Identification of m6A-related lncRNAs as prognostic signature within colon tumor immune microenvironment

BACKGROUND

The current study probed prognosis-related potential for m6A-related lncRNAs signatures within colon tumor immune microenvironment (TIM).

METHODS

After downloading transcriptomic datasets for colon cancer (CC) patients from The Cancer Genome Atlas (TCGA), they were divided, in a 1:1 ratio, within training or test datasets. m6A-related lncRNAs were then scrutinized across such dataset using Pearson correlation assessment before generating a m6A-related lncRNAs prognosis-related model using the training dataset. The latter was then validated with the test and the whole dataset. In addition, we compared the differences of TIM and the estimated IC50 of drug response between the high- and low-risk groups.

RESULTS

Overall survival (OS) resulted as linked with 11 m6A-related lncRNAs, while within the developed prognosis-related model, areas-under-curves were as follows: within training dataset, values at 3-, 4-, and 5-years were 0.777, 0.819, and 0.805, accordingly, and for test one, they were 0.697, 0.682, and 0.706, respectively. Finally, the values for the whole dataset were 0.675 (3-year), 0.682 (4-years), and 0.679 (5-years), accordingly. Moreover, CC cases categorized within low-risk cohort demonstrated enhanced OS (p < .0001), lower metastasis (p = 2e-06) and lower T stage (p = .0067), more instability for microsatellite status (p = .012), and downregulation for PD-L1, PD-1, CTLA-4, LAG3, and HAVCR2 (p < .05). In addition, risk scorings were significantly linked to the degree of infiltrative intensity for CD8 and CD4 (memory resting) T-cells, T-regulatory (Tregs), and Mast cells triggering (p < .05). Patients with low infiltrative propensity for CD4 T-cells also had better OS (p = .016). Moreover, six representative drugs were found to be sensitive for treating CC patients.

CONCLUSION

A robust m6A-related prognostic model with great performances was developed before exploring the TIM characteristics and its potential therapeutic drugs, which might improve the prognosis and therapeutic efficacy.

M6A-related lncRNAs predict clinical outcome and regulate the tumor immune microenvironment in hepatocellular carcinoma

LncRNA N6-methylandenosine (m6A) modification has been shown to be associated with the constitution of the tumor microenvironment (TME) and tumorigenesis. It's essential to understand the mechanisms of lncRNA m6A modification in hepatocellular carcinoma (HCC) and identify relative prognostic predictors to guide therapy and explore potential therapeutic targets. Pearson correlation analysis was performed to identify m6A-related lncRNAs in 374 patients with HCC. Unsupervised cluster analysis of the potential m6A-related lncRNA-based HCC subtypes was conducted, followed by the concurrent analysis of their relationship with TME characteristics, immune checkpoints, immune features, and prognosis through single sample gene set enrichment analysis and ESTIMATE algorithm. Cox regression analyses were performed to screen prognostic m6A-related lncRNA, construct an m6A-related lncRNA signature (m6A-RLRS), and establish an integrated nomogram for the prognosis of patients with HCC. We identified 61 m6A-related lncRNAs and two HCC subtypes defined by consensus cluster of m6A-related lncRNAs with distinct clinical features. Progression-free survival (PFS), three TME-related scores, 15 immune-associated gene sets, and two immune checkpoints expression were found to be significantly different among the two subtypes. Twenty-five prognostic m6A-related lncRNAs were determined, four of which were included to establish an m6A-RLRS with favorable discrimination, and the signature was validated in the validation set and an independent FAHWMU cohort (n = 60). Furthermore, a novel nomogram combining signature and clinical predictors was generated with a C-index of 0.703, and an original ceRNA regulatory network consisting of 9 lncRNAs, 28 miRNAs, and 75 target mRNAs was constructed. Finally, the differential expression of four m6A-related lncRNA was verified by qRT-PCR. In conclusion, m6A-related lncRNA prognostic signature and molecular subtype contributes to accurately predict the prognosis of HCC and provide potential novel therapeutic targets.

An m6A-Related lncRNA Signature Predicts the Prognosis of Hepatocellular Carcinoma

Objective: The purpose of this study was to establish an N6-methylandenosine (m6A)-related long non-coding RNA (lncRNA) signature to predict the prognosis of hepatocellular carcinoma (HCC).

Methods: Pearson correlation analysis was used to identify m6A-related lncRNAs. We then performed univariate Cox regression analysis and least absolute shrinkage and selection operator (LASSO) Cox regression analysis to construct an m6A-related lncRNA signature. Based on the cutoff value of the risk score determined by the X-title software, we divided the HCC patients into high -and low-risk groups. A time-dependent ROC curve was used to evaluate the predictive value of the model. Finally, we constructed a nomogram based on the m6A-related lncRNA signature.

Results: ZEB1-AS1, MIR210HG, BACE1-AS, and SNHG3 were identified to comprise an m6A-related lncRNA signature. These four lncRNAs were upregulated in HCC tissues compared to normal tissues. The prognosis of patients with HCC in the low-risk group was significantly longer than that in the high-risk group. The M6A-related lncRNA signature was significantly associated with clinicopathological features and was established as a risk factor for the prognosis of patients with HCC. The nomogram based on the m6A-related lncRNA signature had a good distinguishing ability and consistency.

Conclusion: We identified an m6A-related lncRNA signature and constructed a nomogram model to evaluate the prognosis of patients with HCC.

Comprehensive analysis of N6-methylandenosine regulators and m6A-related RNAs as prognosis factors in colorectal cancer

Colorectal cancer (CRC) is one of the most common malignancies and has been a leading cause of cancer-related death worldwide in recent years. N6-methyladenosine (m6A) methylation is the most abundant epigenetic modification of various types of RNAs, and it plays a vital role in promoting cancer development. Here, we obtained SNV and transcriptome data of CRC from The Cancer Genome Atlas (TCGA). We demonstrated that most m6A methylation regulators were aberrantly expressed in individuals with CRC. The abnormal expression of m6A regulators was caused by their different copy number variation (CNV) patterns, and alteration of m6A regulators was significantly correlated with prognosis and tumor stage. By using weighted coexpression network analysis (WGCNA), we identified m6A-related long noncoding RNAs (lncRNAs) and mRNAs; then we used least absolute shrinkage and selection operator (LASSO) Cox regression analysis to construct m6A-related lncRNA and mRNA prognostic signatures in the TCGA dataset. Furthermore, a nomogram with clinicopathological features, lncRNA risk scores, and mRNA risk scores was established, which showed a strong ability to forecast the overall survival of the individuals with CRC in training and testing sets. In conclusion, m6A methylation regulators played a vital role in affecting the prognosis of subjects with CRC, and m6A-related lncRNAs and mRNAs revealed underlying mechanisms in CRC tumorigenesis and progression.

Identification of N6-methylandenosine related LncRNAs biomarkers associated with the overall survival of osteosarcoma

PURPOSE

Osteosarcoma (OS) is a differentiation disease caused by the genetic and epigenetic differentiation of mesenchymal stem cells into osteoblasts. OS is a common, highly malignant tumor in children and adolescents. Fifteen to 20 % of the patients find distant metastases at their first visit. The purpose of our study was to identify biomarkers for tracking the prognosis and treatment of OS to improve the survival rate of patients.

MATERIALS AND METHODS

In this study, which was based on Therapeutically Applicable Research to Generate Effective Treatments (TARGET), we searched for m6A related lncRNAs in OS. We constructed a network between lncRNA and m6A, and built an OS prognostic risk model.

RESULTS

We identified 14,581 lncRNAs by using the dataset from TARGET. We obtained 111 m6A-related lncRNAs through a Pearson correlation analysis. A network was built between lncRNA and m6A genes. Eight m6A-related lncRNAs associated with survival were identified through a univariate Cox analysis. A selection operator (LASSO) Cox regression was used to construct a prognostic risk model with six genes (RP11-286E11.1, LINC01426, AC010127.3, DLGAP1-AS2, RP4-657D16.3, AC002398.11) obtained through least absolute shrinkage. We also discovered upregulated levels of DLGAP1-AS2 and m6A methylation in osteosarcoma tissues/cells compared with normal tissues/osteoblasts cells.

CONCLUSION

We constructed a risk score prognosis model of m6A-related lncRNAs (RP11-286E11.1, LINC01426, AC010127.3, DLGAP1-AS2, RP4-657D16.3, AC002398.11) using the dataset downloaded from TRAGET. We verified the value of the model by dividing all samples into test groups and training groups. However, the role of m6A-related lncRNAs in osteosarcoma needs to be further researched by cell and in vivo studies.

N6-Methyladenosine-Related Long Noncoding RNAs as Potential Prognosis Biomarkers for Endometrial Cancer

Purpose

Endometrial cancer (EC) is a common gynaecologic malignancy with an increasing incidence rate and mortality in recent years. N6-methylandenosine (m6A)-related long noncoding RNA (lncRNA) plays a vital role in EC, emerging as one of the most abundant RNA modifications.

Materials and Methods

The Cancer Genome Atlas (TCGA) database and UCSC Xena were used to download data related to EC. Survival and univariate and multifactorial prognostic analyses were performed for m6A-related lncRNAs. The expression levels of the three lncRNAs were verified using q-PCR. A nomogram was used to create a clinical tool to assess overall survival. To investigate the relationship between m6A-related lncRNA and EC, we downloaded differential genes related to EC from the TCGA database and mined three m6A-related lncRNAs, namely SCARNA9, TRAF3IP2-AS1, and AL133243.2. The data were categorized into high- and low-risk groups based on m6A-associated lncRNA.

Results

Survival analysis revealed that the high-risk group had a lower survival rate. Survival analysis of three m6A-associated lncRNAs revealed that cases with high expression of SCARNA9 tended to have a poorer prognosis, whereas the opposite was true for TRAF3IP2-AS1, AL133243.2. Univariate and multifactorial prognostic analyses suggested statistical differences in patients’ age, FIGO stage, pathological grade, risk score, and prognosis of EC, which was confirmed by results of the separate prognostic factor analysis for the three lncRNAs. Risk status was validated as an independent prognostic indicator, and the prognostic nomogram combined patient age, pathological stage, and FIGO classification to assess 3–5-year survival. Cases from high- and low-risk groups were analysed for the tumour microenvironment and immune cell scores, and stromal cell scores were found to be lower in the high-risk group. Correlations were analysed using different databases for immune cell classification.

Conclusion

m6A-related lncRNAs may play a key role in the diagnosis and treatment of EC as targets of prognosis and the immune microenvironment.

Construction of an m6A-related lncRNA pair prognostic signature and prediction of the immune landscape in head and neck squamous cell carcinoma

Background

Mounting evidence indicates that aberrantly expressed N6‐methylandenosine (m6A) modification regulators and long noncoding RNA (lncRNA) influence the development of head and neck squamous cell carcinoma (HNSCC). However, the prognosis of m6A‐related lncRNA (mrlncRNA) in HNSCC has not yet been evaluated.

Methods

We retrieved transcriptome, somatic mutation, and clinical information from The Cancer Genome Atlas database and established a differently expressed mrlncRNA (DEmrlncRNA) pair signature based on least absolute shrinkage and selection operator Cox regression and multivariate Cox analyses. Each sample's risk score was computed premised on the signature, which accurately classified patients into low‐ and high‐risk group by the cut‐off point. The signature was evaluated from the perspective of survival, clinicopathological characteristics, tumor mutation burden (TMB), immune cell infiltration, efficacy of chemotherapeutics, tumor immune microenvironment, and immune checkpoint inhibitor (ICI)‐related genes.

Results

11 DEmrlncRNA pairs were identified and were used to construct the prediction signature. Kaplan–Meier plotter revealed a worse prognosis in high‐risk patients over low‐risk patients (log rank p < 0.001). According to multivariate Cox regression analysis, the hazard ratio of the risk score and 95% confidence interval of 1.722 and (1.488–1.992) (p < 0.001) were obtained. Furthermore, an increased risk score was associated with aggressive clinicopathological features, specific tumor immune infiltration status, increased expression of ICI‐related genes, higher TMB, and higher chemotherapeutics sensitivity (all p < 0.05).

Conclusion

This research demonstrated that the signature premised on DEmrlncRNA pairs was an efficient independent prognostic indicator and may provide a rationale for research on immunotherapeutic and chemotherapeutics strategies for HNSCC patients.

Relationships of N6-Methyladenosine-Related Long Non-Coding RNAs With Tumor Immune Microenvironment and Clinical Prognosis in Lung Adenocarcinoma

Background: Lung adenocarcinoma (LUAD) is the major subtype of lung cancer and is associated with very high mortality. Emerging studies have shown that N6-methyladenosine (m6A)-related long non-coding (lnc) RNAs play crucial roles in tumor prognosis and the tumor immune microenvironment (TME). We aimed to explore the expression patterns of different m6A-related lncRNAs concerning patient prognosis and construct an m6A-related lncRNA prognostic model for LUAD. Methods: The prognostic value of m6A-related lncRNAs was investigated in LUAD samples from The Cancer Genome Atlas (TCGA). Potential prognostic m6A-related lncRNAs were selected by Pearson's correlation and univariate Cox regression analysis. Patients were divided into clusters using principal component analysis and the m6A-related lncRNA prognostic signature was calculated using least absolute shrinkage and selection operator (LASSO) Cox regression analysis. Results: Based on 91 prognostic m6A-related lncRNAs, we identified two m6A-related-lncRNA pattern clusters with different overall survival (OS) and different TMEs. We subsequently verified our findings multidimensionally by constructing a 13 m6A-related lncRNA prognostic signature (m6A-LPS) to calculate the risk score, which was robust in different subgroups. The receiver operating characteristic (ROC) curves and concordance index demonstrated that m6A-LPS harbored a promising ability to predict OS in TCGA data set and independent GSE11969 cohort. The risk score was also related to OS, TME, and clinical stage, and the risk score calculated by our model was also identified as independent prognostic predictive factors for LUAD patients after adjustment for age, smoking, gender, and stage. Enrichment analysis indicated that malignancy and drug resistance-associated pathways were more common in cluster2 (LUAD-unfavorable m6A-LPS). Furthermore, the results indicated that the signaling pathway enriched by the target gene of 13 m6A-related lncRNAs may be associated with metastasis and progression of cancer according to current studies. Conclusion: The current results indicated that different m6A-related-lncRNA patterns could affect OS and TME in patients with LUAD, and the prognostic signature based on 13 m6A-related lncRNAs may help to predict the prognosis in LUAD patients.

Construction and Analysis of a Colorectal Cancer Prognostic Model Based on N6-Methyladenosine-Related lncRNAs

Given the relatively poor understanding of the expression and functional effects of the N6-methyladenosine (m6A) RNA methylation on colorectal cancer (CRC), we attempted to measure its prognostic value and clinical significance. We comprehensively screened 37 m6A-related prognostic long non-coding RNAs (lncRNAs) with significant differences in expression based on 21 acknowledged regulators of m6A modification and data on 473 colorectal cancer tissues and 41 para-cancer tissues obtained from the TCGA database. Accordingly, we classified 473 CRC patients into two clusters by consensus clustering on the basis of significantly different survival outcomes. We also found a potential correlation between m6A-related prognostic lncRNAs and BRAF-KRAS expression, as well as immune cell infiltration. Then, we established a prognostic model by selecting 16 m6A-related prognostic lncRNAs via LASSO Cox analysis and grouped the CRC patients into low- and high-risk groups to calculate risk scores. Then, we performed stratified sampling to validate and confirm our model by categorising the 473 samples into a training group (N = 208) and a testing group (N = 205) in a 1:1 ratio. The survival curve showed a distinct clinical outcome in the low- and high-risk subgroups. We reconfirmed the reliability and independence of the prognostic model through various measures: risk curve, heat map and univariate and multivariate Cox analyses. To ensure that the outcomes were applicable to clinical settings, we performed stratified analyses on different clinical features, such as age, lymph node status and clinical stage. CRC patients with downregulated m6A-related gene expression, lower immune score, distant metastasis, lymph node metastasis or more advanced clinical staging had higher risk scores, indicating less-desirable outcomes. Moreover, we explored the immunology of colorectal cancer cells. The risk score showed positive correlations with eosinophils, M2 macrophages and neutrophils. In summary, our effort revealed the significance of m6A RNA methylation regulators in colorectal cancer, and the prognostic model we constructed may be used as an essential reference for predicting the outcome of CRC patients.

m6A-Related lncRNAs Are Potential Biomarkers for the Prognosis of Metastatic Skin Cutaneous Melanoma

Background: The incidence of skin cutaneous melanoma (SKCM) has risen more rapidly than any other solid tumor in the past few decades. The median survival for metastatic melanoma is only six to nine months and the 5°years survival rate of patients with conventional therapy is less than 5%. Our aim was to reveal the potential molecular mechanism in m6A modification of lncRNA and provide candidate prognostic biomarkers for metastatic SKCM.

Methods: lncRNAs expression level was obtained by re-annotation in TCGA and CCLE datasets. m6A-related lncRNAs were selected though correlation analysis. Univariate cox regression analysis was used to screen out independent prognostic factors. LASSO Cox regression was performed to construct an m6A-related lncRNA model (m6A-LncM). Univariate survival analysis and ROC curve were used to assess the prognostic efficacy of this model and candidate lncRNAs. Enrichment analysis was used to explore the candidate genes’ functions.

Results: We obtained 1,086 common m6A-related lncRNAs after Pearson correlation analysis in both two datasets. 130 out of the 1,086 lncRNAs are independent prognostic factors. 24 crucial lncRNAs were filtered after LASSO Cox regression analysis. All the m6A-LncM and the 24 lncRNAs were related to overall survival. Stratified survival analysis of m6A-LncM showed that the model retains its prognostic efficacy in recurrence, radiation therapy and other subgroups. Enrichment analysis also found that these lncRNAs were immune associated.

Conclusion: Here, we obtained 24 crucial lncRNAs that may be potential biomarkers to predict survival of metastatic SKCM and may provide a new insight to improve the prognosis of it.

N6-Methylandenosine-Related lncRNAs Are Potential Biomarkers for Predicting the Overall Survival of Lower-Grade Glioma Patients

The prognostic value of N6-methylandenosine-related long non-coding RNAs (m6A-related lncRNAs) was investigated in 646 lower-grade glioma (LGG) samples from The Cancer Genome Atlas (TCGA) and the Chinese Glioma Genome Atlas (CGGA) datasets. We implemented Pearson correlation analysis to explore the m6A-related lncRNAs, and then univariate Cox regression analysis was performed to screen their prognostic roles in LGG patients. Twenty-four prognostic m6A-related lncRNAs were identified as prognostic lncRNAs and they were inputted in a least absolute shrinkage and selection operator (LASSO) Cox regression to establish a m6A-related lncRNA prognostic signature (m6A-LPS, including 9 m6A-related prognostic lncRNAs) in the TCGA dataset. Corresponding risk scores of patients were calculated and divided LGG patients into low- and high-risk subgroups by the median value of risk scores in each dataset. The m6A-LPS was validated in the CGGA dataset and it showed a robust prognostic ability in the stratification analysis. Principal component analysis showed that the low- and high-risk subgroups had distinct m6A status. Enrichment analysis indicated that malignancy-associated biological processes, pathways and hallmarks were more common in the high-risk subgroup. Moreover, we constructed a nomogram (based on m6A-LPS, age and World Health Organization grade) that had a strong ability to forecast the overall survival (OS) of the LGG patients in both datasets. We also establish a competing endogenous RNA (ceRNA) network based on seven of the twenty-four m6A-related lncRNAs. Besides, we also detected five m6A-related lncRNA expression levels in 22 clinical samples using quantitative real-time polymerase chain reaction assay.