and 13 with unexplained infertility who reffered to, Avicenna Infertility Clinic, 2018, Tehran, Iran, have been investigated. The seminal metabolomic fingerprinting was evaluated using Raman spectrometer from 100 to 4250 cm-1. The principal component analysis and discriminate analysis methods were used. Results: The total of 26 samples were divided into 20 training and 6 test sets. The Principal component analysis score plot of the training set showed that the data were perfectly divided into two sides of the plot, which statistically approves the direct effect of semen metabolome changes on the Raman spectra. A classification model was constructed by linear discriminant analysis using the training set and evaluated by the test group which resulted in completely correct classification. While three of the six test samples appeared in the fertile group, the rest appeared in the infertile as expected. Conclusion: Metabolic fingerprinting of seminal plasma using Raman spectroscopy combined with chemometric classification methods accurately discriminated between the idiopathic infertile men and the fertile ones and predicted their fertility type. Key words: Semen analysis, Fertility, Raman spectroscopy, Metabolomics.

A German version of the Intermittent Claudication Questionnaire (ICQ): cultural adaptation and validation

VASA ◽  
2012 ◽  
Vol 41 (5) ◽  
pp. 333-342 ◽  
Author(s):  
Kirchberger ◽  
Finger ◽  
Müller-Bühl
Keyword(s):  
Principal Component Analysis ◽  
Intermittent Claudication ◽  
Completion Time ◽  
Short Form ◽  
Principal Component ◽  
Component Analysis ◽  
German Version ◽  
Average Completion Time ◽  
Sf 36 ◽  
Related Quality

Background: The Intermittent Claudication Questionnaire (ICQ) is a short questionnaire for the assessment of health-related quality of life (HRQOL) in patients with intermittent claudication (IC). The objective of this study was to translate the ICQ into German and to investigate the psychometric properties of the German ICQ version in patients with IC. Patients and methods: The original English version was translated using a forward-backward method. The resulting German version was reviewed by the author of the original version and an experienced clinician. Finally, it was tested for clarity with 5 German patients with IC. A sample of 81 patients were administered the German ICQ. The sample consisted of 58.0 % male patients with a median age of 71 years and a median IC duration of 36 months. Test of feasibility included completeness of questionnaires, completion time, and ratings of clarity, length and relevance. Reliability was assessed through a retest in 13 patients at 14 days, and analysis of Cronbach’s alpha for internal consistency. Construct validity was investigated using principal component analysis. Concurrent validity was assessed by correlating the ICQ scores with the Short Form 36 Health Survey (SF-36) as well as clinical measures. Results: The ICQ was completely filled in by 73 subjects (90.1 %) with an average completion time of 6.3 minutes. Cronbach’s alpha coefficient reached 0.75. Intra-class correlation for test-retest reliability was r = 0.88. Principal component analysis resulted in a 3 factor solution. The first factor explained 51.5 of the total variation and all items had loadings of at least 0.65 on it. The ICQ was significantly associated with the SF-36 and treadmill-walking distances whereas no association was found for resting ABPI. Conclusions: The German version of the ICQ demonstrated good feasibility, satisfactory reliability and good validity. Responsiveness should be investigated in further validation studies.

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