A potential biological signature of 7-methylguanosine-related lncRNA to predict the immunotherapy effects in bladder cancer

RNA methylation: A new promising biomaker in cancer liquid biopsy

RNA methylation is a vital epigenetic modification that regulates gene expression by influencing RNA processes such as transcription, degradation, translation, and transport. Aberrant methylation, including modifications like m6A, m5C, m1A, m7G, and m3C, is closely linked to tumorigenesis and progression. Liquid biopsy, a non-invasive technique analyzing tumor markers in body fluids, offers significant potential for early diagnosis and dynamic monitoring. In this context, RNA methylation, due to its tumor-specific properties, is emerging as a valuable marker. However, significant challenges remain in its clinical application. This review explores the roles of RNA methylation in cancer, recent advances in detection technologies, and its potential as a liquid biopsy marker in tumor management. It highlights its promising applications in cancer diagnosis, prognosis, and personalized treatment in the era of precision oncology.

Identification and validation of an m7G-related lncRNAs signature for predicting prognosis, immune response and therapy landscapes in ovarian cancer

Background

Ovarian cancer is the most mortality malignancy in gynecology. N7-methylguanosine (m7G) is one of the most prevalent RNA modifications in the development and progression of cancer. The aim of this study is to investigate the effect of m7G-related lncRNA on ovarian cancer in terms of instruction prognosis and immunotherapy.

Methods

After integrating and processing the RNA expression profiles with the clinical sample information in the TCGA database, we initially screened to the m7G-related lncRNAs by Spearman correlation analysis, and subsequently obtained a prognostic model constructed by five m7G-related lncRNAs with Univariate Cox analysis, LASSO regression analysis, and Multivariate Cox regression analysis, after which we further evaluated and validated the prognostic value of the model using Kaplan-Meier survival analysis, Principal component analysis, Nomogram, and ROC curve. In addition, based on this risk model, we explored the differentially enriched pathways and functions of the high and low risk groups, and characterized the immune cells, immune functions, gene mutations, and drug sensitivity between the two groups.

Results

After a series of rigorous filtering, we finally attained a prognostic risk model consisting of KRT7-AS, USP30-AS1, ZFHX4-AS1, ACAP2-IT1, and TWSG1-DT which is excellent in predicting the prognostic survival of ovarian cancer patients as well as existing as an independent prognostic factor. Moreover, the model has certain relevance in the immune cells and functions between high and low risk groups, and simultaneously, the signature has the role of guiding the option of immunotherapy and chemotherapeutic drugs.

Conclusion

Altogether, our study established a tight connection between m7G-associated lncRNAs and ovarian cancer, with potential that the prognostic patterns contribute to steering the prognosis of ovarian cancer patients, measuring the efficacy of immunotherapeutic approaches, and detecting effective chemotherapeutic agents.