Estimating tumor-specific transcript proportions from mixed bulk samples has potential to inform novel biology. However, estimation accuracy using existing methods in sparse-count data such as microRNA-seq and spatial transcriptomics has yet to be established. We generated a mixed small RNA benchmark dataset to demonstrate analytical challenges. To resolve them, we developed DeMixNB, a semi-reference-based deconvolution model assuming a sum of negative binomial distributions. Applications to miRNA-seq from 856 patients with breast cancer and 3,755 spatial spots from lung cancer generated either clinical or mechanistic insights into tumor cell plasticity. This supports the important utility of DeMixNB to investigate cancer RNomes.

Deconvolution methods have traditionally focused on estimating cell-type proportions in bulk tissues with mRNA profiles. They often overlook the cell transcriptional proportion (i.e., transcriptional activity), which reflects the cell functional states and could be important for understanding disease progression (e.g., tumor). Current methods face additional challenges when analyzing other RNA species, such as miRNAs, due to their low abundance and inherent sparsity. Similar limitations also affect spatial transcriptomics (ST) technologies, which enable in situ gene expression profiling but suffer from high data sparsity and limited resolution, with each spot mimicking a mini-bulk sample. Besides, methods for estimating transcriptional activity in ST data remain underdeveloped. To fill this gap, we extended our semi-reference-based DeMixT framework by incorporating a negative binomial (NB) distribution to account for sparse profiling data, namely DeMixNB. DeMixNB models the observed mixed expression Yig for gene g in sample i as the sum of tumor (Tig) and non-tumor (Nig) components, with each component following an NB distribution parameterized by gene-specific means, dispersion parameters, and sample-specific tumor transcriptomic proportions. The NB framework naturally accommodates the sparsity and overdispersion. DeMixNB runs in two tiers: it first leverages non-tumor reference to estimate the non-tumor component parameters, then uses an Iterated Conditional Modes (ICM) to jointly estimate tumor-specific parameters and tumor proportions in mixed samples. We comprehensively validated DeMixNB from three aspects. First, we simulated data under various settings, including sample sizes, degrees of similarity between tumor and non-tumor components, and the number of distributions mixed in tumors. Across all scenarios, DeMixNB consistently achieved higher accuracy compared to existing methods. Then, to create a controlled experimental benchmark for miRNA analysis, we generated artificial mixtures using HS-5 fibroblasts combined with either wild-type or Dicer1 knockout HCT116 colorectal cancer cells at varying proportions. This design represents real tumor samples with known ground truth. DeMixNB maintained robust performance on sparse miRNA expression. To assess its broader utility, we applied DeMixNB to ST data from multiple cancer types. The estimated transcript proportions showed strong concordance with pathological annotations and revealed cell differentiation patterns not captured by conventional cell-type deconvolution methods. The DeMixT 2.0 framework, featuring DeMixNB, provides a robust solution for deconvolving sparse transcriptomic data. By accurately estimating tumor-specific transcriptional proportions, it enables deeper insight tumor heterogeneity at molecular and spatial levels. Hao Yan, Matthew Montierth, Liyang Xie, Peng Yang, Shuai Guo, Ruonan Li, Xiaoxi Pan, Caner Ercan, Yinyin Yuan, Kinga Németh, George A. Calin, Wenyi Wang. DeMixT 2.0: A deconvolution framework for sparse sequencing data using embedded negative binomial distribution [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2025; Part 1 (Regular Abstracts); 2025 Apr 25-30; Chicago, IL. Philadelphia (PA): AACR; Cancer Res 2025;85(8_Suppl_1):Abstract nr 2480.

microRNAs (miRNAs) are a class of small non-coding RNAs that play a crucial regulatory role in fundamental biological processes and have been implicated in various diseases, including cancer. The first evidence of the cancer-related function of miRNAs was discovered in chronic lymphocytic leukemia (CLL) in the early 2000s. Alterations in miRNA expression have since been shown to strongly influence the clinical course, prognosis, and response to treatment in patients with CLL. Therefore, the identification of specific miRNA alterations not only enhances our understanding of the molecular mechanisms underlying CLL but also holds promise for the development of novel diagnostic and therapeutic strategies. This review aims to provide a comprehensive summary of the current knowledge and recent insights into miRNA dysregulation in CLL, emphasizing its pivotal roles in disease progression, including the development of the lethal Richter syndrome, and to provide an update on the latest translational research in this field.

Abstract Background: MicroRNAs (miRNAs) are small noncoding RNAs that bind mRNA and inhibit translation or encourage degradation. MiRNAs play a significant regulatory role, with approximately 60% of protein-coding genes harboring miRNA binding sites. MiRNA dysregulation is associated with tumor onset, growth, and metastasis. These important regulatory RNAs have been studied as potential cancer biomarkers, yet the widespread adoption of any miRNA signature has been hindered by the variable nature of miRNA targeting, which is highly context specific. Total tumor cell mRNA content, or TmS, has been shown to have potential as a pan-cancer prognostic indicator. Here we expand the TmS framework using deconvolved miRNA expression to calculate TmiS, an estimate of total tumor-cell miRNA content and investigate the utility of TmiS as a prognostic marker in prostate cancer. Methods: Whole exome sequencing and miRNA sequencing, along with clinical annotations were downloaded for prostate cancer patient samples from The Cancer Genome Atlas (TCGA). Sample purity and ploidy were estimated from exomes using ASACT and ABSOLUTE. Tumor miRNA proportion was estimated using our previously developed miRNA deconvolution method DeMixMir, and TmiS, the total miRNA content per haploid genome per tumor cell, was calculated for each sample. Patients were separated by Gleason category and were binarized into High-TmiS and Low-TmiS within samples with Gleason 7 and Gleason 8+ scores separately. Results: We find Low-TmiS patients to have significantly better prognosis in both Gleason 7 (P = 0.04) and Gleason 8+ (P = 0.01) prostate cancer. We confirm that TmiS is indeed an aggregate measure of total miRNA levels, with no strong correlations observed with any one miRNA. We identify differentially expressed miRNAs and mRNAs between high and low TmiS, and find distinct enriched pathways between Gleason 7 and Gleason 8+ samples, and that several of the differentially expressed miRNAs are well-characterized cancer-related transcripts. Conclusions: In spite of their important regulatory function, the variable and context specific nature of miRNA targeting has been a barrier to their implementation as a robust biomarker. We find TmiS to be a measure of total miRNA dysregulation, and that patients with high total miRNA content show poorer survival within prostate samples graded at Gleason 7 (as well as Gleason 8+), which has been a challenging group for further risk classification. In summary, while the consequences of miRNA dysregulation are subtype specific, we find that by measuring total tumor cell miRNA, TmiS has potential as a robust prognostic indicator independent cellular context. Citation Format: Matthew Montierth, Kinga Németh, George A. Calin, Wenyi Wang. TmiS: A prognostic indicator for prostate cancer survival based on total tumor cell miRNA levels [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 1 (Regular Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(6_Suppl):Abstract nr 2993.

Non-coding RNAs (ncRNAs) are a heterogeneous group of transcripts that, by definition, are not translated into proteins. Since their discovery, ncRNAs have emerged as important regulators of multiple biological functions across a range of cell types and tissues, and their dysregulation has been implicated in disease. Notably, much research has focused on the link between microRNAs (miRNAs) and human cancers, although other ncRNAs, such as long non-coding RNAs (lncRNAs) and circular RNAs (circRNAs), are also emerging as relevant contributors to human disease. In this Review, we summarize our current understanding of the roles of miRNAs, lncRNAs and circRNAs in cancer and other major human diseases, notably cardiovascular, neurological and infectious diseases. Further, we discuss the potential use of ncRNAs as biomarkers of disease and as therapeutic targets. In this Review, the authors describe our current knowledge of the role of microRNAs, long non-coding RNAs and circular RNAs in disease, with a focus on cardiovascular, neurological, infectious diseases and cancer. Further, they discuss the potential use of non-coding RNAs as disease biomarkers and as therapeutic targets.

Abstract Measuring tumor heterogeneity is a key issue in modern clinical oncology, with relevance to understanding cancer progression, resistance to therapy, and recurrence. Studies of the transcriptomic landscape in cancer have rapidly advanced in scale, enabled by novel methods and advancing technologies. One such novel method is TmS, a computational estimate of tumor-specific total messenger RNA content from heterogeneous tumor samples, which is calculated by combining genomic and transcriptomic deconvolutions. We have previously shown TmS as a promising pan-cancer biomarker for patient prognosis. Given the fruitful study of tumor cell total messenger RNA content, a logical extension is to investigate the utility of estimating tumor cell content of other RNA species. MicroRNAs (miRNAs) are small noncoding RNAs that regulate mRNA expression, and their dysregulation is a hallmark feature observed across cancers. A computational method to derive tumor cell total miRNA content from bulk sequencing data is especially needed since miRNAs cannot yet be reliably profiled at single cell resolution. Here we develop and benchmark DeMixMir, a new expansion of our reference-free transcriptomic deconvolutional model DeMixT, in order to recover tumor-specific miRNA proportions from mixed samples. For benchmarking, we generated an artificially mixed dataset of small RNA sequencing consisting of a total of 30 samples that were made by mixing HS-5 fibroblast cells with either wildtype or Dicer1 knockout HCT116 colorectal cancer cells. The tumor and fibroblast cell lines were mixed at 5 different proportions, to simulate a broad spectrum of tumor/nontumor cell mixing scenarios, with three independent replicates generated at each mixing ratio. DeMixMir demonstrated high accuracy in estimating the tumor-specific miRNA proportions in both the wildtype and the Dicer1 knockout mixtures. We further calculated tumor cell total miRNA content using DeMixMir output. Our proof-of-concept study suggests estimating tumor cell total miRNA content across tumor tissues at scale is feasible and likely valuable for advancing understanding of the complex roles miRNAs play in the cancer ecosystem. Citation Format: Matthew Montierth, Kinga Nemeth, Xinghua Tao, George Calin, Wenyi Wang. DeMixMir: deconvolution of microRNA sequencing data from heterogeneous tumor samples. [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2023; Part 1 (Regular and Invited Abstracts); 2023 Apr 14-19; Orlando, FL. Philadelphia (PA): AACR; Cancer Res 2023;83(7_Suppl):Abstract nr 3773.

Measuring tumor heterogeneity is a key issue in modern clinical oncology, with relevance to understanding cancer progression, resistance to therapy, and recurrence. Studies of the transcriptomic landscape in cancer have rapidly advanced in scale, enabled by novel methods and advancing technologies. One such novel method is TmS, a computational estimate of tumor-specific total messenger RNA content from heterogeneous tumor samples, which is calculated by combining genomic and transcriptomic deconvolutions. We have previously shown TmS as a promising pan-cancer biomarker for patient prognosis. Given the fruitful study of tumor cell total messenger RNA content, a logical extension is to investigate the utility of estimating tumor cell content of other RNA species. MicroRNAs (miRNAs) are small noncoding RNAs that regulate mRNA expression, and their dysregulation is a hallmark feature observed across cancers. A computational method to derive tumor cell total miRNA content from bulk sequencing data is especially needed since miRNAs cannot yet be reliably profiled at single cell resolution. Here we develop and benchmark DeMixMir, a new expansion of our reference-free transcriptomic deconvolutional model DeMixT, in order to recover tumor-specific miRNA proportions from mixed samples. For benchmarking, we generated an artificially mixed dataset of small RNA sequencing consisting of a total of 30 samples that were made by mixing HS-5 fibroblast cells with either wildtype or Dicer1 knockout HCT116 colorectal cancer cells. The tumor and fibroblast cell lines were mixed at 5 different proportions, to simulate a broad spectrum of tumor/nontumor cell mixing scenarios, with three independent replicates generated at each mixing ratio. DeMixMir demonstrated high accuracy in estimating the tumor-specific miRNA proportions in both the wildtype and the Dicer1 knockout mixtures. We further calculated tumor cell total miRNA content using DeMixMir output. Our proof-of-concept study suggests estimating tumor cell total miRNA content across tumor tissues at scale is feasible and likely valuable for advancing understanding of the complex roles miRNAs play in the cancer ecosystem. Citation Format: Matthew Montierth, Kinga Nemeth, Xinghua Tao, George Calin, Wenyi Wang. DeMixMir: deconvolution of microRNA sequencing data from heterogeneous tumor samples. [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2023; Part 1 (Regular and Invited Abstracts); 2023 Apr 14-19; Orlando, FL. Philadelphia (PA): AACR; Cancer Res 2023;83(7_Suppl):Abstract nr 3773.

EDITORIAL article Front. Endocrinol., 26 September 2023Sec. Molecular and Structural Endocrinology Volume 14 - 2023 | https://doi.org/10.3389/fendo.2023.1264302

Context: DNA demethylation and inhibitory effects of aspirin on pituitary cell proliferation have been demonstrated. Objective: Our aim was to clarify the molecular mechanisms behind the aspirin-related effects in pituitary cells. Methods: DNA methylome and whole transcriptome profile were investigated in RC-4B/C and GH3 pituitary cell lines upon aspirin treatment. Effects of aspirin and a demethylation agent, decitabine, were further tested in vitro. PTTG1 expression in 41 human PitNET samples and whole genome gene and protein expression data of 76 PitNET and 34 control samples (available in Gene Expression Omnibus) were evaluated. Results: Aspirin induced global DNA demethylation and consequential transcriptome changes. Overexpression of Tet enzymes and their cofactor Uhrf2 were identified behind the increase of 5-hydroxymethylcytosine (5hmC). Besides cell cycle, proliferation, and migration effects that were validated by functional experiments, aspirin increased Tp53 activity through p53 acetylation and decreased E2f1 activity. Among the p53 controlled genes, Pttg1 and its interacting partners were downregulated upon aspirin treatment by inhibiting Pttg1 promoter activity. 5hmC positively correlated with Tet1-3 and Tp53 expression, and negatively correlated with Pttg1 expression, which was reinforced by the effect of decitabine. Additionally, high overlap (20.15%) was found between aspirin-regulated genes and dysregulated genes in PitNET tissue samples. Conclusion: A novel regulatory network has been revealed, in which aspirin regulated global demethylation, Tp53 activity, and Pttg1 expression along with decreased cell proliferation and migration. 5hmC, a novel tissue biomarker in PitNET, indicated aspirin antitumoral effect in vitro as well. Our findings suggest the potential beneficial effect of aspirin in PitNET.

In this issue, Traversa et al. [1] reviewed our current knowledge about the role of circular and linear forms of PVT1 non-coding RNA in cancer and human diseases. They highlighted the technical challenges of these studies and raised a potential bias in the publications, which require more attention from researchers.

In vitro monolayer conditions are not able to reproduce the complexity of solid tumors, still, there is scarce information about the 3D cell culture models of endocrine tumor types. Therefore, our aim was to develop in vitro 3D tumor models by different methodologies for adrenocortical carcinoma (H295R), pituitary neuroendocrine tumor (RC-4B/C and GH3) and pheochromocytoma (PC-12). Various methodologies were tested. Cell biological assays (cell viability, proliferation and live cell ratio) and steroid hormone production by HPLC-MS/MS method were applied to monitor cellular well-being. Cells in hanging drops and embedded in matrigel formed multicellular aggregates but they were difficult to handle and propagate for further experiments. The most widely used methods: ultra-low attachment plate (ULA) and spheroid inducing media (SFDM) were not the most viable 3D model of RC-4B/C and GH3 cells that would be suitable for further experiments. Combining spheroid generation with matrigel scaffold H295R 3D models were viable for 7 days, RC-4B/C and GH3 3D models for 7–10 days. ULA and SFDM 3D models of PC-12 cells could be used for further experiments up to 4 days. Higher steroid production in 3D models compared to conventional monolayer culture was detected. Endocrine tumor cells require extracellular matrix as scaffold for viable 3D models that can be one reason behind the lack of the usage of endocrine 3D cultures. Our models help understanding the pathogenesis of endocrine tumors and revealing potential biomarkers and therapeutic targets. They could also serve as an excellent platform for preclinical drug test screening.

Summary Dual inhibition (2i) of Ras–MEK–ERK and GSK3β pathways enables the derivation of embryo stem cells (ESCs) from refractory mouse strains and, for permissive strains, allows ESC derivation with no external protein factor stimuli involvement. In addition, blocking of ERK signalling in 8-cell-stage mouse embryos leads to ablation of GATA4/6 expression in hypoblasts, suggesting fibroblast growth factor (FGF) dependence of hypoblast formation in the mouse. In human, bovine or porcine embryos, the hypoblast remains unaffected or displays slight-to-moderate reduction in cell number. In this study, we demonstrated that segregation of the hypoblast and the epiblast in rabbit embryos is FGF independent and 2i treatment elicits only a limited reinforcement in favour of OCT4-positive epiblast populations against the GATA4-/6-positive hypoblast population. It has been previously shown that TGFβ/Activin A inhibition overcomes the pervasive differentiation and inhomogeneity of rat iPSCs, rat ESCs and human iPSCs while prompting them to acquire naïve properties. However, TGFβ/Activin A inhibition, alone or together with Rho-associated, coiled-coil containing protein kinase (ROCK) inhibition, was not compatible with the viability of rabbit embryos according to the ultrastructural analysis of preimplantation rabbit embryos by electron microscopy. In rabbit models ovulation upon mating allows the precise timing of progression of the pregnancy. It produces several embryos of the desired stage in one pregnancy and a relatively short gestation period, making the rabbit embryo a suitable model to discover the cellular functions and mechanisms of maintenance of pluripotency in embryonic cells and the embryo-derived stem cells of other mammals.

Searchable abstracts of presentations at key conferences in endocrinology ISSN 1470-3947 (print) | ISSN 1479-6848 (online)

Searchable abstracts of presentations at key conferences in endocrinology ISSN 1470-3947 (print) | ISSN 1479-6848 (online)

Background: Cytosine intermediaries 5-methylcytosine (5mC) and 5-hydroxymethylcytosine (5hmC), epigenetic hallmarks, have never been investigated in pituitary neuroendocrine tumors (PitNET). Objective: To examine methylation-demethylation status of global deoxyribonucleic acid (DNA) in PitNET tissues and to assess its correlation with clinical and biological parameters. Materials and Methods: Altogether, 57 PitNET and 25 corresponding plasma samples were collected. 5mC and 5hmC were investigated using liquid chromatography-tandem mass spectrometry. Expression of DNA methyltransferase 1 (DNMT1); tet methylcytosine dioxygenase 1 through 3 (TET1-3); and ubiquitin-like, containing PHD and RING finger domains 1 and 2 (UHRF1-2) were measured by reverse transcription-polymerase chain reaction. Levels of 5hmC and UHRF1-2 were explored by immunohistochemistry. Effect of demethylating agent decitabine was tested on pituitary cell lines. Results: 5hmC/5mC ratio was higher in less differentiated PitNET samples. A negative correlation between Ki-67 proliferation index and 5hmC, 5hmC to 5mC ratio were revealed. Higher 5mC was observed in SF-1 + gonadotroph adenomas with a higher Ki-67 index. Expressions of TET2 and TET3 were significantly higher in adenomas with higher proliferation rate. UHRF1 showed gradually increased expression in higher proliferative adenoma samples, and a significant positive correlation was detected between UHRF2 expression and 5hmC level. Decitabine treatment significantly decreased 5mC and increased 5hmC levels in both cell lines, accompanied with decreased cell viability and proliferation. Conclusion: The demethylation process negatively correlated with proliferation rate and the ratio of 5hmC to 5mC was higher in less differentiated adenomas. Therefore, epigenetic markers can be potential biomarkers for PitNET behavior. Altering the epigenome in adenoma cells by decitabine decreased proliferation, suggesting that this treatment might be a novel medical treatment for PitNET.

Searchable abstracts of presentations at key conferences in endocrinology ISSN 1470-3947 (print) | ISSN 1479-6848 (online)

Searchable abstracts of presentations at key conferences in endocrinology ISSN 1470-3947 (print) | ISSN 1479-6848 (online)

BACKGROUND:Circulating miRNAs in pituitary adenomas would improve patient care, especially as minimally invasive biomarkers of tumor recurrence and progression in nonfunctioning adenoma cases.AIM:Our aim was to investigate plasma miRNA profiles in patients with pituitary adenomas.MATERIALS AND METHODS:A total of 149 plasma and extracellular vesicle (preoperative, early postoperative, and late postoperative) samples were collected from 45 patients with pituitary adenomas. Adenomas were characterized on the basis of anterior pituitary hormones and transcription factors by immunostaining. miRNA next-generation sequencing was performed on 36 samples (discovery set). Individual TaqMan assays were used for validation on an extended sample set. Pituitary adenoma tissue miRNAs were evaluated by TaqMan array and data in the literature.RESULTS:Global downregulation of miRNA expression was observed in plasma samples of pituitary adenomas compared with normal samples. Expression of 29 miRNAs and isomiR variants were able to distinguish preoperative plasma samples from normal controls. miRNAs with altered expression in both plasma and different adenoma tissues were identified. Three, seven, and 66 miRNAs expressed differentially between preoperative and postoperative plasma samples in GH-secreting, FSH/LH+, and hormone-immunonegative groups, respectively. miR‒143-3p was downregulated in late postoperative but not in early postoperative plasma samples compared with preoperative ones exclusively in FSH/LH+ adenomas. The plasma level of miR‒143-3p discriminated these samples with 81.8% sensitivity and 72.3% specificity (area under the curve = 0.79; P = 0.02).CONCLUSIONS:Differentially expressed miRNAs in pituitary adenoma tissues have low abundance in plasma, minimizing their role as biomarkers. Plasma miR‒143-3p level decreased in patients with FSH/LH+ adenomas, indicating successful surgery, but its application for evaluating tumor recurrence needs further investigation.

Searchable abstracts of presentations at key conferences in endocrinology ISSN 1470-3947 (print) | ISSN 1479-6848 (online)

MicroRNAs (miRNAs) are short, single stranded RNA molecules which play regulatory roles through posttranscriptional regulation of their target genes. Based on our current knowledge, more than 30% of the human protein-coding genes are regulated by miRNAs, hence influencing basic cellular mechanisms including cell proliferation, differentiation and cell death. Differential miRNA expression pattern has been detected in many different types of tumors and, recently, several publications have referred to miRNAs as potential therapeutic targets. Through adjustment of miRNA levels by artificial miRNAs administration or miRNA inhibition, we can influence not only one target gene but also complex biological pathways. Pituitary adenoma is the second most frequent intracranial tumor. In spite of this, the molecular mechanism of the pituitary adenoma formation is not yet entirely revealed. Recently, more and more evidences have been found suggesting that miRNAs have an important role in pituitary adenoma pathogenesis. Here, we summarize the recent results related to this role and highlight the therapeutic potentials in pituitary adenomas. Orv Hetil. 2018; 159(7): 252-259.