Microfluidics for Profiling miRNA Biomarker Panels in AI-Assisted Cancer Diagnosis and Prognosis

Abstract

Early detection of cancers and their precise subtyping are essential to patient stratification and effective cancer management. Data-driven identification of expression biomarkers coupled with microfluidics-based detection shows promise to revolutionize cancer diagnosis and prognosis. MicroRNAs play key roles in cancers and afford detection in tissue and liquid biopsies. In this review, we focus on the microfluidics-based detection of miRNA biomarkers in AI-based models for early-stage cancer subtyping and prognosis. We describe various subclasses of miRNA biomarkers that could be useful in machine-based predictive modeling of cancer staging and progression. Strategies for optimizing the feature space of miRNA biomarkers are necessary to obtain a robust signature panel. This is followed by a discussion of the issues in model construction and validation towards producing Software-as-Medical-Devices (SaMDs). Microfluidic devices could facilitate the multiplexed detection of miRNA biomarker panels, and an overview of the different strategies for designing such microfluidic systems is presented here, with an outline of the detection principles used and the corresponding performance measures. Microfluidics-based profiling of miRNAs coupled with SaMD represent high-performance point-of-care solutions that would aid clinical decision-making and pave the way for accessible precision personalized medicine.

Point‐of‐Care Testing for the Detection of MicroRNAs: Towards Liquid Biopsy on a Chip

“…[78,79] Among others, those based on electrochemical and/or optical sensing platforms coupled with suitably designed microfluidics seem to be very promising to this end. Biosensing platforms relying on screen-printed electrodes, field effect transistor (FET), [80,81] surface plasmon resonance (SPR) [75,82,83] or surface-enhanced Raman scattering (SERS) [84][85][86][87] may offer the advantage of an unprecedented high sensitivity and fast detection times. Moreover, they can also be integrated with a second "dummy" sensing platform as the reference to provide a more accurate differential measurement.…”

“…Apart for implementing the sensing devices, chemometric analysis and artificial intelligencesupported data elaboration should be introduced to verify the correlation between the different targets and acquire statistically relevant data (e.g., Ref. [75]).…”

Point‐of‐Care Testing for the Detection of MicroRNAs: Towards Liquid Biopsy on a Chip

“…[78,79] Among others, those based on electrochemical and/or optical sensing platforms coupled with suitably designed microfluidics seem to be very promising to this end. Biosensing platforms relying on screen-printed electrodes, field effect transistor (FET), [80,81] surface plasmon resonance (SPR) [75,82,83] or surface-enhanced Raman scattering (SERS) [84][85][86][87] may offer the advantage of an unprecedented high sensitivity and fast detection times. Moreover, they can also be integrated with a second "dummy" sensing platform as the reference to provide a more accurate differential measurement.…”

COADREADx: A comprehensive algorithmic dissection unravels salient biomarkers and actionable insights into the discrete progression of colorectal cancer

“…The different classes of biomarkers discussed above, including stage-salient genes and monotonically expressed genes, could be used as the feature space to train machine learning (ML) algorithms to solve the binary classification problem of cancer v/s normal samples [20].…”