Targeting treatment resistance: unveiling the potential of RNA methylation regulators and TG-101,209 in pan-cancer neoadjuvant therapy

• Published in: Journal of experimental & clinical cancer research 2024-08, Vol.43 (1), p.232-23
• Main Authors: Zhou, Yaoyao, Liu, Ziyun, Gong, Cheng, Zhang, Jie, Zhao, Jing, Zhang, Xia, Liu, Xiangyu, Li, Bin, Li, Rui, Shi, Zhenyu, Xie, Yongjie, Bao, Li
• Format: Article
• Language: English
• Subjects: Adjuvant treatment; Algorithms; Analysis; Animals; Cancer; Cancer therapies; Cell cycle; Cell growth; Chemotherapy; Clustering; Data mining; DNA sequencing; Drug resistance; Drug Resistance, Neoplasm - genetics; Enzymes; Genes; Genetic transcription; Genomes; Genomics; Health aspects; Humans; Immunotherapy; Machine learning; Medical prognosis; Metastasis; Methylation; Mice; Mortality; Mutation; Neoadjuvant therapy; Neoadjuvant Therapy - methods; Neoplasms - drug therapy; Neoplasms - genetics; Neoplasms - therapy; Nucleotide sequencing; Oncology, Experimental; Pan-cancer; Pancreatic cancer; Patients; Prognosis; Risk factors; RNA; RNA Methylation; RNA methylation regulators; RNA sequencing; TG-101209; Transfer RNA; Treatment resistance; Tumor Microenvironment; Tumorigenesis; Tumors
• ISSN: 0392-9078; 1756-9966; 1756-9966
• DOI: 10.1186/s13046-024-03111-x

Tumor recurrence and mortality rates remain challenging in cancer patients despite comprehensive treatment. Neoadjuvant chemotherapy and immunotherapy aim to eliminate residual tumor cells, reducing the risk of recurrence. However, drug resistance during neoadjuvant therapy is a significant hurdle. Recent studies suggest a correlation between RNA methylation regulators (RMRs) and response to neoadjuvant therapy. Using a multi-center approach, we integrated advanced techniques such as single-cell transcriptomics, whole-genome sequencing, RNA sequencing, proteomics, machine learning, and in vivo/in vitro experiments. Analyzing pan-cancer cohorts, the association between neoadjuvant chemotherapy/immunotherapy effectiveness and RNA methylation using single-cell sequencing was investigated. Multi-omics analysis and machine learning algorithms identified genomic variations, transcriptional dysregulation, and prognostic relevance of RMRs, revealing distinct molecular subtypes guiding pan-cancer neoadjuvant therapy stratification. Our analysis unveiled a strong link between neoadjuvant therapy efficacy and RNA methylation dynamics, supported by pan-cancer single-cell sequencing data. Integration of omics data and machine learning algorithms identified RMR genomic variations, transcriptional dysregulation, and prognostic implications in pan-cancer. High-RMR-expressing tumors displayed increased genomic alterations, an immunosuppressive microenvironment, poorer prognosis, and resistance to neoadjuvant therapy. Molecular investigations and in vivo/in vitro experiments have substantiated that the JAK inhibitor TG-101,209 exerts notable effects on the immune microenvironment of tumors, rendering high-RMR-expressing pan-cancer tumors, particularly in pancreatic cancer, more susceptible to chemotherapy and immunotherapy. This study emphasizes the pivotal role of RMRs in pan-cancer neoadjuvant therapy, serving as predictive biomarkers for monitoring the tumor microenvironment, patient prognosis, and therapeutic response. Distinct molecular subtypes of RMRs aid individualized stratification in neoadjuvant therapy. Combining TG-101,209 adjuvant therapy presents a promising strategy to enhance the sensitivity of high-RMR-expressing tumors to chemotherapy and immunotherapy. However, further validation studies are necessary to fully understand the clinical utility of RNA methylation regulators and their impact on patient outcomes.