Computational Identification and Validation of Non-Synonymous SNPs in Progesterone Receptor Membrane Complex 1 Linked to Lung Cancer

Solaipriya Solairaja; Habeeb Shaik Mohideen; Sivaramakrishnan Venkatabalasubramanian

doi:10.52756/ijerr.2023.v36.006

Solaipriya Solairaja Department of Genetic Engineering, SRM Institute of Science and Technology, Kattankulathur, Kanchipuram, Tamil Nadu, Chennai-603203, India
Habeeb Shaik Mohideen Department of Genetic Engineering, SRM Institute of Science and Technology, Kattankulathur, Kanchipuram, Tamil Nadu, Chennai-603203, India https://orcid.org/0000-0003-4217-5063
Sivaramakrishnan Venkatabalasubramanian Department of Genetic Engineering, SRM Institute of Science and Technology, Kattankulathur, Kanchipuram, Tamil Nadu, Chennai-603203, India https://orcid.org/0000-0002-6498-5561

DOI: https://doi.org/10.52756/ijerr.2023.v36.006

Keywords: Computational, lung cancer, PGRMC1, Progesterone receptor membrane component 1, mutation, single nucleotide polymorphisms

Abstract

Numerous gene polymorphisms have been attributed to Lung cancer, but PGRMC1 (Progesterone receptor membrane component 1) is a lesser-known candidate among them. However, emerging research is slowly suggesting the role of polymorphisms in PGRMC1 gene-associated tumorigenesis. Nevertheless, phenotypic changes still need to be studied. The main aim of this study is to identify the most deleterious nsSNPs (non-synonymous single nucleotide polymorphisms) in PGRMC1 that can potentially increase the susceptibility to lung cancer progression. In this work, we scrutinized highly detrimental nsSNPs for PGRMC1 from the available dbSNP database. We further categorized using the FATHMM server to enlist the nsSNPs, which are driver mutants capable of affecting the function of the PGRMC1 protein. We employed clinical evidence from the COSMIC database for further evaluation and confirming the presence of nsSNPs in lung cancer patients. There are 12 nsSNPs reported in lung cancer patients, which are L32M, R47C, D141N, G20W, S57C, R70P, I89V, G118V, A191D, G95V, E157K, and G168V predicted to damage the functions. Conclusively, through a comprehensive comparison of the outcomes obtained through these computational methods, we identified novel I89V, D120E, G95V and G168V nsSNPs that pose substantial risks to the functionality of the PGRMC1 protein. Focusing on the importance of PGRMC1 in lung cancer, analyzing its function was conceded to unveil the interlink between genetic mutation and phenotypic changes. Thus, this study provides insights into the influence of PGRMC1variants in Lung cancer. The evaluated nsSNPs can significantly aid future research on the gene and its association with lung cancer progression in large population distribution frequencies of genotypes among different subgroups.

Author Biographies

Solaipriya Solairaja, Department of Genetic Engineering, SRM Institute of Science and Technology, Kattankulathur, Kanchipuram, Tamil Nadu, Chennai-603203, India

Department of Genetic Engineering, SRM Institute of Science and Technology, Kattankulathur, Kanchipuram, Tamil Nadu, Chennai-603203

Habeeb Shaik Mohideen, Department of Genetic Engineering, SRM Institute of Science and Technology, Kattankulathur, Kanchipuram, Tamil Nadu, Chennai-603203, India

Department of Genetic Engineering, SRM Institute of Science and Technology, Kattankulathur, Kanchipuram, Tamil Nadu, Chennai-603203

References

Adzhubei, I., Jordan, D. M., & Sunyaev, S. R. (2013). Predicting functional effect of human missense mutations using PolyPhen‐2. Current Protocols in Human Genetics, 76(1), 7–20. https://doi.org/10.1002/0471142905.hg0720s76

Ahmed, I. S., Rohe, H. J., Twist, K. E., Mattingly, M. N., & Craven, R. J. (2010). Progesterone receptor membrane component 1 (Pgrmc1): A heme-1 domain protein that promotes tumorigenesis and is inhibited by a small molecule. Journal of Pharmacology and Experimental Therapeutics, 333(2), 564–573. https://doi.org/10.1124/jpet.109.164210

Arshad, M., Bhatti, A., & John, P. (2018). Identification and in silico analysis of functional SNPs of human TAGAP protein: A comprehensive study. PloS One, 13(1), e0188143. https://doi.org/10.1371/journal.pone.0188143

Avsar, O. (2022). Analysis of missense SNPs in the SLC47A1 and SLC47A2 genes affecting the pharmacokinetics of metformin: Computational approach. Egyptian Journal of Medical Human Genetics, 23(1), 92. https://doi.org/10.1186/s43042-022-00306-9

Bhatnager, R., & Dang, A. S. (2018). Comprehensive in-silico prediction of damage associated SNPs in Human Prolidase gene. Scientific Reports, 8(1), 9430. https://doi.org/10.1038/s41598-018-27789-0

Boga, I., & Bisgin, A. (2022). Real-world applications of tumor mutation burden (TMB) analysis using ctDNA and FFPE samples in various cancer types of Turkish population. International Journal of Experimental Research and Review, 29, 89-93. https://doi.org/10.52756/ijerr.2022.v29.010

Cahill, M. A., & Neubauer, H. (2021). PGRMC proteins are coming of age: A special issue on the role of PGRMC1 and PGRMC2 in metabolism and cancer biology. Cancers, 13(3), 512. https://doi.org/10.3390/cancers13030512.

Capriotti, E., Fariselli, P., & Casadio, R. (2005). I-Mutant2. 0: Predicting stability changes upon mutation from the protein sequence or structure. Nucleic Acids Research, 33(suppl_2), W306–W310. https://doi.org/10.1093/nar/gki375

Choi, Y., & Chan, A. P. (2015). PROVEAN web server: A tool to predict the functional effect of amino acid substitutions and indels. Bioinformatics, 31(16), 2745–2747. https://doi.org/10.1093/bioinformatics/btv195.

Feroz, T., & Islam, M. K. (2023). A computational analysis reveals eight novel high-risk single nucleotide variants of human tumor suppressor LHPP gene. Egyptian Journal of Medical Human Genetics, 24(1), 47. https://doi.org/10.1186/s43042-023-00426-w

Hecht, M., Bromberg, Y., & Rost, B. (2015). Better prediction of functional effects for sequence variants. BMC Genomics, 16(8), 1–12. https://doi.org/10.1186/1471-2164-16-S8-S1

Hossain, M. S., Roy, A. S., & Islam, M. S. (2020). In silico analysis predicting effects of deleterious SNPs of human RASSF5 gene on its structure and functions. Scientific Reports, 10(1), 14542. https://doi.org/10.1038/s41598-020-71457-1

Kosaloglu, Z., Bitzer, J., Halama, N., Huang, Z., Zapatka, M., Schneeweiss, A., … Zörnig, I. (2016). In silico SNP analysis of the breast cancer antigen NY-BR-1. BMC Cancer, 16(1), 1–12. https://doi.org/10.1186/s12885-016-2924-7

Lebrett, M. B., Crosbie, E. J., Smith, M. J., Woodward, E. R., Evans, D. G., & Crosbie, P. A. J. (2021). Targeting lung cancer screening to individuals at greatest risk: The role of genetic factors. Journal of Medical Genetics, 58(4), 217–226. https://doi.org/10.1136/jmedgenet-2020-107399

Lim, E. C., Lim, S. W., Tan, K. J., Sathiya, M., Cheng, W. H., Lai, K.S., & Yap, W.S. (2022). In-silico analysis of deleterious SNPs of FGF4 gene and their impacts on protein structure, function and bladder cancer prognosis. Life, 12(7), 1018. https://doi.org/10.3390/life12071018

Mansouri, M. R., Schuster, J., Badhai, J., Stattin, E.-L., Lösel, R., Wehling, M., & Golovleva, I. (2008). Alterations in the expression, structure and function of progesterone receptor membrane component-1 (PGRMC1) in premature ovarian failure. Human Molecular Genetics, 17(23), 3776–3783. https://doi.org/10.1093/hmg/ddn274

McGuire, M. R., & Espenshade, P. J. (2022). PGRMC1: An enigmatic heme-binding protein. Pharmacology & Therapeutics, 108326. https://doi.org/10.1016/j.pharmthera.2022.108326

Mehta, V., Dey, A., Thakkar, N., Prabhakar, K., Jothimani, G., & Banerjee, A. (2023). Anti-cancer Properties of Dietary Supplement CELNORM against Colon and Lung Cancer: An in vitro preliminary study. International Journal of Experimental Research and Review, 32, 1-14. https://doi.org/10.52756/ijerr.2023.v32.001

Meyer, M. J., Lapcevic, R., Romero, A. E., Yoon, M., Das, J., Beltrán, J. F., & Paccanaro, A. (2016). mutation3D: cancer gene prediction through atomic clustering of coding variants in the structural proteome. Human Mutation, 37(5), 447–456. https://doi.org/10.1002/humu.22963

Paleri, A. V., Cherian, I., Suresh, P. S., & Venkatesh, T. (2020). In silico analysis of COSMIC retrieved P body gene mutations in breast cancer. Gene Reports, 19, 100617. https://doi.org/10.1016/j.genrep.2020.100617

Pavithran, H., & Kumavath, R. (2021). In silico analysis of nsSNPs in CYP19A1 gene affecting breast cancer associated aromatase enzyme. Journal of Genetics, 100(2), 23. https://doi.org/10.1007/s12041-021-01274-6

Pejaver, V., Urresti, J., Lugo-Martinez, J., Pagel, K. A., Lin, G. N., Nam, H.J., & Iakoucheva, L. M. (2020). Inferring the molecular and phenotypic impact of amino acid variants with MutPred2. Nature Communications, 11(1), 5918. https://doi.org/10.1038/s41467-020-19669-x

Pru, J. K. (2022). Pleiotropic actions of PGRMC proteins in cancer. Endocrinology, 163(7), bqac078. https://doi.org/10.1210/endocr/bqac078

Reddy, N. S., & Khanaa, V. (2023). Diagnosing and categorizing of pulmonary diseases using Deep learning conventional Neural network. International Journal of Experimental Research and Review, 31(Spl Volume), 12-22. https://doi.org/10.52756/10.52756/ijerr.2023.v31spl.002

Rogers, M. F., Shihab, H. A., Gaunt, T. R., & Campbell, C. (2017). CScape: A tool for predicting oncogenic single-point mutations in the cancer genome. Scientific Reports, 7(1), 11597. https://doi.org/10.1038/s41598-017-11746-4

Ruan, X., & Mueck, A. O. (2023). Clinical importance of PGRMC1 in hormone responsive breast cancer. Breast Care, 18(3), 172–178. https://doi.org/10.1159/000527969

Saha, A., & Yadav, R. (2023). Study on segmentation and prediction of lung cancer based on machine learning approaches. International Journal of Experimental Research and Review, 30, 1-14. https://doi.org/10.52756/ijerr.2023.v30.001

Sim, N.L., Kumar, P., Hu, J., Henikoff, S., Schneider, G., & Ng, P. C. (2012). SIFT web server: Predicting effects of amino acid substitutions on proteins. Nucleic Acids Research, 40(W1), W452–W457. https://doi.org/10.1093/nar/gks539

Solairaja, S., Ramalingam, S., Dunna, N. R., & Venkatabalasubramanian, S. (2022). Progesterone receptor membrane component 1 and its accomplice: Emerging therapeutic targets in lung cancer. Endocrine, Metabolic & Immune Disorders-Drug Targets (Formerly Current Drug Targets-Immune, Endocrine & Metabolic Disorders), 22(6), 601–611. https://doi.org/10.2174/1871530321666211130145542

Thejer, B. M., Adhikary, P. P., Teakel, S. L., Fang, J., Weston, P. A., Gurusinghe, S., & Ludescher, M. (2020). PGRMC1 effects on metabolism, genomic mutation and CpG methylation imply crucial roles in animal biology and disease. BMC Molecular and Cell Biology, 21, 1–19. https://doi.org/10.1186/s12860-020-00268-z

Venkata Subbiah, H., Ramesh Babu, P., & Subbiah, U. (2020). In silico analysis of non-synonymous single nucleotide polymorphisms of human DEFB1 gene. Egyptian Journal of Medical Human Genetics, 21(1), 1–9. https://doi.org/10.1186/s43042-020-00110-3

Wang, Q., Mehmood, A., Wang, H., Xu, Q., Xiong, Y., & Wei, D.Q. (2019). Computational screening and analysis of lung cancer related non-synonymous single nucleotide polymorphisms on the human kirsten rat sarcoma gene. Molecules, 24(10), 1951. https://doi.org/10.3390/molecules24101951

Wang, Y., Ma, R., Liu, B., Kong, J., Lin, H., Yu, X., & Zhou, B. (2020). SNP rs17079281 decreases lung cancer risk through creating an YY1-binding site to suppress DCBLD1 expression. Oncogene, 39(20), 4092–4102. https://doi.org/10.1038/s41388-020-1278-4.

Zhang, M., Huang, C., Wang, Z., Lv, H., & Li, X. (2020). In silico analysis of non-synonymous single nucleotide polymorphisms (nsSNPs) in the human GJA3 gene associated with congenital cataract. BMC Molecular and Cell Biology, 21(1), 1–13. https://doi.org/10.1186/s12860-020-00252-7

Zhang, R., Akhtar, N., Wani, A. K., Raza, K., & Kaushik, V. (2023). Discovering Deleterious Single Nucleotide Polymorphisms of Human AKT1 Oncogene: An In Silico Study. Life, 13(7), 1532. https://doi.org/10.3390/life13071532