scholarly journals Gene Expression Profiling for the Purposes of Biomarker Discovery in Oral Potentially Malignant Lesions: A Systematic Review

2013 ◽  
Vol 7 ◽  
pp. CMO.S12950 ◽  
Author(s):  
Ahmad A. Abdulmajeed ◽  
Camile S. Farah
Keyword(s):  
Gene Expression ◽  
Biomarker Discovery ◽  
Multiple Gene ◽  
Diagnosis And Prognosis ◽  
Potentially Malignant ◽  
Novel Biomarkers ◽  
Malignant Lesions ◽  
Microarray Studies ◽  
Microarray Analyses ◽  
Selection Of

Early and accurate diagnosis of oral potentially malignant lesions (OPML) is of critical importance in preventing malignant transformation. Although histopathological interpretation of the degree of epithelial dysplasia is considered the gold standard for diagnosis, this method is subjective and lacks sensitivity. Therefore, many attempts have been made to identify objective molecular biomarkers to improve diagnosis. Microarray technology has the advantage of screening the expression of the whole genome making it one of the best tools for searching for novel biomarkers. However, microarray studies of OPMLs are limited, and no review has been published to highlight and compare their findings. In this paper, we systematically review all studies that have incorporated microarray analyses in the investigation of gene profile alterations in OPMLs and suggest a set of commonly dysregulated genes across multiple gene expression profile studies. This list of common genes may help focus selection of markers for further analysis regarding their importance in the diagnosis and prognosis of OPMLs.

scholarly journals Discovery and Validation of Novel Biomarkers for Detection of Epithelial Ovarian Cancer

Cells ◽  
2019 ◽  
Vol 8 (7) ◽  
pp. 713 ◽  
Author(s):  
Kulbe ◽  
Otto ◽  
Darb-Esfahani ◽  
Lammert ◽  
Abobaker ◽  
...  
Keyword(s):  
Gene Expression ◽  
Ovarian Cancer ◽  
Epithelial Ovarian Cancer ◽  
Biomarker Discovery ◽  
Expression Profiles ◽  
Gene Expression Profiles ◽  
Pelvic Mass ◽  
Aurora Kinase ◽  
Malignant Tumours ◽  
Novel Biomarkers

Detection of epithelial ovarian cancer (EOC) poses a critical medical challenge. However, novel biomarkers for diagnosis remain to be discovered. Therefore, innovative approaches are of the utmost importance for patient outcome. Here, we present a concept for blood-based biomarker discovery, investigating both epithelial and specifically stromal compartments, which have been neglected in search for novel candidates. We queried gene expression profiles of EOC including microdissected epithelium and adjacent stroma from benign and malignant tumours. Genes significantly differentially expressed within either the epithelial or the stromal compartments were retrieved. The expression of genes whose products are secreted yet absent in the blood of healthy donors were validated in tissue and blood from patients with pelvic mass by NanoString analysis. Results were confirmed by the comprehensive gene expression database, CSIOVDB (Ovarian cancer database of Cancer Science Institute Singapore). The top 25% of candidate genes were explored for their biomarker potential, and twelve were able to discriminate between benign and malignant tumours on transcript levels (p < 0.05). Among them T-cell differentiation protein myelin and lymphocyte (MAL), aurora kinase A (AURKA), stroma-derived candidates versican (VCAN), and syndecan-3 (SDC), which performed significantly better than the recently reported biomarker fibroblast growth factor 18 (FGF18) to discern malignant from benign conditions. Furthermore, elevated MAL and AURKA expression levels correlated significantly with a poor prognosis. We identified promising novel candidates and found the stroma of EOC to be a suitable compartment for biomarker discovery.

scholarly journals How the RNA isolation method can affect microRNA microarray results.

2011 ◽  
Vol 58 (4) ◽  
Author(s):  
Agnieszka Podolska ◽  
Bogumil Kaczkowski ◽  
Thomas Litman ◽  
Merete Fredholm ◽  
Susanna Cirera
Keyword(s):  
Gene Expression ◽  
Rna Isolation ◽  
Rna Degradation ◽  
Isolation Method ◽  
Guanidine Isothiocyanate ◽  
Microarray Studies ◽  
Microarray Analyses ◽  
Microrna Microarray ◽  
Porcine Brain Tissue

The quality of RNA is crucial in gene expression experiments. RNA degradation interferes in the measurement of gene expression, and in this context, microRNA quantification can lead to an incorrect estimation. In the present study, two different RNA isolation methods were used to perform microRNA microarray analysis on porcine brain tissue. One method is a phenol-guanidine isothiocyanate-based procedure that permits isolation of total RNA. The second method, miRVana™ microRNA isolation, is column based and recovers the small RNA fraction alone. We found that microarray analyses give different results that depend on the RNA fraction used, in particular because some microRNAs appear very sensitive to the RNA isolation method. We conclude that precautions need to be taken when comparing microarray studies based on RNA isolated with different methods.

scholarly journals Bioinformatics analysis of high-throughput data to validate potential novel biomarkers and small molecule drugs for glioblastoma multiforme

2020 ◽  
Vol 48 (7) ◽  
pp. 030006052092454
Author(s):  
Fuwei Qi ◽  
Qing Li ◽  
Xiaojun Lu ◽  
Zhihua Chen
Keyword(s):  
Gene Expression ◽  
Glioblastoma Multiforme ◽  
Molecular Mechanisms ◽  
Interaction Network ◽  
Gene Expression Omnibus ◽  
Protein Protein Interaction ◽  
Diagnosis And Prognosis ◽  
Novel Biomarkers ◽  
Patient Prognosis ◽  
Microarray Datasets

Objective There have been no recent improvements in the glioblastoma multiforme (GBM) outcome, with median survival remaining 15 months. Consequently, the need to identify novel biomarkers for GBM diagnosis and prognosis, and to develop targeted therapies is high. This study aimed to establish biomarkers for GBM pathogenesis and prognosis. Methods In total, 220 overlapping differentially expressed genes (DEGs) were obtained by integrating four microarray datasets from the Gene Expression Omnibus database (GSE4290, GSE12657, GSE15824, and GSE68848). Then a 140-node protein–protein interaction network with 343 interactions was constructed. Results The immune response and cell adhesion molecules were the most significantly enriched functions and pathways, respectively, among DEGs. The designated hub genes ITGB5 and RGS4, which have a high degree of connectivity, were closely correlated with patient prognosis, and GEPIA database mining further confirmed their differential expression in GBM versus normal tissue. We also determined the 20 most appropriate small molecules that could potentially reverse GBM gene expression, Prestwick-1080 was the most promising and had the highest negative scores. Conclusions This study identified ITGB5 and RGS4 as potential biomarkers for GBM diagnosis and prognosis. Insights into molecular mechanisms governing GBM occurrence and progression will help identify alternative biomarkers for clinical practice.

When microspores decide to become embryos — cellular and molecular changesThis review is one of a selection of papers published in the Special Issue on Plant Cell Biology.

2006 ◽  
Vol 84 (4) ◽  
pp. 668-678 ◽  
Author(s):  
K. Peter Pauls ◽  
John Chan ◽  
Grant Woronuk ◽  
Derek Schulze ◽  
Janice Brazolot
Keyword(s):  
Gene Expression ◽  
Cell Biology ◽  
Cell Activation ◽  
Early Stage ◽  
Microspore Culture ◽  
Brassica Napus L ◽  
Embryogenic Cell ◽  
Haploid Embryos ◽  
Microarray Analyses ◽  
Selection Of

Cultured microspores can be induced to develop into fully functional haploid embryos instead of mature pollen. The ability of these cells to change their development in response to environmental stimuli is an exceptional example of totipotency in plants. Discovering the triggers of embryo development in microspore cultures could lead to a greater understanding of the early stages of embryogenesis in plants and might be used to increase the range of crop plants to which microspore culture can be applied for the production of double haploid homozygous lines. The information might also help to define the general characteristics of pluripotent cells in any organism. In this review, the changes that occur in cellular organization and gene expression in early-stage microspore cultures of several species are discussed. Responding cells in these cultures enlarge, their nuclei are repositioned to their cell centres, and their cytoplasms become filled with fragmented vacuoles. We used flow cytometry to track cellular changes in canola ( Brassica napus L.) microspore cultures as well as microarray analyses and real-time PCR to compare gene expression in embryogenic and nonembryogenic cells. A model for embryogenic cell activation in plants that involves alkalinization, Ca2+ signaling, and changes in GTPase activity that lead to significant changes in gene expression is discussed.

Exosome: An Emerging Source of Biomarkers for Human Diseases

2019 ◽  
Vol 19 (6) ◽  
pp. 387-394 ◽  
Author(s):  
Li Xu ◽  
Long-Fei Wu ◽  
Fei-Yan Deng
Keyword(s):  
Breast Milk ◽  
Intercellular Communication ◽  
Translational Medicine ◽  
Disease Diagnosis ◽  
Human Diseases ◽  
Isolation And Identification ◽  
Biomarker Identification ◽  
Biological Features ◽  
Diagnosis And Prognosis ◽  
Novel Biomarkers

Exosomes are 30-120nm long endocytic membrane-derived vesicles, which are secreted by various types of cells and stably present in body fluids, such as plasma, urine, saliva and breast milk. Exosomes participate in intercellular communication. Recently accumulative studies have suggested that exosomes may serve as novel biomarkers for disease diagnosis and prognosis. Herein, we reviewed the biological features of exosomes, technologies for exosome isolation and identification, as well as progress in exosomal biomarker identification, highlighting the relevance of exosome to human diseases and significance and great potential in translational medicine.

scholarly journals Recent Discoveries of Macromolecule- and Cell-Based Biomarkers and Therapeutic Implications in Breast Cancer

2021 ◽  
Vol 22 (2) ◽  
pp. 636
Author(s):  
Hsing-Ju Wu ◽  
Pei-Yi Chu
Keyword(s):  
Breast Cancer ◽  
Normal Breast ◽  
New Drugs ◽  
Cancer Type ◽  
Luminal A ◽  
Luminal B ◽  
Therapeutic Implications ◽  
Whole Cells ◽  
Diagnosis And Prognosis ◽  
Novel Biomarkers

Breast cancer is the most commonly diagnosed cancer type and the leading cause of cancer-related mortality in women worldwide. Breast cancer is fairly heterogeneous and reveals six molecular subtypes: luminal A, luminal B, HER2+, basal-like subtype (ER−, PR−, and HER2−), normal breast-like, and claudin-low. Breast cancer screening and early diagnosis play critical roles in improving therapeutic outcomes and prognosis. Mammography is currently the main commercially available detection method for breast cancer; however, it has numerous limitations. Therefore, reliable noninvasive diagnostic and prognostic biomarkers are required. Biomarkers used in cancer range from macromolecules, such as DNA, RNA, and proteins, to whole cells. Biomarkers for cancer risk, diagnosis, proliferation, metastasis, drug resistance, and prognosis have been identified in breast cancer. In addition, there is currently a greater demand for personalized or precise treatments; moreover, the identification of novel biomarkers to further the development of new drugs is urgently needed. In this review, we summarize and focus on the recent discoveries of promising macromolecules and cell-based biomarkers for the diagnosis and prognosis of breast cancer and provide implications for therapeutic strategies.

scholarly journals Circulating Biomarkers of Colorectal Cancer (CRC)—Their Utility in Diagnosis and Prognosis

2021 ◽  
Vol 10 (11) ◽  
pp. 2391
Author(s):  
Marta Łukaszewicz-Zając ◽  
Barbara Mroczko
Keyword(s):  
Colorectal Cancer ◽  
Inflammatory Mediators ◽  
Colorectal Adenomas ◽  
Global Burden ◽  
Circulating Biomarkers ◽  
Specific Nature ◽  
Diagnosis And Prognosis ◽  
Specific Proteins ◽  
Novel Biomarkers ◽  
Number Of Publications

The global burden of colorectal cancer (CRC) is expected to increase, with 2.2 million new cases and 1.1 million annual deaths by 2030. Therefore, the establishment of novel biomarkers useful in the early diagnosis of CRC is of utmost importance. A number of publications have documented the significance of the overexpression of several specific proteins, such as inflammatory mediators, in CRC progression. However, little is known about the potential utility of these proteins as circulating blood tumor biomarkers of CRC. Therefore, in the present review we report the results of our previous original studies as well as the findings of other authors who investigated whether inflammatory mediators might be used as novel biomarkers in the diagnosis and prognosis of CRC. Our study revealed that among all of the tested proteins, serum M-CSF, CXCL-8, IL-6 and TIMP-1 have the greatest value in the diagnosis and progression of CRC. Serum TIMP-1 is useful in differentiating between CRC and colorectal adenomas, whereas M-CSF and CRP are independent prognostic factors for the survival of patients with CRC. This review confirms the promising significance of these proteins as circulating biomarkers for CRC. However, due to their non-specific nature, further validation of their sensitivity and specificity is required.

scholarly journals The combined detection of Amphiregulin, Cyclin A1 and DDX20/Gemin3 expression predicts aggressive forms of oral squamous cell carcinoma

2021 ◽  
Author(s):  
Ekaterina Bourova-Flin ◽  
Samira Derakhshan ◽  
Afsaneh Goudarzi ◽  
Tao Wang ◽  
Anne-Laure Vitte ◽  
...  
Keyword(s):  
Gene Expression ◽  
Squamous Cell ◽  
Large Scale ◽  
Biomarker Discovery ◽  
Gene Expression Signature ◽  
Predictive Biomarkers ◽  
Specific Gene ◽  
Specific Expression ◽  
Intrinsic Nature ◽  
Independent Cohort

Abstract Background Large-scale genetic and epigenetic deregulations enable cancer cells to ectopically activate tissue-specific expression programmes. A specifically designed strategy was applied to oral squamous cell carcinomas (OSCC) in order to detect ectopic gene activations and develop a prognostic stratification test. Methods A dedicated original prognosis biomarker discovery approach was implemented using genome-wide transcriptomic data of OSCC, including training and validation cohorts. Abnormal expressions of silent genes were systematically detected, correlated with survival probabilities and evaluated as predictive biomarkers. The resulting stratification test was confirmed in an independent cohort using immunohistochemistry. Results A specific gene expression signature, including a combination of three genes, AREG, CCNA1 and DDX20, was found associated with high-risk OSCC in univariate and multivariate analyses. It was translated into an immunohistochemistry-based test, which successfully stratified patients of our own independent cohort. Discussion The exploration of the whole gene expression profile characterising aggressive OSCC tumours highlights their enhanced proliferative and poorly differentiated intrinsic nature. Experimental targeting of CCNA1 in OSCC cells is associated with a shift of transcriptomic signature towards the less aggressive form of OSCC, suggesting that CCNA1 could be a good target for therapeutic approaches.

scholarly journals Lung adenocarcinoma and lung squamous cell carcinoma cancer classification, biomarker identification, and gene expression analysis using overlapping feature selection methods

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Joe W. Chen ◽  
Joseph Dhahbi
Keyword(s):  
Gene Expression ◽  
Feature Selection ◽  
Expression Analysis ◽  
Gene Expression Analysis ◽  
Biomarker Discovery ◽  
Lung Squamous Cell Carcinoma ◽  
Cancer Classification ◽  
Selection Methods ◽  
Biomarker Identification ◽  
Overlapping Method

AbstractLung cancer is one of the deadliest cancers in the world. Two of the most common subtypes, lung adenocarcinoma (LUAD) and lung squamous cell carcinoma (LUSC), have drastically different biological signatures, yet they are often treated similarly and classified together as non-small cell lung cancer (NSCLC). LUAD and LUSC biomarkers are scarce, and their distinct biological mechanisms have yet to be elucidated. To detect biologically relevant markers, many studies have attempted to improve traditional machine learning algorithms or develop novel algorithms for biomarker discovery. However, few have used overlapping machine learning or feature selection methods for cancer classification, biomarker identification, or gene expression analysis. This study proposes to use overlapping traditional feature selection or feature reduction techniques for cancer classification and biomarker discovery. The genes selected by the overlapping method were then verified using random forest. The classification statistics of the overlapping method were compared to those of the traditional feature selection methods. The identified biomarkers were validated in an external dataset using AUC and ROC analysis. Gene expression analysis was then performed to further investigate biological differences between LUAD and LUSC. Overall, our method achieved classification results comparable to, if not better than, the traditional algorithms. It also identified multiple known biomarkers, and five potentially novel biomarkers with high discriminating values between LUAD and LUSC. Many of the biomarkers also exhibit significant prognostic potential, particularly in LUAD. Our study also unraveled distinct biological pathways between LUAD and LUSC.

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