Tumors are comprised of malignant cancer cells and stromal cells which constitute the tumor microenvironment (TME). Previous studies have shown that cancer associated fibroblast (CAF) in TME is an important promoter of tumor initiation and progression. However, the underlying molecular mechanisms by which CAFs influence the growth of colorectal cancer cells (CRCs) have not been clearly elucidated. In this study, by using a non-contact co-culture system between human colorectal fibroblasts (CCD-18-co) and CRCs (LoVo, SW480, and SW620), we found that fibroblasts existing in tumor microenvironment positively influenced the metabolism of colorectal cancer cells, through its autophagy and oxidative stress pathway which were initially induced by neighboring tumor cells. Therefore, our data provided a novel possibility to develop fibroblasts as a potential target to treat CRC.
The prognosis of glioma is significantly correlated with the pathological grades; however, the correlations between the prognostic biomarkers with pathological grades have not been elucidated. S100A11 is involved in a variety of malignant biological processes of tumor, whereas its biological and clinicopathological features on glioma remain unclear. In this study, the S100A11 expression and clinical information were obtained from the public databases (TCGA, GEPIA2) to analyze its correlations with the pathological grade and the prognosis of glioma patients. We then verified the expression of S100A11 by immunohistochemistry staining. The effects of S100A11 on the proliferation of glioma cells were confirmed by cytological function assays (CCK-8, Flow cytometry, Clone formation assay) in vitro, the role of S100A11 in regulation of glioma growth was determined by xenograft model assay. We observed that S100A11 expression positively correlated with the pathological grades, while negatively correlated with the survival time of patients. In cytological analysis, we found the proliferations of glioma cell lines were significantly inhibited in vitro ( P < 0.05) after interfering S100A11 expression via shRNAs. The cell cycle was blocked at G0/G1 stage. The ability of clone formation was significantly decreased, and the tumorigenicity in vivo was weakened ( P < 0.05). In summary, S100A11 was over-expressed in gliomas and positively correlated with the pathological grades. Interfering the expression of S100A11 significantly inhibited the proliferation of glioma in vitro and the tumorigenicity in vivo ( P < 0.05). In conclusion, S100A11 might be considered as a potential biomarker in glioma.
IntroductionPost-neurosurgical bacterial meningitis (PNBM) is a serious complication for patients who receive neurosurgical treatment, but the diagnosis is difficult given the complicated microenvironment orchestrated by sterile brain injury and pathogenic infection. In this study, we explored potential diagnostic biomarkers and immunological features using a proteomics platform.MethodsA total of 31 patients with aneurysmal subarachnoid hemorrhage (aSAH) who received neurosurgical treatment were recruited for this study. Among them, 15 were diagnosed with PNBM. The remaining 16 patients were categorized into the non-PNBM group. Proteomics analysis of the cerebrospinal fluid (CSF) was conducted on the Olink platform, which contained 92 immunity-related molecules.ResultsWe found that the expressions of 27 CSF proteins were significantly different between the PNBM and non-PNBM groups. Of those 27 proteins, 15 proteins were upregulated and 12 were downregulated in the CSF of the PNBM group. The receiver operating characteristic curve analysis indicated that three proteins (pleiotrophin, CD27, and angiopoietin 1) had high diagnostic accuracy for PNBM. Furthermore, we also performed bioinformatics analysis to explore potential pathways and the subcellular localization of the proteins.ConclusionIn summary, we found a cohort of immunity-related molecules that can serve as potential diagnostic biomarkers for PNBM in patients with aSAH. These molecules also provide an immunological profile of PNBM.
In the research of flash flood disaster monitoring and early warning, the Internet of Things is widely used in real-time information collection. There are abnormal situations such as noise, repetition and errors in a large amount of data collected by sensors, which will lead to false alarm, lower prediction accuracy and other problems. Aiming at the characteristic that outliers flow of sensors will cause obvious fluctuation of information entropy, this paper proposes a local outlier detection method based on information entropy and optimized by sliding window and LOF (Local Outlier Factor). This method can be used to improve the data quality, thus improving the accuracy of disaster prediction. The method is applied to data stream processing of water sensor, and the experimental results show that the method can accurately detect outliers. Compared with the existing detection methods that only use data distance to determine, the test positive rate is improved and the false positive rate is reduced.
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