Use of a 6-miRNA panel to distinguish lymphoma from reactive lymphoid hyperplasia

Dear Editor,Lymphoma is a systemic malignancy originating from the lymphatic system,and it accounts for 3–4%of all tumors.In the United States,lymphoma is ranked 5th among the top causes of cancer deaths,and an estimated 80,500 new cases were diagnosed in 2017.1 Successful treatment relies largely on the correct diagnosis and subclassification of lymphoma in surgically excised biopsies based on cell morphology,immunophenotyping,flow cytometry,in situ fluorescent hybridization,and molecular diagnosis.However,the ability to distinguish between reactive lymphoid hyperplasia(RLH)and lymphoma is not an easy task.In routine pathology practice,lymph nodes in formalin-fixed paraffin-embedded(FFPE)sections show reactive changes more frequently than malignant features.Complicating the analysis,many reactive changes display atypical features and often mimic lymphoma,making these benign changes difficult to distinguish from malignant changes.2 New biomarkers that will address this challenge are urgently needed in clinical practice.

Artificial intelligence and its applications in digital hematopathology

The advent of whole-slide imaging,faster image data generation,and cheaper forms of data storage have made it easier for pathologists to manipulate digital slide images and interpret more detailed biological processes in conjunction with clinical samples.In parallel,with continuous breakthroughs in object detection,image feature extraction,image classification and image segmentation,artificial intelligence(AI)is becoming the most beneficial technology for high-throughput analysis of image data in various biomedical imaging disciplines.Integrating digital images into biological workflows,advanced algorithms,and computer vision techniques expands the biologist’s horizons beyond the microscope slide.Here,we introduce recent developments in AI applied to microscopy in hematopathology.We give an overview of its concepts and present its applications in normal or abnormal hematopoietic cells identification.We discuss how AI shows great potential to push the limits of microscopy and enhance the resolution,signal and information content of acquired data.Its shortcomings are discussed,as well as future directions for the field.

Nanoparticles targeting at methylases with high correlation to N^(6)-methyladenosine-related lncRNA signatures as potential therapy of kidney clear cell carcinoma

Clear cell renal cell carcinoma(ccRCC)is a heterogeneous malignancy with poor prognosis.Methylation of the N^(6) position of adenosine(m^(6)A),the most common epigenetic modification in both messenger RNAs and noncoding RNAs,has been reported to regulate the initiation and progression of ccRCC.However,whether and how m^(6)A-related long noncoding RNAs(m^(6)ArlncRNAs)signify the progression of ccRCC remain unclear.We found m^(6)ArlncRNAs are effective signatures illustrating immune landscape and risk stratification in ccRCC.We identified two differently expressed m^(6)ArlncRNAs(DEm^(6)ArlncRNAs),AC008870.2 and EMX2OS,as independent risk factors for overall survival of ccRCC patients,by applying stringent variable selection procedure to data from the Cancer Genome Atlas Kidney Renal Clear Cell Carcinoma project.The risk score generated from the DEm^(6)ArlncRNA expression categorizes patients into either high or low-risk groups,between which,enrichment analysis indicated an enrichment in immune-related pathways.Under different DEm^(6)ArlncRNA transcription pattern,the two risk groups differ in immune cell population composition and expression levels of therapy targeting genes.Nanoparticle is satisfactory strategy to delivering therapeutic drugs.For further clinical translation,we designed a novel nanoparticle delivery system packaged STM2457(STM@8P4 NPs),which selectively inhibits AC008870.2-correlated m^(6)A writer.STM@8P4 NPs loaded drug successfully with uniform particle size,long-term stability and high release efficiency.STM@8P4 NPs can easily enter ccRCC cells and showed a highly efficient ccRCC killing activity in vitro.Our results therefore indicate that m^(6)ArlncRNAs expression can depict tumor microenvironment,predict prognosis for ccRCC patient and give hint to therapeutic strategies in ccRCC.