Metabolomic Fingerprinting for the Detection of Early-Stage Lung Cancer: From the Genome to the Metabolome

Haince, Jean-François; Joubert, Philippe; Bach, Horacio; Bux, Rashid Ahmed; Tappia, Paramjit S.; Ramjiawan, Bram

doi:10.3390/ijms23031215

Cited by 18 publications

(7 citation statements)

References 119 publications

(103 reference statements)

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“…These metabolites have been used to assess the clinical features of ovarian tumours [ 9 ], renal tumours [ 10 ], and pancreatic tumours [ 11 ]. Metabolomic analysis is a low-cost, high-throughput blood-based test that is feasible and affordable for early lung cancer screening in high-risk groups compared to other biomarkers, including molecular targets, in lung cancer patients [ 12 ]. Therefore, for lung cancer, more methods need to be explored to analyse biomarkers with specificity and sensitivity in metabolites.…”

Section: Introductionmentioning

confidence: 99%

Construction of the XGBoost model for early lung cancer prediction based on metabolic indices

Guan

et al. 2023

BMC Med Inform Decis Mak

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Background Lung cancer is a malignant tumour, and early diagnosis has been shown to improve the survival rate of lung cancer patients. In this study, we assessed the use of plasma metabolites as biomarkers for lung cancer diagnosis. In this work, we used a novel interdisciplinary mechanism, applied for the first time to lung cancer, to detect biomarkers for early lung cancer diagnosis by combining metabolomics and machine learning approaches. Results In total, 478 lung cancer patients and 370 subjects with benign lung nodules were enrolled from a hospital in Dalian, Liaoning Province. We selected 47 serum amino acid and carnitine indicators from targeted metabolomics studies using LC‒MS/MS and age and sex demographic indicators of the subjects. After screening by a stepwise regression algorithm, 16 metrics were included. The XGBoost model in the machine learning algorithm showed superior predictive power (AUC = 0.81, accuracy = 75.29%, sensitivity = 74%), with the metabolic biomarkers ornithine and palmitoylcarnitine being potential biomarkers to screen for lung cancer. The machine learning model XGBoost is proposed as an tool for early lung cancer prediction. This study provides strong support for the feasibility of blood-based screening for metabolites and provide a safer, faster and more accurate tool for early diagnosis of lung cancer. Conclusions This study proposes an interdisciplinary approach combining metabolomics with a machine learning model (XGBoost) to predict early the occurrence of lung cancer. The metabolic biomarkers ornithine and palmitoylcarnitine showed significant power for early lung cancer diagnosis.

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Section: Introductionmentioning

confidence: 99%

Construction of the XGBoost model for early lung cancer prediction based on metabolic indices

Guan

et al. 2023

BMC Med Inform Decis Mak

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“…However, its immunogenicity, clearance rate, drug-carrying capacity, and targeting create obstacles and challenges for drug transformation into the lung (S. Li et al, 2021;Srinivasan, Rajabi, & Mousa, 2015). In consequence, it's urgent for better, cheaper, less invasive, and more widespread early lung cancer detection (Haince et al, 2022).…”

Section: Discussionmentioning

confidence: 99%

Circulating circular RNA hsa_circ_0023179 acts as a diagnostic biomarker for non-small-cell lung cancer detection

Zhang

Qin

Peng

et al. 2022

J Cancer Res Clin Oncol

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Background: Lung cancer, the most prevalent cancer-related death worldwide, still lacks the means for early diagnosis. Because of the unique properties of the loop that make it stable in body uids, circular RNAs (circRNAs) as a biomarker becomes a possibility. This research purposed to explore whether hsa_circ_0023179 can be applied as a possible biomarker for the early diagnosis and prognosis of non-small cell lung cancer (NSCLC).Methods: hsa_circ_0023179 was screened by high-throughput sequencing of three pairs of NSCLC tissues and their surrounding tissues. Agarose gel electrophoresis (AGE), Sanger sequencing, exonuclease digestion assay, and actinomycin D were used to a rm the molecular properties of circRNA. Precision determination was performed by placement at room temperature and multiple freezethawing test for methodological evaluation. The expression of hsa_circ_0023179 in tissues, serum, and cells was determined by quantitative real-time polymerase chain reaction (qRT-PCR) to establish the receiver operating characteristic (ROC) curve to assess the diagnostic e cacy of hsa_circ_0023179.Results: hsa_circ_0023179 conforms to the basic properties of circRNA, and the detection method of hsa_circ_0023179 has good stability and repeatability. Its expression was connected to histological type, TNM stage, lymph node metastasis, and distal metastasis in NSCLC tissues, serum, and cells. Compared with traditional tumor markers with higher sensitivity and speci city. A combined diagnosis can signi cantly improve the diagnostic value. The decrease in postoperative expression level suggests some potential for dynamic monitoring.Conclusion: hsa_circ_0023179 might be a promising novel serum marker for the detection and prediction of NSCLC.

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“…Immunovia has developed protein-based pancreatic adenocarcinoma biomarkers which are currently being evaluated in clinical trials. Recent studies used metabolomic biomarkers for differentiating benign and malignant tumors from patients with nonspecific symptoms of early-stage lung cancer [47,48 ▪ ]. Despite an increasing number of cancer metabolomics and proteomics studies, biomarker development is slow and hampered by technical caveats [49].…”

Section: Approaches In Biomarker Discovery and Developmentmentioning

confidence: 99%

Early Detection of Cancers in the Era of Precision Oncology

Tan

Sharma

Das

et al. 2023

Current Opinion in Oncology

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Purpose of reviewThe increasing global incidence of cancer demands innovative cancer detection modalities. The current population-based early cancer detection approaches focus on several major types of cancers (breast, prostate, cervical, lung and colon) at their early stages, however, they generally do not target high-risk individuals at precancerous stages. Recent findingsSome cancers, such as pancreatic cancer, are challenging to detect in their early stages. Therefore, there is a pressing need for improved, accessible, noninvasive, and cost-effective early detection methods. Harnessing cell-free-based biomarker-driven strategies paves a new era of precision diagnosis for multicancer early detection. The majority of these tests are in the early stages and expensive, but these approaches are expected to become cost sensitive in the near future. SummaryThis review provides an overview of early cancer detection strategies, highlighting the methods, challenges, and issues to be addressed to revolutionize and improve global early cancer detection.

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Metabolomic Fingerprinting for the Detection of Early-Stage Lung Cancer: From the Genome to the Metabolome

Cited by 18 publications

References 119 publications

Construction of the XGBoost model for early lung cancer prediction based on metabolic indices

Construction of the XGBoost model for early lung cancer prediction based on metabolic indices

Circulating circular RNA hsa_circ_0023179 acts as a diagnostic biomarker for non-small-cell lung cancer detection

Early Detection of Cancers in the Era of Precision Oncology

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