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Molecular Research on Lung Cancer: Translational Perspectives
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Molecular Research on Lung Cancer: Translational Perspectives

A special issue of International Journal of Molecular Sciences (ISSN 1422-0067). This special issue belongs to the section "Molecular Oncology".

Deadline for manuscript submissions: 30 November 2024 | Viewed by 4003

Special Issue Editor

Dr. Mariaenrica Tinè

E-Mail Website
Guest Editor
Department of Cardiac, Thoracic, Vascular Sciences and Public Health, University of Padova, 35128 Padova, Italy
Interests: obstructive airway diseases; immunology; microbiota

Special Issue Information

Dear Colleagues,

Lung cancer still represents a major global threat in terms of mortality and morbidity. Indeed, the majority of patients are diagnosed with metastatic lung cancer at a delayed stage, and such a delay severely impacts on the patients' prognosis. Nonetheless, the recent discovery of target molecular therapies and immune response modulators has significantly improved the prognosis of patients with advanced-stage disease. The demand for tissue analyses is constantly increasing in order to fully characterize cancer and customize treatment, despite clinicians’ need to limit the invasiveness of diagnostic strategies, especially in frail patients. Easily available biomarkers are now under evaluation to address the urgency of early lung cancer diagnosis and molecular/immunological stratification. Among these, c DNA, extracellular vesicles, and miRNAs have been investigated and, especially when integrated with clinical and radiological data, might represent promising future candidates. For this Special Issue, we invite researchers to contribute either with original research (both in vivo or in vitro studies) or review articles focusing on novel insights on the pathophysiology, treatment options and screening strategies of lung cancer.

  • Viral vectors in cancer treatment;
  • nanoparticles in lung cancer treatment;
  • extracellular vesicles in lung cancer phenotypization;
  • nucleic acids in lung cancer characterization;
  • liquid biopsy in lung cancer;
  • immune check-points in lung cancer;
  • novel molecular targets in non-small-cell lung cancer;
  • noninvasive diagnostic tools in lung cancer.

Dr. Mariaenrica Tinè
Guest Editor

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. International Journal of Molecular Sciences is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. There is an Article Processing Charge (APC) for publication in this open access journal. For details about the APC please see here. Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • lung cancer
  • oncogene
  • tumor microenvironment

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Published Papers (2 papers)

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11 pages, 1596 KiB  
Article
Discriminating Benign from Malignant Lung Diseases Using Plasma Glycosaminoglycans and Cell-Free DNA
by Alvida Qvick, Sinisa Bratulic, Jessica Carlsson, Bianca Stenmark, Christina Karlsson, Jens Nielsen, Francesco Gatto and Gisela Helenius
Int. J. Mol. Sci. 2024, 25(18), 9777; https://doi.org/10.3390/ijms25189777 - 10 Sep 2024
Viewed by 632
Abstract
We aimed to investigate the use of free glycosaminoglycan profiles (GAGomes) and cfDNA in plasma to differentiate between lung cancer and benign lung disease, in a cohort of 113 patients initially suspected of lung cancer. GAGomes were analyzed in all samples using the [...] Read more.
We aimed to investigate the use of free glycosaminoglycan profiles (GAGomes) and cfDNA in plasma to differentiate between lung cancer and benign lung disease, in a cohort of 113 patients initially suspected of lung cancer. GAGomes were analyzed in all samples using the MIRAM® Free Glycosaminoglycan Kit with ultra-high-performance liquid chromatography and electrospray ionization triple quadrupole mass spectrometry. In a subset of samples, cfDNA concentration and NGS-data was available. We detected two GAGome features, 0S chondroitin sulfate (CS), and 4S CS, with cancer-specific changes. Based on the observed GAGome changes, we devised a model to predict lung cancer. The model, named the GAGome score, could detect lung cancer with 41.2% sensitivity (95% CI: 9.2–54.2%) at 96.4% specificity (95% CI: 95.2–100.0%, n = 113). When we combined the GAGome score with a cfDNA-based model, the sensitivity increased from 42.6% (95% CI: 31.7–60.6%, cfDNA alone) to 70.5% (95% CI: 57.4–81.5%) at 95% specificity (95% CI: 75.1–100%, n = 74). Notably, the combined GAGome and cfDNA testing improved the sensitivity, compared to cfDNA alone, especially in ASCL stage I (55.6% vs 11.1%). Our findings show that plasma GAGome profiles can enhance cfDNA testing performance, highlighting the applicability of a multiomics approach in lung cancer diagnostics. Full article
(This article belongs to the Special Issue Molecular Research on Lung Cancer: Translational Perspectives)
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Review

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10 pages, 1875 KiB  
Review
Systematic Review, Meta-Analysis and Radiomics Quality Score Assessment of CT Radiomics-Based Models Predicting Tumor EGFR Mutation Status in Patients with Non-Small-Cell Lung Cancer
by Mehdi Felfli, Yan Liu, Fadila Zerka, Charles Voyton, Alexandre Thinnes, Sebastien Jacques, Antoine Iannessi and Sylvain Bodard
Int. J. Mol. Sci. 2023, 24(14), 11433; https://doi.org/10.3390/ijms241411433 - 14 Jul 2023
Cited by 10 | Viewed by 2612
Abstract
Assessment of the quality and current performance of computed tomography (CT) radiomics-based models in predicting epidermal growth factor receptor (EGFR) mutation status in patients with non-small-cell lung carcinoma (NSCLC). Two medical literature databases were systematically searched, and articles presenting original studies on CT [...] Read more.
Assessment of the quality and current performance of computed tomography (CT) radiomics-based models in predicting epidermal growth factor receptor (EGFR) mutation status in patients with non-small-cell lung carcinoma (NSCLC). Two medical literature databases were systematically searched, and articles presenting original studies on CT radiomics-based models for predicting EGFR mutation status were retrieved. Forest plots and related statistical tests were performed to summarize the model performance and inter-study heterogeneity. The methodological quality of the selected studies was assessed via the Radiomics Quality Score (RQS). The performance of the models was evaluated using the area under the curve (ROC AUC). The range of the Risk RQS across the selected articles varied from 11 to 24, indicating a notable heterogeneity in the quality and methodology of the included studies. The average score was 15.25, which accounted for 42.34% of the maximum possible score. The pooled Area Under the Curve (AUC) value was 0.801, indicating the accuracy of CT radiomics-based models in predicting the EGFR mutation status. CT radiomics-based models show promising results as non-invasive alternatives for predicting EGFR mutation status in NSCLC patients. However, the quality of the studies using CT radiomics-based models varies widely, and further harmonization and prospective validation are needed before the generalization of these models. Full article
(This article belongs to the Special Issue Molecular Research on Lung Cancer: Translational Perspectives)
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