As an essential part of central dogma, RNA delivers genetic and regulatory information and reflects cellular states. Based on high‐throughput sequencing technologies, cumulating data show that various RNA molecules are able to serve as biomarkers for the diagnosis and prognosis of various diseases, for instance, cancer. In particular, detectable in various bio‐fluids, such as serum, saliva and urine, extracellular RNAs (exRNAs) are emerging as non‐invasive biomarkers for earlier cancer diagnosis, tumor progression monitor, and prediction of therapy response. In this review, we summarize the latest studies on various types of RNA biomarkers, especially extracellular RNAs, in cancer diagnosis and prognosis, and illustrate several well‐known RNA biomarkers of clinical utility. In addition, we describe and discuss general procedures and issues in investigating exRNA biomarkers, and perspectives on utility of exRNAs in precision medicine.

Background: Moderate and late preterm (MLPT) birth accounts for the vast majority of preterm births, which is a global public health problem. The association between MLPT and neurobehavioral developmental delays in children and the underlying biological mechanisms need to be further revealed. The “placenta-brain axis” (PBA) provides a new perspective for gene regulation and risk prediction of neurodevelopmental delays in MLPT children. Methods: The authors performed multivariate logistic regression models between MLPT and children’s neurodevelopmental outcomes, using data from 129 MLPT infants and 3136 full-term controls from the Ma’anshan Birth Cohort (MABC). Furthermore, the authors identified the abnormally regulated PBA-related genes in MLPT placenta by bioinformatics analysis of RNA-seq data and RT-qPCR verification on independent samples. Finally, the authors established the prediction model of neurodevelopmental delay in children with MLPT using multiple machine learning models. Results: The authors found an increased risk of neurodevelopmental delay in children with MLPT at 6 months, 18 months, and 48 months, especially in boys. Further verification showed that APOE and CST3 genes were significantly correlated with the developmental levels of gross-motor domain, fine-motor domain, and personal social domain in 6-month-old male MLPT children. Conclusions: These findings suggested that there was a sex-specific association between MLPT and neurodevelopmental delays. Moreover, APOE and CST3 were identified as placental biomarkers. The results provided guidance for the etiology investigation, risk prediction, and early intervention of neurodevelopmental delays in children with MLPT.

Background: Preterm birth (PTB) is the main driver of newborn deaths. The identification of pregnancies at risk of PTB remains challenging, as the incomplete understanding of molecular mechanisms associated with PTB. Although several transcriptome studies have been done on the placenta and plasma from PTB women, a comprehensive description of the RNA profiles from plasma and placenta associated with PTB remains lacking. Methods: Candidate markers with consistent trends in the placenta and plasma were identified by implementing differential expression analysis using placental tissue and maternal plasma RNA-seq datasets, and then validated by RT-qPCR in an independent cohort. In combination with bioinformatics analysis tools, we set up two protein–protein interaction networks of the significant PTB-related modules. The support vector machine (SVM) model was used to verify the prediction potential of cell free RNAs (cfRNAs) in plasma for PTB and late PTB. Results: We identified 15 genes with consistent regulatory trends in placenta and plasma of PTB while the full term birth (FTB) acts as a control. Subsequently, we verified seven cfRNAs in an independent cohort by RT-qPCR in maternal plasma. The cfRNA ARHGEF28 showed consistence in the experimental validation and performed excellently in prediction of PTB in the model. The AUC achieved 0.990 for whole PTB and 0.986 for late PTB. Conclusions: In a comparison of PTB versus FTB, the combined investigation of placental and plasma RNA profiles has shown a further understanding of the mechanism of PTB. Then, the cfRNA identified has the capacity of predicting whole PTB and late PTB.

The rapid growth of wireless electronic devices has raised concerns about the harmful effects of leaked electromagnetic radiation (EMR) on human health. Even though numerous studies have been carried out to explore the biological effects of EMR, no clear conclusions have been drawn about the effect of radio frequency (RF) EMR on oligodendrocytes. To this end, we exposed oligodendroglia and three other types of brain cells to 2.4 GHz EMR for 6 or 48 h at an average input power of 1 W in either a continuous wave (CW-RF) or a pulse-modulated wave (PW-RF, 50 Hz pulse frequency, 1/3 duty cycle) pattern. RNA sequencing, RT-qPCR, and Western blot were used to examine the expression of C/EBPβ and its related genes. Multiple reaction monitoring (MRM) was used to examine the levels of expression of C/EBPβ-interacting proteins. Our results showed that PW-RF EMR significantly increased the mRNA level of C/EBPβ in oligodendroglia but not in other types of cells. In addition, the expression of three isoforms and several interacting proteins and targeted genes of C/EBPβ were markedly changed after 6-h PW-RF but not CW-RF. Our results indicated that RF EMR regulated the expression and functions of C/EBPβ in a waveform- and cell-type-dependent manner.

Background: The dysregulation of RNA binding proteins (RBPs) is involved in tumorigenesis and progression. However, information on the overall function of RNA binding proteins in Uterine Corpus Endometrial Carcinoma (UCEC) remains to be studied. This study aimed to explore Uterine Corpus Endometrial Carcinoma-associated molecular mechanisms and develop an RNA-binding protein-associated prognostic model.Methods: Differently expressed RNA binding proteins were identified between Uterine Corpus Endometrial Carcinoma tumor tissues and normal tissues by R packages (DESeq2, edgeR) from The Cancer Genome Atlas (TCGA) database. Hub RBPs were subsequently identified by univariate and multivariate Cox regression analyses. The cBioPortal platform, R packages (ggplot2), Human Protein Atlas (HPA), and TIMER online database were used to explore the molecular mechanisms of Uterine Corpus Endometrial Carcinoma. Kaplan-Meier (K-M), Area Under Curve (AUC), and the consistency index (c-index) were used to test the performance of our model.Results: We identified 128 differently expressed RNA binding proteins between Uterine Corpus Endometrial Carcinoma tumor tissues and normal tissues. Seven RNA binding proteins genes (NOP10, RBPMS, ATXN1, SBDS, POP5, CD3EAP, ZC3H12C) were screened as prognostic hub genes and used to construct a prognostic model. Such a model may be able to predict patient prognosis and acquire the best possible treatment. Further analysis indicated that, based on our model, the patients in the high-risk subgroup had poor overall survival (OS) compared to those in the low-risk subgroup. We also established a nomogram based on seven RNA binding proteins. This nomogram could inform individualized diagnostic and therapeutic strategies for Uterine Corpus Endometrial Carcinoma.Conclusion: Our work focused on systematically analyzing a large cohort of Uterine Corpus Endometrial Carcinoma patients in the The Cancer Genome Atlas database. We subsequently constructed a robust prognostic model based on seven RNA binding proteins that may soon inform individualized diagnosis and treatment.

BackgroundThe dysregulation of RNA binding proteins (RBPs) is involved in tumorigenesis and progression. However, information on the overall function of RBPs in Uterine Corpus Endometrial Carcinoma (UCEC) remains to be studied. The aims of this study were to explore the associated molecular mechanisms and to develop an RNA binding protein-associated prognostic model for UCEC.MethodsBased on The Cancer Genome Atlas (TCGA) database, differentially expressed RBPs were identified between UCEC tumor tissues and normal tissues. Hub RBPs were then found by univariate and multivariate Cox regression analysis. The cBioPortal platform, R packages (DESeq2, edgeR and ggplot2) and The Human Protein Atlas (HPA) online database were used to explore the molecular mechanisms of UCEC. Kaplan-Meier (K-M), Area Under Curve (AUC), and the consistency index (c-index) were used to test the performance of our model.ResultsIn total, 128 differentially expressed RBPs between UCEC tumor tissues and normal tissues were identified. Seven RBP genes (NOP10, RBPMS, ATXN1, SBDS, POP5, CD3EAP, ZC3H12C) were screened as prognostic hub genes and used to construct a prognostic model, which are particularly important to prospectively predict patient prognosis and help them get treatment accordingly. Further analysis indicated that the patients in the high-risk subgroup had poor overall survival (OS) compared to those in low-risk subgroup based on the model. We also established a nomogram based on seven RBPs, which potentially improved individualized diagnostic and therapeutic strategies for UCEC.ConclusionOur work focused on the systematic analysis of a large cohort of patients in the TCGA database, which allowed us to construct a robust prognostic model based on seven RBPs, that may be of great value in clinical applications.