Novel Metabolic Biomarker for Early Detection and Prognosis to the Patients with Gastric Cardia Adnocarcinoma

Background : Gastric cardia adenocarcinoma (GCA), which has been normalized as type II 17 of adenocarcinoma at esophagogastric junction in western countries. In clinical, most of the 18 GCA patients are lack of early alarming symptoms, more than 90% of GCA patients were 19 diagnosed at advanced stage, resulted in a very poor prognosis, with less than 20% of 5-year 20 survival. Obviously, early detection for GCA plays crucial role in decreasing the high 21 mortality. Metabolomics allows for appraisal of small molecular mass compounds in a 22 biofluid, which may provide a way for finding biomarkers for GCA. 23 Methods : The serum metabolic features of 276 curatively resected GCA patients and 588 24 healthy control participates were collected from the database of State Key Laboratory of 25 Esophageal Cancer Prevention & Treatment and Henan Key Laboratory for Esophageal 26 Cancer Research of The First Affiliated Hospital of Zhengzhou University to discover the 27 metabolic dysregulation by using the ultraperformance liquid chromatography-mass 28 spectrometry (UPLC-MS). Joint pathway analysis with metabolites identified, survival 29 analysis and auxiliary diagnosis metabolites were discussed in present work. 30 Results : A sum of 200 known differential metabolites were obtained with p ＜ 0.05 and fold 31 change FC≥1.25 or FC≤0.8 by comparison GCA and healthy control participates. 12 32 metabolites significant correlated with survival (p ＜ 0.05) and 17 metabolites for potential 33 auxiliary diagnosis(FC ＞ 1.5 or FC ＜ 0.67) for GCA. Dysregulated arginine biosynthesis 34 was an important pathway of GCA. 9 differential metabolites of 12-ketolithocholic acid, 2- 35 Hydroxybutanoic acid, Aldosterone, All-trans-13,14-dihydroretinol, Hododeoxycholic acid, 36 L-histidine, Malonic acid, Prostaglandin E2 and Sphingosine were identified as potential 37 metabolic markers for distinguishing the GCA and healthy control (AUC=0.976, etc. results showed that glycine, proline, and ornithine in the saliva of early The levels of four potential biomarkers such as citrulline and citrulline are lower than those of normal people, and the decrease of citrulline content is contrary to the results of this experimental study. This study found that the citrulline content in tissues was significantly increased, that the catabolism proline and arginine cycle, and proline further

biofluid, which may provide a way for finding biomarkers for GCA.  Results: A sum of 200 known differential metabolites were obtained with p＜0.05 and fold 31 change FC≥1.25 or FC≤0.8 by comparison GCA and healthy control participates. 12 32 metabolites significant correlated with survival (p ＜ 0.05) and 17 metabolites for potential 33 auxiliary diagnosis(FC ＞ 1.5 or FC ＜ 0.67) for GCA. Dysregulated arginine biosynthesis 34 was an important pathway of GCA. 9 differential metabolites of 12-ketolithocholic acid, 2-35 Introduction 50 Endoscopy with iodine staining was widely used for gastric cardia cancer (GCA) and 51 esophageal cancer (EC) screening in high-incidence area. Most endoscopy screening-positive 52 population was found to develop esophageal epithelium lesion, and therefore endured higher 53 risk for developing gastric cardia cancer (GCA) and EC than normal population. However, 54 endoscopic screening may be too costly and invasive for large-scale population, and non- 55 invasive biomarkers may be more applicable and cost effective for population-based 56 screening. In this population-based screening study, we aim to identify potential metabolic 57 biomarkers for early screening of GCA, and establish the optimal early GCA screening 58 model. 59 Gastric cardia adenocarcinoma (GCA) is a cancer which occurs in the gastric cardia area 60 (gastric-esophageal boundary), that originates or mainly occupies within 2 cm of the 61 esophagus and gastric mucosa junction line [1], and it is one of the common malignant tumor 62 of the digestive tract in China. GCA morbidity and mortality have increased in recent 63 years [2].GCA and esophageal squamous cell carcinoma (ESCC) are two common 64 gastrointestinal tumors,and have been called sister cancer owing to their similar 65 characteristics, including to the adjacent anatomical locations, and simultaneously occurrence 66 in clinical practice [3]. And it suggests that they may have similar prognostic molecules 67 mechanism. Early diagnosis, prevention and treatment are the keymeans to reducing the 68 incidence and mortality of GCA, and it is particularly important to find non-injury (serum) 69 early detection indicators. 70 The abnormal metabolism of cancer has been considered an important characteristic of 71 tumors, which could clarify the pathogenesis and provide potential therapeutic targets for 72 clinical treatments (3). According to the Warburg effect, the deregulated energy metabolism 73 of cancer cells may also modify many related metabolic pathways that influence various 74 biological processes, such as cell proliferation and apoptosis. As a common characteristic of 75 cancer cells (4, 5), modified metabolism has been the focus of cancer research. 76 Metabolomics was first proposed by Nicholson Lindon and Holmes in 1999. 77 Metabolomics is a newly developed discipline after genomics, and it is an important part of 78 systems biology [4,5]. Metabolomics is a new discipline that simultaneously conducts 79 qualitative and quantitative analysis of all low molecular weight (relative molecular weight 80 less than 1000 Da) metabolites of a certain organism or cell in a specific physiological and 81 pathological period. The object of metabolomics research is the endogenous metabolites of 82 small molecules in the body. Through high-throughput qualitative and quantitative analysis, 83 we can understand the changes in metabolites in the body. Changes in any physiological, 84 pathological or other factors in the body will cause its metabolic level has changed. Through 85 metabolomics research, the impact of various factors on the body can be analyzed at the 86 overall level, which truly reflects the impact of the body [ (2)Anabolism enhancement of amino acids and lipids, which related to the interruption of the 99 tricarboxylic acid cycle and the increased use of glutamine as a carbon source (increased 100 glutamine uptake and catabolism); (3)Enhancement of the tumor-induced pentose phosphate 101 pathway which increased the cycle of reduced coenzyme II to protect cell from oxidative 102 stress; (4)Consumption and uptake of glucose. 103 In this present work, we characterized the metabolic features of GCA using a 104 nontargeted metabolic profiling strategy based on liquid chromatography-mass spectrometry 105 metabolomics analysis, and a two-phase biomarker development strategy (discovery set and 106 validation set) was applied in 864 subjects including to clinically relevant controls, and 107 univariate statistical analysis and multivariate analysis (MVA) methods were used to identify 108 differential metabolites. The serum of 276 curatively resected GCA patients and 588 healthy 109 people were collected to discover metabolic dysregulation, and a technique was used to 110 establish a novel diagnostic tool. Joint pathway analysis with metabolites identified relevant 111 metabolic pathways and detection biomarkers for GCA.

113
Study participates 114 In this study, 276 GCA patients and 588 non-GCA participants (control) were collected. 115 The GCA patients were diagnosed between 1987 to 2020 and followed up to 2020. We 116 divided the study cohort into training set (138 GCA participates and 363 control participates) 117 and validation set (138 GCA participates and 225 control participates), respectively.Entry 118 criteria for the experimental participates: ①The pathological type was clearly diagnosed by 119 histopathology; ② The pathological type was cardia adenocarcinoma; ③ The age of GCA 120 participates were from18 to90 years old; ④ None of the participates had received Serum collection in the morning on an empty stomach, peripheral venous blood was 138 collected at 7:00 am; blood samples were collected in a centrifuge tube, and left standing at 139 37°C or room temperature for 1 hour for stratification; centrifuged at 3,000 r/min (r=8 cm) at 140 room temperature for 10 min, and taken Transfer the supernatant to a clean centrifuge tube; 141 centrifuge at 12,000 r/min (r=8 cm) at 4 ℃ for 10 min, transfer the supernatant to 1.5 mL 142 centrifuge tubes, 0.2 mL per tube. Store in the refrigerator at -80 ℃, and transport in dry ice. 143 The GCA and non-GCA (control) blood samples were prepared on the ice in subsequent  Finder algorithm was used to identify potential differential metabolites and generate a group

276
Demographic characteristics of the study population 277 A total of 864 participants, consisting of 588 healthy volunteers, 276 patients with GCA 278 from 2 independent cohorts were recruited (Fig. 1A). The principal component analysis of this phase showed differences among samples from 299 normal healthy controls and GCA participants (Fig. 1B), and no significant outliers were 300 observed, which indicated that the stability of the analysis data is good.A PCA was 301 performed on all plasma sample data. The best separation of groups was obtained in the 302 principal components (PC) 1 and 2, which accounted for 15.3% and 6.0% of the whole 303 variance of the data set, respectively.The two sets showed a major overlap but samples from 304 GCA patients had a tendency towards lower scores in PC1, which was remarkable for a 305 heterogeneous cohort with high inter individual variability due to diverse lifestyles, 306 medications and comorbidities. In the PCA obtained in the second validation study, GCA 307 patients were added as another diagnosis group in addition to the test set (Fig. 3B). The best 308 separation between the GCA and control groups was again observed in PC1 and PC2 (25.3% 309 and 8.1% of the observed variance), which means that the special metabolites could clearly 310 separate GCA from normal. Remarkably, an almost complete separation of the control group 311 from the other two could be observed. The GCA patients tended to have higher scores in the 312 PC2 than the liver cirrhosis patients, resulting in a visible separation between these groups. 313 We conducted test set and validation set simultaneously, and they were done this to 314 show that the metabolomic profiles and distribution of the GCA versus control group patients 315 in the test and validation set. And in conclusion, we observed that the similar separation 316 between the two sets, which demonstrated that the two sets were actually comparable, Heatmapof serum metabolomic profilesshowed in Fig. 2 including to the 9,10-DiHOME [(±)9,10-dihydroxy-12Z-octadecenoic acid], 12,13-DiHOME, 366 9-HpODE, 12,13-EpOME [(±)12(13)epoxy-9Z-octadecenoic acid], 9,10-EpOME [(±)9,10-  Fig. 2A. 386 As we mentioned above, we found 3 potential metabolites (2-Hydroxybutanoic acid, 387 All-trans-13,14-dihydroretinol, and Malonic acid) with AUC above 0.5 with P-value below Differential metabolites for survival analysis 395 We obtained 12 metabolites including to 9,10-DiHOME [(±)9,10-dihydroxy-12Z-396 octadecenoic acid], 12,13-DiHOME, Aldosterone, 9-HpODE, 12,13-EpOME 397 [(±)12(13)epoxy-9Z-octadecenoic acid], 9,10-EpOME [(±)9,10-Epoxy-12Z-octadecenoic  be seen that the sample selection and preparation process in the experiment is very important. 532 In this present work, we found 200 metabolites selected from the discovery set and 533 validation set from the statistical analysis based on metabolomics (Table 2S) the y-axis represents the charge-to-mass ratio of the features. Each circle in the cloud plot represents 1 differential feature, and the circle size represents the relative concentration of the feature. Before differential metabolite analysis, rst perform principal component analysis on the grouped samples for difference comparison, and observe the degree of variability between the difference groups and the samples within the group.) (D). Venn diagram displaying the 200 differential metabolitesthat were altered as biomarker candidates from the 2 cohorts in the discovery set and validation set.

Figure 2
Metabolomic pro le of matched early and middle-late stage of GCA for discovery set (A) and validation set (B). Survival analysis. We used the median of the metabolite in GCA as the cut-off value to set the up and down group.

Supplementary Files
This is a list of supplementary les associated with this preprint. Click to download. Table12andTableS1S2.pdf