BIOM-37. ELUCIDATION OF THE TRANSCRIPTOMIC CONSEQUENCES OF KLF4 AND TRAF7 MUTATIONS IN SECRETORY MENINGIOMAS

Abstract Secretory meningioma (SM) is a benign histological meningioma subtype frequently located in the central skull-base, complicating surgical removal, highlighting the need of gentle targeting treatment alternatives. SM tumors arise from the arachnoid layer in the meninges and are associated with a hotspot p.K409Q mutation in KLF4 combined with different mutations in TRAF7. The objective of this study was to identify how KLF4 and TRAF7 mutations alter signaling in SM tumors. The study includes 16 FFPE samples from SM tumors and three arachnoid cysts as normal-tissue reference. All tissues were histologically confirmed, followed by genomic sequencing of KLF4, TRAF7, NF2 and TERT-promoter (C220T/C228T) using a customized NGS panel. Gene expression profiles were analyzed using NanoString Pan-cancer Progression and Pan-cancer Pathways panels, which resulted in measurement of 1,202 unique transcripts over background level. All 16 SM tumors presented the KLF4p.K409Q mutation, of which 12 had mutation in TRAF7 (four being p.N520S), while no cysts had mutations in these genes. Neither tumors nor cysts had NF2 or TERT-promoter mutations. PCA analysis showed different RNA expression profiles for SM tumors and cysts, while TRAF7 mutation was without influence on the overall gene expression patterns in the tumors. Sub-analysis found increased KLF4 expression in TRAF7-mutated SM tumors versus non-mutated tumors and cysts at the RNA level, while TRAF7 expression was stable in all samples. In total 98 genes were found differentially expressed (54 up- and 44 downregulated) with a log2-fold change over 1 and an adjusted p-value under 0.05 in tumors versus cysts. Further analyses are planned to explore and validate these results. In conclusion, we find that the KLF4p.K409Q mutation correlates more strongly with SM histology, than the TRAF7 mutation, and that SM tumors have gene expressions profiles that are distinct from normal arachnoid tissue for a number of disease relevant genes.

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Discovering Distinct Patterns in Gene Expression Profiles

Journal of Integrative Bioinformatics ◽

10.1515/jib-2008-105 ◽

2008 ◽

Vol 5 (2) ◽

Cited By ~ 1

Author(s):

Li Teng ◽

Laiwan Chan

Keyword(s):

Gene Expression ◽

Large Scale ◽

Expression Profiles ◽

Expression Patterns ◽

Gene Expression Profiles ◽

Clustering Methods ◽

Gene Expressions ◽

Real Gene ◽

Large Scale Dataset ◽

Coexpressed Genes

SummaryTraditional analysis of gene expression profiles use clustering to find groups of coexpressed genes which have similar expression patterns. However clustering is time consuming and could be diffcult for very large scale dataset. We proposed the idea of Discovering Distinct Patterns (DDP) in gene expression profiles. Since patterns showing by the gene expressions reveal their regulate mechanisms. It is significant to find all different patterns existing in the dataset when there is little prior knowledge. It is also a helpful start before taking on further analysis. We propose an algorithm for DDP by iteratively picking out pairs of gene expression patterns which have the largest dissimilarities. This method can also be used as preprocessing to initialize centers for clustering methods, like K-means. Experiments on both synthetic dataset and real gene expression datasets show our method is very effective in finding distinct patterns which have gene functional significance and is also effcient.

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Building Gene Networks by Analyzing Gene Expression Profiles

Advanced Methodologies and Technologies in Medicine and Healthcare - Advances in Medical Diagnosis, Treatment, and Care ◽

10.4018/978-1-5225-7489-7.ch003 ◽

2019 ◽

pp. 27-44

Author(s):

Crescenzio Gallo

Keyword(s):

Gene Expression ◽

Gene Expression Data ◽

Gene Networks ◽

Dna Microarrays ◽

Expression Profiles ◽

Expression Patterns ◽

Gene Expression Profiles ◽

Expression Data ◽

Gene Expressions ◽

Over Time

The possible applications of modeling and simulation in the field of bioinformatics are very extensive, ranging from understanding basic metabolic paths to exploring genetic variability. Experimental results carried out with DNA microarrays allow researchers to measure expression levels for thousands of genes simultaneously, across different conditions and over time. A key step in the analysis of gene expression data is the detection of groups of genes that manifest similar expression patterns. In this chapter, the authors examine various methods for analyzing gene expression data, addressing the important topics of (1) selecting the most differentially expressed genes, (2) grouping them by means of their relationships, and (3) classifying samples based on gene expressions.

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Deciphering spatial domains from spatially resolved transcriptomics with adaptive graph attention auto-encoder

10.1101/2021.08.21.457240 ◽

2021 ◽

Author(s):

Kangning Dong ◽

Shihua Zhang

Keyword(s):

Gene Expression ◽

Spatial Information ◽

Expression Profiles ◽

Expression Patterns ◽

Gene Expression Profiles ◽

Identification Accuracy ◽

Spatial Context ◽

Gene Expressions ◽

Spatially Resolved ◽

Spatial Domains

Recent advances in spatially resolved transcriptomics have enabled comprehensive measurements of gene expression patterns while retaining spatial context of tissue microenvironment. Deciphering the spatial context of spots in a tissue needs to use their spatial information carefully. To this end, we developed a graph attention auto- encoder framework STGATE to accurately identify spatial domains by learning low-dimensional latent embeddings via integrating spatial information and gene expression profiles. To better characterize the spatial similarity at the boundary of spatial domains, STGATE adopts an attention mechanism to adaptively learn the similarity of neighboring spots, and an optional cell type-aware module through integrating the pre-clustering of gene expressions. We validated STGATE on diverse spatial transcriptomics datasets generated by different platforms with different spatial resolutions. STGATE could substantially improve the identification accuracy of spatial domains, and denoise the data while preserving spatial expression patterns. Importantly, STGATE could be extended to multiple consecutive sections for reducing batch effects between sections and extracting 3D expression domains from the reconstructed 3D tissue effectively.

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The Association between TIF1 Family Members and Cancer Stemness in Solid Tumors

Cancers ◽

10.3390/cancers13071528 ◽

2021 ◽

Vol 13 (7) ◽

pp. 1528

Author(s):

Patrycja Czerwinska ◽

Nikola Agata Wlodarczyk ◽

Anna Maria Jaworska ◽

Andrzej Adam Mackiewicz

Keyword(s):

Gene Expression ◽

Stem Cell ◽

Solid Tumors ◽

Cancer Progression ◽

Expression Profiles ◽

Expression Patterns ◽

Family Members ◽

The Cancer Genome Atlas ◽

Tumor Type ◽

Cancer Stemness

Cancer progression entails a gradual loss of a differentiated phenotype in parallel with the acquisition of stem cell-like features. Cancer de-differentiation and the acquisition of stemness features are mediated by the transcriptional and epigenetic dysregulation of cancer cells. Here, using publicly available data from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) databases and harnessing several bioinformatic tools, we characterized the association between Transcriptional Intermediary Factor 1 (TIF1) family members and cancer stemness in 27 distinct types of solid tumors. We aimed to define the prognostic value for TIF1 members in predicting a stem cell-like cancer phenotype and patient outcome. Our results demonstrate that high expression of only one member of the TIF1 family, namely TIF1β (also known as Tripartite Motif protein 28, TRIM28) is consequently associated with enriched cancer stemness across the tested solid tumor types, resulting in a worse prognosis for cancer patients. TRIM28 is highly expressed in higher grade tumors that exhibit stem cell-like traits. In contrast to other TIF1 members, only TIF1β/TRIM28-associated gene expression profiles were robustly enriched with stemness markers regardless of the tumor type. Our work demonstrates that TIF1 family members exhibit distinct expression patterns in stem cell-like tumors, despite their structural and functional similarity. Among other TIF1 members, only TRIM28 might serve as a marker of cancer stemness features.

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Building Gene Networks by Analyzing Gene Expression Profiles

Encyclopedia of Information Science and Technology, Fourth Edition ◽

10.4018/978-1-5225-2255-3.ch039 ◽

2018 ◽

pp. 440-454

Author(s):

Crescenzio Gallo

Keyword(s):

Gene Expression ◽

Gene Expression Data ◽

Gene Networks ◽

Dna Microarrays ◽

Expression Profiles ◽

Expression Patterns ◽

Gene Expression Profiles ◽

Expression Data ◽

Gene Expressions ◽

Over Time

The possible applications of modeling and simulation in the field of bioinformatics are very extensive, ranging from understanding basic metabolic paths to exploring genetic variability. Experimental results carried out with DNA microarrays allow researchers to measure expression levels for thousands of genes simultaneously, across different conditions and over time. A key step in the analysis of gene expression data is the detection of groups of genes that manifest similar expression patterns. In this chapter we examine various methods for analyzing gene expression data, addressing the important topics of (1) selecting the most differentially expressed genes, (2) grouping them by means of their relationships, and (3) classifying samples based on gene expressions.

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Comprehensive Comparison of Amnion Stromal Cells and Chorion Stromal Cells by RNA-Seq

International Journal of Molecular Sciences ◽

10.3390/ijms22041901 ◽

2021 ◽

Vol 22 (4) ◽

pp. 1901

Author(s):

Brielle Jones ◽

Chaoyang Li ◽

Min Sung Park ◽

Anne Lerch ◽

Vimal Jacob ◽

...

Keyword(s):

Gene Expression ◽

Stromal Cells ◽

Expression Profiles ◽

Expression Patterns ◽

Principal Component ◽

Differentially Expressed ◽

Inflammatory Factor ◽

Next Generation Sequencing Technology ◽

Angiogenic Potential ◽

Anti Inflammatory

Mesenchymal stromal cells derived from the fetal placenta, composed of an amnion membrane, chorion membrane, and umbilical cord, have emerged as promising sources for regenerative medicine. Here, we used next-generation sequencing technology to comprehensively compare amniotic stromal cells (ASCs) with chorionic stromal cells (CSCs) at the molecular and signaling levels. Principal component analysis showed a clear dichotomy of gene expression profiles between ASCs and CSCs. Unsupervised hierarchical clustering confirmed that the biological repeats of ASCs and CSCs were able to respectively group together. Supervised analysis identified differentially expressed genes, such as LMO3, HOXA11, and HOXA13, and differentially expressed isoforms, such as CXCL6 and HGF. Gene Ontology (GO) analysis showed that the GO terms of the extracellular matrix, angiogenesis, and cell adhesion were significantly enriched in CSCs. We further explored the factors associated with inflammation and angiogenesis using a multiplex assay. In comparison with ASCs, CSCs secreted higher levels of angiogenic factors, including angiogenin, VEGFA, HGF, and bFGF. The results of a tube formation assay proved that CSCs exhibited a strong angiogenic function. However, ASCs secreted two-fold more of an anti-inflammatory factor, TSG-6, than CSCs. In conclusion, our study demonstrated the differential gene expression patterns between ASCs and CSCs. CSCs have superior angiogenic potential, whereas ASCs exhibit increased anti-inflammatory properties.

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Pan-Cancer Transcriptional Models Predicting Chemosensitivity in Human Tumors

Cancer Informatics ◽

10.1177/11769351211002494 ◽

2021 ◽

Vol 20 ◽

pp. 117693512110024

Author(s):

Jason D Wells ◽

Jacqueline R Griffin ◽

Todd W Miller

Keyword(s):

Gene Expression ◽

Expression Profiles ◽

Human Tumors ◽

Molecular Characteristics ◽

Survival Times ◽

Chemotherapy Drugs ◽

Testing Dataset ◽

Cancer Models ◽

Cancer Types ◽

Pan Cancer

Motivation: Despite increasing understanding of the molecular characteristics of cancer, chemotherapy success rates remain low for many cancer types. Studies have attempted to identify patient and tumor characteristics that predict sensitivity or resistance to different types of conventional chemotherapies, yet a concise model that predicts chemosensitivity based on gene expression profiles across cancer types remains to be formulated. We attempted to generate pan-cancer models predictive of chemosensitivity and chemoresistance. Such models may increase the likelihood of identifying the type of chemotherapy most likely to be effective for a given patient based on the overall gene expression of their tumor. Results: Gene expression and drug sensitivity data from solid tumor cell lines were used to build predictive models for 11 individual chemotherapy drugs. Models were validated using datasets from solid tumors from patients. For all drug models, accuracy ranged from 0.81 to 0.93 when applied to all relevant cancer types in the testing dataset. When considering how well the models predicted chemosensitivity or chemoresistance within individual cancer types in the testing dataset, accuracy was as high as 0.98. Cell line–derived pan-cancer models were able to statistically significantly predict sensitivity in human tumors in some instances; for example, a pan-cancer model predicting sensitivity in patients with bladder cancer treated with cisplatin was able to significantly segregate sensitive and resistant patients based on recurrence-free survival times ( P = .048) and in patients with pancreatic cancer treated with gemcitabine ( P = .038). These models can predict chemosensitivity and chemoresistance across cancer types with clinically useful levels of accuracy.

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Gene Expression and Transcriptome Profiling of Changes in a Cancer Cell Line Post-Exposure to Cadmium Telluride Quantum Dots: Possible Implications in Oncogenesis

Dose-Response ◽

10.1177/15593258211019880 ◽

2021 ◽

Vol 19 (2) ◽

pp. 155932582110198

Author(s):

Mohammed S. Aldughaim ◽

Mashael R. Al-Anazi ◽

Marie Fe F. Bohol ◽

Dilek Colak ◽

Hani Alothaid ◽

...

Keyword(s):

Gene Expression ◽

Quantum Dots ◽

Cancer Cells ◽

Cadmium Telluride ◽

Cancer Progression ◽

Expression Profiles ◽

Gene Expression Profiles ◽

Dependent Manner ◽

Cdte Qds ◽

Cadmium Telluride Quantum Dots

Cadmium telluride quantum dots (CdTe-QDs) are acquiring great interest in terms of their applications in biomedical sciences. Despite earlier sporadic studies on possible oncogenic roles and anticancer properties of CdTe-QDs, there is limited information regarding the oncogenic potential of CdTe-QDs in cancer progression. Here, we investigated the oncogenic effects of CdTe-QDs on the gene expression profiles of Chang cancer cells. Chang cancer cells were treated with 2 different doses of CdTe-QDs (10 and 25 μg/ml) at different time intervals (6, 12, and 24 h). Functional annotations helped identify the gene expression profile in terms of its biological process, canonical pathways, and gene interaction networks activated. It was found that the gene expression profiles varied in a time and dose-dependent manner. Validation of transcriptional changes of several genes through quantitative PCR showed that several genes upregulated by CdTe-QD exposure were somewhat linked with oncogenesis. CdTe-QD-triggered functional pathways that appear to associate with gene expression, cell proliferation, migration, adhesion, cell-cycle progression, signal transduction, and metabolism. Overall, CdTe-QD exposure led to changes in the gene expression profiles of the Chang cancer cells, highlighting that this nanoparticle can further drive oncogenesis and cancer progression, a finding that indicates the merit of immediate in vivo investigation.

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Microarray analysis identification of key pathways and interaction network of differential gene expressions during osteogenic differentiation

Human Genomics ◽

10.1186/s40246-020-00293-1 ◽

2020 ◽

Vol 14 (1) ◽

Author(s):

Fatemeh Khodabandehloo ◽

Sara Taleahmad ◽

Reza Aflatoonian ◽

Farzad Rajaei ◽

Zahra Zandieh ◽

...

Keyword(s):

Gene Expression ◽

Stem Cells ◽

Signaling Pathway ◽

Expression Profiles ◽

Wnt Signaling Pathway ◽

Hub Genes ◽

Gene Expressions ◽

Cell Based Therapy ◽

Wnt Pathways ◽

Key Pathways

Abstract Background Adult bone marrow-derived mesenchymal stem cells (BM-MSCs) are multipotent stem cells that can differentiate into three lineages. They are suitable sources for cell-based therapy and regenerative medicine applications. This study aims to evaluate the hub genes and key pathways of differentially expressed genes (DEGs) related to osteogenesis by bioinformatics analysis in three different days. The DEGs were derived from the three different days compared with day 0. Results Gene expression profiles of GSE37558 were obtained from the Gene Expression Omnibus (GEO) database. A total of 4076 DEGs were acquired on days 8, 12, and 25. Gene ontology (GO) enrichment analysis showed that the non-canonical Wnt signaling pathway and lipopolysaccharide (LPS)-mediated signaling pathway were commonly upregulated DEGs for all 3 days. KEGG pathway analysis indicated that the PI3K-Akt and focal adhesion were also commonly upregulated DEGs for all 3 days. Ten hub genes were identified by CytoHubba on days 8, 12, and 25. Then, we focused on the association of these hub genes with the Wnt pathways that had been enriched from the protein-protein interaction (PPI) by the Cytoscape plugin MCODE. Conclusions These findings suggested further insights into the roles of the PI3K/AKT and Wnt pathways and their association with osteogenesis. In addition, the stem cell microenvironment via growth factors, extracellular matrix (ECM), IGF1, IGF2, LPS, and Wnt most likely affect osteogenesis by PI3K/AKT.

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Identification of Gastric Cancer-Related Circular RNA through Microarray Analysis and Bioinformatics Analysis

BioMed Research International ◽

10.1155/2018/2381680 ◽

2018 ◽

Vol 2018 ◽

pp. 1-9 ◽

Cited By ~ 12

Author(s):

Wei Gu ◽

Ying Sun ◽

Xiong Zheng ◽

Jin Ma ◽

Xiao-Ying Hu ◽

...

Keyword(s):

Gene Expression ◽

Gastric Cancer ◽

Microarray Analysis ◽

Cancer Progression ◽

Pathway Analysis ◽

Bioinformatics Analysis ◽

Malignant Tumors ◽

Expression Profiles ◽

Circular Rnas ◽

Target Mrnas

Gastric cancer is one of the common malignant tumors worldwide. Increasing studies have indicated that circular RNAs (circRNAs) play critical roles in the cancer progression and have shown great potential as useful markers and therapeutic targets. However, the precise mechanism and functions of most circRNAs are still unknown in gastric cancer. In the present study, we performed a microarray analysis to detect circRNA expression changes between tumor samples and adjacent nontumor samples. The miRNA expression profiles were obtained from the National Center of Biotechnology Information Gene Expression Omnibus (GEO). The differentially expressed circRNAs and miRNAs were identified through fold change filtering. The interactions between circRNAs and miRNAs were predicted by Arraystar’s home-made miRNA target prediction software. After circRNA-related miRNAs and dysregulated miRNAs were intersected, 23 miRNAs were selected. The target mRNAs of miRNAs were predicted by TarBase v7.0. Gene ontology (GO) enrichment analysis and pathway analysis were performed using standard enrichment computational methods for the target mRNAs. The results of pathway analysis showed that p53 signaling pathway and hippo signal pathway were significantly enriched and CCND2 was a cross-talk gene associated with them. Finally, a circRNA-miRNA-mRNA regulation network was constructed based on the gene expression profiles and bioinformatics analysis results to identify hub genes and hsa_circRNA_101504 played a central role in the network.

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