Deregulation of microRNAs in oral squamous cell carcinoma, a bioinformatics analysis

Expression of DDSR1 Long Non-Coding RNA and Genes Involved in the DNA Damage Response in Sperm with DNA Fragmentation

The molecular mechanism responsible for sperm DNA fragmentation is not fully understood. Therefore, identifying genes related to the response to DNA damage is an important area of research. Recently, the role of long non-coding RNAs (LncRNAs), especially DNA damage-sensitive RNA1 (DDSR1) in male infertility has been highlighted. In this research, a protein-protein interaction network (PPIN) was constructed using the STRING database, and functional classification was conducted using webgestalt servers. Subsequently, a group of 40 males with a high degree of sperm DNA fragmentation (DFI ≥ 25%) was compared to a control group of 20 healthy males with a normal sperm DNA fragmentation rate (DFI < 25%). To assess gene expression, real-time polymerase chain reaction (PCR) analysis was performed on DNA samples obtained from both healthy and infertile males. Our findings revealed that infertile men with an abnormal DFI index showed significantly lower expression levels of the long noncoding RNA DDSR1, as well as the genes BRCA1, MRE11A, RAD51, and NBN, compared to the control group. Pathway analysis of the network proteins using Reactome indicated involvement in crucial cellular processes such as the cell cycle, DNA repair, meiosis, reproduction, and extension of telomeres. In conclusion, the downregulation of LncRNA and genes associated with the DNA damage response in males with an abnormal DFI suggests that these factors may contribute to the development of sperm DNA fragmentation and could potentially serve as diagnostic markers for further investigation in therapeutic interventions in the future.

Hubs and Bottlenecks in Protein-Protein Interaction Networks

Protein-protein interaction networks (PPINs) represent the physical interactions among proteins in a cell. These interactions are critical in all cellular processes, including signal transduction, metabolic regulation, and gene expression. In PPINs, centrality measures are widely used to identify the most critical nodes. The two most commonly used centrality measures in networks are degree and betweenness centralities. Degree centrality is the number of connections a node has in the network, and betweenness centrality is the measure of the extent to which a node lies on the shortest paths between pairs of other nodes in the network. In PPINs, proteins with high degree and betweenness centrality are referred to as hubs and bottlenecks respectively. Hubs and bottlenecks are topologically and functionally essential proteins that play crucial roles in maintaining the network's structure and function. This article comprehensively reviews essential literature on hubs and bottlenecks, including their properties and functions.

A systems biology approach and in vitro experiment indicated Rapamycin targets key cancer and cell cycle-related genes and miRNAs in triple-negative breast cancer cells

An anticancer drug known as Rapamycin acts by inhibiting the mammalian target of the Rapamycin pathway. This agent has recently been investigated for its potential therapeutic benefits in sensitizing drug-resistant breast cancer (BC) treatment. The molecular mechanism underlying these effects, however, is still a mystery. Using a systems biology method and in vitro experiment, this study sought to discover essential genes and microRNAs (miRNAs) targeted by Rapamycin in triple-negative BC (TNBC) cells to aid prospective new medications with less adverse effects in BC treatment. We developed the transcription factor-miRNA-gene and protein-protein interaction networks using the freely accessible microarray data sets. FANMOD and MCODE were utilized to identify critical regulatory motifs, clusters, and seeds. Then, functional enrichment analyses were conducted. Using topological analysis and motif detection, the most important genes and miRNAs were discovered. We used quantitative real-time polymerase chain reaction (qRT-PCR) to examine the effect of Rapamycin on the expression of the selected genes and miRNAs to verify our findings. We performed flow cytometry to investigate Rapamycin's impact on cell cycle and apoptosis. Furthermore, wound healing and migration assays were done. Three downregulated (PTGS2, EGFR, VEGFA) and three upregulated (c-MYC, MAPK1, PIK3R1) genes were chosen as candidates for additional experimental verification. There were also three upregulated miRNAs (miR-92a, miR-16, miR-20a) and three downregulated miRNAs (miR-146a, miR-145, miR-27a) among the six selected miRNAs. The qRT-PCR findings in MDA-MB-231 cells indicated that c-MYC, MAPK1, PIK3R1, miR-92a, miR-16, and miR-20a expression levels were considerably elevated following Rapamycin treatment, whereas PTGS2, EGFR, VEGFA, miR-146a, and miR-145 expression levels were dramatically lowered (p < 0.05). These genes are engaged in cancer pathways, transcriptional dysregulation in cancer, and cell cycle, according to the top pathway enrichment findings. Migration and wound healing abilities of the cells declined after Rapamycin treatment, and the number of apoptotic cells increased. We demonstrated that Rapamycin suppresses cell migration and metastasis in the TNBC cell line. In addition, our data indicated that Rapamycin induces apoptosis in this cell line. The discovered vital genes and miRNAs affected by Rapamycin are anticipated to have crucial roles in the pathogenesis of TNBC and its therapeutic resistance.

Salivary miRNAs as non-invasive biomarkers of hepatocellular carcinoma: a pilot study

BACKGROUND

Improved detection of hepatocellular carcinoma (HCC) is needed, as current detection methods, such as alpha fetoprotein (AFP) and ultrasound, suffer from poor sensitivity. MicroRNAs (miRNAs) are small, non-coding RNAs that regulate many cellular functions and impact cancer development and progression. Notably, miRNAs are detectable in saliva and have shown potential as non-invasive biomarkers for a number of cancers including breast, oral, and lung cancers. Here, we present, to our knowledge, the first report of salivary miRNAs in HCC and compare these findings to patients with cirrhosis, a high-risk cohort for HCC.

METHODS

We performed small RNA sequencing in 20 patients with HCC and 19 with cirrhosis. Eleven patients with HCC had chronic liver disease, and analyses were performed with these samples combined and stratified by the presence of chronic liver disease. P values were adjusted for multiple comparisons using a false discovery rate (FDR) approach and miRNA with FDR P < 0.05 were considered statistically significant. Differential expression of salivary miRNAs was compared to a previously published report of miRNAs in liver tissue of patients with HCC vs cirrhosis. Support vector machines and leave-one-out cross-validation were performed to determine if salivary miRNAs have predictive potential for detecting HCC.

RESULTS

A total of 4,565 precursor and mature miRNAs were detected in saliva and 365 were significantly different between those with HCC compared to cirrhosis (FDR P < 0.05). Interestingly, 283 of these miRNAs were significantly downregulated in patients with HCC. Machine-learning identified a combination of 10 miRNAs and covariates that accurately classified patients with HCC (AUC = 0.87). In addition, we identified three miRNAs that were differentially expressed in HCC saliva samples and in a previously published study of miRNAs in HCC tissue compared to cirrhotic liver tissue.

CONCLUSIONS

This study demonstrates, for the first time, that miRNAs relevant to HCC are detectable in saliva, that salivary miRNA signatures show potential to be highly sensitive and specific non-invasive biomarkers of HCC, and that additional studies utilizing larger cohorts are needed.