Dissecting the Functional Mechanisms of Somatic Copy-Number Alterations Based on Dysregulated ceRNA Networks across Cancers

Potential Role of MAP3K14 in Hepatocellular Carcinoma: A Study Based on Comprehensive Bioinformatical Analysis and Validation

According to reports, MAP3K14 is considered an oncogene and is aberrantly expressed in various types of tumor cells. Its abnormal expression is closely associated with the occurrence and progression of various cancers. MAP3K14 also plays a significant role in the development of non-alcoholic steatohepatitis (NASH)-related hepatocellular carcinoma and its connection to tumor stem cells. The prognostic value of MAP3K14 in HCC, as well as its potential functions and roles, requires further elucidation. We evaluated the potential role of MAP3K14 in HCC based on data mining from a range of public databases. The bioinformatics analysis of TCGA, GEO, TIMER, cBioportal, Kaplan-Meier plotter, MethSurv, ENCORI and CellMiner databases was carried out. The expression of MAP3K14 protein in HCC was detected by immunohistochemical method. The mRNA and protein expression levels of MAP3K14 in tumor tissues were higher than those in normal tissues (p < 0.05). The expression of MAP3K14 was correlated with Pathologic T stage (p=0.026), Pathologic stage (p=0.032), Tumor status (p=0.024) and AFP (p=0.002). HCC patients with high expression of MAP3K14 had poor overall survival (OS), progression free survival (PFS) and recurrence free survival (RFS). Multivariate Cox regression analysis showed that the Pathologic stage (p < 0.001) and MAP3K14 expression levels (p < 0.05) is an independent prognostic factor affecting the survival of patients with liver cancer. GO/KEGG analysis suggested that key biological processes (PI3K-Akt signaling pathway) may be the mechanism promoting HCC development. In addition, MAP3K14 was significantly correlated with the infiltrating levels of B cells, CD8+ T cells, CD4+ T cells, macrophages, neutrophils, and dendritic cells (p < 0.05). MAP3K14 is up-regulated in HCC and is closely related to the prognosis of HCC patients. MAP3K14 may serve as a potential biomarker for poor prognosis of HCC.

Multi-Omics Mining of lncRNAs with Biological and Clinical Relevance in Cancer

In this review, we provide a general overview of the current panorama of mining strategies for multi-omics data to investigate lncRNAs with an actual or potential role as biological markers in cancer. Several multi-omics studies focusing on lncRNAs have been performed in the past with varying scopes. Nevertheless, many questions remain regarding the pragmatic application of different molecular technologies and bioinformatics algorithms for mining multi-omics data. Here, we attempt to address some of the less discussed aspects of the practical applications using different study designs for incorporating bioinformatics and statistical analyses of multi-omics data. Finally, we discuss the potential improvements and new paradigms aimed at unraveling the role and utility of lncRNAs in cancer and their potential use as molecular markers for cancer diagnosis and outcome prediction.

Identifying the personalized driver gene sets maximally contributing to abnormality of transcriptome phenotype in glioblastoma multiforme individuals

High heterogeneity in genome and phenotype of cancer populations made it difficult to apply population-based common driver genes to the diagnosis and treatment of cancer individuals. Characterizing and identifying the personalized driver mechanism for glioblastoma multiforme (GBM) individuals were pivotal for the realization of precision medicine. We proposed an integrative method to identify the personalized driver gene sets by integrating the profiles of gene expression and genetic alterations in cancer individuals. This method coupled genetic algorithm and random walk to identify the optimal gene sets that could explain abnormality of transcriptome phenotype to the maximum extent. The personalized driver gene sets were identified for 99 GBM individuals using our method. We found that genomic alterations in between one and seven driver genes could maximally and cumulatively explain the dysfunction of cancer hallmarks across GBM individuals. The driver gene sets were distinct even in GBM individuals with significantly similar transcriptomic phenotypes. Our method identified MCM4 with rare genetic alterations as previously unknown oncogenic genes, the high expression of which were significantly associated with poor GBM prognosis. The functional experiments confirmed that knockdown of MCM4 could significantly inhibit proliferation, invasion, migration, and clone formation of the GBM cell lines U251 and U118MG, and overexpression of MCM4 significantly promoted the proliferation, invasion, migration, and clone formation of the GBM cell line U87MG. Our method could dissect the personalized driver genetic alteration sets that are pivotal for developing targeted therapy strategies and precision medicine. Our method could be extended to identify key drivers from other levels and could be applied to more cancer types.

Comprehensive analysis of the FOXA1-related ceRNA network and identification of the MAGI2-AS3/DUSP2 axis as a prognostic biomarker in prostate cancer

Background

Prostate cancer (PCa) is the second most common cause of cancer-related deaths in American men. Even though increasing evidence has disclosed the competitive endogenous RNA (ceRNA) regulatory networks among cancers, the complexity and behavior characteristics of the ceRNA network in PCa remain unclear. Our study aimed to investigate the forkhead box A1 (FOXA1)-related ceRNA regulatory network and ascertain potential prognostic markers associated with PCa.

Methods

RNA sequence profiles downloaded from The Cancer Genome Atlas (TCGA) were analyzed to recognize differentially expressed genes (DEGs) derived from tumor and non-tumor adjacent samples as well as FOXA1low and FOXA1high tumor samples. The enrichment analysis was conducted for the dysregulated mRNAs. The network for the differentially expressed long non-coding RNA (lncRNA)-associated ceRNAs was then established. Survival analysis and univariate Cox regression analysis were executed to determine independent prognostic RNAs associated with PCa. The correlation between DUSP2 and immune cell infiltration level was analyzed. Tissue and blood samples were collected to verify our network. Molecular experiments were performed to explore whether DUSP2 is involved in the development of PCa.

Results

A ceRNA network related to FOXA1 was constructed and comprised 18 lncRNAs, 5 miRNAs, and 44 mRNAs. The MAGI2-AS3~has-mir-106a/has-mir-204~DUSP2 ceRNA regulatory network relevant to the prognosis of PCa was obtained by analysis. We markedly distinguished the MAGI2-AS3/DUSP2 axis in the ceRNA. It will most likely become a clinical prognostic model and impact the changes in the tumor immune microenvironment of PCa. The abnormal MAGI2-AS3 expression level from the patients’ blood manifested that it would be a novel potential diagnostic biomarker for PCa. Moreover, down-expressed DUSP2 suppressed the proliferation and migration of PCa cells.

Conclusions

Our findings provide pivotal clues to understanding the role of the FOXA1-concerned ceRNA network in PCa. Simultaneously, this MAGI2-AS3/DUSP2 axis might be a new significant prognostic factor associated with the diagnosis and prognosis of PCa.

Dysregulated ceRNA network modulated by copy number variation-driven lncRNAs in breast cancer: A comprehensive analysis

Breast cancer is a malignancy harmful to physical and mental health in women, with quite high mortality. Copy number variations (CNVs) are vital factors affecting the progression of breast cancer. Detecting CNVs in breast cancer to predict the prognosis of patients has become a promising approach to accurate treatment in recent years. The differential analysis was performed on CNVs of long noncoding RNAs (lncRNAs) as well as the expression of lncRNAs, microRNAs (miRNAs) and mRNAs in normal tissue and breast tumor tissue based on The Cancer Genome Atlas (TCGA) database. The CNV-driven lncRNAs were identified by the Kruskal-Wallis test. Meanwhile, a competitive endogenous RNA (ceRNA) network regulated by CNV-driven lncRNA was constructed. As the Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) analyses revealed, the mRNAs in the dysregulated ceRNA network were mainly enriched in the biological functions and signaling pathways, including the Focal Adhesion-PI3K-Akt-mTOR-signaling pathway, the neuronal system, metapathway biotransformation Phase I and II and blood circulation, etc. The relationship between the CNVs of five lncRNAs and their gene expression in the ceRNA network was analyzed via a chi-square test, which confirmed that except for LINC00243, the expression of four lncRNAs was notably correlated with the CNVs. The survival analysis revealed that only the copy number gain of LINC00536 was evidently related to the poor prognosis of patients. The CIBERSORT algorithm showed that five lncRNAs were correlated with the abundance of immune cell infiltration and immune checkpoints. In a word, by analyzing CNV-driven lncRNAs and the ceRNA network regulated by these lncRNAs, this study explored the mechanism of breast cancer and provided novel insights into new biomarkers.

HELLPAR/RRM2 axis related to HMMR as novel prognostic biomarker in gliomas

BACKGROUND

Gliomas are the most frequent type of central nervous system tumor, accounting for more than 70% of all malignant CNS tumors. Recent research suggests that the hyaluronan-mediated motility receptor (HMMR) could be a novel potential tumor prognostic marker. Furthermore, mounting data has highlighted the important role of ceRNA regulatory networks in a variety of human malignancies. The complexity and behavioural characteristics of HMMR and the ceRNA network in gliomas, on the other hand, remained unknown.

METHODS

Transcriptomic expression data were collected from TCGA, GTEx, GEO, and CGGA database.The relationship between clinical variables and HMMR was analyzed with the univariate and multivariate Cox regression. Kaplan-Meier method was used to assess OS. TCGA data are analyzed and processed, and the correlation results obtained were used to perform GO, GSEA, and ssGSEA. Potentially interacting miRNAs and lncRNAs were predicted by miRWalk and StarBase.

RESULTS

HMMR was substantially expressed in gliomas tissues compared to normal tissues. Multivariate analysis revealed that high HMMR expression was an independent predictive predictor of OS in TCGA and CGGA. Functional enrichment analysis found that HMMR expression was associated with nuclear division and cell cycle. Base on ssGSEA analysis, The levels of HMMR expression in various types of immune cells differed significantly. Bioinformatics investigation revealed the HEELPAR-hsa-let-7i-5p-RRM2 ceRNA network, which was linked to gliomas prognosis. And through multiple analysis, the good predictive performance of HELLPAR/RRM2 axis for gliomas patients was confirmed.

CONCLUSION

This study provides multi-layered and multifaceted evidence for the importance of HMMR and establishes a HMMR-related ceRNA (HEELPAR-hsa-let-7i-5p-RRM2) overexpressed network related to the prognosis of gliomas.

lncRNAs Functioned as ceRNA to Sponge miR-15a-5p Affects the Prognosis of Pancreatic Adenocarcinoma and Correlates With Tumor Immune Infiltration

Pancreatic adenocarcinoma (PAAD) is one of the most common malignant tumors with poor prognosis worldwide. Mounting evidence suggests that the expression of lncRNAs and the infiltration of immune cells have prognostic value for patients with PAAD. We used Gene Expression Omnibus (GEO) database and identified six genes (COL1A2, ITGA2, ITGB6, LAMA3, LAMB3, and LAMC2) that could affect the survival rate of pancreatic adenocarcinoma patients. Based on a series of in silico analyses for reverse prediction of target genes associated with the prognosis of PAAD, a ceRNA network of mRNA (COL1A2, ITGA2, LAMA3, LAMB3, and LAMC2)–microRNA (miR-15a-5p)–long non-coding RNA (LINC00511, LINC01578, PVT1, and TNFRSF14-AS1) was constructed. We used the algorithm “CIBERSORT” to assess the proportion of immune cells and found three overall survival (OS)–associated immune cells (monocytes, M1 macrophages, and resting mast cell). Moreover, the OS-associated gene level was significantly positively associated with immune checkpoint expression and biomarkers of immune cells. In summary, our results clarified that ncRNA-mediated upregulation of OS-associated genes and tumor-infiltration immune cells (monocytes, M1 macrophages M1, and resting mast cell resting) correlated with poor prognosis in PAAD.

Genomic Instability-Related LncRNA Signature Predicts the Prognosis and Highlights LINC01614 Is a Tumor Microenvironment-Related Oncogenic lncRNA of Papillary Thyroid Carcinoma

Background

Genomic instability (GI) is among the top ten characteristics of malignancy. Long non-coding RNAs (lncRNAs) are promising cancer biomarkers that are reportedly involved in GI. So far, the clinical value of GI-related lncRNAs (GIlncs) in papillary thyroid cancer (PTC) has not been clarified.

Methods

Integrative analysis of lncRNA expression and somatic mutation profiles was performed to identify GIlncs. Analysis of differentially expressed lncRNAs in the group with high- and low- cumulative number of somatic mutations revealed significant GIlncs in PTC. Univariate and multivariate Cox proportional hazard regression analyses were performed to identify hub-GIlncs.

Results

A computational model based on four lncRNAs (FOXD2-AS1, LINC01614, AC073257.2, and AC005082.1) was identified as a quantitative index using an in-silicon discovery cohort. GILS score was significantly associated with poor prognosis, as validated in the TCGA dataset and further tested in our local RNA-Seq cohort. Moreover, a combination of clinical characteristics and the composite GILS-clinical prognostic nomogram demonstrates satisfactory discrimination and calibration. Furthermore, the GILS score and FOXD2-AS1, LINC01614, AC073257.2, and AC005082.1 were also associated with driver mutations and multiple clinical-pathological variables, respectively. Moreover, RNA-Seq confirmed the expression patterns of FOXD2-AS1, LINC01614, AC073257.2, and AC005082.1 in PTC and normal thyroid tissues. Biological experiments demonstrated that downregulated or overexpressed LINC01614 affect PTC cell proliferation, migration, and invasion in vitro. Activation of the stromal and immune cell infiltration was also observed in the high LINC01614 group in the PTC microenvironment.

Conclusion

In summary, we identified a signature for clinical outcome prediction in PTC comprising four lncRNAs associated with GI. A better understanding of the GI providing an alternative evaluation of the progression risk of PTC. Our study also demonstrated LINC01614 as a novel oncogenic lncRNA and verified its phenotype in PTC.

Identifying Key Somatic Copy Number Alterations Driving Dysregulation of Cancer Hallmarks in Lower-Grade Glioma

Somatic copy-number alterations (SCNAs) are major contributors to cancer development that are pervasive and highly heterogeneous in human cancers. However, the driver roles of SCNAs in cancer are insufficiently characterized. We combined network propagation and linear regression models to design an integrative strategy to identify driver SCNAs and dissect the functional roles of SCNAs by integrating profiles of copy number and gene expression in lower-grade glioma (LGG). We applied our strategy to 511 LGG patients and identified 98 driver genes that dysregulated 29 cancer hallmark signatures, forming 143 active gene-hallmark pairs. We found that these active gene-hallmark pairs could stratify LGG patients into four subtypes with significantly different survival times. The two new subtypes with similar poorest prognoses were driven by two different gene sets (one including EGFR, CDKN2A, CDKN2B, INFA8, and INFA5, and the other including CDK4, AVIL, and DTX3), respectively. The SCNAs of the two gene sets could disorder the same cancer hallmark signature in a mutually exclusive manner (including E2F_TARGETS and G2M_CHECKPOINT). Compared with previous methods, our strategy could not only capture the known cancer genes and directly dissect the functional roles of their SCNAs in LGG, but also discover the functions of new driver genes in LGG, such as IFNA5, IFNA8, and DTX3. Additionally, our method can be applied to a variety of cancer types to explore the pathogenesis of driver SCNAs and improve the treatment and diagnosis of cancer.

Construction of a Myc-associated ceRNA network reveals a prognostic signature in hepatocellular carcinoma

Hepatocellular carcinoma (HCC) remains an extremely lethal disease worldwide. High-throughput methods have revealed global transcriptome dysregulation; however, a comprehensive investigation of the complexity and behavioral characteristics of the competing endogenous RNA (ceRNA) network in HCC is lacking. In this study, we extracted the transcriptome (RNA) sequencing data of 371 HCC patients from The Cancer Genome Atlas platform. With the comparison of the high Myc expression (Myc^high) tumor and low Myc expression (Myc^low) tumor groups in HCC, we identified 1,125 differentially expressed (DE) mRNAs, 589 long non-coding RNAs (lncRNAs), and 93 microRNAs (miRNAs). DE RNAs predicted the interactions necessary to construct an associated Myc ceRNA network, including 19 DE lncRNAs, 5 miRNAs, and 72 mRNAs. We identified a significant signature (long intergenic non-protein-coding [LINC] RNA 2691 [LINC02691] and LINC02499) that effectively predicted overall survival and had protective effects. The target genes of microRNA (miR)-212-3p predicted to intersect with DE mRNAs included SEC14-like protein 2 (SEC14L2) and solute carrier family 6 member 1 (SLC6A1), which were strongly correlated with survival and prognosis. With the use of the lncRNA-miRNA-mRNA axis, we constructed a ceRNA network containing four lncRNAs (LINC02691, LINC02499, LINC01354, and NAV2 antisense RNA 4), one miRNA (miR-212-3p), and two mRNAs (SEC14L2 and SLC6A1). Overall, we successfully constructed a mutually regulated ceRNA network and identified potential precision-targeted therapies and prognostic biomarkers.

Comprehensive analysis to identify DLEU2L/TAOK1 axis as a prognostic biomarker in hepatocellular carcinoma

Hepatocellular carcinoma (HCC) is one of the deadliest malignant tumors that are harmful to human health. Increasing evidence has underscored the critical role of the competitive endogenous RNA (ceRNA) regulatory networks among various human cancers. However, the complexity and behavior characteristics of the ceRNA network in HCC were still unclear. In this study, we aimed to clarify a phosphatase and tensin homolog (PTEN)-related ceRNA regulatory network and identify potential prognostic markers associated with HCC. The expression profiles of three RNAs (long non-coding RNAs [lncRNAs], microRNAs [miRNAs], and mRNAs) were extracted from The Cancer Genome Atlas (TCGA) database. The DLEU2L-hsa-miR-100-5p/ hsa-miR-99a-5p-TAOK1 ceRNA network related to the prognosis of HCC was obtained by performing bioinformatics analysis. Importantly, we identified the DLEU2L/TAOK1 axis in the ceRNA by using correlation analysis, and it appeared to become a clinical prognostic model by Cox regression analysis. Furthermore, methylation analyses suggested that the abnormal upregulation of the DLEU2L/TAOK1 axis likely resulted from hypomethylation, and immune infiltration analysis showed that the DLEU2L/TAOK1 axis may have an impact on the changes in the tumor immune microenvironment and the development of HCC. In summary, the current study constructing a ceRNA-based DLEU2L/TAOK1 axis might be a novel important prognostic factor associated with the diagnosis and prognosis of HCC.

Knockdown of lncRNA ANRIL suppresses the production of inflammatory cytokines and mucin 5AC in nasal epithelial cells via the miR-15a-5p/JAK2 axis

The incidence of allergic rhinitis (AR) is increasing worldwide. Human nasal epithelial cells (HNECs) are the key cells in the occurrence of AR. Antisense non-coding RNA in the INK4 locus (ANRIL) was discovered to be involved in the progression of AR. However, the mechanism by which ANRIL mediates the progression of AR remains to be determined. The present study aimed to further explore the mechanism by which ANRIL regulates AR. Thereby, HNECs were treated with IL-13 to mimic AR in vitro. The mRNA expression levels of ANRIL, microRNA (miR)-15a-5p, JAK2, mucin 5AC (MUC5AC), granulocyte-macrophage colony-stimulating factor (GM-CSF) and eotaxin-1, and protein expression levels of JAK2, STAT3 and phosphorylated-STAT3 in HNECs were analyzed using reverse transcription-quantitative PCR and western blotting, respectively. ELISAs were used to detect the secretory levels of inflammatory cytokines and mucin in cell supernatants. In addition, a dual luciferase reporter assay was used to confirm the downstream target of ANRIL and the target gene of miR-15a-5p. The results revealed that the secretory levels of eotaxin-1, GM-CSF and MUC5AC were significantly upregulated by IL-13 in the supernatant of HNECs. The expression levels of ANRIL and JAK2 were also upregulated in IL-13-induced HNECs, while the expression levels of miR-15a-5p were downregulated. In addition, ANRIL was identified to bind to miR-15a-5p. The IL-13-induced upregulation of eotaxin-1, GM-CSF and MUC5AC mRNA expression and secretory levels was significantly inhibited by the genetic knockdown of ANRIL, while the miR-15a-5p inhibitor effectively reversed this effect. JAK2 was also discovered to be directly targeted by miR-15a-5p. The overexpression of JAK2 significantly suppressed the therapeutic effect of miR-15a-5p mimics on IL-13-induced inflammation in vitro. In conclusion, the findings of the present study suggested that the genetic knockdown of ANRIL may suppress the production of inflammatory cytokines and mucin in IL-13-treated HNECs via regulation of the miR-15a-5p/JAK2 axis. Thus, ANRIL may serve as a novel target for AR treatment.