Background As one of the most abundant post‐transcriptional mRNA modifications, N6‐methyladenosine (m6A) has attracted extensive attention from scientists. Emerging evidence indicates that m6A modification plays a significant role in cancer‐related signalling pathways. Existing research demonstrates that m6A modifications were also identified in miRNAs and contribute to cancer‐related signalling pathways. Methods A literature retrieval has been performed to collect m6A‐miRNA‐related original articles published in recent years. Later, a systematic analysis has been conducted to abstract and classify the relationships between m6A modification and miRNAs, and their contributions to tumorigenesis and cancer development. Results Accumulating literature provides important insights into multiple relationships between m6A modifications and miRNAs. Mechanically, m6A writer and eraser alter pri‐miRNAs m6A levels, and m6A readers could dually modulate pri‐miRNAs processing and pri‐miRNAs degradation. It is also been demonstrated that miRNAs impair m6A regulators' translation to influence m6A medication function in return. Aberrant expressions of m6A regulators and miRNAs could dysregulate proliferative, apoptosis, cell adhesion‐related, and malignant transformation signalling pathways, and contribute to tumour occurrence and development. Conclusion This review summarizes the interrelationship between m6A modification and miRNAs; highlights the combined effects of each type of m6A regulator and miRNAs in cancers. These findings enhance our understanding of m6A‐miRNAs' multiple interactions and significant modulatory role in tumorigenesis and progression.

Purpose Systemic immune-inflammation index (SII) has been demonstrated to be closely associated with the poor prognosis of nasopharyngeal carcinoma (NPC). However, the role of SII during treatment of NPC has not been reported. This study aimed to determine the prognostic value of SII during treatment for NPC patients. Methods A total of 759 patients diagnosed with NPC were included in this retrospective study (393 in training cohort and 366 in validation cohort). The correlation between variables was analyzed by the chi-squared test, the Fisher’s exact test or the likelihood test. Kaplan–Meier method and log-rank test were used to analyze progression-free survival (PFS) and overall survival (OS). The independent prognostic factors were determined by multivariate analysis of Cox proportional hazards regression model. The uncontrolled risk was analyzed by Logistic regression. Receiver operating characteristic (ROC) curves were used to assess prognostic value. Results The optimal cut-off point for the SII during treatment was 937.32. High SII during treatment group had higher uncontrolled risk than low SII during treatment group (p = 0.008). In multivariate Cox proportional hazard models analysis, SII during treatment was an independent prognostic factor for 5-year PFS (p < 0.001) and 5-year OS (p < 0.001). All results were found in the training cohort and confirmed in the validation cohort. Conclusions The SII during treatment is a promising indicator of predicting the survival in NPC patients, especially the risk of uncontrolled occurrence. By monitoring the SII during treatment, it is possible to better evaluate the treatment effect and formulate personalized treatment.

Background Inflammatory parameters and Epstein–Barr virus (EBV) DNA status have been confirmed to be associated with prognosis in nasopharyngeal carcinoma (NPC) patients. However, there are few in-depth studies on the prognosis of NPC patients with negative EBV DNA. Our study aimed to look for inflammatory biomarkers that can identify disease progression in NPC patients with negative EBV DNA. Methods A total of 795 NPC patients were recruited, and ultimately 325 NPC patients with negative EBV DNA were included in this study (170 in training cohort and 155 in validation cohort). Kaplan–Meier method and log-rank test were used to analyze progression-free survival (PFS) and overall survival (OS). The multivariate analysis of Cox proportional hazards regression model was used to determine the independent prognostic factors. Receiver operating characteristic (ROC) curves were used to assess prognostic value. The logistic regression was used to evaluate the relationship between EBV DNA status and inflammatory parameters. The correlation between clinical characteristics was analyzed by the chi-squared test or the Fisher’s exact test. Results The optimal cutoff point for the SIRI was 1.12. The EBV DNA-negative NPC patients with high SIRI level had worse PFS and OS (all p < 0.001). In multivariate Cox proportional hazard models analysis, SIRI was an independent prognostic factor for PFS and OS (all p < 0.05), and had higher prognostic value than other indicators. Above results were found in the training cohort and confirmed in the validation cohort. In addition, EBV DNA status was not associated with any inflammatory parameters. Conclusions The SIRI can provide more accurate risk stratification and better prognostic prediction for NPC patients with negative EBV DNA.