Glioma

LINC01783 Promotes Glioma Tumorigenesis by Enhancing GATA3 Expression Through CBP-Mediated H3K27 Acetylation to Suppress PTEN Expression

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LINC01783

In vitro and in vivo molecular mechanistic investigations

Study Summary

Type of Study: In vitro and in vivo molecular mechanistic investigation First Author: Shaocai Hao et al. Author Affiliations:

  • Department of Neurosurgery, General Hospital of Ningxia Medical University, Yinchuan, Ningxia, China

Journal: Biofactors DOI: 10.1002/biof.70029 PMID: 40546096 Publication Date: May–June 2025 Title: LINC01783 Promotes Glioma Tumorigenesis by Enhancing GATA3 Expression Through CBP-Mediated H3K27 Acetylation to Suppress PTEN Expression

Purpose

To elucidate the oncogenic function of the long intergenic non-coding RNA LINC01783 in glioma progression, focusing on its effect on GATA3 expression and PTEN suppression via CBP-mediated H3K27 acetylation.

Conclusions

LINC01783 is significantly upregulated in glioma tissues and enhances glioma progression by promoting GATA3 expression through CBP-mediated H3K27 acetylation. This, in turn, transcriptionally represses PTEN, contributing to increased tumor cell proliferation and stemness.

Critical Review

Methodological Weaknesses

  • Sample opacity: No clear details on glioma sample number, subtype stratification, or clinical metadata; undermines reproducibility and clinical significance.
  • In vivo data insufficiently controlled: No information on animal randomization, group sizes, or blinding procedures. Xenograft conclusions are weakly supported.
  • Epigenetic mechanistic oversimplification: Attribution of GATA3 regulation solely to CBP-H3K27ac is unconvincing; alternative pathways and compensatory mechanisms are unexamined.
  • Lack of causal proof: The PTEN axis is emphasized, but whether GATA3 mediates all observed phenotypes is not demonstrated.
  • No translational bridge: No therapeutic agent, inhibitor, or antisense strategy explored. The leap to “potential therapeutic target” is scientifically unfounded.

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Oncolytic virus‑mediated immunomodulation in glioblastoma: Insights from clinical trials and challenges

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In a Review Raziye Piranlioglu *et al.* from

Affiliations Harvey Cushing Neuro‑oncology Laboratories, Dept. Neurosurgery, Brigham and Women’s Hospital, Boston, MA, USA; Dana‑Farber Cancer Institute, Boston, MA, USA

published in *Seminars in Immunology* with the Purpose to synthesize data from clinical trials of oncolytic viruses (OVs) in glioblastoma, evaluating immunomodulatory effects, delivery strategies, and challenges in assessing immune responses. They concluded that Oncolytic virus therapy is well tolerated in GBM trials and can convert the immunosuppressive microenvironment into an immunologically active state. However, limitations in post‑treatment sampling and delivery methods impede full understanding of biological mechanisms.

This review is a rehash of well‑known take‑home messages, offering little in the way of novel synthesis or incisive critique. The authors lean heavily on canonical trials (e.g., oHSV, adenovirus) but fail to integrate preclinical correlates from myeloid-targeting strategies, such as macrophage polarization dynamics or MDSC modulation. There’s no fresh mechanism, no meta‑analysis of response rates, and no exploration of why most trials remain phase I with limited impact. Sample‑scarcity is once again highlighted as a blocker—but no alternative trial designs (e.g., neoadjuvant window cohorts, liquid biopsy) are proposed. In short, the review scratches the surface of challenges without pushing the field forward.

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Maximizing Tumor Resection and Managing Cognitive Attentional Outcomes: Measures of Impact of Awake Surgery in Glioma Treatment

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In a retrospective observational study Zigiotto et al. from the S. Chiara University-Hospital, Azienda Provinciale per i Servizi Sanitari, Trento, published in the Neurosurgery Journal on 64 glioma patients who underwent awake surgery (AwS) or asleep surgery (AsS), with neuropsychological and imaging follow-up. They evaluated the impact of awake surgery on attentional outcomes in glioma patients, and analyzed whether greater extent of tumor resection correlates with transient cognitive (attentional) decline, especially in relation to lesions within the default mode network. Awake surgery allows for more extensive supramaximal resection and is associated with longer overall survival, particularly in patients with glioblastomas. However, it also leads to a higher rate of transient postoperative attentional dysfunction, likely due to resection in attention-related brain networks. The study suggests that patient selection and intraoperative cognitive monitoring should be optimized in future glioma surgery 5).

This retrospective study compares awake versus asleep craniotomy in 64 glioma patients, using simple attention tests before and after surgery. The authors claim that awake craniotomy (AwC) allows more extensive tumor resection and leads to longer survival, albeit at the cost of transient attentional dysfunction.

🧠 1. Conceptual Inflation Meets Methodological Reductionism

The title promises a nuanced exploration of cognitive outcomes. What it delivers is a reduction of “attention” to the Trail Making Test Part A and a visual search task — an embarrassingly narrow lens for such a multidimensional construct. The study purports to evaluate the impact of surgery on attention, yet fails to define attention, stratify its subtypes, or provide any neuropsychological depth. This is not a cognitive study — it’s a surgical paper pretending to be one.

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Preoperative Nomogram-Based Assessment to Identify GBM Patients Who Do not Derive Survival Benefit From GTR Compared to STR

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In a retrospective prognostic modeling study, He et al. from Sichuan Provincial People’s Hospital published in the Academic Radiology a preoperative nomogram to identify glioblastoma patients who do not derive a survival benefit from gross total resection (GTR) compared to subtotal resection (STR), and concluded that patients with nomogram scores below 55 or above 95 gain limited survival advantage from GTR, supporting a more individualized surgical strategy 16).

🎯 Takeaway Message for Neurosurgeons

Don’t let a nomogram tell you not to operate. This study reduces complex glioblastoma surgery to a score — ignoring tumor location, function, biology, and patient context. Use it, at best, as background noise. Surgical judgment, not predictive modeling, should guide the extent of resection. Maximal safe resection remains the standard — not because of scores, but because leaving tumor behind costs lives.

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Clinical outcome and deep learning imaging characteristics of patients treated by radio-chemotherapy for a “molecular” glioblastoma

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In a retrospective observational cohort study, Zerbib et al., from the Department of Radiation Oncology, Institut Universitaire du Cancer de Toulouse Oncopole (IUCT-Oncopole), Claudius Regaud; INSERM UMR 1037, Cancer Research Center of Toulouse (CRCT); IRT Saint-Exupéry; Department of Engineering and Medical Physics, IUCT-Oncopole; Biostatistics & Health Data Science Unit, IUCT-Oncopole; Department of Neuroradiology, Hôpital Pierre-Paul Riquet, CHU Purpan; Department of Medical Oncology & Clinical Research Unit, IUCT-Oncopole; Pathology and Cytology Department, CHU Toulouse, IUCT-Oncopole; CerCo, Université de Toulouse, CNRS, UPS, CHU Purpan; Department of Neurosurgery, Hôpital Pierre-Paul Riquet, CHU Purpan; and University Toulouse III – Paul Sabatier, published in The Oncologist, sought to evaluate and compare the clinical outcomes of patients with molecular glioblastoma (molGB) and histological glioblastoma (histGB) treated with standard radio-chemotherapy. They also assessed whether artificial intelligence (AI) models could accurately distinguish molGB without contrast enhancement (CE) from low-grade gliomas (LGG) using MRI FLAIR imaging features.

Conclusion: Patients with molGB and histGB showed similar overall survival under standard treatment.

  • However, molGB without contrast enhancement (CE) demonstrated a significantly better median overall survival (31.2 vs 18 months).
  • AI models based on FLAIR MRI features were able to differentiate non-enhancing molGB from LGG, achieving a best-performing ROC AUC of 0.85.

→ These findings support the clinical relevance of non-enhancing molGB as a distinct subgroup with better prognosis and highlight the potential diagnostic utility of AI tools in radiologically ambiguous cases.

This study presents itself as cutting-edge — mixing radiotherapy outcomes with artificial intelligence — but beneath the polished language and deep learning jargon lies a set of predictable flaws:

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