Background: Observational epidemiological studies suggest that lung cancer risk may be raised by gastroesophageal reflux disease (GERD); however, the causal relationship between them remains unknown. Our study performed the two‐sample Mendelian randomization (MR) approach to examine the causal relationship between GERD and lung cancer.Methods: Instrument variables were found to be independent single nucleotide polymorphisms (SNPs) that were highly linked with GERD (n = 129,080). Summary data from genome‐wide association studies (GWAS) data were used to determine outcomes for lung cancer (n = 11,348), squamous cell lung cancer (LUSC), and lung adenocarcinoma (LUAD). In this study, three MR statistical techniques (inverse variance weighted (IVW), MR‐Egger, and weighted median) were used to examine the potential causative relationship between GERD and the risk of lung cancer. Cochran's Q test, MR‐Egger intercept test, leave‐one‐out analysis, and the funnel plot were all used in sensitivity analyses.Results: The main IVW method revealed that GERD substantially increased the risk of lung cancer [odds ratio (OR) = 1.37; 95% CI 1.16–1.63, p = 0. 0003], which was also supported by weighted median and MR‐Egger analyses. Using IVW estimate, similar causal relationships were also observed between GERD and LUSC (OR = 1.56; 95% CI 1.26–1.93, p = 5.35 × 10⁻⁵) and LUAD (OR = 1.45; 95% CI 1.09–1.93, p = 0.01). Although potential heterogeneity was observed in some studies, random effect IVW was not violated by the heterogeneity, indicating that the causal effect was robust.Conclusion: GERD was positively associated with the risk of lung cancer, for LUSC and LUAD. This study shed light on a new direction for prevent strategy of lung cancer and therapeutic perspectives in patients with GERD.

The blockade of programmed cell death protein 1 (PD-1) as monotherapy has been widely used in melanoma, but to identify melanoma patients with survival benefit from anti-PD-1 monotherapy is still a big challenge. There is an urgent need for prognostic signatures improving the prediction of immunotherapy responses of these patients. We analyzed transcriptomic data of pre-treatment tumor biopsies and clinical profiles in advanced melanoma patients receiving only anti-PD-1 monotherapy (nivolumab or pembrolizumab) from the PRJNA356761 and PRJEB23709 data sets as the training and validation cohort, respectively. Weighted gene co-expression network analysis was used to identify the key module, then least absolute shrinkage and selection operator was conducted to determine prognostic-related long noncoding RNAs (lncRNAs). Subsequently, the differentially expressed genes between different clusters were identified, and their function and pathway annotation were performed. In this investigation, 92 melanoma patients with complete survival information (51 from training cohort and 41 from validation cohort) were included in our analyses. We initiallyidentified the key module (skyblue) by weighted gene co-expression network analysis, and then identified a 15 predictive lncRNAs (AC010904.2, LINC01126, AC012360.1, AC024933.1, AL442128.2, AC022211.4, AC022211.2, AC127496.5, NARF-AS1, AP000919.3, AP005329.2, AC023983.1, AC023983.2, AC139100.1, and AC012615.4) signature in melanoma patients treated with anti-PD-1 monotherapy by least absolute shrinkage and selection operator in the training cohort. These results were then validated in the validation cohort. Finally, enrichment analysis showed that the functions of differentially expressed genes between two consensus clusters were mainly related to the immune process and treatment. In summary, the 15 lncRNAs signature is a novel effective predictor for prognosis in advanced melanoma patients treated with anti-PD-1 monotherapy.

Objective: The stratification of neuroblastoma (NBL) prognosis remains difficult. RNA-based signatures might be able to predict prognosis, but independent cross-platform validation is still rare. Methods: RNA-Seq-based profiles from NBL patients were acquired and then analyzed. The RNA-Seq prognostic index (RPI) and the clinically adjusted RPI (RCPI) were successively established in the training cohort (TARGET-NBL) and then verified in the validation cohort (GSE62564). Survival prediction was assessed using a time-dependent receiver operating characteristic (ROC) curve and area under the ROC curve (AUC). Functional enrichment analysis of the genes was conducted using bioinformatics methods. Results: In the training cohort, 10 gene pairs were eventually integrated into the RPI. In both cohorts, the high-risk group had poor overall survival (OS) (P < 0.001 and P < 0.001, respectively) and favorable event-free survival (EFS) (P = 0.00032 and P = 0.06, respectively). ROC curve analysis also showed that the RPI predicted OS (60 month AUC values of 0.718 and 0.593, respectively) and EFS (60 month AUC values of 0.627 and 0.852, respectively) well in both the training and validation cohorts. Clinicopathological indicators associated with prognosis in the univariate and multivariate regression analyses were identified and added to the RPI to form the RCPI. The RCPI was also used to divide populations into different risk groups, and the high-risk group had poor OS (P < 0.001 and P < 0.001, respectively) and EFS (P < 0.05 and P < 0.05, respectively). Finally, the RCPI had higher accuracy than the RPI for the prediction of OS (60 month AUC values of 0.730 and 0.852, respectively) and EFS (60 month AUC values of 0.663 and 0.763, respectively) in both the training and validation cohorts. Moreover, these differentially expressed genes may be involved in certain NBL-related events. Conclusions: The RCPI could reliably categorize NBL patients based on different risks of death.

Circular RNAs (circRNAs) are emerging as promising tools in cancer and immunotherapy, with unique characteristics that offer potential therapeutic applications. This review outlines the discovery, biogenesis, and mechanisms of circRNAs, emphasizing their roles in cancer and immune regulation. CircRNAs modulate immune responses by acting as miRNA sponges, binding RNA-binding proteins, or serving as translation templates. These interactions influence T cells, NK cells, macrophages, and immune checkpoints. The review also explores circRNAs' roles in different cancers, focusing on target identification, immune effects, and mechanisms of action. Additionally, it examines circRNA-based therapies, including vaccines, CAR-T cell therapy, and database applications. Despite their potential, technical hurdles must be overcome to advance circRNAs' clinical use in cancer immunotherapy. Future research should focus on addressing these challenges to fully realize the therapeutic potential of circRNAs.

Systematic review and meta-analysis of the efficacy of serum neuron-specific enolase for early small cell lung cancer screening Systematic review and meta-analysis of the efficacy of serum neuron-specific enolase for early small cell lung cancer screening