Characterization of the Prognostic m6A-Related lncRNA Signature in Gastric Cancer

Comprehensive landscape of m6A regulator-related gene patterns and tumor microenvironment infiltration characterization in gastric cancer

The epigenetic regulation of N6-methyladenosine (m6A) has attracted considerable interest in tumor research, but the potential roles of m6A regulator-related genes, remain largely unknown within the context of gastric cancer (GC) and tumor microenvironment (TME). Here, a comprehensive strategy of data mining and computational biology utilizing multiple datasets based on 28 m6A regulators (including novel anti-readers) was employed to identify m6A regulator-related genes and patterns and elucidate their underlying mechanisms in GC. Subsequently, a scoring system was constructed to evaluate individual prognosis and immunotherapy response. Three distinct m6A regulator-related patterns were identified through the unsupervised clustering of 56 m6A regulator-related genes (all significantly associated with GC prognosis). TME characterization revealed that these patterns highly corresponded to immune-inflamed, immune-excluded, and immune-desert phenotypes, and their TME characteristics were highly consistent with different clinical outcomes and biological processes. Additionally, an m6A-related scoring system was developed to quantify the m6A modification pattern of individual samples. Low scores indicated high survival rates and high levels of immune activation, whereas high scores indicated stromal activation and tumor malignancy. Furthermore, the m6A-related scores were correlated with tumor mutation loads and various clinical traits, including molecular or histological subtypes and clinical stage or grade, and the score had predictive values across all digestive system tumors and even in all tumor types. Notably, a low score was linked to improved responses to anti-PD-1/L1 and anti-CTLA4 immunotherapy in three independent cohorts. This study has expanded the important role of m6A regulator-related genes in shaping TME diversity and clinical/biological traits of GC. The developed scoring system could help develop more effective immunotherapy strategies and personalized treatment guidance.

The regulatory mechanism of m6A modification in gastric cancer

To the best of our knowledge, N6-Methyladenosine (m6A) exerts a significant role in the occurrence and development of various tumors. Gastric cancer (GC), originating from the mucosal epithelium in the digestive tract, is the fifth most common cancer and the third most common cause of cancer death around the world. Therefore, it is urgent to explore the specific mechanism of tumorigenesis of GC. As we all know, m6A modification as the most common RNA modification, is involved in the modification of mRNA and ncRNA at the post-transcriptional level, which played a regulatory role in various biological processes. As identified by numerous studies, the m6A modification are able to influence the proliferation, apoptosis, migration, and invasion of GC. What's more, m6A modification are associated with EMT, drug resistance, and aerobic glycolysis in GC. m6A related-ncRNAs may be a valuable biomarker used by the prediction of GC diagnosis in the future. This review summarizes the role of m6A modification in the mechanism of gastric cancer, with the aim of identifying biological progress.

Targeting RNA N6-methyladenosine modification-- a novel therapeutic target for HER2- positive gastric cancer

Gastric cancer is one of the most common cancers and is considered the 5th most frequent occurring cancer worldwide. It has gained great attention from the clinicians and researchers because of high mortality rate. It is generally treated with chemotherapy, radiotherapy, and surgery. Recently, additional treatment options including immunotherapy and targeted therapy and immunotherapy have been developed. However, poor prognosis, limited survival rate of patients, and drug resistance to treatment remain critical problems. To improve treatment options or to overcome the bottleneck of treatment, identification of diagnostic and prognostic markers, determining the most effective therapeutic options, and uncovering the molecular regulations associated with treatment strategies are required. In this regard n6-methyladenosine (m6A) regulation is considered important. This reversible modification plays a crucial role in progression, development and treatment of HER2-positive gastric cancer. Here, we discuss the role of m6A modification in HER2-positive gastric cancer progression through collecting related studies at present. We further discuss the association of m6A modification with therapeutic efficacy in HER2-positive gastric cancer and list some examples. We conclude that modification of m6A can be a new strategy for improving the prognosis and survival rate of HER2-positive gastric cancer patients.

LncRNA OIP5-AS1 Upregulates the Cyclin D2 Levels to Promote Metastasis of Breast Cancer by Targeting miR-150-5p

OBJECTIVE

Breast cancer (BC) is a cancer that seriously affects women's health. BC cell migration increases the mortality of BC patients. Current studies have shown that long noncoding RNAs (LncRNAs) are related to the metastasis mechanism of BC. This study aimed to explore the function and role of LncRNA OIP5-AS1 in BC. And we analyzed its regulatory mechanism and related modification process.

METHODS

Our study analyzed the expression pattern of OIP5-AS1 in BC tissues and cell lines by qRT-PCR. The effects of OIP5-AS1 on the function of BC cells were detected by CCK-8 and transwell experiments. Bioinformatics analysis and double luciferase reporter gene detection were used to confirm the correlation between OIP5-AS1 and miR-150-5p and between miR-150-5p and Cyclin D2 (CCND2). The rescue test analyzed the effect of miR-150-5p regulating OIP5-AS1. In addition, the N6-methyladenosine (m6A) modification process of OIP5-AS1 was analyzed by RNA m6A dot blot, RIP assay, and double luciferase report experiment.

RESULTS

OIP5-AS1 was significantly upregulated in BC tissues and cell lines. OIP5-AS1 knockdown inhibited BC cell viability, migration and invasion. OIP5-AS1 upregulated CCND2 by binding with miR-150-5p. This process affected the metastasis of BC. Higher degree of m6A methylation was confirmed in BC cell lines. There were some binding sites between methyltransferase like 3 (METTL3) and OIP5-AS1. Moreover, the silencing of METTL3 inhibited the OIP5-AS1 expression through decreasing the m6A methylation levels.

CONCLUSIONS

LncRNA OIP5-AS1 promoted cell viability and metastasis of BC cells by targeting miR-150-5p/CCND2 axis. This process was modified by m6A methylation of METTL3.

Identification of potential novel N6-methyladenosine effector-related lncRNA biomarkers for serous ovarian carcinoma: a machine learning-based exploration in the framework of 3P medicine

Background

Serous ovarian carcinoma (SOC) is considered the most lethal gynecological malignancy. The current lack of reliable prognostic biomarkers for SOC reduces the efficacy of predictive, preventive, and personalized medicine (PPPM/3PM) in patients with SOC, leading to unsatisfactory therapeutic outcomes. N6-methyladenosine (m6A) modification-associated long noncoding RNAs (lncRNAs) are effective predictors of SOC. In this study, an effective risk prediction model for SOC was constructed based on m6A modification-associated lncRNAs.

Methods

Transcriptomic data and clinical information of patients with SOC were downloaded from The Cancer Genome Atlas. Candidate lncRNAs were identified using univariate and multivariate and least absolute shrinkage and selection operator-penalized Cox regression analyses. The molecular mechanisms of m6A effector-related lncRNAs were explored via Gene Ontology, pathway analysis, gene set enrichment analysis, and gene set variation analysis (GSVA). The extent of immune cell infiltration was assessed using various algorithms, including CIBERSORT, Microenvironment Cell Populations counter, xCell, European Prospective Investigation into Cancer and Nutrition, and GSVA. The calcPhenotype algorithm was used to predict responses to the drugs commonly used in ovarian carcinoma therapy. In vitro experiments, such as migration and invasion Transwell assays, wound healing assays, and dot blot assays, were conducted to elucidate the functional roles of candidate lncRNAs.

Results

Six m6A effector-related lncRNAs that were markedly associated with prognosis were used to establish an m6A effector-related lncRNA risk model (m6A-LRM) for SOC. Immune microenvironment analysis suggested that the high-risk group exhibited a proinflammatory state and displayed increased sensitivity to immunotherapy. A nomogram was constructed with the m6A effector-related lncRNAs to assess the prognostic value of the model. Sixteen drugs potentially targeting m6A effector-related lncRNAs were identified. Furthermore, we developed an online web application for clinicians and researchers (https://leley.shinyapps.io/OC_m6A_lnc/). Overexpression of the lncRNA RP11-508M8.1 promoted SOC cell migration and invasion. METTL3 is an upstream regulator of RP11-508M8.1. The preliminary regulatory axis METTL3/m6A/RP11-508M8.1/hsa-miR-1270/ARSD underlying SOC was identified via a combination of in vitro and bioinformatic analyses.

Conclusion

In this study, we propose an innovative prognostic risk model and provide novel insights into the mechanism underlying the role of m6A-related lncRNAs in SOC. Incorporating the m6A-LRM into PPPM may help identify high-risk patients and personalize treatment as early as possible.

Prognostic signature analysis and survival prediction of esophageal cancer based on N6-methyladenosine associated lncRNAs

Esophageal cancer (ESCA) has a bad prognosis. Long non-coding RNA (lncRNA) impacts on cell proliferation. However, the prognosis function of N6-methyladenosine (m6A)-associated lncRNAs (m6A-lncRNAs) in ESCA remains unknown. Univariate Cox analysis was applied to investigate prognosis related m6A-lncRNAs, based on which the samples were clustered. Wilcoxon rank and Chi-square tests were adopted to compare the clinical traits, survival, pathway activity and immune infiltration in different clusters where overall survival, clinical traits (N stage), tumor-invasive immune cells and pathway activity were found significantly different. Through least absolute shrinkage and selection operator and proportional hazard (Lasso-Cox) model, five m6A-lncRNAs were selected to construct the prognostic signature (m6A-lncSig) and risk score. To investigate the link between risk score and clinical traits or immunological microenvironments, Chi-square test and Spearman correlation analysis were utilized. Risk score was found connected with N stage, tumor stage, different clusters, macrophages M2, B cells naive and T cells CD4 memory resting. Risk score and tumor stage were found as independent prognostic variables. And the constructed nomogram model had high accuracy in predicting prognosis. The obtained m6A-lncSig could be taken as potential prognostic biomarker for ESCA patients. This study offers a theoretical foundation for clinical diagnosis and prognosis of ESCA.

Angiogenesis-related lncRNAs index: A predictor for CESC prognosis, immunotherapy efficacy, and chemosensitivity

Cervical squamous cell carcinoma and endocervical adenocarcinoma (CESC) is a common gynecologic tumor and patients with advanced and recurrent disease usually have a poor clinical outcome. Angiogenesis is involved in the biological processes of tumors and can promote tumor growth and invasion. In this paper, we created a signature for predicting prognosis based on angiogenesis-related lncRNAs (ARLs). This provides a prospective direction for enhancing the efficacy of immunotherapy in CESC patients. We screened seven OS-related ARLs by univariate and multivariate regression analyses and Lasso analysis and developed a prognostic signature at the same time. Then, we performed an internal validation in the TCGA-CESC cohort to increase the precision of the study. In addition, we performed a series of analyses based on ARLs, including immune cell infiltration, immune function, immune checkpoint, tumor mutation load, and drug sensitivity analysis. Our created signature based on ARLs can effectively predict the prognosis of CESC patients. To strengthen the prediction accuracy of the signature, we built a nomogram by combining signature and clinical features.

Non-coding RNA-Mediated N6-Methyladenosine (m6A) deposition: A pivotal regulator of cancer, impacting key signaling pathways in carcinogenesis and therapy response

The emergence of RNA modifications has recently been considered as critical post-transcriptional regulations which governed gene expression. N6-methyladenosine (m6A) modification is the most abundant type of RNA modification which is mediated by three distinct classes of proteins called m6A writers, readers, and erasers. Accumulating evidence has been made in understanding the role of m6A modification of non-coding RNAs (ncRNAs) in cancer. Importantly, aberrant expression of ncRNAs and m6A regulators has been elucidated in various cancers. As the key role of ncRNAs in regulation of cancer hallmarks is well accepted now, it could be accepted that m6A modification of ncRNAs could affect cancer progression. The present review intended to discuss the latest knowledge and importance of m6A epigenetic regulation of ncRNAs including mircoRNAs, long non-coding RNAs, and circular RNAs, and their interaction in the context of cancer. Moreover, the current insight into the underlying mechanisms of therapy resistance and also immune response and escape mediated by m6A regulators and ncRNAs are discussed.

Prognostic value of RNA methylation-related genes in gastric adenocarcinoma based on bioinformatics

Background

Gastric cancer (GC) is a malignant tumor that originates from the epithelium of the gastric mucosa and has a poor prognosis. Stomach adenocarcinoma (STAD) covers 95% of total gastric cancer. This study aimed to identify the prognostic value of RNA methylation-related genes in gastric cancer.

Methods

In this study, The Cancer Genome Atlas (TCGA)-STAD and GSE84426 cohorts were downloaded from public databases. Patients were classified by consistent cluster analysis based on prognosis-related differentially expressed RNA methylation genes Prognostic genes were obtained by differential expression, univariate Cox and least absolute shrinkage and selection operator (LASSO) analyses. The prognostic model was established and validated in the training set, test set and validation set respectively. Independent prognostic analysis was implemented. Finally, the expression of prognostic genes was affirmed by reverse transcription quantitative PCR (RT-qPCR).

Results

In total, four prognostic genes (ACTA2, SAPCD2, PDK4 and APOD) related to RNA methylation were identified and enrolled into the risk signature. The STAD patients were divided into high- and low-risk groups based on the medium value of the risk score, and patients in the high-risk group had a poor prognosis. In addition, the RNA methylation-relevant risk signature was validated in the test and validation sets, and was authenticated as a reliable independent prognostic predictor. The nomogram was constructed based on the independent predictors to predict the 1/3/5-year survival probability of STAD patients. The gene set enrichment analysis (GSEA) result suggested that the poor prognosis in the high-risk subgroup may be related to immune-related pathways. Finally, the experimental results indicated that the expression trends of RNA methylation-relevant prognostic genes in gastric cancer cells were in agreement with the result of bioinformatics.

Conclusion

Our study established a novel RNA methylation-related risk signature for STAD, which was of considerable significance for improving prognosis of STAD patients and offering theoretical support for clinical therapy.

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.

WTAP-Involved the m6A Modification of lncRNA FAM83H-AS1 Accelerates the Development of Gastric Cancer

The long noncoding RNA FAM83H-antisense RNA 1 (FAM83H-AS1) is involved in gastric cancer (GC) development. This study determined whether FAM83H-AS1 was regulated by N6-methyladenosine (m6A) modifications in GC. Real-time quantitative polymerase chain reaction was performed to determine the expression levels of FAM83H-AS1 and Wilms' tumor 1 associated protein (WTAP). The protein content of WTAP was evaluated using western blotting. To assess the m6A alterations in FAM83H-AS1, methylated RNA immunoprecipitation was performed to identify interactions between WTAP and FAM83H-AS1. Functionally, the proliferation, migration, and invasion of GC cells were measured using a Cell Counting Kit-8 and transwell assays, respectively. High expression levels of FAM83H-AS1 and WTAP were detected in GC samples and there was a positive correlation between them. In addition, WTAP mediates FAM83H-AS1 expression in an m6A-dependent manner. Further investigations indicated that WTAP silencing reversed the cancer-promoting role of FAM83H-AS1 overexpression in GC cell migration, proliferation, and invasion. Our results suggest that WTAP-mediated FAM83H-AS1 promotes GC development via m6A modification. Our findings provide new biomarkers for GC diagnosis and targeted therapy. Wilms' tumor 1 associated protein (WTAP) promotes gastric cancer (GC) by accelerating cell proliferation, migration, and invasion of GC cells via the N6-methyladenosine(m6A) modification of long non coding RNA FAM83H-AS1.

Constructing a novel signature and predicting the immune landscape of colon cancer using N6-methylandenosine-related lncRNAs

Background: Colon cancer (CC) is a prevalent malignant tumor that affects people all around the world. In this study, N6-methylandenosine-related long non-coding RNAs (m6A-related lncRNAs) in 473 colon cancers and 41 adjacent tissues of CC patients from The Cancer Genome Atlas (TCGA) were investigated. Method: The Pearson correlation analysis was conducted to examine the m6A-related lncRNAs, and the univariate Cox regression analysis was performed to screen 38 prognostic m6A-related lncRNAs. The least absolute shrinkage and selection operator (LASSO) regression analysis were carried out on 38 prognostic lncRNAs to develop a 14 m6A-related lncRNAs prognostic signature (m6A-LPS) in CC. The availability of the m6A-LPS was evaluated using the Kaplan-Meier and Receiver Operating Characteristic (ROC) curves. Results: Three m6A modification patterns with significantly different N stages, survival time, and immune landscapes were identified. It has been discovered that the m6A-LPS, which is based on 14 m6A-related lncRNAs (TNFRSF10A-AS1, AC245041.1, AL513550.1, UTAT33, SNHG26, AC092944.1, ITGB1-DT, AL138921.1, AC099850.3, NCBP2-AS1, AL137782.1, AC073896.3, AP006621.2, AC147651.1), may represent a new, promising biomarker with great potential. It was re-evaluated in terms of survival rate, clinical features, tumor infiltration immune cells, biomarkers related to Immune Checkpoint Inhibitors (ICIs), and chemotherapeutic drug efficacy. The m6A-LPS has been revealed to be a novel potential and promising predictor for evaluating the prognosis of CC patients. Conclusion: This study revealed that the risk signature is a promising predictive indicator that may provide more accurate clinical applications in CC therapeutics and enable effective therapy strategies for clinicians.

Analysis of the prognostic significance and potential mechanisms of lncRNAs related to m6A methylation in laryngeal cancer

Objective to investigate the prognostic significance and potential mechanism analysis of m6A methylation-associated lncRNAs in laryngeal cancer. Methods based on the expression of m6A-associated lncRNAs, the samples were divided into two clusters and least absolute value and selection operator (LASSO) regression analysis was performed to build and validate prognostic models. In addition, the relationships between risk scores, clusters, arginine synthase (SMS), tumor microenvironment, clinicopathological features, immune infiltration, immune checkpoints, and tumor mutation burden were analyzed. Finally, the relationship between SMS and m6A-associated IncRNAs was analyzed and SMS-associated pathways were enriched by gene set enrichment analysis (GSEA). Results a total of 95 lncRNAs were associated with the expression of 22 m6A methylation regulators in laryngeal cancer, 14 of which were prognostic lncRNAs. These lncRNAs were divided into two clusters and evaluated. Clinicopathological features did not show significant differences. However, the two clusters differed significantly in terms of naive B cells, memory B cells, naive CD4 T cells, T helper cells and immune score. lASSO regression analysis showed that risk score was a significant predictor of progression-free survival. Conclusion low expression of m6A-related lncRNAs involved in laryngeal cancer development in laryngeal cancer tissues can be used as an indicator to diagnose patients with laryngeal cancer, reduce patient prognosis, be an independent risk factor affecting patient prognosis and be able to assess patient prognosis.

The lncRNA epigenetics: The significance of m6A and m5C lncRNA modifications in cancer

Most of our transcribed RNAs are represented by non-coding sequences. Long non-coding RNAs (lncRNAs) are transcripts with no or very limited protein coding ability and a length >200nt. They can be epigenetically modified. N6-methyladenosine (m6A), N1-methyladenosine (m1A), 5-methylcytosine (m5C), 7-methylguanosine (m7G) and 2’-O-methylation (Nm) are some of the lncRNAs epigenetic modifications. The epigenetic modifications of RNA are controlled by three classes of enzymes, each playing a role in a specific phase of the modification. These enzymes are defined as “writers”, “readers” and “erasers”. m6A and m5C are the most studied epigenetic modifications in RNA. These modifications alter the structure and properties, thus modulating the functions and interactions of lncRNAs. The aberrant expression of several lncRNAs is linked to the development of a variety of cancers and the epigenetic signatures of m6A- or m5C-related lncRNAs are increasingly recognized as potential biomarkers of prognosis, predictors of disease stage and overall survival. In the present manuscript, the most up to date literature is reviewed with the focus on m6A and m5C modifications of lncRNAs and their significance in cancer.

Construction and validation of a bladder cancer risk model based on autophagy-related genes

Autophagy has an important association with tumorigenesis, progression, and prognosis. However, the mechanism of autophagy-regulated genes on the risk prognosis of bladder cancer (BC) patients has not been fully elucidated yet. In this study, we created a prognostic model of BC risk based on autophagy-related genes, which further illustrates the value of genes associated with autophagy in the treatment of BC. We first downloaded human autophagy-associated genes and BC datasets from Human Autophagy Database and The Cancer Genome Atlas (TCGA) database, and finally obtained differential prognosis-associated genes for autophagy by univariate regression analysis and differential analysis of cancer versus normal tissues. Subsequently, we downloaded two datasets from Gene Expression Omnibus (GEO), GSE31684 and GSE15307, to expand the total number of samples. Based on these genes, we distinguished the molecular subtypes (C1, C2) and gene classes (A, B) of BC by consistent clustering analysis. Using the genes merged from TCGA and the two GEO datasets, we conducted least absolute shrinkage and selection operator (LASSO) and multivariate Cox regression analysis to obtain risk genes and construct autophagy-related risk prediction models. The accuracy of this risk prediction model was assessed by receiver operating characteristic (ROC) and calibration curves, and then nomograms were constructed to predict the survival of bladder cancer patients at 1, 3, and 5 years, respectively. According to the median value of the risk score, we divided BC samples into the high- and low-risk groups. Kaplan-Meier (K-M) survival analysis was performed to compare survival differences between subgroups. Then, we used single sample gene set enrichment analysis (ssGSEA) for immune cell infiltration abundance, immune checkpoint genes, immunotherapy response, gene ontology (GO) and Kyoto encyclopedia of genes and genomes (KEGG) pathway analysis, and tumor mutation burden (TMB) analysis for different subgroups. We also applied quantitative real-time polymerase chain reaction (PCR) and immunohistochemistry (IHC) techniques to verify the expression of these six genes in the model. Finally, we chose the IMvigor210 dataset for external validation. Six risk genes associated with autophagy (SPOCD1, FKBP10, NAT8B, LDLR, STMN3, and ANXA2) were finally screened by LASSO regression algorithm and multivariate Cox regression analysis. ROC and calibration curves showed that the model established was accurate and reliable. Univariate and multivariate regression analyses were used to verify that the risk model was an independent predictor. K-M survival analysis indicated that patients in the high-risk group had significantly worse overall survival than those in the low-risk group. Analysis by algorithms such as correlation analysis, gene set variation analysis (GSVA), and ssGSEA showed that differences in immune microenvironment, enrichment of multiple biologically active pathways, TMB, immune checkpoint genes, and human leukocyte antigens (HLAs) were observed in the different risk groups. Then, we constructed nomograms that predicted the 1-, 3-, and 5-year survival rates of different BC patients. In addition, we screened nine sensitive chemotherapeutic drugs using the correlation between the obtained expression status of risk genes and drug sensitivity results. Finally, the external dataset IMvigor210 verified that the model is reliable and efficient. We established an autophagy-related risk prognostic model that is accurate and reliable, which lays the foundation for future personalized treatment of bladder cancer.

The regulatory role of N6-methyladenosine RNA modification in gastric cancer: Molecular mechanisms and potential therapeutic targets

N6-methyladenosinen (m⁶A) methylation is a frequent RNA methylation modification that is regulated by three proteins: "writers", "erasers", and "readers". The m⁶A modification regulates RNA stability and other mechanisms, including translation, cleavage, and degradation. Interestingly, recent research has linked m⁶A RNA modification to the occurrence and development of cancers, such as hepatocellular carcinoma and non-small cell lung cancer. This review summarizes the regulatory role of m⁶A RNA modification in gastric cancer (GC), including targets, the mechanisms of action, and the potential signaling pathways. Our present findings can facilitate our understanding of the significance of m⁶A RNA modification in GC.

ALKBH5-mediated m6A modification of lincRNA LINC02551 enhances the stability of DDX24 to promote hepatocellular carcinoma growth and metastasis

As the most important RNA epigenetic regulation in eukaryotic cells, N6-metheyladenosine (m⁶A) modification has been demonstrated to play significant roles in cancer progression. However, this modification in long intergenic non-coding RNAs (lincRNAs) and the corresponding functions remain elusive. Here, we showed a lincRNA LINC02551 was downregulated by AlkB Homolog 5 (ALKBH5) overexpression in a m⁶A-dependent manner in hepatocellular carcinoma (HCC). Functionally, LINC02551 was required for the growth and metastasis of HCC. Mechanistically, LINC02551, a bona fide m⁶A target of ALKBH5, acted as a molecular adaptor that blocked the combination between DDX24 and a E3 ligase TRIM27 to decrease the ubiquitination and subsequent degradation of DDX24, ultimately facilitating HCC growth and metastasis. Thus, ALKBH5-mediated LINC02551 m⁶A methylation was required for HCC growth and metastasis.

Pan-Cancer Gene Analysis of m6A Modification and Immune Infiltration in Uterine Corpus Endometrial Carcinoma

Objective

This investigation was to test the potential role of m6A-related long non-coding RNAs (lncRNAs) and immune infiltration as crucial factors in the diagnosis and treatment of uterine corpus endometrial cancer (UCEC).

Method

The UCEC RNA-seq data were downloaded in the Cancer Genome Atlas (TCGA, https://portal.gdc.cancer.gov/). There were 587 samples totally, containing 543 UCEC cases and 35 healthy cases. The clinical information of UCEC cases included survival time, survival status, gender, age, stage, and TMN stage. Twenty-three m6A-related genes were found in published journals. The RNA-seq documents of UCEC were downloaded in the Cancer Genome Atlas (TCGA). The hub gene data of UCEC were downloaded from GEPIA2 database. The different packages of R language were applied to calculate and analyze in this research.

Results

Among 587 cases in our study, we discovered 3039 lncRNAs in the TCGA-UCEC database. After the differential analysis, 23 m6A-associated genetics were screened and twenty-one m6A-associated differential genetics were found. In the end, we obtained 20 m6A-related lncRNAs. LNCTAM34A was considered as a predictive gene through univariate and multivariate Cox regression analysis. In addition to the above, patients with high LNCTAM34A expression had better outcomes than those with low LNCTAM34A expression. The high-risk cohort had greater scores of activated dendritic cells (aDCs), B cells, and T cell regulatory (Tregs) than low-risk cohort; in the meanwhile, high-risk cohort had lower scores of DCs and iDCs. Then, the high-risk cohort displayed greater scores in the immune functions of MHC class I, para-inflammation, and type I IFN response than those of low-risk cohort. Among 27 immune-inducible genes, the level of CD244, KIR3DLI, NRP1, PDCD1LG2, and TNFRSF8 was reduced in UCEC samples and the level of CD27, CD28, CD70, CD80, CD86, HAVCR2, ICOS, IDO1, LAIR1, PDCD1, TIGIT, TNFRSF18, -25, -9, -14, and VTCN1 was increased in UCEC samples.

Conclusion

The key role of M6A-related lncRNAs in immune microenvironment in high-risk patients of UCEC. The patients with strong expression of LNCTAM34A have a good prognosis, and LNCTAM34A can be used as a prognostic gene for UCEC. m6A-related lncRNAs can be used as a potential treatment for UCEC. Our observations can be used as a hypothetical basis for future in vitro and animal experiments.

Necroptosis-related lncRNA in lung adenocarcinoma: A comprehensive analysis based on a prognosis model and a competing endogenous RNA network

Background: Necroptosis, an innovative type of programmed cell death, involves the formation of necrosomes and eventually mediates necrosis. Multiple lines of evidence suggest that necroptosis plays a major role in the development of human cancer. However, the role of necroptosis in lung adenocarcinoma (LUAD) remains unclear. In this study, we aimed to construct an NRL-related prognostic model and comprehensively analyze the role of NRL in LUAD.

Methods: A necroptosis-related lncRNA (NRL) signature was constructed in the training cohort and verified in the validation and all cohorts based on The Cancer Genome Atlas database. In addition, a nomogram was developed. The tumor microenvironment (TME), checkpoint, human leukocyte antigen, and m6A methylation levels were compared between low-risk and high-risk groups. Then, we identified five truly prognostic lncRNAs (AC107021.2, AC027117.1, FAM30A, FAM83A-AS1, and MED4-AS1) and constructed a ceRNA network, and four hub genes of downstream genes were identified and analyzed using immune, pan-cancer, and survival analyses.

Results: The NRL signature could accurately predict the prognosis of patients with LUAD, and patients with low risk scores were identified with an obvious “hot” immune infiltration level, which was strongly associated with better prognosis. Based on the ceRNA network, we postulated that NRLs regulated the TME of patients with LUAD via cyclin-dependent kinase (CDK) family proteins.

Conclusion: We constructed an NRL signature and a ceRNA network in LUAD and found that NRLs may modulate the immune microenvironment of LUAD via CDK family proteins.

The Role of N6-Methyladenosine-Associated lncRNAs in the Immune Microenvironment and Prognosis of Colorectal Cancer

Background

The role of N6-methyladenosine long noncoding RNAs (lncRNAs) in colorectal cancer (CRC) is elusive.

Materials and Methods

We identified m6A-associated lncRNAs by using the data gathered from The Cancer Genome Atlas (TCGA) and stratified CRC patients into different subgroups. Cox regression analysis was performed to construct an m6A-associated lncRNA signature. The role of this signature in the immune microenvironment and prognosis was dissected subsequently. Finally, a gene set enrichment analysis (GSEA) was conducted to predict the possible mechanisms based on the signature.

Results

Three m6A-associated clusters were constructed from 866 differentially expressed lncRNAs. Cluster 2 had poor prognosis and low immune cell infiltration. An m6A-associated lncRNA signature consisting of 14 lncRNAs was constructed and recognized as an independent prognostic indicator of CRC by using survival analysis and receiver operating characteristic (ROC) curves. The clinical features and immune cell infiltration status were significantly different in patients stratified by the risk score. Furthermore, GSEA showed that the P53 pathway and natural killer cell-mediated cytotoxicity were more enriched in the low-risk group.

Conclusion

Our data revealed that m6A-associated lncRNAs could be potential prognostic indicators of immunogenicity in CRC.

Predicting prognosis and immune responses in hepatocellular carcinoma based on N7-methylguanosine-related long noncoding RNAs

Background: Hepatocellular carcinoma (HCC), which has high rates of recurrence and metastasis and is the main reason and the most common tumor for cancer mortality worldwide, has an unfavorable prognosis. N7-methylguanosine (m7G) modification can affect the formation and development of tumors by affecting gene expression and other biological processes. In addition, many previous studies have confirmed the unique function of long noncoding RNAs (lncRNAs) in tumor progression; however, studies exploring the functions of m7G-related lncRNAs in HCC patients has been limited.

Methods: Relevant RNA expression information was acquired from The Cancer Genome Atlas (TCGA, https://portal.gdc.cancer.gov), and m7G-related lncRNAs were identified via gene coexpression analysis. Afterward, univariate Cox regression, least absolute shrinkage and selection operator (LASSO) regression, and multivariate regression analyses were implemented to construct an ideal risk model whose validity was verified using Kaplan–Meier survival, principal component, receiver operating characteristic (ROC) curve, and nomogram analyses. In addition, the potential functions of lncRNAs in the novel signature were explored through Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes(KEGG) analyses and gene set enrichment analysis (GSEA). At last, in both risk groups and subtypes classified based on the expression of the risk-related lncRNAs, we analyzed the immune characteristics and drug sensitivity of patients.

Results: After rigorous screening processes, we built a model based on 11 m7G-related lncRNAs for predicting patient overall survival (OS). The results suggested that the survival status of patients with high-risk scores was lower than that of patients with low-risk scores, and a high-risk score was related to malignant clinical features. Cox regression analysis showed that the m7G risk score was an independent prognostic parameter. Moreover, immune cell infiltration and immunotherapy sensitivity differed between the risk groups.

Conclusion: The m7G risk score model constructed based on 11 m7G-related lncRNAs can effectively assess the OS of HCC patients and may offer support for making individualized treatment and immunotherapy decisions for HCC patients.

Identification and Validation of an m6A-Related LncRNA Signature to Predict Progression-Free Survival in Colorectal Cancer

The RNA methylation of N6 adenosine (m6A) plays a crucial role in various biological processes. Strong evidence reveals that the dysregulation of long non-coding RNAs (lncRNA) brings about the abnormality of downstream signaling in multiple ways, thus influencing tumor initiation and progression. Currently, it is essential to discover effective and succinct molecular biomarkers for predicting colorectal cancer (CRC) prognosis. However, the prognostic value of m6A-related lncRNAs for CRC remains unclear, especially for progression-free survival (PFS). Here, we screened 24 m6A-related lncRNAs in 622 CRC patients and identified five lncRNAs (SLCO4A1-AS1, MELTF-AS1, SH3PXD2A-AS1, H19 and PCAT6) associated with patient PFS. Compared to normal samples, their expression was up-regulated in CRC tumors from TCGA dataset, which was validated in 55 CRC patients from our in-house cohort. We established an m6A-Lnc signature for predicting patient PFS, which was an independent prognostic factor by classification analysis of clinicopathologic features. Moreover, the signature was validated in 1,077 patients from six independent datasets (GSE17538, GSE39582, GSE33113, GSE31595, GSE29621, and GSE17536), and it showed better performance than three known lncRNA signatures for predicting PFS. In summary, our study demonstrates that the m6A-Lnc signature is a promising biomarker for forecasting patient PFS in CRC.

Analysis and prognostic significance of tumour immune infiltrates and immune microenvironment of m6A-related lncRNAs in patients with gastric cancer

BACKGROUND

Studies have shown that long noncoding RNAs and N6-methyladenosine play important roles in gastric cancer. The purpose of this study was to determine the correlation and prognostic value of m6A-related lncRNAs and immune infiltration in gastric cancer.

METHODS

We downloaded the clinically related information and RNA-Seq transcriptome data of gastric cancer patients from the TCGA database. Univariate Cox regression analysis and Pearson analysis were used to screen out m6A-related lncRNAs. Consensus cluster analysis was used to divide the sample into two clusters, and LASSO analysis and Cox regression analysis were used to construct a risk scoring model.

RESULTS

A total of 25 lncRNA expression profiles were screened, and gastric cancer patients were divided into different subtypes. Cluster 2 had a better prognosis, but its stromal score, ESTIMATE score and immune score were low. Cluster 1 was rich in resting memory CD4 T cells, regulatory T cells, monocytes, and resting mast cells, and Cluster 2 was rich in activated memory CD4 T cells and follicular helper T cells. Thirteen lncRNAs were selected to construct a risk model, and the prognosis of gastric cancer patients in the high-risk group was poor. The expression of PD-L1 in tumours is significantly higher than that in normal tissues. Univariate and multivariate Cox regression analysis results showed that the overall survival rate was significantly related to stage and the risk score, which can be used as an independent prognostic factor. The results of the heatmap and scatter plot showed that clusters (P = 0.0045) and grade (G1-2, G3, P = 0.0037) were significantly related to prognosis. The relationship between the risk score and immune cell infiltration showed that memory B cells, resting dendritic cells, M0 macrophages, and M2 macrophages were positively correlated with the risk score, while resting mast cells, monocytes, activated NK cells, and follicular helper T cells were negatively correlated with the risk score.

CONCLUSION

The results of this study indicate that m6A-related lncRNAs may play an important role in the prognosis of gastric cancer patients and the tumour immune microenvironment and may provide help for the treatment of gastric cancer patients.

RP11-552D4.1: a novel m6a-related LncRNA associated with immune status in glioblastoma

Glioblastoma (GBM) is the most malignant form of brain cancer in the world. Nevertheless, the survival rate of patients with GBM is extremely low. N6-methyladenosine (m6A) and long noncoding RNAs (lncRNAs) conduct important biological functions in patients’ survival status and the immunotherapeutic response. Here, m6A-related lncRNAs were identified by a co-expression method. Univariate and multivariate Cox regression together with LASSO were applied to establish the risk model. Kaplan-Meier and ROC analysis were applied to evaluate the prediction power of this risk model. Finally, the related immune profiling and chemical sensitivity targets were also investigated. The risk model holding three m6A-related lncRNAs was confirmed as an independent predictor for the prognosis. Furthermore, we found the risk model based on m6A-related lncRNAs is associated with the immune status, immunosuppressive biomarkers, and chemo-sensitivity in GBM patients. The RP11-552D4.1 is found to facilitate neuronal proliferation. This risk model consisted of m6A-related lncRNAs may be available for the clinical interventions in GBM patients.

The role of N6-methyladenosine-modified non-coding RNAs in the pathological process of human cancer

Non-coding RNAs (ncRNAs) account for the majority of the widespread transcripts of mammalian genomes. They rarely encode proteins and peptides, but their regulatory role is crucial in numerous physiological and pathological processes. The m6A (N6-methyladenosine) modification is one of the most common internal RNA modifications in eukaryotes and is associated with all aspects of RNA metabolism. Accumulating researches have indicated a close association between m6A modification and ncRNAs, and suggested m6A-modified ncRNAs played a crucial role in tumor progression. The correlation between m6A modification and ncRNAs offers a novel perspective for investigating the potential mechanisms of cancer pathological processes, which suggests that both m6A modification and ncRNAs are critical prognostic markers and therapeutic targets in numerous malignancies. In the present report, we summarized the interaction between m6A modification and ncRNA, emphasizing how their interaction regulates pathological processes in cancer.

Construction and Validation of an Epigenetic Regulator Signature as A Novel Biomarker For Prognosis, Immunotherapy, And Chemotherapy In Hepatocellular Carcinoma

Background

Epigenetic modification regulates various aspects of cancer biology, from tumor growth and invasion to immune microenvironment modulation. Whether epigenetic regulators (EGRs) can decide tumor malignant degree and risk of immune evasion in liver hepatocellular carcinoma (LIHC) remains unclear.

Method

An EGR signature called “EGRscore” was constructed based on bulk RNA-seq data of EGR in hepatocellular carcinoma (HCC). The correlation between EGRscore and overall survival (OS) was validated in HCC cohorts and other tumor cohorts. Mutation profiles, copy number alterations (CNAs), enriched pathways, and response to immunotherapy and chemotherapy were compared between EGRscore-high and EGRscore-low patients.

Results

We found that EGRscore was associated with OS in HCC as well as several tumors including glioma, uveal melanoma (UVM), and kidney tumors. A mechanism study demonstrated that the distinct mutation profile of TP53 was present in EGRscore-high and EGRscore-low patients. Meanwhile, EGRscore-low patients were characterized with immune cells that promote killing tumors. Furthermore, EGRscore was associated with genes regulating drug resistance in HCC. Finally, we indicated that EGRscore-low patients had higher response rates to immunotherapy and targeted therapy.

Conclusions

EGRscore could be used to distinguish OS, tumor progression, mutation pattern, and immune microenvironment. The present study contributes to improving hepatocellular carcinoma patient prognosis and predicting response to immunotherapy.

Methyladenosine Modification in RNAs: From Regulatory Roles to Therapeutic Implications in Cancer

Simple Summary

Cancer remains a burden to the public health all over the world. An increasing number of studies have concentrated on the role of methyladenosine modifications on cancers. Methyladenosine modifications mainly include N6-methyladenosine (m⁶A), N1-methyladenosine (m¹A), and 2’-O-methyladenosine (m⁶A_m), of which dynamic changes could modulate the metabolism of RNAs in eukaryotic cells. Mounting evidence has confirmed the crucial role of methyladenosine modification in cancer, offering possibilities for cancer therapy. In this review, we discussed the regulatory role of methyladenosine modification on cancer, as well as their potential for treatment.

Abstract

Methyladenosine modifications are the most abundant RNA modifications, including N6-methyladenosine (m⁶A), N1-methyladenosine (m¹A), and 2’-O-methyladenosine (m⁶A_m). As reversible epigenetic modifications, methyladenosine modifications in eukaryotic RNAs are not invariable. Drastic alterations of m⁶A are found in a variety of diseases, including cancers. Dynamic changes of m⁶A modification induced by abnormal methyltransferase, demethylases, and readers can regulate cancer progression via interfering with the splicing, localization, translation, and stability of mRNAs. Meanwhile, m⁶A, m¹A, and m⁶A_m modifications also exert regulatory effects on noncoding RNAs in cancer progression. In this paper, we reviewed recent findings concerning the underlying biomechanism of methyladenosine modifications in oncogenesis and metastasis and discussed the therapeutic potential of methyladenosine modifications in cancer treatments.

The Value of m5C-Related lncRNAs in the Prognostic Assessment and Immunotherapy of Stomach Adenocarcinoma

Long noncoding RNAs (lncRNAs) are closely associated with a variety of tumors, including stomach adenocarcinoma (STAD). However, the role of 5-methylcytosine- (m5C-) related lncRNAs in STAD is still uncertain. This study investigated the value of m5C-related lncRNAs in prognostic evaluation and immunotherapy of STAD. STAD transcriptome sequencing data were downloaded from The Cancer Genome Atlas (TCGA) database, and m5C-related lncRNAs were screened by Pearson correlation analysis. A prognostic m5C-related lncRNA signature (m5CRLSig) associated with STAD was established using univariate and multivariate Cox regression analysis. We constructed a prognostic risk model for STAD with six m5C-related lncRNAs. The receiver operating characteristic (ROC) curve was used to examine the predictive efficacy. Univariate and multifactorial Cox regression analysis and principal component analysis (PCA) validated m5CRLSig as an independent factor of STAD prognosis. The clinicopathological characteristics of the model showed higher risk scores for stages II-IV, grade 3, N1-3, and death status. The calibration curve of a nomogram revealed that the nomogram had an excellent predictive function for survival risk. Furthermore, the expression of six m5C-related lncRNAs in STAD and paracancerous tissues was detected by quantitative real-time PCR (qRT-PCR), which verified the feasibility of the m5CRLSig as a prognostic marker for STAD. m5C-related lncRNAs were linked to multiple immune-associated pathways, according to gene set enrichment analysis (GSEA). CIBERSORT analysis indicated that m5CRLSig was involved in immune cell infiltration. Risk score was associated with immune checkpoint gene expression, immune function scores, and chemotherapeutic drug sensitivity. Therefore, m5CRLSig can efficiently assess the prognosis of STAD patients and can be used as a biological marker for immunotherapy.

Construction of m6A-Related lncRNA Prognostic Signature Model and Immunomodulatory Effect in Glioblastoma Multiforme

Background

Glioblastoma multiforme (GBM), the most prevalent and aggressive of primary malignant central nervous system tumors (grade IV), has a poor clinical prognosis. This study aimed to assess and predict the survival of GBM patients by establishing an m6A-related lncRNA signaling model and to validate its validity, accuracy and applicability.

Methods

RNA sequencing data and clinical data of GBM patients were obtained from TCGA data. First, m6A-associated lncRNAs were screened and lncRNAs associated with overall survival in GBM patients were obtained. Subsequently, the signal model was established using LASSO regression analysis, and its accuracy and validity are further verified. Finally, GO enrichment analysis was performed, and the influence of this signature on the immune regulation response and anticancer drug sensitivity of GBM patients was discussed.

Results

The signature constructed by four lncRNAs AC005229.3, SOX21-AS1, AL133523.1, and AC004847.1 is obtained. Furthermore, the signature proved to be effective and accurate in predicting and assessing the survival of GBM patients and could function independently of other clinical characteristics (Age, Gender and IDH1 mutation). Finally, Immunosuppression-related factors, including APC co-inhibition, T-cell co-inhibition, CCR and Check-point, were found to be significantly up-regulated in GBM patients in the high-risk group. Some chemotherapeutic drugs (Doxorubicin and Methotrexate) and targeted drugs (AZD8055, BI.2536, GW843682X and Vorinostat) were shown to have higher IC50 values in patients in the high-risk group.

Conclusion

We constructed an m6A-associated lncRNA risk model to predict the prognosis of GBM patients and provide new ideas for the treatment of GBM. Further biological experiments can be conducted on this basis to validate the clinical value of the model.

The Interferon Gamma-Related Long Noncoding RNA Signature Predicts Prognosis and Indicates Immune Microenvironment Infiltration in Colon Adenocarcinoma

Colon adenocarcinoma (COAD) is one of the most common clinically malignant tumours of the digestive system, with high incidence and mortality and poor prognosis. Interferon-gamma (IFN-γ) and long noncoding RNAs (lncRNAs) have prognostic values and were closely associated with immune microenvironment in COAD. Thus, identifying IFN-γ-related lncRNAs may be valuable in predicting the survival of patients with COAD. In this study, we identified IFN-γ-related lncRNAs and divided COAD patients from the Cancer Genome Atlas (TCGA) database into training and validation sets. Pearson’s correlation analysis and least absolute shrinkage and selection operator (LASSO) Cox regression were performed to select IFN-γ-related lncRNA-associated prognoses. Thirteen lncRNAs (AC025165.8, AC091633.3, FENDRR, LINC00882, LINC01828, LINC01829, MYOSLID, RP11-154H23.4, RP11-20J15.3, RP11-324L17.1, RP11-342A23.2, RP11-805I24.3, SERTAD4-AS1) were identified to construct an IFN-γ-related lncRNA prognostic signature in TCGA training (n =213) and validation (n =213) cohorts. COAD patient risk scores were calculated and classified into high- and low-risk groups based on the median value of the risk scores in each dataset. We compared the overall survival (OS) of patients stratified by age, gender, and stage. The OS in the high-risk group was significantly shorter than that in the low-risk group. In addition, the clinical nomogram incorporating the prognostic signature and clinical features showed a high concordance index of 0.78 and accurately predicted 1-, 3-, and 5-year survival times among COAD patients in the high- and low-risk groups. Based on the risk model, the high- and low-risk groups exhibited distinct differences in the immune system by gene set enrichment analysis (GSEA) functional annotation, and differentially expressed genes (DEGs) between the high- and low-risk groups were subjected to Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis. We investigated the expression of multiple immune checkpoint genes in the high- and low-risk groups and plotted Kaplan-Meier survival curves, indicating that immune checkpoint genes, such as LAG3 and PD. L1, STING and TIM 3, were also expressed differently between the two risk groups. Subsequently, there were dramatic differences in mutated genes, SNV (single nucleotide variants) classes, variant types and variant allele frequencies between low- and high-risk patients with COAD. Patients stratified by risk scores had different sensitivities to common chemotherapeutic agents. Finally, we used quantitative real-time polymerase chain reaction (qRT-PCR) assays to demonstrate that three lncRNAs were significantly differentially expressed in COAD tissues and adjacent normal tissues. Considered together, a thirteen-lncRNA prognostic signature has great potential to be a prognostic biomarker and could play an essential role in the immune microenvironment of COAD.

N6-Methyladenosine-Related Long Non-Coding RNAs Are Identified as a Potential Prognostic Biomarker for Lung Squamous Cell Carcinoma and Validated by Real-Time PCR

Currently, the precise mechanism by which N6-methyladenosine (m6A) modification of long non-coding RNAs (lncRNAs) promotes the occurrence and development of lung squamous cell carcinoma (LUSC) and influences tumor microenvironment (TME) remains unclear. Therefore, we studied the prognostic value of m6A-related lncRNAs and their relationship with TME in 495 LUSC samples from The Cancer Genome Atlas (TCGA) database. Pearson’s correlation and univariate Cox regression analysis identified 6 m6A-related lncRNAs with prognostic values for LUSC patients. LUSC patients were divided into two subgroups (clusters 1 and 2) using principal component analysis. The expression of PD-L1 was lower in tumor tissues and cluster 2 of LUSC patients. Cluster 2 of LUSC patients had a high immune score, stromal score, and unique immune cell infiltration. The focal adhesion kinase (FAK) pathway and cytokine receptor pathways are enriched in cluster 1. The m6A-related lncRNA prognostic markers (m6A-LPMs) were established using the least absolute shrinkage and selection operator (LASSO) Cox regression analysis. The risk score was calculated by 4 m6A-LPMs and associated with OS, TME, clinicopathological characteristics of LUSC patients. After adjusting for age, gender, and stage, the risk score was also an independent prognostic factor for LUSC patients. Real-time PCR results showed that the expression of 4 m6A-LPMs was consistent with our prediction results. Our study found that 4 m6A-LPMs (AC138035.1, AC243919.2, HORMAD2-AS1, and AL122125.1) are closely associated with LUSC prognosis, in future, they may as novel diagnostic biomarkers for LUSC and provide new immunotherapy targets for LUSC patients.

The Potential Value of m6A RNA Methylation in the Development of Cancers Focus on Malignant Glioma

N6-methyladenosine (m6A) RNA methylation is an epigenetic modification that has emerged in the last few years and has received increasing attention as the most abundant internal RNA modification in eukaryotic cells. m6A modifications affect multiple aspects of RNA metabolism, and m6A methylation has been shown to play a critical role in the progression of multiple cancers through a variety of mechanisms. This review summarizes the mechanisms by which m6A RNA methylation induced peripheral cancer cell progression and its potential role in the infiltration of immune cell of the glioblastoma microenvironment and novel immunotherapy. Assessing the pattern of m6A modification in glioblastoma will contribute to improving our understanding of microenvironmental infiltration and novel immunotherapies, and help in developing immunotherapeutic strategies.

Identification of m6A-Related lncRNA to Predict the Prognosis of Patients with Hepatocellular Carcinoma

Hepatocellular carcinoma (HCC) is the third leading cause of cancer-related death worldwide. In the past decades, HCC treatment has achieved great progress; however, the overall prognosis remains poor. Therefore, it is the need of the hour to identify new prognostic biomarkers which can advance our understanding related to the underlying molecular mechanism of adverse prognosis and apply them to clinical work in prognosis prediction. In the present study, data of 576 HCC patients and 292 normal control cases from TCGA and ICGC databases were enrolled to our bioinformatic analysis. SNHG1 and SNHG3 were identified as overlapping genes in TCGA and ICGC databases using Pearson correlation analysis and univariate Cox regression analysis. Further, we used the median of the SNHG1 and SNHG3 expression values as the cutoff values to define the HCC patient groups with high or low expression level. The subsequent analysis revealed that abnormal high expression of SNHG1 or SNHG3 affected the immune infiltration patterns and the crosstalk among immune cells. Moreover, high expression of SNHG1 or SNHG3 resulted in drug resistant to AKT inhibitor VII, bexarotene, bicalutamide, dasatinib, erlotinib, and gefitinib. In addition, lower tumor neoantigen burden was observed in high SNHG1 or SNHG3 group. Further, we found significant relation between the aberrant upregulation of SNHG1 and SNHG3 in tumor grade and stage. We established a nomogram to systematically predict the 5- and 8-year overall survival of liver cancer patients with good accuracy. Finally, the in vitro assays suggest that SNHG1 and SNHG3 promote the proliferative, migratory, and invasive abilities of HCC cells.

m6A Regulatory Gene-Mediated Methylation Modification in Glioma Survival Prediction

The median survival of patients with gliomas is relatively short. To investigate the epigenetic mechanisms associated with poor survival, we analyzed publicly available datasets from patients with glioma. This analysis revealed 12 prognosis-related m6A regulatory genes that may be responsible for poor prognosis. These genes may be involved in genomic changes inherent to oxidative phosphorylation, adipogenesis, hedgehog signaling, and Myc signaling. We reconstructed a risk model with univariate and multivariate Cox analyses and identified older age and the m6A risk score as independent risk factors for predicting the prognosis of glioma patients, which is associated with glioma immune infiltration. In conclusion, m6A regulatory genes may serve as both reliable biomarkers and potential targets to increase the chance of survival of patients with glioma.

Transcriptome-Wide Dynamics of m6A Methylation in Tumor Livers Induced by ALV-J Infection in Chickens

Avian Leukosis Virus Subgroup J (ALV-J) is a tumorigenic virus with high morbidity and rapid transmission. N6-methyladenosine (m⁶A) is a common epigenetic modification that may be closely related to the pathogenicity of ALV-J. Currently, there are no reports on whether m⁶A modification is related to ALV-J induced tumor formation. In this study, we used methylated RNA immunoprecipitation sequencing (MeRIP-seq) and RNA sequencing (RNA-seq) to examine the differences in m⁶A methylation and gene expression in normal livers and ALV-J-induced tumor livers systematically, with functional enrichment and co-expression analysis. The results identified 6,541 m⁶A methylated peaks, mainly enriched in CDS, and more than 83% of the transcripts contained 1-2 m⁶A peaks. For RNA-seq, 1,896 and 1,757 differentially expressed mRNAs and lncRNAs were identified, respectively. Gene enrichment analysis indicated that they may be involved in biological processes and pathways such as immunology-related and apoptosis. Moreover, we identified 17 lncRNAs, commonly existing in differently expressed methylome and transcriptome. Through co-expression analysis, 126 differentially expressed lncRNAs, and 18 potentially m⁶A-related methyltransferases were finally identified and connected, suggesting that m⁶A modifications might affect gene expression of lncRNAs and play a role in ALV-J induced tumor formation. This study provides the first comprehensive description of the m⁶A expression profile in tumor livers induced by ALV-J infection in chickens, which provides a basis for studying the role of m⁶A modification in ALV-J induced tumorigenesis. This study provides clues for studying the epigenetic etiology and pathogenesis of ALV-J.

m6A-Related lncRNAs Are Potential Prognostic Biomarkers of Cervical Cancer and Affect Immune Infiltration

The correlation of m⁶A-related lncRNAs with the prognosis and immune microenvironment of cervical cancer is not yet clear. In this study, we identified 7 m⁶A-related prognostic lncRNAs by Pearson correlation and univariate Cox regression analyses based on TCGA-cervical cancer dataset. Then, patients were divided into two clusters by consensus clustering based on the 7 m⁶A-related prognostic lncRNA expression. Cluster 1 was characterized by survival and stage disadvantage, enrichment of immunosuppressive and carcinogenic activation pathways. Besides, cluster 1 had higher immunosuppressive factor TGFbeta and lower immune cell infiltration compared with cluster 2. According to the expression of 7 m⁶A-related lncRNA, a 6-m⁶A-related lncRNA risk score model was established in the training set by LASSO regression analysis. The high-risk group had worse overall survival than the low-risk group. No matter in the training or validation sets, the m⁶A-related lncRNA risk score was an independent prognostic factor for overall survival. Meanwhile, we validated the independent prognostic value of risk score in the disease-specific survival and progression-free survival by multivariate Cox analysis. The high-risk group was characterized by higher TGFbeta and regulatory T cell and was rich in malignant pathways. Additionally, we also detected and compared the expression levels of four m⁶A-related prognostic lncRNA in 9 tumor samples and 9 normal tissues using quantitative real-time polymerase chain reaction assay. In conclusion, the novel m⁶A-related lncRNA risk score is a potential prognostic predictor of cervical cancer patients. These 6 m⁶A-related lncRNAs might serve as key mediators of the immune microenvironment and represent promising therapeutic targets for improving cervical cancer prognosis.

Identification of Five N6-Methylandenosine-Related ncRNA Signatures to Predict the Overall Survival of Patients with Gastric Cancer

Noncoding ribonucleic acids (ncRNAs) are involved in various functions in the formation and progression of different tumors. However, the association between N6-methyladenosine-related ncRNAs (m6A-related ncRNAs) and gastric cancer (GC) prognosis remains elusive. As such, this research was aimed at identifying m6A-related ncRNAs (lncRNAs and miRNAs) in GC and developing prognostic models of relevant m6A-related ncRNAs and identifying potential biomarkers regulated by m6A. In this study, the m6A2Target database, Starbase database, and The Cancer Genome Atlas (TCGA) were used to screen m6A-related ncRNAs. And then, we performed integrated bioinformatics analyses to determine prognosis-associated ncRNAs and to develop the m6A-related ncRNA prognostic signature (m6A-NPS) for GC patients. Finally, five m6A-related ncRNAs (including lnc-ARHGAP12, lnc-HYPM-1, lnc-WDR7-11, LINC02266, and lnc-PRIM2-7) were identified to establish m6A-NPS. The predictive power of m6A-NPS was better in the receiver operating characteristic (ROC) curve analysis of the training set (area under the curve (AUC), >0.6). The m6A-NPS could be utilized to classify patients into high- and low-risk cohorts, and the Kaplan-Meier analysis indicated that participants in the high-risk cohort had a poorer prognosis. The entire TCGA dataset substantiated the predictive value of m6A-NPS. Significant differences in TCGA molecular GC subtypes were observed between high- and low-risk cohorts. The ROC curve analysis indicated that m6A-NPS had better predictive power than other clinical characteristics of GC prognosis. Uni- and multivariate regression analyses indicated m6A-NPS as an independent prognostic factor. Furthermore, the m6A status between the low-risk cohort and high-risk cohort was significantly different. Differential genes between them were enriched in multiple tumor-associated signaling pathways. In summary, five m6A-related ncRNA signatures that could forecast the overall survival of patients with GC were identified.

The N6-methyladenosine:mechanisms, diagnostic value, immunotherapy prospec-ts and challenges in gastric cancer

The N6-methyladenosine(m⁶A) RNA modification is important in post-transcriptional regulation of RNA and are regulated reversibly by methyltransferases (writers), demethylases (erasers) and m⁶A recognition proteins (readers). Changes in the structure and function of key RNAs contribute to the development of diseases, particularly tumors. Many abnormal expressions of molecules related to m⁶A RNA methylation modification are discovered in gastric cancer(GC), which changes the methylation level and stability of target genes after transcription, and then regulates related metabolic pathways, affecting the occurrence and progression of GC. Therefore, an in-depth study of m⁶A RNA modification in GC is conducive to the development of new tumor therapies and the achieve of individualized treatment. At present, both basic and clinical studies indicate that m⁶A plays a complex and contentious role in GC. In this paper, we not only review the roles and mechanisms of m⁶A modified related proteins, but also discuss the value of m⁶A modulators in the clinical applications and current challenges of GC, aiming to provide research clues for the early diagnosis and explore the feasibility of m⁶A related proteins as specific targets for GC immunotherapy.

Comprehensive Analysis of an Individualized Immune-Related lncRNA Pair Signature in Gastric Cancer

Long noncoding RNAs (lncRNAs) have diverse functions, including immune regulation. Increasing studies have reported immune-related lncRNAs in the prognosis of multiple cancers. In this study, we developed an individualized signature containing 13 immune-related lncRNA pairs (IRLPs) which could predict the overall survival, disease-free survival, progression-free survival, and disease-specific survival of gastric cancer (GC) patients in The Cancer Genome Atlas (TCGA) cohort, and internal and external validations, signature comparisons, and subgroup analyses further confirmed its superiority, stability, and generalizability. Notably, this signature also showed good applicability in discriminating the prognosis of pan-cancer patients. Then, we constructed and validated a nomogram for overall survival based on the signature and clinical factors, which allowed more accurate predictions of GC prognosis. In addition, we revealed that the low survival rate of patients with high-risk scores may be due to their aggressive clinical features, enriched cancer-related signaling pathways, the infiltration of specific immunosuppressive cells, and low tumor mutation burden. We further predicted obviously worse immunotherapeutic responses in the high-risk groups and identified some candidate compounds targeting GC risk group differentiation. This signature based on the IRLPs may be promising for predicting the survival outcomes and immunotherapeutic responses of GC patients in clinical practice.

Signature of m5C-Related lncRNA for Prognostic Prediction and Immune Responses in Pancreatic Cancer

BACKGROUND

Pancreatic cancer (PC) has a high mortality and dismal prognosis, predicting to be the second most lethal malignancy. 5-Methylcytosine (m5C) and long noncoding RNAs (lncRNAs) are both crucial in the prognostic outcome and immunotherapeutic effect for PC patients. Therefore, we aimed to create an m5C-related lncRNA signature (m5C-LS) for PC patients' prognosis and treatment.

METHODS

Clinicopathological information and RNAseq data were acquired from The Cancer Genome Atlas (TCGA) database. Pearson's correlation analysis was used to extract m5C-related lncRNAs in PC. Univariate, least absolute shrinkage and selection operator (LASSO), and multivariate Cox analyses were adopted to build an m5C-LS. Kaplan-Meier (K-M), principal component analysis (PCA), and nomogram were utilized to assess model accuracy. In addition, we explored the model's possible immunotherapeutic responses and drug sensitivity targets.

RESULTS

Three m5C-related lncRNAs were finally established to construct the risk signature, which has a good and independent predictive ability for PC patients. Based on the m5C-LS, patients were classified into the low- and high-m5C-LS group, with the latter having a worse prognosis. Furthermore, the m5C-LS allowed us to better discriminate the immunotherapeutic responses of PC patients in different subgroups.

CONCLUSIONS

Our study constructed an m5C-LS and established a nomogram model that accurately predicted the prognosis of PC patients, as well as provides promising immunotherapeutic strategies in the future.

Identification of a novel m6A-related lncRNA pair signature for predicting the prognosis of gastric cancer patients

Background

Accumulating studies have demonstrated that lncRNAs play vital roles in the prognosis of gastric cancer (GC); however, the prognostic value of N6-methyladenosine-related lncRNAs has not been fully reported in GC. This study aimed to construct and validate an m6A-related lncRNA pair signature (m6A-LPS) for predicting the prognosis of GC patients.

Methods

GC cohort primary data were downloaded from The Cancer Genome Atlas. We analysed the coexpression of m6A regulators and lncRNAs to identify m6A-related lncRNAs. Based on cyclical single pairing along with a 0-or-1 matrix and least absolute shrinkage and selection operator-penalized regression analyses, we constructed a novel prognostic signature of m6A-related lncRNA pairs with no dependence upon specific lncRNA expression levels. All patients were divided into high-risk and low-risk group based on the median risk score. The predictive reliability was evaluated in the testing dataset and whole dataset with receiver operating characteristic (ROC) curve analysis. Gene set enrichment analysis was used to identify potential pathways.

Results

Fourteen m6A-related lncRNA pairs consisting of 25 unique lncRNAs were used to construct the m6A-LPS. Kaplan–Meier analysis showed that the high-risk group had poor prognosis. The area under the curve for 5-year overall survival was 0.906, 0.827, and 0.882 in the training dataset, testing dataset, and whole dataset, respectively, meaning that the m6A-LPS was highly accurate in predicting GC patient prognosis. The m6A-LPS served as an independent prognostic factor for GC patients after adjusting for other clinical factors (p < 0.05). The m6A-LPS had more accuracy and a higher ROC value than other prognostic models for GC. Functional analysis revealed that high-risk group samples mainly showed enrichment of extracellular matrix receptor interactions and focal adhesion. Moreover, N-cadherin and vimentin, known biomarkers of epithelial–mesenchymal transition, were highly expressed in high-risk group samples. The immune infiltration analysis showed that resting dendritic cells, monocytes, and resting memory CD4 T cells were significantly positively related to the risk score. Thus, m6A-LPS reflected the infiltration of several types of immune cells.

Conclusions

The signature established by pairing m6A-related lncRNAs regardless of expression levels showed high and independent clinical prediction value in GC patients.

METTL3 promotes oxaliplatin resistance of gastric cancer CD133+ stem cells by promoting PARP1 mRNA stability

Oxaliplatin is the first-line regime for advanced gastric cancer treatment, while its resistance is a major problem that leads to the failure of clinical treatments. Tumor cell heterogeneity has been considered as one of the main causes for drug resistance in cancer. In this study, the mechanism of oxaliplatin resistance was investigated through in vitro human gastric cancer organoids and gastric cancer oxaliplatin-resistant cell lines and in vivo subcutaneous tumorigenicity experiments. The in vitro and in vivo results indicated that CD133+ stem cell-like cells are the main subpopulation and PARP1 is the central gene mediating oxaliplatin resistance in gastric cancer. It was found that PARP1 can effectively repair DNA damage caused by oxaliplatin by means of mediating the opening of base excision repair pathway, leading to the occurrence of drug resistance. The CD133+ stem cells also exhibited upregulated expression of N6-methyladenosine (m6A) mRNA and its writer METTL3 as showed by immunoprecipitation followed by sequencing and transcriptome analysis. METTTL3 enhances the stability of PARP1 by recruiting YTHDF1 to target the 3'-untranslated Region (3'-UTR) of PARP1 mRNA. The CD133+ tumor stem cells can regulate the stability and expression of m6A to PARP1 through METTL3, and thus exerting the PARP1-mediated DNA damage repair ability. Therefore, our study demonstrated that m6A Methyltransferase METTL3 facilitates oxaliplatin resistance in CD133+ gastric cancer stem cells by Promoting PARP1 mRNA stability which increases base excision repair pathway activity.

Characterization of m6A-related lncRNA signature in neuroblastoma

N6-methyladenosine (m6A) constitutes one of the most common modifications in mRNA, rRNA, tRNA, microRNA, and long-chain noncoding RNA. The influence of modifications of m6A on the stability of RNA depends upon the expression of methyltransferase ("writer") and demethylase ("eraser") and m6A binding protein ("reader"). In this study, we identified a set of m6A-related lncRNA expression profiles in neuroblastoma (NBL) based on the Therapeutically Applicable Research to Generate Effective Treatments (TARGET) program. Thereupon, we identified two subgroups of neuroblastoma (high-risk group and low-risk group) by applying consensus clustering to m6A RNA methylation regulators ("Readers,", "Writer," and "Erase"). Relative to the low-risk group, the high-risk group correlates with a poorer prognosis. Moreover, the present study also revealed that the high-risk group proves to be significantly positively enriched in the tumor-related signaling pathways, including the P53 signaling pathway, cell cycle, and DNA repair. This finding indicates that these molecular prognostic markers may also be potentially valuable in early diagnosis, which provides a new research direction for the study of molecular mechanisms underlying the development of NBL. In conclusion, this study constructed a new model of NBL prognosis based on m6a-associated lncRNAs. Ultimately, this model is helpful for stratification of prognosis and development of treatment strategies.

Long Non-Coding RNA Neighbor of BRCA1 Gene 2: A Crucial Regulator in Cancer Biology

Long non-coding RNAs (lncRNAs) are involved in fundamental biochemical and cellular processes. The neighbor of BRCA1 gene 2 (NBR2) is a long intergenic non-coding RNA (lincRNA) whose gene locus is adjacent to the tumor suppressor gene breast cancer susceptibility gene 1 (BRCA1). In human cancers, NBR2 expression is dysregulated and correlates with clinical outcomes. Moreover, NBR2 is crucial for glucose metabolism and affects the proliferation, survival, metastasis, and therapeutic resistance in different types of cancer. Here, we review the precise molecular mechanisms underlying NBR2-induced changes in cancer. In addition, the potential application of NBR2 in the diagnosis and treatment of cancer is also discussed, as well as the challenges of exploiting NBR2 for cancer intervention.

Identification of a N6-Methyladenosine (m6A)-Related lncRNA Signature for Predicting the Prognosis and Immune Landscape of Lung Squamous Cell Carcinoma

Background

m6A-related lncRNAs emerged as potential targets for tumor diagnosis and treatment. This study aimed to identify m6A-regulated lncRNAs in lung squamous cell carcinoma (LUSC) patients.

Materials and Methods

RNA sequencing and the clinical data of LUSC patients were downloaded from The Cancer Genome Atlas (TCGA) database. The m6A-related lncRNAs were identified by using Pearson correlation assay. Univariate and multivariate Cox regression analyses were utilized to construct a risk model. The performance of the risk model was validated using Kaplan–Meier survival analysis and receiver operating characteristics (ROC). Immune estimation of LUSC was downloaded from TIMER, and the correlations between the risk score and various immune cells infiltration were analyzed using various methods. Differences in immune functions and expression of immune checkpoint inhibitors and m6A regulators between high-risk and low-risk groups were further explored. Finally, Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) analyses were utilized to explore the biological functions of AL122125.1.

Results

A total of 351 m6A-related lncRNAs were obtained from TCGA. Seven lncRNAs demonstrated prognostic values. A further multivariate Cox regression assay constructed a risk model consisting of two lncRNAs (AL122125.1 and HORMAD2-AS1). The Kaplan–Meier analysis and area under the curve indicated that this risk model could be used to predict the prognosis of LUSC patients. The m6A-related lncRNAs were immune-associated. There were significant correlations between risk score and immune cell infiltration, immune functions, and expression of immune checkpoint inhibitors. Meanwhile, there were significant differences in the expression of m6A regulators between the high- and low-risk groups. Moreover, GO and KEGG analyses revealed that the upregulated expression of AL122125.1 was tumor-related.

Conclusion

In this study, we constructed an m6A-related lncRNA risk model to predict the survival of LUSC patients. This study could provide a novel insight to the prognosis and treatment of LUSC patients.

Prognostic Value of N6-Methyladenosine-Related lncRNAs in Early-Stage Colorectal Cancer: Association With Immune Cell Infiltration and Chemotherapeutic Drug Sensitivity

Purpose: Accumulating evidence indicates that N6-methyladenosine-related long non-coding RNAs (m6A-related lncRNAs) play a crucial role in the occurrence and development of several cancers. We aimed to explore the potential role of m6A-related lncRNA signatures in predicting prognosis for early-stage (stages I and II) colorectal cancer (CRC).

Methods: m6A-related lncRNA data were obtained from The Cancer Genome Atlas. Univariate Cox regression analysis was used to screen for prognostic m6A-related lncRNAs. Immune characteristics were analyzed in different subgroups created via unsupervised clustering analysis. Next, patients were randomly divided into training and test cohorts. In the training cohort, least absolute shrinkage and selection operator (LASSO) regression was performed to establish a prognostic model. The predictive value of the signature was evaluated in the training and test cohorts. Drug sensitivity was also examined.

Results: A total of 1,478 m6A-related lncRNAs were identified. Two subgroups were created based on the expression of seven prognostic m6A-related lncRNAs. Prognosis was worse for cluster 1 than for cluster 2, and cluster 1 was characterized by increased numbers of M2 macrophages, decreased numbers of memory B cells, and higher expression of checkpoint genes when compared with cluster 2. Five m6A-related lncRNAs were selected to establish a risk prediction signature via LASSO regression. The 3 years overall survival (OS) was higher in the low-risk group than in the high-risk group. The area under the curve at 1, 2, and 3 years was 0.929, 0.954, and 0.841 in the training cohort and 0.664, 0.760, and 0.754 in the test cohort, respectively. Multivariate Cox regression analysis suggests that the risk score was an independent predictor of OS in both the training and test cohorts. A prognostic nomogram based on the five m6A-related lncRNAs and their clinical features was built and verified. The high-risk group was more sensitive to chemotherapeutic drugs (camptothecin and cisplatin) than the low-risk group.

Conclusion: We identified two molecular subgroups of early-stage CRC with unique immune features based on seven prognostic m6A-related lncRNAs. Subsequent analyses demonstrated the usefulness of a five m6A-related lncRNA signature as a potential indicator of prognosis in patients with early-stage CRC.

Construction and Characterization of Long Non-Coding RNA-Associated Networks to Reveal Potential Prognostic Biomarkers in Human Lung Adenocarcinoma

Lung adenocarcinoma (LUAD) is one type of the malignant tumors with high morbidity and mortality. The molecular mechanism of LUAD is still unclear. Studies demonstrate that lncRNAs play crucial roles in LUAD tumorigenesis and can be used as prognosis biomarkers. Thus, in this study, to identify more robust biomarkers of LUAD, we firstly constructed LUAD-related lncRNA-TF network and performed topological analyses for the network. Results showed that the network was a scale-free network, and some hub genes with high clinical values were identified, such as lncRNA RP11-173A16 and TF ZBTB37. Module analysis on the network revealed one close lncRNA module, which had good prognosis performance in LUAD. Furthermore, through integrating ceRNAs strategy and TF regulatory information, we identified some lncRNA-TF positive feedback loops. Prognostic analysis revealed that ELK4- and BDP1-related feedback loops were significant. Secondly, we constructed the lncRNA-m6A regulator network by merging all the high correlated lncRNA-m6A regulator pairs. Based on the network analysis results, some key m6A-related lncRNAs were identified, such as MIR497HG, FENDRR, and RP1-199J3. We also investigated the relationships between these lncRNAs and immune cell infiltration. Results showed that these m6A-related lncRNAs were high correlated with tumor immunity. All these results provide a new perspective for the diagnostic biomarker and therapeutic target identification of LUAD.

m6A Regulator-Mediated Methylation Modification Model Predicts Prognosis, Tumor Microenvironment Characterizations and Response to Immunotherapies of Clear Cell Renal Cell Carcinoma

BACKGROUND

This study aims to establish an N6-methyladenosine (m6A) RNA methylation regulators-mediated methylation model and explore its role in predicting prognostic accuracy of immune contexture and characterizations of clear cell renal cell carcinoma (ccRCC).

METHODS

The m6A modification subclasses (m6AMS) were identified by unsupervised cluster analysis and three clusters were determined by consensus clustering algorithm in a discovering cohort. Testing and real-world validation cohorts were used to identify predictive responses for immune checkpoint therapies (ICTs) of m6AMS.

RESULTS

Prognostic implications landscape of m6A regulators in cancers and its differential expression levels in ccRCC patients were identified. Based on discovering cohort, ccRCC were automatically divided into three m6AMS, and cluster 3 showed significant worse survival than cluster 1/2. Importantly, it was found that the immune checkpoint molecules expression was significantly elevated in cluster 3. Besides, m6A scoreLow group (cluster 1&2) have significantly elevated TIDE score compared with m6A scoreHigh group (cluster 3). There was conspicuous tertiary lymphoid tissue, aggressive phenotype, elevated glycolysis, expression of PD-L1, abundance of CD8+ T cells, CD4+ FOXP3+ Treg cells and TCRn immune cells infiltration in the high m6A score group. Interestingly, there are significantly increased patients with clinical benefit in m6A scoreHigh group in 368 patients receiving ICTs from testing IMvigor210 (n = 292) and validation FUSCC (n = 55) cohorts.

CONCLUSION

Our discovery highlights the relationship between tumor epigenetic heterogeneity and immune contexture. Immune-rejection cluster 3 has pro-tumorigenic immune infiltration, and shows significant clinical benefits for ccRCC patients receiving ICTs, enabling patient selection for future clinical treatment.