Characterization of long non-coding RNA and messenger RNA profiles in laryngeal cancer by weighted gene co-expression network analysis

Neuron stress-related genes serve as new biomarkers in hypothalamic tissue following high fat diet

Objective

Energy homeostasis is modulated by the hypothalamic is essential for obesity progression, however, the gene expression profiling remains to be fully understood.

Methods

GEO datasets were downloaded from the GEO website and analyzed by the R packages to obtain the DEGs. And, the WGCNA analysis and PPI networks of co-expressed DEGs were designed using STRING to get key genes. In addition, the single-cell sequencing datasets and GTEx database were utilized to receive the neuron-stress genes from the key genes. Further, high-fat diet (HFD)-induced hypothalamic tissue of mice was used as an animal model to validate the mRNA up-regulation of neuron-stress genes. In addition, the Bmi1 gene was identified as a hub gene through the LASSO model and nomogram analysis. Western blot confirmed the high expression of Bmi1 in hypothalamic tissue of HFD mice and PA-stimulated microglia. Immunofluorescence staining showed that HFD induced the activation of microglia and the expression of Bmi1 in hypothalamic tissue.

Results

We found that six genes (Sacm1l, Junb, Bmi1, Erbb4, Dkc1, and Suv39h1) are neuron stress-related genes and increased in the HFD-induced mice obesity model, Bmi1gene was identified as a key genes that can reflect the pathophysiology of obesity.

Conclusions

Our research depicted a comprehensive activation map of cell abnormality in the obese hypothalamus and Bim1 may be a diagnostic marker in the clinic, which provides a new perspective and basis for investigating the pathogenesis of obesity.

Combined expression of JHDM1D/KDM7A gene and long non-coding RNA RP11-363E7.4 as a biomarker for urothelial cancer prognosis

Bladder cancer is the tenth most frequently diagnosed cancer globally. Classification of high- or low-grade tumors is based on cytological differentiation and is an important prognostic factor. LncRNAs regulate gene expression and play critical roles in the occurrence and development of cancer, however, there are few reports on their diagnostic value and co-expression levels with genes, which may be useful as specific biomarkers for prognosis and therapy in bladder cancer. Thus, we performed a marker lesion study to investigate whether gene/lncRNA expression in urothelial carcinoma tissues may be useful in differentiating low-grade and high-grade tumors. RT-qPCR was used to evaluate the expression of the JHDM1D gene and the lncRNAs CTD-2132N18.2, SBF2-AS1, RP11-977B10.2, CTD-2510F5.4, and RP11-363E7.4 in 20 histologically diagnosed high-grade and 10 low-grade tumors. A protein-to-protein interaction network between genes associated with JHDM1D gene was constructed using STRING website. The results showed a moderate (positive) correlation between CTD-2510F5.4 and CTD2132N18.2. ROC curve analyses showed that combined JHDM1D and RP11-363E7.4 predicted tumor grade with an AUC of 0.826, showing excellent accuracy. In conclusion, the results indicated that the combined expression of JHDM1D and RP11-363E7.4 may be a prognostic biomarker and a promising target for urothelial tumor therapy.

Long noncoding RNA study: Genome-wide approaches

Long noncoding RNAs (lncRNAs) have been confirmed to play a crucial role in various biological processes across several species. Though many efforts have been devoted to the expansion of the lncRNAs landscape, much about lncRNAs is still unknown due to their great complexity. The development of high-throughput technologies and the constantly improved bioinformatic methods have resulted in a rapid expansion of lncRNA research and relevant databases. In this review, we introduced genome-wide research of lncRNAs in three parts: (i) novel lncRNA identification by high-throughput sequencing and computational pipelines; (ii) functional characterization of lncRNAs by expression atlas profiling, genome-scale screening, and the research of cancer-related lncRNAs; (iii) mechanism research by large-scale experimental technologies and computational analysis. Besides, primary experimental methods and bioinformatic pipelines related to these three parts are summarized. This review aimed to provide a comprehensive and systemic overview of lncRNA genome-wide research strategies and indicate a genome-wide lncRNA research system.

KIF26B-AS1 Regulates TLR4 and Activates the TLR4 Signaling Pathway to Promote Malignant Progression of Laryngeal Cancer

Laryngeal cancer is one of the highest incidence, most prevalently diagnosed head and neck cancers, making it critically necessary to probe effective targets for laryngeal cancer treatment. Here, real-time quantitative reverse transcription PCR (qRT-PCR) and western blot analysis were used to detect gene expression levels in laryngeal cancer cell lines. Fluorescence in situ hybridization (FISH) and subcellular fractionation assays were used to detect the subcellular location. Functional assays encompassing Cell Counting Kit-8 (CCK-8), 5-ethynyl-2'-deoxyuridine (EdU), transwell and wound healing assays were performed to examine the effects of target genes on cell proliferation and migration in laryngeal cancer. The in vivo effects were proved by animal experiments. RNA-binding protein immunoprecipitation (RIP), RNA pulldown and luciferase reporter assays were used to investigate the underlying regulatory mechanisms. The results showed that KIF26B antisense RNA 1 (KIF26B-AS1) propels cell proliferation and migration in laryngeal cancer and regulates the toll-like receptor 4 (TLR4) signaling pathway. KIF26B-AS1 also recruits FUS to stabilize TLR4 mRNA, consequently activating the TLR4 signaling pathway. Furthermore, KIF26B-AS1 plays an oncogenic role in laryngeal cancer via upregulating TLR4 expression as well as the FUS/TLR4 pathway axis, findings which offer novel insight for targeted therapies in the treatment of laryngeal cancer patients.

Prognostic value of lncRNAs related to fatty acid metabolism in lung adenocarcinoma and their correlation with tumor microenvironment based on bioinformatics analysis

Background

As a key regulator of metabolic pathways, long non-coding RNA (lncRNA) has received much attention for its relationship with reprogrammed fatty acid metabolism (FAM). This study aimed to investigate the role of the FAM-related lncRNAs in the prognostic management of patients with lung adenocarcinoma (LUAD) using bioinformatics analysis techniques.

Methods

We obtained LUAD-related transcriptomic data and clinical information from The Cancer Genome Atlas (TCGA) database. The lncRNA risk models associated with FMA were constructed by single-sample gene set enrichment analysis (ssGSEA), weighted gene co-expression network (WGCNA), differential expression analysis, overlap analysis, and Cox regression analysis. Kaplan-Meier (K-M) and receiver operating characteristic (ROC) curves were utilized to assess the predictive validity of the risk model. Gene set variation analysis (GSVA) revealed molecular mechanisms associated with the risk model. ssGSEA and microenvironment cell populations-counter (MCP-counter) demonstrated the immune landscape of LUAD patients. The relationships between lncRNAs, miRNAs, and mRNAs were predicted by using LncBase v.2 and miRTarBase. The lncRNA-miRNA-mRNA regulatory network was visualized with Cytoscape v3.4.0. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis was performed using DAVID v6.8. Quantitative real-time fluorescence PCR (qRT-PCR) was performed to verify the expression levels of the prognostic lncRNAs.

Results

We identified 249 differentially expressed FMA-related lncRNAs in TCGA-LUAD, six of which were used to construct a risk model with appreciable predictive power. GSVA results suggested that the risk model may be involved in regulating fatty acid synthesis/metabolism, gene repair, and immune/inflammatory responses in the LUAD process. Immune landscape analysis demonstrated a lower abundance of immune cells in the high-risk group of patients associated with poor prognosis. Moreover, we predicted 279 competing endogenous RNA (ceRNA) mechanisms for 6 prognostic lncRNAs with 39 miRNAs and 201 mRNAs. Functional enrichment analysis indicated that the ceRNA network may be involved in the process of LUAD by participating in genomic transcription, influencing the cell cycle, and regulating tissue and organogenesis. In vitro experiments showed that prognostic lncRNA CTA-384D8.35, lncRNA RP5-1059L7.1, and lncRNA Z83851.4 were significantly upregulated in LUAD primary tumor tissues, while lncRNA RP11-401P9.4, lncRNA CTA-384D8.35, and lncRNA RP11-259K15.2 were expressed at higher levels in paraneoplastic tissues.

Conclusion

In summary, the prognostic factors identified in this study can be used as potential biomarkers for clinical applications. ceRNA network construction provides a new vision for the study of LUAD pathogenesis.

Methylated RNA Immunoprecipitation Sequencing Reveals the m6A Landscape in Oral Squamous Cell Carcinoma

N6-methyladenosine (m⁶A) is the most common epigenetic modification existing in eukaryocyte transcripts. However, genes related to m⁶A modification in oral squamous cell carcinoma (OSCC) are still unclear. Here, methylated RNA immunoprecipitation sequencing (MeRIP-Seq) was performed to map the m6A landscape in OSCC and corresponding controls. The m⁶A peaks are always distributed in the junction of the 3′-untranslated regions (3′-UTRs) and the coding sequences (CDS) of mRNAs, as well as the entire genome of long noncoding RNA (lncRNA). Furthermore, enrichment analysis showed that differentially methylated genes were significantly enriched in NF-kappa B signaling pathway, Hedgehog signaling pathway, etc. In summary, our findings reveal the landscape of m⁶A modification on mRNAs and lncRNAs in OSCC, which may provide key clues for the precision-targeted therapy of OSCC.

Serum-derived extracellular vesicles mediate Smad4 expression through shuttling microRNA-27a in the progression of laryngeal squamous cell carcinoma

Serum-derived extracellular vesicles (EVs) containing non-coding RNAs have been indicated to serve as diagnostic and prognostic biomarkers for laryngeal squamous cell carcinoma (LSCC), while their functional role remains to be explored. Here, we summarize the possible mechanism explaining the laryngeal carcinogenesis and the associated changes with the involvement of extracellular microRNA (miR)-27a from serum of LSCC patients. Serum-derived EVs from LSCC patients were found to increase the proliferative activity and decreased the apoptotic activity of LSCC cells. miRNA microarrays revealed that miR-27a expression was elevated after EV treatment. miR-27a expression was elevated in LSCC tissues and predicted a poor prognosis for patients. Downregulation of miR-27a inhibited the effect of EVs to reduce the activity of LSCC cells in vitro and to suppress tumor development in vivo. miR-27a targeted SMAD family member 4 (Smad4) to mediate the Wnt/β-catenin pathway, which was induced under the influence of EVs. Smad4 was downregulated in LSCC tissues, and simultaneous overexpression of miR-27a and Smad4 resulted in reduced cell activity and tumorigenicity. In conclusion, serum-derived EVs support the laryngeal carcinogenesis at least partially via transferring miR-27a. miR-27a targets Smad4 and is a biomarker to predict LSCC prognosis.

LncRNA NCK1-AS1 Promotes Malignant Cellular Phenotypes of Laryngeal Squamous Cell Carcinoma via miR-137/NCK1 Axis

Increasing evidence demonstrates that many long noncoding RNAs (lncRNAs) are implicated with the development of laryngeal squamous cell carcinoma (LSCC). As shown by bioinformatics analysis, lncRNA non-catalytic region of tyrosine kinase adaptor protein 1-antisense 1 (NCK1-AS1) is upregulated in tissues of head and neck squamous cell carcinoma. The study aimed to explore the role and mechanism of NCK1-AS1 in LSCC. NCK1-AS1 expression in LSCC cells was evaluated by reverse transcription qPCR. The viability, proliferation, invasion, migration, and apoptosis of LSCC cells with indicated transfection were evaluated by CCK-8 assays, Ethynyl deoxyuridine incorporation assays, Transwell assays, wound healing assays, and TUNEL assays, respectively. Subcellular fractionation assays were performed to evaluate the cellular distribution of NCK1-AS1 and NCK1. NCK1 protein level in LSCC cells with indicated transfection was quantified by western blotting. The binding relation between miR-137 and NCK1-AS1 (or NCK1) were determined using RNA immunoprecipitation assays and luciferase reporter assays. NCK1-AS1 was highly expressed in LSCC cell lines. NCK1-AS1 depletion suppressed LSCC cell viability, proliferation, invasion, and migration while enhancing cell apoptosis. NCK1, an adjacent gene of NCK1-AS1, is also highly expressed in LSCC cells and was positively regulated by NCK1-AS1. Moreover, NCK1-AS1 interact with miR-137 to upregulate NCK1 expression. NCK1 was the downstream target of miR-137 and was negatively correlated to miR-137. In addition, overexpressed NCK1 reversed the suppressive impact of NCK1-AS1 depletion on malignant behaviors of LSCC cells. NCK1-AS1 contributes to LSCC cellular behaviors by upregulating NCK1 via interaction with miR-137.

Identification of prognostic biomarkers for papillary thyroid carcinoma by a weighted gene co‐expression network analysis

Aim

Methods

Results

Conclusions

Analysis of Gene Co-Expression Network to Identify the Role of CD8 + T Cell Infiltration-Related Biomarkers in High-Grade Glioma

Background

High-grade glioma is a type of heterogeneous lethal brain tumor most common in adults. At present, immune checkpoint inhibitors (ICIs) are being considered for first-line therapeutics for malignant GBM. Nonetheless, molecular markers for malignant GBM are unavailable at present. As a result, it is important to explore molecular markers related to immunity for GBM.

Materials and Methods

The present study adopted a deconvolution algorithm for quantifying immunocyte composition and measuring gene expression, and used weighted gene co-expression network analysis (WGCNA) to analyze GBM expression data obtained from Gene Expression Omnibus (GEO), Chinese Glioma Genome Atlas (CGGA), and the Cancer Genome Atlas Glioblastoma Multiforme (TCGA-GBM) databases. Thereafter, key CD8+ T cell infiltration-related genes and modules were identified, and database analysis was conducted to verify the therapeutic and immune features of the selected genes.

Results

From this study, CD8+ T cell-related modules were identified. By using consistent clustering analysis, two panels of genes (red and green) with the highest correlation with CD8+ T cells infiltration were used to construct high-, low-expression groups, silent and/or mixed group of T cell infiltrations. In the high and low CD8+ T cell infiltration groups, a total of 535 differential genes were obtained, of which ten genes (RPS5, RPS6, FAU, RPS19, RPS23, RPS15A, RPS29, RPS14, RPS16, RPS27A) were identified through protein–protein interactions and co-expression network analysis. Post Cox regression and Kaplan–Meier (K-M) survival analysis, RPS5, RPS6, and RPS16 were selected as candidate prognostic biomarkers related to CD8+ T cells.

Conclusion

The three associated genes RPS5, RPS6, and RPS16 were markedly related to degree of T cell infiltration and immune-related activated. We identified their potential biomarkers and therapeutic targets associated with the extent of CD8+ T cell infiltration in GBM.

A Signature for Smoking Status of Coronary Heart Disease Patients through Weighted Gene Coexpression Network Analysis

Background. Smoking is one of the risk factors of coronary heart disease (CHD), while its underlying mechanism is less well defined. Purpose. To identify and testify 6 key genes of CHD related to smoking through weighted gene coexpression network analysis (WGCNA), protein-protein interaction (PPI) network analysis, and pathway analysis. Methods. CHD patients’ samples were first downloaded from Gene Expression Omnibus (GEO). Then, genes of interest were obtained after analysis of variance (ANOVA). Thereafter, 23 coexpressed modules that were determined after genes with similar expression were incorporated via WGCNA. The biological functions of genes in the modules were researched by enrichment analysis. Pearson correlation analysis and PPI network analysis were used to screen core genes related to smoking in CHD. Results. The violet module was the most significantly associated with smoking ( $r=-0.28$ , $p=0.006$ ). Genes in this module mainly participated in biological functions related to the heart. Altogether, 6 smoking-related core genes were identified through bioinformatics analyses. Their expressions in animal models were detected through the animal experiment. Conclusion. This study identified 6 core genes to serve as underlying biomarkers for monitoring and predicting smoker’s CHD risk.

Identification of biomarkers related to glycolysis with weighted gene co-expression network analysis in oral squamous cell carcinoma

BACKGROUND

Oral squamous cell carcinoma (OSCC) is the most common tumor in the oral cavity and maxillofacial region. Increasing evidence suggests that aerobic glycolysis plays an important role in the occurrence, development, and prognosis of OSCC. Therefore, the identification of biomarkers related to glycolysis in OSCC represents considerable potential for improving its treatment.

METHODS

In the present study, a single-sample gene-set enrichment analysis (ssGSEA) algorithm with weighted gene co-expression network analysis (WGCNA) were used to quantify the degree of glycolysis and identify key modules with the greatest correlation with glycolysis.

RESULTS

Glycolytic scores significantly correlated with prognosis. In the key module 5 HUB genes were finally selected, which displayed a robust predictive effect. The expressions of key genes were associated with glycolysis.

CONCLUSIONS

The research comprehensively analyzed the glycolysis of OSCC and identified several biomarkers related to glycolysis. These biomarkers may represent potential therapeutic targets for future OSCC therapy.

Identification of Key Genes Related to CD8+ T-Cell Infiltration as Prognostic Biomarkers for Lung Adenocarcinoma

Background

CD8+ T cells are one of the central effector cells in the immune microenvironment. CD8+ T cells play a vital role in the development and progression of lung adenocarcinoma (LUAD). This study aimed to explore the key genes related to CD8+ T-cell infiltration in LUAD and to develop a novel prognosis model based on these genes.

Methods

With the use of the LUAD dataset from The Cancer Genome Atlas (TCGA), the differentially expressed genes (DEGs) were analyzed, and a co-expression network was constructed by weighted gene co-expression network analysis (WGCNA). Combined with the CIBERSORT algorithm, the gene module in WGCNA, which was the most significantly correlated with CD8+ T cells, was selected for the subsequent analyses. Key genes were then identified by co-expression network analysis, protein–protein interactions network analysis, and least absolute shrinkage and selection operator (Lasso)-penalized Cox regression analysis. A risk assessment model was built based on these key genes and then validated by the dataset from the Gene Expression Omnibus (GEO) database and multiple fluorescence in situ hybridization experiments of a tissue microarray.

Results

Five key genes (MZT2A, ALG3, ATIC, GPI, and GAPDH) related to prognosis and CD8+ T-cell infiltration were identified, and a risk assessment model was established based on them. We found that the risk score could well predict the prognosis of LUAD, and the risk score was negatively related to CD8+ T-cell infiltration and correlated with the advanced tumor stage. The results of the GEO database and tissue microarray were consistent with those of TCGA. Furthermore, the risk score was higher significantly in tumor tissues than in adjacent lung tissues and was correlated with the advanced tumor stage.

Conclusions

This study may provide a novel risk assessment model for prognosis prediction and a new perspective to explore the mechanism of tumor immune microenvironment related to CD8+ T-cell infiltration in LUAD.

Use of bioinformatic database analysis and specimen verification to identify novel biomarkers predicting gastric cancer metastasis

Background: Gastric cancer (GC) is a common gastrointestinal tumor, and its metastasis has led to a significant increase in the death rate. The mechanisms of GC metastasis remain unclear.

Methods: The differentially expressed genes (DmRs) and lncRNAs (DlncRs) of GC were selected from The Cancer Genome Atlas (TCGA) database. We applied the weighted gene co-expression network analysis (WGCNA) to construct co-expression modules related with GC metastasis. Gene ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) method analyzed the functional regions and signal pathways of genes in vital modules. DmRs-DlncRs co-expression network were drawn for finding out hub nodes. Survival analyses of significant biomarkers were analyzed by Kaplan-Meier (KM) method. Finally, the expressions of selected biomarkers were validated in cell lines and caner tissues by quantitative real-time PCR (qRT-PCR), in GC tissue microarray by Fluorescence in situ hybridization (FISH).

Results: 4776 DmRs and 213 DlncRs were involved the construction of WGCNA network, and MEyellow module was identified to have more significant correlation with GC metastasis. DmRs and DlncRs of MEyellow module were proved to be involved in the processes of cancer pathogenesis by GO and KEGG pathway analysis. Through the DmRs-DlncRs co-expression network, 7 DmRs and 1 DlncRs were considered as hub nodes. Besides, the high expression of TIMD4, CETP, KRT27, PTGDS, FAM30A was worse than low expression in GC patients survival, respectively; However, LRRC26 was opposite trend. FAM30A and TIMD4 were all significant biomarkers of GC survival and hub genes. Simultaneously, TIMD4, CETP, KRT27, PTGDS, FAM30A were increased in GC cell lines and tissues compared with GES-1 and normal tissues, respectively; the expression of LRRC26 was reduced in GC cell lines and tissues.

Conclusion: This study identified 6 genes as new biomarkers affecting the metastasis of GC. Especially, FAM30A and TIMD4 might be an effective marker for predicting the prognosis and a potential-therapeutic target in GC.

The Identification of Childhood Asthma Progression-Related lncRNAs and mRNAs Suitable as Biomarkers Using Weighted Gene Coexpression Network Analysis

Background

Asthma is a common chronic respiratory disease in children, seriously affecting children's health and growth. This bioinformatics study aimed to identify potential RNA candidates closely associated with childhood asthma development within current gene databases.

Methods

GSE65204 and GSE19187 datasets were screened and downloaded from the NCBI GEO database. Differentially expressed long noncoding RNAs (DE-lncRNAs) and mRNAs (DE-mRNAs) were identified using the Bioconductor limma package in R, and these DE-mRNAs were used to perform biological process (BP) and Kyoto Encyclopedia of Genes and Genomes (KEGG) analyses. Thereafter, weighted gene coexpression network analysis (WGCNA) was utilized to screen the modules directly related to childhood asthma, and a coexpression network of DE-lncRNAs and DE-mRNAs was built. Finally, principal component analysis (PCA) was performed.

Results

In total, 7 DE-lncRNAs and 1060 DE-mRNAs, as well as 7 DE-lncRNAs and 1027 DE-mRNAs, were identified in GSE65204 and GSE19187, respectively. After comparison, 336 overlapping genes had the same trend of expression, including 2 overlapped DE-lncRNAs and 334 overlapped DE-mRNAs. These overlapped DE-mRNAs were enriched in 28 BP and 12 KEGG pathways. Eleven modules were obtained in GSE65204, and it was found that the purple, black, and yellow modules were significantly positively correlated with asthma development. Subsequently, a coexpression network including 63 DE-mRNAs and 2 DE-lncRNAs was built, and five KEGG pathways, containing 8 genes, were found to be directly associated with childhood asthma. The PCA further verified these results.

Conclusion

LncRNAs LINC01559 and SNHG8 and mRNAs VWF, LAMB3, LAMA4, CAV1, ALDH1A3, SMOX, GNG4, and PPARG were identified as biomarkers associated with the progression of childhood asthma.

Weighted gene co-expression network analysis of the association between upregulated AMD1, EN1 and VGLL1 and the progression and poor prognosis of breast cancer

Breast cancer is the most prevalent malignancy among females, but the molecular mechanisms involved in its pathogenesis and progression have remained to be fully elucidated. The aim of the present study was to identify novel potential therapeutic targets for breast cancer. The dataset GSE76275 was downloaded from the Gene Expression Omnibus database and weighted gene co-expression network analysis (WGCNA) was performed to identify hub genes. Furthermore, the dataset GSE25055, containing gene expression data and clinical information, was downloaded to validate the expression and survival association of these hub genes. In addition, the datasets GSE25065 and GSE42568 were used to validate the association between hub gene expression levels and clinical features. Immunohistochemistry (IHC), reverse transcription-quantitative PCR, as well as proliferation, migration, invasion and apoptosis assays, were used to verify gene expression and function. A total of 4,052 genes were selected for WGCNA and 18 modules were established; the red module was identified as the key module, as it had a strong positive correlation with the tumor grade. Survival analyses of hub genes [S-adenosylmethionine decarboxylase proenzyme (AMD1), homeobox protein engrailed-1 (EN1) and vestigial-like protein (VGLL1)] indicated that higher levels of gene expression were associated with poor prognosis of patients with breast cancer. This association was based on survival analysis of GSE25055 using the Kaplan-Meier plotter tool. Expression validation revealed that the upregulation of hub genes was associated with advanced tumor grade and malignant molecular subtype (basal-like). IHC results from the Human Protein Atlas also demonstrated that protein expression levels of the hub genes were higher in tumor tissues compared with those in adjacent normal tissues. Furthermore, the expression levels of AMD1, EN1 and VGLL1 were strongly correlated with each other. These results demonstrated that AMD1 is highly expressed in breast cancer tissues and cells and AMD1 knockdown decreased the proliferation and metastatic potential, while increasing apoptosis of breast cancer cells. These results suggested that AMD1, EN1 and VGLL1 are likely to contribute to breast cancer progression and unfavorable prognosis.

The clinical significance of interleukin 24 and its potential molecular mechanism in laryngeal squamous cell carcinoma

Interleukin 24 (IL24) has been documented to be highly expressed in several cancers, but its role in laryngeal squamous cell carcinoma (LSCC) remains unclarified. In this study, to reveal the function and its clinical significance of IL24 in LSCC, multiple detecting methods were used comprehensively. IL24 protein expression was remarkably higher in LSCC (n= 49) than non-cancerous laryngeal controls (n= 26) as detected by in-house immunohistochemistry. Meanwhile, the IL24 mRNA expression was also evaluated based on high throughput data from Gene Expression Omnibus, The Cancer Genome Atlas, ArrayExpress and Oncomine databases. Consistently with the protein level, IL24 mRNA expression level was also predominantly upregulated in LSCC (n= 172) compared to non-cancerous laryngeal tissues (n= 81) with the standard mean difference (SMD) being 1.25 and the area under the curve (AUC) of the summary receiver operating characteristic (sROC) being 0.89 (95% CI = 0.86-0.92). Furthermore, the related genes of IL24 and the differentially expressed genes (DEGs) of LSCC were intersected and sent for Gene ontology (GO) enrichment, Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway, and the protein-protein interaction (PPI) analyses. In the GO annotation, the top terms of biological process (BP), cellular component (CC) and molecular function (MF) were extracellular matrix organization, extracellular matrix, cytokine activity, respectively. The top pathway of KEGG was ECM-receptor interaction. The PPI networks indicated the top hub genes of IL24-related genes in LSCC were SERPINE1, TGFB1, MMP1, MMP3, CSF2, and ITGA5. In conclusion, upregulating expression of IL24 may enhance the occurrence of LSCC, which owns prospect diagnostic ability and therapeutic significance in LSCC.

Construction of Competitive Endogenous RNA Network and Verification of 3-Key LncRNA Signature Associated With Distant Metastasis and Poor Prognosis in Patients With Clear Cell Renal Cell Carcinoma

Clear cell renal cell carcinoma (ccRCC) is a common malignancy with high distant metastasis rate. Long non-coding RNAs (LncRNAs) are reported to be upregulated or downregulated in multiple cancers and play a crucial role in the metastasis of tumors or prognosis. Therefore, the purpose of our study is to construct a prognostic signature for ccRCC based on distant metastasis-related lncRNAs and explore the involved potential competitive endogenous RNA (ceRNA) network. The differentially expressed genes (DEGs) screened from the database of the cancer genome atlas (TCGA) were used to construct a co-expression network and identify the distant metastasis-related module by weighted gene co-expression network analysis (WGCNA). Key genes with metastatic and prognostic significance were identified through rigorous screening, including survival analysis, correlation analysis, and expression analyses in stage, grade, and distant metastasis, and were verified in the data set of gene expression omnibus (GEO) and the database from gene expression profiling interactive analysis (GEPIA). The potential upstream miRNAs and lncRNAs were predicted via five online databases and LncBase. Here, we constructed a ceRNA network of key genes that are significantly associated with the distant metastasis and prognosis of patients with ccRCC. The distant metastasis-related lncRNAs were used to construct a risk score model through the univariate, least absolute shrinkage selection operator (LASSO), and multivariate Cox regression analyses, and the patients were divided into high- and low-risk groups according to the median of the risk score. The Kaplan–Meier survival analysis demonstrated that mortality was significantly higher in the high-risk group than in the low-risk group. Considering the other clinical phenotype, the Cox regression analyses indicated that the lncRNAs model could function as an independent prognostic factor. Quantitative real-time (qRT)-PCR in the tissues and cells of ccRCC verified the high-expression level of three lncRNAs. Gene set enrichment analysis (GSEA) revealed that the lncRNA prognostic signature was mainly enriched in autophagy- and immune-related pathways, indicating that the autophagy and immune functions may play an important role in the distant metastasis of ccRCC. In summary, the constructed distant metastasis-related lncRNA signature could independently predict prognosis in patients with ccRCC, and the related ceRNA network provided a new sight on the potential mechanism of distant metastasis and a promising therapeutic target for ccRCC.

Identification of LncRNA Prognostic Markers for Ovarian Cancer by Integration of Co-expression and CeRNA Network

Background

Ovarian cancer (OC), one of the most prevalent gynecological malignancies, is characterized by late detection and dismal prognosis. Recent studies show that long non-coding RNAs (lncRNAs) in competitive endogenous RNA (ceRNA) networks influence immune infiltration and cancer prognosis. However, the function of lncRNA in OC immune infiltration and prognosis remains unclear.

Methods

Transcriptomes of 378 OC samples and clinical data were retrieved from the TCGA repository. Modules related to immune cells were identified using weighted gene co-expression network analysis (WGCNA). Functional enrichment analysis and survival analysis were then performed for the identification of immune-related lncRNAs in the brown module using Cox regression model. Finally, a ceRNA network was constructed by using the lncRNAs and mRNAs from the brown module.

Results

We found lncRNAs and mRNAs in the brown module to be significantly associated with immune cells in OC and identified 4 lncRNAs as potential OC prognostic markers. We further established that lncRNAs in the ceRNA network influence OC immune infiltration and prognosis by regulating miRNA, ultimately modulating mRNA levels.

Conclusion

We have identified 4 lncRNAs as independent immune prognostic factors for OC. Furthermore, our findings offer novel insight into lncRNAs as OC immune and prognostic biomarkers.

Identification of key modules and genes associated with breast cancer prognosis using WGCNA and ceRNA network analysis

Breast cancer is one of the leading causes of cancer-associated mortality in women worldwide and has become a major public health problem. Although the definitive cause of breast cancer is not known, many genes sensitive to breast cancer have been detected using advanced technologies. Our study identified 3301 differentially expressed lncRNAs and mRNAs between tumor and normal samples from The Cancer Genome Atlas database. Based on the gene expression analysis and clinical traits as well as weighted gene co-expression network analysis, the co-expression Brown module was found to be key for breast cancer prognosis. A total of 453 genes in the Brown module were used for functional enrichment, protein-protein interaction analysis, lncRNA-miRNA-mRNA ceRNA network, and lncRNA-RNA binding protein-mRNA network construction. GRM4, SSTR2, PARD6B, PRR15, COX6C, and lncRNA DSCAM-AS1 were the hub genes according to protein-protein interaction, lncRNA-miRNA-mRNA and lncRNA-RNA binding protein-mRNA network. Their high expression was found to be correlated with breast cancer development, according to multiple databases. In conclusion, this study provides a framework of the co-expression gene modules of breast cancer and identifies several important biomarkers in breast cancer development and prognosis.

LINC01235-TWIST2 feedback loop facilitates epithelial-mesenchymal transition in gastric cancer by inhibiting THBS2

Although the anomalous expression of long non-coding RNAs (lncRNAs) has been extensively investigated in numerous carcinomas including gastric cancer (GC), their function remains unclear. The aim of our study was to explore the role of LINC01235 in GC. We used real-time quantitative PCR (RT-qPCR) to measure the expression of LINC01235 and twist family bHLH transcription factor 2 (TWIST2) in GC tissues. Scratch and transwell assays were performed to evaluate cellular capacity for migration and invasion. Gene relationships were explored by Weighted Gene Co-Expression Network Analysis (WGCNA). We measured TWIST2, thrombospondin 2 (THBS2) and epithelial-mesenchymal transition (EMT)-related proteins with western blot. We also used Pearson correlation analysis and the Kaplan-Meier method to detect associations among genes and overall survival. We found that LINC01235 was upregulated in GC tissues and cells. LINC01235 down-regulation restricted migration and invasion. Interestingly, we found the LINC01235-TWIST2-THBS2 axis induced EMT. Additionally, TWIST2 upregulated LINC01235 transcription in luciferase and chromatin immunoprecipitation (ChIP) assays. Bioinformatics analysis showed that microRNA (miR)-6852-5p might be a key gene involved in the regulation of TWIST2 by LINC01235. The LINC01235-TWIST2 positive feedback loop mainly affected migration and invasion of GC cells, which suggests it may serve as a potential therapeutic target in gastric cancer.

13-lncRNAs Signature to Improve Diagnostic and Prognostic Prediction of Hepatocellular Carcinoma

BACKGROUND

Hepatocellular carcinoma (HCC) is a common type of cancer with a high mortality rate and is usually detected at the middle or late stage, missing the optimal treatment period. The current study aims to identify potential long non-coding RNA (lncRNAs) biomarkers that contribute to the diagnosis and prognosis of HCC.

METHODS

The differentially expressed lncRNAs (DElncRNAs) in HCC patients were detected from the Cancer Genome Atlas (TCGA) dataset. LncRNAs signature was screened by LASSO regression, univariate, and multivariate Cox regression. The models for predicting diagnosis and prognosis were established, respectively. The prognostic model was evaluated by Kaplan-Meier survival curve receiver operating characteristic (ROC) curve and stratified analysis. The diagnostic model was validated by ROC. The lncRNAs signature was further demonstrated by functional enrichment analysis.

RESULTS

We found the 13-lncRNAs signature that had a good performance in predicting prognosis and could help to improve the value of diagnosis. In the training set, testing set, and entire cohort, the low-risk group had longer survival than the high-risk group (median OS: 3124 vs. 649 days, 2456 vs. 770 days and 3124 vs. 755 days). It performed well in 1-, 3-, and 5-year survival prediction. 13-lncRNAs-based risk score, age, and race were good predictors of prognosis. The AUC of diagnosis was 0.9487, 0.9265, and 0.9376, respectively. Meanwhile, the 13-lncRNAs were involved in important pathways, including the cell cycle and multiple metabolic pathways.

CONCLUSION

In our study, the 13-lncRNAs signature may be a potential marker for the prognosis of HCC and improve the diagnosis.

Analyzing the lncRNA, miRNA, and mRNA-associated ceRNA networks to reveal potential prognostic biomarkers for glioblastoma multiforme

Background

Glioblastoma multiforme (GBM) is the most seriously brain tumor with extremely poor prognosis. Recent research has demonstrated that competitive endogenous RNA (ceRNA) network which long noncoding RNAs (lncRNAs) act as microRNA (miRNA) sponges to regulate mRNA expression were closely related to tumor development. However, the regulatory mechanisms and functional roles of ceRNA network in the pathogenesis of GBM are remaining poorly understood.

Methods

In this study, we systematically analyzed the expression profiles of lncRNA and mRNA (GSE51146 dataset) and miRNA (GSE65626 dataset) from GEO database. Then, we constructed a ceRNA network with the dysregulated genes by bioinformatics methods. The TCGA and GSE4290 dataset were used to confirm the expression and prognostic value of candidate mRNAs.

Results

In total, 3413 differentially expressed lncRNAs and mRNAs, 305 differentially expressed miRNAs were indentified in GBM samples. Then a ceRNA network containing 3 lncRNAs, 5 miRNAs, and 60 mRNAs was constructed. The overall survival analysis of TCGA databases indicated that two mRNAs (C1s and HSD3B7) were remarkly related with the prognosis of GBM.

Conclusion

The ceRNA network may increase our understanding to the pathogenesis of GBM. In general, the candidate mRNAs from the ceRNA network can be predicted as new therapeutic targets and prognostic biomarkers for GBM.

Aberrantly Expressed lncRNAs and mRNAs of Osteogenically Differentiated Mesenchymal Stem Cells in Ossification of the Posterior Longitudinal Ligament

Ectopic bone formation is the chief characteristic of ossification of the posterior longitudinal ligament (OPLL). Emerging evidence has revealed that long non-coding RNAs (lncRNAs) can regulate the osteogenic differentiation of mesenchymal stem cells (MSCs), which are the main cells responsible for bone formation. However, the role of lncRNAs in the pathogenesis of OPLL remains unclear. In this study, 725 aberrantly expressed lncRNAs and 664 mRNAs in osteogenically differentiated MSCs from OPLL patients (OPLL MSCs) were identified by microarrays and confirmed by qRT-PCR assays. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analyses showed that the most enriched pathways included the p53, JAK-STAT, and PI3K-Akt signaling pathways. The co-expression network showed the interactions between the aberrantly expressed lncRNAs and mRNAs in OPLL MSCs, and the potential targets and transcription factors of the lncRNAs were predicted. Our research demonstrated the aberrantly expressed lncRNA and mRNA and the potential regulatory networks involved in the ectopic bone formation of OPLL. These findings imply that lncRNAs may play a vital role in OPLL, which provides a new perspective on the pathogenesis of OPLL.

lncRNA IGKJ2-MALLP2 suppresses LSCC proliferation, migration, invasion, and angiogenesis by sponging miR-1911-3p/p21

Because advanced laryngeal squamous cell carcinoma (LSCC) is diagnosed as a malignant tumor with a poor prognosis, the associated mechanisms still need to be further investigated. As key players in the development and progression of LSCC, lncRNAs have attracted increasing attention from many researchers. In this study, a novel lncRNA termed IGKJ2‐MALLP2 was identified and investigated for its effects on the development of LSCC. IGKJ2‐MALLP2 expression was confirmed by RT‐qPCR in 78 pairs of tissues and human laryngeal carcinoma cell lines. The results of this study showed that the expression of IGKJ2‐MALLP2 was reduced in LSCC tissues and displayed close relationships with tumor stage, lymph node metastasis, and clinical stage. Using a dual‐luciferase reporter assay, the ability of miR‐1911‐3p to bind both IGKJ2‐MALLP2 and p21 mRNA was demonstrated. IGKJ2‐MALLP2 could upregulate p21 expression by competitively binding miR‐1911‐3p. Moreover, IGKJ2‐MALLP2 effectively hindered the invasion, migration, and proliferation of AMC‐HN‐8 and TU212 tumor cells. Furthermore, its high expression could hinder the secretion of VEGF‐A and suppress angiogenesis. As revealed by the results of in vitro experiments, IGKJ2‐MALLP2 overexpression could restrict tumor growth and blood vessel formation in a xenograft model of LSCC. As indicated from the mentioned findings, IGKJ2‐MALLP2, which mediates p21 expression by targeting miR‐1911‐3p, was capable of regulating LSCC progression and could act as an underlying therapeutic candidate to treat LSCC.

Mechanism of Astragalus membranaceus in the treatment of laryngeal cancer based on gene co-expression network and molecular docking

Astragalus membranaceus (HUANG QI, HQ) is a kind of traditional Chinese medicine. Researchers have widely concerned its antitumor effect. At present, there is still a lack of research on the treatment of laryngeal cancer with HQ. In this study, we integrated data from the weighted gene co-expression network of laryngeal cancer samples and the components and targets of HQ. A new method for dividing PPI network modules is proposed. Important targets of HQ treatment for laryngeal cancer were obtained through the screening of critical modules. These nodes performed differential expression analysis and survival analysis through external data sets. GSEA enrichment analysis reveals pathways for important targets participation. Finally, molecular docking screened active ingredients in HQ that could interact with important targets. Combined with the laryngeal cancer gene co expression network and HQ PPI network, we obtained the critical module related to laryngeal cancer. Among them, MMP1, MMP3, and MMP10 were chosen as important targets. External data sets demonstrate that their expression in tumor samples is significantly higher than in normal samples. The survival time of patients with high expression group was significantly shortened, which is a negative factor for prognosis. GSEA enrichment analysis found that they are mainly involved in tumor-related pathways such as ECM receptor interaction and Small cell lung cancer. The docking results show that the components that can well bind to important targets of HQ are quercetin, rutin, and Chlorogenic acid, which may be the primary mechanism of the anti-cancer effect of HQ. These findings provide a preliminary research basis for Chinese medicine treatment of laryngeal cancer and offer ideas to related drug design.

Identification of biomarkers related to CD8+ T cell infiltration with gene co-expression network in clear cell renal cell carcinoma

Clear cell renal cell carcinoma (ccRCC) is an extremely common kind of kidney cancer in adults. Immunotherapy and targeted therapy are particularly effective at treating ccRCC. In this study, weighted gene co-expression network analysis and a deconvolution algorithm that quantifies the cellular composition of immune cells were used to analyze ccRCC expression data from the Gene Expression Omnibus database, and identify modules related to CD8⁺ T cells. Ten hub genes (LCK, CD2, CD3D, CD3G, IRF1, IFNG, CCR5, CD8A, CCL5, and CXCL9) were identified by co-expression network and protein-protein interactions network analysis. Datasets obtained from The Cancer Genome Atlas were analyzed and the data revealed that the hub genes were meaningfully up-regulated in tumor tissues and correlated with promotion of tumor progression. After Kaplan-Meier analysis and Oncomine meta-analysis, CCL5 was selected as a prognostic biomarker. Finally, the experimental results show that reduced expression of CCL5 decreased cell proliferation and invasion in the ccRCC cell line. Various analyses were performed and verified, CCL5 is a potential biomarker and therapeutic target which related to CD8⁺ T cell infiltration in ccRCC.