BC-miR: Monitoring Breast Cancer-Related miRNA Profile in Blood Sera—A Prosperous Approach for Tumor Detection

Borsos, Barbara N.; Páhi, Zoltán Gábor; Újfaludi, Zsuzsanna; Sükösd, Farkas; Nikolényi, Alíz; Bankó, Sarolta; Pankotai-Bodó, Gabriella; Oláh-Németh, Orsolya; Pankotai, Tibor

doi:10.3390/cells11172721

Cited by 13 publications

(8 citation statements)

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“…For quantitative evaluation of various clustering settings, we employed a popular heuristic approach called Elbow Method28 to determine an optimal number of clusters for our dataset. For qualitative evaluation, various visualisation methods, such as dendrogram and heatmap, were employed 29–32. We then leverage the domain knowledge of subject matter experts (with clinicians in the hospital) to check if each cluster poses a unique and meaningful characteristic.…”

Section: Methodsmentioning

confidence: 99%

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Data-driven phenomapping for novel classification for cardiovascular outcomes compared with traditional obesity index: Tehran Lipid and Glucose Study

et al. 2023

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ObjectiveThis study aimed to propose a data-driven framework for classification of at-risk people for cardiovascular outcomes regarding obesity and metabolic syndrome.DesignA population-based prospective cohort study with a long-term follow-up.SettingData from the Tehran Lipid and Glucose Study (TLGS) were interrogated.Participants12 808 participants of the TLGS cohort, aged ≥20 years who have followed for over 15 years were assessed.Main outcome measuresData for 12 808 participants, aged ≥20 years who have followed for over 15 years, collected through TLGS as a prospective, population-based cohort study, were analysed. Feature engineering followed by hierarchical clustering was used to determine meaningful clusters and novel endophenotypes. Cox regression was used to demonstrate the clinical validity of phenomapping. The performance of endophenotype compared with traditional classifications was evaluated by the value of Akaike information criterion/Bayesian information criterion. R software V.4.2 was employed.ResultsThe mean age was 42.1±14.9 years, 56.2% were female, 13.1%, 2.8% and 6.2% had experienced cardiovascular disease (CVD), CVD mortality and hard CVD, respectively. Low-risk cluster compared with the high risk had significant difference in age, body mass index, waist-to-hip ratio, 2 hours post load plasma glucose, triglyceride, triglycerides to high density lipoprotein ratio, education, marital status, smoking and the presence of metabolic syndrome. Eight distinct endophenotypes were detected with significantly different clinical characteristics and outcomes.ConclusionPhenomapping resulted in a novel classification of population with cardiovascular outcomes, which can, better, stratify individuals into homogeneous subclasses for prevention and intervention as an alternative of traditional methods solely based on either obesity or metabolic status. These findings have important clinical implications for a particular part of the Middle Eastern population for which it is a common practice to use tools/evidence derived from western populations with substantially different backgrounds and risk profiles.

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Section: Methodsmentioning

confidence: 99%

“…For qualitative evaluation, various visualisation methods, such as dendrogram and heatmap, were employed. [29][30][31][32] We then leverage the domain knowledge of subject matter experts (with clinicians in the hospital) to check if each cluster poses a unique and meaningful characteristic.…”

Section: Exploratory Data Analysismentioning

confidence: 99%

Data-driven phenomapping for novel classification for cardiovascular outcomes compared with traditional obesity index: Tehran Lipid and Glucose Study

et al. 2023

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“…In this scenario, miRNAs appear as liquid biopsy biomarkers for early and noninvasive BC detection and prognosis [157][158][159][160]. These biomarkers come from both free-circulating miR-NAs or miRNAs within EVs [161][162][163]. Thanks to the implementation of high-throughput technologies, such as microarray and NGS, thousands of miRNAs in BC samples can be profiled.…”

Section: An Artificial Intelligence Approach To Precision Medicinementioning

confidence: 99%

Extracellular Vesicles and Artificial Intelligence: Unique Weapons against Breast Cancer

Serretiello,

Smimmo,

Ballini

et al. 2024

Applied Sciences

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Breast cancer (BC) caused 685,000 deaths globally in 2020, earning the title of the most common type of tumor among females. With a multifactorial genesis, BC is influenced by several factors such as age, genetic and epigenetic predisposition, and an individual’s exposome, and its classification is based on morphological/histological, invasiveness, and molecular futures. Extracellular vesicles (EVs) are cell-derived lipid-bilayer-delimited nanoparticles, which are distinguishable by size, genesis, and the markers expressed in exosomes (40 to 150 nm), microvesicles (40 to 10,000 nm), and apoptotic bodies (100–5000 nm). Produced in physiological and pathological cellular contexts, EVs are shuttles of biological material and are implicated in cell-to-cell communications, thus attracting significant interest in diagnostic and drug delivery research. We report and discuss the latest evidence regarding the important role of EVs in BC, deepening their implication in tumorigenesis and metastatic mechanisms. On the other hand, the use of BC-derived EVs as prognostic biomarkers and therapeutic approaches is undergoing investigation. Hence, EVs have become new weapons in precision medicine; however, only with the support of advanced algorithms such as artificial intelligence (AI) can we develop a wide range of information. Looking ahead, it is possible to see the application of AI in the prognosis and diagnosis of different pathologies.

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“…Combinations of four miRNAs (miR-1246, miR-206, miR-24, and miR-373) were reported to have a sensitivity of 98%, a specificity of 96%, and an accuracy of 97% for BC detection [ 451 ]. Very recently, the miR-15a, miR-16, and miR-221 combination turned out to be promising for BC diagnostic [ 452 ].…”

Section: Exosomes As Relevant Breast Cancer Biological Markersmentioning

confidence: 99%

Extracellular Vesicles in Breast Cancer: From Biology and Function to Clinical Diagnosis and Therapeutic Management

Loric

Denis

Desbène

et al. 2023

IJMS

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Breast cancer (BC) is the first worldwide most frequent cancer in both sexes and the most commonly diagnosed in females. Although BC mortality has been thoroughly declining over the past decades, there are still considerable differences between women diagnosed with early BC and when metastatic BC is diagnosed. BC treatment choice is widely dependent on precise histological and molecular characterization. However, recurrence or distant metastasis still occurs even with the most recent efficient therapies. Thus, a better understanding of the different factors underlying tumor escape is mainly mandatory. Among the leading candidates is the continuous interplay between tumor cells and their microenvironment, where extracellular vesicles play a significant role. Among extracellular vesicles, smaller ones, also called exosomes, can carry biomolecules, such as lipids, proteins, and nucleic acids, and generate signal transmission through an intercellular transfer of their content. This mechanism allows tumor cells to recruit and modify the adjacent and systemic microenvironment to support further invasion and dissemination. By reciprocity, stromal cells can also use exosomes to profoundly modify tumor cell behavior. This review intends to cover the most recent literature on the role of extracellular vesicle production in normal and cancerous breast tissues. Specific attention is paid to the use of extracellular vesicles for early BC diagnosis, follow-up, and prognosis because exosomes are actually under the spotlight of researchers as a high-potential source of liquid biopsies. Extracellular vesicles in BC treatment as new targets for therapy or efficient nanovectors to drive drug delivery are also summarized.

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BC-miR: Monitoring Breast Cancer-Related miRNA Profile in Blood Sera—A Prosperous Approach for Tumor Detection

Cited by 13 publications

References 50 publications

Data-driven phenomapping for novel classification for cardiovascular outcomes compared with traditional obesity index: Tehran Lipid and Glucose Study

Data-driven phenomapping for novel classification for cardiovascular outcomes compared with traditional obesity index: Tehran Lipid and Glucose Study

Extracellular Vesicles and Artificial Intelligence: Unique Weapons against Breast Cancer

Extracellular Vesicles in Breast Cancer: From Biology and Function to Clinical Diagnosis and Therapeutic Management

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