Epigenetic-dysregulated lncRNAs in Prostate Cancer Based on Multi-omics Analysis

Abstract

Background: The purpose of this study was to investigate the relationship between Long non-coding RNAs (lncRNAs) expression and epigenetic changes, and explore the prognostic value of these findings in patients with prostate cancer. Methods: In this study, we systematically compared the histone modification and methylation regions on lncRNAs promoter and enhancer elements by multiomics. We analyzed the distribution characteristics of histone modified apparent lncRNAs promoters and enhancers in the genome. Single sample Gene Set Enrichment Analysis (ssGSEA) and Kyoto Encyclopedia of Gene and Genomes (KEGG) were used to analyze the functional enrichment of epigenetic dysregulated lncRNAs. The prostate cancer associated lncRNAs were downloaded from lnc2cancer V3.0 and survival analysis was performed. Results: We found 1124 epigenetic-dysregulated lncRNAs (epi-lncRNAs) and 9734 epigenetic-dysregulated protein coding genes (epi-PCGs) in prostate cancer, and the abnormal frequency of lncRNAs was much lower than that of PCGs. H3K4me3, H3K4me1, H3K27ac and H3K27me3 abnormally modified histones accounted for a large proportion and were mainly concentrated in the promoter region. Epigenetic abnormal lncRNAs affects the biological specific molecular function of prostate cancer by changing the abnormal modification of histone. The abnormal expression of lncRNAs caused by abnormal modification of H3K36me3 may be an important factor in the occurrence of prostate cancer. Finally, based on lncRNAs analysis of the prognosis of prostate cancer samples, three lncRNAs (HAR1A, SNHG12 and SNHG17) were identified as prognostic markers of prostate cancer.Conclusions: These findings may help to better understand the abnormal epigenetic regulation of lncRNAs expression in prostate cancer.

Evaluating the Efficacy of ChatGPT as a Patient Education Tool in Prostate Cancer: Multimetric Assessment

“…Our findings appear to differ from Coskun and colleagues', where ChatGPT-3 had accuracy issues using queries generated from the European Association of Urology Patient Information [15]. Interestingly, Zheng and colleagues discovered that ChatGPT-4 can offer suitable counselling on disease prevention and screening for prostate cancer patients [16]. These differences may represent the rapid evolution of the algorithm as our testing utilised the newer model.…”

“…Others have also highlighted such bias, noting that 51% of training data for major Large Language Models (LLMs) is US-sourced [43,44]. The disparities between ChatGPT-3 and ChatGPT-4 highlight the continual advancement and refinement in the underlying technology, reinforcing the need for periodic assessment and validation as newer models emerge [4,16]. Conversely, a lack of validated and reproducible tools to make reliable quality assessments of NPLTs is likely to play a role in varied results within this juvenile domain of clinical research [45].…”

“…To address the AI output, this criteria was modified to seven questions (of original 15) on a scale of one to three, utilising questions 3-9 (Appendix 3). Based on previous DISCERN quality assessment in the literature, each output was scored as very poor (6), poor (7-9), fair (8)(9)(10)(11)(12), good (13)(14)(15) and excellent (16)(17)(18) quality patient education material [26,27].…”

“…Despite these qualities, the accuracy and reliability of AI chatbot medical information must still be assessed using rigorous evaluation tools. Only a handful of studies have begun to test ChatGPT's applicability in prostate cancer, one testing its knowledge directly with questions and statements [15] and another assessing its appropriateness in screening recommendations [16]. However, a significant knowledge gap persists in understanding the quality and safety of information patients receive from ChatGPT-4 for common internet queries.…”