The Construction and Analysis of ceRNA Network and Patterns of Immune Infiltration in Mesothelioma With Bone Metastasis

Construction of a novel nomogram based on competing endogenous RNAs and tumor-infiltrating immune cells for prognosis prediction in elderly patients with colorectal cancer

Competitive endogenous RNAs (ceRNAs) and tumor-infiltrating immune cells play essential roles in colorectal cancer (CRC) tumorigenesis. However, their prognostic role in elderly patients with CRC is unclear. Gene expression profiles and clinical information for elderly patients with CRC were downloaded from The Cancer Genome Atlas. Univariate, LASSO, and multivariate Cox regression analyses were utilized for screening key ceRNAs and prevent overfitting. A total of 265 elderly patients with CRC were included. We constructed a novel ceRNA network consisting of 17 lncRNAs, 35 miRNAs, and 5 mRNAs. We established three prognosis predictive nomograms based on four key ceRNAs (ceRNA nomogram), five key immune cells (immune cell nomogram), and their combination (ceRNA-immune cell nomogram). Among them, the ceRNA-immune cell nomogram had the best accuracy. Furthermore, the areas under the curve of the ceRNA-immune cell nomogram were also significantly greater than the TNM stage at 1 (0.818 vs. 0.693), 3 (0.865 vs. 0.674), and 5 (0.832 vs. 0.627) years. Co-expression analysis revealed that CBX6 was positively correlated with activated dendritic cells (R = 0.45, p < 0.01), whereas negatively correlated with activated mast cells (R =- 0.43, p < 0.01). In conclusion, our study constructed three nomograms to predict prognosis in elderly patients with CRC, among which the ceRNA-immune cell nomogram had the best prediction accuracy. We inferred that the mechanism underlying the regulation of activated dendritic cells and mast cells by CBX6 might play a crucial role in tumor development and prognosis of elderly patients with CRC.

Survival analysis and development of a prognostic nomogram for patients with malignant mesothelioma in different anatomic sites

Background

Malignant mesothelioma (MMe) is a rare and fatal cancer with a poor prognosis. Our study aimed to compare the overall survival (OS) of MMe patients across various sites and develop a prognostic model to provide a foundation for individualized management of MMe patients.

Methods

From the Surveillance, Epidemiology, and End Results (SEER) database, 1,772 individuals with malignant mesothelioma (MMe) were identified. The X-tile software was used to identify the optimal cut-off point for continuous variables. The Kaplan–Meier method was employed to compare the survival of MMe across different sites. The Cox proportional hazards model was applied to identify the independent risk factors of overall survival (OS) and a nomogram was constructed.

Results

In the survival analysis, MMe originating from the reproductive organs and hollow organs showed a relatively better prognosis than those originating from soft tissue, solid organs, and pleura. Age, gender, location, histological type, grade of differentiation, extent of disease, lymph node status, lymph node ratio (LNR), and chemotherapy were all found to be independent risk variables for the prognosis of MMe patients (P&lt;0.05) in a multivariate Cox analysis and were included in the construction of nomogram. In the training and testing sets, the C-index of the nomogram was 0.701 and 0.665, respectively, and the area under the ROC curve (AUROC) of the 1-, 3-, and 5-year overall survival rate was 0.749, 0.797, 0.833 and 0.730, 0.800, 0.832, respectively. The calibration curve shows that the nomogram is well-calibrated.

Conclusions

This is the first research to examine the prognosis of MMe patients based on the location. However, previous studies often focused on malignant pleural mesothelioma or malignant peritoneal mesothelioma with high incidence. Furthermore, a nomograph with good prediction efficiency was established according to the variables that influence patient survival outcomes, which provides us with a reference for clinical decision-making.

Correlation between Genes of the ceRNA Network and Tumor-Infiltrating Immune Cells and Their Biomarker Screening in Kidney Renal Clear Cell Carcinoma

This study aimed to using bioinformatics tools, qPCR, and the immunohistochemical analysis to find out factors related to the early diagnosis and prognosis of kidney renal clear cell carcinoma (KIRC). The expression profiles of lncRNA, miRNA, and mRNA of KIRC were downloaded from The Cancer Genome Atlas database. A ceRNA regulatory network was constructed based on the interaction between these three differentially expressed genes. The CIBERSORT deconvolution algorithm was used to analyze the differential distribution of 22 types of immune cells. The Kaplan–Meier survival and Cox analyses were used to screen genes of the ceRNA network and also immune cell subtypes related to the clinical and prognostic prediction of KIRC. Co-expression regulatory relationships were found among LINC01426, LINC00894, CCNA2, L1 cell adhesion molecule (L1CAM), and T follicular helper cells, which served as potential biomarkers. The results of quantitative reverse transcriptase-polymerase chain reaction showed that LINC01426 was upregulated while L1CAM was downregulated in KIRC, but no difference was found in the expression levels of LINC00894 and CCNA2 in cancer and adjacent samples. The immunohistochemical analysis showed that T follicular helper cells were more concentrated in core tissues and metastases of KIRC. In a word, co-expression relationships were found among LINC01426, L1CAM, and T follicular helper cells, and they may serve as biomarkers for early diagnosis and prognostic evaluation of KIRC.

Tumor-Derived Exosomes Modulate Primary Site Tumor Metastasis

Tumor-derived exosomes (TDEs) are actively produced and released by tumor cells and carry messages from tumor cells to healthy cells or abnormal cells, and they participate in tumor metastasis. In this review, we explore the underlying mechanism of action of TDEs in tumor metastasis. TDEs transport tumor-derived proteins and non-coding RNA to tumor cells and promote migration. Transport to normal cells, such as vascular endothelial cells and immune cells, promotes angiogenesis, inhibits immune cell activation, and improves chances of tumor implantation. Thus, TDEs contribute to tumor metastasis. We summarize the function of TDEs and their components in tumor metastasis and illuminate shortcomings for advancing research on TDEs in tumor metastasis.

A Prognostic Model Using Immune-Related Genes for Colorectal Cancer

There is evidence suggesting that immune genes play pivotal roles in the development and progression of colorectal cancer (CRC). Colorectal carcinoma patient data from The Cancer Genome Atlas (TCGA) and the Gene Expression Omnibus (GEO) were randomly classified into a training set, a test set, and an external validation set. Differentially expressed gene (DEG) analyses, univariate Cox regression, and the least absolute shrinkage and selection operator (LASSO) were used to identify survival-associated immune genes and develop a prognosis model. Receiver operating characteristic (ROC) analysis and principal component analysis (PCA) were used to evaluate the discrimination of the risk models. The model genes predicted were verified using the Human Protein Atlas (HPA) databases, colorectal cell lines, and fresh CRC and adjacent tissues. To understand the relationship between IRGs and immune invasion and the TME, we analyzed the content of immune cells and scored the TME using CIBERSORT and ESTIMATE algorithms. Finally, we predicted the potential sensitive chemotherapeutic drugs in different risk score groups by the Genomics of Drug Sensitivity in Cancer (GDSC). A total of 491 IRGs were screened, and 14 IRGs were identified to be significantly related to overall survival (OS) and applied to construct an immune-related gene (IRG) prognostic signature (IRGSig) for CRC patients. Calibration plots showed that nomograms have powerful predictive ability. PCA and ROC analysis further verified the predictive value of this fourteen-gene prognostic model in three independent databases. Furthermore, we discovered that the tumor microenvironment changed significantly during the tumor development process, from early to middle to late stage, which may be an essential factor for tumor deterioration. Finally, we selected six commonly used chemotherapeutic drugs that have the potential to be useful in the treatment of CRC. Altogether, immune genes were used to construct a prognosis model for CRC patients, and a variety of methods were used to test the accuracy of this model. In addition, we explored the immune mechanisms of CRC through immune cell infiltration and TME in CRC. Furthermore, we assessed the therapeutic sensitivity of many commonly used chemotherapeutic medicines in individuals with varying risk factors. Finally, the immune risk model and immune mechanism of CRC were thoroughly investigated in this paper.

Prognostic Bone Metastasis-Associated Immune-Related Genes Regulated by Transcription Factors in Mesothelioma

Mesothelioma (MESO) is a mesothelial originate neoplasm with high morbidity and mortality. Despite advancement in technology, early diagnosis still lacks effectivity and is full of pitfalls. Approaches of cancer diagnosis and therapy utilizing immune biomarkers and transcription factors (TFs) have attracted more and more attention. But the molecular mechanism of these features in MESO bone metastasis has not been thoroughly studied. Utilizing high-throughput genome sequencing data and lists of specific gene subsets, we performed several data mining algorithm. Single-sample Gene Set Enrichment Analysis (ssGSEA) was applied to identify downstream immune cells. Potential pathways involved in MESO bone metastasis were identified using Gene Oncology (GO) analysis, Kyoto Encyclopedia of Genes and Genomes (KEGG) analysis, Gene Set Variation Analysis (GSVA), Gene Set Enrichment Analysis (GSEA), and Cox regression analysis. Ultimately, a model to help early diagnosis and to predict prognosis was constructed based on differentially expressed immune-related genes between bone metastatic and nonmetastatic MESO groups. In conclusion, immune-related gene SDC2, regulated by TFs TCF7L1 and POLR3D, had an important role on immune cell function and infiltration, providing novel biomarkers and therapeutic targets for metastatic MESO.

RNA-based therapies in inherited retinal diseases

Inherited retinal diseases (IRDs) are a genetically and phenotypically heterogeneous group of genetic eye disorders. There are more than 300 disease entities, and together this group of disorders affects millions of people globally and is a frequent cause of blindness or low-vision certification. However, each type is rare or ultra-rare. Characteristically, the impaired vision in IRDs is due to retinal photoreceptor dysfunction and loss resulting from mutation in a gene that codes for a retinal protein. Historically, IRDs have been considered incurable and individuals living with these blinding conditions could be offered only supportive care. However, the treatment landscape for IRDs is beginning to evolve. Progress is being made, driven by improvements in understanding of genotype-phenotype relationships, through advances in molecular genetic testing and retinal imaging. Alongside this expanding knowledge of IRDs, the current era of precision medicine is fueling a growth in targeted therapies. This has resulted in the first treatment for an IRD being approved. Several other therapies are currently in development in the IRD space, including RNA-based therapies, gene-based therapies (such as augmentation therapy and gene editing), cell therapy, visual prosthetics, and optogenetics. RNA-based therapies are a novel approach within precision medicine that have demonstrated success, particularly in rare diseases. Three antisense oligonucleotides (AONs) are currently in development for the treatment of specific IRD subtypes. These RNA-based therapies bring several key advantages in the setting of IRDs, and the potential to bring meaningful vision benefit to individuals living with inherited blinding disorders. This review will examine the increasing breadth and relevance of RNA-based therapies in clinical medicine, explore the key features that make AONs suitable for treating genetic eye diseases, and provide an overview of the three-leading investigational AONs in clinical trials.

Identification of Novel Biomarkers for Predicting Prognosis and Immunotherapy Response in Head and Neck Squamous Cell Carcinoma Based on ceRNA Network and Immune Infiltration Analysis

Objectives

Patients with head and neck squamous cell carcinoma (HNSCC) have poor prognosis and show poor responses to immune checkpoint (IC) inhibitor (ICI) therapy. Competing endogenous RNA (ceRNA) networks, tumor-infiltrating immune cells (TIICs), and ICIs may influence tumor prognosis and response rates to ICI therapy. This study is aimed at identifying prognostic and IC-related biomarkers and key TIIC signatures to improve prognosis and ICI therapy response in HNSCC patients.

Methods and Results

Ninety-five long noncoding RNAs (lncRNAs), microRNAs (miRNAs), and 1746 mRNAs were identified using three independent methods. We constructed a ceRNA network and estimated the proportions of 22 immune cell subtypes. Ten ceRNAs were related to prognosis according to Kaplan–Meier analysis. Two risk signatures based, respectively, on nine ceRNAs (ANLN, CFL2, ITGA5, KDELC1, KIF23, NFIA, PTX3, RELT, and TMC7) and three immune cell types (naïve B cells, neutrophils, and regulatory T cells) via univariate Cox regression, least absolute shrinkage and selection operator, and multivariate Cox regression analyses could accurately and independently predict the prognosis of HNSCC patients. Key mRNAs in the ceRNA network were significantly correlated with naïve B cells and regulatory T cells and with stage, grade, and immune and molecular subtype. Eight IC genes exhibited higher expression in tumor tissues and were correlated with eight key mRNAs in the ceRNA network in HNSCC patients with different HPV statuses according to coexpression and TIMER 2.0 analyses. Most drugs were effective in association with expression of these key signatures (ANLN, CFL2, ITGA5, KIF23, NFIA, PTX3, RELT, and TMC7) based on GSCALite analysis. The prognostic value of key biomarkers and associations between key ceRNAs and IC genes were validated using online databases. Eight key ceRNAs were confirmed to predict response to ICI in other cancers based on TIDE analysis.

Conclusions

We constructed two risk signatures to accurately predict prognosis in HNSCC. Key IC-related signatures may be associated with response to ICI therapy. Combinations of ICIs with inhibitors of eight key mRNAs may improve survival outcomes of HNSCC patients.

Construction of a ceRNA network in hepatocellular carcinoma and comprehensive analysis of immune infiltration patterns

BACKGROUND

Hepatocellular carcinoma (HCC) is a type of refractory malignant tumor with high fatality rate. Currently, immunotherapy and competitive endogenous RNA (ceRNA) are research hotspots in HCC, but the relationship between ceRNA and the immune microenvironment in HCC is unclear.

METHODS

Firstly, a differentially expressed circRNA-miRNA-mRNA network was constructed from the GEO database, and functional enrichment analysis was performed. Next, combine the TCGA database to construct a ceRNA prognosis-related subnetwork. Establish a risk prediction model based on the mRNA in the sub-network, and evaluate the impact of the model on the prognosis. Use clinical samples to verify the expression of genes in the model. Finally, we analyzed the distribution of tumor infiltrating immune cells (TIC) in HCC, and explored the correlation between mRNAs in the ceRNA sub-network and immune infiltration.

RESULTS

We used the HCC ceRNA network (including 12 circRNA, 5 miRNA, and 8 mRNA) as a starting point for the identification of target genes (PSMD10, ESR1 and PPARGC1A) in the ceRNA prognosis-related subnetwork to establish a risk prediction model and elucidated its important role in predicting the poor prognosis of HCC. The differences in mRNA expression verified by clinical samples are consistent with the database. In addition, we found that the mRNAs in the ceRNA prognosis subnetwork are closely related to different types of TICs and immune checkpoints.

CONCLUSIONS

This study is expected to serve as a reference for the study of mechanisms underlying liver cancer, the screening of prognostic markers and the evaluation of the immune response.

Construction of a competing endogenous RNA network related to the prognosis of cholangiocarcinoma and comprehensive analysis of the immunological correlation

Background

Cholangiocarcinoma (CCA) is a malignant tumor of the digestive system, with occult onset in the early stage, a high degree of malignancy in the late stage, and poor prognosis. At present, the pathogenesis of CCA is not clear, and there is a lack of effective immunotherapy. The purpose of this study was to identify the potential regulatory mechanism of CCA and analyze the possibility of its related immunotherapy.

Methods

The circular RNAs (circRNAs) expression profile data of CCA was downloaded from the Gene Expression Omnibus (GEO) database; the miRNA and mRNA expression profile data of CCA were downloaded from The Cancer Genome Atlas (TCGA) database. Prognostic factors were screened by univariate Cox regression analysis, and the competing endogenous RNA (ceRNA) network was constructed via survival analysis. Multivariate Cox analysis was used to screen the independent prognostic factors and construct a prognostic correlation subnetwork. Analyzing the tumor microenvironment of CCA and survival analysis were performed according to the score of the microenvironment, and the distribution of tumor infiltrating immune cells (TICs) in CCA was calculated using the CIBERSORT algorithm. We explored the expression pattern of the target genes in pan-cancer, and the correlation between the key genes in the ceRNA subnetwork, TICs and immune checkpoints was analyzed using an online database. Finally, the expression levels of target genes were validated based on the Human Protein Atlas (HPA) databases.

Results

We screened four circRNAs, 10 miRNAs, and 17 mRNAs with significant differences, and constructed the ceRNA network. Independent prognostic factors were screened by multivariate Cox regression analysis, and a subnetwork containing five nodes (hsa_circ_0002073→hsa-mir-4524a-3p→SLC16A3/SLC35E4/DDX4) was constructed. Further analysis showed that SLC16A3 was not only an independent posterior factor of CCA, but was also closely correlated with immune cells, immune checkpoints, and immunotherapy, and had a certain regulatory effect on the tumor microenvironment.

Conclusions

Our study identified a novel prognostic marker of CCA, SLC16A3, and revealed the regulatory role of SLC16A3 in the tumor microenvironment, which is expected to provide new insights for the early diagnosis, prognosis, and targeted therapy of CCA.

Potential biomarkers of abnormal osseointegration of implants in type II diabetes mellitus

Background

Type II diabetes mellitus (T2DM) is an important risk factor for osseointegration of implants. The aim of this study was to explore key genes of T2DM affecting bone metabolism through bioinformatic analysis of published RNA sequencing data, identify potential biomarkers, and provide a reference for finding the molecular mechanism of abnormal osseointegration caused by T2DM.

Methods

We identified differentially expressed mRNAs and miRNAs from the Gene Expression Omnibus database using the R package ‘limma’ and analysed the predicted target genes using Kyoto Encyclopedia of Genes and Genomes pathway enrichment analysis and Gene Ontology analysis. At the same time, miRNA–mRNA interactions were explored using miRWalk 2.0.

Results

We constructed an miRNA-gene regulatory network and a protein–protein interaction network. The enrichment pathways of differentially expressed mRNAs included extracellular matrix receptor interactions, protein digestion and absorption, the PI3K-Akt signalling pathway, cytokine–cytokine receptor interactions, chemokine signalling pathways, and haematopoietic cell lineage functions. We analysed the expression of these differentially expressed mRNAs and miRNAs in T2DM rats and normal rats with bone implants and identified Smpd3, Itga10, and rno-mir-207 as possible key players in osseointegration in T2DM.

Conclusion

Smpd3, Itga10, and rno-mir-207 are possible biomarkers of osseointegration in T2DM. This study sheds light on the possible molecular mechanism of abnormal osseointegration caused by bone metabolism disorder in T2DM.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12903-021-01939-9.

Construction of a ceRNA Network and Analysis of Tumor Immune Infiltration in Pancreatic Adenocarcinoma

Pancreatic adenocarcinoma (PAAD) is characterized by high malignancy, frequent metastasis, and recurrence with an unfavorable prognosis. This study is aimed at constructing a prognostic model for tumor-infiltrating immune cells and a competing endogenous RNA (ceRNA) network in PAAD and analyzing susceptibilities of chemotherapy and immunotherapy of PAAD. Gene expression profiles and clinical information of PAAD were downloaded from The Cancer Genome Atlas (TCGA) database and divided into the tumor group and the normal group. A total of five PAAD survival-related key genes in the ceRNA network and three survival-related immune infiltrating cells were uncovered, and two survival risk models and nomograms were constructed. The efficiency and performance of the two models were verified using multi-index area under the curve analysis at different time points, decision curve analysis, and calibration curves. Co-expression analysis showed that LRRC1, MIR600HG, and RNF166 in the ceRNA network and tumor-infiltrating immune cells including CD8 T cells and M1 macrophages were likely related to the PAAD prognosis, and the expression of key ceRNA-related genes was experimently validated in tissues and cell lines by RT-qPCR. Patients with low risk scores for key genes in the ceRNA network displayed a positive response to anti-programmed death-1 (PD-1) treatment and greater sensitivity to chemotherapeutic drugs such as docetaxel, lapatinib, and paclitaxel. More importantly, our results suggested that the IC50 values of gemcitabine in PAAD were not significantly different between the high and low risk groups. The expression levels of immune checkpoints were significantly different in the high-risk and low-risk groups. The prognostic model, nomogram, and drug analysis may provide an essential reference for PAAD patient management in the clinic.

Identification of prognostic and bone metastasis​-related alternative splicing signatures in mesothelioma

Mesothelioma (MESO) is an infrequent tumor derived from mesothelial cells of pleura, peritoneum, pericardium, and tunica vaginalis testis. Despite advancement in technologies and better understanding of tumor progression mechanism, the prognosis of MESO remains poor. The role of alternative splicing events (ASEs) in the oncogenesis, tumor metastasis and drug resistance has been widely discussed in multiple cancers. But the prognosis and potential therapeutic value of ASEs in MESO were not clearly studied by now. We constructed a prognostic model using RNA sequencing data and matched ASE data of MESO patients obtained from the TCGA and TCGASpliceSeq database. A total of 3,993 ASEs were identified associated with overall survival using Cox regression analysis. Eight of them were finally figured out to institute the model by lasso regression analysis. The risk score of the model can predict the prognosis independently. Among the identified 390 splicing factors (SF), HSPA1A and DDX3Y was significantly associated with 43 OS-SEs. Among these OS-SEs, SNX5-58744-AT (p = 0.048) and SNX5-58745-AT (p = 0.048) were significantly associated with bone metastasis. Co-expression analysis of signal pathways and SNX5-58744-AT, SNX5-58745-AT was also depicted using GSVA. Finally, we proposed that splicing factor (SF) HSPA1A could regulate SNX5-58744-AT (R = -0.414) and SNX5-58745-AT (R = 0.414) through the pathway "Class I MHC mediated antigen processing and presentation" (R = 0.400). In this way, tumorigenesis and bone metastasis of MESO were controlled.

Integrated lncRNA and mRNA Transcriptome Analyses in the Ovary of Cynoglossus semilaevis Reveal Genes and Pathways Potentially Involved in Reproduction

Long non-coding RNAs (lncRNAs) have been reported to be involved in multiple biological processes. However, the roles of lncRNAs in the reproduction of half-smooth tongue sole (Cynoglossus semilaevis) are unclear, especially in the molecular regulatory mechanism driving ovarian development and ovulation. Thus, to explore the mRNA and lncRNA mechanisms regulating reproduction, we collected tongue sole ovaries in three stages for RNA sequencing. In stage IV vs. V, we identified 312 differentially expressed (DE) mRNAs and 58 DE lncRNAs. In stage V vs. VI, we identified 1,059 DE mRNAs and 187 DE lncRNAs. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analyses showed that DE mRNAs were enriched in ECM-receptor interaction, oocyte meiosis and steroid hormone biosynthesis pathways. Furthermore, we carried out gene set enrichment analysis (GSEA) to identify potential reproduction related-pathways additionally, such as fatty metabolism and retinol metabolism. Based on enrichment analysis, DE mRNAs with a potential role in reproduction were selected and classified into six categories, including signal transduction, cell growth and death, immune response, metabolism, transport and catabolism, and cell junction. The interactions of DE lncRNAs and mRNAs were predicted according to antisense, cis-, and trans-regulatory mechanisms. We constructed a competing endogenous RNA (ceRNA) network. Several lncRNAs were predicted to regulate genes related to reproduction including cyp17a1, cyp19a1, mmp14, pgr, and hsd17b1. The functional enrichment analysis of these target genes of lncRNAs revealed that they were involved in several signaling pathways, such as the TGF-beta, Wnt signaling, and MAPK signaling pathways and reproduction related-pathways such as the progesterone-mediated oocyte maturation, oocyte meiosis, and GnRH signaling pathway. RT-qPCR analysis showed that two lncRNAs (XR_522278.2 and XR_522171.2) were mainly expressed in the ovary. Dual-fluorescence in situ hybridization experiments showed that both XR_522278.2 and XR_522171.2 colocalized with their target genes cyp17a1 and cyp19a1, respectively, in the follicular cell layer. The results further demonstrated that lncRNAs might be involved in the biological processes by modulating gene expression. Taken together, this study provides lncRNA profiles in the ovary of tongue sole and further insight into the role of lncRNA involvement in regulating reproduction in tongue sole.

Construction and analysis of a ceRNA network and patterns of immune infiltration in bladder cancer

Background

Bladder cancer (BC) is the ninth most common malignant tumor, accounting for an estimate of 549,000 new BC cases and 200,000 BC-related deaths worldwide in 2018. The prognosis of BC has not substantially improved despite significant advances in the diagnosis and treatment of the disease.

Methods

The RNA sequencing (RNA-seq) data and clinical information of BC patients were downloaded from The Cancer Genome Atlas (TCGA) database. The Cell-type Identification By Estimating Relative Subsets Of RNA Transcripts (CIBERSORT) algorithm was used to assess immune infiltration. The survival analyses were performed using the selected components of a ceRNA network and selected immune cell types by least absolute shrinkage and selection operator (LASSO) Cox regression to calculate the risk score. The accuracy of prognosis prediction was determined by receiver operating characteristic (ROC) curves, survival curves, and nomograms. Finally, the correlation analysis was performed to investigate the relationships between the signature components of the ceRNA network and the immune cell signature.

Results

Two completed survival analyses included selected components of the ceRNA network (ELN, SREBF1, DSC2, TTLL7, DIP2C, SATB1, hsa-miR-20a-5p, and hsa-miR-29c-3p) and selected immune cell types (M0 macrophages, M2 macrophages, resting mast cells, and neutrophils). ROC curves, survival curves (all P values <0.05), nomograms, and calibration curves indicated that the accuracy of the two survival analyses was acceptable. Moreover, the correlations between TTLL7 and resting mast cells (R=0.24, P<0.001), DSC2 and resting mast cells (R=−0.23, P<0.001), ELN and resting mast cells (R=0.44, P<0.001), and hsa-miR-29c-3p and M0 macrophages (R=−0.29, P<0.001) were significant, indicating that interactions of these factors may play significant roles in the prognosis of BC.

Conclusions

TTLL7, DSC2, ELN, hsa-miR-29c-3p, resting mast cells, and M0 macrophages may play an important role in the development of BC. However, additional studies are needed to confirm this hypothesis.

Combined Analysis of RNA Sequence and Microarray Data Reveals a Competing Endogenous RNA Network as Novel Prognostic Markers in Malignant Pleural Mesothelioma

Malignant pleural mesothelioma (MPM) is a highly aggressive cancer with short survival time. Unbalanced competing endogenous RNAs (ceRNAs) have been shown to participate in the tumor pathogenesis and served as biomarkers for the clinical prognosis. However, the comprehensive analyses of the ceRNA network in the prognosis of MPM are still rarely reported. In this study, we obtained the transcriptome data of the MPM and the normal samples from TCGA, EGA, and GEO databases and identified the differentially expressed (DE) mRNAs, lncRNAs, and miRNAs. The functions of the prognostic genes and the overlapped DEmRNAs were further annotated by the multiple enrichment analyses. Then, the targeting relationships among lncRNA–miRNA and miRNA–mRNA were predicted and calculated, and a prognostic ceRNA regulatory network was established. We included the prognostic 73 mRNAs and 13 miRNAs and 26 lncRNAs into the ceRNA network. Moreover, 33 mRNAs, three miRNAs, and seven lncRNAs were finally associated with prognosis, and a model including seven mRNAs, two lincRNAs, and some clinical factors was finally established and validated by two independent cohorts, where CDK6 and SGMS1-AS1 were significant to be independent prognostic factors. In addition, the identified co-expressed modules associated with the prognosis were overrepresented in the ceRNA network. Multiple enrichment analyses showed the important roles of the extracellular matrix components and cell division dysfunction in the invasion of MPM potentially. In summary, the prognostic ceRNA network of MPM was established and analyzed for the first time and these findings shed light on the function of ceRNAs and revealed the potential prognostic and therapeutic biomarkers of MPM.

The Construction and Analysis of Tumor-Infiltrating Immune Cells and ceRNA Networks in Bladder Cancer

Background

Bladder cancer (BLCA) is the 11th most common malignancy worldwide. Although significant improvements have been made in screening, diagnosis, and precise management in recent years, the prognosis of BLCA remains bleak.

Objectives

This study aimed to investigate the prognostic significance of tumor-infiltrating immune cells and construct ceRNA networks in BLCA patients.

Methods

The expression data of BLCA patients were obtained from The Cancer Genome Atlas (TCGA) database. A competing endogenous RNA (ceRNA) network was constructed to identify the hub genes involved in the prognosis of BLCA. The CIBERSORT algorithm was utilized to investigate the infiltration levels of 22 subsets of immune cells. Ultimately, the nomogram was generated to visualize the survival probability of each patient, with the calibration curve being performed to assess its performance. Furthermore, the Pearson correlation test was used to explore the correlation between the identified hub genes in the ceRNA network and the prognostic-related immune cells.

Results

A total of eight elements in the ceRNA network were considered as key members and correlated with the prognosis of BLCA, including ELN, SREBF1, DSC2, TTLL7, DIP2C, SATB1, hsa-miR-20a-5p, and hsa-miR-29c-3p. T cells CD8, T cells follicular helper (Tfh), and neutrophils were identified as independent prognostic factors in BLCA. The co-expression analysis showed that there was a significant correlation between the identified hub genes and immune cells.

Conclusion

Our results suggest that the mechanism of hsa-miR-29c-3p regulates the expression of ELN and DSC2, and the infiltration of Tfh and neutrophils might play pivotal roles in the progression of BLCA.

Identification of differentially expressed genes, signaling pathways and immune infiltration in rheumatoid arthritis by integrated bioinformatics analysis

Background

The disability rate associated with rheumatoid arthritis (RA) ranks high among inflammatory joint diseases. However, the cause and potential molecular events are as yet not clear. Here, we aimed to identify differentially expressed genes (DEGs), pathways and immune infiltration involved in RA utilizing integrated bioinformatics analysis and investigating potential molecular mechanisms.

Materials and methods

The expression profiles of GSE55235, GSE55457, GSE55584 and GSE77298 were downloaded from the Gene Expression Omnibus database, which contained 76 synovial membrane samples, including 49 RA samples and 27 normal controls. The microarray datasets were consolidated and DEGs were acquired and further analyzed by bioinformatics techniques. Gene ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analyses of DEGs were performed using R (version 3.6.1) software, respectively. The protein-protein interaction (PPI) network of DEGs were developed utilizing the STRING database. Finally, the CIBERSORT was used to evaluate the infiltration of immune cells in RA.

Results

A total of 828 DEGs were recognized, with 758 up-regulated and 70 down-regulated. GO and KEGG pathway analyses demonstrated that these DEGs focused primarily on cytokine receptor activity and relevant signaling pathways. The 30 most firmly related genes among DEGs were identified from the PPI network. The principal component analysis showed that there was a significant difference between the two tissues in infiltration immune.

Conclusion

This study shows that screening for DEGs, pathways and immune infiltration utilizing integrated bioinformatics analyses could aid in the comprehension of the molecular mechanisms involved in RA development. Besides, our study provides valuable data related to DEGs, pathways and immune infiltration of RA and may provide new insight into the understanding of molecular mechanisms.

Comprehensive analysis of the relationship between competitive endogenous RNA (ceRNA) networks and tumor infiltrating-cells in hepatocellular carcinoma

Background

The innovation of immune checkpoint blockade (ICB) represents a promising shift in the treatment of advanced hepatocellular carcinoma (HCC). However, response to ICB has varied largely due to the high tumor heterogeneity and complex tumor microenvironment (TME). The competitive endogenous RNA (ceRNA) network also plays an important role in tumor occurrence and progression, but its relation with tumor-infiltrating immune cells (TICs) remains largely unexplored in HCC. The overriding objective of our study was thus to construct a prognosis-related risk model and to further evaluate the relationship between ceRNA networks and TICs.

Methods

Differentially expressed gene (DEG) analysis was performed to identify the differentially expressed RNAs. Lasso and multivariable Cox regression analyses were used to construct risk models, which were assessed by the area under the receiver operating characteristic curve (AUC of ROC) and Kaplan-Meier (K-M) curves. Then, a single-sample gene set enrichment analysis (ssGSEA) algorithm was adopted to dissect the TICs in HCC samples. Nomograms were constructed and calibration curves were used to verify the discrimination and accuracy of the nomograms. Finally, integration analysis was performed to validate the correlation of ceRNA and TICs.

Results

In the study, 7 differentially expressed RNAs [5 messenger RNA s (mRNAs) and 2 micro RNAs (miRNAs)] were incorporated to construct a ceRNA risk model. The AUC of the 1-, 3-, and 5-year overall survival (OS) were 0.784, 0.685, and 0.691 respectively. Likewise, 7 types TICs were in the TICs signature model and the AUC of the 1-, 3-, and 5-year OS were 0.706, 0.731, and 0.721 respectively. The integration analysis showed that 7 pairs of mRNA-TICs and 1 pair of miRNA-TICs had a close relation (all correlation coefficients >0.2, P<0.001).

Conclusions

Through constructing two risk models based on ceRNA network and TICs, we identified the hub RNAs and key TICs in the progression and prognosis of HCC, and further explored the relationship between ceRNA and TME. Importantly, targeting these hub RNAs may facilitate the remodeling of the TME and be a potential therapeutic alternative to enhancing the response to ICB, thus improving the prognosis of HCC patients.

The Role of Extracellular Vesicles (EVs) in the Epigenetic Regulation of Bone Metabolism and Osteoporosis

Extracellular vesicles (EVs) are complex phospholipidic structures actively released by cells. EVs are recognized as powerful means of intercellular communication since they contain many signaling molecules (including lipids, proteins, and nucleic acids). In parallel, changes in epigenetic processes can lead to changes in gene function and finally lead to disease onset and progression. Recent breakthroughs have revealed the complex roles of non-coding RNAs (microRNAs (miRNAs) and long non-coding RNAs (lncRNAs)) in epigenetic regulation. Moreover, a substantial body of evidence demonstrates that non-coding RNAs can be shuttled among the cells and tissues via EVs, allowing non-coding RNAs to reach distant cells and exert systemic effects. Resident bone cells, including osteoclasts, osteoblasts, osteocytes, and endothelial cells, are tightly regulated by non-coding RNAs, and many of them can be exported from the cells to neighboring ones through EVs, triggering pathological conditions. For these reasons, researchers have also started to exploit EVs as a theranostic tool to address osteoporosis. In this review, we summarize some recent findings regarding the EVs’ involvement in the fine regulation of non-coding RNAs in the context of bone metabolism and osteoporosis.

Characterization of Dysregulated lncRNA-Associated ceRNA Network Reveals Novel lncRNAs With ceRNA Activity as Epigenetic Diagnostic Biomarkers for Osteoporosis Risk

The altered expression of long non-coding RNAs (lncRNAs) has been implicated in the development and human diseases. However, functional roles and regulatory mechanisms of lncRNA as competing endogenous RNAs (ceRNAs) in osteoporosis and their potential clinical implication for osteoporosis risk are largely unexplored. In this study, we performed integrated analysis for paired expression profiles and regulatory relationships of dysregulated lncRNAs, mRNAs, and miRNAs based on “ceRNA hypothesis,” and constructed an osteoporosis-related dysregulated miRNA-mediated lncRNA–mRNA ceRNA network (DysCeNet) composed of 105 nodes (including eight miRNAs, 24 mRNAs, and 73 lncRNAs) and 515 edges. Functional analysis suggested that the DysCeNet was involved in known osteoporosis or bone metabolism-related biological processes and pathways. Then, we performed random forest-based feature selection for 73 lncRNAs with ceRNA activity and identified 25 of 73 lncRNAs as potential diagnostic biomarkers. A random forest-based classifier composed of 25 lncRNA biomarkers (RF-25lncRNA) was developed for predicting osteoporosis risk. Performance evaluation with the leave-one-out cross-validation (LOOCV) procedure showed that the RF-25lncRNA achieved a good performance in distinguishing high- and low-bone mineral density (BMD) subjects in different osteoporosis datasets. Our study for the first time revealed a global view of lncRNA-associated ceRNA regulation in osteoporosis and provided novel lncRNAs with ceRNA activity as candidate epigenetic diagnostic biomarkers for early detection of osteoporosis risk.