S100 family members (S100s) are small molecular EF hand calcium binding proteins and widely expressed in many tissues and organs. S100s are shown to be biomarkers of disease progression and prognosis in various types of cancers. Nevertheless, the expression patterns, function, and prognostic values of S100s and its association with tumor-infiltrating immune cells in pancreatic adenocarcinoma (PAAD) patients have not been systematically clarified. We explored the expression and roles of the entire 20 S100s in PAAD patients by using the following public databases: Oncomine, gene expression profiling interactive analysis, cBioPortal, Metascape, search tool for recurring instances of neighboring genes, Tumor IMmune Estimation Resource, and GeneMANIA. The S100A2/A3/A4/A6/A8/A9/A10/A11/A13/A14/A16/B/P mRNA expressions were significantly upregulated in PAAD patients. The mRNA expression of S100A3/A4/A5/A6/A10/A11/A14/A16/Z were significantly negatively related with the tumor stage in PAAD patients. We found that the S100A2/A3/A5/A10/A11/A14/A16 were significantly correlated with poor overall survival, whereas the increased levels of S100A1/B/G/Z were strongly associated with good overall survival. We found significant correlations among S100s and tumor-infiltrating immune cells. Cox proportional risk models revealed that B cells, Dendritic cells and S100A1/A5/A6/A8/A9/A13/A14 were significantly related with outcomes in PAAD patients. These results suggest that S100A2/A3/A10/A11/A14/A16 may serve as new diagnostic and prognostic biomarkers for PAAD patients and provide new clues for immunotherapy in PAAD patients.

N6-methyladenosine (m6A) serves as a major RNA methylation modification and impacts the initiation and progression of various human cancers through diverse mechanisms. It has been reported that m6A RNA methylation is involved in different physiological and pathological processes, including stem cell differentiation and motility, immune response, cellular stress, tissue renewal and viral infection. In this review, the m6A modification and its regulatory functions in a few major cancers is introduced. The detection approaches for the m6A sites identification are discussed. Additionally, the potential of the RNA m6A modification in clinical application is discussed.

Objective. ETS1 and ETS2, the main ETS family of transcription factors, have been found to act as downstream effectors of the RAS/MAPK pathway. This study explores the expression and prognostic values of ETS1 and ETS2 across cancers. We also aimed to explore the significance of ETS1 and ETS2 expression in normal immune cells with relation to tumorigenesis. Methods. The expression of ETS1 and ETS2 was examined in the HPA and GEPIA2 databases. The KM plotter was applied to examine prognostic value of ETS1 and ETS2. Correlation between ETS1/ETS2 and infiltrating immune cells and immune checkpoints was assessed using TIMER2.0. The mutation landscape of ETS1/ETS2 was explored using the cBioPortal. STRING and GEPIA2 were used to screen ETS1/ETS2 binding and correlated genes. Enrichr was applied to perform GO and KEGG enrichment analyses. Results. ETS1 showed enhanced expression in lymphoid tissue, while ETS2 showed low tissue specificity. ETS1 was increased in 12 and decreased in 6 cancers, while ETS2 was increased in 4 and decreased in 13 cancers. Both ETS1 and ETS2 were favorable prognostic markers in LIHC and KIRC, while they showed different prognostic roles in more cancers. ETS1 showed stronger correlation with several infiltrating immune cells and immune checkpoints compared with ETS2. Both ETS1 and ETS2 harbored low mutation ratio. ETS1 interacting and correlated genes were enriched in GO terms in response to cadmium ion and response to oxidative stress, while those of ETS2 were enriched in transcription regulation. Conclusion. ETS1 and ETS2 showed different patterns in expression, prognostic values, correlation with immune infiltrating, and immune checkpoints. ETS1 and ETS2 play distinct roles across cancer.

Point-of-care testing (POCT) of blood cell count (BCC) is an emerging approach that allows laypersons to identify and count whole blood cells through simple manipulation. To date, POCTs for BCC were mainly achieved by "stationary" images through blood smears or single-laity arranged cells in the microwell, making it difficult to obtain statistically sufficient numbers of cells. In this work, we present a fully integrated POCT device solely using "in-flow" imaging of 3 μL fingertip whole blood for improved identification and counting accuracy of BCC analysis. A miniaturized magnetic stirring module was integrated to maintain the temporal stability of cell concentration. A relatively high throughput (∼8000 cells/min) with a 30-fold dilution ratio of whole blood can be tested for as long as 1 h to examine sufficient numbers of cells, and the subclass cell concentration keeps constant. To improve the identification accuracy, multi-frame "in-flow" imaging was used to track the cell motion trails with multi-angle morphology analysis. This proof-of-concept was then validated with healthy whole blood samples and 75 cases of clinical patients with abnormal concentrations of red blood cells (RBCs), white blood cells (WBCs), and platelets (PLT). The average precision (AP) value of WBCs identification was improved from 0.8622 to 0.9934 using the multi-frame analysis method. And the high fitting degrees (>0.98) between our POCT device and the commercial clinical equipment indicated good agreement. This POCT device is user-friendly and cost-effective, making it a potential tool for diagnosing abnormal blood cell morphology or concentration in the field setting.

Introduction: Inflammatory bowel disease (IBD) is a chronic relapsing and remitting disease with a rising incidence globally. Circulating exosomes play great roles in IBD pathogenesis through exosomal cargoes, especially impacting the function of endothelial barriers. Transendothelial electrical resistance (TEER) measurement is a widely used non-invasive and label-free strategy to monitor endothelial barrier function in vitro . This study established a well-designed microfluidic device to monitor the TEER changes of endothelial cellular barrier on-chip after treated with exosome derived from IBD serum. Methods: The chip comprised two layers of microfluidic chambers with top layer for the perfusion of medium to maintain the nutrition and pressure during cell culture, and bottom layer for the extracellular matrix mimic using hydrogel, which are separated by a semipermeable membrane that permitted the formation of endothelial cell barrier. Four electrodes independent from the outlets were integrated to the chip for TEER detection. In vivo mouse models mouse models and proteome profiling were performed to finding relevant regulators. Results: With this platform, significant decrease of TEER was detected, indicating that IBD serum exosome impact the endothelial cellular barrier on-chip. In vivo mouse models, IBD serum exosome treated group showed great higher DAI scores, shorter colons, more severe histological features, and higher levers of S100A8 expression, promoting the disease progress. Proteome profiling showed that TFRC and ANXA5 have great potentials as novel regulators in IBD. Discussion: This in-house customized microfluidic chip emulates the endothelial barrier microenvironment and enables the TEER monitoring, and can be used to investigate endothelial barrier function in vitro . IBD serum exosome promote the severity of disease.

目的 筛选炎症性肠病患者血清外泌体中的差异蛋白,分析其功能及生物学过程.方法 选取 2021 年 12 月至 2022 年 3月在郑州大学第一附属医院住院确诊的炎症性肠病患者(炎症性肠病组)与健康体检者(健康对照组),各 3 例,收集患者血清样本,采用外泌体提取试剂盒提取血清外泌体,采用 4D-Label-free定量蛋白质组学技术对两者血清中的外泌体蛋白进行鉴定和定量分析,筛选出差异蛋白,通过生物信息学分析差异蛋白的来源及生物学功能.结果 共鉴定到 5 765 个肽段,891 个蛋白,以差异倍数>2 且P<0.05 为标准共筛选出 55 种差异蛋白,与健康对照组相比,炎症性肠病组上调蛋白 38 种,下调蛋白 17 种.生物信息学分析这些差异蛋白主要集中在细胞内过程、刺激应答、生物学调节、代谢过程等生物学过程,与神经变性、细菌及病毒感染相关,还参与了 Ras信号、钙信号、cCMP-PKG信号等信号通路.结论 4D-Label-free定量蛋白质组学技术筛选出的差异蛋白可能作为炎症性肠病早期诊断的血清学标志物及治疗靶点.

We previously reported the inhibitory effects of microRNA-26a (miR-26a) on the conversion of pyruvate to acetyl coenzyme A in glucose metabolism by directly targeting pyruvate dehydrogenase protein X component in colorectal cancer (CRC) cells (Chen B et al., BMC Cancer 2014). Here, using microRNA in situ hybridization, we confirmed that miR-26a levels were elevated in 77 human CRC tissue samples and further investigated the key miR-26a-mediated metabolic regulation elements and signaling pathways in CRC cells through quantitative proteomic dissection combined with cancer cell biology and biochemical loss-of-function analysis. We found that AKT transcription signaling was a target pathway via miR-26a-mediated deacetylation modification of Ras-responsive element-binding protein 1 (RREB1) at the Lys-60 residue. miR-26a improved the deacetylation level of RREB1, thus contributing to RREB1 binding to the AKT1 promoter to activate AKT transcription and its related signaling pathway in glycolysis. Moreover, miR-26a promoted CRC tumorigenesis in CRC cells and subcutaneous xenograft mice. Thus, miR-26a is a key regulator of CRC tumorigenesis that mediates the deacetylation modification of RREB1 to enhance AKT1 transcription and downstream target gene expression in glycolysis for CRC growth.

Colorectal cancer (CRC) is the third leading cause of cancer-related death in China. It usually originates from the non-cancerous neoplasm polyps of the colon or rectal epithelium. Some polyps will evolve into precancerous lesions and eventually turn into colorectal cancer, Early screening and removal of adenomas can reduce the risk of colorectal cancer if screened. Unfortunately, more than 60% of colorectal cancer cases are attributed to missed polyps. Therefore, a deep learning network referred to as the faster_rcnn_inception_resnet_v2 model was introduced for the localization and classification of precancerous lesions. It enables high-precision classification of polyps and adenomas under white light endoscopic images. The Mean Average Precision reached 90.645% when the Intersection over Union is set to 0.5. As an aid to clinicians, the model can improve the detection rate of adenomas and the diagnostic accuracy of early CRC.

Intestinal metaplasia is a precancerous lesion, gastric cancer is a very common malignant tumor, and many people die every year from stomach cancer. Early diagnosis of gastric cancer is critical to reducing patient mortality and overall medical burden. However, traditional endoscopic intestinal metaplasia on the gastric mucosa lacks specific performance. Consequently, subtle changes in precancerous intestinal metaplasia are not obvious limiting diagnostic accuracy. As a clinical computer aid in the diagnosis of early gastric cancer, a deep learning framework model called W-Deeplab was proposed for the identification and localization of intestinal metaplasia lesions. It achieves high-precision semantic segmentation of endoscopic images. As a computer aid to clinicians, it can improve the accuracy and efficiency of intestinal metaplasia diagnosis and reduce misdiagnosis.

Microfluidic devices and electrical techniques based on electrical impedance and viscoelastic changes can be used in the measurement of cancer cells in the epithelial-mesenchymal transition (EMT) process. Electrical impedance and transmission time were investigated when a single CRC cell passed the measurable tiny channel which was designed with width smaller than the average diameter of a single cell. The experimental data showed that the electrical impedance characteristics and viscoelasticity of the cells were changed after PMA induction. This property is expected to become a new biomarker for studying EMT.

Colorectal cancer is one of the most common cancers in China. The occurrence of most colorectal cancer is closely related to colorectal polyps. Colonoscopy is the gold standard for the diagnosis of intestinal lesions. Usually, existing colonoscopy is performed by physicians to determine the location of polyps by observing the results of detection with the naked eye. The detection rate of polyps is also affected by the doctor’s experience, fatigue, detection rate, and other factors, so there is a certain degree of polyp missed detection. Therefore, to improve diagnostic accuracy and reduce the rate of missed diagnosis, the paper proposes an improved_ssd model based on deep learning. The model is extended from the ssd_inception_v2 model, and the inception_v2 basic framework is used to extract features from multiple dimensions and fuse them, which improve the accuracy of polyp location. The test results show that the AP of this method is 94.92%, the accuracy is 96.04%, the sensitivity is 93.67%, and the specificity is 98.36%. This method realizes the accurate localization of polyps in colonoscopy and provides a reference for doctors' diagnosis.

Esophageal cancer is one of the most common malignant tumors which responses for about 400,000 deaths each year. Early identifying lesions is critical for reducing esophageal cancer mortality and the overall esophageal cancer burden. However, identification of early esophageal cancerous lesions can be very challenging for clinicians owing to the mild clinical symptoms and lack of specificity of esophageal cancer. Consequently, precancer or subtle early neoplastic changes may not be evident, limiting the diagnostic accuracy. As a clinical assistance for early esophageal cancer identification, a deep learning framework referred to as the M-Deeplab model was proposed for the localization and recognition of esophageal mucosa lesion. The proposed M-Deeplab model was extended from the Deeplabv3+ model by employing an encoder-decoder structure for accuracy improvement. It achieves high-precision semantic segmentation for different staining degrees and different sizes of endoscopic images. The overall accuracy reaches 97.31% and the MIoU reaches 92.09%. Moreover, it takes only 0.05s to judge one image by the M-Deeplab model. The M-Deeplab model exhibits good performance both in accuracy and speed for early esophageal cancerous lesions identification, comparable to the experienced clinicians. As an assistance for the clinicians, the proposed model could possibly increase the early esophageal cancer diagnosis accuracy and decrease the misdiagnosis.

Clinical microbial identification plays an important role in optimizing the management of infectious diseases and provides diagnostic and therapeutic support for clinical management. Microbial proteomic research is aimed at identifying proteins associated with microbial activity, which has facilitated the discovery of microbial physiology changes and host–pathogen interactions during bacterial infection and antimicrobial therapy. Here, we summarize proteomic-driven progresses of host–microbial pathogen interactions at multiple levels, mass spectrometry-based microbial proteome identification for clinical diagnosis, and antimicrobial therapy. Proteomic technique progresses pave new ways towards effective prevention and drug discovery for microbial-induced infectious diseases.

BACKGROUND:Reprogramming energy metabolism has been an emerging hallmark of cancer cells. MicroRNAs play important roles in glucose metabolism.METHODS:The targets of microRNA-26a (miR-26a) were predicted by bioinformatics tools. The efficacy of miR-26a binding the 3'-untranslated region (UTR) of pyruvate dehydrogenase protein X component (PDHX) mRNA was evaluated using a dual-luciferase reporter assay. The PDHX expression at the mRNA and protein level in several colon cancer cell lines was quantified with real-time PCR and Western blot analysis respectively. The effects of miR-26a on glucose metabolism were determined by detecting the content of glucose consumption, production of lactate, pyruvate, and acetyl-coenzyme A.RESULTS:The expression of miR-26a is inversely associated with the level of its targeting protein PDHX in several colon cancer cell lines with different malignancy potentials. MiR-26a inhibits PDHX expression by direct targeting the 3'-UTR of PDHX mRNA. The glucose consumption and lactate concentration were both greatly increased in colon cancer cells than the normal colon mucosal epithelia under physiological conditions. The overexpression of miR-26a in HCT116 cells efficiently improved the accumulation of pyruvate and decreased the production of acetyl coenzyme A. Meanwhile the inhibition of miR-26a expression induced inverse biological effects.CONCLUSIONS:MiR-26a regulates glucose metabolism of colorectal cancer cells by direct targeting the PDHX, which inhibits the conversion of pyruvate to acetyl coenzyme A in the citric acid cycle.

In tumor microenvironment, interactions among multiple cell types are critical for cancer progression. Secreted proteins are responsible for crosstalk among these cells within tumor microenvironment. To elucidate the interactions of tumor and epithelia, we co-cultured colon cancer cell line HT29 with normal human colon mucosal epithelial cell line NCM460 to mimic tumor microenvironment in vitro and investigated the differential expression pattern of secretome. A quantitative proteomics approach based on stable isotope labeling by amino acids in cell culture (SILAC) and LC-mass spectrometry was used for secretome analysis. Totally 45 proteins were altered over 2-fold in co-cultured cellular supernatants between equal amounts of NCM460 and HT29 cells, compared with mono-cultured conditions. These differential secreted proteins involve in multiple tumor-associated biological functions. The secretion level and acting pattern of acrogranin, IGFBP6 and vimentin were changed along with different co-cultured cell number ratios between NCM460 and HT29 cells, simulating early, middle or advanced stage of colon cancer. Therefore, a quantitative secretome profiling based on a co-culture system can track secreted protein changes and their associated biological roles between tumor and epithelia, which gives a new insight on communications between tumor and epithelia as well as cancer biotherapy by inhibiting cell interactions.Biological significanceTumor microenvironment is a complex system and comprised of cancer cells and host stromal cells. The growth and progression of tumor have been recognized were affected by multidirectional interactions of secreted proteins (secretome), which were produced by the cells within tumor microenvironment. Focus on general secreted molecules of living cells via proteomic tools, is promising for investigating cell communication. Stable isotope labeling by amino acids in cell culture (SILAC) is a metabolic labeling strategy for quantitative analysis, which is gaining popularity because of its ease of implementation, the high quality of quantitative data obtained, robustness and compatibility with existing experimental workflows. Therefore, SILAC-based quantitative secretome analysis was employed for investigating interactions between epithelia and tumor by in vitro modulating colon cancer microenvironment with established co-culture system, which simplified the complexity of cancer microenvironment also tracked secreted protein changes and their associated biological roles between epithelia and cancer cells. A series of tumor associated secreted proteins was quantitated and investigated in our study. So, the results give a new insight on communications between tumor and epithelia as well as cancer biotherapy by inhibiting interactions of them. (c) 2013 The Authors. Published by Elsevier B.V. All rights reserved.

Advanced studies of microRNAs (miRNAs) have revealed their manifold biological functions, including control of cell proliferation, cell cycle and cell death. However, it seems that their roles as key regulators of metabolism have drawn more and more attention in the recent years. Cancer cells display increased metabolic autonomy in comparison to non-transformed cells, taking up nutrients and metabolizing them in pathways that support growth and proliferation. MiRNAs regulate cell metabolic processes through complicated mechanisms, including directly targeting key enzymes or transporters of metabolic processes and regulating transcription factors, oncogenes / tumor suppressors as well as multiple oncogenic signaling pathways. MiRNAs like miR-375, miR-143, miR-14 and miR-29b participate in controlling cancer cell metabolism by regulating the expression of genes whose protein products either directly regulate metabolic machinery or indirectly modulate the expression of metabolic enzymes, serving as master regulators, which will hopefully lead to a new therapeutic strategy for malignant cancer. This review focuses on miRNA regulations of cancer cell metabolism,including glucose uptake, glycolysis, tricarboxylic acid cycle and insulin production, lipid metabolism and amino acid biogenesis, as well as several oncogenic signaling pathways. Furthermore, the challenges of miRNA-based strategies for cancer diagnosis, prognosis and therapeutics have been discussed.