Single-cell RNA sequencing analysis reveals the heterogeneity and effect of TAMs in colorectal cancer
Bibliographic record
Abstract
Colorectal cancer (CRC) is a prevalent and malignant tumor of the digestive system, characterized by high incidence and mortality rates. This study aimed to investigate the heterogeneity of the tumor microenvironment (TME) and the involvement of immune cells in CRC. Single-cell RNA sequencing (scRNA-seq) data obtained from the Gene Expression Omnibus database were used to analyze and identify six major cell types across normal, core, and border tumor samples. A total of 27 414 cells from various regions of patients with CRC were selected for subsequent analyses. Cellular interaction analysis revealed that differential signaling pathways between the TME and normal tissues, with several pathways involving interactions between myeloid cells and epithelial cells. Myeloid cells were extracted and classified into six subtypes based on markers identified in the literature. Monocle3 revealed the trajectory of tumor-associated macrophages (TAMs) and identified genes associated with pseudotime. Single-Cell ENrichment analysis for Interpreting Cellular Heterogeneity analysis identified specific regulons and target genes associated with TAMs. This study reanalyzed single-cell RNA-sequencing data and provided insights into the heterogeneity of the TME, particularly in relation to the role of TAMs.
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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 | 0.000 | 0.000 |
| 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) | 0.000 | 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".