N7-methylguanosine-related lncRNAs: Predicting the prognosis and diagnosis of colorectal cancer in the cold and hot tumors

Abstract

Background: 7-Methylguanosine(m7G) contributes greatly to its pathogenesis and progression in colorectal cancer. We proposed building a prognostic model of m7G-related LncRNAs. Our prognostic model was used to identify differences between hot and cold tumors.Methods: The study included 647 colorectal cancer patients (51 cancer-free patients and 647 cancer patients) from The Cancer Genome Atlas (TCGA). We identified m7G-related prognostic lncRNAs by employing the univariate Cox regression method. Assessments were conducted using univariate Cox regression, multivariate Cox regression, receiver operating characteristics (ROC), nomogram, calibration curves, and Kaplan-Meier analysis. All of these procedures were used with the aim of confirming the validity and stability of the model. Besides these two analyses, we also conducted half-maximal inhibitory concentration (IC50), immune analysis, principal component analysis (PCA), and gene set enrichment analysis (GSEA). The entire set of m7G-related (lncRNAs) with respect to cold and hot tumors has been divided into two clusters for further discussion of immunotherapy.Results: The risk model was constructed with 17 m7G-related lncRNAs. A good correlation was found between the calibration plots and the prognosis prediction in the model. By assessing IC50 in a significant way across risk groups, systemic treatment can be guided. By using clusters, it may be possible to distinguish hot and cold tumors effectively and to aid in specific therapeutic interventions. Cluster 1 was identified as having the highest response to immunotherapy drugs and thus was identified as the hot tumor.Conclusion: This study shows that 17 m7G-related lncRNA can be used in clinical settings to predict prognosis and use them to determine whether a tumor is cold or hot in colorectal cancer and improve the individualization of treatment.

Predicting prognosis, immunotherapy and distinguishing cold and hot tumors in clear cell renal cell carcinoma based on anoikis-related lncRNAs

“…Therefore, it is crucial to distinguish between cold tumors and hot tumors and convert the former into the latter. It has been reported that different clusters are related to different tumor immune microenvironments and can distinguish hot and cold tumors (29)(30)(31)(32). Therefore, we divided the patients into 2 clusters based on the R package CC, and the results indicated that the higher CD8 + T immune cells infiltration of cluster 1.…”

“…Despite the important value of the risk score we established for tumor prognosis and systemic treatment, it cannot effectively identify hot and cold tumors. Several studies have proven that cluster analysis can be applied to distinguish hot and cold tumors and guide immunotherapy (29)(30)(31)(32). Based on the expression levels of Anoikis-Related lncRNAs, ccRCC patients were classified into two clusters using consensus clustering analysis (Figure 10A, Supplementary Figure 2).…”

Histone acetylation-related IncRNA: Potential biomarkers for predicting prognosis and immune response in lung adenocarcinoma, and distinguishing hot and cold tumours

“…Although our risk model can predict patient prognosis and guide drug treatment, it cannot identify hot and cold tumours. Clusters can distinguish between hot and cold tumours and guide immunotherapy (52)(53)(54)(55). Therefore, we divided the patients into three clusters.…”

“…Different immune microenvironments have different subtypes, resulting in different immunotherapy effects (50,51). Clusters can distinguish between hot and cold tumours and guide immunotherapy (52)(53)(54)(55). Therefore, we used the R package 'ConsensusClusterPlus' to group the patients into three clusters based on cluster analysis.…”

Emerging role of RNA modification and long noncoding RNA interaction in cancer

“…They show potential value in predicting prognosis, drug sensitivity, and immunotherapy response in LIHC patients [ 196 ]. Seventeen m7G-related lncRNAs have also been reported in CRC, which can be used to predict prognosis in the clinical setting and to determine whether the tumor is cold or hot in CRC to improve the individualization of treatment [ 197 ]. In addition, there are similar studies in ESCC, UCEC, CM, and LUAD [ 198 – 201 ] as shown in Table 2 .…”