Unique circulating microRNA profiles in epidemic Kaposi's sarcoma

The miRNomics of antiretroviral therapy-induced obesity

Human immunodeficiency virus (HIV) is a retrovirus that incorporates its genetic material into the host's chromosome. The resulting diseases and related conditions constitute a global health problem as there are no treatments to eliminate HIV from an infected individual. However, the potent, complex, and active antiretroviral therapy (ART) strategies have been able to successfully inhibit HIV replication in patients. Unfortunately, obesity following ART is frequent among HIV-infected patients. The mechanism underlying ART-induced obesity is characterized based on expression of traditional markers such as genes and proteins. However, little is known about, yet another key component of molecular biology known as microRNAs (miRNAs). Micro-RNAs are ~ 22 base-long non-coding nucleotides capable of regulating more than 60% of all human protein-coding genes. The interest in miRNA molecules is increasing and their roles in HIV and obesity are beginning to be apparent. In this review, we provide an overview of HIV and its associated diseases, ART-induced obesity, and discuss the roles and plausible benefits of miRNAs in regulating obesity genes in HIV-infected patients. Understanding the roles of miRNAs in ART-induced obesity will aid in tracking the disease progression and designing beneficial therapeutic approaches.

Investigation of key miRNAs and Target-mRNA in Kaposi's sarcoma using bioinformatic methods

Kaposi's sarcoma (KS) is the second most common tumor in human immunodeficiency virus (HIV) infected patients worldwide. While many miRNAs have been confirmed to be involved in KS biological processes, no relevant studies have combined miRNA and mRNA expression profiles using KS patient tissue biopsies. In this study, we performed transcriptome sequencing on tumor and normal tissues from four KS patients and identified differentially expressed mRNA and miRNA, further performed target gene prediction and enrichment analysis. 19,551 target-mRNAs were identified by predicting 106 miRNAs, with 553 overlapping with 571 significantly differentially expressed mRNAs. Enrichment analysis showed significant involvement of the Ubiquitin-mediated proteolysis pathway. Additionally, the miRNA-mRNA interaction network was established, and the topological score of Cytohubba's algorithm was calculated for comparison with three other datasets. The Mutual Clustering Coefficient (MCC) scoring ranking placed ZBTB34, NFIB, and RORA as the top three mRNAs, while hsa-miR-16-5p, hsa-miR-27a-3p, hsa-miR-340-5p, hsa-miR-182-5p, and hsa-miR-186-5p ranked as the top five miRNAs. Hsa-miR-101-3p is the only miRNA that appears both in the top 10 MCC scores and at the intersection of the other two datasets. Finally, qRT-PCR was used to validate the findings at the cellular level. In summary, the miRNA analysis results indicated that hsa-miR-101-3p could be used as a potential diagnostic or therapeutic marker in future studies. Moreover, the mRNA analysis results suggested that the histone binding pathways involved in mRNAs and ubiquitin-related biological processes were closely associated with KS and could serve as promising biomarkers for the diagnosis and treatment of this disease.

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.