Reconsideration of the laminin receptor 67LR in colorectal cancer cells
Bibliographic record
Abstract
The 67 kDa laminin receptor (67LR) was identified as the first laminin receptor shown to be involved in the carcinogenesis of various cancers, including colorectal cancer. While the exact composition of this 67 kDa receptor remains unknown, it has been reported to be formed by the 37 kDa ribosomal protein SA (RPSA) covalently attached to another unidentified protein. The goal of this study was to clarify the molecular structure of 67LR to enhance our understanding of its role in malignancies. Using cell fractionation of colorectal cancer cells, the 67 kDa immunoreactive protein corresponding to 67LR was found in the soluble protein fraction, while some of the 37 kDa RPSA exhibited plasma membrane-like properties. Proteomic analysis of the 67 kDa fraction revealed the absence of RPSA but identified the β-galactosidase-related 67 kDa elastin-binding protein (67EBP), another laminin binding receptor which presents amino acid sequence similarities that can explain the immune cross reactivity with RPSA. The downregulation of β-galactosidase through short hairpin RNA (shRNA) led to a reduction in both 67LR and 67EBP immunoreactive proteins, confirming the misidentification of 67LR and 67EBP in colorectal cancer cells. Based on these findings, we propose to redefine the 67LR as the RPSA-containing laminin receptor (RCLR) to avoid confusion with the 67EBP.
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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) | 0.001 | 0.001 |
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".