The momentous role of N6-methyladenosine in lung cancer

METTL3 -mediated m6A modification of circ_0000620 regulates cisplatin sensitivity and apoptosis in lung adenocarcinoma via the MiR-216b-5p/KRAS axis

Circular RNAs (circRNAs) are stable non-coding RNAs characterized by the absence of the conventional 5' cap and 3' polyadenylated tail structure. Its involvement in various aspects of cancers underscores its significance in oncology. Elevated expression of circ_0000620 was observed in both lung adenocarcinoma (LUAD) tissues and cell lines. In vitro, experiments demonstrated that the downregulation of circ_0000620 increased cisplatin sensitivity and promoted cell apoptosis while suppressing malignant characteristics such as cell migration and proliferation. Further investigation into the mechanism underlying the increased expression of circ_0000620 revealed that Methyltransferase 3, N6-Adenosine-Methyltransferase Complex Catalytic Subunit (METTL3) mediates the m6A methylation modification of circ_0000620, thereby promoting its stability and expression. Furthermore, circ_0000620 modulates the miR-216b-5p/KRAS axis to influence apoptosis and cisplatin sensitivity in both A549 and H1299 cell lines. These findings were corroborated by in vivo nude mouse experiments, which showed that knockdown of circ_0000620 inhibited tumor growth and proliferation. In summary, METTL3 plays a role in regulating the stability of circ_0000620 expression, and circ_0000620 exerts its effects on LUAD apoptosis and cisplatin sensitivity through the miR-216b-5p/KRAS signaling pathway.

Development of m6A/m5C/m1A regulated lncRNA signature for prognostic prediction, personalized immune intervention and drug selection in LUAD

Research indicates that there are links between m6A, m5C and m1A modifications and the development of different types of tumours. However, it is not yet clear if these modifications are involved in the prognosis of LUAD. The TCGA-LUAD dataset was used as for signature training, while the validation cohort was created by amalgamating publicly accessible GEO datasets including GSE29013, GSE30219, GSE31210, GSE37745 and GSE50081. The study focused on 33 genes that are regulated by m6A, m5C or m1A (mRG), which were used to form mRGs clusters and clusters of mRG differentially expressed genes clusters (mRG-DEG clusters). Our subsequent LASSO regression analysis trained the signature of m6A/m5C/m1A-related lncRNA (mRLncSig) using lncRNAs that exhibited differential expression among mRG-DEG clusters and had prognostic value. The model's accuracy underwent validation via Kaplan-Meier analysis, Cox regression, ROC analysis, tAUC evaluation, PCA examination and nomogram predictor validation. In evaluating the immunotherapeutic potential of the signature, we employed multiple bioinformatics algorithms and concepts through various analyses. These included seven newly developed immunoinformatic algorithms, as well as evaluations of TMB, TIDE and immune checkpoints. Additionally, we identified and validated promising agents that target the high-risk mRLncSig in LUAD. To validate the real-world expression pattern of mRLncSig, real-time PCR was carried out on human LUAD tissues. The signature's ability to perform in pan-cancer settings was also evaluated. The study created a 10-lncRNA signature, mRLncSig, which was validated to have prognostic power in the validation cohort. Real-time PCR was applied to verify the actual manifestation of each gene in the signature in the real world. Our immunotherapy analysis revealed an association between mRLncSig and immune status. mRLncSig was found to be closely linked to several checkpoints, such as IL10, IL2, CD40LG, SELP, BTLA and CD28, which could be appropriate immunotherapy targets for LUAD. Among the high-risk patients, our study identified 12 candidate drugs and verified gemcitabine as the most significant one that could target our signature and be effective in treating LUAD. Additionally, we discovered that some of the lncRNAs in mRLncSig could play a crucial role in certain cancer types, and thus, may require further attention in future studies. According to the findings of this study, the use of mRLncSig has the potential to aid in forecasting the prognosis of LUAD and could serve as a potential target for immunotherapy. Moreover, our signature may assist in identifying targets and therapeutic agents more effectively.

Upregulation of M6A Reader HNRNPA2B1 Associated with Poor Prognosis and Tumor Progression in Lung Adenocarcinoma

BACKGROUND

Lung cancer is the most prevalent malignancy worldwide, and lung adenocarcinoma (LUAD) accounts for a substantial proportion of all cases. N6-methyladenosine (m6A) is the most frequent post-transcriptional modification in mRNAs that also plays a role in cancer development. Heterogeneous nuclear ribonucleoprotein A2/B1 (HNRNPA2B1) is a reader of m6A modification, which can affect tumor invasion, migration, and proliferation.

OBJECTIVES

The purpose of this study was to explore the prognostic factors of LUAD based on m6A through bioinformatics analysis.

MATERIALS AND METHODS

The expression levels and prognostic significance of HNRNPA2B1 in LUAD were analyzed on the basis of data extracted from the UALCAN, GEPIA, NCBI-GEO, Human Protein Atlas, STRING, miRDB, TargetScan, PROMO, Starbase, UCSC Xena browser, TIMER, and TISIDB databases. HNRNPA2B1 protein and mRNA levels in several LUAD cell lines were detected by western blotting and qRT-PCR. CCK8, wound-healing and transwell assays were performed to evaluate the proliferation, invasion, and migration abilities of LUAD cells.

RESULTS

HNRNPA2B1 mRNA was found to be significantly overexpressed in LUAD tissues, and its high levels correlated with poor OS and DFS. The genes co-expressed with HNRNPA2B1 were related to mRNA production, cell cycle, and histone binding. To determine the mechanistic basis of HNRNPA2B1 in LUAD, we next predicted the microRNAs and transcription factors that were directly associated with HNRNPA2B1, as well as copy number changes. In addition, it was found that HNRNPA2B1 expression was significantly related to CD4+ T cells, neutrophils, lymphocytes, immunomodulators, and chemokines. Besides, knocking down HNRNPA2B1 in the LUAD cells led to a significant reduction in their proliferation, invasion, and migration rates in vitro.

CONCLUSION

Elevated HNRNPA2B1 is a risk factor in LUAD and portends a poor prognosis.

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.

Global burden of cancers attributable to tobacco smoking, 1990-2019: an ecological study

Aim and background

Identifying risk factors for cancer initiation and progression is the cornerstone of the preventive approach to cancer management and control (EPMA J. 4(1):6, 2013). Tobacco smoking is a well-recognized risk factor for initiation and spread of several cancers. The predictive, preventive, and personalized medicine (PPPM) approach to cancer management and control focuses on smoking cessation as an essential cancer prevention strategy. Towards this end, this study examines the temporal patterns of cancer burden due to tobacco smoking in the last three decades at global, regional, and national levels.

Data and methods

The data pertaining to the burden of 16 cancers attributable to tobacco smoking at global, regional, and national levels were procured from the Global Burden of Disease 2019 Study. Two main indicators, deaths and disability-adjusted life years (DALYs), were used to describe the burden of cancers attributable to tobacco smoking. The socio-economic development of countries was measured using the socio-demographic index (SDI).

Results

Globally, deaths due to neoplasms caused by tobacco smoking increased from 1.5 million in 1990 to 2.5 million in 2019, whereas the age-standardized mortality rate (ASMR) decreased from 39.8/100,000 to 30.6/100,000 and the age-standardized DALY rate (ASDALR) decreased from 948.9/100,000 to 677.3/100,000 between 1990 and 2019. Males accounted for approximately 80% of global deaths and DALYs in 2019. Populous regions of Asia and a few regions of Europe account for the largest absolute burden, whereas countries in Europe and America have the highest age-standardized rates of cancers due to tobacco smoking. In 8 out of 21 regions, there were more than 100,000 deaths due to cancers attributable to tobacco smoking led by East Asia, followed by Western Europe in 2019. The regions of Sub-Saharan Africa (except southern region) had one of the lowest absolute counts of deaths, DALYs, and age-standardized rates. In 2019, tracheal, bronchus, and lung (TBL), esophageal, stomach, colorectal, and pancreatic cancer were the top 5 neoplasms attributable to tobacco smoking, with different burdens in regions as per their development status. The ASMR and ASDALR of neoplasms due to tobacco smoking were positively correlated with SDI, with pairwise correlation coefficient of 0.55 and 0.52, respectively.

Conclusion

As a preventive tool, tobacco smoking cessation has the biggest potential among all risk factors for preventing millions of cancer deaths every year. Cancer burden due to tobacco smoking is found to be higher in males and is positively associated with socio-economic development of countries. As tobacco smoking begins mostly at younger ages and the epidemic is unfolding in several parts of the world, more accelerated efforts are required towards tobacco cessation and preventing youth from entering this addiction. The PPPM approach to medicine suggests that not only personalized and precision medicine must be provided to cancer patients afflicted by tobacco smoking but personalized and targeted preventive solutions must be provided to prevent initiation and progression of smoking.

Supplementary Information

The online version contains supplementary material available at 10.1007/s13167-022-00308-y.

Role of m6A modification in dysregulation of Wnt/β-catenin pathway in cancer

N6-methyladenosine (m⁶A) modification is the most abundant post-transcriptional regulation of RNAs in eukaryotes. Dysregulation of m⁶A readers, writers, and erasers can significantly promote tumorigenesis by altering the expression of various genes. Wnt/β-catenin is an evolutionarily conserved signaling pathway that has recently been linked to the pathogenesis of many cancers. Given the significance of this pathway in regulating normal tissue homeostasis and stem cell differentiation, a subtle understanding of the molecular mechanism underlying its dysregulation is required for effective targeting. There is mounting evidence that m⁶A regulators are highly implicated in the dysregulation of the Wnt/β-catenin signaling pathway. Since m⁶A regulators can affect Wnt pathway components and dysregulation of either leads to carcinogenesis, this study aims to clarify the relationship between m⁶A regulators and the Wnt/β-catenin signaling pathway to investigate their combined impact on tumorigenesis.

Roles and therapeutic implications of m6A modification in cancer immunotherapy

Recent studies have demonstrated that N6-methyladenosine (m6A), the most abundant, dynamic, and reversible epigenetic RNA modification in eukaryotes, is regulated by a series of enzymes, including methyltransferases (writers), demethylases (erasers), and m6A recognition proteins (readers). Aberrant regulation of m6A modification is pivotal for tumorigenesis, progression, invasion, metastasis, and apoptosis of malignant tumors. Immune checkpoint inhibitors (ICIs) has revolutionized cancer treatment, as recognized by the 2018 Nobel Prize in Medicine and Physiology. However, not all cancer patients response to ICI therapy, which is thought to be the result of intricate immune escape mechanisms. Recently, numerous studies have suggested a novel role for m6A epigenetic modification in the regulation of tumor immune evasion. Herein, we review the relevant mechanisms of m6A regulators in regulating various key signaling pathways in cancer biology and how m6A epigenetic modifications regulate the expression of immune checkpoints, opening a new window to understand the roles and mechanisms of m6A epigenetic modifications in regulating tumor immune evasion. In addition, we highlight the prospects and development directions of future combined immunotherapy strategies based on m6A modification targeting, providing directions for promoting the treatment outcomes of immune checkpoint inhibitors.

Influencing factors and prediction methods of radiotherapy and chemotherapy in patients with lung cancer based on logistic regression analysis

Logistic regression analysis has widespread applications in clinical disease diagnosis, but it has not yet been applied to assess the acceptance of radiotherapy and chemotherapy in patients with lung cancer. A prediction model was established to investigate the influencing factors of radiotherapy and chemotherapy in lung cancer patients in order to provide useful information for clinicians to develop targeted and effective treatment. A sample was admitted of lung cancer patients to Binzhou Medical University Hospital stays from January 2020 to June 2021. After investigating doctors, nurses, patients, managers and conducting expert demonstration, the questionnaire was formed. The questionnaire was filled out by the patient or the patient's family members. The factors in the questionnaire data of patients accepting and not accepting radiotherapy and chemotherapy were compared for univariate analysis, and the significantly different single factor were analyzed by multifactor logistic regression analysis, explored the influencing factors of radiotherapy and chemotherapy in lung cancer patients established a predictive model and drew the receiver operating characteristic curve (ROC curve). The factors of two groups had statistically significant differences or no statistically significant differences. After multifactor logistic regression analysis was conducted, own personality, self-care ability, disease course classification, own attitude towards disease treatment, and family attitude towards disease treatment were included in the influencing factors of radiotherapy and chemotherapy in patients with lung cancer. Then, a predictive model was established. The area under the ROC curve of the predicted model was 0.973, the 95% confidence interval was 0.952-0.995, the optimal critical value was 0.832, the sensitivity was 91.84%, the specificity was 89.09%, and the accuracy was 90.85%. Based on logistic regression analysis, the prediction model could predict the extent of accepting radiotherapy and chemotherapy in patients with lung cancer. Understanding the factors related to patients with lung cancer accepting radiotherapy and chemotherapy could provide useful information for the targeted and effective treatment by clinicians.

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.

Development and validation of the potential biomarkers based on m6A-related lncRNAs for the predictions of overall survival in the lung adenocarcinoma and differential analysis with cuproptosis

BACKGROUND

The treatment and prognosis of lung adenocarcinoma (LUAD) remains a challenge. The study aimed to conduct a systematic analysis of the predictive capacity of N6-methyladenosine (m6A)-related long non-coding RNAs (lncRNAs) in the prognosis of LUAD.

METHODS

594 samples were totally selected from a dataset from The Cancer Genome Atlas. The identification of prognostic m6A-related lncRNAs were performed by Pearson correlation analysis and Cox regression analysis. Systematic analyses, including cluster analysis, survival analysis, and immuno-correlated analysis, were conducted. A prognosis model was built from the optimized subset of m6A-related lncRNAs. The assessment of model was performed by survival analysis, and receiver operating characteristic (ROC) curve. Finally, the risk score of patients with LUAD calculated by the prognosis model was implemented by the analysis of Cox regression. Differential analysis was for further evaluation of the cuproptosis-related genes in two risk sets.

RESULTS

These patients were grouped into two clusters according to the expression levels of 22 prognostic m6A-related lncRNAs. The patients with LUAD in cluster 2 was significantly worse in the overall survival (OS) (P = 0.006). Three scores calculated by the ESTIMATE methods in cluster 2 were significantly lower. After the least absolute shrinkage and selection operator algorithm, 10 prognostic m6A-related lncRNAs were totally selected to construct the final model to obtain the risk score. Then the area under the ROC curve of the prognosis model for 1, 3, and 5-year OS was 0.767, 0.709, and 0.736 in the training set, and 0.707, 0.691, and 0.675 in the test set. The OS of the low-risk cohort was significantly higher than that of the high-risk cohort in both the training set (P < 0.001) and test set (P < 0.001). After the analysis of Cox regression, the risk score [Hazard ratio (HR) = 5.792; P < 0.001] and stage (HR = 1.576; P < 0.001) were both considered as independent indicators of prognosis for LUAD. The expression levels of five cuproptosis-related genes were significantly different in two risk sets.

CONCLUSIONS

The study constructed a predictive model for the OS of patients with LUAD and these OS-related m6A-lncRNAs might have potential roles in LUAD progression.

Comprehensive Analysis of Immune Cell Infiltration of m6a-Related lncRNA in Lung Squamous Cell Carcinoma and Construction of Relevant Prognostic Models

Lung squamous cell carcinoma (LUSC) is the main cause of cancer-related mortality. Some studies demonstrate that m6a and long noncoding RNA (lncRNA) are vital in the pathogenesis of LUSC. In this study, we aimed to further understand the prognostic value of m6a-related lncRNAs in LUSC and their role in the immune microenvironment. For this, we obtained LUSC transcriptome and clinical data from the TCGA database. Further, the identified m6a-related and prognostically relevant lncRNAs were clustered into groups based on prognostic lncRNA expression. Further analysis of the differences between clusters was performed. Five m6A-related lncRNAs were used for model construction using the LASSO regression. The receiver-operating characteristic curve (ROC curves) and decision curve analysis (DCA) were used to assess the model accuracy. Finally, the model was validated using polymerase chain reaction (PCR). We identified 12 m6a-related lncRNAs that were associated with prognosis and were lowly expressed in tumors. Cytotoxic T-lymphocyte-associated protein 4 (CTLA4) highly correlated with prognostic genes, and differential analysis indicated that it was highly expressed in the tumor group and cluster 1. In cluster 2 TIME, tumor cells were less pure and more immune, and stromal-associated cells were present. A prognostic model was constructed based on five m6a-lncRNAs. The area under the curve (AUC) was >0.5 in test group and train group. The PCR results showed that the genes in the prognostic model were lowly expressed in the tumor and were statistically significant ( $p<0.05$ ). We noted that m6a-lncRNAs were strongly associated with LUSC prognosis and the immune microenvironment. Thus, PRC1-AS1, AL132780.2, AC013731.1, SNHG30, and AL358472.2 can be considered as new targets for the treatment of patients with LUSC.

m6A Topological Transition Coupled to Developmental Regulation of Gene Expression During Mammalian Tissue Development

N6-methyladenosine (m6A) is the most prevalent internal modification and reversible epitranscriptomic mark in messenger RNAs (mRNAs) and plays essential roles in a variety of biological processes. However, the dynamic distribution patterns of m6A and their significance during mammalian tissue development are poorly understood. Here, we found that based on m6A distribution patterns, protein-coding genes were classified into five groups with significantly distinct biological features and functions. Strikingly, comparison of the m6A methylomes of multiple mammalian tissues between fetal and adult stages revealed dynamic m6A topological transition during mammalian tissue development, and identified large numbers of genes with significant m6A loss in 5′UTRs or m6A gain around stop codons. The genes with m6A loss in 5′UTRs were highly enriched in developmental stage-specific genes, and their m6A topological transitions were strongly associated with gene expression regulation during tissue development. The genes with m6A gain around the stop codons were associated with tissue-specific functions. Our findings revealed the existence of different m6A topologies among protein-coding genes that were associated with distinct characteristics. More importantly, these genes with m6A topological transitions were crucial for tissue development via regulation of gene expression, suggesting the importance of dynamic m6A topological transitions during mammalian tissue development.

A Novel M6A-Related Genes Signature Can Impact the Immune Status and Predict the Prognosis and Drug Sensitivity of Lung Adenocarcinoma

Lung adenocarcinoma (LUAD) is a primary cause of cancer-related death around the world and has a poor outcome and high incidence. Treatment options are, however, restricted. One of the most critical factors in cancer and metastasis is the N6-methyladenine (m6A) alteration on RNA. This modification could alter gene expression and even function at numerous levels, such as the stability, translocation and translation of RNA splicing. This study aimed to construct an m6A-related genes signature to accurately predict the prognosis of LUAD patients. From TCGA datasets, the LUAD patient data and m6A-related genes were retrieved. LUAD patients’ mutational features and differentially expressed genes (DEGs) were investigated. An univariate and LASSO model with m6A-related genes were constructed for the prediction of outcomes in LUAD. It was possible to develop a prognostic nomogram that could quantitatively predict LUAD patients’ overall survival chances at 1, 3, and 5 years. Research into biological processes and cell pathways was carried out using GSEA. This study found six m6A-related DEGs in LUAD patients, and three of these DEGs(HNRNPC, IGFBP3 and IGF2BP1) were linked to the clinical outcomes of LUAD patients. We found that the overall survival rate for all LUAD patients with high-risk subgroup was considerably lower. According to ROC curves, the prognostic signature demonstrated a high degree of accuracy in predicting future outcomes. In addition, we created a novel nomogram achieved great accuracy with this one as well. The researchers also found that the novel signature might favorably modulate the immune response, and high-risk scores samples were more susceptible to numerous chemotherapeutic medicines. Overall, we developed a m6A-related gene prognostic signature that effectively predicted outcomes of LUAD patients and gave an immunological perspective for creating customized therapeutics.

A Prognostic Risk Score Based on Hypoxia-, Immunity-, and Epithelialto-Mesenchymal Transition-Related Genes for the Prognosis and Immunotherapy Response of Lung Adenocarcinoma

Background: Lung adenocarcinoma (LUAD), the most common subtype of non-small cell lung cancer (NSCLC), is associated with poor prognosis. However, current stage-based clinical methods are insufficient for survival prediction and decision-making. This study aimed to establish a novel model for evaluating the risk of LUAD based on hypoxia, immunity, and epithelial-mesenchymal transition (EMT) gene signatures.

Methods: In this study, we used data from TCGA-LUAD for the training cohort and GSE68465 and GSE72094 for the validation cohorts. Immunotherapy datasets GSE135222, GSE126044, and IMvigor210 were obtained from a previous study. Using bioinformatic and machine algorithms, we established a risk model based on hypoxia, immune, and EMT gene signatures, which was then used to divide patients into the high and low risk groups. We analyzed differences in enriched pathways between the two groups, following which we investigated whether the risk score was correlated with stemness scores, genes related to m6A, m5C, m1A and m7G modification, the immune microenvironment, immunotherapy response, and multiple anti-cancer drug sensitivity.

Results: Overall survival differed significantly between the high-risk and low-risk groups (HR = 4.26). The AUCs for predicting 1-, 3-, and 5-year survival were 0.763, 0.766, and 0.728, respectively. In the GSE68465 dataset, the HR was 2.03, while the AUCs for predicting 1-, 3-, and 5-year survival were 0.69, 0.651, and 0.618, respectively. The corresponding values in the GSE72094 dataset were an HR of 2.36 and AUCs of 0.653, 0.662, and 0.749, respectively. The risk score model could independently predict OS in patients with LUAD, and highly correlated with stemness scores and numerous m6A, m5C, m1A and m7G modification-related genes. Furthermore, the risk model was significantly correlated with multiple immune microenvironment characteristics. In the GSE135222 dataset, the HR was 4.26 and the AUC was 0.702. Evaluation of the GSE126044 and IMvigor210 cohorts indicated that PD-1/PD-LI inhibitor treatment may be indicated in patients with low risk scores, while anti-cancer therapy with various drugs may be indicated in patients with high risk scores.

Conclusion: Our novel risk model developed based on hypoxia, immune, and EMT gene signatures can aid in predicting clinical prognosis and guiding treatment in patients with LUAD.

An N6-methyladenosine and target genes-based study on subtypes and prognosis of lung adenocarcinoma

Purpose: Lung adenocarcinoma (LUAD) is a highly lethal subtype of primary lung cancer with a poor prognosis. N6-methyladenosine (m⁶A), the most predominant form of RNA modification, regulates biological processes and has critical prognostic implications for LUAD. Our study aimed to mine potential target genes of m⁶A regulators to explore their biological significance in subtyping LUAD and predicting survival. Methods: Using gene expression data from TCGA database, candidate target genes of m⁶A were predicted from differentially expressed genes (DEGs) in tumor based on M⁶A2 Target database. The survival-related target DEGs identified by Cox-regression analysis was used for consensus clustering analysis to subtype LUAD. Uni-and multi-variable Cox regression analysis and LASSO Cox-PH regression analysis were used to select the optimal prognostic genes for constructing prognostic score (PS) model. Nomogram encompassing PS score and independent prognostic factors was built to predict 3-year and 5-year survival probability. Results: We obtained 2429 DEGs in tumor tissue, within which, 1267 were predicted to m⁶A target genes. A prognostic m⁶A-DEGs network of 224 survival-related target DEGs was established. We classified LUAD into 2 subtypes, which were significantly different in OS time, clinicopathological characteristics, and fractions of 12 immune cell types. A PS model of five genes (C1QTNF6, THSD1, GRIK2, E2F7 and SLCO1B3) successfully split the training set or an independent GEO dataset into two subgroups with significantly different OS time (p < 0.001, AUC = 0.723; p = 0.017, AUC = 0.705).A nomogram model combining PS status, pathologic stage, and recurrence was built, showing good performance in predicting 3-year and 5-year survival probability (C-index = 0.708, 0.723, p-value = 0). Conclusion: Using candidate m⁶A target genes, we obtained two molecular subtypes and designed a reliable five-gene PS score model for survival prediction in LUAD.

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.

m6A regulator-mediated RNA methylation modification patterns and immune microenvironment infiltration characterization in severe asthma

N6‐methyladenosine (m6A) modification is one of the most prevalent RNA modification forms of eukaryotic mRNA and is an important post‐transcriptional mechanism for regulating genes. However, the role of m6A modification in the regulation of severe asthma has never been reported. Thus, we aimed to investigate the m6A regulator‐mediated RNA methylation modification patterns and immune microenvironment infiltration characterization in severe asthma. In this study, 87 healthy controls and 344 severe asthma cases from the U‐BIOPRED (Unbiased Biomarkers for the Prediction of Respiratory Disease Outcomes) programme were used to systematically evaluate the m6A modification patterns mediated by 27 m6A regulators and to investigate the effects of m6A modification on immune microenvironment characteristics. We found that 16 m6A regulators were abnormal and identified two key m6A regulators (YTHDF3 and YTHDC1) and three m6A modification patterns. The study of infiltration characteristics of immune microenvironment found that pattern 2 had more infiltrating immune cells and more active immune response. Besides, it was found that the eosinophils which are very important for severe asthma were affected by YTHDF3 and EIF3B. We also verified key m6A regulators with merip‐seq and found that they were mainly distributed in exons and enriched in 3′UTR. In conclusion, our findings suggested that m6A modification plays a key role in severe asthma, and may be able to guide the future strategy of immunotherapy.

A Risk Score Model Incorporating Three m6A RNA Methylation Regulators and a Related Network of miRNAs-m6A Regulators-m6A Target Genes to Predict the Prognosis of Patients With Ovarian Cancer

Ovarian cancer (OC) is the leading cause of cancer-related death among all gynecological tumors. N6-methyladenosine (m6A)-related regulators play essential roles in various tumors, including OC. However, the expression of m6A RNA methylation regulators and the related regulatory network in OC and their correlations with prognosis remain largely unknown. In the current study, we obtained the genome datasets of OC from GDC and GTEx database and analyzed the mRNA levels of 21 key m6A regulators in OC and normal human ovarian tissues. The expression levels of 7 m6A regulators were lower in both the OC tissues and the high-stage group. Notably, the 5-year survival rate of patients with OC presenting low VIRMA expression or high HNRNPA2B1 expression was higher than that of the controls. Next, a risk score model based on the three selected m6A regulators (VIRMA, IGF2BP1, and HNRNPA2B1) was built by performing a LASSO regression analysis, and the moderate accuracy of the risk score model to predict the prognosis of patients with OC was examined by performing ROC curve, nomogram, and univariate and multivariate Cox regression analyses. In addition, a regulatory network of miRNAs-m6A regulators-m6A target genes, including 2 miRNAs, 3 m6A regulators, and 47 mRNAs, was constructed, and one of the pathways, namely, miR-196b-5p-IGF2BP1-PTEN, was initially validated based on bioinformatic analysis and assay verification. These results demonstrated that the risk score model composed of three m6A RNA methylation regulators and the related network of miRNAs-m6A regulators-m6A target genes is valuable for predicting the prognosis of patients with OC, and these molecules may serve as potential biomarkers or therapeutic targets in the future.

Metabolic Changes in Early-Stage Non-Small Cell Lung Cancer Patients after Surgical Resection

Simple Summary

Considerable progress in the treatment of non–small cell lung cancer (NSCLC) has been made possible by large-scale technologies that scan the gene expression in tumor cells. While gene expression is informative, it is the changes to cellular metabolism that directly affect the initiation and the progression of the disease. Altered metabolic processes in cancer include how the tumor cells handle fat, proteins, and sugar, produce energy, divide (grow), or migrate. We have used nuclear magnetic resonance and mass spectrometry to survey and document the metabolic changes in blood and urine samples collected from NSCLC patients before and after their lung tumors were surgically removed. We found several molecular compounds that changed in abundance in the blood or urine after surgery, many of which are related to cancer cell metabolism. Further documentation of these changes in large patient populations will lead to non-invasive ways to screen, diagnose, or monitor disease progression in lung cancer patients.

Abstract

Metabolic alterations in malignant cells play a vital role in tumor initiation, proliferation, and metastasis. Biofluids from patients with non–small cell lung cancer (NSCLC) harbor metabolic biomarkers with potential clinical applications. In this study, we assessed the changes in the metabolic profile of patients with early-stage NSCLC using mass spectrometry and nuclear magnetic resonance spectroscopy before and after surgical resection. A single cohort of 35 patients provided a total of 29 and 32 pairs of urine and serum samples, respectively, pre-and post-surgery. We identified a profile of 48 metabolites that were significantly different pre- and post-surgery: 17 in urine and 31 in serum. A higher proportion of metabolites were upregulated than downregulated post-surgery (p < 0.01); however, the median fold change (FC) was higher for downregulated than upregulated metabolites (p < 0.05). Purines/pyrimidines and proteins had a larger dysregulation than other classes of metabolites (p < 0.05 for each class). Several of the dysregulated metabolites have been previously associated with cancer, including leucyl proline, asymmetric dimethylarginine, isopentenyladenine, fumaric acid (all downregulated post-surgery), as well as N6-methyladenosine and several deoxycholic acid moieties, which were upregulated post-surgery. This study establishes metabolomic analysis of biofluids as a path to non-invasive diagnostics, screening, and monitoring in NSCLC.

MicroRNA-449a delays lung cancer development through inhibiting KDM3A/HIF-1α axis

Background

It has been established that microRNA (miR)-449a is anti-tumorigenic in cancers, including lung cancer. Therefore, this study further explored miR-449a-mediated mechanism in lung cancer, mainly focusing on lysine demethylase 3A/hypoxia-induced factor-1α (KDM3A/HIF-1α) axis.

Methods

miR-449a, KDM3A and HIF-1α levels in lung cancer tissues and cell lines (A549, H1299 and H460) were measured. Loss- and gain-of-function assays were performed and then cell proliferation, cell cycle, apoptosis, invasion and migration were traced. The relationship between KDM3A, miR-449a and HIF-1α was verified. Tumor growth in vivo was also monitored.

Results

Both lung cancer tissues and cells exhibited reduced miR-449a and raised KDM3A and HIF-1α levels. miR-449a interacted with KDM3A; HIF-1α could bind with KDM3A. Up-regulating miR-449a hindered while suppressing miR-449a induced lung cancer development via mediating HIF-1α. Elevating KDM3A promoted cellular aggression while down-regulating KDM3A had the opposite effects. Up-regulating KDM3A or HIF-1α negated up-regulated miR-449a-induced effects on cellular growth in lung cancer. Restoring miR-449a impaired tumorigenesis in vivo in lung cancer.

Conclusion

It is eventually concluded that miR-449a delays lung cancer development through suppressing KDM3A/HIF-1α axis.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12967-021-02881-8.

m6A RNA Methylation in Systemic Autoimmune Diseases-A New Target for Epigenetic-Based Therapy?

The general background of autoimmune diseases is a combination of genetic, epigenetic and environmental factors, that lead to defective immune reactions. This erroneous immune cell activation results in an excessive production of autoantibodies and prolonged inflammation. During recent years epigenetic mechanisms have been extensively studied as potential culprits of autoreactivity. Alike DNA and proteins, also RNA molecules are subjected to an extensive repertoire of chemical modifications. N6-methyladenosine is the most prevalent form of internal mRNA modification in eukaryotic cells and attracts increasing attention due to its contribution to human health and disease. Even though m6A is confirmed as an essential player in immune response, little is known about its role in autoimmunity. Only few data have been published up to date in the field of RNA methylome. Moreover, only selected autoimmune diseases have been studied in respect of m6A role in their pathogenesis. In this review, I attempt to present all available research data regarding m6A alterations in autoimmune disorders and appraise its role as a potential target for epigenetic-based therapies.

The roles of histone variants in fine-tuning chromatin organization and function

Histones serve to both package and organize DNA within the nucleus. In addition to histone post-translational modification and chromatin remodelling complexes, histone variants contribute to the complexity of epigenetic regulation of the genome. Histone variants are characterized by a distinct protein sequence and a selection of designated chaperone systems and chromatin remodelling complexes that regulate their localization in the genome. In addition, histone variants can be enriched with specific post-translational modifications, which in turn can provide a scaffold for recruitment of variant-specific interacting proteins to chromatin. Thus, through these properties, histone variants have the capacity to endow specific regions of chromatin with unique character and function in a regulated manner. In this Review, we provide an overview of recent advances in our understanding of the contribution of histone variants to chromatin function in mammalian systems. First, we discuss new molecular insights into chaperone-mediated histone variant deposition. Next, we discuss mechanisms by which histone variants influence chromatin properties such as nucleosome stability and the local chromatin environment both through histone variant sequence-specific effects and through their role in recruiting different chromatin-associated complexes. Finally, we focus on histone variant function in the context of both embryonic development and human disease, specifically developmental syndromes and cancer.