Clinical significance of pre-surgical serum lipid levels in patients with glioblastoma

Blood lipid metabolic biomarkers are emerging as significant prognostic indicators for survival in cancer patients

BACKGROUND

Dyslipidemia is a common comorbidity in patients with cancer, yet the impact of abnormal lipid levels on tumor prognosis remains contentious. This study was conducted to synthesize the current evidence regarding the prognostic utility of blood lipid levels, including high-density lipoprotein cholesterol (HDL-C), low-density lipoprotein cholesterol (LDL-C), total cholesterol (TC), triglycerides (TG), apolipoprotein A1 (ApoA1), and apolipoprotein B (ApoB), in predicting overall survival (OS) and disease-free survival (DFS) in cancer patients.

METHODS

A comprehensive literature search was performed across electronic databases to assess the associations between blood lipid levels and OS or DFS in cancer patients. Pooled hazard ratios (HRs) and 95% confidence intervals (CIs) were calculated to analyze the data. The research protocol was previously submitted to the International Prospective Register of Systematic Reviews (PROSPERO): CRD42023458597.

RESULTS

Our study represents the largest and most extensive evaluation of the prognostic significance of blood lipid levels in cancer to date. It includes a meta-analysis of 156 eligible studies involving 85,173 cancer patients. The findings revealed a significant association between elevated levels of HDL-C, TC, and ApoA1 and improved OS and DFS in cancer patients. In contrast, no significant relationships were identified between LDL-C, TG, and ApoB levels and the OS or DFS of cancer patients.

CONCLUSION

Blood lipids, particularly HDL-C, TC, and ApoA1, emerge as accessible and cost-effective biomarkers that may aid in assessing survival outcomes in cancer patients and potentially inform clinical decision-making.

Integrating Multi-Omics Analysis for Enhanced Diagnosis and Treatment of Glioblastoma: A Comprehensive Data-Driven Approach

The most aggressive primary malignant brain tumor in adults is glioblastoma (GBM), which has poor overall survival (OS). There is a high relapse rate among patients with GBM despite maximally safe surgery, radiation therapy, temozolomide (TMZ), and aggressive treatment. Hence, there is an urgent and unmet clinical need for new approaches to managing GBM. The current study identified modules (MYC, EGFR, PIK3CA, SUZ12, and SPRK2) involved in GBM disease through the NeDRex plugin. Furthermore, hub genes were identified in a comprehensive interaction network containing 7560 proteins related to GBM disease and 3860 proteins associated with signaling pathways involved in GBM. By integrating the results of the analyses mentioned above and again performing centrality analysis, eleven key genes involved in GBM disease were identified. ProteomicsDB and Gliovis databases were used for determining the gene expression in normal and tumor brain tissue. The NetworkAnalyst and the mGWAS-Explorer tools identified miRNAs, SNPs, and metabolites associated with these 11 genes. Moreover, a literature review of recent studies revealed other lists of metabolites related to GBM disease. The enrichment analysis of identified genes, miRNAs, and metabolites associated with GBM disease was performed using ExpressAnalyst, miEAA, and MetaboAnalyst tools. Further investigation of metabolite roles in GBM was performed using pathway, joint pathway, and network analyses. The results of this study allowed us to identify 11 genes (UBC, HDAC1, CTNNB1, TRIM28, CSNK2A1, RBBP4, TP53, APP, DAB1, PINK1, and RELN), five miRNAs (hsa-mir-221-3p, hsa-mir-30a-5p, hsa-mir-15a-5p, hsa-mir-130a-3p, and hsa-let-7b-5p), six metabolites (HDL, N6-acetyl-L-lysine, cholesterol, formate, N, N-dimethylglycine/xylose, and X2. piperidinone) and 15 distinct signaling pathways that play an indispensable role in GBM disease development. The identified top genes, miRNAs, and metabolite signatures can be targeted to establish early diagnostic methods and plan personalized GBM treatment strategies.

Serum lipids concentration on prognosis of high-grade glioma

OBJECTIVE

To investigate the effect of serum lipids concentration on the prognosis of high-grade glioma patients undergoing postoperative radiotherapy.

METHODS

Retrospective analysis of the patients with high-grade glioma who received postoperative Intensity Modulated Radiotherapy between 13 May 2013 and 12 September 2018 was performed. The patients were grouped according to the average values of serum total cholesterol, LDL, and HDL concentration in peripheral blood (before surgery, 6 months after therapy). Cox proportional hazards model was performed to determine whether the total cholesterol concentration, LDL concentration, and HDL concentration in peripheral blood before therapy and their changes after therapy were factors influencing the prognosis.

RESULTS

The results of COX regression analysis showed that the independent prognostic factors of high-grade glioma patients were pathological grade, the extent of resection, serum cholesterol concentration pre-surgery, and the change of LDL concentration from pre-surgery to post-therapy. The prognosis of patients with high serum total cholesterol concentration before therapy was worse than those of patients with low total cholesterol concentration. The 5-year survival rate and the median survival time of patients with high serum total cholesterol concentration before therapy were 4.9% and 23.6 months, but the low cholesterol concentration group were 19.6% and 24.5 months, respectively. Besides, the average serum LDL concentration in high-grade glioma patients gradually increased after therapy. The 5-year survival rate of patients and the median survival time with elevated LDL concentration after therapy is 11.8% and 20.4 months, but the reduced LDL concentration group was 16.7% and 28.4 months, respectively. The total cholesterol and LDL concentration increased significantly after therapy in Grade IV patients while Grade III patients did not.

CONCLUSIONS

The cholesterol concentration before therapy and LDL concentration change from pre-surgery to post-therapy are the factors that affect the prognosis of high-grade glioma patients who have undergone postoperative radiotherapy. In the final analysis, the high serum cholesterol pre-surgery and the increased in serum LDL concentration from pre-surgery to post-therapy were associated with worse survival of patients.

Lipid Alterations in Glioma: A Systematic Review

Gliomas are highly lethal tumours characterised by heterogeneous molecular features, producing various metabolic phenotypes leading to therapeutic resistance. Lipid metabolism reprogramming is predominant and has contributed to the metabolic plasticity in glioma. This systematic review aims to discover lipids alteration and their biological roles in glioma and the identification of potential lipids biomarker. This systematic review was conducted using the preferred reporting items for systematic reviews and meta-analyses (PRISMA) guidelines. Extensive research articles search for the last 10 years, from 2011 to 2021, were conducted using four electronic databases, including PubMed, Web of Science, CINAHL and ScienceDirect. A total of 158 research articles were included in this study. All studies reported significant lipid alteration between glioma and control groups, impacting glioma cell growth, proliferation, drug resistance, patients' survival and metastasis. Different lipids demonstrated different biological roles, either beneficial or detrimental effects on glioma. Notably, prostaglandin (PGE2), triacylglycerol (TG), phosphatidylcholine (PC), and sphingosine-1-phosphate play significant roles in glioma development. Conversely, the most prominent anti-carcinogenic lipids include docosahexaenoic acid (DHA), eicosapentaenoic acid (EPA), and vitamin D3 have been reported to have detrimental effects on glioma cells. Furthermore, high lipid signals were detected at 0.9 and 1.3 ppm in high-grade glioma relative to low-grade glioma. This evidence shows that lipid metabolisms were significantly dysregulated in glioma. Concurrent with this knowledge, the discovery of specific lipid classes altered in glioma will accelerate the development of potential lipid biomarkers and enhance future glioma therapeutics.

Understanding the effect of categorization of a continuous predictor with application to neuro-oncology

Many neuro-oncology studies commonly assess the association between a prognostic factor (predictor) and disease or outcome, such as the association between age and glioma. Predictors can be continuous (eg, age) or categorical (eg, race/ethnicity). Effects of categorical predictors are frequently easier to visualize and interpret than effects of continuous variables. This makes it an attractive, and seemingly justifiable, option to subdivide the continuous predictors into categories (eg, age <50 years vs age ≥50 years). However, this approach results in loss of information (and power) compared to the continuous version. This review outlines the use cases for continuous and categorized predictors and provides tips and pitfalls for interpretation of these approaches.

Lack of association between modifiable exposures and glioma risk: a Mendelian randomization analysis

BACKGROUND

The etiological basis of glioma is poorly understood. We have used genetic markers in a Mendelian randomization (MR) framework to examine if lifestyle, cardiometabolic, and inflammatory factors influence the risk of glioma. This methodology reduces bias from confounding and is not affected by reverse causation.

METHODS

We identified genetic instruments for 37 potentially modifiable risk factors and evaluated their association with glioma risk using data from a genome-wide association study of 12 488 glioma patients and 18 169 controls. We used the estimated odds ratio of glioma associated with each of the genetically defined traits to infer evidence for a causal relationship with the following exposures:Lifestyle and dietary factors-height, plasma insulin-like growth factor 1, blood carnitine, blood methionine, blood selenium, blood zinc, circulating adiponectin, circulating carotenoids, iron status, serum calcium, vitamins (A1, B12, B6, E, and 25-hydroxyvitamin D), fatty acid levels (monounsaturated, omega-3, and omega-6) and circulating fetuin-A;Cardiometabolic factors-birth weight, high density lipoprotein cholesterol, low density lipoprotein cholesterol, total cholesterol, total triglycerides, basal metabolic rate, body fat percentage, body mass index, fasting glucose, fasting proinsulin, glycated hemoglobin levels, diastolic and systolic blood pressure, waist circumference, waist-to-hip ratio; andInflammatory factors- C-reactive protein, plasma interleukin-6 receptor subunit alpha and serum immunoglobulin E.

RESULTS

After correction for the testing of multiple potential risk factors and excluding associations driven by one single nucleotide polymorphism, no significant association with glioma risk was observed (ie, PCorrected > 0.05).

CONCLUSIONS

This study did not provide evidence supporting any of the 37 factors examined as having a significant influence on glioma risk.

Sanbo Scoring System, Based on Age and Pre-treatment Hematological Markers, is a Non-invasive and Independent Prognostic Predictor for Patients with Primary Glioblastomas: A Retrospective Multicenter Study

Various hematological markers are associated with survival in patients with glioblastomas (GBMs), as they reflect inflammation and nutrition status. However, single markers are insufficient for predicting prognosis in GBM, and a comprehensive scoring system is needed. In this study, we developed a simple, inexpensive, and non-invasive scoring system, referred to as the Sanbo Scoring System (SSS), to predict survival in patients with GBMs. Patients with GBM were retrospectively assigned to two independent cohorts at Sanbo Brain Hospital and National Cancer Center/Cancer Hospital. Clinical records, including age, routine blood tests, biochemistry and coagulation examinations, and IDH-1 status, were collected. In total, 274 and 87 patients with GBMs at Sanbo Brain Hospital and National Cancer Center/Cancer Hospital were included as derivation and validation cohorts, retrospectively. We developed the SSS based on data for the derivation cohort, i.e., age, neutrophil-to-lymphocyte ratio (NLR), platelet-to-lymphocyte ratio (PLR), albumin-to-globulin ratio (AGR), and fibrinogen levels. These patients were divided into three groups that differed with respect to age, inflammation-nutrition status, and overall survival (p < 0.001), i.e., SSS 0, 1, and 2. NLR, PLR, and fibrinogen levels were lower and AGR was higher in the SSS 2 group than in the other groups, indicating better inflammation and nutrition statuses. Additionally, the longest overall survival was observed in this group. A multivariate analysis showed that SSS was an independent prognostic factor. The validation cohort supported all the results. SSS was a simple, non-invasive, and effective scoring system, and independently predicted survival in GBMs.

Systematic Review and Meta-Analysis of the Prognostic Value of Serum High-Density Lipoprotein Cholesterol Levels for Solid Tumors

Numerous studies have demonstrated that serum high-density lipoprotein cholesterol (HDL-C) levels correlate strongly with cancer patient survival. However, other studies have had the opposite results. We therefore conducted a systematic review and meta-analysis to assess the prognostic value of HDL-C levels in people with cancer. We searched PubMed, Embase, and the Cochrane Library (last update by December 28, 2017) for studies evaluating the effect of serum HDL-C levels on cancer patient prognosis. Data from 25 studies covering13,140 patients were included. Combined hazard ratios (HRs) for overall survival (OS) and disease-free survival (DFS) were assessed using fixed-effects and random-effects models. High serum HDL-C levels were associated with better OS (pooled HR = 0.70; 95% confidence interval (CI) (0.60-0.82). In the subgroup, the relative high level of HDL-C yielded a favorable outcome in most of tumor types. However, in the nasopharyngeal carcinoma subgroup, the correlation was not significant (combined HR = 1.31; 95% CI (0.91-1.90)). High serum HDL-C levels were associated with better DFS (pooled HR = 0.64; 95% confidence interval (CI) (0.50-0.81)). This meta-analysis demonstrates that high serum HDL-C levels are associated with better OS in patients with solid tumors, but not nasopharyngeal carcinoma; and high serum HDL-C levels are associated with better DFS.