Immunoglobulin kappa and immunoglobulin lambda are required for expression of the anti-apoptotic molecule Bcl-xL in human colorectal cancer tissue
Bibliographic record
Abstract
OBJECTIVE: Aberrant expression of immunoglobulin (Ig) by cancer cells has been documented in a number of malignant tumors but its biological significance is unclear. Cancer cells overexpress anti-apoptotic molecules such as Bcl-xL. The present study aimed to examine the role of expression of Ig light-chain Igk and Iglambda in maintaining the high levels of Bcl-xL in colorectal cancer cells. MATERIAL AND METHODS: Thirty patients with colorectal cancer were recruited to this study. Expression of Igk, Iglambda and Bcl-xL in surgically removed cancer tissue was examined by immunohistochemistry and/or flow cytometry. Using the HT29 cell line as a study platform, RNA interference (RNAi) was employed to knock out the genes of Igk and Iglambda in the cancer cell line; the expression of Bcl-xL in HT29 cells was subsequently analyzed. RESULTS: Human colorectal cancer cells, but not normal colorectal tissue, expressed both Igk and Iglambda in the cytoplasm. High levels of Bcl-xL were detected in cancer cells. Using RNAi to knock out the genes of Igk and/or Iglambda, Bcl-xL expression in HT29 cells was significantly suppressed and the cells became apoptotic. CONCLUSION: The results suggest that expression of Igk and Iglambda is required to stabilize Bcl-xL expression in cancer cells.
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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) | 0.002 | 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".