Current Status and Future Perspectives of Liquid Biopsy in Small Cell Lung Cancer

Advances in adoptive cell therapies in small cell lung cancer

Small cell lung cancer (SCLC) is an aggressive tumor characterized by early metastasis and resistance to treatment, making it a prime target for therapeutic investigation. The current standard of care for frontline treatment involves a combination of chemotherapeutic agents and immune checkpoint inhibitors (ICIs), though durability of response remains limited. The genetic heterogeneity of SCLC also complicates the development of new therapeutic options. Adoptive cell therapies show promise by targeting specific mutations in order to increase efficacy and minimize toxicity. There has been significant investigation in three therapeutic classes for application towards SCLC: antibody drug conjugates (ADCs), bispecific T-cell engagers (BiTEs), and chimeric antigen receptor (CAR)-T cell therapies. This review summarizes the recent advances and challenges in the development of adoptive cell therapies. Genetic targets such as delta-like ligand 3 (DLL3), trophoblast cell surface antigen 2 (Trop2), B7-H3 (CD276), gangliosides disialoganglioside GD2 (GD2) and ganglioside GM2 (GM2) have been found to be expressed in SCLC, which makes them prime targets for therapy development. While investigated therapies such as rovalpituzumab tesirine (Rova-T) have failed, several insights from these trials have led to the development of compelling new agents such as sacituzumab govitecan (SG), ifinatamab deruxtecan (I-DXd), tarlatamab, and DLL3-targeted CAR-T cells. Advancing development of molecular testing and improving targeted approaches remain integral to pushing forward the progress of adoptive cell therapies in SCLC.

Small cell lung cancer unveiled: Exploring the untapped resource of circulating tumor cells-derived organoids

Small cell lung cancer (SCLC) remains a challenge in oncology due to its aggressive behavior and dismal prognosis. Despite advances in treatments, novel strategies are urgently needed. Enter liquid biopsy-a game-changer in SCLC management. This revolutionary non-invasive approach allows for the analysis of circulating tumor cells (CTCs), offering insights into tumor behavior and treatment responses. Our review focuses on a groundbreaking frontier: harnessing CTCs to create three-dimensional (3D) organoid models. These models, derived from CTCs that break away from the primary tumor or metastatic locations, hold immense potential for revolutionizing cancer research, especially in SCLC. We explore the essential conditions for successfully establishing CTC-derived organoids-a transformative approach with profound implications for personalized medicine. Our evaluation spans diverse isolation techniques, shedding light on their advantages and limitations. Furthermore, we uncover the critical factors governing the cultivation of 3D organoids from CTCs, meticulously mimicking the tumor microenvironment. This review comprehensively elucidates the molecular characterization of these organoids, showcasing their potential in identifying treatment targets and predicting responses. In essence, our review amalgamates cutting-edge methodologies for isolating CTCs, establishing transformative CTC-derived organoids, and characterizing their molecular landscape. This represents a promising frontier for advancing personalized medicine in the complex realm of SCLC management and holds significant implications for translational research.

Genomic alterations and transcriptional phenotypes in circulating free DNA and matched metastatic tumor

BACKGROUND

Profiling circulating cell-free DNA (cfDNA) has become a fundamental practice in cancer medicine, but the effectiveness of cfDNA at elucidating tumor-derived molecular features has not been systematically compared to standard single-lesion tumor biopsies in prospective cohorts of patients. The use of plasma instead of tissue to guide therapy is particularly attractive for patients with small cell lung cancer (SCLC), due to the aggressive clinical course of this cancer, which makes obtaining tumor biopsies exceedingly challenging.

METHODS

In this study, we analyzed a prospective cohort of 49 plasma samples obtained before, during, and after treatment from 20 patients with recurrent SCLC. We conducted cfDNA low-pass whole genome sequencing (0.1X coverage), comparing it with time-point matched tumor characterized using whole-exome (130X) and transcriptome sequencing.

RESULTS

A direct comparison of cfDNA and tumor biopsy revealed that cfDNA not only mirrors the mutation and copy number landscape of the corresponding tumor but also identifies clinically relevant resistance mechanisms and cancer driver alterations not detected in matched tumor biopsies. Longitudinal cfDNA analysis reliably tracks tumor response, progression, and clonal evolution. Sequencing coverage of plasma DNA fragments around transcription start sites showed distinct treatment-related changes and captured the expression of key transcription factors such as NEUROD1 and REST in the corresponding SCLC tumors. This allowed for the prediction of SCLC neuroendocrine phenotypes and treatment responses.

CONCLUSIONS

cfDNA captures a comprehensive view of tumor heterogeneity and evolution. These findings have significant implications for the non-invasive stratification of SCLC, a disease currently treated as a single entity.

Integrative Stacking Machine Learning Model for Small Cell Lung Cancer Prediction Using Metabolomics Profiling

Background: Small cell lung cancer (SCLC) is an extremely aggressive form of lung cancer, characterized by rapid progression and poor survival rates. Despite the importance of early diagnosis, the current diagnostic techniques are invasive and restricted. Methods: This study presents a novel stacking-based ensemble machine learning approach for classifying small cell lung cancer (SCLC) and non-small cell lung cancer (NSCLC) using metabolomics data. The analysis included 191 SCLC cases, 173 NSCLC cases, and 97 healthy controls. Feature selection techniques identified significant metabolites, with positive ions proving more relevant. Results: For multi-class classification (control, SCLC, NSCLC), the stacking ensemble achieved 85.03% accuracy and 92.47 AUC using Support Vector Machine (SVM). Binary classification (SCLC vs. NSCLC) further improved performance, with ExtraTreesClassifier reaching 88.19% accuracy and 92.65 AUC. SHapley Additive exPlanations (SHAP) analysis revealed key metabolites like benzoic acid, DL-lactate, and L-arginine as significant predictors. Conclusions: The stacking ensemble approach effectively leverages multiple classifiers to enhance overall predictive performance. The proposed model effectively captures the complementary strengths of different classifiers, enhancing the detection of SCLC and NSCLC. This work accentuates the potential of combining metabolomics with advanced machine learning for non-invasive early lung cancer subtype detection, offering an alternative to conventional biopsy methods.

Emerging advances in defining the molecular and therapeutic landscape of small-cell lung cancer

Small-cell lung cancer (SCLC) has traditionally been considered a recalcitrant cancer with a dismal prognosis, with only modest advances in therapeutic strategies over the past several decades. Comprehensive genomic assessments of SCLC have revealed that most of these tumours harbour deletions of the tumour-suppressor genes TP53 and RB1 but, in contrast to non-small-cell lung cancer, have failed to identify targetable alterations. The expression status of four transcription factors with key roles in SCLC pathogenesis defines distinct molecular subtypes of the disease, potentially enabling specific therapeutic approaches. Overexpression and amplification of MYC paralogues also affect the biology and therapeutic vulnerabilities of SCLC. Several other attractive targets have emerged in the past few years, including inhibitors of DNA-damage-response pathways, epigenetic modifiers, antibody-drug conjugates and chimeric antigen receptor T cells. However, the rapid development of therapeutic resistance and lack of biomarkers for effective selection of patients with SCLC are ongoing challenges. Emerging single-cell RNA sequencing data are providing insights into the plasticity and intratumoural and intertumoural heterogeneity of SCLC that might be associated with therapeutic resistance. In this Review, we provide a comprehensive overview of the latest advances in genomic and transcriptomic characterization of SCLC with a particular focus on opportunities for translation into new therapeutic approaches to improve patient outcomes.

Clinical application of ctDNA in early diagnosis, treatment and prognosis of patients with non-small cell lung cancer

Lung cancer is one of the most common malignancies worldwide, with non-small cell lung cancer (NSCLC) being the most common type. As understanding of precise treatment options for NSCLC deepens, circulating tumor DNA (ctDNA) has emerged as a potential biomarker that has become a research hotspot and may represent a new approach for the individualized diagnosis and treatment of NSCLC. This article reviews the applications of ctDNA for the early screening of patients with NSCLC, guiding targeted therapy and immunotherapy, evaluating chemotherapy and postoperative efficacy, assessing prognosis and monitoring recurrence. With the in-depth study of the pathogenesis of NSCLC, plasma ctDNA may become an indispensable part of the precise treatment of NSCLC, which has great clinical application prospects.

Genomic alterations and transcriptional phenotypes in circulating tumor DNA and matched metastatic tumor

Background

Profiling circulating cell-free DNA (cfDNA) has become a fundamental practice in cancer medicine, but the effectiveness of cfDNA at elucidating tumor-derived molecular features has not been systematically compared to standard single-lesion tumor biopsies in prospective cohorts of patients. The use of plasma instead of tissue to guide therapy is particularly attractive for patients with small cell lung cancer (SCLC), a cancer whose aggressive clinical course making it exceedingly challenging to obtain tumor biopsies.

Methods

Here, a prospective cohort of 49 plasma samples obtained before, during, and after treatment from 20 patients with recurrent SCLC, we study cfDNA low pass whole genome (0.1X coverage) and exome (130X) sequencing in comparison with time-point matched tumor, characterized using exome and transcriptome sequencing.

Results

Direct comparison of cfDNA versus tumor biopsy reveals that cfDNA not only mirrors the mutation and copy number landscape of the corresponding tumor but also identifies clinically relevant resistance mechanisms and cancer driver alterations not found in matched tumor biopsies. Longitudinal cfDNA analysis reliably tracks tumor response, progression, and clonal evolution. Genomic sequencing coverage of plasma DNA fragments around transcription start sites shows distinct treatment-related changes and captures the expression of key transcription factors such as NEUROD1 and REST in the corresponding SCLC tumors, allowing prediction of SCLC neuroendocrine phenotypes and treatment responses.

Conclusions

These findings have important implications for non-invasive stratification and subtype-specific therapies for patients with SCLC, now treated as a single disease.

Construction of a Diagnostic Model for Small Cell Lung Cancer Combining Metabolomics and Integrated Machine Learning

BACKGROUND

To date, no study has systematically explored the potential role of serum metabolites and lipids in the diagnosis of small cell lung cancer (SCLC). Therefore, we aimed to conduct a case-cohort study that included 191 cases of SCLC, 91 patients with lung adenocarcinoma, 82 patients with squamous cell carcinoma, and 97 healthy controls.

METHODS

Metabolomics and lipidomics were applied to analyze different metabolites and lipids in the serum of these patients. The SCLC diagnosis model (d-model) was constructed using an integrated machine learning technology and a training cohort (n = 323) and was validated in a testing cohort (n=138).

RESULTS

Eight metabolites, including 1-mristoyl-sn-glycero-3-phosphocholine, 16b-hydroxyestradiol, 3-phosphoserine, cholesteryl sulfate, D-lyxose, dioctyl phthalate, DL-lactate and Leu-Phe, were successfully selected to distinguish SCLC from controls. The d-model was constructed based on these 8 metabolites and showed improved diagnostic performance for SCLC, with the area under curve (AUC) of 0.933 in the training cohort and 0.922 in the testing cohort. Importantly, the d-model still had an excellent diagnostic performance after adjusting the stage and related clinical variables and, combined with the progastrin-releasing peptide (ProGRP), showed the best diagnostic performance with 0.975 of AUC for limited-stage patients.

CONCLUSION

This study is the first to analyze the difference between metabolomics and lipidomics and to construct a d-model to detect SCLC using integrated machine learning. This study may be of great significance for the screening and early diagnosis of SCLC patients.

A meta-analysis of circulating tumor DNA as a survival indicator in small cell lung cancer patients

A high level of circulating tumor DNA (ctDNA) has been linked to poor survival in patients with certain solid tumors. In spite of this, it is still unclear whether ctDNA is associated with poor survival in small cell lung cancer (SCLC). To investigate the above association, we conducted a systematic review and meta-analysis. PubMed, Web of Science, Cochrane's Library, and Embase were searched for relevant cohort studies from the inception of the databases to November 28, 2022. Data collection, literature search, and statistical analysis were carried out independently by two authors. To account for heterogeneity, we used a random-effects model. In this meta-analysis, 391 patients with SCLC were identified, and the data were pooled from nine observational studies and followed for 11.4 to 25.0 months. A high ctDNA was associated with worse overall survival (OS, risk ratio [RR] 2.50, 95% confidence interval [CI]1.85 to 3.38, p < 0.001; I2 = 25%) and progression-free survival (PFS, RR 2.33, 95% CI 1.48 to 3.64, p < 0.001, I2 = 42%). Subgroup analyses retrieved consistent results in prospective and retrospective studies, in studies with ctDNA measured with polymerase chain reaction or next-generation sequencing, and in studies analyzed with univariate or multivariate regression models. Studies suggest that ctDNA may be an important factor in predicting poor OS and PFS in SCLC patients.

A 15-Gene-Based Risk Signature for Predicting Overall Survival in SCLC Patients Who Have Undergone Surgical Resection

Small cell lung cancer (SCLC) is a malignancy with a poor prognosis whose treatment has not progressed for decades. The survival benefit of surgery and the selection of surgical candidates are still controversial in SCLC. This study is the first report to identify transcriptomic alterations associated with prognosis and propose a gene expression-based risk signature that can be used to predict overall survival (OS) in SCLC patients who have undergone potentially curative surgery. An integrative transcriptome analysis of three gene expression datasets (GSE30219, GSE43346, and GSE149507) revealed 1734 up-regulated and 2907 down-regulated genes. Cox-Mantel test, Cox regression, and Lasso regression analyses were used to identify genes to be included in the risk signature. EGAD00001001244 and GSE60052-cohorts were used for internal and external validation, respectively. Overall survival was significantly poorer in patients with high-risk scores compared to the low-risk group. The discriminatory performance of the risk signature was superior to other parameters. Multivariate analysis showed that the risk signature has the potential to be an independent predictor of prognosis. The prognostic genes were enriched in pathways including regulation of transcription, cell cycle, cell metabolism, and angiogenesis. Determining the roles of the identified prognostic genes in the pathogenesis of SCLC may contribute to the development of new treatment strategies. The risk signature needs to be validated in a larger cohort of patients to test its usefulness in clinical decision-making.

A plasma miRNA-based classifier for small cell lung cancer diagnosis

Introduction

Small cell lung cancer (SCLC) is characterized by poor prognosis and challenging diagnosis. Screening in high-risk smokers results in a reduction in lung cancer mortality, however, screening efforts are primarily focused on non-small cell lung cancer (NSCLC). SCLC diagnosis and surveillance remain significant challenges. The aberrant expression of circulating microRNAs (miRNAs/miRs) is reported in many tumors and can provide insights into the pathogenesis of tumor development and progression. Here, we conducted a comprehensive assessment of circulating miRNAs in SCLC with a goal of developing a miRNA-based classifier to assist in SCLC diagnoses.

Methods

We profiled deregulated circulating cell-free miRNAs in the plasma of SCLC patients. We tested selected miRNAs on a training cohort and created a classifier by integrating miRNA expression and patients' clinical data. Finally, we applied the classifier on a validation dataset.

Results

We determined that miR-375-3p can discriminate between SCLC and NSCLC patients, and between SCLC and Squamous Cell Carcinoma patients. Moreover, we found that a model comprising miR-375-3p, miR-320b, and miR-144-3p can be integrated with race and age to distinguish metastatic SCLC from a control group.

Discussion

This study proposes a miRNA-based biomarker classifier for SCLC that considers clinical demographics with specific cut offs to inform SCLC diagnosis.

Plasma Extracellular Vesicle Long RNA in Diagnosis and Prediction in Small Cell Lung Cancer

(1) Introduction: The aim of this study was to identify the plasma extracellular vesicle (EV)-specific transcriptional profile in small-cell lung cancer (SCLC) and to explore the application value of plasma EV long RNA (exLR) in SCLC treatment prediction and diagnosis. (2) Methods: Plasma samples were collected from 57 SCLC treatment-naive patients, 104 non-small-cell lung cancer (NSCLC) patients and 59 healthy participants. The SCLC patients were divided into chemo-sensitive and chemo-refractory groups based on the therapeutic effects. The exLR profiles of the plasma samples were analyzed by high-throughput sequencing. Bioinformatics approaches were used to investigate the differentially expressed exLRs and their biofunctions. Finally, a t-signature was constructed using logistic regression for SCLC treatment prediction and diagnosis. (3) Results: We obtained 220 plasma exLRs profiles in all the participants. Totals of 5787 and 1207 differentially expressed exLRs were identified between SCLC/healthy controls, between the chemo-sensitive/chemo-refractory groups, respectively. Furthermore, we constructed a t-signature that comprised ten exLRs, including EPCAM, CCNE2, CDC6, KRT8, LAMB1, CALB2, STMN1, UCHL1, HOXB7 and CDCA7, for SCLC treatment prediction and diagnosis. The exLR t-score effectively distinguished the chemo-sensitive from the chemo-refractory group (p = 9.268 × 10^-9) with an area under the receiver operating characteristic curve (AUC) of 0.9091 (95% CI: 0.837 to 0.9811) and distinguished SCLC from healthy controls (AUC: 0.9643; 95% CI: 0.9256-1) and NSCLC (AUC: 0.721; 95% CI: 0.6384-0.8036). (4) Conclusions: This study firstly characterized the plasma exLR profiles of SCLC patients and verified the feasibility and value of identifying biomarkers based on exLR profiles in SCLC diagnosis and treatment prediction.

Liquid Biopsies in Cancer Diagnosis, Monitoring and Prognosis

Liquid biopsy has emerged as new tool for detecting clinically relevant genetic alterations in cancer patients [...].

The Role of Genomics and Proteomics in Lung Cancer Early Detection and Treatment

Lung cancer is the leading cause of cancer-related death worldwide, with non-small-cell lung cancer (NSCLC) being the primary type. Unfortunately, it is often diagnosed at advanced stages, when therapy leaves patients with a dismal prognosis. Despite the advances in genomics and proteomics in the past decade, leading to progress in developing tools for early diagnosis, targeted therapies have shown promising results; however, the 5-year survival of NSCLC patients is only about 15%. Low-dose computed tomography or chest X-ray are the main types of screening tools. Lung cancer patients without specific, actionable mutations are currently treated with conventional therapies, such as platinum-based chemotherapy; however, resistances and relapses often occur in these patients. More noninvasive, inexpensive, and safer diagnostic methods based on novel biomarkers for NSCLC are of paramount importance. In the current review, we summarize genomic and proteomic biomarkers utilized for the early detection and treatment of NSCLC. We further discuss future opportunities to improve biomarkers for early detection and the effective treatment of NSCLC.

Plasma cell-free DNA and circulating tumor cells as prognostic biomarkers in small cell lung cancer patients

BACKGROUND

Lack of biomarkers for treatment selection and monitoring in small cell lung cancer (SCLC) patients with the limited therapeutic options, result in poor outcomes. Therefore, new prognostic biomarkers are needed to improve their management. The prognostic value of cell-free DNA (cfDNA) and circulating tumor cells (CTCs) have been less explored in SCLC.

METHODS

We quantified cfDNA in 46 SCLC patients at different times during first-line of chemotherapy or chemo-immunotherapy. Moreover, CTCs were analyzed in 21 patients before therapy onset using CellSearch® system. The possible association between both biomarkers and patients' outcomes was investigated in order to develop a prognostic model.

RESULTS

High cfDNA levels before therapy were associated with shorter progression-free survival (PFS) and overall survival (OS). Furthermore, cfDNA levels at 3 weeks and at progression disease were also associated with patients' outcomes. Multivariate analyses confirmed the independence of cfDNA levels as a prognostic biomarker. Finally, the three-risk category prognostic model developed included Eastern Cooperative Oncology Group Performance Status (ECOG PS), gender and baseline cfDNA levels was associated with a higher risk of progression and death.

CONCLUSIONS

We confirmed the prognostic utility of cfDNA quantitative analysis in SCLC patients before and during therapy. Our novel risk prognostic model in clinical practice will allow to identify patients who could benefit with actual therapies.

Small-cell lung cancer brain metastasis: From molecular mechanisms to diagnosis and treatment

Lung cancer is the most malignant human cancer worldwide, also with the highest incidence rate. However, small-cell lung cancer (SCLC) accounts for 14 % of all lung cancer cases. Approximately 10 % of patients with SCLC have brain metastasis at the time of diagnosis, which is the leading cause of death of patients with SCLC worldwide. The median overall survival is only 4.9 months, and a long-tern cure exists for patients with SCLC brain metastasis due to limited common therapeutic options. Recent studies have enhanced our understanding of the molecular mechanisms leading to meningeal metastasis, and multimodality treatments have brought new hopes for a better cure for the disease. This review aimed to offer an insight into the cellular processes of different metastatic stages of SCLC revealed by the established animal models, and into the major diagnostic methods of SCLC. Additionally, it provided in-depth information on the recent advances in SCLC treatments, and highlighted several new models and biomarkers with promises to improve the prognosis of SCLC.

Accurate treatment of small cell lung cancer: Current progress, new challenges and expectations

Small cell lung cancer (SCLC) is a deadly disease with poor prognosis. Fast growing speed, inclination to metastasis, enrichment in cancer stem cells altogether constitute its aggressive nature. In stark contrast to non-small cell lung cancer (NSCLC) that strides vigorously on the road to precision oncology, SCLC has been on the embryonic path to achieve effective personalized treatments. The survival of patients with SCLC have not been improved greatly, which could be possibly due to our inadequate understanding of genetic alterations of SCLC. Recently, encouraging effects have been observed in patients with SCLC undergoing immunotherapy. However, exciting results have only been observed in a small fraction of patients with SCLC, warranting biomarkers predictive of responses as well as novel therapeutic strategies. In addition, SCLC has previously been viewed to be homogeneous. However, perspectives have been changed thanks to the advances in sequencing techniques and platforms, which unfolds the complex heterogeneity of SCLC both genetically and non-genetically, rendering the treatment of SCLC a further step forward into the precision era. To outline the road of SCLC towards precision oncology, we summarize the progresses and achievements made in precision treatment in SCLC in genomic, transcriptomic, epigenetic, proteomic and metabolic dimensions. Moreover, we conclude relevant therapeutic vulnerabilities in SCLC. Clinically tested drugs and clinical trials have also been demonstrated. Ultimately, we look into the opportunities and challenges ahead to advance the individualized treatment in pursuit of improved survival for patients with SCLC.

[Research Progress on the Application of Liquid Biopsy in the Diagnosis and Treatment of Small Cell Lung Cancer]

Small cell lung cancer (SCLC) is a malignant tumor with strong invasiveness and high mortality. It has the characteristics of easy metastasis, fast growth, high degree of malignancy and strong invasiveness. The prognosis of patients is generally poor. The current clinical diagnosis of SCLC is mainly based on tissue biopsy, which is invasive, long cycle time and high cost. In recent years, liquid biopsy has been gradually applied because of its non-invasive, comprehensive and real-time characteristics that traditional tissue biopsy does not have. The main detection objects of liquid biopsy include circulating tumor DNA (ctDNA), circulating tumor cells (CTCs) and exosomes in peripheral blood. The application of liquid biopsy in the clinical treatment of SCLC will help clinicians to improve the detailed diagnosis of SCLC patients, as well as the timely control and response to the treatment response of patients.
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Current and Future Perspectives of Cell-Free DNA in Liquid Biopsy

A liquid biopsy is a minimally invasive or non-invasive method to analyze a range of tumor material in blood or other body fluids, including circulating tumor cells (CTCs), cell-free DNA (cfDNA), messenger RNA (mRNA), microRNA (miRNA), and exosomes, which is a very promising technology. Among these cancer biomarkers, plasma cfDNA is the most widely used in clinical practice. Compared with a tissue biopsy of traditional cancer diagnosis, in assessing tumor heterogeneity, a liquid biopsy is more reliable because all tumor sites release cfDNA into the blood. Therefore, a cfDNA liquid biopsy is less invasive and comprehensive. Moreover, the development of next-generation sequencing technology makes cfDNA sequencing more sensitive than a tissue biopsy, with higher clinical applicability and wider application. In this publication, we aim to review the latest perspectives of cfDNA liquid biopsy clinical significance and application in cancer diagnosis, treatment, and prognosis. We introduce the sequencing techniques and challenges of cfDNA detection, analysis, and clinical applications, and discuss future research directions.

Tuning Surface Properties via Plasma Treatments for the Improved Capture of MicroRNA Biomarkers

Advanced materials could bring about fundamental improvements in the evolution of innovative analytical devices, i.e., biosensors or lab-on-a-chip devices, in particular in the context of liquid biopsies. Here, plasma deposition processes were tested for the introduction of primary amines on silicon surfaces by tuning the amounts and availability of amino-charged residues. Different binary (CH₄/NH₃) and ternary (CH₄/NH₃/H₂ and CH₄/NH₃/N₂) mixtures of gases were used as feeds for the plasma treatments. The obtained surfaces were fully characterized for their chemical and physical properties before their use as capture materials in a functional test. Synthetic and fluorescently conjugated microRNA-21 (miR-21) was selected as the target molecule. The capture of miR-21 increased linearly with the increase in amino nitrogen measured on surfaces. The surface showing the most promising performance was further analyzed in different conditions, i.e., varying pH and time of incubation, incubation with different microRNAs, and possible elution of captured microRNAs. The apparent pH range of primary amines present on the surfaces was around 3.5–4. Positively charged surfaces prepared via PE-CVD were, therefore, demonstrated as being suitable materials for the capture of microRNA biomarkers, paving the way for their inclusion in biomedical devices for the purification and analysis of circulating biomarkers.