MicroRNAs are functional, 22 nt, noncoding RNAs that negatively regulate gene expression. Disturbance of microRNA expression may play a role in the initiation and progression of certain diseases. A microRNA expression signature has been identified that is associated with pancreatic cancer. This has been accomplished with the application of real‐time PCR profiling of over 200 microRNA precursors on specimens of human pancreatic adenocarcinoma, paired benign tissue, normal pancreas, chronic pancreatitis and nine pancreatic cancer cell lines. Hierarchical clustering was able to distinguish tumor from normal pancreas, pancreatitis and cell lines. The PAM algorithm correctly classified 28 of 28 tumors, 6 of 6 normal pancreas and 11 of 15 adjacent benign tissues. One hundred microRNA precursors were aberrantly expressed in pancreatic cancer or desmoplasia (p < 0.01), including microRNAs previously reported as differentially expressed in other human cancers (miR‐155, miR‐21, miR‐221 and miR‐222) as well as those not previously reported in cancer (miR‐376a and miR‐301). Most of the top aberrantly expressed miRNAs displayed increased expression in the tumor. Expression of the active, mature microRNA was validated using a real‐time PCR assay to quantify the mature microRNA and Northern blotting. Reverse transcription in situ PCR showed that three of the top differentially expressed miRNAs (miR‐221, ‐376a and ‐301) were localized to tumor cells and not to stroma or normal acini or ducts. Aberrant microRNA expression may offer new clues to pancreatic tumorigenesis and may provide diagnostic biomarkers for pancreatic adenocarcinoma.

Although machine learning (ML) has shown promise across disciplines, out-of-sample generalizability is concerning. This is currently addressed by sharing multi-site data, but such centralization is challenging/infeasible to scale due to various limitations. Federated ML (FL) provides an alternative paradigm for accurate and generalizable ML, by only sharing numerical model updates. Here we present the largest FL study to-date, involving data from 71 sites across 6 continents, to generate an automatic tumor boundary detector for the rare disease of glioblastoma, reporting the largest such dataset in the literature (n = 6, 314). We demonstrate a 33% delineation improvement for the surgically targetable tumor, and 23% for the complete tumor extent, over a publicly trained model. We anticipate our study to: 1) enable more healthcare studies informed by large diverse data, ensuring meaningful results for rare diseases and underrepresented populations, 2) facilitate further analyses for glioblastoma by releasing our consensus model, and 3) demonstrate the FL effectiveness at such scale and task-complexity as a paradigm shift for multi-site collaborations, alleviating the need for data-sharing.

Purpose: MicroRNA (miRNA) is a new class of small, noncoding RNA. The purpose of this study was to determine if miRNAs are differentially expressed in hepatocellular carcinoma (HCC). Experimental Design: More than 200 precursor and mature miRNAs were profiled by real-time PCR in 43 and 28 pairs of HCC and adjacent benign liver, respectively, and in normal liver specimens. Results: Several miRNAs including miR-199a, miR-21, and miR-301 were differentially expressed in the tumor compared with adjacent benign liver. A large number of mature and precursor miRNAs were up-regulated in the adjacent benign liver specimens that were both cirrhotic and hepatitis-positive compared with the uninfected, noncirrhotic specimens (P < 0.01). Interestingly, all of the miRNAs in this comparison had increased expression and none were decreased. The expression of 95 randomly selected mRNAs was not significantly altered in the cirrhotic and hepatitis-positive specimens, suggesting a preferential increase in the transcription of miRNA. Comparing the miRNA expression in the HCC tumors with patient's survival time revealed two groups of patients; those with predominantly lower miRNA expression and poor survival and those with predominantly higher miRNA expression and good survival (P < 0.05). A set of 19 miRNAs significantly correlated with disease outcome. A number of biological processes including cell division, mitosis, and G₁-S transition were predicted to be targets of the 19 miRNAs in this group. Conclusion: We show that a global increase in the transcription of miRNA genes occurs in cirrhotic and hepatitis-positive livers and that miRNA expression may prognosticate disease outcome in HCC.

Our previous study described a real-time PCR method to quantify microRNA (miRNA) precursors using SYBR green detection [T. D. Schmittgen, J. Jiang, Q. Liu and L. Yang (2004) Nucleic Acids Res., 32, e43]. The present study adapted the assay to a 384-well format and expanded it to include primers to 222 human miRNA precursors. TaqMan minor groove binder probes were used to discriminate nearly identical members of the let-7 family of miRNA isoforms. The miRNA precursor expression was profiled in 32 human cell lines from lung, breast, colorectal, hematologic, prostate, pancreatic, and head and neck cancers. Some miRNA precursors were expressed at similar levels in many of the cell lines, while others were differentially expressed. Clustering analysis of the miRNA precursor expression data revealed that most of the cell lines clustered into their respective tissues from which each cell line was ostensibly derived. miRNA precursor expression by PCR paralleled the mature miRNA expression by northern blotting for most of the conditions studied. Our study provides PCR primer sequences to all of the known human miRNA precursors as of December 2004 and provides a database of the miRNA precursor expression in many commonly used human cancer cell lines.

Very little is known regarding regulation of microRNA (miRNA) biogenesis in normal tissues, tumors, and cell lines. Here, we profiled the expression of 225 precursor and mature miRNAs using real-time PCR and compared the expression levels to determine the processing patterns. RNA from 22 different human tissues, 37 human cancer cell lines, and 16 pancreas and liver tissues/tumors was profiled. The relationship between precursor and mature miRNA expression fell into the following four categories: (1) a direct correlation exists between the precursor and mature miRNA expression in all cells/tissues studied; (2) direct correlation of the precursor and mature miRNA exists, yet the expression is restricted to specific cell lines or tissues; (3) there is detectable expression of mature miRNA in certain cells and tissues while the precursor is expressed in all or most cells/tissues; or (4) both precursor and mature miRNA are not expressed. Pearson correlation between the precursor and mature miRNA expression was closer to one for the tissues but was closer to zero for the cell lines, suggesting that processing of precursor miRNAs is reduced in cancer cell lines. By using Northern blotting, we show that many of these miRNAs (e.g., miR-31, miR-105 and miR-128a) are processed to the precursor, but in situ hybridization analysis demonstrates that these miRNA precursors are retained in the nucleus. We provide a database of the levels of precursor and mature miRNA in a variety of cell types. Our data demonstrate that a large number of miRNAs are transcribed but are not processed to the mature miRNA.

The Rembrandt brain cancer dataset includes 671 patients collected from 14 contributing institutions from 2004–2006. It is accessible for conducting clinical translational research using the open access Georgetown Database of Cancer (G-DOC) platform. In addition, the raw and processed genomics and transcriptomics data have also been made available via the public NCBI GEO repository as a super series GSE108476. Such combined datasets would provide researchers with a unique opportunity to conduct integrative analysis of gene expression and copy number changes in patients alongside clinical outcomes (overall survival) using this large brain cancer study.