Combinatorial Blood Platelets-Derived circRNA and mRNA Signature for Early-Stage Lung Cancer Detection

D’Ambrosi, Silvia; Giannoukakos, Stavros; Antunes-Ferreira, Mafalda; Pedraz-Valdunciel, Carlos; Bracht, Jillian Wilhelmina Paulina; Potie, Nicolas; Giménez‐Capitán, Ana; Hackenberg, Michael; Fernández, Alberto; Molina-Vila, Miguel Ángel; Rosell, Rafael; Würdinger, Thomas; Koppers-Lalić, Danijela

doi:10.3390/ijms24054881

Cited by 12 publications

(6 citation statements)

References 47 publications

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“…5c ). Similar to previous work assessing circRNAs as a lung cancer biomarker 94 , combining circRNA expression with gene expression showed a small improvement in predictive ability over gene expression alone in both PPMI (AUC = 0.85 vs 0.84, Fig. 5c ) and ICICLE-PD (AUC = 0.60 vs 0.59, Fig.…”

Section: Discussionsupporting

confidence: 83%

Early-stage idiopathic Parkinson’s disease is associated with reduced circular RNA expression

Whittle,

Izuogu,

Lowes

et al. 2024

npj Parkinsons Dis.

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Neurodegeneration in Parkinson’s disease (PD) precedes diagnosis by years. Early neurodegeneration may be reflected in RNA levels and measurable as a biomarker. Here, we present the largest quantification of whole blood linear and circular RNAs (circRNA) in early-stage idiopathic PD, using RNA sequencing data from two cohorts (PPMI = 259 PD, 161 Controls; ICICLE-PD = 48 PD, 48 Controls). We identified a replicable increase in TMEM252 and LMNB1 gene expression in PD. We identified novel differences in the expression of circRNAs from ESYT2, BMS1P1 and CCDC9, and replicated trends of previously reported circRNAs. Overall, using circRNA as a diagnostic biomarker in PD did not show any clear improvement over linear RNA, minimising its potential clinical utility. More interestingly, we observed a general reduction in circRNA expression in both PD cohorts, accompanied by an increase in RNASEL expression. This imbalance implicates the activation of an innate antiviral immune response and suggests a previously unknown aspect of circRNA regulation in PD.

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Section: Discussionsupporting

confidence: 83%

Early-stage idiopathic Parkinson’s disease is associated with reduced circular RNA expression

Whittle,

Izuogu,

Lowes

et al. 2024

npj Parkinsons Dis.

View full text Add to dashboard Cite

show abstract

“…5c). Similar to previous work assessing circRNAs as a lung cancer biomarker 90 , combining circRNA expression with gene expression showed a small improvement in predictive ability over gene expression alone in both PPMI (AUC = 0.85 vs 0.84, Fig. 5c) and ICICLE-PD (AUC = 0.60 vs 0.59, Fig.…”

Section: Discussionsupporting

confidence: 83%

Parkinson’s disease is associated with an imbalance in circular RNA expression

Hudson

Whittle

Izuogu

et al. 2023

Preprint

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Neurodegeneration in Parkinson’s disease (PD) precedes diagnosis by years. Early neurodegeneration may be reflected in RNA levels and measurable as a biomarker. Here, we present the largest quantification of whole blood linear and circular RNAs (circRNA) in early-stage PD, using RNA sequencing data from two cohorts (PPMI = 259 PD, 161 Controls; ICICLE-PD = 48 PD, 48 Controls). We identified a replicable increase in TMEM252 and LMNB1 gene expression in PD. We identified novel differences in the expression of circRNAs from ESYT2, BMS1P1 and CCDC9, and replicated trends of previously reported circRNAs. Overall, using circRNA as a diagnostic biomarker in Parkinson’s did not show any clear improvement over linear RNA. More interestingly, we observed a general reduction in circRNA expression in both PD cohorts, accompanied by an increase in RNASEL expression. This imbalance implicates the activation of an innate antiviral immune response and suggests a previously unknown aspect of circRNA regulation in PD.

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“…Notably, the study involved four seasoned thoracic radiologists, demonstrating a comprehensive and expert-driven evaluation of the recommended approach. In [5], Silvia D'Ambrosi et al gave a presentation on a study that described a combinatory signature of circRNA and mRNA derived from blood platelets for the early identification of lung cancer. Researchers used a computational framework that leverages nCounter analysis of data and machine learning to investigate platelet mRNA and circRNA for the diagnosis of lung cancer.…”

Section: Literature Reviewmentioning

confidence: 99%

Lrfe: A Novel Local Response Feature Elimination Process for Identification of Lung Cancer Cells

Chintha Vishnu Vardhana Reddy

2024

jes

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One of the main causes of cancer-related mortality across the globe is lung cancer. Early-stage lung cancer frequently exhibits no symptoms, which delays diagnosis until the illness has progressed. Before symptoms manifest, screening and early detection techniques can aid in the early diagnosis and treatment of lung cancer. Many olden research papers have implemented image processing and few latest papers have implemented computer vision techniques to detect lung cancer. Particularly when dealing with minor or subtle anomalies, image processing algorithms may not be able to detect lung cancer lesions with sufficient sensitivity and specificity. It is still difficult to increase the detection algorithms' accuracy and dependability, especially when dealing with early-stage lesions or situations where attributes overlap. It takes a lot of processing power, such as high-performance GPUs and enormous memory capacities, to train deep learning models, particularly large-scale convolutional neural networks (CNNs). In this proposed research, the model pre-processes the images using the ostu and sober filter mechanisms because Otsu's approach adjusts to the features of the input image, including noise, contrast, and lighting fluctuations. It is capable of handling images with varying dynamic ranges and intensity distributions without depending on pre-established threshold settings. When it comes to image noise, the Sobel filter is more resilient than other edge detection methods. It produces clearer edge maps and fewer false detections by determining the gradient magnitude, which amplifies edge information while suppressing noise. The features are extracted using the tuned AlexNet pre-trained model, in AlexNet there is a layer known as “Layer-wise Relevance Propagation”. By giving each pixel or feature in the input image a relevance score, the LRP layer offers fine-grained feature attribution. This makes it possible to analyze in great depth which particular elements or areas of the input image are most important for the network to forecast, which helps to clarify the underlying patterns that the network has learned. At last, the extracted features are further reduced using the enhanced feature elimination method. By iteratively selecting subsets of features based on their importance, RFE helps to identify the most relevant features for the classification task. Integrating RFE with SVM classifiers can lead to improved model performance by focusing on the subset of features that are most discriminative and informative for the classification problem.

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Combinatorial Blood Platelets-Derived circRNA and mRNA Signature for Early-Stage Lung Cancer Detection

Cited by 12 publications

References 47 publications

Early-stage idiopathic Parkinson’s disease is associated with reduced circular RNA expression

Early-stage idiopathic Parkinson’s disease is associated with reduced circular RNA expression

Parkinson’s disease is associated with an imbalance in circular RNA expression

Lrfe: A Novel Local Response Feature Elimination Process for Identification of Lung Cancer Cells

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