16 Identification of LncRNAs as Therapeutic Targets in Chronic Lymphocytic Leukemia Simay Dolaner1,2, Harpreet Kaur2, Mohit Mazumder2, Julia Panov2,3, Elia Brodsky2 1Department of Molecular Biology and Genetics, Bahcesehir University, Istanbul, Turkey 2Pine Biotech, New Orleans, USA 3Tauber Bioinformatics Research Center, Haifa, Israel ABSTRACT: Chronic Lymphocytic Leukemia (CLL) is a type of blood cancer that has a very heterogeneous biological background and diverse treatment strategies. However, a small part of this malignancy may disappear without receiving any treatment, known as “spontaneous re- gression”, which occurs as a result of a poorly investigated mechanism. Exposing the underlying causes of this condition can lead to a novel treatment approach for CLL. In this article, we applied in-silico analysis on total RNA expression data from 24 CLL samples to determine possible reg- ulatory mechanisms of spontaneous regression in CLL. These were first selected by comparing spontaneous regression with progressive samples of CLL at the transcriptional level using two unsupervised machine learning algorithms, i.e., Principal Component Analysis (PCA) and Hier- archical Clustering. Subsequently, the DESeq2 algorithm was used to scrutinize only statisti- cally significant (adjusted p-value < 0.01) RNA transcripts that can differentiate both conditions. Here, at first, we have elucidated 870 significantly differentially expressed protein-coding genes that were involved in the biogenesis and processing of RNA. Consequently, these findings led our study to investigate non-coding RNA, and 33 long non-coding RNAs (lncRNAs) were found to be significantly differentially expressed among two conditions based on differential gene ex- pression analysis. Further, our analysis in the current study suggested lncRNAs, PTPN22-AS1, PCF11-AS1, SYNGAP1-AS1, PRRT3-AS1, and H1FX-AS1 as potential therapeutic targets to trigger spontaneous regression. Ultimately, the results presented here reveal new insights into spontaneous regression and its relationship with non-coding RNAs, particularly lncRNAs. KEYWORDS: Chronic Lymphocytic Leukemia, spontaneous regression, therapeutic targets, LncRNAs, transcriptomics © 2021 Dolaner, Kaur, Mazumder, Panov, Brodsky. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits the user to copy, distribute, and transmit the work provided that the original authors and source are credited. INTRODUCTION According to the American Cancer Society sta- tistics, leukemia is the second leading blood cancer, with roughly 60,000 new cases identified in 2020 [1]. Leukemia has different subtypes, which are classified in the context of the tumor origin [2]. The most common variety in adults is chronic lymphocytic leukemia (CLL), which is a lymphoid malignancy due to failed apoptosis and aggressive proliferation of mature B cells [3]. These cells circulate through the blood as non-proliferating cells or arrested cells in the G0/G1 phase of the cell cycle and may affect the function of normal cells in other organs [4]. Columbia Undergraduate Science Journal Vol. 15, 2021 Dolaner et. al. 17 CLL has a highly diverse biological and clinical background for each patient that determines the stage of the disease [5]. Although there are sev- eral stages of CLL classified according to their genetic background and B cell number, one of the most intriguing concepts is known as spon- taneous regression, which is the disappearance of the tumor over time either without any treat- ment or with treatment that is categorized as insufficient to have an impact on the tumor [6]. Spontaneous regression can be seen in 1-2% of all CLL patients, and it is a phe- nomenon that is poorly understood [7]. In this process, cells that proliferate uncontrolla- bly are transmitted to the quiescent state so that the tumor disappears partially or com- pletely with time [6]. Spontaneous regres- sion is not a common feature of cancer cells and is regulated by mechanisms that are not well-understood. If such mechanisms can be determined, target biological molecules that have a specific role in disease progression can be identified and manipulated in vitro. Current strategies aim to inhibit BCR signaling, which is crucial for the survival of the B-cells, and chemokine signaling that creates survival signals and attracts leukemic cells to communicate with its microenvironment [5]. Moreover, activation of apoptosis pathways via blocking BCL2 activity, an anti-apoptotic protein, which is highly expressed in leukemic cells, is included in the aforementioned strate- gies [8]. Although these types of targeted ther- apies improve the outcome of the patients, the heterogeneity of the leukemia microenviron- ment reinforces the necessity of new targets. As the targeted therapies may have an impact on tumor surroundings and affect the other cells found in the tumor microenvironment, triggering spontaneous regression mechanisms may im- prove the strategies as well as patient outcome. In CLL, the most frequent chromo- somal abnormalities and somatic mutations on the protein-coding region of the genome have been distinguished as a result of ge- nomic sequencing, and disrupted cellular pathways are identified using next-generation sequencing of mRNA expression [9]. In spite of this progress, nearly 20% of CLL cells do not show chromosomal abnormalities or ge- nomic variation. Therefore, researchers re- cently shifted their focus to the non-coding region of the genome and regulatory RNA molecules, especially long non-coding RNAs, which are deregulated in many cancers [10]. Long non-coding RNAs (lncRNAs) are a subgroup of non-coding RNAs which are longer than 200 nucleotides and encom- pass thousands of diverse transcripts in hu- mans [11]. There are approximately 100,000 known lncRNAs, and this quantity is ex- panding each year with the new studies [12]. LncRNAs play a significant role in gene regulation, controlling multiple cellular mecha- nisms involved in tumor progression. They are involved in epigenetic regulation of gene ex- pression via histone modification, DNA meth- ylation, or acetylation. Specifically, these epi- genetic regulations may include recruitment of histone remodeling complexes, interaction with histone methyltransferases and demeth- ylases to regulate DNA methylation, or his- tone acetyltransferases and deacetylases to modulate the acetylation [13]. Furthermore, lncRNAs may regulate gene expression at the transcriptional level by recruiting transcription factors [14]. Moreover, lncRNAs can produce hybrids or act as scaffolds through interac- tion with proteins to regulate expression at the post-translational level, including regula- tion of phosphorylation and ubiquitination [15]. In tumor development, lncRNAs can serve as either tumor suppressors, oncogenes, or even both at the same time for some cancer types [16]. Analysis of lncRNA expression pat- terns had led to the identification of putative bio- markers such as HOTAIR, H19, and DLEU1/2 [17]. Specifically, HOTAIR serves as an onco- gene by inducing invasiveness and metastasis in several cancers via recruiting a demethylase [13]. Since this lncRNA is highly expressed in aggressive tumors, it is considered as a bio- Columbia Undergraduate Science Journal Vol. 15, 2021 Dolaner et. al. 18 marker and a possible therapeutic target for many cancers [18]. DLEU1/2 epigenetically regulate the tumor suppressors and are delet- ed in CLL cells, which results in the progression of the CLL. This allows it to serve as a biomark- er in the diagnosis [19]. On the other hand, H19 has a dual role in different cancers by stimu- lating distinct mechanisms through transcrip- tion factors, which makes it a target that needs to be studied separately for each cancer [13]. Furthermore, lncRNAs are known to play an important role in cell differentiation and tissue specificity [20]. However, characteriza- tion may be compelling because lncRNAs are transcribed in different loci and localized dis- tinctly. More importantly, lncRNA expression is generally tissue-specific and can be detected under certain conditions [21]. As the lncRNAs are differentially expressed, their roles and ac- tivities can be identified in disease conditions. Due to the diverse functions of ln- cRNAs, novel studies are concentrated on the identification of lncRNAs as therapeu- tic targets. As the expression of these mol- ecules is tissue and disease-specific, this specificity makes them excellent targets compared to protein-coding genes [22]. In the scope of this project, the trigger mechanism behind the spontaneous regres- sion process is investigated at the transcrip- tomic level to identify a pattern of lncRNA expression that would explain cell “deci- sion” by comparing CLL tissue at the sponta- neous regression and the progressive states. METHODS Dataset The dataset of this project was generated by Kwok et al. (2020) and published as a BioProj- ect on the NCBI with the accession number PRJNA535508 (Supplementary Notes 1) [6]. Transcriptome data contains raw reads of RNA sequencing from the Illumina Nextseq 550 plat- form by using paired-end sequencing. In this study, the authors compared multiple samples from multiple subtypes of chronic lymphocytic leukemia that can be seen in Table I. In our proj- ect, spontaneous regression and progressive states were chosen for further analysis to inves- tigate expression variation specifically involved in the regression mechanism in CLL cells. Table I. Dataset of the BioProject RNA-seq Raw Data Processing Raw reads were used to construct an RNA se- quencing pipeline that contains pre-processing using Trimmomatic, mapping of reads on refer- ence transcriptome with Bowtie2-t, and quan- tification using RNA-Seq by Expectation-Max- imization (RSEM) algorithms with T-Bioinfo server (Supplementary Notes 1) as represent- ed in Figure 1. Briefly, in order to get gene ex- pression levels, duplicated sequences which resulted in PCR amplification were removed by PCR clean considering best coverage. Then, Trimmomatic was used to remove adaptor se- quences and poor-quality data at the end of the sequence. Mapping of these clean reads on transcriptome was performed by using the Bow- tie2-t algorithm based on the reference genome (GRCh38). Bowtie2-t is an option of the Bowtie, which is a mapping algorithm and aligns short reads according to the seed approach [23]. Fi- nally, for the quantification of gene expression, the RSEM algorithm was used with FPKM nor- malization to obtain the gene expression table. Columbia Undergraduate Science Journal Vol. 15, 2021 Dolaner et. al. 19 Figure 1: RNA-seq Data Processing Pipeline to generate an RNA expression table on T-Bioinfo Server Exploratory Analysis Exploratory analysis facilitated the examina- tion of variation between all samples, includ- ing healthy and diseased patients, in order to select a comparison parameter for further analysis. Thus, the visual outputs were used to determine the patterns. Data was explored by using principal component analysis (PCA), which is a dimensionality reduction technique that discerns the variability between the sam- ples [24]. PCA was performed twice, both for all the samples and for the spontaneous re- gression and the progressive samples to be able to observe the improvement on principle components. Hierarchical Clustering, which finds patterns among the samples using sim- ilarity measures [25], made it possible to un- derstand the clustering aspects of spontaneous regression and progressive samples based on their gene expression, especially after the se- lection of statistically significant genes, which will be explained in the following section. Differential Gene Expression Analysis The Differential Gene Expression analy- sis was conducted by contrasting spon- taneous regression with progressive CLL samples. Separate studies were performed for protein-coding and non-protein cod- ing transcripts to understand the mecha- nisms involved in spontaneous regression. The differential gene expression (DGE) pipe- line, which includes pre-processing, mapping with HiSat2, RNA expression quantification using HTseq, and differential gene expression analysis with DESeq2 algorithm was construct- ed by using the T-Bioinfo server (Figure 2). Here, initially, PCR cleaning was performed by considering coverage to get rid of the duplicat- ed sequences generated via PCR amplifica- tion. To eliminate the adaptors and bad quality sequences from the data, the Trimmomatic al- gorithm was used. Next, the HiSat2 was utilized for the mapping of sequence reads to the refer- ence genome (GRCh38) by taking into account the splice junctions. Then, the HTseq algorithm quantified the gene expression through over- lapping reads and generated a gene expression table in the form of count values. Finally, differ- ential gene expression analysis was performed by using the DESeq2 algorithm which gives the expression differences between two groups us- ing the shrinkage estimators [26]. DESeq2 pro- vides results, which include p-value, log 2-fold change, and adjusted p-value. Then, an ad- justed p-value or False Discovery Rate (FDR) that is a standard statistical value utilized for multiple testing correction, is calculated to eliminate the false-positive results [26, 27]. The T-Bioinfo server uses a one- step approach for DGE analysis and com- bines several methods. Although DESeq2 can be used for both normalization and sta- tistical analysis, the T-Bioinfo server pro- vides an additional approach, which includes gene set enrichment analysis (GSEA) to dis- cover related pathways and processes [28]. Selection of Significant Genes The significant genes were identified by their p-adjusted values and log2 fold change. The de- termined threshold for the adjusted p-value was 0.01 and the log2 fold change was ±1 in non-cod- ing RNAs and ±1.5 in protein-coding genes. Columbia Undergraduate Science Journal Vol. 15, 2021 Dolaner et. al. 20 Figure 2: Differential Gene Expression Analysis Pipeline Using the T-Bioinfo Server Gene Ontology Analysis The enrichment analysis was done via us- ing the Enrichr platform for the protein-cod- ing genes [29]. Respectively, the GO and KEGG pathways were considered for the observed upregulated and downregulated genes in spontaneous regression samples. Data Visualization The gene expression patterns were observed by heatmaps. Furthermore, PCA and H-Clus- tering were repeated with the selected signif- icant RNA transcripts to make a comparison as before and after, and see an improvement on the basis of variance. Visualization was performed independently for protein-cod- ing genes and non-protein coding genes. RESULTS Variation is Detected Between Sponta- neous Regression and the Progressive Samples The exploratory analysis using the PCA revealed that there was a variation between the spon- taneous regression and the progressive state. Based on Figure 3A, it can be seen that healthy samples and the diseased sam- ples are well separated, which means there is significant variation between these groups. When the spontaneous regression and pro- gressive samples were examined in a specif- ic scatter plot, it was obvious that there was an improvement in principal components, and the two groups were clearly separated from each other (Figure 3B). After the observed variation between the two groups, the reason for this difference and its impacts could be investigated at the level of gene regulation. Figure 3. Principal Component Analysis, (A) Healthy samples versus Diseased samples (B) Spontaneous Regression (Red) versus Pro- gressive Samples (Purple) Revealed Pathways Involved in Biogenesis and Processing of RNA Based on DESeq2 analysis, expression levels of 870 protein-coding genes were identified as significantly different between the two groups with p-adjusted values (<0.01) and log2 fold change (+/- 1.5) (Supplementary Table 1). The heatmap of the protein-coding genes shows the gene expression patterns between spon- taneous regression and progressive samples (Figure 4A). It is obvious that most of the genes were upregulated in spontaneous regression while they downregulated in progressive ones. To understand the biological importance of path- ways, gene ontology analysis was performed. Interestingly, although there were different path- ways in upregulated genes, many represented biological pathways involved in biogenesis and processing of RNA, and mRNA translation (Fig- ure 4B). In addition, identified pathways also included ribosome-related pathways, which im- ply translation of protein-coding RNAs is affect- ed. The same concept could be detected with Columbia Undergraduate Science Journal Vol. 15, 2021 Dolaner et. al. 21 Figure 4. Data visualization of protein-coding genes (A) Gene expression patterns of protein-coding genes. Heatmap exposes the downregulated and upreg- ulated genes among the samples (B) Gene ontology analysis of protein-coding genes. Pathway analysis shows the downregulated and upregulated pathways in the spontaneous regression samples, respectively. Arrows indicate the RNA related pathways (C) Representation of principal component analysis with 3D scatter plots. First PCA shows the separation of spontaneous regression (SR) and progressive (P) samples before the selection of the protein-coding genes, second PCA represents the separation of SR and P samples after the selection of the protein-cod- ing genes, and last PCA indicates the true variability source considering four outliers (D) Hierarchical Clustering results as dendrograms. Each dendrogram point out the related scatter plots that are placed above and reveal the clustering perspective. Red boxes indicate the SR samples, and the other ones are the P samples A B C D Columbia Undergraduate Science Journal Vol. 15, 2021 Dolaner et. al. 22 the downregulated genes as presented in Fig- ure 4B. Consequently, these findings led this research to investigate the non-coding RNAs, which was the second branch of this study. There was a Diversity Among the Sponta- neous Regression Samples According to the first principal component, spon- taneous regression had a lot of variability within its own group (Figure 4C). This variation could be observed from the dendrograms in Figure 4D. Correspondingly, spontaneous regression samples had two diverse groups. The clinical data of these samples, therefore, needed to be examined to interpret them as outliers or true variability sources. However, no difference was detected between the four outliers and the oth- er samples. This meant the difference had to be in their biological background. That is why these four diverse samples were selected in comparison with progressive samples to see the most significant difference between the two groups in the context of expression patterns. As a result, it can be seen in Figure 4C that there was a significant improvement with the well-separated two groups after the selec- tion of true variability source. Additionally, bet- ter separation could also be observed from the dendrogram in Figure 4D that four samples and the progressive samples have distinct branch- es compared to the remaining twelve samples. Identified Non-Coding RNAs were Novel Transcripts Based on differential gene expression analysis, 33 lncRNAs, which are significantly expressed, were identified with the specific parameters (Supplementary Table 2). Each lncRNA was in- vestigated by their Ensembl ID and identified as novel transcripts. When the PCA was repeated with selected lncRNAs, clear separation could be seen among the spontaneous regression and the progressive samples (Figure 5A). Even though there was no dramatic improvement in principal components, hierarchical clustering results were highly distinctive. It can be seen in Figure 5B that the two groups cluster separately in the dendrogram, which implies these signifi- cant genes might predict tumor characteristics. To be able to observe the gene ex- pression pattern of the lncRNAs, a heatmap was generated. As shown in Figure 5C, most of the genes were upregulated in progressive samples compared to spontaneous regression. So far, pathway analysis of differential- ly expressed protein-coding genes in sponta- neous regression samples has demonstrated that global gene expression in these samples might be modulated at the transcriptional and post-transcriptional level, and non-pro- tein coding genes have shown that lncRNAs are differentially expressed in these sam- ples. To make a biologically relevant inter- pretation, the analysis will regard lncRNAs identified as statistically most significant. DISCUSSION Although spontaneous tumor regression is rare, it is a phenomenon that can be observed in some types of cancer, such as neuroblas- toma, renal cell carcinoma, lung cancer, lym- phoma, and leukemia [30]. To be able to in- crease the occurrence rate of this mechanism, detailed studies should be conducted, and the related pathways should be examined. In summary, we used RNA-seq datasets from CLL patients containing a total of 16 spon- taneous regression and 8 progressive state samples. First of all, we looked for variation and pattern among these two groups by using PCA and H-Clustering based on gene expres- sion. After the detection of variation, we hypoth- esized that the reason for this difference should be at the gene expression level. Therefore, we applied differential gene expression analysis by comparing spontaneous regression and pro- gressive tumor states. We identified 870 pro- tein-coding genes which were significantly dif- ferentially expressed and also were associated with RNA-related pathways based on gene on- tology analysis. Depending on these findings, we also investigated non-coding RNAs and Columbia Undergraduate Science Journal Vol. 15, 2021 Dolaner et. al. 23 Figure 5. Data visualization of non-coding RNAs (A) Representation of principal component analysis with 3D scatter plots. Initial PCA indicates the separa- tion of spontaneous regression (SR) and progressive (P) samples before the selection of the non-coding RNAs and second PCA shows the separation of SR and P samples after the selection of the non-coding RNAs (B) Hierarchical Clustering results as dendrograms. Each dendrogram specify the related scatter plots that are located above and expose the clustering perspective. Red boxes demonstrate the SR samples, and the other ones are the P samples (C) Gene expression patterns of non-coding RNAs. Heatmap represents the downregulated and upregu- lated genes between the samples A B C Columbia Undergraduate Science Journal Vol. 15, 2021 Dolaner et. al. 24 identified significantly expressed 33 lncRNAs. This paper has highlighted that new therapeutic strategies may involve lncRNAs to trigger the spontaneous regression phenomena in can- cer cells. Since the lncRNAs can regulate the important cellular mechanisms during cancer progression, identification of disease-specific lncRNAs may enlighten the way of new treat- ment strategies. Therefore, a detailed literature review for each lncRNA and their sense mR- NAs had been done. Even though the detailed explanation can be found in Supplementary Table 2, identified lncRNAs are involved in sev- eral pathways including tumor growth, metas- tasis, cell survival, and regulation of the tumor microenvironment such as the immune system. Previous studies showed that identified two lncRNAs, the PRRT3-AS1 and H1FX-AS1, were focused on cancer progression [31, 32, 33, 34]. Both lncRNAs were upregulated in the progressive state compared to spontaneous re- gression, which indicates that these lncRNAs were functioning as tumor enhancers in CLL cells. Li et al. (2020) showed that the lncRNA PRRT3-AS1 is upregulated in prostate cancer and targets the PPARγ gene by binding its 3’ end, which leads to regulation of the Akt/mTOR signaling pathway [31]. The same study also re- vealed that the down-regulation of PRRT3-AS1 inhibits prostate cancer progression by regulat- ing cell proliferation, migration, and apoptosis. Moreover, knocking down this lncRNA triggers autophagy via the mTOR pathway [31]. Anoth- er study suggested that PRRT3-AS1 is esti- mated as an immune-related lncRNA involved in PPAR signaling and can be used as a poten- tial target in glioblastoma [32]. In light of these findings, it can be said that the PRRT3-AS1 functions as a tumor promoter gene in prostate cancer and glioblastoma. Since it is revealed that this lncRNA is highly expressed in the progressive state of CLL, further studies may clarify the related pathways and novel targets. Moreover, downregulation of this lncRNA might promote spontaneous regression by repressing cell proliferation and inducing apoptosis. According to Shi et al. (2020), lncRNA H1FX-AS1 is downregulated in cervical can- cer, and low expression is correlated with poor prognosis and linked to tumor size as well as metastasis. In silico analysis predicted that the possible target of the H1FX-AS1 was the miR- 324-3p, and it was binding and regulating the DACT1 [33]. Furthermore, the same research included overexpression studies, which re- vealed that the high expression levels of this lncRNA significantly reduced the proliferation and invasiveness, and activated the apoptosis pathways. H1FX-AS1, therefore, is identified as a tumor suppressor gene in cervical cancer [33]. On the contrary, high expression levels of lncRNA H1FX-AS1 are associated with a poor prognosis in gastric cancer. It is predicted that this lncRNA was related to epithelial to mesen- chymal transition (EMT) and metastasis path- ways [34]. Additionally, in-silico prediction anal- ysis indicated several targets of the H1FX-AS1 lncRNA, such as H1FX, COPG1, and MIR6826 that can be used as potential therapeutic tar- gets in gastric cancer [34]. Since H1FX-AS1 is upregulated in the progressive state of CLL, the precise roles of this lncRNA should be ex- amined for CLL cells. Besides, considering the studies on gastric cancer, downregulation of H1FX-AS1 may trigger the spontaneous re- gression through inactivation of metastasis. As the other 31 lncRNAs were novel transcripts, several examples could be consid- ered by examining the sense protein-coding genes of the lncRNAs to reveal the significance and possible functions of these lncRNAs. Thereby, the specified lncRNAs and their sense protein-coding genes can be targeted for further analysis and treatment strategies. The first one is the lncRNA PT- PN22-AS1 that was upregulated in the sponta- neous regression samples. It is known that the B cell receptor signaling is vital for the growth and survival of the leukemic cells [35]. These signaling pathways can be stimulated with di- verse tyrosine kinases as well as phospha- tases. PTPN22 is a protein tyrosine phospha- Columbia Undergraduate Science Journal Vol. 15, 2021 Dolaner et. al. 25 tase specifically expressed in immune cells. It can function as an enhancer or suppressor of BCR and TCR signaling by regulating phos- phorylation status [36]. According to studies, the PTPN22 gene is upregulated in CLL cells, and this upregulation results in attenuation of the apoptosis signals produced by the BCR and stimulation of the AKT activity that generates a survival signal [35]. This regulation reveals that the cancer cells which express autoreactive BCRs can escape from apoptosis by upregu- lating the PTPN22 gene. Therefore, downreg- ulation of this gene through the lncRNA PT- PN22-AS1 at multiple levels may trigger the spontaneous regression in CLL by eliminating autoreactive B cells in the context of immune tolerance. So, if the upregulation of this lncRNA can be induced by an external factor, it could be used as a therapeutic target for CLL cells. The next lncRNA would be PCF11-AS1, and it is also upregulated in spontaneous regres- sion. Alternative polyadenylation (APA) leads to transcription of diverse isoforms at the RNA 3’ end that affects the functioning of encoded pro- teins. This mechanism needs multicomponent protein complexes, and one of the protein com- plexes is called CFII [37]. This complex com- prises the PCF11 gene that is a cleavage and polyadenylation factor subunit, and regulates the transcription termination and RNA 3’ end maturation. Studies show that this gene partic- ularly regulates the alternative polyadenylation of the genes associated with the WNT pathway as an oncogene in neuroblastoma cells [38]. Accordingly, the downregulation of this gene results in diminished cell growth as well as in- vasiveness. Interestingly, one of the studies indicates that spontaneously regressed neuro- blastomas express the PCF11 gene at low lev- els compared to highly progressive neuroblasto- mas [39]. Basically, downregulation of this gene at the epigenetic or post-transcriptional level through the lncRNA PCF11-AS1 may induce the spontaneous regression in CLL via ceasing the cell growth. Thus, the upregulation of this lncRNA can be used as a therapeutic strategy. Another lncRNA is the SYNGAP1-AS1 which is upregulated in progressive samples. Mutant RAS genes stimulate the GTP-bound state which constitutively activates RAS signal- ing in metastatic cells [40]. RasGAPs inactivate RAS signaling by converting active GTP-bound into its inactive state [41]. SYNGAP1, also known as RASA5, is a member of the RasGAP family and functions as a tumor suppressor gene [42, 43]. Studies proved that the RASA5 gene is epigenetically disrupted in multiple can- cer types by promoter methylation, and gain of function assays exposed that RASA5 expres- sion leads to a reduction in metastasis by regu- lating EMT and cell stemness [43]. As it is known that the lncRNAs can regulate gene expression at the epigenetic level through methylation, this epigenetic silencing may be due to the lncRNA SYNGAP1-AS1. So, the upregulation of the SYNGAP1-AS1 can knock down this gene at the epigenetic level by methylating the promot- er region leading to aggressive tumor progres- sion in CLL patients. Even though further stud- ies are essential, targeting this lncRNA may also trigger spontaneous regression through metastasis and cell stemness pathways, and enhance our understanding of this mechanism. As a result, the deduction that can be made from these exemplary lncRNAs is that the detailed examination of the identified novel transcripts can be used to reveal the mecha- nisms that lead to spontaneous regression, as well as to identify these lncRNAs as tar- gets for small molecule inhibitors or activators so that the spontaneous regression mecha- nism can be triggered in other cancer types. CONCLUSION These findings suggest that regulation through the lncRNAs might have a major role in cells’ fate, and their detailed examination can enlight- en the way of discovery of the possible ther- apeutic targets. In prospective studies, each lncRNA should be investigated to perceive their functional pathways by over-expression and suppression studies in various cancer types Columbia Undergraduate Science Journal Vol. 15, 2021 Dolaner et. al. 26 in order to understand their specific roles. Ad- ditionally, the following study which considers these findings should combine the protein-cod- ing genes and non-protein coding genes. Since these two groups revealed significant results independently, their combination can lead to future selection and determination of particular pathways which affected each other. This study had certain limitations that need to be overcome to conduct more efficient studies. Spontaneous regression samples had diverse biological backgrounds, which in- creased the demand for the detailed investi- gation of these samples. Furthermore, it was problematic to identify the significant path- ways through differential gene expression al- gorithms due to this variability. Finally, as the amount of the samples was limited, more com- prehensive research is required to verify these findings and make a distinctive interpretation. AUTHOR INFORMATION Corresponding Author *Simay Dolaner (College of Engineering and Natural Sciences, Department of Molecular Biology and Genetics, Bahcesehir University, Istanbul, Turkey) simay.dolaner@bahcesehir.edu.tr Author Contributions Simay Dolaner performed the data analysis and produced the figures and manuscript. Dr. Harpreet Kaur provided technical assistance with analysis and pipelines. Elia Brodsky and Dr. Mohit Mazumder added project direc- tion and provided guidance. Dr. Julia Pan- ov provided expert feedback for the project. Competing Interests The authors declare no competing financial and non-financial interests. SUPPLEMENTARY DOCUMENTS Supplementary Notes 1, which includes external sources and related links mentioned in the Meth- ods section, can be found in the following link. https://www.dropbox.com/s/mlo17y1yjdxfekm The list of the significantly differentially ex- pressed protein-coding genes can be found in Supplementary Table 1 through the following link. https://www.dropbox.com/s/zc1oaryr3zyhxlf Supplementary Table 2, which contains the name, Ensembl ID, and the pos- sible functions of the 33 significant ln- cRNAs, can be found in the following link. https://www.dropbox.com/s/4p67jf4d54if2b8 ACKNOWLEDGMENTS This research was performed within the frame- work of a research fellowship program and was supported by Pine Biotech. Data pro- cessing and visualization pipelines were run with the T-Bioinfo Server. I would like to thank Elia Brodsky, Dr. Harpreet Kaur, Dr. Mohit Mazumder, and Dr. Julia Panov for their ex- pertise and guidance throughout the project. ABBREVIATIONS CLL: Chronic Lymphocytic Leukemia NCBI: National Center for Biotechnology Infor- mation BCR: B Cell Receptor TCR: T Cell Receptor PCA: Principal Component Analysis H-Cluster: Hierarchical Clustering GO: Gene Ontology KEGG: Kyoto Encyclopedia of Genes and Genomes lncRNA: Long Non-Coding RNA PRRT3-AS1: Antisense to PRRT3 H1FX-AS1: Antisense to H1FX PTPN22-AS1: Antisense to PTPN22 PCF11-AS1: Antisense to PCF11 SYNGAP1-AS1: Antisense to SYNGAP1 EMT: Epithelial to Mesenchymal Transition REFERENCES [1] American Cancer Society. Cancer Statistics Center. https://cancerstatisticscenter.cancer. Columbia Undergraduate Science Journal Vol. 15, 2021 Dolaner et. al. 27 org. 2020 [2] A. A. Ferrando, C. López-Otín, Clonal evo- lution in leukemia. Nature Medicine, 23(10), 1135–1145. doi:10.1038/nm.4410 (2017). [3] M. V. Haselager, A. P. Kater, E. Eldering, Proliferative Signals in Chronic Lymphocytic Leukemia; What Are We Missing? 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