Identification of prognostic burns-related indicators and microRNA biosignatures in burns patients with inhalation injury

The study was approved by the Health and Wellness Research Ethics Committee (HW-REC), Cape Peninsula University of Technology (CPUT), and the Stellenbosch University (SU) Health Research Ethics Committee (reference: CPUT/HW-REC 2015/H15; CPUT/HW-REC 2017/H20). Student ethics approval was also granted by the CPUT REC (CPUT/HW-REC 2017/H20). Site approval was provided from the Chief Executive Office and Medical Services Manager/Research Coordinator to conduct research at TBH, Tygerberg, CPT, WC, SA in accordance with the Provincial Policy and TBH Notice No 40/2009 (reference: CPUT/HW-REC 2015/H15). The study was also registered with the WC Government National Health Research Database (reference: 2016RP18364).Study aspects involves (1) medical-files based data to (i) observe epidemiological alignment of inhalation injury with similar clinical settings and other LMICs (comparative tests between parameter subgroups), (ii) determine clinical markers for mortality and the significance of inhalation injury in relation to mortality (using Fisher's Exact test, Spearman and/or Pearson's correlation coefficient and partial least squares regress) and (iii) determine clinical markers for inhalation injury (using Fisher's Exact test, Spearman and/or Pearson's correlation coefficient and partial least squares regress). These were performed on the data set named: 'Demographic, injury, and clinical data of all samples' in Data set 1 or Data set 1.In addition, (2) human whole blood was used for RNA sequencing to determine predictive miRNAs for inhalation injury by using (i) the Illumina platform for RNA sequencing, (ii) sRNA bench and Bowtie for sequence alignment to human genome, (iii) EdgeR and DeSeq2 pipelines for differential expression analysis, and (iv) the Fisher's exact test for comparison between DE miRNAs between mild and severe inhalation injury. The data sets named 'Demographic, injury, clinical, and total RNA-related data of all exemplar samples' and 'Demographic, injury, clinical, and total RNA-related of all exemplar samples that passed QC for Sequencing' were the samples used for RNA sequencing.Finally, (3) DE miRNA meeting the threshold criteria, i.e., overlapped between EdgeR and DeSeq2, fold change ≤1.5 and Padj value (i) miRNet (for target gene networks), (ii) STRING and (iii) Cytoscape (protein-protein networks), (iv) Cytoscape's MCODE and cytoHubba plugin (for top clusters and hub genes). In turn the top hub genes were subjected to the following functional enrichment pathway analysis: (v) Gene ontology (GO) biological processes (BP) terms, (vi) Kyoto Encyclopedia of Genes and Genomes (KEGG), (vii) Reactome, and (viii) PANTHER pathways. All were performed using the online EnrichR platform. The related data sets named 'Data sets all' or 'Data set 2.1 - DE miRNA EdgeR' and 'Data set 2.1 - DE miRNA DeSeq2' was used to determine the DE miRNA meeting the threshold criteria and subsequently used for the aforementioned computational analysis.