Small Non-coding RNA Abundance in Adrenocortical Carcinoma: A Footprint of a Rare Cancer

Expression profiles of small non-coding RNAs in breast cancer tumors characterize clinicopathological features and show prognostic and predictive potential

Precision medicine approaches are required for more effective therapies for cancer. As small non-coding RNAs (sncRNAs) have recently been suggested as intriguing candidates for cancer biomarkers and have shown potential also as novel therapeutic targets, we aimed at profiling the non-miRNA sncRNAs in a large sample set to evaluate their role in invasive breast cancer (BC). We used small RNA sequencing and 195 fresh-frozen invasive BC and 22 benign breast tissue samples to identify significant associations of small nucleolar RNAs, small nuclear RNAs, and miscellaneous RNAs with the clinicopathological features and patient outcome of BC. Ninety-six and five sncRNAs significantly distinguished (Padj < 0.01) invasive local BC from benign breast tissue and metastasized BC from invasive local BC, respectively. Furthermore, 69 sncRNAs significantly associated (Padj < 0.01) with the tumor grade, hormone receptor status, subtype, and/or tumor histology. Additionally, 42 sncRNAs were observed as candidates for prognostic markers and 29 for predictive markers for radiotherapy and/or tamoxifen response (P < 0.05). We discovered the clinical relevance of sncRNAs from each studied RNA type. By introducing new sncRNA biomarker candidates for invasive BC and validating the potential of previously described ones, we have guided the way for further research that is warranted for providing novel insights into BC biology.

Controlled variable selection in Weibull mixture cure models for high-dimensional data

Medical breakthroughs in recent years have led to cures for many diseases. The mixture cure model (MCM) is a type of survival model that is often used when a cured fraction exists. Many have sought to identify genomic features associated with a time-to-event outcome which requires variable selection strategies for high-dimensional spaces. Unfortunately, currently few variable selection methods exist for MCMs especially when there are more predictors than samples. This study develops high-dimensional penalized Weibull MCMs, which allow for identification of prognostic factors associated with both cure status and/or survival. We demonstrated how such models may be estimated using two different iterative algorithms. The model-X knockoffs method was combined with these algorithms to control the false discovery rate (FDR) in variable selection. Through extensive simulation studies, our penalized MCMs have been shown to outperform alternative methods on multiple metrics and achieve high statistical power with FDR being controlled. In an acute myeloid leukemia (AML) application with gene expression data, our proposed approach identified 14 genes associated with potential cure and 12 genes with time-to-relapse, which may help inform treatment decisions for AML patients.

Spindle and Kinetochore-Associated Complex is Associated With Poor Prognosis in Adrenocortical Carcinoma

INTRODUCTION

The spindle and kinetochore-associated (SKA) complex, composed of three subunits (SKA1, SKA2, and SKA3), stabilizes spindle microtubule attachment to the kinetochore (KT) in the middle stage of mitosis. High expression of this complex is associated with poor prognosis for several tumors. However, the potential role of SKA complex overexpression in rare malignant diseases, such as adrenocortical carcinoma (ACC), has not been well investigated.

MATERIALS AND METHODS

In this study, we used several databases to explore the relationship between SKA subunit expression and prognosis in ACC patients. Gene ontology (GO) and Kyoto Encyclopedia of Genes and Genome (KEGG) databases were used to analyze enriched pathways in ACC.

RESULTS

The results suggest that each of the three SKA subunits are overexpressed in ACC and that high expression is correlated with poor patient prognosis. Overexpression of the SKA complex is associated with the expression of organelle fission, nuclear division, and chromosome segregation pathways. Furthermore, differential expression of hub genes for proteins that interact physically or functionally with the SKA complex (CCNB2, UBE2C, BUB1B, TPX2, CCNA2, CDCA8, CCNB1, MELK, TOP2A, and KIF2C) revealed additional potential biomarkers for ACC.

CONCLUSIONS

Our findings provide additional understanding of the mechanisms of ACC and suggest an approach for biomarker discovery using publicly available resources.

The Diagnostic, Prognostic and Therapeutic Role of miRNAs in Adrenocortical Carcinoma: A Systematic Review

Adrenocortical carcinoma (ACC) is a rare endocrine malignancy with a dismal prognosis and a high rate of recurrence and mortality. Therapeutic options are limited. In some cases, the distinction of ACCs from benign adrenal neoplasms with the existing widely available pathological and histopathological tools is difficult. Thus, new biomarkers have been tested. We conducted a review of the recent literature on the advances of the diagnostic, prognostic and therapeutic role of miRNAs on ACC patients. More than 10 miRNAs validated by multiple studies were found to present a diagnostic and prognostic role for ACC patients, from which miR-483-5p and miR-195 were the most frequently met biomarkers. In particular, upregulation of miR-483-5p and downregulation of miR-195 were the most commonly validated molecular alterations. Unfortunately, data on the therapeutic role of miRNA are still scarce and limited mainly at the experimental level. Thus, the role of miRNA regulation in ACC remains an area of active research.

Artificial Intelligence in Epigenetic Studies: Shedding Light on Rare Diseases

More than 7,000 rare diseases (RDs) exist worldwide, affecting approximately 350 million people, out of which only 5% have treatment. The development of novel genome sequencing techniques has accelerated the discovery and diagnosis in RDs. However, most patients remain undiagnosed. Epigenetics has emerged as a promise for diagnosis and therapies in common disorders (e.g., cancer) with several epimarkers and epidrugs already approved and used in clinical practice. Hence, it may also become an opportunity to uncover new disease mechanisms and therapeutic targets in RDs. In this “big data” age, the amount of information generated, collected, and managed in (bio)medicine is increasing, leading to the need for its rapid and efficient collection, analysis, and characterization. Artificial intelligence (AI), particularly deep learning, is already being successfully applied to analyze genomic information in basic research, diagnosis, and drug discovery and is gaining momentum in the epigenetic field. The application of deep learning to epigenomic studies in RDs could significantly boost discovery and therapy development. This review aims to collect and summarize the application of AI tools in the epigenomic field of RDs. The lower number of studies found, specific for RDs, indicate that this is a field open to expansion, following the results obtained for other more common disorders.

Identification of Distinct Heterogenic Subtypes and Molecular Signatures Associated with African Ancestry in Triple Negative Breast Cancer Using Quantified Genetic Ancestry Models in Admixed Race Populations

Triple negative breast cancers (TNBCs) are molecularly heterogeneous, and the link between their aggressiveness with African ancestry is not established. We investigated primary TNBCs for gene expression among self-reported race (SRR) groups of African American (AA, n = 42) and European American (EA, n = 33) women. RNA sequencing data were analyzed to measure changes in genome-wide expression, and we utilized logistic regressions to identify ancestry-associated gene expression signatures. Using SNVs identified from our RNA sequencing data, global ancestry was estimated. We identified 156 African ancestry-associated genes and found that, compared to SRR, quantitative genetic analysis was a more robust method to identify racial/ethnic-specific genes that were differentially expressed. A subset of African ancestry-specific genes that were upregulated in TNBCs of our AA patients were validated in TCGA data. In AA patients, there was a higher incidence of basal-like two tumors and altered TP53, NFB1, and AKT pathways. The distinct distribution of TNBC subtypes and altered oncologic pathways show that the ethnic variations in TNBCs are driven by shared genetic ancestry. Thus, to appreciate the molecular diversity of TNBCs, tumors from patients of various ancestral origins should be evaluated.

MicroRNA dysregulation interplay with childhood abdominal tumors

Abdominal tumors (AT) in children account for approximately 17% of all pediatric solid tumor cases, and frequently exhibit embryonal histological features that differentiate them from adult cancers. Current molecular approaches have greatly improved the understanding of the distinctive pathology of each tumor type and enabled the characterization of novel tumor biomarkers. As seen in abdominal adult tumors, microRNAs (miRNAs) have been increasingly implicated in either the initiation or progression of childhood cancer. Moreover, besides predicting patient prognosis, they represent valuable diagnostic tools that may also assist the surveillance of tumor behavior and treatment response, as well as the identification of the primary metastatic sites. Thus, the present study was undertaken to compile up-to-date information regarding the role of dysregulated miRNAs in the most common histological variants of AT, including neuroblastoma, nephroblastoma, hepatoblastoma, hepatocarcinoma, and adrenal tumors. Additionally, the clinical implications of dysregulated miRNAs as potential diagnostic tools or indicators of prognosis were evaluated.

MiR-195 and miR-497 suppress tumorigenesis in lung cancer by inhibiting SMURF2-induced TGF-β receptor I ubiquitination

SMURF2 is a member of the HECT family of E3 ubiquitin ligases that have important roles as a negative regulator of transforming growth factor‐β (TGF‐β) signaling through ubiquitin‐mediated degradation of TGF‐β receptor I. However, the regulatory mechanism of SMURF2 is largely unknown. In this study, we identified that micro(mi)R‐195 and miR‐497 putatively target SMURF2 using several target prediction databases. Both miR‐195 and miR‐497 bind to the 3′‐UTR of the SMURF2 mRNA and inhibit SMURF2 expression. Furthermore, miR‐195 and miR‐497 regulate SMURF2‐dependent TβRI ubiquitination and cause the activation of the TGF‐β signaling pathway in lung cancer cells. Upregulation of miR‐195 and miR‐497 significantly reduced cell viability and colony formation through the activation of TGF‐β signaling. Interestingly, miR‐195 and miR‐497 also reduced the invasion ability of lung cancer cells when cells were treated with TGF‐β1. Subsequent in vivo studies in xenograft nude mice model revealed that miR‐195 and miR‐497 repress tumor growth. These findings demonstrate that miR‐195 and miR‐497 act as a tumor suppressor by suppressing ubiquitination‐mediated degradation of TGF‐β receptors through SMURF2, and suggest that miR‐195 and miR‐497 are potential therapeutic targets for lung cancer.

RNA-seq analysis of LPS-induced transcriptional changes and its possible implications for the adrenal gland dysregulation during sepsis

Activation of the adrenal gland stress response is of utmost importance to survive sepsis. Experimental and clinical evidence exists demonstrating that adrenal gland often develops functional and structural damage due to sepsis with mechanisms remaining largely unknown. In the present study, we have used RNA Sequencing (RNA-Seq) technology to analyze changes in adrenal transcriptome elucidated by bacterial LPS. We aimed to find particularly alterations in genes that were previously not reported to be involved in the adrenal gland dysregulation in contexts of sepsis. Our results demonstrate that systemic administration of LPS significantly altered expression of 8458 genes as compared to saline injected animals. The subsequent quality and functional analysis of these gene signatures revealed that LPS-induced highly homogenous transcriptional response in total upregulating 4312 and downregulating 4146 genes. Furthermore, functional annotation analysis together with gene enrichment set analysis (GSEA) clearly demonstrated that adrenal response to LPS involved alterations in multiple pathways related to the inflammatory response along with previously unexplored activation of the hypoxia pathway. In addition, LPS strongly downregulated genes involved in the adrenal homeostasis, development, and regeneration. Those alterations were subsequently verified in clinically relevant cecal ligation and puncture (CLP)-induced sepsis model. Collectively, our study demonstrates that RNA-seq is a very useful method that can be applied to search for new unexplored pathways potentially involved in adrenal gland dysregulation during sepsis.

Analysis of Expression Pattern of snoRNAs in Different Cancer Types with Machine Learning Algorithms

Small nucleolar RNAs (snoRNAs) are a new type of functional small RNAs involved in the chemical modifications of rRNAs, tRNAs, and small nuclear RNAs. It is reported that they play important roles in tumorigenesis via various regulatory modes. snoRNAs can both participate in the regulation of methylation and pseudouridylation and regulate the expression pattern of their host genes. This research investigated the expression pattern of snoRNAs in eight major cancer types in TCGA via several machine learning algorithms. The expression levels of snoRNAs were first analyzed by a powerful feature selection method, Monte Carlo feature selection (MCFS). A feature list and some informative features were accessed. Then, the incremental feature selection (IFS) was applied to the feature list to extract optimal features/snoRNAs, which can make the support vector machine (SVM) yield best performance. The discriminative snoRNAs included HBII-52-14, HBII-336, SNORD123, HBII-85-29, HBII-420, U3, HBI-43, SNORD116, SNORA73B, SCARNA4, HBII-85-20, etc., on which the SVM can provide a Matthew’s correlation coefficient (MCC) of 0.881 for predicting these eight cancer types. On the other hand, the informative features were fed into the Johnson reducer and repeated incremental pruning to produce error reduction (RIPPER) algorithms to generate classification rules, which can clearly show different snoRNAs expression patterns in different cancer types. The analysis results indicated that extracted discriminative snoRNAs can be important for identifying cancer samples in different types and the expression pattern of snoRNAs in different cancer types can be partly uncovered by quantitative recognition rules.

A novel four‑snoRNA signature for predicting the survival of patients with uveal melanoma

Uveal melanoma (UM), the predominant histological subtype of intraocular malignant tumors in adults, often results in high rates of mortality; effective prognostic signatures used to predict the survival of patients with UM are limited. Small nucleolar RNAs (snoRNAs) are emerging as important regulators in the processes of carcinogenesis and tumor progression, but knowledge of their application as prognostic markers in UM is limited. In the present study, the expression profiles of snoRNAs in UM were determined; a total of 60 snoRNAs were notably associated with the overall survival of patients with UM via univariate Cox survival analysis. Subsequently, a prognostic signature based on four snoRNAs was proposed, which retained their prognostic significance determined by a multivariate Cox survival analysis. The formula is as follows: ACA17 * (‑1.602) + ACA45 * 0.803 + HBII‑276 * 0.603 + SNORD12 * 1.348. Furthermore, the results of in silico analysis indicated that perturbation of the phototransduction, GABAergic synapse and amphetamine addiction pathways may be the potential molecular mechanisms underlying the poor prognosis of patients with UM. Collectively, the present study proposed a potential prognostic signature for patients with UM and the prospective mechanisms at the genome‑wide level were determined.