The dark matter of the human genome and its role in human cancers

Computational identification of ultra-conserved elements in the human genome: a hypothesis on homologous DNA pairing

Thousands of prolonged sequences of human ultra-conserved non-coding elements (UCNEs) share only one common feature: peculiarities in the unique composition of their dinucleotides. Here we investigate whether the numerous weak signals emanating from these dinucleotide arrangements can be used for computational identification of UCNEs within the human genome. For this purpose, we analyzed 4272 UCNE sequences, encompassing 1 393 448 nucleotides, alongside equally sized control samples of randomly selected human genomic sequences. Our research identified nine different features of dinucleotide arrangements that enable differentiation of UCNEs from the rest of the genome. We employed these nine features, implementing three Machine Learning techniques - Support Vector Machine, Random Forest, and Artificial Neural Networks - to classify UCNEs, achieving an accuracy rate of 82-84%, with specific conditions allowing for over 90% accuracy. Notably, the strongest feature for UCNE identification was the frequency ratio between GpC dinucleotides and the sum of GpG and CpC dinucleotides. Additionally, we investigated the entire pool of 31 046 SNPs located within UCNEs for their representation in the ClinVar database, which catalogs human SNPs with known phenotypic effects. The presence of UCNE-associated SNPs in ClinVar aligns with the expectation of a random distribution, emphasizing the enigmatic nature of UCNE phenotypic manifestation.

Extracellular vesicles carry transcriptional 'dark matter' revealing tissue-specific information

From eukaryotes to prokaryotes, all cells secrete extracellular vesicles (EVs) as part of their regular homeostasis, intercellular communication, and cargo disposal. Accumulating evidence suggests that small EVs carry functional small RNAs, potentially serving as extracellular messengers and liquid-biopsy markers. Yet, the complete transcriptomic landscape of EV-associated small RNAs during disease progression is poorly delineated due to critical limitations including the protocols used for sequencing, suboptimal alignment of short reads (20-50 nt), and uncharacterized genome annotations-often denoted as the 'dark matter' of the genome. In this study, we investigate the EV-associated small unannotated RNAs that arise from endogenous genes and are part of the genomic 'dark matter', which may play a key emerging role in regulating gene expression and translational mechanisms. To address this, we created a distinct small RNAseq dataset from human prostate cancer & benign tissues, and EVs derived from blood (pre- & post-prostatectomy), urine, and human prostate carcinoma epithelial cell line. We then developed an unsupervised data-based bioinformatic pipeline that recognizes biologically relevant transcriptional signals irrespective of their genomic annotation. Using this approach, we discovered distinct EV-RNA expression patterns emerging from the un-annotated genomic regions (UGRs) of the transcriptomes associated with tissue-specific phenotypes. We have named these novel EV-associated small RNAs as 'EV-UGRs' or "EV-dark matter". Here, we demonstrate that EV-UGR gene expressions are downregulated by ∼100 fold (FDR < 0.05) in the circulating serum EVs from aggressive prostate cancer subjects. Remarkably, these EV-UGRs expression signatures were regained (upregulated) after radical prostatectomy in the same follow-up patients. Finally, we developed a stem-loop RT-qPCR assay that validated prostate cancer-specific EV-UGRs for selective fluid-based diagnostics. Overall, using an unsupervised data driven approach, we investigate the 'dark matter' of EV-transcriptome and demonstrate that EV-UGRs carry tissue-specific Information that significantly alters pre- and post-prostatectomy in the prostate cancer patients. Although further validation in randomized clinical trials is required, this new class of EV-RNAs hold promise in liquid-biopsy by avoiding highly invasive biopsy procedures in prostate cancer.

Therapeutic significance of long noncoding RNAs in estrogen receptor-positive breast cancer

About 70% of cases of breast cancer are compromised by Estrogen-positive breast cancer. Through its regulation of several processes, including cell proliferation, cell cycle progression, and apoptosis, Estrogen signaling plays a pivotal role in the genesis and progression of this particular kind of breast cancer. One of the best treatment strategies for treating Estrogen-positive breast cancer is blocking Estrogen signaling. However, patients' treatment failure is mainly caused by the emergence of resistance and metastases, necessitating the development of novel therapeutic targets. Numerous studies have shown long noncoding RNAs (lncRNAs) to play a role in Estrogen-mediated carcinogenesis. These lncRNAs interact with co-regulators and the Estrogen signaling cascade components, primarily due to Estrogen activation. Vimentin and E-cadherin are examples of epithelial-to-mesenchymal transition markers, and they regulate genes involved in cell cycle progression, such as Cyclins, to affect the growth, proliferation, and metastasis of Estrogen-positive breast cancer. Furthermore, a few of these lncRNAs contribute to developing resistance to chemotherapy, making them more desirable targets for enhancing results. Thus, to shed light on the creation of fresh approaches for treating this cancer, this review attempts to compile recently conducted studies on the relationship between lncRNAs and the advancement of Estrogen-positive breast cancer.

PURPL Promotes M2 Macrophage Polarization in Lung Cancer by Regulating RBM4/xCT Signaling

Lung cancer is the most common malignancy worldwide. Long non-coding RNA (lncRNA) p53 upregulated regulator of P53 levels (PURPL) is abnormally in various cancers. However, the reports on its roles in lung cancer are limited. The purpose of present study is to investigate the potentials of lncRNA PURPL in lung cancer. PURPL and mRNA expression was determined using real-time reverse transcriptase-polymerase chain reaction (RT-qPCR). The location of PURPL was detected using RNA fluorescence in situ hybridization (FISH) assay. Protein expression was detected using western blot. Cellular functions were determined using flow cytometry. The interaction between PURPL and RNA-binding motif 4 (RBM4) was confirmed using RNA immunoprecipitation (RIP) assay. PURPL was overexpressed in lung cancer cells and patients. Overexpressed PURPL promoted M2 macrophage polarization and suppressed ferroptosis. Additionally, PURPL maintained the mRNA stability of cystine glutamate reverse transporter (xCT) via regulating RBM4. xCT knockdown antagonized the effects of overexpressed PURPL and inhibited M2 macrophage polarization via inducing macrophage ferroptosis. PURPL/RBM4/xCT axis promoted M2 macrophage polarization in lung cancer. Therefore, PURPL may be a potential target of lung cancer.

Non-coding RNAs transcribed from ultra-conserved regions (T-UCRs) are differentially expressed in dental follicle tissues of impacted mandibular third molars

INTRODUCTION

Transcribed ultra-conserved regions (T-UCRs) are a new class of long non-coding RNA molecules transcribed from ultra-conserved regions (UCRs) of the human genome. T-UCRs are extremely conserved in the human, rat, and mouse genomes. Deletions of genomic areas containing UCRs resulted in live mice that developed without discernible phenotypes, implying that T-UCRs are involved in developmental processes. In addition, there is increasing evidence that dental follicle tissues exhibit various cellular alterations involving deregulation of protein-coding genes and non-coding RNAs. Accordingly, the main objective of the present study was to determine the clinical significance and distinct expression signatures of non-coding RNA molecules transcribed from ultra-conserved regions in dental follicle samples.

MATERIALS AND METHODS

From March 2021 to December 2022, a total 42 patients who referred to clinic of oral and maxillofacial surgery department with the indications of impacted mandibular third molar extraction from 38^th and 48^th positions were enrolled for the study. For the analysis of T-UCR expression levels, real-time quantitative reverse transcription PCR method was used.

RESULTS

Findings of the present study indicated that T-UCRs are distinctly expressed in dental follicle tissues of impacted mandibular third molars. The expression of uc.38, uc.112, and uc.338 was found to be significantly increased in the dental follicles of impacted mandibular third molars, indicating a clinical significance of these molecules. In addition, no differences in T-UCR expression were found as a function of demographic factors.

CONCLUSIONS

Collectively, transcribed ultra-conserved elements, such as uc.38, uc.112, and uc.338, are considerably deregulated in the dental follicle tissues of impacted mandibular third molars and might be responsible for the molecular changes acquired by dental follicle tissues of impacted mandibular third molars.