for their comments. Financial support from the Rodney L. White Center at the Wharton School is gratefully acknowledged. We are responsible for any errors. The views expressed herein are those of the authors and not necessarily those of the National Bureau of Economic Research.
Xenotransplantation of human cancers into immunodeficient mice is a very useful approach for studying human tumor biology. However, the occasional occurrence of lymphomagenesis in some mice can spoil the model and must be investigated in detail. We found that a high percentage (32.5%, 26/80) of cancer patient-derived xenografts (PDXs) resembled lymphoma in NOD/SCID mice. Of the 26 xenografts, 23 were human-derived expressing human CD45 (hCD45+) and proved to be of the B-cell subtype (CD3-/CD20+), and they were all positive for Epstein - Barr virus (EBV). The remaining 3 xenografts proved to be mouse-derived for both hCD45- and negative amplification of a human gene. The most interesting finding is that gastric cancer had much higher rates (24/126, 19.0%) of lymphoma formation in the PDX model than did colorectal cancer (1/43, 2.3%). Statistical analysis revealed that cancer type and inflammation in the parent tumor are significantly associated with lymphomagenesis. Further validation discovered lymphomagenesis by inoculating only gastritis mucosa. Therefore, our findings suggest that it is necessary to take precautions when directly xenografting cancer tissues with remarkable baseline inflammation, such as gastric cancer into immunodeficient NOD/SCID strains. Further, the established xenograft models should be validated by both leukocyte markers and human gene signatures.
Motivation
The studies have indicated that not only microRNAs (miRNAs) or long non-coding RNAs (lncRNAs) play important roles in biological activities, but also their interactions affect the biological process. A growing number of studies focus on the miRNA–lncRNA interactions, while few of them are proposed for plant. The prediction of interactions is significant for understanding the mechanism of interaction between miRNA and lncRNA in plant.
Results
This article proposes a new method for fulfilling plant miRNA–lncRNA interaction prediction (PmliPred). The deep learning model and shallow machine learning model are trained using raw sequence and manually extracted features, respectively. Then they are hybridized based on fuzzy decision for prediction. PmliPred shows better performance and generalization ability compared with the existing methods. Several new miRNA–lncRNA interactions in Solanum lycopersicum are successfully identified using quantitative real time–polymerase chain reaction from the candidates predicted by PmliPred, which further verifies its effectiveness.
Availability and implementation
The source code of PmliPred is freely available at http://bis.zju.edu.cn/PmliPred/.
Supplementary information
Supplementary data are available at Bioinformatics online.
BackgroundImmune checkpoint blockade targeting PD-1/PD-L1 has shown efficacy in several types of cancers. However, the correlation between PD-L1/PD-1 expression and the specific clinicopathological features in papillary thyroid carcinoma (PTC) has not been investigated.MethodsWe examined the immunohistochemical expression of PD-L1, PD-1, and BRAF V600E on whole-tissue sections from 126 cases of primary PTC more than 1 cm in size. The correlation between the PD-L1/PD-1 expression and the clinicopathological features was evaluated.
ResultsPD-L1 was positively expressed in 53.2% PTCs, and its expression was positively correlated with rich tumor-infiltrating lymphocytes (TILs), background chronic lymphocytic thyroiditis (CLT), female gender, absence of psammoma bodies, and PD-1 expression. Among these parameters, rich TILs, female gender, and absence of psammoma bodies were independent factors affecting PD-L1 expression on the multivariate logistic regression analysis. PD-1 expression was detected in the TILs and was positively correlated with rich TILs, background CLT, and absence of stromal calcification. Lack of stromal calcification was an independent factor affecting PD-1 expression. Neither PD-L1 nor PD-1 expression showed significant correlation with BRAF V600E expression.ConclusionsOur results show that the distinctive pathological features of PTCs, including TILs, background CLT, female gender, psammoma bodies, and stromal calcification, are useful parameters for predicting PD-L1 or PD-1 expression.
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