NCT 02096835.

Background NOD-like receptors affect multiple stages of cancer progression in many malignancies. NACHT, LRR, and PYD domain-containing protein 7 (NLRP7) is a member of the NOD-like receptor family, although its role in tumorigenesis remains unclear. By analyzing clinical samples, we found that NLRP7 protein levels were upregulated in colorectal cancer (CRC). We proposed the hypothesis that a high level of NLRP7 in CRC may promote tumor progression. Here, we further investigated the role of NLRP7 in CRC and the underlying mechanism. Methods NLRP7 expression in human CRC and adjacent non-tumorous tissues was examined by quantitative real-time polymerase chain reaction (qRT-PCR), western blotting, and immunohistochemistry. The effect of NLRP7 in CRC progression was investigated in vitro and in vivo. Proteins interacting with NLRP7 were identified by immunoprecipitation and mass spectrometry analysis while immunofluorescence staining revealed the cellular location of the proteins. Cellular ubiquitination and protein stability assays were applied to demonstrate the ubiquitination effect on NLRP7. Cloning and mutagenesis were used to identify a lysine acceptor site that mediates NLRP7 ubiquitination. Cytokines/chemokines affected by NLRP7 were identified by RNA sequencing, qRT-PCR, and enzyme-linked immunosorbent assay. Macrophage phenotypes were determined using qRT-PCR, flow cytometry, and immunohistochemistry. Results NLRP7 protein levels, but not mRNA levels, were upregulated in CRC, and increased NLRP7 protein expression was associated with poor survival. NLRP7 promoted tumor cell proliferation and metastasis in vivo and in vitro and interacted with ubiquitin-specific protease 10, which catalyzed its deubiquitination in CRC cells. NLRP7 stability and protein levels in CRC cells were modulated by ubiquitination and deubiquitination, and NLRP7 was involved in the ubiquitin-specific protease 10 promotion of tumor progression and metastasis in CRC. K379 was an important lysine acceptor site that mediates NLRP7 ubiquitination in CRC cells. In CRC, NLRP7 promoted the polarization of pro-tumor M2-like macrophages by inducing the secretion of C-C motif chemokine ligand 2. Furthermore, NLRP7 promoted NF-κB nuclear translocation and activation of C-C motif chemokine ligand 2 transcription. Conclusions We showed that NLRP7 promotes CRC progression and revealed an as-yet-unidentified mechanism by which NLRP7 induces the polarization of pro-tumor M2-like macrophages. These results suggest that NLRP7 could serve as a biomarker and novel therapeutic target for the treatment of CRC.

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Background: Colorectal cancer (CRC) is the third most common cause of cancer deaths worldwide. Numerous studies have reported that circular RNAs (circRNAs) have important functions in CRC. It was first thought that circRNAs were non-coding RNA; however, more recently they were discovered to encode peptides and play a pivotal role in cancer development and progression. It was shown that most circRNAs possess coding potential; however, not all of them can truly encode peptides. Therefore, a practical strategy to scan for coding circRNAs is needed.Method: Sequence analyses included open reading frame (ORF) prediction, coding peptide prediction, and the identification of unique sequences. Then, experimental assays were used to verify the coded peptides, liquid chromatography-tandem mass spectrometry (LC-MS/MS) was introduced to detect sequences of circRNAs with coding potential, and Western blot was used to identify the encoded peptides. Finally, the functions of the circRNAs were primarily explored.Result: An efficient strategy for searching circRNAs with coding potential was created. We verified this schedule using public databases and LC-MS/MS, then two of these circRNAs were selected for further verification. We used commercial antibodies that can also identify the predicted peptides to test the coded peptides. The functions of the circRNAs were explored primarily, and the results showed that they were mainly involved in the promotion of proliferation and invasion ability.Discussion: We have constructed an efficient strategy of scanning circRNAs with coding potential. Our strategy helped to provide a more convenient pathway for identifying circRNA-derived peptides, which can be a potential therapeutic target or a diagnostic biomarker.

330 Background: Gastrointestinal (GI) cancers totally account for more than one third of the cancerous deaths, yet there is no cost-effective blood-based assay for the early detection of GI cancers. We sought to develop GutSeer, a noninvasive test based on cell-free DNA (cfDNA) methylation and fragmentation signatures derived from one single targeted DNA methylation sequencing panel, for early detection and localization of five major GI cancers, including colorectal (CC), gastric (GC), liver (LC), esophageal (EC), and pancreatic cancer (PC). Methods: A DNA methylation targeted sequencing panel with 1656 target regions was designed. It was then verified in a large cohort of retrospective cancer and control plasma samples for feature selection and modeling. The participants were randomly divided into a training cohort and a validation cohort in a 1:1 ratio. DNA methylation and fragmentomic features were calculated based on GutSeer sequencing data. An ensemble stacked machine learning approach was built to classify cancer and healthy samples in training cohort and tested in validation cohort. We also constructed a TOO model to predict the tissue of origin of detected cancer samples. Results: To develop GutSeer assay, we have enrolled and tested a total of 1844 retrospective plasma samples (787 healthy, 342 LC, 239 GC, 209 EC, 180 CC, and 87 PC), over half of the cancer samples were diagnosed with early-stage disease (TNM stage I 35.6%; stage II 23.3%; stage III 21.7%; stage IV 12.5%). Cancer- vs-healthy model was built on training cohort and tested in validation cohort, achieving an AUC of 0.94 (sensitivity=77.7%, specificity=96.4%) with methylation features, and 0.95 (sensitivity=77.1%, specificity=95.9%) with fragmentomic features. Combining these features could achieve AUC of 0.963 (sensitivity = 86.2%, specificity = 96.7%). For individual cancer types, the sensitivity was 93.3% (CC), 81.1% (EC), 70.3% (GC), 96.5% (LC) and 86.4% (PC), respectively. For predicted cancer samples, we achieved an 82% top-one (66.7% CC, 87.0% GC/EC, 89.0% LC, 63.2% PC) and 95.2% top-two (86,9% CC, 98.2% GC/EC, 97.6% LC, 89.5% PC) TOO accuracy (ACC, accuracy of predicting the most likely, and the top 2 most likely tissue or organ types where the identified cancer was located, respectively) in validation cohort with TOO model combined all features. Conclusions: Based on a single targeted DNA methylation sequencing assay, GutSeer, which combined cfDNA methylation and fragmentomic signatures, could detect and localize the major five GI cancers with high accuracy but low cost. Although this is a pilot study with limited sample size, GutSeer demonstrated the potential to be further optimized into non-invasive diagnostics for blood-based early screening and diagnosis for GI cancers.

Background: The CUGBP1 (CELF1) is differentially expressed in liver metastasis and no liver metastasis colorectal cancers (CRC) tissues and the function of CUGBP1 in CRC is still unclear.Methods: Five cases of colorectal adenocarcinoma and 6 cases of liver metastatic CRC lesions were collected and subjected to cDNA microarray and bioinformatical analyses. The quantitative reverse transcription-polymerase chain reaction (qRT-PCR) was used to confirm the result. Cell function assays were used to study the function of CUGBP1, and the western blot was used to discover the change of the downstream molecules.Results: CUGBP1 was significantly elevated in liver metastatic CRC lesions. Besides, the CUGBP1 can promote proliferation, colony formation, invasion, metastasis abilities as well as increase the apoptosis rates of CRC cells. ERBB2 was positively related to the CUGBP1. Western blot results found that silence of CUGBP1 decreased the protein level of p-AKT and p-ERK without influence the expression level of total protein of AKT and ERK.Conclusions: CUGBP1 can promote liver metastasis of CRC by promoting the phosphorylation of AKT and ERK through the ErbB signaling pathway. CUGBP1 is a potential biomarker for early detection of CRC and maybe a novel therapeutic target of CRC treatment, especially in liver metastasis.

4169 Background: Five major gastrointestinal (GI) cancers - colorectal (CRC), gastric (GC), liver (LC), esophageal (EC), and pancreatic cancer (PC) - are responsible for hundreds of thousands of mortalities annually worldwide. Unfortunately, there is a lack of cost-effective, blood-based screening method for their early detection. To address this issue, we aimed to develop GutSeer, a noninvasive, targeted methylation sequencing-based test by leveraging methylation and fragmentomic signatures carried by cell-free DNA (cfDNA). Methods: The panel of GutSeer consists of 1656 target regions which were either differentially methylated between healthy and cancer samples, or distinctively methylated in a specific GI cancer. Cancer and healthy participants were recruited and randomly divided into a training and a validation cohort. Their plasma DNA samples were analyzed to generate DNA methylation and fragmentomic features. These multi-dimensional features were integrated to build ensemble stacked machine learning models to differentiate cancer against healthy, and to determine the tissue-of-origin (TOO) of the cancer. Results: A total of 1844 cases (787 healthy, 342 LC, 239 GC, 209 EC, 180 CRC, and 87 PC cases) were recruited for this study. A cancer- vs-healthy model achieved an AUC of 0.94 and 0.95 (sensitivity of 77.7% and 77.1% under the specificity around 96%) using either methylation or fragmentomic features only, respectively. Combining both methylation and fragmentomic features further improved performances, achieving an AUC of 0.96 (sensitivity = 86.2% at a specificity of 96.7%). For individual type of cancer, GutSeer has a sensitivity of 93.3% for CRC, 81.1% for EC, 70.3% for GC, 96.5% for LC, and 86.4% for PC. An independent test using 629 benign cases as controls achieved a specificity of 87.1%. A separate TOO model was built using all features and achieved an overall accuracy of 82% for all cancer cases (66.7% for CRC, 87.0% for GC and EC combined, 89.0% for LC, and 63.2% for PC). Same as the cancer detection model, using multi-dimensional features in TOO prediction yielded higher accuracy than when models using only methylation or fragmentomics features (accuracy = 75.6% or 75.4%, respectively). When compared with whole-genome sequencing (WGS) based approaches, GutSeer showed a comparable performance in cancer detection but a higher accuracy in TOO identification, further confirming its effectiveness for detection of GI cancers. Conclusions: GutSeer, a non-invasive test integrating multi-dimensional features, was demonstrated to detect and localize the 5 main types of GI cancer with high accuracy. Our results further showed that a reasonably sized panel can perform comparably or even better than WGS-based methods in cancer detection and TOO localization, indicating GutSeer may be a low-cost solution for blood-based early screening for GI cancers.

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