Integrated plasma and exosome long noncoding RNA profiling is promising for diagnosing non-small cell lung cancer
© 2023 Walter de Gruyter GmbH, Berlin/Boston..
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.
| Medienart: |
E-Artikel |
|---|
| Erscheinungsjahr: |
2023 |
|---|---|
| Erschienen: |
2023 |
| Enthalten in: |
Zur Gesamtaufnahme - volume:61 |
|---|---|
| Enthalten in: |
Clinical chemistry and laboratory medicine - 61(2023), 12 vom: 27. Nov., Seite 2216-2228 |
| Sprache: |
Englisch |
|---|
| Beteiligte Personen: |
Wang, Na [VerfasserIn] |
|---|
| Links: |
|---|
| Themen: |
Biomarkers, Tumor |
|---|
| Anmerkungen: |
Date Completed 26.10.2023 Date Revised 12.11.2023 published: Electronic-Print Citation Status MEDLINE |
|---|
| doi: |
10.1515/cclm-2023-0291 |
|---|
| funding: |
|
|---|---|
| Förderinstitution / Projekttitel: |
|
| PPN (Katalog-ID): |
NLM358888905 |
|---|
| LEADER | 01000naa a22002652 4500 | ||
|---|---|---|---|
| 001 | NLM358888905 | ||
| 003 | DE-627 | ||
| 005 | 20231226075713.0 | ||
| 007 | cr uuu---uuuuu | ||
| 008 | 231226s2023 xx |||||o 00| ||eng c | ||
| 024 | 7 | |a 10.1515/cclm-2023-0291 |2 doi | |
| 028 | 5 | 2 | |a pubmed24n1196.xml |
| 035 | |a (DE-627)NLM358888905 | ||
| 035 | |a (NLM)37387637 | ||
| 040 | |a DE-627 |b ger |c DE-627 |e rakwb | ||
| 041 | |a eng | ||
| 100 | 1 | |a Wang, Na |e verfasserin |4 aut | |
| 245 | 1 | 0 | |a Integrated plasma and exosome long noncoding RNA profiling is promising for diagnosing non-small cell lung cancer |
| 264 | 1 | |c 2023 | |
| 336 | |a Text |b txt |2 rdacontent | ||
| 337 | |a ƒaComputermedien |b c |2 rdamedia | ||
| 338 | |a ƒa Online-Ressource |b cr |2 rdacarrier | ||
| 500 | |a Date Completed 26.10.2023 | ||
| 500 | |a Date Revised 12.11.2023 | ||
| 500 | |a published: Electronic-Print | ||
| 500 | |a Citation Status MEDLINE | ||
| 520 | |a © 2023 Walter de Gruyter GmbH, Berlin/Boston. | ||
| 520 | |a 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 | ||
| 520 | |a 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 | ||
| 520 | |a 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) | ||
| 520 | |a 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 | ||
| 650 | 4 | |a Journal Article | |
| 650 | 4 | |a NSCLC | |
| 650 | 4 | |a clinical data | |
| 650 | 4 | |a diagnosis | |
| 650 | 4 | |a exosome lncRNAs | |
| 650 | 4 | |a plasma lncRNAs | |
| 650 | 7 | |a RNA, Long Noncoding |2 NLM | |
| 650 | 7 | |a Biomarkers, Tumor |2 NLM | |
| 700 | 1 | |a Yao, Cong |e verfasserin |4 aut | |
| 700 | 1 | |a Luo, Changliang |e verfasserin |4 aut | |
| 700 | 1 | |a Liu, Shaoping |e verfasserin |4 aut | |
| 700 | 1 | |a Wu, Long |e verfasserin |4 aut | |
| 700 | 1 | |a Hu, Weidong |e verfasserin |4 aut | |
| 700 | 1 | |a Zhang, Qian |e verfasserin |4 aut | |
| 700 | 1 | |a Rong, Yuan |e verfasserin |4 aut | |
| 700 | 1 | |a Yuan, Chunhui |e verfasserin |4 aut | |
| 700 | 1 | |a Wang, Fubing |e verfasserin |4 aut | |
| 773 | 0 | 8 | |i Enthalten in |t Clinical chemistry and laboratory medicine |d 1998 |g 61(2023), 12 vom: 27. Nov., Seite 2216-2228 |w (DE-627)NLM095304118 |x 1437-4331 |7 nnns |
| 773 | 1 | 8 | |g volume:61 |g year:2023 |g number:12 |g day:27 |g month:11 |g pages:2216-2228 |
| 856 | 4 | 0 | |u http://dx.doi.org/10.1515/cclm-2023-0291 |3 Volltext |
| 912 | |a GBV_USEFLAG_A | ||
| 912 | |a GBV_NLM | ||
| 951 | |a AR | ||
| 952 | |d 61 |j 2023 |e 12 |b 27 |c 11 |h 2216-2228 | ||