• Different states of stemness of glioblastoma stem cells sustain glioblastoma subtypes indicating novel clinical biomarkers and high-efficacy customized therapies
• Preoperative Immune-Inflammatory Status of the Patients With Newly-Diagnosed Glioblastoma – Could It Genuinely Predict Their Survival?
• Intratumoral thrombosis as a histological biomarker for predicting epidermal growth factor receptor alteration and poor prognosis in patients with glioblastomas
• Translocator protein (18kDA) (TSPO) marks mesenchymal glioblastoma cell populations characterized by elevated numbers of tumor-associated macrophages
• Surgical management and outcome of newly diagnosed glioblastoma without contrast enhancement (‘low grade appearance’) – a report of the RANO resect group
• Characterization of tumor microenvironment in glioblastoma multiforme identifies ITGB2 as a key immune and stromal related regulator in glial cell types
• Angiopoietin4 (ANGPT4) expression and potential mechanisms in carcinogenesis: current achievements and perspectives
• Development and validation a prognostic model based on natural killer T cells marker genes for predicting prognosis and characterizing immune status in glioblastoma through integrated analysis of single-cell and bulk RNA sequencing

Glioblastoma (GBM) is an aggressive form of brain cancer, and predicting patient outcomes is a complex task. Prognostic markers are factors or characteristics that can help healthcare professionals estimate a patient’s likely disease course and survival. In the context of glioblastoma, several prognostic markers have been studied. Here are some of them:

Age: Increasing age is a well-established negative prognostic marker in glioblastoma. Older patients often have poorer outcomes.

Karnofsky Performance Score (KPS): This is a measure of a patient’s functional impairment and general well-being. A lower KPS score is associated with poorer prognosis.

Extent of Surgical Resection: The degree to which the tumor can be surgically removed is a significant prognostic factor. More extensive resection is associated with better outcomes.

O6-methylguanine-DNA Methyltransferase (MGMT) Methylation: Methylation of the MGMT gene promoter is associated with better responses to chemotherapy and improved survival.

Corticosteroid Use: The use of corticosteroids is associated with poor prognosis in glioblastoma patients.

Density of White Matter Tracts: Recent research has suggested that the density of white matter tracts in the brain near the tumor may be a prognostic marker. Lower tract density may indicate longer survival.

STAT5b: STAT5b activation is associated with poorer survival in glioblastoma patients.

SPTSSA Expression: SPTSSA expression may serve as a prognostic biomarker for glioma patients.

Lymphopenia: Baseline lymphopenia (a low lymphocyte count) is associated with worse overall survival in elderly glioblastoma patients.

c-Met and VEGFR2: Overexpression of c-Met and VEGFR2 may predict poorer responses to anti-angiogenic therapies in glioblastoma.

SII (Systemic Immune-Inflammation Index) and AGR (Albumin-to-Globulin Ratio): High SII and low AGR values are promising prognostic markers for identifying high-grade glioma (HGG) patients with poor prognoses.

ALK (Anaplastic Lymphoma Kinase): The role of ALK as a prognostic marker in glioblastoma is not well-established and remains controversial.

ATP-Binding Cassette Transporters: The activity of ATP-binding cassette transporters may impact prognosis by reducing drug penetration into tumor cells, but their use as isolated prognostic markers is not supported.

Tumor Geometry: Tumor shape and geometric heterogeneity can be used as prognostic markers. Patients with tumors exhibiting certain geometric characteristics may have better prognoses.

Neurologic Status: A proposed neurologic index may help predict poor outcomes in glioblastoma patients receiving tumor resection.

It’s important to note that glioblastoma is a complex and heterogeneous disease, and multiple factors can influence patient outcomes. Prognostic markers are used in combination to provide a more accurate prediction of prognosis. Additionally, ongoing research continues to uncover new markers and refine our understanding of glioblastoma prognosis.

Prognostic markers in glioblastoma are complex. In addition to previously recognized prognostic variables such as age and Karnofsky performance score, tumor size, total resection and proliferative index were identified as predictors of survival in a series of patients with glioblastoma multiforme ¹⁾.

Many reports on glioblastoma multiforme discuss the prognostic impact of anatomical features such as cysts, necrotic changes, extent of edema or subependymal spread of tumor cells.

The most consistent and well-described clinical prognostic factors associated with poor survival include: increasing age, poor performance status (PS), low degree of surgical resection of the tumor, and the use of corticosteroids ^{2) 3) 4) 5)}

Salvalaggio et al. examined two Groups of Patients: The first group, called the “discovery cohort,” included 112 patients from Italy who had surgery between February 2015 and November 2020. The second group, known as the “replicative cohort,” included 70 patients from Germany who had surgery between September 2012 and November 2015.

What They Measured: The researchers were interested in something called “white matter tracts” in the patients’ brains. They measured how dense or crowded these tracts were in the area where the GBM was located.

Main Findings:

In the first group (discovery cohort), they found that the density of these white matter tracts was related to how long patients lived after surgery. When the tracts were less dense, patients tended to live longer.

This relationship between white matter tract density and survival was stronger and more consistent compared to other factors that are commonly used to predict how GBM patients will do, like age, performance status, a specific type of DNA change (O6-methylguanine-DNA methyltransferase methylation), and how much of the tumor was removed during surgery. They confirmed these findings in the second group (replicative cohort), which makes the results more reliable. Using the density of white matter tracts, they were able to predict whether a patient would have a higher or lower chance of surviving for at least 18 or 24 months after surgery with a high level of accuracy. Conclusion: This study suggests that the density of white matter tracts in the area around the GBM may be a useful predictor of how long patients with GBM will live after surgery. It could be valuable in clinical trials and medical practice to help doctors make decisions about treatment and prognosis.

In simple terms, the study found that the structure of certain brain pathways is related to how long patients with brain cancer live after surgery. This could be a helpful tool for doctors when treating these patients. ⁶⁾.

STAT5b is frequently activated in Glioblastoma and correlates inversely with patient survival. It does not contribute to the growth and resistance of these tumors and is thus rather a potential prognostic marker than a therapeutic target in these tumors ⁷⁾.

SPTSSA expression might be used as a prognostic biomarker for glioma and a potential target for novel glioma therapy ⁸⁾

Baseline lymphopenia is associated with worse OS, which may be considered a prognostic biomarker for elderly glioblastoma outcome patients ⁹⁾

c-Met and VEGFR2 overexpression have a role in the development of glioblastoma early resistance and might predict poorer responses to anti-angiogenic therapies. ¹⁰⁾

Liang et al., demonstrated that high SII and low AGR values may serve as promising prognostic markers to identify HGG patients with poor prognosis ¹¹⁾.

Data on the prognostic role of ALK in Glioblastoma are very limited and remain controversial ^{12) 13) 14)}.

The activity of ATP-binding cassette transporters severely reduces the amount of therapeutics that penetrates the tumor cells. Roy et al. hypothesized that ABC transporter expression could correlate with survival surrogates. They assessed the expression of four commonly expressed ABC transporters in GBM samples and investigated if mRNA levels could serve as a prognostic biomarker.

The expression of the four ABC transporters evaluated would not be suitable prognostic biomarkers. They believe that when estimating prognosis, the plethora of mechanisms implicated in chemoresistance should be analyzed as a multi-facetted entity rather than isolated units ¹⁵⁾.

Patients with tumours having small geometric heterogeneity and/or spherical rim widths had significantly better prognosis. These imaging biomarkers have a strong individual and combined prognostic value for Glioblastoma patients ^16) 17).

Multi-channel MR image derived texture features, tumor shape, and volumetric features, and patient age were obtained for 163 Glioblastoma patients. In order to assess the impact of tumor shape features on OS prediction, two feature sets, with and without tumor shape features, were created. For the feature set with tumor shape features, the mean prediction error (MPE) was 14.6 days and its 95% confidence interval (CI) was 195.8 days. For the feature set excluding shape features, the MPE was 17.1 days and its 95% CI was observed to be 212.7 days. The coefficient of determination (R2) value obtained for the feature set with shape features was 0.92, while it was 0.90 for the feature set excluding shape features. Although marginal, the inclusion of shape features improves OS prediction in Glioblastoma patients. The proposed OS prediction method using regression provides good accuracy and overcomes the limitations of Glioblastoma OS classification, like choosing data-derived or pre-decided thresholds to define the OS groups. Graphical abstract Two feature sets: with and without tumor shape features were extracted from T1-weighted contrast-enhanced, T2-weighted and FLAIR MRI. These feature sets were analyzed using the Mean Prediction Error (MPE) and its 95% Confidence Interval (CI) obtained from the Bland-Altman plot, along with the coefficient of determination (R2) value to assess the impact of tumor shape features on overall survival prediction of glioblastoma multiforme patients ¹⁸⁾.

Neurologic status is one of the major prognostic factors; however, no consensus exists on a clinical index for predicting patient outcomes.

One proposed neurologic index enables significant identification of glioblastoma patients receiving tumor resection with poor outcomes, independent of other common prognostic factors. Using the index provides a preoperative predictor of prognosis in glioblastoma patients receiving tumor resection ¹⁹⁾.

What are prognostic markers used for in medicine? a) To diagnose diseases b) To predict the likely outcome of a disease in a patient c) To treat diseases d) To prevent diseases

In glioblastoma, which of the following factors were identified as predictors of survival in addition to age and performance score? a) Blood pressure and cholesterol levels b) Tumor size and total resection c) DNA mutations and tumor grade d) Blood type and genetic markers

What did Salvalaggio et al. measure in the brains of glioblastoma patients to assess prognosis? a) Blood flow b) White matter tract density c) Tumor size d) Brain volume

What was the main finding of the study conducted by Salvalaggio et al. regarding white matter tracts? a) White matter tract density was unrelated to patient survival. b) Patients with denser white matter tracts tended to live longer. c) White matter tracts were unrelated to tumor location. d) White matter tracts were unrelated to age.

Which of the following is NOT mentioned as a clinical prognostic factor associated with poor survival in glioblastoma? a) Increasing age b) Poor performance status (PS) c) Extent of surgical resection d) Use of corticosteroids

What is the potential role of STAT5b in glioblastoma? a) It contributes to tumor growth and resistance. b) It is a therapeutic target for glioblastoma. c) It is a prognostic marker. d) It has no role in glioblastoma.

What is SPTSSA expression potentially used for in glioma? a) Diagnosis b) Prognostic marker c) Treatment d) Disease prevention

What is lymphopenia considered as in elderly glioblastoma outcome patients? a) A marker of good prognosis b) A marker of disease progression c) A prognostic biomarker for poor outcome d) A diagnostic marker

What do c-Met and VEGFR2 overexpression predict in glioblastoma? a) Positive response to anti-angiogenic therapies b) Increased tumor size c) Resistance to chemotherapy d) No impact on patient outcomes

Which statement about ALK as a prognostic marker in glioblastoma is true? a) It is a well-established prognostic marker. b) Data on its prognostic role are limited and controversial. c) It is a therapeutic target for glioblastoma. d) It is not associated with patient survival.

What are prognostic markers used for in medicine? Answer: b) To predict the likely outcome of a disease in a patient

In glioblastoma, which of the following factors were identified as predictors of survival in addition to age and performance score? Answer: b) Tumor size and total resection

What did Salvalaggio et al. measure in the brains of glioblastoma patients to assess prognosis? Answer: b) White matter tract density

What was the main finding of the study conducted by Salvalaggio et al. regarding white matter tracts? Answer: b) Patients with denser white matter tracts tended to live longer.

Which of the following is NOT mentioned as a clinical prognostic factor associated with poor survival in glioblastoma? Answer: d) Use of corticosteroids

What is the potential role of STAT5b in glioblastoma? Answer: c) It is a prognostic marker.

What is SPTSSA expression potentially used for in glioma? Answer: b) Prognostic marker

What is lymphopenia considered as in elderly glioblastoma outcome patients? Answer: c) A prognostic biomarker for poor outcome

What do c-Met and VEGFR2 overexpression predict in glioblastoma? Answer: a) Positive response to anti-angiogenic therapies

Which statement about ALK as a prognostic marker in glioblastoma is true? Answer: b) Data on its prognostic role are limited and controversial.