Next Generation of Ovarian Cancer Detection Using Aptamers

Abreu, Rayane da Silva; Antunes, Deborah; Moreira, Aline dos Santos; Passetti, Fábio; Mendonça, Julia Badaró; Araújo, Natássia Silva de; Sassaro, Tayanne Felippe; Alberto, Anael Viana Pinto; Carrossini, Nina; Fernandes, Priscila Valverde; Costa, Mayla Abrahim; Guimarães, Ana Carolina Ramos; Degrave, Wim; Waghabi, Mariana Caldas

doi:10.3390/ijms24076315

Cited by 8 publications

(6 citation statements)

References 59 publications

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“…SMPDL3B (marginal p-value=8.8×10 -182 , network-adjusted permutation p-value < 10 -4 ) and SLC34A2 (marginal p-value=1.9×10 -190 , network-adjusted permutation p-value < 10 -4 ) were confirmed in the OC occurrence study, TRAFD1 (marginal p-value = 0.0013, network-adjusted permutation p-value < 10 -4 ) and CHD1L (marginal p-value = 0.0021, network-adjusted permutation p-value < 10 -4 ) were confirmed in the 5-year survival study, and FANCI (marginal p-value = 0.0016, network-adjusted permutation p- value < 10 -4 ) was confirmed in the clinical stage study. Expression of SMPDL3B was found related to specific aptamers for ovarian tumors, such as AptaC2 and AptaC4, through molecular docking [34]. SLC34A2 overexpression was reported related to development and progression of OC, brain cancer, and pancreatic cancer [35].…”

Section: Resultsmentioning

confidence: 99%

Transcriptomic Gene Network Profiling and Weak Signal Detection for Prediction of Ovarian Cancer Occurrence, Survival, and Severity by Integrating Bulk and Single-cell RNAseq Data

Li,

Sardiu,

Koestler

et al. 2023

Preprint

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BackgroundOvarian cancer (OC) is a significant gynecological malignancy characterized by its high mortality rate, poor long-term survival rate, and late-stage diagnosis. OC is the 5th leading cause of cancer death among woman and counts 2.1% of all cancer death. OC survival rates are much lower than other cancers that affect woman. Its 5-year survival rate is less than 50%. Only ∼17% of OC patients are diagnosed within the early stage. The majority are diagnosed at an advanced stage, making early detection and effective treatment critical challenges. Currently, the identified OC predictive genes are still very sparse, resulting in pool prognostic performance. There exists unmet needs to identify novel prognostic gene biomarkers for OC occurrence, survival, and clinical stages to promote the likelihood of survival and to perform optimal treatments or therapeutic strategies at the earliest stage possible.MethodsPrevious RNAseq analysis on OC focused on detecting differentially expressed (DE) genes only. Many genes, although having weak marginal differential effects, may still exude strong predictive effects on disease outcomes though regulating other DE genes. In this work, we employed a new machine learning method, netLDA, to detect such predictive coregulating genes with weak marginal DE effects for predicting OC occurrence, 5-year survival, and clinical stage. The netLDA detects predictive gene networks (PGN) containing strong DE genes as hub genes and detects coregulating weak genes within the PGNs. The network structures of the detected PGNs along with the strong and weak genes therein are then used in outcome prediction on test datasets.ResultsWe identified different sets of signature genes for OC occurrence, survival, and clinical stage. Previously identified prognostic genes, such asEPCAM, UBE2C, CHD1L, TP53,CD24,WFDC2, andFANCI,were confirmed. We also identified novel predictive coregulating weak genes includingGIGYF2, GNPAT, RAD54L, andELL.Many of the detected predictive gene networks and coregulating weak genes therein overlapped with OC-related biological pathways such as KEGGtight junction, ribosome, andcell cyclepathways. The detection and incorporation of the gene networks and weak genes significantly improved the prediction performance. Cellular mapping of selected feature genes using single-cell RNAseq data further revealed the heterogeneous expression distributions of the signature genes on different cell types.ConclusionsWe established a transcriptomic gene network profile for OC prediction. The novel genes detected provide new targets for early diagnostics and new drug development for OC.

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Section: Resultsmentioning

confidence: 99%

Transcriptomic Gene Network Profiling and Weak Signal Detection for Prediction of Ovarian Cancer Occurrence, Survival, and Severity by Integrating Bulk and Single-cell RNAseq Data

Li,

Sardiu,

Koestler

et al. 2023

Preprint

View full text Add to dashboard Cite

show abstract

“…After selecting aptamers with specificity and affinity for BC tumor cells, it is important to understand how the aptamer interacts with its targets. In this scenario, in silico methodologies are gaining more and more prominence, as they allow for the analysis of the recognition and binding stability of aptamer-target [34,35]. The aptamer interaction with its target depends entirely on its three-dimensional (3D) structure.…”

Section: Discussionmentioning

confidence: 99%

Aptamer-Based Recognition of Breast Tumor Cells: A New Era for Breast Cancer Diagnosis

de Araújo,

Moreira,

Abreu

et al. 2024

IJMS

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Breast cancer is one of the leading causes of death among women worldwide and can be classified into four major distinct molecular subtypes based on the expression of specific receptors. Despite significant advances, the lack of biomarkers for detailed diagnosis and prognosis remains a major challenge in the field of oncology. This study aimed to identify short single-stranded oligonucleotides known as aptamers to improve breast cancer diagnosis. The Cell-SELEX technique was used to select aptamers specific to the MDA-MB-231 tumor cell line. After selection, five aptamers demonstrated specific recognition for tumor breast cell lines and no binding to non-tumor breast cells. Validation of aptamer specificity revealed recognition of primary and metastatic tumors of all subtypes. In particular, AptaB4 and AptaB5 showed greater recognition of primary tumors and metastatic tissue, respectively. Finally, a computational biology approach was used to identify potential aptamer targets, which indicated that CSKP could interact with AptaB4. These results suggest that aptamers are promising in breast cancer diagnosis and treatment due to their specificity and selectivity.

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“…30 Many kinds of SELEX have been developed, based on both the target and process, including cell-SELEX, tissue-SELEX, and HT-SELEX. [31][32][33] All of these methods, however, maintain the basic logic of SELEX, including the continuous extraction of high-affinity aptamers and the discarding of low-affinity aptamers. 34 Altering the conditions of SELEX, such as temperature or binding buffer, would yield different types of aptamer.…”

Section: Discussionmentioning

confidence: 99%

A TSHR-Targeting Aptamer in Monocytes Correlating with Clinical Activity in TAO

Cao,

Zhang,

Chen

et al. 2024

IJN

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Background Manifestations of thyroid-associated ophthalmopathy (TAO) vary greatly. Few tools and indicators are available to assess TAO, restricting personalized diagnosis and treatment. Aim To identify an aptamer targeting thyroid-stimulating hormone receptor (TSHR) and utilize this aptamer to evaluate clinical activity in patients with TAO. Methods An aptamer targeting TSHR was developed by exponential enrichment and systematic evaluation of TSHR ligands. After truncation and optimization, the affinity, equilibrium dissociation constant, and serum stability of this aptamer were evaluated. The affinity of the TSHR-targeting aptamer to isolated fibrocytes was assessed, as was aptamer internalization by fibrocytes. The mechanism of binding was determined by molecular docking. The correlation between disease manifestations and the percentage of TSHR-positive cells was assessed by correlation analysis. Results The aptamer TSHR-21-42 was developed to bind to TSHR, with the equilibrium dissociation constant being 71.46 Kd. Isolated fibrocytes were shown to bind TSHR-21-42 through TSHR, with its affinity maintained at various temperatures and ion concentrations. TSHR-21-42 could compete with anti-TSHR antibody, both for binding site to TSHR and uptake by cells after binding. In addition, TSHR-21-42 could bind to leukocytes in peripheral blood, with this binding differing in patients with TAO and healthy control subjects. The percentage of TSHR-positive monocytes, as determined by binding of TSHR-21-42, correlated positively with clinical activity score in patients with TAO, indicating that TSHR-21-42 binding could assess the severity of TAO. Conclusion This aptamer targeting TSHR may be used to objectively assess disease activity in patients with TAO, by evaluating the percentages of TSHR positive cells in peripheral blood.

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Next Generation of Ovarian Cancer Detection Using Aptamers

Cited by 8 publications

References 59 publications

Transcriptomic Gene Network Profiling and Weak Signal Detection for Prediction of Ovarian Cancer Occurrence, Survival, and Severity by Integrating Bulk and Single-cell RNAseq Data

Transcriptomic Gene Network Profiling and Weak Signal Detection for Prediction of Ovarian Cancer Occurrence, Survival, and Severity by Integrating Bulk and Single-cell RNAseq Data

Aptamer-Based Recognition of Breast Tumor Cells: A New Era for Breast Cancer Diagnosis

A TSHR-Targeting Aptamer in Monocytes Correlating with Clinical Activity in TAO

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