Research

An integrated approach for the understanding of association of JAK1 mutations in Chronic Myeloid Leukemia

Rowaid M. Qahwaji ¹, Ibraheem Ashankyty ^1,*, Eman F. Bahakeem ¹, Mohammed H. Qari ², and Mohammad Mobashir ^3,*

¹  Department of Medical Laboratory Technology, Faculty of Applied Medical Sciences, King Abdulaziz University, Jeddah 22233, Saudi Arabia; rgahwajy@kau.edu.sa (R.M.G.); ishankyty@kau.edu.sa (I.M.A.); amo0onh95@hotmail.com (E.F.B.);

²  Hematology Department, College of Medicine, King Abdulaziz University, Jeddah 22233, Saudi Arabia; drqari200@gmail.com (M.H.Q.).

³    Department of Biomedical Laboratory Science, Faculty of Natural Sciences, Norwegian University of Science and Technology, (NTNU) N-7491 Trondheim, Norway; mohammad.mobashir@ntnu.no (M.M.)

*    Correspondence: mohammad.mobashir@ntnu.no (M.M.) and ishankyty@kau.edu.sa (I.M.A.)

 Abstract: JAKs are the class of protein tyrosine kinases that connect with intracellular cytokine receptors and become active when the extra-cellular ligand-receptor interaction is trans-phosphorylated. By assisting tumor cells in squeezing through tiny capillaries and tissues and spreading to different regions of the body, it plays crucial functions in JAK/STAT signaling, particularly in immunity, cell proliferation, apoptosis, and tumor metastasis. Mutations in the JAK1 gene have already been connected to a variety of hema-tological cancers and other cancer types. The onset and course of chronic myeloid leukemia (CML) may be linked to certain muta-tions in the JAK1 gene. The previous findings make it more interesting to study the association of the JAK1 gene with the Philadel-phia chromosome (Ph. Ch.) in CML disease since it may become a way to suppress by gene-targeting therapies. There exists knowledge gap regarding the effect of JAK mutations in CML patients overall/disease-free/event-free survival rates which also further reflects the importance of our study especially in Saudi Arabia. Thus, we have potentially focused on JAK1 mutations in CML patients by using sequencing techniques for the potential exons and also by using in-silico approach by using publicly avail-able datasets. Here, we have explored the association of JAK1 with potential genes and the critical biological functions by using network-level understanding and the putative herbal drugs for targeting purpose. Our results show potential difference in the se-lected patients in terms of the sequences and JAK1 may act as a master regulator of critical biological functions and their compo-nents. Furthermore, the integrated approach leads to the conclusion that ATM, JAK1, TP53, KRAS, and BRAF as the most critical genes and KRAS and TP53 show maximum binding affinity (-8.41 kcal/mol (quercetin with KRAS), -8.17 kcal/mol (resveratrol with KRAS), -8.08 kcal/mol (apigenin with TP53).

Keywords: JAK1; mutations; chronic myeloid leukemia (CML); pharmacogenomics; herbal drugs; network medicine; clinical relevance

1. Introduction

Cancer is a highly complex, heterogeneous, and robust disease and depending on the organs it may be leukemia, lymphoma, sarcoma, carcinoma, melanoma, or glioma. Through the lymphatic and circulatory systems, these aberrant cells have the ability to spread to other body areas and infiltrate adjacent tissues[1-10]. Cancer is not one illness; it includes more than 200 unique cancer forms that affect more than 60 different body organs. Genetic, biochemical, and environmental variables are known to play potential roles in the development of cancer. The biological factor, which includes being infected with viruses that might cause cancer, the genetic factor, which is mutations, and the environmental factor, which includes exposure to chemicals and radiation. Based on its origins, cancer could be grouped into five main groups, and within each group there are other cancer types: leukemia, lymphoma and myeloma, sarcoma, carcinoma, and brain and spinal cord cancers[11-24].

Hematological cancers include leukemia, lymphoma, and myeloma in their classification. Thomas Hodgkin first described the first hematological malignancy before 187 years passed; after then, further hematological cancers have been described and categorized. The fundamental classification of a heterogeneous group of diseases, hematological malignancies, derive from cells in primary lymphoid organs like the bone marrow (BM) or secondary lymphoid organs like the lymphatic system[25-38]. The clinical and biochemical characteristics of hematological malignancies vary significantly. Hematological neoplasms are divided into two primary categories: myeloid and lymphoid neoplasms. The World Health Organization (WHO) (https://www.who.int/) categorized haematological neoplasms according to the afflicted lineage. Every lineage separates into a variety of diseases. Leukemia, lymphoma, and plasma cell neoplasms are the three main categories of haematological malignancies.

JAK (Janus Tyrosine Kinases) gene family members include Tyrosine kinase 2 (TYK2), JAK1, JAK2, and JAK3. Tyrosine kinases (TKs) are intracellular, nonreceptor proteins that are encoded by JAK genes[39-43]. Because of this, many of the operations of the cell are controlled by enzymes found inside the cells, which speed up various reactions through the phosphorylation process. Numerous growth factors, cytokines, interleukin-4, IL-10, and IL-2, are signaled by the TKs. All of these growth factors are crucial for the control, proliferation, differentiation, and maturation of hematopoietic precursor cells. JAK protein plays a part in the JAK/STAT signaling pathway as well. This pathway allows information to be delivered to the nucleus for gene expression. Additionally, it is crucial for immunity, cell division, cell death, and tumor development. Normally, cytokine signals including interferon, interleukin, and certain growth factors activate the JAK/STAT signaling pathway[44-49]. The JAK molecules will get activated as soon as the cytokine signal attaches to its receptors. The JAK molecules can then activate the STAT molecules after they have been attracted to the receptor. Last but not least, the activated STAT molecules will enter the nucleus to start the transcription of several genes involved in numerous biological processes, including cell growth and survival. This pathway’s deregulation has been associated to a variety of cancers, including haematological malignancies[26, 27, 50-52].

The c-Abelson (abl) protooncogene on chromosome 9 reciprocally translocates with the 5′ sequence of the truncated break point cluster region on chromosome 22 to cause CML, a haematological malignancy. BCR-ABL, a functional oncoprotein p210 that results from the fusion of two genes, is a constitutively active tyrosine kinase that stimulates a number of signaling pathways involved in cell proliferation. Tyrosine kinase inhibitors (TKIs) that target BCR-ABL, such as imatinib, nilotinib, and ponatinib, effectively slow the progression of CML[53-58]. Drug resistance brought on by BCR-ABL overexpression and mutations is still a problem, though. Chemical molecules or compounds created by living things are referred to as natural products (NPs). Due to their low toxicity and cost-effectiveness, they are growing in importance as a research topic for the development of new cancer drugs. Many NPs, including alkaloids, flavonoids, terpenoids, polyketides, lignans, and saponins, inhibit CML cell proliferation and trigger apoptosis, according to a number of lines of research. NPs can overcome CML cells’ multi-drug resistance (MDR) as well as differentiating them into the monocyte/erythroid lineage[59, 60]. For this study, we have chosen the compounds apigenin, quercetin, and resveratrol, which are now the subjects of much research. The chosen medications have demonstrated several traits thought to be anticancer and may influence putative pathways linked to cancer. Many fruits, vegetables, leaves, seeds, and grains contain these NDs; notable foods that contain significant levels of them include kale, capers, and red onions. Because quercetin activates caspase-3, inhibits Akt, mTOR, and ERK phosphorylation, decreases β-catenin, and stabilizes HIF-1α stabilization, it increases apoptosis and autophagy in cancer. Additionally, it prevents the spread of cancer cells by lowering the release of VEGF and MMP. Apigenin’s capacity to affect many cell signaling pathways, including as tumor suppressor genes, angiogenesis, apoptosis, cell cycle, inflammation, apoptosis, PI3K/AKT, NF-κB, MAPK/ERK, and STAT3, confirms its anti-cancer potential[61-64]. It has been shown that resveratrol affects the carcinogenesis process at several stages. Additionally, it has been demonstrated to kill a number of human cancer cells by means of processes for programmed cell death, including necroptosis, autophagy, and apoptosis.

The term “clinical heterogeneity” describes the situation in which some patients respond to a therapy while others do not, or in which some patients respond well to a treatment while others do not, or in which some individuals have a short half-life and others a long half-life. Biology’s word for heterogeneity describes the range of biological and molecular characteristics that cells from different patients exhibit[65-70]. The current techniques of treatment need to be improved because the people undergoing therapy have severe, complex conditions like cancer, neurological disorders, and diabetes. Thus, there is an urgent need for improved, customized medications that could lessen the undesirable side effects. Pharmacogenetics and pharmacogenomics research focuses on how inheritance affects how each person reacts to medication, a phenotype that can range from potentially fatal adverse drug reactions to an equally catastrophic lack of therapeutic efficacy[71-86]. Pharmacogenetics and pharmacogenomics are important components of the “individualized medicine” philosophy. Early pharmacogenetic research were primarily concerned with monogenic traits, typically including genetic variety in drug metabolism. Because of the convergence of pharmacogenetics’ successes with the rapidly advancing field of human genomes, pharmacogenetics has developed into pharmacogenomics. Drug response research is a systems-based field that is expanding beyond genomics to encompass pharmacotranscriptomics and pharmacometabolomics.

2. Methods

2.1. Basic clinical relevance and patient information and sanger sequencing of JAK1: The study was approved by King Abdulaziz University Hospital (KAUH) ethical committee for permission to collect the samples from patients visiting the Hematology Clinic.  Each participant has provided with the detailed information about the aim of the study and asked to signed the consent approved by the KAUH ethical committee. Following the instruction of the treating physician, Professor Mohammed Gari, fourteen CML patients and one JMML patient were selected for this study, they already diagnosed and confirmed as CML patients, and they are under treatment. As control, one healthy individual was selected to use his blood as a control. This subject was healthy individual coming for blood donation at KAUH. We make sure he was not coming to donate to any of the patients selected for the study and has no relatives suffering from CML

2.2. Blood collection and deoxyribonucleic acid (DNA) extraction

A venous blood sample was collected from those patients in 4.5mL EDTA (ethylenediaminetetraacetic acid) tube. Blood samples were immediately transported to the lab for immediate DNA extraction or kept in the refrigerator (40 Degrees Celsius (◦c)) for not more than 4 days. Total genomic DNA was extracted using a commercially available kit (QlAamp® Blood Mini Kit) according to the manufacturer’s instructions.

2.3. DNA concentration assessment

The extracted DNA was assessed using the Nanodrop 2000 spectrophotometer (Thermo Scientific) at the core facility in King Fahad Medical Research Center (KFMRC).

2.4. DNA amplification

The extracted DNA was amplified by Polymerase Chain Reaction (PCR) using (Top Taq DNA Polymerase Kit, Qiagen (Manufacturer)) PCR kit with group of primers (JAK1 Exon 6, 11, 14 and 15) from (SynBio Technologies).

2.5. GEL electrophoresis

PCR products were visualized and the PCR product size was determined by running the PCR on 1% agarose gel.

2.6. PCR products purification

PCR product was purified and cleaned by the PCR Fragment Purification Kit (Dongsheng biotech, DSBIOTM) following the manufacturer’s instruction.

2.7. Sanger sequencing

DNA sequencing was used for the JAK1 gene sequence. Sanger sequencing was used for determining the nucleotide sequence of DNA to look for possible JAK1 gene mutation. The sequencing was performed using the Applied Biosystems™ Sanger Sequencing Kit. The sequencing was performed on the PCR product using the same primer used in the PCR reactions. The PCR products were sued as DNA template and there are two reactions are performed one with the forward primer and the other with the reverse primer.

2.8. Data analysis

BioEdit was used to analyzed and compared the sequences with the reference genes sequence. The statistically significant of the mutations was tested by Mutation Taster.

      2.9. Bioinformatics analysis details

In this study, we proposed to investigate role of JAK1 in CML and identify potential targets and herbal drugs that are able to disrupt the activated signaling pathways. For JAK1 network preparation, FunCoup PPI network database was used. For JAK1 genes enriched pathway analysis KEGG database and GECIP toolbox was implemented[52, 87-89]. To explore the clinical relevance of JAK1, Protein Atlas database was used to show the prognostic summary, its expression in immune cells, and different types of cancers[90-96].

FunCoup2.0 has been used throughout the entire investigation to generate JAK1 networks, and cytoscape has been utilized to display the networks. MATLAB has been used for the majority of our code and calculations. Protein complexes, protein-protein physical interactions, metabolic processes, and signaling pathways are only a few examples of the four classes of functional coupling or relationships that FunCoup predicts[52, 61, 97-106].

2.10. Docking approach:

The protein sequences were obtained from the UniProt database (www.uniprot.org). Pubchem was used to obtain the 3D structures of herbal drugs from PubChem in SDF format. The structure of proteins and ligands was visualized using PyMol. The Swiss Model website (www.swissmodel.org) was used to do homology modelling on the aforementioned proteins. Based on the results of the GMQE, QMEANDisCo, and QMEAN Z-score analyses, the model structures have been chosen. Greater values indicate higher expected quality in the GMQE (Global Model Quality Estimate) and QMEANDisCo global scales for overall model quality, which range from 0 to 1. After hydrogen atoms were inserted, the energy of proteins’ 3D structures was reduced using the Swiss PDB Viewer with all parameters set to default[97, 107-111].

The total number of active sites, as well as information on their amino acid sequence, cavity locations, and cavity average volume, were determined using a search engine. As a result, the Discovery Studio and CASTp servers were used to forecast the binding pocket of each of the aforementioned proteins using the default probe radius (1.4 Å).

Utilizing PyRx, the molecular docking experiment was carried out (AutoDock Vina). For docking, the atomic coordinates of the protein and its ligand were converted to pdbqt files. Using AutoDock Vina and grid box dimensions with specified spacing and size pointing in the x, y, and z directions, the binding pocket was produced. The docking studies employed the default parameters. The most advantageous binding configurations of the compounds were chosen based on the lowest binding free energy (delta G), the quantity of hydrogen bonds, and other hydrophobic interactions. Among the several interactions that Discovery Studio and PyMol looked into were hydrogen bonds, carbon-hydrogen bonds, van der Waals interactions, pi-sigma, pi-sulfur, alkyl, pi-alkyl, pi-alkyl, pi-pi T-shaped, and halogen bonds[109, 112-114].

3. Results

3.1. Patient information and Sanger sequencing of JAK1: In terms of prognostic profile, it appears that JAK1 is clinically highly relevant in case of renal, colorectal, and breast cancer (Figure 1a). In terms of its expression in different immune cells, NK cell, netrophil, basophil, eosinophil, PMBC, and regulatory cell display maximum expression level (Figure 1b). While JAK1 seems to dominantly ex-pressed in colorectal, endometrial, ovarian, testis, lymphoma, and pancreatic cancer (Figure 1c).  Fifteen patients were included in this study, fourteen of them were confirmed as CML patients and the last one was confirmed as juvenile myelomonocytic leukemia (JMML) or in another name chronic myelomonocytic leukemia (CMML) patient. Nine of the participants were female while the other six were males, with a mean of age 52 Y.O. and they were from different ethnic origins. Their initial complete blood count (CBC) results at the date of diagnosis were much similar to each other, unexplained increase in the WBCs count, decrease in the RBCs and the Hb and a normal platelets (PLTs) count, with a mean of the blood counts 232 Thousand per cubic milliliter (K/ul), 3 Million per micro-liter (M/ul), 9 Gram per deciliter (g/dl) and 338 K/ul, respectively.

BCR- ABL expression was tested by qualitative PCR since it is considered as the main criterion in diagnosis CML. The initial BCR-ABL result at the date of diagnosis was positive in all of the participants., Seven out of the 15 pa-tients became BCR-ABL negative after treatment plan but they follow the treatment plan even if the result became negative to achieve the completely deep remission state. Regarding the disease information, nine of the patients were in the chronic phase of the disease, the other six were in the accelerated phase. None of the patients was in the blast crisis. Since five of the patients were reporting a resis-tant to a certain medication while the other ten patients don not. Therefore, there is a variation in the treatment pro-vided. Six of the patients were on Imatinib, four were on Dasatinib, four were on Nilotinib and the last one was on Hydroxyurea only (Table 1).

Table 1. Patient information

We have analyzed all the previously reported mutational exons (Table 2) of JAK1 by the Sanger sequencing method. From this, we found two genetic alterations in exon 6 and 15 of the JAK1 gene. Direct sequencing resulted in two silent mutations in exon 6 and exon 15: (c.579T>C and c.2049C>T). The single nucleotide polymorphism (SNPs) are T to C transition at nucleotide 579 (c.579T>C) in exon 6 and a C to T transition at nucleotide 2049 (c.2049C>T) (as shown in Figure 1a, 1b, and 1c) in exon 15 (Figure 1d, 1e, and 1f). The details of all the sequences as chromatogram were shown in Supplementary Data 1 and 2. Moreover, we have also retrieved the mutational impact of JAK1 in human disease from UniProt where there are three mutations shown on the protein sequence.

Table 2. Mutational summary for JAK1

Polymorphism




Silent

15

2, 5, 7, 8, 14

c.2049C>T

No AA changes

Single base exchange

Homozygous mutation




Protein features might be affected




Splice site changes

Polymorphism




Silent

The above mentioned mutations were detected in our study while mutations-associated with disease shown in previous studies (fetched from www.uniprot.org) are below:

VAR_084991

Position: 634aa

A > D

Constitutive gain of function resulting in increased receptor signaling pathway via JAK-STAT; no effect on protein abundance.

VAR_084992

Position: 703aa

S > I

Increased activation of protein kinase activity; constitutive gain of function through the transactivation of associated JAK kinases; increased receptor signaling pathway via JAK-STAT

VAR_041715

Position: 973aa

N > K

in dbSNP:rs34680086

Figure 1. DNA sequencing of the JAK1 gene in CML patient (exon 6 and 15). (a) JAK1 gene (Exon 6) reference sequence (b) JAK1 gene (Exon 6) patient No. 1 sequence shows a nucleotide change at 579 (c.579T>C). (c) Mutation Taster report of Exon 6 mutation. (d) JAK1 gene (Exon 15) reference sequence (e) JAK1 gene (Exon 15) patient No. 2, 5, 7, 8 and 14 sequences show a nucleotide change at 2049 (c.2049C>T). (f) Mutation Taster report of Exon 15 mutation.

3.2. JAK1 appear to connect critical genes and potential biological functions: As we presented the patients details and Sanger sequencing of the human samples, now we first performed clinical relevance analysis of the target gene JAK1 by using human protein atlas database (Figure 2). Here, we could see theprognostic summary of JAK1 in renal, colorectal, and breast cancer as highly significant (2a). In terms of JAK1 expression in immune cells, it is dominantly expressed in NK cell, neutrophil, and basophil (Figure 2b) while in terms of different types of cancers, it is highly expressed in colorectal cancer followed by endometril cancer, ovarian cancer, and lymphoma (Figure 2c).

Figure 2. Clinical relavance of JAK1 by using Protein Atlas database. (a) Prognostic summary showing its clinical impact as highly significant in renal, colorectal, and breast cancer. (b) JAK1 expression in different immune cells. (c) JAK1 expression in different types of cancers.

Finally, we have performed in-silico analysis for the basic understanding of JAK1 gene in terms of its association with other genes and biological functions and pathway. First of all, we have used FunCoup PPI network database to predict the JAK1 PPI interactors (Figure 3a and Supplementary Table 1) and then for all the interactors including JAK1, pathway enrichment analysis was performed (Figure 3b and Supplementary Table 1). JAK1, DDR1, and CSNK2B shows the maximum connectivity in JAK1 network. MAPK, ErbB, Ras, cytokine signaling, FoxO, PI3K-Akt, and Jak-STAT were among the highly enriched pathways for JAK1 gene network. Among the list of enriched pathways, there are a large number of pathways which are well-known to be associated with different types of cancers including CML. ErbB, PI3K-AKT, FoxO, JAK-STAT, NK cell-mediated cytotoxicity, HIF-1, Wnt, VEGF, TLR, mTOR, Cell cycle, TNF, Hippo, NF-kB, and TGF-B signaling pathways are among the well known different types of cancer signaling pathways (Supplementary data 1).

Figure 3. Network-level understanding of JAK1 gene and the enriched pathways. (a) The network of JAK1 genes retrieved from FunCoup network database where circle represents genes and edges for the connectivity. The size of genes is linked with the number of connections. Higher connections means larger node size and lesser connections means smaller node. (b) Enriched pathways for JAK1 and all the genes.

After performing the basic analysis of JAK1, we have performed the mutational analysis of genes in case of CML for which TCGA database was utilized by using cBioPortal (Figure 4). From mutational analysis (Figure 4a), we observe that SF3B1, ATM, NOTCH1, TP53, CHD2, and POT1 as the extremely high mutated genes and the JAK1 and JAK2 seems to be quite less mutated which have added after selecting top 50 mutated genes because our study focuses on JAK1. Based on the network of genes and the associated/inferred pathway network (Figure 4b) analysis, ATM, JAK1, TP53, KRAS, and BRAF appear to be the most critical genes because they comparatively control critical biological pathways and the genes which we have processed for possible herbal drug-target predicion.

Figure 4. (a) Mutational analysis performed for CML samples from TCGA database. Here, we presented the top 50 genes and the two JAKs with the percentage of mutations. (b) Network of top 50 genes and the JAKs followed by inferred KEGG pathways.

• Prediction of putative herbal drug-targets in case of CML:

After analyzing the network-level understanding of JAK1, associated genes, and the pathways, we performed drug-target anlysis for which we have used herbal drugs (apigenin, resveratrol, and quercetin) to target the selective genes/proteins (ATM, JAK1, TP53, KRAS, BRAF) which appear highly connected with more genes and the pathways (Figure 5).

Figure 5. The overall significant molecular docking result of different proteins (such as ATM, JAK1, KRAS, TP53 and BRAF) with Apigenin and Quercetin represented as a pictorial presentation. The protein-ligand complexes are shown as helical ribbon structure in presence of respective drugs (a) ATM::Apigenin (b) JAK1::Quercetin (c) KRAS::Quercetin, (d) TP53::Quercetin, and (e) BRAF::Apigenin. The 3D H-bonds surface representation as donor and acceptor region of proteins and binding state of drugs in their respective predicted binding pockets. 2D representation of drug interactions within the predicted catalytic pocket forming different types of covalent and non-covalent bonds with multiple residues.

For more details, we have also presented the summary Table (Table 3) for docking outcomes. In Table 3, the binding affinity (delta G) were presented for the respective docking outcomes. Here, we observe that KRAS with maximum binding affinity with both two herbal drugs (quercetin, resveratrol, and apigenin) and TP53 also shows binding affinity of -9.2Kcal/mol with quercetin. Almost all the binding energy values appear to be less than -7.1Kcal/mol which means, we could say that all the selected target proteins could have the potential to bind with the three selected herbal drugs where KRAS and TP53 show maximum binding affinity.

Table 3. List of protein and ligand with their respective binding energies.

4. Discussion

In this work, JAK1 mutations in CML patients may have received the majority of our attention. Using an in-silico methodology, we looked at its relationships with possible genes and key biological processes. Our findings point to potential differences between the chosen patients in terms of sequencing, and JAK1 may function as a master regulator of vital biological processes and the elements that make them up. Furthermore, the combined technique revealed that the most important genes are ATM, JAK1, TP53, KRAS, and BRAF, and that KRAS and TP53 had the highest binding affinity.

The lack of the knowledge of the genetic landscape of CML patients worldwide and particularly in Saudi Arabia, which CML (BCR-ABL Positive) accounts of 1.2%  and CML not otherwise specified (NOS) which mean that the BCR-ABL status is unknown accounts of 13.0 %  Out of others leukemia type[115], reflect the importance of such studies. This study aimed to improve the scientific knowledge about the presence and the association of the JAK1 gene with the Ph. Ch. in CML disease. Initially, the mean age of infected with CML is much similar to the other studies, it affected the elderly people with a mean age of 40 and our mean of age is 52 years old. The initial blood counts between the CML patients also is much similar to the other studies, the leukocytosis and the anaemia[116, 117]. But the PLTs count shows a variation in our studies the majority of the patients shows normal PLTs count while in other studies the patients show thrombocytosis[118-123]. Like the previous studies, all CML patients are BCR-ABL positive and it is considered as a diagnostic criterion for this disease. JAK/STAT signaling pathway activated easily by different cytokines, once the JAK activate the STAT transcription factors will persistently be activated. The role of the JAK/STAT signaling pathway in the survival, proliferation and differentiation of our cells reflect the importance of studying their status in hematological malignancies[45]. Although several cancers show constitutive STAT activation, without reporting any activating mutations in JAK or STAT. Mutational studies have shown that the mutations in exon 11 of the JAK1 gene can point to molecular target for novel therapies since it connotes a poor unfavorable prognosis in T-ALL patients. JAK1 reported mutations in exon 14 have shown an association with poor response to leukemia therapy, frequent relapse of the disease, and reduced the overall survival rate in B-ALL and T-ALL patients. Other mutational studies have shown that the mutations in exon 14 of the JAK1 gene could result in a gain of the protein function, in addition to their response to JAK inhibitor treatment to inhibit JAK/STAT signaling pathway in T-cell lymphoma[124-129].

There are a number of approaches where different methods have been implemented for various studies related to different cancer types and many of them have explored potential putative biomarkers. In this study, we found two silent mutations (c.579T>C and c.2049C>T) in JAK1 in CML patients. these two silent mutations have been reported previously in hemangioblastomas patients with no amino acid changes (c.2049C>T; p.S683S) and (c.579T>C; p.A193A)[130-134]. In conclusion, we report here two silent mutations in exon 6 and 15 of the JAK1 gene in six CML patients. In spite of the low frequency of mutation, we suggest that mutation of the JAK1 gene may contribute to the development and progression of CML and CMML by altering activation of the JAK-STAT signaling pathway. Further mutational analysis and whole exon sequencing of the JAK1 gene will broaden our understanding of its role in CML and CMML.

Using the human protein atlas database, we also conducted a clinical relevance analysis of the target gene JAK1 (Figure 2). The prognostic summary of JAK1 in colorectal, breast, and renal cancer is shown to be significantly important in this instance (2a). When it comes to immune cells, NK cells, neutrophils, and basophils are the ones that express JAK1 most (Figure 2b). When it comes to cancer types, colorectal cancer has the highest expression of JAK1, followed by ovarian, endometrial, and lymphoma malignancies (Figure 2c).

The main goal of the study was to investigate the mutations on JAK1 in the patient samples while to make it more imperative we extended our study in next direction to check the direct the interactors of JAK1 and their pathway. Which means we wanted to answer a question that is “what will be the impact if there is mutation on JAK1 in terms of biological function”. Thus, we extended our study where in-silico approach was implemented. Here, we used the protein interaction network database to find the JAK1 interactors and the associated pathways with JAK1 and its interactors which shows the functional relevance of JAK1. Afterward, we predicted the top ranked interactor and performed the docking of the interactor with NPs. The NDs presented here for docking shows promising binding affinity such as -8.41 kcal/mol (quercetin with KRAS), -8.17 kcal/mol (resveratrol with KRAS), -8.08 kcal/mol (apigenin with TP53), -7.78 kcal/mol (apigenin with KRAS), -7.75 kcal/mol (quercetin with TP53), and -7.41 kcal/mol (resveratrol with TP53). We have also applied these drugs in different cancer types for in-silico prediction of binding affinity[52, 61, 97, 100, 102, 104, 135].

Furthermore, this field is gradually moving past the “translational interface” into the clinic, where it is being incorporated into the process of creating new medications and coming under regulatory control. The history of pharmacogenetics and pharmacogenomics, scientific advances that have aided this field’s advancement, the incorporation of transcriptomic and metabolomics data into efforts to comprehend and predict variation in drug response phenotypes, as well as challenges with the “translation” of this important field of biomedical science into the clinic, will all be covered in the article.

5. Conclusions

A limited number of included patients and the variation in the ethnic group considered as one of our study limitations that must overcome in future studies. In conclusion, the present study is one of the first studies in Saudi Arabia that look for the association between JAK1 gene and CML patients. So, our results provide several benefits to both patients and scientific communities and help us to a better understanding of this association. From integrated approach, we conclude that ATM, JAK1, TP53, KRAS, and BRAF as the most critical genes and we could also say that all the selected target proteins (ATM, JAK1, TP53, KRAS, and BRAF) could have the potential to bind with the three selected herbal drugs where KRAS and TP53 show maximum binding affinity.

Supplementary Materials: The following supporting information have been also attached: Supplementary Table 1 and Supplementary Data 1.

Author Contributions: Conceptualization, R.M.Q., I.M.A., E.F.B., M.H.Q., and M.M.; methodology, R.M.Q., I.M.A., E.F.B., M.H.Q., and M.M.; validation, R.M.Q., I.M.A., and M.M.; formal analysis, M.M.; investigation, I.M.A. and M.M.; resources, I.M.A. and M.M.; data curation, I.M.A. and M.M.; writing—original draft preparation, R.M.Q., I.M.A., E.F.B., M.H.Q., and M.M.; writing—review and editing, I.M.A. and M.M.; visualization, M.M.; supervision, I.M.A. and M.M.; project administration, I.M.A. and M.M.; funding acquisition, I.M.A. All authors have read and agreed to the published version of the manuscript.

Funding: This research received no external funding.

Institutional Review Board Statement: The study was conducted in accordance with the Declaration of Helsinki, and approved by the Institutional Review Board (or Ethics Committee) of Unit of Biomedical Ethics and Research Committee, King Abdulaziz University, KSA (protocol code 699-19 and 27/11/2019).

Informed Consent Statement: Informed consent was obtained from all subjects involved in the study. Written informed consent has been obtained from the patient(s) to publish this paper.

Data Availability Statement: We have already uploaded the supplementary data with this manuscript.

Acknowledgments: We acknowledge Department of Medical Laboratory Technology, Faculty of Applied Medical Sciences, King Abdulaziz University, Jeddah 22233, Saudi Arabia and Department of Biomedical Laboratory Science, Faculty of Natural Sciences, Norwegian University of Science and Technology (NTNU), N-7491, Trondheim, Norway for providing us the facility and resource to carry out the study.

Conflicts of Interest: The authors declare no conflict of interest. The funders had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript; or in the decision to publish the results.

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