m6A-related Genes Research Articles

N6-methyladenosine-related genes contribute to malignant progression, have clinical prognostic and neoadjuvant treatments response impact for breast cancer

N6-methyladenosine (m6A) methylation dysregulation contributes to tumorigenesis and breast cancer development. This study intends to conduct a comprehensive analysis for determining the clinical significance of m6A-related genes and establishing m6A-related gene-based risk signature to predict the clinical outcomes and neoadjuvant treatments response for breast cancer patients. The m6Avar database was utilized for downloading the m6A-regulated genes. The Cancer Genome Atlas (TCGA) database was utilized for downloading breast cancer patients’ RNA-Seq data and clinicopathological information. For determining the differentially expressed m6A-related gene, a one-way analysis of variance (ANOVA) was conducted. The interaction and correlation of m6A-related genes were evaluated using search tool for the retrieval of interacting genes/proteins (STRING) and Spearmen test. For determining clusters of breast cancer patients with different clinical outcomes, a consensus clustering analysis was conducted. We screened differentially expressed genes and functional enrichment pathways between subgroups utilizing gene ontology (GO) and Kyoto encyclopedia of genes and genomes (KEGG). We constructed and verified a prognostic signature utilizing Cox regression analysis as well as a least absolute shrinkage and selection operator (LASSO) regression model. 286 genes were detected as significantly differentially expressed in different stages, including 3 m6A RNA methylation regulators, Wilms tumor 1-associating protein (WTAP), YT521-B homology (YTH) domain containing 2 (YTHDC2), and YTH domain family 2 (YTHDF2). A 13-gene prognostic signature was constructed and could predict the overall survival and the neoadjuvant treatments response in breast cancer patients. We categorized breast cancer patients into four groups based on m6A-associated RNAs expression. Significant differences were found in the overall survivals among the four clusters of patients. The biological processes and the key signaling pathways closely related to breast cancer have a close connection to the four clusters. This study confirmed that the m6A-related genes expression levels were highly associated with prognosis and neoadjuvant treatment response in breast cancer and constructed an effective m6A-related gene-based risk signature for predicting the prognosis of patients with breast cancer.

Identification of m6A-related lncRNAs for thyroid cancer recurrence

Although the prognosis of thyroid cancer (THCA) is generally good, many patients have a high risk of recurrence after treatment. N6-methyladenosine (m6A)-related long noncoding RNAs (lncRNAs) have been extensively studied in recent years. However, the potential of m6A-related lncRNAs to predict recurrence in THCA is unknown. RNA sequencing (RNA-seq) data and clinical information for THCA were downloaded from The Cancer Genome Atlas (TCGA). Differentially expressed lncRNAs (DELs) were identified using the R package DESeq2. A coexpression network based on m6A-related genes and lncRNAs was constructed. The CIBERSORT algorithm and gene set enrichment analysis (GSEA) were used for immune-infiltrating cell estimation and clustering functional enrichment analysis, respectively. A Kaplan-Meier plot was used for prognostic analysis based on m6A-associated lncRNA risk patterns. The expression of lncRNAs in recurrent and nonrecurrent THCA tissues was analyzed by real-time quantitative polymerase chain reaction (RT-qPCR). A network of m6A-related lncRNAs containing 8 lncRNAs was constructed with good predictive power for recurrence in THCA. A total of 3 clusters were obtained, and cluster 1 was most associated with THCA recurrence. We found significantly lower levels of CD8 T cells and follicular helper T cells, and significantly higher levels of dendritic cells (DCs), M2 macrophages, resting DCs, regulatory T cells, and mast cells in cluster 1 patients. Pathway analysis revealed significant enrichment in natural killer cell-mediated cytotoxicity, butyrate metabolism, and cell adhesion molecules in cluster 1. The m6A-related lncRNA risk model was effective in predicting progression-free survival (PFS) in patients with THCA recurrence. RT-qPCR analysis based on 40 THCA clinical samples from our center found the risk model to be a good predictor of recurrence in THCA patients. In summary, m6A-related lncRNAs may provide a novel predictive method for prognostic relapse in THCA patients.

M6A RNA methylation modification and tumor immune microenvironment in lung adenocarcinoma.

Lung adenocarcinoma is one of the deadliest tumors. Studies have shown that N6-methyladenosine RNA methylation regulators, as a dynamic chemical modification, affect the occurrence and development of lung adenocarcinoma. To investigate the relationship between mutations and expression levels of m6A regulators in lung adenocarcinoma, we investigated the mutations and expression levels of 38 m6A regulators. We found that mutations in m6A regulatory factors did not affect the changes in expression levels, and 19 differentially expressed genes were identified. All tumor samples were classified into two subtypes based on the expression levels of 19 differentially expressed m6A-regulated genes. Survival analysis showed significant differences in survival between the two subtypes. To explore the relationship between immune cell infiltration and survival in both subtypes, we calculated the infiltration of 23 immune cells in both subtypes, and we found that the subtype with high immune cell infiltration had better survival. We found that subtypes with low tumor purity and high stromal and immune scores had better survival. The m6A-related immune genes were identified by taking the intersection of differentially expressed genes and immune genes in the two isoforms and calculating the Pearson correlation coefficients between the intersecting immune genes and the differentially expressed m6A-regulated genes. Finally, a prognostic model associated with m6A and associated with immunity was developed using prognostic genes screened from m6A-associated immune genes. The predictive power of the model was evaluated and our model was able to achieve good prediction.

Characteristics of m6A-related LncRNAs in breast cancer as prognostic biomarkers and immunotherapy.

N6-methyladenosine (m6A) is a common RNA modification in coding and non-coding RNAs and plays an important role in the occurrence and development of breast cancer (BC). However, the role of m6A-related lncRNAs in breast cancer prognosis is unclear. This study aimed to help verify the biological function of m6A-related lncRNAs in breast cancer prognosis through bio-informatics techniques. First, we screened 18 m6A-related lncRNAs from the TCGA database: AL137847.1, AC137932.2, OTUD6B-AS1, MORF4L2-AS1, AC078846.1, AC012442.1, AL118556.1, AL138955.1, AC009754.1, AC024257.4, AL391095.1, AC024270.3, AC087392.1, LINC02649, AC090948.2, AL158212.1, ITGA6-AS1, AL133243.2 and constructed a risk-prognosis model based on this. Based on the model's median risk score, BC patients were divided into high-risk and low-risk groups. Then, the predictive value of the model was verified by Cox regression, Lasso regression, Kaplan-Meier curve and ROC curve analysis, and biological differences between the two groups were verified by GO enrichment analysis, tumor mutation burden, immune indications and in vitro tests. Importantly, the risk score of this prognostic model is an excellent independent prognostic factor, and m6A regulators are differentially expressed in patients with different risks. In addition, based on patients' different sensitivities to drugs, some drug candidates for different risk populations are screened to provide targets for breast cancer treatment. The difference in immune function between high-risk and low-risk patients also affected the sensitivity to immunotherapy. In the validation of clinical samples, we analyzed the expression of relevant lncRNAs in different risk groups and speculated the possible impact on the prognosis of breast cancer patients. The risk assessment tool built based on the full analysis of these m6A-related genes and m6A-related lncRNA libraries, as well as the m6A-related lncRNAs, has a high prognostic prediction ability, which may provide a supplementary screening method for accurately judging the prognosis of BC and a new perspective for personalized treatment of breast cancer patients.

Constructing and validating of m6a-related genes prognostic signature for stomach adenocarcinoma and immune infiltration: Potential biomarkers for predicting the overall survival.

Stomach adenocarcinoma (STAD) arises from the mutations of stomach cells and has poor overall survival. Chemotherapy is commonly indicated for patients with stomach cancer following surgical resection. The most prevalent alteration that affects cancer growth is N6-methyladenosine methylation (m6A), although the possible function of m6A in STAD prognosis is not recognized. The research measured predictive FRGs in BLCA samples from the TCGA and GEO datasets. Data on the stemness indices (mRNAsi), gene mutations, copy number variations (CNV), tumor mutation burden (TMB), and corresponding clinical characteristics were obtained from TCGA and GEO. STAD from TCGA and GEO at 24 m6A was investigated. Lasso regression was used to construct the prediction model to assess the m6A prognostic signals in STAD. In addition, the correlation between m6a and immune infiltration in STAD patients was discussed using GSVA and ssGSEA analysis. Based on these genes, GO and KEGG analyses were performed to identify key biological functions and key pathways. A significant relationship was discovered between numerous m6A clusters and the tumor immune microenvironment, as well as three m6A alteration patterns with different clinical outcomes. Furthermore, GSVA and ssGSEA showed that m6A clusters were significantly associated with immune infiltration in the STAD. The low-m6Ascore group had a lower immunotherapeutic response than the high-m6Ascore group. ICIs therapy was more effective in the group with a higher m6Ascore. Three writers (VIRMA, ZC3H13, and METTL3) showed significantly lower expression, whereas five authors (METTL14, METTL16, WTAP, RBM15, and RBM15B) showed considerably higher expression. Three readers (YTHDC2, YTHDF2, and LRPPRC) had higher levels of expression, whereas eleven readers (YTHDC1, YTHDF1, YTHDF3, HNRNPC, FMR1, HNRNPA2B1, IGFBP1, IGFBP2, IGFBP3, and RBMX) had lower levels. As can be observed, the various types of m6 encoders have varied ramifications for STAD control. STAD occurrence and progression are linked to m6A-genes. Corresponding prognostic models help forecast the prognosis of STAD patients. m6A-genes and associated immune cell infiltration in the tumor microenvironment (TME) may serve as potential therapeutic targets in STAD, which requires further trials. In addition, the m6a-related gene signature offers a viable alternative to predict bladder cancer, and these m6A-genes show a prospective research area for STAD targeted treatment in the future.

HNRNPC, a predictor of prognosis and immunotherapy response based on bioinformatics analysis, is related to proliferation and invasion of NSCLC cells

BackgroundLittle is known about the relationship between N6-methyladenosine (m6A)-related genes and tumor immune microenvironment (TIME) in non-small cell lung cancer (NSCLC). It is unclear which m6A regulators are essential for NSCLC progression. The aim of this work was to excavate the role of m6A-related genes in the TIME and progression of NSCLC.MethodsBased on bioinformatics analysis, heterogeneous nuclear ribonucleoprotein C (HNRNPC) was considered as the most influential m6A regulator. Further study was investigated using patient samples, stable cell lines, and xenograft mice models.ResultsThe differentially expressed profiles of m6A-related genes were established in NSCLC, and the NSCLC samples were clustered into two subtypes with different immune infiltration and survival time. Next, we found that the risk score (RS) based on m6A-related genes was a predictor of prognosis and immunotherapy response for NSCLC, in which HNRNPC was considered as the most influential m6A regulator. In NSCLC patients, we confirmed that HNRNPC predicted poor prognosis and correlated with tumor invasion and lymph node metastasis. RNA-seq data revealed that HNRNPC was involved in cell growth, cell migration, extracellular matrix organization and angiogenesis. In vitro, we verified that HNRNPC knockdown attenuated the cell proliferation, clonogenicity, invasion and migration. In vivo, HNRNPC knockdown inhibited the tumor growth and lung metastasis. Additionally, HNRNPC knockdown was associated with high CD8 + T cell infiltration, along with elevated CD4 + T cell infiltration, collagen production and angiogenesis.ConclusionsM6A regulator HNRNPC, a predictor of prognosis and immunotherapy response based on bioinformatics analysis, is related to proliferation and invasion of NSCLC cells.

Construction of a ferroptosis-related signature based on seven lncRNAs for prognosis and immune landscape in clear cell renal cell carcinoma

BackgroundRecent studies have demonstrated that long non-coding RNAs (lncRNAs) are involved in regulating tumor cell ferroptosis. However, prognostic signatures based on ferroptosis-related lncRNAs (FRLs) and their relationship to the immune microenvironment have not been comprehensively explored in clear cell renal cell carcinoma (ccRCC).MethodsIn the present study, the expression profiles of ccRCC were acquired from The Cancer Genome Atlas (TCGA) database; 459 patient specimens and 69 adjacent normal tissues were randomly separated into training or validation cohorts at a 7:3 ratio. We identified 7 FRLs that constitute a prognostic signature according to the differential analysis, correlation analysis, univariate regression, and least absolute shrinkage and selection operator (LASSO) Cox analysis. To identify the independence of risk score as a prognostic factor, univariate and multivariate regression analyses were also performed. Furthermore, CIBERSORT was conducted to analyze the immune infiltration of patients in the high-risk and low-risk groups. Subsequently, the differential expression of immune checkpoint and m6A genes was analyzed in the two risk groups.ResultsA 7-FRLs prognostic signature of ccRCC was developed to distinguish patients into high-risk and low-risk groups with significant survival differences. This signature has great prognostic performance, with the area under the curve (AUC) for 1, 3, and 5 years of 0.713, 0.700, 0.726 in the training set and 0.727, 0.667, and 0.736 in the testing set, respectively. Moreover, this signature was significantly associated with immune infiltration. Correlation analysis showed that risk score was positively correlated with regulatory T cells (Tregs), activated CD4 memory T cells, CD8 T cells and follicular helper T cells, whereas it was inversely correlated with monocytes and M2 macrophages. In addition, the expression of fourteen immune checkpoint genes and nine m6A-related genes varied significantly between the two risk groups.ConclusionWe established a novel FRLs-based prognostic signature for patients with ccRCC, containing seven lncRNAs with precise predictive performance. The FRLs prognostic signature may play a significant role in antitumor immunity and provide a promising idea for individualized targeted therapy for patients with ccRCC.

Comprehensive analysis of ERCC3 prognosis value and ceRNA network in AML.

Acute myeloid leukemia (AML) is a hematological malignancy with high molecular and clinical heterogeneity, and is the most common type of acute leukemia in adults. Due to limited treatment options, AML is prone to relapse and has a poor prognosis. Excision repair cross-complementing 3 (ERCC3) is an important member of nucleotide excision repair (NER) that is overexpressed in types of solid cancers and potentially regarded as a prognostic factor. However, its role in AML remains unclear. The purpose of this study was to explore ERCC3 expression and functions in AML. The Cancer Genome Atlas (TCGA) and GEO (Gene Expression Omnibus) were used to test the accuracy of ERCC3 expression levels for AML diagnosis. Using online databases and R packages, we also explored the signaling pathway, epigenetic regulation, infiltration of immune cells, clinical prognostic value, and ceRNA network in AML. Our results revealed that ERCC3 expression was increased in AML and that high ERCC3 expression had good value for disease-free survival and overall survival in AML patients who underwent allogeneic hematopoietic stem cell transplantation (allo-HSCT). We found that ERCC3 and co-expressed genes were mainly involved in chemical carcinogenesis/reactive oxygen species, ubiquitin-mediated protein degradation and oxidative phosphorylation. In addition, almost all the m6A-related coding genes (except GF2BP1) were positively associated with ERCC3 expression. We also constructed a ceRNA regulatory network containing ERCC3 in AML and identified 6 pairs of ceRNA networks, indicating that ERCC3 expression is regulated by a noncoding RNA system. This study demonstrated that ERCC3 was overexpressed in AML and that high ERCC3 expression can be considered a biomarker conducive to allo-HSCT in AML patients.

The risk of COVID-19 can be predicted by a nomogram based on m6A-related genes.

The expression of m6A-related genes and their significance in COVID-19 patients are still unknown. The GSE177477 and GSE157103 datasets of the Gene Expression Omnibus were used to extract RNA-seq data. The expression of 26 m6A-related genes and immune cell infiltration in COVID-19 patients were analyzed. Finally, we built and validated a nomogram model to predict the risk of COVID-19 infection. There were significant differences in 11 m6A regulatory factors between patients with COVID-19 and healthy individuals. The classification of disease subtypes based on m6A-related gene levels can be distinguished. COVID-19 patients in GSE177477 were classified into two categories based on m6A-related genes. The patients in cluster A were all symptomatic, while those in cluster B were asymptomatic. A significant correlation was also found between immune cells and m6A-related genes. Finally, seven m6A-related disease-characteristic genes, HNRNPA2B1, ELAVL1, RBM15, RBM15B, YTHDC1, HNRNPC, and WTAP, were screened to construct a nomogram model for predicting risk. The calibration curve, decision curve analysis, and clinical impact curve analysis were used to show that the nomogram model was effective and had a high net efficacy for risk prediction. m6A-related genes were correlated with immune cells. The nomogram model effectively predicted COVID-19 risk. Moreover, m6A-related genes may be associated with the presence or absence of symptoms in COVID-19 patients.

Bioinformatics analysis of the correlation between m6A RNA methylation regulators and the immune infiltration and prognosis of bladder cancer.

To analyze the effect of N6-methyladenosine (m6A) RNA methylation regulators on the immune infiltration and prognosis of bladder cancer (BC). We explored the related signaling pathways and prognosis-related genes to provide candidate targets for the treatment and prognostic evaluation of BC. After downloading BC data from The Cancer Genome Atlas (TCGA) database, the expressions of m6A-related genes were obtained. We then performed correlation and sample cluster analysis of the m6A methylation regulator genes as well as difference comparison and survival analysis for the clustered patients using R software. Gene set enrichment analysis (GSEA) was carried out on cluster-grouped samples. Finally, the prognosis-related genes of BC among the m6A methylation regulators were screened. Genomic alterations in the m6A regulators were linked to a poor BC prognosis. HNRNPA2B1, HNRNPC, IGF2BP2, RBM15, YTHDF1, and YTHDF2 were found to be associated with advanced clinical stages of BC. Furthermore, the current study revealed that the levels of the m6A regulators were related to the expression levels and immune infiltration levels of immune regulators [immunosuppressive factors, immunostimulators, and major histocompatibility complex (MHC) molecules] in BC. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analyses suggested that in addition to the relevant immune responses, m6A regulators were involved in the poor prognosis of BC via their participation in blood vessels through regulatory RNA binding, telomeric DNA binding, microRNA (miRNA) binding, negative regulation of messenger RNA (mRNA) processing, negative regulation of DNA biosynthesis, branches of morphogenesis, positive regulation of the Notch receptor target gene transcription, etc. The expression of m6A RNA methylation regulators is closely linked to immune infiltration and prognosis in BC. Thus, it can be utilized as a potential molecular target for the treatment and prognostic assessment of BC.

Prognostic analysis of m6A-related genes as potential biomarkers in idiopathic pulmonary fibrosis.

Idiopathic pulmonary fibrosis (IPF) is a progressive, fatal lung disease with limited treatment options. N6-methyladenosine (m6A) is a reversible RNA modification and has been implicated in various biological processes. However, there are few studies on m6A in IPF. This project mainly explores the prognostic value of m6A-related genes as potential biomarkers in IPF, in order to establish a set of accurate prognostic prediction model. In this study, we used GSE28042 dataset in GEO database to screen out 218 m6A-related candidate genes with high IPF correlation and high differential expression through differentially expressed gene analysis, WGCNA and m6A correlation analysis. The genes associated with the prognosis of IPF were screened out by univariate Cox regression analysis, LASSO analysis, and multivariate Cox regression analysis, and the multivariate Cox model of prognostic risk of related genes was constructed. We found that RBM11, RBM47, RIC3, TRAF5 and ZNF14 were key genes in our model. Finally, the prognostic prediction ability and independent prognostic characteristics of the risk model were evaluated by survival analysis and independent prognostic analysis, and verified by the GSE93606 dataset, which proved that the prognostic risk model we constructed has a strong and stable prediction efficiency.

Expression of m6A Methylation Regulator in Osteoarthritis and Its Prognostic Markers.

Osteoarthritis (OA) is a multifactorial disorder, in which genetic factors are strongly associated with its development. However, the pathogenesis of OA is still unclear, and recently it has been observed that epigenetic modifications are also involved in the pathogenesis of OA. This study aims to study the potential role of m6A-related genes in the occurrence and development of OA. We downloaded the OA expression profile data (GSE55235) from the Gene Expression Omnibus database. First, function enrichment analysis of 17 representative m6A methylation regulatory factors was performed using the DAVID database and Metascape online tool. Then, we analyzed the expression of 17 m6A methylation regulatory factors in OA and the correlation between regulatory factors using Perl software. Finally, receiver operating characteristic (ROC) curve analysis and the area under the ROC curve were used to evaluate the diagnostic effectiveness of m6A-related genes for discriminating patients with OA and healthy. We first identified that 12 of the 17 genes were differentially expressed in OA. ALKBH1, EIF3, IGF2BP3, WTAP, and YTHDC1 were associated with early diagnosis and prognosis of OA. m6A RNA methylation regulator factors are key players in the progression of OA and have potential role in the stratification of prognosis and the formulation of treatment strategies.

Characterization of immune microenvironment infiltration and m6A regulator-mediated RNA methylation modification patterns in osteoarthritis.

Few studies have been reported the potential role of N6-methyladenosine (m6A) modification in osteoarthritis (OA). We investigated the patterns of m6A modification in the immune microenvironment of OA. We evaluated the m6A modification patterns based on 22 m6A regulators in 139 OA samples and systematically associated these modification patterns with immune cell infiltration characteristics. The function of m6A phenotype-related differentially expressed genes (DEGs) was investigated using gene enrichment analysis. An m6A score model was constructed using principal component analysis (PCA), and an OA prediction model was established based on the key m6A regulators. We used real-time PCR analysis to detect the changes of gene expression in the cell model of OA. Healthy and OA samples showed significant differences in the expression of m6A regulators. Nine key m6A regulators, two m6A modification patterns, m6A-related genes and two gene clusters were identified. Some m6A regulators had a strong correlation with each other. Gene clusters and m6A clusters have high similarity, and cluster A corresponds to a high m6A score. Immunocytes infiltration differed significantly between the two clusters, with the m6A cluster B and gene cluster B having more types of infiltrating immunocytes than cluster A. The predictive model can also predict the progression of OA through m6A regulators expression. The results of real-time PCR analysis showed that the gene expression in the cell model of OA is similar to that of the m6A cluster B. Our study reveals for the first time the potential regulatory mechanism of m6A modification in the immune microenvironment of OA. This study also sheds new light on the pathogenesis of OA.

N6-methyladenosine regulators-related immune genes enable predict graft loss and discriminate T-cell mediate rejection in kidney transplantation biopsies for cause.

The role of m6A modification in kidney transplant-associated immunity, especially in alloimmunity, still remains unknown. This study aims to explore the potential value of m6A-related immune genes in predicting graft loss and diagnosing T cell mediated rejection (TCMR), as well as the possible role they play in renal graft dysfunction. Renal transplant-related cohorts and transcript expression data were obtained from the GEO database. First, we conducted correlation analysis in the discovery cohort to identify the m6A-related immune genes. Then, lasso regression and random forest were used respectively to build prediction models in the prognosis and diagnosis cohort, to predict graft loss and discriminate TCMR in dysfunctional renal grafts. Connectivity map (CMap) analysis was applied to identify potential therapeutic compounds for TCMR. The prognostic prediction model effectively predicts the prognosis and survival of renal grafts with clinical indications (P< 0.001) and applies to both rejection and non-rejection situations. The diagnostic prediction model discriminates TCMR in dysfunctional renal grafts with high accuracy (area under curve = 0.891). Meanwhile, the classifier score of the diagnostic model, as a continuity index, is positively correlated with the severity of main pathological injuries of TCMR. Furthermore, it is found that METTL3, FTO, WATP, and RBM15 are likely to play a pivotal part in the regulation of immune response in TCMR. By CMap analysis, several small molecular compounds are found to be able to reverse TCMR including fenoldopam, dextromethorphan, and so on. Together, our findings explore the value of m6A-related immune genes in predicting the prognosis of renal grafts and diagnosis of TCMR.

Identification and validation of an m6A-related gene signature to predict prognosis and evaluate immune features of breast cancer.

Breast cancer is the most prevalent cancer, and it is accompanied by high heterogeneity. N6-methyladenosine (m6A) modification significantly contributes to breast cancer tumorigenesis and progression. However, how m6A-related genes affect the clinical outcomes and tumor immune microenvironment (TIME) of breast cancer is largely unknown. Our study developed an m6A-related gene signature on the basis of The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) databases. The m6A-related gene signature was constructed using univariate, least absolute shrinkage and selection operator (LASSO), and multivariate Cox regression analyses. Breast cancer patients were classified into low- and high-risk groups depending on the median risk score. The reliability and efficiency of the signature were validated using Kaplan-Meier analysis, receiver operating characteristic (ROC) curves, and principal component analysis (PCA). The risk score was validated as an independent indicator associated with overall survival, and a nomogram model was created to estimate the overall survival of patients with breast cancer. Functional annotation suggested that the risk score had a strong relationship with immune-related pathways. Different proportions of immune cell infiltration between the two groups were evaluated using various algorithms. The high-risk group had higher immune checkpoint expression levels. We discovered that one of the 6 prognostic genes, TMEM71, was downregulated in breast cancer tissues. In vitro experiments indicated that overexpression of TMEM71 suppressed breast cancer cell proliferation and migration. In conclusion, the m6A-related gene signature may be a sensitive biomarker for overall survival prediction and guide the individualized treatment for breast cancer patients.

Potential prognostic value of a eight ferroptosis-related lncRNAs model and the correlative immune activity in oral squamous cell carcinoma

BackgroundTo investigate the prognostic value of ferroptosis-related long noncoding RNAs (lncRNAs) in oral squamous cell carcinoma (OSCC) and to construct a prognostic risk and immune activity model.MethodsWe obtained clinical and RNA-seq information on OSCC patient data in The Cancer Genome Atlas (TCGA) Genome Data Sharing (GDC) portal. Through a combination of a differential analysis, Pearson correlation analysis and Cox regression analysis, ferroptosis-related lncRNAs were identified, and a prognostic model was established based on these ferroptosis-related lncRNAs. The accuracy of the model was evaluated via analyses based on survival curves, receiver operating characteristic (ROC) curves, and clinical decision curve analysis (DCA). Univariate Cox and multivariate Cox regression analyses were performed to evaluate independent prognostic factors. Then, the infiltration and functional enrichment of immune cells in high- and low-risk groups were compared. Finally, certain small-molecule drugs that potentially target OSCC were predicted via use of the L1000FWD database.ResultsThe prognostic model included 8 ferroptosis-related lncRNAs (FIRRE, LINC01305, AC099850.3, AL512274.1, AC090246.1, MIAT, AC079921.2 and LINC00524). The area under the ROC curve (AUC) was 0.726. The DCA revealed that the risk score based on the prognostic model was a better prognostic indicator than other clinical indicators. The multivariate Cox regression analysis showed that the risk score was an independent prognostic factor for OSCC. There were differences in immune cell infiltration, immune functions, m6A-related gene expression levels, and signal pathway enrichment between the high- and low-risk groups. Subsequently, several small-molecule drugs were predicted for use against differentially expressed ferroptosis-related genes in OSCC.ConclusionsWe constructed a new prognostic model of OSCC based on ferroptosis-related lncRNAs. The model is valuable for prognostic prediction and immune evaluation, laying a foundation for the study of ferroptosis-related lncRNAs in OSCC.

The M6A Methyltransferase METTL14 Promotes Myelodysplastic Syndromes Development Via PI3K/Akt Signaling Pathway

Background: RNA N6-methyladenosine (m6A) plays a critical role in normal and malignant hematopoiesis. The m6A methyltransferases METTL3 and METTL14 were reported as oncogenes in acute myeloid leukemia. Epigenetic dysregulation is a prominent biological feature of myelodysplastic syndromes (MDS), however, whether RNA methylation dysregulation exists in MDS, and what's the functions of m6A methyltransferases are largely unknown. Methods: The global m6A modification level of RNA in bone marrow was detected in MDS patients by colorimetric method, and the expression level of METTL14 in bone marrow of MDS patients was detected by q-PCR. Both gain- and loss-of-function experiments were performed to study the role of METTL14 in MDS. M6A sequencing and RNA sequencing were examined to explore the potential signaling pathways and target genes that mediated METTL14 function in MDS. Results: We detected obvious RNA m6A dysregulation in bone marrow of MDS patients compared with normal controls, and found that the up-regulation of the global m6A modification is closely related to higher-risk category and worse survival of MDS patients. By analyzing the MDS public database (GSE_58831) and q-PCR data of MDS patients in our center, we found that among 23 classical m6A related genes, m6A methyltransferase METTL14 was the key factor causing the m6A dysregulation in MDS: high expression of METTL14 was associated with higher disease risk stratification, more bone marrow blasts, poorer prognosis, and higher response rates to hypomethylating agents (HMAs). In order to explore the functions of METTL14 in MDS, we performed gain- and loss-of-function experiments in MDS cell line MDSL in vitro. It was observed that the expression of METTL14 promoted the cell proliferation and clone formation ability of MDS cells by inducing cell apoptosis and differentiation. More interestingly, we found that HMAs down-regulated the level of global m6A modification by inhibiting METTL14 expression, and the expression of METTL14 could increase the sensitivity of MDS cells to HMAs in MDS cells. To search for upstream regulatory genes of METTL14, we analyzed the ChIPBase online database and identified ELF1 might be the potential upstream transcription factor. The relationship between ELF1 and METTL14 was further confirmed by ELF1-overexpressed and ELF1-knowndowned MDSL cell lines. To explore the relationship between gene mutations and METTL14, we analyzed the correlations between next generation sequencing (NGS) data and METTL14 expression level in MDS patients in our center, and found that the METTL14 expression level was significantly increased in MDS patients carrying TP53 gene mutations (P<0.05). The increased METTL14 and ELF1 expression were all observed in MDS cell lines with introduction of TP53 missense mutations (M237I and Y220C) or TP53 knockdown. Mechanistically, m6A sequencing and RNA-seq sequencing revealed METTL14 promoted MDS development via PI3K/Akt signaling pathway, and the potential target genes involved in PI3K/Akt signaling pathway were ANGPT1, CDK6, FGFR2, GNG11, IGF1R et al. Conclusion： Herein, we reported RNA m6A methylation dysregulation did exist in MDS, and m6A methyltransferase METTL14 was an important regulatory factor. Higher METTL14 expression was associated with higher-risk stratification, poorer survival, and better response to HMAs in MDS patients. METTL14 was required for the cell growth and response to HMAs in MDS cells. TP53 mutations were significantly correlated with the up-regulation of METTL14 expression level, and TP53 mutations could induce METTL14 overexpression by regulating transcription factor ELF1. Mechanistically, METTL14 exerts its oncogenic role via PI3K/Akt signaling pathway. Our findings highlighted that METTL14 could potentially be a novel therapeutic target in MDS especially those with TP53 mutations. Figure 1View largeDownload PPTFigure 1View largeDownload PPT Close modal

The RNA demethylase ALKBH5 promotes the progression and angiogenesis of lung cancer by regulating the stability of the LncRNA PVT1

BackgroundN6-methyladenosine (m6A) is the most common posttranscriptional modification of RNA and plays critical roles in human cancer progression. However, the biological function of m6A methylation requires further studied in cancer, especially in tumor angiogenesis.MethodsA public database was used to analyze the expression and overall survival of ALKBH5 and PVT1 in lung cancer patients. CCK-8 and colony formation assays were performed to detect cell proliferation, a transwell assay was used to assess cell migration, and a tube formation assay was performed to assess angiogenic potential in vitro. A zebrafish lung cancer xenograft model was used to verify the function of ALKBH5 and PVT1 in vivo. Western blot assays were used to measure the relative protein expression in lung cancer cells. SRAMP predictor analysis and RNA stability experiments were used to examine the potential m6A modification.ResultsBioinformatics analysis showed that the expression levels of m6A-related genes were changed significantly in lung cancer tissues compared with normal lung tissues. We then identified that ALKBH5 was upregulated in lung cancer tissues and associated with poor prognosis of lung cancer patients by analyzing a public database. Knockdown of ALKBH5 inhibited the proliferation and migration of cultured lung cancer cell lines. Zebrafish lung cancer xenografts showed that ALKBH5 silencing also suppressed the growth and metastasis of lung cancer cells. Moreover, knockdown of ALKBH5 inhibited the angiogenesis of lung cancer in vitro and in vivo. Mechanistic studies showed that knockdown of ALKBH5 decreased the expression and stability of PVT1 in lung cancer cells. We next observed that PVT1 promoted the progression of lung cancer cells in vitro and in vivo and regulated the expression of VEGFA and angiogenesis in lung cancer. Finally, rescue experiments revealed that ALKBH5 regulated the proliferation, migration and angiogenesis of lung cancer cells, partially through PVT1.ConclusionOur results demonstrate that ALKBH5 promotes the progression and angiogenesis of lung cancer by regulating the expression and stability of PVT1, which provides a potential prognostic and therapeutic target for lung cancer patients.

Construction of N-7 methylguanine-related mRNA prognostic model in uterine corpus endometrial carcinoma based on multi-omics data and immune-related analysis

N-7 methylguanine (m7G) is one of the most common RNA base modifications in post-transcriptional regulation, which participates in multiple processes such as transcription, mRNA splicing and translation during the mRNA life cycle. However, its expression and prognostic value in uterine corpus endometrial carcinoma (UCEC) have not been systematically studied. In this paper, the data such as gene expression profiles, clinical data of UCEC patients, somatic mutations and copy number variants (CNVs) are obtained from the cancer genome atlas (TCGA) and UCSC Xena. By analyzing the expression differences of m7G-related mRNA in UCEC and plotting the correlation network maps, a risk score model composed of four m7G-related mRNAs (NSUN2, NUDT3, LARP1 and NCBP3) is constructed using least absolute shrinkage and selection operator (LASSO), univariate and multivariate Cox regression in order to identify prognosis and immune response. The correlation of clinical prognosis is analyzed between the m7G-related mRNA and UCEC via Kaplan–Meier method, receiver operating characteristic (ROC) curve, principal component analysis (PCA), t-SNE, decision curve analysis (DCA) curve and nomogram etc. It is concluded that the high risk is significantly correlated with (P < 0.001) the poorer overall survival (OS) in patients with UCEC. It is one of the independent risk factors affecting the OS. Differentially expressed genes are identified by R software in the high and low risk groups. The functional analysis and pathway enrichment analysis have been performed. Single sample gene set enrichment analysis (ssGSEA), immune checkpoints, m6A-related genes, tumor mutation burden (TMB), stem cell correlation, tumor immune dysfunction and rejection (TIDE) scores and drug sensitivity are also used to study the risk model. In addition, we have obtained 3 genotypes based on consensus clustering, which are significantly related to (P < 0.001) the OS and progression-free survival (PFS). The deconvolution algorithm (CIBERSORT) is applied to calculate the proportion of 22 tumor infiltrating immune cells (TIC) in UCEC patients and the estimation algorithm (ESTIMATE) is applied to work out the number of immune and matrix components. In summary, m7G-related mRNA may become a potential biomarker for UCEC prognosis, which may promote UCEC occurrence and development by regulating cell cycles and immune cell infiltration. It is expected to become a potential therapeutic target of UECE.

ZNF320 is a hypomethylated prognostic biomarker involved in immune infiltration of hepatocellular carcinoma and associated with cell cycle.

Hepatocellular carcinoma (HCC) is one of the most deadly and common malignant cancers around the world, and the prognosis of HCC patients is not optimistic. ZNF320 belongs to Krüppel like zinc finger gene family. However, no studies have focused on the influence of ZNF320 in HCC. We first analyzed ZNF320 expression in HCC by using data from TCGA and ICGC, then conducted a joint analysis with TIMER and UALCAN, and validated by immunohistochemistry in clinical HCC samples. Then we applied UALCAN to explore the correlation between ZNF320 expression and clinicopathological characteristics. Consequently, using Kaplan-Meier Plotter analysis and the Cox regression, we can predict the prognostic value of ZNF320 for HCC patients. Next, the analysis by GO, KEGG, and GSEA revealed that ZNF320 was significantly correlated to cell cycle and immunity. Finally, TIMER and GEPIA analysis verified that ZNF320 expression is closely related to tumor infiltrating immune cells (TIIC), including B cells, CD8+ T cells, CD4+ T cells, macrophages, neutrophils, and dendritic cells. The analysis of the TCGA and ICGC data sets revealed that ZNF320 expression was significantly correlated with m6A related genes (RBMX, YTHDF1, and METTL3). In conclusion, ZNF320 may be a prognostic biomarker related to immunity as a candidate for liver cancer.