Startseite Integrated plasma and exosome long noncoding RNA profiling is promising for diagnosing non-small cell lung cancer
Artikel
Lizenziert
Nicht lizenziert Erfordert eine Authentifizierung

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 und Fubing Wang EMAIL logo
Veröffentlicht/Copyright: 3. Juli 2023
Veröffentlichen auch Sie bei De Gruyter Brill

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.

References

1. Ganti, AK, Klein, AB, Cotarla, I, Seal, B, Chou, E. Update of incidence, prevalence, survival, and initial treatment in patients with non-small cell lung cancer in the US. JAMA Oncol 2021;7:1824–32. https://doi.org/10.1001/jamaoncol.2021.4932.Suche in Google Scholar PubMed PubMed Central

2. American Society of Clinical Oncology. Lung cancer – non-small cell – statistics: cancer. Net Editorial Board; 2022. https://www.cancer.net/cancer-types/lung-cancer-non-small-cell/statistics [Accessed 8 Aug 2022].Suche in Google Scholar

3. Duma, N, Santana-Davila, R, Molina, JR. Non-small cell lung cancer: epidemiology, screening, diagnosis, and treatment. Mayo Clin Proc 2019;94:1623–40. https://doi.org/10.1016/j.mayocp.2019.01.013.Suche in Google Scholar PubMed

4. Ma, C, Hu, K, Ullah, I, Zheng, Q-K, Zhang, N, Sun, Z-G. Molecular mechanisms involving the sonic hedgehog pathway in lung cancer therapy: recent advances. Front Oncol 2022;12:729088. https://doi.org/10.3389/fonc.2022.729088.Suche in Google Scholar PubMed PubMed Central

5. Mendoza, TR, Kehl, KL, Bamidele, O, Williams, LA, Shi, Q, Cleeland, CS, et al.. Assessment of baseline symptom burden in treatment-naïve patients with lung cancer: an observational study. Support Care Cancer 2019;27:3439–47. https://doi.org/10.1007/s00520-018-4632-0.Suche in Google Scholar PubMed

6. Gridelli, C, Rossi, A, Carbone, DP, Guarize, J, Karachaliou, N, Mok, T, et al.. Non-small-cell lung cancer. Nat Rev Dis Prim 2015;1:15009. https://doi.org/10.1038/nrdp.2015.9.Suche in Google Scholar PubMed

7. Aberle, DR, Adams, AM, Berg, CD, Black, WC, Clapp, JD, Fagerstrom, RM, et al.. Reduced lung-cancer mortality with low-dose computed tomographic screening. N Engl J Med 2011;365:395–409. https://doi.org/10.1056/nejmoa1102873.Suche in Google Scholar PubMed PubMed Central

8. Humphrey, LL, Deffebach, M, Pappas, M, Baumann, C, Artis, K, Mitchell, JP, et al.. Screening for lung cancer with low-dose computed tomography: a systematic review to update the US Preventive services task force recommendation. Ann Intern Med 2013;159:411–20. https://doi.org/10.7326/0003-4819-159-6-201309170-00690.Suche in Google Scholar PubMed

9. Gerlinger, M, Rowan, AJ, Horswell, S, Math, M, Larkin, J, Endesfelder, D, et al.. Intratumor heterogeneity and branched evolution revealed by multiregion sequencing. N Engl J Med 2012;366:883–92. https://doi.org/10.1056/nejmoa1113205.Suche in Google Scholar PubMed PubMed Central

10. Ten Haaf, K, Jeon, J, Tammemägi, MC, Han, SS, Kong, CY, Plevritis, SK, et al.. Risk prediction models for selection of lung cancer screening candidates: a retrospective validation study. PLoS Med 2017;14:e1002277. https://doi.org/10.1371/journal.pmed.1002277.Suche in Google Scholar PubMed PubMed Central

11. Li, K, Hüsing, A, Sookthai, D, Bergmann, M, Boeing, H, Becker, N, et al.. Selecting high-risk individuals for lung cancer screening: a prospective evaluation of existing risk models and eligibility criteria in the German EPIC cohort. Cancer Prev Res 2015;8:777–85. https://doi.org/10.1158/1940-6207.capr-14-0424.Suche in Google Scholar PubMed

12. Crosbie, PA, Balata, H, Evison, M, Atack, M, Bayliss-Brideaux, V, Colligan, D, et al.. Second round results from the Manchester ‘Lung Health Check’ community-based targeted lung cancer screening pilot. Thorax 2019;74:700–4. https://doi.org/10.1136/thoraxjnl-2018-212547.Suche in Google Scholar PubMed PubMed Central

13. Weber, M, Yap, S, Goldsbury, D, Manners, D, Tammemagi, M, Marshall, H, et al.. Identifying high risk individuals for targeted lung cancer screening: independent validation of the PLCO(m2012) risk prediction tool. Int J Cancer 2017;141:242–53. https://doi.org/10.1002/ijc.30673.Suche in Google Scholar PubMed

14. Ostrowski, M, Marjanski, T, Dziedzic, R, Kocot, S, Rzyman, W. P1.11-08 are risk prediction models superior over standard criteria for lung cancer screening in europe? Macroscale simulation on large polish cohort. J Thorac Oncol 2019;14:S518. https://doi.org/10.1016/j.jtho.2019.08.1081.Suche in Google Scholar

15. Tammemägi, MC, Katki, HA, Hocking, WG, Church, TR, Caporaso, N, Kvale, PA, et al.. Selection criteria for lung-cancer screening. N Engl J Med 2013;368:728–36. https://doi.org/10.1056/nejmoa1211776.Suche in Google Scholar PubMed PubMed Central

16. Muller, DC, Johansson, M, Brennan, P. Lung cancer risk prediction model incorporating lung function: development and validation in the UK biobank prospective cohort study. J Clin Oncol 2017;35:861–9. https://doi.org/10.1200/jco.2016.69.2467.Suche in Google Scholar PubMed

17. Sin, DD, Tammemagi, CM, Lam, S, Barnett, MJ, Duan, X, Tam, A, et al.. Pro-surfactant protein B as a biomarker for lung cancer prediction. J Clin Oncol 2013;31:4536–43. https://doi.org/10.1200/jco.2013.50.6105.Suche in Google Scholar

18. Shiels, MS, Pfeiffer, RM, Hildesheim, A, Engels, EA, Kemp, TJ, Park, JH, et al.. Circulating inflammation markers and prospective risk for lung cancer. J Natl Cancer Inst 2013;105:1871–80. https://doi.org/10.1093/jnci/djt309.Suche in Google Scholar PubMed PubMed Central

19. Cecchini, MJ, Yi, ES. Liquid biopsy is a valuable tool in the diagnosis and management of lung cancer. J Thorac Dis 2020;12:7048–56. https://doi.org/10.21037/jtd.2020.04.20.Suche in Google Scholar PubMed PubMed Central

20. Han, J, LaVigne, CA, Jones, BT, Zhang, H, Gillett, F, Mendell, JT. A ubiquitin ligase mediates target-directed microRNA decay independently of tailing and trimming. Science 2020;370. https://doi.org/10.1126/science.abc9546.Suche in Google Scholar PubMed PubMed Central

21. Bridges, MC, Daulagala, AC, Kourtidis, A. LNCcation: lncRNA localization and function. J Cell Biol 2021;220. https://doi.org/10.1083/jcb.202009045.Suche in Google Scholar PubMed PubMed Central

22. Zhou, M, Guo, M, He, D, Wang, X, Cui, Y, Yang, H, et al.. A potential signature of eight long non-coding RNAs predicts survival in patients with non-small cell lung cancer. J Transl Med 2015;13:231. https://doi.org/10.1186/s12967-015-0556-3.Suche in Google Scholar PubMed PubMed Central

23. Schmidt, LH, Spieker, T, Koschmieder, S, Schäffers, S, Humberg, J, Jungen, D, et al.. The long noncoding MALAT-1 RNA indicates a poor prognosis in non-small cell lung cancer and induces migration and tumor growth. J Thorac Oncol 2011;6:1984–92. https://doi.org/10.1097/jto.0b013e3182307eac.Suche in Google Scholar PubMed

24. Li, C, Lv, Y, Shao, C, Chen, C, Zhang, T, Wei, Y, et al.. Tumor-derived exosomal lncRNA GAS5 as a biomarker for early-stage non-small-cell lung cancer diagnosis. J Cell Physiol 2019;234:20721–7. https://doi.org/10.1002/jcp.28678.Suche in Google Scholar PubMed

25. Tao, Y, Tang, Y, Yang, Z, Wu, F, Wang, L, Yang, L, et al.. Exploration of serum exosomal LncRNA TBILA and AGAP2-AS1 as promising biomarkers for diagnosis of non-small cell lung cancer. Int J Biol Sci 2020;16:471–82. https://doi.org/10.7150/ijbs.39123.Suche in Google Scholar PubMed PubMed Central

26. Chen, Q, Zhu, C, Jin, Y, Si, X, Jiao, W, He, W, et al.. Plasma long non-coding RNA RP11-438N5.3 as a novel biomarker for non-small cell lung cancer. Cancer Manag Res 2020;12:1513–21. https://doi.org/10.2147/cmar.s237024.Suche in Google Scholar

27. Lin, Y, Leng, Q, Zhan, M, Jiang, F. A plasma long noncoding RNA signature for early detection of lung cancer. Transl Oncol 2018;11:1225–31. https://doi.org/10.1016/j.tranon.2018.07.016.Suche in Google Scholar PubMed PubMed Central

28. Huang, L, Rong, Y, Tang, X, Yi, K, Qi, P, Hou, J, et al.. Engineered exosomes as an in situ DC-primed vaccine to boost antitumor immunity in breast cancer. Mol Cancer 2022;21:45. https://doi.org/10.1186/s12943-022-01515-x.Suche in Google Scholar PubMed PubMed Central

29. Ren, S, Wang, F, Shen, J, Sun, Y, Xu, W, Lu, J, et al.. Long non-coding RNA metastasis associated in lung adenocarcinoma transcript 1 derived miniRNA as a novel plasma-based biomarker for diagnosing prostate cancer. Eur J Cancer 2013;49:2949–59. https://doi.org/10.1016/j.ejca.2013.04.026.Suche in Google Scholar PubMed

30. Chakraborty, S, Andrieux, G, Hasan, AMM, Ahmed, M, Hosen, MI, Rahman, T, et al.. Harnessing the tissue and plasma lncRNA-peptidome to discover peptide-based cancer biomarkers. Sci Rep 2019;9:12322. https://doi.org/10.1038/s41598-019-48774-1.Suche in Google Scholar PubMed PubMed Central

31. Riffo-Campos, AL, Perez-Hernandez, J, Martinez-Arroyo, O, Ortega, A, Flores-Chova, A, Redon, J, et al.. Biofluid specificity of long non-coding RNA profile in hypertension: relevance of exosomal fraction. Int J Mol Sci 2022;23:5199. https://doi.org/10.3390/ijms23095199.Suche in Google Scholar PubMed PubMed Central

32. Wang, J, Cao, B, Gao, Y, Chen, Y-H, Feng, J. Exosome-transported lncRNA H19 regulates insulin-like growth factor-1 via the H19/let-7a/insulin-like growth factor-1 receptor axis in ischemic stroke. Neural Regener Res 2023;18. https://doi.org/10.4103/1673-5374.357901.Suche in Google Scholar PubMed PubMed Central

33. Sun, Z, Yang, S, Zhou, Q, Wang, G, Song, J, Li, Z, et al.. Emerging role of exosome-derived long non-coding RNAs in tumor microenvironment. Mol Cancer 2018;17:82. https://doi.org/10.1186/s12943-018-0831-z.Suche in Google Scholar PubMed PubMed Central

34. Luo, A, Lan, X, Qiu, Q, Zhou, Q, Li, J, Wu, M, et al.. LncRNA SFTA1P promotes cervical cancer progression by interaction with PTBP1 to facilitate TPM4 mRNA degradation. Cell Death Dis 2022;13:936. https://doi.org/10.1038/s41419-022-05359-7.Suche in Google Scholar PubMed PubMed Central

35. Wang, M, Zhang, Z, Pan, D, Xin, Z, Bu, F, Zhang, Y, et al.. Circulating lncRNA UCA1 and lncRNA PGM5-AS1 act as potential diagnostic biomarkers for early-stage colorectal cancer. Biosci Rep 2021;41. https://doi.org/10.1042/bsr20211115.Suche in Google Scholar PubMed PubMed Central

36. Groeper, C, Gambazzi, F, Zajac, P, Bubendorf, L, Adamina, M, Rosenthal, R, et al.. Cancer/testis antigen expression and specific cytotoxic T lymphocyte responses in non small cell lung cancer. Int J Cancer 2007;120:337–43. https://doi.org/10.1002/ijc.22309.Suche in Google Scholar PubMed

37. Chen, Y, Zitello, E, Guo, R, Deng, Y. The function of LncRNAs and their role in the prediction, diagnosis, and prognosis of lung cancer. Clin Transl Med 2021;11:e367. https://doi.org/10.1002/ctm2.367.Suche in Google Scholar PubMed PubMed Central

38. Weber, DG, Johnen, G, Casjens, S, Bryk, O, Pesch, B, Jöckel, KH, et al.. Evaluation of long noncoding RNA MALAT1 as a candidate blood-based biomarker for the diagnosis of non-small cell lung cancer. BMC Res Notes 2013;6:518. https://doi.org/10.1186/1756-0500-6-518.Suche in Google Scholar PubMed PubMed Central

39. Zhao, T, Khadka, VS, Deng, Y. Identification of lncRNA biomarkers for lung cancer through integrative cross-platform data analyses. Aging 2020;12:14506–27. https://doi.org/10.18632/aging.103496.Suche in Google Scholar PubMed PubMed Central

40. Guida, F, Sun, N, Bantis, LE, Muller, DC, Li, P, Taguchi, A, et al.. Assessment of lung cancer risk on the basis of a biomarker panel of circulating proteins. JAMA Oncol 2018;4:e182078. https://doi.org/10.1001/jamaoncol.2018.2078.Suche in Google Scholar PubMed PubMed Central

41. Badowski, C, He, B, Garmire, LX. Blood-derived lncRNAs as biomarkers for cancer diagnosis: the Good, the Bad and the Beauty. NPJ Precis Oncol 2022;6:40. https://doi.org/10.1038/s41698-022-00283-7.Suche in Google Scholar PubMed PubMed Central

42. Li, MX, Wang, HY, Yuan, CH, Ma, ZL, Jiang, B, Li, L, et al.. KLHDC7B-DT aggravates pancreatic ductal adenocarcinoma development via inducing cross-talk between cancer cells and macrophages. Clin Sci 2021;135:629–49. https://doi.org/10.1042/cs20201259.Suche in Google Scholar

43. Ge, X, Peng, X, Li, M, Ji, F, Chen, J, Zhang, D. OGT regulated O-GlcNacylation promotes migration and invasion by activating IL-6/STAT3 signaling in NSCLC cells. Pathol Res Pract 2021;225:153580. https://doi.org/10.1016/j.prp.2021.153580.Suche in Google Scholar PubMed

44. Du, D, Shen, X, Zhang, Y, Yin, L, Pu, Y, Liang, G. Expression of long non-coding RNA SFTA1P and its function in non-small cell lung cancer. Pathol Res Pract 2020;216:153049. https://doi.org/10.1016/j.prp.2020.153049.Suche in Google Scholar PubMed

45. Zhu, B, Finch-Edmondson, M, Leong, KW, Zhang, X, Mitheera, V, Lin, QXX, et al.. LncRNA SFTA1P mediates positive feedback regulation of the Hippo-YAP/TAZ signaling pathway in non-small cell lung cancer. Cell Death Dis 2021;7:369. https://doi.org/10.1038/s41420-021-00761-0.Suche in Google Scholar PubMed PubMed Central

46. Liu, W, Liu, P, Gao, H, Wang, X, Yan, M. Long non-coding RNA PGM5-AS1 promotes epithelial-mesenchymal transition, invasion and metastasis of osteosarcoma cells by impairing miR-140-5p-mediated FBN1 inhibition. Mol Oncol 2020;14:2660–77. https://doi.org/10.1002/1878-0261.12711.Suche in Google Scholar PubMed PubMed Central

47. Zhuo, E, Cai, C, Liu, W, Li, K, Zhao, W. Downregulated microRNA-140-5p expression regulates apoptosis, migration and invasion of lung cancer cells by targeting zinc finger protein 800. Oncol Lett 2020;20:390. https://doi.org/10.3892/ol.2020.12253.Suche in Google Scholar PubMed PubMed Central

48. Du, L, Gao, Y. PGM5-AS1 impairs miR-587-mediated GDF10 inhibition and abrogates progression of prostate cancer. J Transl Med 2021;19:12. https://doi.org/10.1186/s12967-020-02572-w.Suche in Google Scholar PubMed PubMed Central


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

Artikel in diesem Heft

  1. Frontmatter
  2. Editorial
  3. Adherence to recommendations and clinical practice guidelines: not an easy task
  4. Reviews
  5. Comparison of interferon-gamma production between TB1 and TB2 tubes of QuantiFERON-TB Gold Plus: a meta-analysis
  6. Review on adherence of the literature to official recommendations on albuminuria harmonization and standardization
  7. Opinion Paper
  8. The total testing process harmonization: the case study of SARS-CoV-2 serological tests
  9. IFCC Paper
  10. Quality standards and internal quality control practices in medical laboratories: an IFCC global survey of member societies
  11. Guidelines and Recommendations
  12. Toolkit for emerging technologies in laboratory medicine
  13. Recommendations for the study of monoclonal gammopathies in the clinical laboratory. A consensus of the Spanish Society of Laboratory Medicine and the Spanish Society of Hematology and Hemotherapy. Part I: Update on laboratory tests for the study of monoclonal gammopathies
  14. Recommendations for the study of monoclonal gammopathies in the clinical laboratory. A consensus of the Spanish Society of Laboratory Medicine and the Spanish Society of Hematology and Hemotherapy. Part II: Methodological and clinical recommendations for the diagnosis and follow-up of monoclonal gammopathies
  15. Genetics and Molecular Diagnostics
  16. The MBL2 genotype relates to COVID-19 severity and may help to select the optimal therapy
  17. General Clinical Chemistry and Laboratory Medicine
  18. The stability of C-peptide and insulin in plasma and serum samples under different storage conditions
  19. Self-sampling of blood using a topper and pediatric tubes; a prospective feasibility study for PSA analysis using 120 prostate cancer patients
  20. Albumin determined by bromocresol green leads to erroneous results in routine evaluation of patients with chronic kidney disease
  21. Uncertainty in measurement and the renal tubular reabsorption of phosphate
  22. Urine transfer devices may impact urinary particle results: a pre-analytical study
  23. Analytical and clinical validation of a blood progranulin ELISA in frontotemporal dementias
  24. Early changes in S100B maternal blood levels can predict fetal intrauterine growth restriction
  25. Reference Values and Biological Variations
  26. Interpreting two TSH results from the same patient
  27. Cancer Diagnostics
  28. Integrated plasma and exosome long noncoding RNA profiling is promising for diagnosing non-small cell lung cancer
  29. Diagnostic accuracy of extended HPV DNA genotyping and its application for risk-based cervical cancer screening strategy
  30. Infectious Diseases
  31. Performance evaluation of SARS-CoV-2 antigen detection in the post-pandemic era: multi-laboratory assessment
  32. Serum sPD-L1 levels are elevated in patients with viral diseases, bacterial sepsis or in patients with impaired renal function compared to healthy blood donors
  33. Letters to the Editor
  34. Results of the first survey of the EFLM Task Force Preparation of Labs for Emergencies (TF-PLE)
  35. Thrombocytopenia and hyperinflammation are induced by extracellular histones circulating in blood
  36. Bilirubin color interference on prothrombin time and activated partial thromboplastin time tests assessed in patients with liver disease
  37. Laboratory response to paradigm change in hemophilia treatment
  38. MALDI-ISD mass spectrometry analysis as a simple and reliable tool to detect post-translational modifications of hemoglobin variants: the case of Hb Raleigh
  39. Method validation for a greener approach to the quantification of 25-hydroxy vitamin D3 in patient serum using supported liquid extraction and liquid chromatography-tandem mass spectrometry
  40. Interest of minigene splicing reporter assay in familial hypobetalipoproteinemia genetic diagnosis: the example of the missense mutation APOB c.1468C>T
  41. Laboratory medicine unveiling an unusual cause of D-lactic acidosis as the trigger of decompensation of a rare inborn error of metabolism
Heruntergeladen am 17.9.2025 von https://www.degruyterbrill.com/document/doi/10.1515/cclm-2023-0291/html
Button zum nach oben scrollen