Phytohormones-mediated strategies for mitigation of heavy metals toxicity in plants focused on sustainable production.

KEY MESSAGE: In this manuscript, authors reviewed and explore the information on beneficial role of phytohormones to mitigate adverse effects of heavy metals toxicity in plants. Global farming systems are seriously threatened by heavy metals (HMs) toxicity, which can result in decreased crop yields, impaired food safety, and negative environmental effects. A rise in curiosity has been shown recently in creating sustainable methods to reduce HMs toxicity in plants and improve agricultural productivity. To accomplish this, phytohormones, which play a crucial role in controlling plant development and adaptations to stress, have emerged as intriguing possibilities. With a particular focus on environmentally friendly farming methods, the current review provides an overview of phytohormone-mediated strategies for reducing HMs toxicity in plants. Several physiological and biochemical activities, including metal uptake, translocation, detoxification, and stress tolerance, are mediated by phytohormones, such as melatonin, auxin, gibberellin, cytokinin, ethylene, abscisic acid, salicylic acid, and jasmonates. The current review offers thorough explanations of the ways in which phytohormones respond to HMs to help plants detoxify and strengthen their resilience to metal stress. It is crucial to explore the potential uses of phytohormones as long-term solutions for reducing the harmful effects of HMs in plants. These include accelerating phytoextraction, decreasing metal redistribution to edible plant portions, increasing plant tolerance to HMs by hormonal manipulation, and boosting metal sequestration in roots. These methods seek to increase plant resistance to HMs stress while supporting environmentally friendly agricultural output. In conclusion, phytohormones present potential ways to reduce the toxicity of HMs in plants, thus promoting sustainable agriculture.

iProX in 2021: connecting proteomics data sharing with big data.

The rapid development of proteomics studies has resulted in large volumes of experimental data. The emergence of big data platform provides the opportunity to handle these large amounts of data. The integrated proteome resource, iProX (https://www.iprox.cn), which was initiated in 2017, has been greatly improved with an up-to-date big data platform implemented in 2021. Here, we describe the main iProX developments since its first publication in Nucleic Acids Research in 2019. First, a hyper-converged architecture with high scalability supports the submission process. A hadoop cluster can store large amounts of proteomics datasets, and a distributed, RESTful-styled Elastic Search engine can query millions of records within one second. Also, several new features, including the Universal Spectrum Identifier (USI) mechanism proposed by ProteomeXchange, RESTful Web Service API, and a high-efficiency reanalysis pipeline, have been added to iProX for better open data sharing. By the end of August 2021, 1526 datasets had been submitted to iProX, reaching a total data volume of 92.42TB. With the implementation of the big data platform, iProX can support PB-level data storage, hundreds of billions of spectra records, and second-level latency service capabilities that meet the requirements of the fast growing field of proteomics.

The protein-protein interaction ontology: for better representing and capturing the biological context of protein interaction.

BACKGROUND: With the rapid increase in the amount of Protein-Protein Interaction (PPI) data, the establishment of an event-centered PPI ontology that contains temporal and spatial vocabularies is urgently needed to clarify PPI biological annotations. In this paper, we propose a precisely designed schema - PPIO (PPI Ontology) for representing the biological context of PPIs. RESULTS: Inspired by the event model and the distinct characteristics of PPI events, PPIO consists of six core aspects of the information required for reporting a PPI event, including the interactor (who), the biological process (when), the subcellular location (where), the interaction type (how), the biological function (what) and the detection method (which). PPIO is implemented through the integration of appropriate terms from the corresponding vocabularies/ontologies, e.g., Gene Ontology, Protein Ontology, PSI-MI/MOD, etc. To assess PPIO, an approach based on PPIO in developed to extract PPI biological annotations from an open standard corpus "BioCreAtIvE-PPI". The experiment results demonstrate PPIO's high performance, a precision of 0.69, a recall of 0.72 and an F-score of 0.70. CONCLUSIONS: PPIO is a well-constructed essential ontology in the interpretation of PPI biological context. The results of the experiments conducted on the BioCreAtIvE corpus demonstrate that PPIO is able to facilitate PPI annotation extraction from biomedical literature effectively and enrich essential annotation for PPIs.

iProX: an integrated proteome resource.

Sharing of research data in public repositories has become best practice in academia. With the accumulation of massive data, network bandwidth and storage requirements are rapidly increasing. The ProteomeXchange (PX) consortium implements a mode of centralized metadata and distributed raw data management, which promotes effective data sharing. To facilitate open access of proteome data worldwide, we have developed the integrated proteome resource iProX (http://www.iprox.org) as a public platform for collecting and sharing raw data, analysis results and metadata obtained from proteomics experiments. The iProX repository employs a web-based proteome data submission process and open sharing of mass spectrometry-based proteomics datasets. Also, it deploys extensive controlled vocabularies and ontologies to annotate proteomics datasets. Users can use a GUI to provide and access data through a fast Aspera-based transfer tool. iProX is a full member of the PX consortium; all released datasets are freely accessible to the public. iProX is based on a high availability architecture and has been deployed as part of the proteomics infrastructure of China, ensuring long-term and stable resource support. iProX will facilitate worldwide data analysis and sharing of proteomics experiments.

Systematic analysis of missing proteins provides clues to help define all of the protein-coding genes on human chromosome 1.

Our first proteomic exploration of human chromosome 1 began in 2012 (CCPD 1.0), and the genome-wide characterization of the human proteome through public resources revealed that 32-39% of proteins on chromosome 1 remain unidentified. To characterize all of the missing proteins, we applied an OMICS-integrated analysis of three human liver cell lines (Hep3B, MHCC97H, and HCCLM3) using mRNA and ribosome nascent-chain complex-bound mRNA deep sequencing and proteome profiling, contributing mass spectrometric evidence of 60 additional chromosome 1 gene products. Integration of the annotation information from public databases revealed that 84.6% of genes on chromosome 1 had high-confidence protein evidence. Hierarchical analysis demonstrated that the remaining 320 missing genes were either experimentally or biologically explainable; 128 genes were found to be tissue-specific or rarely expressed in some tissues, whereas 91 proteins were uncharacterized mainly due to database annotation diversity, 89 were genes with low mRNA abundance or unsuitable protein properties, and 12 genes were identifiable theoretically because of a high abundance of mRNAs/RNC-mRNAs and the existence of proteotypic peptides. The relatively large contribution made by the identification of enriched transcription factors suggested specific enrichment of low-abundance protein classes, and SRM/MRM could capture high-priority missing proteins. Detailed analyses of the differentially expressed genes indicated that several gene families located on chromosome 1 may play critical roles in mediating hepatocellular carcinoma invasion and metastasis. All mass spectrometry proteomics data corresponding to our study were deposited in the ProteomeXchange under the identifiers PXD000529, PXD000533, and PXD000535.

Design optimization of office building envelope by developed farmland fertility algorithm for energy saving.

This study focuses on designing sustainable buildings with a specific emphasis on reducing energy consumption and optimizing costs. To address the time-consuming nature of simulation software like TRNSYS and Energy Plus, a novel meta-heuristic optimization algorithm called the Developed Optimization Algorithm of Farmland Fertility (DFFA) is provided. The DFFA algorithm is utilized to optimize parameters related to the building envelope, encompassing walls, windows, and glass curtain walls, aiming to minimize energy demand and construction expenses. Comparative analysis with other approaches such as EPO, LOA, MVO, and FFA demonstrates significant reductions in energy consumption and building design costs achieved by employing the proposed algorithm. Furthermore, the DFFA algorithm yields the desired results within fewer iterations. By increasing the surface area of the glass curtain wall and total window space, improvements in natural ventilation and interior lighting are observed. Despite similar window opening measurements across the compared methods, the suggested algorithm surpasses others when it comes to overall cost and energy efficiency. The total cost reduction compared to the initial design amounts to 39 %. Thus, the DFFA algorithm proves to be more effective in conserving energy in buildings compared to other analyzed procedures. This research serves as a case study and presents a promising method applicable to designing various building types under different weather conditions in future projects.

SPINK1 Overexpression Correlates with Hepatocellular Carcinoma Treatment Resistance Revealed by Single Cell RNA-Sequencing and Spatial Transcriptomics.

Low efficacy of treatments and chemoresistance are challenges in addressing refractory hepatocellular carcinoma (HCC). SPINK1, an oncogenic protein, is frequently overexpressed in many HCC cases. However, the impact of SPINK1 on HCC treatment resistance remains poorly understood. Here, we elucidate the functions of SPINK1 on HCC therapy resistance. Analysis of SPINK1 protein level reveals a correlation between elevated SPINK1 expression and unfavorable prognosis. Furthermore, intercellular variations in SPINK1 expression levels are observed. Subsequent examination of single cell RNA-sequencing data from two HCC cohorts further suggest that SPINK1-high cells exhibit heightened activity in drug metabolic pathways compared to SPINK1-low HCC cells. High SPINK1 expression is associated with reduced sensitivities to both chemotherapy drugs and targeted therapies. Moreover, spatial transcriptomics data indicate that elevated SPINK1 expression correlates with non-responsive phenotype during treatment with targeted therapy and immune checkpoint inhibitors. This is attributed to increased levels of drug metabolic regulators, especially CES2 and CYP3A5, in SPINK1-high cells. Experimental evidence further demonstrates that SPINK1 overexpression induces the expression of CES2 and CYP3A5, consequently promoting chemoresistance to sorafenib and oxaliplatin. In summary, our study unveils the predictive role of SPINK1 on HCC treatment resistance, identifying it as a potential therapeutic target for refractory HCC.

Negative pressure pulmonary edema after laparoscopic cholecystectomy: A case report and literature review.

RATIONALE: Negative pressure pulmonary edema (NPPE) is an acute onset of non-cardiogenic interstitial pulmonary edema, commonly seen among surgical patients after extubation from general aneasthesia. It is mainly caused by rapid inspiration with acute upper airway obstruction resulting in significant negative thoracic pressure. PATIENT CONCERNS: A 24-year-old female patient who underwent laparoscopic cholecystectomy under general anesthesia and developed NPPE postoperatively. DIAGNOSES: Her main clinical manifestation was coughing up pink foamy sputum; postoperative CT showed increased texture in both lungs and bilateral ground glass opacities. INTERVENTIONS: Diuretics and steroids were used, and symptomatic supportive treatments such as oxygen were given. OUTCOMES: After treatment, on the fourth post-operative day, her symptoms were relieved and her vital signs were stable enough for her to be discharged. LESSONS: Although this is a rare and severe complication, the prognosis of NPPE is good when it is managed with proper diagnosis and treatment.

Establishment of a cholangiocarcinoma risk evaluation model based on mucin expression levels.

BACKGROUND: Cholangiocarcinoma (CCA) is a highly malignant cancer, characterized by frequent mucin overexpression. MUC1 has been identified as a critical oncogene in the progression of CCA. However, the comprehensive understanding of how the mucin family influences CCA progression and prognosis is still incomplete. AIM: To investigate the functions of mucins on the progression of CCA and to establish a risk evaluation formula for stratifying CCA patients. METHODS: Single-cell RNA sequencing data from 14 CCA samples were employed for elucidating the roles of mucins, complemented by bioinformatic analyses. Subsequent validations were conducted through spatial transcriptomics and immunohistochemistry. The construction of a risk evaluation model utilized the least absolute shrinkage and selection operator regression algorithm, which was further confirmed by independent cohorts and diverse data types. RESULTS: CCA tumor cells with elevated levels of MUC1 and MUC4 showed activated nucleotide metabolic pathways and increased invasiveness. MUC5AC-high cells were found to promote CCA progression through WNT signaling. MUC5B-high cells exhibited robust cellular oxidation activities, leading to resistance against antitumoral treatments. MUC13-high cells were observed to secret chemokines, recruiting and transforming macrophages into the M2-polarized state, thereby suppressing antitumor immunity. MUC16-high cells were found to promote tumor progression through interleukin-1/nuclear factor kappa-light-chain-enhancer of activated B cells signaling upon interaction with neutrophils. Utilizing the expression levels of these mucins, a risk factor evaluation formula for CCA was developed and validated across multiple cohorts. CCA samples with higher risk factors exhibited stronger metastatic potential, chemotherapy resistance, and poorer prognosis. CONCLUSION: Our study elucidates the functional mechanisms through which mucins contribute to CCA development, and provides tools for risk stratification in CCA.

Gallbladder cancer masquerading as xanthogranulomatous cholecystitis: a case report and literature review.

Xanthogranulomatous cholecystitis (XGC) is a rare type of cholecystitis that, despite being benign poses diagnostic challenges due to its low prevalence and need for consensus on diagnostic criteria. Consequently, distinguishing XGC from gallbladder cancer (GBC) is challenging, leading to clinical misdiagnoses. This article presents a case where a patient initially diagnosed with GBC was later found to have XGC.

BioLadder: A bioinformatic platform primarily focused on proteomic data analysis.

BioLadder (https://www.bioladder.cn/) is an online data analysis platform designed for proteomics research, which includes three classes of experimental data analysis modules and four classes of common data analysis modules. It allows for a variety of proteomics analyses to be conducted easily and efficiently. Additionally, most modules can also be utilized for the analysis of other omics data. To facilitate user experience, we have carefully designed four different kinds of functions for customers to quickly and accurately utilize the relevant analysis modules.

LiverAtlas: a unique integrated knowledge database for systems-level research of liver and hepatic disease.

BACKGROUND: A large amount of liver-related physiological and pathological data exist in publicly available biological and bibliographic databases, which are usually far from comprehensive or integrated. Data collection, integration and mining processes pose a great challenge to scientific researchers and clinicians interested in the liver. METHOD: To address these problems, we constructed LiverAtlas (http://liveratlas.hupo.org.cn), a comprehensive resource of biomedical knowledge related to the liver and various hepatic diseases by incorporating 53 databases. RESULTS: In the present version, LiverAtlas covers data on liver-related genomics, transcriptomics, proteomics, metabolomics and hepatic diseases. Additionally, LiverAtlas provides a wealth of manually curated information, relevant literature citations and cross-references to other databases. Importantly, an expert-confirmed Human Liver Disease Ontology, including relevant information for 227 types of hepatic disease, has been constructed and is used to annotate LiverAtlas data. Furthermore, we have demonstrated two examples of applying LiverAtlas data to identify candidate markers for hepatocellular carcinoma (HCC) at the systems level and to develop a systems biology-based classifier by combining the differential gene expression with topological features of human protein interaction networks to enhance the ability of HCC differential diagnosis. CONCLUSION: LiverAtlas is the most comprehensive liver and hepatic disease resource, which helps biologists and clinicians to analyse their data at the systems level and will contribute much to the biomarker discovery and diagnostic performance enhancement for liver diseases.

The effects of calcination on the electrochemical properties of manganese oxides.

Three different crystalline forms of Mn3O4 were successfully prepared by a liquid phase method with different additives. Using XRD, SEM, EDS, BET, compacted density and electrochemical analysis, the effects of different additives on the morphology, phase composition, surface characteristics, specific surface area, electrochemical and other physical and chemical properties of manganese oxides were investigated. The results showed that the rod type Mn3O4 was prepared by mixing ammonia water and anhydrous ethanol in a 1 : 1 ratio and an appropriate amount of cetylmethyl ammonium bromide as the additive. The rod-type Mn3O4 showed a maximum specific surface area of 63.87 m2 g-1 and has the advantages of low compaction density, no introduction of other impurities, and high adsorption potential. It also has excellent electrochemical performance and an impedance of 240 Ω. The specific capacity was as high as 666.5 mA h g-1 at 1C current density and 382.2 mA h g-1 after 200 cycles. The results also showed that the electrochemical performance of Mn2O3 prepared at 700 °C from the rod-type Mn3O4 was the best. When it was used as the anode material of a lithium-ion battery, it showed a high specific capacity of 712.1 mA h g-1 after 200 cycles. Therefore, the rod-type Mn2O3 material has the characteristics of high capacity, low cost and environmental friendliness and is a promising candidate anode material for lithium-ion batteries.

Dynamic atlas of immune cells reveals multiple functional features of macrophages associated with progression of pulmonary fibrosis.

Idiopathic pulmonary fibrosis (IPF) is a chronic interstitial lung disease with a high mortality rate and unclarified aetiology. Immune response is elaborately regulated during the progression of IPF, but immune cells subsets are complicated which has not been detailed described during IPF progression. Therefore, in the current study, we sought to investigate the role of immune regulation by elaborately characterize the heterogeneous of immune cells during the progression of IPF. To this end, we performed single-cell profiling of lung immune cells isolated from four stages of bleomycin-induced pulmonary fibrosis-a classical mouse model that mimics human IPF. The results revealed distinct components of immune cells in different phases of pulmonary fibrosis and close communication between macrophages and other immune cells along with pulmonary fibrosis progression. Enriched signals of SPP1, CCL5 and CXCL2 were found between macrophages and other immune cells. The more detailed definition of the subpopulations of macrophages defined alveolar macrophages (AMs) and monocyte-derived macrophages (mo-Macs)-the two major types of primary lung macrophages-exhibited the highest heterogeneity and dynamic changes in expression of profibrotic genes during disease progression. Our analysis suggested that Gpnmb and Trem2 were both upregulated in macrophages and may play important roles in pulmonary fibrosis progression. Additionally, the metabolic status of AMs and mo-Macs varied with disease progression. In line with the published data on human IPF, macrophages in the mouse model shared some features regarding gene expression and metabolic status with that of macrophages in IPF patients. Our study provides new insights into the pathological features of profibrotic macrophages in the lung that will facilitate the identification of new targets for disease intervention and treatment of IPF.

Effectiveness of kumquat decoction for the improvement of cough caused by SARS-CoV-2 Omicron variants, a multicentre, prospective observational study.

BACKGROUND: Kumquat decoction is a traditional Chinese medicine formula and has been widely used to alleviate the coronavirus disease 2019 (COVID-19)-related cough in China. However, the effectiveness and safety of kumquat decoction remain unclear. PURPOSE: To assess the effectiveness and safety of kumquat decoction for COVID-19-related cough. STUDY DESIGN: A multicentre, prospective observational study. METHODS: We enrolled consecutive patients with mild-to-moderate COVID-19 from December 31, 2022, to January 3, 2023, during the Omicron phase in Yangshuo County, China. The primary outcome was the time from study baseline to sustained cough resolution by the last follow-up day on January 31, 2023. The effectiveness was evaluated by Cox proportional hazards models based on propensity score analyses. The secondary outcomes were the resolution of cough and other COVID-19-related symptoms by Days 3, 5, and 7. RESULTS: Of 1434 patients, 671 patients were excluded from the analysis of cough resolution. Among the remaining 763 patients, 481 (63.04%) received kumquat decoction, and 282 (36.96%) received usual care. The median age was 38.0 (interquartile range [IQR] 29.0, 50.0) years, and 55.7% were women. During a median follow-up of 7.000 days, 68.2% of patients in the kumquat group achieved sustained cough resolution (93.77 per 1000 person-days) compared to 39.7% in the usual care group (72.94 per 1000 person-days). The differences in restricted mean survival time (RMST) (kumquat decoction minus usual care group) for cough resolution were -0.742 days (95% CI, -1.235 to -0.250, P = 0.003) on Day 7. In the main analysis using propensity-score matching, the adjusted hazard ratio (HR) for cough resolution (kumquat decoction vs. usual care group) was 1.94 (95% CI, 1.48 to 2.53, P < 0.001). Similar findings were found in multiple sensitivity analyses. In addition, the use of kumquat decoction was associated with the resolution of cough, and a stuffy nose on Days 5 and 7, as well as the resolution of sore throat on Day 7 following medication. CONCLUSION: In this study among patients with COVID-19-related cough, receiving kumquat decoction was associated with an earlier resolution of cough symptoms.

Study on the Adsorption Mechanism of Cobalt and Nickel in Manganese Sulfate by δ-MnO2.

Manganese has excellent performance in removing metal ions from aqueous solutions, but there are few studies on the adsorption and removal of heavy metal impurities in metal salt solutions. In this paper, the adsorption of cobalt and nickel ions in MnSO4 solution by δ-MnO2 prepared from two different manganese sources was studied. The optimum adsorption conditions were as follows: When the concentration of Mn2+ was 20 g/L, δ-MnO2 addition was 10 g/L, Co2+ concentration was 80 mg/L, Ni2+ concentration was 80 mg/L, reaction time was 60 min, reaction temperature was 80 °C, and pH value was 7, the adsorption rate of Co2+ and Ni2+ reached more than 80%. The manganese dioxide adsorbed by heavy metals was analyzed and detected. The results showed that MnOOH appeared in the phases of both kinds of δ-MnO2, and their morphologies were dense rod-like structures with different lengths and flake-like structures of fine particles. Co and Ni were distributed on the surface and gap of MnO2 particles, and the atomic percentage of Co was slightly higher than that of Ni. The new vibration peaks appeared near wave numbers of 2668.32, 1401.00, and 2052.19 cm-1, which were caused by the complexation of cations such as Co2 + and Ni2 + with hydroxyl groups. Some cobalt and nickel appeared on the surface of δ-MnO2, and the surface oxygen increased after adsorption. The above characterization revealed that the adsorption of cobalt and nickel in manganese sulfate by δ-MnO2 was realized by the reaction of its surface hydroxyl with metal ions (M) to form ≡SOMOH.

Metabolic detection of malignant brain gliomas through plasma lipidomic analysis and support vector machine-based machine learning.

BACKGROUND: Most malignant brain gliomas (MBGs) are associated with dismal outcomes, mainly due to their late diagnosis. Current diagnostic methods for MBGs are based on imaging and histological examination, which limits their early detection. Here, we aimed to identify reliable plasma lipid biomarkers for non-invasive diagnosis for MBGs. METHODS: Untargeted lipidomic analysis was firstly performed using a discovery cohort (n=107). The data were processed by a support vector machine (SVM)-based discriminating model to retrieve a panel of candidate biomarkers. Then, a targeted quantification method was developed, and the SVM-based diagnostic model was constructed using a training cohort (n=750) and tested using a test cohort (n=225). Finally, the performance of the diagnostic model was further evaluated in an independent validation cohort (n=920) enrolled from multiple medical centers. FINDINGS: A panel of 11 plasma lipids was identified as candidate biomarkers with an accuracy of 0.999. The diagnostic model developed achieved a high performance in distinguishing MBGs patients from normal controls with an area under the receiver-operating characteristic curve (AUC) of 0.9877 and 0.9869 in the training and test cohorts, respectively. In the validation cohort, the 11 lipid panel still achieved an accuracy of 0.9641 and an AUC of 0.9866. INTERPRETATION: The present study demonstrates the applicability and robustness of utilizing a machine learning algorithm to analyze lipidomic data for efficient and reliable biomarker screening. The 11 lipid biomarkers show great potential for the non-invasive diagnosis of MBGs with high throughput. FUNDING: A full list of funding bodies that contributed to this study can be found in the Acknowledgments section.

Discovery of bladder cancer biomarkers in paired pre- and postoperative urine samples.

BACKGROUND: Bladder cancer (BC), a common cancer of the urinary system, has a low mortality but an extremely high recurrence rate. Patients who have undergone initial surgical treatment often undergo frequent prognostic examinations with a substantial burden of discomfort and costs. Urine samples can reflect early disease processes in the urinary system and may be an excellent source of biomarkers. METHODS: In the present study, we used the liquid chromatography with tandem mass spectrometry (LC-MS/MS) to perform proteomic analysis of pre- and postoperative urine samples from patients with stage III BC to identify biomarkers of cancer prognosis. Candidate biomarkers from proteomic analysis were simultaneously validated using western blotting in an independent cohort and immunohistochemical (IHC) staining, combined with gene expression data of BC samples in The Cancer Genome Atlas (TCGA). RESULTS: The comparison of pre- and postoperative urine samples from the same patients led to the discovery of several significantly differentially expressed proteins, whose functions could be closely related to the occurrence and development of BC. We confirmed a representative group of candidate biomarker molecules, such as cadherin-related family member 2 (CDHR2), heat shock protein beta-1 (HSP27), and heterogeneous nuclear ribonucleoproteins A2/B1 (HNRNPA2B1). CONCLUSIONS: The candidate biomarker molecules can distinguish between pre- and postoperative urine samples, and alterations in their expression levels are significantly associated with recurrence rates in patients with BC. Therefore, these molecules may become useful biomarkers for the monitoring and prognosis of BC.

Mutations in the coat complex II component SEC23B promote colorectal cancer metastasis.

Metastasis is the leading cause of death for colorectal cancer (CRC). However, the protein transport process involved in CRC metastasis remains unclear. In this report, we use whole-exome sequencing and bioinformatics analysis to identify somatic mutations in CRC samples and found mutations of the protein transport gene Sec23 homolog B (SEC23B) in patients with metachronous liver metastasis. We show that deletion of SEC23B suppresses the membrane localization of adhesion proteins and augments cell mobility. SEC23B mutations either cause a premature stop (C649T) or impair its protein transport activity (C1467G and T488C + G791A + G2153A). Furthermore, SEC23B mutations inhibit the transport of epithelial cell adhesion molecule (EPCAM) and CD9 molecule, thereby attenuating cell adhesion and promoting invasiveness both in vitro and in vivo. Taken together, these data demonstrate the important impact of SEC23B mutations on metastasis, and we propose that SEC23B is a potential suppressor of CRC metastasis.