MiRNA expression profiling in adenocarcinoma and squamous cell lung carcinoma reveals both common and specific deregulated microRNAs

MicroRNA-140-3p inhibits proliferation and promotes apoptosis in non-small cell lung cancer by targeting MDIG

BACKGROUND

MicroRNAs (miRNAs) are associated with cancer progression. MiR-140-3p is a tumor suppressor. Nevertheless, its function in non-small cell lung cancer (NSCLC) is unclear.

METHODS

MiR-140-3p expression in NSCLC clinical specimens was examined using the TCGA database and real-time PCR. NSCLC cell proliferation and apoptosis were investigated after the miRNA overexpression. Then, mineral dust-induced gene (MDIG) levels in NSCLC clinical specimens were monitored by real-time PCR and western blotting. Bioinformatics predicated the binding of miR-140-3p to MDIG, and their relationship was validated by luciferase reporter assay. The miR-140-3p/MDIG axis was further validated through rescue experiments. The involvement of STAT3 signaling in the actions of miR-140-3p/MDIG axis was investigated.

RESULTS

MiR-140-3p was decreased in NSCLC tissues and negatively correlated with MDIG expression. Additionally, it was also lower in high-grade specimens than in low-grade ones. MiR-140-3p restrained cell proliferation, facilitated apoptosis, and inhibited STAT3 signaling in NSCLC. Interestingly, MDIG was a target of this miRNA. Furthermore, MDIG upregulation abolished miR-140-3p's effect on cell proliferation, apoptosis, and STAT3 pathway in NSCLC cells.

CONCLUSION

MiR-140-3p restrained NSCLC development through the regulation of the STAT3 pathway by targeting MDIG. This axis may be a promising target for NSCLC treatment.

miR-122 and miR-21 are Stable Components of miRNA Signatures of Early Lung Cancer after Validation in Three Independent Cohorts

Several panels of circulating miRNAs have been reported as potential biomarkers of early lung cancer, yet the overlap of components between different panels is limited, and the universality of proposed biomarkers has been minimal across proposed panels. To assess the stability of the diagnostic potential of plasma miRNA signature of early lung cancer among different cohorts, a panel of 24 miRNAs tested in the frame of one lung cancer screening study (MOLTEST-2013, Poland) was validated with material collected in the frame of two other screening studies (MOLTEST-BIS, Poland; and SMAC, Italy) using the same standardized analytical platform (the miRCURY LNA miRNA PCR assay). On analysis of selected miRNAs, two associated with lung cancer development, miR-122 and miR-21, repetitively differentiated healthy participants from individuals with lung cancer. Additionally, miR-144 differentiated controls from cases specifically in subcohorts with adenocarcinoma. Other tested miRNAs did not overlap in the three cohorts. Classification models based on neither a single miRNA nor multicomponent miRNA panels (24-mer and 7-mer) showed classification performance sufficient for a standalone diagnostic biomarker (AUC, 75%, 71%, and 53% in MOLTEST-2013, SMAC, and MOLTEST-BIS, respectively, in the 7-mer model). The performance of classification in the MOLTEST-BIS cohort with the lowest contribution of adenocarcinomas was increased when only this cancer type was considered (AUC, 60% in 7-mer model).

MicroRNA‑mediated regulation in lung adenocarcinoma: Signaling pathways and potential therapeutic implications (Review)

Lung adenocarcinoma (LUAD) poses a significant global health burden owing to its high incidence rate and unfavorable prognosis, driven by frequent recurrence and drug resistance. Understanding the biological mechanisms underlying LUAD is imperative to developing advanced therapeutic strategies. Recent research has highlighted the role of dysregulated microRNAs (miRNAs) in LUAD progression through diverse signaling pathways, including the Wnt and AKT pathways. Of particular interest is the novel pathological mechanism involving the interaction between competing endogenous RNAs (ceRNAs) and miRNAs. This review critically analyzed the impact of aberrant miRNA expression on LUAD development, shedding light on the associated signaling pathways. It also highlighted the emerging significance of ceRNA‑miRNA interactions in LUAD pathogenesis. Elucidating the intricate regulatory networks involving miRNAs and ceRNAs presents a promising avenue for the development of potential therapeutic interventions and diagnostic biomarkers in LUAD. Further research in this area is essential to advance precision medicine approaches and improve patient outcomes.

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.

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.

Long non-coding RNAs in lung cancer: Unraveling the molecular modulators of MAPK signaling

Lung cancer (LC) continues to pose a significant global medical burden, necessitating a comprehensive understanding of its molecular foundations to establish effective treatment strategies. The mitogen-activated protein kinase (MAPK) signaling system has been scientifically associated with LC growth; however, the intricate regulatory mechanisms governing this system remain unknown. Long non-coding RNAs (lncRNAs) are emerging as crucial regulators of diverse cellular activities, including cancer growth. LncRNAs have been implicated in LC, which can function as oncogenes or tumor suppressors, and their dysregulation has been linked to cancer cell death, metastasis, spread, and proliferation. Due to their involvement in critical pathophysiological processes, lncRNAs are gaining attention as potential candidates for anti-cancer treatments. This article aims to elucidate the regulatory role of lncRNAs in MAPK signaling in LC. We provide a comprehensive review of the key components of the MAPK pathway and their relevance in LC, focusing on aberrant signaling processes associated with disease progression. By examining recent research and experimental findings, this article examines the molecular mechanisms through which lncRNAs influence MAPK signaling in lung cancer, ultimately contributing to tumor development.

miRdisNET: Discovering microRNA biomarkers that are associated with diseases utilizing biological knowledge-based machine learning

During recent years, biological experiments and increasing evidence have shown that microRNAs play an important role in the diagnosis and treatment of human complex diseases. Therefore, to diagnose and treat human complex diseases, it is necessary to reveal the associations between a specific disease and related miRNAs. Although current computational models based on machine learning attempt to determine miRNA-disease associations, the accuracy of these models need to be improved, and candidate miRNA-disease relations need to be evaluated from a biological perspective. In this paper, we propose a computational model named miRdisNET to predict potential miRNA-disease associations. Specifically, miRdisNET requires two types of data, i.e., miRNA expression profiles and known disease-miRNA associations as input files. First, we generate subsets of specific diseases by applying the grouping component. These subsets contain miRNA expressions with class labels associated with each specific disease. Then, we assign an importance score to each group by using a machine learning method for classification. Finally, we apply a modeling component and obtain outputs. One of the most important outputs of miRdisNET is the performance of miRNA-disease prediction. Compared with the existing methods, miRdisNET obtained the highest AUC value of .9998. Another output of miRdisNET is a list of significant miRNAs for disease under study. The miRNAs identified by miRdisNET are validated via referring to the gold-standard databases which hold information on experimentally verified microRNA-disease associations. miRdisNET has been developed to predict candidate miRNAs for new diseases, where miRNA-disease relation is not yet known. In addition, miRdisNET presents candidate disease-disease associations based on shared miRNA knowledge. The miRdisNET tool and other supplementary files are publicly available at: https://github.com/malikyousef/miRdisNET.

Metabolic Pathways in Breast Cancer Reprograming: An Insight to Non-Coding RNAs

Cancer cells reprogram their metabolisms to achieve high energetic requirements and produce precursors that facilitate uncontrolled cell proliferation. Metabolic reprograming involves not only the dysregulation in glucose-metabolizing regulatory enzymes, but also the enzymes engaging in the lipid and amino acid metabolisms. Nevertheless, the underlying regulatory mechanisms of reprograming are not fully understood. Non-coding RNAs (ncRNAs) as functional RNA molecules cannot translate into proteins, but they do play a regulatory role in gene expression. Moreover, ncRNAs have been demonstrated to be implicated in the metabolic modulations in breast cancer (BC) by regulating the metabolic-related enzymes. Here, we will focus on the regulatory involvement of ncRNAs (microRNA, circular RNA and long ncRNA) in BC metabolism, including glucose, lipid and glutamine metabolism. Investigation of this aspect may not only alter the approaches of BC diagnosis and prognosis, but may also open a new avenue in using ncRNA-based therapeutics for BC treatment by targeting different metabolic pathways.