Comprehensive analysis of anoikis-related long non-coding RNA immune infiltration in patients with bladder cancer and immunotherapy

Abstract

BackgroundAnoikis is a form of programmed cell death or programmed cell death(PCD) for short. Studies suggest that anoikis involves in the decisive steps of tumor progression and cancer cell metastasis and spread, but what part it plays in bladder cancer remains unclear. We sought to screen for anoikis-correlated long non-coding RNA (lncRNA) so that we can build a risk model to understand its ability to predict bladder cancer prognosis and the immune landscape.MethodsWe screened seven anoikis-related lncRNAs (arlncRNAs) from The Cancer Genome Atlas (TCGA) and designed a risk model. It was validated through ROC curves and clinicopathological correlation analysis, and demonstrated to be an independent factor of prognosis prediction by uni- and multi-COX regression. In the meantime, Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analysis, immune infiltration, and half-maximal inhibitory concentration prediction (IC50) were implemented with the model. Moreover, we divided bladder cancer patients into three subtypes by consensus clustering analysis to further study the differences in prognosis, immune infiltration level, immune checkpoints, and drug susceptibility.ResultWe designed a risk model of seven arlncRNAs, and proved its accuracy using ROC curves. COX regression indicated that the model might be an independent prediction factor of bladder cancer prognosis. KEGG enrichment analysis showed it was enriched in tumors and immune-related pathways among the people at high risk. Immune correlation analysis and drug susceptibility results indicated that it had higher immune infiltration and might have a better immunotherapy efficacy for high-risk groups. Of the three subtypes classified by consensus clustering analysis, cluster 3 revealed a positive prognosis, and cluster 2 showed the highest level of immune infiltration and was sensitive to most chemistries. This is helpful for us to discover more precise immunotherapy for bladder cancer patients.ConclusionIn a nutshell, we found seven arlncRNAs and built a risk model that can identify different bladder cancer subtypes and predict the prognosis of bladder cancer patients. Immune-related and drug sensitivity researches demonstrate it can provide individual therapeutic schedule with greater precision for bladder cancer patients.

Deciphering the prognostic features of bladder cancer through gemcitabine resistance and immune-related gene analysis and identifying potential small molecular drug PIK-75

“…7 B). Remarkably, these findings aligned with a previous study to some extent [ 6 ], providing additional support for the significance and applicability of the gene signature in influencing medication decisions for BCa patients. Consequently, our gene signature held valuable potential for guiding the selection of chemotherapy and targeted agents for BCa patients.…”

Long Noncoding RNA LINC01614 is a Diagnostic and Prognostic Marker for Breast Cancer

“…For instance, KEYNOTE-001 showed that patients with more CD8+ T cell infiltration responded better to pembrolizumab than those with less infiltration [27]. Moreover, some studies reported that lncRNA could influence the infiltration level of tumor-infiltrating immune cells [28,29]. Therefore, a currently acknowledged method, CIBERSORT, was utilized to evaluate the association between LINC01614 expression and tumor-infiltrating immune cells [30].…”

Comprehensive analysis of anoikis‐related lncRNAs for predicting prognosis and response of immunotherapy in hepatocellular carcinoma

“…These models demonstrated remarkable predictive performance and were successfully implemented as a means of stratifying patients for personalised treatment. Additionally, Sun et al uncovered potential associations between anoikis-related lncRNAs and bladder cancer [21]. Similarly, the process of anoikis is also known to play a crucial role in the metastasis of HCC.…”