A spatial transcriptomics study of MES-like and mono/macro cells in gliomas
Bibliographic record
Abstract
Gliomas, including both glioblastoma multiforme (GBM) and lower-grade glioma (LGG), present a substantial challenge in neuro-oncology because of genetic heterogeneity and unsatisfactory prognosis. This study aimed to conduct a comprehensive multi-omics analysis of gliomas using various bioinformatics approaches to identify potential therapeutic targets and prognostic markers. A comprehensive analysis was conducted on 1327 sequencing data samples alongside their relevant clinical information sourced from The Cancer Genome Atlas (TCGA) pertaining to glioblastoma (GBM), low-grade glioma (LGG), the Chinese Glioma Genome Atlas (CCGA) and University of California Santa Cruz Xena (UCSC Xena) datasets. These tools were employed for gene expression profiling, survival analysis, and cell communication mapping. Spatial transcriptomics revealed the localization of mesenchymal (MES)-like malignant tumors, and drug sensitivity analysis was performed to evaluate responses to quinpirole and meropenem. Additionally, the Tumor Immune Dysfunction and Exclusion (TIDE) framework was utilized to gauge the responsiveness to immunotherapy. The MES-like malignant and monocyte/macrophage (mono/macro) cell subsets showed high hallmark scores, playing key roles in the tumor microenvironment. MES-like malignant marker gene scores correlated with overall survival across datasets, whereas mono/macro marker gene scores were significant in the TCGA-LGG and CCGA datasets. Key interactions between these cell types were found, especially with CD14-ITGB2, LGALS1-CD69, and APOE-TREM2. The mono/macro cell subset demonstrated better immune therapy responsiveness, as indicated by lower TIDE scores. Spatial transcriptomics revealed that MES-like malignant tumors are predominantly localized in four distinct regions, with the marker genes CHI3L1 and ADM confirming these locations. Drug sensitivity analysis revealed differential responses of the MES-like malignant cell subset to quinpirole and meropenem. Our results offer fresh perspectives on the differential roles of MES-like malignant and monocyte/macrophage cell subsets in tumor progression and immune modulation, providing novel insights into glioma biology.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".