Liaison of Dimethylated arginine between PDL1 and its ligand expressions in Gastric Cancer

Liaison of Dimethylated arginine between PDL1 and its ligand expressions in Gastric Cancer | 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 Research Article Liaison of Dimethylated arginine between PDL1 and its ligand expressions in Gastric Cancer Priyatma ., Shyam Prakash, Govind K Makharia, Siddhartha D Gupta, and 5 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6800273/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 Gastric cancer is one of the most common oncological diseases. It can develop in any part of the stomach and spread to other organs, especially in the lungs, liver and oesophagus. Programmed death-1 (PD1), a cell-surface molecule, is involved in a process of dimethylation of arginine and dysregulates the production of nitric oxide in peripheral tissues. Hence, a disruption of the PD1 and PD-L1 axis in patients with severe gastritis. While DDAH activity is normally involved in the processing of neovascularisation, angiogenesis, even in the metastatic phase, the bioavailability of NO and their activity behaviour either interact synergistically or target the PDL1/PD1 activation towards the striking of tumoral activity in patients with gastritis. Therefore, a question naturally arises to understand the dimethylation process of arginine with regards to ADMA and SDMA in conjunction with claudin(s) and involvement of PD-L1 expression and mitochondrial dysregulation in GC. We observed abnormal production of NO levels and reduction of mitochondrial DNA copy numbers in GC patients. Significantly decreased levels of ADMA and excessive influx of arginase activity were assessed in GC patients. PD-L1 expression was significantly higher in GC patients, while suboptimal expression of PD-L1 is in disease control. The abnormal influx of dimethylated arginine and MMP-7 were associated and interlinked with the production of nitric oxide levels. Their association could be with the nitrigenic pathway and possible ways to damage cell surface molecules in GC. Overall, the disruption of the ADMA-SDMA equilibrium fails to maintain the PD1/ PD-L1 axis in GC patients. Therefore, claudin-4, MMP-7, and PD-L1 mRNA overexpression were found in GC, and subsequently, ADMA levels and mitochondrial DNA copy numbers were drastically decreased. Thus, these variables have potential associations to identify novel biomarkers for the diagnosis and therapeutic management of GC. Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Highlights ► ADMA/SDMA ratio and claudin-4 mRNA expression were predominantly active at immune checkpoint sites, coordinating the progression of gastric cancer. MMP-7 mRNA and PD-L1 mRNA could be a possible way in the decay of mitochondrial DNA copy numbers, and ionised calcium was elevated in GC patients. What is already known about this subject? Anti-PDL-1 is currently being used in the management of various cancers. ►Extensive accumulation of CgA in gastric cancer patients. ►ADMA is a catalytic source for the generation of excessive arginine. How might it impact clinical practice in the foreseeable future? ADMA levels and PD-L1 expressions could be a regulatory determinant to provoke the progression of GC. Lower copy numbers of mitochondrial DNA (mtDNA) and the ADMA/SDMA ratio could be a novel approach for the dimethylation process in the severity of GC. Inactivation of DDAH1 promotes abnormal behaviour of nitric oxide and disrupted ionised calcium channel, which may lead to GC. Novelty : Excessive Nitric oxide production blocks the Ca ++ channel, and MMP-7 mRNA was overexpressed in GC. Mitochondrial DNA copy number was significantly reduced in GC patients. Decreased levels of ADMA are unable to activate DDAH1, an enzyme for the optimisation of arginine metabolism in GC. PD-L1 activity decompensation occurs with the interaction of reactive nitrogen species (RNS) on the intra-entero lining of stomach cell walls that may disrupt the microenvironment in tissues/ adjacent cells due to the Ca++ permeability and subsequently decrease mitochondrial DNA copy numbers in GC. Abnormal Chromogranin A (CgA) and ADMA/SDMA ratio were significantly associated with PD-L1 in GC patients. Introduction Gastric cancer (GC) is the fourth most common oncological abnormality after breast, skin, and lung cancer 1 . Programmed cell death factor 1 (PD-1) and its ligand, i.e., PD-L1/PD-L2, are normally involved as a negative regulator in various tumors. Depending on overall survival rates, the PD-L1 expression is firmly associated with in-depth features, such as invasion and lymphatic metastasis, in cancer patients. Monoclonal PD-L1 antibody has been effectively reported in melanoma, non-small cell lung carcinoma and many other cancers. However, they are poorly defined in stomach-oriented tumors. PD-L1 mRNA is involved in various cancer cells and actively induces the host T cells’ evasion 3 . Blockade of PD-1/PD-L1 in cancer cells has been extensively studied in anti-tumour immunity and tumor growth inhibition in cancer patients 4 . The regulation of PD-L1 expression remains largely unknown in GC. However, some studies have been reported on advanced-stage cancer, at the junctions of adenocarcinoma with immune system evasion, and on the checkpoints of immune-expressed molecules 5 . The trials of metabolic programming are recognized as a hallmark of cancer6 and progression due to the Warburg effect 7 . Though the targeted and non-targeted metabolic profiles at advanced-stage arginine are the key substrates involved repeatedly and dysregulated at varying rates across the types of cancer, they remain constant over a period in cancer. Either they reflect in a heterogeneous manner or progress towards the end stage of cancer 8 , 9 . Nitriginic enzymes have been frequently associated with reactive nitrogen species and dysregulate the nitric oxide behaviour depending on ADMA or SDMA concentration in various cancers 10 – 13 . However, the nitrigenic pathway is not yet clear, and its role is still ambiguous 14 in gastric cancer. Arginine is, however, more competitive to immune enhancement and restricted to cancer cell progression at the metastatic state in later advanced stages 15 . However, the precise role of arginine is still not clear in gastric cancer 16 , 17 . The competitive ways of arginine utilization during NO synthesis in cancers 8 . It is still not yet proven that NO inhibition is under debate and dimethylation of arginine synthesis, while derivatives of arginine, i.e. asymmetric, symmetric dimethylarginines (ADMA and SDMA) and protein arginine methyltransferases (PRMTs), are actively involved in various cancers 18 , 19 . Asymmetric dimethylarginine is usually metabolized to citrulline and dimethylamine (DMA) in the presence of dimethylarginine dimethyl amino-hydrolases (DDAHs) 20 through numerous derivatives that are associated with inflammatory sites and dysregulated nitric oxides and DDAH1 activity in cancer patients. Studies have shown arginine methylation of cellular proteins actively interacting with the abnormal influx of ADMA in different types of cells, such as endothelial and smooth muscle cells, Endogenous L-arginine, asymmetric dimethylarginine (ADMA), and SDMA in gastric mucosal cells 21 , 22 . This growing evidence is still inadequate and unclear on the accumulation of ADMA and its bioavailability in gastric cancer cells/tissues. Mitochondrial functioning and ionization of calcium irregularities have been linked in several cancers. Hence, intracellular calcium is needed to support an efficient repair or reinforcement of tight junctional proteins. Tight junctional proteins such as Claudin (s), ZO1, and Zonulin are localized to apical regions, and their expressions have a prognostic role in some cancer patients 24 . Various tight junctional proteins (Claudin (s), ZO1, and Zonulin) are adequately localised to apical regions, where their remodelling mechanism remains unclear. Moreover, mitochondrial DNA copy number (mtDNA-CN) alterations have been reported in cancers and are being applied for the assessment of cancer progression 23 . The Pathomical pathway of Gastric cancer in respect to PD-L1, MMP-7 and ROS has been shown in Fig. 1 . In this study, we tried to connect in detail the TJ proteins (Claudin 4,7,18) and mtDNA-CN variations along with NO synthesis, ADMA, SDMA, PD-L1, and MMP-7 mRNA, which could provide pathogenic insights and potential therapeutic molecules for the assessment of the early stages of GC. Results All patients were newly diagnosed with gastric carcinoma on histopathological examination and endoscopic procedure in the department of Gastroenterology, AIIMS, New Delhi, India. The demographic profile of all groups based on the inclusion and exclusion criteria of subjects has been categorised in Table 1 . The clinical, biochemical, and haematological examinations were done in all the groups of patients, as shown in Table 2 . The adjacent tissues from the stomach of GC patients were also collected, as well as the tissues from the diseased control, for histopathological examination. Table 1 Demographic Profile of Gastric Cancer and Disease Control Sr No Characteristics Gastric cancer (n = 25) Disease Control (n = 30) Healthy control (n = 20) 1 Age (years) 56.2 ± 8.8 45.9 ± 10.8 31.3 ± 6.5 2 Male 20 (75%) 18 (60%) 10 (50%) 3 Female 5(25%) 12 (40%) 10 (50%) 4 Diarrhoea 3 (15%) 2 (6.6%) None 5 Constipation 2 (10%) 6(20%) 2 (10%) 6 Pain abdomen 20 (100%) 12 (40%) None 7 Nausea 4 (20%) 4 (13.3%) None 8 Joint pain 2 (10%) 9 (30%) None 9 Pallor 6 (30%) 7 (23.3%) None 10 Fatigue 7 (35%) 9 (30%) None 11 Heart burn 18 (90%) 16 (53.3%) None 12 Weight loss 8 (40%) 3 (10%) None Table 2 Baseline Characteristics of study groups Hematological and Biochemical profile: Clinical parameters GC (n = 25) Mean ± SD DC (n = 30) Mean ± SD Healthy Control (n = 20) Mean ± SD P values Hematological Profile Heamoglobin (g/dl) 11.56 ± 1.66 12.5 ± 1.0 12.9 ± 2.1 0.007 Hematocrit % 33.15 ± 5.03 39 ± 3.2 40.5 ± 5.8 0.004 MCV (fL) 85.34 ± 5.45 84.7 ± 4.0 83.9 ± 9.6 0.667 MCHC (g/dl) 31.8 ± 1.37 33.4 ± 1.8 31.9 ± 1.6 0.773 Total leukocyte count (10 3 /mm) 3 7.13 ± 1.27 6.8 ± 1.3 7.2 ± 2.1 0.276 Platelets (10 3 /mm 3 ) 170 ± 79.13 189.1 ± 50.3 206.9 ± 60.6 0.002 ESR 26.5 ± 8.30 16.2 ± 5.1 15.1 ± 11 0.001 Biochemical Profile Serum urea (mg%) 26.27 ± 8.19 24.7 ± 3.9 22.7 ± 5.6 0.318 Creatinine (mg%) 0.82 ± 0.2 0.81 ± 0.1 0.74 ± 0.2 0.343 Calcium (mg%) 8.86 ± 0.49 8.9 ± 0.5 9.2 ± 0.3 0.047 Bilirubin (mg%) 0.6 ± 0.2 0.7 ± 0.2 0.7 ± 0.5 0.42 Total protein (gm%) 6.86 ± 0.55 7.8 ± 0.4 7.3 ± 0.3 0.00 ALT (IU/L) 26.36 ± 12.45 27.6 ± 5.9 27.3 ± 19 0.47 AST (IU/L) 24.81 ± 6.00 24.9 ± 8.1 25.3 ± 9.3 0.799 ALP (IU/L) 227.9 ± 84.98 232.7 ± 38.3 237.5 ± 37.1 0.767 Pathomic Profile CgA (ng/ml) 1008 ± 374.2 111.2 ± 42.8 28.7 ± 12.5 0.003 MMP-7 (pg/ml) 1643.1 ± 2113.9 131.7 ± 69.4 16.62 ± 8.62 0.001 Arginase activity (nM/min/mg) 126 ± 44.62 42 ± 16.6 39.2 ± 21.84 0.0003 DDAH 1 (pg/ml) 460 ± 218.2 188 ± 106 128.5 ± 96.6 0.0001 NO (µg/ml) 19.2 ± 8.5 8.5 ± 1.4 7.1 ± 1.4 0.001 i) Correlation of NO levels correlates with poor prognosis of patients with GC: Nitric oxide levels were significantly higher in GC comparatively disease control and healthy subjects. Serum NO levels were higher in GC patients, as shown in Fig. 2 (A). ii) eNOS-mediated NO production in GC Functional role of NO was assessed in GC patients with the eNOS mRNA expression in 25 GC tissues and adjacent gastric mucosal tissues. eNOS mRNA was significantly down-regulated in tissues of GC patients. iii) ADMA and SDMA mediate NOs expression in GC: ADMA levels were significantly lower in GC as compared with disease control and healthy control; perhaps no significant difference was seen between disease controls and healthy control, as shown in Fig. 2 (B). SDMA levels were significantly lower in GC as compared with disease control and healthy control, as shown in Fig. 2 (C). The pairwise comparison of the ADMA and SDMA ratio was 21% lower in gastric cancer as compared to disease control, as shown in Fig. 2 (D). Furthermore, median serum MMP-7 level in GC was [697.3 (IQR 185.85-2689.1)] and [114.15(IQR 92.6-131.47)] in GC and DC, respectively, as shown in Fig. 2 (E), and they were statistically significant (p < 0.05). The serum levels of MMP-7 were 131.7 ± 69.4 pg/ml in DC and 1643.1 ± 2113.9 pg/ml in gastric cancer, which was statistically significant (p < 0.001). The functional role of PD-L1 was higher depending on the severity of the tumour stages in GC. PD-L1 was highly expressed in the (Grade III and IV) stage of GC patients than in lower grades of gastric cancer, i.e., less than grade II in GC patients. PD-L1 showed significantly higher expressions in grade III and grade IV of tumours than in lower grades of tumours (p < 0.050, p < 0.010, respectively), as shown in the amplification plot as well as in the melt curve (Fig. 3 (A)). The overall 3.6-fold change of PD-L1 expression was observed in GC patients. The amplification profile at the grade-wise level is shown in Fig. 3 (A). The GAPDH was used as an internal control. PD-L1 expression in human gastric carcinoma tissues and non-neoplastic mucosae The mRNA expression of PD-L1 was 6.22 ± 5.2 in GC, while the mean relative expression was 4.08 ± 8.17 in adjacent non-neoplastic tissue (Fig. 3 (B)). The significant (p < 0.05) expression of MMP-7 mRNA was exceedingly higher in GC (n = 19) as compared with DC (n = 17), while suboptimal expression of MMP-7 mRNA was seen in 6 patients with GC and 13 patients with disease control. The relative expression of MMP-7 mRNA was observed as 4.37 ± 8.16 in GC, and the mean expression values were 0.06 ± 0.14 in DC. The observed delta Ct difference values were 12.82 ± 2.53 in disease control and 6.50 ± 3.14 in gastric cancer. Therefore, up-regulation of MMP-7 mRNA expression was positively associated in Gastric cancer patients as shown in Fig. 3 (C). Claudin-4, 7 and 18 mRNA expressions in Gastric cancer and Disease control Claudins are the backbone of tight junctions and play a key role in barrier activities and permeability of small molecules and ions. The differential expression of Claudin-4 mRNA was overexpressed in GC compared to DC (Fig. 4 (A)). However, the non-significant (p < 0.54) expression of Claudin-18 was found in GC as compared with DC (Fig. 4 (B)). Arginase enhances Chromogranin (CgA) in GC through DDAH activity In conjunction with arginase activity, Chromogranin A was significantly higher in GC patients as compared to disease control, as well as from healthy subjects (p < 0.001), while DDAH1 activities were lowered in GC patients, as shown in Table 2 . The relative expression of different genes in Gastric cancer and Disease control patients was found as 50th percentile (min-max) as shown in Table 3 (A). The Mitochondrial DNA copy number is shown in Table 3 (B) (gastric cancer and disease control patients). Table 3 A) Relative expression of different genes; B) Mitochondrial DNA copy number in Gastric cancer and Disease control patients. Target Gene GC DC p value A Claudin-4 mRNA 9.89 (0.39–67.18) 0.70 (0.04–6.96) 0.005 Claudin-7 mRNA 0.271 (0.02–8.93) 1.02 (0.02–7.41) 0.64 Claudin-18 mRNA 0.76 (0.02–1.27) 0.77 (0.07–1.23) 0.54 PD-L1 mRNA 4.11 (0.03–16.91) 0.73 (0.11–32.22) 0.25 MMP 7 mRNA 0.450 (0.001–3.29) 0.003 (0.0002-0.18) 0.04 eNOS mRNA 0.02 (0.004–1.25) 2.03 (0.09–15.90) 0.05 B mtDNA copy number 60.40 ± 30.43 80.57 ± 30.85 0.05 Correlation of PD-L1 with other factors in Gastric Cancer ADMA/SDMA ratio and PD-L1 correlation had positive effects in GC patients (Fig. 5 A). The significance (p < 0.0001). Similarly, ADMA and PD-L1 correlation had positive effects in GC patients (Fig. 5 (B)). Although a decreasing pattern of ADMA and increasing expressions of PD-L1 mRNA had an antagonistic effect in GC. Furthermore, the correlation analysis between PD-L1 and NO was found to be positively associated in GC patients (Fig. 5 (C)). The positive correlations were observed between MMP-7 and PD-L1, as shown in Fig. 5 (D). Correlation studies of NO and MMP-7 were positively associated with each other in gastric cancer patients (Fig. 5 (E)). MMP7 mRNA was superficially expressed in GC, which was more than 3-fold above that of the disease controls, while a borderline difference was seen in healthy controls. Claudin-4, claudin-7 and claudin-18 mRNA expressions were correlated (r=-0.08) (r = 0.24) (r=-0.25) respectively and shown in Fig. 5 I, 5 G, and 5 F with PD-L1 mRNA expression. A positive correlation was observed between Claudin-7 and PD-L1, Claudin-4 and Claudin-18 (r = 0.22), Claudin-4 and PD-L1 (r=-0.25), and Claudin-7 and Claudin-18 r = 0.75 were strongly correlated in GC, as shown in Fig. 5 (G), 5(H), 5(I) and 5(J) respectively. Discussion GC represents one of the most common malignancies worldwide, ranking fourth after lung cancer, breast cancer and colorectal cancer. Even, it is still the second most common cause of death due to gastric cancer. It is attributed to changes in lifestyle, higher consumption of tobacco or smoking, alcohol, and increased stress levels in the population 25 . Our study explains that PD-L1 expression was more highly pronounced in higher-grade tumours, i.e., grade III and IV, than in lower-grade tumours in patients. PD-L1 is a coregulatory ligand highly expressed depending upon the severity of inflamed tissues at the lining of stomach cells, which is responsible for inhibiting immune responses in GC. These inhibitory responses rely on binding surface T lymphocytes and inducing T cells to process specific apoptosis mechanisms. In this way, tumour cells are capable of upregulating PD-L1 expression by activation of inhibitory signals. Similar observations on PDL1 expressions have already been reported in various cancers, including breast, ovarian, pancreatic, oesophageal, colorectal and gastric cancer 5 , 26 – 29 . PDL1 mRNA expression has also been reported to be associated with poor prognosis in GC patients. MMP-7 mRNA expressions were associated more than two-fold with the severity of GC, while other studies have shown no association in GC 30 . Interaction analysis between MMP-7 mRNA and ADMA/SDMA ratio has revealed a similar opinion in the progression of GC as we have observed in this study. We have observed a strong association with the severity of disease and higher grades of GC tumors. These associations suggest that interactions between ADMA regulation, metabolism, export and import could be a critical determinant of intracellular levels of ADMA and the NOS substrate, as well as L-Arginine availability. These functional defects could be accompanied by increased total body NO generation, decreased circulating levels of ADMA and indices of reactive nitrogen species (RNS), and occur due to impaired endothelial uptake of L-arginine. We found that increased NO production was present in gastric cancer patients, which could relatively alter the oxidant and antioxidant homeostatic status. Since NO has a dual role in disease progression at higher concentrations for long durations and releases peroxinitrite that directly or indirectly damages DNA, leading to mutations as well as progression in gastric cancer. Moreover, the ADMA/SDMA ratio was found to be significantly associated with MMP-7 mRNA expression, as compared between GC and HC, but no difference was found in DC subjects. Similarly, serum MMP-7 were significantly raised in GC patients as compared with DC and HC. The loss of the TJ structure caused by aberrant expression of claudin proteins suggested that there is a diffusion process of nutrient absorption along with other associated factors that could be involved in the survival and proliferation of cancer cells. The present study inferred that the expression of claudin 4, 7 and 18 was altered in GC and DC patients, and their expression was associated with metastasis. DDAH1 accelerates the GC malignant process by upregulating the PD-L1 expression: Increased level of DDAH1 suppresses the intracellular ADMA by the activation of eNOS expressions, which simultaneously accelerates PD-L1 activity at the inflammatory sites. PD-L1 levels were significantly higher in GC patients. A significant correlation was seen with increasing severity in the Grades of the GC patient sample grade III. The significant decrease of ADMA levels in GC patients and the increased level of NO; however, the exact mechanism is still under way in the progression of GC. ADMA levels were significantly decreased by the upregulation of iNOS mRNA expression in GC. Mechanistically, DDAH1-mediated NO activation is actively involved in the regulation of arginase activity in GC. Arginine is converted to polyamines and induces matrix metalloproteinases for removing the physical barrier that stops tumor cells from invasions. On the other hand, with the coordination of the ADMA/SDMA ratio, the PDL-1 is overexpressed, mediated by MMP-7 mRNA, with the active involvement of claudin-4 and claudin-18, which leads to the severity of GC. Overall, disrupted arginine dimethylation, mitochondrial dysfunction, and PD-1/PDL-1 signalling underscore a complex interplay and disrupt vascular regulation, angiogenesis and immune evasion mechanisms in GC. Claudin-4, MMP-7, and PD-L1 mRNA, alongside ADMA and mtDNA levels, offer promising avenues for developing novel diagnostic and therapeutic strategies for GC. Limitations: Insufficient information was available on the nature of intratumor heterogeneity and sampling issues while collecting biopsy specimens. Most patients were visiting the OPD in the end-stage or chronic stage, during a new diagnosis of GC. While recruiting patients for the series, we considered only cases with at least two biopsy specimens of different lesions (average number of biopsies per lesion: 1–2) due to ethical constraints to avoid multiple biopsy procedures in GC patients. As per national and international guidelines, this number (biopsies/tissue pieces) was reduced for evaluation in GC/GEC. Moreover, it was difficult for small preinvasive lesions to be taken into routine diagnosis for patient care. Thus, PD-L1 intratumor heterogeneity should be investigated in future studies considering surgical series of GC/GEC. Materials and methods Patient recruitments: The recruitment of patients was done in the department of Gastroenterology and Dr BRA IRCH, AIIMS, New Delhi, at the time of total or subtotal Gastrectomy from 2016–2019, who underwent the endoscopic procedure for gastric cancer diagnosis. Blood and tissue samples were collected from the OPDs and endoscopy lab for molecular and biochemical tests, and samples were stored at -80°C until analysis. Histologic evaluation was done based on Laurens' classification 31 for tumor grades and diagnosis of patients. The study was approved by the AIIMS ethics committee (IECPG/88/30.12.2015, RT-11/27.01.2016, dated 29.01.2016), and informed consents were obtained from all subjects who had participated in the study as per the Declaration of Helsinki. Biopsies were collected from the targeted regions and also from adjacent localized regions of the same patient. Stomach biopsies were also collected in disease control patients, such as dyspepsia. All procedures were followed as per the standard outlined in the protocol declaration. Histological evaluation of all biopsies was done for categorization of GC stages. Of these, 78% of biopsies were moderately well differentiated, and 8% were designated signet ring cell types. Overall, 36% of patients were diagnosed with low-grade cancer (grades I and II), and 64% of patients were diagnosed with high-grade cancer (grades III and IV). The age range of all recruited patients was 36–62 years. Arginase activity Arginase activity in the tissue lysate was measured by the spectrophotometric method of an intermediate product formed from the arginase reaction with the arginine in accordance with the manufacturer’s instructions of a photometric kit (ab 180877), eLab Science USA. All tissue samples were processed as per the protocol defined for extraction and in triplicate. The reaction complex was measured at 570 nm in kinetic mode for 30 min at 37°C using a Cary 100 spectrophotometer (Agilent, USA). The measured activities were represented in terms of protein content (mg). RNA extraction, cDNA synthesis and Q-PCR RNA was extracted from frozen specimens using TRIzol TM reagent (Thermo Fisher Scientific, Inc., Waltham, MA, USA) according to the manufacturer’s protocol. The RNA concentration was measured in a Nanodrop ND 1000 spectrophotometer (Nanodrop Technologies). The isolated RNA was aliquoted and stored at -80°C until use. cDNA Synthesis Total RNA (5µg) was reverse transcribed using 1µM random primer, 200/U/µl of Superscript II reverse transcriptase (Thermo Fisher Scientific), 1µl of Ribolock RNase inhibitor (20U/µl) and 2µl of 10 mM dNTP (Thermo Fisher Scientific) for cDNA preparation in a total volume of 20µl. The reaction mixture was kept at 42°C for one hour, and then the reaction was terminated at 72°C by keeping it for 5 minutes. Real-time PCR (Agilent Technologies, CA, USA) was done using SYBR chemistry with the following primer pairs designed from Beacon Designer 5.1 Software (Premier Biosoft, Palo Alto, CA) for each target gene, as shown in Table 2 , and were synthesised by IDT, Canada. The primer sequence of PD-L1 (F-5′-CCAAGGCGCAGATCAAAGAGA-3′; R-5′-AGGACCCAGACTAGCAGCA-3′), MMP-7 (F-5′-CATTTGATGGGCCAGGAAAAC-3′; R- 5′-GCAGCATACAGGAAGTTAATCC-3′), eNOS (F-5′-CGGCATCACCAGGAAGAAGA-3′; R-5′- CATGAGCGAGGCGGAGAT-3′), Claudin-4 (F-5′-AGCTCTGTGGCCTCAGGACTCT-3′; R-5′-CTCTTCTTAAATTACAA-3′), Claudin-7 (F-5′-ATGGCCAACTCGGGCCTGCAACTG-3′; R-5′-AGTGATGAATAGTC ACACGTATTCCTTGGAGGAATT-3′), Claudin-18 (F-5′CGGGCGGCCAGGATCATGTC-3′; R- 5′- ACTGCCTGCAGCATGGCTGG-3′), mtDNA (F- 5′-TGGCCATGGGTATGTTG TTA-3′;R-5′-TCTCTGCTCCCCACCTCTAAGT-3′),GAPDH (F-5’-ACAGTCAGCCGCAT CTTC − 3’; R-5’-GCCCAATACGACCAAATC-3’). PD-L1 mRNA expression : The 20µl reaction mixture was prepared using 4µl of cDNA, 10µl of 2X Sybr PCR master mix (Promega, USA), 1µl of forward primer (10 pM) and reverse primer (10 pM) each. The thermal condition for Q-PCR was as 94°C for 5 min, 1 cycle; at 94°C for 30 sec, at 52°C for 30 sec, 72°C for 20 sec 40 cycles and data collection at 94°C for 15 sec, at 60°C for 20 sec, 94°C for 15 sec one cycles. The reaction was carried out in a Q-PCR system (Agilent Aria Mx, US) for PD-L1 mRNA gene amplification. MMP-7 mRNA expression : The 20µl reaction mixture was prepared using 4µl of cDNA, 10µl of 2X Sybr PCR master mix (Promega, USA), 1µl of forward primer (10 pM) and reverse primer (10 pM) each. The thermal condition for Q-PCR was as 94°C for 5 min, 1 cycle; at 94°C for 30 sec, at 52°C for 30 sec, 72°C for 20 sec 40 cycles & data collection at 94°C for 15 sec, at 60°C for 20 sec, 94°C for 15 sec one cycles. The reaction was carried out in a Q-PCR system (Agilent Aria Mx) for PD-L1 mRNA gene amplification. Claudin-4 mRNA expression : The 20µl reaction mixture was prepared using 4µl of cDNA, 10µl of 2X Sybr PCR master mix, 1µl of forward primer (10 pM) and reverse primer (10 pM) each. The thermal condition for Q-PCR was as 94°C for 5 min, 1 cycle; at 94°C for 30 sec, at 52°C for 30 sec, 72°C for 20 sec 40 cycles & data collection at 94°C for 15 sec, at 60°C for 20 sec, 94°C for 15 sec one cycles. The reaction was carried out in a Q-PCR system (Agilent Aria Mx) for PD-L1 mRNA gene amplification. Claudin-7 mRNA expression : The 20µl reaction mixture was prepared using 4µl of cDNA, 10µl of 2X Sybr PCR master mix, 1µl of forward primer (10 pM) and reverse primer (10 pM) each. The thermal condition for Q-PCR was as 94°C for 5 min, 1 cycle; at 94°C for 30 sec, at 52°C for 30 sec, 72°C for 20 sec 40 cycles & data collection at 94°C for 15 sec, at 60°C for 20 sec, 94°C for 15 sec one cycles. The reaction was carried out in a Q-PCR system (Agilent Aria Mx, US) for PD-L1 mRNA gene amplification. Claudin-18 mRNA expression : The 20µl reaction mixture was prepared using 4µl of cDNA, 10µl of 2X Sybr PCR master mix (Promega, USA), 1µl of forward primer (10 pM) and reverse primer (10 pM) each. The thermal condition for Q-PCR was as 94°C for 5 min, 1 cycle; at 94°C for 30 sec, at 56°C for 30 sec, 72°C for 20 sec 40 cycles & data collection at 94°C for 15 sec, at 60°C for 20 sec, 94°C for 15 sec one cycles. The reaction was carried out in a Q-PCR system (Agilent Aria Mx, US) for PD-L1 mRNA gene amplification. GAPDH mRNA expression : The housekeeping gene GAPDH was used as an internal control for amplification. The 20µl reaction mixture was prepared using 4µl of cDNA, 10µl of 2X Sybr PCR master mix, 1µl of forward primer (10 pM) and reverse primer (10 pM) each. The thermal condition for Q-PCR was as 94°C for 5 min, 1 cycle; at 94°C for 30 sec, at 52°C for 30 sec, 72°C for 20 sec 40 cycles & data collection at 94°C for 15 sec, at 60°C for 20 sec, 94°C for 15 sec one cycles. The reaction was carried out in a Q-PCR system (Agilent Aria Mx) for PD-L1 mRNA gene amplification. mtDNA copy number: For mtDNA copy number, DNA was extracted using the salt extraction method 32 . Real-time reaction was done using the SYBR green chemistry for the amplification. The reaction mix was prepared in a total of 20µl reaction, which includes gene-specific forward and reverse primers (5 pM), 10µl SYBR mix (2X), template up to 500 ng and nuclease-free water was added to adjust the volume. β-Globin and nuclear DNA genes were used as an internal control for mitochondrial DNA copy numbers. The thermal condition for Q-PCR was as follows: 94°C for 10 min, 1 cycle; at 94°C for 30 sec, at 52°C for 30 sec, 40 cycles & data collection at 94°C for 15 sec, at 60°C for 20 sec, 94°C for 15 sec, one cycle. The amplified product was quantified, results were calculated in terms of copy number (mtDNA) 33 . ADMA and SDMA assay: Plasma ADMA and SDMA were analyzed by HPLC (Agilent, Infinity 1260) as described by Teerlink et al., with minor modifications to the method 34 . The sample derivatisation process was initiated as per the defined injector programme reaction mixture, such as 5µl borate buffer, followed by the addition of 5µl sample, 0.5µl OPA, 0.5µl FMOC and 20µl of water, with the flow rate of solvents at 1.2 ml/minute and buffer gradient. 10µl of the reaction mixture was injected after mixing of the sample and reagents in the autosampler. Detection was performed at an excitation wavelength of 254nm and an emission cutoff filter of 324 nm. Finally, the chromatogram was generated, and the peak area was used for the quantification of ADMA and SDMA estimation in samples. DDAH-1 assay Assay was performed by following the manufacturer's instructions of the DDAH activity assay kit (Abcam). Measured the absorbance in a microplate reader at 466 nm at RT. Calculation: Determine DDAH activity in the sample (s) using the following equations: DDAH (mU/mg) = (OD Sample – ODSBC) / (OD (Spiked Sample) – OD Sample) x 2 x T x C (nmol/min*mg) Nitric Oxide Assay Plasma total nitrite and nitrate levels were measured with use of the Griess reagent 35 . The Griess reagent consists of sulphanilamide and N-(1-naphthyl) ethylenediamine. Photometric measurement of the azo product was done at 540nm. Statistical analysis The sample size was calculated keeping in view the available data on GC by applying a statistical formula, so that in each group, the sample size was 20. The patients’ characteristics were analyzed by the Mann-Whitney U and chi-square tests. Continuous variables are expressed as the mean ± SD and median and interquartile range for skewed variables. Meanwhile, the SPSS 19.0 computer software (SPSS Inc., Chicago, IL, USA) was used to carry out statistical analysis, including Student’s t test and nonparametric tests. Nonparametric tests have been applied for age and sex while comparing groups and within groups, and the Mann-Whitney U test has also been done. Comparison between the control and groups was made with the Kruskal-Wallis equality-of-population rank test, and Bonferroni correction was applied. A p-value of < 0.05 is considered statistically significant. Declarations Conflict of interest The authors declared no competing interests. Ethics Statement The Institutional Research Ethics Committee of All India Institute of Medical Sciences, New Delhi, approved the ethical use of human subjects for this study (Ref: IECPG/88/30.12.2015, RT-11/27.01.2016, dated 29.01.2016). Written informed consent was taken from all patients, duly signed, for participation in the study. Funding Statement: No specific grant for the study, and we have used consumables and reagents from the AIIMS intramural and ICMR-funded projects. Author Contribution Priyatma, Shyam, Govind, Siddhartha for acquisition of data, Priyatma, Shyam, Arulselvi and Siddhartha analysis and interpretation of data, statistical analysis and drafting of the manuscript; Shyam for technical and material support; Shyam, Priyatma, Peush, Sanjay and Ranjit for study concept and design, analysis and interpretation of data, drafting of the manuscript, obtained funding and study supervision. All authors read and approved the final manuscript. Acknowledgement The Director General, Indian Council of Medical Research, Director AIIMS Delhi, for intramural grant support, and providing the Institutional support such as lab infrastructure, library facility, and all lab colleagues who have supported all the time for the sample management, etc. Data Availability The material described in the manuscript, including all relevant data, will be freely available to any researcher to use for non-commercial purposes without breaching participant confidentiality. Author affiliations 1 Department of Laboratory Medicine, 2 Department of Gastroenterology, 3 Department of Pathology, 4 Department of Gastrointestinal Surgery, 5 Department of Medical Oncology, IRCH Dr BRA, 6 Department of Radiology, IRCH Dr BRA, 7 Department of Laboratory Medicine, JPN Trauma, and 8 Department of Biostatistics, All India Institute of Medical Sciences, Ansari Nagar, New Delhi, India- 110029. Contributors Priyatma, Shyam, Govind, Siddarth for acquisition of data, Priyatma, Shyam, Arulselvi and Siddarth analysis and interpretation of data, statistical analysis and drafting of the manuscript; Shyam for technical and material support; Shyam, Priyatma, Peush, Sanjay and Ranjan for study concept and design, analysis and interpretation of data, drafting of the manuscript, obtained funding and study supervision. All authors read and approved the final manuscript. References Machlowska J, Baj J, Sitarz M, Maciejewski R, Sitarz R. Gastric cancer: Epidemiology, risk factors, classification, genomic characteristics and treatment strategies. Int J Mol Sci. 2020;21. 10.3390/ijms21114012 . Butte MJ, Keir ME, Phamduy TB, Sharpe AH, Freeman GJ. Programmed Death-1 Ligand 1 Interacts Specifically with the B7-1 Costimulatory Molecule to Inhibit T Cell Responses. Immunity. 2007;27:111–22. Boussiotis VA. Molecular and Biochemical Aspects of the PD-1 Checkpoint Pathway. N Engl J Med. 2016;375:1767–78. Han Y, Liu D, Li L. PD-1/PD-L1 pathway: current researches in cancer. 2020 Hudson K, Cross N, Jordan-Mahy N, Leyland R. The Extrinsic and Intrinsic Roles of PD-L1 and Its Receptor PD-1: Implications for Immunotherapy Treatment. Front Immunol. 2020;11. 10.3389/fimmu.2020.568931 . Liberti MV, Locasale JW. The Warburg Effect: How Does it Benefit Cancer Cells? Trends Biochem Sci. 2016;41:211–8. Yuan LW, Yamashita H, Seto Y. Glucose metabolism in gastric cancer: The cutting-edge. World J Gastroenterol. 2016;22:2046–59. Tain YL, Hsu CN. Toxic dimethylarginines: Asymmetric dimethylarginine (ADMA) and symmetric dimethylarginine (SDMA). Toxins (Basel). 2017;9. 10.3390/toxins9030092 . Du T, Han J. Arginine Metabolism and Its Potential in Treatment of Colorectal Cancer. Front Cell Dev Biol. 2021;9. 10.3389/fcell.2021.658861 . Bollenbach A, Schutte AE, Kruger R, Tsikas D. An ethnic comparison of arginine dimethylation and cardiometabolic factors in healthy black and white youth: The ASOS and African-PREDICT studies. J Clin Med. 2020;9. 10.3390/jcm9030844 . McEvoy MA, Attia JR, Oldmeadow C, Holliday E, Smith WT, Mangoni AA, et al. Serum L-arginine and endogenous methylarginine concentrations predict irritable bowel syndrome in adults: A nested case-control study. United Eur Gastroenterol J. 2021;9:809–18. Durante W, Johnson FK, Johnson RA, ARGINASE. A CRITICAL REGULATOR OF NITRIC OXIDE SYNTHESIS AND VASCULAR FUNCTION. Tsikas D, Bollenbach A, Hanff E, Kayacelebi AA. Asymmetric dimethylarginine (ADMA), symmetric dimethylarginine (SDMA) and homoarginine (hArg): The ADMA, SDMA and hArg paradoxes. Cardiovasc Diabetol. 2018;17. 10.1186/s12933-017-0656-x . Bollenbach A, Huneau JF, Mariotti F, Tsikas D. Asymmetric and symmetric protein arginine dimethylation: Concept and postprandial effects of high-fat protein meals in healthy overweight men. Nutrients. 2019;11. 10.3390/nu11071463 . Oliva-Damaso E, Oliva-Damaso N, Rodriguez-Esparragon F, Payan J, Baamonde-sLaborda E, Gonzalez-Cabrera F, et al. Asymmetric (ADMA) and symmetric (SDMA) dimethylarginines in chronic kidney disease: A clinical approach. Int J Mol Sci. 2019;20. 10.3390/ijms20153668 . Pozzesi N, Fierabracci A, Liberati AM, Martelli MP, Ayroldi E, Riccardi C, et al. Role of caspase-8 in thymus function. Cell Death Differ. 2014;21:226–33. Rojas J, Chávez Castillo M, Cabrera M, Bermúdez V, Joselyn Rojas C. Glucococorticoid-Induced Death of Pancreatic Beta Cells: An Organized Chaos. Online, 2015 http://www.serena.unina.it/index.php/ Chachaj A, Wiśniewski J, Rybka J, Butrym A, Biedroń M, Krzystek-Korpacka M, et al. Asymmetric and symmetric dimethylarginines and mortality in patients with hematological malignancies—A prospective study. PLoS ONE. 2018;13. 10.1371/journal.pone.0197148 . Hulin JA, Gubareva EA, Jarzebska N, Rodionov RN, Mangoni AA, Tommasi S. Inhibition of Dimethylarginine Dimethylaminohydrolase (DDAH) Enzymes as an Emerging Therapeutic Strategy to Target Angiogenesis and Vasculogenic Mimicry in Cancer. Front Oncol. 2020;9. 10.3389/fonc.2019.01455 . Guo Q, Xu J, Huang Z, Yao Q, Chen F, Liu H, et al. ADMA mediates gastric cancer cell migration and invasion via Wnt/β-catenin signaling pathway. Clin Transl Oncol. 2021;23:325–34. Bednarz-Misa I, Fleszar MG, Fortuna P, Lewandowski Ł, Mierzchała-Pasierb M, Diakowska D, et al. Altered l-arginine metabolic pathways in gastric cancer: Potential therapeutic targets and biomarkers. Biomolecules. 2021;11. 10.3390/biom11081086 . Sanada Y, Oue N, Mitani Y, Yoshida K, Nakayama H, Yasui W. Down-regulation of the claudin-18 gene, identified through serial analysis of gene expression data analysis, in gastric cancer with an intestinal phenotype. J Pathol. 2006;208:633–42. Radzak SMA, Khair SZNM, Ahmad F, Patar A, Idris Z, Yusoff AAM. Insights regarding mitochondrial DNA copy number alterations in human cancer (Review). Int J Mol Med. 2022;50. 10.3892/IJMM.2022.5160 . Hashimoto I, Oshima T. Claudins and Gastric Cancer: An Overview. Cancers (Basel). 2022;14. 10.3390/cancers14020290 . Zali H, Rezaei-Tavirani M, Azodi M. Gastroenterology and Hepatology From Bed to Bench. 2011. Zhou Y-J, Li G, Wang J, Liu M, Wang Z, Song Y et al. PD-L1: expression regulation. 2023. 10.1097/BS9.0000000000000149 Akinleye A, Rasool Z. Immune checkpoint inhibitors of PD-L1 as cancer therapeutics. J Hematol Oncol. 2019;12. 10.1186/s13045-019-0779-5 . Wang X, Teng F, Kong L, Yu J. PD-L1 expression in human cancers and its association with clinical outcomes. Onco Targets Ther. 2016;9:5023–39. Sundar R, Smyth EC, Peng S, Yeong JPS, Tan P. Predictive Biomarkers of Immune Checkpoint Inhibition in Gastroesophageal Cancers. Front Oncol. 2020;10. 10.3389/fonc.2020.00763 . Zhang Y, Qin L, Ma X, Wang Y, Wu Y, Jiang J. Coexpression of Matrix Metalloproteinase-7 and Tissue Inhibitor of Metalloproteinase-1 as a Prognostic Biomarker in Gastric Cancer. Dis Markers 2020; 2020. 10.1155/2020/8831466 Zhao L-Y, Wang J-J, Zhao Y-L, Chen X-Z, Yang K, Chen X-L, et al. Superiority of Tumor Location-Modified Lauren Classification System for Gastric Cancer: A Multi-Institutional Validation Analysis. Ann Surg Oncol. 2018;25:3257–63. Miller SA, Dykes DD, Polesky HF. A simple salting out procedure for extracting DNA from human nucleated cells. Nucleic Acids Res. 1988;16:1215–1215. Rooney J, Ryde I, Sanders L, Howlett E, Germ K, Mayer G et al. PCR Based Determination of Mitochondrial DNA Copy Number in Multiple Species. 10.1007/978-1-4939-1875-1_3 Široká R, Trefil L, Racek J, Cibulka R. Comparison of asymmetric dimethylarginine detection - HPLC and ELISA methods (technical brief). Klin Biochem Metab. 2006;14:111–3. Archer S. Measurement of nitric oxide in biological models. FASEB J. 1993;7:349–60. 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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-6800273","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":481894739,"identity":"33352a53-2faf-4dd0-8939-4172cc2a8953","order_by":0,"name":"Priyatma .","email":"","orcid":"","institution":"All India Institute of Medical Sciences","correspondingAuthor":false,"prefix":"","firstName":"Priyatma","middleName":"","lastName":".","suffix":""},{"id":481894740,"identity":"b059f2ac-14ef-4110-bfc7-ce377d44547e","order_by":1,"name":"Shyam Prakash","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA50lEQVRIiWNgGAWjYHACZiA+wMAGoj4AMRs7KVoYZ4C0MBOrBczigfHxAXPpw48Nfvy5I8/Hzp382ebXNnk+ZgbGDx9zcGux7EszTuxte2bYxsy7TTq37zaQwcAsOXMbbi0GZxiMD/A2HGYEaWHO7bkNZAC9w4tXC/vng3/+HLYHatn82bLntj0RWniMk3nYDicCtWyQZvhxO5GgFssenmJj2bbDySCHSfY23AYyGJvx+sWch32z5Js/h23n95/d/OHHn9u289ubD374iM9hKDzGNjDZgFs9hhaGP3gVj4JRMApGwQgFAA3PTltgeY1tAAAAAElFTkSuQmCC","orcid":"","institution":"All India Institute of Medical Sciences","correspondingAuthor":true,"prefix":"","firstName":"Shyam","middleName":"","lastName":"Prakash","suffix":""},{"id":481894741,"identity":"e6313465-6165-42b0-a156-155d2e8975dc","order_by":2,"name":"Govind K Makharia","email":"","orcid":"","institution":"All India Institute of Medical Sciences","correspondingAuthor":false,"prefix":"","firstName":"Govind","middleName":"K","lastName":"Makharia","suffix":""},{"id":481894742,"identity":"bd61618b-fc32-4a04-bad3-07b8d897dec4","order_by":3,"name":"Siddhartha D Gupta","email":"","orcid":"","institution":"All India Institute of Medical Sciences","correspondingAuthor":false,"prefix":"","firstName":"Siddhartha","middleName":"D","lastName":"Gupta","suffix":""},{"id":481894745,"identity":"721abab5-e2fb-42eb-ab77-302ba5d7a9ae","order_by":4,"name":"Peush Shani","email":"","orcid":"","institution":"All India Institute of Medical Sciences","correspondingAuthor":false,"prefix":"","firstName":"Peush","middleName":"","lastName":"Shani","suffix":""},{"id":481894748,"identity":"2a9bb4c6-7170-4d08-9dd8-0b61365d6c7d","order_by":5,"name":"Ranjit K Sahoo","email":"","orcid":"","institution":"All India Institute of Medical Sciences","correspondingAuthor":false,"prefix":"","firstName":"Ranjit","middleName":"K","lastName":"Sahoo","suffix":""},{"id":481894750,"identity":"611976d5-c4d6-4147-a707-ad3eb52241cd","order_by":6,"name":"Sanjay Thulkar","email":"","orcid":"","institution":"All India Institute of Medical Sciences","correspondingAuthor":false,"prefix":"","firstName":"Sanjay","middleName":"","lastName":"Thulkar","suffix":""},{"id":481894751,"identity":"214dacbd-33a9-4607-91a5-c181ba7a6d84","order_by":7,"name":"Arulselvi S","email":"","orcid":"","institution":"All India Institute of Medical Sciences","correspondingAuthor":false,"prefix":"","firstName":"Arulselvi","middleName":"","lastName":"S","suffix":""},{"id":481894752,"identity":"83bfc9d0-3d1f-417a-96d2-aa2ff0e40150","order_by":8,"name":"Ravinder M Pandey","email":"","orcid":"","institution":"All India Institute of Medical Sciences","correspondingAuthor":false,"prefix":"","firstName":"Ravinder","middleName":"M","lastName":"Pandey","suffix":""}],"badges":[],"createdAt":"2025-06-02 08:38:26","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-6800273/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-6800273/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":86625659,"identity":"e4dcc196-a4a4-4183-8de4-5aad8394829a","added_by":"auto","created_at":"2025-07-14 05:13:00","extension":"jpg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":33407,"visible":true,"origin":"","legend":"\u003cp\u003ePathomical pathway of Gastric cancer.\u003c/p\u003e","description":"","filename":"Picture1.jpg","url":"https://assets-eu.researchsquare.com/files/rs-6800273/v1/c0453a57e51abd4dd95c06f0.jpg"},{"id":86625660,"identity":"fb0baad0-2571-47c5-9f94-39bb6083b53e","added_by":"auto","created_at":"2025-07-14 05:13:00","extension":"jpg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":126414,"visible":true,"origin":"","legend":"\u003cp\u003eA) Nitric oxide levels in GC, DC and healthy controls; B) SDMA level in GC; C) Concentration of ADMA in µM/L; D) ADMA/SDMA ratio in three groups; E) MMP-7 concentration in GC and DC\u003c/p\u003e","description":"","filename":"Picture2.jpg","url":"https://assets-eu.researchsquare.com/files/rs-6800273/v1/aa3d2a9065848a9540cbb843.jpg"},{"id":86625661,"identity":"ffcf42e2-53bd-4c10-8f24-635feb8f5f7c","added_by":"auto","created_at":"2025-07-14 05:13:00","extension":"jpg","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":91095,"visible":true,"origin":"","legend":"\u003cp\u003eA) Amplification plot and Melt curve analysis of various grades of GC Furthermore PD-L1 mRNA expression have clearly shown in box plot; B) mRNA expression levels of PD-L1 in human gastric cancer and non-neoplastic mucosae. The median quartile of PD-L1 expression was \u0026gt;50% while the lower quartile was \u0026lt;15% and the upper quartile was not more than 35%; C) MMP 7 mRNA in GC and DC.\u003c/p\u003e","description":"","filename":"Picture3.jpg","url":"https://assets-eu.researchsquare.com/files/rs-6800273/v1/3d69db0479af46c5af947c12.jpg"},{"id":86627478,"identity":"ce6fe3ab-a769-4d47-b3db-94f4c41be96c","added_by":"auto","created_at":"2025-07-14 05:37:32","extension":"jpg","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":72840,"visible":true,"origin":"","legend":"\u003cp\u003eA) mRNA expression levels of CLDN-4 \u0026amp; 7 in human gastric cancer and non-neoplastic mucosae of disease control; B) mRNA expression levels of CLDN-18 in human gastric cancer and non-neoplastic mucosae of disease control.\u003c/p\u003e","description":"","filename":"Picture4.jpg","url":"https://assets-eu.researchsquare.com/files/rs-6800273/v1/f96f52fc6fa0f21e66035b8e.jpg"},{"id":86625678,"identity":"29e86c5e-eca5-46bc-a1d5-762d72540c10","added_by":"auto","created_at":"2025-07-14 05:13:01","extension":"jpg","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":197594,"visible":true,"origin":"","legend":"\u003cp\u003eA) Correlation of PD-L1 mRNA expression and ADMA /SDMA ratio in gastric cancer; B) Correlation of PD-L1 mRNA expression and ADMA level in gastric cancer; C) Correlation analysis between PD-L1 mRNA and NO level in Gastric cancer; D) Correlation analysis between MMP 7 concentration and PD-L1 in GC; E) Correlation analysis of MMP-7 mRNA expression and NO value. Green shade is for GC and Blue colour is for DC; F) correlation of PD-L1 mRNA expression and claudin 18 mRNA expression in gastric cancer samples; G) Correlation analysis between claudin 7 and PD-L1 mRNA expression; H) Correlation analysis between claudin 18 and claudin 4 mRNA expression; I) Correlation analysis between claudin 4 and PD-L1 mRNA expression; J) Correlation analysis between claudin 7 and claudin 18 mRNA.\u003c/p\u003e","description":"","filename":"Picture5.jpg","url":"https://assets-eu.researchsquare.com/files/rs-6800273/v1/9a1ab9f505d7357e772358ea.jpg"},{"id":88486382,"identity":"9bd881df-d857-41cf-be02-90c52dccf4a8","added_by":"auto","created_at":"2025-08-07 03:08:27","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1660663,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6800273/v1/bc6b3d4d-5377-4b57-bbe6-381a3d60d552.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Liaison of Dimethylated arginine between PDL1 and its ligand expressions in Gastric Cancer","fulltext":[{"header":"Highlights","content":"\u003cp\u003e► ADMA/SDMA ratio and claudin-4 mRNA expression were predominantly active at immune checkpoint sites, coordinating the progression of gastric cancer. MMP-7 mRNA and PD-L1 mRNA could be a possible way in the decay of mitochondrial DNA copy numbers, and ionised calcium was elevated in GC patients. \u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eWhat is already known about this subject?\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAnti-PDL-1 is currently being used in the management of various cancers.\u003c/p\u003e\n\u003cp\u003e►Extensive accumulation of CgA in gastric cancer patients.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e►ADMA is a catalytic source for the generation of excessive arginine.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eHow might it impact clinical practice in the foreseeable future?\u003c/strong\u003e\u003c/p\u003e\n\u003cul\u003e\n \u003cli\u003eADMA levels and PD-L1 expressions could be a regulatory determinant to provoke the progression of GC.\u003c/li\u003e\n \u003cli\u003eLower copy numbers of mitochondrial DNA (mtDNA) and the ADMA/SDMA ratio could be a novel approach for the dimethylation process in the severity of GC.\u0026nbsp;\u003c/li\u003e\n \u003cli\u003eInactivation of DDAH1 promotes abnormal behaviour of nitric oxide and disrupted ionised calcium channel, which may lead to GC.\u0026nbsp;\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003e\u003cstrong\u003eNovelty\u003c/strong\u003e:\u003c/p\u003e\n\u003cul\u003e\n \u003cli\u003eExcessive Nitric oxide production blocks the Ca\u003csup\u003e++\u003c/sup\u003e channel, and MMP-7 mRNA was overexpressed in GC. \u0026nbsp;\u003c/li\u003e\n \u003cli\u003eMitochondrial DNA copy number was significantly reduced in GC patients.\u003c/li\u003e\n \u003cli\u003eDecreased levels of ADMA are unable to activate DDAH1, an enzyme for the optimisation of arginine metabolism in GC.\u003c/li\u003e\n \u003cli\u003ePD-L1 activity decompensation occurs with the interaction of reactive nitrogen species (RNS) on the intra-entero lining of stomach cell walls that may disrupt the microenvironment in tissues/ adjacent cells due to the Ca++ permeability and subsequently decrease mitochondrial DNA copy numbers in GC. Abnormal Chromogranin A (CgA) and ADMA/SDMA ratio were significantly associated with PD-L1 in GC patients.\u003c/li\u003e\n\u003c/ul\u003e"},{"header":"Introduction","content":"\u003cp\u003eGastric cancer (GC) is the fourth most common oncological abnormality after breast, skin, and lung cancer\u003csup\u003e\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e\u003c/sup\u003e. Programmed cell death factor 1 (PD-1) and its ligand, i.e., PD-L1/PD-L2, are normally involved as a negative regulator in various tumors. Depending on overall survival rates, the PD-L1 expression is firmly associated with in-depth features, such as invasion and lymphatic metastasis, in cancer patients. Monoclonal PD-L1 antibody has been effectively reported in melanoma, non-small cell lung carcinoma and many other cancers. However, they are poorly defined in stomach-oriented tumors. PD-L1 mRNA is involved in various cancer cells and actively induces the host T cells\u0026rsquo; evasion\u003csup\u003e\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e\u003c/sup\u003e. Blockade of PD-1/PD-L1 in cancer cells has been extensively studied in anti-tumour immunity and tumor growth inhibition in cancer patients\u003csup\u003e\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e\u003c/sup\u003e. The regulation of PD-L1 expression remains largely unknown in GC. However, some studies have been reported on advanced-stage cancer, at the junctions of adenocarcinoma with immune system evasion, and on the checkpoints of immune-expressed molecules\u003csup\u003e\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e\u003c/sup\u003e. The trials of metabolic programming are recognized as a hallmark of cancer6 and progression due to the Warburg effect\u003csup\u003e\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e\u003c/sup\u003e. Though the targeted and non-targeted metabolic profiles at advanced-stage arginine are the key substrates involved repeatedly and dysregulated at varying rates across the types of cancer, they remain constant over a period in cancer. Either they reflect in a heterogeneous manner or progress towards the end stage of cancer \u003csup\u003e\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e,\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e\u003c/sup\u003e. Nitriginic enzymes have been frequently associated with reactive nitrogen species and dysregulate the nitric oxide behaviour depending on ADMA or SDMA concentration in various cancers\u003csup\u003e\u003cspan additionalcitationids=\"CR11 CR12\" citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e\u003c/sup\u003e. However, the nitrigenic pathway is not yet clear, and its role is still ambiguous\u003csup\u003e\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e\u003c/sup\u003e in gastric cancer. Arginine is, however, more competitive to immune enhancement and restricted to cancer cell progression at the metastatic state in later advanced stages\u003csup\u003e\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e\u003c/sup\u003e. However, the precise role of arginine is still not clear in gastric cancer\u003csup\u003e\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e,\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e\u003c/sup\u003e. The competitive ways of arginine utilization during NO synthesis in cancers\u003csup\u003e\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e\u003c/sup\u003e. It is still not yet proven that NO inhibition is under debate and dimethylation of arginine synthesis, while derivatives of arginine, i.e. asymmetric, symmetric dimethylarginines (ADMA and SDMA) and protein arginine methyltransferases (PRMTs), are actively involved in various cancers\u003csup\u003e\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e,\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e\u003c/sup\u003e. Asymmetric dimethylarginine is usually metabolized to citrulline and dimethylamine (DMA) in the presence of dimethylarginine dimethyl amino-hydrolases (DDAHs)\u003csup\u003e\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e\u003c/sup\u003e through numerous derivatives that are associated with inflammatory sites and dysregulated nitric oxides and DDAH1 activity in cancer patients. Studies have shown arginine methylation of cellular proteins actively interacting with the abnormal influx of ADMA in different types of cells, such as endothelial and smooth muscle cells, Endogenous L-arginine, asymmetric dimethylarginine (ADMA), and SDMA in gastric mucosal cells\u003csup\u003e\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e,\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e\u003c/sup\u003e. This growing evidence is still inadequate and unclear on the accumulation of ADMA and its bioavailability in gastric cancer cells/tissues. Mitochondrial functioning and ionization of calcium irregularities have been linked in several cancers. Hence, intracellular calcium is needed to support an efficient repair or reinforcement of tight junctional proteins. Tight junctional proteins such as Claudin (s), ZO1, and Zonulin are localized to apical regions, and their expressions have a prognostic role in some cancer patients\u003csup\u003e\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e\u003c/sup\u003e. Various tight junctional proteins (Claudin (s), ZO1, and Zonulin) are adequately localised to apical regions, where their remodelling mechanism remains unclear. Moreover, mitochondrial DNA copy number (mtDNA-CN) alterations have been reported in cancers and are being applied for the assessment of cancer progression\u003csup\u003e\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e\u003c/sup\u003e. The Pathomical pathway of Gastric cancer in respect to PD-L1, MMP-7 and ROS has been shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e. In this study, we tried to connect in detail the TJ proteins (Claudin 4,7,18) and mtDNA-CN variations along with NO synthesis, ADMA, SDMA, PD-L1, and MMP-7 mRNA, which could provide pathogenic insights and potential therapeutic molecules for the assessment of the early stages of GC.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e"},{"header":"Results","content":"\u003cp\u003eAll patients were newly diagnosed with gastric carcinoma on histopathological examination and endoscopic procedure in the department of Gastroenterology, AIIMS, New Delhi, India. The demographic profile of all groups based on the inclusion and exclusion criteria of subjects has been categorised in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e. The clinical, biochemical, and haematological examinations were done in all the groups of patients, as shown in Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e. The adjacent tissues from the stomach of GC patients were also collected, as well as the tissues from the diseased control, for histopathological examination.\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eDemographic Profile of Gastric Cancer and Disease Control\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=\"left\" 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\u003eSr No\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eCharacteristics\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eGastric cancer (n\u0026thinsp;=\u0026thinsp;25)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eDisease Control (n\u0026thinsp;=\u0026thinsp;30)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003eHealthy control (n\u0026thinsp;=\u0026thinsp;20)\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eAge (years)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e56.2\u0026thinsp;\u0026plusmn;\u0026thinsp;8.8\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e45.9\u0026thinsp;\u0026plusmn;\u0026thinsp;10.8\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e31.3\u0026thinsp;\u0026plusmn;\u0026thinsp;6.5\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eMale\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e20 (75%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e18 (60%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e10 (50%)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eFemale\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e5(25%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e12 (40%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e10 (50%)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eDiarrhoea\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e3 (15%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e2 (6.6%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eNone\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eConstipation\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e2 (10%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e6(20%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e2 (10%)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e6\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003ePain abdomen\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e20 (100%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e12 (40%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eNone\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e7\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eNausea\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e4 (20%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e4 (13.3%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eNone\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e8\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eJoint pain\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e2 (10%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e9 (30%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eNone\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e9\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003ePallor\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e6 (30%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e7 (23.3%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eNone\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e10\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eFatigue\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e7 (35%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e9 (30%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eNone\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e11\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eHeart burn\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e18 (90%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e16 (53.3%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eNone\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e12\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eWeight loss\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e8 (40%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e3 (10%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eNone\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\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\u003eBaseline Characteristics of study groups Hematological and Biochemical profile:\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=\"left\" 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\u003eClinical parameters\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eGC (n\u0026thinsp;=\u0026thinsp;25)\u003c/p\u003e\u003cp\u003eMean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eDC (n\u0026thinsp;=\u0026thinsp;30)\u003c/p\u003e\u003cp\u003eMean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eHealthy Control (n\u0026thinsp;=\u0026thinsp;20)\u003c/p\u003e\u003cp\u003eMean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003eP values\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"5\" nameend=\"c5\" namest=\"c1\"\u003e\u003cp\u003eHematological Profile\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eHeamoglobin (g/dl)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e11.56\u0026thinsp;\u0026plusmn;\u0026thinsp;1.66\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e12.5\u0026thinsp;\u0026plusmn;\u0026thinsp;1.0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e12.9\u0026thinsp;\u0026plusmn;\u0026thinsp;2.1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.007\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eHematocrit %\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e33.15\u0026thinsp;\u0026plusmn;\u0026thinsp;5.03\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e39\u0026thinsp;\u0026plusmn;\u0026thinsp;3.2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e40.5\u0026thinsp;\u0026plusmn;\u0026thinsp;5.8\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.004\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMCV (fL)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e85.34\u0026thinsp;\u0026plusmn;\u0026thinsp;5.45\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e84.7\u0026thinsp;\u0026plusmn;\u0026thinsp;4.0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e83.9\u0026thinsp;\u0026plusmn;\u0026thinsp;9.6\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.667\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMCHC (g/dl)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e31.8\u0026thinsp;\u0026plusmn;\u0026thinsp;1.37\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e33.4\u0026thinsp;\u0026plusmn;\u0026thinsp;1.8\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e31.9\u0026thinsp;\u0026plusmn;\u0026thinsp;1.6\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.773\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eTotal leukocyte count (10\u003csup\u003e3\u003c/sup\u003e/mm)\u003csup\u003e3\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e7.13\u0026thinsp;\u0026plusmn;\u0026thinsp;1.27\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e6.8\u0026thinsp;\u0026plusmn;\u0026thinsp;1.3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e7.2\u0026thinsp;\u0026plusmn;\u0026thinsp;2.1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.276\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePlatelets (10\u003csup\u003e3\u003c/sup\u003e/mm\u003csup\u003e3\u003c/sup\u003e)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e170\u0026thinsp;\u0026plusmn;\u0026thinsp;79.13\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e189.1\u0026thinsp;\u0026plusmn;\u0026thinsp;50.3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e206.9\u0026thinsp;\u0026plusmn;\u0026thinsp;60.6\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.002\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eESR\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e26.5\u0026thinsp;\u0026plusmn;\u0026thinsp;8.30\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e16.2\u0026thinsp;\u0026plusmn;\u0026thinsp;5.1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e15.1\u0026thinsp;\u0026plusmn;\u0026thinsp;11\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"5\" nameend=\"c5\" namest=\"c1\"\u003e\u003cp\u003eBiochemical Profile\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSerum urea (mg%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e26.27\u0026thinsp;\u0026plusmn;\u0026thinsp;8.19\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e24.7\u0026thinsp;\u0026plusmn;\u0026thinsp;3.9\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e22.7\u0026thinsp;\u0026plusmn;\u0026thinsp;5.6\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.318\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCreatinine (mg%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.82\u0026thinsp;\u0026plusmn;\u0026thinsp;0.2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.81\u0026thinsp;\u0026plusmn;\u0026thinsp;0.1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.74\u0026thinsp;\u0026plusmn;\u0026thinsp;0.2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.343\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCalcium (mg%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e8.86\u0026thinsp;\u0026plusmn;\u0026thinsp;0.49\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e8.9\u0026thinsp;\u0026plusmn;\u0026thinsp;0.5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e9.2\u0026thinsp;\u0026plusmn;\u0026thinsp;0.3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.047\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eBilirubin (mg%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.6\u0026thinsp;\u0026plusmn;\u0026thinsp;0.2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.7\u0026thinsp;\u0026plusmn;\u0026thinsp;0.2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.7\u0026thinsp;\u0026plusmn;\u0026thinsp;0.5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.42\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eTotal protein (gm%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e6.86\u0026thinsp;\u0026plusmn;\u0026thinsp;0.55\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e7.8\u0026thinsp;\u0026plusmn;\u0026thinsp;0.4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e7.3\u0026thinsp;\u0026plusmn;\u0026thinsp;0.3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.00\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eALT (IU/L)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e26.36\u0026thinsp;\u0026plusmn;\u0026thinsp;12.45\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e27.6\u0026thinsp;\u0026plusmn;\u0026thinsp;5.9\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e27.3\u0026thinsp;\u0026plusmn;\u0026thinsp;19\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.47\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAST (IU/L)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e24.81\u0026thinsp;\u0026plusmn;\u0026thinsp;6.00\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e24.9\u0026thinsp;\u0026plusmn;\u0026thinsp;8.1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e25.3\u0026thinsp;\u0026plusmn;\u0026thinsp;9.3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.799\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eALP (IU/L)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e227.9\u0026thinsp;\u0026plusmn;\u0026thinsp;84.98\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e232.7\u0026thinsp;\u0026plusmn;\u0026thinsp;38.3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e237.5\u0026thinsp;\u0026plusmn;\u0026thinsp;37.1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.767\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"5\" nameend=\"c5\" namest=\"c1\"\u003e\u003cp\u003ePathomic Profile\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCgA (ng/ml)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1008\u0026thinsp;\u0026plusmn;\u0026thinsp;374.2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e111.2\u0026thinsp;\u0026plusmn;\u0026thinsp;42.8\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e28.7\u0026thinsp;\u0026plusmn;\u0026thinsp;12.5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.003\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMMP-7 (pg/ml)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1643.1\u0026thinsp;\u0026plusmn;\u0026thinsp;2113.9\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e131.7\u0026thinsp;\u0026plusmn;\u0026thinsp;69.4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e16.62\u0026thinsp;\u0026plusmn;\u0026thinsp;8.62\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eArginase activity (nM/min/mg)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e126\u0026thinsp;\u0026plusmn;\u0026thinsp;44.62\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e42\u0026thinsp;\u0026plusmn;\u0026thinsp;16.6\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e39.2\u0026thinsp;\u0026plusmn;\u0026thinsp;21.84\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.0003\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eDDAH 1 (pg/ml)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e460\u0026thinsp;\u0026plusmn;\u0026thinsp;218.2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e188\u0026thinsp;\u0026plusmn;\u0026thinsp;106\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e128.5\u0026thinsp;\u0026plusmn;\u0026thinsp;96.6\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.0001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNO (\u0026micro;g/ml)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e19.2\u0026thinsp;\u0026plusmn;\u0026thinsp;8.5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e8.5\u0026thinsp;\u0026plusmn;\u0026thinsp;1.4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e7.1\u0026thinsp;\u0026plusmn;\u0026thinsp;1.4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e\u003ch2\u003ei) Correlation of NO levels correlates with poor prognosis of patients with GC:\u003c/h2\u003e\u003cp\u003eNitric oxide levels were significantly higher in GC comparatively disease control and healthy subjects. Serum NO levels were higher in GC patients, as shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e(A).\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003c/div\u003e\n\u003ch3\u003eii) eNOS-mediated NO production in GC\u003c/h3\u003e\n\u003cp\u003eFunctional role of NO was assessed in GC patients with the eNOS mRNA expression in 25 GC tissues and adjacent gastric mucosal tissues. eNOS mRNA was significantly down-regulated in tissues of GC patients.\u003c/p\u003e\n\u003ch3\u003eiii) ADMA and SDMA mediate NOs expression in GC:\u003c/h3\u003e\n\u003cp\u003eADMA levels were significantly lower in GC as compared with disease control and healthy control; perhaps no significant difference was seen between disease controls and healthy control, as shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e(B). SDMA levels were significantly lower in GC as compared with disease control and healthy control, as shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e(C). The pairwise comparison of the ADMA and SDMA ratio was 21% lower in gastric cancer as compared to disease control, as shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e(D). Furthermore, median serum MMP-7 level in GC was [697.3 (IQR 185.85-2689.1)] and [114.15(IQR 92.6-131.47)] in GC and DC, respectively, as shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e(E), and they were statistically significant (p\u0026thinsp;\u0026lt;\u0026thinsp;0.05). The serum levels of MMP-7 were 131.7\u0026thinsp;\u0026plusmn;\u0026thinsp;69.4 pg/ml in DC and 1643.1\u0026thinsp;\u0026plusmn;\u0026thinsp;2113.9 pg/ml in gastric cancer, which was statistically significant (p\u0026thinsp;\u0026lt;\u0026thinsp;0.001). The functional role of PD-L1 was higher depending on the severity of the tumour stages in GC. PD-L1 was highly expressed in the (Grade III and IV) stage of GC patients than in lower grades of gastric cancer, i.e., less than grade II in GC patients. PD-L1 showed significantly higher expressions in grade III and grade IV of tumours than in lower grades of tumours (p\u0026thinsp;\u0026lt;\u0026thinsp;0.050, p\u0026thinsp;\u0026lt;\u0026thinsp;0.010, respectively), as shown in the amplification plot as well as in the melt curve (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e(A)). The overall 3.6-fold change of PD-L1 expression was observed in GC patients. The amplification profile at the grade-wise level is shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e(A). The GAPDH was used as an internal control.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\n\u003ch3\u003ePD-L1 expression in human gastric carcinoma tissues and non-neoplastic mucosae\u003c/h3\u003e\n\u003cp\u003eThe mRNA expression of PD-L1 was 6.22\u0026thinsp;\u0026plusmn;\u0026thinsp;5.2 in GC, while the mean relative expression was 4.08\u0026thinsp;\u0026plusmn;\u0026thinsp;8.17 in adjacent non-neoplastic tissue (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e(B)). The significant (p\u0026thinsp;\u0026lt;\u0026thinsp;0.05) expression of MMP-7 mRNA was exceedingly higher in GC (n\u0026thinsp;=\u0026thinsp;19) as compared with DC (n\u0026thinsp;=\u0026thinsp;17), while suboptimal expression of MMP-7 mRNA was seen in 6 patients with GC and 13 patients with disease control. The relative expression of MMP-7 mRNA was observed as 4.37\u0026thinsp;\u0026plusmn;\u0026thinsp;8.16 in GC, and the mean expression values were 0.06\u0026thinsp;\u0026plusmn;\u0026thinsp;0.14 in DC. The observed delta Ct difference values were 12.82\u0026thinsp;\u0026plusmn;\u0026thinsp;2.53 in disease control and 6.50\u0026thinsp;\u0026plusmn;\u0026thinsp;3.14 in gastric cancer. Therefore, up-regulation of MMP-7 mRNA expression was positively associated in Gastric cancer patients as shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e(C).\u003c/p\u003e\n\u003ch3\u003eClaudin-4, 7 and 18 mRNA expressions in Gastric cancer and Disease control\u003c/h3\u003e\n\u003cp\u003eClaudins are the backbone of tight junctions and play a key role in barrier activities and permeability of small molecules and ions. The differential expression of Claudin-4 mRNA was overexpressed in GC compared to DC (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e(A)). However, the non-significant (p\u0026thinsp;\u0026lt;\u0026thinsp;0.54) expression of Claudin-18 was found in GC as compared with DC (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e(B)).\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cdiv id=\"Sec8\" class=\"Section2\"\u003e\u003ch2\u003eArginase enhances Chromogranin (CgA) in GC through DDAH activity\u003c/h2\u003e\u003cp\u003eIn conjunction with arginase activity, Chromogranin A was significantly higher in GC patients as compared to disease control, as well as from healthy subjects (p\u0026thinsp;\u0026lt;\u0026thinsp;0.001), while DDAH1 activities were lowered in GC patients, as shown in Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e.\u003c/p\u003e\u003cp\u003eThe relative expression of different genes in Gastric cancer and Disease control patients was found as 50th percentile (min-max) as shown in Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e(A). The Mitochondrial DNA copy number is shown in Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e(B) (gastric cancer and disease control patients).\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\u003eA) Relative expression of different genes; B) Mitochondrial DNA copy number in Gastric cancer and Disease control patients.\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=\"char\" char=\".\" 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=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eTarget Gene\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eGC\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eDC\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003ep value\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"5\" rowspan=\"6\"\u003e\u003cp\u003e\u003cb\u003eA\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eClaudin-4 mRNA\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e9.89 (0.39\u0026ndash;67.18)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.70 (0.04\u0026ndash;6.96)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.005\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eClaudin-7 mRNA\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.271 (0.02\u0026ndash;8.93)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e1.02 (0.02\u0026ndash;7.41)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.64\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eClaudin-18 mRNA\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.76 (0.02\u0026ndash;1.27)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.77 (0.07\u0026ndash;1.23)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.54\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003ePD-L1 mRNA\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e4.11 (0.03\u0026ndash;16.91)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.73 (0.11\u0026ndash;32.22)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.25\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eMMP 7 mRNA\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.450 (0.001\u0026ndash;3.29)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.003 (0.0002-0.18)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.04\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eeNOS mRNA\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.02 (0.004\u0026ndash;1.25)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e2.03 (0.09\u0026ndash;15.90)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.05\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eB\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003emtDNA copy number\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e60.40\u0026thinsp;\u0026plusmn;\u0026thinsp;30.43\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e80.57\u0026thinsp;\u0026plusmn;\u0026thinsp;30.85\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.05\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\n\u003ch3\u003eCorrelation of PD-L1 with other factors in Gastric Cancer\u003c/h3\u003e\n\u003cp\u003eADMA/SDMA ratio and PD-L1 correlation had positive effects in GC patients (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eA). The significance (p\u0026thinsp;\u0026lt;\u0026thinsp;0.0001). Similarly, ADMA and PD-L1 correlation had positive effects in GC patients (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e(B)). Although a decreasing pattern of ADMA and increasing expressions of PD-L1 mRNA had an antagonistic effect in GC. Furthermore, the correlation analysis between PD-L1 and NO was found to be positively associated in GC patients (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e(C)). The positive correlations were observed between MMP-7 and PD-L1, as shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e(D).\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eCorrelation studies of NO and MMP-7 were positively associated with each other in gastric cancer patients (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e(E)). MMP7 mRNA was superficially expressed in GC, which was more than 3-fold above that of the disease controls, while a borderline difference was seen in healthy controls. Claudin-4, claudin-7 and claudin-18 mRNA expressions were correlated (r=-0.08) (r\u0026thinsp;=\u0026thinsp;0.24) (r=-0.25) respectively and shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eI, \u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eG, and \u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eF with PD-L1 mRNA expression. A positive correlation was observed between Claudin-7 and PD-L1, Claudin-4 and Claudin-18 (r\u0026thinsp;=\u0026thinsp;0.22), Claudin-4 and PD-L1 (r=-0.25), and Claudin-7 and Claudin-18 r\u0026thinsp;=\u0026thinsp;0.75 were strongly correlated in GC, as shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e(G), 5(H), 5(I) and 5(J) respectively.\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eGC represents one of the most common malignancies worldwide, ranking fourth after lung cancer, breast cancer and colorectal cancer. Even, it is still the second most common cause of death due to gastric cancer. It is attributed to changes in lifestyle, higher consumption of tobacco or smoking, alcohol, and increased stress levels in the population\u003csup\u003e\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e\u003c/sup\u003e. Our study explains that PD-L1 expression was more highly pronounced in higher-grade tumours, i.e., grade III and IV, than in lower-grade tumours in patients. PD-L1 is a coregulatory ligand highly expressed depending upon the severity of inflamed tissues at the lining of stomach cells, which is responsible for inhibiting immune responses in GC. These inhibitory responses rely on binding surface T lymphocytes and inducing T cells to process specific apoptosis mechanisms. In this way, tumour cells are capable of upregulating PD-L1 expression by activation of inhibitory signals. Similar observations on PDL1 expressions have already been reported in various cancers, including breast, ovarian, pancreatic, oesophageal, colorectal and gastric cancer\u003csup\u003e\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e,\u003cspan additionalcitationids=\"CR27 CR28\" citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e\u003c/sup\u003e. PDL1 mRNA expression has also been reported to be associated with poor prognosis in GC patients.\u003c/p\u003e\u003cp\u003eMMP-7 mRNA expressions were associated more than two-fold with the severity of GC, while other studies have shown no association in GC\u003csup\u003e\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e\u003c/sup\u003e. Interaction analysis between MMP-7 mRNA and ADMA/SDMA ratio has revealed a similar opinion in the progression of GC as we have observed in this study. We have observed a strong association with the severity of disease and higher grades of GC tumors. These associations suggest that interactions between ADMA regulation, metabolism, export and import could be a critical determinant of intracellular levels of ADMA and the NOS substrate, as well as L-Arginine availability. These functional defects could be accompanied by increased total body NO generation, decreased circulating levels of ADMA and indices of reactive nitrogen species (RNS), and occur due to impaired endothelial uptake of L-arginine. We found that increased NO production was present in gastric cancer patients, which could relatively alter the oxidant and antioxidant homeostatic status. Since NO has a dual role in disease progression at higher concentrations for long durations and releases peroxinitrite that directly or indirectly damages DNA, leading to mutations as well as progression in gastric cancer. Moreover, the ADMA/SDMA ratio was found to be significantly associated with MMP-7 mRNA expression, as compared between GC and HC, but no difference was found in DC subjects. Similarly, serum MMP-7 were significantly raised in GC patients as compared with DC and HC. The loss of the TJ structure caused by aberrant expression of claudin proteins suggested that there is a diffusion process of nutrient absorption along with other associated factors that could be involved in the survival and proliferation of cancer cells. The present study inferred that the expression of claudin 4, 7 and 18 was altered in GC and DC patients, and their expression was associated with metastasis.\u003c/p\u003e\u003cdiv id=\"Sec11\" class=\"Section2\"\u003e\u003ch2\u003eDDAH1 accelerates the GC malignant process by upregulating the PD-L1 expression:\u003c/h2\u003e\u003cp\u003eIncreased level of DDAH1 suppresses the intracellular ADMA by the activation of eNOS expressions, which simultaneously accelerates PD-L1 activity at the inflammatory sites. PD-L1 levels were significantly higher in GC patients. A significant correlation was seen with increasing severity in the Grades of the GC patient sample grade III. The significant decrease of ADMA levels in GC patients and the increased level of NO; however, the exact mechanism is still under way in the progression of GC.\u003c/p\u003e\u003cp\u003eADMA levels were significantly decreased by the upregulation of iNOS mRNA expression in GC. Mechanistically, DDAH1-mediated NO activation is actively involved in the regulation of arginase activity in GC. Arginine is converted to polyamines and induces matrix metalloproteinases for removing the physical barrier that stops tumor cells from invasions. On the other hand, with the coordination of the ADMA/SDMA ratio, the PDL-1 is overexpressed, mediated by MMP-7 mRNA, with the active involvement of claudin-4 and claudin-18, which leads to the severity of GC.\u003c/p\u003e\u003cp\u003eOverall, disrupted arginine dimethylation, mitochondrial dysfunction, and PD-1/PDL-1 signalling underscore a complex interplay and disrupt vascular regulation, angiogenesis and immune evasion mechanisms in GC. Claudin-4, MMP-7, and PD-L1 mRNA, alongside ADMA and mtDNA levels, offer promising avenues for developing novel diagnostic and therapeutic strategies for GC.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec12\" class=\"Section2\"\u003e\u003ch2\u003eLimitations:\u003c/h2\u003e\u003cp\u003eInsufficient information was available on the nature of intratumor heterogeneity and sampling issues while collecting biopsy specimens. Most patients were visiting the OPD in the end-stage or chronic stage, during a new diagnosis of GC. While recruiting patients for the series, we considered only cases with at least two biopsy specimens of different lesions (average number of biopsies per lesion: 1\u0026ndash;2) due to ethical constraints to avoid multiple biopsy procedures in GC patients. As per national and international guidelines, this number (biopsies/tissue pieces) was reduced for evaluation in GC/GEC. Moreover, it was difficult for small preinvasive lesions to be taken into routine diagnosis for patient care. Thus, PD-L1 intratumor heterogeneity should be investigated in future studies considering surgical series of GC/GEC.\u003c/p\u003e\u003c/div\u003e"},{"header":"Materials and methods","content":"\u003cdiv id=\"Sec14\" class=\"Section2\"\u003e\u003ch2\u003ePatient recruitments:\u003c/h2\u003e\u003cp\u003eThe recruitment of patients was done in the department of Gastroenterology and Dr BRA IRCH, AIIMS, New Delhi, at the time of total or subtotal Gastrectomy from 2016\u0026ndash;2019, who underwent the endoscopic procedure for gastric cancer diagnosis. Blood and tissue samples were collected from the OPDs and endoscopy lab for molecular and biochemical tests, and samples were stored at -80\u0026deg;C until analysis. Histologic evaluation was done based on Laurens' classification 31 for tumor grades and diagnosis of patients. The study was approved by the AIIMS ethics committee (IECPG/88/30.12.2015, RT-11/27.01.2016, dated 29.01.2016), and informed consents were obtained from all subjects who had participated in the study as per the Declaration of Helsinki. Biopsies were collected from the targeted regions and also from adjacent localized regions of the same patient. Stomach biopsies were also collected in disease control patients, such as dyspepsia. All procedures were followed as per the standard outlined in the protocol declaration. Histological evaluation of all biopsies was done for categorization of GC stages. Of these, 78% of biopsies were moderately well differentiated, and 8% were designated signet ring cell types. Overall, 36% of patients were diagnosed with low-grade cancer (grades I and II), and 64% of patients were diagnosed with high-grade cancer (grades III and IV). The age range of all recruited patients was 36\u0026ndash;62 years.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec15\" class=\"Section2\"\u003e\u003ch2\u003eArginase activity\u003c/h2\u003e\u003cp\u003eArginase activity in the tissue lysate was measured by the spectrophotometric method of an intermediate product formed from the arginase reaction with the arginine in accordance with the manufacturer\u0026rsquo;s instructions of a photometric kit (ab 180877), eLab Science USA. All tissue samples were processed as per the protocol defined for extraction and in triplicate. The reaction complex was measured at 570 nm in kinetic mode for 30 min at 37\u0026deg;C using a Cary 100 spectrophotometer (Agilent, USA). The measured activities were represented in terms of protein content (mg).\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec16\" class=\"Section2\"\u003e\u003ch2\u003eRNA extraction, cDNA synthesis and Q-PCR\u003c/h2\u003e\u003cp\u003eRNA was extracted from frozen specimens using TRIzol TM reagent (Thermo Fisher Scientific, Inc., Waltham, MA, USA) according to the manufacturer\u0026rsquo;s protocol. The RNA concentration was measured in a Nanodrop ND 1000 spectrophotometer (Nanodrop Technologies). The isolated RNA was aliquoted and stored at -80\u0026deg;C until use.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec17\" class=\"Section2\"\u003e\u003ch2\u003ecDNA Synthesis\u003c/h2\u003e\u003cp\u003eTotal RNA (5\u0026micro;g) was reverse transcribed using 1\u0026micro;M random primer, 200/U/\u0026micro;l of Superscript II reverse transcriptase (Thermo Fisher Scientific), 1\u0026micro;l of Ribolock RNase inhibitor (20U/\u0026micro;l) and 2\u0026micro;l of 10 mM dNTP (Thermo Fisher Scientific) for cDNA preparation in a total volume of 20\u0026micro;l. The reaction mixture was kept at 42\u0026deg;C for one hour, and then the reaction was terminated at 72\u0026deg;C by keeping it for 5 minutes. Real-time PCR (Agilent Technologies, CA, USA) was done using SYBR chemistry with the following primer pairs designed from Beacon Designer 5.1 Software (Premier Biosoft, Palo Alto, CA) for each target gene, as shown in Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e, and were synthesised by IDT, Canada. The primer sequence of PD-L1 (F-5\u0026prime;-CCAAGGCGCAGATCAAAGAGA-3\u0026prime;; R-5\u0026prime;-AGGACCCAGACTAGCAGCA-3\u0026prime;), MMP-7 (F-5\u0026prime;-CATTTGATGGGCCAGGAAAAC-3\u0026prime;; R- 5\u0026prime;-GCAGCATACAGGAAGTTAATCC-3\u0026prime;), eNOS (F-5\u0026prime;-CGGCATCACCAGGAAGAAGA-3\u0026prime;; R-5\u0026prime;- CATGAGCGAGGCGGAGAT-3\u0026prime;), Claudin-4 (F-5\u0026prime;-AGCTCTGTGGCCTCAGGACTCT-3\u0026prime;; R-5\u0026prime;-CTCTTCTTAAATTACAA-3\u0026prime;), Claudin-7 (F-5\u0026prime;-ATGGCCAACTCGGGCCTGCAACTG-3\u0026prime;; R-5\u0026prime;-AGTGATGAATAGTC ACACGTATTCCTTGGAGGAATT-3\u0026prime;), Claudin-18 (F-5\u0026prime;CGGGCGGCCAGGATCATGTC-3\u0026prime;; R- 5\u0026prime;- ACTGCCTGCAGCATGGCTGG-3\u0026prime;), mtDNA (F- 5\u0026prime;-TGGCCATGGGTATGTTG TTA-3\u0026prime;;R-5\u0026prime;-TCTCTGCTCCCCACCTCTAAGT-3\u0026prime;),GAPDH (F-5\u0026rsquo;-ACAGTCAGCCGCAT CTTC \u0026minus;\u0026thinsp;3\u0026rsquo;; R-5\u0026rsquo;-GCCCAATACGACCAAATC-3\u0026rsquo;).\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec18\" class=\"Section2\"\u003e\u003ch2\u003e\u003cb\u003ePD-L1 mRNA expression\u003c/b\u003e:\u003c/h2\u003e\u003cp\u003eThe 20\u0026micro;l reaction mixture was prepared using 4\u0026micro;l of cDNA, 10\u0026micro;l of 2X Sybr PCR master mix (Promega, USA), 1\u0026micro;l of forward primer (10 pM) and reverse primer (10 pM) each. The thermal condition for Q-PCR was as 94\u0026deg;C for 5 min, 1 cycle; at 94\u0026deg;C for 30 sec, at 52\u0026deg;C for 30 sec, 72\u0026deg;C for 20 sec 40 cycles and data collection at 94\u0026deg;C for 15 sec, at 60\u0026deg;C for 20 sec, 94\u0026deg;C for 15 sec one cycles. The reaction was carried out in a Q-PCR system (Agilent Aria Mx, US) for PD-L1 mRNA gene amplification.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec19\" class=\"Section2\"\u003e\u003ch2\u003e\u003cb\u003eMMP-7 mRNA expression\u003c/b\u003e:\u003c/h2\u003e\u003cp\u003eThe 20\u0026micro;l reaction mixture was prepared using 4\u0026micro;l of cDNA, 10\u0026micro;l of 2X Sybr PCR master mix (Promega, USA), 1\u0026micro;l of forward primer (10 pM) and reverse primer (10 pM) each. The thermal condition for Q-PCR was as 94\u0026deg;C for 5 min, 1 cycle; at 94\u0026deg;C for 30 sec, at 52\u0026deg;C for 30 sec, 72\u0026deg;C for 20 sec 40 cycles \u0026amp; data collection at 94\u0026deg;C for 15 sec, at 60\u0026deg;C for 20 sec, 94\u0026deg;C for 15 sec one cycles. The reaction was carried out in a Q-PCR system (Agilent Aria Mx) for PD-L1 mRNA gene amplification.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec20\" class=\"Section2\"\u003e\u003ch2\u003e\u003cb\u003eClaudin-4 mRNA expression\u003c/b\u003e:\u003c/h2\u003e\u003cp\u003eThe 20\u0026micro;l reaction mixture was prepared using 4\u0026micro;l of cDNA, 10\u0026micro;l of 2X Sybr PCR master mix, 1\u0026micro;l of forward primer (10 pM) and reverse primer (10 pM) each. The thermal condition for Q-PCR was as 94\u0026deg;C for 5 min, 1 cycle; at 94\u0026deg;C for 30 sec, at 52\u0026deg;C for 30 sec, 72\u0026deg;C for 20 sec 40 cycles \u0026amp; data collection at 94\u0026deg;C for 15 sec, at 60\u0026deg;C for 20 sec, 94\u0026deg;C for 15 sec one cycles. The reaction was carried out in a Q-PCR system (Agilent Aria Mx) for PD-L1 mRNA gene amplification.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec21\" class=\"Section2\"\u003e\u003ch2\u003e\u003cb\u003eClaudin-7 mRNA expression\u003c/b\u003e:\u003c/h2\u003e\u003cp\u003eThe 20\u0026micro;l reaction mixture was prepared using 4\u0026micro;l of cDNA, 10\u0026micro;l of 2X Sybr PCR master mix, 1\u0026micro;l of forward primer (10 pM) and reverse primer (10 pM) each. The thermal condition for Q-PCR was as 94\u0026deg;C for 5 min, 1 cycle; at 94\u0026deg;C for 30 sec, at 52\u0026deg;C for 30 sec, 72\u0026deg;C for 20 sec 40 cycles \u0026amp; data collection at 94\u0026deg;C for 15 sec, at 60\u0026deg;C for 20 sec, 94\u0026deg;C for 15 sec one cycles. The reaction was carried out in a Q-PCR system (Agilent Aria Mx, US) for PD-L1 mRNA gene amplification.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec22\" class=\"Section2\"\u003e\u003ch2\u003e\u003cb\u003eClaudin-18 mRNA expression\u003c/b\u003e:\u003c/h2\u003e\u003cp\u003eThe 20\u0026micro;l reaction mixture was prepared using 4\u0026micro;l of cDNA, 10\u0026micro;l of 2X Sybr PCR master mix (Promega, USA), 1\u0026micro;l of forward primer (10 pM) and reverse primer (10 pM) each. The thermal condition for Q-PCR was as 94\u0026deg;C for 5 min, 1 cycle; at 94\u0026deg;C for 30 sec, at 56\u0026deg;C for 30 sec, 72\u0026deg;C for 20 sec 40 cycles \u0026amp; data collection at 94\u0026deg;C for 15 sec, at 60\u0026deg;C for 20 sec, 94\u0026deg;C for 15 sec one cycles. The reaction was carried out in a Q-PCR system (Agilent Aria Mx, US) for PD-L1 mRNA gene amplification.\u003c/p\u003e\u003cdiv id=\"Sec23\" class=\"Section3\"\u003e\u003ch2\u003e\u003cb\u003eGAPDH mRNA expression\u003c/b\u003e:\u003c/h2\u003e\u003cp\u003eThe housekeeping gene GAPDH was used as an internal control for amplification. The 20\u0026micro;l reaction mixture was prepared using 4\u0026micro;l of cDNA, 10\u0026micro;l of 2X Sybr PCR master mix, 1\u0026micro;l of forward primer (10 pM) and reverse primer (10 pM) each. The thermal condition for Q-PCR was as 94\u0026deg;C for 5 min, 1 cycle; at 94\u0026deg;C for 30 sec, at 52\u0026deg;C for 30 sec, 72\u0026deg;C for 20 sec 40 cycles \u0026amp; data collection at 94\u0026deg;C for 15 sec, at 60\u0026deg;C for 20 sec, 94\u0026deg;C for 15 sec one cycles. The reaction was carried out in a Q-PCR system (Agilent Aria Mx) for PD-L1 mRNA gene amplification.\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv id=\"Sec24\" class=\"Section2\"\u003e\u003ch2\u003emtDNA copy number:\u003c/h2\u003e\u003cp\u003eFor mtDNA copy number, DNA was extracted using the salt extraction method \u003csup\u003e\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e\u003c/sup\u003e. Real-time reaction was done using the SYBR green chemistry for the amplification. The reaction mix was prepared in a total of 20\u0026micro;l reaction, which includes gene-specific forward and reverse primers (5 pM), 10\u0026micro;l SYBR mix (2X), template up to 500 ng and nuclease-free water was added to adjust the volume. β-Globin and nuclear DNA genes were used as an internal control for mitochondrial DNA copy numbers. The thermal condition for Q-PCR was as follows: 94\u0026deg;C for 10 min, 1 cycle; at 94\u0026deg;C for 30 sec, at 52\u0026deg;C for 30 sec, 40 cycles \u0026amp; data collection at 94\u0026deg;C for 15 sec, at 60\u0026deg;C for 20 sec, 94\u0026deg;C for 15 sec, one cycle. The amplified product was quantified, results were calculated in terms of copy number (mtDNA)\u003csup\u003e\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e\u003cdiv id=\"Sec25\" class=\"Section3\"\u003e\u003ch2\u003eADMA and SDMA assay:\u003c/h2\u003e\u003cp\u003ePlasma ADMA and SDMA were analyzed by HPLC (Agilent, Infinity 1260) as described by Teerlink et al., with minor modifications to the method \u003csup\u003e\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e\u003c/sup\u003e. The sample derivatisation process was initiated as per the defined injector programme reaction mixture, such as 5\u0026micro;l borate buffer, followed by the addition of 5\u0026micro;l sample, 0.5\u0026micro;l OPA, 0.5\u0026micro;l FMOC and 20\u0026micro;l of water, with the flow rate of solvents at 1.2 ml/minute and buffer gradient. 10\u0026micro;l of the reaction mixture was injected after mixing of the sample and reagents in the autosampler. Detection was performed at an excitation wavelength of 254nm and an emission cutoff filter of 324 nm. Finally, the chromatogram was generated, and the peak area was used for the quantification of ADMA and SDMA estimation in samples.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec26\" class=\"Section3\"\u003e\u003ch2\u003eDDAH-1 assay\u003c/h2\u003e\u003cp\u003eAssay was performed by following the manufacturer's instructions of the DDAH activity assay kit (Abcam). Measured the absorbance in a microplate reader at 466 nm at RT. Calculation: Determine DDAH activity in the sample (s) using the following equations: DDAH (mU/mg) = (OD Sample \u0026ndash; ODSBC) / (OD (Spiked Sample) \u0026ndash; OD Sample) x 2 x T x C (nmol/min*mg)\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec27\" class=\"Section3\"\u003e\u003ch2\u003eNitric Oxide Assay\u003c/h2\u003e\u003cp\u003ePlasma total nitrite and nitrate levels were measured with use of the Griess reagent\u003csup\u003e\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e\u003c/sup\u003e. The Griess reagent consists of sulphanilamide and N-(1-naphthyl) ethylenediamine. Photometric measurement of the azo product was done at 540nm.\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv id=\"Sec28\" class=\"Section2\"\u003e\u003ch2\u003eStatistical analysis\u003c/h2\u003e\u003cp\u003eThe sample size was calculated keeping in view the available data on GC by applying a statistical formula, so that in each group, the sample size was 20. The patients\u0026rsquo; characteristics were analyzed by the Mann-Whitney U and chi-square tests. Continuous variables are expressed as the mean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD and median and interquartile range for skewed variables. Meanwhile, the SPSS 19.0 computer software (SPSS Inc., Chicago, IL, USA) was used to carry out statistical analysis, including Student\u0026rsquo;s t test and nonparametric tests. Nonparametric tests have been applied for age and sex while comparing groups and within groups, and the Mann-Whitney U test has also been done. Comparison between the control and groups was made with the Kruskal-Wallis equality-of-population rank test, and Bonferroni correction was applied. A p-value of \u0026lt;\u0026thinsp;0.05 is considered statistically significant.\u003c/p\u003e\u003c/div\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003ch2\u003eConflict of interest\u003c/h2\u003e\u003cp\u003eThe authors declared no competing interests.\u003c/p\u003e\u003c/p\u003e\u003cp\u003e\u003ch2\u003eEthics Statement\u003c/h2\u003e\u003cp\u003e The Institutional Research Ethics Committee of All India Institute of Medical Sciences, New Delhi, approved the ethical use of human subjects for this study (Ref: IECPG/88/30.12.2015, RT-11/27.01.2016, dated 29.01.2016). Written informed consent was taken from all patients, duly signed, for participation in the study.\u003c/p\u003e\u003c/p\u003e\u003ch2\u003eFunding Statement:\u003c/h2\u003e\u003cp\u003eNo specific grant for the study, and we have used consumables and reagents from the AIIMS intramural and ICMR-funded projects.\u003c/p\u003e\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003ePriyatma, Shyam, Govind, Siddhartha for acquisition of data, Priyatma, Shyam, Arulselvi and Siddhartha analysis and interpretation of data, statistical analysis and drafting of the manuscript; Shyam for technical and material support; Shyam, Priyatma, Peush, Sanjay and Ranjit for study concept and design, analysis and interpretation of data, drafting of the manuscript, obtained funding and study supervision. All authors read and approved the final manuscript.\u003c/p\u003e\u003ch2\u003eAcknowledgement\u003c/h2\u003e\u003cp\u003eThe Director General, Indian Council of Medical Research, Director AIIMS Delhi, for intramural grant support, and providing the Institutional support such as lab infrastructure, library facility, and all lab colleagues who have supported all the time for the sample management, etc.\u003c/p\u003e\u003ch2\u003eData Availability\u003c/h2\u003e\u003cp\u003e The material described in the manuscript, including all relevant data, will be freely available to any researcher to use for non-commercial purposes without breaching participant confidentiality.\u003c/p\u003e\u003cp\u003e\u003cstrong\u003eAuthor affiliations\u003c/strong\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003csup\u003e1\u003c/sup\u003eDepartment of Laboratory Medicine, \u003csup\u003e2\u003c/sup\u003eDepartment of Gastroenterology, \u003csup\u003e3\u003c/sup\u003eDepartment of Pathology, \u003csup\u003e4\u003c/sup\u003eDepartment of Gastrointestinal Surgery, \u003csup\u003e5\u003c/sup\u003eDepartment of Medical Oncology, IRCH Dr BRA, \u003csup\u003e6\u003c/sup\u003eDepartment of Radiology, IRCH Dr BRA, \u003csup\u003e7\u003c/sup\u003eDepartment of Laboratory Medicine, JPN Trauma, and \u003csup\u003e8\u003c/sup\u003eDepartment of Biostatistics, All India Institute of Medical Sciences, Ansari Nagar, New Delhi, India- 110029. Contributors Priyatma, Shyam, Govind, Siddarth for acquisition of data, Priyatma, Shyam, Arulselvi and Siddarth analysis and interpretation of data, statistical analysis and drafting of the manuscript; Shyam for technical and material support; Shyam, Priyatma, Peush, Sanjay and Ranjan for study concept and design, analysis and interpretation of data, drafting of the manuscript, obtained funding and study supervision. All authors read and approved the final manuscript.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eMachlowska J, Baj J, Sitarz M, Maciejewski R, Sitarz R. Gastric cancer: Epidemiology, risk factors, classification, genomic characteristics and treatment strategies. 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FASEB J. 1993;7:349\u0026ndash;60.\u003c/span\u003e\u003c/li\u003e\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":"","lastPublishedDoi":"10.21203/rs.3.rs-6800273/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6800273/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eGastric cancer is one of the most common oncological diseases. It can develop in any part of the stomach and spread to other organs, especially in the lungs, liver and oesophagus. Programmed death-1 (PD1), a cell-surface molecule, is involved in a process of dimethylation of arginine and dysregulates the production of nitric oxide in peripheral tissues. Hence, a disruption of the PD1 and PD-L1 axis in patients with severe gastritis. While DDAH activity is normally involved in the processing of neovascularisation, angiogenesis, even in the metastatic phase, the bioavailability of NO and their activity behaviour either interact synergistically or target the PDL1/PD1 activation towards the striking of tumoral activity in patients with gastritis. Therefore, a question naturally arises to understand the dimethylation process of arginine with regards to ADMA and SDMA in conjunction with claudin(s) and involvement of PD-L1 expression and mitochondrial dysregulation in GC.\u003c/p\u003e\u003cp\u003eWe observed abnormal production of NO levels and reduction of mitochondrial DNA copy numbers in GC patients. Significantly decreased levels of ADMA and excessive influx of arginase activity were assessed in GC patients. PD-L1 expression was significantly higher in GC patients, while suboptimal expression of PD-L1 is in disease control. The abnormal influx of dimethylated arginine and MMP-7 were associated and interlinked with the production of nitric oxide levels. Their association could be with the nitrigenic pathway and possible ways to damage cell surface molecules in GC. Overall, the disruption of the ADMA-SDMA equilibrium fails to maintain the PD1/ PD-L1 axis in GC patients. Therefore, claudin-4, MMP-7, and PD-L1 mRNA overexpression were found in GC, and subsequently, ADMA levels and mitochondrial DNA copy numbers were drastically decreased. Thus, these variables have potential associations to identify novel biomarkers for the diagnosis and therapeutic management of GC.\u003c/p\u003e","manuscriptTitle":"Liaison of Dimethylated arginine between PDL1 and its ligand expressions in Gastric Cancer","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-07-14 05:12:56","doi":"10.21203/rs.3.rs-6800273/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":"6c47ca3b-41e3-44fc-86c7-afc3303c352e","owner":[],"postedDate":"July 14th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2025-08-07T03:08:10+00:00","versionOfRecord":[],"versionCreatedAt":"2025-07-14 05:12:56","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-6800273","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-6800273","identity":"rs-6800273","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}