A novel prognostic model of gastric cancer patients based on cachexia status by using plasma exosome-derived miRNAs

A novel prognostic model of gastric cancer patients based on cachexia status by using plasma exosome-derived miRNAs | 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 A novel prognostic model of gastric cancer patients based on cachexia status by using plasma exosome-derived miRNAs Xunliang Jiang, Ke Wang, Jingyuan Wang, Yaoting Li, Yu Jiang, and 6 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-2437588/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 Background: Emerging evidence shows that serum biomarkers are closely associated with the prognosis of gastric cancer. Cachexia represents systemic nutritional and metabolic statuses. This study aimed to clinically validate the predictive value of serum biomarkers and cachexia, and to identify a potential biomarker for the early diagnosis of cachexia. Methods: This study included patients with gastric cancer who received curative treatment with no other nonneoplastic cachexia. The eligible population was randomized into training (70%) and test (30%) cohorts.A univariate and multivariate Cox proportional-hazards regression model was used to construct a gastric cancer prognosis model. The predictive and discriminative abilities of the model were evaluated using Kaplan–Meier (K–M) and receiver operating characteristic (ROC) curves. A nomogram was constructed based on the factors identified using the prognostic model, and the corresponding calibration curve was used to validate the accuracy of the nomogram. Exosomal microRNAs (miRNAs) were screened for the early diagnosis of cachexia via whole-genome sequencing, and the clinical samples were used for verification. Results: This study included 1101 eligible patients with gastric cancer. There were 330 (29.97%) patients with cachexia and 771 (70.03%) without cachexia. Univariate Cox regression analysis identified the following prognostic factors: body mass index; cachexia; nutritional risk screening scale-2002 (NRS2002) score; serum albumin, carcinoembryonic antigen (CEA), carbohydrate antigen 19-9(CA19-9), and carbohydrate antigen 125 (CA125) levels, and red blood cell count. Multivariate Cox regression analysis identified cachexia and CEA, CA19-9, and serum albumin levels as the independent risk factors for overall survival (OS; p < 0.05). The K–M curve indicated that the OS of high-risk patients was significantly lower than that of low-risk patients. The areas under the curve of the 1-, 2-, and 3-year prognostic models were 81.13%, 78.49%, and 76.23%, respectively (79.01%, 78.61%, and 75.34% for the test cohort, respectively). Finally, the corresponding nomogram was used to predict the OS of patients with gastric cancer. The calibration curve showed the best agreement between predictions and actual observations. Furthermore, plasma exosomal miR-432-5p was identified as a biomarker for the early diagnosis of cachexia via whole-gene sequencing to make up for the lack of methods for the early diagnosis of cachexia. Conclusions: Serum biomarker levels and cachexia status are clinically significant in patients with gastric cancer. We constructed a novel prognostic model based on serum biomarker levels and cachexia. A novel nomogram constructed using this model may predict OS in patients with gastric cancer alone. Furthermore, we identified a novel plasma exosomal biomarker, miR-432-5p, for the early diagnosis of cachexia. Cachexia Gastric cancer Prognostic model Nomogram Exosome Biomarker Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Introduction Gastric cancer is one of the most common malignant tumors worldwide, with morbidity and mortality rates among the top five highest in the world [ 1 ]. According to cancer statistics reported in 2020, more than 1.08 million people are newly diagnosed with cancer each year and approximately 770,000 people die from gastric cancer[ 1 ]. Gastric cancer is more common in less developed regions, particularly developing countries and regions with relatively poor hygiene and medical conditions. East Asia, particularly China, accounts for approximately 50% of the global cases [ 2 ]. With continuous advancements in medical technologies worldwide, the mortality rate of gastric cancer has been decreasing. However, owing to the high incidence of gastric cancer and difficulty of early diagnosis, improving the survival rate and quality of life of patients with gastric cancer remains challenging. Gastric cancer is a multifactorial disease with an abysmal prognosis. The 5-year survival rate of patients with a delayed diagnosis is < 10%, whereas that of patients with an early diagnosis is 85% [ 3 ]. One of the reasons for such a large difference in the survival rates is severe deficiencies in the nutritional status of the patients [ 4 ]. Serum biomarkers are significantly associated with disease prognosis and can complement the current TNM staging[ 5 ]. A previous study identified an increased carcinoembryonic antigen (CEA) as an independent risk factor for poor prognosis in patients with early-stage gastric cancer [ 6 ]. In recurrent gastric cancer, particularly in the case of lymph node recurrence, the double-positive status of CEA and carbohydrate antigen 19 − 9 (CA19-9) may negatively affect the disease prognosis [ 7 ]. Recent evidence suggests that CA19-9 and alpha-fetoprotein (AFP) levels are independent predictors of gastric cancer prognosis, and the combined assessment of CA19-9, AFP, and CA125 levels is valuable for the evaluation of gastric cancer prognosis [ 8 ]. Futhermore, CA19-9 level combined with neutrophil–lymphocyte ratio can better predict the overall survival (OS) of patients with gastric cancer after surgery [ 9 ]. In recent years, assessment of patients’ nutritional status has become increasingly important in clinical practice. Albumin, a potent protein produced by hepatocytes, has been widely used as a serum nutritional biomarker for predicting death among critically ill patients[ 10 ]. Low albumin levels are often associated with adverse effects, poor response to chemotherapy, and shortened OS and progression-free survival in patients with advanced unresectable gastric cancer [ 11 ]. However, these serum biomarkers only reflect the substances released by tumor cells and cannot fully reflect the metabolic and nutritional statuses of a patient’s entire body. Cachexia is a multifactorial metabolic syndrome characterized by severe weight loss and muscle wasting (with or without fat loss) [ 12 ]. Cachexia caused by tumors, known as tumor cachexia , is the most common type of cachexia. The incidence of cachexia can be as high as 80% in patients with gastric cancer and can directly lead to the death of at least 20% of patients with cancer [ 13 , 14 ]. In long-term clinical practice, cachexia assessment can easily be overlooked. Patients with significant weight loss enter a stage of refractory cachexia, which results in severe nutritional deficiencies. However, at this stage, no effective clinical intervention can be performed; moreover, simple nutritional support is not beneficial. Systemic inflammation and severe malnutrition are common in patients with cancer[ 15 , 16 ]. These conditions substantially increase patients’ susceptibility to tumor growth, tumor cell spread, and drug resistance, resulting in a significantly reduced patient survival rate [ 17 – 19 ]. Accurate and individualized prognostic assessment of patients with gastric cancer is crucial for surgeons to formulate better treatment strategies. Nutritional status assessment is a comprehensive evaluation of patient response tolerance, drug resistance, and postoperative rehabilitation. Therefore, cachexia can be considered an important indicator for prognostic evaluation. The body secretes biomarkers in the serum in response to release of circulating substances by tumor cells. Cachexia is diagnosed based on the characterization of systemic nutritional status and assessment of metabolic disorders. Thus, it is reasonable and feasible to combine these factors to improve the predictive power of prognostic factors for patients with gastric cancer. Cachexia is a multifactorial metabolic disorder syndrome manifesting in end-stage cancer patients, and there has been a lack of simple, accurate diagnostic criteria for a long time. Weight loss alone does not fully reflect the pathophysiological changes and clinical impact of cachexia. Merely assessing the body weight and ignoring other manifestations often result in the exclusion of patients with refractory cachexia at the time of diagnosis [ 20 , 21 ]. Thus, identifying more sensitive diagnostic indicators is essential. As the main components of liquid biopsy, exosomes have the advantages of high concentration, stability, ease of collection, and being rich in tumor genome and mutation characteristics. Thus, exosomes are important in predicting new gastric cancer occurrences and therapeutic targets [ 22 ]. Therefore, developing novel methods for the early prediction and diagnosis of cachexia based on liquid biopsy and exosomal microRNAs (miRNAs) detection is clinically significant[ 23 ]. In this study, we developed a simple, personalized prognosis prediction model for patients with gastric cancer based on the assessment of serum biomarkers and nutritional metabolism (cachexia). This model may enable clinicians to more effectively evaluate the prognosis of gastric cancer and formulate more optimized treatment strategies. We determined the risk factors associated with the prognosis of gastric cancer using univariate Cox regression analysis and constructed a new prognostic analysis model using multivariate Cox regression. Analysis of the area under the receiver operating characteristic (ROC) curve exhibited high sensitivity and specificity. Subsequently, we constructed a novel nomogram that included cachexia and CEA, CA19-9, and serum albumin levels for the preoperative personalized prediction of OS in patients with gastric cancer. Finally, we identified a plasma exosomal miRNA via whole-genome sequencing. This miRNA can be obtained via liquid biopsy and used to efficiently and consistently predict the occurrence of gastric cancer cachexia at an early stage. Methods Patients and Study Design This retrospective study included patients with gastric cancer who received radical treatment at the First Affiliated Hospital of Air Force Military Medical University (Shaanxi, China) between December 2016 and December 2018. The inclusion criteria were as follows: (1) patients with gastric cancer as the only primary carcinoma, (2) patients with gastric cancer for whom complete follow-up and basic clinical data were available, (3) patients who underwent radical gastrectomy for gastric cancer, (4) patients with available postoperative data, such as the TNM stage determined based on the seventh or eighth edition of TNM staging and follow-up data for > 36 months, and (5) patients who provided informed consent for participation. Patients who received neoadjuvant therapy, such as radiotherapy or chemotherapy, before surgery were excluded as these treatments may affect disease prognosis. Patients with other tumors or nonneoplastic cachexia were also excluded as these conditions may influence the development of gastric cancer models. The diagnostic criteria for cachexia were as follows: (1) weight loss of > 5% in the past 6 months, (2) body mass index (BMI) of 2%, (3) anthropometric measurement of the muscle area in the middle of the upper arm (men, < 32 cm 2 ; women, < 18 cm 2 ), (4) skeletal muscle index of the limbs (men, < 7.26 kg/m 2 ; women, < 5.45 kg/m 2 ), and (5) computed tomography-based lumbar skeletal muscle index (men, < 55 cm 2 /m 2 ; women, < 39 cm 2 /m 2 ). The presence of any one of the aforementioned criteria indicates cachexia. The study was reviewed on June 7, 2021, and approved by the Medical Ethics Committee of the First Affiliated Hospital of Air Force Military Medical University (approval number: KY20212018-F-2). Informed consent was obtained from the patients before surgery. Follow-Up Patients with gastric cancer were evaluated every 3 months in the first year following surgery and every 6 months thereafter via telephonic and outpatient follow-ups. Follow-up re-examinations included assessment of the patients’ basic symptoms and physical, imaging, and serological examinations. The primary endpoint of this study was OS (days), defined as the time from the diagnosis of gastric cancer to death from any cause. Blood Sample Peripheral venous blood was collected from the patients on the day of surgery. Within 30 min of collection, the blood samples were centrifuged at a low speed (1000 g) for 15 min at 4°C, and the supernatant (plasma) was collected and labeled. All plasma samples were stored at − 80°C. Exosome and Exosomal miRNA Extraction Plasma exosomes were extracted from the patients using an exosome rapid extraction kit (exoRNeasy middle extraction kit, QIAGEN, Germany). The operation method is carried out according to the manufacturer’s instructions. Statistical Analysis All statistical analyses were performed using SPSS version 26 (IBM Corp., Armonk, NY, USA) or R software version 4.0.4 ( https://www.r-project.org/ ). The measurement data conforming to a normal distribution were analyzed using Student’s t -test or one-way analysis of variance and expressed as means ± standard deviations. Categorical data were analyzed using χ 2 or Fisher’s exact test and expressed as frequency or percentage. A restricted cubic spline test was performed on the transformed data to verify the linear relationship between variables and outcomes. Univariate Cox regression analysis was used to determine the risk factors associated with gastric cancer prognosis. Multivariate Cox proportional-hazards regression analysis was used to construct a prognostic model for gastric cancer. The risk model with the smallest Akaike information criterion (AIC) value was identified using a backward and stepdown process. Kaplan–Meier (K–M) and ROC curves were used to evaluate the accuracy and specificity of the model. We constructed a nomogram based on the factors identified using the prognostic model and used the corresponding 1-, 2-, and 3-year calibration curves to verify the accuracy of the nomogram. The following R language packages were used: “ggplot2,” “survminer,” “rms,” “survival,” “timeROC,” “rmda,” “dplyr,” “rfsrc,” “randomForestSRC,” “ggRandomForests,” and “compareGroups.” All tests were two-sided, and a p value of < 0.05 was considered statistically significant. *p < 0.05,**p < 0.01, ***p < 0.001 Results Clinical Characteristics of the Patients A total of 1240 patients with gastric cancer hospitalized between December 2016 and December 2018 Were included in this study. All patients underwent radical gastrectomy for gastric cancer and postoperative pathological examination for confirmation diagnosis. The patients and their families were informed about the study design and significance. They agreed to cooperate and be followed up for study purposes. Of the 1240 patients included in this study, 33 had no survival duration data, 59 were lost to follow-up, and 47 had incomplete basic data. Finally, 1101 patients were included for a follow-up period of approximately 50 months. The baseline data of the cohort are presented in Supplementary Table S1 . The design of this study was as follows: the population that met the inclusion and exclusion criteria was randomly divided into training and test cohorts at a ratio of 7:3. The model was filtered and optimized in the training cohort. Univariate Cox proportional-hazards regression analysis was used to screen for prognostic factors, and multivariate Cox regression analysis was used to determine the final stable model. ROC and K–M curves were used to verify the model’s predictive ability in the two datasets, respectively. Finally, a new nomogram was constructed based on the key factors identified using the model; the nomogram was then evaluated using the calibration curve. The obtained Cox prognostic model was validated using the Random Survival Forest Prognostic Model ( Supplementary Figure S1 ). Clinical Baseline Characteristics of the Training and Test cohorts The training and test cohorts included 770 and 331 patients with gastric cancer, respectively, with median survival durations of 37.17 and 38.17 months. No significant differences were observed between the two cohorts in terms of any baseline characteristics. Table 1 presents the detailed baseline characteristics of the two cohorts. To better fit the gastric cancer prognostic model and avoid the zero value, we transformed the original serum marker values by ln(values + 1). Table 1 Basic clinical features in the training and testing cohorts Characteristics Train cohort Test cohort P value No. of cases 770(69.9) 331(30.1) Survival status (%) 0.491 Dead 237 (30.78) 95 (28.70) Alive 533 (69.22) 236 (71.30) Cachexia (%) 0.616 Yes 236 (30.65) 104 (31.42) No 534 (69.35) 227 (68.58) Survival time(month) 37.17 (17.50) 38.18 (17.16) 0.379 Age(year) 58.06 (10.58) 57.20(11.20) 0.225 Sex (%) 0.300 Male 585 (75.97) 241 (72.81) Female 185 (24.0.) 90 (27.19) NRS2002 0.374 ≥3 514 (66.75) 230 (69.49) ༜3 256 (33.25) 101 (30.51) BMI (kg/m2) 22.87 (3.12) 22.79 (3.31) 0.731 CA19-9 21.57 (27.84) 24.29(31.84) 0.154 CEA 4.59 (7.37) 4.46 (7.65) 0.788 Hemoglobin 127.41 (23.96) 127.76 (24.07) 0.827 Albumin 37.33 (6.05) 38.07 (6.19) 0.065 T stage (%) 0.231 1 187 (24.29) 90 (27.19) 2 117 (15.19) 50 (15.11) 3 172 (22.34) 85 (25.68) 4 294 (38.18) 106 (32.02) M stage (%) 0.353 0 744 (96.62) 316 (95.47) 1 26 (3.38) 15 (4.53) N stage (%) 0.690 0 323 (41.95) 146 (44.11) 1 125 (16.23) 59 (17.82) 2 114 (14.81) 43 (12.99) 3 208 (27.01) 83 (25.08) BMI, body mass index; CEA, carcinoembryonic antigen; CA19-9, carbohydrate antigen 19 − 9; NRS2002,nutritional risk screening scale-2002; Prognostic Value of Clinical Features The 1-, 2-, and 3-year survival rates of the training cohort were 81.69%, 74.68%, and 68.96%, respectively, whereas those of the test cohort were 85.20%, 75.23%, and 71.30%, respectively. Table 2 presents the results of the univariate Cox regression analysis of the training set; NRS2002 score (hazard ratio [HR], 1.464; p = 0.01) and cachexia (HR, 4.565; p < 0.001) were identified as the risk factors and BMI (HR, 0.913; p < 0.001), serum albumin level (HR, 0.141; p < 0.001), and red blood cell count (HR, 0.161; p < 0.001) were identified as the protective factors predicting the OS of patients with gastric cancer. Regarding the prognostic analysis of the tumor biomarkers, three common markers—CEA (HR, 1.642; p < 0.001), CA19-9 (HR, 1.936; p < 0.001), and CA125 (HR, 1.334; p < 0.001) levels—were identified as the causes of poor prognosis of gastric cancer. The results of the prognostic analysis of the TNM staging system were consistent with the traditional theory. Table 2 Univariate Cox regression analysis for GC training cohort Variables β HR 95% Cl P value Sex(Male vs Female) -0.202 0.817 0.598–1.117 0.206 Age 0.007 1.007 0.995–1.019 0.272 BMI -0.092 0.913 0.873–0.954 < 0.001 Cachexia 1.518 4.565 3.516–5.927 < 0.001 TNM(Ⅰ,Ⅱvs. Ⅲ,IV) 1.175 3.240 2.412–4.351 < 0.001 NRS2002 0.381 1.464 1.096–1.957 0.010 Albumin -1.959 0.141 0.066–0.301 < 0.001 CEA 0.496 1.642 1.419–1.899 < 0.001 CA199 0.661 1.936 1.700-2.205 < 0.001 CA125 Glucose 0.288 0.265 1.334 1.303 1.155–1.542 0.831–2.045 < 0.001 0.249 WBC 0.168 1.183 0.854–1.639 0.312 RBC -1.825 0.161 0.064–0.404 < 0.001 CHD -0.047 0.954 0.521–1.748 0.879 Hypertension 0.040 1.041 0.763–1.419 0.801 Diabetes -0.077 0.926 0.607–1.412 0.720 Smoking history -0.111 0.895 0.693–1.155 0.394 Alcohol drinking 0.105 1.111 0.779–1.584 0.561 GC, gastric cancer; HR, hazard ration; BMI, body mass index; ACD, Acute Coronary Disease; CEA, carcinoembryonic antigen; CA19-9, carbohydrate antigen 19 − 9; CA125, carbohydrate antigen 125; WBC, leukocyte ,white blood cell ; RBC, Erythrocyte/Red Blood Cell CHD, coronary heart disease Through multivariate Cox analysis, cachexia and CEA, CA19-9, and serum albumin levels were identified as significant independent risk factors for OS (Table 3 ). Therefore, we combined these four factors to construct an optimal model based on a backward and stepdown process with the smallest AIC. Table 3 Multivariate Cox regression analysis for GC training cohort Variables Coefficient HR 95% Cl P value Cachexia 0.977 2.655 1.950–3.616 < 0.001 CEA 0.165 1.179 1.006–1.382 0.009 CA19-9 0.356 1.427 1.234–1.650 0.042 Albumin -1.058 0.347 0.157–0.769 < 0.001 CEA, carcinoembryonic antigen; CA19-9, carbohydrate antigen 19 − 9; Construction of the Prognostic Model for Gastric Cancer We used a multivariate Cox regression model to screen for optimal variables and models based on a backward and stepdown process. Four variables were identified: cachexia and CEA, CA19-9, and serum albumin levels. The regression model calculated the coefficients corresponding to the four factors (Table 3 ). Based on the coefficients and variables, the following model formula was established to calculate the risk score of patients with gastric cancer: Risk score = 0.977 ⋅ cachexia (where 0 = No and 1 = Yes ) + 0.165 ⋅ CEA level + 0.356 ⋅ CA19-9 level − 1.058 ⋅ albumin level Assessment of the Prognostic Model We divided each cohort into two groups based on the optimal cutoff point indicated by the “survminer” R language package: patients with a value higher than the cutoff point were classified into the high-risk group while the others were classified into the low-risk group. For the training cohort, the cutoff point was − 2.1049; 199 and 571 patients were classified into the high-risk and low-risk groups, respectively. The 3-year survival rates of the high- and low-risk groups were 37.69% and 80.21%, respectively, indicating a significant difference ( p < 0.0001). The K–M curve clearly showed that the survival duration of the low-risk group was much longer than that of the high-risk group ( p < 0.0001; Fig. 1 A). Similarly, for the test cohort, the cutoff point was − 2.0812; the high-risk and low-risk groups included 103 and 228 patients, respectively. The 3-year survival rates of the two groups differed significantly (40.78% and 85.09% for the high-risk and low-risk groups,respectively; Fig. 1 B). These results indicate that the prognostic model can significantly divide the population into high- and low-risk groups. The ROC curve was used for correlation analysis to evaluate the predictive power of the prognostic model. In the training cohort, the areas under the curve (AUCs) of this risk model for 1-, 2-, and 3-year survival were 81.13%, 78.49%, and 76.23%, respectively (Fig. 2 A),whereas in the test cohort, the corresponding values were 79.01%, 78.61%, and 75.34%, respectively (Fig. 2 B). We further examined each index of the model and traditional TNM staging index using the ROC curve. The results showed that the AUC values of the prognostic model were significantly higher than the aforementioned individual metrics ( Supplementary Figure S2 ). This indicates that the prognostic model exhibits better discriminative ability and fitting performance than the traditional TNM staging system. In summary, we developed a new prognostic model that included cachexia and the CEA, CA19-9, and serum albumin levels. The model demonstrated a satisfactory predictive performance with a high sensitivity and specificity for predicting the prognosis of gastric cancer. Nomograms and Calibration Curves We further constructed a novel nomogram (Fig. 3 A). After combining the data of the training and test cohorts (n = 1101), the nomogram was constructed based on the following four independent risk factors: cachexia and CEA, CA19-9, and albumin levels. First, we obtained the corresponding nomogram scores based on the patients’ four indicators. Subsequently, we summed the scores of each item to calculate the total score. The total score was finally projected onto the corresponding 1-, 2-, and 3-year survival rates. We plotted 1-, 2-, and 3-year calibration curves to verify the accuracy of the nomogram predictions (Fig. 3 B). The nomogram constructed in this study accurately predicted patient survival and exhibited the best agreement between predictions and actual observations. In the nomogram, total scores were obtained by summing up individual scores from the respective variables, and higher scores indicated poorer survival. In the calibration diagram, the nearer distance of the red or blue dots to the diagonal line, the more accurate is the nomogram’s predictive ability. Random Survival Forest Prognostic Model For the abovementioned Cox regression model constructed, we further constructed a random survival forest prognosis model for validation. We constructed the model using the “rfsrc” function of the R language to examine the effect of each variable on disease prognosis and patient survival. A total of 500 binary survival trees were generated using the model. As the number of survival trees increases, the model prediction error rate decreases significantly. When the number of survival trees increases to a certain number, the error rate curve stabilizes ( Supplementary Figure S3A ). Performing variable screening based on machine learning can help us better understand the importance of variables, and the variable importance (VIMP) and minimal depth methods are commonly used to screen variables in random survival forest models. The results showed that cachexia and CA19-9, CEA, and serum albumin levels ranked in the top four in both variable screening methods ( Supplementary Figure S3B ). Thus, the gastric cancer prognosis model developed based on Cox proportional-hazards regression model demonstrated a satisfactory predictive performance. Identification of Exosomes Previous studies have identified the importance of cachexia in prognostic models for patients with gastric cancer. However, early diagnosis of cachexia remains challenging. To compensate for the lack of early diagnostic capacity for cachexia, we identified a potential biomarker of cachexia via whole-genome sequencing, which may better predict the occurrence of cachexia at an early stage. A total of 105 patients were evaluated, all of whom were subjected to the extraction of exosomes using an exosome extraction kit (exoRNeasy). The results of exosome identification were as follows: transmission electron microscopy revealed that the exosomes were complete in shape and uniform in density (Fig. 4 A). Nanoparticle tracking analysis revealed that the particle diameter of the exosomes was approximately 120 nm, which is in the normal range of exosome diameters (Fig. 4 B). Western blotting results indicated that the protein expression of all exosome-positive markers (CD9, CD81, and TSG101) of the samples were in the normal range (Fig. 4 C). Plasma Exosome Whole-Genome Sequencing To determine the association between exosomal miRNAs and gastric cancer cachexia, we performed whole-genome sequencing of plasma exosomes obtained from 5 of the abovementioned 105 patients (two gastric cancer patients without cachexia and three gastric cancer patients with cachexia were included in the G1 and G2 groups, respectively). We detected 670 exosomal miRNAs, including 666 known miRNAs and 4 newly predicted miRNAs. Futher, 24 miRNAs were differentially expressed. Among these, 12 were upregulated and 12 were downregulated. Figures 5 A and 5 B present the volcano plot and cluster analysis results of differentially expressed miRNAs, respectively. Gastric Cancer Cachexia-Related Exosomal miRNA We used quantitative RT-PCR (qRT-PCR) to verify these exosomal miRNAs in an independent validation cohort comprising 37 gastric cancer patients without cachexia and 32 gastric cancer patients with cachexia. The expression of hsa-miR-432-5p was upregulated in plasma-derived exosomes obtained from patients with cachexia compared with those obtained from patients without cachexia (Fig. 5 C). Subseuently, we validated the reliability of hsa-miR-432-5p for the early diagnosis of cachexia in this independent sample cohort using ROC curves. The results showed that the AUC for hsa-miR-432-5p achieved a value of 0.8043 (Fig. 5 D). Based on these findings, exosomal hsa-miR-432-5p was significantly highly expressed in patients with cachexia, suggesting that plays an important role in the development of gastric cancer cachexia. Therefore, exosomal hsa-miR-432-5p has the potential as a biomarker for predicting the occurrence of gastric cancer cachexia, which may provide new insights and help develop novel methods for the early prediction and diagnosis of gastric cancer cachexia. Furthermore, we evaluated the expression of hsa-miR-432-5p in tumor tissues and adjacent tissues of the same patient. As expected, the expression of hsa-miR-432-5p tended to decrease in adjacent tissues (Fig. 5 E), which may mean that the exosomal miRNA comes from tumor cells. Similarly, a trend consistent with this result was observed in the comparison of tumor and normal tissues based on data obtained from The Cancer Genome Atlas database (Fig. 5 F). We further performed gene ontology (GO) analysis of the miRNA target genes differentially expressed in exosomes; 260 annotations were obtained for differentially expressed miRNAs. The GO analysis revealed that the differentially expressed miRNA target genes were mainly concentrated in cellular processes, biological regulations, metabolisms, tumors, and other processes ( Supplementary Figure S4 ). The pathways of the target genes were analyzed using Kyoto Encyclopedia of Genes and Genome pathway analysis (KEGG). A total of 179 annotations of differentially expressed genes were obtained. The differentially expressed miRNA target genes were mainly related to tumors ( Supplementary Figure S5 ).These results indicate that the differentially expressed miRNAs were significantly correlated with tumors and their biological regulation metabolism and that they can be transported to multiple body parts, including the skeletal muscle, through the exosomes released by tumor tissues, thus playing regulatory role. Discussion In recent decades, the decline in the incidence of Helicobacter pylori infection, improvement in lifestyle habits, and continuous development of targeted therapy and immunotherapy have contributed to the decline in the incidence and mortality of gastric cancer in most parts of the world[ 24 ]. However, the 5-year OS (even the 3-year OS) and quality of life of patients with gastric cancer are far from satisfactory, particularly for those with stage IV cancer[ 3 ]. In most parts of the world, the 5-year survival rate of patients with gastric cancer is nearly 20–30%, which is significantly lower than that of patients with other malignant tumors[ 13 ]. Gastric cancer symptoms usually appear at an advanced stage, which often leads to a poor prognosis. Patient prognoses must be accurately assessed, and individualized treatment options must be adopted to improve the patients’ quality of life. Increasing evidence suggests that malnutrition significantly shortens the survival duration of patients, particularly those with cancer. The crucial role of serum biomarkers in the management of advanced disease and in the prognoses of various cancers is constantly being investigated. In this study, we evaluated the association between cachexia status and serum biomarker levels and the 3-year OS of patients with gastric cancer. We further developed an innovative risk prediction model based on the results of cachexia diagnosis and serum biomarker assessment. The ROC curve exhibited the exact consistency of the model. Ultimately, a novel nomogram was constructed based on the independent risk factors identified, with a great potential for broad clinical applications. A previous study reported that 50–90% of patients with malignant tumors experience weight loss and malnutrition, particularly those with malignant digestive tract tumors and gastric cancer [ 14 ]. The high incidence of malnutrition in patients with gastric cancer is related to the location of the tumor. At least 20% of patients die from malnutrition and related complications rather than malignancy. Quality of life, prognosis, and survival differ markedly between well-nourished and malnourished patients. However, to the best of our knowledge, there is no current gold standard for the accurate assessment of patient survival and disease prognosis. Studies on the nutritional assessment of patients with gastric cancer are scarce. Since its inception, the concept of cachexia has been widely used in the management of advanced disease, particularly in patients with advanced cancer. Cachexia can comprehensively reflect the patient’s protein and energy balance. Severity of cachexia can be classified according to energy storage and protein consumption. In clinical practice, weight loss has been used as an evaluation index for many years because of the lack of accurate diagnostic criteria. This limits cachexia diagnosis in term of nutritional assessment, prognosis evaluation, and quality of life assessment in patients with cancer. The standardization of cachexia has become increasingly clear in recent years, and it has been increasingly applied in clinical practice. Patient-generated subjective global assessments have been reported to be a promising screening tool for cachexia [ 25 ]. Furthermore, additional features such as anorexia, muscle loss, fatigue, and other indicators are also gradually being used to screen for cachexia. In their recent study, Zhang et al. reported that systemic inflammation is significantly associated with the survival of patients with tumor cachexia [ 26 ]. Using serum and urine metabolomics, Yang et al. developed a unique diagnostic model for tumor cachexia[ 27 ]. All of the aforementioned methods may continuously improve the clinical application value of cachexia. However, to the best of our knowledge, no studies to date have evaluated the use of cachexia diagnosis combined with the assessment of other indicators to predict disease prognosis and patient survival. Serum biomarkers are widely used to determine cancer prognosis. Several studies have demonstrated that an elevated CEA level is an independent risk factor for the poor prognosis of patients with early-stage gastric cancer, and the upregulated expression of postoperative serum CEA is closely associated with tumor recurrence [ 28 ]. CA19-9 is also an important biomarker of gastric cancer. An elevated serum level of CA19-9 indicates that the patient has an increased risk of tumor metastasis and a decreased survival rate, making it an important prognostic factor in gastric cancer. The measurement of serum CA19-9 level is also often combined with that of AFP, CEA, and CA125 levels to determine the prognosis of gastric cancer. A recent study has demonstrated that the assessment of anti-HP antibody level combined with that of CA19-9 and CEA levels is highly valuable in determining postoperative recurrence, metastasis, and death risk in patients with early-stage gastric cancer [ 29 ]. Albumin reflects the systemic nutritional status of patients and has become a mature serum marker. In clinical practice, the measurement of certain biochemical indicators combined with that of serum albumin level is widely used to evaluate the survival of patients with tumor. The fibrinogen–albumin ratio can be used as a prognostic factor for first-line chemotherapy in patients with advanced gastric cancer. The ratio of C-reactive protein and albumin levels reflects the prognosis of gastric cancer [ 30 ]. Similarly, combined D-dimer level may be useful for predicting patients’ response to first-line chemotherapy and prognosis of advanced gastric cancer [ 31 ]. Therefore, we combined cachexia diagnosis with serum biomarker assessment to predict gastric cancer prognosis and patient survival. To compensate for the bias resulting from single-factor modeling, a more accurate and stable model must be established. Unlike previous studies, we innovatively used continuous serum biomarker values rather than “negative” or “positive” binary results. As mentioned above, cachexia is difficult to detect at an early stage; moreover, conventional treatment is ineffective after a delayed diagnosis. Thus, the early diagnosis of cachexia has become a challenge for many clinicians. The continuous development of liquid biopsy technology and advancements in exosome research, has made it possible to identify a marker with high concentration, stability, easy collection, and high sensitivity and specificity. Exosomes can carry miRNAs into the circulatory system and promote cell-to-cell and tissue-to-tissue connections through paracrine, autocrine, and endocrine methods. With the discovery of exosomal miRNAs, many miRNAs have been confirmed to be involved in inflammatory responses, inducing metastasis, mediating cancer invasion, and participating in protein synthesis and degradation pathways in the skeletal muscle [ 32 ]. He et al. found that exosomes secreted by pancreatic and lung cancer cells delivered miR-21 to muscle cells through the blood and induced apoptosis of these muscle cells. Exosomal miR-21 regulates the recognition and activation of Toll-like receptor 7 in mouse myoblasts, thereby promoting myocyte apoptosis. Hudson et al. demonstrated that exosomal miR-182 inhibits muscle atrophy induced by the overexpression of FOXO3 in skeletal muscle cells[ 33 ]. Furthermore, miR-21 and miR-29 inhibit protein synthesis and promote protein degradation by activating the nuclear factor-κB signaling pathway[ 34 ]. Taken together, exosomal miRNAs are involved in many regulatory pathways in muscle cells. The most important feature of cachexia is the continuous loss of the skeletal muscle, which may be accompanied by fat wastage. The skeletal muscle constitutes approximately 40% of the body weight and is important for locomotion and metabolic homeostasis. However, there is no report that exosomal miRNAs cause skeletal muscle loss and lead to the development of cachexia. Thus, this study is the first to identify a correlation between plasma-derived exosomal hsa-miR-432-5p and gastric cancer cachexia, which is highly expressed in patients with gastric cancer cachexia, and has better sensitivity and specificity for the early diagnosis. Therefore, plasma-derived exosomal hsa-miR-432-5p has the potential for use as a biomarker for gastric cancer cachexia. Our study has some limitations. Because this was a retrospective, single-center study, we presume a certain degree of selection bias; moreover, the model needs to be validated at other hospitals. In the future, we would like to gradually expand the number of studies and increase cooperation units to further validate the findings of our model and optimize the model using data obtained from large-sample, multicenter studies. In conclusion, we developed a prognostic model based on cachexia diagnosis combined with serum biomarker assessment for patients with gastric cancer. The model exhibited a satisfactory predictive power. A novel nomogram was constructed to predict OS in patients with gastric cancer alone. The four independent factors included in the prediction model developed in this study are relatively easy to assess in clinical practice. They can be used to accurately predict the postoperative OS of patients with gastric cancer. When weight loss is not up to the standard, only the judgment based on cachexia has a considerable influence on individual subjective factors. However, the detection of exosomal miRNAs may largely make up for this shortcoming. The nomogram constructed in this study by combining four independent factors has a consistent clinical application value. Conclusion Serum biomarker levels and cachexia status are clinically significant in patients with gastric cancer. We developed a novel prognostic model that included serum biomarker levels and nutrient metabolism evaluation index (cachexia). A novel nomogram constructed using this model may predict OS in patients with gastric cancer alone. In addtiton, we identified a novel plasma exosomal biomarker, miR-432-5p, for the early diagnosis of cachexia. Combining multiple markers to diagnose cachexia may be another valuable strategy. Nevertheless, further multicenter, international studies are needed to consolidate our findings. Declarations Data Availability Statement The raw data supporting the conclusions of this article will be made available by the authors, without undue reservation. Source data are provided with this paper. RNA-seq raw dada and processed expression matrix are uploaded to GEO DataSets under accession code GSE221666 ( secure token for reviewers: sdwxscyatbkhbur ). All other data analyzed or generated in this study are provided along with the article. Author contributions J.L and R.Z designed the study, obtained the fundings and supervised all the work. X.J was the main preson responsible for most of the experiments, with the help and supervision of K.W, J.W and Y.L and Y.J provided technical support of cytology experiments. Y.D and Y.L contributed to the data curation. N.W and Y.Q provided formal analysis. R.C performed the bioinformatic analysis. X.J wrote the first draft of the manuscript that was revised critically for important intellectual content by J.L and R.Z and approved by all the authors. Acknowledgments None Funding The work was financed in part by grants from the National Natural Science Foundation of China (81672751), the Key Research and Development Program of Shaanxi (2019SF-010) and the Shaanxi Provincial Science Fund for Distinguished Young Scholars (2023-JC-JQ-66). Conflict of interest The authors have declared no conflicts of interest. Ethical Approval The study was reviewed on June 7, 2021, and approved by the Medical Ethics Committee of the First Affiliated Hospital of Air Force Military Medical University (approval number: KY20212018-F-2). Follow the guidelines of the Ethics Committee of the First Affiliated Hospital of the Air Force Military Medical University on human research. Informed consent was obtained from the patients before surgery. All patients agreed to participate in the study. References Siegel RL, Miller KD, Jemal A, Cancer statistics. 2020. CA: a cancer journal for clinicians. 2020; 70: 7–30. Karimi P, Islami F, Anandasabapathy S, Freedman ND, Kamangar F. Gastric cancer: descriptive epidemiology, risk factors, screening, and prevention. Cancer epidemiology, biomarkers & prevention: a publication of the American Association for Cancer Research, cosponsored by the American Society of Preventive Oncology. 2014; 23:700–13. Yang L, Ying X, Liu S, Lyu G, Xu Z, Zhang X et al. Gastric cancer: Epidemiology, risk factors and prevention strategies. Chinese journal of cancer research = Chung-kuo yen cheng yen chiu. 2020; 32:695–704. Jin G, Lv J, Yang M, Wang M, Zhu M, Wang T, et al. Genetic risk, incident gastric cancer, and healthy lifestyle: a meta-analysis of genome-wide association studies and prospective cohort study. Lancet Oncol. 2020;21:1378–86. Duffy MJ, Lamerz R, Haglund C, Nicolini A, Kalousová M, Holubec L, et al. Tumor markers in colorectal cancer, gastric cancer and gastrointestinal stromal cancers: European group on tumor markers 2014 guidelines update. Int J Cancer. 2014;134:2513–22. Feng F, Tian Y, Xu G, Liu Z, Liu S, Zheng G, et al. Diagnostic and prognostic value of CEA, CA19-9, AFP and CA125 for early gastric cancer. BMC Cancer. 2017;17:737. Moriyama J, Oshima Y, Nanami T, Suzuki T, Yajima S, Shiratori F, et al. Prognostic impact of CEA/CA19-9 at the time of recurrence in patients with gastric cancer. Surg Today. 2021;51:1638–48. Feng F, Sun L, Liu Z, Liu S, Zheng G, Xu G, et al. Prognostic values of normal preoperative serum cancer markers for gastric cancer. Oncotarget. 2016;7:58459–69. Guo L, Wang Q, Chen K, Liu HP, Chen X. Prognostic Value of Combination of Inflammatory and Tumor Markers in Resectable Gastric Cancer. J Gastrointest surgery: official J Soc Surg Aliment Tract. 2021;25:2470–83. Kapoor A, Dhandapani S, Gaudihalli S, Dhandapani M, Singh H, Mukherjee KK. Serum albumin level in spontaneous subarachnoid haemorrhage: More than a mere nutritional marker! Br J Neurosurg. 2018;32:47–52. Oh SE, Choi MG, Seo JM, An JY, Lee JH, Sohn TS, et al. Prognostic significance of perioperative nutritional parameters in patients with gastric cancer. Clinical nutrition (Edinburgh. Scotland). 2019;38:870–6. Fearon K, Strasser F, Anker SD, Bosaeus I, Bruera E, Fainsinger RL, et al. Definition and classification of cancer cachexia: an international consensus. Lancet Oncol. 2011;12:489–95. Pressoir M, Desné S, Berchery D, Rossignol G, Poiree B, Meslier M, et al. Prevalence, risk factors and clinical implications of malnutrition in French Comprehensive Cancer Centres. Br J Cancer. 2010;102:966–71. Guo ZQ, Yu JM, Li W, Fu ZM, Lin Y, Shi YY, et al. Survey and analysis of the nutritional status in hospitalized patients with malignant gastric tumors and its influence on the quality of life. Supportive care in cancer: official journal of the Multinational Association of Supportive Care in Cancer. 2020;28:373–80. Mantovani A, Cancer. Inflaming metastasis Nature. 2009;457:36–7. Hébuterne X, Lemarié E, Michallet M, de Montreuil CB, Schneider SM, Goldwasser F. Prevalence of malnutrition and current use of nutrition support in patients with cancer. JPEN J Parenter Enter Nutr. 2014;38:196–204. Diakos CI, Charles KA, McMillan DC, Clarke SJ. Cancer-related inflammation and treatment effectiveness. Lancet Oncol. 2014;15:e493–503. Mantovani A, Allavena P, Sica A, Balkwill F. Cancer-related inflammation. Nature. 2008;454:436–44. Kim AJ, Hong DS, George GC. Diet-related interventions for cancer-associated cachexia. J Cancer Res Clin Oncol. 2021;147:1443–50. Argilés JM, López-Soriano FJ, Toledo M, Betancourt A, Serpe R, Busquets S. The cachexia score (CASCO): a new tool for staging cachectic cancer patients. J cachexia sarcopenia muscle. 2011;2:87–93. Ebner N, von Haehling S. Silver linings on the horizon: highlights from the 10th Cachexia Conference. Journal of cachexia, sarcopenia and muscle. 2018; 9: 176 – 82. Lobb RJ, Lima LG, Möller A, Exosomes. Key mediators of metastasis and pre-metastatic niche formation. Semin Cell Dev Biol. 2017;67:3–10. Kalluri R, LeBleu VS. The biology, function, and biomedical applications of exosomes. New York, NY): Science; 2020. p. 367. Gambardella V, Castillo J, Tarazona N, Gimeno-Valiente F, Martínez-Ciarpaglini C, Cabeza-Segura M, et al. The role of tumor-associated macrophages in gastric cancer development and their potential as a therapeutic target. Cancer Treat Rev. 2020;86:102015. Cong M, Song C, Xu H, Song C, Wang C, Fu Z, et al. The patient-generated subjective global assessment is a promising screening tool for cancer cachexia. BMJ supportive & palliative care; 2020. Zhang Q, Song MM, Zhang X, Ding JS, Ruan GT, Zhang XW, et al. Association of systemic inflammation with survival in patients with cancer cachexia: results from a multicentre cohort study. J cachexia sarcopenia muscle. 2021;12:1466–76. Yang QJ, Zhao JR, Hao J, Li B, Huo Y, Han YL, et al. Serum and urine metabolomics study reveals a distinct diagnostic model for cancer cachexia. J cachexia sarcopenia muscle. 2018;9:71–85. Locker GY, Hamilton S, Harris J, Jessup JM, Kemeny N, Macdonald JS, et al. ASCO 2006 update of recommendations for the use of tumor markers in gastrointestinal cancer. J Clin oncology: official J Am Soc Clin Oncol. 2006;24:5313–27. Gong X, Zhang H. Diagnostic and prognostic values of anti-helicobacter pylori antibody combined with serum CA724, CA19-9, and CEA for young patients with early gastric cancer. J Clin Lab Anal. 2020;34:e23268. Yu Q, Li KZ, Fu YJ, Tang Y, Liang XQ, Liang ZQ, et al. Clinical significance and prognostic value of C-reactive protein/albumin ratio in gastric cancer. Annals of surgical treatment and research. 2021;100:338–46. Zhang L, Wang Z, Xiao J, Zhang Z, Li H, Li F et al. Prognostic Value of Albumin to D-Dimer Ratio in Advanced Gastric Cancer. Journal of oncology. 2021; 2021: 9973743. Argilés JM, Busquets S, Stemmler B, López-Soriano FJ. Cancer cachexia: understanding the molecular basis. Nat Rev Cancer. 2014;14:754–62. Hudson MB, Rahnert JA, Zheng B, Woodworth-Hobbs ME, Franch HA, Price SR. miR-182 attenuates atrophy-related gene expression by targeting FoxO3 in skeletal muscle. Am J Physiol Cell Physiol. 2014;307:C314–9. Marinho R, Alcântara PSM, Ottoch JP, Seelaender M. Role of Exosomal MicroRNAs and myomiRs in the Development of Cancer Cachexia-Associated Muscle Wasting. Front Nutr. 2017;4:69. Additional Declarations No competing interests reported. Supplementary Files supplementarymaterial.docx Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. 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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-2437588","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":166468834,"identity":"531dfac9-302a-4fa9-928d-777a2e047592","order_by":0,"name":"Xunliang Jiang","email":"","orcid":"","institution":"Fourth Military Medical University","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Xunliang","middleName":"","lastName":"Jiang","suffix":""},{"id":166468835,"identity":"73711126-dbd3-4a39-bea7-425b484b4386","order_by":1,"name":"Ke Wang","email":"","orcid":"","institution":"Fourth Military Medical 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Li","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA90lEQVRIiWNgGAWjYBACxmYwZcHDxt7/weADkEGsFgkePp4DBoUzgAxiLZNgkJNIMPjMA2QQBMztvAcfF/ySkGGTSEjcbFMjIWNwI/cAw4+KbXgcxpdsPLNPgoeN58Fh45xjEjwGN/ISGHvO3MajhcdMmrcHqIU9sc04hw2kJceAmbENrxbz32AtDMnsvy3+EafFjJnnB1ALRxqDMWMbUVr4kqV5G0B+OcNg2Av0lOSZNwYH8fnFsP/swc88f2zs5dt7GAx+fLOx5zueY/jgRwUeLQ3AuGNsQxJROMDAcACneiCQZwBF9x9kkQZ86kfBKBgFo2AkAgBxzUzRWrZZTgAAAABJRU5ErkJggg==","orcid":"","institution":"Fourth Military Medical University","correspondingAuthor":true,"submittingAuthor":false,"prefix":"","firstName":"Jipeng","middleName":"","lastName":"Li","suffix":""}],"badges":[],"createdAt":"2023-01-03 05:59:09","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-2437588/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-2437588/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":31498904,"identity":"4d7ce5d4-d553-4c30-ad07-5abbea8b9c55","added_by":"auto","created_at":"2023-01-12 19:12:21","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":171680,"visible":true,"origin":"","legend":"\u003cp\u003eKaplan–Meier curves for the 3-year overall survival rate of high- and low-risk patients with gastric cancer\u003c/p\u003e\n\u003cp\u003e(A) Survival curves of the training cohort; the cutoff value was −2.1049.\u003c/p\u003e\n\u003cp\u003e(B) Survival curves of the test cohort; the cutoff value was −2.0812.\u003c/p\u003e","description":"","filename":"Figure1.png","url":"https://assets-eu.researchsquare.com/files/rs-2437588/v1/d2d0d600ac420e26867a0527.png"},{"id":31499901,"identity":"95c010e2-8b52-4d44-a07a-defe192a29fe","added_by":"auto","created_at":"2023-01-12 19:20:22","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":227124,"visible":true,"origin":"","legend":"\u003cp\u003eEvaluation of the prognostic model; ROC, receiver operating characteristic curve\u003c/p\u003e\n\u003cp\u003e(A) ROC curves of the training cohort. The areas under the curves of the 1-, 2-, and 3-year prognostic models were 81.13%, 78.49%, and 76.23%,respectively.\u003c/p\u003e\n\u003cp\u003e(B) ROC curves of the test cohort. The areas under the curves of the 1-, 2-, and 3-year prognostic models were 79.01%, 78.61%, and 75.34%,respectively\u003c/p\u003e","description":"","filename":"Figure2.png","url":"https://assets-eu.researchsquare.com/files/rs-2437588/v1/b49e5a223ecf5366ac460865.png"},{"id":31500127,"identity":"965a9c09-10f4-48f4-bf03-1ec764a42d4b","added_by":"auto","created_at":"2023-01-12 19:28:21","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":332786,"visible":true,"origin":"","legend":"\u003cp\u003eConstruction of the nomogram and calibration curve. \u003cbr\u003e\n (A) Nomogram constructed based on cachexia and the carcinoembryonic antigen, carbohydrate antigen 19-9, and albumin levels for predicting the overall survival of patients with gastric cancer.\u003cbr\u003e\n (B) 1-, 2- and 3-year calibration diagram used to verify the accuracy of the nomogram predictions.\u003c/p\u003e\n\u003cp\u003eIn the nomogram, total scores were obtained by summing up individual scores from the respective variables, and higher scores indicated poorer survival. In the calibration diagram, the nearer distance of the red or blue dots to the diagonal line, the more accurate is the nomogram’s predictive ability.\u003c/p\u003e","description":"","filename":"Figure3.png","url":"https://assets-eu.researchsquare.com/files/rs-2437588/v1/be7b13dc1e39a2f998586e2f.png"},{"id":31498908,"identity":"b6454df4-4281-45d6-84e1-c450ababe3c9","added_by":"auto","created_at":"2023-01-12 19:12:22","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":805228,"visible":true,"origin":"","legend":"\u003cp\u003eResults of plasma exosome identification\u003c/p\u003e\n\u003cp\u003e(A) Transmission electron microscopy revealed that the exosome is complete in shape and uniform in density.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e(B) Nanoparticle tracking analysis (NTA) revealed that the exosome diameter was about 120 nm (i.e., within the normal exosome diameter range). \u0026nbsp;\u003c/p\u003e\n\u003cp\u003e(C) Western blotting results indicated that the exosome positive markers (CD9, CD81, and TSG101) of the samples were normally expressed.\u003c/p\u003e","description":"","filename":"Figure4.png","url":"https://assets-eu.researchsquare.com/files/rs-2437588/v1/6dcc3ed8df91f42c9e7a0607.png"},{"id":31500128,"identity":"6e4725e8-b7dd-413d-8097-60f9db01d78f","added_by":"auto","created_at":"2023-01-12 19:28:22","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":567913,"visible":true,"origin":"","legend":"\u003cp\u003eResults of the analysis of differentially expressed miRNAs\u003c/p\u003e\n\u003cp\u003e(A) Volcano plot results of differentially expressed miRNAs\u003c/p\u003e\n\u003cp\u003e(B) Cluster analysis results of differentially expressed miRNAs. The results of 24 differentially expressed miRNAs are shown.\u003c/p\u003e\n\u003cp\u003e(C) Relative expression of miR-432-5p between patients with and without cachexia. The data are expressed as mean ± SD. ***p \u0026lt; 0.001.\u003c/p\u003e\n\u003cp\u003e(D) ROC curves to evaluate the sensitivity and specificity of miR-432-5p\u003c/p\u003e\n\u003cp\u003e(E) The expression of hsa-miR-432-5p in tumor tissues and adjacent tissues of the same patient. **p \u0026lt; 0.01.\u003c/p\u003e\n\u003cp\u003e(F) The expression of hsa-miR-432-5p between normal and tumor tissues in TCGA database. *p \u0026lt; 0.05.\u003c/p\u003e","description":"","filename":"Figure5.png","url":"https://assets-eu.researchsquare.com/files/rs-2437588/v1/bc3fca465abdf4a324ca37c8.png"},{"id":31574573,"identity":"c7aad85f-768e-4b34-8cb1-2687cfda085c","added_by":"auto","created_at":"2023-01-14 13:29:29","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1418652,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-2437588/v1/a949635a-304c-424e-9c94-647932aa0cfd.pdf"},{"id":31498909,"identity":"05ed2d20-6fc2-4b36-97ea-e49d91f44353","added_by":"auto","created_at":"2023-01-12 19:12:22","extension":"docx","order_by":8,"title":"","display":"","copyAsset":false,"role":"supplement","size":1244834,"visible":true,"origin":"","legend":"","description":"","filename":"supplementarymaterial.docx","url":"https://assets-eu.researchsquare.com/files/rs-2437588/v1/c09b417aab853cd2f192bcc9.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"A novel prognostic model of gastric cancer patients based on cachexia status by using plasma exosome-derived miRNAs","fulltext":[{"header":"Introduction","content":"\u003cp\u003eGastric cancer is one of the most common malignant tumors worldwide, with morbidity and mortality rates among the top five highest in the world [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]. According to cancer statistics reported in 2020, more than 1.08\u0026nbsp;million people are newly diagnosed with cancer each year and approximately 770,000 people die from gastric cancer[\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]. Gastric cancer is more common in less developed regions, particularly developing countries and regions with relatively poor hygiene and medical conditions. East Asia, particularly China, accounts for approximately 50% of the global cases [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. With continuous advancements in medical technologies worldwide, the mortality rate of gastric cancer has been decreasing. However, owing to the high incidence of gastric cancer and difficulty of early diagnosis, improving the survival rate and quality of life of patients with gastric cancer remains challenging. Gastric cancer is a multifactorial disease with an abysmal prognosis. The 5-year survival rate of patients with a delayed diagnosis is \u0026lt;\u0026thinsp;10%, whereas that of patients with an early diagnosis is 85% [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]. One of the reasons for such a large difference in the survival rates is severe deficiencies in the nutritional status of the patients [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eSerum biomarkers are significantly associated with disease prognosis and can complement the current TNM staging[\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]. A previous study identified an increased carcinoembryonic antigen (CEA) as an independent risk factor for poor prognosis in patients with early-stage gastric cancer [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]. In recurrent gastric cancer, particularly in the case of lymph node recurrence, the double-positive status of CEA and carbohydrate antigen 19\u0026thinsp;\u0026minus;\u0026thinsp;9 (CA19-9) may negatively affect the disease prognosis [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]. Recent evidence suggests that CA19-9 and alpha-fetoprotein (AFP) levels are independent predictors of gastric cancer prognosis, and the combined assessment of CA19-9, AFP, and CA125 levels is valuable for the evaluation of gastric cancer prognosis [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]. Futhermore, CA19-9 level combined with neutrophil\u0026ndash;lymphocyte ratio can better predict the overall survival (OS) of patients with gastric cancer after surgery [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]. In recent years, assessment of patients\u0026rsquo; nutritional status has become increasingly important in clinical practice. Albumin, a potent protein produced by hepatocytes, has been widely used as a serum nutritional biomarker for predicting death among critically ill patients[\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]. Low albumin levels are often associated with adverse effects, poor response to chemotherapy, and shortened OS and progression-free survival in patients with advanced unresectable gastric cancer [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]. However, these serum biomarkers only reflect the substances released by tumor cells and cannot fully reflect the metabolic and nutritional statuses of a patient\u0026rsquo;s entire body.\u003c/p\u003e \u003cp\u003eCachexia is a multifactorial metabolic syndrome characterized by severe weight loss and muscle wasting (with or without fat loss) [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]. Cachexia caused by tumors, known as \u003cem\u003etumor cachexia\u003c/em\u003e, is the most common type of cachexia. The incidence of cachexia can be as high as 80% in patients with gastric cancer and can directly lead to the death of at least 20% of patients with cancer [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e, \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]. In long-term clinical practice, cachexia assessment can easily be overlooked. Patients with significant weight loss enter a stage of refractory cachexia, which results in severe nutritional deficiencies. However, at this stage, no effective clinical intervention can be performed; moreover, simple nutritional support is not beneficial. Systemic inflammation and severe malnutrition are common in patients with cancer[\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e, \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e]. These conditions substantially increase patients\u0026rsquo; susceptibility to tumor growth, tumor cell spread, and drug resistance, resulting in a significantly reduced patient survival rate [\u003cspan additionalcitationids=\"CR18\" citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e]. Accurate and individualized prognostic assessment of patients with gastric cancer is crucial for surgeons to formulate better treatment strategies. Nutritional status assessment is a comprehensive evaluation of patient response tolerance, drug resistance, and postoperative rehabilitation. Therefore, cachexia can be considered an important indicator for prognostic evaluation. The body secretes biomarkers in the serum in response to release of circulating substances by tumor cells. Cachexia is diagnosed based on the characterization of systemic nutritional status and assessment of metabolic disorders. Thus, it is reasonable and feasible to combine these factors to improve the predictive power of prognostic factors for patients with gastric cancer.\u003c/p\u003e \u003cp\u003eCachexia is a multifactorial metabolic disorder syndrome manifesting in end-stage cancer patients, and there has been a lack of simple, accurate diagnostic criteria for a long time. Weight loss alone does not fully reflect the pathophysiological changes and clinical impact of cachexia. Merely assessing the body weight and ignoring other manifestations often result in the exclusion of patients with refractory cachexia at the time of diagnosis [\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e, \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e]. Thus, identifying more sensitive diagnostic indicators is essential. As the main components of liquid biopsy, exosomes have the advantages of high concentration, stability, ease of collection, and being rich in tumor genome and mutation characteristics. Thus, exosomes are important in predicting new gastric cancer occurrences and therapeutic targets [\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e]. Therefore, developing novel methods for the early prediction and diagnosis of cachexia based on liquid biopsy and exosomal microRNAs (miRNAs) detection is clinically significant[\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eIn this study, we developed a simple, personalized prognosis prediction model for patients with gastric cancer based on the assessment of serum biomarkers and nutritional metabolism (cachexia). This model may enable clinicians to more effectively evaluate the prognosis of gastric cancer and formulate more optimized treatment strategies. We determined the risk factors associated with the prognosis of gastric cancer using univariate Cox regression analysis and constructed a new prognostic analysis model using multivariate Cox regression. Analysis of the area under the receiver operating characteristic (ROC) curve exhibited high sensitivity and specificity. Subsequently, we constructed a novel nomogram that included cachexia and CEA, CA19-9, and serum albumin levels for the preoperative personalized prediction of OS in patients with gastric cancer. Finally, we identified a plasma exosomal miRNA via whole-genome sequencing. This miRNA can be obtained via liquid biopsy and used to efficiently and consistently predict the occurrence of gastric cancer cachexia at an early stage.\u003c/p\u003e"},{"header":"Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003ePatients and Study Design\u003c/h2\u003e \u003cp\u003eThis retrospective study included patients with gastric cancer who received radical treatment at the First Affiliated Hospital of Air Force Military Medical University (Shaanxi, China) between December 2016 and December 2018. The inclusion criteria were as follows: (1) patients with gastric cancer as the only primary carcinoma, (2) patients with gastric cancer for whom complete follow-up and basic clinical data were available, (3) patients who underwent radical gastrectomy for gastric cancer, (4) patients with available postoperative data, such as the TNM stage determined based on the seventh or eighth edition of TNM staging and follow-up data for \u0026gt;\u0026thinsp;36 months, and (5) patients who provided informed consent for participation. Patients who received neoadjuvant therapy, such as radiotherapy or chemotherapy, before surgery were excluded as these treatments may affect disease prognosis. Patients with other tumors or nonneoplastic cachexia were also excluded as these conditions may influence the development of gastric cancer models.\u003c/p\u003e \u003cp\u003eThe diagnostic criteria for cachexia were as follows: (1) weight loss of \u0026gt;\u0026thinsp;5% in the past 6 months, (2) body mass index (BMI) of \u0026lt;\u0026thinsp;20 kg/m\u003csup\u003e2\u003c/sup\u003e and any degree of weight loss of \u0026gt;\u0026thinsp;2%, (3) anthropometric measurement of the muscle area in the middle of the upper arm (men, \u0026lt;\u0026thinsp;32 cm\u003csup\u003e2\u003c/sup\u003e; women, \u0026lt;\u0026thinsp;18 cm\u003csup\u003e2\u003c/sup\u003e), (4) skeletal muscle index of the limbs (men, \u0026lt;\u0026thinsp;7.26 kg/m\u003csup\u003e2\u003c/sup\u003e; women, \u0026lt;\u0026thinsp;5.45 kg/m\u003csup\u003e2\u003c/sup\u003e), and (5) computed tomography-based lumbar skeletal muscle index (men, \u0026lt;\u0026thinsp;55 cm\u003csup\u003e2\u003c/sup\u003e/m\u003csup\u003e2\u003c/sup\u003e; women, \u0026lt;\u0026thinsp;39 cm\u003csup\u003e2\u003c/sup\u003e/m\u003csup\u003e2\u003c/sup\u003e). The presence of any one of the aforementioned criteria indicates cachexia.\u003c/p\u003e \u003cp\u003e The study was reviewed on June 7, 2021, and approved by the Medical Ethics Committee of the First Affiliated Hospital of Air Force Military Medical University (approval number: KY20212018-F-2). Informed consent was obtained from the patients before surgery.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003eFollow-Up\u003c/h2\u003e \u003cp\u003ePatients with gastric cancer were evaluated every 3 months in the first year following surgery and every 6 months thereafter via telephonic and outpatient follow-ups. Follow-up re-examinations included assessment of the patients\u0026rsquo; basic symptoms and physical, imaging, and serological examinations. The primary endpoint of this study was OS (days), defined as the time from the diagnosis of gastric cancer to death from any cause.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003eBlood Sample\u003c/h2\u003e \u003cp\u003ePeripheral venous blood was collected from the patients on the day of surgery. Within 30 min of collection, the blood samples were centrifuged at a low speed (1000 g) for 15 min at 4\u0026deg;C, and the supernatant (plasma) was collected and labeled. All plasma samples were stored at \u0026minus;\u0026thinsp;80\u0026deg;C.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003eExosome and Exosomal miRNA Extraction\u003c/h2\u003e \u003cp\u003ePlasma exosomes were extracted from the patients using an exosome rapid extraction kit (exoRNeasy middle extraction kit, QIAGEN, Germany). The operation method is carried out according to the manufacturer\u0026rsquo;s instructions.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003eStatistical Analysis\u003c/h2\u003e \u003cp\u003eAll statistical analyses were performed using SPSS version 26 (IBM Corp., Armonk, NY, USA) or R software version 4.0.4 (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.r-project.org/\u003c/span\u003e\u003cspan address=\"https://www.r-project.org/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e). The measurement data conforming to a normal distribution were analyzed using Student\u0026rsquo;s \u003cem\u003et\u003c/em\u003e-test or one-way analysis of variance and expressed as means\u0026thinsp;\u0026plusmn;\u0026thinsp;standard deviations. Categorical data were analyzed using χ\u003csup\u003e2\u003c/sup\u003e or Fisher\u0026rsquo;s exact test and expressed as frequency or percentage. A restricted cubic spline test was performed on the transformed data to verify the linear relationship between variables and outcomes. Univariate Cox regression analysis was used to determine the risk factors associated with gastric cancer prognosis. Multivariate Cox proportional-hazards regression analysis was used to construct a prognostic model for gastric cancer. The risk model with the smallest Akaike information criterion (AIC) value was identified using a backward and stepdown process. Kaplan\u0026ndash;Meier (K\u0026ndash;M) and ROC curves were used to evaluate the accuracy and specificity of the model. We constructed a nomogram based on the factors identified using the prognostic model and used the corresponding 1-, 2-, and 3-year calibration curves to verify the accuracy of the nomogram.\u003c/p\u003e \u003cp\u003eThe following R language packages were used: \u0026ldquo;ggplot2,\u0026rdquo; \u0026ldquo;survminer,\u0026rdquo; \u0026ldquo;rms,\u0026rdquo; \u0026ldquo;survival,\u0026rdquo; \u0026ldquo;timeROC,\u0026rdquo; \u0026ldquo;rmda,\u0026rdquo; \u0026ldquo;dplyr,\u0026rdquo; \u0026ldquo;rfsrc,\u0026rdquo; \u0026ldquo;randomForestSRC,\u0026rdquo; \u0026ldquo;ggRandomForests,\u0026rdquo; and \u0026ldquo;compareGroups.\u0026rdquo; All tests were two-sided, and a \u003cem\u003ep\u003c/em\u003e value of \u0026lt;\u0026thinsp;0.05 was considered statistically significant. *p\u0026thinsp;\u0026lt;\u0026thinsp;0.05,**p\u0026thinsp;\u0026lt;\u0026thinsp;0.01, ***p\u0026thinsp;\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/div\u003e"},{"header":"Results","content":"\u003cdiv class=\"Section2\" id=\"Sec9\"\u003e\n \u003ch2\u003eClinical Characteristics of the Patients\u003c/h2\u003e\n \u003cp\u003eA total of 1240 patients with gastric cancer hospitalized between December 2016 and December 2018 Were included in this study. All patients underwent radical gastrectomy for gastric cancer and postoperative pathological examination for confirmation diagnosis. The patients and their families were informed about the study design and significance. They agreed to cooperate and be followed up for study purposes. Of the 1240 patients included in this study, 33 had no survival duration data, 59 were lost to follow-up, and 47 had incomplete basic data. Finally, 1101 patients were included for a follow-up period of approximately 50 months. The baseline data of the cohort are presented in \u003cstrong\u003eSupplementary Table S1\u003c/strong\u003e.\u003c/p\u003e\n \u003cp\u003eThe design of this study was as follows: the population that met the inclusion and exclusion criteria was randomly divided into training and test cohorts at a ratio of 7:3. The model was filtered and optimized in the training cohort. Univariate Cox proportional-hazards regression analysis was used to screen for prognostic factors, and multivariate Cox regression analysis was used to determine the final stable model. ROC and K\u0026ndash;M curves were used to verify the model\u0026rsquo;s predictive ability in the two datasets, respectively. Finally, a new nomogram was constructed based on the key factors identified using the model; the nomogram was then evaluated using the calibration curve. The obtained Cox prognostic model was validated using the Random Survival Forest Prognostic Model (\u003cstrong\u003eSupplementary Figure S1\u003c/strong\u003e).\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv class=\"Section2\" id=\"Sec10\"\u003e\n \u003ch2\u003eClinical Baseline Characteristics of the Training and Test cohorts\u003c/h2\u003e\n \u003cp\u003eThe training and test cohorts included 770 and 331 patients with gastric cancer, respectively, with median survival durations of 37.17 and 38.17 months. No significant differences were observed between the two cohorts in terms of any baseline characteristics. Table \u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e presents the detailed baseline characteristics of the two cohorts. To better fit the gastric cancer prognostic model and avoid the zero value, we transformed the original serum marker values by ln(values\u0026thinsp;+\u0026thinsp;1).\u003c/p\u003e\n \u003cdiv class=\"gridtable\"\u003e\u0026nbsp;\u003ctable border=\"1\" id=\"Tab1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eBasic clinical features in the training and testing cohorts\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003ccolgroup cols=\"4\"\u003e\u003c/colgroup\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eCharacteristics\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eTrain cohort\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eTest cohort\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eP value\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNo. of cases\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e770(69.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e331(30.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSurvival status (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.491\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eDead\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e237 (30.78)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e95 (28.70)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAlive\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e533 (69.22)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e236 (71.30)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCachexia (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.616\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e236 (30.65)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e104 (31.42)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e534 (69.35)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e227 (68.58)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSurvival time(month)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e37.17 (17.50)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e38.18 (17.16)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.379\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAge(year)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e58.06 (10.58)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e57.20(11.20)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.225\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSex (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.300\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMale\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e585 (75.97)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e241 (72.81)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eFemale\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e185 (24.0.)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e90 (27.19)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNRS2002\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.374\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026ge;3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e514 (66.75)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e230 (69.49)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e༜3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e256 (33.25)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e101 (30.51)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eBMI (kg/m2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e22.87 (3.12)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e22.79 (3.31)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.731\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCA19-9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e21.57 (27.84)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e24.29(31.84)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.154\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCEA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e4.59 (7.37)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e4.46 (7.65)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.788\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eHemoglobin\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e127.41 (23.96)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e127.76 (24.07)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.827\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAlbumin\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e37.33 (6.05)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e38.07 (6.19)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.065\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eT stage (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.231\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e187 (24.29)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e90 (27.19)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e117 (15.19)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e50 (15.11)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e172 (22.34)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e85 (25.68)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e294 (38.18)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e106 (32.02)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eM stage (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.353\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e744 (96.62)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e316 (95.47)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e26 (3.38)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e15 (4.53)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eN stage (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.690\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e323 (41.95)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e146 (44.11)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e125 (16.23)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e59 (17.82)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e114 (14.81)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e43 (12.99)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e208 (27.01)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e83 (25.08)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003ctfoot\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"4\"\u003eBMI, body mass index; CEA, carcinoembryonic antigen;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"4\"\u003eCA19-9, carbohydrate antigen 19\u0026thinsp;\u0026minus;\u0026thinsp;9; NRS2002,nutritional risk screening scale-2002;\u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tfoot\u003e\n \u003c/table\u003e\n \u003c/div\u003e\n\u003c/div\u003e\n\u003cdiv class=\"Section2\" id=\"Sec11\"\u003e\n \u003ch2\u003ePrognostic Value of Clinical Features\u003c/h2\u003e\n \u003cp\u003eThe 1-, 2-, and 3-year survival rates of the training cohort were 81.69%, 74.68%, and 68.96%, respectively, whereas those of the test cohort were 85.20%, 75.23%, and 71.30%, respectively. Table \u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003e presents the results of the univariate Cox regression analysis of the training set; NRS2002 score (hazard ratio [HR], 1.464; \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.01) and cachexia (HR, 4.565; \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001) were identified as the risk factors and BMI (HR, 0.913; \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001), serum albumin level (HR, 0.141; \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001), and red blood cell count (HR, 0.161; \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001) were identified as the protective factors predicting the OS of patients with gastric cancer. Regarding the prognostic analysis of the tumor biomarkers, three common markers\u0026mdash;CEA (HR, 1.642; \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001), CA19-9 (HR, 1.936; \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001), and CA125 (HR, 1.334; \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001) levels\u0026mdash;were identified as the causes of poor prognosis of gastric cancer. The results of the prognostic analysis of the TNM staging system were consistent with the traditional theory.\u003c/p\u003e\n \u003cdiv class=\"gridtable\"\u003e\u0026nbsp;\u003ctable border=\"1\" id=\"Tab2\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eUnivariate Cox regression analysis for GC training cohort\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003ccolgroup cols=\"5\"\u003e\u003c/colgroup\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eVariables\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e\u0026beta;\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eHR\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e95% Cl\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eP\u0026nbsp;value\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSex(Male vs Female)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-0.202\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.817\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.598\u0026ndash;1.117\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.206\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAge\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.007\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.007\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.995\u0026ndash;1.019\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.272\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eBMI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-0.092\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.913\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.873\u0026ndash;0.954\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u0026lt;\u0026nbsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCachexia\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.518\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e4.565\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e3.516\u0026ndash;5.927\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u0026lt;\u0026nbsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eTNM(Ⅰ,Ⅱvs. Ⅲ,IV)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.175\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e3.240\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2.412\u0026ndash;4.351\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u0026lt;\u0026nbsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNRS2002\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.381\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.464\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.096\u0026ndash;1.957\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.010\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAlbumin\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-1.959\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.141\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.066\u0026ndash;0.301\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u0026lt;\u0026nbsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCEA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.496\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.642\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.419\u0026ndash;1.899\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u0026lt;\u0026nbsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCA199\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.661\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.936\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.700-2.205\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u0026lt;\u0026nbsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCA125\u003c/p\u003e\n \u003cp\u003eGlucose\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.288\u003c/p\u003e\n \u003cp\u003e0.265\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.334\u003c/p\u003e\n \u003cp\u003e1.303\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.155\u0026ndash;1.542\u003c/p\u003e\n \u003cp\u003e0.831\u0026ndash;2.045\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u0026lt;\u0026nbsp;0.001\u003c/p\u003e\n \u003cp\u003e0.249\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eWBC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.168\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.183\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.854\u0026ndash;1.639\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.312\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eRBC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-1.825\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.161\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.064\u0026ndash;0.404\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u0026lt;\u0026nbsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCHD\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-0.047\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.954\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.521\u0026ndash;1.748\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.879\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eHypertension\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.040\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.041\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.763\u0026ndash;1.419\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.801\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eDiabetes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-0.077\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.926\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.607\u0026ndash;1.412\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.720\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSmoking history\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-0.111\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.895\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.693\u0026ndash;1.155\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.394\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAlcohol drinking\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.105\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.111\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.779\u0026ndash;1.584\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.561\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003ctfoot\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"5\"\u003eGC, gastric cancer; HR, hazard ration; BMI, body mass index;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"5\"\u003eACD, Acute Coronary Disease; CEA, carcinoembryonic antigen;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"5\"\u003eCA19-9, carbohydrate antigen 19\u0026thinsp;\u0026minus;\u0026thinsp;9; CA125, carbohydrate antigen 125;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"5\"\u003eWBC, leukocyte ,white blood cell ; RBC, Erythrocyte/Red Blood Cell\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"5\"\u003eCHD, coronary heart disease\u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tfoot\u003e\n \u003c/table\u003e\n \u003c/div\u003e\n \u003cp\u003eThrough multivariate Cox analysis, cachexia and CEA, CA19-9, and serum albumin levels were identified as significant independent risk factors for OS (Table \u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003e). Therefore, we combined these four factors to construct an optimal model based on a backward and stepdown process with the smallest AIC.\u003c/p\u003e\n \u003cdiv class=\"gridtable\"\u003e\u0026nbsp;\u003ctable border=\"1\" id=\"Tab3\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eMultivariate Cox regression analysis for GC training cohort\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003ccolgroup cols=\"5\"\u003e\u003c/colgroup\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eVariables\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eCoefficient\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eHR\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e95% Cl\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eP\u0026nbsp;value\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCachexia\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.977\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2.655\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.950\u0026ndash;3.616\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u0026lt;\u0026nbsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCEA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.165\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.179\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.006\u0026ndash;1.382\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.009\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCA19-9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.356\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.427\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.234\u0026ndash;1.650\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.042\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAlbumin\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-1.058\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.347\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.157\u0026ndash;0.769\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u0026lt;\u0026nbsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003ctfoot\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"5\"\u003eCEA, carcinoembryonic antigen; CA19-9, carbohydrate antigen 19\u0026thinsp;\u0026minus;\u0026thinsp;9;\u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tfoot\u003e\n \u003c/table\u003e\n \u003c/div\u003e\n\u003c/div\u003e\n\u003cdiv class=\"Section2\" id=\"Sec12\"\u003e\n \u003ch2\u003eConstruction of the Prognostic Model for Gastric Cancer\u003c/h2\u003e\n \u003cp\u003eWe used a multivariate Cox regression model to screen for optimal variables and models based on a backward and stepdown process. Four variables were identified: cachexia and CEA, CA19-9, and serum albumin levels. The regression model calculated the coefficients corresponding to the four factors (Table \u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003e). Based on the coefficients and variables, the following model formula was established to calculate the risk score of patients with gastric cancer:\u003c/p\u003e\n \u003cp\u003eRisk score\u0026thinsp;=\u0026thinsp;0.977 \u0026sdot; cachexia (where 0\u0026thinsp;=\u0026thinsp;No and 1\u0026thinsp;=\u0026thinsp;Yes )\u0026thinsp;+\u0026thinsp;0.165 \u0026sdot; CEA level\u0026thinsp;+\u0026thinsp;0.356 \u0026sdot; CA19-9 level\u0026thinsp;\u0026minus;\u0026thinsp;1.058 \u0026sdot; albumin level\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv class=\"Section2\" id=\"Sec13\"\u003e\n \u003ch2\u003eAssessment of the Prognostic Model\u003c/h2\u003e\n \u003cp\u003eWe divided each cohort into two groups based on the optimal cutoff point indicated by the \u0026ldquo;survminer\u0026rdquo; R language package: patients with a value higher than the cutoff point were classified into the high-risk group while the others were classified into the low-risk group. For the training cohort, the cutoff point was \u0026minus;\u0026thinsp;2.1049; 199 and 571 patients were classified into the high-risk and low-risk groups, respectively. The 3-year survival rates of the high- and low-risk groups were 37.69% and 80.21%, respectively, indicating a significant difference (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.0001). The K\u0026ndash;M curve clearly showed that the survival duration of the low-risk group was much longer than that of the high-risk group (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.0001; Fig. \u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003eA). Similarly, for the test cohort, the cutoff point was \u0026minus;\u0026thinsp;2.0812; the high-risk and low-risk groups included 103 and 228 patients, respectively. The 3-year survival rates of the two groups differed significantly (40.78% and 85.09% for the high-risk and low-risk groups,respectively; Fig. \u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003eB). These results indicate that the prognostic model can significantly divide the population into high- and low-risk groups.\u003c/p\u003e\n \u003cp\u003eThe ROC curve was used for correlation analysis to evaluate the predictive power of the prognostic model. In the training cohort, the areas under the curve (AUCs) of this risk model for 1-, 2-, and 3-year survival were 81.13%, 78.49%, and 76.23%, respectively (Fig. \u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003eA),whereas in the test cohort, the corresponding values were 79.01%, 78.61%, and 75.34%, respectively (Fig. \u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003eB). We further examined each index of the model and traditional TNM staging index using the ROC curve. The results showed that the AUC values of the prognostic model were significantly higher than the aforementioned individual metrics (\u003cstrong\u003eSupplementary Figure S2\u003c/strong\u003e). This indicates that the prognostic model exhibits better discriminative ability and fitting performance than the traditional TNM staging system.\u003c/p\u003e\n \u003cp\u003eIn summary, we developed a new prognostic model that included cachexia and the CEA, CA19-9, and serum albumin levels. The model demonstrated a satisfactory predictive performance with a high sensitivity and specificity for predicting the prognosis of gastric cancer.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv class=\"Section2\" id=\"Sec14\"\u003e\n \u003ch2\u003eNomograms and Calibration Curves\u003c/h2\u003e\n \u003cp\u003eWe further constructed a novel nomogram (Fig. \u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003eA). After combining the data of the training and test cohorts (n\u0026thinsp;=\u0026thinsp;1101), the nomogram was constructed based on the following four independent risk factors: cachexia and CEA, CA19-9, and albumin levels. First, we obtained the corresponding nomogram scores based on the patients\u0026rsquo; four indicators. Subsequently, we summed the scores of each item to calculate the total score. The total score was finally projected onto the corresponding 1-, 2-, and 3-year survival rates. We plotted 1-, 2-, and 3-year calibration curves to verify the accuracy of the nomogram predictions (Fig. \u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003eB). The nomogram constructed in this study accurately predicted patient survival and exhibited the best agreement between predictions and actual observations.\u003c/p\u003e\n \u003cp\u003eIn the nomogram, total scores were obtained by summing up individual scores from the respective variables, and higher scores indicated poorer survival. In the calibration diagram, the nearer distance of the red or blue dots to the diagonal line, the more accurate is the nomogram\u0026rsquo;s predictive ability.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv class=\"Section2\" id=\"Sec15\"\u003e\n \u003ch2\u003eRandom Survival Forest Prognostic Model\u003c/h2\u003e\n \u003cp\u003eFor the abovementioned Cox regression model constructed, we further constructed a random survival forest prognosis model for validation. We constructed the model using the \u0026ldquo;rfsrc\u0026rdquo; function of the R language to examine the effect of each variable on disease prognosis and patient survival. A total of 500 binary survival trees were generated using the model. As the number of survival trees increases, the model prediction error rate decreases significantly. When the number of survival trees increases to a certain number, the error rate curve stabilizes (\u003cstrong\u003eSupplementary Figure S3A\u003c/strong\u003e). Performing variable screening based on machine learning can help us better understand the importance of variables, and the variable importance (VIMP) and minimal depth methods are commonly used to screen variables in random survival forest models. The results showed that cachexia and CA19-9, CEA, and serum albumin levels ranked in the top four in both variable screening methods (\u003cstrong\u003eSupplementary Figure S3B\u003c/strong\u003e). Thus, the gastric cancer prognosis model developed based on Cox proportional-hazards regression model demonstrated a satisfactory predictive performance.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv class=\"Section2\" id=\"Sec16\"\u003e\n \u003ch2\u003eIdentification of Exosomes\u003c/h2\u003e\n \u003cp\u003ePrevious studies have identified the importance of cachexia in prognostic models for patients with gastric cancer. However, early diagnosis of cachexia remains challenging. To compensate for the lack of early diagnostic capacity for cachexia, we identified a potential biomarker of cachexia via whole-genome sequencing, which may better predict the occurrence of cachexia at an early stage.\u003c/p\u003e\n \u003cp\u003eA total of 105 patients were evaluated, all of whom were subjected to the extraction of exosomes using an exosome extraction kit (exoRNeasy). The results of exosome identification were as follows: transmission electron microscopy revealed that the exosomes were complete in shape and uniform in density (Fig. \u003cspan class=\"InternalRef\"\u003e4\u003c/span\u003eA). Nanoparticle tracking analysis revealed that the particle diameter of the exosomes was approximately 120 nm, which is in the normal range of exosome diameters (Fig. \u003cspan class=\"InternalRef\"\u003e4\u003c/span\u003eB). Western blotting results indicated that the protein expression of all exosome-positive markers (CD9, CD81, and TSG101) of the samples were in the normal range (Fig. \u003cspan class=\"InternalRef\"\u003e4\u003c/span\u003eC).\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv class=\"Section2\" id=\"Sec17\"\u003e\n \u003ch2\u003ePlasma Exosome Whole-Genome Sequencing\u003c/h2\u003e\n \u003cp\u003eTo determine the association between exosomal miRNAs and gastric cancer cachexia, we performed whole-genome sequencing of plasma exosomes obtained from 5 of the abovementioned 105 patients (two gastric cancer patients without cachexia and three gastric cancer patients with cachexia were included in the G1 and G2 groups, respectively). We detected 670 exosomal miRNAs, including 666 known miRNAs and 4 newly predicted miRNAs. Futher, 24 miRNAs were differentially expressed. Among these, 12 were upregulated and 12 were downregulated. Figures \u003cspan class=\"InternalRef\"\u003e5\u003c/span\u003eA and 5\u003cstrong\u003eB\u003c/strong\u003epresent the volcano plot and cluster analysis results of differentially expressed miRNAs, respectively.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv class=\"Section2\" id=\"Sec18\"\u003e\n \u003ch2\u003eGastric Cancer Cachexia-Related Exosomal miRNA\u003c/h2\u003e\n \u003cp\u003eWe used quantitative RT-PCR (qRT-PCR) to verify these exosomal miRNAs in an independent validation cohort comprising 37 gastric cancer patients without cachexia and 32 gastric cancer patients with cachexia. The expression of hsa-miR-432-5p was upregulated in plasma-derived exosomes obtained from patients with cachexia compared with those obtained from patients without cachexia (Fig. \u003cspan class=\"InternalRef\"\u003e5\u003c/span\u003eC). Subseuently, we validated the reliability of hsa-miR-432-5p for the early diagnosis of cachexia in this independent sample cohort using ROC curves. The results showed that the AUC for hsa-miR-432-5p achieved a value of 0.8043 (Fig. \u003cspan class=\"InternalRef\"\u003e5\u003c/span\u003eD). Based on these findings, exosomal hsa-miR-432-5p was significantly highly expressed in patients with cachexia, suggesting that plays an important role in the development of gastric cancer cachexia. Therefore, exosomal hsa-miR-432-5p has the potential as a biomarker for predicting the occurrence of gastric cancer cachexia, which may provide new insights and help develop novel methods for the early prediction and diagnosis of gastric cancer cachexia.\u003c/p\u003e\n \u003cp\u003eFurthermore, we evaluated the expression of hsa-miR-432-5p in tumor tissues and adjacent tissues of the same patient. As expected, the expression of hsa-miR-432-5p tended to decrease in adjacent tissues (Fig. \u003cspan class=\"InternalRef\"\u003e5\u003c/span\u003eE), which may mean that the exosomal miRNA comes from tumor cells. Similarly, a trend consistent with this result was observed in the comparison of tumor and normal tissues based on data obtained from The Cancer Genome Atlas database (Fig. \u003cspan class=\"InternalRef\"\u003e5\u003c/span\u003eF).\u003c/p\u003e\n \u003cp\u003eWe further performed gene ontology (GO) analysis of the miRNA target genes differentially expressed in exosomes; 260 annotations were obtained for differentially expressed miRNAs. The GO analysis revealed that the differentially expressed miRNA target genes were mainly concentrated in cellular processes, biological regulations, metabolisms, tumors, and other processes (\u003cstrong\u003eSupplementary Figure S4\u003c/strong\u003e). The pathways of the target genes were analyzed using Kyoto Encyclopedia of Genes and Genome pathway analysis (KEGG). A total of 179 annotations of differentially expressed genes were obtained. The differentially expressed miRNA target genes were mainly related to tumors (\u003cstrong\u003eSupplementary Figure S5\u003c/strong\u003e).These results indicate that the differentially expressed miRNAs were significantly correlated with tumors and their biological regulation metabolism and that they can be transported to multiple body parts, including the skeletal muscle, through the exosomes released by tumor tissues, thus playing regulatory role.\u003c/p\u003e\n\u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eIn recent decades, the decline in the incidence of \u003cem\u003eHelicobacter pylori\u003c/em\u003e infection, improvement in lifestyle habits, and continuous development of targeted therapy and immunotherapy have contributed to the decline in the incidence and mortality of gastric cancer in most parts of the world[\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e]. However, the 5-year OS (even the 3-year OS) and quality of life of patients with gastric cancer are far from satisfactory, particularly for those with stage IV cancer[\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]. In most parts of the world, the 5-year survival rate of patients with gastric cancer is nearly 20\u0026ndash;30%, which is significantly lower than that of patients with other malignant tumors[\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]. Gastric cancer symptoms usually appear at an advanced stage, which often leads to a poor prognosis. Patient prognoses must be accurately assessed, and individualized treatment options must be adopted to improve the patients\u0026rsquo; quality of life. Increasing evidence suggests that malnutrition significantly shortens the survival duration of patients, particularly those with cancer. The crucial role of serum biomarkers in the management of advanced disease and in the prognoses of various cancers is constantly being investigated. In this study, we evaluated the association between cachexia status and serum biomarker levels and the 3-year OS of patients with gastric cancer. We further developed an innovative risk prediction model based on the results of cachexia diagnosis and serum biomarker assessment. The ROC curve exhibited the exact consistency of the model. Ultimately, a novel nomogram was constructed based on the independent risk factors identified, with a great potential for broad clinical applications.\u003c/p\u003e \u003cp\u003eA previous study reported that 50\u0026ndash;90% of patients with malignant tumors experience weight loss and malnutrition, particularly those with malignant digestive tract tumors and gastric cancer [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]. The high incidence of malnutrition in patients with gastric cancer is related to the location of the tumor. At least 20% of patients die from malnutrition and related complications rather than malignancy. Quality of life, prognosis, and survival differ markedly between well-nourished and malnourished patients. However, to the best of our knowledge, there is no current gold standard for the accurate assessment of patient survival and disease prognosis. Studies on the nutritional assessment of patients with gastric cancer are scarce. Since its inception, the concept of cachexia has been widely used in the management of advanced disease, particularly in patients with advanced cancer. Cachexia can comprehensively reflect the patient\u0026rsquo;s protein and energy balance. Severity of cachexia can be classified according to energy storage and protein consumption. In clinical practice, weight loss has been used as an evaluation index for many years because of the lack of accurate diagnostic criteria. This limits cachexia diagnosis in term of nutritional assessment, prognosis evaluation, and quality of life assessment in patients with cancer. The standardization of cachexia has become increasingly clear in recent years, and it has been increasingly applied in clinical practice. Patient-generated subjective global assessments have been reported to be a promising screening tool for cachexia [\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e]. Furthermore, additional features such as anorexia, muscle loss, fatigue, and other indicators are also gradually being used to screen for cachexia. In their recent study, Zhang et al. reported that systemic inflammation is significantly associated with the survival of patients with tumor cachexia [\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e]. Using serum and urine metabolomics, Yang et al. developed a unique diagnostic model for tumor cachexia[\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e]. All of the aforementioned methods may continuously improve the clinical application value of cachexia. However, to the best of our knowledge, no studies to date have evaluated the use of cachexia diagnosis combined with the assessment of other indicators to predict disease prognosis and patient survival.\u003c/p\u003e \u003cp\u003eSerum biomarkers are widely used to determine cancer prognosis. Several studies have demonstrated that an elevated CEA level is an independent risk factor for the poor prognosis of patients with early-stage gastric cancer, and the upregulated expression of postoperative serum CEA is closely associated with tumor recurrence [\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e]. CA19-9 is also an important biomarker of gastric cancer. An elevated serum level of CA19-9 indicates that the patient has an increased risk of tumor metastasis and a decreased survival rate, making it an important prognostic factor in gastric cancer. The measurement of serum CA19-9 level is also often combined with that of AFP, CEA, and CA125 levels to determine the prognosis of gastric cancer. A recent study has demonstrated that the assessment of anti-HP antibody level combined with that of CA19-9 and CEA levels is highly valuable in determining postoperative recurrence, metastasis, and death risk in patients with early-stage gastric cancer [\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e]. Albumin reflects the systemic nutritional status of patients and has become a mature serum marker. In clinical practice, the measurement of certain biochemical indicators combined with that of serum albumin level is widely used to evaluate the survival of patients with tumor. The fibrinogen\u0026ndash;albumin ratio can be used as a prognostic factor for first-line chemotherapy in patients with advanced gastric cancer. The ratio of C-reactive protein and albumin levels reflects the prognosis of gastric cancer [\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e]. Similarly, combined D-dimer level may be useful for predicting patients\u0026rsquo; response to first-line chemotherapy and prognosis of advanced gastric cancer [\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e]. Therefore, we combined cachexia diagnosis with serum biomarker assessment to predict gastric cancer prognosis and patient survival. To compensate for the bias resulting from single-factor modeling, a more accurate and stable model must be established. Unlike previous studies, we innovatively used continuous serum biomarker values rather than \u0026ldquo;negative\u0026rdquo; or \u0026ldquo;positive\u0026rdquo; binary results.\u003c/p\u003e \u003cp\u003eAs mentioned above, cachexia is difficult to detect at an early stage; moreover, conventional treatment is ineffective after a delayed diagnosis. Thus, the early diagnosis of cachexia has become a challenge for many clinicians. The continuous development of liquid biopsy technology and advancements in exosome research, has made it possible to identify a marker with high concentration, stability, easy collection, and high sensitivity and specificity. Exosomes can carry miRNAs into the circulatory system and promote cell-to-cell and tissue-to-tissue connections through paracrine, autocrine, and endocrine methods. With the discovery of exosomal miRNAs, many miRNAs have been confirmed to be involved in inflammatory responses, inducing metastasis, mediating cancer invasion, and participating in protein synthesis and degradation pathways in the skeletal muscle [\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e]. He et al. found that exosomes secreted by pancreatic and lung cancer cells delivered miR-21 to muscle cells through the blood and induced apoptosis of these muscle cells. Exosomal miR-21 regulates the recognition and activation of Toll-like receptor 7 in mouse myoblasts, thereby promoting myocyte apoptosis. Hudson et al. demonstrated that exosomal miR-182 inhibits muscle atrophy induced by the overexpression of FOXO3 in skeletal muscle cells[\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e]. Furthermore, miR-21 and miR-29 inhibit protein synthesis and promote protein degradation by activating the nuclear factor-κB signaling pathway[\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e]. Taken together, exosomal miRNAs are involved in many regulatory pathways in muscle cells. The most important feature of cachexia is the continuous loss of the skeletal muscle, which may be accompanied by fat wastage. The skeletal muscle constitutes approximately 40% of the body weight and is important for locomotion and metabolic homeostasis. However, there is no report that exosomal miRNAs cause skeletal muscle loss and lead to the development of cachexia. Thus, this study is the first to identify a correlation between plasma-derived exosomal hsa-miR-432-5p and gastric cancer cachexia, which is highly expressed in patients with gastric cancer cachexia, and has better sensitivity and specificity for the early diagnosis. Therefore, plasma-derived exosomal hsa-miR-432-5p has the potential for use as a biomarker for gastric cancer cachexia.\u003c/p\u003e \u003cp\u003eOur study has some limitations. Because this was a retrospective, single-center study, we presume a certain degree of selection bias; moreover, the model needs to be validated at other hospitals. In the future, we would like to gradually expand the number of studies and increase cooperation units to further validate the findings of our model and optimize the model using data obtained from large-sample, multicenter studies.\u003c/p\u003e \u003cp\u003eIn conclusion, we developed a prognostic model based on cachexia diagnosis combined with serum biomarker assessment for patients with gastric cancer. The model exhibited a satisfactory predictive power. A novel nomogram was constructed to predict OS in patients with gastric cancer alone. The four independent factors included in the prediction model developed in this study are relatively easy to assess in clinical practice. They can be used to accurately predict the postoperative OS of patients with gastric cancer. When weight loss is not up to the standard, only the judgment based on cachexia has a considerable influence on individual subjective factors. However, the detection of exosomal miRNAs may largely make up for this shortcoming. The nomogram constructed in this study by combining four independent factors has a consistent clinical application value.\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eSerum biomarker levels and cachexia status are clinically significant in patients with gastric cancer. We developed a novel prognostic model that included serum biomarker levels and nutrient metabolism evaluation index (cachexia). A novel nomogram constructed using this model may predict OS in patients with gastric cancer alone. In addtiton, we identified a novel plasma exosomal biomarker, miR-432-5p, for the early diagnosis of cachexia. Combining multiple markers to diagnose cachexia may be another valuable strategy. Nevertheless, further multicenter, international studies are needed to consolidate our findings.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003eData Availability Statement\u003c/p\u003e\n\u003cp\u003eThe raw data supporting the conclusions of this article will be made available by the authors, without undue reservation. Source data are provided with this paper. RNA-seq raw dada and processed expression matrix are uploaded to GEO DataSets under accession code GSE221666 ( secure token for reviewers: sdwxscyatbkhbur ). All other data analyzed or generated in this study are provided along with the article.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eJ.L and R.Z designed the study, obtained the fundings and supervised all the work. X.J was the main preson responsible for most of the experiments, with the help and supervision of K.W, J.W and Y.L and Y.J provided technical support of cytology experiments. Y.D and Y.L contributed to the data curation. N.W and Y.Q provided formal analysis. R.C performed the bioinformatic analysis. X.J wrote the first draft of the manuscript that was revised critically for important intellectual content by J.L and R.Z and approved by all the authors.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgments\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNone\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe work was financed in part by grants from the National Natural Science Foundation of China (81672751), the Key Research and Development Program of Shaanxi (2019SF-010) and the Shaanxi Provincial Science Fund for Distinguished Young Scholars (2023-JC-JQ-66).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConflict of interest\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors have declared no conflicts of interest.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthical Approval\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe study was reviewed on June 7, 2021, and approved by the Medical Ethics Committee of the First Affiliated Hospital of Air Force Military Medical University (approval number: KY20212018-F-2).\u0026nbsp;Follow the guidelines of the Ethics Committee of the First Affiliated Hospital of the Air Force Military Medical University on human research. Informed consent was obtained from the patients before surgery.\u0026nbsp;All patients agreed to participate in the study.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eSiegel RL, Miller KD, Jemal A, Cancer statistics. 2020. CA: a cancer journal for clinicians. 2020; 70: 7\u0026ndash;30.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKarimi P, Islami F, Anandasabapathy S, Freedman ND, Kamangar F. Gastric cancer: descriptive epidemiology, risk factors, screening, and prevention. Cancer epidemiology, biomarkers \u0026amp; prevention: a publication of the American Association for Cancer Research, cosponsored by the American Society of Preventive Oncology. 2014; 23:700\u0026ndash;13.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eYang L, Ying X, Liu S, Lyu G, Xu Z, Zhang X et al. Gastric cancer: Epidemiology, risk factors and prevention strategies. Chinese journal of cancer research = Chung-kuo yen cheng yen chiu. 2020; 32:695\u0026ndash;704.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eJin G, Lv J, Yang M, Wang M, Zhu M, Wang T, et al. Genetic risk, incident gastric cancer, and healthy lifestyle: a meta-analysis of genome-wide association studies and prospective cohort study. Lancet Oncol. 2020;21:1378\u0026ndash;86.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eDuffy MJ, Lamerz R, Haglund C, Nicolini A, Kalousov\u0026aacute; M, Holubec L, et al. Tumor markers in colorectal cancer, gastric cancer and gastrointestinal stromal cancers: European group on tumor markers 2014 guidelines update. Int J Cancer. 2014;134:2513\u0026ndash;22.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eFeng F, Tian Y, Xu G, Liu Z, Liu S, Zheng G, et al. Diagnostic and prognostic value of CEA, CA19-9, AFP and CA125 for early gastric cancer. BMC Cancer. 2017;17:737.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMoriyama J, Oshima Y, Nanami T, Suzuki T, Yajima S, Shiratori F, et al. Prognostic impact of CEA/CA19-9 at the time of recurrence in patients with gastric cancer. Surg Today. 2021;51:1638\u0026ndash;48.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eFeng F, Sun L, Liu Z, Liu S, Zheng G, Xu G, et al. Prognostic values of normal preoperative serum cancer markers for gastric cancer. Oncotarget. 2016;7:58459\u0026ndash;69.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eGuo L, Wang Q, Chen K, Liu HP, Chen X. Prognostic Value of Combination of Inflammatory and Tumor Markers in Resectable Gastric Cancer. J Gastrointest surgery: official J Soc Surg Aliment Tract. 2021;25:2470\u0026ndash;83.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKapoor A, Dhandapani S, Gaudihalli S, Dhandapani M, Singh H, Mukherjee KK. Serum albumin level in spontaneous subarachnoid haemorrhage: More than a mere nutritional marker! Br J Neurosurg. 2018;32:47\u0026ndash;52.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eOh SE, Choi MG, Seo JM, An JY, Lee JH, Sohn TS, et al. Prognostic significance of perioperative nutritional parameters in patients with gastric cancer. Clinical nutrition (Edinburgh. Scotland). 2019;38:870\u0026ndash;6.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eFearon K, Strasser F, Anker SD, Bosaeus I, Bruera E, Fainsinger RL, et al. Definition and classification of cancer cachexia: an international consensus. Lancet Oncol. 2011;12:489\u0026ndash;95.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003ePressoir M, Desn\u0026eacute; S, Berchery D, Rossignol G, Poiree B, Meslier M, et al. Prevalence, risk factors and clinical implications of malnutrition in French Comprehensive Cancer Centres. Br J Cancer. 2010;102:966\u0026ndash;71.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eGuo ZQ, Yu JM, Li W, Fu ZM, Lin Y, Shi YY, et al. Survey and analysis of the nutritional status in hospitalized patients with malignant gastric tumors and its influence on the quality of life. Supportive care in cancer: official journal of the Multinational Association of Supportive Care in Cancer. 2020;28:373\u0026ndash;80.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMantovani A, Cancer. Inflaming metastasis Nature. 2009;457:36\u0026ndash;7.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eH\u0026eacute;buterne X, Lemari\u0026eacute; E, Michallet M, de Montreuil CB, Schneider SM, Goldwasser F. Prevalence of malnutrition and current use of nutrition support in patients with cancer. JPEN J Parenter Enter Nutr. 2014;38:196\u0026ndash;204.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eDiakos CI, Charles KA, McMillan DC, Clarke SJ. Cancer-related inflammation and treatment effectiveness. Lancet Oncol. 2014;15:e493\u0026ndash;503.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMantovani A, Allavena P, Sica A, Balkwill F. Cancer-related inflammation. Nature. 2008;454:436\u0026ndash;44.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKim AJ, Hong DS, George GC. Diet-related interventions for cancer-associated cachexia. J Cancer Res Clin Oncol. 2021;147:1443\u0026ndash;50.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eArgil\u0026eacute;s JM, L\u0026oacute;pez-Soriano FJ, Toledo M, Betancourt A, Serpe R, Busquets S. The cachexia score (CASCO): a new tool for staging cachectic cancer patients. J cachexia sarcopenia muscle. 2011;2:87\u0026ndash;93.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eEbner N, von Haehling S. Silver linings on the horizon: highlights from the 10th Cachexia Conference. Journal of cachexia, sarcopenia and muscle. 2018; 9: 176 \u0026ndash; 82.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLobb RJ, Lima LG, M\u0026ouml;ller A, Exosomes. Key mediators of metastasis and pre-metastatic niche formation. Semin Cell Dev Biol. 2017;67:3\u0026ndash;10.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKalluri R, LeBleu VS. The biology, function, and biomedical applications of exosomes. New York, NY): Science; 2020. p. 367.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eGambardella V, Castillo J, Tarazona N, Gimeno-Valiente F, Mart\u0026iacute;nez-Ciarpaglini C, Cabeza-Segura M, et al. The role of tumor-associated macrophages in gastric cancer development and their potential as a therapeutic target. Cancer Treat Rev. 2020;86:102015.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eCong M, Song C, Xu H, Song C, Wang C, Fu Z, et al. The patient-generated subjective global assessment is a promising screening tool for cancer cachexia. BMJ supportive \u0026amp; palliative care; 2020.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eZhang Q, Song MM, Zhang X, Ding JS, Ruan GT, Zhang XW, et al. Association of systemic inflammation with survival in patients with cancer cachexia: results from a multicentre cohort study. J cachexia sarcopenia muscle. 2021;12:1466\u0026ndash;76.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eYang QJ, Zhao JR, Hao J, Li B, Huo Y, Han YL, et al. Serum and urine metabolomics study reveals a distinct diagnostic model for cancer cachexia. J cachexia sarcopenia muscle. 2018;9:71\u0026ndash;85.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLocker GY, Hamilton S, Harris J, Jessup JM, Kemeny N, Macdonald JS, et al. ASCO 2006 update of recommendations for the use of tumor markers in gastrointestinal cancer. J Clin oncology: official J Am Soc Clin Oncol. 2006;24:5313\u0026ndash;27.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eGong X, Zhang H. Diagnostic and prognostic values of anti-helicobacter pylori antibody combined with serum CA724, CA19-9, and CEA for young patients with early gastric cancer. J Clin Lab Anal. 2020;34:e23268.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eYu Q, Li KZ, Fu YJ, Tang Y, Liang XQ, Liang ZQ, et al. Clinical significance and prognostic value of C-reactive protein/albumin ratio in gastric cancer. Annals of surgical treatment and research. 2021;100:338\u0026ndash;46.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eZhang L, Wang Z, Xiao J, Zhang Z, Li H, Li F et al. Prognostic Value of Albumin to D-Dimer Ratio in Advanced Gastric Cancer. Journal of oncology. 2021; 2021: 9973743.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eArgil\u0026eacute;s JM, Busquets S, Stemmler B, L\u0026oacute;pez-Soriano FJ. Cancer cachexia: understanding the molecular basis. Nat Rev Cancer. 2014;14:754\u0026ndash;62.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHudson MB, Rahnert JA, Zheng B, Woodworth-Hobbs ME, Franch HA, Price SR. miR-182 attenuates atrophy-related gene expression by targeting FoxO3 in skeletal muscle. Am J Physiol Cell Physiol. 2014;307:C314\u0026ndash;9.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMarinho R, Alc\u0026acirc;ntara PSM, Ottoch JP, Seelaender M. Role of Exosomal MicroRNAs and myomiRs in the Development of Cancer Cachexia-Associated Muscle Wasting. Front Nutr. 2017;4:69.\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":"Cachexia, Gastric cancer, Prognostic model, Nomogram, Exosome, Biomarker","lastPublishedDoi":"10.21203/rs.3.rs-2437588/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-2437588/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cstrong\u003eBackground:\u003c/strong\u003eEmerging evidence shows that serum biomarkers are closely associated with the prognosis of gastric cancer. Cachexia represents systemic nutritional and metabolic statuses. This study aimed to clinically validate the predictive value of serum biomarkers and cachexia, and to identify a potential biomarker for the early diagnosis of cachexia.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMethods: \u003c/strong\u003eThis study included patients with gastric cancer who received curative treatment with no other nonneoplastic cachexia. The eligible population was randomized into training (70%) and test (30%) cohorts.A univariate and multivariate Cox proportional-hazards regression model was used to construct a gastric cancer prognosis model. The predictive and discriminative abilities of the model were evaluated using Kaplan–Meier (K–M) and receiver operating characteristic (ROC) curves. A nomogram was constructed based on the factors identified using the prognostic model, and the corresponding calibration curve was used to validate the accuracy of the nomogram. Exosomal microRNAs (miRNAs) were screened for the early diagnosis of cachexia via whole-genome sequencing, and the clinical samples were used for verification.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eResults: \u003c/strong\u003eThis study included 1101 eligible patients with gastric cancer. There were 330 (29.97%) patients with cachexia and 771 (70.03%) without cachexia. Univariate Cox regression analysis identified the following prognostic factors: body mass index; cachexia; nutritional risk screening scale-2002 (NRS2002) score; serum albumin, carcinoembryonic antigen (CEA), carbohydrate antigen 19-9(CA19-9), and carbohydrate antigen 125 (CA125) levels, and red blood cell count. Multivariate Cox regression analysis identified cachexia and CEA, CA19-9, and serum albumin levels as the independent risk factors for overall survival (OS;\u003cem\u003ep\u003c/em\u003e \u0026lt; 0.05). The K–M curve indicated that the OS of high-risk patients was significantly lower than that of low-risk patients. The areas under the curve of the 1-, 2-, and 3-year prognostic models were 81.13%, 78.49%, and 76.23%, respectively (79.01%, 78.61%, and 75.34% for the test cohort, respectively). Finally, the corresponding nomogram was used to predict the OS of patients with gastric cancer. The calibration curve showed the best agreement between predictions and actual observations. Furthermore, plasma exosomal miR-432-5p was identified as a biomarker for the early diagnosis of cachexia via whole-gene sequencing to make up for the lack of methods for the early diagnosis of cachexia.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConclusions: \u003c/strong\u003eSerum biomarker levels and cachexia status are clinically significant in patients with gastric cancer. We constructed a novel prognostic model based on serum biomarker levels and cachexia. A novel nomogram constructed using this model may predict OS in patients with gastric cancer alone. Furthermore, we identified a novel plasma exosomal biomarker, miR-432-5p, for the early diagnosis of cachexia.\u003c/p\u003e","manuscriptTitle":"A novel prognostic model of gastric cancer patients based on cachexia status by using plasma exosome-derived miRNAs","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2023-01-12 19:12:16","doi":"10.21203/rs.3.rs-2437588/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":"c194ee0c-ddbe-4cf2-807e-a08aadf2e550","owner":[],"postedDate":"January 12th, 2023","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2023-02-10T02:14:17+00:00","versionOfRecord":[],"versionCreatedAt":"2023-01-12 19:12:16","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-2437588","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-2437588","identity":"rs-2437588","version":["v1"]},"buildId":"7rjqhiLT3MXkJMwkYKINL","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}