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Licensed Unlicensed Requires Authentication Published by De Gruyter July 3, 2023

Integrated plasma and exosome long noncoding RNA profiling is promising for diagnosing non-small cell lung cancer

  • Na Wang , Cong Yao , Changliang Luo , Shaoping Liu , Long Wu , Weidong Hu , Qian Zhang , Yuan Rong EMAIL logo , Chunhui Yuan EMAIL logo and Fubing Wang EMAIL logo

Abstract

Objectives

Non-small cell lung cancer (NSCLC) accounts for more than 80 % of all lung cancers, and its 5-year survival rate can be greatly improved by early diagnosis. However, early diagnosis remains elusive because of the lack of effective biomarkers. In this study, we aimed to develop an effective diagnostic model for NSCLC based on a combination of circulating biomarkers.

Methods

Tissue-deregulated long noncoding RNAs (lncRNAs) in NSCLC were identified in datasets retrieved from the Gene Expression Omnibus (GEO, n=727) and The Cancer Genome Atlas (TCGA, n=1,135) databases, and their differential expression was verified in paired local plasma and exosome samples from NSCLC patients. Subsequently, LASSO regression was used to screen for biomarkers in a large clinical population, and a logistic regression model was used to establish a multi-marker diagnostic model. The area under the receiver operating characteristic (ROC) curve (AUC), calibration plots, decision curve analysis (DCA), clinical impact curves, and integrated discrimination improvement (IDI) were used to evaluate the efficiency of the diagnostic model.

Results

Three lncRNAs-PGM5-AS1, SFTA1P, and CTA-384D8.35 were consistently expressed in online tissue datasets, plasma, and exosomes from local patients. LASSO regression identified nine variables (Plasma CTA-384D8.35, Plasma PGM5-AS1, Exosome CTA-384D8.35, Exosome PGM5-AS1, Exosome SFTA1P, Log10CEA, Log10CA125, SCC, and NSE) in clinical samples that were eventually included in the multi-marker diagnostic model. Logistic regression analysis revealed that Plasma CTA-384D8.35, exosome SFTA1P, Log10CEA, Exosome CTA-384D8.35, SCC, and NSE were independent risk factors for NSCLC (p<0.01), and their results were visualized using a nomogram to obtain personalized prediction outcomes. The constructed diagnostic model demonstrated good NSCLC prediction ability in both the training and validation sets (AUC=0.97).

Conclusions

In summary, the constructed circulating lncRNA-based diagnostic model has good NSCLC prediction ability in clinical samples and provides a potential diagnostic tool for NSCLC.


Corresponding authors: Yuan Rong, Department of Laboratory Medicine, Zhongnan Hospital of Wuhan University, Wuhan 430071, P.R. China; and Center for Single-Cell Omics and Tumor Liquid Biopsy, Zhongnan Hospital of Wuhan University, Wuhan 430071, P.R. China, E-mail: ; Chunhui Yuan, Department of Laboratory Medicine, Wuhan Children’s Hospital (Wuhan Maternal and Child Healthcare Hospital), Tongji Medical College, Huazhong University of Science & Technology, Wuhan 430016, P.R. China, E-mail: ; and Fubing Wang, Department of Laboratory Medicine, Zhongnan Hospital of Wuhan University, Wuhan 430071, P.R. China; Center for Single-Cell Omics and Tumor Liquid Biopsy, Zhongnan Hospital of Wuhan University, Wuhan 430071, P.R. China; and Wuhan Research Center for Infectious Diseases and Cancer, Chinese Academy of Medical Sciences, Wuhan 430071, P.R. China, Phone: +86+18971509896, E-mail:
Na Wang, Cong Yao and Changliang Luo contributed equally to this work.

Funding source: Creative Research Groups of Hubei Provincial Natural Science Foundation

Award Identifier / Grant number: No.2022CFA005

Funding source: Zhongnan Hospital of Wuhan University Medical Science and Technology Innovation Platform Construction Support Project

Award Identifier / Grant number: No. PTXM2021019

Funding source: medical Sci-Tech innovation platform of Zhongnan Hospital

Award Identifier / Grant number: No. PTXM2021001

Funding source: Medical Top-talented youth development project of Hubei Province and the Health Commission of Hubei Province scientific research project

Award Identifier / Grant number: No. WJ2021M172

  1. Research funding: This work was supported by the research fund from Creative Research Groups of Hubei Provincial Natural Science Foundation (No.2022CFA005), medical Sci-Tech innovation platform of Zhongnan Hospital (No. PTXM2021001), the Fundamental Research Funds for the Central Universities (No. 2042021kf0227), Medical Top-talented youth development project of Hubei Province and the Health Commission of Hubei Province scientific research project (No. WJ2021M172) and Zhongnan Hospital of Wuhan University Medical Science and Technology Innovation Platform Construction Support Project (No. PTXM2021019).

  2. Author contributions: All authors have accepted responsibility for the entire content of this manuscript and approved its submission.

  3. Competing interests: Authors state no conflict of interest.

  4. Informed consent: Informed consent was obtained from all individuals included in this study.

  5. Ethical approval: Research involving human subjects complied with all relevant national regulations, institutional policies and is in accordance with the tenets of the Helsinki Declaration (as revised in 2013), and has been approved by the Research Ethics Committees of Zhongnan Hospital and Renmin Hospital of Wuhan University (Ethical Approval number: 2021054).

  6. Data availability: The datasets generated and/or analyzed in the current study are available from the corresponding author upon reasonable request.

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Supplementary Material

This article contains supplementary material (https://doi.org/10.1515/cclm-2023-0291).


Received: 2023-03-20
Accepted: 2023-06-20
Published Online: 2023-07-03
Published in Print: 2023-11-27

© 2023 Walter de Gruyter GmbH, Berlin/Boston

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