N6-Methylandenosine-Related lncRNAs Predict Prognosis and Immunotherapy Response in Bladder Cancer

Bladder Cancer in Exosomal Perspective: Unraveling New Regulatory Mechanisms

Bladder cancer, a prevalent malignant neoplasm of the urinary tract, exhibits escalating morbidity and mortality rates. Current diagnosis standards rely on invasive and costly cystoscopy and histopathology, underscoring the urgency for non-invasive, high-throughput, and cost-effective novel diagnostic techniques to ensure timely detection and standardized treatment. Recent years have witnessed the rise of exosome research in bladder cancer studies. Exosomes contain abundant bioactive molecules that can help elucidate the intricate mechanisms underlying bladder cancer pathogenesis and metastasis. Exosomes hold potential as biomarkers for early bladder cancer diagnosis while also serving as targeted drug delivery vehicles to enhance treatment efficacy and mitigate adverse effects. Furthermore, exosome analyses offer insights into the complex molecular signaling networks implicated in bladder cancer progression, revealing novel therapeutic targets. This review provides a comprehensive overview of prevalent exosome isolation techniques and highlights the promising clinical utility of exosomes in both diagnostic and therapeutic applications in bladder cancer management.

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.

Fatty acid metabolism-related lncRNAs are potential biomarkers for survival prediction in clear cell renal cell carcinoma

Metabolic reprogramming of energy is a newly recognized characteristic of cancer. In our current investigation, we examined the possible predictive importance of long noncoding RNAs (lncRNAs) associated to fatty acid metabolism in clear cell renal cell carcinoma (ccRCC). We conducted an analysis of the gene expression data obtained from patients diagnosed with ccRCC using the Cancer Genome Atlas (TCGA) database and the ArrayExpress database. We performed a screening to identify lncRNAs that are differentially expressed in fatty acid metabolism. Based on these findings, we developed a prognostic risk score model using these fatty acid metabolism-related lncRNAs. We then validated this model using Cox regression analysis, Kaplan-Meier survival analysis, and principal-component analysis (PCA). Furthermore, the prognostic risk score model was successfully validated using both the TCGA cohort and the E-MTAB-1980 cohort. We utilized gene set variation analysis (GSVA) and gene set enrichment analysis (GSEA) to determine the correlation between fatty acid metabolism and the PPAR signaling pathway in patients with ccRCC at various clinical stages and prognoses. We have discovered compelling evidence of the interaction between immune cells in the tumor microenvironment and tumor cells, which leads to immune evasion and resistance to drugs. This was achieved by the utilization of advanced techniques such as the CIBERSORT method, ESTIMATE R package, ssGSEA algorithm, and TIMER database exploration. Ultimately, we have established a network of competing endogenous RNA (ceRNA) that is related to fatty acid metabolism. The findings of our study suggest that medicines focused on fatty acid metabolism could be clinically significant for individuals with ccRCC. The utilization of this risk model, which is centered around the lncRNAs associated with fatty acid metabolism, could potentially provide valuable prognostic information and hold immunotherapeutic implications for patients with ccRCC.

N6-Methyladenosine (m6A) Modification in Natural Immune Cell-Mediated Inflammatory Diseases

The post-transcriptional N6-methyladenosine (m6A) modification of RNA influences stability, transport, and translation with implications for various physiological and pathological processes. Immune cell development, differentiation, and activation are also thought to be regulated by m6A and affect host defense against pathogens and inflammatory response with impacts on infectious, neoplastic, autoimmune, cardiovascular, hepatic, and osteal diseases. The current review summarizes recent research on m6A in monocyte/macrophages, neutrophils, dendritic cells, natural killer cells, and microglia and gives insights into epigenetic modifications of the immune system and novel therapeutic strategies for immune-related diseases.

Creation of a Prognostic Model Using Cuproptosis-Associated Long Noncoding RNAs in Hepatocellular Carcinoma

Cuproptosis is an unusual form of cell death caused by copper accumulation in mitochondria. Cuproptosis is associated with hepatocellular carcinoma (HCC). Long noncoding RNAs (LncRNAs) have been shown to be effective prognostic biomarkers, yet the link between lncRNAs and cuproptosis remains unclear. We aimed to build a prognostic model of lncRNA risk and explore potential biomarkers of cuproptosis in HCC. Pearson correlations were used to derive lncRNAs co-expressed in cuproptosis. The model was constructed using Cox, Lasso, and multivariate Cox regressions. Kaplan-Meier survival analysis, principal components analysis, receiver operating characteristic curve, and nomogram analyses were carried out for validation. Seven lncRNAs were identified as prognostic factors. A risk model was an independent prognostic predictor. Among these seven lncRNAs, prostate cancer associated transcript 6 (PCAT6) is highly expressed in different types of cancer, activating Wnt, PI3K/Akt/mTOR, and other pathways; therefore, we performed further functional validation of PCAT6 in HCC. Reverse transcription-polymerase chain reaction results showed that PCAT6 was aberrantly highly expressed in HCC cell lines (HepG2 and Hep3B) compared to LO2 (normal hepatocytes). When its expression was knocked down, cells proliferated and migrated less. PCAT6 might be a potential biomarker for predicting prognosis in HCC.

Novel insights into the multifaceted roles of m6A-modified LncRNAs in cancers: biological functions and therapeutic applications

N6-methyladenosine (m⁶A) is considered as the most common and important internal transcript modification in several diseases like type 2 diabetes, schizophrenia and especially cancer. As a main target of m⁶A methylation, long non-coding RNAs (lncRNAs) have been proved to regulate cellular processes at various levels, including epigenetic modification, transcriptional, post-transcriptional, translational and post-translational regulation. Recently, accumulating evidence suggests that m⁶A-modified lncRNAs greatly participate in the tumorigenesis of cancers. In this review, we systematically summarized the biogenesis of m⁶A-modified lncRNAs and the identified m⁶A-lncRNAs in a variety of cancers, as well as their potential diagnostic and therapeutic applications as biomarkers and therapeutic targets, hoping to shed light on the novel strategies for cancer treatment.

An m7G-related lncRNA signature predicts prognosis and reveals the immune microenvironment in bladder cancer

Bladder cancer (BC) is a representative malignant tumor type, and the significance of N7-methyguanosine (m7G)-related lncRNAs in BC is still unclear. Utilizing m7G-related lncRNAs, we developed a prognostic model to evaluate BC's prognosis and tumor immunity. First, we selected prognostic lncRNAs related to m7G by co-expression analysis and univariate Cox regression and identified two clusters by consensus clustering. The two clusters differed significantly in terms of overall survival, clinicopathological factors, and immune microenvironment. Then, we further constructed a linear stepwise regression signature by multivariate Cox and least absolute shrinkage and selection operator (LASSO) regression analysis. Patients fell into high-risk (HR) and low-risk (LR) groups considering the train group risk score. HR group had worse prognoses when stratified by clinicopathological factors. The receiver operating curve (ROC) suggested that the signature had a better prognostic value. Tumor mutation burden (TMB) showed a negative relevance to the risk score, and patients with low TMB presented a better prognosis. Validation of the signature was carried out with multivariate and univariate Cox regression analysis, nomogram, principal component analysis (PCA), C-Index, and quantitative reverse transcriptase PCR (qRT-PCR). Finally, the gene set enrichment analysis (GSEA) demonstrated the enrichment of tumor-related pathways in HR groups, and single-sample gene set enrichment analysis (ssGSEA) indicated a close association of risk score with tumor immunity. According to the drug sensitivity test, the signature could predict the effects of conventional chemotherapy drugs. In conclusion, our study indicates the close relevance of m7G-related lncRNAs to BC, and the established risk signature can effectively evaluate patient prognosis and tumor immunity and is expected to become a novel prognostic marker for BC patients.

Identification and validation of the pyroptosis-related long noncoding rna signature to predict the prognosis of patients with bladder cancer

Bladder cancer ranked the second most frequent tumor among urological malignancies. This work investigated bladder cancer prognosis, including the relevance of pyroptosis-related long noncoding RNA (lncRNA) in it and its potential roles. The Cancer Genome Atlas database offered statistics on lncRNAs and clinical data from 411 bladder cancer patients. Pearson correlation analysis was used to evaluate pyroptosis-related lncRNAs. To explore prognosis-associated lncRNAs, we performed univariate Cox regression, least absolute shrinkage and selection operator regression analyses, as well as the Kaplan-Meier method. Multivariate Cox analysis was leveraged to establish the risk score model. Afterward, a nomogram was constructed according to the risk score and clinical variables. Finally, to investigate the potential functions of pyroptosis-related lncRNAs, gene set enrichment analysis was employed. Eleven pyroptosis-related lncRNAs were screened to be closely associated with patients prognosis. On this foundation, a risk score model was created to classify patients into high and low risk groups. The signature was shown to be an independent prognostic factor (P < .001) with an area under the curve of 0.730. Then a nomogram was established including risk scores and clinical characteristics. The nomogram prediction effect is excellent, with a concordance index of 0.86. The 11-lncRNAs signature was associated with the supervision of oxidative stress, epithelial-mesenchymal transition, cell adhesion, TGF-β, and Wingless and INT-1 signaling pathway, according to the gene set enrichment analysis. Our findings indicate that pyroptosis-related lncRNAs, which may affect tumor pathogenesis in many ways, might be exploited to assess the prognosis of bladder cancer patients.

The Cancer Genome Atlas (TCGA) based m6A methylation-related genes predict prognosis in rectosigmoid cancer

N6-methyladenosine (m6A) methylation plays an important role in the occurrence and development of tumors. This study aimed to explore the effects of m6A methylation regulatory genes on rectosigmoid cancer (RSC). RNA-seq data and related clinical information in The Cancer Genome Atlas database were analyzed. The Wilcoxon test was used to analyze the different expression levels of m6A methylation regulatory genes between the tumor and normal samples. Least absolute shrinkage and selection operator Cox regression analysis was used to construct a risk prognosis model between the m6A methylation regulatory genes and RSC. The median risk score was used to classify RSC patients into high and low-risk groups. Kaplan-Meier survival analysis and receiver operating characteristic curves were used to evaluate the sensitivity and specificity of the prediction model. The expression of m6A methylation regulation genes was different between the tumor and normal samples, 6 genes were overexpressed in tumor and 2 genes were down-regulated. Four m6A methylation regulatory genes, YTHDF3, KIAA1429, ALKBH5 and METTL3, were screened by least absolute shrinkage and selection operator Cox regression analysis. The overall survival of high-risk group was significantly lower than that of low-risk group (P = 4.681 × 10-4). The area under the curve value in the receiver operating characteristic curve was 0.935, indicating that the prediction model was effective. Univariate and multivariate Cox regression were used to test the effectiveness of the model. m6A methylation regulators YTHDF3, KIAA1429, ALKBH5, and METTL3 can be used to construct predictive models to predict overall survival in different clinical subgroups of RSC patients.

m6A-related lncRNA signature for predicting prognosis and immune response in head and neck squamous cell carcinoma

OBJECTIVES

N6-methyladenosine (m⁶A) and long non-coding RNAs (lncRNAs) significantly impact the prognosis and the response to immunotherapy in head and neck squamous cell carcinoma (HNSCC). Therefore, this study aimed to develop an m⁶A-related lncRNA (m⁶AlncRNA) model for predicting the prognosis and the immunotherapeutic response in HNSCC.

METHODS

We identified the m⁶AlncRNAs and constructed a risk assessment signature by using univariable Cox, Least Absolute Shrinkage and Selection Operator (LASSO), and multivariate Cox regression analyses. The Kaplan-Meier analysis, receiver-operating characteristic (ROC) curves, principal component analysis (PCA), decision curve analysis (DCA), consistency index (C-index), and nomogram were applied to assess the risk model. Finally, we investigated the predictability of this model in prognosis and response to immunotherapy and evaluated various novel compounds for the clinical treatment of HNSCC.

RESULTS

HNSCC patients were assigned to high- and low-risk groups based on the median risk scores, and the high- and low-risk groups had different clinical features, tumor immune infiltration status, tumor immune dysfunction and exclusion (TIDE), tumor mutational burden (TMB), sensitivity to novel potential compounds, and immunotherapeutic response.

CONCLUSIONS

The model we developed was accurate and efficient in predicting the prognosis of patients with HNSCC. It was also sensitive in stratifying HNSCC patients with good response to immunotherapy. Therefore, our study provided insight into elucidating the processes and mechanisms of m⁶AlncRNAs.

A Novel Prognostic Chemokine-Related lncRNAs Signature Associated with Immune Landscape in Colon Adenocarcinoma

Chemokines have been reported to be involved in tumorigenesis and progression and can also modulate the tumor microenvironment. However, it is still unclear whether chemokine-related long noncoding RNAs (lncRNAs) can affect the prognosis of colon adenocarcinoma (COAD). We summarized chemokine-related genes and downloaded RNA-seq and clinical data from The Cancer Genome Atlas (TCGA) database. A total of 52 prognostic chemokine-related lncRNAs were screened by univariate Cox regression analysis; patients were grouped according to cluster analysis results. Lasso regression analysis was applied to determine chemokine-related lncRNAs to construct a risk model for further research. This study first investigated the differences between the prognosis and immune status of two chemokine-related lncRNAs clusters by consensus clustering. Then, using various algorithms, we obtained ten chemokine-related lncRNAs to construct a new prognostic chemokine-related lncRNAs risk model. The risk model's predictive efficiency, validity, and accuracy were further validated and determined in the test and training cohorts. Furthermore, this risk model played a vital role in predicting immune cell infiltration, immune checkpoint gene expression, tumor mutational burden (TMB), immunotherapy score, and drug sensitivity in COAD patients. These findings elucidated the critical role of novel prognostic chemokine-related lncRNAs in prognosis, immune landscape, and drug therapy, thereby providing valuable insights for prognosis assessment and personalized treatment strategies for COAD patients.

m6A-immune-related lncRNA prognostic signature for predicting immune landscape and prognosis of bladder cancer

Background

N6-methyladenosine (m6A) related long noncoding RNAs (lncRNAs) may have prognostic value in bladder cancer for their key role in tumorigenesis and innate immunity.

Methods

Bladder cancer transcriptome data and the corresponding clinical data were acquired from the Cancer Genome Atlas (TCGA) database. The m6A-immune-related lncRNAs were identified using univariate Cox regression analysis and Pearson correlation analysis. A risk model was established using least absolute shrinkage and selection operator (LASSO) Cox regression analyses, and analyzed using nomogram, time-dependent receiver operating characteristics (ROC) and Kaplan–Meier survival analysis. The differences in infiltration scores, clinical features, and sensitivity to Talazoparib of various immune cells between low- and high-risk groups were investigated.

Results

Totally 618 m6A-immune-related lncRNAs and 490 immune-related lncRNAs were identified from TCGA, and 47 lncRNAs of their intersection demonstrated prognostic values. A risk model with 11 lncRNAs was established by Lasso Cox regression, and can predict the prognosis of bladder cancer patients as demonstrated by time-dependent ROC and Kaplan–Meier analysis. Significant correlations were determined between risk score and tumor malignancy or immune cell infiltration. Meanwhile, significant differences were observed in tumor mutation burden and stemness-score between the low-risk group and high-risk group. Moreover, high-risk group patients were more responsive to Talazoparib.

Conclusions

An m6A-immune-related lncRNA risk model was established in this study, which can be applied to predict prognosis, immune landscape and chemotherapeutic response in bladder cancer.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12967-022-03711-1.

m6A-induced lncDBET promotes the malignant progression of bladder cancer through FABP5-mediated lipid metabolism

The limited effect of adjuvant therapy for advanced bladder cancer (BCa) leads to a poor prognosis. Increasing evidence has shown that RNA N6-methyladenosine (m⁶A) modification plays important functional roles in tumorigenesis. Nevertheless, the role and mechanism of m6A-modified noncoding RNAs (ncRNAs) in BCa remain largely unknown.

Methods: RT-PCR, western blotting and ONCOMINE dataset were used to determine the dominant m⁶A-related enzyme in BCa. M⁶A-lncRNA epitranscriptomic microarray was used to screen candidate targets of METTL14. RT-PCR, MeRIP and TCGA dataset were carried out to confirm the downstream target of METTL14. CHIRP/MS was conducted to identify the candidate proteins binding to lncDBET. RT-PCR, western blotting, RIP and KEGG analysis were used to confirm the target of lncDBET. The levels of METTL14, lncDBET and FABP5 were tested in vitro and in vivo. CCK-8, EdU, transwell and flow cytometry assays were performed to determine the oncogenic function of METTL14, lncDBET and FABP5, and their regulatory networks.

Results: We identified that the m⁶A level of total RNA was elevated and that METTL14 was the dominant m6A-related enzyme in BCa. m⁶A modification mediated by METTL14 promoted the malignant progression of BCa by promoting the expression of lncDBET. Upregulated lncDBET activated the PPAR signalling pathway to promote the lipid metabolism of cancer cells through direct interaction with FABP5, thus promoting the malignant progression of BCa in vitro and in vivo.

Conclusions: Our study establishes METTL14/lncDBET/FABP5 as a critical oncogenic axis in BCa.

Identification of N6-methylandenosine related lncRNA signatures for predicting the prognosis and therapy response in colorectal cancer patients

Despite recent advances in surgical and multimodal therapies, the overall survival (OS) of advanced colorectal cancer (CRC) patients remains low. Thus, discerning sensitive prognostic biomarkers to give the optimistic treatment for CRC patients is extremely critical. N6-methyladenosine (m6A) and long noncoding RNAs (lncRNAs) play an important role in CRC progression. Nonetheless, few studies have focused on the impact of m6A-related lncRNAs on the prognosis, tumor microenvironment (TME) and treatment of CRC. In this study, 1707 m6A-related lncRNAs were identified through Pearson correlation analysis and Weighted co-expression network analysis (WGCNA) using The Cancer Genome Atlas (TCGA) cohort. Then, 28 m6A-related prognostic lncRNAs were screened by univariate Cox regression analysis, followed by identifying two clusters by consensus clustering analysis. A prognostic model consisted of 8 lncRNA signatures was constructed by the least absolute shrinkage and selection operator (LASSO). Kaplan–Meier curve analysis and a nomogram were performed to investigate the prognostic ability of this model. The risk score of prognostic model act as an independent risk factor for OS rate. Functional enrichment analysis indicated that lncRNA signatures related tumor immunity. The low-risk group characterized by increased microsatellite instability-high (MSI-H), mutation burden, and immunity activation, indicated favorable odds of OS. Moreover, the lncRNA signatures were significantly associated with the cancer stem cell (CSC) index and drug sensitivity. In addition, 3 common immune genes shared by the lncRNA signatures were screened out. We found that these immune genes were widely distributed in 2 cell types of TME. Finally, a ceRNA network was constructed to identify ZEB1-AS1 regulatory axis in CRC. We found that ZEB1-AS1 was significantly overexpressed in tumor tissues, and was related to the metastasis of EMT and the chemoresistance of 5-Fu in CRC. Therefore, our study demonstrated the important role of m6A-related lncRNAs in TME remodeling. Moreover, these results illustrated the levels of ZEB1-AS1 might be valuable for predicting the progression and prognosis of CRC, and further provided a new target for the diagnosis and treatment of CRC patients.

N6-Methyladenosine (m6A)-Related lncRNAs Are Potential Signatures for Predicting Prognosis and Immune Response in Lung Squamous Cell Carcinoma

Background

Despite increasing understanding of m⁶A-related lncRNAs in lung cancer, the role of m⁶A-related lncRNAs in the prognosis and treatment of lung squamous cell carcinoma is poorly understood to date. Thus, the current study aims to elucidate its role and build a model to predict the prognosis of LUSC patients.

Materials and Methods

The data of the current study were accessed from the TCGA database. Pearson correlation analysis was performed to identify lncRNAs correlated to m⁶A. Next, an m⁶A-related lncRNAs risk model was built using a single factor, least absolute association, selection operator, and multivariate Cox regression analysis.

Results

The relevance between 23 m⁶A genes and 14,056 lncRNAs is shown by Pearson correlation analysis by Sankey diagram. Multivariate Cox regression analysis determined that 11 m⁶A-lncRNAs show predictive potential in prognosis, which is confirmed by the consistency index, Kaplan–Meier analysis, principal component analysis, and ROC curve. Additionally, the immune analysis showed that the enrichment of immune cells, major histocompatibility complex molecules, and immune checkpoints in the high and low-risk subgroups were markedly disparate, with the high-risk group showing a stronger immune escape ability and a worse response to immunotherapy.

Conclusion

In conclusion, the risk model based on m⁶A-related lncRNAs showed great promise in predicting the prognosis and the efficacy of immunotherapy.

Construction and verification of a novel hypoxia-related lncRNA signature related with survival outcomes and immune microenvironment of bladder urothelial carcinoma by weighted gene co-expression network analysis

Background: Bladder urothelial carcinoma (BLCA) is a common malignant tumor with the greatest recurrence rate of any solid tumor. Hypoxia is crucial in the growth and immune escape of malignant tumors. To predict clinical outcomes and immunological microenvironment of patients with BLCA, a hypoxia-related long non-coding RNA (HRlncRNA) signature was established.

Methods: The Cancer Genome Atlas (TCGA) provided us with the differentially expressed profile of HRlncRNAs as well as clinical data from patients with BLCA, and we used weighted gene co-expression network analysis (WGCNA) to identify gene modules associated with malignancies.

Results: Finally, Cox analysis revealed that HRlncRNAs, which comprised 13 lncRNAs, were implicated in the predictive signature. The training, testing, and overall cohorts of BLCA patients were divided into the low-risk group and high-risk group based on the median of the risk score. The Kaplan–Meier curves revealed that BLCA patients with a high-risk score had a poor prognosis, and the difference between subgroups was statistically significant. The receiver operating characteristic curves revealed that this signature outperformed other strategies in terms of predicting ability. Multivariate analysis revealed that the risk score was an independent prognostic index for overall survival (HR = 1.411; 1.259–1.582; p < 0.001). Then, a nomogram with clinicopathological features and risk score was established. This signature could effectively enhance the capacity to predict survival, according to the calibration plots, stratification, and clinical analysis. The majority of Kyoto Encyclopedia of Genes and Genomes (KEGG) were WNT, MAPK, and ERBB signaling pathways. Two groups had different immune cell subtypes, immune checkpoints, immunotherapy response, and anti-tumor drug sensitivity, which might result in differing survival outcomes. We then validated the differential expression of signature-related genes between tumor and normal tissues using TCGA paired data.

Conclusion: This prognostic signature based on 13 HRlncRNAs may become a novel and potential prognostic biomarker, providing more accurate clinical decision-making and effective treatment for BLCA patients.

Predicting Prognosis and Distinguishing Cold and Hot Tumors in Bladder Urothelial Carcinoma Based on Necroptosis-Associated lncRNAs

Background

In reference to previous studies, necroptosis played an important role in cancer development. Our team decided to explore the potential prognostic values of long non-coding RNAs (lncRNAs) associated with necroptosis in bladder urothelial carcinoma (BLCA) and their relationship with the tumor microenvironment (TME) and the immunotherapeutic response for accurate dose.

Methods

To obtain the required data, bladder urothelial carcinoma transcriptome data were searched from Cancer Genome Atlas (TCGA) (https://portal.gdc.cancer.gov/). We used co-expression analysis, differential expression analysis, and univariate Cox regression to screen out prognostic lncRNAs associated with necroptosis in BLCA. Then the least absolute shrinkage and selection operator (LASSO) was conducted to construct the necroptosis-associated lncRNAs model. Based on this model, we also performed the Kaplan–Meier analysis and time-dependent receiver operating characteristics (ROC) to estimate the prognostic power of risk score. Multivariate and univariate Cox regression analysis were performed to build up a nomogram. Calibration curves, and time-dependent ROC were also conducted to evaluate nomogram. Principal component analysis (PCA) revealed a difference between high- and low-risk groups. In addition, we explored immune analysis, gene set enrichment analyses (GSEA), and evaluation of the half-maximal inhibitory concentration (IC50) in constructed model. Finally, the entire samples were divided into three clusters based on model of necroptosis-associated lncRNAs to further compare immunotherapy in cold and hot tumors.

Results

A model was built up based on necroptosis-associated lncRNAs. The model revealed good consistence between calibration plots and prognostic prediction. The area of 1-, 3-, and 5-year OS under the ROC curve (AUC) were 0.707, 0.679, and 0.675. Risk groups could be helpful for systemic therapy due to the markedly diverse IC50 between risk groups. To our delight, clusters could effectively identify cold and hot tumors, which would be beneficial to accurate mediation. Clusters 2 and 3 were considered the hot tumor, which was more sensitive to immunotherapeutic drugs.

Conclusions

The outcomes of our study suggested that necroptosis-associated lncRNAs could effectively predict patients with BLCA prognosis, which may be helpful for distinguishing the cold and hot tumors and improving individual treatment of BLCA.

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.

Four Types of RNA Modification Writer-Related lncRNAs Are Effective Predictors of Prognosis and Immunotherapy Response in Serous Ovarian Carcinoma

Serous ovarian carcinoma (SOC) is a gynecological malignancy with high mortality rates. Currently, there is a lack of reliable biomarkers for accurate SOC patient prognosis. Here, we analyzed SOC RNA-Seq data from The Cancer Genome Atlas (TCGA) to identify prognostic biomarkers. Through the pearson correlation analysis, univariate Cox regression analysis, and LASSO-penalized Cox regression analysis, we identified nine lncRNAs significantly associated with four types of RNA modification writers (m6A, m1A, APA, and A-I) and with the prognosis of SOC patients (P <0.05). Six writer-related lncRNAs were ultimately selected following multivariate Cox analysis. We established a risk prediction model based on these six lncRNAs and evaluated its prognostic value in multiple groups (training set, testing set, and entire set). Our risk prediction model could effectively predict the prognosis of SOC patients with different clinical characteristics and their responses to immunotherapy. Lastly, we validated the predictive reliability and sensitivity of the lncRNA-based model via a nomogram. This study explored the association between RNA modification writer-related lncRNAs and SOC prognosis, providing a potential complement for the clinical management of SOC patients.

RNA-Binding Protein IGF2BP1 Associated With Prognosis and Immunotherapy Response in Lung Adenocarcinoma

N6-methyladenosine (m⁶A) is the most common modification in eukaryotic RNAs and plays a vital role in the tumorigenesis and metastasis of various cancers. However, a comprehensive study of m⁶A methylation regulators in lung adenocarcinoma (LUAD) is still lacking. The present study aimed to systematically explore the role of m⁶A methylation regulators in LUAD. RNA sequencing data of 20 m⁶A methylation regulators and clinical data of LUAD patients were downloaded from The Cancer Genome Atlas (TCGA) database. The prognosis value of m⁶A methylation regulators in LUAD was evaluated using the Gene Expression Profiling Interactive Analysis (GEPIA) and PrognoScan database. The correlation between IGF2BP1 and immune infiltrates in LUAD was investigated via CIBERSORT and Tumor Immune Estimation Resource (TIMER). A total of 15 m⁶A modification regulators were significantly abnormally expressed in LUAD tissues. Survival analysis revealed that four genes (HNRNPC, HNRNPA2B1, IGF2BP1, and IGF2BP3) were significantly associated with poor prognosis in LUAD. Multivariate Cox regression analysis showed that only IGF2BP1 was an independent predictor of LUAD after adjusting common clinical parameters. The mutation rates of m⁶A modification regulators in LUAD were less than 10%. Further analysis revealed that IGF2BP1 expression was significantly correlated with immune infiltration, the expression of immune checkpoints, and tumor mutational burden (TMB) in LUAD. Our findings suggest that IGF2BP1 is an independent predictor and related to immunotherapy response in LUAD, which maybe a potential novel biomarker for LUAD prognosis and the status of tumor immunity.