Chinese Medical Sciences Journal ›› 2020, Vol. 35 ›› Issue (3): 239-247.doi: 10.24920/003662
• Original Article • Previous Articles Next Articles
Novel Long Non-coding RNA Markers for Prognostic Prediction of Patients with Bladder Cancer
Li Wenxing1, 2, Zhang Yanli3, 4, *(
)
- 1Department of Diagnosis, Heze Medical College, Heze, Shandong 274000, China
2Department of Urology, Affiliated Hospital of Heze Medical College, Heze, Shandong 274000, China
3School of Life Sciences, Tsinghua University, Beijing 100084, China
4School of Veterinary Medicine, Huazhong Agricultural University, Wuhan 430072, China
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Received:2019-10-16Published:2020-09-30Online:2020-09-25 -
Contact:Zhang Yanli E-mail:yanlizhang12@mail.tsinghua.edu.cn
Cite this article
Li Wenxing, Zhang Yanli. Novel Long Non-coding RNA Markers for Prognostic Prediction of Patients with Bladder Cancer[J].Chinese Medical Sciences Journal, 2020, 35(3): 239-247.
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Table 1
Overall information of seven novel prognosis related lncRNAs"
| Ensembl ID | Gene symbol | Hazard ratio | P value | - logL | AIC |
|---|---|---|---|---|---|
| ENSG00000248256.1 | OCIAD1-AS1 | 0.352498 | 0.000426 | 313.55 | 629.1 |
| ENSG00000260331.1 | RP11-111J6.2 | 0.124571 | 0.000367 | 309.8 | 623.61 |
| ENSG00000222035.3 | AC079354.3 | 132322.9 | 9.81E-06 | 304.4 | 614.79 |
| ENSG00000231652.2 | RP11-553A21.3 (AL590428.1) | 28.95384 | 4.98E-05 | 300.78 | 609.56 |
| ENSG00000256948.1 | RP11-598F7.3 | 0.324872 | 0.012715 | 293.77 | 597.55 |
| ENSG00000265787.1 | CYP4F35P | 0.473391 | 0.001861 | 291.21 | 594.41 |
| ENSG00000255503.1 | RP11-113K21.4 | 0.15136 | 0.000223 | 289.07 | 592.15 |
Table 1
Figure 1.
Chromosome location and potential target genes of two antisense lncRNAs. A. RP11-553A21.3 (also named AL590428.1), an antisense RNA is located in the promoter region of CD109. B. OCIAD1-AS1, an antisense RNA, is located in the gene body of OCIAD1."
Figure 1.
Figure 2.
Co-expressed protein coding genes (PCGs) for prognostic lncRNAs and their KEGG pathway enrichment network. Top 100 positively (A) or negatively correlated PCGs (B), and top ten KEGG pathways of positively (C) or negatively correlated PCGs (D) are presented. The significance of each term is represented by the length and color of the bar graph. Interaction networks of top ten KEGG pathways of positively (E) or negatively correlated PCGs (F) are shown."
Figure 2.
Figure 3.
LncRNA risk score analysis in the training data. A. Distribution of risk scores; B. Distribution of survival time; C. Relative expression levels of the seven lncRNAs related to prognosis; D. The Kaplan-Meier curve used the median risk score as a cut-off point to divide patients into the high-risk group and low-risk group."
Figure 3.
Figure 4.
Determination of the optimal threshold and validation of the model. A. The sensitivity and specificity of survival time were analyzed by Receiver Operating Characteristic. The red dot represents the risk score at the optimal cut-off point. B. In the Kaplan-Meier curve, the optimal cut-off point is used to estimate the patient’s survival time in the training data. C. Kaplan-Meier curve is used to estimate the survival time of all patients. D. Kaplan-Meier curve is used to estimate patient’s survival time in the test data."
Figure 4.
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