Evidence map›Paper›PMID 39500988›Full record

ArticleScientific reports2024

Exploration on the construction of a bladder cancer prognostic model based on disulfidptosis-related lncRNAs and its clinical significance.

Xian-Cun Hu, Qi-Ying Yu, Hai-Ping Ding, Feng Xiao, Chun-Yan Gu

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Article in Scientific reports, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

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0cells of the map it votes in
1citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

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3 · Its place in the literature

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1 citing paper in PubMed.

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4 · The record

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5 · Who and what money

Authors and funding

5 authors.

Xian-Cun Hu *Medical School of Nantong University, Nantong, 226007, Jiangsu, China.
Qi-Ying Yu *Cancer Research Center, Nantong Tumor Hospital, Affiliated Tumor Hospital of Nantong University, Nantong, 226361, Jiangsu, China.
Hai-Ping DingMedical School of Nantong University, Nantong, 226007, Jiangsu, China.
Feng XiaoDepartment of Pathology, Nantong Third People's Hospital, Affiliated Nantong Hospital 3 of Nantong University, Nantong, 226006, Jiangsu, China. xfeng_1981@163.com.
Chun-Yan GuDepartment of Pathology, Nantong Third People's Hospital, Affiliated Nantong Hospital 3 of Nantong University, Nantong, 226006, Jiangsu, China. gcy72123@163.com.

Funding

Nantong Health and Wellness Committee MS2022069Nantong Health and Wellness Committee MS2023070Nantong Science and Technology Bureau JC22022025
6 · The paper itself

Abstract

Disulfidptosis is a novel programmed cell death mode that has been reported to play a role in oncogenesis. Increasing evidences suggest that the long non-coding RNAs (lncRNAs) play crucial roles in the initiation and progression of bladder cancer (BLCA). However, the role and prognostic value of disulfidptosis-related lncRNAs in BLCA remain unknown.The aim of this study was to construct and validate a disulfidptosis-related lncRNA risk model for predicting the prognosis of BLCA patients. A risk model consisting of 5 disulfidptosis-related lncRNAs was developed to predict the prognosis of BLCA patients. The overall survival (OS) of BLCA patients in the high-risk group was significantly shorter than that in the low-risk group (P < 0.05). The effectiveness of this model was validated using the receiver operating characteristic (ROC) curve analysis, and this model proved superior in prognostic accuracy compared with other clinical features. Furthermore, the tumor immune dysfunction and exclusion (TIDE) score in the high-risk group was significantly higher than that in the low-risk group, suggesting that the high-risk group had a less favorable response to immunotherapy. Simultaneously, patients in the low-risk group exhibited significantly higher sensitivity to CTLA-4 monoclonal antibody therapy compared to those in the high-risk group, suggesting potential benefits of immunotherapy for patients in the low-risk group. The combination of high risk and low tumor mutational burden (TMB) could further shortened the OS of BLCA patients. Lastly, the drug sensitivity analysis revealed that the BLCA cells in the high-risk group showed an increased sensitivity to cisplatin, sunitinib, cetuximab, axitinib, docetaxel, saracatinib, vinblastine and pazopanib compared with those in the low-risk group. According to the Quantitative real time PCR results, we found that five lncRNAs of the risk model were more highly expressed in BCa cell lines than human immortalized uroepithelial cell line. The disulfidptosis-related lncRNA risk model has a valuable effect in assessing the prognosis of BLCA patients.

Indexed as

RNA, Long NoncodingUrinary Bladder NeoplasmsAgedApoptosisBiomarkers, TumorClinical RelevanceFemaleGene Expression Regulation, NeoplasticHumansMaleMiddle AgedPrognosisROC CurveBiomarkers, TumorRNA, Long NoncodingBladder cancerDisulfidptosislncRNAPrognosisTCGA database

Identifiers

PMID39500988
PMCPMC11538480

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.