Gazing at The PubMed Reports on CRISPR Tools in Medical Research: A Text-Mining Study

Singha DL^1* and Sahu J^2*
*Correspondence: Singha DL, Department of Biotechnology, Assam Agriculture University, Jorhat, Assam, India, Tel: 0376 234 0001, Email: Sahu J, Department of Mycology and Plant Pathology, Institute of Agricultural Sciences, Banaras Hindu University, Varanasi, Uttar Pradesh, India, Tel: 0376 234 0001, Email:

Keywords

CRISPR; Cancer; Tumour; Hereditary disease; PubMed; MeSH; Text-mining

Introduction

CRISPR tool has the ability to manipulate the nucleotides at precisely desired location. Therefore, CRISPR is becoming an important tool in medical research as well as in agriculture. Various groundbreaking developments have been done in medical research employing CRISPR tool for studying diseases such as cancer, tumour and inherited genetic diseases including hereditary retinal degeneration, HIV infection, inborn genetic disorders, and recessive genetic diseases [1-5]. To study any disease, availability of disease models is the crucial stage for understanding the disease and its associated pathology. CRISPR mediated approach is the easiest, versatile and mostly employed method in medical research for developing such disease models. Here, we present a meta-analysis to identify the mostly studied diseases in medical research using CRISPR tool from the available reports on PubMed.

Literature Review

Text mining search on PubMed for "CRISPR & Medicine" in the "Title and Abstract" section retrieved a total of 306 records of diseases (MeSH terms) associated with the help of R scripts and PubTator tool [6]. PubTator is an automated, web-based text mining tool for assisting biocuration. PubTator is publicly available tool. The text-mining search resulted in 137 MeSH terms and 196 PubMed IDs which were then analysed using Cytoscape to map the association between PubMed IDs and MeSH terms (Figure 1) [7]. Cytoscape is a publicly available software for integrated biomolecular interaction networks that visually integrate the network with expression profiles, phenotypes to databases of functional annotations. Based on the interaction networks degree distribution, the node, D009369 was found to be the most studied disease (degree=37) which corresponds to cancer and tumor research (Supplementary Table 1).

The study revealed cancer and tumor research (D009369) as the most prominently studied area using CRISPR tool. Numerous important approaches were developed using CRISPR tool to understand the complex cancer genome in the field of cancer research including precision cancer mouse models such as germline models, somatic genome editing models, lymphoma models and mouse models for drug treatment [1]. In addition, inactivation of mutations created by cancer was possible utilizing CRISPR tool [2]. Furthermore, new approaches such as patient-derived stem cell organoids are effective cancer and infectious diseases models for the development of novel targeted therapies [3]. Apart from mouse models, study of cancer in other animal models using direct in vivo CRISPR screens will be more beneficial to identify the cause of the disease [4]. Models of cancer research were also developed using human cell lines. Human scarless isogenic cell models of cancer variants were developed with CRISPR/ Cas9 gene editing along with high-throughput Kompetitive Allele- Specific PCR genotyping technology [5]. Another report on cancer research using CRISPR tool revealed that inhibition of STAG1 and STAG2 in several cancer cell lines reduces cell proliferation [8]. These are the promising therapeutic targets in cancer research. Moreover, identification of biomarkers and therapeutic targets is a critical goal in cancer research. Potential biomarker such as N-glycoproteins and O-6-methylguanine-DNA methyltransferase gene were developed for cancer research using CRISPR-Cas9–mediated vulnerability screen [9,10]. Presence of an altered form of tumor suppressor gene, p53 is considered as a common characteristic of many cancer cells. Utilizing the CRISPR tool, the normal p53 phenotype in tumour cells were restored by replacing the mutant p53 gene with a functional copy [11]. Along with cancer research, tumor research (D009369) is also mostly studied area as revealed by text-mining approach. A strategy has been developed to study the tumour mutations employing CRISPR/Cas9 to develop personalized medicine [2]. On the gene level, the altered form of tumour suppressor gene p53, present in most of the cancer cells were restored using CRISPR approach [11].

Discussion

Other emerging tumour suppressor genes are the STAG1 and STAG2, a core component of cohesion. Through a vector-free CRISPR system, double gene knockout of STAG1/STAG2 significantly reduces tumour cell proliferation [8]. Among the potential strategies developed, specific targeting of viral cancer drivers and oncogenes were one of them [12]. The tumor sphere formation was reduced in breast cancer cells using the CRISPR gene targeting [13]. Moreover, a gene target therapeutic platform was developed by taking the datasets of cancer and tumour using CRISPR tool on epidermal growth factor that effectively reduced the oncogenicity in thyroid cancer [14]. Similarly, using CRISPR mediated loss of dihydrotestosterone-inactivation activity promotes prostate cancer castration resistance [15]. Thus, CRISPR approach has many potential applications in medical research for studying cancer and tumour diseases.

Conclusion

With the availability of huge literature on public domains, it has become very difficult to review them manually. However, approaches such as scientometrics and text-mining have become very powerful data mining tools which grabbed the attention of researchers from any field, especially due to increasing number of inter-disciplinary researches. The current study analysed the reports related to use of CRISPR tools in medical research thereby revealing cancer and tumour diseases (D009369) as the most prominently studied area. Also, this approach for identifying the mostly studied area using text mining can be useful to other important areas within very short duration. Overall, the review work revealed that CRISPR plays important role in medical research for disease diagnosis, treatment as well as disease study.

Acknowledgement

Authors would like to acknowledge CSIR-HRDG for the financial support to Dhanawantari L. Singha in the form of CSIR-HRDG Research Associate award.

References

1. Mou H, Kennedy Z, Anderson DG, Yin H, Xue W (2015) Precision cancer mouse models through genome editing with CRISPR-Cas9. Genome Med 7: 53.
2. Gebler C, Lohoff T, Paszkowski-Rogacz M, Mircetic J, Chakraborty D, et al. (2017) Inactivation of Cancer Mutations Utilizing CRISPR/Cas9. JNCI J Natl Cancer Inst 109: djw183.
3. Liu F, Huang J, Ning J, Ning B, Liu Z, et al. (2016) Drug discovery via human- derived stem cell organoids. Front Pharmacol 7: 1-12.
4. Chow RD, Chen S (2018) Cancer CRISPR Screens in vivo. Trends in Cancer 4: 349-358.
5. Coggins NB, Stultz J, O’Geen H, Carvajal-Carmona LG, Segal DJ (2017) Methods for scarless, selection- free generation of human cells and allele- specific functional analysis of disease-associated snps and variants of uncertain significance. Sci Rep 7: 15044.
6. Wei CH, Kao HY, Lu Z (2013) PubTator: A web-based text mining tool for assisting biocuration. Nucleic Acids Res. 41: W518-W522.
7. Shannon P, Markiel A, Ozier O, Baliga NS, Wang JT, et al. (2003) Cytoscape: A software environment for integrated models of biomolecular interaction networks. Genome Res. 13: 2498-2504.
8. Benedetti L, Cereda M, Monteverde LA, Desai N, Ciccarelli FD (2017) Synthetic lethal interaction between the tumour suppressor STAG2 and its paralog STAG1. Oncotarget 8: 37619-37632.
9. Rolland DCM, Basrur V, Jeon YK, McNeil-Schwalm C, Fermin D, et al. (2017) Functional proteogenomics reveals biomarkers and therapeutic targets in lymphomas. PNAS 114: 6581-6586.
10. Mari-Alexandre J, Diaz-Lagares A, Villalba M, Juan O, Crujeiras AB, et al. (2017) Translating cancer epigenomics into the clinic: Focus on lung cancer. Transl Res 189: 76-92.
11. Chira S, Gulei D, Hajitou A, Berindan-Neagoe I (2018) Restoring the p53 ‘Guardian’ phenotype in p53-deficient tumor cells with CRISPR/Cas9. Trends in Biotechnol 36: 653-660.
12. Oppel F, Schürmann M, Goon P, Albers AE, Sudhoff H (2018) Specific targeting of oncogenes using CRISPR technology. Cancer Res 78: 5506-5512.
13. Pian L, Wen X, Kang L, Li Z, Nie Y, et al. (2018) Targeting the IGF1R pathway in Breast Cancer using antisense lncRNA-mediated promoter cis competition. Mol Ther-Nucl Acids 12: 105-117.
14. Huang LC, Tam KW, Liu WN, Lin CY, Hsu KW, et al. (2018) CRISPR/Cas9 genome editing of epidermal growth factor receptor sufficiently abolished oncogenicity in anaplastic thyroid cancer. Disease Markers 3835783: 1-14.
15. Zhu Z, Chung YM, Sergeeva O, Kepe V, Berk M, et al. (2018) Loss of dihydrotestosterone-inactivation activity promotes prostate cancer castration resistance detectable by functional imaging. J Biol Chem 293: 17829-17837.