Identification of a ten-long noncoding RNA signature for predicting the survival and immune status of patients with bladder urothelial carcinoma based on the GEO database: a superior machine learning model

Relevance of HOTAIR rs920778 and rs12826786 Genetic Variants in Bladder Cancer Risk and Survival

The long non-coding RNA HOX transcript antisense intergenic RNA (HOTAIR) is associated with oncogenic features in bladder cancer and is predictive of poor clinical outcomes in patients diagnosed with this disease. In this study, we evaluated the impact of the HOTAIR single nucleotide polymorphisms rs920778 and rs12826786 on bladder cancer risk and survival. This case-control study included 106 bladder cancer patients and 199 cancer-free controls. Polymorphisms were evaluated through PCR-restriction fragment length polymorphism. The odds ratio and 95% confidence intervals were tested using univariable and multivariable logistic regressions. The effects on patient survival were evaluated using the log-rank test and Cox regression models. Our data showed that the HOTAIR rs920778 and rs12826786 genetic variants are not associated with the risk of developing bladder cancer. Nevertheless, survival analyses suggested that the HOTAIR rs920778 TT genotype and rs12826786 CC genotype are associated with increased survival in male bladder cancer patients and in patients, both male and female, who have primary tumors with a pathological stage of pT2. Together, these results suggest that, despite not being associated with bladder cancer risk, HOTAIR rs920778 and rs12826786 polymorphisms might represent new prognostic factors in this type of cancer. This is particularly important as these polymorphisms might be easily evaluated in bladder cancer patients in a minimally invasive manner to better predict their clinical outcomes.

Assessment of Prostate and Bladder Cancer Genomic Biomarkers Using Artificial Intelligence: a Systematic Review

PURPOSE OF REVIEW

The aim of the systematic review is to assess AI's capabilities in the genetics of prostate cancer (PCa) and bladder cancer (BCa) to evaluate target groups for such analysis as well as to assess its prospects in daily practice.

RECENT FINDINGS

In total, our analysis included 27 articles: 10 articles have reported on PCa and 17 on BCa, respectively. The AI algorithms added clinical value and demonstrated promising results in several fields, including cancer detection, assessment of cancer development risk, risk stratification in terms of survival and relapse, and prediction of response to a specific therapy. Besides clinical applications, genetic analysis aided by the AI shed light on the basic urologic cancer biology. We believe, our results of the AI application to the analysis of PCa, BCa data sets will help to identify new targets for urological cancer therapy. The integration of AI in genomic research for screening and clinical applications will evolve with time to help personalizing chemotherapy, prediction of survival and relapse, aid treatment strategies such as reducing frequency of diagnostic cystoscopies, and clinical decision support, e.g., by predicting immunotherapy response. These factors will ultimately lead to personalized and precision medicine thereby improving patient outcomes.

Emerging Roles of Long Noncoding RNAs in Immuno-Oncology

Long noncoding RNAs (lncRNAs), defined as ncRNAs no longer than 200 nucleotides, play an important role in cancer development. Accumulating research on lncRNAs offers a compelling new aspect of genome modulation, in which they are involved in chromatin remodeling, transcriptional and post-transcriptional regulation, and cross-talk with other nucleic acids. Increasing evidence suggests that lncRNAs reshape the tumor microenvironment (TME), which accounts for tumor development and progression. At the same time, the insightful findings on lncRNAs in immune recognition and evasion in tumor-infiltrating immune cells raise concerns with regard to immuno-oncology. In this review, we describe the essential characteristics of lncRNAs, elucidate functions of immune components engaged in tumor surveillance, and present some instructive examples in this new area.

Multi-Omics analysis identifies a lncRNA-related prognostic signature to predict bladder cancer recurrence

Bladder cancer (BLCA) is one of the most common cancers worldwide with high recurrence rate. Hence, we intended to establish a recurrence-related long non-coding RNA (lncRNA) model of BLCA as a potential biomarker based on multi-omics analysis. Multi-omics data including copy number variation (CNV) data, mutation annotation files, RNA expression profiles and clinical data of The Cancer Genome Atlas (TCGA) BLCA cohort (303 cases) and GSE31684 (93 cases) were downloaded from public database. With multi-omics analysis, twenty lncRNAs were identified as the candidates related with BLCA recurrence, CNVs and mutations in training set. Ten-lncRNA signature were established using least absolute shrinkage and selection operation (LASSO) and Cox regression. Then, various survival analysis was used to assess the power of lncRNA model in predicting BLCA recurrence. The results showed that the recurrence-free survival time of high-risk group was significantly shorter than that of low-risk group in training and testing sets, and the predictive value of ten-lncRNA signature was robust and independent of other clinical variables. Gene Set Enrichment Analysis (GSEA) showed this signature were associated with immune disorders, indicating this signature may be involved in tumor immunology. After compared with the other reported lncRNA signatures, ten-lncRNA signature was validated as a superior prognostic model in predicting the recurrence of BLCA. The effectiveness of the model was also evaluated in bladder cancer samples via qRT-PCR. Thus, the novel ten-lncRNA signature, constructed based on multi-omics data, had robust prognostic power in predicting the recurrence of BLCA and potential clinical implications as biomarkers.