m6A Modification in Coding and Non-coding RNAs: Roles and Therapeutic Implications in Cancer

N6-methyladenosine (m6A) is considered the most common, abundant, and conserved internal transcript modification, especially in eukaryotic messenger RNA (mRNA). m6A is installed by m6A methyltransferases (METTL3/14, WTAP, RBM15/15B, VIRMA and ZC3H13, termed “writers”), removed by demethylases (FTO, ALKBH5, and ALKBH3, termed “erasers”), and recognized by m6A-binding proteins (YTHDC1/2, YTHDF1/2/3, IGF2BP1/2/3, HNRNP, and eIF3, termed “readers”). Accumulating evidence suggests that m6A RNA methylation greatly impacts RNA metabolism and is involved in the pathogenesis of many kinds of diseases, including cancers. In this review, we focus on the physiological functions of m6A modification and its related regulators, as well as on the potential biological roles of these elements in human tumors.

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The potential role of RNA N6-methyladenosine in Cancer progression

N6-methyladenosine (m6A) is one of the most common RNA modifications in eukaryotes, mainly in messenger RNA (mRNA). Increasing evidence shows that m6A methylation modification acts an essential role in various physiological and pathological bioprocesses. Noncoding RNAs (ncRNAs), including miRNAs, lncRNAs and circRNAs, are known to participate in regulating cell differentiation, angiogenesis, immune response, inflammatory response and carcinogenesis. m6A regulators, such as METTL3, ALKBH5 and IGF2BP1 have been reported to execute a m6A-dependent modification of ncRNAs involved in carcinogenesis. Meanwhile, ncRNAs can target or modulate m6A regulators to influence cancer development. In this review, we provide an insight into the interplay between m6A modification and ncRNAs in cancer.

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Novel insights into the interplay between m6A modification and noncoding RNAs in cancer.

LncRNA CDKN2B-AS1 promotes tumor growth and metastasis of human hepatocellular carcinoma by targeting let-7c-5p/NAP1L1 axis.

Tumor microenvironment (TME) has been reported to exhibit a crucial effect in lung cancer. Therefore, this study was aimed to investigate the genes associated with TME and develop a risk score to predict the overall survival (OS) of patients with lung adenocarcinoma (LUAD) based on these genes. The immune and stromal scores were generated by the ESTIMATE algorithm for LUAD patients in The Cancer Genome Atlas (TCGA) database. Differentially expressed gene and weighted gene co-expression network analyses were used to derive immune- and stromal-related genes. The Least Absolute Shrinkage and Selection Operator (LASSO)-Cox regression was applied for further selection and the selected genes were inputted into stepwise regression to develop TME-related risk score (TMErisk) which was further validated in Gene Expression Omnibus (GEO) datasets. TMErisk-related biological phenotypes were analyzed in function enrichment, tumor immune signature, and tumor mutation signature. The patient’s response to immunotherapy was inferred by the tumor immune dysfunction and exclusion (TIDE) score and immunophenoscore (IPS). According to our results, TMErisk was developed based on SERPINE1, CX3CR1, CD200R1, GBP1, IRF1, STAP1, LOX, and OR7E47P. Furthermore, high TMErisk was identified as a poor factor for OS in TCGA and GEO datasets, as well as in subgroup analysis with different gender, smoking status, age, race, anatomic site, therapies, and tumor-node-metastasis (TNM) stages. Higher TMErisk is also associated negatively with the abundance of B cells, CD4+ T cells, CD8+ T cells, neutrophils, macrophages, and other stromal or immune cells. Several genes of the human leukocyte antigen (HLA) family and immune checkpoints were less expressed in the high-TMErisk group. Mutations of 19 genes occurred more frequently in the high-TMErisk group. These mutations may be associated with TME change and indicate patients’ response to immunotherapy. According to our analyses, a lower TMErisk score may indicate better response and OS outcome of immunotherapy.

A risk model developed based on tumor microenvironment predicts overall survival and associates with tumor immunity of patients with lung adenocarcinoma.

Accumulating evidence implies that N6-methyladenosine (m6A) methylation participated in the tumorigenesis of gastric cancer (GC). Here we synthetically analyzing the prognostic value and expression profile of seven m6A methylation-relevant genes through silico analysis of sequencing data downloaded from The Cancer Genome Atlas, Kaplan-Meier plotter, and Gene Expression Omnibus database. We explored the methyltransferase-like 3 (METTL3) expression in GC cell line and tumor tissues by reverse transcription quantitative polymerase chain reaction and western blot analysis. The m6A methylation status of total RNA was measured by m6A RNA methylation quantification kit. Small interfering RNA was used to establish METTL3 knockdown cell lines. We also measure the proliferation and migration capability GC cell. Furthermore, we detect the epithelial cell mesenchymal transition marker and m6A methylation level after METTL3 knock down. Our result revealed that METTL3 was significantly increased in GC tissues compared with control in big crowd data sets. Survival analysis showed that METTL3 serve as a poor prognostic factor for GC patients. The expression level of METTL3 gradually increased with the progress of tumor stage and grade. GFI1 is an important transcription factor associated with METTL3. We verified the up-trend of METTL3 in messenger RNA and protein expression and observed a significant increase in the m6A methylation status of total RNA in the GC cells and tissues. METTL3 knockdown inhibited total RNA m6A methylation level, as well as cell proliferation and migration capacity. Moreover, METTL3 knockdown decreased α-smooth muscle actin. Taken together, our finding revealed that m6A methylation writer METTL3 serve as an oncogene in tumorigenesis of GC.

Dysregulated N6-methyladenosine methylation writer METTL3 contributes to the proliferation and migration of gastric cancer.

Cutaneous malignant melanoma (hereafter called melanoma) is one of the most aggressive cancers with increasing incidence and mortality rates worldwide. In this study, we performed a systematic investigation of the tumor microenvironmental and genetic factors associated with melanoma to identify prognostic biomarkers for melanoma. We calculated the immune and stromal scores of melanoma patients from the Cancer Genome Atlas (TCGA) using the ESTIMATE algorithm and found that they were closely associated with patients' prognosis. Then the differentially expressed genes were obtained based on the immune and stromal scores, and prognostic immune-related genes further identified. Functional analysis and the protein-protein interaction network further revealed that these genes enriched in many immune-related biological processes. In addition, the abundance of six infiltrating immune cells was analyzed using prognostic immune-related genes by TIMER algorithm. The unsupervised clustering analysis using immune-cell proportions revealed eight clusters with distinct survival patterns, suggesting that dendritic cells were most abundant in the microenvironment and CD8+ T cells and neutrophils were significantly related to patients' prognosis. Finally, we validated these genes in three independent cohorts from the Gene Expression Omnibus database. In conclusion, this study comprehensively analyzed the tumor microenvironment and identified prognostic immune-related biomarkers for melanoma.

Comprehensive analysis of prognostic immune-related genes in the tumor microenvironment of cutaneous melanoma.

The digestive system cancers are aggressive cancers with the highest mortality worldwide. In this study, we undertook a systematic investigation of the tumor immune microenvironment to identify diagnostic and prognostic biomarkers. The fraction of 22 immune cell types of patients were estimated using CIBERSORT. The least absolute shrinkage and selection operator (LASSO) analysis was carried out to identify important immune predictors. By comparing immune cell compositions in 801 tumor samples and 46 normal samples, we constructed the diagnostic immune score (DIS), showing high specificity and sensitivity in the training (area under the receiver operating characteristic curve [AUC] = 0.929), validation (AUC = 0.935), and different cancer type cohorts (AUC > 0.70 for all). We also established the prognostic immune score (PIS), which was an effective prognostic factor for relapse-free survival in training, validation, and entire cohorts (P < .05). In addition, PIS provided a higher net benefit than TNM stage. A composite nomogram was built based on PIS and patients' clinical information with well-fitted calibration curves (c-index = 0.84). We further used other cohorts from Gene Expression Omnibus databases and obtained similar results, confirming the reliability and validity of the DIS and PIS. In addition, the unsupervised clustering analysis using immune cell proportions revealed 6 immune subtypes, suggesting that the immune types defined as having relatively high levels of M0 or/and M1 macrophages were the high-risk subtypes of relapse. In conclusion, this study comprehensively analyzed the tumor immune microenvironment and identified DIS and PIS for digestive system cancers.

https://onlinelibrary.wiley.com/doi/pdf/10.1111/cas.14216

Immune cell infiltration as a biomarker for the diagnosis and prognosis of digestive system cancer

In vitro evaluation of nanoplastics using human lung epithelial cells, microarray analysis and co-culture model.

Background Lung adenocarcinoma (LUAD), mainly originated in lung glandular cells, is the most frequent pathological type of lung cancer and the 5-year survival rate of LUAD patients is still very low. Therefore, we aim to identify a long noncoding RNA (lncRNA)-related signature as the sensitive and novel prognostic biomarkers. Methods The associations between survival outcome and the intersection of lncRNAs were obtained from The Cancer Genome Atlas (TCGA) database. By the univariate and multivariate Cox analyses, key lncRNAs were identified to construct the prognostic model. The model was estimated by survival analysis and receiver operating characteristic curve, and verified by the Kaplan-Meier (K-M) plotter and quantitative reverse-transcription polymerase chain reaction (qRT-PCR). Functional enrichment analysis was also performed. Results A four-lncRNA signature (CEBPA-AS1, GVINP1, MIR31HG, and RAET1K) was developed after Cox analysis. The power of the four-lncRNA prognostic signature was effective in the TCGA database. The results from by the K-M plotter and qRT-PCR validation were consistent with our TCGA bioinformatics results. Furthermore, Gene Ontology and pathway analysis revealed the tumorigenic and prognostic function of the four lncRNAs. Conclusions By mining the TCGA data, we built a four-lncRNA signature, which could effectively predict prognosis of LUAD. In the future, an independent cohort is needed to validate our findings. Impact The four-lncRNA signature could become potential prognostic indicator of LUAD in the future.

https://onlinelibrary.wiley.com/doi/pdf/10.1002/jcb.27428

Tong Liu

Papers

Dysregulated N6-methyladenosine methylation writer METTL3 contributes to the proliferation and migration of gastric cancer.

Comprehensive analysis of prognostic immune-related genes in the tumor microenvironment of cutaneous melanoma.

Immune cell infiltration as a biomarker for the diagnosis and prognosis of digestive system cancer

In vitro evaluation of nanoplastics using human lung epithelial cells, microarray analysis and co-culture model.

Molecular characterization of lung adenocarcinoma: A potential four–long noncoding RNA prognostic signature