CircATP13A1 (hsa_circ_0000919) promotes cell proliferation and metastasis and inhibits cell apoptosis in pancreatic ductal adenocarcinoma via the miR-186/miR-326/HMGA2 axis: implications for novel therapeutic targets.

Pancreatic ductal adenocarcinoma (PDAC) is a notoriously aggressive malignancy with a survival rate of merely 9%. The prognosis in patients with PDAC is relatively poor, particularly in patients with advanced distant metastases. However, the mechanisms of PDAC progression remain elusive. Circular RNAs (circRNAs) have been implicated in the development of various malignancies, including PDAC. Therefore, this study aimed to investigate how a novel circRNA, circATP13A1, regulates PDAC progression. We used the GEO database to determine circATP13A1 expression levels in cancer and adjacent cells and employed the limma package of R software to identify differentially expressed circRNAs. We detected the expression of circATP13A1, miR-186, and miR-326 using qRT-PCR and investigated the effect of circATP13A1 on cell proliferation, migration, invasion, and apoptosis in vitro using the Cell Counting Kit-8 (CCK-8), the transwell migration assay, and the flow cytometry assay. We then performed RNA pull-down assay, RNA immunoprecipitation (RIP), and Western blot to verify the interaction between circATP13A1, miR-186, miR-326, and HMGA2. Moreover, we used a naked mice model to determine how circATP13A1 affects tumor growth and progression in vivo. Loss and gain of function analyses revealed that circATP13A1 upregulation promotes cell proliferation, migration, invasion and tumor growth both in vitro and in vivo, which results in PDAC progression and poor prognosis in patients. CircATP13A1 knockdown significantly impaired cell proliferation and migration of PDAC cell lines. Additionally, circATP13A1 knockdown significantly increased the expression of miR-186 and miR-326, while reducing the expression of HMGA2 (P < 0.05), indicating that miR-186 and miR-326 are downstream targets of circATP13A1. Rescue experiments support the interactions between circATP13A1, miR-186, miR-326, and HMGA2. In conclusion, we demonstrated that circATP13A1 sponges the miR-186/miR-326/HMGA2/axis, acting as an oncogene to promote PDAC development.

Investigation polarimetric scattering of light from the randomly rough surface based on the calculation of the Mueller matrix.

As the transmission matrix of scattering and incident light, the Mueller matrix reflects the polarimetric scattering characteristics of the rough surface, providing a significant reference for the study of light scattering. Currently, few calculations of the Mueller matrix for a two-dimensional randomly rough surface have been carried out by numerical methods. In this paper, we use six polarization states of incident light and calculate their scattering polarization states numerically by finite-difference time-domain method and obtain the rough surface Mueller matrix by combination. To verify the accuracy of the calculated Mueller matrix, the polarization state of the scattering light obtained by simulation is compared with the predicted result, and the maximum relative error is 0.0635, yielding a good result. In addition, we use this method to obtain the Mueller matrix at different incidence angles and investigate the polarization scattering characteristics. The results show that the derived parameters of the Mueller matrix of different media at different incidence angles have distinct trends. This polarization scattering property obtained from the Mueller matrix can be effectively applied to target recognition, material detection, and other fields.

The Role of Extracellular Vesicles in Colorectal Cancer.

Extracellular vesicles (EVs) are a class of spherical vesicles that are produced by active secretion of cells and encapsulated by phospholipid bilayers. In recent years, numerous studies have shown that EVs play pivotal roles in the regulation of intercellular communication between colorectal cancer (CRC) cells and target cells, and can regulate the proliferation, metastasis, and infiltration of tumor cells by regulating the microenvironment of tumor cells. EVs carry specific molecular substances in source CRC cells and are expected to serve as new molecular markers for the detection of cancers. This review highlights the current state of research and progress of potentially incorporating EVs in the diagnosis and treatment of CRC.

Investigation of trends in gut microbiome associated with colorectal cancer using machine learning.

Background: The rapid growth of publications on the gut microbiome and colorectal cancer (CRC) makes it feasible for text mining and bibliometric analysis. Methods: Publications were retrieved from the Web of Science. Bioinformatics analysis was performed, and a machine learning-based Latent Dirichlet Allocation (LDA) model was used to identify the subfield research topics. Results: A total of 5,696 publications related to the gut microbiome and CRC were retrieved from the Web of Science Core Collection from 2000 to 2022. China and the USA were the most productive countries. The top 25 references, institutions, and authors with the strongest citation bursts were identified. Abstracts from all 5,696 publications were extracted for a text mining analysis that identified the top 50 topics in this field with increasing interest. The colitis animal model, expression of cytokines, microbiome sequencing and 16s, microbiome composition and dysbiosis, and cell growth inhibition were increasingly noticed during the last two years. The 50 most intensively investigated topics were identified and further categorized into four clusters, including "microbiome sequencing and tumor," "microbiome compositions, interactions, and treatment," "microbiome molecular features and mechanisms," and "microbiome and metabolism." Conclusion: This bibliometric analysis explores the historical research tendencies in the gut microbiome and CRC and identifies specific topics of increasing interest. The developmental trajectory, along with the noticeable research topics characterized by this analysis, will contribute to the future direction of research in CRC and its clinical translation.

A LASSO-based model to predict central lymph node metastasis in preoperative patients with cN0 papillary thyroid cancer.

Introduction: Central lymph node metastasis (CLNM) is common in papillary thyroid carcinoma (PTC). Prophylactic central lymph node dissection (PCLND) in clinically negative central compartment lymph node (cN0) PTC patients is still controversial. How to predict CLNM before the operation is very important for surgical decision making. Methods: In this article, we retrospectively enrolled 243 cN0 PTC patients and gathered data including clinical characteristics, ultrasound (US) characteristics, pathological results of fine-needle aspiration (FNA), thyroid function, eight gene mutations, and immunoenzymatic results. Least absolute shrinkage and selection operator (LASSO) analysis was used for data dimensionality reduction and feature analysis. Results: According to the results, the important predictors of CLNM were identified. Multivariable logistic regression analysis was used to establish a new nomogram prediction model. The receiver operating characteristic (ROC) curve, calibration curve, and decision curve analysis (DCA) curve were used to evaluate the performance of the new prediction model. Discussion: The new nomogram prediction model was a reasonable and reliable model for predicting CLNM in cN0 PTC patients, but further validation is warranted.

Intraductal oncocytic papillary neoplasm arising in Peutz-Jeghers Syndrome bile duct: a unique case report.

BACKGROUND: Peutz-Jeghers syndrome (PJS) is a rare, autosomal dominant disorder caused by germline mutations of STK11/LKB1, with an increased risk of tumors at multiple sites. Intraductal oncocytic papillary neoplasm (IOPN) is a unique subtype of intraductal papillary neoplasm of the bile duct (IPNB) defined by a premalignant neoplasm with intraductal papillary or villous growth of biliary-type epithelium. IOPN has a distinct mutation profile compared with both IPNB and intraductal papillary mucinous neoplasm (IPMN). CASE PRESENTATION: We herein describe the case of a 44-year-old woman who presented as polyps in the intestinal lumen of sigmoid colon and a 3.1 × 2.1 cm mass in the left lobe of liver. Gross feature revealed a cystic papillary mass and the neoplasm had a clear boundary with the surrounding liver tissue. Histology revealed complex papillary structures, a small amount of fine fibrovascular cores and immunohistochemistry showed extensive positive for MUC5AC, MUC6, CD117. Therefore, histological and immunohistochemical examination of the liver tumor suggested the diagnosis of IOPN. Next-generation sequencing (NGS) revealed other than STK11 germline mutation, the tumor also harbors GNAS somatic mutation at codon 478 and EGFR amplification. CONCLUSION: To our knowledge, this is the first report of IOPN arising in PJS. This case enlarges the spectrum of PJS related tumors and genetic rearrangements in IOPN.

Citrus green fruit detection via improved feature network extraction.

Introduction: It is crucial to accurately determine the green fruit stage of citrus and formulate detailed fruit conservation and flower thinning plans to increase the yield of citrus. However, the color of citrus green fruits is similar to the background, which results in poor segmentation accuracy. At present, when deep learning and other technologies are applied in agriculture for crop yield estimation and picking tasks, the accuracy of recognition reaches 88%, and the area enclosed by the PR curve and the coordinate axis reaches 0.95, which basically meets the application requirements.To solve these problems, this study proposes a citrus green fruit detection method that is based on improved Mask-RCNN (Mask-Region Convolutional Neural Network) feature network extraction. Methods: First, the backbone networks are able to integrate low, medium and high level features and then perform end-to-end classification. They have excellent feature extraction capability for image classification tasks. Deep and shallow feature fusion is used to fuse the ResNet(Residual network) in the Mask-RCNN network. This strategy involves assembling multiple identical backbones using composite connections between adjacent backbones to form a more powerful backbone. This is helpful for increasing the amount of feature information that is extracted at each stage in the backbone network. Second, in neural networks, the feature map contains the feature information of the image, and the number of channels is positively related to the number of feature maps. The more channels, the more convolutional layers are needed, and the more computation is required, so a combined connection block is introduced to reduce the number of channels and improve the model accuracy. To test the method, a visual image dataset of citrus green fruits is collected and established through multisource channels such as handheld camera shooting and cloud platform acquisition. The performance of the improved citrus green fruit detection technology is compared with those of other detection methods on our dataset. Results: The results show that compared with Mask-RCNN model, the average detection accuracy of the improved Mask-RCNN model is 95.36%, increased by 1.42%, and the area surrounded by precision-recall curve and coordinate axis is 0.9673, increased by 0.3%. Discussion: This research is meaningful for reducing the effect of the image background on the detection accuracy and can provide a constructive reference for the intelligent production of citrus.

Pathological Nodal Staging Score for Gastric Signet Ring Cell Carcinoma: A Clinical Tool of Adequate Nodal Staging.

Background: Gastric signet ring cell carcinoma (GSRCC) is a subset of gastric cancer with distinct histological and inconsistent prognosis outcome. Currently, the association between the adequate regional lymph node and proper nodal staging in GSRCC is rarely noticed. Materials and methods: Clinical data of GSRCC were retrieved from the Surveillance, Epidemiology, and End Results database. Beta-binomial distribution model was employed for the estimation of the probability of missing nodal disease, followed by the development of a nodal staging score (NSS). Results: A total of 561 GSRCC patients were included in this study, with 193 in lymph node-negative and 368 in lymph node-positive diagnoses. As the number of examined lymph nodes increased, the probability of missing nodal disease decreased rapidly, with T stage-specific curves. The probability of missing nodal disease in T4 was lower than that in T1. NSS calculation indicated that T1 stage patients commonly had NSS > 0.8. However, with the NSS of T2−T4 to reach 0.8, the number of examined lymph node was required to be larger than 12 in T2, 17 in T3 and 27 in T4. NSS ≥ 0.75 (quantile 75%) subgroup in T2−T4 subgroups tended to have better outcome; however, without significant prognostic value. Conclusions: NSS is served as a reliable and feasible tool in adequate nodal staging of GSRCC with statistical basis and provides further evidence for clinical decision making.

Landscape of Artificial Intelligence in Breast Cancer (2000-2021): A Bibliometric Analysis.

BACKGROUND: Breast cancer remains one of the leading malignancies in women with distinct clinical heterogeneity and intense multidisciplinary cooperation. Remarkable progresses have been made in artificial intelligence (AI). A bibliometric analysis was taken to characterize the current picture of development of AI in breast cancer. MATERIALS AND METHODS: Search process was performed in the Web of Science Core Collection database with analysis and visualization performed by R software, VOSviewer, CiteSpace and Gephi. Latent Dirichlet Allocation (LDA), a machine learning based algorithm, was used for analysis of topic terms. RESULTS: A total of 511 publications in the field of AI in breast cancer were retrieved between 2000 to 2021. A total of 103 publications were from USA with 2482 citations, making USA the leading country in the field of AI in breast cancer, followed by China. Mem Sloan Kettering Canc Ctr, Radboud Univ Nijmegen, Peking Univ, Sichuan Univ, ScreenPoint Med BV, Lund Univ, Duke Univ, Univ Chicago, Harvard Med Sch and Univ Texas MD Anderson Canc Ctr were the leading institutions in the field of AI in breast cancer. AI, breast cancer and classification, mammography were the leading keywords. LDA topic modeling identified top fifty topics relating the AI in breast cancer. A total of five primary clusters were found within the network of fifty topics, including radiology feature, lymph node diagnosis and model, pathological tissue and image, dataset classification and machine learning, gene expression and survival. CONCLUSIONS: This research depicted AI studies in breast cancer and presented insightful topic terms with future perspective.

Identification of Key Genes and Pathways Involved in Circulating Tumor Cells in Colorectal Cancer.

BACKGROUND: Characterization of the features associated with circulating tumor cells (CTCs) is one of major interests for predicting clinical outcome of colorectal cancer (CRC) patients. However, the molecular features of CTCs remain largely unclear. METHODS: For identification of key genes and pathways, GSE31023, contained CTCs from six metastatic CRC patients and three controls, was retrieved for differentially expressed gene (DEG) analysis. Protein-protein interaction networks of DEGs were constructed. Hub genes from the network were prognostic analyzed, as well as the association with tumor-infiltrating immune cells. RESULTS: 1353 DEGs were identified between the CTC and control groups, with 403 genes upregulated and 950 downregulated. 32 pathways were significantly enriched in KEGG, with ribosome pathway as top. The top 10 hub genes were included, including eukaryotic translation elongation factor 2 (EEF2), ribosomal protein S2 (RPS2), ribosomal protein S5 (RPS5), ribosomal protein L3 (RPL3), ribosomal protein S3 (RPS3), ribosomal protein S14 (RPS14), ribosomal protein SA (RPSA), eukaryotic translation elongation factor 1 alpha 1 (EEF1A1), ribosomal protein S15a (RPS15A), and ribosomal protein L4 (RPL4). The correlation between CD4+ T cells and RPS14 (correlation = -0.5) was the highest in colon cancer while CD8+ T and RPS2 (correlation = -0.53) was the highest in rectal cancer. CONCLUSION: This study identified potential role of ribosome pathway in CTC, providing further insightful therapeutic targets and biomarkers for CRC.

Development and Validation of a BMI-Based Figure Rating Scale for Chinese Adolescents.

Figure rating scales (FRS) have been widely used to measure body dissatisfaction. In the current study, an FRS based on body mass index (BMI) and body shape of Chinese adolescents (C-BMI-FRS) was developed and validated. The perceived actual figure chosen from C-BMI-FRS by 2,237 Chinese adolescents aged 15-18 years old correlated strongly with BMI (r = .83 in girls and r = .80 in boys). Additionally, there was a strong relation between actual and ideal figure discrepancy (AID) scores and measures of body satisfaction (r = -.54 in girls, r = -.28 in boys) and eating disorder symptoms (r = .54 in girls, and r = .52 in boys). There also were moderate associations of AID scores with self-esteem (r = -.16) and negative affect (r = .18) in girls. Test-retest reliability over an 8- to 10 -week interval exceeded .57 for actual figure, ideal figure, and AID scores. Overall, findings support the utility of C-BMI-FRS as a measure of body dissatisfaction among Chinese adolescents.

Validation of the Chinese Version of the Body Image Concern Inventory.

The Body Image Concern Inventory (BICI) was developed to assess dysmorphic appearance concern and has been found to be a reliable and valid instrument in Western societies. To examine the psychometric properties of a new Chinese BICI, the BICI was administered to 1,231 Chinese young adults (Study 1) and 47 female patients with eating disorders and 56 matched controls (ED; Study 2). In study 1, Cronbach's alpha of .92 and test-retest reliability of .73 over a 6-month interval was observed for the total scale. Confirmatory factor analysis supported a 3-factor model for the BICI: avoidant behaviors (AB), safety behaviors against perceived flaws (SB), and negative appearance evaluation (NE). In study 2, ED patients scored significantly higher on the BICI total and three subscale scores than controls. In addition, AB best differentiated ED patients and matched controls (Cohen's d = 1.52); SB best differentiated between the non-clinical female and male groups (Cohen's d = 0.75); NE was most closely associated with level of negative affect and subjective well-being (inverse relationship) in both clinical and non-clinical groups. In conclusion, the Chinese BICI is a reliable and valid tool for evaluating dysmorphic appearance concern among Chinese speakers.

The role of AI technology in prediction, diagnosis and treatment of colorectal cancer.

Artificial intelligence (AI) is a fascinating new technology that incorporates machine learning and neural networks to improve existing technology or create new ones. Potential applications of AI are introduced to aid in the fight against colorectal cancer (CRC). This includes how AI will affect the epidemiology of colorectal cancer and the new methods of mass information gathering like GeoAI, digital epidemiology and real-time information collection. Meanwhile, this review also examines existing tools for diagnosing disease like CT/MRI, endoscopes, genetics, and pathological assessments also benefitted greatly from implementation of deep learning. Finally, how treatment and treatment approaches to CRC can be enhanced when applying AI is under discussion. The power of AI regarding the therapeutic recommendation in colorectal cancer demonstrates much promise in clinical and translational field of oncology, which means better and personalized treatments for those in need.

Bibliometric Evaluation of Publications (2000-2020) on the Prognosis of Gastric Cancer.

Background: Gastric cancer remains a global malignancy. The role of bibliometric analysis is increasingly valued. It is feasible and necessary to perform a bibliometric analysis to regurgitate studies in the prognosis of gastric cancer. Materials and methods: Web of Science was selected for the dataset resource. Articles published between 2000 and 2020 within the database of Web of Science Core Collection were included with predefined search terms. CiteSpace version 5.7.R1 and R software program version 4.0.3 were used for bibliometric analysis with parameters extrapolated from included studies. Results: A total of 1721 articles were included from 2000 to 2020 with remarkably increasing trends. China (n=1183), Japan (n=218), and South Korea (n=119) showed the most publications. SUN YAT SEN University, FUDAN University, and NANJING MED University were the top institutions with most publications. Keywords with strongest citation bursts between 2000 and 2020 were characterized. Particularly, "statistics", "resistance", "mortality", "lncrna", "diagnosis", "outcome", "migration", "promote," and "regulatory t cell" were the latest rising keywords since 2017, indicating possible study trends ahead. Several articles showed strongest citation bursts, including Jemal A. CA-CANCER J CLIN, Van Cutsem E. LANCET, and Japanese Gastric Cancer Association GASTRIC CANCER. Conclusion: This bibliometric analysis provides a thought-provoking, insightful result concerning the trajectory of research development in prognosis of gastric cancer with a future perspective.

Role of rehabilitation amidst the COVID-19 pandemic: a review.

COVID-19 remains globally a highly infectious disease targeting multiple organs. Rehabilitation is increasingly valued among the supportive care fields to combat COVID-19 as currently definitive curative treatment remains largely absent. This narrative review is to address rehabilitation related topics associated with the treatment of COVID-19 patients. Nosocomial spread remains a high risk for healthcare workers, with comparable high ratios of exposed workers suffering from the disease with more severe clinical course. Primary principle of rehabilitation is to protect rehabilitation physicians and cover all person-to-person interactions. Translating perspectives are encouraged through each multidisciplinary approach. Rehabilitation for the outpatient remains a potential beneficial approach. Artificial intelligence can potentially provide aid and possible answers to important problems that may emerge involving COVID-19. The real value of rehabilitation in COVID-19 may be very impactful and beneficial for patient's physical and mental health.

Detection and Clinical Value of Circulating Tumor Cells as an Assisted Prognostic Marker in Colorectal Cancer Patients.

BACKGROUND: Circulating tumor cells (CTCs) are cells that have been shed into the vasculature from a primary tumor and circulate in the bloodstream. It has been suggested that detecting CTCs could help the clinician to detect early metastasis or recurrence more effectively. This trial sets out to assess the detection and clinical value of CTCs as an assisted prognostic marker in patients with colon cancer and rectal cancer. METHODS: A prospective cohort of patients with colorectal cancer (CRC) was enrolled from July 2015 to February 2018 in Shanghai Minimally Invasive Surgery Center, Shanghai, China. In this study, 149 patients with CRC were enrolled and underwent surgical treatment. There were 79 cases of colon cancer and 70 cases of rectal cancer, including 93 males and 56 females. To investigate the correlativity and clinical value of CTCs, the patients were statistically analyzed in different subgroups: colon cancer group vs rectal cancer group, and left hemicolon cancer group vs right hemicolon cancer group. RESULTS: The results of analysis comparing CTC counts and clinical pathological features in colon and rectal cancer indicated that with increased tumor stage, the number of CTCs also increased, with significant statistical differences. CTC counts in patients with colon and rectal cancer showed positive correlations with TNM staging (P=0.001, 0.013, respectively), T staging (P=0.021, 0.001), N staging (P=0.014, 0.035) and M staging (P=0.018, 0.203). Detection of serum biomarkers in CTC-positive and CTC-negative groups indicated a significantly increasing expression in the CTC-positive group. To confirm the correlations between CTCs and histoembryological differences, analysis was conducted with the patients in two subgroups: left hemicolon cancer group and right hemicolon cancer group. The results showed that the positive rate of CTCs increased in both groups with the increase in tumor stage. The survival analysis indicated that there was a steep gradient in survival in the follow-up period, particularly in the CTC-positive group (P=0.000). Risk assessment curves showed that the change escalated more rapidly in the CTC-positive group. Furthermore, with the increase in T stage, changes in the survival curve and risk curve escalated more rapidly in the CTC-positive group. CONCLUSION: It was confirmed that in the left hemicolon cancer group, a much higher coincidence rate could be found on CTC-positive rate and clinicopathological features, than in the right hemicolon cancer group. The sensitivity of CTCs may be related to the histoembryological location of the tumor, lymphatic metastasis and the depth of infiltration. Monitoring CTCs may have value in evaluating clinical staging and estimating clinical prognosis.

Development and validation of a Surveillance, Epidemiology, and End Results (SEER)-based prognostic nomogram for predicting survival in elderly patients with gastric cancer after surgery.

BACKGROUND: Elderly gastric cancer (ELGC) remains one of the intensively investigated topics during the last decades. To establish a comprehensive nomogram for effective clinical practice and assessment is of significance. This study is designed to develop a prognostic nomogram for ELGC both in overall survival (OS) and cancer-specific survival (CSS). METHODS: The recruited cases were from the Surveillance, Epidemiology, and End Results (SEER) database and input for the construction of nomogram. RESULTS: A total of 4,414 individuals were recruited for this study, of which 2,208 were randomly in training group and 2,206 were in validation group. In univariate analysis of OS, significant variables (P<0.05) included age, marital status, grade, American Joint Committee on Cancer (AJCC) tumor-node-metastasis (TNM) stage, bone/brain/liver/lung metastasis and tumor size. In univariate analysis of CSS, significant variables (P<0.05) included age, grade, AJCC TNM stage, bone/brain/liver/lung metastasis and tumor size. In multivariate analysis of OS, sex, age, race, grade, TNM stage, lung metastasis and tumor size were considered as the significant variables and subjected to the establishment of nomogram. In multivariable analysis of CSS, age, grade, TNM, tumor size were considered as the significant variables and input to the establishment of nomogram. Sex, age, race, grade, TNM stage, lung metastasis and tumor size were included for the establishment of nomogram in OS while age, grade, TNM, tumor size were included to the establishment of nomogram in CSS. C-index, decision curve analysis (DCA) and the area under the curve (AUC) showed distinct value of newly established nomogram models. Both OS and CSS nomograms showed higher statistic power over the AJCC stage. CONCLUSIONS: This study established and validated novel nomogram models of OS and CSS for ELGC based on population dataset.

A greater lymph node yield is required during pathological examination in microsatellite instability-high gastric cancer.

BACKGROUND: The impact of microsatellite status on lymph node (LN) yield during lymphadenectomy and pathological examination has never been assessed in gastric cancer (GC). In this study, we aimed to appraise the association between microsatellite instability-high (MSI-H) and LN yield after curative gastrectomy. METHODS: We retrospectively analyzed 1757 patients with GC undergoing curative gastrectomy and divided them into two groups: MSI-H (n = 185(10.5%)) and microsatellite stability (MSS) (n = 1572(89.5%)), using a five-Bethesda-marker (NR-24, BAT-25, BAT-26, CAT-25, MONO-27) panel. The median LN count and the percentage of specimens with a minimum of 16 LNs (adequate LN ratio) were compared between the two groups. The log odds (LODDS) of positive LN count (PLNC) to negative LN count (NLNC) and the target LN examined threshold (TLNT(x%)) were calculated in both groups. RESULTS: Statistically significant differences were found in the median LN count between MSI-H and MSS groups for the complete cohort (30 vs. 28, p = 0.031), for patients undergoing distal gastrectomy (DG) (30 vs. 27, p = 0.002), for stage II patients undergoing DG (34 vs. 28, p = 0.005), and for LN-negative patients undergoing DG (28 vs. 24, p = 0.002). MSI-H was an independent factor for higher total LN count in patients undergoing DG (p = 0.011), but it was not statistically correlated to the adequate LN ratio. Statistically significant differences in PLNC, NLNC and LODDS were found between MSI-H GC and MSS GC (all p < 0.001). The TLNT(90%) for MSI-H and MSS groups were 31 and 25, respectively. TLNT(X%) of MSI-H GC was always higher than that of MSS GC regardless of the given value of X%. CONCLUSIONS: MSI-H was associated with higher LN yield in patients undergoing gastrectomy for GC. Although MSI-H did not affect the adequacy of LN harvest, we speculate that a greater lymph node yield is required during pathological examination in MSI-H GC.