Prognostic value of circulating microRNAs in upper tract urinary carcinoma

Montalbo, Ruth; Izquierdo, Laura; Ingelmo-Torres, Mercedes; Lozano, Juan José; Capitán, David; Alcaraz, Antonio; Mengual, Lourdes

doi:10.18632/oncotarget.24672

Cited by 20 publications

(12 citation statements)

References 39 publications

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“…In Annex B of the Additional file 1, we present a detailed analysis of the top 50 miRNAs in the signature, showing cancer study type, reference and circulating sample type used for measuring the expression. 23 miRNAs in the signature do not appear in the surveys, but they are mentioned in recent research papers, as promising research leads whose role may need further corroboration (we put the mature sequence as they appear in the study): miR-211 [54], miR-135a [55], miR-3678-3p [56], miR-204 [57], miR-1228 [58], miR-374b [59], miR-424 [60] miR-217-5p [60] miR-3613-5p [61], miR-124 [62], miR-1277-5p [63] miR-190 [64], miR-934 [65], miR-490 [66], miR-1247 [67], miR-199b [68], miR-135a [55], miR-503 [69], miR-584 [70], miR-137-3p [71], and miR-103 [72].…”

Section: Resultsmentioning

confidence: 99%

Automatic discovery of 100-miRNA signature for cancer classification using ensemble feature selection

et al. 2019

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Background MicroRNAs (miRNAs) are noncoding RNA molecules heavily involved in human tumors, in which few of them circulating the human body. Finding a tumor-associated signature of miRNA, that is, the minimum miRNA entities to be measured for discriminating both different types of cancer and normal tissues, is of utmost importance. Feature selection techniques applied in machine learning can help however they often provide naive or biased results. Results An ensemble feature selection strategy for miRNA signatures is proposed. miRNAs are chosen based on consensus on feature relevance from high-accuracy classifiers of different typologies. This methodology aims to identify signatures that are considerably more robust and reliable when used in clinically relevant prediction tasks. Using the proposed method, a 100-miRNA signature is identified in a dataset of 8023 samples, extracted from TCGA. When running eight-state-of-the-art classifiers along with the 100-miRNA signature against the original 1046 features, it could be detected that global accuracy differs only by 1.4%. Importantly, this 100-miRNA signature is sufficient to distinguish between tumor and normal tissues. The approach is then compared against other feature selection methods, such as UFS, RFE, EN, LASSO, Genetic Algorithms, and EFS-CLA. The proposed approach provides better accuracy when tested on a 10-fold cross-validation with different classifiers and it is applied to several GEO datasets across different platforms with some classifiers showing more than 90% classification accuracy, which proves its cross-platform applicability. Conclusions The 100-miRNA signature is sufficiently stable to provide almost the same classification accuracy as the complete TCGA dataset, and it is further validated on several GEO datasets, across different types of cancer and platforms. Furthermore, a bibliographic analysis confirms that 77 out of the 100 miRNAs in the signature appear in lists of circulating miRNAs used in cancer studies, in stem-loop or mature-sequence form. The remaining 23 miRNAs offer potentially promising avenues for future research.

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Section: Resultsmentioning

confidence: 99%

Automatic discovery of 100-miRNA signature for cancer classification using ensemble feature selection

et al. 2019

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“…The dysregulation of lipid metabolism, especially the abnormal elevation of LDL-C, is considered to be the main pathological basis of ASCVD [16]. At present, the circulating level of LDL-C is the most extensive diagnostic indicator for assessing and predicting the risk of atherosclerosis, and it has been globally acknowledged that reducing circulating LDL-C levels will effectively help in preventing ASCVD [14, 17]. Therefore, early intervention to inhibit circulating LDL-C levels has causally become more and more important.…”

Section: Discussionmentioning

confidence: 99%

“…In fact, an increasing number of studies have shown that noncoding ribonucleic acids could be used for the early diagnosis of cardiovascular diseases and even play vital roles in the evaluation of therapeutic effects or prognosis [12, 13]. Wang GK et al demonstrated that plasma miR-208a might be a novel biomarker for acute myocardial infarction [14]. Fang Y et al found that miR-10a could regulate pro-inflammatory phenotypes in atherosclerosis-susceptible endothelium [15].…”

Section: Introductionmentioning

confidence: 99%

Several circulating miRNAs related to hyperlipidemia and atherosclerotic cardiovascular diseases

Chen

Wang

et al. 2019

Lipids Health Dis

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Background In recent years, an increasing number of studies have proved that circulating miRNAs could be used for the early diagnosis of cardiovascular diseases and even play vital roles in the evaluation of therapeutic effects or prognosis. This study was conducted to examine the correlation between serum microRNAs and hyperlipidemia to provide a theoretical basis for the early screening and intervention of atherosclerotic cardiovascular diseases (ASCVD). Methods The serum samples and clinical data of 122 patients with hyperlipidemia and 168 healthy subjects were collected. Related clinical information was statistically analyzed for the two groups. Expression of circulating miRNAs was detected by miRNA microarray analysis and further verified by reverse transcription-quantitative PCR (RT-qPCR). Results Statistical analysis of clinical information revealed a significant difference in the incidence of ASCVD between the two groups. The MiRNA microarray analysis ( n = 10) showed 22 miRNAs with significantly different expression, among which 12 showed upregulation, and the others showed downregulation. Those possessing obvious differences and stable expression in the miRNA microarray, including miRNA-191-3p, miRNA-933, and miRNA-425-3p, were chosen for further investigation using RT-qPCR. The results demonstrated that several miRNAs were related to lipid metabolism disorders, especially miRNA-933. The area under the curve (AUC) of miRNA-933 in distinguishing the hyperlipidemia and ASCVD patients was 0.739 (95% CI, 0.682–0.795; P < 0.01) and 0.703 (95% CI, 0.643–0.763, P < 0.01), respectively. Conclusions In conclusion, miRNA-191-3p, miRNA-933, and miRNA-425-3p may be depressed in the peripheral circulation of patients with lipid metabolism disorders (mainly LDL). Circulating miRNA-933 could be a feasible predictor for ASCVD at the early stage. Electronic supplementary material The online version of this article (10.1186/s12944-019-1046-z) contains supplementary material, which is available to authorized users.

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“…In 2014, a miRNA quality control (miRQC) study that evaluated 12 different technology platforms was published, which reported that the majority of platforms detected fewer than 90 miRNAs in human serum 19 . Since the miRQC study, newer platforms have emerged and reagent updates have been provided for several pre-existing platforms, but their relative performance in human biofluids is unclear 24 – 33 . Hence, an updated evaluation of miRNA quantitation platforms with a specific focus on human serum and plasma will be helpful.…”

Section: Introductionmentioning

confidence: 99%

Systematic evaluation of multiple qPCR platforms, NanoString and miRNA-Seq for microRNA biomarker discovery in human biofluids

Hong¹,

Zhou²,

Zou³

et al. 2021

Sci Rep

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Aberrant miRNA expression has been associated with many diseases, and extracellular miRNAs that circulate in the bloodstream are remarkably stable. Recently, there has been growing interest in identifying cell-free circulating miRNAs that can serve as non-invasive biomarkers for early detection of disease or selection of treatment options. However, quantifying miRNA levels in biofluids is technically challenging due to their low abundance. Using reference samples, we performed a cross-platform evaluation in which miRNA profiling was performed on four different qPCR platforms (MiRXES, Qiagen, Applied Biosystems, Exiqon), nCounter technology (NanoString), and miRNA-Seq. Overall, our results suggest that using miRNA-Seq for discovery and targeted qPCR for validation is a rational strategy for miRNA biomarker development in clinical samples that involve limited amounts of biofluids.

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Prognostic value of circulating microRNAs in upper tract urinary carcinoma

Cited by 20 publications

References 39 publications

Automatic discovery of 100-miRNA signature for cancer classification using ensemble feature selection

Automatic discovery of 100-miRNA signature for cancer classification using ensemble feature selection

Several circulating miRNAs related to hyperlipidemia and atherosclerotic cardiovascular diseases

Systematic evaluation of multiple qPCR platforms, NanoString and miRNA-Seq for microRNA biomarker discovery in human biofluids

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