Circulating miRNAs and lncRNAs serve as biomarkers for early colorectal cancer diagnosis.

BACKGROUND: Colorectal cancer (CRC), the third most prevalent and lethal disease, accounted for approximately 1.9 million new cases and claimed nearly 861,000 lives in 2018. It is imperative to develop a minimally invasive diagnostic technique for early identification of CRC. This would facilitate the selection of patient populations most suitable for clinical trials, monitoring disease progression, assessing treatment effectiveness, and enhancing overall patient care. Utilizing blood as a biomarker source is advantageous due to its minimal discomfort for patients, enabling better integration into clinical and follow-up trials. Recent findings indicate that long noncoding RNAs (lncRNAs) and microRNAs (miRNAs) are detectable in the blood of cancer patients, proving crucial in diagnosing various malignancies. METHODS: In this case-control study, we collected plasma samples from 30 patients diagnosed with colorectal cancer (CRC) and 30 healthy volunteers. Following RNA extraction, we measured the expression levels of specific biomolecules, including miR-410, miR-211, miR-139, miR-197, lncRNA UICLM, lncRNA FEZF1-AS1, miR-129, lncRNA CCAT1, lncRNA BBOX1-AS1, and lncRNA LINC00698, using real-time quantitative polymerase chain reaction (RT-qPCR). The obtained data underwent analysis using the Mann-Whitney test for non-parametric data and the T-test for parametric data. RESULTS: The level of miR-410, miR-211, miR-139, miR-197, lncRNA UICLM, lncRNA FEZF1-AS1 were significantly higher in patients with CRC than healthy controls (p < .05). Meanwhile, the level of miR-129, lncRNA CCAT1, lncRNA BBOX1-AS1, and lncRNA LINC00698 were higher in healthy controls than in CRC patients (p < .05). CONCLUSION: MicroRNA (miRNA) and long noncoding RNAs (lncRNAs) have recently emerged as detectable entities in the blood of cancer patients, playing crucial roles in diagnosing various malignancies. However, their specific relevance in the diagnosis of colorectal cancer (CRC) remains underexplored. This study aimed to investigate miRNA and lncRNA profiles in the plasma fraction of human blood to discern significant differences in content and expression levels between CRC patients and healthy individuals. Our cohort comprised 30 CRC patients and 30 healthy controls, with no statistically significant differences (p < 0.05) in age or gender observed between the two groups. Noteworthy is the uniqueness of our study, as we identified a panel of three significant microRNAs and one significant lncRNA, providing a more reliable prediction compared to existing molecular markers in diagnosing CRC. The four genes examined, including miR-211, miR-129, miR-197, and lncRNA UICLM, demonstrated impeccable results in terms of sensitivity and specificity, suggesting their potential candidacy for inclusion in diagnostic panels. Further validation in a larger statistical population is recommended to confirm the robustness of these genes as promising markers for colorectal cancer diagnosis.

Salivary level of microRNA-146a and microRNA-155 biomarkers in patients with oral lichen planus versus oral squamous cell carcinoma.

BACKGROUND: Oral lichen planus (OLP) is a chronic inflammatory disease of the oral mucosa, which has potential for malignant transformation. MicroRNAs play an important role in immunopathogenesis of OLP, and may be used for prediction of its malignant transformation. This study aimed to assess the salivary level of microRNA-146a and microRNA-155 biomarkers in patients with OLP and oral squamous cell carcinoma (OSCC). METHODS: In this case-control study, unstimulated saliva samples were collected from 60 patients, including 15 patients with dysplastic OLP, 15 OLP patients without dysplasia, 15 patients with OSCC, and 15 healthy controls according to the Navazesh technique. After RNA extraction, the expression of microRNA-146a and microRNA-155 was quantified by real-time quantitative polymerase chain reaction (RT-qPCR). The data were analyzed by the Kruskal-Wallis and Dunn-Bonferroni tests. RESULTS: The difference in expression of microRNA-146a and microRNA-155 among the four groups was significant (P < 0.05). Pairwise comparisons of the groups showed significantly higher expression of microRNA-146a in OLP (P = 0.004) and dysplastic OLP (P = 0.046) patients compared with the control group. Up-regulation of this biomarker in OSCC patients was not significant compared with the control group (P = 0.076). Up-regulation of micro-RNA-155 was only significant in OLP group, compared with the control group (P = 0.009). No other significant differences were found (P > 0.05). CONCLUSION: Considering the altered expression of MicroRNA-146a and microRNA-155 in dysplastic OLP and OSCC, their altered expression may serve as an alarming sign of malignancy. However, further investigations are still required.

Peptidomimetics in cancer targeting.

The low efficiency of treatment strategies is one of the main obstacles to developing cancer inhibitors. Up to now, various classes of therapeutics have been developed to inhibit cancer progression. Peptides due to their small size and easy production compared to proteins are highly regarded in designing cancer vaccines and oncogenic pathway inhibitors. Although peptides seem to be a suitable therapeutic option, their short lifespan, instability, and low binding affinity for their target have not been widely applicable against malignant tumors. Given the peptides' disadvantages, a new class of agents called peptidomimetic has been introduced. With advances in physical chemistry and biochemistry, as well as increased knowledge about biomolecule structures, it is now possible to chemically modify peptides to develop efficient peptidomimetics. In recent years, numerous studies have been performed to the evaluation of the effectiveness of peptidomimetics in inhibiting metastasis, angiogenesis, and cancerous cell growth. Here, we offer a comprehensive review of designed peptidomimetics to diagnose and treat cancer.

Gene expression microarray classification using PCA-BEL.

In this paper, a novel hybrid method is proposed based on Principal Component Analysis (PCA) and Brain Emotional Learning (BEL) network for the classification tasks of gene-expression microarray data. BEL network is a computational neural model of the emotional brain which simulates its neuropsychological features. The distinctive feature of BEL is its low computational complexity which makes it suitable for high dimensional feature vector classification. Thus BEL can be adopted in pattern recognition in order to overcome the curse of dimensionality problem. In the experimental studies, the proposed model is utilized for the classification problems of the small round blue cell tumors (SRBCTs), high grade gliomas (HGG), lung, colon and breast cancer datasets. According to the results based on 5-fold cross validation, the PCA-BEL provides an average accuracy of 100%, 96%, 98.32%, 87.40% and 88% in these datasets respectively. Therefore, they can be effectively used in gene-expression microarray classification tasks.

Practical emotional neural networks.

In this paper, we propose a limbic-based artificial emotional neural network (LiAENN) for a pattern recognition problem. LiAENN is a novel computational neural model of the emotional brain that models emotional situations such as anxiety and confidence in the learning process, the short paths, the forgetting processes, and inhibitory mechanisms of the emotional brain. In the model, the learning weights are adjusted by the proposed anxious confident decayed brain emotional learning rules (ACDBEL). In engineering applications, LiAENN is utilized in facial detection, and emotion recognition. According to the comparative results on ORL and Yale datasets, LiAENN shows a higher accuracy than other applied emotional networks such as brain emotional learning (BEL) and emotional back propagation (EmBP) based networks.

A novel single neuron perceptron with universal approximation and XOR computation properties.

We propose a biologically motivated brain-inspired single neuron perceptron (SNP) with universal approximation and XOR computation properties. This computational model extends the input pattern and is based on the excitatory and inhibitory learning rules inspired from neural connections in the human brain's nervous system. The resulting architecture of SNP can be trained by supervised excitatory and inhibitory online learning rules. The main features of proposed single layer perceptron are universal approximation property and low computational complexity. The method is tested on 6 UCI (University of California, Irvine) pattern recognition and classification datasets. Various comparisons with multilayer perceptron (MLP) with gradient decent backpropagation (GDBP) learning algorithm indicate the superiority of the approach in terms of higher accuracy, lower time, and spatial complexity, as well as faster training. Hence, we believe the proposed approach can be generally applicable to various problems such as in pattern recognition and classification.