Normalizing genes for quantitative RT-PCR in differentiating human intestinal epithelial cells and adenocarcinomas of the colon
Bibliographic record
Abstract
As for other mRNA measurement methods, quantitative RT-PCR results need to be normalized relative to stably expressed genes. Widely used normalizing genes include beta-actin and glyceraldehyde-3-phosphate dehydrogenase. It has, however, become clear that these and other normalizing genes can display modulated patterns of expression across tissue types and during complex cellular processes such as cell differentiation and cancer progression. Our objective was to set the basis for identifying normalizing genes that displayed stable expression during enterocytic differentiation and between healthy tissue and adenocarcinomas of the human colon. We thus identified novel potential normalizing genes using previously generated cDNA microarray data and examined the alterations of expression of two of these genes as well as seven commonly used normalizing genes during the enterocytic differentiation process and between matched pairs of resection margins and primary carcinomas of the human colon using real-time RT-PCR. We found that ribosomal phosphoprotein P0 was particularly stable in all intestinal epithelial cell extracts, thereby representing a particularly robust housekeeping reference gene for the assessment of gene expression during the human enterocytic differentiation process. On the other hand, beta-2-microglobulin generated the best score as a normalizing gene for comparing human colon primary carcinomas with their corresponding normal mucosa of the resection margin, although others were found to represent acceptable alternatives. In conclusion, we identified and characterized specific normalizing genes that should significantly improve quantitative mRNA studies related to both the differentiation process of the human intestinal epithelium and adenocarcinomas of the human colon. This approach should also be useful to validate normalizing genes in other intestinal contexts.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.004 | 0.003 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 0.002 |
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".