Identification of biomarkers for the early detection of non-small cell lung cancer: a systematic review and meta-analysis

Biomarkers for the detection of circulating tumor cells

Circulating tumor cells (CTCs) have emerged as a key biomarker in cancer detection and prognosis, and their molecular profiling is gaining importance in precision oncology. Liquid biopsies, which allow the extraction of CTCs, circulating tumor DNA (ctDNA) or cell-free DNA (cfDNA), have measurable advantages over traditional tissue biopsies, especially when molecular material is difficult to obtain. However, this method is not without limitations. Difficulties in differentiating between primary and metastatic lesions, uncertain predictive values and the complexity of the biomarkers used can prove challenging. Recently, high cell heterogeneity has been identified as the main obstacle to achieving high diagnostic accuracy. Because not all cells undergo epithelial-mesenchymal transition (EMT) at the same time, there is a large population of hybrid CTCs that express both epithelial and mesenchymal markers. Since traditional diagnostic tools primarily detect epithelial markers, they are often unable to detect cells with a hybrid phenotype; therefore, additional markers may be required to avoid false negatives. In this review, we summarize recent reports on emerging CTCs markers, with particular emphasis on their use in cancer diagnosis. Most of them, including vimentin, TWIST1, SNAI1, ZEB1, cadherins, CD44, TGM2, PD-L1 and GATA, hold promise for the detection of CTCs, but are also implicated in cancer progression, metastasis, and therapeutic resistance. Therefore, understanding the nature and drivers of epithelial-mesenchymal plasticity (EMP) is critical to advancing our knowledge in this field.

Prioritizing gut microbial SNPs linked to immunotherapy outcomes in NSCLC patients by integrative bioinformatics analysis

BACKGROUND

The human gut microbiome has emerged as a potential modulator of treatment efficacy for different cancers, including non-small cell lung cancer (NSCLC) patients undergoing immune checkpoint inhibitor (ICI) therapy. In this study, we investigated the association of gut microbial variations with response against ICIs by analyzing the gut metagenomes of NSCLC patients.

METHODS

Strain identification from the publicly available metagenomes of 87 NSCLC patients, treated with nivolumab and collected at three different timepoints (T0, T1, and T2), was performed using StrainPhlAn3. Variant calling and annotations were performed using Snippy and associations between microbial genes and genomic variations with treatment responses were evaluated using MaAsLin2. Supervised machine learning models were developed to prioritize single nucleotide polymorphisms (SNPs) predictive of treatment response. Structural bioinformatics approaches were employed using MUpro, I-Mutant 2.0, CASTp and PyMOL to access the functional impact of prioritized SNPs on protein stability and active site interactions.

RESULTS

Our findings revealed the presence of strains for several microbial species (e.g., Lachnospira eligens) exclusively in Responders (R) or Non-responders (NR) (e.g., Parabacteroides distasonis). Variant calling and annotations for the identified strains from R and NR patients highlighted variations in genes (e.g., ftsA, lpdA, and nadB) that were significantly associated with the NR status of patients. Among the developed models, Logistic Regression performed best (accuracy > 90% and AUC ROC > 95%) in prioritizing SNPs in genes that could distinguish R and NR at T0. These SNPs included Ala168Val (lpdA) in Phocaeicola dorei and Tyr233His (lpdA), Leu330Ser (lpdA), and His233Arg (obgE) in Parabacteroides distasonis. Lastly, structural analyses of these prioritized variants in objE and lpdA revealed their involvement in the substrate binding site and an overall reduction in protein stability. This suggests that these variations might likely disrupt substrate interactions and compromise protein stability, thereby impairing normal protein functionality.

CONCLUSION

The integration of metagenomics, machine learning, and structural bioinformatics provides a robust framework for understanding the association between gut microbial variations and treatment response, paving the way for personalized therapies for NSCLC in the future. These findings emphasize the potential clinical implications of microbiome-based biomarkers in guiding patient-specific treatment strategies and improving immunotherapy outcomes.

Biomarkers Suitable for Early Detection of Intrathoracic Cancers in Primary Care: A Systematic Review

Intrathoracic cancers, including lung cancer, mesothelioma, and thymoma, present diagnostic challenges in primary care. Biomarkers could resolve some challenges. We synthesized evidence on biomarker performance for intrathoracic cancer detection in low-prevalence settings. A search in Embase and MEDLINE included studies that recruited participants with suspected intrathoracic cancer and reported on at least one diagnostic measure for a validated, noninvasive biomarker. Studies were excluded if participants were recruited based on a preestablished diagnosis. A total of 52 studies were included, reporting on 108 individual biomarkers and panels. Carcinoembryonic antigen, CYFRA 21-1, and VEGF were evaluated for lung cancer and mesothelioma. For lung cancer, carcinoembryonic antigen and CYFRA 21-1 were the most studied, with AUCs of 0.48 to -0.90 and 0.48 to -0.83, respectively. Pro-gastrin-releasing peptide (Pro-GRP) and neuron-specific enolase (NSE) had the highest negative predictive values (NPV) (98.2% and 96.9%, respectively), whereas Early Cancer Detection Test - Lung (Early CDT) and miRNA signature classifier panels showed NPVs of 99.3% and 99.0%, respectively, in smokers. For mesothelioma, fibrillin-3 and mesothelin plus osteopontin had AUCs of 0.93 and 0.91, respectively. Thymoma panels (binding AcHR + StrAb and binding AcHR + modulating AcHR + StrAb) had 100% NPVs in patients with myasthenia gravis. The review highlights the performance of some biomarkers. However, few were evaluated in low-prevalence settings. Further evaluation is necessary before implementing these biomarkers for intrathoracic cancers in primary care.

Glycosylation in cancer as a source of biomarkers

INTRODUCTION

Glycosylation, the process of glycan synthesis and attachment to target molecules, is a crucial and common post-translational modification (PTM) in mammalian cells. It affects the protein's hydrophilicity, charge, solubility, structure, localization, function, and protection from proteolysis. Aberrant glycosylation in proteins can reveal new detection and therapeutic Glyco-biomarkers, which help to improve accurate early diagnosis and personalized treatment. This review underscores the pivotal role of glycans and glycoproteins as a source of biomarkers in human diseases, particularly cancer.

AREAS COVERED

This review delves into the implications of glycosylation, shedding light on its intricate roles in cancer-related cellular processes influencing biomarkers. It is underpinned by a thorough examination of literature up to June 2024 in PubMed, Scopus, and Google Scholar; concentrating on the terms: (Glycosylation[Title/Abstract]) OR (Glycan[Title/Abstract]) OR (glycoproteomics[Title/Abstract]) OR (Proteoglycans[Title/Abstract]) OR (Glycomarkers[Title/Abstract]) AND (Cancer[Title/Abstract]) AND ((Diagno*[Title/Abstract]) OR (Progno*[Title/Abstract])).

EXPERT OPINION

Glyco-biomarkers enhance early cancer detection, allow early intervention, and improve patient prognoses. However, the abundance and complex dynamic glycan structure may make their scientific and clinical application difficult. This exploration of glycosylation signatures in cancer biomarkers can provide a detailed view of cancer etiology and instill hope in the potential of glycosylation to revolutionize cancer research.

Enhanced Lung Cancer Detection Using a Combined Ratio of Antigen-Autoantibody Immune Complexes against CYFRA 21-1 and p53

The early detection of lung cancer (LC) improves patient outcomes, but current methods have limitations. Autoantibodies against tumor-associated antigens have potential as early biomarkers. This study evaluated the 9G testTM Cancer/Lung, measuring circulating complexes of two antigen-autoantibody immune complexes (AIC) against their respective free antigens (CYFRA 21-1 and p53) for LC diagnosis. We analyzed 100 LC patients and 119 healthy controls using the 9G testTM Cancer/Lung, quantifying the levels of AICs (CYFRA 21-1-Anti-CYFRA 21-1 autoantibody immune complex (CIC) and p53-Anti-p53 autoantibody immune complex (PIC)), free antigens (CYFRA 21-1 and p53), and ratios of AICs/antigens (LC index). The levels of the CICs and PICs were significantly elevated in LC compared to the controls (p < 0.0062 and p < 0.0026), while free antigens showed no significant difference. The CIC/CYFRA 21-1 and PIC/p53 ratios were also significantly higher in LC (all, p < 0.0001). The LC index, when combining both ratios, exhibited the best diagnostic performance with an area under the curve (AUC) of 0.945, exceeding individual CICs, PICs, and free antigens (AUCs ≤ 0.887). At a cut-off of 3.60, the LC index achieved 81% sensitivity and 95% specificity for LC diagnosis. It detected early-stage (Stage I-II) LC with 87.5% sensitivity, exceeding its performance in advanced stages (72.7%). The LC index showed no significant differences based on age, gender, smoking status (former, current, or never smoker), or pack years smoked. The LC index demonstrates promising potential for early LC diagnosis, exceeding conventional free antigen markers.

Microbes for lung cancer detection: feasibility and limitations

As the second most common cancer in the world, the development of lung cancer is closely related to factors such as heredity, environmental exposure, and lung microenvironment, etc. Early screening and diagnosis of lung cancer can be helpful for the treatment of patients. Currently, CT screening and histopathologic biopsy are widely used in the clinical detection of lung cancer, but they have many disadvantages such as false positives and invasive operations. Microbes are another genome of the human body, which has recently been shown to be closely related to chronic inflammatory, metabolic processes in the host. At the same time, they are important players in cancer development, progression, treatment, and prognosis. The use of microbes for cancer therapy has been extensively studied, however, the diagnostic role of microbes is still unclear. This review aims to summarize recent research on using microbes for lung cancer detection and present the current shortcomings of microbes in collection and detection. Finally, it also looks ahead to the clinical benefits that may accrue to patients in the future about screening and early detection.

Blood miRNAs as Potential Diagnostic Biomarkers for Chronic Obstructive Pulmonary Disease: A Meta-Analysis

Purpose

Investigate the efficacy of blood microRNAs (miRNAs) as diagnostic biomarkers for Chronic Obstructive Pulmonary Disease (COPD).

Patients and Methods

We conducted a comprehensive search in English and Chinese databases, selecting studies based on predetermined criteria. Diagnostic parameters like summarized sensitivity (SSEN), summarized specificity (SSPE), summarized positive likelihood ratio (SPLR), summarized negative likelihood ratio (SNLR), and diagnostic odds ratio (DOR), and area under the curve (AUC) of the summary receiver operating characteristic (SROC) curves were analyzed using a bivariate model. Each parameter was accompanied by a 95% confidence interval (CI).

Results

Eighteen high-quality studies were included. For diagnosing COPD with blood miRNAs, the SSEN was 0.83 (95% CI 0.76-0.89), SSPE 0.76 (95% CI 0.70-0.82), SPLR 3.50 (95% CI 2.66-4.60), SNLR 0.22 (95% CI 0.15-0.33), DOR 15.72 (95% CI 8.58-28.77), and AUC 0.86 (95% CI 0.82-0.88). In acute exacerbations, SSEN was 0.85 (95% CI 0.76-0.91), SSPE 0.80 (95% CI 0.73-0.86), SPLR 4.26 (95% CI 3.05-5.95), SNLR 0.19 (95% CI 0.12-0.30), DOR 22.29 (95% CI 11.47-43.33), and AUC 0.89 (95% CI 0.86-0.91).

Conclusion

Blood miRNAs demonstrate significant accuracy in diagnosing COPD, both in general and during acute exacerbations, suggesting their potential as reliable biomarkers.