Systematic review and meta-analysis of dysregulated microRNAs derived from liquid biopsies as biomarkers for amyotrophic lateral sclerosis

The discovery of disease-specific biomarkers, such as microRNAs (miRNAs), holds the potential to transform the landscape of Amyotrophic Lateral Sclerosis (ALS) by facilitating timely diagnosis, monitoring treatment response, and accelerating drug discovery. Such advancement could ultimately improve the quality of life and survival rates for ALS patients. Despite more than a decade of research, no miRNA biomarker candidate has been translated into clinical practice. We conducted a systematic review and meta-analysis to quantitatively synthesize data from original studies that analyzed miRNA expression from liquid biopsies via PCR and compared them to healthy controls. Our analysis encompasses 807 miRNA observations from 31 studies, stratified according to their source tissue. We identified consistently dysregulated miRNAs in serum (hsa-miR-3665, -4530, -4745-5p, -206); blood (hsa-miR-338-3p, -183-5p); cerebrospinal fluid (hsa-miR-34a-3p); plasma (hsa-miR-206); and neural-enriched extracellular vesicles from plasma (hsa-miR-146a-5p, -151a-5p, -10b-5p, -29b-3p, and -4454). The meta-analyses provided further support for the upregulation of hsa-miR-206, hsa-miR-338-3p, hsa-miR-146a-5p and hsa-miR-151a-5p, and downregulation of hsa-miR-183-5p, hsa-miR-10b-5p, hsa-miR-29b-3p, and hsa-miR-4454 as consistent indicators of ALS across independent studies. Our findings provide valuable insights into the current understanding of miRNAs' dysregulated expression in ALS patients and on the researchers' choices of methodology. This work contributes to the ongoing efforts towards discovering disease-specific biomarkers.

Identifying driver genes involving gene dysregulated expression, tissue-specific expression and gene-gene network

BACKGROUND

Cancer as a kind of genomic alteration disease each year deprives many people's life. The biggest challenge to overcome cancer is to identify driver genes that promote the cancer development from a huge amount of passenger mutations that have no effect on the selective growth advantage of cancer. In order to solve those problems, some researchers have started to focus on identification of driver genes by integrating networks with other biological information. However, more efforts should be needed to improve the prediction performance.

METHODS

Considering the facts that driver genes have impact on expression of their downstream genes, they likely interact with each other to form functional modules and those modules should tend to be expressed similarly in the same tissue. We proposed a novel model named by DyTidriver to identify driver genes through involving the gene dysregulated expression, tissue-specific expression and variation frequency into the human functional interaction network (e.g. human FIN).

RESULTS

This method was applied on 974 breast, 316 prostate and 230 lung cancer patients. The consequence shows our method outperformed other five existing methods in terms of Fscore, Precision and Recall values. The enrichment and cociter analysis illustrate DyTidriver can not only identifies the driver genes enriched in some significant pathways but also has the capability to figure out some unknown driver genes.

CONCLUSION

The final results imply that driver genes are those that impact more dysregulated genes and express similarly in the same tissue.

Long non-coding RNAs in ovarian cancer

Long non-coding RNAs (lncRNAs) refer to functional cellular RNAs molecules longer than 200 nucleotides in length. Unlike microRNAs, which have been widely studied, little is known about the enigmatic role of lncRNAs. However, lncRNAs have motivated extensively attention in the past few years and are emerging as potentially important regulators in pathological processes, including in cancer. We now understand that lncRNAs play role in cancer through their interactions with DNA, protein, and RNA in many instances. Moreover, accumulating evidence has recognized that large classes of lncRNAs are functional for ovarian cancer. Nevertheless, the biological phenomena and molecular mechanisms of lncRNAs in ovarian cancer remain to be better identified. In this review, we outline the dysregulated expression of lncRNAs and their potential clinical implications in ovarian cancer, with a particular emphasis on discussing the well characterized mechanisms underlying lncRNAs in ovarian cancer.