Immunohistochemistry Datasets Research Articles

A comprehensive analysis of different types of databases reveals that CDH1 mRNA and E-cadherin protein are not downregulated in most carcinoma tissues and carcinoma cell lines

BackgroundThe CDH1 gene codes for the epithelial-cadherin (E-cad) protein, which is embedded in the plasma membrane of epithelial cells to form adherens junctions. E-cad is known to be essential for maintaining the integrity of epithelial tissues, and the loss of E-cad has been widely considered a hallmark of metastatic cancers enabling carcinoma cells to acquire the ability to migrate and invade nearby tissues. However, this conclusion has come under scrutiny.MethodsTo assess how CDH1 and E-cad expression changes during cancer progression, we analyzed multiple large transcriptomics, proteomics, and immunohistochemistry datasets on clinical cancer samples and cancer cell lines to determine the CDH1 mRNA and E-cad protein expression profiles in tumor and normal cells.ResultsIn contrast to the textbook knowledge of the loss of E-cad during tumor progression and metastasis, the levels of CDH1 mRNA and E-cad protein are either upregulated or remain unchanged in most carcinoma cells compared to normal cells. In addition, the CDH1 mRNA upregulation occurs in the early stages of tumor development and the levels remain elevated as tumors progress to later stages across most carcinoma types. Furthermore, E-cad protein levels are not downregulated in most metastatic tumor cells compared to primary tumor cells. The CDH1 mRNA and E-cad protein levels are positively correlated, and the CDH1 mRNA levels are positively correlated to cancer patient’s survival. We have discussed potential mechanisms underlying the observed expression changes in CDH1 and E-cad during tumor progression.ConclusionsCDH1 mRNA and E-cadherin protein are not downregulated in most tumor tissues and cell lines derived from commonly occurring carcinomas. The role of E-cad in tumor progression and metastasis may have previously been oversimplified. CDH1 mRNA levels may serve as a reliable biomarker for the diagnosis of some tumors (such as colon and endometrial carcinomas) due to the marked upregulation of CDH1 mRNA in the early stages of tumor development of these carcinomas.

TNFRSF11B Suppresses Memory CD4+ T Cell Infiltration in the Colon Cancer Microenvironment: A Multiomics Integrative Analysis.

BackgroundColorectal cancer is a lethal cancer worldwide. Due to the low tumor mutation burden and low proportion of tumor-infiltrating lymphocytes in the microenvironment of most patients, innovative immunotherapeutic approaches need to be identified.MethodsUsing the TCGA-COAD dataset (n = 514), we identified TNFRSF11B as a prognostic factor of colon cancer. An immunohistochemistry (IHC) dataset (n = 86), 290 single colorectal cancer cells (GSE81861), and 31 paired colon cancer transcriptional datasets were further applied to validate the function of TNFRSF11B, which was confirmed via fluorescence-activated cell sorting (FACS) analysis.ResultsA risk score system consisting of eight immune-related genes (IRGs) (FGFR2, ZC3HAV1L, TNFRSF11B, CD79A, IGHV3-11, IGHV3-21, IGKV2D-30, and IGKV6D-21) was constructed to predict the prognosis of colon cancer patients. Only TNFRSF11B was closely correlated with late-stage lymph node metastasis and worse survival outcomes (p = 0.010, p = 0.014, and p = 0.0061). In our IHC dataset, 72.09% (62/86) of the colon cancer patients had TNFRSF11B overexpression with significantly shorter overall survival times (p = 0.072). High TNFRSF11B expression typically had a later TNM stage (p = 0.067), a higher frequency of lymph node (p = 0.029) and lymphovascular (p = 0.007) invasion, and a higher incidence of pneumonia (p = 0.056) than their counterparts. The expression of six genes (KRT18, ARPC5L, ACTG1, ARPC2, EZR, and YWHAZ) related to pathogenic E. coli infection was simultaneously increased with TNFRSF11B overexpression via gene set enrichment analysis (GSEA). These genes are involved in the regulation of the actin cytoskeleton, shigellosis, bacterial invasion of epithelial cells, and Salmonella infection. Finally, only activated memory CD4+ T cells (p = 0.017) were significantly decreased in the high TNFRSF11B expression group via CIBERSORT comparison, which was confirmed by TIMER2.0 analysis of the TCGA-COAD dataset. We also performed FACS analysis to show that TNFRSF11B decreased the infiltration of central memory CD4+ T cells and effector memory CD4+ T cells in the colorectal cancer microenvironment (all p <0.001).ConclusionTNFRSF11B acts as a prognostic factor for colon cancer patients and could affect the colon cancer immune response. TNFRSF11B was closely related to lymph node invasion and pathogenic E. coli. infection, which may negatively affect memory-activated CD4+ T cell infiltration in colon cancer.

TNFRSF11B Remodel Immune Response in Colon Cancer Microenvironment: A Multi-Center Study

Background: Colorectal cancer is the lethal cancer in the world, due to the low tumor mutation burden and low proportion of tumor-infiltrating lymphocytes in the microenvironment of most patients, innovative immunotherapeutic approaches need to be identified. Methods: Using TCGA-COAD dataset (n = 514), we identified TNFRSF11B as a prognostic factor of colon cancer. Immunohistochemistry (IHC) dataset (n = 86), 290 single colorectal cancer cells (GSE81861) and 31 paired colon cancer transcriptional dataset were further applied to validate the function of TNFRSF11B, which was confirmed via fluorescence-activated cell sorting (FACS) analysis. Results: A risk scores system consisting of eight IRGs was constructed to predict the prognosis of colon cancer patients. Only TNFRSF11B correlated with TNM-stage, lymph node metastasis and prognosis. In our IHC dataset, High TNFRSF11B expression had a later TNM-stage, higher frequency of lymph node and lymphovascular invasion. Six genes (KRT18, ARPC5L, ACTG1, ARPC2, EZR, YWHAZ) related to pathogenic E. coli. infection were simultaneously increased with TNFRSF11B overexpression via GESA analysis. Finally, only activated memory CD4+ T cells (p =0.017) were significantly decreased in high TNFRSF11B expression group, which was supported by our FACS findings that TNFRSF11B downregulate the immune activity of central memory CD4 + T cells and effector memory CD4+ T cells (all p <0.001). Conclusion: TNFRSF11B, acts as a prognostic factor for colon cancer patient, could affect colon cancer immune response. TNFRSF11B was closely related with lymph node invasion and pathogenic E. coli. infection, which might negatively affect memory activated CD4+ T cell infiltration in colon cancer. Funding: This work was supported Joint Funds for the innovation of science and Technology, Fujian province (Grant number: 2018Y9203 to Jun-rong Zhang, 2018Y92042 to Xian-qiang Chen); Startup Fund for scientific research, Fujian Medical University (Grant number: 2019QH1016 to Ping Hou); Education Department of Fujian Province Young and middle-aged teacher education research project (Grant number: JAT190180 to Ping Hou, JT180181 to Jun-rong Zhang). Declaration of Interest: The authors declare that they have no competing interests. Ethical Approval: The validation of TNFRSF11B in paraffin-embedded colorectal cancer tissues were supported by general surgery of Fujian Medical University Union Hospital from January 2013 to December 2017. The study protocol was approved by the Institutional Review Board of Fujian Medical University Union Hospital, and all patients provided written informed consent.

GOcats: A tool for categorizing Gene Ontology into subgraphs of user-defined concepts.

Gene Ontology is used extensively in scientific knowledgebases and repositories to organize a wealth of biological information. However, interpreting annotations derived from differential gene lists is often difficult without manually sorting into higher-order categories. To address these issues, we present GOcats, a novel tool that organizes the Gene Ontology (GO) into subgraphs representing user-defined concepts, while ensuring that all appropriate relations are congruent with respect to scoping semantics. We tested GOcats performance using subcellular location categories to mine annotations from GO-utilizing knowledgebases and evaluated their accuracy against immunohistochemistry datasets in the Human Protein Atlas (HPA). In comparison to term categorizations generated from UniProt's controlled vocabulary and from GO slims via OWLTools' Map2Slim, GOcats outperformed these methods in its ability to mimic human-categorized GO term sets. Unlike the other methods, GOcats relies only on an input of basic keywords from the user (e.g. biologist), not a manually compiled or static set of top-level GO terms. Additionally, by identifying and properly defining relations with respect to semantic scope, GOcats can utilize the traditionally problematic relation, has_part, without encountering erroneous term mapping. We applied GOcats in the comparison of HPA-sourced knowledgebase annotations to experimentally-derived annotations provided by HPA directly. During the comparison, GOcats improved correspondence between the annotation sources by adjusting semantic granularity. GOcats enables the creation of custom, GO slim-like filters to map fine-grained gene annotations from gene annotation files to general subcellular compartments without needing to hand-select a set of GO terms for categorization. Moreover, GOcats can customize the level of semantic specificity for annotation categories. Furthermore, GOcats enables a safe and more comprehensive semantic scoping utilization of go-core, allowing for a more complete utilization of information available in GO. Together, these improvements can impact a variety of GO knowledgebase data mining use-cases as well as knowledgebase curation and quality control.

Segmentation of glandular epithelium in colorectal tumours to automatically compartmentalise IHC biomarker quantification: A deep learning approach.

In this paper, we propose a method for automatically annotating slide images from colorectal tissue samples. Our objective is to segment glandular epithelium in histological images from tissue slides submitted to different staining techniques, including usual haematoxylin-eosin (H&E) as well as immunohistochemistry (IHC). The proposed method makes use of Deep Learning and is based on a new convolutional network architecture. Our method achieves better performances than the state of the art on the H&E images of the GlaS challenge contest, whereas it uses only the haematoxylin colour channel extracted by colour deconvolution from the RGB images in order to extend its applicability to IHC. The network only needs to be fine-tuned on a small number of additional examples to be accurate on a new IHC dataset. Our approach also includes a new method of data augmentation to achieve good generalisation when working with different experimental conditions and different IHC markers. We show that our methodology enables to automate the compartmentalisation of the IHC biomarker analysis, results concurring highly with manual annotations.

A multilevel framework to reconstruct anatomical 3D models of the hepatic vasculature in rat livers

The intricate (micro)vascular architecture of the liver has not yet been fully unravelled. Although current models are often idealized simplifications of the complex anatomical reality, correct morphological information is instrumental for scientific and clinical purposes. Previously, both vascular corrosion casting (VCC) and immunohistochemistry (IHC) have been separately used to study the hepatic vasculature. Nevertheless, these techniques still face a number of challenges such as dual casting in VCC and limited imaging depths for IHC. We have optimized both techniques and combined their complementary strengths to develop a framework for multilevel reconstruction of the hepatic circulation in the rat. The VCC and micro-CT scanning protocol was improved by enabling dual casting, optimizing the contrast agent concentration, and adjusting the viscosity of the resin (PU4ii). IHC was improved with an optimized clearing technique (CUBIC) that extended the imaging depth for confocal microscopy more than five-fold. Using in-house developed software (DeLiver), the vascular network - in both VCC and IHC datasets - was automatically segmented and/or morphologically analysed. Our methodological framework allows 3D reconstruction and quantification of the hepatic circulation, ranging from the major blood vessels down to the intertwined and interconnected sinusoids. We believe that the presented framework will have value beyond studies of the liver, and will facilitate a better understanding of various parenchymal organs in general, in physiological and pathological circumstances.

Multimodal detection of GM2 and GM3 lipid species in the brain of mucopolysaccharidosis type II mouse by serial imaging mass spectrometry and immunohistochemistry.

Mucopolysaccharidosis type II (Hunter's disease) mouse model (IdS-KO) was investigated by both imaging mass spectrometry (IMS) and immunohistochemistry (IHC) performed on the same tissue sections. For this purpose, IdS-KO mice brain sections were coated with sublimated 1,5-diaminonaphtalene and analyzed by high spatial resolution IMS (5μm) and anti-GM3 IHC on the same tissue sections to characterize the ganglioside monosialated ganglioside (GM) deposits found in Hunter's disease. IMS analysis have found that two species of GM3 and GM2 that are only different due to the length of their fatty acid residue (stearic or arachidic residue) were overexpressed in the IdS-KO mice compared to a control mouse. GM3 and GM2 were characterized by on-tissue exact mass and MS/MS compared to a GM3 standard. Realignment of both IMS and IHC data sets further confirmed the observed regioselective signal previously detected by providing direct correlation of the IMS image for the two GM3 overly expressed MS signals with the anti-GM3 IHC image. Furthermore, these regioselective GM MS signals were also found to have highly heterogeneous distributions within the GM3-IHC staining. Some deposits showed high content in GM3 and GM2 stearic species (r = 0.74) and others had more abundant GM3 and GM2 arachidic species (r = 0.76). Same-section analysis of Hunter's disease mouse model by both high spatial resolution IMS and IHC provides a more in-depth analysis of the composition of the GM aggregates while providing spatial distribution of the observed molecular species. Graphical Abstract Ganglioside imaging mass spectrometry followed by immunohistochemistry performed on the same tissue section.