## 2 Materials and Methods
### Functional Enrichment Analysis  
Gene enrichment analysis was performed via g:Profiler web tool ([[https://bilt.cs.ut.ee/gprofiler/gost](https://bilt.cs.ut.ee/gprofiler/gost)]([https://bilt.cs.ut.ee/gprofiler/gost](https://bilt.cs.ut.ee/gprofiler/gost)) (accessed on 2 February 2021), ranking genes according to their absolute fold change and using Bonferroni-Hochberg FDR of 0.05 as threshold [47; 49]. Kyoto Encyclopedia of Genes and Genomes (KEGG) ([[https://www.genome.jp/kegg/](https://www.genome.jp/kegg/)]([https://www.genome.jp/kegg/](https://www.genome.jp/kegg/)) (accessed on 2 February 2021) pathway analysis, WikiPathways analysis ([[https://www.wikipathways.org/index.php/WikiPathways](https://www.wikipathways.org/index.php/WikiPathways)]([https://www.wikipathways.org/index.php/WikiPathways](https://www.wikipathways.org/index.php/WikiPathways)) (accessed on 2 February 2021), and Reactome ([[https://reactome.org/](https://reactome.org/)]([https://reactome.org/](https://reactome.org/)) (accessed on 2 February 2021) pathway analysis of differentially expressed coding genes was performed. Moreover, Gene Ontology (GO) analysis for biological processes, cellular components, and molecular function [50; 51], and CORUM analysis were executed and represented with ClueGO app ([[http://www.cytoscape.org/](http://www.cytoscape.org/)]([http://www.cytoscape.org/](http://www.cytoscape.org/)) (accessed on 4 February 2021), a Cytoscape plug-in developed to facilitate the biological interpretation and to visualize functionally grouped terms [52]. Moreover, regulatory motifs in DNA with TRANSFAC, miRNAs targets from miRTarBase and human disease phenotypes from Human Phenotype Ontology (HP) analyses were performed [49]. The R software was used to generate heatmaps (heatmap.2 function from the R ggplots package), PCA plot (pcomp function from the R ggplots package), volcano plots [53], and dotplot graphs (ggplot2 library).  
RNA Secondary Structure Prediction, Pairwise Alignment, Subcellular Localization, and Functional Annotation  
Long non-coding RNAs (RNAs) were selected considering their homology with human GRCh37 genome using MGI web tool ([[http://www.informatics.jax.org/](http://www.informatics.jax.org/)]([http://www.informatics.jax.org/](http://www.informatics.jax.org/)) (accessed on 4 February 2021) and were subjected to further analysis. RNA secondary structure of selected lncRNAs was predicted using RNA Fold web server ([[http://ma.tbi.univie.ac.at/cgi-bin/RNAWebSuite/RNAfold.cgi](http://ma.tbi.univie.ac.at/cgi-bin/RNAWebSuite/RNAfold.cgi)]([http://ma.tbi.univie.ac.at/cgi-bin/RNAWebSuite/RNAfold.cgi](http://ma.tbi.univie.ac.at/cgi-bin/RNAWebSuite/RNAfold.cgi)) (accessed on 7 February 2021) with default settings. Pairwise alignment was computed using the Geneious software with default settings (Geneious version 2020.1 created by Biomarkers. Available from [[https://www.geneious.com](https://www.geneious.com)]([https://www.geneious.com](https://www.geneious.com)) (accessed on 8 February 2021). Subcellular localization was predicted through the LnLocator software [54] while functional annotation was performed on AnnoLnc2 database [55].