In a new mouse model of multiple myeloma, mice expressing the human versions of six proteins important for hematopoietic function were able to support the growth of primary human multiple myeloma xenografts, including both preneoplastic and malignant plasma cells. Most human cancers, including myeloma, are preceded by a precursor state. There is an unmet need for in vivo models to study the interaction of human preneoplastic cells in the bone marrow microenvironment with non-malignant cells. Here, we genetically humanized mice to permit the growth of primary human preneoplastic and malignant plasma cells together with non-malignant cells in vivo. Growth was largely restricted to the bone marrow, mirroring the pattern in patients with myeloma. Xenografts captured the genomic complexity of parental tumors and revealed additional somatic changes. Moreover, xenografts from patients with preneoplastic gammopathy showed progressive growth, suggesting that the clinical stability of these lesions may in part be due to growth controls extrinsic to tumor cells. These data demonstrate a new approach to investigate the entire spectrum of human plasma cell neoplasia and illustrate the utility of humanized models for understanding the functional diversity of human tumors.

Extracellular vesicles (EVs) are small lipid bilayer-delimited particles that are naturally released from cells into body fluids, and therefore can travel and convey regulatory functions in the distal parts of the body. EVs can transmit paracrine signaling by carrying over cytokines, chemokines, growth factors, interleukins (ILs), transcription factors, and nucleic acids such as DNA, mRNAs, microRNAs, piRNAs, lncRNAs, sn/snoRNAs, mtRNAs and circRNAs; these EVs travel to predecided destinations to perform their functions. While mesenchymal stem cells (MSCs) have been shown to improve healing and facilitate treatments of various diseases, the allogenic use of these cells is often accompanied by serious adverse effects after transplantation. MSC-produced EVs are less immunogenic and can serve as an alternative to cellular therapies by transmitting signaling or delivering biomaterials to diseased areas of the body. This review article is focused on understanding the properties of EVs derived from different types of MSCs and MSC–EV-based therapeutic options. The potential of modern technologies such as 3D bioprinting to advance EV-based therapies is also discussed.

Cancer is the second leading cause of death in the United States and is a major public health concern worldwide. Basic, clinical and epidemiological research is leading to improved cancer detection, prevention, and outcomes. Recent technological advances have allowed unbiased and comprehensive screening of genome-wide gene expression. Small non-coding RNAs (sncRNAs) have been shown to play an important role in biological processes and could serve as a diagnostic, prognostic and therapeutic biomarker for specific diseases. Recent findings have begun to reveal and enhance our understanding of the complex architecture of sncRNA expression including miRNAs, piRNAs, lncRNAs, sn/snoRNAs and their relationships with biological systems. We used publicly available small RNA sequencing data that was derived from 24 triple negative breast cancers (TNBC) and 14 adjacent normal tissue samples to remap various types of sncRNAs. We found a total of 55 miRNAs were aberrantly expressed (p<0.005) in TNBC samples (8 miRNAs upregulated; 47 downregulated) compared to adjacent normal tissues whereas the original study reported only 25 novel miRs. In this study, we used pathway analysis of differentially expressed miRNAs which revealed TGF-beta signaling pathways to be profoundly affected in the TNBC samples. Furthermore, our comprehensive re-mapping strategy allowed us to discover a number of other differentially expressed sncRNAs including piRNAs, lncRNAs, sn/snoRNAs, rRNAs, miscRNAs and nonsense-mediated decay RNAs. We believe that our sncRNA analysis workflow is extremely comprehensive and suitable for discovery of novel sncRNAs changes, which may lead to the development of innovative diagnostic and therapeutic tools for TNBC.

Hepatocellular carcinoma (HCC) was the fifth leading cause of cancer death in men and eighth leading cause of death in women in the United States in 2017. In our study, we sought to identify sncRNAs in various stages of development of HCC. We obtained publicly available small RNA-seq data derived from patients with cirrhosis (n = 14), low-grade dysplastic nodules (LGDN, n = 9), high grade dysplastic nodules (HGDN, n = 6), early hepatocellular carcinoma (eHCC, n = 6), and advanced hepatocellular carcinoma (HCC, n = 20), along with healthy liver tissue samples (n = 9). All samples were analyzed for various types of non-coding RNAs using PartekFlow software. We remapped small RNA-seq to miRBase to obtain differential expressions of miRNAs and found 87 in cirrhosis, 106 in LGDN, 59 in HGDN, 80 in eHCC, and 133 in HCC. Pathway analysis of miRNAs obtained from diseased samples compared to normal samples showed signaling pathways in the microRNA dependent EMT, CD44, and others. Additionally, we analyzed the data sets for piRNAs, lncRNAs, circRNAs, and sno/mt-RNAs. We validated the in silico data using human HCC samples with NanoString miRNA global expression. Our results suggest that publically available data is a valuable resource for sncRNA identification in HCC progression (FDR set to <0.05 for all samples) and that a data mining approach is useful for biomarker development.

Background: Improved healthcare and recent breakthroughs in technology have substantially reduced cancer mortality rates worldwide. Recent advancements in next-generation sequencing (NGS) have allowed genomic analysis of the human transcriptome. Now, using NGS we can further look into small non-coding regions of RNAs (sncRNAs) such as microRNAs (miRNAs), Piwi-interacting-RNAs (piRNAs), long non-coding RNAs (lncRNAs), and small nuclear/nucleolar RNAs (sn/snoRNAs) among others. Recent studies looking at sncRNAs indicate their role in important biological processes such as cancer progression and predict their role as biomarkers for disease diagnosis, prognosis, and therapy. Results: In the present study, we data mined publically available small RNA sequencing data from colorectal tissue samples of eight matched patients (benign, tumor, and metastasis) and remapped the data for various small RNA annotations. We identified aberrant expression of 13 miRNAs in tumor and metastasis specimens [tumor vs benign group (19 miRNAs) and metastasis vs benign group (38 miRNAs)] of which five were upregulated, and eight were downregulated, during disease progression. Pathway analysis of aberrantly expressed miRNAs showed that the majority of miRNAs involved in colon cancer were also involved in other cancers. Analysis of piRNAs revealed six to be over-expressed in the tumor vs benign cohort and 24 in the metastasis vs benign group. Only two piRNAs were shared between the two cohorts. Examining other types of small RNAs [sn/snoRNAs, mt_rRNA, miscRNA, nonsense mediated decay (NMD), and rRNAs] identified 15 sncRNAs in the tumor vs benign group and 104 in the metastasis vs benign group, with only four others being commonly expressed. Conclusion: In summary, our comprehensive analysis on publicly available small RNA-seq data identified multiple differentially expressed sncRNAs during colorectal cancer progression at different stages compared to normal colon tissue. We speculate that deciphering and validating the roles of sncRNAs may prove useful in colorectal cancer prognosis, diagnosis, and therapy.