MATERIAL AND METHODS: Differential gene expression was identified using the "limma" package in R. Prognosis-related LncRNAs were identified via univariate Cox regression analysis, while a prognostic model was crafted using multivariate Cox regression analysis. Survival analysis was conducted using Kaplan-Meier curves. The precision of the prognostic model was assessed through ROC analysis. Subsequently, the Tumor Immune Dysfunction and Exclusion (TIDE) algorithm were executed on the TCGA dataset via the TIDE database. Fractions of 24 types of immune cell infiltration were obtained from NCI Cancer Research Data Commons using deconvolution techniques. The protein expression levels encoded by specific genes were obtained through the TPCA database.
RESULTS: In this research, we have identified 85 LncRNAs associated with TP53 mutations and developed a corresponding signature referred to as TP53MLncSig. Kaplan-Meier analysis revealed a lower 3-year survival rate in high-risk patients (46.9%) compared to low-risk patients (74.2%). The accuracy of the prognostic TP53MLncSig was further evaluated by calculating the area under the ROC curve. The analysis yielded a 5-year ROC score of 0.793, confirming its effectiveness. Furthermore, a higher score for TP53MLncSig was found to be associated with an increased response rate to immune checkpoint blocker (ICB) therapy (p = .005). Patients possessing high-risk classification exhibited lower levels of P53 protein expression and higher levels of genomic instability.
CONCLUSION: The present study aimed to identify and validate LncRNAs associated with TP53 mutations. We constructed a prognostic model that can predict chemosensitivity and response to ICB therapy in HCC patients. This novel approach sheds light on the role of LncRNAs in TP53 mutation and provides valuable resources for analyzing patient prognosis and treatment selection.
RESULTS: Sequence data were obtained for both A. dorsata and H. itama. The raw sequence data for A. dorsata was 5 Mb, which was assembled into 5 contigs with a size of 6,098,728 bp, an N50 of 15,534, and a GC average of 57.42. Similarly, the raw sequence data for H. itama was 6.3 Mb, which was assembled into 11 contigs with a size of 7,642,048 bp, an N50 of 17,180, and a GC average of 55.38. In the honey sample of A. dorsata, we identified five different plant/pollen species, with only one of the five species exhibiting a relative abundance of less than 1%. For H. itama, we identified seven different plant/pollen species, with only three of the species exhibiting a relative abundance of less than 1%. All of the identified plant species were native to Peninsular Malaysia, especially the East Coast area of Terengganu.
DATA DESCRIPTION: Our data offers valuable insights into honey's geographical and botanical origin and authenticity. Metagenomic studies could help identify the plant species that honeybees forage and provide preliminary data for researchers studying the biological development of A. dorsata and H. itama. The identification of various flowers from the eDNA of honey that are known for their medicinal properties could aid in regional honey with accurate product origin labeling, which is crucial for guaranteeing product authenticity to consumers.