Heyue Jin’s research while affiliated with Anhui Medical University and other places

Small for gestational age (SGA) infants affected by placental insufficiency are exposed to the risk of stillbirth and long-term complications. Based on RNA-seq and mass spectrometry, we identified dysregulated RNAs and proteins from the comparisons of SGA placental tissues and controls. We revealed two SGA-relevant co-expression modules (SRMs) that also significantly distinguished SGA from controls. Then we performed an integrated analysis of transcriptomic and proteomic profiles to trace their links to SGA as well as their significant correlations. For the core functional molecules we screened, we revealed their potential upstream regulators and validated them experimentally in an independent cohort. Overall, we pointed out insights into different molecular pathways for the pathological mechanisms of SGA and indicated potential target molecules that may be drivers of placental aberrations in the SGA infants.

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.

Gastrointestinal (GI) cancer includes many cancer types, such as esophageal, liver, gastric, pancreatic, and colorectal cancer. As the cornerstone of personalized medicine for GI cancer, liquid biopsy based on noninvasive biomarkers provides promising opportunities for early diagnosis and dynamic treatment management. Recently, a growing number of studies have demonstrated the potential of cell‐free RNA (cfRNA) as a new type of noninvasive biomarker in body fluids, such as blood, saliva, and urine. Meanwhile, transcriptomes based on high‐throughput RNA detection technologies keep discovering new cfRNA biomarkers. In this review, we introduce the origins and applications of cfRNA, describe its detection and qualification methods in liquid biopsy, and summarize a comprehensive list of cfRNA biomarkers in different GI cancer types. Moreover, we also discuss perspective studies of cfRNA to overcome its current limitations in clinical applications. This article is categorized under: RNA in Disease and Development > RNA in Disease

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.

Non-coding RNAs (ncRNAs) are transcribed from the genome and do not encode proteins. In recent years, ncRNAs have attracted increasing attention as critical participants in gene regulation and disease pathogenesis. Different categories of ncRNAs, which mainly include microRNAs (miRNAs), long non-coding RNAs (lncRNAs), and circular RNAs (circRNAs), are involved in the progression of pregnancy, while abnormal expression of placental ncRNAs impacts the onset and development of adverse pregnancy outcomes (APOs). Therefore, we reviewed the current status of research on placental ncRNAs and APOs to further understand the regulatory mechanisms of placental ncRNAs, which provides a new perspective for treating and preventing related diseases.

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.

Background The 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.Methods Based 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.Conclusion Our 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.