Prognostic value of microRNAs in lung cancer: A systematic review and meta-analysis

MicroRNAs in lung cancer: their role in tumor progression, biomarkers, diagnostic, prognostic, and therapeutic relevance

MicroRNAs (miRNAs) are a class of small non-coding RNAs which are associated with post-transcriptional regulation of gene expression. Dysfunction or aberrant expression of miRNAs is predominant in various malignancies including lung cancer. Lung cancer is one of the commonest causes of cancer-related death worldwide, with a five-year survival of only 10-20%. The present review summarizes the current understanding of the role of miRNAs in the development and progression of human lung cancer and their therapeutic potential. Also, we briefly discuss the canonical biogenetic pathway of miRNAs followed by a detailed illustration on how miRNAs regulate human lung cancer progression in various ways. Furthermore, we focus on how miRNAs contribute to the crosstalk between cancer cells and different cells in the tumor microenvironment in the context of lung cancer. Finally, we illustrate how different miRNAs are used as a prognostic and diagnostic biomarker for lung cancer and the ongoing miRNA-associated clinical trials. In conclusion, we discuss how targeting miRNAs can be a potential therapeutic means in the treatment of human lung cancer.

Liquid biopsy utilizing miRNA in patients with advanced breast cancer treated with cyclin‑dependent kinase 4/6 inhibitors

Cyclin-dependent kinase 4/6 inhibitors (CDK4/6is) are the mainstay of treatment of hormone receptor+/human epidermal growth factor receptor 2-patients with advanced breast cancer (ABC). Despite improvements in overall survival, most patients experience disease progression. Biomarkers derived from a liquid biopsy are appealing for their potential to detect resistance to treatment earlier than computed tomography imaging. However, clinical data concerning microRNAs (miRNAs/miRs) in the context of CDK4/6is are lacking. Thus, the present study assessed the use of miRNAs in patients with ABC treated with CDK4/6is. Patients treated for ABC with CDK4/6is between June and August 2022 were eligible. miRNA expression analyses were performed using a TaqMan™ low-density miRNA array. A total of 80 consecutive patients with ABC treated with CDK4/6is at Maria Sklodowska-Curie National Research Institute of Oncology (Gliwice, Poland) were assessed, with 14 patients diagnosed with progressive disease at the time of sampling, 55 patients exhibited clinical benefit from CDK4/6i treatment and 11 patients were at the beginning of CDK4/6i treatment. Patients with disease progression had significantly higher levels of miR-21 (P=0.027), miR-34a (P=0.011), miR-193b (P=0.032), miR-200a (P=0.027) and miR-200b (P=0.003) compared with patients who benefitted from CDK4/6i treatment. Significantly higher levels of miR-34a expression were observed in patients with progressive disease than in patients beginning treatment (P=0.031). The present study demonstrated the potential innovative role of circulating miRNAs during CDK4/6i treatment. Plasma-based expression of miR-21, -34a, -193b, -200a and -200b effectively distinguished patients with ABC who responded to CDK4/6i treatment from patients who were resistant. However, longitudinal studies are required to verify the predictive and prognostic potential of miRNA.

Potential of miR-181a-5p and miR-630 as clinical biomarkers in NSCLC

BACKGROUND

The development of drug resistance and high mortality rates are the major problems observed in non-small cell lung cancer (NSCLC). Biomarkers indicating and predicting disease development towards these unfavorable directions are therefore on high demand. Many studies have demonstrated that changes in miRNAs expression may be associated with a response to treatment and disease prognosis, thus suggesting its potential biomarker value for a broad spectrum of clinical applications. The aim of the present study was to investigate the expression level of miR-181a-5p, miR-630, and its targets in NSCLC tumor tissue and plasma samples; and to analyze its association with NSCLC patient's response to treatment and disease prognosis.

METHODS

The study was performed in 89 paired tissue specimens and plasma samples obtained from NSCLC patients who underwent surgical treatment at the Department of Thoracic Surgery and Oncology of the National Cancer Institute. Analysis of miR-181a-5p and miR-630 expression was performed by qRT-PCR using TaqMan miRNA specific primers. Whereas BCL2, LMO3, PTEN, SNAI2, WIF1 expression levels were identified with KAPA SYBR FAST qPCR Kit. Each sample was examined in triplicate and calculated following the 2-ΔΔCt method. When the p-value was less than 0.05, the differences were considered statistically significant.

RESULTS

It was found that miR-181a-5p and miR-630 expression levels in NSCLC tissue and plasma samples were significantly decreased compared with control samples. Moreover, patients with low miR-181a-5p expression in tumor tissue and plasma had longer PFS rates than those with high miRNA expression. Decreased miR-630 expression in tumor was statistically significantly associated with better NSCLC patients' OS. In addition, the expression of miR-181a-5p, as well as miR-630 in tumor tissue, are the statistically significant variables for NSCLC patients' OS. Moreover, in NSCLC patient plasma samples circulating miR-181a-5p can be evaluated as significant independent prognostic factors for OS and PFS.

CONCLUSIONS

Our findings indicate the miR-181a-5p and miR-630 expression levels have the potential to prognose and predict and therefore improve the treatment individualization and the outcome of NSCLC patients. Circulating miR-181a-5p has the potential clinical value as a non-invasive biomarker for NSCLC.

Computational method using heterogeneous graph convolutional network model combined with reinforcement layer for MiRNA-disease association prediction

BACKGROUND

A large number of evidences from biological experiments have confirmed that miRNAs play an important role in the progression and development of various human complex diseases. However, the traditional experiment methods are expensive and time-consuming. Therefore, it is a challenging task that how to develop more accurate and efficient methods for predicting potential associations between miRNA and disease.

RESULTS

In the study, we developed a computational model that combined heterogeneous graph convolutional network with enhanced layer for miRNA-disease association prediction (HGCNELMDA). The major improvement of our method lies in through restarting the random walk optimized the original features of nodes and adding a reinforcement layer to the hidden layer of graph convolutional network retained similar information between nodes in the feature space. In addition, the proposed approach recalculated the influence of neighborhood nodes on target nodes by introducing the attention mechanism. The reliable performance of the HGCNELMDA was certified by the AUC of 93.47% in global leave-one-out cross-validation (LOOCV), and the average AUCs of 93.01% in fivefold cross-validation. Meanwhile, we compared the HGCNELMDA with the state‑of‑the‑art methods. Comparative results indicated that o the HGCNELMDA is very promising and may provide a cost‑effective alternative for miRNA-disease association prediction. Moreover, we applied HGCNELMDA to 3 different case studies to predict potential miRNAs related to lung cancer, prostate cancer, and pancreatic cancer. Results showed that 48, 50, and 50 of the top 50 predicted miRNAs were supported by experimental association evidence. Therefore, the HGCNELMDA is a reliable method for predicting disease-related miRNAs.

CONCLUSIONS

The results of the HGCNELMDA method in the LOOCV (leave-one-out cross validation, LOOCV) and 5-cross validations were 93.47% and 93.01%, respectively. Compared with other typical methods, the performance of HGCNELMDA is higher. Three cases of lung cancer, prostate cancer, and pancreatic cancer were studied. Among the predicted top 50 candidate miRNAs, 48, 50, and 50 were verified in the biological database HDMMV2.0. Therefore; this further confirms the feasibility and effectiveness of our method. Therefore, this further confirms the feasibility and effectiveness of our method. To facilitate extensive studies for future disease-related miRNAs research, we developed a freely available web server called HGCNELMDA is available at http://124.221.62.44:8080/HGCNELMDA.jsp .

MiRNAs in Lung Cancer: Diagnostic, Prognostic, and Therapeutic Potential

Lung cancer is the dominant emerging factor in cancer-related mortality around the globe. Therapeutic interventions for lung cancer are not up to par, mainly due to reoccurrence/relapse, chemoresistance, and late diagnosis. People are currently interested in miRNAs, which are small double-stranded (20–24 ribonucleotides) structures that regulate molecular targets (tumor suppressors, oncogenes) involved in tumorigeneses such as cell proliferation, apoptosis, metastasis, and angiogenesis via post-transcriptional regulation of mRNA. Many studies suggest the emerging role of miRNAs in lung cancer diagnostics, prognostics, and therapeutics. Therefore, it is necessary to intensely explore the miRNOME expression of lung tumors and the development of anti-cancer strategies. The current review focuses on the therapeutic, diagnostic, and prognostic potential of numerous miRNAs in lung cancer.

Diagnostic and prognostic potential of eight whole blood microRNAs for equine sarcoid disease

MicroRNAs have been proposed as biomarkers for equine sarcoids, the most prevalent equine skin tumors globally. This study served to validate the diagnostic and prognostic potential of whole blood microRNAs identified in a previous study for long-term equine sarcoid diagnosis and outcome prediction. Based on findings of a clinical examination at the age of 3 years and a follow-up following a further 5–12 years, 32 Franches-Montagnes and 45 Swiss Warmblood horses were assigned to four groups: horses with regression (n = 19), progression (n = 9), new occurrences of sarcoid lesions (n = 19) and tumor-free control horses (n = 30). The expression levels for eight microRNAs (eca-miR-127, eca-miR-432, eca-miR-24, eca-miR-125a-5p, eca-miR-134, eca-miR-379, eca-miR-381, eca-miR-382) were analyzed through reverse transcription quantitative polymerase chain reaction in whole blood samples collected on initial examination. Associations of sex, breed, diagnosis, and prognosis with microRNA expression levels were examined using multivariable analysis of variance. Sex and breed influenced the expression level of five and two microRNAs, respectively. Eca-miR-127 allowed discrimination between sarcoid-affected and tumor-free horses. No variation in microRNA expression was found when comparing horses with sarcoid regression and progression. Expression levels of eca-miR-125a-5p and eca-miR-432 varied in male horses that developed sarcoids throughout the study period in comparison to male control horses. While none of the investigated miRNAs was validated for predicting the prognosis of sarcoid regression / progression within young horses with this condition, two miRNAs demonstrated potential to predict if young male (though not female) tumor-free horse can develop sarcoids within the following years. Sex- and breed- biased miRNAs exist within the equine species and have an impact on biomarker discovery.

Circulating Biomarkers for Early Stage Non-Small Cell Lung Carcinoma Detection: Supplementation to Low-Dose Computed Tomography

Lung cancer is currently the leading cause of cancer death in both developing and developed countries. Given that lung cancer has poor prognosis in later stages, it is essential to achieve an early diagnosis to maximize patients’ overall survival. Non-small cell lung cancer (NSCLC) is the most common form of primary lung cancer in both smokers and non-smokers. The current standard screening method, low‐dose computed tomography (LDCT), is the only radiological method that demonstrates to have mortality benefits across multiple large randomized clinical trials (RCT). However, these RCTs also found LDCT to have a significant false positive rate that results in unnecessary invasive biopsies being performed. Due to the lack of both sensitive and specific screening methods for the early detection of lung cancer, there is an urgent need for alternative minimally or non-invasive biomarkers that may provide diagnostic, and/or prognostic information. This has led to the identification of circulating biomarkers that can be readily detectable in blood and have been extensively studied as prognosis markers. Circulating microRNA (miRNA) in particular has been investigated for these purposes as an augmentation to LDCT, or as direct diagnosis of lung cancer. There is, however, a lack of consensus across the studies on which miRNAs are the most clinically useful. Besides miRNA, other potential circulating biomarkers include circulating tumor cells (CTCs), circulating tumor DNA (ctDNAs) and non-coding RNAs (ncRNAs). In this review, we provide the current outlook of several of these biomarkers for the early diagnosis of NSCLC.

Combined embedding model for MiRNA-disease association prediction

BACKGROUND

Cumulative evidence from biological experiments has confirmed that miRNAs have significant roles to diagnose and treat complex diseases. However, traditional medical experiments have limitations in time-consuming and high cost so that they fail to find the unconfirmed miRNA and disease interactions. Thus, discovering potential miRNA-disease associations will make a contribution to the decrease of the pathogenesis of diseases and benefit disease therapy. Although, existing methods using different computational algorithms have favorable performances to search for the potential miRNA-disease interactions. We still need to do some work to improve experimental results.

RESULTS

We present a novel combined embedding model to predict MiRNA-disease associations (CEMDA) in this article. The combined embedding information of miRNA and disease is composed of pair embedding and node embedding. Compared with the previous heterogeneous network methods that are merely node-centric to simply compute the similarity of miRNA and disease, our method fuses pair embedding to pay more attention to capturing the features behind the relative information, which models the fine-grained pairwise relationship better than the previous case when each node only has a single embedding. First, we construct the heterogeneous network from supported miRNA-disease pairs, disease semantic similarity and miRNA functional similarity. Given by the above heterogeneous network, we find all the associated context paths of each confirmed miRNA and disease. Meta-paths are linked by nodes and then input to the gate recurrent unit (GRU) to directly learn more accurate similarity measures between miRNA and disease. Here, the multi-head attention mechanism is used to weight the hidden state of each meta-path, and the similarity information transmission mechanism in a meta-path of miRNA and disease is obtained through multiple network layers. Second, pair embedding of miRNA and disease is fed to the multi-layer perceptron (MLP), which focuses on more important segments in pairwise relationship. Finally, we combine meta-path based node embedding and pair embedding with the cost function to learn and predict miRNA-disease association. The source code and data sets that verify the results of our research are shown at https://github.com/liubailong/CEMDA .

CONCLUSIONS

The performance of CEMDA in the leave-one-out cross validation and fivefold cross validation are 93.16% and 92.03%, respectively. It denotes that compared with other methods, CEMDA accomplishes superior performance. Three cases with lung cancers, breast cancers, prostate cancers and pancreatic cancers show that 48,50,50 and 50 out of the top 50 miRNAs, which are confirmed in HDMM V2.0. Thus, this further identifies the feasibility and effectiveness of our method.

Clinico-Pathological Importance of miR-146a in Lung Cancer

Lung cancer is a well-known malignant tumor of the respiratory tract, which has caused a significant level of damage to human health in the 21st century. Micro-RNAs (miRNAs) are tiny, non-coding RNA stem-loop structures with a length of roughly 20–25 nucleotides that function as powerful modulators of mRNA and protein products of a gene. miRNAs may modulate many biological processes involving growth, differentiation, proliferation, and cell death and play a key role in the pathogenesis of various types of malignancies. Several accumulating pieces of evidence have proven that miRNA, especially miR-146a, are crucial modulators of innate immune response sequences. A novel and exciting cancer research field has involved miRNA for the detection and suppression of cancer. However, the actual mechanism which is adopted by these miRNA is still unclear. miRNAs have been used as a cancer-associated biomarker in several studies, suggesting their altered expression in various cancers compared to the normal cells. The amount of expression of miRNA can also be used to determine the stage of the disease, aiding in early detection. In breast, pancreatic, and hepatocellular carcinoma, and gastric cancer, cancer cell proliferation and metastasis has been suppressed by miR-146a. Changes in miR-146a expression levels have biomarker importance and possess a high potential as a therapeutic target in lung cancer. It retards epithelial-mesenchymal transition and promotes the therapeutic action of anticancer agents in lung cancer. Studies have also suggested that miR-146a affects gene expression through different signaling pathways viz. TNF-α, NF-κB and MEK-1/2, and JNK-1/2. Further research is required for understanding the molecular mechanisms of miR-146a in lung cancer. The potential role of miR-146a as a diagnostic marker of lung cancer must also be analyzed. This review summarizes the tumor-suppressing, anti-inflammatory, and antichemoresistive nature of miR-146a in lung cancer.

Clinical Utility of microRNAs in Exhaled Breath Condensate as Biomarkers for Lung Cancer

This study represents a novel proof of concept of the clinical utility of miRNAs from exhaled breath condensate (EBC) as biomarkers of lung cancer (LC). Genome-wide miRNA profiling and machine learning analysis were performed on EBC from 21 healthy volunteers and 21 LC patients. The levels of 12 miRNAs were significantly altered in EBC from LC patients where a specific signature of miR-4507, miR-6777-5p and miR-451a distinguished these patients with high accuracy. Besides, a distinctive miRNA profile between LC adenocarcinoma and squamous cell carcinoma was observed, where a combined panel of miR-4529-3p, miR-8075 and miR-7704 enabling discrimination between them. EBC levels of miR-6777-5p, 6780a-5p and miR-877-5p predicted clinical outcome at 500 days. Two additional miRNA signatures were also associated with other clinical features such as stage and invasion status. Dysregulated EBC miRNAs showed potential target genes related to LC pathogenesis, including CDKN2B, PTEN, TP53, BCL2, KRAS and EGFR. We conclude that EBC miRNAs might allow the identification, stratification and monitorization of LC, which could lead to the development of precision medicine in this and other respiratory diseases.

Prognosis prediction model for conversion from mild cognitive impairment to Alzheimer's disease created by integrative analysis of multi-omics data

BACKGROUND

Mild cognitive impairment (MCI) is a precursor to Alzheimer's disease (AD), but not all MCI patients develop AD. Biomarkers for early detection of individuals at high risk for MCI-to-AD conversion are urgently required.

METHODS

We used blood-based microRNA expression profiles and genomic data of 197 Japanese MCI patients to construct a prognosis prediction model based on a Cox proportional hazard model. We examined the biological significance of our findings with single nucleotide polymorphism-microRNA pairs (miR-eQTLs) by focusing on the target genes of the miRNAs. We investigated functional modules from the target genes with the occurrence of hub genes though a large-scale protein-protein interaction network analysis. We further examined the expression of the genes in 610 blood samples (271 ADs, 248 MCIs, and 91 cognitively normal elderly subjects [CNs]).

RESULTS

The final prediction model, composed of 24 miR-eQTLs and three clinical factors (age, sex, and APOE4 alleles), successfully classified MCI patients into low and high risk of MCI-to-AD conversion (log-rank test P = 3.44 × 10^-4 and achieved a concordance index of 0.702 on an independent test set. Four important hub genes associated with AD pathogenesis (SHC1, FOXO1, GSK3B, and PTEN) were identified in a network-based meta-analysis of miR-eQTL target genes. RNA-seq data from 610 blood samples showed statistically significant differences in PTEN expression between MCI and AD and in SHC1 expression between CN and AD (PTEN, P = 0.023; SHC1, P = 0.049).

CONCLUSIONS

Our proposed model was demonstrated to be effective in MCI-to-AD conversion prediction. A network-based meta-analysis of miR-eQTL target genes identified important hub genes associated with AD pathogenesis. Accurate prediction of MCI-to-AD conversion would enable earlier intervention for MCI patients at high risk, potentially reducing conversion to AD.

Predicting MiRNA-disease associations by multiple meta-paths fusion graph embedding model

BACKGROUND

Many studies prove that miRNAs have significant roles in diagnosing and treating complex human diseases. However, conventional biological experiments are too costly and time-consuming to identify unconfirmed miRNA-disease associations. Thus, computational models predicting unidentified miRNA-disease pairs in an efficient way are becoming promising research topics. Although existing methods have performed well to reveal unidentified miRNA-disease associations, more work is still needed to improve prediction performance.

RESULTS

In this work, we present a novel multiple meta-paths fusion graph embedding model to predict unidentified miRNA-disease associations (M2GMDA). Our method takes full advantage of the complex structure and rich semantic information of miRNA-disease interactions in a self-learning way. First, a miRNA-disease heterogeneous network was derived from verified miRNA-disease pairs, miRNA similarity and disease similarity. All meta-path instances connecting miRNAs with diseases were extracted to describe intrinsic information about miRNA-disease interactions. Then, we developed a graph embedding model to predict miRNA-disease associations. The model is composed of linear transformations of miRNAs and diseases, the means encoder of a single meta-path instance, the attention-aware encoder of meta-path type and attention-aware multiple meta-path fusion. We innovatively integrated meta-path instances, meta-path based neighbours, intermediate nodes in meta-paths and more information to strengthen the prediction in our model. In particular, distinct contributions of different meta-path instances and meta-path types were combined with attention mechanisms. The data sets and source code that support the findings of this study are available at https://github.com/dangdangzhang/M2GMDA .

CONCLUSIONS

M2GMDA achieved AUCs of 0.9323 and 0.9182 in global leave-one-out cross validation and fivefold cross validation with HDMM V2.0. The results showed that our method outperforms other prediction methods. Three kinds of case studies with lung neoplasms, breast neoplasms, prostate neoplasms, pancreatic neoplasms, lymphoma and colorectal neoplasms demonstrated that 47, 50, 49, 48, 50 and 50 out of the top 50 candidate miRNAs predicted by M2GMDA were validated by biological experiments. Therefore, it further confirms the prediction performance of our method.

Suppression of non-small cell lung cancer migration and invasion by hsa-miR-486-5p via the TGF-β/SMAD2 signaling pathway

Non-small cell lung cancer (NSCLC) is the leading cause of cancer-related death worldwide. SMAD family member 2 (SMAD2) is a key element downstream of the transforming growth factor beta (TGF-β) signaling pathway that regulates cancer metastasis by promoting the epithelial-mesenchyme transition (EMT). MicroRNA miR-486-5p is a tumor suppressor in NSCLC progression. However, it remains unclear whether miR-486-5p is implicated in TGF-β signaling and EMT in NSCLC. In the present study, high expression of SMAD2 mRNA was detected in NSCLC tissues and cell lines, and was associated with poor survival of patients with NSCLC. By contrast, miR-486-5p was downregulated in NSCLC tissues and cell lines. In silico prediction showed that SMAD2 was a potential target of miR-486-5p. The prediction was verified using a dual-luciferase reporter assay. Transwell assays showed that knockdown of SMAD2 inhibited TGF-β-induced EMT and migration and invasion in NSCLC cells. Similarly, miR-486-5p overexpression suppressed TGF-β-induced EMT and migration and invasion of NSCLC cells. The present study provides a new insight into the role of miR-486-5p in regulating TGF-β-mediated EMT and invasion in NSCLC.

Enhancing the ligand efficiency of anti-HIV compounds targeting frameshift-stimulating RNA

Ribosomal frameshifting, a process whereby a translating ribosome is diverted from one reading frame to another on a contiguous mRNA, is an important regulatory mechanism in biology and an opportunity for therapeutic intervention in several human diseases. In HIV, ribosomal frameshifting controls the ratio of Gag and Gag-Pol, two polyproteins critical to the HIV life cycle. We have previously reported compounds able to selectively bind an RNA stemloop within the Gag-Pol mRNA; these compounds alter the production of Gag-Pol in a manner consistent with increased frameshifting. Importantly, they also display antiretroviral activity in human T-cells. Here, we describe new compounds with significantly reduced molecular weight, but with substantially maintained affinity and anti-HIV activity. These results suggest that development of more "ligand efficient" enhancers of ribosomal frameshifting is an achievable goal.