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Published on: October 30, 2013
Comprehensive analysis of anoikis-related long non-coding RNA immune infiltration in patients with bladder cancer and
Yao-Yu Zhang1,2, Xiao-Wei Li1, Xiao-Dong Li1,2
1Department of Urology, The General Hospital of Western Theater Command, Chengdu, China.
Background:
Anoikis 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.
Methods:
We 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.
Result:
We 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.
Conclusion:
In 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.
Insights
This study identifies seven anoikis-related long non-coding RNAs (lncRNAs) and develops a risk model to predict bladder cancer prognosis and guide immunotherapy. The model aids in classifying bladder cancer subtypes for personalized treatment strategies.
Area of Science:
- Oncology
- Molecular Biology
- Genomics
Background:
- Anoikis, a form of programmed cell death, is implicated in tumor progression and metastasis.
- The role of anoikis in bladder cancer development and spread remains largely undefined.
- Identifying key molecular players like long non-coding RNAs (lncRNAs) is crucial for understanding bladder cancer biology.
Purpose of the Study:
- To screen for anoikis-related long non-coding RNAs (lncRNAs) associated with bladder cancer.
- To develop and validate a predictive risk model for bladder cancer prognosis and immune landscape.
- To explore potential therapeutic strategies based on identified molecular subtypes.
Main Methods:
- Screening of seven anoikis-related lncRNAs (arlncRNAs) using The Cancer Genome Atlas (TCGA) data.
- Development and validation of a risk model using ROC curves and COX regression analysis.
- Consensus clustering to classify bladder cancer subtypes, followed by immune infiltration and drug sensitivity analysis.
Main Results:
- A seven-arlncRNA risk model accurately predicted bladder cancer prognosis and identified independent predictive factors.
- High-risk patients showed enrichment in tumor and immune-related pathways, with higher immune infiltration and potential for immunotherapy.
- Three distinct patient subtypes were identified, with one showing favorable prognosis and another exhibiting high immune infiltration and chemosensitivity.
Conclusions:
- The developed risk model effectively predicts bladder cancer prognosis and aids in subtype classification.
- Findings suggest potential for personalized immunotherapy and treatment strategies based on the identified arlncRNAs and patient subtypes.
- This research provides a foundation for precision medicine approaches in bladder cancer management.
