Human colorectal cancer-derived carcinoma associated fibroblasts promote CD44-mediated adhesion of colorectal cancer cells to endothelial cells by secretion of HGF.

BACKGROUND: Carcinoma-associated fibroblasts (CAFs) are dominant components of tumor microenvironment, which has been reported to promote development, progression, and metastasis of cancer. However, the role of CAFs during adhesion process remains unknown. It has been hypothesized that CAFs contribute to adhesion to endothelial cells of colorectal cancer (CRC) via HGF/c-Met pathway. METHODS: Clinical specimen and orthotopic liver metastasis model was used to investigate association between CD44 expression and propensity of metastasis in CRC. Human CRC derived cancer associated fibroblasts was isolated and its effect on migration and adhesion of CRC cells was investigated. We also confirm the conclusion on animal metastasis model. RESULTS: In this study, clinical specimen and orthotopic liver metastatic model indicated that overexpression of CD44 is associated with CRC metastasis, and we found that colorectal cancer-derived CAFs (CC-CAFs) increased the adhesion and migration of CRC cells in vitro through up-regulation of CD44, we also found that CC-CAFs promoted adhesion and liver or lung metastasis in vivo. Mechanistically, we found that the expression of HGF increased tenfolds compared CC-CAFs with adjacent normal fibroblasts, and HGF promoted adhesion through up-regulation of CD44 via HGF/c-MET signal pathway. CONCLUSIONS: These results indicated that CC-CAFs-derived HGF induced up-regulation of CD44 which mediated adhesion of CRC cells to endothelial cells, and subsequently resulted in enhancement of metastasis of CRC cells, it could provide a novel therapeutic or preventive target.

The number of mesogastria containing metastatic lymph nodes predicts gastric cancer prognosis.

BACKGROUND: Previous studies have established the existence of the mesogastrium, dividing it into 6 sections. The mesogastrium is identified during surgery and used in surgical practice. The aim of the present study was to further investigate its role in gastric cancer prognosis. METHODS: Between January 2014 and January 2018, patients from the Tongji Hospital were included in this post hoc analysis, including data from a randomized clinical study (DCGC01; http://www. CLINICALTRIALS: gov, NCT01978444). Mesogastria containing metastatic lymph nodes were referred to as metastatic mesogastria. Pathology reports were examined to assess metastases in the mesogastrium. Survival was assessed using Kaplan-Meier curves and multivariable Cox models. RESULTS: Among the 479 patients, 230 (48.0%) had no lymph node metastasis, 34 (7.1%) had 1 metastatic mesogastrium, and 215 (44.9%) had 2 to 6 metastatic mesogastria. Multivariate analysis showed that the number of metastatic mesogastria and N stages were independent risk factors for patient prognosis. In general, a higher metastatic mesogastrium number is positively correlated with a worse prognosis. For identical N stages, 5-year survival rates for patients with 2 to 6 metastatic mesogastria were significantly lower than those for patients with 1 metastatic mesogastrium. CONCLUSION: The number of metastatic mesogastria serves as an independent prognostic factor from the N stage.

Predicting bowel function after diverting stoma closure in patients with rectal cancer.

Background: Patients with rectal cancer undergoing laparoscopic anterior resection and diverting stomas often suffer from bowel dysfunction after stoma closure, impairing their quality of life. This study aims to develop a machine learning tool to predict bowel function after diverting stoma closure. Methods: Clinicopathological data and post-operative follow-up information from patients with mid-low rectal cancer after diverting stoma closure were collected and analyzed. Patients were randomly divided into training and test sets in a 7:3 ratio. A machine learning model was developed in the training set to predict major low anterior resection syndrome (LARS) and evaluated in the test set. Decision curve analysis (DCA) was used to assess clinical utility. Results: The study included 396 eligible patients who underwent laparoscopic anterior resection and diverting stoma in Tongji Hospital affiliated with Huazhong University of Science and Technology from 1 January 2012 to 31 December 2020. The interval between stoma creation and closure, neoadjuvant therapy, and body mass index were identified as the three most crucial characteristics associated with patients experiencing major LARS in our cohort. The machine learning model achieved an area under the receiver operating characteristic curve (AUC) of 0.78 [95% confidence interval (CI): 0.74-0.83] in the training set (n=277) and 0.74 (95% CI: 0.70-0.79) in the test set (n=119), and area under the precision-recall curve (AUPRC) of 0.73 and 0.69, respectively, with sensitivity of 0.67 and specificity of 0.66 for the test set. DCA confirmed clinical applicability. Conclusions: This study developed a machine learning model to predict major LARS in rectal cancer patients after diverting stoma closure, aiding their decision-making and counseling.

Artificial intelligence assists surgeons' decision-making of temporary ileostomy in patients with rectal cancer who have received anterior resection.

BACKGROUND: Due to the difficult evaluation of the risk of anastomotic leakage (AL) after rectal cancer resection, the decision to perform a temporary ileostomy is not easily distinguishable. The aim of the present study was to develop an artificial intelligence (AI) model for identifying the risk of AL to assist surgeons in the selective implementation of a temporary ileostomy. MATERIALS AND METHODS: The data from 2240 patients with rectal cancer who received anterior resection were collected, and these patients were divided into one training and two test cohorts. Five AI algorithms, such as support vector machine (SVM), logistic regression (LR), Naive Bayes (NB), stochastic gradient descent (SGD) and random forest (RF) were employed to develop predictive models using clinical variables and were assessed using the two test cohorts. RESULTS: The SVM model indicated good discernment of AL, and might have increased the implementation of temporary ileostomy in patients with AL in the training cohort (p < 0.001). Following the assessment of the two test cohorts, the SVM model could identify AL in a favorable manner, which performed with positive predictive values of 0.150 (0.091-0.234) and 0.151 (0.091-0.237), and negative predictive values of 0.977 (0.958-0.988) and 0.986 (0.969-0.994), respectively. It is important to note that the implementation of temporary ileostomy in patients without AL would have been significantly reduced (p < 0.001) and which would have been significantly increased in patients with AL (p < 0.05). CONCLUSION: The model (https://alrisk.21cloudbox.com/) indicated good discernment of AL, which may be used to assist the surgeon's decision-making of performing temporary ileostomy.

Machine learning model for prediction of low anterior resection syndrome following laparoscopic anterior resection of rectal cancer: A multicenter study.

BACKGROUND: Low anterior resection syndrome (LARS) severely impairs patient postoperative quality of life, especially major LARS. However, there are few tools that can accurately predict major LARS in clinical practice. AIM: To develop a machine learning model using preoperative and intraoperative factors for predicting major LARS following laparoscopic surgery of rectal cancer in Chinese populations. METHODS: Clinical data and follow-up information of patients who received laparoscopic anterior resection for rectal cancer from two medical centers (one discovery cohort and one external validation cohort) were included in this retrospective study. For the discovery cohort, the machine learning prediction algorithms were developed and internally validated. In the external validation cohort, we evaluated the trained model using various performance metrics. Further, the clinical utility of the model was tested by decision curve analysis. RESULTS: Overall, 1651 patients were included in the present study. Anastomotic height, neoadjuvant therapy, diverting stoma, body mass index, clinical stage, specimen length, tumor size, and age were the risk factors associated with major LARS. They were used to construct the machine learning model to predict major LARS. The trained random forest (RF) model performed with an area under the curve of 0.852 and a sensitivity of 0.795 (95%CI: 0.681-0.877), a specificity of 0.758 (95%CI: 0.671-0.828), and Brier score of 0.166 in the external validation set. Compared to the previous preoperative LARS score model, the current model exhibited superior predictive performance in predicting major LARS in our cohort (accuracy of 0.772 for the RF model vs 0.355 for the preoperative LARS score model). CONCLUSION: We developed and validated a robust tool for predicting major LARS. This model could potentially be used in the clinic to identify patients with a high risk of developing major LARS and then improve the quality of life.

Comprehensive abdominal composition evaluation of rectal cancer patients with anastomotic leakage compared with body mass index-matched controls.

BACKGROUND: Anastomotic leakage (AL) is a fatal complication in patients with rectal cancer after undergoing anterior resection. However, the role of abdominal composition in the development of AL has not been studied. AIM: To investigate the relationship between abdominal composition and AL in rectal cancer patients after undergoing anterior resection. METHODS: A retrospective case-matched cohort study was conducted. Complete data for 78 patients with AL were acquired and this cohort was defined as the AL group. The controls were matched for the same sex and body mass index (± 1 kg/m2). Parameters related to abdominal composition including visceral fat area (VFA), subcutaneous fat area (SFA), subcutaneous fat thickness (SFT), skeletal muscle area (SMA), skeletal muscle index (SMI), abdominal circumference (AC), anterior to posterior diameter of abdominal cavity (APD), and transverse diameter of abdominal cavity (TD) were evaluated based on computed tomography (CT) images using the following Hounsfield Unit (HU) thresholds: SFA: -190 to -30, SMA: -29 to 150, and VFA: -150 to -20. The significance of abdominal composition-related parameters was quantified using feature importance analysis; an artificial intelligence method was used to evaluate the contribution of each included variable. RESULTS: Two thousand two hundred and thirty-eight rectal cancer patients who underwent anterior resection from 2010 to 2020 in a large academic hospital were investigated. Finally, 156 cases were enrolled in the study. Patients in the AL group showed longer operative time (225.03 ± 55.29 vs 207.17 ± 40.80, P = 0.023), lower levels of preoperative hemoglobin (123.32 ± 21.17 vs 132.60 ±1 6.31, P = 0.003) and albumin (38.34 ± 4.01 vs 40.52 ± 3.97, P = 0.001), larger tumor size (4.07 ± 1.36 vs 2.76 ± 1.28, P < 0.001), and later cancer stage (P < 0.001) compared to the controls. Patients who developed AL exhibited a larger VFA (125.68 ± 73.59 vs 97.03 ± 57.66, P = 0.008) and a smaller APD (77.30 ± 23.23 vs 92.09 ± 26.40, P < 0.001) and TD (22.90 ± 2.23 vs 24.21 ± 2.90, P = 0.002) compared to their matched controls. Feature importance analysis revealed that TD, APD, and VFA were the three most important abdominal composition-related features. CONCLUSION: AL patients have a higher visceral fat content and a narrower abdominal structure compared to matched controls.

Application of Machine Learning for Predicting Anastomotic Leakage in Patients with Gastric Adenocarcinoma Who Received Total or Proximal Gastrectomy.

Anastomotic leakage is a life-threatening complication in patients with gastric adenocarcinoma who received total or proximal gastrectomy, and there is still no model accurately predicting anastomotic leakage. In this study, we aim to develop a high-performance machine learning tool to predict anastomotic leakage in patients with gastric adenocarcinoma received total or proximal gastrectomy. A total of 1660 cases of gastric adenocarcinoma patients who received total or proximal gastrectomy in a large academic hospital from 1 January 2010 to 31 December 2019 were investigated, and these patients were randomly divided into training and testing sets at a ratio of 8:2. Four machine learning models, such as logistic regression, random forest, support vector machine, and XGBoost, were employed, and 24 clinical preoperative and intraoperative variables were included to develop the predictive model. Regarding the area under the receiver operating characteristic curve (AUC), sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV), and accuracy, random forest had a favorable performance with an AUC of 0.89, a sensitivity of 81.8% and specificity of 82.2% in the testing set. Moreover, we built a web app based on random forest model to achieve real-time predictions for guiding surgeons' intraoperative decision making.

Cancer-associated fibroblasts enhance tumor-associated macrophages enrichment and suppress NK cells function in colorectal cancer.

Cancer-associated fibroblasts (CAFs) and tumor-associated macrophages (TAMs) are important components of the tumor microenvironment, which have been reported to localize in colorectal carcinomas where they promote tumor progression. One of the crucial effects they exerted is immune-suppression, which was reported recently, however, the overall mechanism has not been fully addressed. In this study, it was shown that TAMs were enriched in colorectal cancer, and their infiltration was associated with VCAM-1 expression. Human colorectal cancer-derived CAFs can promote the adhesion of monocytes by up-regulating VCAM-1 expression in colorectal cancer cells. Furthermore, CAFs can attract monocytes by secreting IL-8 rather than SDF-1 and subsequently promote M2 polarization of macrophages, which synergize with CAFs in suppressing the functioning of natural killer (NK) cells. It was also found that CAFs promoted M2 macrophages recruitment in tumor tissue in vivo, and after VCAM-1 knocking-down in tumor cells or depletion of macrophages, the pro-tumor effect of CAFs was partly abolished, but no change was observed in NK cells infiltration. Collectively, the findings in this work show that TAMs and CAFs function synergistically in the tumor microenvironment and have the capacity to regulate NK cells in colorectal cancer and this presents a novel mechanism.

Down-regulation of Cav1.3 in auditory pathway promotes age-related hearing loss by enhancing calcium-mediated oxidative stress in male mice.

In this study, age related Cav1.3 expression in cochlea and auditory cortex of C57BL/6J male mice was evaluated. It was found that the expression of Cav1.3 in cochlea decreased with aging whereas this phenomenon was not observed in neuron of auditory cortex. The correlation between decreased expression of Cav1.3 and age-related hearing losses was studied in vitro, after Cav1.3 was knocked out, the rate of apoptosis of hair cells increased after being subjected to ROS stresses, accompanied with enhanced senescence. Further, Cav1.3 knock down also interfered with the electrophysiology of hair cells. The effect was further confirmed in vivo, after Cav1.3 knocked down by injection of AAV, hearing impairment was observed in C57BL/6J male mice subjected to senescence and this was accompanied by increased loss of hair cells in cochlea. The effect was further confirmed in 3D organ culture, increased loss of hair cells after Cav1.3 was knocked down under ROS stresses.Mechanistically, Cav1.3 knock out resulted in decreased intracellular calcium which subsequently reduced the inactivation of ROS from complex I, and finally resulted in increased intracellular ROS and enhanced senescence.Collectively, these findings confirmed that Cav1.3 could protect cells in auditory pathway from oxidative stresses, and decreased expression of Cav1.3 in auditory pathway could contribute to hearing losses by enhancement of calcium-mediated oxidative stress.