Cohesive transcriptomic and in vitro approach revealed genes involved in inhibition of G0/G1, G2 phase of cell cycle pathway of stomach cancer cells by geraniol treatment

Cohesive transcriptomic and in vitro approach revealed genes involved in inhibition of G0/G1, G2 phase of cell cycle pathway of stomach cancer cells by geraniol treatment | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Article Cohesive transcriptomic and in vitro approach revealed genes involved in inhibition of G0/G1, G2 phase of cell cycle pathway of stomach cancer cells by geraniol treatment Haribalan Perumalsamy, Shadi Rahimi, Anandapadmanaban Gokulanathan, and 6 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4127451/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract The effects of geraniol on cell cycle related pathway in AGS using RNA sequencing have not been explored and it is largely unknown. In this study, we isolated geraniol from Cymbopogon martini (palmarosa) essential oil using various spectroscopic analyses. At first, we carried out the cytotoxicity of geraniol on AGS cells. In-depth RNA sequencing analysis showed that geraniol negatively regulated genes that specifically initiate double-strand break repair via DNA replication, mitotic G1, G2/M transition, and S phases in cell cycle, eventually leading to induce apoptosis. Additionally, we validated the interaction of geraniol with the cell cycle related genes using docking, Florescence-activated cell sorting (FACS) and quantitative polymerase chain reaction (qPCR) analysis. Overall, the present investigation shows that geraniol interacts with specific target genes involved in the cell cycle process and induce cell death in the stomach cancer cells, which can be suggested as the potential treatment for stomach cancer. Biological sciences/Cancer Biological sciences/Cell biology Biological sciences/Computational biology and bioinformatics Geraniol stomach cancer cell lines RNAseq docking cell cycle FACS Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 Figure 8 1. Introduction Recently, gastric cancer is one of the major health concerns across the globe. Their occurrences are highly regional specific and more profound in Eastern Asia. It is also considered as the 5th most prevalent cancer in the world, and 4th most deadly cancer 1 . Although there are many variants of stomach cancer, adenocarcinoma is the most common type of cancer. Gastric cancer can be caused by a number of different etiological factors, including radiation exposure, adenomatous polyps, chronic atrophic gastritis, smoking, alcohol use, obesity, and prior gastric surgery 2 . Gender, age, ethnicity and geography are considered as the most important factors next to diet and Helicobacter pylori infection. The most common treatment options for gastric cancer include chemotherapy, radiotherapy and surgery. However, they exhibit their own disadvantages such as toxicity to normal cells, and resistance to anticancer drugs 3 . Monoterpenes are a large group of secondary metabolites that performs numerous biological functions and contains various biological properties including antioxidants, anti-inflammatory, antitumor, and antidiabetic effects 4 . Chemically, monoterpenes are made of 2 isoprene units formed by five carbons joined together head to tail. These biochemically active units are di-phosphate esters, isopentyl diphosphate, and dimethylallyl diphosphate 5 . Monoterpenes are further classified as acyclic, monocyclic, bicyclic, and iridoid glycosides. These are found primarily as active ingredients in essential oils and fixed oils derived from plants and other sources. 6,7 . Among various monoterpenes, geraniol is an acyclic isoprenoid commonly occur in the natural oils of fragrant herbal plants. Tumor proliferation is one of the critical steps of cancer formation that has been well established. It is the stepwise mechanism that cells perform to multiply. The cell cycle typically undergoes four different phases such as G1, S, G2 and M. Numerous studies on geraniol have shown to inhibit cell cycle in various cancer cell lines including prostate 8 , pancreatic cancer 9 , breast cancer 10 , oral cancer 11 and skin 12 cell lines. A recent study showed that geraniol can inhibit cell proliferation by inhibiting JNK/ERK signaling pathway and thus promoting apoptosis in the gastric cell line 13 . However, the target genes that are involved in the cell cycle process are crucial for the discovery of biomarkers and to identify the drug targets. Hence, we used Next Generation Sequencing (NGS) to analyze large-scale screening transcriptomes with or without geraniol treatment to unravel genes contribute to anticancer related properties. Specifically, NGS analysis combined with bioinformatics data mining tools offers the stage to concurrently interpret various genes, categorize the targets and its relations after treatments in stomach cancer cell lines. Overall, our study stands unique from already published study where, we claim geraniol can inhibit cell cycle by interacting with various target gene interactions to promote apoptosis in stomach cancer cell lines. 2. Methods 2.1 Cell line, reagents, and chemicals The cells line such as stomach cancer cell line (AGS: ATCC-CRL-1739) and lung fibroblast (MRC-5: ATCC-CCL-171) were cultured in RPMI 1640 or DMEM medium consist of 10% fetal bovine serum (FBS) and 1% antibiotic-antimycotic solution at 37°C in 5% CO2 and 95% air. In this study, the following materials were used: 0.5% trypsin-ethylenediaminetetraacetic acid in water (Invitrogen, Grand Island, NY, USA), culture medium, serum, and phosphate buffer (Life Technologies, Grand Island, NY), and antibiotic-antimycotic solution. The 3-(4,5-dimethylthiazol-2-yl)-2,5-diphenyl tetrazolium bromide (MTT) was purchased from Sigma-Aldrich (St. Louis, MO) with reagent-grade quality. Similarly, the palmarosa essential oil was acquired from Berjé (Carteret, NJ). The other materials and reagents utilized in the present study were easily accessible and of reagent-grade quality. 2.2 Cell treatments and MTT assay The anti-proliferative activity of geraniol was assessed in both AGS and MRC-5 cell lines using a MTT assay. As previously stated 14,15 the MTT assay was performed accordingly with proper protocols. Cisplatin is used as positive control and was formulated similar to geraniol. The DMSO solution is used as the negative control. In 96-well plate 2x 10 4 cells were seeded and incubated for 12 h to reach optimum cellular environment for the treatment. Dose-dependent concentration of geraniol (0 to 100 µg in 0.1% DMSO) was treated with three replicates. DMSO was used as a similar concentration for the negative control. After 24 h of treatment, cells were washed with 1X phosphate-buffered saline (PBS) and 100 µl of fresh cell culture medium consisting of MTT stock solution was added (5 mg/mL). Then plates were incubated 4–6 hours and further steps were performed as previously described 14,15 . The VersaMax microplate reader was used to record optical density values at 560 nm and 670 nm respectively. 2.3 Medium pressure liquid chromatography (MPLC) To fractionize main active components from Cymbopogon martini , crude palmarosa oil, proper solvent system conditions are required. As a result, we used thin layer chromatography (TLC) to confirm the suitable solvent system and picked up a combination of hexane and ethyl acetate with different gradient ratios (H:E; 10:0, 9:1, 8:2, 7:3, 6:4, 4:6, 1:9, 0:10 by volume). For this study, 5 g of palmarosa oil was used to isolate the secondary metabolites as previously described 16 using MPLC (Biotage Isolera apparatus) equipped with a UV detector at 254 nm and 365 nm and a column cartridge SNAP (100 g silica gel) with a volume of 132 mL. We combined similar column fractions together and labeled a number of groups based on Rf values of TLC data generated with hexane and ethyl acetate (8:2 by volume). Further separation and purification were accomplished using preparative high-performance liquid chromatography (HPLC) using a 7.8 mm i.d. 300 mm Bondapak C18 (Waters, Milford, MA, USA) column with an acetonitrile and water (80:20 by volume) mobile phase at a flow rate of 1 mL min − 1 . Finally, we extracted purified active constituent 1 (geraniol; 24 g) for further spectroscopic characterization to establish structural validation. 2.4 Gas-chromatography (GC) and gas-chromatography mass spectroscopy (GC-MS) Meanwhile, GC (Agilent 7890A gas chromatography system; Agilent, Wilmington, DE,) and GC-MS (Clarus 680T gas chromatograph-mass spectrometer; PerkinElmer, Fort Belvoir, VA) were used to identify the primary volatile elements of palmarosa oil. The GC and GC-MS conditions were employed as stated previously 17–19 . The 0.25 mm i.d. ( d f = 0.25 µm) DB-5 MS capillary column length of 30 m was used to separate active constituents more precisely with nitrogen carrier gas flow rate of 1mL/min. Similarly, we employed similar column specifications for GC-MS and the flow velocity of the helium carrier gas was 24.4 cm/s (30°C) at a split ratio of 1:50. Each volatile peak from GC mass spectra (Table 1 ) has been identified by comparing mass spectra libraries from mass spectrometry data center, NIST). Table 1. Primer lists used in this study S.no Gene name Primer sequence Forward Reverse 1 ESPL1 CACTTGGCACATTGCCCTTC CACCCAGCTCCCAAGTAAGA 2 CDK1 ACACCACTTGTCCCTCTAAGAT GTGGGTGGGAAGCCCATTTA 3 BUB1B AGCTGAAAGGACCAGCCATC TCCCGTACCTACCTCAGAGC 4 CCNB2 GGTGTGTGCCGTCATGTAAA GAGGGTGGCAACTAGTGCTT 5 TTK TGCAAAAGTGCCTTGGGAGA ACCAACAAGTTGGCCCAGAA 6 PKMYT1 AGCCACAAGTGGCACAATCA CCACCTCGTGACACATCCAA 7 MCM3 TGGATCCTCAAAACAGCAGACA GTGGGTGGTTGGCATTTTCC 8 MCM6 CTTCCCGGATTTCAACCCCT GCCTGTCTGAGCCTAGTTGT 9 CDC6 GCAGCGTTTGTTCTCCCTTG ACGACTTGTTTAACTAACTGTGGTC 10 CDC45 GGGGGTGTTGGGGTTATCAG GCCAACTTGCTTGGTTCCAG 11 GADD45B TGTCAACAAAGTCGGGCTGA TTAGTGGAGCCTCCCTCTCC 12 SDF2L1 AGAGATGGTTACTGAGCGCC TCAAGGCTGTGTGGATTCCC 13 β-ACTIN CATCACTATCGGCAATGAGC GACAGCACTGTGTTGGCATA 2.5 RNA isolation The 5x10 4 cells were cultured in 6-well plates and treated for 24h with geraniol based on IC 50 value in order to achieve differential expression of total genes for better profiling of RNAseq. Then treated and untreated cells were washed twice with ice-cold PBS to remove leftover medium and FBS. Then the cells were centrifuged at 1000 rpm for 3 min to remove any dead cells. The total RNA of AGS cells from geraniol treated and untreated groups was extracted using a TRIzol Reagent Kit (Invitrogen, Carlsbad, CA, USA). The purity of RNA was assessed using the NanoDrop® (Thermo Scientific, Wilmington, DE, USA) and integrity of RNA was measured using the RNA Nano 6000 Assay Kit of the Bioanalyzer 2100 system (Agilent Technologies, CA, USA). 2.6 RNA-Sequencing analysis 1 µg of RNA was used as input material for the preparation of RNA samples. To prepare sequencing libraries for Illumina®, we followed the manufacturer's instructions when using the NEBNext® UltraTM RNA Library Prep Kit. The addition of index codes was used to attribute sequences to samples. After receiving the raw data from Illumina® sequencing, the unprocessed data undergone sequential processing to determine the transcript sequence length, distribution, GC content, and total read counts ( Figure S1 ). R package, edgeR or DESeq2 20,21 was used to identify differentially expressed RNAs based on fold changes < 2 or fold changes < 0.5 and p values < 0.05. Differentially expressed genes were identified using the TopHat, Cufflinks, and Cuffdiff and the expression plots were generated using CummeRbund 22 . The GO term enrichment analysis was conducted using the Bioconductor package GOstats to examine differentially expressed genes (DEG) biological significance 23 . KEGG pathway enrichment analyses of DEGs were conducted using the gene Answers package 24 . Network data integration, analysis, and visualization performed by the open source software Cytoscape (v. 3.10.1) 25 . For significant enrichment, p < 0.05 was considered statistically significant. 2.7 Molecular docking Molecular docking was employed to determine the binding affinity between two molecules. The targeted receptors BUB1B (PDB ID: 3SI5), CDC45 (PDB ID: 5DGO), CDK1 (PDB ID: 4Y72), ESPL1 (PDB ID: 7NJ0), MCM6 (PDB ID: 6XTY), and TTK (PDB ID: 2ZMC) were obtained from the RCSB Protein Data Bank. The Schrödinger Suite 2023-3 was utilized for protein preparation and docking. Geraniol (CID_637566) was designated as the ligand during the docking process. The 3D and 2D interactions between the targeted receptors and geraniol were visualized using the Schrödinger Suite 2023-3 2.8 Fluorescence activated cell sorting (FACS) analysis A flow cytometric analysis was conducted to determine the different phases of the cell cycle and the population percentage of before and after 12 or 24 hours of geraniol treatment. The AGS cancer cells were grown in 6-well culture plates and adhered overnight. Dose dependent treatment groups of geraniol (5 and 10 µg/mL) were incubated for 12 or 24 hours. Untreated control samples received 0.1% DMSO. After the treatment durations, cells were washed twice with 1X PBS and centrifuged to cell pellets for flow cytometry analysis. Following the geraniol extraction, the cells were stained for 15 min at room temperature with Annexin-V-FLUOS (Roche Diagnostics, Laval, QC) and PI (1 mg/ml) in detection buffer (10 mM HEPES, 140 mM NaCl, and 5 mM CaCl 2 ) and analyzed by flow cytometry. Cell cycle analysis was performed using a FACSCalibur flow cytometer equipped with 488 nm (blue), 405 nm (violet), and 640 nm (red) solid state laser light (BD Biosciences, San Jose, CA, USA). The percentage of cells in the different cell cycle phases was determined using the BD Biosciences Cell Quest acquisition software. 2.9 cDNA construction and quantitative reverse transcription polymerase chain reaction (qRT-PCR) expression analysis 1 µ g of total RNA was used to develop the cDNA using ABi First Strand cDNA Synthesis Kit (Thermo Scientific, USA). The real-time experiments were performed in triplicate as described previously 26 . The primers used in this study are listed in Table 1 . The Student's t-test was performed to evaluate statistically significant data. 2.10 Statistical analysis The data are presented as the mean ± standard deviation of triplicate experiments, and statistical significance was determined by P < 0.05 when comparing treated and untreated cells. Statistical analysis of cell viability and geraniol treatment activity was conducted using one-way analysis of variance (ANOVA) followed by Tukey's post hoc test, were used to analyze the cell cycle data using R software, version 4.3.1 27 . 3. Results 3.1 Isolation and characterization of geraniol The HPLC spectrum showed the major peak with high purity at the retention time 18.86 min and confirmed as geraniol from palmarosa oil (Fig. 1 a). Furthermore, electron ionization mass spectrometry (EI-MS) was used to confirm their molecular weight as denoted 154 [M] + (100), 138 (13.8), 109 ( 35 ), 81 (19.3), 69 ( 3 ), 63 (7.2), 55 (6.3) (Fig. 1 b). Furthermore, nuclear magnetic resonance (NMR) spectroscopy was used to identify the proton and carbon chain of bioactive component geraniol. Geraniol was discovered using the following evidence: an oil that is colorless(methanol): ë max nm = 245; 1 H NMR (DMSO, 600 MHz): δ 1.70 (3H, d, J = 0.65 Hz), 1.75 (3H, d, J = 1.1 Hz), 2.00 (2H, s), 2.18 (3H, m), 4.99 (2H, s), 5.02 (3H, s), 5.18 (1H, m), 5.83 (1H, t). 13 C NMR (CDCl 3 , 150 MHz): δ 134.6 s, 132.6 s, 124.1 d, 123.5 d, 51.8 t, 40.7 t, 25.4 t, 23.6 q, 18.2 q, 17.3 q (Fig. 1 c). Additionally, GC-MS analysis was performed to elucidate volatile compounds from palmarosa oil as listed in Table 2 . The major constituents were identified with specific retention time as follows; geraniol (73.5%), geranyl acetate (12%), linalool (3%), caryophyllene (2.2%) and β-cis-Ocimene (1.4%). Table 2 Chemical constituents of palmorosa oil identified by gas chromatography (GC) and gas chromatography-mass spectrometry (GC-MS) Compound RT a (min) % Area 2,2-Dimethoxybutane 16.13 0.3 α-Myrcene 23.95 0.7 Ocimene 25.28 0.3 β-cis-Ocimene 25.58 1.4 Linalool 27.06 3.0 Nerol 29.77 0.3 Geraniol 30.39 73.5 Citral 31.00 0.4 Geranyl acetate 32.51 12.0 Caryophyllene 34.36 2.2 a Retention time. 3.2 Anti-proliferative effects of geraniol A dose-dependent concentration of geraniol was treated in both AGS and normal lung cells (MRC-5) and the IC50 value was calculated. To determine the effect of geraniol on AGS cells, it was also compared to cisplatin. Geraniol had an IC 50 value of 6.75 µg/mL on AGS cells and cisplatin had an IC 50 value of 9.89 µg/mL ( Figure S2 ). Similarly, geraniol exhibited non-significant toxicity (93 µg/mL) towards MRC-5, whereas cisplatin exhibited a mild toxic effect (18.4 µg/mL). 3.3 Transcriptomic analysis of geraniol in AGS cell lines The RNAseq analysis revealed significant differences in the expression of genes between the control and treated groups (Fig. 2 a). The majority of the genes in the treated groups were highly expressed, indicating that geraniol exhibited significant changes in transcript of stomach cancer lines. Specifically, our transcriptomic approach revealed a total of 22,488 genes following treatment with geraniol. Among them, 3296 genes were significantly differentially expressed compared to the control groups. The DEGs analysis revealed that 178 genes were significantly upregulated while 144 were significantly downregulated (Fig. 2 b). Figure 2 c depicts a volcano graph that lists both up and downregulated genes with denoted substantial expression. Genes such as XAF1, SOGA3, NPY4R, ITGB2-AS1, FBBP, FAM231D, EIF3CL , and ARL17B were significantly downregulated. Whereas, genes including PHOSPHO2, KLHL23, LOC100101148, KIAA0408, HSPA6, FAM65B, FAM47E, STBD1 , and CRYAB were upregulated (Fig. 2 d). The gene ontology (GO) enrichment analysis of expressed genes showed biological, chemical, and molecular functions (Fig. 2 e). The blue color in each node of the GO analysis showed significant expression, and most of them were involved in cell cycle, DNA replication, and mitotic regulation of cell cycle in biological processes. At cellular component levels, most of them are involved in chromosome condensation and elongation, spindle formation, MCM complex formation, etc. Whereas at the molecular level, they showed function in ATP-dependent regulation, DNA replication, and helicase activity. Overall, our RNAseq transcriptome analysis revealed significant genes and their expression in AGS cell lines after the treatment of geraniol. Furthermore, extensive analysis was required to proceed with gene ontology and the proposed molecular pathway detection. 3.4 Network analysis We performed cytoscape analysis for an interaction network, which showed an unified conceptual framework, to understand the biomolecular interaction between DEGs (Fig. 3 a). Our findings showed that most of the genes were involved in various molecular pathways that were analyzed based on the number of genes involved and fold enrichment scores. In specific, pathways such as cell cycle, DNA replication, proteosome, MAPK signaling pathway, extracellular matrix (ECM) receptor interaction, ferroptosis, and cancer-related genes were proposed (Fig. 3 b). Further, we identified that the genes such as ESPL1, BUB1B, CCNB2, GADD45, PKMYT1, CDC25A, MCM3, MCM6, MCM10, TTK, CDC25B, CDC7, CDC6, CCNB1, CDC45, WEE1, CDK1, MCM5, E2F1, BUB1, SMC1A, MCM2, CDC20, SDF2L1 , and CCNA2 were mostly involved in cell cycle pathways, showing interaction between them ( Fig. 3 c ) . Similarly, for DNA replication, POLD1, MCM2, MCM3, MCM4, MCM5, MCM6, DNA2 , LIG1, POLA1, POLA2 , and POLE genes have strong interactions with each other ( Fig. 3 c ) . Furthermore, other proposed pathways such as the proteasome, MAPK signaling pathway, ECM-receptor interaction, ferroptosis, and cancer-related pathways showed significant signaling interaction with their unique genes ( Fig. 3 c ) . 3.5 In-depth profiling of cell cycle analysis after geraniol treatment In-depth profiling of cell cycle pathway analysis was carried out and visualized under different phases of the cell cycle since geraniol is mostly involved in cell cycle pathways of stomach cancer lines. Our RNAseq analysis showed that most of the DEGs involved in cell cycle were downregulated, indicating that geraniol is significantly involved in the inhibition of AGS cell proliferation. The DEGs such as CCNA2 , CDC20, SMC1A, BUB1, BUB1B, E2F1, WEE1, CDC45, CCNB1, CCNB2, CDC6, CDC7, CDC25A, CDC25B, TTK, MCM2, MCM3, MCM5, MCM6, MCM10, PKMYT1, CDK1 and ESPL1 were downregulated while other genes such as SDF2L1 and GADD45B were upregulated respectively (Fig. 4 a). The uniform manifold approximation and projection (UMAP) analysis showed genes involved in different phases and biological functions of the cell cycle, including mitosis G1, G2/M and S phases ( Fig. 4 b ) . Each point represents the expression of genes involved in cell cycle pathways. In addition, more similar gene sets are positioned closer together to denote diversity in their function based on the p-value from the enrichment calculation. The colors of each group represent the differentiation of one from another, and the darker and larger points denote the most significantly differentially expressed ones (Fig. 4 b). Similarly, the hexagonal canvas plot showed a significant pathway of expressed genes based on the Bioplanet_2019 set library. Each hexagon denotes a single proposed pathway, and the highlighted blue is the most significantly involved, followed by light blue, and gray colored represents least significant (Fig. 4 c). The number of each hexagon explained the genes involved in the proposed pathway and their function. Correspondingly, Fig. 4 d also depicts a Manhattan plot of mostly differentially expressed genes based on log10 (p-value) corresponding to the enrichment of the input gene set. It represents enrichment analysis of significant transcripts participated in cell cycle, mitotic G1-G1/Sphases, G2/M checkpoints, metaphase-anaphase transition, DNA replication, G2/M transition, and G1to S cell cycle (Fig. 4 d). GO analysis showed the biological, cellular, and molecular function of genes involved in the cell cycle (Fig. 4 e-g). The biological process clearly showed positive regulation of the cell cycle and division. It initiates double-strand break repair via DNA replication, mitotic G1, G2/M transition, and S phases (Fig. 4 e ) . Similarly, cellular components clearly denote the formation of Cdc45-MCM-GINS (CMG) complexes, which represent the formation of double stranded DNA replication, intracellular membrane bounded organelles, mitotic spindles, and anaphase-promoting complexes. Also, it initiates amino acid complexes, including serine/threonine protein kinase complexes, which are mainly involved in mitotic cell division (Fig. 4 f). At the molecular level, most of the genes are involved in the DNA replication process, including 3’and 5’ DNA helicase activity, single stranded DNA binding, and cell dependent protein serine/threonine regulation (Fig. 4 g). The detailed information on each proposed cell cycle pathway and their respective genes are depicted in Table 3 . Table 3 Cell cycle Term P-value Adjusted P-value Combined Score Genes Cell cycle 4.42E-39 7.43E-37 96570.08829 GADD45B; BUB1B; MCM10; TTK; CDC7; CDC6; PKMYT1; SMC1A; CDC25A; CDC25B; CDC20; CCNA2; CCNB2; CCNB1; WEE1; CDC45; ESPL1; E2F1; CDK1; MCM3; MCM5; MCM6; BUB1; MCM2 G2/M checkpoints 1.42E-29 1.20E-27 47838.95406 MCM10; CDC7; CDC6; CDC25A; CCNB2; CCNB1; WEE1; CDC45; CDK1; MCM3; MCM5; MCM6; MCM2 Cell cycle checkpoints 3.94E-28 2.21E-26 18441.38147 BUB1B; MCM10; CDC7; CDC6; CDC25A; CDC20; CCNB2; CCNB1; WEE1; CDC45; CDK1; MCM3; MCM5; MCM6; MCM2 Mitotic G1-G1/S phases 3.81E-27 1.60E-25 15098.10538 MCM10; CDC7; CDC6; PKMYT1; CDC25A; CCNA2; CCNB1; WEE1; CDC45; E2F1; CDK1; MCM3; MCM5; MCM6; MCM2 DNA replication 4.18E-22 1.40E-20 6421.643046 BUB1B; MCM10; CDC7; CDC6; SMC1A; CDC20; CCNA2; CDC45; E2F1; MCM3; MCM5; MCM6; BUB1; MCM2 Cyclin A/B1-associated events during G2/M transition 1.09E-20 3.05E-19 61702.47931 CCNA2; CCNB2; CCNB1; WEE1; CDK1; PKMYT1; CDC25A; CDC25B G1 to S cell cycle control 2.77E-19 6.64E-18 9954.67398 CCNB1; WEE1; CDC45; E2F1; CDK1; MCM3; MCM5; MCM6; CDC25A; MCM2 DNA replication pre-Initiation 4.96E-18 1.04E-16 6775.902517 CCNA2; CDC45; E2F1; MCM3; MCM10; CDC7; MCM5; CDC6; MCM6; MCM2 Activation of the pre-replicative complex 9.81E-18 1.83E-16 16714.80509 CDC45; MCM3; MCM10; CDC7; MCM5; CDC6; MCM6; MCM2 S phase 6.12E-17 1.03E-15 4848.983268 CCNA2; WEE1; CDC45; MCM3; MCM5; CDC6; MCM6; CDC25A; CDC25B; MCM2 Mitotic G2-G2/M phases 7.03E-16 1.07E-14 5006.486955 CCNA2; CCNB2; CCNB1; WEE1; E2F1; CDK1; PKMYT1; CDC25A; CDC25B 3.6 Visualization of geraniol targeted proteins The molecular docking evaluation study demonstrated the effectiveness of geraniol binding to its respective receptors. In this study we performed more than 12 proteins which involved in the cell cycle pathway, however, binding affinity was used as the criterion to select the most favorable docked complexes. Geraniol displayed binding affinities when interacting with ESPL1 (-3.695), CDK1 (-3.6268), BUB1B (-3.306), TTK (-2.793), MCM6 (-2.626), and CDC45 (-2.386) kcal/mol, respectively (Table 4 ). Table 4 Geraniol-binding affinities score of the selected protein Protein ID Compound Docking Score BUB1B 3SI5 CID_637566 (Geraniol) -3.306 CDC45 5DGO -2.386 CDK1 4Y72 -3.628 ESPL1 7NJ0 -3.695 MCM6 6XTY -2.626 TTK 2ZMC -2.793 Among these complexes, ESPL1, CDK1, and BUB1B showed the most promising binding affinities. ESPL1 displayed the highest score, with key interacting residues including GLU (1006), SER (1003), LEU (1001), LEU (998), ILE (940), ILE (937), GLU (936), HIE (1630), GLN (1631), ARG (1638), THR (1634), LEU (2016), and GLN (2013) (Fig. 5 a). CDK1 exhibited the second-highest binding affinities, with critical residues such as PHE (147), ASP (146), ALA (145), LEU (55), VAL (64), ALA ( 31 ), LYS ( 33 ), PHE (80), VAL ( 18 ), GLY ( 11 ), ILE ( 10 ), ASP (86), GLN (132), ASN (133), and LEU (135) (Fig. 5 b). BUB1B, with the third-highest binding affinities, interacted with GLN (153), SER (157), GLU (160), GLU (161), PRO (135), TYR (139), ARG (130), LYS (127), LEU (126), and ASN (123) (Fig. 5 c). Similarly, the other proteins including TTK, MCM6, and CDC45 also showed considerable binding affinities as shown in Fig. 5 d-f. This study suggests that geraniol has the potential to serve as a promising candidate for targeting these three proteins. 3.7 Validation of cell cycle analysis by FACS Based on the RNAseq results, it was suggested that geraniol highly influences the cell cycle pathway which needs further validation upon experiment analysis. Therefore, we performed cell cycle analysis using flow cytometry to confirm at which phase cell cycle was arrested after geraniol treatment. In both 12 and 24 h after treatment experiments, the time and dose dependent experiment analysis revealed significant differences in genes as observed after geraniol treatment with 5 and 10 𝛍g/mL (Fig. 6 ). Next, we observed and assigned different stages of the cell cycle, including initial phase M1 (GO/G1), stationary phase M2 (S), secondary phase M3 (G2), and necrosis phase M4 (apoptosis). The untreated group showed well grown M1 (35.21%), M2 (12.76%), M3 (13.69%), and reduced necrosis phases (8.14%). Whereas, the geraniol (5 and 10 𝛍g/mL) treated groups showed a reduced number of M1 (20.31; 16.22%) and M2 phases (11.22; 23.13%) and an increased number of M4 (23.45; 28.66%), which may denote the induction of cell death in stomach cancer cell lines (Fig. 6 ). Similarly, at 24 hours, exposure to 5 𝛍g/mL showed induction of apoptotic mediated cell death in M4 phase (28.66%) compared to the untreated control group (7.12%). Whereas substantial cellular death was noticed at 10 𝛍g/mL at M4 phase (71.62%), which showed significant differences compared with the untreated control (7.12%) (Fig. 6 ). These results suggested that geraniol inhibits cellular proliferation as well as induction of cellular mediated apoptotic cell death in stomach cancer cell lines. 3.8 Validation of DEGs by qRT-PCR Further evaluation was conducted for substantially differentially expressed genes that are involved in the cell cycle pathway. Validating the most significant selective genes is necessary in order to confirm the influences on cell cycle pathways and to confirm in silico RNAseq analysis. For that reason, we designed the forward and reverse primers for each selected gene listed in Table 2 and performed the qRT-PCR. The AGS cells were treated with dose-dependent geraniol (5 and 10 µg/mL) and RNA was isolated from the treated groups, respectively. Then we performed qRT-PCR based on the manufacturer's protocol with well-designed primers for each gene. Our qRT-PCR results showed that most of them were downregulated, as denoted by RNAseq results (Fig. 4 a). In particular, ESPL1, BUB1B, TTK, PKMYT1, CDC6 , and CDC45 were significantly downregulated by 10 µg/mL treatment (Fig. 7 ). Simultaneously, among the genes, two of them ( GADD45B and SDF2L1 ) showed substantial upregulation, which was similar to RNAseq results. 4. Discussion The anticancer effect of geraniol was shown against various types of cancer cells including prostate, renal, colon, oral, skin, endothelium, stomach, endometrial, breast, pancreatic, lung, leukemia, and liver cancer cell lines 28 . It was recently depicted that the geraniol could induce stomach cancer cell death through production of mitochondrial ROS and inhibition of phosphorylation of mitogen-activated protein kinase (p38, MAPK, JNK, and ERK1/2) signaling pathway 13 . In a different investigation, geraniol enhanced peroxiredoxin-1 expression in human gastric epithelial cells, which had a protective effect on Helicobacter pylori-induced human gastric cancer signaling. 29 . However, in this study, we for the first time investigated cell cycle arrest as the potential underlying mechanism behind the cytotoxicity of geraniol against AGS stomach cancer cell line through RNA sequencing and FACS. Given that the geraniol is the major chemical constituent of palmarosa oil 30 , we extracted and purified geraniol as the main active component of palmarosa oil (Fig. 1 ). We further treated the AGS cells with geraniol and examined the effect of geraniol on changes in the expression of cell cycle related genes through RNAseq. We found the significant differential expression of genes involved in cell cycle, DNA replication, and mitotic regulation of cell cycle in geraniol-treated AGS cells (Fig. 2 ). This result is in consistent with the effectiveness of geraniol in promoting cell cycle arrest demonstrated in prostate, colon, breast, liver cancer cells 31–34 . As it was demonstrated in Fig. 4 and Fig. 7 , most of the DEGs related to cell cycle including CCNA2 , E2F1, CDC45, CCNB1, CCNB2, CDC6, CDC7, CDC25A, CDC25B, MCM2, MCM3, MCM5, MCM6, MCM10 , and CDK1 were downregulated in geraniol-treated AGS cells. Additionally, our docking analysis results for geraniol showed favorable docked complexes as well as most promising binding affinities to cell cycle proteins including ESPL1, CDK1, BUBIB, TTK, MCM6 and CDC45 (Fig. 5 ). Cell division cycle 6 ( CDC6 ), CDC7 , and CDC45 are responsible for the activation of DNA replication and partake in checkpoint controls that warrant DNA replication is completed before the initiation of mitosis. CDC25A is crucial for progress from G1 to the S phase of the cell cycle while CDC25B is obligatory for G2 to M phases cell cycle progress. Downregulation of CDC genes including CDC6 , CDC7 , CDC45 , CDC25A , and CDC25B might result in cell cycle arrest at G1 and G2 phases in geraniol-treated AGS cells. Cyclin and cyclin dependent kinase 1 (CDK1) is also involved in the control of the cell cycle at the G1/S (start) and G2/M (mitosis) transitions. The downregulation of cyclins such as CCNA2 , CCNB1 , and CCNB2 as well as CDK1 might result in cell cycle arrest at G1 and G2 phases in geraniol-treated AGS cells. It is concordant with the cell cycle arrest at G1 phase, and a considerable arrest at G2 phase, in geraniol-treated PC-3 cells deep-rooted by the diminished expression levels of cyclins A, B, D and E and CDK1 and CDK4 , as well as by the elevated levels of p21Cip1 and p27Kip1 35,36 . The E2F transcription factor 1 (E2F1) is the key cell cycle regulator and targets genes encoding proteins that regulate cell cycle progression through the G1/S transition 37 . Downregulation of this gene in geraniol-treated AGS cells could induce G1 arrest. It was previously shown that the geraniol could suppress prostate cancer growth through down-regulation of E2F transcription factor 8 (E2F8) 31 . Mini-chromosome maintenance proteins (MCM) are required for the initiation of eukaryotic genome replication. It was previously proposed that MCM profiling could be used to predict treatment response and prognosis in breast cancer patients. 38 . Thus, downregulation of MCM2 , MCM3 , MCM5 , MCM6 , and MCM10 genes in geraniol-treated AGS cells could be attributed to the efficiency of geraniol treatment for stomach cancer. There was increase in growth arrest and DNA damage inducible beta ( GADD45B ) upon geraniol-treated AGS cells. It is well known that GADD45B group gene’stranscript levels are increased upon stressful growth arrest conditions and/or while treating with DNA-damaging agents. They most probably express together and function in conjugation to inhibit cell growth. Induced expression of GADD45B gene could suppress growth of hepatocellular carcinoma 39 . The high transcript expression of GADD45B significantly heightened the chemo-sensitivity and induced apoptosis mediated cell death via mitogen-activated protein kinase (MAPK) pathway in advanced prostate cancer 40 . Hence, induced expression of GADD45B by geraniol could result in apoptosis, as increased number of AGS cells at M4 phase and induction of cell death were evident after geraniol treatment (Fig. 6 ). Overall, the geraniol treatment caused apoptosis mediated cell death in the stomach cancer cells by de-promoting genes involved in DNA replication and cell cycle progression as well as induction of GADD45B . 5. Conclusion Finally, based on our RNAseq data analysis, we concluded that geraniol negatively affected most of the genes involved in the cell proliferation by influencing the cell cycle pathway, which led to the induction of apoptosis. Based on our findings, we proposed the cell cycle, DNA replication, MAPK signaling, and the ubiquitin mediated proteolysis pathways as the potential pathways affected by geraniol treatment (Fig. 8 ). The genes highlighted in red in the pathway map are the genes with significant differential expression based on our RNAseq data analysis. Further, we validated the up and downregulated genes and proteins involved in different phases of the cell cycle by qRT-PCR analysis and docking studies. FACS analysis also confirmed the cell cycle arrest by geraniol treatment. Based on these results, this study suggests that the geraniol can be used as the potential treatment for stomach cancer. Declarations Conflict of interest The authors declare no competing financial interest. Declaration of Competing Interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper. CRediT authorship contribution statement HP and SRB performed most of the experiments. HP and SRB acquired and analyzed the experimental data. SD performed docking analysis. HP and GA performed data analysis. All authors helped in drafting the manuscript. HP, SR, and SRB finalized the manuscript. All authors contributed and approved the submitted version of the article. Funding The authors extend their appreciation to the Researcher’s supporting project number (RSP2023R349) King Saud University, Riyadh, Saudi Arabia Author Contribution HP and SRB performed most of the experiments. HP and SRB acquired and analyzed the experimental data. SD performed docking analysis. HP and GA performed data analysis. All authors helped in drafting the manuscript. HP, SR, and SRB finalized the manuscript. All authors contributed and approved the submitted version of the article. Data Availability The datasets used and/or analyzed during the current study available from the corresponding author on reasonable request. References WHO. The Global Cancer Observatory - All cancers. Int. Agency Res. Cancer - WHO 419 , 199–200 (2020). Christian, N. What is stomach cancer? What is gastric cancer? Medilexicon Int. LtD 1–14 (2015). Khan, T. et al. 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GADD45B Is a Potential Diagnostic and Therapeutic Target Gene in Chemotherapy-Resistant Prostate Cancer. Front. Cell Dev. Biol. 9 , 1–12 (2021). Additional Declarations No competing interests reported. Supplementary Files FigureS1.tif FigureS2.tif Graphicalabstract.png Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-4127451","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":286734148,"identity":"5d594ec8-e28f-4f63-8140-9aedfb092f3a","order_by":0,"name":"Haribalan Perumalsamy","email":"","orcid":"","institution":"Hanyang University","correspondingAuthor":false,"prefix":"","firstName":"Haribalan","middleName":"","lastName":"Perumalsamy","suffix":""},{"id":286734149,"identity":"daa2dadb-9b46-47b0-a2b8-af57b24beb5a","order_by":1,"name":"Shadi Rahimi","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA0ElEQVRIiWNgGAWjYDACCSBKYLBhMGBgbiBJSxpQCyMpWhgYDpOgRX5288MbD/eclzdnP9i64eOeOgb5dgJaDe4cM7ZIeHbbcGdPYtvNGc+A1p05QECLRIKZRMKB2wkGBxLbbvMcOAASIeCwGenfgFrOJRicf9h2+88BoMPmPyDgmRs5IFsOJBjcANrCcIAZKEJAh8GNnGKLhAPJhhtuPGy72XPgMI/BGcIO23jzxwE7eYPzycdu/DhQJyfffoCANeiAh0T1o2AUjIJRMAqwAQCneE34RopCYQAAAABJRU5ErkJggg==","orcid":"","institution":"Chalmers University of Technology","correspondingAuthor":true,"prefix":"","firstName":"Shadi","middleName":"","lastName":"Rahimi","suffix":""},{"id":286734150,"identity":"9d8f5c17-21dd-425d-96f8-995d19960faa","order_by":2,"name":"Anandapadmanaban Gokulanathan","email":"","orcid":"","institution":"","correspondingAuthor":false,"prefix":"","firstName":"Anandapadmanaban","middleName":"","lastName":"Gokulanathan","suffix":""},{"id":286734151,"identity":"a545d8b6-7aaa-4068-80d7-288f623c054c","order_by":3,"name":"Vuluchala Jyothiraditya","email":"","orcid":"","institution":"Saveetha Medical College and Hospital, Saveetha Institute of Medical and Technical Sciences","correspondingAuthor":false,"prefix":"","firstName":"Vuluchala","middleName":"","lastName":"Jyothiraditya","suffix":""},{"id":286734152,"identity":"589eb13a-73ab-4d88-9cc8-bc34817fc754","order_by":4,"name":"Sanjeevram Dhandapani","email":"","orcid":"","institution":"Kyung Hee University","correspondingAuthor":false,"prefix":"","firstName":"Sanjeevram","middleName":"","lastName":"Dhandapani","suffix":""},{"id":286734153,"identity":"51f4be86-6dea-4bbd-a297-65839dbbbf21","order_by":5,"name":"Alia Almoajel","email":"","orcid":"","institution":"King Saud University","correspondingAuthor":false,"prefix":"","firstName":"Alia","middleName":"","lastName":"Almoajel","suffix":""},{"id":286734154,"identity":"2d6e9198-c772-4d28-bfee-35f86b71ebc0","order_by":6,"name":"Sivakumar Allur Subramanian","email":"","orcid":"","institution":"Hallym University","correspondingAuthor":false,"prefix":"","firstName":"Sivakumar","middleName":"Allur","lastName":"Subramanian","suffix":""},{"id":286734155,"identity":"269d52e1-0746-4b07-819b-e5f3f2fcd0e3","order_by":7,"name":"Mohamed Farouk Elsadek","email":"","orcid":"","institution":"King Saud University","correspondingAuthor":false,"prefix":"","firstName":"Mohamed","middleName":"Farouk","lastName":"Elsadek","suffix":""},{"id":286734156,"identity":"e7a42662-eff0-42d9-aecb-f142e05ba055","order_by":8,"name":"Sri Renukadevi Balusamy","email":"","orcid":"","institution":"Sejong University","correspondingAuthor":false,"prefix":"","firstName":"Sri","middleName":"Renukadevi","lastName":"Balusamy","suffix":""}],"badges":[],"createdAt":"2024-03-19 05:29:19","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-4127451/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-4127451/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":54159322,"identity":"fd65cb68-0660-4254-bb8c-bba5d7d1379f","added_by":"auto","created_at":"2024-04-05 12:50:35","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":547461,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eSpectroscopic analysis of geraniol extracted and purified from palmarosa oil\u003c/strong\u003e. (\u003cstrong\u003ea\u003c/strong\u003e) HPLC chromatogram of geraniol showing as major peak. (\u003cstrong\u003eb\u003c/strong\u003e) Molecular weight of geraniol was confirmed using electron ionization mass spectroscopy (EI-MS). (\u003cstrong\u003ec\u003c/strong\u003e) \u003csup\u003e1\u003c/sup\u003eH NMR and \u003csup\u003e13\u003c/sup\u003eC NMR of geraniol with structural validation.\u003c/p\u003e","description":"","filename":"Figure1.png","url":"https://assets-eu.researchsquare.com/files/rs-4127451/v1/5bf0473a198c3509ac55e607.png"},{"id":54159328,"identity":"4155ef70-77fd-453b-9767-ce705d4eb22f","added_by":"auto","created_at":"2024-04-05 12:50:37","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":814407,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eTranscriptomic analysis of AGS cell line in response to geraniol treatment\u003c/strong\u003e. (\u003cstrong\u003ea\u003c/strong\u003e) Gene expression profile of AGS cells with or without treatment with geraniol; (\u003cstrong\u003eb\u003c/strong\u003e) Venn diagram analysis showed expressed genes in four differences groups; (\u003cstrong\u003ec\u003c/strong\u003e) Volcano map distinguished substantial up and downregulated genes from non-significant genes which was calculated by log2 fold changes; (\u003cstrong\u003ed\u003c/strong\u003e) Expression of total DEGs from both untreated control and geraniol treated groups. The substantial genes were highlighted with their names; (\u003cstrong\u003ee\u003c/strong\u003e) Gene ontology enrichment analysis performed at various levels of biochemical process, cellular component and molecular functions. Relevant to panel b and c, substantial up and downregulated genes are marked with red and blue color, respectively.\u003c/p\u003e","description":"","filename":"Figure2.png","url":"https://assets-eu.researchsquare.com/files/rs-4127451/v1/b9d401a21615378f4c5daabe.png"},{"id":54159326,"identity":"738c30b8-dd80-43f1-bdbc-0ecfd3dc8d71","added_by":"auto","created_at":"2024-04-05 12:50:37","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":793957,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eNetwork analysis of DEGs in response to geraniol treatment\u003c/strong\u003e. (\u003cstrong\u003ea\u003c/strong\u003e) Network analysis of total DEGs from with or without treatment groups; (\u003cstrong\u003eb\u003c/strong\u003e) Proposed molecular pathways of significant DEGs based on number of genes involved and log10 (FDR) values; (\u003cstrong\u003ec\u003c/strong\u003e) Network analysis of selected genes from each pathways and observed signaling interaction between the genes.\u003c/p\u003e","description":"","filename":"Figure3.png","url":"https://assets-eu.researchsquare.com/files/rs-4127451/v1/8cad2152070b3269997c981a.png"},{"id":54160366,"identity":"764f7fa3-6f3b-4097-9656-4be8687fb9bf","added_by":"auto","created_at":"2024-04-05 12:58:37","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":644976,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eComprehensive analysis of cell cycle pathways involved in response to geraniol treatment\u003c/strong\u003e. (\u003cstrong\u003ea\u003c/strong\u003e) List of DEGs involved in cell cycle pathways; (\u003cstrong\u003eb\u003c/strong\u003e) Uniform Manifold Approximation and Projection (UMAP) analysis explored the expression of gene clusters involved in different phases of cell cycle pathway; (\u003cstrong\u003ec\u003c/strong\u003e) Hexagonal graph revealed proposed pathway list based on significant priority denoted with color differences (blue to gray); (\u003cstrong\u003ed\u003c/strong\u003e) Manhattan plot showed the priority list of genes involved in different pathways; (\u003cstrong\u003ee\u003c/strong\u003e) Gene ontology enrichment analysis of DEGs mainly involved in cell cycle pathway showed their biochemical process, cellular component and molecular function.\u003c/p\u003e","description":"","filename":"Figure4.png","url":"https://assets-eu.researchsquare.com/files/rs-4127451/v1/8b9a4b81b2d4bf8b44b912cf.png"},{"id":54159334,"identity":"040fd326-30a6-465d-8582-99989071a0d7","added_by":"auto","created_at":"2024-04-05 12:50:38","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":849263,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eMolecular docking of cell cycle proteins and geraniol. \u003c/strong\u003eBinding affinity of ESPL1 (a), CDK1 (b), BUB1B (c), TKK (d), MCM6 (e), CDC45 (f) with geraniol showed docked complexes and interacting residues.\u003c/p\u003e","description":"","filename":"Figure5.png","url":"https://assets-eu.researchsquare.com/files/rs-4127451/v1/6291aa9500ff009df45ca4df.png"},{"id":54159325,"identity":"7d9639ce-511a-4359-98ca-d8df46ca0a4d","added_by":"auto","created_at":"2024-04-05 12:50:36","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":575626,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eCell cycle analysis by FACS analysis\u003c/strong\u003e. Cells at different phases of cell cycle with their population percentage with or without treatment with geraniol at 12 and 24 hours.\u003c/p\u003e","description":"","filename":"Figure6.png","url":"https://assets-eu.researchsquare.com/files/rs-4127451/v1/8ccdda94c5b6bf471dd16131.png"},{"id":54159332,"identity":"e219f372-48ec-41b6-ad06-a2f4372503d9","added_by":"auto","created_at":"2024-04-05 12:50:38","extension":"png","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":246479,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eValidation of DEGs identified from RNAseq analysis by qRT-PCR analysis\u003c/strong\u003e.\u003c/p\u003e","description":"","filename":"Figure7.png","url":"https://assets-eu.researchsquare.com/files/rs-4127451/v1/0aa65d35f30005b27c5174b1.png"},{"id":54159331,"identity":"004cc189-9147-4b4b-8b84-6f628d3339d1","added_by":"auto","created_at":"2024-04-05 12:50:37","extension":"png","order_by":8,"title":"Figure 8","display":"","copyAsset":false,"role":"figure","size":1212509,"visible":true,"origin":"","legend":"\u003cp\u003eOverall schematic representation of pathways that are involved in geraniol treatment of stomach cancer cell lines.\u003c/p\u003e","description":"","filename":"Figure8.png","url":"https://assets-eu.researchsquare.com/files/rs-4127451/v1/2ab064db13f2f047be8f58f5.png"},{"id":66045325,"identity":"a73fdd99-d10a-4e96-adfa-52e2fddfc6d5","added_by":"auto","created_at":"2024-10-07 06:54:07","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":6924071,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4127451/v1/c062e1b6-d1ec-4393-8cf2-6f668f498668.pdf"},{"id":54159333,"identity":"c30d22be-1e38-4e41-8b10-d70fc0c40a4e","added_by":"auto","created_at":"2024-04-05 12:50:38","extension":"tif","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":47389704,"visible":true,"origin":"","legend":"","description":"","filename":"FigureS1.tif","url":"https://assets-eu.researchsquare.com/files/rs-4127451/v1/6971bc0facf7b3d6433c48e1.tif"},{"id":54159330,"identity":"17f13284-a8c3-4cbf-bfb5-b33c67986290","added_by":"auto","created_at":"2024-04-05 12:50:37","extension":"tif","order_by":2,"title":"","display":"","copyAsset":false,"role":"supplement","size":281596,"visible":true,"origin":"","legend":"","description":"","filename":"FigureS2.tif","url":"https://assets-eu.researchsquare.com/files/rs-4127451/v1/72d4b0b9bb3a2ac605bd6bff.tif"},{"id":54159329,"identity":"bb8dcc39-c18f-4c86-b209-09990c5f32ed","added_by":"auto","created_at":"2024-04-05 12:50:37","extension":"png","order_by":3,"title":"","display":"","copyAsset":false,"role":"supplement","size":3386528,"visible":true,"origin":"","legend":"","description":"","filename":"Graphicalabstract.png","url":"https://assets-eu.researchsquare.com/files/rs-4127451/v1/ce40e23ae607b7c646cc1f81.png"}],"financialInterests":"No competing interests reported.","formattedTitle":"Cohesive transcriptomic and in vitro approach revealed genes involved in inhibition of G0/G1, G2 phase of cell cycle pathway of stomach cancer cells by geraniol treatment","fulltext":[{"header":"1. Introduction","content":"\u003cp\u003eRecently, gastric cancer is one of the major health concerns across the globe. Their occurrences are highly regional specific and more profound in Eastern Asia. It is also considered as the 5th most prevalent cancer in the world, and 4th most deadly cancer \u003csup\u003e1\u003c/sup\u003e. Although there are many variants of stomach cancer, adenocarcinoma is the most common type of cancer. Gastric cancer can be caused by a number of different etiological factors, including radiation exposure, adenomatous polyps, chronic atrophic gastritis, smoking, alcohol use, obesity, and prior gastric surgery \u003csup\u003e2\u003c/sup\u003e. Gender, age, ethnicity and geography are considered as the most important factors next to diet and \u003cem\u003eHelicobacter pylori\u003c/em\u003e infection. The most common treatment options for gastric cancer include chemotherapy, radiotherapy and surgery. However, they exhibit their own disadvantages such as toxicity to normal cells, and resistance to anticancer drugs \u003csup\u003e3\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eMonoterpenes are a large group of secondary metabolites that performs numerous biological functions and contains various biological properties including antioxidants, anti-inflammatory, antitumor, and antidiabetic effects \u003csup\u003e4\u003c/sup\u003e. Chemically, monoterpenes are made of 2 isoprene units formed by five carbons joined together head to tail. These biochemically active units are di-phosphate esters, isopentyl diphosphate, and dimethylallyl diphosphate \u003csup\u003e5\u003c/sup\u003e. Monoterpenes are further classified as acyclic, monocyclic, bicyclic, and iridoid glycosides. These are found primarily as active ingredients in essential oils and fixed oils derived from plants and other sources. \u003csup\u003e6,7\u003c/sup\u003e. Among various monoterpenes, geraniol is an acyclic isoprenoid commonly occur in the natural oils of fragrant herbal plants.\u003c/p\u003e \u003cp\u003eTumor proliferation is one of the critical steps of cancer formation that has been well established. It is the stepwise mechanism that cells perform to multiply. The cell cycle typically undergoes four different phases such as G1, S, G2 and M. Numerous studies on geraniol have shown to inhibit cell cycle in various cancer cell lines including prostate \u003csup\u003e8\u003c/sup\u003e, pancreatic cancer \u003csup\u003e9\u003c/sup\u003e, breast cancer \u003csup\u003e10\u003c/sup\u003e, oral cancer \u003csup\u003e11\u003c/sup\u003e and skin \u003csup\u003e12\u003c/sup\u003e cell lines. A recent study showed that geraniol can inhibit cell proliferation by inhibiting JNK/ERK signaling pathway and thus promoting apoptosis in the gastric cell line \u003csup\u003e13\u003c/sup\u003e. However, the target genes that are involved in the cell cycle process are crucial for the discovery of biomarkers and to identify the drug targets. Hence, we used Next Generation Sequencing (NGS) to analyze large-scale screening transcriptomes with or without geraniol treatment to unravel genes contribute to anticancer related properties. Specifically, NGS analysis combined with bioinformatics data mining tools offers the stage to concurrently interpret various genes, categorize the targets and its relations after treatments in stomach cancer cell lines. Overall, our study stands unique from already published study where, we claim geraniol can inhibit cell cycle by interacting with various target gene interactions to promote apoptosis in stomach cancer cell lines.\u003c/p\u003e"},{"header":"2. Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003e2.1 Cell line, reagents, and chemicals\u003c/h2\u003e \u003cp\u003eThe cells line such as stomach cancer cell line (AGS: ATCC-CRL-1739) and lung fibroblast (MRC-5: ATCC-CCL-171) were cultured in RPMI 1640 or DMEM medium consist of 10% fetal bovine serum (FBS) and 1% antibiotic-antimycotic solution at 37\u0026deg;C in 5% CO2 and 95% air. In this study, the following materials were used: 0.5% trypsin-ethylenediaminetetraacetic acid in water (Invitrogen, Grand Island, NY, USA), culture medium, serum, and phosphate buffer (Life Technologies, Grand Island, NY), and antibiotic-antimycotic solution. The 3-(4,5-dimethylthiazol-2-yl)-2,5-diphenyl tetrazolium bromide (MTT) was purchased from Sigma-Aldrich (St. Louis, MO) with reagent-grade quality. Similarly, the palmarosa essential oil was acquired from Berj\u0026eacute; (Carteret, NJ). The other materials and reagents utilized in the present study were easily accessible and of reagent-grade quality.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003e2.2 Cell treatments and MTT assay\u003c/h2\u003e \u003cp\u003eThe anti-proliferative activity of geraniol was assessed in both AGS and MRC-5 cell lines using a MTT assay. As previously stated \u003csup\u003e14,15\u003c/sup\u003e the MTT assay was performed accordingly with proper protocols. Cisplatin is used as positive control and was formulated similar to geraniol. The DMSO solution is used as the negative control. In 96-well plate 2x 10\u003csup\u003e4\u003c/sup\u003e cells were seeded and incubated for 12 h to reach optimum cellular environment for the treatment. Dose-dependent concentration of geraniol (0 to 100 \u0026micro;g in 0.1% DMSO) was treated with three replicates. DMSO was used as a similar concentration for the negative control. After 24 h of treatment, cells were washed with 1X phosphate-buffered saline (PBS) and 100 \u0026micro;l of fresh cell culture medium consisting of MTT stock solution was added (5 mg/mL). Then plates were incubated 4\u0026ndash;6 hours and further steps were performed as previously described \u003csup\u003e14,15\u003c/sup\u003e. The VersaMax microplate reader was used to record optical density values at 560 nm and 670 nm respectively.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003e2.3 Medium pressure liquid chromatography (MPLC)\u003c/h2\u003e \u003cp\u003eTo fractionize main active components from \u003cem\u003eCymbopogon martini\u003c/em\u003e, crude palmarosa oil, proper solvent system conditions are required. As a result, we used thin layer chromatography (TLC) to confirm the suitable solvent system and picked up a combination of hexane and ethyl acetate with different gradient ratios (H:E; 10:0, 9:1, 8:2, 7:3, 6:4, 4:6, 1:9, 0:10 by volume). For this study, 5 g of palmarosa oil was used to isolate the secondary metabolites as previously described \u003csup\u003e16\u003c/sup\u003e using MPLC (Biotage Isolera apparatus) equipped with a UV detector at 254 nm and 365 nm and a column cartridge SNAP (100 g silica gel) with a volume of 132 mL. We combined similar column fractions together and labeled a number of groups based on Rf values of TLC data generated with hexane and ethyl acetate (8:2 by volume).\u003c/p\u003e \u003cp\u003eFurther separation and purification were accomplished using preparative high-performance liquid chromatography (HPLC) using a 7.8 mm i.d. 300 mm Bondapak C18 (Waters, Milford, MA, USA) column with an acetonitrile and water (80:20 by volume) mobile phase at a flow rate of 1 mL min\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e. Finally, we extracted purified active constituent 1 (geraniol; 24 g) for further spectroscopic characterization to establish structural validation.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003e2.4 Gas-chromatography (GC) and gas-chromatography mass spectroscopy (GC-MS)\u003c/h2\u003e \u003cp\u003eMeanwhile, GC (Agilent 7890A gas chromatography system; Agilent, Wilmington, DE,) and GC-MS (Clarus 680T gas chromatograph-mass spectrometer; PerkinElmer, Fort Belvoir, VA) were used to identify the primary volatile elements of palmarosa oil. The GC and GC-MS conditions were employed as stated previously \u003csup\u003e17\u0026ndash;19\u003c/sup\u003e. The 0.25 mm i.d. (\u003cem\u003ed\u003c/em\u003e\u003csub\u003ef\u003c/sub\u003e = 0.25 \u0026micro;m) DB-5 MS capillary column length of 30 m was used to separate active constituents more precisely with nitrogen carrier gas flow rate of 1mL/min. Similarly, we employed similar column specifications for GC-MS and the flow velocity of the helium carrier gas was 24.4 cm/s (30\u0026deg;C) at a split ratio of 1:50. Each volatile peak from GC mass spectra (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e) has been identified by comparing mass spectra libraries from mass spectrometry data center, NIST).\u003c/p\u003e \u003cp\u003e\u003cstrong\u003eTable 1. Primer lists used in this study\u003c/strong\u003e\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"592\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd width=\"7.094594594594595%\" rowspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eS.no\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.66891891891892%\" rowspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eGene name\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"80.23648648648648%\" colspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003ePrimer sequence\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"49.05263157894737%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eForward\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"50.94736842105263%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eReverse\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"7.094594594594595%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e1\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.66891891891892%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003eESPL1\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"39.358108108108105%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eCACTTGGCACATTGCCCTTC\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"40.87837837837838%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eCACCCAGCTCCCAAGTAAGA\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"7.094594594594595%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e2\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.66891891891892%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003eCDK1\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"39.358108108108105%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eACACCACTTGTCCCTCTAAGAT\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"40.87837837837838%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eGTGGGTGGGAAGCCCATTTA\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"7.094594594594595%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e3\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.66891891891892%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003eBUB1B\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"39.358108108108105%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eAGCTGAAAGGACCAGCCATC\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"40.87837837837838%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eTCCCGTACCTACCTCAGAGC\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"7.094594594594595%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e4\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.66891891891892%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003eCCNB2\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"39.358108108108105%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eGGTGTGTGCCGTCATGTAAA\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"40.87837837837838%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eGAGGGTGGCAACTAGTGCTT\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"7.094594594594595%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e5\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.66891891891892%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003eTTK\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"39.358108108108105%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eTGCAAAAGTGCCTTGGGAGA\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"40.87837837837838%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eACCAACAAGTTGGCCCAGAA\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"7.094594594594595%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e6\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.66891891891892%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003ePKMYT1\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"39.358108108108105%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eAGCCACAAGTGGCACAATCA\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"40.87837837837838%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eCCACCTCGTGACACATCCAA\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"7.094594594594595%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e7\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.66891891891892%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003eMCM3\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"39.358108108108105%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eTGGATCCTCAAAACAGCAGACA\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"40.87837837837838%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eGTGGGTGGTTGGCATTTTCC\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"7.094594594594595%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e8\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.66891891891892%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003eMCM6\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"39.358108108108105%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eCTTCCCGGATTTCAACCCCT\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"40.87837837837838%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eGCCTGTCTGAGCCTAGTTGT\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"7.094594594594595%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e9\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.66891891891892%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003eCDC6\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"39.358108108108105%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eGCAGCGTTTGTTCTCCCTTG\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"40.87837837837838%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eACGACTTGTTTAACTAACTGTGGTC\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"7.094594594594595%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e10\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.66891891891892%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003eCDC45\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"39.358108108108105%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eGGGGGTGTTGGGGTTATCAG\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"40.87837837837838%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eGCCAACTTGCTTGGTTCCAG\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"7.094594594594595%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e11\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.66891891891892%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003eGADD45B\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"39.358108108108105%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eTGTCAACAAAGTCGGGCTGA\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"40.87837837837838%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eTTAGTGGAGCCTCCCTCTCC\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"7.094594594594595%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e12\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.66891891891892%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003eSDF2L1\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"39.358108108108105%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eAGAGATGGTTACTGAGCGCC\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"40.87837837837838%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eTCAAGGCTGTGTGGATTCCC\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"7.094594594594595%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e13\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.66891891891892%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003e\u0026beta;-ACTIN\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"39.358108108108105%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eCATCACTATCGGCAATGAGC\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"40.87837837837838%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eGACAGCACTGTGTTGGCATA\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e \u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003e2.5 RNA isolation\u003c/h2\u003e \u003cp\u003eThe 5x10\u003csup\u003e4\u003c/sup\u003e cells were cultured in 6-well plates and treated for 24h with geraniol based on IC\u003csub\u003e50\u003c/sub\u003e value in order to achieve differential expression of total genes for better profiling of RNAseq.\u0026nbsp;Then treated and untreated cells were washed twice with ice-cold PBS to remove leftover medium and FBS. Then the cells were centrifuged at 1000 rpm for 3 min to remove any dead cells. The total RNA of AGS cells from geraniol treated and untreated groups was extracted using a TRIzol Reagent Kit (Invitrogen, Carlsbad, CA, USA). The purity of RNA was assessed using the NanoDrop\u0026reg; (Thermo Scientific, Wilmington, DE, USA) and integrity of RNA was measured using the RNA Nano 6000 Assay Kit of the Bioanalyzer 2100 system (Agilent Technologies, CA, USA).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003e2.6 RNA-Sequencing analysis\u003c/h2\u003e \u003cp\u003e1 \u0026micro;g of RNA was used as input material for the preparation of RNA samples. To prepare sequencing libraries for Illumina\u0026reg;, we followed the manufacturer's instructions when using the NEBNext\u0026reg; UltraTM RNA Library Prep Kit. The addition of index codes was used to attribute sequences to samples. After receiving the raw data from Illumina\u0026reg; sequencing, the unprocessed data undergone sequential processing to determine the transcript sequence length, distribution, GC content, and total read counts (\u003cb\u003eFigure \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e\u003c/b\u003e). R package, edgeR or DESeq2 \u003csup\u003e20,21\u003c/sup\u003e was used to identify differentially expressed RNAs based on fold changes\u0026thinsp;\u0026lt;\u0026thinsp;2 or fold changes\u0026thinsp;\u0026lt;\u0026thinsp;0.5 and \u003cem\u003ep\u003c/em\u003e values\u0026thinsp;\u0026lt;\u0026thinsp;0.05. Differentially expressed genes were identified using the TopHat, Cufflinks, and Cuffdiff and the expression plots were generated using CummeRbund \u003csup\u003e22\u003c/sup\u003e. The GO term enrichment analysis was conducted using the Bioconductor package GOstats to examine differentially expressed genes (DEG) biological significance \u003csup\u003e23\u003c/sup\u003e. KEGG pathway enrichment analyses of DEGs were conducted using the gene Answers package \u003csup\u003e24\u003c/sup\u003e. Network data integration, analysis, and visualization performed by the open source software Cytoscape (v. 3.10.1) \u003csup\u003e25\u003c/sup\u003e. For significant enrichment, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05 was considered statistically significant.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec9\" class=\"Section2\"\u003e \u003ch2\u003e2.7 Molecular docking\u003c/h2\u003e \u003cp\u003eMolecular docking was employed to determine the binding affinity between two molecules. The targeted receptors BUB1B (PDB ID: 3SI5), CDC45 (PDB ID: 5DGO), CDK1 (PDB ID: 4Y72), ESPL1 (PDB ID: 7NJ0), MCM6 (PDB ID: 6XTY), and TTK (PDB ID: 2ZMC) were obtained from the RCSB Protein Data Bank. The Schr\u0026ouml;dinger Suite 2023-3 was utilized for protein preparation and docking. Geraniol (CID_637566) was designated as the ligand during the docking process. The 3D and 2D interactions between the targeted receptors and geraniol were visualized using the Schr\u0026ouml;dinger Suite 2023-3\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec10\" class=\"Section2\"\u003e \u003ch2\u003e2.8 Fluorescence activated cell sorting (FACS) analysis\u003c/h2\u003e \u003cp\u003eA flow cytometric analysis was conducted to determine the different phases of the cell cycle and the population percentage of before and after 12 or 24 hours of geraniol treatment. The AGS cancer cells were grown in 6-well culture plates and adhered overnight. Dose dependent treatment groups of geraniol (5 and 10 \u0026micro;g/mL) were incubated for 12 or 24 hours. Untreated control samples received 0.1% DMSO. After the treatment durations, cells were washed twice with 1X PBS and centrifuged to cell pellets for flow cytometry analysis. Following the geraniol extraction, the cells were stained for 15 min at room temperature with Annexin-V-FLUOS (Roche Diagnostics, Laval, QC) and PI (1 mg/ml) in detection buffer (10 mM HEPES, 140 mM NaCl, and 5 mM CaCl\u003csub\u003e2\u003c/sub\u003e) and analyzed by flow cytometry. Cell cycle analysis was performed using a FACSCalibur flow cytometer equipped with 488 nm (blue), 405 nm (violet), and 640 nm (red) solid state laser light (BD Biosciences, San Jose, CA, USA). The percentage of cells in the different cell cycle phases was determined using the BD Biosciences Cell Quest acquisition software.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003e2.9 cDNA construction and quantitative reverse transcription polymerase chain reaction (qRT-PCR) expression analysis\u003c/h2\u003e \u003cp\u003e1 \u003cem\u003e\u0026micro;\u003c/em\u003eg of total RNA was used to develop the cDNA using ABi First Strand cDNA Synthesis Kit (Thermo Scientific, USA). The real-time experiments were performed in triplicate as described previously \u003csup\u003e26\u003c/sup\u003e. The primers used in this study are listed in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e. The Student's t-test was performed to evaluate statistically significant data.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003e2.10 Statistical analysis\u003c/h2\u003e \u003cp\u003eThe data are presented as the mean\u0026thinsp;\u0026plusmn;\u0026thinsp;standard deviation of triplicate experiments, and statistical significance was determined by \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05 when comparing treated and untreated cells. Statistical analysis of cell viability and geraniol treatment activity was conducted using one-way analysis of variance (ANOVA) followed by Tukey's post hoc test, were used to analyze the cell cycle data using R software, version 4.3.1 \u003csup\u003e27\u003c/sup\u003e.\u003c/p\u003e \u003c/div\u003e"},{"header":"3. Results","content":"\u003cdiv id=\"Sec14\" class=\"Section2\"\u003e \u003ch2\u003e3.1 Isolation and characterization of geraniol\u003c/h2\u003e \u003cp\u003eThe HPLC spectrum showed the major peak with high purity at the retention time 18.86 min and confirmed as geraniol from palmarosa oil (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003ea). Furthermore, electron ionization mass spectrometry (EI-MS) was used to confirm their molecular weight as denoted 154 [M]\u003csup\u003e+\u003c/sup\u003e (100), 138 (13.8), 109 (\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e), 81 (19.3), 69 (\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e), 63 (7.2), 55 (6.3) (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eb). Furthermore, nuclear magnetic resonance (NMR) spectroscopy was used to identify the proton and carbon chain of bioactive component geraniol. Geraniol was discovered using the following evidence: an oil that is colorless(methanol): \u0026euml;\u003csub\u003emax\u003c/sub\u003e nm\u0026thinsp;=\u0026thinsp;245; \u003csup\u003e1\u003c/sup\u003eH NMR (DMSO, 600 MHz): δ 1.70 (3H, d, \u003cem\u003eJ\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.65 Hz), 1.75 (3H, d, \u003cem\u003eJ\u003c/em\u003e\u0026thinsp;=\u0026thinsp;1.1 Hz), 2.00 (2H, s), 2.18 (3H, m), 4.99 (2H, s), 5.02 (3H, s), 5.18 (1H, m), 5.83 (1H, t). \u003csup\u003e13\u003c/sup\u003eC NMR (CDCl\u003csub\u003e3\u003c/sub\u003e, 150 MHz): δ 134.6 s, 132.6 s, 124.1 d, 123.5 d, 51.8 t, 40.7 t, 25.4 t, 23.6 q, 18.2 q, 17.3 q (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003ec).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eAdditionally, GC-MS analysis was performed to elucidate volatile compounds from palmarosa oil as listed in Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e. The major constituents were identified with specific retention time as follows; geraniol (73.5%), geranyl acetate (12%), linalool (3%), caryophyllene (2.2%) and β-cis-Ocimene (1.4%).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eChemical constituents of palmorosa oil identified by gas chromatography (GC) and gas chromatography-mass spectrometry (GC-MS)\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"3\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCompound\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eRT\u003csup\u003e\u003cem\u003ea\u003c/em\u003e\u003c/sup\u003e (min)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003e% Area\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2,2-Dimethoxybutane\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e16.13\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.3\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eα-Myrcene\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e23.95\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.7\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOcimene\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e25.28\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.3\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eβ-cis-Ocimene\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e25.58\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.4\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLinalool\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e27.06\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e3.0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNerol\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e29.77\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.3\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eGeraniol\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003e30.39\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e73.5\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCitral\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e31.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.4\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGeranyl acetate\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e32.51\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e12.0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCaryophyllene\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e34.36\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e2.2\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"3\"\u003e\u003csup\u003e\u003cem\u003ea\u003c/em\u003e\u003c/sup\u003e Retention time.\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec15\" class=\"Section2\"\u003e \u003ch2\u003e3.2 Anti-proliferative effects of geraniol\u003c/h2\u003e \u003cp\u003eA dose-dependent concentration of geraniol was treated in both AGS and normal lung cells (MRC-5) and the IC50 value was calculated. To determine the effect of geraniol on AGS cells, it was also compared to cisplatin. Geraniol had an IC\u003csub\u003e50\u003c/sub\u003e value of 6.75 \u0026micro;g/mL on AGS cells and cisplatin had an IC\u003csub\u003e50\u003c/sub\u003e value of 9.89 \u0026micro;g/mL (\u003cb\u003eFigure \u003cspan refid=\"MOESM2\" class=\"InternalRef\"\u003eS2\u003c/span\u003e\u003c/b\u003e). Similarly, geraniol exhibited non-significant toxicity (93 \u0026micro;g/mL) towards MRC-5, whereas cisplatin exhibited a mild toxic effect (18.4 \u0026micro;g/mL).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec16\" class=\"Section2\"\u003e \u003ch2\u003e3.3 Transcriptomic analysis of geraniol in AGS cell lines\u003c/h2\u003e \u003cp\u003eThe RNAseq analysis revealed significant differences in the expression of genes between the control and treated groups (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003ea). The majority of the genes in the treated groups were highly expressed, indicating that geraniol exhibited significant changes in transcript of stomach cancer lines. Specifically, our transcriptomic approach revealed a total of 22,488 genes following treatment with geraniol. Among them, 3296 genes were significantly differentially expressed compared to the control groups. The DEGs analysis revealed that 178 genes were significantly upregulated while 144 were significantly downregulated (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eb). Figure\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003ec depicts a volcano graph that lists both up and downregulated genes with denoted substantial expression. Genes such as \u003cem\u003eXAF1, SOGA3, NPY4R, ITGB2-AS1, FBBP, FAM231D, EIF3CL\u003c/em\u003e, and \u003cem\u003eARL17B\u003c/em\u003e were significantly downregulated. Whereas, genes including \u003cem\u003ePHOSPHO2, KLHL23, LOC100101148, KIAA0408, HSPA6, FAM65B, FAM47E, STBD1\u003c/em\u003e, and \u003cem\u003eCRYAB\u003c/em\u003e were upregulated (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003ed). The gene ontology (GO) enrichment analysis of expressed genes showed biological, chemical, and molecular functions (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003ee). The blue color in each node of the GO analysis showed significant expression, and most of them were involved in cell cycle, DNA replication, and mitotic regulation of cell cycle in biological processes. At cellular component levels, most of them are involved in chromosome condensation and elongation, spindle formation, MCM complex formation, etc. Whereas at the molecular level, they showed function in ATP-dependent regulation, DNA replication, and helicase activity. Overall, our RNAseq transcriptome analysis revealed significant genes and their expression in AGS cell lines after the treatment of geraniol. Furthermore, extensive analysis was required to proceed with gene ontology and the proposed molecular pathway detection.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec17\" class=\"Section2\"\u003e \u003ch2\u003e3.4 Network analysis\u003c/h2\u003e \u003cp\u003eWe performed cytoscape analysis for an interaction network, which showed an unified conceptual framework, to understand the biomolecular interaction between DEGs (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003ea). Our findings showed that most of the genes were involved in various molecular pathways that were analyzed based on the number of genes involved and fold enrichment scores. In specific, pathways such as cell cycle, DNA replication, proteosome, MAPK signaling pathway, extracellular matrix (ECM) receptor interaction, ferroptosis, and cancer-related genes were proposed (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eb). Further, we identified that the genes such as \u003cem\u003eESPL1, BUB1B, CCNB2, GADD45, PKMYT1, CDC25A, MCM3, MCM6, MCM10, TTK, CDC25B, CDC7, CDC6, CCNB1, CDC45, WEE1, CDK1, MCM5, E2F1, BUB1, SMC1A, MCM2, CDC20, SDF2L1\u003c/em\u003e, and \u003cem\u003eCCNA2\u003c/em\u003e were mostly involved in cell cycle pathways, showing interaction between them \u003cb\u003e(\u003c/b\u003eFig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003ec\u003cb\u003e)\u003c/b\u003e. Similarly, for DNA replication, \u003cem\u003ePOLD1, MCM2, MCM3, MCM4, MCM5, MCM6, DNA2\u003c/em\u003e, \u003cem\u003eLIG1, POLA1, POLA2\u003c/em\u003e, and \u003cem\u003ePOLE\u003c/em\u003e genes have strong interactions with each other \u003cb\u003e(\u003c/b\u003eFig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003ec\u003cb\u003e)\u003c/b\u003e. Furthermore, other proposed pathways such as the proteasome, MAPK signaling pathway, ECM-receptor interaction, ferroptosis, and cancer-related pathways showed significant signaling interaction with their unique genes \u003cb\u003e(\u003c/b\u003eFig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003ec\u003cb\u003e)\u003c/b\u003e.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec18\" class=\"Section2\"\u003e \u003ch2\u003e3.5 In-depth profiling of cell cycle analysis after geraniol treatment\u003c/h2\u003e \u003cp\u003eIn-depth profiling of cell cycle pathway analysis was carried out and visualized under different phases of the cell cycle since geraniol is mostly involved in cell cycle pathways of stomach cancer lines. Our RNAseq analysis showed that most of the DEGs involved in cell cycle were downregulated, indicating that geraniol is significantly involved in the inhibition of AGS cell proliferation. The DEGs such as \u003cem\u003eCCNA2\u003c/em\u003e, \u003cem\u003eCDC20, SMC1A, BUB1, BUB1B, E2F1, WEE1, CDC45, CCNB1, CCNB2, CDC6, CDC7, CDC25A, CDC25B, TTK, MCM2, MCM3, MCM5, MCM6, MCM10, PKMYT1, CDK1\u003c/em\u003e and \u003cem\u003eESPL1\u003c/em\u003e were downregulated while other genes such as \u003cem\u003eSDF2L1\u003c/em\u003e and \u003cem\u003eGADD45B\u003c/em\u003e were upregulated respectively (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003ea).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eThe uniform manifold approximation and projection (UMAP) analysis showed genes involved in different phases and biological functions of the cell cycle, including mitosis G1, G2/M and S phases \u003cb\u003e(\u003c/b\u003eFig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eb\u003cb\u003e)\u003c/b\u003e. Each point represents the expression of genes involved in cell cycle pathways. In addition, more similar gene sets are positioned closer together to denote diversity in their function based on the p-value from the enrichment calculation. The colors of each group represent the differentiation of one from another, and the darker and larger points denote the most significantly differentially expressed ones (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eb). Similarly, the hexagonal canvas plot showed a significant pathway of expressed genes based on the Bioplanet_2019 set library. Each hexagon denotes a single proposed pathway, and the highlighted blue is the most significantly involved, followed by light blue, and gray colored represents least significant (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003ec). The number of each hexagon explained the genes involved in the proposed pathway and their function.\u003c/p\u003e \u003cp\u003eCorrespondingly, Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003ed also depicts a Manhattan plot of mostly differentially expressed genes based on log10 (p-value) corresponding to the enrichment of the input gene set. It represents enrichment analysis of significant transcripts participated in cell cycle, mitotic G1-G1/Sphases, G2/M checkpoints, metaphase-anaphase transition, DNA replication, G2/M transition, and G1to S cell cycle (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003ed). GO analysis showed the biological, cellular, and molecular function of genes involved in the cell cycle (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003ee-g). The biological process clearly showed positive regulation of the cell cycle and division. It initiates double-strand break repair via DNA replication, mitotic G1, G2/M transition, and S phases (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003ee\u003cb\u003e)\u003c/b\u003e. Similarly, cellular components clearly denote the formation of Cdc45-MCM-GINS (CMG) complexes, which represent the formation of double stranded DNA replication, intracellular membrane bounded organelles, mitotic spindles, and anaphase-promoting complexes. Also, it initiates amino acid complexes, including serine/threonine protein kinase complexes, which are mainly involved in mitotic cell division (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003ef). At the molecular level, most of the genes are involved in the DNA replication process, including 3\u0026rsquo;and 5\u0026rsquo; DNA helicase activity, single stranded DNA binding, and cell dependent protein serine/threonine regulation (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eg). The detailed information on each proposed cell cycle pathway and their respective genes are depicted in Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eCell cycle\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTerm\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eP-value\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eAdjusted P-value\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eCombined Score\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eGenes\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCell cycle\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e4.42E-39\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e7.43E-37\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e96570.08829\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cem\u003eGADD45B; BUB1B; MCM10; TTK; CDC7; CDC6; PKMYT1; SMC1A; CDC25A; CDC25B; CDC20; CCNA2; CCNB2; CCNB1; WEE1; CDC45; ESPL1; E2F1; CDK1; MCM3; MCM5; MCM6; BUB1; MCM2\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eG2/M checkpoints\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.42E-29\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.20E-27\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e47838.95406\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cem\u003eMCM10; CDC7; CDC6; CDC25A; CCNB2; CCNB1; WEE1; CDC45; CDK1; MCM3; MCM5; MCM6; MCM2\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCell cycle checkpoints\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3.94E-28\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2.21E-26\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e18441.38147\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cem\u003eBUB1B; MCM10; CDC7; CDC6; CDC25A; CDC20; CCNB2; CCNB1; WEE1; CDC45; CDK1; MCM3; MCM5; MCM6; MCM2\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMitotic G1-G1/S phases\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3.81E-27\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.60E-25\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e15098.10538\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cem\u003eMCM10; CDC7; CDC6; PKMYT1; CDC25A; CCNA2; CCNB1; WEE1; CDC45; E2F1; CDK1; MCM3; MCM5; MCM6; MCM2\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDNA replication\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e4.18E-22\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.40E-20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e6421.643046\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cem\u003eBUB1B; MCM10; CDC7; CDC6; SMC1A; CDC20; CCNA2; CDC45; E2F1; MCM3; MCM5; MCM6; BUB1; MCM2\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCyclin A/B1-associated events during G2/M transition\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.09E-20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3.05E-19\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e61702.47931\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cem\u003eCCNA2; CCNB2; CCNB1; WEE1; CDK1; PKMYT1; CDC25A; CDC25B\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eG1 to S cell cycle control\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2.77E-19\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e6.64E-18\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e9954.67398\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cem\u003eCCNB1; WEE1; CDC45; E2F1; CDK1; MCM3; MCM5; MCM6; CDC25A; MCM2\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDNA replication pre-Initiation\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e4.96E-18\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.04E-16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e6775.902517\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cem\u003eCCNA2; CDC45; E2F1; MCM3; MCM10; CDC7; MCM5; CDC6; MCM6; MCM2\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eActivation of the pre-replicative complex\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e9.81E-18\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.83E-16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e16714.80509\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cem\u003eCDC45; MCM3; MCM10; CDC7; MCM5; CDC6; MCM6; MCM2\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eS phase\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e6.12E-17\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.03E-15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e4848.983268\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cem\u003eCCNA2; WEE1; CDC45; MCM3; MCM5; CDC6; MCM6; CDC25A; CDC25B; MCM2\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMitotic G2-G2/M phases\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e7.03E-16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.07E-14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e5006.486955\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cem\u003eCCNA2; CCNB2; CCNB1; WEE1; E2F1; CDK1; PKMYT1; CDC25A; CDC25B\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec19\" class=\"Section2\"\u003e \u003ch2\u003e3.6 Visualization of geraniol targeted proteins\u003c/h2\u003e \u003cp\u003eThe molecular docking evaluation study demonstrated the effectiveness of geraniol binding to its respective receptors. In this study we performed more than 12 proteins which involved in the cell cycle pathway, however, binding affinity was used as the criterion to select the most favorable docked complexes. Geraniol displayed binding affinities when interacting with ESPL1 (-3.695), CDK1 (-3.6268), BUB1B (-3.306), TTK (-2.793), MCM6 (-2.626), and CDC45 (-2.386) kcal/mol, respectively (Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab4\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eGeraniol-binding affinities score of the selected protein\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eProtein\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eID\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eCompound\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eDocking Score\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBUB1B\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3SI5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\" morerows=\"5\" rowspan=\"6\"\u003e \u003cp\u003eCID_637566 (Geraniol)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-3.306\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCDC45\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e5DGO\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-2.386\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCDK1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e4Y72\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-3.628\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eESPL1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e7NJ0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-3.695\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMCM6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e6XTY\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-2.626\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTTK\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2ZMC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-2.793\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eAmong these complexes, ESPL1, CDK1, and BUB1B showed the most promising binding affinities. ESPL1 displayed the highest score, with key interacting residues including GLU (1006), SER (1003), LEU (1001), LEU (998), ILE (940), ILE (937), GLU (936), HIE (1630), GLN (1631), ARG (1638), THR (1634), LEU (2016), and GLN (2013) (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003ea). CDK1 exhibited the second-highest binding affinities, with critical residues such as PHE (147), ASP (146), ALA (145), LEU (55), VAL (64), ALA (\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e), LYS (\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e), PHE (80), VAL (\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e), GLY (\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e), ILE (\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e), ASP (86), GLN (132), ASN (133), and LEU (135) (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eb). BUB1B, with the third-highest binding affinities, interacted with GLN (153), SER (157), GLU (160), GLU (161), PRO (135), TYR (139), ARG (130), LYS (127), LEU (126), and ASN (123) (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003ec). Similarly, the other proteins including TTK, MCM6, and CDC45 also showed considerable binding affinities as shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003ed-f. This study suggests that geraniol has the potential to serve as a promising candidate for targeting these three proteins.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec20\" class=\"Section2\"\u003e \u003ch2\u003e3.7 Validation of cell cycle analysis by FACS\u003c/h2\u003e \u003cp\u003eBased on the RNAseq results, it was suggested that geraniol highly influences the cell cycle pathway which needs further validation upon experiment analysis. Therefore, we performed cell cycle analysis using flow cytometry to confirm at which phase cell cycle was arrested after geraniol treatment. In both 12 and 24 h after treatment experiments, the time and dose dependent experiment analysis revealed significant differences in genes as observed after geraniol treatment with 5 and 10 \u0026#120525;g/mL (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003e). Next, we observed and assigned different stages of the cell cycle, including initial phase M1 (GO/G1), stationary phase M2 (S), secondary phase M3 (G2), and necrosis phase M4 (apoptosis). The untreated group showed well grown M1 (35.21%), M2 (12.76%), M3 (13.69%), and reduced necrosis phases (8.14%). Whereas, the geraniol (5 and 10 \u0026#120525;g/mL) treated groups showed a reduced number of M1 (20.31; 16.22%) and M2 phases (11.22; 23.13%) and an increased number of M4 (23.45; 28.66%), which may denote the induction of cell death in stomach cancer cell lines (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003e). Similarly, at 24 hours, exposure to 5 \u0026#120525;g/mL showed induction of apoptotic mediated cell death in M4 phase (28.66%) compared to the untreated control group (7.12%). Whereas substantial cellular death was noticed at 10 \u0026#120525;g/mL at M4 phase (71.62%), which showed significant differences compared with the untreated control (7.12%) (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003e). These results suggested that geraniol inhibits cellular proliferation as well as induction of cellular mediated apoptotic cell death in stomach cancer cell lines.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec21\" class=\"Section2\"\u003e \u003ch2\u003e3.8 Validation of DEGs by qRT-PCR\u003c/h2\u003e \u003cp\u003eFurther evaluation was conducted for substantially differentially expressed genes that are involved in the cell cycle pathway. Validating the most significant selective genes is necessary in order to confirm the influences on cell cycle pathways and to confirm \u003cem\u003ein silico\u003c/em\u003e RNAseq analysis. For that reason, we designed the forward and reverse primers for each selected gene listed in Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e and performed the qRT-PCR. The AGS cells were treated with dose-dependent geraniol (5 and 10 \u0026micro;g/mL) and RNA was isolated from the treated groups, respectively. Then we performed qRT-PCR based on the manufacturer's protocol with well-designed primers for each gene. Our qRT-PCR results showed that most of them were downregulated, as denoted by RNAseq results (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003ea). In particular, \u003cem\u003eESPL1, BUB1B, TTK, PKMYT1, CDC6\u003c/em\u003e, and \u003cem\u003eCDC45\u003c/em\u003e were significantly downregulated by 10 \u0026micro;g/mL treatment (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003e). Simultaneously, among the genes, two of them (\u003cem\u003eGADD45B\u003c/em\u003e and \u003cem\u003eSDF2L1\u003c/em\u003e) showed substantial upregulation, which was similar to RNAseq results.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e"},{"header":"4. Discussion","content":"\u003cp\u003eThe anticancer effect of geraniol was shown against various types of cancer cells including prostate, renal, colon, oral, skin, endothelium, stomach, endometrial, breast, pancreatic, lung, leukemia, and liver cancer cell lines \u003csup\u003e28\u003c/sup\u003e. It was recently depicted that the geraniol could induce stomach cancer cell death through production of mitochondrial ROS and inhibition of phosphorylation of mitogen-activated protein kinase (p38, MAPK, JNK, and ERK1/2) signaling pathway \u003csup\u003e13\u003c/sup\u003e. In a different investigation, geraniol enhanced peroxiredoxin-1 expression in human gastric epithelial cells, which had a protective effect on Helicobacter pylori-induced human gastric cancer signaling. \u003csup\u003e29\u003c/sup\u003e. However, in this study, we for the first time investigated cell cycle arrest as the potential underlying mechanism behind the cytotoxicity of geraniol against AGS stomach cancer cell line through RNA sequencing and FACS. Given that the geraniol is the major chemical constituent of palmarosa oil \u003csup\u003e30\u003c/sup\u003e, we extracted and purified geraniol as the main active component of palmarosa oil (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eWe further treated the AGS cells with geraniol and examined the effect of geraniol on changes in the expression of cell cycle related genes through RNAseq.\u0026nbsp;We found the significant differential expression of genes involved in cell cycle, DNA replication, and mitotic regulation of cell cycle in geraniol-treated AGS cells (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). This result is in consistent with the effectiveness of geraniol in promoting cell cycle arrest demonstrated in prostate, colon, breast, liver cancer cells \u003csup\u003e31\u0026ndash;34\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eAs it was demonstrated in Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e and Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003e, most of the DEGs related to cell cycle including \u003cem\u003eCCNA2\u003c/em\u003e, \u003cem\u003eE2F1, CDC45, CCNB1, CCNB2, CDC6, CDC7, CDC25A, CDC25B, MCM2, MCM3, MCM5, MCM6, MCM10\u003c/em\u003e, and \u003cem\u003eCDK1\u003c/em\u003e were downregulated in geraniol-treated AGS cells. Additionally, our docking analysis results for geraniol showed favorable docked complexes as well as most promising binding affinities to cell cycle proteins including ESPL1, CDK1, BUBIB, TTK, MCM6 and CDC45 (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eCell division cycle 6 (\u003cem\u003eCDC6\u003c/em\u003e), \u003cem\u003eCDC7\u003c/em\u003e, and \u003cem\u003eCDC45\u003c/em\u003e are responsible for the activation of DNA replication and partake in checkpoint controls that warrant DNA replication is completed before the initiation of mitosis. \u003cem\u003eCDC25A\u003c/em\u003e is crucial for progress from G1 to the S phase of the cell cycle while \u003cem\u003eCDC25B\u003c/em\u003e is obligatory for G2 to M phases cell cycle progress. Downregulation of CDC genes including \u003cem\u003eCDC6\u003c/em\u003e, \u003cem\u003eCDC7\u003c/em\u003e, \u003cem\u003eCDC45\u003c/em\u003e, \u003cem\u003eCDC25A\u003c/em\u003e, and \u003cem\u003eCDC25B\u003c/em\u003e might result in cell cycle arrest at G1 and G2 phases in geraniol-treated AGS cells.\u003c/p\u003e \u003cp\u003eCyclin and cyclin dependent kinase 1 (CDK1) is also involved in the control of the cell cycle at the G1/S (start) and G2/M (mitosis) transitions. The downregulation of cyclins such as \u003cem\u003eCCNA2\u003c/em\u003e, \u003cem\u003eCCNB1\u003c/em\u003e, and \u003cem\u003eCCNB2\u003c/em\u003e as well as \u003cem\u003eCDK1\u003c/em\u003e might result in cell cycle arrest at G1 and G2 phases in geraniol-treated AGS cells. It is concordant with the cell cycle arrest at G1 phase, and a considerable arrest at G2 phase, in geraniol-treated PC-3 cells deep-rooted by the diminished expression levels of \u003cem\u003ecyclins A, B, D and E\u003c/em\u003e and \u003cem\u003eCDK1\u003c/em\u003e and \u003cem\u003eCDK4\u003c/em\u003e, as well as by the elevated levels of p21Cip1 and p27Kip1 \u003csup\u003e35,36\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eThe \u003cem\u003eE2F transcription factor 1 (E2F1)\u003c/em\u003e is the key cell cycle regulator and targets genes encoding proteins that regulate cell cycle progression through the G1/S transition \u003csup\u003e37\u003c/sup\u003e. Downregulation of this gene in geraniol-treated AGS cells could induce G1 arrest. It was previously shown that the geraniol could suppress prostate cancer growth through down-regulation of \u003cem\u003eE2F transcription factor 8 (E2F8)\u003c/em\u003e \u003csup\u003e31\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eMini-chromosome maintenance proteins (MCM) are required for the initiation of eukaryotic genome replication. It was previously proposed that MCM profiling could be used to predict treatment response and prognosis in breast cancer patients. \u003csup\u003e38\u003c/sup\u003e. Thus, downregulation of \u003cem\u003eMCM2\u003c/em\u003e, \u003cem\u003eMCM3\u003c/em\u003e, \u003cem\u003eMCM5\u003c/em\u003e, \u003cem\u003eMCM6\u003c/em\u003e, and \u003cem\u003eMCM10\u003c/em\u003e genes in geraniol-treated AGS cells could be attributed to the efficiency of geraniol treatment for stomach cancer.\u003c/p\u003e \u003cp\u003eThere was increase in growth arrest and DNA damage inducible beta (\u003cem\u003eGADD45B\u003c/em\u003e) upon geraniol-treated AGS cells. It is well known that \u003cem\u003eGADD45B\u003c/em\u003e group gene\u0026rsquo;stranscript levels are increased upon stressful growth arrest conditions and/or while treating with DNA-damaging agents. They most probably express together and function in conjugation to inhibit cell growth. Induced expression of \u003cem\u003eGADD45B\u003c/em\u003e gene could suppress growth of hepatocellular carcinoma \u003csup\u003e39\u003c/sup\u003e. The high transcript expression of \u003cem\u003eGADD45B\u003c/em\u003e significantly heightened the chemo-sensitivity and induced apoptosis mediated cell death via mitogen-activated protein kinase (MAPK) pathway in advanced prostate cancer \u003csup\u003e40\u003c/sup\u003e. Hence, induced expression of \u003cem\u003eGADD45B\u003c/em\u003e by geraniol could result in apoptosis, as increased number of AGS cells at M4 phase and induction of cell death were evident after geraniol treatment (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eOverall, the geraniol treatment caused apoptosis mediated cell death in the stomach cancer cells by de-promoting genes involved in DNA replication and cell cycle progression as well as induction of \u003cem\u003eGADD45B\u003c/em\u003e.\u003c/p\u003e"},{"header":"5. Conclusion","content":"\u003cp\u003eFinally, based on our RNAseq data analysis, we concluded that geraniol negatively affected most of the genes involved in the cell proliferation by influencing the cell cycle pathway, which led to the induction of apoptosis. Based on our findings, we proposed the cell cycle, DNA replication, MAPK signaling, and the ubiquitin mediated proteolysis pathways as the potential pathways affected by geraniol treatment (Fig.\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e8\u003c/span\u003e). The genes highlighted in red in the pathway map are the genes with significant differential expression based on our RNAseq data analysis. Further, we validated the up and downregulated genes and proteins involved in different phases of the cell cycle by qRT-PCR analysis and docking studies. FACS analysis also confirmed the cell cycle arrest by geraniol treatment. Based on these results, this study suggests that the geraniol can be used as the potential treatment for stomach cancer.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e"},{"header":"Declarations","content":"\u003ch2\u003eConflict of interest\u003c/h2\u003e \u003cp\u003eThe authors declare no competing financial interest.\u003c/p\u003e \u003ch2\u003eDeclaration of Competing Interest\u003c/h2\u003e \u003cp\u003eThe authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.\u003c/p\u003e\u003ch2\u003eCRediT authorship contribution statement\u003c/h2\u003e \u003cp\u003eHP and SRB performed most of the experiments. HP and SRB acquired and analyzed the experimental data. SD performed docking analysis. HP and GA performed data analysis. All authors helped in drafting the manuscript. HP, SR, and SRB finalized the manuscript. All authors contributed and approved the submitted version of the article.\u003c/p\u003e \u003c/p\u003e\u003ch2\u003eFunding\u003c/h2\u003e \u003cp\u003eThe authors extend their appreciation to the Researcher\u0026rsquo;s supporting project number (RSP2023R349) King Saud University, Riyadh, Saudi Arabia\u003c/p\u003e\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003eHP and SRB performed most of the experiments. HP and SRB acquired and analyzed the experimental data. SD performed docking analysis. HP and GA performed data analysis. All authors helped in drafting the manuscript. HP, SR, and SRB finalized the manuscript. All authors contributed and approved the submitted version of the article.\u003c/p\u003e\u003ch2\u003eData Availability\u003c/h2\u003e\u003cp\u003eThe datasets used and/or analyzed during the current study available from the corresponding author on reasonable request.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eWHO. 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Biol.\u003c/em\u003e \u003cstrong\u003e9\u003c/strong\u003e, 1\u0026ndash;12 (2021).\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Geraniol, stomach cancer cell lines, RNAseq, docking, cell cycle, FACS","lastPublishedDoi":"10.21203/rs.3.rs-4127451/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-4127451/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eThe effects of geraniol on cell cycle related pathway in AGS using RNA sequencing have not been explored and it is largely unknown. In this study, we isolated geraniol from \u003cem\u003eCymbopogon martini\u003c/em\u003e (palmarosa) essential oil using various spectroscopic analyses. At first, we carried out the cytotoxicity of geraniol on AGS cells. In-depth RNA sequencing analysis showed that geraniol negatively regulated genes that specifically initiate double-strand break repair via DNA replication, mitotic G1, G2/M transition, and S phases in cell cycle, eventually leading to induce apoptosis. Additionally, we validated the interaction of geraniol with the cell cycle related genes using docking, Florescence-activated cell sorting (FACS) and quantitative polymerase chain reaction (qPCR) analysis. Overall, the present investigation shows that geraniol interacts with specific target genes involved in the cell cycle process and induce cell death in the stomach cancer cells, which can be suggested as the potential treatment for stomach cancer.\u003c/p\u003e","manuscriptTitle":"Cohesive transcriptomic and in vitro approach revealed genes involved in inhibition of G0/G1, G2 phase of cell cycle pathway of stomach cancer cells by geraniol treatment","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-04-05 12:50:30","doi":"10.21203/rs.3.rs-4127451/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"7cbba5a9-1856-4c24-aaa4-5728699a6728","owner":[],"postedDate":"April 5th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[{"id":30182178,"name":"Biological sciences/Cancer"},{"id":30182179,"name":"Biological sciences/Cell biology"},{"id":30182180,"name":"Biological sciences/Computational biology and bioinformatics"}],"tags":[],"updatedAt":"2024-10-07T06:53:28+00:00","versionOfRecord":[],"versionCreatedAt":"2024-04-05 12:50:30","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-4127451","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-4127451","identity":"rs-4127451","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}