Downregulated miR-18a and miR-92a synergistically suppress non-small cell lung cancer via targeting Sprouty 4

Epigenetic regulation of hepatocellular carcinoma progression: MicroRNAs as therapeutic, diagnostic and prognostic factors

Hepatocellular carcinoma (HCC), a significant challenge for public healthcare systems in developed Western countries including the USA, Canada, and the UK, is influenced by different risk factors including hepatitis virus infections, alcoholism, and smoking. The disruption in the balance of microRNAs (miRNAs) plays a vital function in tumorigenesis, given their function as regulators in numerous signaling networks. These miRNAs, which are mature and active in the cytoplasm, work by reducing the expression of target genes through their impact on mRNAs. MiRNAs are particularly significant in HCC as they regulate key aspects of the tumor, like proliferation and invasion. Additionally, during treatment phases such as chemotherapy and radiotherapy, the levels of miRNAs are key determinants. Pre-clinical experiments have demonstrated that altered miRNA expression contributes to HCC development, metastasis, drug resistance, and radio-resistance, highlighting related molecular pathways and processes like MMPs, EMT, apoptosis, and autophagy. Furthermore, the regulatory role of miRNAs in HCC extends beyond their immediate function, as they are also influenced by other epigenetic factors like lncRNAs and circular RNAs (circRNAs), as discussed in recent reviews. Applying these discoveries in predicting the prognosis of HCC could mark a significant advancement in the therapy of this disease.

Advances in applications of artificial intelligence algorithms for cancer-related miRNA research

MiRNAs are a class of small non-coding RNAs, which regulate gene expression post-transcriptionally by partial complementary base pairing. Aberrant miRNA expressions have been reported in tumor tissues and peripheral blood of cancer patients. In recent years, artificial intelligence algorithms such as machine learning and deep learning have been widely used in bioinformatic research. Compared to traditional bioinformatic tools, miRNA target prediction tools based on artificial intelligence algorithms have higher accuracy, and can successfully predict subcellular localization and redistribution of miRNAs to deepen our understanding. Additionally, the construction of clinical models based on artificial intelligence algorithms could significantly improve the mining efficiency of miRNA used as biomarkers. In this article, we summarize recent development of bioinformatic miRNA tools based on artificial intelligence algorithms, focusing on the potential of machine learning and deep learning in cancer-related miRNA research.

Role of SPRY4 in health and disease

SPRY4 is a protein encoding gene that belongs to the Spry family. It inhibits the mitogen-activated protein kinase (MAPK) signaling pathway and plays a role in various biological functions under normal and pathological conditions. The SPRY4 protein has a specific structure and interacts with other molecules to regulate cellular behavior. It serves as a negative feedback inhibitor of the receptor protein tyrosine kinases (RTK) signaling pathway and interferes with cell proliferation and migration. SPRY4 also influences inflammation, oxidative stress, and cell apoptosis. In different types of tumors, SPRY4 can act as a tumor suppressor or an oncogene. Its dysregulation is associated with the development and progression of various cancers, including colorectal cancer, glioblastoma, hepatocellular carcinoma, perihilar cholangiocarcinoma, gastric cancer, breast cancer, and lung cancer. SPRY4 is also involved in organ development and is associated with ischemic diseases. Further research is ongoing to understand the expression and function of SPRY4 in specific tumor microenvironments and its potential as a therapeutic target.

A label-free activatable biosensor for in situ detection of exosomal microRNAs based on DNA-AgNCs and hairpin type nucleic acid probes

Exosomal microRNA (miRNA) is a potential biomarker for cancer diagnosis, metastasis, and treatment. In situ detection of exosomal miRNA is an attractive option due to its simplicity and high accuracy. However, in situ exosomal miRNA detection has encountered challenges because of the low target abundance of targets and limited probe permeability. Herein, a label-free and activatable biosensor was developed for in situ exosomal miRNA assays by utilizing hairpin-shaped nucleic acid probes and DNA-hosted silver nanoclusters (DNA-AgNCs). The probe is directly internalized into the exosomes, and then hybridized with the target miRNA-21. Subsequently, the DNA-AgNCs are pulled closer to the G-rich sequence, ultimately leading to in situ red fluorescence activation. The biosensor not only can detect exosomal miRNA-21 but also distinguish cancer cells from normal cells. Under optimal reaction conditions, the detection limit (LOD) of exosomal miRNA-21 is 1.53 × 107 particles per mL. Furthermore, DNA-AgNCs are used as label-free signal elements for in situ detection of exosomal miRNAs for the first time, expanding the application of nanomaterials in this field. This strategy does not require tedious RNA extraction steps and expensive instruments, and may develop into a non-invasive diagnostic tool for ovarian cancer.

tRNA-derived fragments: mechanism of gene regulation and clinical application in lung cancer

Lung cancer, being the most widespread and lethal form of cancer globally, has a high incidence and mortality rate primarily attributed to challenges associated with early detection, extensive metastasis, and frequent recurrence. In the context of lung cancer development, noncoding RNA molecules have a crucial role in governing gene expression and protein synthesis. Specifically, tRNA-derived fragments (tRFs), a subset of noncoding RNAs, exert significant biological influences on cancer progression, encompassing transcription and translation processes as well as epigenetic regulation. This article primarily examines the mechanisms by which tRFs modulate gene expression and contribute to tumorigenesis in lung cancer. Furthermore, we provide a comprehensive overview of the current bioinformatics analysis of tRFs in lung cancer, with the objective of offering a systematic and efficient approach for studying the expression profiling, functional enrichment, and molecular mechanisms of tRFs in this disease. Finally, we discuss the clinical significance and potential avenues for future research on tRFs in lung cancer. This paper presents a comprehensive systematic review of the existing research findings on tRFs in lung cancer, aiming to offer improved biomarkers and drug targets for clinical management of lung cancer.

Recent advances on high-efficiency of microRNAs in different types of lung cancer: a comprehensive review

Carcinoma of the lung is among the most common types of cancer globally. Concerning its histology, it is categorized as a non-small cell carcinoma (NSCLC) and a small cell cancer (SCLC) subtype. MicroRNAs (miRNAs) are a member of non-coding RNA whose nucleotides range from 19 to 25. They are known to be critical regulators of cancer via epigenetic control of oncogenes expression and by regulating tumor suppressor genes. miRNAs have an essential function in a tumorous microenvironment via modulating cancer cell growth, metastasis, angiogenesis, metabolism, and apoptosis. Moreover, a wide range of information produced via several investigations indicates their tumor-suppressing, oncogenic, diagnostic assessment, and predictive marker functions in different types of lung malignancy. miRNA mimics or anti-miRNAs can be transferred into a lung cancer cell, with possible curative implications. As a result, miRNAs hold promise as targets for lung cancer treatment and detection. In this study, we investigate the different functions of various miRNAs in different types of lung malignancy, which have been achieved in recent years that show the lung cancer-associated regulation of miRNAs expression, concerning their function in lung cancer beginning, development, and resistance to chemotherapy, also the probability to utilize miRNAs as predictive biomarkers for therapy reaction.

Expression of miR-92a is associated with the prognosis in non-small cell lung cancer: An observation study

With the development of molecular biology technology, the discovery of microRNAs (miRNAs) has provided new ideas for the diagnosis, treatment, and prognosis of lung cancer and laid a foundation for the study of this malignancy. To assess the potential prognostic value of miR-92a as a new biomarker in non-small cell lung cancer (NSCLC) via clinical evaluation, a total of 100 patients with NSCLC admitted to the Respiratory and Intensive Care Department of Suining Central Hospital in Sichuan Province between August 2007 and April 2020 were retrospectively analyzed. The correlation between miR-92a expression and prognosis of patients with NSCLC was also evaluated in the present study. The expression level of miR-92a was measured by PT-PCR and in situ hybridization. Chi-square test was adopted to explore the relationship of miR-92a expression and clinical features. Kaplan-Meier survival curves were plotted to delineate the overall survival rate of patients with NSCLC. Cox regression analysis was performed to evaluate the prognostic significance of miR-92a expression in NSCLC. The miR-92a expression in NSCLC tissue samples was significantly higher than that in normal lung tissues (P < .001) and significantly correlated with the Eastern Cooperative Oncology Group score, histological type, and distant metastasis (P < .05). Survival curve revealed that patients with NSCLC and high miR-92a expression had relatively higher mortality than those with low PAK4 expression (P = .001). Cox regression analysis explained that miR-92a expression was associated with the prognosis of patients with NSCLC (HR = 1.8, 95% CI: 1.0-3.2, P = .036). In summary, miR-92a was highly expressed in NSCLC tissues and could act as a prognostic factor for patients with NSCLC. These results illustrate that miR-92a expression plays an important role in the invasion and metastasis of NSCLC, and miR-92a can be used as a new biomarker to determine the prognosis of this cancer.