Profiles of immune infiltration in abdominal aortic aneurysm and their associated marker genes: a gene expression-based study
Bibliographic record
Abstract
Immune-mediated inflammation plays a key role in the pathology of abdominal aortic aneurysm (AAA). We aimed to use a computational approach to profile the immune infiltration patterns and related core genes in AAA samples based on the overexpression of gene signatures. The microarray datasets of AAA and normal abdominal tissues were acquired from gene expression omnibus (GEO) database. We evaluated the composition of immune infiltrates through microenvironment cell populations (MCP)-counter. Weighted gene correlation network analysis (WGCNA) was employed to construct the co-expression network and extract gene information in the most relevant module. Functional and pathway enrichment analysis was performed and immune infiltration related core genes were screened. AAA tissues had a higher level of infiltration by cytotoxic lymphocytes, NK cells, T cells, fibroblasts, myeloid dendritic cells, and neutrophils than normal aorta. The red module was strongly correlated with the infiltrating levels of T cells and cytotoxic lymphocytes. Gene ontology (GO) and pathway analyses revealed that genes in the most relevant module were mainly enriched in T cell activation, regulation of lymphocyte activation, cytokine-cytokine receptor interaction, and chemokine signaling pathway, etc. The expression of GZMK, CCL5, GZMA, CD2, and EOMES showed significant correlations with cytotoxic lymphocytes, while CD247, CD2, CD6, RASGRP1, and CD48 expression were positively associated with T cell infiltration. In conclusion, we comprehensively analyzed profiles of infiltrated immune cells in AAA tissues and their associated marker genes. Our data may provide a novel clue to indicate the underlying molecular mechanisms of AAA formation in terms of immune infiltration.
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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 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| 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".