Stem/progenitor cell marker expression in clear cell renal cell carcinoma: a potential relationship with the immune microenvironment to be explored
Bibliographic record
Abstract
Abstract Background Clear cell renal cell carcinoma (ccRCC) is a markedly heterogeneous disease in many aspects, including the tumour microenvironment. Our previous study showed the importance of the tumour microenvironment in ccRCC xeno-transplant success rates. In order to better understand the potential relationship between TICs and the immune microenvironment, we employed a multi-modal approach, examining RNA and protein expression (flow cytometry, immunohistochemistry). Methods We first examined the gene expression pattern of 18 stem/progenitor marker genes in the cancer genome atlas (TCGA) ccRCC cohort. Flow cytometry was next employed to examine lineage-specific expression levels of stem/progenitor markers and immune population makeup in six, disaggregated, primary ccRCC specimens. Immunohistochemistry was performed on a commercial ccRCC tissue microarray (TMA). Results The 18 genes differed with respect to their correlation patterns with one another and to their prognostic significance. By flow cytometry, correlating expression frequency of 12 stem/progenitor markers and CD10 resulted in two clusters—one with CD10 (marker of proximal tubular differentiation), and second cluster containing mostly mesenchymal stem cell (MSC) markers, including CD146. In turn, these clusters differed with respect to their correlation with different CD45 + lineage markers and their expression of immune checkpoint pathway proteins. To confirm these findings, four stem/progenitor marker expression patterns were compared with CD4, CD8 and CD20 in a ccRCC TMA which showed a number of similar trends with respect to frequency of the different tumour-infiltrating leukocytes. Conclusion Taken together, we observed heterogeneous but patterned expression levels of different stem/progenitor markers. Our results suggest a non-random relationship between their expression patterns with the immune microenvironment populations in ccRCC.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 teacher head, 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".