High-Throughput Gene Expression and Mutation Profiling: Current Methods and Future Perspectives

Karn, Thomas

doi:10.1159/000357461

Cited by 16 publications

(11 citation statements)

References 59 publications

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“…A series of high-throughput methods have changed cancer research and therapy 2–4. Expression profiling on a genomic scale led to the classification of the intrinsic subtypes of breast cancer, thus allowing for the prediction of disease progression and patient stratification 3.…”

Section: Introductionmentioning

confidence: 99%

Expression and functionality of TRPV1 in breast cancer cells

Weber¹,

Al-Refae²,

Wölk³

et al. 2016

BCTT

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Transient receptor potential (TRP) channels contribute to the regulation of intracellular calcium, which can promote cancer hallmarks in cases of dysregulation of gene transcription and calcium-dependent pro-proliferative or anti-apoptotic mechanisms. Several studies have begun to elucidate the roles of TRPV1, TRPV6, TRPM8, and TRPC1 in cancer progression; however, no study has examined the expression profiles of human TRP channels in breast cancer on a large scale. This study focused on the expression and functionality of TRPV1, a nonselective cation channel that was found to be expressed in different carcinoma tissues. Next-generation sequencing analyses revealed the expression of TRPV1 in several native breast cancer tissues, which was subsequently validated via reverse transcriptase-polymerase chain reaction. Activation of TRPV1 by its ligand capsaicin was associated with the growth inhibition of some cancer cell types; however, the signaling components involved are complex. In this study, stimulation by the TRPV1 agonist, capsaicin, of SUM149PT cells, a model system for the most aggressive breast cancer subtype, triple-negative breast cancer, led to intracellular calcium signals that were diminished by the specific TRPV1 antagonist, capsazepin. Activation of TRPV1 by capsaicin caused significant inhibition of cancer cell growth and induced apoptosis and necrosis. In conclusion, the current study revealed the expression profiles of human TRP channels in 60 different breast cancer tissues and cell lines and furthermore validated the antitumor activity of TRPV1 against SUM149PT breast cancer cells, indicating that activation of TRPV1 could be used as a therapeutic target, even in the most aggressive breast cancer types.

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Section: Introductionmentioning

confidence: 99%

Expression and functionality of TRPV1 in breast cancer cells

Weber¹,

Al-Refae²,

Wölk³

et al. 2016

BCTT

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“…Invasive breast cancer encompasses at least four major molecular subtypes which differ by their expression of estrogen (ER) and progesterone (PR) receptors, HER2, and the proliferative status of the tumor [7][8][9]. This gene expression based subtype classification is also supported by whole genome sequencing studies [10,11]. Similar approaches were already applied to adopt these molecular subtypes also for DCIS [12,13] but the numbers of samples analyzed are still very small.…”

Section: Molecular Markers As Prognostic Factors In Dcis and Small Inmentioning

confidence: 99%

Molecular Markers as Prognostic Factors in DCIS and Small Invasive Breast Cancers

Sänger

Engels²,

Graf

et al. 2014

Geburtshilfe Frauenheilkd

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!Ductal carcinoma in situ (DCIS) accounts for up to half of screen-detected breast cancers and thus constitutes a major public health problem. Despite effective current treatment many patients with DCIS are either over-or undertreated because of the paucity of precise models to predict recurrence or progression. The combination of clinical and molecular factors as already applied for invasive disease may help to build such models also for DCIS. We compared 53 DCIS (36.6 %) and 92 (63.4 %) invasive breast cancer cases and found no significant differences in age, receptor status of ER, PR, and HER2, and the use of radiotherapy. Interestingly, the proportion of disseminated tumor cells (DTC) did also not significantly differ between DCIS and invasive cases (p = 0.57). A negative PR status was associated with the detection of DTCs (p = 0.026). We then compared relationships of clinical parameters and biomarkers with patientsʼ prognosis in 43 DCIS and 40 small invasive tumors ≤ 5 mm (T1a). ER negativity was associated with shorter relapse free survival in the complete cohort (p = 0.004) and showed a trend in both subgroups (p = 0.053 for DCIS and p = 0.046 for T1a, respectively). In conclusion, we found markedly similar properties of both DCIS and small invasive breast cancers with respect to the distribution of several parameters as well as to the prognostic value of biomarkers. DCIS with a luminal phenotype seem to be characterized by a favourable prognosis. Interessanterweise unterschied sich auch die Häufigkeit der Detektion disseminierter Tumorzellen (DTZ) nicht signifikant zwischen DCIS und invasiven Fällen (p = 0,57). Ein negativer Progesteronrezeptor-Status war mit dem DTZ-Nachweis assoziiert (p = 0,026). Untersucht wurde ebenfalls der Zusammenhang von klinischen Parametern und Biomarkern mit der Prognose bei 43 DCIS und 40 invasiven T1a-Karzinomen. Negativität für den Östrogenrezeptor zeigte hierbei einen signifikanten Zusammenhang zu einem kürzeren krankheitsfreien Intervall in der Gesamtkohorte (p = 0,004) und einen Trend in beiden Subgruppen (p = 0,053 bei DCIS bzw. p = 0,046 bei T1a). Zusammenfassend fanden wir sehr ähnliche Charakteristika bez. der Verteilung verschiedener Parameter und des prognostischen Werts von Biomarkern sowohl bei DCIS als auch invasiven Karzinomen. DCIS mit einem luminalen Phänotyp scheint durch eine günstigere Prognose gekennzeichnet zu sein. Zusammenfassung

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“…If there is a potential correlation associated with the patterns of gene expression that it is a direct indication of co-regulation of gene [5]. At present, it is believed that there are 10000-100000 genes present in one microarray data [6]. It could be even more for advance technologies of microarray data.…”

Section: Introductionmentioning

confidence: 99%

Novel modelling of clustering for enhanced classification performance on gene expression data

Sudha¹,

Girijamma²

2020

IJECE

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Gene expression data is popularized for its capability to disclose various disease conditions. However, the conventional procedure to extract gene expression data itself incorporates various artifacts that offer challenges in diagnosis a complex disease indication and classification like cancer. Review of existing research approaches indicates that classification approaches are few to proven to be standard with respect to higher accuracy and applicable to gene expression data apart from unaddresed problems of computational complexity. Therefore, the proposed manuscript introduces a novel and simplified model capable using Graph Fourier Transform, Eigen Value and vector for offering better classification performance considering case study of microarray database, which is one typical example of gene expression data. The study outcome shows that proposed system offers comparatively better accuracy and reduced computational complexity with the existing clustering approaches.

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High-Throughput Gene Expression and Mutation Profiling: Current Methods and Future Perspectives

Cited by 16 publications

References 59 publications

Expression and functionality of TRPV1 in breast cancer cells

Expression and functionality of TRPV1 in breast cancer cells

Molecular Markers as Prognostic Factors in DCIS and Small Invasive Breast Cancers

Novel modelling of clustering for enhanced classification performance on gene expression data

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