In this study, a new triplex hairpin oligosensor was developed for the determination of a breast cancer biomarker using silicon quantum dots (Si QD) (λex = 370 nm, λem = 482 nm) as donor and gold nanoparticles (GNP) as an acceptor in a FRET (fluorescence resonance energy transfer) mechanism. In the triplex hairpin oligosensor, a triplex-forming oligonucleotide (TFO) labeled with Si QD and a single-strand DNA labeled with GNP form a hairpin shape with a triplex structure at the hairpin stem. In a turn-on mechanism, the triplex hairpin stem is opened in the presence of sequence-specific miRNA-155 which leads to the release of the Si QD-labeled TFO probe and recovery of the fluorescence signal. About 80 % of the fluorescence intensity of the Si QD-TFO is quenched in the triplex hairpin structure of the oligosensor and in the presence of 800 pM miRNA-155, the fluorescence signal recovered to 57.7 % of its initial value. The LOD of about 10 pM was obtained. The designed triplex-based biosensor can discriminate concentrations of breast cancer biomarkers with high selectivity.

MicroRNA (miRNA) has broad application prospects in the early detection of various cancers. In this work, a SPRi/SERS dual-mode biosensor was developed on the same gold chip by AuNPs as the reinforcing medium. High throughput and sensitivity detection of three typical cervical cancer markers miRNA21, miRNA124 and miRNA143 were achieved based on the sandwich structure of polyA blocks-DNA capture probe/target miRNA/AuNPs-assistant probe or SERS nanoprobes. AuNPs greatly improved the SPR response due to mass increase and more sensitive refractive index changes. Meanwhile, due to the LSPR effect of AuNPs, the signal of SERS nanoprobe can be amplified. The miRNAs were detected in serum to verify its practicality. SPRi achieved detection of three miRNAs simultaneously. LODs were 6.3 fM, 5.3 fM and 4.6 fM, respectively, and wide dynamic response range of 500 pM-10 nM. While SERS assay ensured high sensitivity with LODs as low as 1 fM, 0.8 fM and 1.2 fM, respectively, and with the recoveries in the range of 90.0 %-100.2 %. The redundant detection signals of the two modes can provide more reliable data to prevent false positive or false negative detection, and have great application prospects in detection of cancer-related nucleic acids in early stage of disease.

The discovery of miRNAs and their role in disease represent a significant breakthrough that has stimulated and propelled research on miRNAs as targets for diagnosis and therapy. Cardiovascular disease is an area where the restrictions of early diagnosis and conventional pharmacotherapy are evident and deserve attention. Therefore, miRNA-based drugs have significant potential for development. Research and its application can make considerable progress, as seen in preclinical and clinical trials. The use of miRNAs is still experimental but has a promising role in diagnosing and predicting a variety of acute coronary syndrome presentations. Its use, either alone or in combination with currently available biomarkers, might be adopted soon, particularly if there is diagnostic ambiguity. In this review, we examine the current understanding of miRNAs as possible targets for diagnosis and treatment in the cardiovascular system. We report on recent advances in recognising and characterising miRNAs with a focus on clinical translation. The latest challenges and perspectives towards clinical application are discussed.

High-risk benign breast tumors are known to develop breast cancer at high rates. However, it is still controversial whether they should be removed during diagnosis or followed up until cancer development becomes evident. Therefore, this study sought to identify circulating microRNAs (miRNAs) that could serve as detection markers of cancers arising from high-risk benign tumors. Small RNA-seq was performed using plasma samples collected from patients with early-stage breast cancer (CA) and high-risk (HB), moderate-risk (MB), and no-risk (Be) benign breast tumors. Proteomic profiling of CA and HB plasma was performed to investigate the underlying functions of the identified miRNAs. Our findings revealed that four miRNAs, hsa-mir-128-3p, hsa-mir-421, hsa-mir-130b-5p, and hsa-mir-28-5p, were differentially expressed in CA vs. HB and had diagnostic power to discriminate CA from HB with AUC scores greater than 0.7. Enriched pathways based on the target genes of these miRNAs indicated their association with IGF-1. Furthermore, the Ingenuity Pathway Analysis performed on the proteomic data revealed that the IGF-1 signaling pathway was significantly enriched in CA vs. HB. In conclusion, these findings suggest that these miRNAs could potentially serve as biomarkers for detecting early-stage breast cancer from high-risk benign tumors by monitoring IGF signaling-induced malignant transformation.

Finding reliable miRNA markers and revealing their potential mechanisms will play an important role in the diagnosis and treatment of NSCLC. Most existing computational methods for identifying miRNA biomarkers only consider the expression variation of miRNAs or rely heavily on training sets. These deficiencies lead to high false-positive rates. The independent regulatory model is an important complement to traditional models of co-regulation and is more impervious to the dataset. In addition, previous studies of miRNA mechanisms in the development of non-small cell lung cancer (NSCLC) have mostly focused on the post-transcriptional level and did not distinguish between NSCLC subtypes. For the above problems, we improved mainly in two areas: miRNA identification based on both the NOG network and biological functions of miRNA target genes; and the construction of a 4-node directed competitive regulatory network to illustrate the mechanisms. NSCLC was classified as lung adenocarcinoma (LUAD) and lung squamous cell carcinoma (LUSC) in this work. One miRNA biomarker of LUAD (miR-708-5p) and four of LUSC (miR-183-5p, miR-140-5p, miR-766-5p, and miR-766-3p) were obtained. They were validated using literature and external datasets. The ceRNA-hub-FFL involving transcription factors (TFs), microRNAs (miRNAs), mRNAs, and long non-coding RNAs (lncRNAs) was constructed. There were multiple interactions among these components within the net at the transcriptional, post-transcriptional, and protein levels. New regulations were revealed by the network. Meanwhile, the network revealed the reasons for the previous conflicting conclusions on the roles of CD44, ACTB, and ITGB1 in NSCLC, and demonstrated the necessity of typing studies on NSCLC. The novel miRNA markers screening method and the 4-node directed competitive ceRNA-hub-FFL network constructed in this work can provide new ideas for screening tumor markers and understanding tumor development mechanisms in depth.

We examined miRNA biomarkers for ALS extracted from extracellular vesicles in blood samples using a large and diverse patient and control population. Different blood collection and storage protocols by different investigators could impact repeatability of miRNA analysis. We tested the hypotheses that miRNA extracted from extracellular vesicles using immunoaffinity purification techniques are robust and repeatable across investigators, laboratories and in a broad ALS population.
De-identified patient blood plasma samples obtained from the U.S. National ALS Biorepository were compared with plasma from non-ALS controls. Extracellular vesicles were extracted and isolated using L1CAM immunoaffinity purification. Total RNA was extracted, and miRNA quantified using qPCR following careful quality control measures. Gene fold expressions of eight miRNAs were compared using a Mann-Whitney two-tailed test.
One hundred blinded, blood plasma samples were analyzed. Thirty-five men and 15 women with ALS were compared with controls consisting of 30 men and 20 women. None of the ALS patient cohort reported family members with ALS suggesting sporadic ALS. Five of the eight biomarkers previously published were found to significantly discriminate ALS patient samples from control samples.
The methods used in this study provide a repeatable measure of miRNA biomarkers that statistically differentiate ALS patient samples from control samples. The broad inclusion criteria for both the ALS patient cohort and controls along with the collection of blood samples by different investigators suggest that these methods are robust and represent good candidates for further research and development aimed at clinical application.

Bone tumor is a kind of rare cancer, the location of which is mainly in bone tissue as well as cartilage tissue. Bone tumor is mainly classified into benign and malignant types. The survival rate of patients with bone tumors can be considerably improved by early detection, and the danger of amputation caused by bone tumors can be greatly reduced. In this study, we first screened the top 25% serum miRNAs with the greatest variance in patients with malignant and benign bone tumor and healthy individuals. The expression of serum miRNAs in patients with bone tumor was then examined using unsupervised clustering and PCA, and the results revealed that the overall expression of serum miRNAs was ineffective in distinguishing patients with benign/malignant bone tumors. Subsequently, we screened 19 miRNA biomarkers that could be used to determine the benign/malignant bone tumor of patients by LASSO logistic regression. These genes were validated using ROC curves. Results showed that there were 11 miRNAs that could accurately distinguish benign/malignant bone tumor alone. These 11 miRNAs were, namely, hsa-miR-192-5p, hsa-miR-137, hsa-miR-142-3p, hsa-miR-155-3p, hsa-miR-1205, hsa-miR-1273a, hsa-miR-3187-3p, hsa-miR-1255b-2-3p, hsa-miR-1288-5p, hsa-miR-6836-5p, and hsa-miR-6862-5p. Next, we established a diagnostic model using logistic regression and validated the diagnostic model using ROC curves; the result of which showed that the model had good diagnostic efficacy. Then, we also verified that the diagnostic model established by these 11 miRNAs could distinguish patients with benign/malignant bone tumor using unsupervised clustering as well as PCA. Finally, by using qPCR, we validated the expression of 11 miRNAs in the serum of patients with malignant and benign bone tumors, as well as healthy volunteers. The results were consistent with the trend of miRNAs expression in public databases. In summary, we examined the differential expression of serum miRNAs in individuals with benign and malignant bone tumors and discovered 11 miRNA biomarkers that could be utilized to discriminate between the two.

UNASSIGNED: MicroRNAs (miRNAs) are post-transcriptional regulators with potential as biomarkers for cancer management. Data-driven competing endogenous RNA (ceRNA) network modeling is an effective way to decipher the complex interplay between miRNAs and spongers. However, there are currently no general rules for ceRNA network-based biomarker prioritization.
UNASSIGNED: In this study, a novel bioinformatics model was developed by integrating gene expression with multivariate miRNA-target data for ceRNA network-based biomarker discovery. Compared with traditional methods, the structural vulnerability in the human long non-coding RNA (lncRNA)-miRNA-messenger RNAs (mRNA) network was comprehensively analyzed, and the single-line regulatory or competing mode among miRNAs, lncRNAs, and mRNAs was characterized and quantified as statistical evidence for miRNA biomarker identification. The application of this model to prostate cancer (PCa) metastasis identified a total of 12 miRNAs as putative biomarkers from the metastatic PCa-specific lncRNA-miRNA-mRNA network and nine of them have been previously reported as biomarkers for PCa metastasis. The receiver operating characteristic curve and cell line qRT-PCR experiments demonstrated the power of miR-26b-5p, miR-130a-3p, and miR-363-3p as novel candidates for predicting PCa metastasis. Moreover, PCa-associated pathways such as prostate cancer signaling, ERK/MAPK signaling, and TGF-β signaling were significantly enriched by targets of identified miRNAs, indicating the underlying mechanisms of miRNAs in PCa carcinogenesis.
UNASSIGNED: A novel ceRNA-based bioinformatics model was proposed and applied to screen candidate miRNA biomarkers for PCa metastasis. Functional validations using human samples and clinical data will be performed for future translational studies on the identified miRNAs.

Cervical cancer is a global public health subject as it affects women in the reproductive ages, and accounts for the second largest burden among cancer patients worldwide with an unforgiving 50% mortality rate. Relatively scant awareness and limited access to effective diagnosis have led to this enormous disease burden, calling for point-of-care, minimally invasive diagnosis methods. Here, an end-to-end quantitative unified pipeline for diagnosis has been developed, beginning with identification of optimal biomarkers, concurrent design of toehold switch sensors, and finally simulation of the designed diagnostic circuits to assess performance. Using miRNA expression data in the public domain, we identified miR-21-5p and miR-20a-5p as blood-based miRNA biomarkers specific to early-stage cervical cancer employing a multi-tier algorithmic screening. Synthetic riboregulators called toehold switches specific to the biomarker panel were then designed. To predict the dynamic range of toehold switches for use in genetic circuits as biosensors, we used a generic grammar of these switches, and built a neural network model of dynamic range using thermodynamic features derived from mRNA secondary structure and interaction. Second-generation toehold switches were used to overcome the design challenges associated with miRNA biomarkers. The resultant model yielded an adj. R2 ∼0.71, outperforming earlier models of toehold-switch dynamic range. Reaction kinetics modelling was performed to predict the sensitivity of the second-generation toehold switches to the miRNA biomarkers. Simulations showed a linear response between 10 nM and 100 nM before saturation. Our study demonstrates an end-to-end computational workflow for the efficient design of genetic circuits geared towards the effective detection of unique genomic/nucleic-acid signatures. The approach has the potential to replace iterative experimental trial and error, and focus time, money, and efforts. All software including the toehold grammar parser, neural network model and reaction kinetics simulation are available as open-source software (https://github.com/SASTRA-iGEM2019) under GNU GPLv3 licence.

Background: Prostate cancer (PCa) is occurred with increasing incidence and heterogeneous pathogenesis. Although clinical strategies are accumulated for PCa prevention, there is still a lack of sensitive biomarkers for the holistic management in PCa occurrence and progression. Based on systems biology and artificial intelligence, translational informatics provides new perspectives for PCa biomarker prioritization and carcinogenic survey. Methods: In this study, gene expression and miRNA-mRNA association data were integrated to construct conditional networks specific to PCa occurrence and progression, respectively. Based on network modeling, hub miRNAs with significantly strong single-line regulatory power were topologically identified and those shared by the condition-specific network systems were chosen as candidate biomarkers for computational validation and functional enrichment analysis. Results: Nine miRNAs, i.e., hsa-miR-1-3p, hsa-miR-125b-5p, hsa-miR-145-5p, hsa-miR-182-5p, hsa-miR-198, hsa-miR-22-3p, hsa-miR-24-3p, hsa-miR-34a-5p, and hsa-miR-499a-5p, were prioritized as key players for PCa management. Most of these miRNAs achieved high AUC values (AUC > 0.70) in differentiating different prostate samples. Among them, seven of the miRNAs have been previously reported as PCa biomarkers, which indicated the performance of the proposed model. The remaining hsa-miR-22-3p and hsa-miR-499a-5p could serve as novel candidates for PCa predicting and monitoring. In particular, key miRNA-mRNA regulations were extracted for pathogenetic understanding. Here hsa-miR-145-5p was selected as the case and hsa-miR-145-5p/NDRG2/AR and hsa-miR-145-5p/KLF5/AR axis were found to be putative mechanisms during PCa evolution. In addition, Wnt signaling, prostate cancer, microRNAs in cancer etc. were significantly enriched by the identified miRNAs-mRNAs, demonstrating the functional role of the identified miRNAs in PCa genesis. Conclusion: Biomarker miRNAs together with the associated miRNA-mRNA relations were computationally identified and analyzed for PCa management and carcinogenic deciphering. Further experimental and clinical validations using low-throughput techniques and human samples are expected for future translational studies.