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Background: Tumor tissue origin detection is of great importance in determining the appropriate course of treatment for cancer patients. Classifiers based on gene expression and DNA methylation profiles have been confirmed to be feasible and reliable to predict the tumor primary. However, few works have been performed to compare the performance of these classifiers based on different profiles. Methods: Using gene expression and DNA methylation profiles from The Cancer Genome Atlas (TCGA) project, eight machine learning methods were employed for the tumor tissue origin detection. We then evaluated the predictive performance using DNA methylation, mRNA, microRNA (miRNA) and long non-coding RNA (lncRNA) expression profiles in a comparative manner. A statistical method was introduced to select the most informative CpG sites. Results: We found that LASSO is the most predictive models based on various profiles. Further analyses indicated that the results derived from DNA methylation (overall accuracy: 97.77%) are better than those derived from mRNA expression (overall accuracy: 88.01%), microRNA expression (overall accuracy: 91.03%) and lncRNA expression (overall accuracy: 95.7%). It has been suggested that we can achieve an overall accuracy >90% using only 1,000 methylated CpG sites for prediction. Conclusion: In this work, we comprehensively evaluated the performance of classifiers based on different profiles for the tumor origin detection. Our findings demonstrated the effectiveness of DNA methylation as biomarker for tracing tumor tissue origin using LASSO and neural network.

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With the recent advancement in omics and molecular techniques, a wealth of new molecular biomarkers have become available for the diagnosis and classification of primary Sjögren's syndrome (pSS) patients. However, whether these biomarkers are universal is of great interest to us. In this study, we used various methods to obtain shared biomarkers derived from multiple tissue in pSS patients and to explore their relationship with immune microenvironment alterations. First we identified differentially expressed genes (DEGs) between pSS and healthy controls utilizing nine mRNA microarray datasets obtained from the Gene Expression Omnibus (GEO). Then, shared biomarkers were filtered out using robust rank aggregation (RRA), data integration analysis, weighted gene co-expression network analysis (WGCNA), and least absolute selection and shrinkage operator (LASSO) regression; their roles in pSS and association with changes in the immune microenvironment were also analyzed. In addition, these biomarkers were further confirmed with both the testing set and immunohistochemistry (IHC). As a result, ten biomarkers, i.e., EPSTI1, IFI44, IFIT1, IFIT2, IFIT3, MX1, OAS1, PARP9, SAMD9L and TRIM22, were identified. Receiver operating characteristic (ROC) curves showed that the ten genes could discriminate pSS from controls. Gene set enrichment analysis (GSEA) showed that the enrichment of immune-related gene sets was significant in pSS patients with high expression of either biomarker. Furthermore, the association between some immunocytes and these biomarkers was identified. In the two distinct molecular patterns of pSS patients based on the expressions of these biomarkers, the proportions of immunocytes were significantly different. Our study identified shared biomarkers of multi-tissue origin and revealed their relationship with altered immune microenvironment in pSS patients. These markers not only have diagnostic implications but also provide potential immunotherapeutic targets for the clinical treatment of pSS patients.

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Stresses associated with changes in diet or environmental disturbances are common situations that fish encounter during their lifetime. The stability and ease of measuring microRNAs (miRNAs) present in biological fluids make these molecules particularly interesting biomarkers for non-lethal assessment of stress in animals. Rainbow trout were exposed for four weeks to abiotic stress (moderate hypoxia) and/or nutritional stress (a high-carbohydrate/low-protein diet). Blood plasma and epidermal mucus were sampled at the end of the experiment, and miRNAs were assessed using small RNA sequencing. We identified four miRNAs (miR-122-5p, miR-184-3p, miR-192-5p and miR-194a-5p) and three miRNAs (miR-210-3p, miR-153a-3p and miR-218c-5p) that accumulated in response to stress in blood plasma and epidermal mucus, respectively. In particular, the abundance of miR-210-3p, a hypoxamiR in mammals, increased strongly in the epidermal mucus of rainbow trout subjected to moderate hypoxia, and can thus be considered a relevant biomarker of hypoxic stress in trout. We explored the contribution of 22 tissues/organs to the abundance of circulating miRNAs (c-miRNAs) in blood plasma and epidermal mucus influenced by the treatments. Some miRNAs were tissue-specific, while others were distributed among several tissues. Some c-miRNAs (e.g., miR-210-3p, miR184-3p) showed similar variations in both tissues and fluids, while others showed an inverse trend (e.g., miR-122-5p) or no apparent relationship (e.g. miR-192-5p, miR-194a-5p. Overall, these results demonstrate that c-miRNAs can be used as non-lethal biomarkers to study stress in fish. In particular, the upregulation of miR-210-3p in epidermal mucus induced by hypoxia demonstrates the potential of using epidermal mucus as a matrix for identifying non-invasive biomarkers of stress. This study provides information about the tissue sources of c-miRNAs and highlights the potential difficulty in relating variations in miRNA abundance in biological fluids to that in tissues.

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For twenty-five years, attempts have been made to use MSCs in the treatment of various diseases due to their regenerative and immunomodulatory properties. However, the results are not satisfactory. Assuming that MSCs can be replaced in some therapies by the active factors they produce, the immortalized MSCs line was established from human adipose tissue (HATMSC1) to produce conditioned media and test its regenerative potential in vitro in terms of possible clinical application. The production of biologically active factors by primary MSCs was lower compared to the HATMSC1 cell line and several factors were produced only by the cell line. It has been shown that an HATMSC1-conditioned medium increases the proliferation of various cell types, augments the adhesion of cells and improves endothelial cell function. It was found that hypoxia during culture resulted in an augmentation in the pro-angiogenic factors production, such as VEGF, IL-8, Angiogenin and MCP-1. The immunomodulatory factors caused an increase in the production of GM-CSF, IL-5, IL-6, MCP-1, RANTES and IL-8. These data suggest that these factors, produced under different culture conditions, could be used for different medical conditions, such as in regenerative medicine, when an increased concentration of pro-angiogenic factors may be beneficial, or in inflammatory diseases with conditioned media with a high concentration of immunomodulatory factors.

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Current research data reveal microenvironment as a significant modifier of physical functions, pathologic changes, as well as the therapeutic effects of stem cells. When comparing regeneration potential of various stem cell types used for cytotherapy and tissue engineering, mesenchymal stem cells (MSCs) are currently the most attractive cell source for bone and tooth regeneration due to their differentiation and immunomodulatory potential and lack of ethical issues associated with their use. The microenvironment of donors and recipients selected in cytotherapy plays a crucial role in regenerative potential of transplanted MSCs, indicating interactions of cells with their microenvironment indispensable in MSC-mediated bone and dental regeneration. Since a variety of MSC populations have been procured from different parts of the tooth and tooth-supporting tissues, MSCs of dental origin and their achievements in capacity to reconstitute various dental tissues have gained attention of many research groups over the years. This review discusses recent advances in comparative analyses of dental MSC regeneration potential with regards to their tissue origin and specific microenvironmental conditions, giving additional insight into the current clinical application of these cells.

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Gene expression profiling holds great potential as a new approach to histological diagnosis and precision medicine of cancers of unknown primary (CUP). Batch effects and different data types greatly decrease the predictive performance of biomarker-based algorithms, and few methods have been widely applied to identify tissue origin of CUP up to now. To address this problem and assist in more precise diagnosis, we have developed a gene expression rank-based majority vote algorithm for tissue origin diagnosis of CUP (TOD-CUP) of most common cancer types. Based on massive tissue-specific RNA-seq data sets (10 553) found in The Cancer Genome Atlas (TCGA), 538 feature genes (biomarkers) were selected based on their gene expression ranks and used to predict tissue types. The top scoring pairs (TSPs) classifier of the tumor type was optimized by the TCGA training samples. To test the prediction accuracy of our TOD-CUP algorithm, we analyzed (1) two microarray data sets (1029 Agilent and 2277 Affymetrix/Illumina chips) and found 91% and 94% prediction accuracy, respectively, (2) RNA-seq data from five cancer types derived from 141 public metastatic cancer tumor samples and achieved 94% accuracy and (3) a total of 25 clinical cancer samples (including 14 metastatic cancer samples) were able to classify 24/25 samples correctly (96.0% accuracy). Taken together, the TOD-CUP algorithm provides a powerful and robust means to accurately identify the tissue origin of 24 cancer types across different data platforms. To make the TOD-CUP algorithm easily accessible for clinical application, we established a Web-based server for tumor tissue origin diagnosis (http://ibi. zju.edu.cn/todcup/).

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Transport of bioactive cargo of microvesicles (MVs) into target cells can affect their fate and behavior and change their microenvironment. We assessed the effect of MVs derived from human immortalized mesenchymal stem cells of adipose tissue-origin (HATMSC2-MVs) on the biological activity of the ovarian cancer cell lines ES-2 (clear cell carcinoma) and OAW-42 (cystadenocarcinoma). The HATMSC2-MVs were characterized using dynamic light scattering (DLS), transmission electron microscopy, and flow cytometry. The anti-tumor properties of HATMSC2-MVs were assessed using MTT for metabolic activity and flow cytometry for cell survival, cell cycle progression, and phenotype. The secretion profile of ovarian cancer cells was evaluated with a protein antibody array. Both cell lines internalized HATMSC2-MVs, which was associated with a decreased metabolic activity of cancer cells. HATMSC2-MVs exerted a pro-apoptotic and/or necrotic effect on ES-2 and OAW-42 cells and increased the expression of anti-tumor factors in both cell lines compared to control. In conclusion, we confirmed an effective transfer of HATMSC2-MVs into ovarian cancer cells that resulted in the inhibition of cell proliferation via different pathways, apoptosis and/or necrosis, which, with high likelihood, is related to the presence of different anti-tumor factors secreted by the ES-2 and OAW-42 cells.

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BACKGROUND: Mesenchymal stromal cell (MSC) - based therapies are emerging as promising treatment of various autoimmune diseases, however the utility of different MSC tissue sources remains elusive. We aimed to characterize MSC from different origins, namely bone marrow (BM), adipose tissue (AT) and umbilical cord (UC) and determine their functional effects on normal human lung fibroblasts (NHLF). METHODS: BM-, AT- or UC-MSC were isolated each from 3 different healthy donors. The gene expression and protein secretion were analyzed at basal level, along with TNFα-, IL-1ß- and SAA- stimulated cells using real-time PCR and Luminex technology. Effect of conditioned medium (CM) from different MSC sources on migration was determined with wound scratch assay, while mitotic and apoptotic rates were studied using immunofluorescence microscopy. RESULTS: BM-MSC expressed highest basal mRNA levels of SDF1 and VCAM-1, while other genes were similarly expressed between MSC origins. TNFα priming of AT-MSC gained a prominent increase in IDO1 and CCL5 gene expression, with 928-fold and 4396-fold changes, respectively. Among all tissue sources, basal UC-MSC released highest protein levels of most measured analytes, including IL-6, IL-8, MCP-1, ICAM1, HGF, MMP1 and CH3L1. BM- and AT-MSC derived CM enhanced wound closure in NHLF, while an opposite effect was observed with UC-MSC derived CM. Our data also suggests that MSC-CM could contribute to decreased mitotic potential and increased apoptotic rate in lung fibroblasts. CONCLUSIONS: Our study highlights origin-specific MSC profile differences and emphasizes a heterogenic response of different MSC to inflammatory stimuli.

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Gelatin, as a by-product of the meat industry, is extracted from bone and skin of mainly bovine and porcine origins. It is used widely in the food, drug, and cosmetic industries. Authenticity testing methods can be used to confirm whether labelled ingredients are present in the product. Generally, studies on gelatin are concerned mainly with determining species, but detecting tissue origin is also important from religious, health, and commercial perspectives. In the present study, for the first time, liquid chromatography/mass spectrometry (HPLC/MS) was used to differentiate bovine bone gelatin from gelatin derived from bovine skin. Tryptic-digested gelatins were measured using HPLC/MS and, subsequently, two powerful chemometrics approaches (i.e., PCA and PLS-DA) were used to classify samples as either skin or bone gelatins. Origin of bovine gelatins in different test samples were predicted accurately using this method. The results showed both the stability and reliability of the proposed procedure.

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Urine-derived stem cells (USCs) have shown potentials for the treatment of skeletal and urological disorders. Based on published literature and our own data, USCs consist of heterogeneous populations of cells. In this paper, we identify and characterize two morphologically distinct subpopulations of USCs from human urine samples, named as spindle-shaped USCs (SS-USCs) and rice-shaped USCs (RS-USCs) respectively. The two subpopulations showed similar clone-forming efficiency, while SS-USCs featured faster proliferation, higher motility, and greater potential for osteogenic and adipogenic differentiation, RS-USCs showed greater potential for chondrogenic differentiation. POU5F1 was strongly expressed in both subpopulations, but MYC was weakly expressed. Both subpopulations showed similar patterns of CD24, CD29, CD34, CD44, CD73, CD90 and CD105 expression, while a higher percentage of RS-USCs were positive for CD133. SS-USCs were positive for VIM, weakly positive for SLC12A1 and UMOD, and negative for KRT18, NPHS1, AQP1 and AQP2, indicating a renal mesenchyme origin; while RS-USCs are positive for VIM, partially positive for KRT18, NPHS1, AQP1, SLC12A1 and UMOD, and negative for AQP2, indicating a nephron tubule origin. The above results can facilitate understanding of the biological characteristics of subpopulations of USCs, and provide a basis for further research and applications of such cells.

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Synchronous multifocal tumors are common in the hepatobiliary and pancreatic system but because of similarities in their histological features, oncologists have difficulty in identifying their precise tissue clonal origin through routine histopathological methods. To address this problem and assist in more precise diagnosis, we developed a computational approach for tissue origin diagnosis based on naive Bayes algorithm (TOD-Bayes) using ubiquitous RNA-Seq data. Massive tissue-specific RNA-Seq data sets were first obtained from The Cancer Genome Atlas (TCGA) and ∼1,000 feature genes were used to train and validate the TOD-Bayes algorithm. The accuracy of the model was >95% based on tenfold cross validation by the data from TCGA. A total of 18 clinical cancer samples (including six negative controls) with definitive tissue origin were subsequently used for external validation and 17 of the 18 samples were classified correctly in our study (94.4%). Furthermore, we included as cases studies seven tumor samples, taken from two individuals who suffered from synchronous multifocal tumors across tissues, where the efforts to make a definitive primary cancer diagnosis by traditional diagnostic methods had failed. Using our TOD-Bayes analysis, the two clinical test cases were successfully diagnosed as pancreatic cancer (PC) and cholangiocarcinoma (CC), respectively, in agreement with their clinical outcomes. Based on our findings, we believe that the TOD-Bayes algorithm is a powerful novel methodology to accurately identify the tissue origin of synchronous multifocal tumors of unknown primary cancers using RNA-Seq data and an important step toward more precision-based medicine in cancer diagnosis and treatment.

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Purpose To investigate the clinicopathological characteristics,diagnosis and differential diagnosis of benign metastasizing leiomyoma (BML).Methods The clinicopathological data in 6 patients with BML were collected.All cases of BLM were investigated by HE and immunohistochemistry of EnVision method.Results All cases were female,with age of 33 -65 years,and had undergone myomectomy.5 cases had lung metastasis,including abdominal wall metastasis and spinal metastasis in each of the 1 cases,and another case had inguinal metastasis.Morphology showed that the tumor cells were spindle without obvious atypia,nuclear mitoses and necrosis,some cases were cellular.Immunohistochemical staining showed that the tumor cells were positive for SMA,SM-MHC,desmin,ER,PR,vimentin,while negative for S-100,CD117,CD34.Ki-67 label index were less than 5％.3 patients were alive with tumor and 3 patients were alive without tumor in the follow up of 18,28,40,31,36,80 months.Conclusion BML often occurs in female patients that undergone uterine myomectomy.The lung is the most common site of metastasis,often accompanied by other sites.The disease progresses slowly,and most patients have a longer survival time.

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Approximately 3 to 5％ of newly diagnosed metastatic cancers are of unknown primary tissue origin due to difficulties identifying a primary tumor using standard diagnostic approaches.MicroRNAs (miRNAs) have recently been demonstrated to be able to assist pathologist with improved accuracy in diagnosing cancers of unknown primary origin (CUP).In this short commentary,we will highlight some of the recent advancements in miRNA based cancer diagnosis as well as some future directions for the field.

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Background and purpose:Cancer of unknown primary (CUP) represents approximately 5%~10%of malignant neoplasms. For CUP patients, identiifcation of tumor origin allows for more speciifc therapeutic regimens and improves outcomes.Methods:By retrieving the gene expression data from ArrayExpress and Gene Expression Omnibus data repositories, we established a comprehensive gene expression database of 5 800 tumor samples encom-passing 22 main tumor types. The support vector machine-recursive feature elimination algorithm was used for feature selection and classiifcation modelling. We further optimized the RNA isolation and real-time quantitative polymerase chain reaction (RTQ-PCR) methods for candidate gene expression proifling and applied the RTQ-PCR assays to a set of formalin-fixed, paraffin-embedded tumor samples.Results:Based on the pan-cancer transcriptome database, we identiifed a list of 96-tumor speciifc genes, including common tumor markers, such as cadherin 1 (CDH1), kallikrein-re-lated peptidase 3 (KLK3), and epidermal growth factor receptor (EGFR). Furthermore, we successfully translated the microarray-based gene expression signature to the RTQ-PCR assays, which allowed an overall success rate of 88.4% (95%CI: 83.2%-92.4%) in classifying 22 different tumor types of 206 formalin-fixed, paraffin-embedded samples. Conclusion:The 96-gene RTQ-PCR assay represents a useful tool for accurately identifying tumor origins. The assay uses RTQ-PCR and routine formalin-ifxed, paraffn-embedded samples, making it suitable for rapid clinical adoption.