Immune Infiltrates of m6A RNA Methylation-Related lncRNAs and Identification of PD-L1 in Patients With Primary Head and Neck Squamous Cell Carcinoma

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.

RNA 5-Methylcytosine Regulator NSUN3 promotes tumor progression through regulating immune infiltration in head and neck squamous cell carcinoma

OBJECTIVE

This study aimed to determine whether the RNA, 5-methylcytosine (m5C), is involved in the progression of head and neck squamous cell carcinoma (HNSCC).

MATERIALS AND METHODS

We used least absolute shrinkage and selection operator to establish a prognostic score (PS) model based on the m5C regulator expression. Immune scores were calculated using the estimation of stromal and immune cells in malignant tumor tissues using expression data. The biological functions of the m5C regulator, NOP2/Sun RNA methyltransferase 3 (NSUN3), were thoroughly investigated in vitro and in vivo.

RESULTS

The PS model acted as efficient prognostic factors in HNSCC. The expression of NSUN3, with the maximum weight, was found to be upregulated and indicated a poor prognosis. Meanwhile, NSUN3 knockdown inhibited the tumor proliferation and growth both in vitro and in vivo. High PS status was negatively correlated with CD8+ T, γδ+ T, and M1 macrophage percentages. NSUN3 knockdown increased the infiltration of M1 macrophages but decreased the percentage of M2 macrophages.

CONCLUSIONS

The PS index is a novel and promising biomarker for predicting the prognosis and immune infiltration microenvironment in HNSCC. Moreover, NSUN3 plays a key role in this process and may serve as a potential therapeutic target for HNSCC.

A senescence-related lncRNA signature predicts prognosis and reflects immune landscape in HNSCC

OBJECTIVE

Long noncoding RNAs (lncRNAs) regulate cancer cell senescence in many cancers. However, their specific involvement in head and neck squamous cell carcinoma (HNSCC) remains unclear. We are looking for an ingenious prognostic signature that utilizes senescence-related lncRNAs (SRlncRNAs) to predict prognosis and provide insights into the immune landscape in HNSCC.

MATERIALS AND METHODS

HNSCC clinical and Cellular senescence genes information were collected from The Cancer Genome Atlas and Human Aging Genomic Resources. Then we performed Cox and Lasso regression to locate SRlncRNAs related to the prognosis of HNSCC and built a predictive signature. Further, prognosis assessment, potential mechanisms, and immune status were assessed by Kaplan-Meier analysis, Gene Set Enrichment Analysis (GSEA), and CIBERSORT, respectively.

RESULTS

A prognosis prediction model based on sixteen SRlncRNAs was identified and internally validated. Then, patients with high-risk scores suffered an unfavorable overall survival (All p < 0.05). The risk score, age, and stage were independent prognostic parameters (all p < 0.001). Our model has good predictive ability (The AUC (area under the curves) 1-year = 0.707, AUC3-year = 0.748 and AUC5-year = 0.779). Subsequently, GESA revealed SRlncRNAs regulated immune responses. Patients in the high-risk group had higher tumor mutation burden and Tumor Immune Dysfunction and Exclusion but lower levels of 37 immune checkpoint genes, immune scores, and immune cells like CD8 + T cells, follicular helper T cells, and regulatory T cells.

CONCLUSIONS

A prognostic model based on SRlncRNAs is the potential target for improving immunotherapy outcomes for HNSCC.

The pyroptosis-related signature predicts prognosis and influences the tumor immune microenvironment in dedifferentiated liposarcoma

Background

Dedifferentiated liposarcoma (DDL), a member of malignant mesenchymal tumors, has a high local recurrence rate and poor prognosis. Pyroptosis, a newly discovered programmed cell death, is tightly connected with the progression and outcome of tumor.

Objective

The aim of this study was to explore the role of pyroptosis in DDL.

Methods

We obtained the RNA sequencing data from The Cancer Genome Atlas (TCGA) and Genotype-Tissue Expression databases to identify different pyroptosis-related genes (PRGs) expression pattern. An unsupervised method for clustering based on PRGs was performed. Based on the result of cluster analysis, we researched clinical outcomes and immune microenvironment between clusters. The differentially expressed genes (DEGs) between the two clusters were used to develop a prognosis model by the LASSO Cox regression method, followed by the performance of functional enrichment analysis and single-sample gene set enrichment analysis. All of the above results were validated in the Gene Expression Omnibus (GEO) dataset.

Results

Forty-one differentially expressed PRGs were found between tumor and normal tissues. A consensus clustering analysis based on PRGs was conducted and classified DDL patients into two clusters. Cluster 2 showed a better outcome, higher immune scores, higher immune cells abundances, and higher expression levels in numerous immune checkpoints. DEGs between clusters were identified. A total of 5 gene signatures was built based on the DEGs and divided all DDL patients of the TCGA cohort into low-risk and high-risk groups. The low-risk group indicates greater inflammatory cell infiltration and better outcome. For external validation, the survival difference and immune landscape between the two risk groups of the GEO cohort were also significant. Receiver operating characteristic curves implied that the risk model could exert its function as an outstanding predictor in predicting DDL patients' prognoses.

Conclusion

Our findings revealed the clinical implication and key role in tumor immunity of PRGs in DDL. The risk model is a promising predictive tool that could provide a fundamental basis for future studies and individualized immunotherapy.

A new marker constructed from immune-related lncRNA pairs can be used to predict clinical treatment effects and prognosis: in-depth exploration of underlying mechanisms in HNSCC

BACKGROUND

Long non-coding RNA (lncRNA) plays a vital role in tumor proliferation, migration, and treatment. Since it is challenging to standardize the gene expression levels detected by different platforms, the signatures composed of many immune-related single lncRNAs are still inaccurate. Utilizing a gene pair formed of two immune-related lncRNAs and strategically assigning values can effectively meet the demand for a higher-accuracy dual biomarker combination.

METHODS

Co-expression and differential expression analyses were performed on immune genes and lncRNAs data from The Cancer Genome Atlas and the ImmPort database to obtain differentially expressed immune-related lncRNAs for pairwise pairing. The prognostic-related differentially expressed immune-related lncRNAs (PR-DE-irlncRNAs) pairs were then identified by univariate Cox regression and used for lasso regression to construct a prognostic model. Various methods were used to validate the predictive prognostic performance of the model. Additionally, we explored the potential guiding value of the model in immunotherapy and chemotherapy and constructed a nomogram suitable for efficient prognosis prediction. Mechanistic exploration of anti-tumor immunity and mutational perspectives are also included. We also analyzed the correlation between the model and immune checkpoint inhibitors (ICIs)-related, N6-methyadenosine (m6A)-related, and multidrug resistance genes.

RESULTS

We used a total of 20 pairs of PR-DE-irlncRNAs to create a prognosis model. Quantitative real-time polymerase chain reaction experiments further verified the abnormal expression of 11 lncRNAs in HNSCC cells. Various methods have confirmed the excellent performance of the model in predicting patient prognosis. We reasoned that lncRNAs/TP53 mutation might play a positive/negative anti-tumor role through the immune system by multi-perspective analyses. Finally, it was found that the prognostic model was closely related to immunotherapy and chemotherapy as well as the expression of ICIs/m6A/multidrug resistance-related genes.

CONCLUSION

The prognostic model performs excellently in predicting the prognosis of patients and provides the potential value of practical guidance for treatment.

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.

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.

A prognostic signature based on cuprotosis-related long non-coding RNAs predicts the prognosis and sensitivity to chemotherapy in patients with colorectal cancer

Cuprotosis, a newly proposed mechanism of cell death, can trigger acute oxidative stress that leads to cell death by mediating protein lipidation in the tricarboxylic acid cycle. However, cuprotosis-related long non-coding RNAs (CRLNCs) and their relationship with prognosis and the immunological landscape of colorectal cancer (CRC) are unclear. We have developed a lncRNA signature to predict survival time, immune infiltration, and sensitivity to chemotherapy. CRLNCs were screened using the Cor function of the R software and the differentially expressed lncRNAs were collected with the limma package. Differentially expressed long non-coding RNAs (lncRNAs) associated with prognosis were selected using univariate regression analysis. A prognostic signature was developed using the least absolute shrinkage and selection operator (LASSO) and multivariate regression analysis. Patients with CRC were divided into two groups based on the risk score. The low-risk group had a more favorable prognosis, higher expression of immune checkpoints, and a higher level of immune cell infiltration compared with the high-risk group. Furthermore, there was a close association between the risk score and the clinical stage, tumor mutational burden, cancer stem cell index, and microsatellite instability. We also assessed chemotherapy response in the two risk groups. Our study analyzed the role of CRLNCs in CRC and provided novel targets and strategies for CRC chemotherapy and immunotherapy.

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.

Deciphering the expression patterns of homologous recombination-related lncRNAs identifies new molecular subtypes and emerging therapeutic opportunities in epithelial ovarian cancer

Epithelial ovarian cancer (EOC) is the leading killer among women with gynecologic malignancies. Homologous recombination deficiency (HRD) has attracted increasing attention due to its significant implication in the prediction of prognosis and response to treatments. In addition to the germline and somatic mutations of homologous recombination (HR) repair genes, to widely and deeply understand the molecular characteristics of HRD, we sought to screen the long non-coding RNAs (lncRNAs) with regard to HR repair genes and to establish a prognostic risk model for EOC. Herein, we retrieved the transcriptome data from the Genotype-Tissue Expression Project (GTEx) and The Cancer Genome Atlas (TCGA) databases. HR-related lncRNAs (HRRlncRNAs) associated with prognosis were identified by co-expression and univariate Cox regression analyses. The least absolute shrinkage and selection operator (LASSO) and multivariate stepwise Cox regression were performed to construct an HRRlncRNA risk model containing AC138904.1, AP001001.1, AL603832.1, AC138932.1, and AC040169.1. Next, Kaplan−Meier analysis, time-dependent receiver operating characteristics (ROC), nomogram, calibration, and DCA curves were made to verify and evaluate the model. Gene set enrichment analysis (GSEA), immune analysis, and prediction of the half-maximal inhibitory concentration (IC₅₀) in the risk groups were also analyzed. The calibration plots showed a good concordance with the prognosis prediction. ROCs of 1-, 3-, and 5-year survival confirmed the well-predictive efficacy of this model in EOC. The risk score was used to divide the patients into high-risk and low-risk subgroups. The low-risk group patients tended to exhibit a lower immune infiltration status and a higher HRD score. Furthermore, consensus clustering analysis was employed to divide patients with EOC into three clusters based on the expression of the five HRRlncRNAs, which exhibited a significant difference in checkpoints’ expression levels and the tumor microenvironment (TME) status. Taken together, the results of this project supported that the five HRRlncRNA models might function as a biomarker and prognostic indicator with respect to predicting the PARP inhibitor and immune treatment in EOC.

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.

Cuproptosis-Related lncRNAs are Biomarkers of Prognosis and Immune Microenvironment in Head and Neck Squamous Cell Carcinoma

Background: Cuproptosis is a new type of cell death that induces protein toxic stress and eventually leads to cell death. Hence, regulating cuproptosis in tumor cells is a new therapeutic approach. However, studies on cuproptosis-related long noncoding RNA (lncRNA) in head and neck squamous cell carcinoma (HNSC) have not been found. This study aimed to explore the cuproptosis-related lncRNAs prognostic marker and their relationship to immune microenvironment in HNSC by using bioinformatics methods.

Methods: RNA sequencing, genomic mutations, and clinical data of TCGA_HNSC were downloaded from The Cancer Genome Atlas. HNSC patients were randomly assigned to either a training group or a validation cohort. The least absolute shrinkage and selection operator Cox regression and multivariate Cox regression models were used to determine the prognostic model in the training cohort, and its independent prognostic effect was further confirmed in the validation and entire cohorts.

Results: Based on previous literature, we collected 19 genes associated with cuproptosis. Afterward, 783 cuproptosis-related lncRNAs were obtained through coexpression. Cox model revealed and constructed eight cuproptosis-related lncRNAs prognostic marker (AL132800.1, AC090587.1, AC079160.1, AC011462.4, AL157888.1, GRHL3-AS1, SNHG16, and AC021148.2). Patients were divided into high- and low-risk groups based on the median risk score. The Kaplan–Meier survival curve revealed that the overall survival between the high- and low-risk groups was statistically significant. The receiver operating characteristic curve and principal component analysis demonstrated the accurate prognostic ability of the model. Univariate and multivariate Cox regression analysis showed that risk score was an independent prognostic factor. In addition, we used multivariate Cox regression to establish a nomogram of the predictive power of prognostic markers. The tumor mutation burden showed significant differences between the high- and low-risk groups. HNSC patients in the high-risk group responded better to immunotherapy than those in the low-risk group. We also found that risk scores were significantly associated with drug sensitivity in HNSC.

Conclusion: In summary, our study identified eight cuprotosis-related lncRNAs signature of HNSC as the prognostic predictor, which may be promising biomarkers for predicting the benefit of HNSC immunotherapy as well as drug sensitivity.

M6 A Demethylase FTO Regulates Cisplatin Resistance of Gastric Cancer by Modulating Autophagy Activation via ULK1

Drug resistance is an important factor for treatment failure of gastric cancer. N⁶‐methyladenosine (m⁶A) is the predominant mRNA internal modification in eukaryotes. The roles of m⁶A modification in drug resistance of gastric cancer remains unclear. In the present study, the m⁶A methylated RNA level was significantly decreased while the expression of m⁶A demethylase fat mass and obesity‐associated protein (FTO) was obviously elevated in cisplatin‐resistant (SGC‐7901/DDP) gastric cancer cells. Knockdown of FTO reversed cisplatin resistance of SGC‐7901/DDP cells both in vitro and in vivo, which was attributed to the inhibition of Unc‐51‐like kinase 1 (ULK1)‐mediated autophagy. Mechanistically, ULK1 expression was regulated in an FTO‐m⁶A‐dependent and YTHDF2‐mediated manner. Collectively, our findings indicate that the FTO/ULK1 axis exerts crucial roles in cisplatin resistance of gastric cancer.

Comprehensive analysis of m6A regulator-based methylation modification patterns characterized by distinct immune profiles in colon adenocarcinomas

Mounting evidences have indicated that RNA N⁶-methyladenosine (m⁶A) modification played important roles in tumor formation and growth. However, it is rarely reported that m⁶A modifications are involved in the immune regulation and tumor microenvironment (TME) formation. In this study, we aimed to investigate the correlation between m⁶A modifications and TME regulation of colon adenocarcinoma (COAD) by bioinformatic analysis. NMF algorithm was applied to carry out consensus molecular subtype analysis on 36 selected m⁶A regulators regarding methylation modification, to identify m⁶A modification patterns and characteristics of m⁶A related genes in colon adenocarcinoma (COAD). Further, the relative infiltration levels of different immune cell subsets were quantified by ssGSEA and CIBERSORT algorithms, and a m⁶Sig scoring scheme was constructed to predict the prognosis and evaluate the response to immunotherapy in the patients with COAD. Among 579 COAD samples, we identified three different m⁶A modification patterns which were related to different biological pathways and clinical outcomes. Then, a scoring scheme termed "m⁶Sig score" was developed based on m⁶A-related characteristic genes, and was utilized to score patients with COAD into groups. We found that COAD patients with lower m⁶Sig scores exhibited prolonged survival and potentiated immune infiltration, which were associated with higher tumor mutation load, lower PD-L1 expression, and higher mutation rates of SMG (such as TTN and KRAS). Moreover, analysis regarding evaluation of immune response revealed that the patients with lower m⁶Sig scores had higher Immunophenoscore. Collectively, our study provided in depth insight into the interactions between m⁶A modification and regulation of TME. In addition, the quantitative evaluation of m⁶A modification patterns in our results may have implications in further immunotherapy for individual COAD patients.

Role of main RNA modifications in cancer: N6-methyladenosine, 5-methylcytosine, and pseudouridine

Cancer is one of the major diseases threatening human life and health worldwide. Epigenetic modification refers to heritable changes in the genetic material without any changes in the nucleic acid sequence and results in heritable phenotypic changes. Epigenetic modifications regulate many biological processes, such as growth, aging, and various diseases, including cancer. With the advancement of next-generation sequencing technology, the role of RNA modifications in cancer progression has become increasingly prominent and is a hot spot in scientific research. This review studied several common RNA modifications, such as N⁶-methyladenosine, 5-methylcytosine, and pseudouridine. The deposition and roles of these modifications in coding and noncoding RNAs are summarized in detail. Based on the RNA modification background, this review summarized the expression, function, and underlying molecular mechanism of these modifications and their regulators in cancer and further discussed the role of some existing small-molecule inhibitors. More in-depth studies on RNA modification and cancer are needed to broaden the understanding of epigenetics and cancer diagnosis, treatment, and prognosis.

The role of long non-coding RNAs in the pathogenesis of head and neck squamous cell carcinoma

Head and neck cancers are a heterogeneous collection of malignancies of the upper aerodigestive tract, salivary glands, and thyroid. However, the molecular mechanisms underlying the carcinogenesis of head and neck squamous cell carcinomas (HNSCCs) remain poorly understood. Over the past decades, overwhelming evidence has demonstrated the regulatory roles of long non-coding RNAs (lncRNAs) in tumorigenesis, including HNSCC. Notably, these lncRNAs have vital roles in gene regulation and affect various aspects of cellular homeostasis, including proliferation, survival, and metastasis. They exert regulating functions by interacting with nucleic acids or proteins and affecting cancer cell signaling. LncRNAs represent a burgeoning field of cancer research, and we are only beginning to understand the importance and complicity of lncRNAs in HNSCC. In this review, we summarize the deregulation and function of lncRNAs in human HNSCC. We also review the working mechanism of lncRNAs in HNSCC pathogenesis and discuss the potential application of lncRNAs as diagnostic/prognostic tools and therapeutic targets in human HNSCC.

Downregulation of Methyltransferase-Like 14 Promotes Ovarian Cancer Cell Proliferation Through Stabilizing TROAP mRNA

Altered expression levels of the proteins that regulate N6-methyladenosine (m⁶A) RNA methylation, including methyltransferase-like 14 (METTL14), are associated with cancer development. Based on our analysis of m⁶A methylation regulators using The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) datasets, we focused on the regulatory role of METTL14 in ovarian cancer. We performed bioinformatics and survival analyses with these datasets and also used METTL14-overexpressing SKOV-3 ovarian cancer cells for in vitro studies. Trophinin associated protein (TROAP) siRNA and treatment with or without actinomycin D was used in the cells for qRT-PCR, western blot, cDNA microarray, cell viability, colony formation, luciferase gene reporter, methylated RNA immunoprecipitation (MeRIP)-qPCR, total RNA methylation, and RNA stability assays. Additionally, ovarian cancer and normal tissue samples were analyzed by immunohistochemistry, qRT-PCR, and western blot assays. The TCGA and GEO data confirmed copy number variations (CNVs) of these m⁶A RNA methylation regulators in ovarian cancer tissues. Furthermore, reduced METTL14 expression was associated with alterations in CNVs as well as poor patient survival in ovarian cancer. Moreover, the METTL14 and m⁶A RNA methylation levels were both significantly reduced in ovarian cancer tissues than in normal tissues. Restoration of METTL14 expression suppresses ovarian cancer cell proliferation by inhibition of TROAP expression. Further in vivo and in vitro experiments confirmed that METTL14 is a negative regulator of ovarian cancer cell proliferation via TROAP expression and that m⁶A RNA methylation regulates TROAP mRNA stability. In conclusion, METTL14 overexpression decreased ovarian cancer proliferation by inhibition of TROAP expression via an m⁶A RNA methylation-dependent mechanism.

The role of RNA m6A methylation in lipid metabolism

The m6A methylation is the most numerous modification of mRNA in mammals, coordinated by RNA m6A methyltransferases, RNA m6A demethylases, and RNA m6A binding proteins. They change the RNA m6A methylation level in their specific manner. RNA m6A modification has a significant impact on lipid metabolic regulation. The "writer" METTL3/METTL14 and the "eraser" FTO can promote the accumulation of lipids in various cells by affecting the decomposition and synthesis of lipids. The "reader" YTHDF recognizes m6A methylation sites of RNA and regulates the target genes' translation. Due to this function that regulates lipid metabolism, RNA m6A methylation plays a pivotal role in metabolic diseases and makes it a great potential target for therapy.

N6-Methyladenosine RNA Modification in the Tumor Immune Microenvironment: Novel Implications for Immunotherapy

N6-methyladenosine (m⁶A) methylation is one of the most common modifications of RNA in eukaryotic cells, and is mainly regulated by m⁶A methyltransferases (writers), m⁶A demethylases (erasers), and m⁶A binding proteins (readers). Recently, accumulating evidence has shown that m⁶A methylation plays crucial roles in the regulation of the tumor immune microenvironment, greatly impacting the initiation, progression, and metastasis processes of various cancers. In this review we first briefly summarizes the m⁶A-related concepts and detection methods, and then describes in detail the associations of m⁶A methylation modification with various tumor immune components especially immune cells (e.g., regulatory T cells, dendritic cells, macrophages, and myeloid-derived suppressor cells) in a variety of cancers. We discuss the relationship between m⁶A methylation and cancer occurrence and development with the involvement of tumor immunity highlighted, suggesting novel markers and potential targets for molecular pathological diagnosis and immunotherapy of various cancers.

Roles of Major RNA Adenosine Modifications in Head and Neck Squamous Cell Carcinoma

Head and neck squamous cell carcinoma (HNSCC) is the sixth most common cancer malignancy worldwide and is known to have poor prognosis. The pathogenesis behind the development of HNSCC is not fully understood. Modifications on RNA are involved in many pathophysiological processes, such as tumor development and inflammation. Adenosine-related RNA modifications have shown to be linked to cancer and may play a role in cancer occurrence and development. To date, there are at least 170 different chemical RNA modifications that modify coding and non-coding RNAs (ncRNAs). These modifications affect RNA stability and transcription efficiency. In this review, we focus on the current understanding of the four major RNA adenosine modifications (N⁶-Methyladenosine, N¹-Methyladenosine, Alternative Polyadenylation Modification and A-to-I RNA editing) and their potential molecular mechanisms related to HNSCC development and progression. We also touch on how these RNA modifications affect treatment of HNSCCs.

Identification and Validation of the Pyroptosis-Related Molecular Subtypes of Lung Adenocarcinoma by Bioinformatics and Machine Learning

Lung cancer remains the leading cause of cancer death globally, with lung adenocarcinoma (LUAD) being its most prevalent subtype. Due to the heterogeneity of LUAD, patients given the same treatment regimen may have different responses and clinical outcomes. Therefore, identifying new subtypes of LUAD is important for predicting prognosis and providing personalized treatment for patients. Pyroptosis-related genes play an essential role in anticancer, but there is limited research investigating pyroptosis in LUAD. In this study, 33 pyroptosis gene expression profiles and clinical information were collected from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) databases. By bioinformatics and machine learning analyses, we identified novel subtypes of LUAD based on 10 pyroptosis-related genes and further validated them in the GEO dataset, with machine learning models performing up to an AUC of 1 for classifying in GEO. A web-based tool was established for clinicians to use our clustering model (http://www.aimedicallab.com/tool/aiml-subphe-luad.html). LUAD patients were clustered into 3 subtypes (A, B, and C), and survival analysis showed that B had the best survival outcome and C had the worst survival outcome. The relationships between pyroptosis gene expression and clinical characteristics were further analyzed in the three molecular subtypes. Immune profiling revealed significant differences in immune cell infiltration among the three molecular subtypes. GO enrichment and KEGG pathway analyses were performed based on the differential genes of the three subtypes, indicating that differentially expressed genes (DEGs) were involved in multiple cellular and biological functions, including RNA catabolic process, mRNA catabolic process, and pathways of neurodegeneration-multiple diseases. Finally, we developed an 8-gene prognostic model that accurately predicted 1-, 3-, and 5-year overall survival. In conclusion, pyroptosis-related genes may play a critical role in LUAD, and provide new insights into the underlying mechanisms of LUAD.

Analysis of the prognostic significance and potential mechanisms of lncRNAs associated with m6A methylation in papillary thyroid carcinoma

BACKGROUND

m6A methylation-related long non-coding RNAs (lncRNAs) play a significant role in the progression of various tumors and can be used as prognostic markers. However, whether m6A-related lncRNAs also play the same function as prognostic markers in papillary thyroid carcinoma (PTC) remains unclear.

METHODS

Consensus cluster analysis was performed to divide PTC samples obtained from The Cancer Genome Atlas database into two clusters according to the expression of m6A-related lncRNAs. Then, the least absolute shrinkage and selection operator (LASSO) regression analysis was performed to create and verify a prognostic model. Furthermore, the relationship among risk scores, clusters, programmed death-ligand 1 (PD-L1), tumor microenvironment (TME), clinicopathological characteristics, immune infiltration, immune checkpoint, and tumor mutation burden (TMB) was analyzed. In addition, a nomogram was created, and subsequently, the drug sensitivity of lncRNAs in the prognostic model was analyzed. Finally, the relationship between these lncRNAs and prognosis in pan-cancer was investigated.

RESULTS

The prognosis, RAS, BRAF, M, and TME were found to be different in two clusters. The prognostic model included three lncRNAs: PSMG3-AS1, BHLHE40-AS1, and AC016747.3. The risk score was associated with clusters, PD-L1, tumor microenvironment, clinicopathological characteristics, immune cell infiltration, immune checkpoint, and TMB, and thus, risk score was confirmed as useful prognostic indicator. Differentially expressed lncRNAs are involved in many malignancies and can be identified as cancer prognostic makers.

CONCLUSION

According to our research, we can regard m6A-related lncRNAs involved in the procession of PTC as a biomarker of progression-free survival for PTC patients, and pan-cancer.

Construction of Prognostic Risk Model of 5-Methylcytosine-Related Long Non-Coding RNAs and Evaluation of the Characteristics of Tumor-Infiltrating Immune Cells in Breast Cancer

Purpose: The role of 5-methylcytosine-related long non-coding RNAs (m5C-lncRNAs) in breast cancer (BC) remains unclear. Here, we aimed to investigate the prognostic value, gene expression characteristics, and correlation between m5C-lncRNA risk model and tumor immune cell infiltration in BC. Methods: The expression matrix of m5C-lncRNAs in BC was obtained from The Cancer Genome Atlas database, and the lncRNAs were analyzed using differential expression analysis as well as univariate and multivariate Cox regression analysis to eventually obtain BC-specific m5C-lncRNAs. A risk model was developed based on three lncRNAs using multivariate Cox regression and the prognostic value, accuracy, as well as reliability were verified. Gene set enrichment analysis (GSEA) was used to analyze the Kyoto Encyclopedia of Genes and Genomes signaling pathway enrichment of the risk model. CIBERSORT algorithm and correlation analysis were used to explore the characteristics of the BC tumor-infiltrating immune cells. Finally, reverse transcription-quantitative polymerase chain reaction was performed to detect the expression level of three lncRNA in clinical samples. Results: A total of 334 differential m5C-lncRNAs were identified, and three BC-specific m5C-lncRNAs were selected, namely AP005131.2, AL121832.2, and LINC01152. Based on these three lncRNAs, a highly reliable and specific risk model was constructed, which was proven to be closely related to the prognosis of patients with BC. Therefore, a nomogram based on the risk score was built to assist clinical decisions. GSEA revealed that the risk model was significantly enriched in metabolism-related pathways and was associated with tumor immune cell infiltration based on the analysis with the CIBERSORT algorithm. Conclusion: The efficient risk model based on m5C-lncRNAs associated with cancer metabolism and tumor immune cell infiltration could predict the survival prognosis of patients, and AP005131.2, AL121832.2, and LINC01152 could be novel biomarkers and therapeutic targets for BC.