Gene expression profiling with principal component analysis depicts the biological continuum from essential thrombocythemia over polycythemia vera to myelofibrosis

2012 ◽  
Vol 40 (9) ◽  
pp. 771-780.e19 ◽  
Author(s):  
Vibe Skov ◽  
Mads Thomassen ◽  
Caroline H. Riley ◽  
Morten K. Jensen ◽  
Ole Weis Bjerrum ◽  
...  
Keyword(s):  
Gene Expression ◽  
Principal Component Analysis ◽  
Gene Expression Profiling ◽  
Polycythemia Vera ◽  
Essential Thrombocythemia ◽  
Expression Profiling ◽  
Principal Component ◽  
Component Analysis

scholarly journals Effects of Collagen–Glycosaminoglycan Mesh on Gene Expression as Determined by Using Principal Component Analysis-Based Unsupervised Feature Extraction

Polymers ◽  
2021 ◽  
Vol 13 (23) ◽  
pp. 4117
Author(s):  
Y-h. Taguchi ◽  
Turki Turki
Keyword(s):  
Gene Expression ◽  
Principal Component Analysis ◽  
Feature Extraction ◽  
Cell Lines ◽  
Time Course ◽  
Expression Profiles ◽  
Principal Component ◽  
Component Analysis ◽  
Altered Expression ◽  
Unsupervised Feature Extraction

The development of the medical applications for substances or materials that contact cells is important. Hence, it is necessary to elucidate how substances that surround cells affect gene expression during incubation. In the current study, we compared the gene expression profiles of cell lines that were in contact with collagen–glycosaminoglycan mesh and control cells. Principal component analysis-based unsupervised feature extraction was applied to identify genes with altered expression during incubation in the treated cell lines but not in the controls. The identified genes were enriched in various biological terms. Our method also outperformed a conventional methodology, namely, gene selection based on linear regression with time course.

Gender Counts: Defining the Core Genetic Profile of Polycythemia Vera.

Blood ◽  
2004 ◽  
Vol 104 (11) ◽  
pp. 660-660 ◽  
Author(s):  
Jerry L. Spivak ◽  
Chunfa Jie ◽  
Donna M. Williams ◽  
Alison R. Moliterno
Keyword(s):  
Gene Expression ◽  
Gene Expression Profiling ◽  
Polycythemia Vera ◽  
Expression Profiling ◽  
Extramedullary Hematopoiesis ◽  
Genetic Profile ◽  
Cd34 Cells ◽  
Differential Gene ◽  
Gender Based ◽  
Gender Specific

Abstract Intra-abdominal venous thrombosis and exuberant extramedullary hematopoiesis leading to painful splenomegaly and hepatomegaly are two of the most serious consequences of polycythemia vera. Although polycythemia vera is slightly more common in men, both of these complications appear to be more common in women, in whom the disease also presents at an earlier age. We previously reported that polycythemia vera peripheral blood (pb) CD34+ cells could be distinguished from their immunophenotypically similar normal counterparts by gene expression profiling. We, therefore, hypothesized that phenotypic differences in disease behavior between men and women with polycythemia vera were a consequence of gender-based genotypic differences and have employed gene expression profiling to examine this issue. For this purpose, cRNA was prepared from the total RNA of pb CD34+ cells of eleven polycythemia vera patients and six normal controls purified by immunomagnetic bead chromatography. The cRNA was hybridized to an Affymetrix HU133 high-density oligonucleotide microarray chip representing 22,000 genes. Approximately 30–45 % of chip genes were recorded as present in the RNA samples. GC-RMA (Robust Multiarray Analysis) was used for normalization, to adjust for probe effects and for signal estimation. A parametric empirical Bayes statistical modeling method was used for differential gene expression analysis between the patients and controls. A posterior probability of >0.5 was taken to indicate significant differential gene expression. As previously reported, all polycythemia vera patients could be unequivocally distinguished from the controls, indicating that gene expression profiling can be employed as diagnostic test for polycythemia vera. With respect to gender, comparing male patients with male controls, 1106 genes were differentially expressed in the patient group; comparing female patients with female controls, 461 genes were differentially expressed in the patient group. Using unsupervised hierarchical clustering, the control patients segregated as a single group while the polycythemia vera patients segregated into two groups based on exuberant extramedullary hematopoiesis but this segregation was not gender-based despite the marked differences in gender-specific gene expression. Importantly, however, after eliminating gender-specific genes, only 93 genes were concordantly expressed in the male and female patients (59 up regulated and 34 down regulated). This small group appears to represent core genes in polycythemia vera whose behavior may be modified by gender-specific genes but the specific up regulation or repression of which is essential for disease expression. These data indicate that gender-specific effects on gene expression must be addressed in order to determine the basic genetic signature of a hematologic malignancy and they also define a core genetic profile in polycythemia vera.

Blood-based gene expression profiling to reveal potential response biomarkers for immunotherapy in advanced lung cancer.

2020 ◽  
Vol 38 (15_suppl) ◽  
pp. e15155-e15155
Author(s):  
Jie Wu ◽  
Weihua Huang ◽  
Changhong Yin ◽  
Yee Him Cheung ◽  
Debra Abrams ◽  
...  
Keyword(s):  
Gene Expression ◽  
Lung Cancer ◽  
Gene Expression Profiling ◽  
Expression Profiling ◽  
Time Course ◽  
Γδ T Cells ◽  
Principal Component ◽  
Genetic Mutation ◽  
Differentially Expressed ◽  
Advanced Lung Cancer

e15155 Background: Novel immunotherapies are becoming a viable option for advanced lung cancer treatment. High-throughput gene expression profiling (GEP) has enabled better understanding of patient responsiveness to targeted immunotherapy. However, non-invasive blood-based signatures are needed to monitor and predict response. Methods: To evaluate the predictive and prognostic role of blood-based GEP, we designed a longitudinal study by enrolling 15 patients with advanced lung cancers. A total of 65 samples were collected for RNA sequencing, ~4 blood specimens per patient, before and during anti-PD-1 treatment. We used multiple analyses, including time-course differential gene expression, principal component, lymphocyte compartment deconvolution, and genetic mutation, to search for and assess potential predictive and prognostic aspects. Results: Of 15 patients, 11 were classified as Responders (partial responders) and four were Non-Responders (one stable and three progressive diseases). Our analyses demonstrated: 1) By comparing baseline GEPs from Responders vs. Non-Responders before the first treatment, we identified potential markers (e.g., LY6E is significantly lower expressed in Responders, with Log2 Fold change = -3.44 and p = 1.83E-04) that can be used as predictors of responsiveness of the patients; 2) Immunoglobulin subunits- and T cell receptor complex-related genes were differentially expressed in Responders (DAVID analysis, p = 6.7E-3 and 2.1E-2, respectively), but not in Non-responders; 3) γδ T cells in the lymphocyte compartment were relatively increased in Responders; 4) Despite a different set of genes differentially expressed at different time points, the biggest GEP changes were at ~ week 6, after the second treatment. Additionally, we observed certain genes consistently up- or down-regulated through the whole course of treatment. Furthermore, after the first treatment, genes in the immune response pathway were regulated to different directions in Responders and Non-Responders. For example, interleukin receptor genes, such as IL18R1 and IL18RAP, and CD24 were down-regulated in Responders, but up-regulated in Non-Responders (p = 0.042, 0.023 and 0.044 respectively, t-test for the differential expression in these two groups). Conclusions: The utility of blood GEP to identify predictive and prognostic factors for precision immunotherapy is encouraging. Nevertheless, these results, predictive of the anti-PD-1 clinical response, are preliminary and need to be validated in a larger cohort.

scholarly journals Gene Expression Data Classification With Kernel Principal Component Analysis

2005 ◽  
Vol 2005 (2) ◽  
pp. 155-159 ◽  
Author(s):  
Zhenqiu Liu ◽  
Dechang Chen ◽  
Halima Bensmail
Keyword(s):  
Gene Expression ◽  
Principal Component Analysis ◽  
Gene Expression Data ◽  
Human Tumor ◽  
Principal Component ◽  
Component Analysis ◽  
Kernel Principal Component Analysis ◽  
Support Vector ◽  
Expression Data ◽  
Nonlinear Version

One important feature of the gene expression data is that the number of genesMfar exceeds the number of samplesN. Standard statistical methods do not work well whenN<M. Development of new methodologies or modification of existing methodologies is needed for the analysis of the microarray data. In this paper, we propose a novel analysis procedure for classifying the gene expression data. This procedure involves dimension reduction using kernel principal component analysis (KPCA) and classification with logistic regression (discrimination). KPCA is a generalization and nonlinear version of principal component analysis. The proposed algorithm was applied to five different gene expression datasets involving human tumor samples. Comparison with other popular classification methods such as support vector machines and neural networks shows that our algorithm is very promising in classifying gene expression data.

scholarly journals Joint Lp-Norm and L2,1-Norm Constrained Graph Laplacian PCA for Robust Tumor Sample Clustering and Gene Network Module Discovery

2021 ◽  
Vol 12 ◽  
Author(s):  
Xiang-Zhen Kong ◽  
Yu Song ◽  
Jin-Xing Liu ◽  
Chun-Hou Zheng ◽  
Sha-Sha Yuan ◽  
...  
Keyword(s):  
Gene Expression ◽  
Principal Component Analysis ◽  
Gene Network ◽  
Principal Component ◽  
Graph Laplacian ◽  
Component Analysis ◽  
Network Module ◽  
Tumor Sample ◽  
Lp Norm ◽  
Module Discovery

The dimensionality reduction method accompanied by different norm constraints plays an important role in mining useful information from large-scale gene expression data. In this article, a novel method named Lp-norm and L2,1-norm constrained graph Laplacian principal component analysis (PL21GPCA) based on traditional principal component analysis (PCA) is proposed for robust tumor sample clustering and gene network module discovery. Three aspects are highlighted in the PL21GPCA method. First, to degrade the high sensitivity to outliers and noise, the non-convex proximal Lp-norm (0 &lt; p &lt; 1)constraint is applied on the loss function. Second, to enhance the sparsity of gene expression in cancer samples, the L2,1-norm constraint is used on one of the regularization terms. Third, to retain the geometric structure of the data, we introduce the graph Laplacian regularization item to the PL21GPCA optimization model. Extensive experiments on five gene expression datasets, including one benchmark dataset, two single-cancer datasets from The Cancer Genome Atlas (TCGA), and two integrated datasets of multiple cancers from TCGA, are performed to validate the effectiveness of our method. The experimental results demonstrate that the PL21GPCA method performs better than many other methods in terms of tumor sample clustering. Additionally, this method is used to discover the gene network modules for the purpose of finding key genes that may be associated with some cancers.

scholarly journals Effects of the collagen–glycosaminoglycan mesh on gene expression as determined by using principal component analysis-based unsupervised feature extraction

2021 ◽  
Author(s):  
Y-h. Taguchi ◽  
Turki Turki
Keyword(s):  
Gene Expression ◽  
Principal Component Analysis ◽  
Feature Extraction ◽  
Cell Lines ◽  
Time Course ◽  
Expression Profiles ◽  
Principal Component ◽  
Component Analysis ◽  
Altered Expression ◽  
Unsupervised Feature Extraction

AbstractDevelopment of the medical applications for substances or materials that contact the cells is important. Hence, it is necessary to elucidate how substance that surround cells affect the gene expression during incubation. Here, we compared the gene expression profiles of cell lines that were in contact with the collagen–glycosaminoglycan mesh and control cells. Principal component analysis-based unsupervised feature extraction was applied to identify genes with altered expression during incubation in the treated cell lines but not in the controls. The identified genes were enriched in various biological terms. Our method also outperformed a conventional methodology, namely, gene selection based on linear regression with time course.

Gene Expression Profile Analysis of Pediatric MDS Patients Correlates with FAB Classification and Has Prognostic Relevance

Blood ◽  
2008 ◽  
Vol 112 (11) ◽  
pp. 2695-2695
Author(s):  
Silvia Bresolin ◽  
Luca Trentin ◽  
Geertruy Kronnie ◽  
Laura Sainati ◽  
Marco Zecca ◽  
...  
Keyword(s):  
Gene Expression ◽  
Principal Component Analysis ◽  
Myeloid Leukemia ◽  
De Novo ◽  
Gene Expression Signature ◽  
Principal Component ◽  
Component Analysis ◽  
Secondary Aml ◽  
Fab Classification ◽  
De Novo Aml

Abstract Myelodysplatic syndromes (MDS) are rare malignant haematopoietic stem cell disorders in children. They have the propensity to transform into acute myeloid leukemia and, for this reason they can be considered as a pre-leukemia condition. In this study we analyzed the gene expression profile (GEP) of a large cohort of pediatric patients: 14 MDS [6 refractory cytopenias (RC), 6 refractory anemias with excess blasts (RAEB) and 2 refractory anemias with excess blasts in transformation (RAEB-t)], 50 de-novo acute myeloid leukemia (AML) and 6 normal bone marrow (BM) aspirates. Furthermore, in 5 cases, samples were available for analysis both at diagnosis and at time of secondary AML progression. Gene expression analysis was performed on the Affymetrix HG U133 Plus 2.0 oligonucleotide microarrays using Partek software packages and the leukemia classifier version 7(LCver7). Statistical analyses were performed to determine the correlation between the gene expression signature and MDS subtype. Unsupervised hierarchical clustering analysis separated the majority of MDS cases from the diagnostic AML samples and placed the normal BM specimens into the MDS cluster. Remarkably, all the MDS cases that evolved into an AML within one year, except one, clustered together inside the diagnostic AML group. Performing principal component analysis (PCA) we observed that MDS samples were clustered between the group of normal BM and AML samples. Moreover the RC samples were located proximal to the cluster of normal BM samples while RAEB and RAEB-t specimens were nearest to the AML samples cluster (Fig 1). Further, we classified the MDS samples using the LCver7 classifier, an algorithm developed inside the MILE (Microarray Innovation In LEukemia) study that gives an overall cross-validation accuracy of &gt;95% for distinct sub-classes of pediatric and adult leukemias using gene expression profiles. The 14 MDS samples had 57.2 % and 42.8 % AML and non AML-like signatures, respectively. The Fisher exact test showed that there was a statistical concordance (p=0.008) between the FAB classification and the gene expression signature. In fact, 83% of RC patients had a non AML-like signature whereas only 17% had an AML-like signature. On the contrary 85% of the RAEB and RAEB-t patients had an AML-like signature. In conclusion, the results of unsupervised analysis not only demonstrated that gene expression technology is able to distinguish between MDS, AML and normal BM samples but, in addition, GEP can identify an AML-like signature in samples at diagnosis of MDS, with a higher risk of AML-evolution, allowing to identify a group of patients that could be eligible for a more intensive treatment. Fig.1. Principal component analysis (PCA) of MDS, AML and normal BM samples. Red: MDS samples, Blue: de novo AML, Green: secondary AML, Violet: normal BM. RC samples are placed closer to the normal BM specimens. The three MDS patients that will evolve into secondary AML are included into the green ellipsoids. Fig.1. Principal component analysis (PCA) of MDS, AML and normal BM samples. Red: MDS samples, Blue: de novo AML, Green: secondary AML, Violet: normal BM. RC samples are placed closer to the normal BM specimens. The three MDS patients that will evolve into secondary AML are included into the green ellipsoids.

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