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Identification of Driver Mutations in MicroRNAs for Early Diagnosis of Lung Cancer

Article Information

Ramsha Nasir, Nisar A Shar*

Department of Biomedical Engineering, NED University of Engineering and Technology, Karachi, Pakistan

*Corresponding Authors: Nisar A Shar, Department of Biomedical Engineering, NED University of Engineering and Technology, Karachi, Pakistan

Received: 22 May 2020; Accepted: 29 July 2020; Published: 22 September 2020

Citation: Ramsha Nasir, Nisar A Shar. Identification of Driver Mutations in MicroRNAs for Early Diagnosis of Lung Cancer. Journal of Bioinformatics and Systems Biology 3 (2020): 074-080.

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Abstract

Over the past years, it has been discovered that microRNA possesses a critical role in lung cancer related deaths worldwide. Different research on molecular biology of lung cancer has improved our understanding that determines the behavior of malignant cells which has encouraged the discovery of driver mutations. The identification of driver mutations in lung cancer has led to a paradigm shift and provides us with new direction towards personalized therapies with improved survival rates. Therefore, it is of interest to explore microRNA driver mutations in this context. Hence, in this research different consistent point mutations are identified in candidate microRNAs at different sites and different numbers of transcription factors binding at these consistent mutations are examined. The highest consistency of mutation was 18 found in mir-23A at the genomic point (13880331) where total 5 transcription factors were binding. The second highest consistency was 12 found in different miRNAs at different genomic locals where 1-2 transcription factors were binding whereas; mir-25 is the only having consistency of 7 at genomic point (100089125) with 1 transcription factor attachment. The result of consistent mutation in regulatory regions indicates that these mutated sites may serve as potential biomarkers for lung cancer. Moreover, the comprehensive utilization of these microRNAs mutations, different drugs can be designed that mainly target these specified mutated loci and would be helpful in treating lung cancer.

Keywords

Lung cancer; MicroRNA; Transcription factors; Regulatory regions; Consistency

Lung cancer articles, MicroRNA articles, Transcription factors articles, Regulatory regions articles, Consistency articles

Article Details

1. Introduction

Lung cancer is one of the leading cancer related mortality [1]. It is characterized by genetic diversity with relatively few persistent mutations, occurring at high frequency [2]. In this condition, the cells begin to divide in the lungs uncontrollably. These abnormal cells obstruct the functioning of lung that ultimately reduces the ability of a person to breath. Normally, our body has check and balance system by which our body programs cells to grow and die at certain stages of life cycle in order to avoid overgrowth of cells. Whereas in cancer, this instruction is overrides, causing cells to multiply and grow exponentially. This overgrowth of abnormal cells, leads to the development of lung cancer.

Moreover, many of these mutations are associated with several environment and heredity factors [3]. The changes in phenotype from normal lung cell to malignant occur in a series of genetic alterations and identification of these mutations has the potential to provide further therapeutic opportunities [4]. However, in recent years the vast improvements have been made in molecular biology that helps researchers to understand and underpins lung cancer. This has lead to a great improvement in prevention of diseases, treatments and early detection of tumors [5, 6]. The symptoms of lung cancer may comprise coughing with blood, stained mucus, persistent cough, wheezing sound, unexplained weight loss, breathlessness, pain when coughing or breathing [7]. The risk factors that are involved to take part in the development of lung cancer are smoking, asbestos fiber, pollutant air, exposure to radon, ceramics’, and fireworks [8]. These factors mutate the genome and some of these mutations may play aggressive role in driving lung cancer.

MicroRNAs also known as miRNAs or miRs, are short, non-coding, single stranded RNAs 20-25 nucleotide long [9]. The main function of MicroRNA is to regulate gene expressions post -transcriptionally [10]. MicroRNAs comprise of 1-2% of all genes [11]. The first microRNA was discovered as non-coding small RNA as growth regulator of Caenorhabditis elegans (lin-4 and let-7) [12]. MicroRNAs are unable to code proteins, but acquiring significant catalytic, regulatory and structural functions. Thus, microRNAs are capable of increasing and decreasing expressions of genes by binding to the untranslated region (3′-UTR) of their target messenger RNA (mRNA) and repress the production of protein by destabilizing the mRNA [13, 14], also known as gene silencing.

Different researches have proved that altered expression of miRNAs in tumors may also act as novel oncomiRs [15] and tumor suppressors [16]. These abnormal expression levels of microRNAs are characterized as a result of aberrant expressions of precursor microRNAs transcript. These aberrant expressions of genes have been observed to cause different abnormalities and cancers such as prostate, lung, breast, gastric, colorectal, lymphoma, leukemia, melanoma, and hepatocellular carcinoma [17]. Additionally to their role in physiological conditions, studies indicate that microRNAs is also playing significant role in the pathological mechanisms of neurodegenerative diseases [18].

2. Materials and Methods

2.1 Identification of Non-Coding Mutations in Non-coding variants

In this mutational analysis, the initial step is to identify mutations in non-coding variants. For this purpose, non-coding variant file is downloaded from COSMIC database (release v87) and overlapped with 100kb extended genomic locations of candidate miRNAs using bedtools intersect command. The non-coding variants file contains complete data of non-coding mutations. Genomic locations of microRNAs are identified from Ensemble genome browser GRch38. The genomic locations are extended, prior to overlapping because, it has been found in almost 70% of cases, that regulatory region of a gene lie within 100 kb [19]. The overlapped result determines vastly repetitive mutational regions in candidate miRNAs. Therefore; we developed python code to obtain non-repetitive mutations to identify unique sites of interest.

2.2 Identification of consistent mutations in Non-Coding variants

In the next step, we identified consistent mutations in non-coding variants by overlapping repetitive and non-repetitive file. It is also not necessary that all consistent point mutations possess the potential of driving lung cancer. It might be possible that these mutated sites are just repeating itself because of any interruption of any cellular process like DNA repair mechanism. Some of them can be passenger mutations because throughout the life of human beings many mutations can occur randomly, and many of the mutations occur due to several environmental factors, and few mutations are inherited from parents to their offspring’s. In any case, a portion of these consistent mutations can be driver mutations.

2.3 Identification of Consistent Non-Coding Mutations at IMR90 transcription factors Binding Sites

Non-coding region of DNA contain regulatory regions that are capable of increasing and decreasing the gene expressions. At these regulatory regions transcription factors bind which can either enhance or suppress the process of translation. Different studies indicate that mutations inside regulatory region are extensively significant in cancers and other pathogenic diseases. To further characterize and shortlist candidates, IMR90 Transcription factors (TFs) are mapped that are downloaded from UCSC ENCODE with identified consistent mutations to examine the point mutations in regulatory regions. IMR90 is the cancer cell line of human lung. There are total 10 Transcription Factors (TFs) for IMR90 cells out of which 5 (CTCF, MAFK, RAD21, POL, CEBPB) were downloaded from UCSC ENCODE (hg19). The files are then converted to GRCH38/hg38 genome coordinates using UCSC liftover tool. The overlapped result recognized significant TFs’ binding sites (TFBS) as well as the number of TFs binding at these consistent point mutations.

3. Results and Discussion

MIicroRNA are crucial in various biological processes such as regulatory functions, cellular differentiation, development and apoptosis. In cancers, mutations in non-coding regions can cause dysregulation of oncogenes and tumor suppressors. Measures of function in the non-coding genome usually reflect on binding of transcription factors. In this study, we identified potential driver mutations in candidate microRNAs. As shown in Figure 1, all non-coding mutations in microRNAs are first identified by intersecting non-coding variants with 100kb extended genomic locations of each microRNA using bedtools intersect command. This recognized repetitive mutations therefore; we used python code to extract non-repetitive mutations. In the next step, consistencies in mutations are identified by intersecting repetitive and non-repetitive files. Higher the consistencies of mutations have higher probabilities of driving lung cancer.

After the identification of consistencies, we map total five Transcription factors (CEBPB, CTCF, MAFK, POL2, RAD21) of IMR90 (lung cancer cell line) with consistent mutations of microRNA using bedtools intersect command. The result of intersecting consistent point mutations with Transcription factors of IMR90 cells examine number of Transcription Factors binding as well as significant Transcription Factors binding sites as shown in Table 1 (A, B).  Highest consistency is found in MIR-23A as compared to other microRNA. At the highest consistency of 18 at a point mutation of (13880331) total 5 TFs are binding in MIR-23A. Recognition of this persistent mutated loci of non-coding variants, propose that it can play significant role in driving lung cancer and more likely to influence regulatory elements of genes that ultimately cause disruption in translation process because, this precise loci is having the highest probability of causing lung cancer.

MIR NAMES

CHR:

STAR-END

MUTATED SITE

CONSISTENCY

NO. OF TFs BINDING

TFs BINDING

MIR-23A

chr19

13836587-13836659

13880331

18

5

CEBPB, CTCF, MAFK, POL2, RAD21

MIR-21

chr17

59841266-59841337

59838122

12

1

POL2

59838178

12

1

POL2

MIR-23A

chr19

13836587-13836659

13829976

12

1

POL2

13830024

13830145

12

1

POL2

12

1

POL2

13831407

12

1

POL2

13831773

12

1

POL2

13836478

12

1

POL2

13836558

12

1

POL2

MIR-451A

chr17

28861369-28861440

28854934

12

2

POL2, RAD21

28879226

12

2

RAD21

MIR-372

chr19

53787890-53787956

53772987

12

2

CTCF

53773139

12

2

CTCF

53773148

12

2

CTCF

MIR-16

chr3

160404745-160404825

160449652

12

2

POL2.RAD21

MIR-25

chr7

100093560-100093643

100101813

12

2

CEBPB, POL2

MIR-100

chr11

122152229-122158308

122115592

12

1

CEBPB

Table 1A: Consistent non-coding mutations at IMR90 TF binding sites at specific positions.

MIR NAMES

CHR:

START-END

MUTATED SITE

CONSISTENCY

NO. OF TFs BINDING

TFs

BINDING

MIR-328

chr16

67202321-67202395

67180020

12

1

CTCF

MIR-21

chr17

59841266-59841337

59841436

12

1

POL2

MIR-23A

chr19

13836587-13836659

13789265

12

1

CEBPB

13820939

12

1

CEBPB

MIR-155

chr21

25573980-25574044

25607308

12

1

POL2

25607316

12

1

POL2

MIR-145

chr5

149430646-149430733

149517936

12

1

MAFK

MIR-25

chr7

100093560-100093643

100089125

7

1

POL2

100091199

6

1

POL2

100091597

6

1

POL2

100091833

6

1

CTCF

100093937

6

1

MAFK

100094036

6

1

MAFK

100101469

6

1

MAFK

100148875

6

1

CEBPB

100148922

6

1

CEBPB

MIR-182

chr7

129770383-129770492

129833002

6

1

MAFK

129865187

6

1

MAFK

MIR-126

chr9

136670602-136670686

136758603

6

1

CTCF

MIR-370

chr14

100811139-101011213

100845392

6

1

POL2

100862420

6

1

MAFK

Table 1B: Consistent non-coding mutations at IMR90 TF binding sites at specific positions.

At the consistency of 12 in different microRNAs, 1-2 TFs are binding. While, at least consistency of 6 in MIR-25, -182, -126, and MIR -370, only 1 TF is binding. The results of least consistency with least TFs are binding on mutated sites. It indicates that although, these point mutations are present in regulatory regions and possesses the potential of causing lung cancer, but having less probability as compared to the mutated sites having higher consistency in regulatory regions. The mutations at Transcription factors binding sites can be termed as driver mutations. The obtained results suggest, as these mutations are consistent and present at regulatory regions consequently; they can significantly affect genes expression level and might play a considerable role in driving lung cancer. It means either these genes are excessively expressed or unexpressed. Hence, these point mutations in regulatory regions can serve as potential biomarker for lung cancer. However, further evaluation is needed for the verification of acquired results to identify up to which extent these consistent mutations are involved in driving lung cancer.

fortune-biomass-feedstock

Figure 1: Methodology of finding driver mutations in microRNAs comprises of three steps mainly. In the initial step, non-coding mutations in non-coding variants are identified by overlapping cosmic non-coding variants with microRNA genomic locations. Overlapped result yields highly repetitive mutations. These repetitive mutations file is then given as input to the python code and the non-repetitive file is generated as output. The second step is the identification of consistency which is obtained by overlapping repetitive and non-repetitive mutations files. In the final step, consistent non-coding mutations in regulatory regions are identified.

4. Conclusion

The exploration for driver mutations in cancer genome is a crucial step in the progress towards personalized approaches to cancer treatments. The ability of miRNAs to regulate gene expression levels and their key role in progression of lung cancer endow with new direction for research on prognostic and predictive biomarkers [20]. By recognizing the molecular variations in microRNA responsible for driving lung cancers, drugs can be designed that particularly targets deregulated or mutated genes. Furthermore, new approaches may be designed that averts the damage or even restore these mutated regions of DNA due to which these drivers are formed. Hence, it is of importance to evaluate these driver microRNA mutations for further consideration.

Acknowledgement

NAS and RN are thankful to National Center in Big Data and Cloud computing (Genomics Lab) NED University of Engineering and Technology for their tremendous support in this study.

Conflict of Interest

The authors declare no conflict of interest.

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