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Research article
Xuefeng Zhang
Weihai central hospital
Corresponding Author
ORCiD: https://orcid.org/0000-0001-6115-9064
License:
This work is licensed under a CC BY 4.0 License. Read Full License
Background and Objective:
There is significant heterogeneity between cellular composition and patient outcome in prostate cancer (PCa). Accumulating evidence shows that long noncoding RNAs (lncRNAs) possess great potential in the diagnosis and prognosis of PCa with biological and clinical significance. Therefore, this study aimed to construct an lncRNA-based signature to more accurately predict the prognosis of different PCa patients, so as to improve patient management and prognosis.
Methods
The Cancer Genome Atlas (TCGA) database was used to download RNA-seq expression data together with the clinical information of 499 PCa tissue samples as well as 52 corresponding non-carcinoma tissue samples. Differently expressed lncRNAs (DElncRNAs) were selected based on tumor tissues and non-carcinoma samples. Through univariate and multivariate Cox regression analysis, this study constructed a 4 lncRNAs-based prognosis nomogram for the classification and prediction of survival risk in patients with PCa. The receiver operating characteristic (ROC) curve was plotted for detecting and validating our prediction model sensitivity and specificity. In addition, univariate as well as multivariate Cox regression was conducted to examine whether the constructed lncRNA signature’s prediction ability was independent of additional clinicopathological variables (like age, Gleason score, N stage, T stage and M stage) among PCa cases. Possible biological functions for those prognostic lncRNAs were predicted through gene ontology (GO) together with Kyoto Encyclopedia of Genes and Genomes (KEGG) analysis on those 4 protein-coding genes (PCGs) related to lncRNAs.
Results
A total of 451 differently expressed lncRNAs (DElncRNAs) related to the overall survival (OS) rate for PCa cases were screened from 3838 lncRNAs in the TCGA database. Four lncRNAs (HOXB-AS3, YEATS2-AS1, LINC01679, PRRT3-AS1) were extracted after univariate as well as multivariate COX regression analysis for classifying patients into high and low-risk groups by different OS rates. As suggested by ROC analysis, our proposed model showed high sensitivity and specificity. Independent prognostic capability of the model from other clinicopathological factors was indicated through further analysis. Based on functional enrichment, those action sites for prognostic lncRNAs were mostly located in the extracellular matrix and cell membrane, and their functions are mainly associated with the adhesion, activation and transport of the components across the extracellular matrix or cell membrane.
Conclusion
Our current study successfully identifies a novel four-lncRNA candidate, which can provide more convincing evidence for prognosis in addition to the traditional clinicopathological indicators to predict the PCa survival, and laying the foundation for offering potentially novel therapeutic treatment. Additionally, this study sheds more lights on the PCa-related molecular mechanisms.
Keywords
prostate cancer, lncRNA, Prognostic signature, Survival Analysis, TCGA
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A new preprint design is coming!
We have improved readability, clarity, accessibility, and opportunities for engagement.
This is a preprint, a preliminary version of a manuscript that has not completed peer review at a journal. Research Square does not conduct peer review prior to posting preprints. The posting of a preprint on this server should not be interpreted as an endorsement of its validity or suitability for dissemination as established information or for guiding clinical practice.
Research article
Xuefeng Zhang
Weihai central hospital
Corresponding Author
ORCiD: https://orcid.org/0000-0001-6115-9064
Badges
Prescreen
This manuscript passed our Prescreen assessment
Complete author information
Appropriate declaration statements
Potential risks to human health
Prescreen
History
CURRENT STATUS: Posted
Version 1
Posted 18 Aug, 2020
No community comments so far
Metrics
Comments: 0
PDF Downloads: ...
HTML Views: ...
License:
This work is licensed under a CC BY 4.0 License. Read Full License
Background and Objective:
There is significant heterogeneity between cellular composition and patient outcome in prostate cancer (PCa). Accumulating evidence shows that long noncoding RNAs (lncRNAs) possess great potential in the diagnosis and prognosis of PCa with biological and clinical significance. Therefore, this study aimed to construct an lncRNA-based signature to more accurately predict the prognosis of different PCa patients, so as to improve patient management and prognosis.
Methods
The Cancer Genome Atlas (TCGA) database was used to download RNA-seq expression data together with the clinical information of 499 PCa tissue samples as well as 52 corresponding non-carcinoma tissue samples. Differently expressed lncRNAs (DElncRNAs) were selected based on tumor tissues and non-carcinoma samples. Through univariate and multivariate Cox regression analysis, this study constructed a 4 lncRNAs-based prognosis nomogram for the classification and prediction of survival risk in patients with PCa. The receiver operating characteristic (ROC) curve was plotted for detecting and validating our prediction model sensitivity and specificity. In addition, univariate as well as multivariate Cox regression was conducted to examine whether the constructed lncRNA signature’s prediction ability was independent of additional clinicopathological variables (like age, Gleason score, N stage, T stage and M stage) among PCa cases. Possible biological functions for those prognostic lncRNAs were predicted through gene ontology (GO) together with Kyoto Encyclopedia of Genes and Genomes (KEGG) analysis on those 4 protein-coding genes (PCGs) related to lncRNAs.
Results
A total of 451 differently expressed lncRNAs (DElncRNAs) related to the overall survival (OS) rate for PCa cases were screened from 3838 lncRNAs in the TCGA database. Four lncRNAs (HOXB-AS3, YEATS2-AS1, LINC01679, PRRT3-AS1) were extracted after univariate as well as multivariate COX regression analysis for classifying patients into high and low-risk groups by different OS rates. As suggested by ROC analysis, our proposed model showed high sensitivity and specificity. Independent prognostic capability of the model from other clinicopathological factors was indicated through further analysis. Based on functional enrichment, those action sites for prognostic lncRNAs were mostly located in the extracellular matrix and cell membrane, and their functions are mainly associated with the adhesion, activation and transport of the components across the extracellular matrix or cell membrane.
Conclusion
Our current study successfully identifies a novel four-lncRNA candidate, which can provide more convincing evidence for prognosis in addition to the traditional clinicopathological indicators to predict the PCa survival, and laying the foundation for offering potentially novel therapeutic treatment. Additionally, this study sheds more lights on the PCa-related molecular mechanisms.
Figure 1
Figure 2
Figure 3
Figure 4
Figure 5
Figure 6
Figure 7
Figure 8
Figure 9
Figure 10
This is a list of supplementary files associated with this preprint. Click to download.
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