N6-methylandenosine-related lncRNAs play a key role in the prognosis and immune microenvironment of colon cancer

doi:10.21203/rs.3.rs-2237805/v1

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N6-methylandenosine-related lncRNAs play a key role in the prognosis and immune microenvironment of colon cancer

https://doi.org/10.21203/rs.3.rs-2237805/v1

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Long noncoding RNAs (lncRNA) plays major roles in cancer. Few researchers have investigated the correlation between N 6-methyladenosine-related lncRNAs and immune cell infiltration in colon cancer. This study aims at exploring immune cell infiltration in the tumor microenvironment and clarify its relationship with the clinicopathological parameters of colon cancer. This study conducted a bioinformatics study on the regulatory mechanism of m6A-related lncRNA in colon cancer and screened out 13 m6A-related lncRNAs. Different colon cancer patients have different lncRNA subtypes in terms of overall survival, The overall survival of cluster2 is higher. The prognostic risk score of m6A-related lncRNA is closely related to clinicopathological characteristics (Grade), two subtypes and immune cell infiltration.

N6-methyladenosine

colon cancer

lncRNA

nomogram

immunotherapy

Colon cancer is a common intestinal malignancy, which ranks third in terms of incidence, but second in terms of mortality of all types of cancers worldwide ^[1]. Furthermore, although colon cancer is thought of as a disease primarily of developed countries, rapid rises in incidence are occurring in countries undergoing economic development and changes in diet and lifestyle ^[2]. Therefore, there is a need to explore alternative prognostic markers.

RNA modification is an indispensable player in various processes of biological cellular activities. N⁶-methyladenosine (m⁶A) is a chemical modification present in multiple RNA species ^[3–6].N⁶-methyladenosine(m⁶A) is catalyzed by methyltransferase complexes (writers), reversed by demethylases (erasers) and functionally facilitated by m⁶A-binding proteins (readers). N⁶-methyladenosine(m⁶A) participates in carcinogenesis and tumor progression ^[7–10].

Long noncoding RNAs (lncRNA) plays major roles in cancer. Due to their genome-wide expression patterns in a variety of tissues and their tissue-specific expression characteristics, lncRNAs hold strong promise as novel biomarkers and therapeutic targets for cancer ^[11–14].Recent studies have reported the functions and mechanisms of lncRNAs involved in colon oncogenesis^[15–17].However, there are few studies on the role of lncRNAs in m6A modification in colon cancer. Immune cell score analysis improve the prognostic evaluation of colon cancer patients. A new study shows Immunoscore had the highest relative contribution to the risk of all clinical parameters in the classification of colon cancer ^[18].

Regrettably, few researchers have investigated the correlation between N 6-methyladenosine-related lncRNAs and immune cell infiltration in colon cancer. Thus, it is important to analyze immune cell infiltration in the tumor microenvironment and clarify its relationship with the clinicopathological parameters of colon cancer.

Datasets

All data, including colon cancer gene expression profiles and clinical data, were downloaded from the TCGA website through the Genomic Data Commons Data Portal (https://portal.gdc.cancer.gov/) up to March 2021 ^[19].Patients with missing survival information were not included in the subsequent steps of the analysis.

Identification Of M6a Related Lncrnas

We extracted the expression matrix of 23 m6A RNA methylation regulators based on a previous study, including writers (METTL3, METTL14, METTL16, WTAP, VIRMA, RBM15, RBM15B, and ZC3H13), erasers (FTO and ALKBH5), and readers (YTHDC1, YTHDC2, IGF2BP1, IGF2BP2, IGF2BP3, YTHDF1, YTHDF2, YTHDF3, HNRNPC, LRPPRC, HNRNPA2B1, FMR1, and RBMX)^[20].N 6-methyladenosine-related gene expression data were extracted with the limma package in R software. Next, co-expression analysis (with | Pearson’s R| > 0.6 and p < 0.001) was carried out to identify the correlation among N 6-methyladenosine-related gene expression and lncRNAs. By this method, we acquired the expression data of N 6-methyladenosine-related lncRNAs through the limma package in R software. Moreover, a network plot was depicted via the igraph package to visualize the correlation. Expression data of N 6-methyladenosine-related lncRNAs were combined with clinical survival data via the limma package. Prognostic-related lncRNAs were extracted, and the confidence interval and hazard ratio were calculated by the survival package. Visualization of univariate Cox regression analysis was achieved by drawing forest plots. The differential expression data of prognosis-related N 6-methyladenosine-related lncRNAs between tumor and normal tissues were acquired through the limma package, pheatmap package, reshape2 package and ggpubr package in R software. P value < 0.05 was considered statistically significant. We drafted heatmaps and boxplots.

The Role Of N 6-methyladenosine-related Lncrnas

First, we sorted prognosis-related N 6-methyladenosine-related lncRNAs into two subtypes ^[21–22], cluster 1 and cluster 2, via ConsensusClusterPlus and limma packages according to the expression of lncRNAs. The calculation method was clusterAlg = “km” and clusterNum = “2”. We executed survival analysis according to lncRNA subtypes via survminer and survival packages to evaluate the prognostic value of N 6-methyladenosine-related lncRNAs. To identify differentially expressed prognosis-related lncRNAs in each cluster and analyze the relationship between lncRNAs and clinicopathological parameters, the pheatmap package was utilized, and a heatmap was depicted. Differential expression of target genes in related subtypes and different types of tissue was identified via the limma package. To clarify the correlation between the target genes and prognostic N 6-methyladenosine-related lncRNAs in colon cancer, gene correlation analysis was conducted via the limma package. P value < 0.05 was considered statistically significant.

The Role Of Immune Cell Infiltration And The Tumor Microenvironment

To explore and calculate the infiltration level of different immune cell types in the samples, the preprocessCore, limma and e1071 packages were utilized, and the relative number infiltrating immune cells was obtained. The analysis of the tumor microenvironment was carried out via the estimate and limma packages, and the stromal score, immune score and estimation core were determined. The above scores were inversely related to tumor purity. Differential analysis of immune cell infiltration in different clusters was carried out by the limma package, and vioplot was used to visualize the differences. Additionally, the infiltration level of each type of immune cell in different clusters of colon cancer was analyzed via the limma package, and a boxplot was drawn. Immediately, we conducted differential analysis of the tumor microenvironment in different subtypes of samples by the limma package according to the immune score, estimate core and stromal score, and corresponding boxplots were generated to further explore the purity of tumor cells in different types.

N 6-methyladenosine-related Lncrna Prognostic Model

Lasso regression was carried out to construct a prognostic model. All samples were divided into a high-risk group and a low-risk group according to the median value of the risk score of prognostic N 6-methyladenosine-related lncRNAs. Training (50%) and test (50%) groups were used for the lasso regression, and related plots were obtained. The survival curves of different groups were depicted, in which high-risk and low-risk groups were compared. To evaluate the accuracy of our model for predicting the survival of patients with the disease, a corresponding ROC curve was obtained via the time ROC package. A risk curve was generated in line with the risk score, survival status and risk associated with N 6-methyladenosine-related lncRNAs. Independent prognostic analysis was conducted to evaluate whether our model was independent of other clinical prognostic factors that affect patient outcomes. Multivariate and univariate analyses were carried out, and the hazard ratio was calculated. Model validation for clinical groups was utilized to test and verify whether our model could be applied to different clinical groups. Risk and clinical correlation analysis was carried out to clarify related high-risk and low-risk N 6-methyladenosine-related lncRNAs and expound the correlation of clinical characteristics and our prognostic risk model. A heatmap was generated via the pheatmap package and limma package. Boxplots of risk and clinical relevance were obtained to evaluate the relationship between risk and the clinical data. Genetic differential analysis was performed to assess the expression difference of target genes in different risk groups in our model in colon cancer. Correlation analysis of risk and immune cells was conducted to evaluate the relationship between immune cells and the risk score. A scatter plot was generated to visualize this nexus and assess whether immune cells were beneficial or detrimental.

Identification of m6A Related lncRNAs

m6A-related gene expression data were extracted from collated transcriptomic data to discriminate mRNAs and lncRNAs. Next, co-expression analysis was performed to identify the correlation between m6A-related gene expression and lncRNAs. A network plot was generated to visualize this correlation (Fig. 1A). The univariate Cox regression analysis was visualized as a forest plot (Fig. 1B). LncRNAs were considered to be m6A prognosis-related lncRNAs when P < 0.05. The differential expression of prognosis-related m6A lncRNAs between tumor and normal tissues was acquired, and heatmaps and box plots were obtained, as shown in Fig. 2A and B. There were 14 differentially expressed m6A prognosis-related lncRNAs between tumor and normal tissues. Some of these lncRNAs were highly expressed in tumors, while others were highly expressed in normal tissue (P < 0.05).

The Role Of M6a Lncrnas

According to the expression of lncRNAs, to distribute the samples evenly and avoid too few or too many samples in individual groups, we classified lncRNAs into two types: cluster 1 and cluster 2 in Fig. 3. Survival analysis according to the lncRNA subtypes was undertaken to evaluate the prognostic value of m6A-lncRNAs, and the survival rate of cluster 2 was higher than that of cluster 1, as shown in Fig. 4. Differences in the expression of target genes in the related subtypes and in different types of tissue specimens are shown in Fig. 5. PD-L1 expression was higher in the tumor than in normal tissue (p < 0.001). Gene correlation analysis was conducted to ascertain the correlation between the target gene and the prognostic m6A-lncRNAs in colon cancer (Fig. 6).

The Role Of Immune Cell Infiltration And The Tumor Microenvironment

To explore and calculate the infiltration of different immune cells in the samples, analysis of the tumor microenvironment was carried out. Differential analysis of immune cell infiltration in different clusters was carried out, and a vioplot was generated, as shown in Fig. 7A. We conducted a differential analysis of the tumor microenvironment in different subtypes of samples, and corresponding boxplots were generated to further explore the purity of tumor cells in the different types, as shown in(Fig. 7B-D). All of the scores were higher in cluster 1 (P < 0.05).

M6a-related Lncrna Prognostic Model

Lasso regression was carried out to construct a prognostic model. All samples were divided into a high-risk group and a low-risk group according to the median value of the risk score of the prognostic m6A lncRNA. A training group (50%) and a test group (50%) were used for the lasso regression and related plots were generated, as shown in Fig. 8A + 10B. The survival curves of the different groups are depicted, and the high-risk and low-risk groups are compared in Fig. 8C + 10D. In both the test group and the training group, the survival rate of the low-risk subtype was higher than that of the high-risk subtype (P < 0.05). To evaluate the accuracy of our model for predicting the survival of patients with the disease, the corresponding ROC curve was generated via the time-ROC package (Fig. 8E + F). The ROC curve analysis revealed that both AUC values were > 0.5, indicating the considerable accuracy of our model for predicting the survival of patients with the disease. A risk curve was generated, and the survival status and risk of m6A lncRNAs were assessed based on the curve (Fig. 9). As the risk score increased, the number of deaths increased, and the ratio of high risk increased. Independent prognostic analysis was conducted to evaluate whether our model was independent of other clinical prognostic factors that affect patient outcomes (Fig. 10). Genetic differential analysis was conducted to assess the expression difference of target genes in the different risk groups in our model in colon cancer, as shown in Fig. 11. The expression of PD-L1 in no difference between different risk group (P = 0.83). Figure 12A shows the expressions of these 13 m6A-related lncRNAs in the high- and low-risk groups, respectively. And expound the correlation of clinical characteristics and our prognostic risk model in Fig. 12B-G. Both cluster, immune score, stage and TNM were closely related to risk score (P < 0.05). A scatter plot was depicted to visualize this nexus and assess whether immune cells were beneficial or detrimental (Fig. 13). T cells regulatory (Tregs) were positively related to the risk score, R > 0 and P < 0.05. Both T cells CD4 memory activated, Dendritic cells activated, Eosinophils, Mast cells activated, T cells C memory activated, and Neutrophils are negatively related with risk score, R < 0 and P < 0.05.

This study analyzed m6A-related lncRNAs related to the clinical characteristics of colon cancer patients and found that 13 lncRNAs are risk factors. The risk score constructed from 13 m6A-related lncRNAs is also significantly related to overall survival, clusters and Grade (G1-2, G3) are significantly related to the prognosis, and Cluster 1 is related to the high-risk group, and its prognosis is poor. This shows that the risk model based on m6A-related LncRNA is reliable.

A number of studies in recent years have shown that m6A can play an important role in cancer immunity through different regulatory factors ^[23–26]. The m6A regulatory factor in colon cancer has determined three m6A modification modes and found that these three m6A modification modes are closely related to the immunophenotype ^[27]. The tumor immune microenvironment plays an important role in immunotherapy. This study found that cluster2 is related with the immune cell and has low activation in stromal cells. The results of the relationship between risk score and immune cell infiltration showed that: T cells regulatory (Tregs) were positively related to the risk score. Both T cells CD4 memory activated, Dendritic cells activated, Eosinophils, Mast cells activated, T cells C memory activated, and Neutrophils are negatively related with risk score (P < 0.05). In summary, we have analyzed the stromal cells and immune infiltrating cells in the tumor immune microenvironment, and the results showed that m6A-related LncRNA was involved in the reprogramming of the tumor immune microenvironment.

The risk mode including 13 lncRNAs, the patients are divided into train group and test group, besides, the train group and test group are divided into high risk group and low risk group according the median risk score respectively, and the prognosis of low risk group is better than the high risk group, besides the ROC of the risk model are great, with the risk score increases, the number of deaths increases, the ratio of high-risk increases, this indicating our risk model is reliable. Our risk model is also an independent prognostic factor of overall survival, and risk model have the insignificant value compared with TNM stage, so the risk model can be widely used in clinical practice in the future.

This study still has some limitations. First, the effectiveness of this model still needs to be verified in many external queues. Secondly, the m6A-related lncRNA selected in this study needs to be verified by further functional experiments. It is necessary to further reveal the regulatory network between m6A and LncRNA.

In conclusion, this study conducted a bioinformatics study on the regulatory mechanism of m6A-related lncRNA in colon cancer and screened out 13 m6A-related lncRNAs. Different colon cancer patients have different lncRNA subtypes in terms of overall survival, The overall survival of cluster2 is higher. The prognostic risk score of m6A-related lncRNA is closely related to clinicopathological characteristics (Grade), two subtypes and immune cell infiltration.

Data Availability Statement

The datasets generated for this study can be found in the https://portal.gdc.cancer.gov.

Author Contributions

TC designed the study. HT collected the clinical information, gene expression data, analysis data, and wrote the manuscript and TC provided revisions. All authors contributed to the article and approved the submitted version.

Ethics approval and consent to participate

TCGA project is a public database and patients included in the database have provided informed consent for the use of their data. Users can freely download the relevant data for research purposes and publish relevant articles. As our study is based on open-source data, no ethical issues or conflicts of interest are reported.

Consent for publication

Not applicable.

Competing interests

The authors declare that they have no competing interests.

Acknowledgement

We would like to acknowledge the contributions from the TCGA project.

Funding

No financial support was received for this study.

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No competing interests reported.

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Version 1

posted

You are reading this latest preprint version

N6-methylandenosine-related lncRNAs play a key role in the prognosis and immune microenvironment of colon cancer

Status:

Version 1

Abstract

Figures

Introduction

Materials And Methods

Datasets

Identification Of M6a Related Lncrnas

The Role Of N 6-methyladenosine-related Lncrnas

The Role Of Immune Cell Infiltration And The Tumor Microenvironment

N 6-methyladenosine-related Lncrna Prognostic Model

Results

Identification of m6A Related lncRNAs

The Role Of M6a Lncrnas

The Role Of Immune Cell Infiltration And The Tumor Microenvironment

M6a-related Lncrna Prognostic Model

Discussion

Declarations

Data Availability Statement

Author Contributions

Ethics approval and consent to participate

Consent for publication

Competing interests

Acknowledgement

Funding

References

Additional Declarations

Status:

Version 1