Preoperative systemic inflammatory markers in low- and high-grade gliomas: A retrospective analysis of 171 patients

Evaluation of the preoperative neutrophil-to-lymphocyte ratio as a predictor of the micropapillary component of stage IA lung adenocarcinoma

OBJECTIVE

To assess the ability of markers of inflammation to identify the solid or micropapillary components of stage IA lung adenocarcinoma and their effects on prognosis.

METHODS

We performed a retrospective study of clinicopathologic data from 654 patients with stage IA lung adenocarcinoma collected between 2013 and 2019. Logistic regression analysis was used to identify independent predictors of these components, and we also evaluated the relationship between markers of inflammation and recurrence.

RESULTS

Micropapillary-positive participants had high preoperative neutrophil-to-lymphocyte ratios. There were no significant differences in the levels of markers of systemic inflammation between the participants with or without a solid component. Multivariate analysis showed that preoperative neutrophil-to-lymphocyte ratio (odds ratio [OR] = 2.094; 95% confidence interval [CI], 1.668-2.628), tumor size (OR = 1.386; 95% CI, 1.044-1.842), and carcinoembryonic antigen concentration (OR = 1.067; 95% CI, 1.017-1.119) were independent predictors of a micropapillary component. There were no significant correlations between markers of systemic inflammation and the recurrence of stage IA lung adenocarcinoma.

CONCLUSIONS

Preoperative neutrophil-to-lymphocyte ratio independently predicts a micropapillary component of stage IA lung adenocarcinoma. Therefore, the potential use of preoperative neutrophil-to-lymphocyte ratio in the optimization of surgical strategies for the treatment of stage IA lung adenocarcinoma should be further studied.

The role of preoperative hematological inflammatory markers as a predictor of meningioma grade: A systematic review and meta-analysis

Background

Inflammatory processes play an important role in the aggressiveness of a tumor. However, the relationship between inflammatory markers in meningioma grade is not well known. Knowledge of preoperative meningioma grade plays an important role in the prognosis and treatment of this tumor. This study aims to assess preoperative hematological inflammatory markers as a predictor of the pathological grade of meningioma.

Methods

To ensure comprehensive retrieval of relevant studies, we searched the following key databases, PubMed, Science Direct, and Biomed Central, with evidence related to preoperative hematological inflammatory markers among meningioma up to September 2023. The studies involved were selected based on established eligibility criteria. The analysis in this study uses Review Manager 5.4.

Results

Six studies were obtained from the search results. The total number of patients 2789 (469 high-grade meningioma and 2320 low-grade meningioma) analysis shows elevated neutrophil-to-lymphocyte ratio (NLR) (mean difference [MD]: 0.29; 95% confidence interval [CI] 0.13-0.45; P = 0.0004), monocyte-to-lymphocyte ratio (MLR) (MD: 0.02; 95% CI 0.00-0.04; P = 0.003), and low lymphocyte-to-monocyte ratio (LMR) (MD: -0.82; 95% CI -1.46--0.18; P = 0.005) significantly associated with high-grade meningioma compared to low-grade meningioma. No significant correlation between high-grade and low-grade meningioma based on platelet-lymphocyte ratio value is observed.

Conclusion

The parameters of NLR, MLR, and LMR have been found to be cost-effective preoperative methods that demonstrate potential value in the prediction of meningioma grade. To enhance the reliability of the findings, it is imperative to do further prospective study.

Preoperative Immune-Inflammatory Status of the Patients With Newly-Diagnosed Glioblastoma - Could It Genuinely Predict Their Survival?

BACKGROUND

Glioblastoma multiforme (GBM) is the most aggressive brain tumor affecting adult patients, with an extremely reduced overall survival despite rapid diagnosis and treatment. Therefore, it is crucial to establish accurate and affordable markers that allow an individualized approach to GBM patients. Serum biomarkers could be the most accessible, as complete blood counts should be performed on all GBM patients before undergoing any surgical and/or pharmacological treatment. However, their prognostic role is still unclear. Our study aims to assess the influence of various hematological markers of inflammation in predicting the outcome of GBM patients.

MATERIAL AND METHODS

We retrospectively analyzed all adult patients diagnosed with primary glioblastoma in the Neurosurgery Department of the Emergency Clinical County Hospital of Târgu Mureș, Romania, from January 2017 until December 2019. We aimed to discover whether the immune/inflammatory status of the patients before receiving any kind of pharmacological or surgical treatment influenced their overall survival.

RESULTS

Our study showed that pre-therapeutic elevated white blood count could predict reduced overall survival in not otherwise specified subtype (NOS) of GBMs (HR 0.4153, 95% CI 0.1825-0.9449, p 0.0362). Furthermore, patients with increased systemic immune response index (SIRI) had much larger tumors at the time of diagnosis (p 0.0359). In wild type, isocitrate dehydrogenase subpopulation (IDHwt), the higher values of neutrophil-to-lymphocyte ratio (NLR, p 0.0412), platelet-to-lymphocyte ratio (PLR, p 0.0376) and monocyte-to-lymphocyte ratio (MLR, p 0.0412) were related to more advanced age at the moment of diagnosis. Moreover, our results revealed a weakly positive association between tumor size and NLR values in the NOS group (Spearman r 0.3212, p 0.0493).

CONCLUSIONS

Our study does not provide enough evidence for the immune/inflammatory status of GBM patients to be used as an efficient prognostic marker to guide the therapeutic approach.

The Role of Preoperative Neutrophil, Platelet, and Monocyte to Lymphocyte Ratios as Independent Prognostic Factors for Patient Survival in WHO 2021 Glioblastoma: A Single-Center Retrospective Study

Introductions

Immuno-oncology is a rapidly developing field wherein tumor-immune system interactions can be harnessed for diagnostics. Herein, we set out to establish the role of the immune system response, as measured by preoperative neutrophil, platelet, and monocyte to lymphocyte ratios (NLR, PLR, and MLR) as prognostic markers for patient survival based on the newly defined criteria for glioblastoma (GBM).

Materials and methods

The study included patients diagnosed with GBM at a four-year interval. Exclusion criteria were patients subject to reoperation in the time period; tumors in more than one system; a history of hematological and autoimmune diseases; and cases with infectious or other inflammatory conditions. Data regarding patient demographics and preoperative blood counts were pulled from patient records and compared to postoperative survival.

Results

A total of 22 patients fit the established criteria, with a male to female ratio of 2.14:1, a mean age of 66.23 years, and a mean survival of 255.72 days (8.04 months, range 24-801 days). Eight patients had an elevation of NLR and five of PLR, with no statistical correlation to survival. Six patients had an increase in MLR with a statistically significant (p=0.0044) shorter postoperative survival. Synergic increases in NLR and PLR did not show significance, while synergic increases with MLR showed no added benefit.

Conclusion

Preoperative MLR, but not NLR or PLR, is a promising independent biomarker for patient survival in GBM. It is suggested that elevations in these ratios directly correlate to tumor biological potential.

Ensemble based machine learning approach for prediction of glioma and multi-grade classification

Glioma is the most pernicious cancer of the nervous system, with histological grade influencing the survival of patients. Despite many studies on the multimodal treatment approach, survival time remains brief. In this study, a novel two-stage ensemble of an ensemble-type machine learning-based predictive framework for glioma detection and its histograde classification is proposed. In the proposed framework, five characteristics belonging to 135 subjects were considered: human telomerase reverse transcriptase (hTERT), chitinase-like protein (YKL-40), interleukin 6 (IL-6), tissue inhibitor of metalloproteinase-1 (TIMP-1) and neutrophil/lymphocyte ratio (NLR). These characteristics were examined using distinctive ensemble-based machine learning classifiers and combination strategies to develop a computer-aided diagnostic system for the non-invasive prediction of glioma cases and their grade. In the first stage, the analysis was conducted to classify glioma cases and control subjects. Machine learning approaches were applied in the second stage to classify the recognised glioma cases into three grades, from grade II, which has a good prognosis, to grade IV, which is also known as glioblastoma. All experiments were evaluated with a five-fold cross-validation method, and the classification results were analysed using different statistical parameters. The proposed approach obtained a high value of accuracy and other statistical parameters compared with other state-of-the-art machine learning classifiers. Therefore, the proposed framework can be utilised for designing other intervention strategies for the prediction of glioma cases and their grades.

Blood-Based Biomarkers for Glioma in the Context of Gliomagenesis: A Systematic Review

BACKGROUND

Gliomas are the most common and aggressive tumors of the central nervous system. A robust and widely used blood-based biomarker for glioma has not yet been identified. In recent years, a plethora of new research on blood-based biomarkers for glial tumors has been published. In this review, we question which molecules, including proteins, nucleic acids, circulating cells, and metabolomics, are most promising blood-based biomarkers for glioma diagnosis, prognosis, monitoring and other purposes, and align them to the seminal processes of cancer.

METHODS

The Pubmed and Embase databases were systematically searched. Biomarkers were categorized in the identified biomolecules and biosources. Biomarker characteristics were assessed using the area under the curve (AUC), accuracy, sensitivity and/or specificity values and the degree of statistical significance among the assessed clinical groups was reported.

RESULTS

7,919 references were identified: 3,596 in PubMed and 4,323 in Embase. Following screening of titles, abstracts and availability of full-text, 262 articles were included in the final systematic review. Panels of multiple biomarkers together consistently reached AUCs >0.8 and accuracies >80% for various purposes but especially for diagnostics. The accuracy of single biomarkers, consisting of only one measurement, was far more variable, but single microRNAs and proteins are generally more promising as compared to other biomarker types.

CONCLUSION

Panels of microRNAs and proteins are most promising biomarkers, while single biomarkers such as GFAP, IL-10 and individual miRNAs also hold promise. It is possible that panels are more accurate once these are involved in different, complementary cancer-related molecular pathways, because not all pathways may be dysregulated in cancer patients. As biomarkers seem to be increasingly dysregulated in patients with short survival, higher tumor grades and more pathological tumor types, it can be hypothesized that more pathways are dysregulated as the degree of malignancy of the glial tumor increases. Despite, none of the biomarkers found in the literature search seem to be currently ready for clinical implementation, and most of the studies report only preliminary application of the identified biomarkers. Hence, large-scale validation of currently identified and potential novel biomarkers to show clinical utility is warranted.

ELAVL1 Role in Cell Fusion and Tunneling Membrane Nanotube Formations with Implication to Treat Glioma Heterogeneity

Homotypic and heterotypic cell fusions via permanent membrane fusions and temporal tunneling nanotube formations in the glioma microenvironment were recently documented in vitro and in vivo and mediate glioma survival, plasticity, and recurrence. Chronic inflammation, a hypoxic environment, aberrant mitochondrial function, and ER stress due to unfolded protein accumulation upregulate cell fusion events, which leads to tumor heterogeneity and represents an adaptive mechanism to promote tumor cell survival and plasticity in cytotoxic, nutrient-deprived, mechanically stressed, and inflammatory microenvironments. Cell fusion is a multistep process, which consists of the activation of the cellular stress response, autophagy formation, rearrangement of cytoskeletal architecture in the areas of cell-to-cell contacts, and the expression of proinflammatory cytokines and fusogenic proteins. The mRNA-binding protein of ELAV-family HuR is a critical node, which orchestrates the stress response, autophagy formation, cytoskeletal architecture, and the expression of proinflammatory cytokines and fusogenic proteins. HuR is overexpressed in gliomas and is associated with poor prognosis and treatment resistance. Our review provides a link between the HuR role in the regulation of cell fusion and tunneling nanotube formations in the glioma microenvironment and the potential suppression of these processes by different classes of HuR inhibitors.