Circulation immune cell landscape in canonical pathogenesis of colorectal adenocarcinoma by CyTOF analysis.

Current studies on the immune microenvironment of colorectal cancer (CRC) were mostly limited to the tissue level, lacking relevant studies in the peripheral blood, and failed to describe its alterations in the whole process of adenocarcinoma formation, especially of adenoma carcinogenesis. Here, we constructed a large-scale population cohort and used the CyTOF to explore the changes of various immune cell subsets in peripheral blood of CRC. We found monocytes and basophils cells were significantly higher in adenocarcinoma patients. Compared with early-stage CRC, effector CD4+T cells and naive B cells were higher in patients with lymph node metastasis, whereas the basophils were lower. We also performed random forest algorithm and found monocytes play the key role in carcinogenesis. Our study draws a peripheral blood immune cell landscape of the occurrence and development of CRC at the single-cell level and provides a reference for other researchers.

Clinical evaluation of a multitarget fecal immunochemical test-sDNA test for colorectal cancer screening in a high-risk population: a prospective, multicenter clinical study.

Colorectal cancer (CRC) is a major malignancy threatening the health of people in China and screening could be effective for preventing the occurrence and reducing the mortality of CRC. We conducted a multicenter, prospective clinical study which recruited 4,245 high-risk CRC individuals defined as having positive risk-adapted scores or fecal immunochemical test (FIT) results, to evaluate the clinical performance of the multitarget fecal immunochemical and stool DNA (FIT-sDNA) test for CRC screening. Each participant was asked to provide a stool sample prior to bowel preparation, and FIT-sDNA test and FIT were performed independently of colonoscopy. We found that 186 (4.4%) were confirmed to have CRC, and 375 (8.8%) had advanced precancerous neoplasia among the high CRC risk individuals. The sensitivity of detecting CRC for FIT-sDNA test was 91.9% (95% CI, 86.8-95.3), compared with 62.4% (95% CI, 54.9-69.3) for FIT (P < 0.001). The sensitivity for detecting advanced precancerous neoplasia was 63.5% (95% CI, 58.3-68.3) for FIT-sDNA test, compared with 30.9% (95% CI, 26.3-35.6) for FIT (P < 0.001). Multitarget FIT-sDNA test detected more colorectal advanced neoplasia than FIT. Overall, these findings indicated that in areas with limited colonoscopy resources, FIT-sDNA test could be a promising further risk triaging modality to select patients for colonoscopy in CRC screening.

Hepatoid adenocarcinoma-Clinicopathological features and molecular characteristics.

Hepatoid adenocarcinoma (HAC) is a rare, malignant, extrahepatic tumor with histologic features similar to those of hepatocellular carcinoma. HAC is most often associated with elevated alpha-fetoprotein (AFP). HAC can occur in multiple organs, including the stomach, esophagus, colon, pancreas, lungs, and ovaries. HAC differs greatly from typical adenocarcinoma in terms of its biological aggression, poor prognosis, and clinicopathological characteristics. However, the mechanisms underlying its development and invasive metastasis remain unclear. The purpose of this review was to summarize the clinicopathological features, molecular traits, and molecular mechanisms driving the malignant phenotype of HAC, in order to support the clinical diagnosis and treatment of HAC.

OLA1 promotes colorectal cancer tumorigenesis by activation of HIF1α/CA9 axis.

BACKGROUND: Obg-like ATPase 1 (OLA1) is a highly conserved GTPase, which was over expressed in a variety of malignant tumors, but its role in colorectal cancer (CRC) was poorly studied. PATIENTS AND METHODS: Three public CRC gene databases were applied for OLA1 mRNA expression detection. The clinical data of 111 CRC patients were retrospectively collected from the Second Affiliated Hospital of Zhejiang University (SAHZU) for OLA1 protein expression and Kaplan-Meier Survival analysis. OLA1 stably knocked out CRC cell lines were conducted by CRISPR-Cas9 for experiments in vitro and in vivo. RESULTS: OLA1 was highly expressed in 84% CRC compared to matched surrounding tissues. Patients with OLA1 high expression had a significantly lower 5-year survival rate (47%) than those with OLA1 low expression (75%). OLA1 high expression was an independent factor of poor prognosis in CRC patients. OLA1-KO CRC cell lines showed lower ability of growth and tumorigenesis in vitro and in vivo. By mRNA sequence analysis, we found 113 differential express genes in OLA1-KO cell lines, of which 63 were hypoxic related. HIF1α was a key molecule in hypoxic regulation. Further molecular mechanisms showed HIF1α /CA9 mRNA and/or protein levels were heavily downregulated in OLA1-KO cell lines, which could explain the impaired tumorigenesis. According to previous studies, HIF1α was a downstream gene of GSK3ß, we verified GSK3ß was over-activated in OLA1-KO cell lines. CONCLUSION: OLA1 was a new gene that was associated with carcinogenesis and poor outcomes in CRC by activation of HIF1α/CA9 axis, which may be interpreted by GSK3ß.

The Clinicopathological Characteristics of Alpha-Fetoprotein-Producing Adenocarcinoma of the Gastrointestinal Tract-A Single-Center Retrospective Study.

Alpha-fetoprotein (AFP)-producing adenocarcinoma from the gastrointestinal tract (APA-GI) is a rare type of highly malignant tumor with a poor prognosis. It may originate from any site along the GI tract with similar clinicopathological characteristics. As limited research had ever described the characteristics of APA-GI, the present article intends to systemically investigate the clinicopathological characteristics of APA-GI from a single center's retrospective study to deepen the understanding of the disease. A total of 177 patients pathologically diagnosed with APA-GI between 2010 and 2017 at the Second Affiliated Hospital of Zhejiang University, School of Medicine, were included. Also, clinical data of 419 gastric cancers and 609 colorectal cancers from The Cancer Genome Atlas database were also extracted. Clinical information of patients from Second Affiliated Hospital of Zhejiang University, School of Medicine, was collected, and a median follow-up of 14.5 months was performed to investigate clinical characteristics of APA-GI. For the pathological characteristics of APA-GI, hematoxylin-eosin sections were reviewed, and immunohistochemistry of AFP was performed. The results showed that the primary tumor could develop through the whole GI tract, including the esophagus (0.6%), stomach (83.1%), duodenum (1.1%), ileum (0.6%), appendix (0.6%), colon (5.1%), and rectum (7.9%). Hepatoid adenocarcinoma is the main pathological feature of APA-GI. AFP expression level in tumor tissue was not strictly associated with serum AFP or hepatoid differentiation. The prognosis of APA-GI was worse than that of common adenocarcinoma of the GI tract and liver metastasis, and high AFP levels suggest poor prognosis in patients with APA-GI. Therefore, the present study was the first research to systemically explore the clinicopathological characteristics of APA-GI. APA-GI occurs through the whole GI tract with a significantly worse prognosis than common adenocarcinoma of GI. APA-GI should be regarded as one kind of disease for its similar clinicopathological characteristics within patients.

An automated detection system for colonoscopy images using a dual encoder-decoder model.

Conventional computer-aided detection systems (CADs) for colonoscopic images utilize shape, texture, or temporal information to detect polyps, so they have limited sensitivity and specificity. This study proposes a method to extract possible polyp features automatically using convolutional neural networks (CNNs). The objective of this work aims at building up a light-weight dual encoder-decoder model structure for polyp detection in colonoscopy Images. This proposed model, though with a relatively shallow structure, is expected to have the capability of a similar performance to the methods with much deeper structures. The proposed CAD model consists of two sequential encoder-decoder networks that consist of several CNN layers and full connection layers. The front end of the model is a hetero-associator (also known as hetero-encoder) that uses backpropagation learning to generate a set of reliably corrupted labeled images with a certain degree of similarity to a ground truth image, which eliminates the need for a large amount of training data that is usually required for medical images tasks. This dual CNN architecture generates a set of noisy images that are similar to the labeled data to train its counterpart, the auto-associator (also known as auto-encoder), in order to increase the successor's discriminative power in classification. The auto-encoder is also equipped with CNNs to simultaneously capture the features of the labeled images that contain noise. The proposed method uses features that are learned from open medical datasets and the dataset of Zhejiang University (ZJU), which contains around one thousand images. The performance of the proposed architecture is compared with a state-of-the-art detection model in terms of the metrics of the Jaccard index, the DICE similarity score, and two other geometric measures. The improvements in the performance of the proposed model are attributed to the effective reduction in false positives in the auto-encoder and the generation of noisy candidate images by the hetero-encoder.

Fast-track multidisciplinary treatment versus conventional treatment for colorectal cancer: a multicenter, open-label randomized controlled study.

BACKGROUND: Laparoscopic surgery, fast-track perioperative treatment and XELOX chemotherapy are effective strategies for shortening the duration of hospital stay for cancer patients. This trial aimed to clarify the safety and efficacy of the fast-track multidisciplinary treatment (FTMDT) model compared to conventional surgery combined with chemotherapy in Chinese colorectal cancer patients. METHODS: This trial was a prospective randomized controlled study with a 2 × 2 balanced factorial design and was conducted at six hospitals. Patients in group 1 (FTMDT) received fast-track perioperative treatment and XELOX adjuvant chemotherapy. Patients in group 2 (conventional treatment) received conventional perioperative treatment and mFOLFOX6 adjuvant chemotherapy. Subgroups 1a and 2a had laparoscopic surgery and subgroups 1b and 2b had open surgery. The primary endpoint was total length of hospital stay during treatment. RESULTS: A total of 374 patients were randomly assigned to the four subgroups, and 342 patients were finally analyzed, including 87 patients in subgroup 1a, 85 in subgroup 1b, 86 in subgroup 2a, and 84 in subgroup 2b. The total hospital stay of group 1 was shorter than that of group 2 [13 days, (IQR, 11-17 days) vs. 23.5 days (IQR, 15-42 days), P = 0.0001]. Compared to group 2, group 1 had lower surgical costs, fewer in-hospital complications and faster recovery (all P < 0.05). Subgroup 1a showed faster surgical recovery than that of subgroup 1b (all P < 0.05). There was no difference in 5-year overall survival between groups 1 and 2 [87.1% (95% CI, 80.7-91.5%) vs. 87.1% (95% CI, 80.8-91.4%), P = 0.7420]. CONCLUSIONS: The FTMDT model, which integrates laparoscopic surgery, fast-track treatment, and XELOX chemotherapy, was the superior model for enhancing the recovery of Chinese patients with colorectal cancer. TRIAL REGISTRATION: ClinicalTrials.gov: NCT01080547 , registered on March 4, 2010.

POPCORN: A web service for individual PrognOsis prediction based on multi-center clinical data CollabORatioN without patient-level data sharing.

BACKGROUND AND OBJECTIVE: Clinical prognosis prediction plays an important role in clinical research and practice. The construction of prediction models based on electronic health record data has recently become a research focus. Due to the lack of external validation, prediction models based on single-center, hospital-specific datasets may not perform well with datasets from other medical institutions. Therefore, research investigating prognosis prediction model construction based on a collaborative analysis of multi-center electronic health record data could increase the number and coverage of patients used for model training, enrich patient prognostic features and ultimately improve the accuracy and generalization of prognosis prediction. MATERIALS AND METHODS: A web service for individual prognosis prediction based on multi-center clinical data collaboration without patient-level data sharing (POPCORN) was proposed. POPCORN focuses on solving key issues in multi-center collaborative research based on electronic health record systems; these issues include the standardization of clinical data expression, the preservation of patient privacy during model training and the effect of case mix variance on the prediction model construction and application. POPCORN is based on a multivariable meta-analysis and a Bayesian framework and can construct suitable prediction models for multiple clinical scenarios that can effectively adapt to complex clinical application environments. RESULTS: POPCORN was validated using a joint, multi-center collaborative research network between China and the United States with patients diagnosed with colorectal cancer. The performance of the models based on POPCORN was comparable to that of the standard prognosis prediction model; however, POPCORN did not expose raw patient data. The prediction models had similar AUC, but the BMA model had the lowest ECI across all prediction models, indicating that this model had better calibration performance than the other models, especially for patients in Chinese hospitals. CONCLUSIONS: The POPCORN system can build prediction models that perform well in complex clinical application scenarios and can provide effective decision support for individual patient prognostic predictions.

Time-dependent and nonlinear effects of prognostic factors in nonmetastatic colorectal cancer.

The survival risk following curative surgery for nonmetastatic colorectal cancer (CRC) may be over- or underestimated due to a lack of attention to nonlinear effects and violation of the proportional hazards assumption. In this paper, we aimed to detect and interpret the shape of time-dependent and nonlinear effects to improve the predictive performance of models of prognoses in nonmetastatic CRC patients. Data for nonmetastatic CRC patients diagnosed between 2004 and 2012 were obtained from the Surveillance Epidemiology End Results registry. Time-dependent and nonlinear effects were tested and plotted. A nonlinear model that used random survival forests was implemented. The estimated 5-year cancer-specific death rate was 17.95% (95% CI, 17.70-18.20%). Tumor invasion depth, lymph node status, age at diagnosis, tumor grade, histology and tumor site were significantly associated with cancer-specific death. Nonlinear and time-dependent effects on survival were detected. Positive lymph node number had a larger effect per unit of measurement at low values than at high values, whereas age at diagnosis showed the opposite pattern. Moreover, nonproportional hazards were detected for all covariates, indicating that the contributions of these risks to survival outcomes decreased over time. The nonlinear model predicted prognoses more accurately (C-index: 0.7934, 0.7933-0.7934) than did the Fine and Gray model (C-index: 0.7550, 0.7510-0.7583). The three-dimensional cumulative incidence curves derived from nonlinear model were used to identify the change points of the risk trends. It would be useful to implement these findings in treatment plans and follow-up surveillance in nonmetastatic CRC patients.

[Distribution and regulation of Panx1 protein].

Pannexin1 (Panx1) is a subtype of the newly discovered gap junction proteins named Pannexin(s). Panx1 is widely expressed in the nervous system, cardiovascular system, etc and can form non-selective and large conductance hemichannel. It has been demonstrated that various conditions can regulate Panx1 open and affect the body's physiological function via macromolecular substance (for instance, ATP) releasing. This review gives a detailed introduction to the main distribution of Panxl, summarizes the open and inhibition condition of Panx1, and finally, prospects directions of future research.