scholarly journals Normal tissue content impact on the GBM molecular classification

2020 ◽  
Author(s):  
Rodrigo Madurga ◽  
Noemí García-Romero ◽  
Beatriz Jiménez ◽  
Ana Collazo ◽  
Francisco Pérez-Rodríguez ◽  
...  
Keyword(s):  
Normal Tissue ◽  
In Silico Analysis ◽  
Molecular Classification ◽  
Gene Signature ◽  
Normal Brain ◽  
Normal Cells ◽  
Final Model ◽  
Tumor Subtypes ◽  
Tissue Content

Abstract Molecular classification of glioblastoma has enabled a deeper understanding of the disease. The four-subtype model (including Proneural, Classical, Mesenchymal and Neural) has been replaced by a model that discards the Neural subtype, found to be associated with samples with a high content of normal tissue. These samples can be misclassified preventing biological and clinical insights into the different tumor subtypes from coming to light. In this work, we present a model that tackles both the molecular classification of samples and discrimination of those with a high content of normal cells. We performed a transcriptomic in silico analysis on glioblastoma (GBM) samples (n = 810) and tested different criteria to optimize the number of genes needed for molecular classification. We used gene expression of normal brain samples (n = 555) to design an additional gene signature to detect samples with a high normal tissue content. Microdissection samples of different structures within GBM (n = 122) have been used to validate the final model. Finally, the model was tested in a cohort of 43 patients and confirmed by histology. Based on the expression of 20 genes, our model is able to discriminate samples with a high content of normal tissue and to classify the remaining ones. We have shown that taking into consideration normal cells can prevent errors in the classification and the subsequent misinterpretation of the results. Moreover, considering only samples with a low content of normal cells, we found an association between the complexity of the samples and survival for the three molecular subtypes.

scholarly journals Cell Differentiation Trajectory-Associated Molecular Classification of Osteosarcoma

Genes ◽  
2021 ◽  
Vol 12 (11) ◽  
pp. 1685
Author(s):  
Ankai Xu ◽  
Chao Qian ◽  
Jinti Lin ◽  
Wei Yu ◽  
Jiakang Jin ◽  
...  
Keyword(s):  
Cell Differentiation ◽  
Molecular Subtypes ◽  
Molecular Classification ◽  
Gene Signature ◽  
Gene Expression Omnibus ◽  
Tumor Stem Cell ◽  
Osteosarcoma Cell ◽  
Rna Seq ◽  
Individual Survival

This study aims to investigate the differentiation trajectory of osteosarcoma cells and to construct molecular subtypes with their respective characteristics and generate a multi-gene signature for predicting prognosis. Integrated single-cell RNA-sequencing (scRNA-seq) data, bulk RNA-seq data and microarray data from osteosarcoma samples were used for analysis. Via scRNA-seq data, time-related as well as differentiation-related genes were recognized as osteosarcoma tumor stem cell-related genes (OSCGs). In Gene Expression Omnibus (GEO) cohort, osteosarcoma patients were classified into two subtypes based on prognostic OSCGs and it was found that molecular typing successfully predicted overall survival, tumor microenvironment and immune infiltration status. Further, available drugs for influencing osteosarcoma via prognostic OSCGs were revealed. A 3-OSCG-based prognostic risk score signature was generated and by combining other clinic-pathological independent prognostic factor, stage at diagnosis, a nomogram was established to predict individual survival probability. In external independent TARGET cohort, the molecular types, the 3-gene signature as well as nomogram were validated. In conclusion, osteosarcoma cell differentiation occupies a crucial position in many facets, such as tumor prognosis and microenvironment, suggesting promising therapeutic targets for this disease.

Molecular classification of hepatoblastoma and prognostic value of the HB 16-gene signature

Hepatology ◽  
2017 ◽  
Vol 66 (4) ◽  
pp. 1351-1352 ◽  
Author(s):  
Marie-Annick Buendia ◽  
Carolina Armengol ◽  
Stefano Cairo
Keyword(s):  
Prognostic Value ◽  
Molecular Classification ◽  
Gene Signature

Unsupervised Analysis of Gene Expression Profile of 37 Human Myeloma Cell Lines Identifies 5 Classes of HMCL Characterized by a Set of 213 Genes Related to Molecular Classification of Patients with Multiple Myeloma

Blood ◽  
2008 ◽  
Vol 112 (11) ◽  
pp. 2702-2702
Author(s):  
Jerome Moreaux ◽  
Bernard Klein ◽  
Thierry Reme ◽  
Michel Jourdan ◽  
Sophie Maiga ◽  
...  
Keyword(s):  
Expression Profile ◽  
Molecular Level ◽  
Molecular Classification ◽  
Gene Signature ◽  
Myeloma Cell ◽  
Cancer Testis Antigens ◽  
Group A ◽  
Group D ◽  
Cancer Testis

Abstract Multiple myeloma (MM) is a malignant plasma cell disorder characterized by a wide diversity from the molecular level up to the treatment response. Diversity is supported by cytogenetic abnormalities, especially by non random hyperdiploidy or translocations of IgH locus with recurrent partners. This classification based on cytogenetic aberrations has been confirmed at the molecular level leading to the molecular classification of patients (Zhan et al, Blood 2006 108:2020). This classification defines 7 groups of patients, 5 being linked to cytogenetic abnormalities, CD-1 (cyclin D1/D3–1), CD-2 (cyclin D1/D3–2), HY (hyperdiploid), MS (MMSET), MF (MAF) and 2 to functional characteristics, LB (low bone disease) and PR (proliferation). In the present work, we have addressed the study of mRNA expression profile (global expression profile, GEP) of a very large panel of human myeloma cell lines (HMCL) (n=37) in order to define whether HMCL were or not representative of patient’s diversity. Unsupervised hierarchic clustering of 37 HMCL identified 5 distinct classes (groups A to E). SAM analysis (with 1000 permutations) revealed a set of 213 genes with a FDR ≤ 1% and a ratio ≥ 2. Group A (n=8) gathered HMCL with t(11,14) translocation and genes expressed by CD-1 and CD-2 groups of patients. HMCL of groups B (n=6) and C (n=7) were both IL-6 dependent HMCL exclusively. They were mainly characterized by expression of more than 10 genes coding for cancer testis antigens (GAGE, MAGE, SSX). Group C had also an IL6 signature (IL6, SOCS3) found in one group of HY patients reflecting the presence of several HY HMCL within this group (CHNG et al, Cancer Res 2007, 67/2982). Both groups B and C were related to the PR group. Group D (n=8) had a Maf (Maf, Integrin-beta7) and group E (n=8) a WHSC1 gene signature and gathered HMCL with t(14;16) or t(4;14) translocation, respectively. GSEA analysis revealed very similar results: group A presented a gene signature linked to glycerophospholipid metabolism. Group B overexpressed genes indicative of a plasmablastic signature (Tarte et al., Blood2003,102:592). Group C was characterized by a gene signature linked to the IL-6 pathway and cancer testis antigens, group D by a gene signature relative to the MF group and to the TACIhigh patient’s group. Group E presented a gene signature relative to the MS group. Moreover, in this set of 213 genes, we identified new genes with strong prognosic value for newly-diagnosed patients treated with high dose chemotherapy (both EFS and OS). These data show that HMCL have kept molecular signature of primary myeloma cells in good agreement with their genomic abnormalities (mainly 14q32 translocation). Interestingly, two groups of HMCL matched with PR group suggesting that PR group could be related to two different pathways of proliferation. This GEP analysis showed that heterogeneity of HMCL is representative of patient’s diversity. For the first time, this study establishes at the molecular level the relevance of HMCL to study myeloma and should help to identify target genes in the context of heterogeneity. This large panel of HMCL should be useful to study mechanisms of drug action as well as predict drug efficiency with respect to diversity.

Molecular classification of pituitary adenomas

2015 ◽  
Author(s):  
Antonio Pico ◽  
Laura Sanchez-Tejada ◽  
Ruth Sanchez-Ortiga ◽  
Rosa Camara ◽  
Cristina Lamas ◽  
...  
Keyword(s):  
Pituitary Adenomas ◽  
Molecular Classification

scholarly journals Molecular Classification and Tumor Microenvironment Characterization of Gallbladder Cancer by Comprehensive Genomic and Transcriptomic Analysis

Cancers ◽  
2021 ◽  
Vol 13 (4) ◽  
pp. 733
Author(s):  
Nobutaka Ebata ◽  
Masashi Fujita ◽  
Shota Sasagawa ◽  
Kazuhiro Maejima ◽  
Yuki Okawa ◽  
...  
Keyword(s):  
Tumor Microenvironment ◽  
Gallbladder Cancer ◽  
Epithelial To Mesenchymal Transition ◽  
Molecular Classification ◽  
Mesenchymal Transition ◽  
Elevated Expression ◽  
Fresh Frozen ◽  
Therapeutic Decision Making

Gallbladder cancer (GBC), a rare but lethal disease, is often diagnosed at advanced stages. So far, molecular characterization of GBC is insufficient, and a comprehensive molecular portrait is warranted to uncover new targets and classify GBC. We performed a transcriptome analysis of both coding and non-coding RNAs from 36 GBC fresh-frozen samples. The results were integrated with those of comprehensive mutation profiling based on whole-genome or exome sequencing. The clustering analysis of RNA-seq data facilitated the classification of GBCs into two subclasses, characterized by high or low expression levels of TME (tumor microenvironment) genes. A correlation was observed between gene expression and pathological immunostaining. TME-rich tumors showed significantly poor prognosis and higher recurrence rate than TME-poor tumors. TME-rich tumors showed overexpression of genes involved in epithelial-to-mesenchymal transition (EMT) and inflammation or immune suppression, which was validated by immunostaining. One non-coding RNA, miR125B1, exhibited elevated expression in stroma-rich tumors, and miR125B1 knockout in GBC cell lines decreased its invasion ability and altered the EMT pathway. Mutation profiles revealed TP53 (47%) as the most commonly mutated gene, followed by ELF3 (13%) and ARID1A (11%). Mutations of ARID1A, ERBB3, and the genes related to the TGF-β signaling pathway were enriched in TME-rich tumors. This comprehensive analysis demonstrated that TME, EMT, and TGF-β pathway alterations are the main drivers of GBC and provides a new classification of GBCs that may be useful for therapeutic decision-making.

scholarly journals MultiKOC: Multi-One-Class Classifier Based K-Means Clustering

Algorithms ◽  
2021 ◽  
Vol 14 (5) ◽  
pp. 134
Author(s):  
Loai Abdallah ◽  
Murad Badarna ◽  
Waleed Khalifa ◽  
Malik Yousef
Keyword(s):  
Clustering Algorithm ◽  
Main Idea ◽  
Molecular Classification ◽  
Positive Sample ◽  
Classification Problems ◽  
Multiple Cancer ◽  
Multiple Tumor ◽  
One Class Classifier ◽  
The Given

In the computational biology community there are many biological cases that are considered as multi-one-class classification problems. Examples include the classification of multiple tumor types, protein fold recognition and the molecular classification of multiple cancer types. In all of these cases the real world appropriately characterized negative cases or outliers are impractical to achieve and the positive cases might consist of different clusters, which in turn might lead to accuracy degradation. In this paper we present a novel algorithm named MultiKOC multi-one-class classifiers based K-means to deal with this problem. The main idea is to execute a clustering algorithm over the positive samples to capture the hidden subdata of the given positive data, and then building up a one-class classifier for every cluster member’s examples separately: in other word, train the OC classifier on each piece of subdata. For a given new sample, the generated classifiers are applied. If it is rejected by all of those classifiers, the given sample is considered as a negative sample, otherwise it is a positive sample. The results of MultiKOC are compared with the traditional one-class, multi-one-class, ensemble one-classes and two-class methods, yielding a significant improvement over the one-class and like the two-class performance.

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