Quantitative Proteomics Analysis of Sporadic Medullary Thyroid Cancer Reveals FN1 as a Potential Novel Candidate Prognostic Biomarker

The Ubiquitin Ligase CHIP Accelerates Papillary Thyroid Carcinoma Metastasis via the Transgelin-Matrix Metalloproteinase-9 Axis

The carboxyl-terminus of Hsp70-interacting protein (CHIP) plays crucial roles in tumorigenesis and immunity, with previous studies suggesting a double-edged sword in thyroid cancer. However, its precise functions and underlying molecular mechanisms in thyroid cancer remained unclear. Here, we demonstrate through immunohistochemistry (IHC) that CHIP expression progressively increases from normal thyroid tissue to primary papillary thyroid carcinoma (PTC) and lymph node metastases, with CHIP levels positively correlating with lymph node metastasis (P = 0.006). Moreover, CHIP overexpression enhanced thyroid cancer cell migration and invasion without significantly affecting cell viability. Tandem mass tag (TMT)-based LC-MS/MS analysis revealed that CHIP-regulated differentially expressed proteins, notably transgelin, were predominantly associated with metastasis-related pathways. Western blot, qPCR, and TCGA-THCA cohort data confirmed that CHIP regulates transgelin expression at the protein but not the genetic level. Mechanistically, CHIP promotes extracellular matrix degradation through the transgelin-matrix metalloproteinase-9 (MMP-9) axis, thereby facilitating PTC progression. Collectively, our findings indicate that CHIP expression was closely related to the progression and metastasis of PTC, suggesting that CHIP functions as a novel tumor oncoprotein in PTC via the transgelin-MMP-9 signaling axis.

TGFBR3 inhibits progression of papillary thyroid cancer by inhibiting the PI3K/AKT pathway and EMT

Background

Transforming growth factor beta receptor III (TGFBR3) has been shown to play a tumor-suppressive role in a variety of cancers. However, its role in papillary thyroid cancer (PTC) remains unknown.

Method

TGFBR3 expression levels in PTC were analyzed utilizing The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) databases. Edu, wound healing, and Transwell assays were used to evaluate cell proliferation, migration, and invasion. Transcriptome sequencing, quantitative real-time reverse transcription-polymerase chain reaction (qRT-PCR), and Western blotting were used to detect the underlying mechanism of TGFBR3 in PTC progression.

Result

This study demonstrated that TGFBR3 expression was significantly down-regulated in PTC compared to normal thyroid tissues. Low expression of TGFBR3 was associated with poor prognosis of patients with PTC. Furthermore, TGFBR3 expression positively correlated with thyroid differentiation score. In investigating the biological impact of TGFBR3 overexpression in PTC cell lines, we found that the proliferation, migration, and invasion of PTC cells were significantly inhibited in response to TGFBR3 overexpression. Moreover, we also demonstrated that overexpression of TGFBR3 inhibited the PI3K/AKT pathway and epithelial-mesenchymal transformation processes. Lastly, TGFBR3 expression was found to be involved in tumor immune infiltration, highlighting its potential influence on immune dynamics within the tumor microenvironment in PTC.

Conclusion

TGFBR3 plays a tumor-suppressive role in PTC progression by inhibiting the PI3K/AKT pathway and epithelial mesenchymal transformation.

Single-cell disulfidptosis regulator patterns guide intercellular communication of tumor microenvironment that contribute to kidney renal clear cell carcinoma progression and immunotherapy

Background

Disulfidptosis, an emerging type of programmed cell death, plays a pivotal role in various cancer types, notably impacting the progression of kidney renal clear cell carcinoma (KIRC) through the tumor microenvironment (TME). However, the specific involvement of disulfidptosis within the TME remains elusive.

Methods

Analyzing 41,784 single cells obtained from seven samples of KIRC through single-cell RNA sequencing (scRNA-seq), this study employed nonnegative matrix factorization (NMF) to assess 24 disulfidptosis regulators. Pseudotime analysis, intercellular communication mapping, determination of transcription factor activities (TFs), and metabolic profiling of the TME subgroup in KIRC were conducted using Monocle, CellChat, SCENIC, and scMetabolism. Additionally, public cohorts were utilized to predict prognosis and immune responses within the TME subgroup of KIRC.

Results

Through NMF clustering and differential expression marker genes, fibroblasts, macrophages, monocytes, T cells, and B cells were categorized into four to six distinct subgroups. Furthermore, this investigation revealed the correlation between disulfidptosis regulatory factors and the biological traits, as well as the pseudotime trajectories of TME subgroups. Notably, disulfidptosis-mediated TME subgroups (DSTN+CD4T-C1 and FLNA+CD4T-C2) demonstrated significant prognostic value and immune responses in patients with KIRC. Multiple immunohistochemistry (mIHC) assays identified marker expression within both cell clusters. Moreover, CellChat analysis unveiled diverse and extensive interactions between disulfidptosis-mediated TME subgroups and tumor epithelial cells, highlighting the TNFSF12-TNFRSF12A ligand-receptor pair as mediators between DSTN+CD4T-C1, FLNA+CD4T-C2, and epithelial cells.

Conclusion

Our study sheds light on the role of disulfidptosis-mediated intercellular communication in regulating the biological characteristics of the TME. These findings offer valuable insights for patients with KIRC, potentially guiding personalized immunotherapy approaches.

Paving the path toward multi-omics approaches in the diagnostic challenges faced in thyroid pathology

INTRODUCTION

Despite advancements in diagnostic methods, the classification of indeterminate thyroid nodules still poses diagnostic challenges not only in pre-surgical evaluation but even after histological evaluation of surgical specimens. Proteomics, aided by mass spectrometry and integrated with artificial intelligence and machine learning algorithms, shows great promise in identifying diagnostic markers for thyroid lesions.

AREAS COVERED

This review provides in-depth exploration of how proteomics has contributed to the understanding of thyroid pathology. It discusses the technical advancements related to immunohistochemistry, genetic and proteomic techniques, such as mass spectrometry, which have greatly improved sensitivity and spatial resolution up to single-cell level. These improvements allowed the identification of specific protein signatures associated with different types of thyroid lesions.

EXPERT COMMENTARY

Among all the proteomics approaches, spatial proteomics stands out due to its unique ability to capture the spatial context of proteins in both cytological and tissue thyroid samples. The integration of multi-layers of molecular information combining spatial proteomics, genomics, immunohistochemistry or metabolomics and the implementation of artificial intelligence and machine learning approaches, represent hugely promising steps forward toward the possibility to uncover intricate relationships and interactions among various molecular components, providing a complete picture of the biological landscape whilst fostering thyroid nodule diagnosis.

Integrated analysis of transcription factor-mRNA-miRNA regulatory network related to immune characteristics in medullary thyroid carcinoma

Background

Medullary thyroid carcinoma (MTC), a thyroid C cell-derived malignancy, is poorly differentiated and more aggressive than papillary, follicular and oncocytic types of thyroid cancer. The current therapeutic options are limited, with a third of population suffering resistance. The differential gene expression pattern among thyroid cancer subtypes remains unclear. This study intended to explore the exclusive gene profile of MTC and construct a comprehensive regulatory network via integrated analysis, to uncover the potential key biomarkers.

Methods

Multiple datasets of thyroid and other neuroendocrine tumors were obtained from GEO and TCGA databases. Differentially expressed genes (DEGs) specific in MTC were identified to construct a transcription factor (TF)-mRNA-miRNA network. The impact of the TF-mRNA-miRNA network on tumor immune characteristics and patient survival was further explored by single-sample GSEA (ssGSEA) and ESTIMATE algorithms, as well as univariate combined with multivariate analyses. RT-qPCR, cell viability and apoptosis assays were performed for in vitro validation.

Results

We identified 81 genes upregulated and 22 downregulated in MTC but not in other types of thyroid tumor compared to the normal thyroid tissue. According to the L1000CDS2 database, potential targeting drugs were found to reverse the expressions of DEGs, with panobinostat (S1030) validated effective for tumor repression in MTC by in vitro experiments. The 103 DEGs exclusively seen in MTC were involved in signal release, muscle contraction, pathways of neurodegeneration diseases, neurotransmitter activity and related amino acid metabolism, and cAMP pathway. Based on the identified 15 hub genes, a TF-mRNA-miRNA linear network, as well as REST-cored coherent feed-forward loop networks, namely REST-KIF5C-miR-223 and REST-CDK5R2-miR-130a were constructed via online prediction and validation by public datasets and our cohort. Hub-gene, TF and miRNA scores in the TF-mRNA-miRNA network were related to immune score, immune cell infiltration and immunotherapeutic molecules in MTC as well as in neuroendocrine tumor of lung and neuroblastoma. Additionally, a high hub-gene score or a low miRNA score indicated good prognoses of neuroendocrine tumors.

Conclusion

The present study uncovers underlying molecular mechanisms and potential immunotherapy-related targets for the pathogenesis and drug discovery of MTC.

Circ_0101692 knockdown retards the development of clear cell renal cell carcinoma through miR-384/FN1 pathway

PURPOSE

Circular RNA_0101692 (circ_0101692) is overexpressed in clear cell renal cell carcinoma (ccRCC) by microarray analyses. However, its function and action mechanism in ccRCC tumorigenesis is still elusive.

METHODS

Western blotting and qRT-PCR were executed to assess the circ_0101692, miR-384 and FN1 expression in ccRCC cells and tissues. Target relationships among them were determined via dual luciferase reporter and/or RNA immunoprecipitation assays. Cell proliferation was evaluated by CCK-8 assay. Caspase-3 activity assay was utilized to analyze cell apoptosis. To find out whether ccRCC cells might migrate, a transwell assay was performed. To assess the effects of circ_0101692 on tumor development in vivo, a mouse xenograft model was used.

RESULTS

High expression of circ_0101692 and FN1, and decreased miR-384 were determined in ccRCC. Cell growth, migration and viability were decreased whereas cell apoptosis was stimulated when circ_0101692 was knockdown. miR-384 inhibitor transfection attenuated the inhibiting impacts of circ_0101692 silencing on ccRCC cell progression. FN1 deletion further inverted the cancer-promoting effect of miR-384 downregulation on cell viability and migration. In addition, circ_0101692 could sponge miR-384 to relieve the inhibition of miR-384 on FN1 in ccRCC.

CONCLUSIONS

Circ_0101692 targeted miR-384/FN1 axis to facilitate cell proliferation, migration and repress apoptosis, thereby accelerating the development of ccRCC. This points out that circ_0101692/miR-384/FN1 axis might be a prospective target implemented for the future treatment of ccRCC.

Identification of potential diagnostic and prognostic biomarkers for papillary thyroid microcarcinoma (PTMC) based on TMT-labeled LC-MS/MS and machine learning

OBJECTIVES

To explore the molecular mechanisms underlying aggressive progression of papillary thyroid microcarcinoma and identify potential biomarkers.

METHODS

Samples were collected and sequenced using tandem mass tag-labeled liquid chromatography-tandem mass spectrometry. Differentially expressed proteins (DEPs) were identified and further analyzed using Mfuzz and protein-protein interaction analysis (PPI). Parallel reaction monitoring (PRM) and immunohistochemistry (IHC) were performed to validate the DEPs.

RESULTS

Five thousand, two hundred and three DEPs were identified and quantified from the tumor/normal comparison group or the N1/N0 comparison group. Mfuzz analysis showed that clusters of DEPs were enriched according to progressive status, followed by normal tissue, tumors without lymphatic metastases, and tumors with lymphatic metastases. Analysis of PPI revealed that DEPs interacted with and were enriched in the following metabolic pathways: apoptosis, tricarboxylic acid cycle, PI3K-Akt pathway, cholesterol metabolism, pyruvate metabolism, and thyroid hormone synthesis. In addition, 18 of the 20 target proteins were successfully validated with PRM and IHC in another 20 paired validation samples. Based on machine learning, the five proteins that showed the best performance in discriminating between tumor and normal nodules were PDLIM4, ANXA1, PKM, NPC2, and LMNA. FN1 performed well in discriminating between patients with lymph node metastases (N1) and N0 with an AUC of 0.690. Finally, five validated DEPs showed a potential prognostic role after examining The Cancer Genome Atlas database: FN1, IDH2, VDAC1, FABP4, and TG. Accordingly, a nomogram was constructed whose concordance index was 0.685 (confidence interval: 0.645-0.726).

CONCLUSIONS

PDLIM4, ANXA1, PKM, NPC2, LMNA, and FN1 are potential diagnostic biomarkers. The five-protein nomogram could be a prognostic biomarker.

Artificial intelligence defines protein-based classification of thyroid nodules

Determination of malignancy in thyroid nodules remains a major diagnostic challenge. Here we report the feasibility and clinical utility of developing an AI-defined protein-based biomarker panel for diagnostic classification of thyroid nodules: based initially on formalin-fixed paraffin-embedded (FFPE), and further refined for fine-needle aspiration (FNA) tissue specimens of minute amounts which pose technical challenges for other methods. We first developed a neural network model of 19 protein biomarkers based on the proteomes of 1724 FFPE thyroid tissue samples from a retrospective cohort. This classifier achieved over 91% accuracy in the discovery set for classifying malignant thyroid nodules. The classifier was externally validated by blinded analyses in a retrospective cohort of 288 nodules (89% accuracy; FFPE) and a prospective cohort of 294 FNA biopsies (85% accuracy) from twelve independent clinical centers. This study shows that integrating high-throughput proteomics and AI technology in multi-center retrospective and prospective clinical cohorts facilitates precise disease diagnosis which is otherwise difficult to achieve by other methods.

A Community-Driven, Openly Accessible Molecular Pathway Integrating Knowledge on Malignant Pleural Mesothelioma

Malignant pleural mesothelioma (MPM) is a highly aggressive malignancy mainly triggered by exposure to asbestos and characterized by complex biology. A significant body of knowledge has been generated over the decades by the research community which has improved our understanding of the disease toward prevention, diagnostic opportunities and new treatments. Omics technologies are opening for additional levels of information and hypotheses. Given the growing complexity and technological spread of biological knowledge in MPM, there is an increasing need for an integrating tool that may allow scientists to access the information and analyze data in a simple and interactive way. We envisioned that a platform to capture this widespread and fast-growing body of knowledge in a machine-readable and simple visual format together with tools for automated large-scale data analysis could be an important support for the work of the general scientist in MPM and for the community to share, critically discuss, distribute and eventually advance scientific results. Toward this goal, with the support of experts in the field and informed by existing literature, we have developed the first version of a molecular pathway model of MPM in the biological pathway database WikiPathways. This provides a visual and interactive overview of interactions and connections between the most central genes, proteins and molecular pathways known to be involved or altered in MPM. Currently, 455 unique genes and 247 interactions are included, derived after stringent manual curation of an initial 39 literature references. The pathway model provides a directly employable research tool with links to common databases and repositories for the exploration and the analysis of omics data. The resource is publicly available in the WikiPathways database (Wikipathways : WP5087) and continues to be under development and curation by the community, enabling the scientists in MPM to actively participate in the prioritization of shared biological knowledge.

Asporin Interacts With HER2 to Promote Thyroid Cancer Metastasis via the MAPK/EMT Signaling Pathway

Approximately 85% of histological subtypes of thyroid cancer are papillary thyroid cancer (PTC), and the morbidity and mortality of PTC patients rapidly increased due to lymph node metastases or distant metastasis. Therefore, it needs to distill an enhanced understanding of the pathogenesis of PTC patients with lymph node metastases or distant metastasis. We employed the TMT-based quantitative proteomics approach to identify and analyze differentially expressed proteins in PTC with different degrees of lymph node metastases. Compared with paired normal tissues, asporin is overexpressed in PTC-N0, PTC-N1a, and PTC-N1b tumorous tissues via proteomics, western blotting, and immunohistochemistry assays. Functionally, asporin is mainly expressed in the extracellular matrix, cell membrane, and cytoplasm of PTC tumorous tissues, and promotes thyroid cancer cell proliferation, migration, and invasion. Mechanistically, asporin, interacting with HER2, co-localizes HER2 on the cell membrane and cytoplasm, and the asporin/HER2/SRC/EGFR axis upregulate the expression of EMT-activating transcription factors through the MAPK signaling pathway. Clinically, asporin can be regarded as a serological biomarker to identify PTC patients with or without lymph node metastasis, and high expression of asporin in PTC tumorous tissues is a risk factor for poor prognosis.

The Overexpression of Fibronectin 1 Promotes Cancer Progression and Associated with M2 Macrophages Polarization in Head and Neck Squamous Cell Carcinoma Patients

Purpose

This study aimed to investigate the biological roles of fibronectin 1 (FN1) in head and neck squamous cell carcinoma (HNSCC) and its effects on macrophage M2 polarization.

Methods

We analyzed FN1 expression pattern and examined its clinical relevance in HNSCC progression by bioinformatic analysis. Small interfering RNA (siRNA) was utilized to silence FN1 in HNSCC cells. Cell counting kit-8 (CCK-8) assay, colony formation assay, Transwell assay and wound healing assay were performed to reveal the effect of FN1 on malignant behaviors of HNSCC cells. Moreover, a co-culture model of macrophages and HNSCC cells was established to investigate whether FN1 induce macrophage M2 polarization. Finally, we used bioinformatic methods to explore the possible FN1-related pathways in HNSCC.

Results

FN1 is significantly overexpressed in HNSCC patients and has been obviously correlated with higher pathological stage and poor prognosis. Downregulation of FN1 suppressed the proliferation, migration and invasion of HNSCC cells, and inhibited macrophage M2 polarization in vitro. In addition, “PI3K-Akt” and “MAPK” signaling pathways may be involved in the malignant process of FN1 in HNSCC.

Conclusion

The overexpression of FN1 promotes HNSCC progression and induces macrophages M2 polarization. FN1 may serve as a promising prognostic biomarker and therapeutic target in HNSCC.

GATA6‑induced FN1 activation promotes the proliferation, invasion and migration of oral squamous cell carcinoma cells

GATA binding protein 6 (GATA6) is a transcription factor involved in cell fate decision making and tissue morphogenesis and serves a significant role in the progression of a number of types of cancer. The present study aimed to investigate the role and mechanisms underlying the effects of GATA6 in oral squamous cell carcinoma (OSCC). The expression levels of GATA6 were determined in a number of OSCC cell lines and the expression of GATA6 was knocked down to evaluate its role in the proliferation, invasion and migration of OSCC cells. Subsequently, the association between GATA6 and fibronectin 1 (FN1) was investigated using bioinformatics and further verified using dual‑luciferase reporter and chromosomal immunoprecipitation assays. Following the overexpression of FN1 in OSCC cells with GATA6 silencing, functional assays were performed to assess the mechanisms underlying GATA6 in OSCC progression. The results of the present study indicated that OSCC cells exhibited markedly upregulated expression levels of GATA6, while knockdown of GATA6 inhibited the proliferation, colony formation, invasion and migration of OSCC cells. In addition, GATA6 regulated FN1 expression levels by binding to the FN1 promoter. The suppressive effects of GATA6 knockdown on the proliferation, colony formation, invasion and migration of OSCC cells were abolished following FN1 overexpression. In conclusion, the findings of the present study demonstrated that GATA6 promoted the malignant development of OSCC cells by binding to the FN1 promotor. These results may contribute to further understanding the pathogenesis of OSCC and provide potential therapeutic targets for the clinical treatment of OSCC.

Over-Expression and Prognostic Significance of FN1, Correlating With Immune Infiltrates in Thyroid Cancer

Background

Thyroid cancer (THCA) is a malignancy affecting the endocrine system, which currently has no effective treatment due to a limited number of suitable drugs and prognostic markers.

Methods

Three Gene Expression Omnibus (GEO) datasets were selected to identify differentially expressed genes (DEGs) between THCA and normal thyroid samples using GEO2R tools of National Center for Biotechnology Information. We identified hub gene FN1 using functional enrichment and protein-protein interaction network analyses. Subsequently, we evaluated the importance of gene expression on clinical prognosis using The Cancer Genome Atlas (TCGA) database and GEO datasets. MEXPRESS was used to investigate the correlation between gene expression and DNA methylation; the correlations between FN1 and cancer immune infiltrates were investigated using CIBERSORT. In addition, we assessed the effect of silencing FN1 expression, using an in vitro cellular model of THCA. Immunohistochemical(IHC) was used to elevate the correlation between CD276 and FN1.

Results

FN1 expression was highly correlated with progression-free survival and moderately to strongly correlated with the infiltration levels of M2 macrophages and resting memory CD4+ T cells, as well as with CD276 expression. We suggest promoter hypermethylation as the mechanism underlying the observed changes in FN1 expression, as 20 CpG sites in 507 THCA cases in TCGA database showed a negative correlation with FN1 expression. In addition, silencing FN1 expression suppressed clonogenicity, motility, invasiveness, and the expression of CD276 in vitro. The correlation between FN1 and CD276 was further confirmed by immunohistochemical.

Conclusion

Our findings show that FN1 expression levels correlate with prognosis and immune infiltration levels in THCA, suggesting that FN1 expression be used as an immunity-related biomarker and therapeutic target in THCA.

Comprehensive analysis of abnormal expression, prognostic value and oncogenic role of the hub gene FN1 in pancreatic ductal adenocarcinoma via bioinformatic analysis and in vitro experiments

Background

Pancreatic ductal adenocarcinoma (PDAC) is one of the most commonly diagnosed cancers with a poor prognosis worldwide. Although the treatment of PDAC has made great progress in recent years, the therapeutic effects are still unsatisfactory. Methods. In this study, we identified differentially expressed genes (DEGs) between PDAC and normal pancreatic tissues based on four Gene Expression Omnibus (GEO) datasets (GSE15471, GSE16515, GSE28735 and GSE71729). A protein–protein interaction (PPI) network was established to evaluate the relationship between the DEGs and to screen hub genes. The expression levels of the hub genes were further validated through the Gene Expression Profiling Interactive Analysis (GEPIA), ONCOMINE and Human Protein Atlas (HPA) databases, as well as the validation GEO dataset GSE62452. Additionally, the prognostic values of the hub genes were evaluated by Kaplan–Meier plotter and the validation GEO dataset GSE62452. Finally, the mechanistic roles of the most remarkable hub genes in PDAC were examined through in vitro experiments.

Results

We identified the following nine hub genes by performing an integrated bioinformatics analysis: COL1A1, COL1A2, FN1, ITGA2, KRT19, LCN2, MMP9, MUC1 and VCAN. All of the hub genes were significantly upregulated in PDAC tissues compared with normal pancreatic tissues. Two hub genes (FN1 and ITGA2) were associated with poor overall survival (OS) rates in PDAC patients. Finally, in vitro experiments indicated that FN1 plays vital roles in PDAC cell proliferation, colony formation, apoptosis and the cell cycle.

Conclusions

In summary, we identified two hub genes that are associated with the expression and prognosis of PDAC. The oncogenic role of FN1 in PDAC was first illustrated by performing an integrated bioinformatic analysis and in vitro experiments. Our results provide a fundamental contribution for further research aimed finding novel therapeutic targets for overcoming PDAC.

SPP1 and FN1 are significant gene biomarkers of tongue squamous cell carcinoma

Tongue squamous cell carcinoma (TSCC) is one of the most common malignant tumor types in the oral and maxillofacial region. The etiology and pathogenesis behind TSCC is complicated. In the present study, three gene expression profiles, namely GSE31056, GSE13601 and GSE78060, were downloaded from the Gene Expression Omnibus (GEO). The GEO2R online tool was utilized to identify differentially expressed genes (DEGs) between TSCC and normal tissue samples. Furthermore, a protein-protein interaction (PPI) network was constructed and hub genes were validated and analyzed. A total of 83 common DEGs were obtained in three datasets, including 48 upregulated and 35 downregulated genes. Pathway enrichment analysis indicated that DEGs were primarily enriched in cell adhesion, extracellular matrix (ECM) organization, and proteolysis. A total of 63 nodes and 218 edges were included in the PPI network. The top 11 candidate hub genes were acquired, namely plasminogen activator urokinase (PLAU), signal transducer and activator of transcription 1, C-X-C motif chemokine ligand 12, matrix metallopeptidase (MMP) 13, secreted phosphoprotein 1 (SPP1), periostin, MMP1, MMP3, fibronectin 1 (FN1), serpin family E member 1 and snail family transcriptional repressor 2. Overall, 83 DEGs and 11 hub genes were screened from TSCC and normal individuals using bioinformatics and microarray technology. These genes may be used as diagnostic and therapeutic biomarkers for TSCC. In addition, SPP1 and FNl were identified as potential biomarkers for the progression of TSCC.

A Five-Gene Prognostic Nomogram Predicting Disease-Free Survival of Differentiated Thyroid Cancer

Background

Differentiated thyroid cancer (DTC) is the most common type of thyroid tumor with a high recurrence rate. Here, we developed a nomogram to effectively predict postoperative disease-free survival (DFS) in DTC patients.

Methods

The mRNA expressions and clinical data of DTC patients were downloaded from the Cancer Genome Atlas (TCGA) and the Gene Expression Omnibus (GEO) database. Seventy percent of patients were randomly selected as the training dataset, and thirty percent of patients were classified into the testing dataset. Multivariate Cox regression analysis was adopted to establish a nomogram to predict 1-year, 3-year, and 5-year DFS rate of DTC patients.

Results

A five-gene signature comprised of TENM1, FN1, APOD, F12, and BTNL8 genes was established to predict the DFS rate of DTC patients. Results from the concordance index (C-index), area under curve (AUC), and calibration curve showed that both the training dataset and the testing dataset exhibited good prediction ability, and they were superior to other traditional models. The risk score and distant metastasis (M) of the five-gene signature were independent risk factors that affected DTC recurrence. A nomogram that could predict 1-year, 3-year, and 5-year DFS rate of DTC patients was established with a C-index of 0.801 (95% CI: 0.736, 0.866).

Conclusion

Our study developed a prediction model based on the gene expression and clinical characteristics to predict the DFS rate of DTC patients, which may be applied to more accurately assess patient prognosis and individualized treatment.

Downregulation of fibronectin 1 attenuates ATRA-induced inhibition of cell migration and invasion in neuroblastoma cells

Neuroblastoma (NB) is the most common malignant extra cranial solid tumors in children. It has been well established that retinoic acid (RA) inhibits proliferation of neuroblastoma (NB) by blocking cells at G1 phase of the cell cycle. Clinically, RA has been successfully used to treat NB patients. However, the precise mechanism underlying the potent action of RA-treated NB is not fully explored. In this work, we carried out a gene expression profiling by RNA sequencing on all-trans retinoic acid (ATRA)-treated NB cells. Cancer-related pathway enrichment and subsequent protein-protein interaction (PPI) network analysis identified fibronectin 1 (FN1) as one of the central molecules in the network, which was significantly upregulated during ATRA treatment. In addition, we found that although downregulation of FN1 had no significant effects on either cell proliferation or cell cycle distributions in the presence or absence of ATRA, it increased cell migration and invasion in NB cells and partially blocked ATRA-induced inhibition of cell migration and invasion in SY5Y NB cells. Consistent with this finding, FN1 expression levels in NB patients positively correlate with their overall survivals. Taken together, our data suggest that FN1 is a potential target for effective ATRA treatment on NB patients, likely by facilitating ATRA-induced inhibition of cell migration and invasion.

Identification of the EMT-Related Genes Signature for Predicting Occurrence and Progression in Thyroid Cancer

BACKGROUND

The detection rate of thyroid cancer (TC) has been continuously improved due to the development of detection technology. Epithelial-mesenchymal transition (EMT) is thought to be closely related to the malignant progression of tumors. However, the relationship between EMT-related genes (ERGs) characteristics and the diagnosis and prognosis of TC patients has not been studied.

METHODS

Four datasets from Gene Expression Omnibus (GEO) were used to perform transcriptomic profile analysis. The overlapping differentially expressed ERGs (DEERGs) were analyzed using the R package "limma". Then, the hub genes, which had a higher degree, were identified by the protein-protein interaction (PPI) network. Gene expression analysis between the TC and normal data, the disease-free survival (DFS) analysis of TC patients from The Cancer Genome Atlas Thyroid Cancer (TCGA-THCA) cohort, function analysis, and immunohistochemistry (IHC) were performed to verify the importance of the hub genes. Finally, a prognostic risk scoring was constructed to predict DFS in patients with the selected genes.

RESULTS

A total of 43 DEERGs were identified and 10 DEERGs were considered hub ERGs, which had a high degree of connectivity in the PPI network. Then, the differential expressions of FN1, ITGA2, and KIT between TC and normal tissues were verified in the TCGA-THCA cohort and their protein expressions were also verified by IHC. DFS analysis indicated upregulations of FN1 expression (P<0.01) and ITGA2 expression (P<0.01) and downregulation of KIT expression (P=0.01) increased risks of decreased DFS for TCGA-THCA patients. Besides, by building a prognostic risk scoring model, we found that the DFS of TCGA-THCA patients was significantly worse in high-risk groups.

CONCLUSION

In summary, these hub ERGs were potential biomarkers for diagnosis and prognosis of TC, which can provide a basis for further exploring the efficacy of EMT in patients with TC.

Weighted correlation network analysis identifies FN1, COL1A1 and SERPINE1 associated with the progression and prognosis of gastric cancer

BACKGROUND

Gastric cancer (GC) is one of the most deadliest tumours worldwide, and its prognosis remains poor.

OBJECTIVE

This study aims to identify and validate hub genes associated with the progression and prognosis of GC by constructing a weighted correlation network.

METHODS

The gene co-expression network was constructed by the WGCNA package based on GC samples and clinical data from the TCGA database. The module of interest that was highly related to clinical traits, including stage, grade and overall survival (OS), was identified. GO and KEGG pathway enrichment analyses were performed using the clusterprofiler package in R. Cytoscape software was used to identify the 10 hub genes. Differential expression and survival analyses were performed on GEPIA web resources and verified by four GEO datasets and our clinical gastric specimens. The receiver operating characteristic (ROC) curves of hub genes were plotted using the pROC package in R. The potential pathogenic mechanisms of hub genes were analysed using gene set enrichment analysis (GSEA) software.

RESULTS

A total of ten modules were detected, and the magenta module was identified as highly related to OS, stage and grade. Enrichment analysis of magenta module indicated that ECM-receptor interaction, focal adhesion, PI3K-Akt pathway, proteoglycans in cancer were significantly enriched. The PPI network identified ten hub genes, namely COL1A1, COL1A2, FN1, POSTN, THBS2, COL11A1, SPP1, MMP13, COMP, and SERPINE1. Three hub genes (FN1, COL1A1 and SERPINE1) were finally identified to be associated with carcinogenicity and poor prognosis of GC, and all were independent risk factors for GC. The area under the curve (AUC) values of FN1, COL1A1 and SERPINE1 for the prediction of GC were 0.702, 0.917 and 0.812, respectively. GSEA showed that three hub genes share 15 common upregulated biological pathways, including hypoxia, epithelial mesenchymal transition, angiogenesis, and apoptosis.

CONCLUSION

We identified FN1, COL1A1 and SERPINE1 as being associated with the progression and poor prognosis of GC.

Identification of Potential BRAF Inhibitor Joint Therapy Targets in PTC based on WGCAN and DCGA

As the most common mutation in papillary thyroid cancer (PTC), B-type Raf kinase V600E mutation (BRAFV600E ) has become an important target for the clinical treatment of PTC. However, the clinical application still faces the problem of resistance to BRAF inhibitors (BRAFi). Therefore, exploring BRAFV600E-associated prognostic factors to providing potential joint targets is important for combined targeted therapy with BRAFi. In this study, we combined transcript data and clinical information from 199 BRAF wild-type (BRAFWT ) patients and 283 BRAFV600E mutant patients collected from The Cancer Genome Atlas (TCGA), and screened 455 BRAFV600E- associated genes through differential analysis and weighted gene co-expression network analysis. Based on these BRAFV600E -associated genes, we performed functional enrichment analysis and co-expression differential analysis and constructed a core co-expression network. Next, genes in the differential co-expression network were used to predict drugs for therapy in the crowd extracted expression of differential signatures (CREEDS) database, and the key genes were selected based on the hub co-expression network through survival analyses and receiver operating characteristic (ROC) curve analyses. Finally, we obtained eight BRAFV600E -associated biomarkers with both prognostic and diagnostic values as potential BRAFi joint targets, including FN1, MET, SLC34A2, NGEF, TBC1D2, PLCD3, PROS1, and NECTIN4. Among these genes, FN1, MET, PROS1, and TBC1D2 were validated through GEO database. Two novel biomarkers, PROS1 and TBC1D2, were further validated by qRT-PCR experiment. Besides, we obtained four potential targeted drugs that could be used in combination with BRAFi to treat PTC, including MET inhibitor, ERBB3 inhibitor, anti-NaPi2b antibody-drug conjugate, and carboplatin through literature review. The study provided potential drug targets for combination therapy with BRAFi for PTC to overcome the drug resistance for BRAFi.

Transcriptomic Changes of Murine Visceral Fat Exposed to Intermittent Hypoxia at Single Cell Resolution

Intermittent hypoxia (IH) is a hallmark of obstructive sleep apnea (OSA) and induces metabolic dysfunction manifesting as inflammation, increased lipolysis and insulin resistance in visceral white adipose tissues (vWAT). However, the cell types and their corresponding transcriptional pathways underlying these functional perturbations are unknown. Here, we applied single nucleus RNA sequencing (snRNA-seq) coupled with aggregate RNA-seq methods to evaluate the cellular heterogeneity in vWAT following IH exposures mimicking OSA. C57BL/6 male mice were exposed to IH and room air (RA) for 6 weeks, and nuclei from vWAT were isolated and processed for snRNA-seq followed by differential expressed gene (DEGs) analyses by cell type, along with gene ontology and canonical pathways enrichment tests of significance. IH induced significant transcriptional changes compared to RA across 14 different cell types identified in vWAT. We identified cell-specific signature markers, transcriptional networks, metabolic signaling pathways, and cellular subpopulation enrichment in vWAT. Globally, we also identify 298 common regulated genes across multiple cellular types that are associated with metabolic pathways. Deconvolution of cell types in vWAT using global RNA-seq revealed that distinct adipocytes appear to be differentially implicated in key aspects of metabolic dysfunction. Thus, the heterogeneity of vWAT and its response to IH at the cellular level provides important insights into the metabolic morbidity of OSA and may possibly translate into therapeutic targets.

Current status of the prognostic molecular markers in medullary thyroid carcinoma

Medullary thyroid cancer (MTC) is a rare thyroid malignancy, which arises from parafollicular C-cells. It occurs in the hereditary or sporadic form. Hereditary type is a consequence of activation of the RET proto-oncogene by germline mutations, whereas about 80% of sporadic MTC tumors harbor somatic, mainly RET or rarely RAS mutations. According to the current ATA guidelines, a postoperative MTC risk stratification and long-term follow-up are mainly based on histopathological data, including tumor stage, the presence of lymph node and/or distant metastases (TNM classification), and serum concentration of two biomarkers: calcitonin (Ctn) and carcinoembryonic antigen (CEA). The type of RET germline mutation also correlates with MTC clinical characteristics. The most common and the best known RET mutation in sporadic MTC, localized at codon 918, is related to a more aggressive MTC course and poorer survival. However, even if histopathological or clinical features allow to predict a long-term prognosis, they are not sufficient to select the patients showing aggressive MTC courses requiring immediate treatment or those, who are refractory to different therapeutic methods. Besides the RET gene mutations, there are currently no other reliable molecular prognostic markers. This review summarizes the present data of genomic investigation on molecular prognostic factors in medullary thyroid cancer.

Identification of Potential Core Genes Associated With the Progression of Stomach Adenocarcinoma Using Bioinformatic Analysis

Purpose

Stomach adenocarcinoma (STAD) is one of the most frequently diagnosed cancer in the world with both high mortality and high metastatic capacity. Therefore, the present study aimed to investigate novel therapeutic targets and prognostic biomarkers that can be used for STAD treatment.

Materials and Methods

We acquired four original gene chip profiles, namely GSE13911, GSE19826, GSE54129, and GSE65801 from the Gene Expression Omnibus (GEO). The datasets included a total of 114 STAD tissues and 110 adjacent normal tissues. The GEO2R online tool and Venn diagram software were used to discriminate differentially expressed genes (DEGs). Gene ontology (GO) and Kyoto Encyclopedia of Gene and Genome (KEGG) enriched pathways were also performed for annotation and visualization with DEGs. The STRING online database was used to identify the functional interactions of DEGs. Subsequently, we selected the most significant DEGs to construct the protein-protein interaction (PPI) network and to reveal the core genes involved. Finally, the Kaplan-Meier Plotter online database and Gene Expression Profiling Interactive Analysis (GEPIA) were used to analyze the prognostic information of the core DEGs.

Results

A total of 114 DEGs (35 upregulated and 79 downregulated) were identified, which were abnormally expressed in the GEO datasets. GO analysis demonstrated that the majority of the upregulated DEGs were significantly enriched in collagen trimer, cell adhesion, and identical protein binding. The downregulated DEGs were involved in extracellular space, digestion, and inward rectifier potassium channel activity. Signaling pathway analysis indicated that upregulated DEGs were mainly enriched in receptor interaction, whereas downregulated DEGs were involved in gastric acid secretion. A total of 80 DEGs were screened into the PPI network complex, and one of the most important modules with a high degree was detected. Furthermore, 10 core genes were identified, namely COL1A1, COL1A2, FN1, COL5A2, BGN, COL6A3, COL12A1, THBS2, CDH11, and SERPINH1. Finally, the results of the prognostic information further demonstrated that all 10 core genes exhibited significantly higher expression in STAD tissues compared with that noted in normal tissues.

Conclusion

The multiple molecular mechanisms of these novel core genes in STAD are worthy of further investigation and may reveal novel therapeutic targets and biomarkers for STAD treatment.

Novel circulating protein biomarkers for thyroid cancer determined through data-independent acquisition mass spectrometry

Background

Distinguishing between different types of thyroid cancers (TC) remains challenging in clinical laboratories. As different tumor types require different clinical interventions, it is necessary to establish new methods for accurate diagnosis of TC.

Methods

Proteomic analysis of the human serum was performed through data-independent acquisition mass spectrometry for 29 patients with TC (stages I–IV): 13 cases of papillary TC (PTC), 10 cases of medullary TC (MTC), and six cases follicular TC (FTC). In addition, 15 patients with benign thyroid nodules (TNs) and 10 healthy controls (HCs) were included in this study. Subsequently, 17 differentially expressed proteins were identified in 291 patients with TC, including 247 with PTC, 38 with MTC, and six with FTC, and 69 patients with benign TNs and 176 with HC, using enzyme-linked immunosorbent assays.

Results

In total, 517 proteins were detected in the serum samples using an Orbitrap Q-Exactive-plus mass spectrometer. The amyloid beta A4 protein, apolipoprotein A-IV, gelsolin, contactin-1, gamma-glutamyl hydrolase, and complement factor H-related protein 1 (CFHR1) were selected for further analysis. The median serum CFHR1 levels were significantly higher in the MTC and FTC groups than in the PTC and control groups (P < 0.001). CFHR1 exhibited higher diagnostic performance in distinguishing patients with MTC from those with PTC (P < 0.001), with a sensitivity of 100.0%, specificity of 85.08%, area under the curve of 0.93, and detection cut-off of 0.92 ng/mL.

Conclusion

CFHR1 may serve as a novel biomarker to distinguish PTC from MTC with high sensitivity and specificity.

Identifying hub genes of papillary thyroid carcinoma in the TCGA and GEO database using bioinformatics analysis

Background

Thyroid carcinoma (THCA) is a common endocrine malignant tumor. Papillary carcinoma with low degree of malignancy and good prognosis is the most common. It can occur at any age, but it is more common in young adults. Although the mortality rate is decreased due to early diagnosis, the survival rate varies depending on the type of tumor. Therefore, the purpose of this study is to identify hub biomarkers and novel therapeutic targets for THCA.

Methods

The GSE3467, GSE3678, GSE33630 and GSE53157 were obtained from the GEO database, including 100 thyroid tumors and 64 normal tissues to obtain the intersection of differentially expressed genes, and a protein-protein interaction network was constructed to obtain the HUB gene. The corresponding overall survival information from The Cancer Genome Atlas Project-THCA was then included in this research. The signature mechanism was studied by analyzing the gene ontology and the Kyoto Encyclopedia of Genes and Genome database.

Results

In this research, we identified eight candidate genes (FN1, CCND1, CDH2, CXCL12, MET, IRS1, DCN and FMOD) from the network. Also, expression verification and survival analysis of these candidate genes based on the TCGA database indicate the robustness of the above results. Finally, our hospital samples validated the expression levels of these genes.

Conclusion

The research identified eight mRNA (four up–regulated and four down–regulated) which serve as signatures and could be a potential prognostic marker of THCA.

New Classification of Macrophages in Plaques: a Revolution

PURPOSE

Macrophages play vital roles in the development of atherosclerosis in responding to lipid accumulation and inflammation. Macrophages were classified as inflammatory (M1) and alternatively activated (M2) macrophage types based on results of in vitro experiments. On the other hand, the composition of macrophages in vivo is more complex and remains unresolved. This review summarizes the transcriptional variations of macrophages in atherosclerosis plaques that were discovered by single-cell RNA sequencing (scRNA-seq) to better understand their contribution to atherosclerosis.

RECENT FINDINGS

ScRNA-seq provides a more detailed transcriptional landscape of macrophages in atherosclerosis, which challenges the traditional view. By mining the data of GSE97310, we discovered the transcriptional variations of macrophages in LDLR^-/- mice that were fed with high-fat diet (HFD) for 11 and 20 weeks. Cells were represented in a two-dimensional tSNE plane and clusters were identified and annotated via Seurat and SingleR respectively, which were R toolkits for single-cell genomics. The results showed that in healthy conditions, Trem2^hi (high expression of triggering receptors expressed on myeloid cells 2)-positive, inflammatory, and resident-like macrophages make up 68%, 18%, and 6% of total macrophages respectively. When mice were fed with HFD for 11 weeks, Trem2^hi, monocytes, and monocyte-derived dendritic cells take possession of 40%, 18%, and 17% of total macrophages respectively. After 20 weeks of HFD feeding, Trem2^hi, inflammatory, and resident-like macrophages occupied 12%, 37%, and 35% of total macrophages respectively. The phenotypes of macrophages are very different from the previous studies. In general, Trem2^hi macrophages are the most abundant population in healthy mice, while the proportion of monocytes increases after 11 weeks of HFD. Most importantly, inflammatory and resident-like macrophages make up 70% of the macrophage populations after 20 weeks of HFD. These strongly indicate that inflammatory and resident-like macrophages promote the progression of atherosclerosis plaques.

In silico analysis of the immune microenvironment in bladder cancer

BACKGROUND

Infiltrating immune and stromal cells are vital components of the bladder cancer (BC) microenvironment, which can significantly affect BC progression and outcome. However, the contribution of each subset of tumour-infiltrating immune cells is unclear. The objective of this study was to perform cell phenotyping and transcriptional profiling of the tumour immune microenvironment and analyse the association of distinct cell subsets and genes with BC prognosis.

METHODS

Clinical data of 412 patients with BC and 433 transcription files for normal and cancer tissues were downloaded from The Cancer Genome Atlas. The CIBERSORT algorithm was used to determine the relative abundance of 22 immune cell types in each sample and the ESTIMATE algorithm was used to identify differentially expressed genes within the tumour microenvironment of BC, which were subjected to functional enrichment and protein-protein interaction (PPI) analyses. The association of cell subsets and differentially expressed genes with patient survival and clinical parameters was examined by Cox regression analysis and the Kaplan-Meier method.

RESULTS

Resting natural killer cells and activated memory CD4+ and CD8+ T cells were associated with favourable patient outcome, whereas resting memory CD4+ T cells were associated with poor outcome. Differential expression analysis revealed 1334 genes influencing both immune and stromal cell scores; of them, 97 were predictive of overall survival in patients with BC. Among the top 10 statistically significant hub genes in the PPI network, CXCL12, FN1, LCK, and CXCR4 were found to be associated with BC prognosis.

CONCLUSION

Tumour-infiltrating immune cells and cancer microenvironment-related genes can affect the outcomes of patients and are likely to be important determinants of both prognosis and response to immunotherapy in BC.

MetaOmGraph: a workbench for interactive exploratory data analysis of large expression datasets

The diverse and growing omics data in public domains provide researchers with tremendous opportunity to extract hidden, yet undiscovered, knowledge. However, the vast majority of archived data remain unused. Here, we present MetaOmGraph (MOG), a free, open-source, standalone software for exploratory analysis of massive datasets. Researchers, without coding, can interactively visualize and evaluate data in the context of its metadata, honing-in on groups of samples or genes based on attributes such as expression values, statistical associations, metadata terms and ontology annotations. Interaction with data is easy via interactive visualizations such as line charts, box plots, scatter plots, histograms and volcano plots. Statistical analyses include co-expression analysis, differential expression analysis and differential correlation analysis, with significance tests. Researchers can send data subsets to R for additional analyses. Multithreading and indexing enable efficient big data analysis. A researcher can create new MOG projects from any numerical data; or explore an existing MOG project. MOG projects, with history of explorations, can be saved and shared. We illustrate MOG by case studies of large curated datasets from human cancer RNA-Seq, where we identify novel putative biomarker genes in different tumors, and microarray and metabolomics data from Arabidopsis thaliana. MOG executable and code: http://metnetweb.gdcb.iastate.edu/ and https://github.com/urmi-21/MetaOmGraph/.

Clinico-pathologic and dynamic prognostic factors in sporadic and familial medullary thyroid carcinoma: an Israeli multi-center study

OBJECTIVE

Multiple clinical, pathological and biochemical variables, including the response to initial treatment, are associated with medullary thyroid carcinoma (MTC) prognosis. Studies that include separate analyses of familial and sporadic MTC patients followed for long period are scarce. This study evaluated the association between baseline clinico-pathologic variables and response to initial treatment and short- and long-term disease outcomes in sporadic and familial MTC.

METHODS

Patients treated for MTC at four tertiary medical centers were retrospectively analyzed. Clinical and pathological data were collected. The outcomes measured included disease persistence 1 year after diagnosis, disease persistence at last follow-up, disease-related mortality (DRM) and all-cause mortality.

RESULTS

The study enrolled 193 patients (mean age: 48.9 ± 18.7, 44.7% males), of whom 18.1% were familial cases. The mean follow-up period was 10.1 ± 9.4 years (8.5 ± 8.1 in sporadic and 16.9 ± 11.6 in familial MTC). Disease persistence 1-year after diagnosis and at last follow-up was detected in 56.1 and 60.4% patients, respectively. All-cause and DRM were 28.5 and 12.6%, respectively. Extra-thyroidal extension (ETE) and distant metastases (DM) were associated with disease persistence at last follow-up. ETE and DM were also significantly associated with DRM. Complete remission 1 year after diagnosis had high correlation with no evidence of disease at last follow-up (Cramer's V measure of association 0.884, P < 0.001) and with 100% disease-specific survival (Cramer's V measure of association 0.38, P < 0.001).

CONCLUSIONS

Apart from clinico-pathologic parameters, close correlation was found between 1-year status and long-term prognosis. These results underscore the importance of combining classical and dynamic factors for both sporadic and familial MTC prognostication and treatment decision making.

Identification of key genes and pathways of thyroid cancer by integrated bioinformatics analysis

Thyroid cancer is a common endocrine malignancy with a rapidly increasing incidence worldwide. Although its mortality is steady or declining because of earlier diagnoses, its survival rate varies because of different tumour types. Thus, the aim of this study was to identify key biomarkers and novel therapeutic targets in thyroid cancer. The expression profiles of GSE3467, GSE5364, GSE29265 and GSE53157 were downloaded from the Gene Expression Omnibus database, which included a total of 97 thyroid cancer and 48 normal samples. After screening significant differentially expressed genes (DEGs) in each data set, we used the robust rank aggregation method to identify 358 robust DEGs, including 135 upregulated and 224 downregulated genes, in four datasets. Gene Ontology and Kyoto Encyclopaedia of Genes and Genomes pathway enrichment analyses of DEGs were performed by DAVID and the KOBAS online database, respectively. The results showed that these DEGs were significantly enriched in various cancer-related functions and pathways. Then, the STRING database was used to construct the protein-protein interaction network, and modules analysis was performed. Finally, we filtered out five hub genes, including LPAR5, NMU, FN1, NPY1R, and CXCL12, from the whole network. Expression validation and survival analysis of these hub genes based on the The Cancer Genome Atlas database suggested the robustness of the above results. In conclusion, these results provided novel and reliable biomarkers for thyroid cancer, which will be useful for further clinical applications in thyroid cancer diagnosis, prognosis and targeted therapy.

Translational Metabolomics: Current Challenges and Future Opportunities

Metabolomics is one of the latest omics technologies that has been applied successfully in many areas of life sciences. Despite being relatively new, a plethora of publications over the years have exploited the opportunities provided through this data and question driven approach. Most importantly, metabolomics studies have produced great breakthroughs in biomarker discovery, identification of novel metabolites and more detailed characterisation of biological pathways in many organisms. However, translation of the research outcomes into clinical tests and user-friendly interfaces has been hindered due to many factors, some of which have been outlined hereafter. This position paper is the summary of discussion on translational metabolomics undertaken during a peer session of the Australian and New Zealand Metabolomics Conference (ANZMET 2018) held in Auckland, New Zealand. Here, we discuss some of the key areas in translational metabolomics including existing challenges and suggested solutions, as well as how to expand the clinical and industrial application of metabolomics. In addition, we share our perspective on how full translational capability of metabolomics research can be explored.

Genome-wide identification and analysis of A-to-I RNA editing events in the malignantly transformed cell lines from bronchial epithelial cell line induced by α-particles radiation

Adenosine (A) to inosine (I) RNA editing is the most prevalent RNA editing mechanism in humans and plays critical roles in tumorigenesis. However, the effects of radiation on RNA editing were poorly understood, and a deeper understanding of the radiation-induced cancer is imperative. Here, we analyzed BEP2D (a human bronchial epithelial cell line) and radiation-induced malignantly transformed cell lines with next generation sequencing. By performing an integrated analysis of A-to-I RNA editing, we found that single-nucleotide variants (SNVs) might induce the downregulation of ADAR2 enzymes, and further caused the abnormal occurrence of RNA editing in malignantly transformed cell lines. These editing events were significantly enriched in differentially expressed genes between normal cell line and malignantly transformed cell lines. In addition, oncogenes CTNNB1 and FN1 were highly edited and significantly overexpressed in malignantly transformed cell lines, thus may be responsible for the lung cancer progression. Our work provides a systematic analysis of RNA editing from cell lines derived from human bronchial epithelial cells with high-throughput RNA sequencing and DNA sequencing. Moreover, these results provide further evidence for RNA editing as an important tumorigenesis mechanism.

Identification of Serum miRNA-423-5p Expression Signature in Somatotroph Adenomas

Circulating miRNAs are novel disease biomarkers that are valuable for diagnosis and prognosis. But the circulating miRNAs profile in somatotroph adenomas is still unknown. Therefore, serum exosomal miRNAs expression profiling in somatotroph adenomas was performed on 6 somatotroph adenomas and 6 normal controls. From the exosomal miRNAs expression profiling, we found 169 miRNAs differently expressed between somatotroph adenomas and healthy pituitary samples (p< 0.05, FC > 2). Among the 169 miRNAs, miR-423-5p was expressed lower in somatotroph adenomas than in healthy pituitary samples, which was proved by miRSCan Panel Chip™ qPCR. PTTG1 and SYT1 were the target mRNAs of miR-423-5p, and transcriptomics and proteomics profile both indicated the high expression of PTTG1 and SYT1 in somatotroph adenomas. H-scores were 223.1 ± 34.7 for PTTG1 and 163.4 ± 42.3 for SYT1 in 62 somatotroph adenomas specimens and 84.2 ± 21.3 for PTTG1 and 47.4 ± 17.2 for SYT1 in 6 healthy pituitary specimens by IHC. miR-423-5p inhibited the expression of SYT1 and PTTG1 at the mRNA and protein levels. Dual luciferase reporter gene assay shown was significantly reduced in the presence of miR-423-5p in GH3 cells transfected with wild-type PTTG1 3'UTR luciferase reporter plasmid but not reduced when transfected with the mutation PTTG1 3'UTR luciferase reporter plasmid (p<0.01). In vitro experiments showed that miR-423-5p induced cell apoptosis, inhibited cell proliferation, and reduced growth hormone release and migration of GH3 cells. The activity of miR-423-5p in GH3 cell was nearly blocked by its inhibitor. These results verified the central role of low miR-423-5p in promoting tumorigenesis in somatotroph adenomas. PTTG1 may act as biomarkers for clinical treatment of somatotroph adenomas.