Glaucoma is a chronic neurodegenerative disease, which is the leading cause of irreversible blindness worldwide. As a response to high intraocular pressure, the clinical and molecular glaucoma biomarkers indicate the biological state of the visual system. Classical and uncovering novel biomarkers of glaucoma development and progression, follow-up, and monitoring the response to treatment are key objectives to improve vision outcomes. While the glaucoma imaging field has successfully validated biomarkers of disease progression, there is still a considerable need for developing new biomarkers of early glaucoma, that is, at the preclinical and initial glaucoma stages. Outstanding clinical trials and animal-model study designs, innovative technology, and analytical approaches in bioinformatics are essential tools to successfully uncover novel glaucoma biomarkers with a high potential for translation into clinical practice. To better understand the clinical and biochemical–molecular–genetic glaucoma pathogenesis, we conducted an analytical, observational, and case-comparative/control study in 358 primary open-angle glaucoma (POAG) patients and 226 comparative-control individuals to collect tears, aqueous humor, and blood to process for identifying POAG biomarkers by exploring several biological pathways, such as inflammation, neurotransmitter/neurotrophin alteration, oxidative stress, gene expression, miRNAs fingerprint and its biological targets, and vascular endothelial dysfunction. We are incredibly enthusiastic about gathering as much information as possible on POAG biomarkers to learn how this information can be used to better steer the diagnosis and therapy of glaucoma to prevent blindness in the predictable future.

On the day of processing, samples were defrosted and prepared for RNA extraction using the miRNeasy Mini Kit (QIAGEN Inc., Hilden, Germany). Briefly, purification is based on spin column chromatography using a proprietary resin as the separation matrix. Small RNAs are separated from other cellular components such as proteins without the use of phenol or chloroform. The quality and quantity of total RNA obtained from tears was assessed using a Bioanalyzer 2100 (Agilent® Technologies, Inc., Santa Clara, CA, USA), and the RNA 6000 Nano Kit (Agilent® Technologies, Inc.). RNA libraries were prepared using NEBNext® Multiplex Small RNA Library Prep Set for Illumina® (#E7300 y #7580; New England BioLabs®, Inc., Ipswich, MA, USA), according to the manufacturer’s protocol https://international.neb.com/protocols /2018/03/27/protocol-for-use-with-nebnext-small-rna-library-prep-set-for-illumina-e7300-e7580-e7560-e7330. According to the guidelines for low RNA concentration samples, the adapters and RT primers were diluted 1:2 with nuclease-free water and 15 cycles were used for the amplification by PCR. The indexed libraries were purified using the QIAquick® PCR Purification Kit (#28104, QIAGEN®, Hilden, Germany). Library quality control was assessed using a 4200 TapeStation (Agilent® Technologies, Inc.) and High Sensitivity D1000 Kit (Agilent® Technologies, Inc.). The miRNA fraction of each library (120–200 bp) was collected using the Pippin Prep System (Sage Science, Inc., Beverly, MA, USA) following the manufacturer’s guidelines and using 3% agarose dye-free gel cassettes with internal standards (Marker P) (Sage Science # CDP3010). miRNAs were quantified using a 4200 TapeStation (Agilent® Technologies, Inc.) and High Sensitivity D1000 Kit (Agilent® Technologies, Inc.) prior to normalization and pooling. Sequencing was performed on a NextSeq 500 System (Illumina, Inc., San Diego, CA, USA) with a Mid-Output flow cell for 150-cycle reads obtaining about 3.5 million reads per sample. FASTQ file quality was assessed using FASTQC tool (https://www.bioinformatics.babraham.ac.uk/projects/fastqc/). Adapters and low liability reads were removed. At this point, non-coding RNAs previously described in the ENSEMBL database were selected and characterized. Statistical analyses were performed using Limma and edgeR packages deposited in Bioconductor (www.bioconductor.org). A predictive analysis based on receiver operating characteristic (ROC) curves was performed to select those miRNAs showing an area under the curve (AUC) greater than 0.75. Subsequently, an analysis of the main components (PCA) was performed.