Comprehensive transcriptomic analysis of Papillary Thyroid Cancer: potential biomarkers associated with tumor progression

Abstract Background Identification of stage-specific prognostic/predictive biomarkers could lead to the more efficient clinical management in papillary thyroid carcinoma (PTC). The main objective of this study was to characterize the stage-specific deregulations in gene and miRNA expression in PTC and also to identify potential prognostic biomarkers.Methods 495 RNASeq and 499 miRNASeq PTC samples (stage I-IV) as well as, respectively, 56 and 57 normal samples were retrieved from The Cancer Genome Atlas (TCGA). DESeq2 was used to identify deregulation of genes and miRNAs between sequential stages of PTC. To identify the minority of patients who progress from stage I to higher stages, we additionally performed a clustering analysis focused on the stage I RNASeq data. Moreover a validation study was done on an independent PTC RNASeq study.Results Large amount of heterogeneity in gene expression patterns was observed in stage I PTC patients. LTF and PLA2R1 were identified as two promising biomarkers that exhibited down-regulation in a small subgroup of stage I (both in TCGA and in the validation data set) and in the majority of stage IV patients (in TCGA data set). hsa-miR-205, hsa-miR-509-2, hsa-miR-514-1 and hsa-miR-514-2 were also identified as up-regulated miRNAs in both PTC patients with stage I and stage III.Conclusion Common gene expression alterations in early and advanced stages of PTC, could be used for individualized risk stratification and personalized treatment approach.

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Gene Expression Patterns Unveil New Insights in Papillary Thyroid Cancer

Medicina ◽

10.3390/medicina55080500 ◽

2019 ◽

Vol 55 (8) ◽

pp. 500 ◽

Cited By ~ 2

Author(s):

Saftencu ◽

Braicu ◽

Cojocneanu ◽

Buse ◽

Irimie ◽

...

Keyword(s):

Gene Expression ◽

Papillary Thyroid Carcinoma ◽

Thyroid Carcinoma ◽

Clinical Relevance ◽

Expression Patterns ◽

Gene Expression Signature ◽

Specific Gene ◽

Endocrine Tumors ◽

Papillary Thyroid ◽

Data Set

Background and objectives: Papillary thyroid carcinoma is the most frequent variety of all malignant endocrine tumors. It represents a heterogeneous malignancy with various clinical outcomes, emphasizing the need to identify powerful biomarkers with clinical relevance. Materials and Methods: Available gene expression data (level 3) for thyroid cancers were downloaded from the Cancer Genome Atlas (TCGA), followed by bioinformatic analyses performed on the data set. Results: Based on gene expression analysis, we were able to identify common and specific gene signatures for the three main types of papillary thyroid carcinoma (classical, follicular variant, and tall-cell). The survival rate was not significantly different among the main subtypes, but we were able to identify a biological adhesion signature with impact in patient prognostic. Conclusions: Taken together, the gene expression signature and particular adhesion signature, along with ITGA10 and MSLN in particular, could be used as a prognostic tool with important clinical relevance.

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Identification of biomarkers and functional modules from genomic data in stage-wise breast cancer

Current Bioinformatics ◽

10.2174/1574893615999200922123104 ◽

2020 ◽

Vol 15 ◽

Author(s):

Athira K ◽

Vrinda C ◽

Sunil Kumar P V ◽

Gopakumar G

Keyword(s):

Breast Cancer ◽

Expression Patterns ◽

Differential Expression Analysis ◽

Predictive Biomarkers ◽

Stage I ◽

Stage Ii ◽

Stage Iii ◽

Functional Modules ◽

Incidence And Mortality ◽

Multiple Stages

Background: Breast cancer is the most common cancer in women across the world, with high incidence and mortality rates. Being a heterogeneous disease, gene expression profiling based analysis plays a significant role in understanding breast cancer. Since expression patterns of patients belonging to the same stage of breast cancer vary considerably, an integrated stage-wise analysis involving multiple samples is expected to give more comprehensive results and understanding of breast cancer. Objective: The objective of this study is to detect functionally significant modules from gene co-expression network of cancerous tissues and to extract prognostic genes related to multiple stages of breast cancer. Methods: To achieve this, a multiplex framework is modelled to map the multiple stages of breast cancer, which is followed by a modularity optimization method to identify functional modules from it. These functional modules are found to enrich many Gene Ontology terms significantly that are associated with cancer. Result and Discussion: predictive biomarkers are identified based on differential expression analysis of multiple stages of breast cancer. Conclusion: Our analysis identified 13 stage-I specific genes, 12 stage-II specific genes, and 42 stage-III specific genes that are significantly regulated and could be promising targets of breast cancer therapy. That apart, we could identify 29, 18 and 26 lncRNAs specific to stage I, stage II and stage III respectively.

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Heterogenous nature of gene expression patterns in BRAF-like papillary thyroid carcinomas with BRAFV600E

Endocrine ◽

10.1007/s12020-019-02063-z ◽

2019 ◽

Vol 66 (3) ◽

pp. 607-613

Author(s):

Yuji Nagayama ◽

Hiroyuki Mishima

Keyword(s):

Gene Expression ◽

Expression Patterns ◽

Gene Expression Patterns ◽

Papillary Thyroid ◽

Thyroid Carcinomas

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Gene expression profiling of epigenetic chromatin modification enzymes and histone marks by cigarette smoke: implications for COPD and lung cancer

AJP Lung Cellular and Molecular Physiology ◽

10.1152/ajplung.00253.2016 ◽

2016 ◽

Vol 311 (6) ◽

pp. L1245-L1258 ◽

Cited By ~ 19

Author(s):

Isaac K. Sundar ◽

Irfan Rahman

Keyword(s):

Gene Expression ◽

Lung Cancer ◽

Dna Methylation ◽

Cigarette Smoke ◽

Histone Modifications ◽

Expression Patterns ◽

Chromatin Modification ◽

Gene Expression Patterns ◽

Validation Data ◽

Histone Marks

Chromatin-modifying enzymes mediate DNA methylation and histone modifications on recruitment to specific target gene loci in response to various stimuli. The key enzymes that regulate chromatin accessibility for maintenance of modifications in DNA and histones, and for modulation of gene expression patterns in response to cigarette smoke (CS), are not known. We hypothesize that CS exposure alters the gene expression patterns of chromatin-modifying enzymes, which then affects multiple downstream pathways involved in the response to CS. We have, therefore, analyzed chromatin-modifying enzyme profiles and validated by quantitative real-time PCR (qPCR). We also performed immunoblot analysis of targeted histone marks in C57BL/6J mice exposed to acute and subchronic CS, and of lungs from nonsmokers, smokers, and patients with chronic obstructive pulmonary disease (COPD). We found a significant increase in expression of several chromatin modification enzymes, including DNA methyltransferases, histone acetyltransferases, histone methyltransferases, and SET domain proteins, histone kinases, and ubiquitinases. Our qPCR validation data revealed a significant downregulation of Dnmt1, Dnmt3a, Dnmt3b, Hdac2, Hdac4, Hat1, Prmt1, and Aurkb. We identified targeted chromatin histone marks (H3K56ac and H4K12ac), which are induced by CS. Thus CS-induced genotoxic stress differentially affects the expression of epigenetic modulators that regulate transcription of target genes via DNA methylation and site-specific histone modifications. This may have implications in devising epigenetic-based therapies for COPD and lung cancer.

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Integrative transcriptome data mining for identification of core lncRNAs in breast cancer

PeerJ ◽

10.7717/peerj.7821 ◽

2019 ◽

Vol 7 ◽

pp. e7821 ◽

Cited By ~ 7

Author(s):

Xiaoming Zhang ◽

Jing Zhuang ◽

Lijuan Liu ◽

Zhengguo He ◽

Cun Liu ◽

...

Keyword(s):

Breast Cancer ◽

Gene Expression ◽

Prognostic Value ◽

Early Stage ◽

Expression Profiles ◽

Diagnostic Value ◽

The Cancer Genome Atlas ◽

Functional Enrichment ◽

Differentially Expressed ◽

Data Set

Background Cumulative evidence suggests that long non-coding RNAs (lncRNAs) play an important role in tumorigenesis. This study aims to identify lncRNAs that can serve as new biomarkers for breast cancer diagnosis or screening. Methods First, the linear fitting method was used to identify differentially expressed genes from the breast cancer RNA expression profiles in The Cancer Genome Atlas (TCGA). Next, the diagnostic value of all differentially expressed lncRNAs was evaluated using a receiver operating characteristic (ROC) curve. Then, the top ten lncRNAs with the highest diagnostic value were selected as core genes for clinical characteristics and prognosis analysis. Furthermore, core lncRNA-mRNA co-expression networks based on weighted gene co-expression network analysis (WGCNA) were constructed, and functional enrichment analysis was performed using the Database for Annotation, Visualization and Integrated Discovery (DAVID). The differential expression level and diagnostic value of core lncRNAs were further evaluated by using independent data set from Gene Expression Omnibus (GEO). Finally, the expression status and prognostic value of core lncRNAs in various tumors were analyzed based on Gene Expression Profiling Interactive Analysis (GEPIA). Results Seven core lncRNAs (LINC00478, PGM5-AS1, AL035610.1, MIR143HG, RP11-175K6.1, AC005550.4, and MIR497HG) have good single-factor diagnostic value for breast cancer. AC093850.2 has a prognostic value for breast cancer. AC005550.4 and MIR497HG can better distinguish breast cancer patients in early-stage from the advanced-stage. Low expression of MAGI2-AS3, LINC00478, AL035610.1, MIR143HG, and MIR145 may be associated with lymph node metastasis in breast cancer. Conclusion Our study provides candidate biomarkers for the diagnosis and prognosis of breast cancer, as well as a bioinformatics basis for the further elucidation of the molecular pathological mechanism of breast cancer.

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Diverse oncogenic fusions and distinct gene expression patterns define the genomic landscape of pediatric papillary thyroid carcinoma

Cancer Research ◽

10.1158/0008-5472.can-21-0761 ◽

2021 ◽

pp. canres.0761.2021

Author(s):

Ana Stosic ◽

Fabio Fuligni ◽

Nathaniel D. Anderson ◽

Scott Davidson ◽

Richard de Borja ◽

...

Keyword(s):

Gene Expression ◽

Papillary Thyroid Carcinoma ◽

Thyroid Carcinoma ◽

Expression Patterns ◽

Gene Expression Patterns ◽

Papillary Thyroid ◽

Distinct Gene ◽

Genomic Landscape

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Predicting Complete Remission of Acute Myeloid Leukemia: Machine Learning Applied to Gene Expression

Cancer Informatics ◽

10.1177/1176935119835544 ◽

2019 ◽

Vol 18 ◽

pp. 117693511983554 ◽

Cited By ~ 5

Author(s):

Ophir Gal ◽

Noam Auslander ◽

Yu Fan ◽

Daoud Meerzaman

Keyword(s):

Gene Expression ◽

Machine Learning ◽

Acute Myeloid Leukemia ◽

Complete Remission ◽

Myeloid Leukemia ◽

Statistical Significance ◽

Expression Patterns ◽

Pathway Enrichment Analysis ◽

Data Set ◽

Acute Myeloid

Machine learning (ML) is a useful tool for advancing our understanding of the patterns and significance of biomedical data. Given the growing trend on the application of ML techniques in precision medicine, here we present an ML technique which predicts the likelihood of complete remission (CR) in patients diagnosed with acute myeloid leukemia (AML). In this study, we explored the question of whether ML algorithms designed to analyze gene-expression patterns obtained through RNA sequencing (RNA-seq) can be used to accurately predict the likelihood of CR in pediatric AML patients who have received induction therapy. We employed tests of statistical significance to determine which genes were differentially expressed in the samples derived from patients who achieved CR after 2 courses of treatment and the samples taken from patients who did not benefit. We tuned classifier hyperparameters to optimize performance and used multiple methods to guide our feature selection as well as our assessment of algorithm performance. To identify the model which performed best within the context of this study, we plotted receiver operating characteristic (ROC) curves. Using the top 75 genes from the k-nearest neighbors algorithm (K-NN) model ( K = 27) yielded the best area-under-the-curve (AUC) score that we obtained: 0.84. When we finally tested the previously unseen test data set, the top 50 genes yielded the best AUC = 0.81. Pathway enrichment analysis for these 50 genes showed that the guanosine diphosphate fucose (GDP-fucose) biosynthesis pathway is the most significant with an adjusted P value = .0092, which may suggest the vital role of N-glycosylation in AML.

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Characterizing the heterogeneity in 5-aminolevulinic acid–induced fluorescence in glioblastoma

Journal of Neurosurgery ◽

10.3171/2019.2.jns183128 ◽

2020 ◽

Vol 132 (6) ◽

pp. 1706-1714 ◽

Cited By ~ 4

Author(s):

Damian A. Almiron Bonnin ◽

Matthew C. Havrda ◽

Myung Chang Lee ◽

Linton Evans ◽

Cong Ran ◽

...

Keyword(s):

Gene Expression ◽

Tumor Tissue ◽

Aminolevulinic Acid ◽

Expression Profiles ◽

Expression Patterns ◽

Protoporphyrin Ix ◽

Progression Free Survival ◽

The Cancer Genome Atlas ◽

Diagnostic Challenge ◽

Biological Basis

OBJECTIVE5-aminolevulinic acid (5-ALA)–induced protoporphyrin IX (PpIX) fluorescence is an effective surgical adjunct for the intraoperative identification of tumor tissue during resection of high-grade gliomas. The use of 5-ALA-induced PpIX fluorescence in glioblastoma (GBM) has been shown to double the extent of gross-total resection and 6-month progression-free survival. The heterogeneity of 5-ALA-induced PpIX fluorescence observed during surgery presents a technical and diagnostic challenge when utilizing this tool intraoperatively. While some regions show bright fluorescence after 5-ALA administration, other regions do not, despite that both regions of the tumor may be histopathologically indistinguishable. The authors examined the biological basis of this heterogeneity using computational methods.METHODSThe authors collected both fluorescent and nonfluorescent GBM specimens from a total of 14 patients undergoing surgery and examined their gene expression profiles.RESULTSIn this study, the authors found that the gene expression patterns characterizing fluorescent and nonfluorescent GBM surgical specimens were profoundly different and were associated with distinct cellular functions and different biological pathways. Nonfluorescent tumor tissue tended to resemble the neural subtype of GBM; meanwhile, fluorescent tumor tissue did not exhibit a prominent pattern corresponding to known subtypes of GBM. Consistent with this observation, neural GBM samples from The Cancer Genome Atlas database exhibited a significantly lower fluorescence score than nonneural GBM samples as determined by a fluorescence gene signature developed by the authors.CONCLUSIONSThese results provide a greater understanding regarding the biological basis of differential fluorescence observed intraoperatively and can provide a basis to identify novel strategies to maximize the effectiveness of fluorescence agents.

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