Classification of diffuse large B cell lymphoma gene expression data based on two-layer particle swarm optimization

2013 ◽  
Author(s):  
Yajie Liu ◽  
Xinling Shi ◽  
Guoliang Huang ◽  
Baolei Li ◽  
Lei Zhao
Keyword(s):  
Gene Expression ◽  
Particle Swarm Optimization ◽  
Gene Expression Data ◽  
Cell Lymphoma ◽  
B Cell Lymphoma ◽  
Expression Data ◽  
Swarm Optimization ◽  
Large B Cell Lymphoma ◽  
Large B Cell

scholarly journals Prognostic Efficacy of the RTN1 Gene in Patients with Diffuse Large B-Cell Lymphoma

2021 ◽  
Author(s):  
Mohamad Zamani-Ahmadmahmudi ◽  
Seyed Mahdi Nassiri ◽  
Amir Asadabadi
Keyword(s):  
Gene Expression ◽  
Clinical Outcome ◽  
Gene Expression Data ◽  
Proportional Hazards ◽  
Cell Lymphoma ◽  
B Cell Lymphoma ◽  
Cox Proportional Hazards ◽  
Expression Data ◽  
Large B Cell Lymphoma ◽  
Large B Cell

Abstract Gene expression profiling has been vastly used to extract the genes that can predict the clinical outcome in patients with diverse cancers, including diffuse large B-cell lymphoma (DLBCL). With the aid of bioinformatics and computational analysis on gene expression data, various prognostic gene signatures for DLBCL have been recently developed. The major drawback of the previous signatures is their inability to correctly predict survival in external data sets. In other words, they are not reproducible in other datasets. Hence, in this study, we sought to determine the gene(s) that can reproducibly and robustly predict survival in patients with DLBCL. Gene expression data were extracted from 7 datasets containing 1636 patients (GSE10846 [n=420], GSE31312 [n=470], GSE11318 [n=203], GSE32918 [n=172], GSE4475 [n=123], GSE69051 [n=157], and GSE34171 [n=91]). Genes significantly associated with overall survival were detected using the univariate Cox proportional hazards analysis with a P value <0.001 and a false discovery rate (FDR) <5%. Thereafter, significant genes common between all the datasets were extracted. Additionally, chromosomal aberrations in the corresponding region of the final common gene(s) were evaluated as copy number alterations using the single nucleotide polymorphism (SNP) data of 570 patients with DLBCL (GSE58718 [n=242], GSE57277 [n=148], and GSE34171 [n=180]). Our results indicated that reticulon family gene 1 (RTN1) was the only gene that met our rigorous pipeline criteria and associated with a favorable clinical outcome in all the datasets (P<0.001, FDR<5%). In the multivariate Cox proportional hazards analysis, this gene remained independent of the routine international prognostic index components (i.e., age, stage, lactate dehydrogenase level, Eastern Cooperative Oncology Group [ECOG] performance status, and number of extranodal sites) (P<0.0001). Furthermore, no significant chromosomal aberration was found in the RTN1 genomic region (14q23.1: Start 59,595,976/ End 59,870,966).

scholarly journals Reliable subtype classification of diffuse large B-cell lymphoma samples from GELA LNH2003 trials using the Lymph2Cx gene expression assay

Haematologica ◽  
2017 ◽  
Vol 102 (10) ◽  
pp. e404-e406 ◽  
Author(s):  
Jean-Philippe Jais ◽  
Thierry Jo Molina ◽  
Philippe Ruminy ◽  
David Gentien ◽  
Cecile Reyes ◽  
...  
Keyword(s):  
Gene Expression ◽  
B Cell ◽  
Cell Lymphoma ◽  
B Cell Lymphoma ◽  
Gene Expression Assay ◽  
Subtype Classification ◽  
Large B Cell Lymphoma ◽  
Large B Cell

scholarly journals Artificial Neural Network Analysis of Gene Expression Data Predicted Non-Hodgkin Lymphoma Subtypes with High Accuracy

2021 ◽  
Vol 3 (3) ◽  
pp. 720-739
Author(s):  
Joaquim Carreras ◽  
Rifat Hamoudi
Keyword(s):  
Neural Network ◽  
Gene Expression ◽  
Hodgkin Lymphoma ◽  
Cell Lymphoma ◽  
B Cell Lymphoma ◽  
High Accuracy ◽  
Expression Data ◽  
Non Hodgkin Lymphoma ◽  
Large B Cell Lymphoma ◽  
Large B Cell

Predictive analytics using artificial intelligence is a useful tool in cancer research. A multilayer perceptron neural network used gene expression data to predict the lymphoma subtypes of 290 cases of non-Hodgkin lymphoma (GSE132929). The input layer included both the whole array of 20,863 genes and a cancer transcriptome panel of 1769 genes. The output layer was lymphoma subtypes, including follicular lymphoma, mantle cell lymphoma, diffuse large B-cell lymphoma, Burkitt lymphoma, and marginal zone lymphoma. The neural networks successfully classified the cases consistent with the lymphoma subtypes, with an area under the curve (AUC) that ranged from 0.87 to 0.99. The most relevant predictive genes were LCE2B, KNG1, IGHV7_81, TG, C6, FGB, ZNF750, CTSV, INGX, and COL4A6 for the whole set; and ARG1, MAGEA3, AKT2, IL1B, S100A7A, CLEC5A, WIF1, TREM1, DEFB1, and GAGE1 for the cancer panel. The characteristic predictive genes for each lymphoma subtypes were also identified with high accuracy (AUC = 0.95, incorrect predictions = 6.2%). Finally, the topmost relevant 30 genes of the whole set, which belonged to apoptosis, cell proliferation, metabolism, and antigen presentation pathways, not only predicted the lymphoma subtypes but also the overall survival of diffuse large B-cell lymphoma (series GSE10846, n = 414 cases), and most relevant cancer subtypes of The Cancer Genome Atlas (TCGA) consortium including carcinomas of breast, colorectal, lung, prostate, and gastric, melanoma, etc. (7441 cases). In conclusion, neural networks predicted the non-Hodgkin lymphoma subtypes with high accuracy, and the highlighted genes also predicted the survival of a pan-cancer series.

scholarly journals Prognostic Efficacy of the RTN1 Gene in Patients with Diffuse Large B-Cell Lymphoma

2019 ◽  
Author(s):  
Mohamad Zamani-Ahmadmahmudi ◽  
Fatemeh Soltani-Nezhad ◽  
Amir Asadabadi
Keyword(s):  
Gene Expression ◽  
Clinical Outcome ◽  
Proportional Hazards ◽  
Cell Lymphoma ◽  
B Cell Lymphoma ◽  
Cox Proportional Hazards ◽  
Expression Data ◽  
Survival Group ◽  
Large B Cell Lymphoma ◽  
Large B Cell

Abstract Background Gene expression profiling has been vastly used to extract genes that can predict the clinical outcome in patients with diverse cancers, including diffuse large B-cell lymphoma (DLBCL). With the aid of bioinformatics and computational analysis on gene expression data, various prognostic gene signatures for DLBCL have been recently developed. The major drawback of the previous signatures is their inability to correctly predict survival in external data sets. In other words, they are not reproducible in other datasets. Hence, in this study, we sought to determine the gene(s) that can reproducibly and robustly predict survival in patients with DLBCL. Methods Gene expression data were extracted from 7 datasets containing 1636 patients (GSE10846 [n=420], GSE31312 [n=470], GSE11318 [n=203], GSE32918 [n=172], GSE4475 [n=123], GSE69051 [n=157], and GSE34171 [n=91]). Genes significantly associated with overall survival were detected using the univariate Cox proportional hazards analysis with a P value <0.001 and a false discovery rate (FDR) <5%. Thereafter, significant genes common between all the datasets were extracted. Additionally, chromosomal aberrations in the corresponding region of final common gene(s) were evaluated as copy number alterations using the single nucleotide polymorphism (SNP) data of 570 patients with DLBCL (GSE58718 [n=242], GSE57277 [n=148], and GSE34171 [n=180]). The results were experimentally confirmed using the quantitative real-time PCR (qRT-PCR) analysis. Results Our results indicated that reticulon family gene 1 (RTN1) was the only gene that met our rigorous pipeline criteria and associated with a favorable clinical outcome in all the datasets (P<0.001, FDR<5%). In the multivariate Cox proportional hazards analysis, this gene remained independent of the routine international prognostic index components (i.e., age, stage, lactate dehydrogenase level, Eastern Cooperative Oncology Group [ECOG] performance status, and number of extranodal sites) (P<0.0001). Our experimental step confirmed the results and revealed that the expression of RTN1 in the long-survival group was significantly higher than that in the short-survival group. Furthermore, no significant chromosomal aberration was found in the RTN1 genomic region (14q23.1: Start 59,595,976/ End 59,870,966). Conclusion In light of the results of present study, RTN1 can be considered a potential prognostic gene that can robustly predict survival in patients with DLBCL.

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