Comprehensive circulating microRNA profile as a supersensitive biomarker for early-stage lung cancer screening

Development of a sensitive disease-screening model using comprehensive circulating microRNA profiles in dogs: A pilot study

In the veterinary field, the utility of disease-identification models that use comprehensive circulating microRNA (miRNA) profiles produced through measurements based on next-generation sequencing (NGS) remains unproven. To integrate NGS technology with automated machine learning (autoML) to create a comprehensive circulating miRNA profile and to assess the clinical utility of a disease-screening model derived from this profile. The study involved dogs diagnosed with or being treated for various diseases, including tumors, across multiple veterinary clinics (n = 254), and healthy dogs without apparent diseases (n = 91). miRNA was extracted from EDTA-treated plasma, and a comprehensive analysis was conducted of one million reads per sample using NGS. Then autoML technology was applied to develop a diagnostic model based on miRNA. Among these models, the one with the highest performance was chosen for evaluation. The diagnostic model, based on the comprehensive circulating miRNA profile developed in this study, achieved an AUC score of 0.89, with a sensitivity of 85 % and a specificity of 88 % for the disease samples. The miRNA-based diagnostic model demonstrated high sensitivity for disease groups and has the potential to be an effective screening test. This study indicates that a comprehensive miRNA profile in dog plasma could serve as a highly sensitive blood biomarker.

Prediction of Pathologic Complete Response in Esophageal Squamous Cell Carcinoma Using Preoperative Serum Small Ribonucleic Acid Obtained After Neoadjuvant Chemoradiotherapy

BACKGROUND

The authors hypothesized that small ribonucleic acid (sRNA) obtained from blood samples after neoadjuvant therapy from patients treated with neoadjuvant chemoradiation therapy (NACRT) could serve as a novel biomarker for predicting pathologic complete response (pCR).

METHODS

This study included 99 patients treated with esophagectomy after NACRT between March 2010 and October 2021 whose blood samples were collected between the end of NACRT and surgery. Next-generation sequencing (NGS) was used to analyze sRNAs from the blood samples. A predictive model for pCR comprising micro-RNA isoforms (isomiR), transfer RNA (tRNA)-derived sRNAs (tsRNAs), and clinical factors was constructed using cross-validation.

RESULTS

Of the 99 patients, pCR was diagnosed for 30 and non-pCR for 69 of the patients. Among sRNAs, the isomiRs of let-7b and miR-93 and the tsRNA group derived from tRNA-Gly-CCC/GCC were identified as predictive factors. The clinical factors included a decrease in the maximum standardized uptake value (SUVmax) at the primary site, clinical complete response post-NACRT, preoperative biopsy, and post-NACRT carcinoembryonic antigen levels. The combined predictive model for pCR (C-PM) was established using the three sRNAs and four clinical factors. The area under the curve for the C-PM was 0.84, which was a significant factor in the multivariate analysis (odds ratio, 89.41; 95 % confidence interval 8.1-987.5; p < 0.001).

CONCLUSIONS

Pathologic complete response after NACRT can be predicted by a predictive model constructed from preoperative clinical factors obtained via minimally invasive procedures and sRNA identified by NGS. Preoperative pCR prediction may influence treatment decision-making after NACRT.

Improved lung cancer classification by employing diverse molecular features of microRNAs

MiRNAs are edited or modified in multiple ways during their biogenesis pathways. It was reported that miRNA editing was deregulated in tumors, suggesting the potential value of miRNA editing in cancer classification. Here we extracted three types of miRNA features from 395 LUAD and control samples, including the abundances of original miRNAs, the abundances of edited miRNAs, and the editing levels of miRNA editing sites. Our results show that eight classification algorithms selected generally had better performances on combined features than on the abundances of miRNAs or editing features of miRNAs alone. One feature selection algorithm, i.e., the DFL algorithm, selected only three features, i.e., the frequencies of hsa-miR-135b-5p, hsa-miR-210-3p and hsa-mir-182_48u (an edited miRNA), from 316 training samples. Seven classification algorithms achieved 100% accuracies on these three features for 79 independent testing samples. These results indicate that the additional information of miRNA editing is useful in improving the classification of LUAD samples.

Serum Insights: Leveraging the Power of miRNA Profiling as an Early Diagnostic Tool for Non-Small Cell Lung Cancer

Non-small cell lung cancer is the predominant form of lung cancer and is associated with a poor prognosis. MiRNAs implicated in cancer initiation and progression can be easily detected in liquid biopsy samples and have the potential to serve as non-invasive biomarkers. In this study, we employed next-generation sequencing to globally profile miRNAs in serum samples from 71 early-stage NSCLC patients and 47 non-cancerous pulmonary condition patients. Preliminary analysis of differentially expressed miRNAs revealed 28 upregulated miRNAs in NSCLC compared to the control group. Functional enrichment analyses unveiled their involvement in NSCLC signaling pathways. Subsequently, we developed a gradient-boosting decision tree classifier based on 2588 miRNAs, which demonstrated high accuracy (0.837), sensitivity (0.806), and specificity (0.859) in effectively distinguishing NSCLC from non-cancerous individuals. Shapley Additive exPlanations analysis improved the model metrics by identifying the top 15 miRNAs with the strongest discriminatory value, yielding an AUC of 0.96 ± 0.04, accuracy of 0.896, sensitivity of 0.884, and specificity of 0.903. Our study establishes the potential utility of a non-invasive serum miRNA signature as a supportive tool for early detection of NSCLC while also shedding light on dysregulated miRNAs in NSCLC biology. For enhanced credibility and understanding, further validation in an independent cohort of patients is warranted.

Small RNA Deep Sequencing of Circulating Small RNAs Discovers a Unique Panel of microRNAs as Feasible and Reliable Biomarkers of Non-Small Cell Lung Cancers in Northern Thailand

OBJECTIVE

This study aimed to assess the practicality and reliability of utilizing microRNAs (miRNAs) as a potential screening and diagnosing tool for non-small cell lung cancers (NSCLCs) in Northern Thailand.

METHODS

Small RNA sequencing and a literature review was performed to obtain a list of serum miRNA candidates. Serum levels of these selected miRNA candidates were measured in patients with NSCLC and healthy volunteers by real-time RT-PCR and receiver operating characteristic curve (ROC) were used to assess diagnostic performance.

RESULTS

Sequencing data revealed 148 known miRNAs and 230 novel putative miRNAs in serum samples; 19 serum miRNAs were significantly downregulated and 242 were upregulated. Seven miRNAs selected according to sequencing data and 11 miRNAs according to previous reports were evaluated in training cohort (45 lung cancer patients, 26 controls) and 6 miRNAs were found differentially expressed (p < 0.05, Mann Whitney U test) and associated (p < 0.05, Chi-square test) with NSCLC development. Further analysis and verification identified an optimal combination of 4 miRNAs composed of hsa-miR23a, hsa-miR26b, hsa-miR4488 and novel-130 to provide the optimal AUC of 0.901±0.034. Detection of serum miRNA by real-time RT-PCR showed good reproducibility with the coefficient of variation (CV) ≤ 4%. The optimal screening miRNAs panel was primarily identified through sequencing data of local patient population, thus indicating that the etiology of NSCLCs may differ from one population to other and thus require a unique panel of miRNAs for their identification.

CONCLUSION

Circulating miRNA is a feasible screening tool for NSCLCs. Nevertheless, populations with different lung cancer etiology may need to identify their own most suitable miRNA panel.