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. 2022 Jul 19:13:918705.
doi: 10.3389/fgene.2022.918705. eCollection 2022.

Recognition of Glycometabolism-Associated lncRNAs as Prognosis Markers for Bladder Cancer by an Innovative Prediction Model

Dongdong Tang  1   2 , Yangyang Li  2 , Ying Tang  2   3 , Haoxiang Zheng  2 , Weihan Luo  2   3 , Yuqing Li  2   3 , Yingrui Li  2 , Zhiping Wang  1 , Song Wu  1   2   3   4
Affiliations

Recognition of Glycometabolism-Associated lncRNAs as Prognosis Markers for Bladder Cancer by an Innovative Prediction Model

Dongdong Tang et al. Front Genet. .

Abstract

The alteration of glycometabolism is a characteristic of cancer cells. Long non-coding RNAs (lncRNAs) have been documented to occupy a considerable position in glycometabolism regulation. This research aims to construct an effective prediction model for the prognosis of bladder cancer (BC) based on glycometabolism-associated lncRNAs (glyco-lncRNAs). Pearson correlation analysis was applied to get glyco-lncRNAs, and then, univariate cox regression analysis was employed to further filtrate survival time-associated glyco-lncRNAs. Multivariate cox regression analysis was utilized to construct the prediction model to divide bladder cancer (BC) patients into high- and low-risk groups. The overall survival (OS) rates of these two groups were analyzed using the Kaplan-Meier method. Next, gene set enrichment analysis and Cibersortx were used to explore the enrichment and the difference in immune cell infiltration, respectively. pRRophetic algorithm was applied to explore the relation between chemotherapy sensitivity and the prediction model. Furthermore, reverse transcriptase quantitative polymerase chain reaction was adopted to detect the lncRNAs constituting the prediction signature in tissues and urine exosomal samples of BC patients. A powerful model including 6 glyco-lncRNAs was proposed, capable of suggesting a risk score for each BC patient to predict prognosis. Patients with high-risk scores demonstrated a shorter survival time both in the training cohort and testing cohort, and the risk score could predict the prognosis without depending on the traditional clinical traits. The area under the receiver operating characteristic curve of the risk score was higher than that of other clinical traits (0.755 > 0.640, 0.485, 0.644, or 0.568). The high- and low-risk groups demonstrated very distinct immune cells infiltration conditions and gene set enriched terms. Besides, the high-risk group was more sensitive to cisplatin, docetaxel, and sunitinib. The expression of lncRNA AL354919.2 featured with an increase in low-grade patients and a decrease in T3-4 and Stage III-IV patients. Based on the experiment results, lncRNA AL355353.1, AC011468.1, and AL354919.2 were significantly upregulated in tumor tissues. This research furnishes a novel reference for predicting the prognosis of BC patients, assisting clinicians with help in the choice of treatment.

Keywords: bioinformatics; bladder cancer; glycometabolism; lncRNA; prognosis prediction.

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Conflict of interest statement

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Figures

FIGURE 1
FIGURE 1
The flowchart of this study. TCGA, the cancer genome atlas; BC, bladder cancer; lncRNA, long non-coding RNA; glyco-lncRNAs, glycometabolism associated lncRNAs; GEO, the gene expression omnibus; ROC, receiver operating characteristic; PCA, principal component analysis; GSEA, gene enrichment analysis.
FIGURE 2
FIGURE 2
The selected glyco-lncRNAs related to the prognosis of BC patients. The lncRNAs with red front were involved in construct the prediction model.
FIGURE 3
FIGURE 3
The accuracy of the prognostic model in training and testing cohort. (A) The distribution of risk score in TCGA BLCA cohort. (B) The survival status of high- and low-risk groups in TCGA BLCA cohort. (C) The expression of the 6 glyco-lncRNAs between the high- and low-risk groups in TCGA BLCA cohort. (D) Kaplan-Meier survival curve of the high- and low-risk groups in TCGA BLCA cohort. (E–H) The distribution of risk score, survival status, expression status of the 6 glyco-lncRNAs and Kaplan-Meier survival curve of the high- and low-risk groups in GSE154261 cohort.
FIGURE 4
FIGURE 4
The risk score can predict prognosis independently. (A) Univariate COX analysis and (B) Multivariate COX analysis results of the risk score and common clinical characters. (C) The AUC of risk score and common clinical characters. (D) 1-, 3-, and 5-years ROC curves of the risk score.
FIGURE 5
FIGURE 5
The high- and low-risk groups with different glycometabolism statuses. (A–C) PCA between the high- and low-risk groups on basis of whole gene sets, glycometabolism gene sets and glyco-lncRNAs. (D) PCA between the high- and low-risk groups according to the 6 glyco-lncRNAs.
FIGURE 6
FIGURE 6
The high- and low-risk groups demonstrate different immune cell infiltration status (A) and enriched pathways of KEGG (B).
FIGURE 7
FIGURE 7
Discrepancy of drug sensitivity between high- and low-risk groups. The IC50 of cisplatin (A), docetaxel (B), sunitinib (C), methotrexate (D), pyrimethamine (E), gemcitabine (F) between high- and low-risk groups.
FIGURE 8
FIGURE 8
The association of 6 glyco-lncRNAs with clinical characters. The relation of the 6glyco-lncRNAs with age (A), tumor grade (B), distant metastasis status (C), lymphatic metastasis status (D), UICC stage (E), and T stage (F).
FIGURE 9
FIGURE 9
Tumor tissues had an obvious high-expression of AL355353.1, AC011468.1, and AL354919.2. (A) The expression of the 6 glyco-lncRNAs in BC tissues and paired normal tissues. (B) Measurement of particle size of the urine exosomes. (C) Representative electron microscopy image of the urine exosomes from BC patients. (D) Western blot analysis of the exosomal protein markers CD9, CD63, and CD81. *p < 0.05, **p < 0.01.
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