Proteome Analysis and Tissue Microarray for Profiling Protein Markers Associated with Lymph Node Metastasis in Colorectal Cancer
Bibliographic record
Abstract
BACKGROUND: Understanding the proteins associated with lymph node metastasis (LNM) in colorectal cancer (CRC) will benefit us in the prediction of CRC prognosis and provide us new potential targets in the intervention of CRC. The aim of this study is to investigate the LNM-associated proteins and to evaluate the clinicopathological characteristics of these target proteins' expression in CRC. METHODS: Fresh tumor and paired normal mucosa from five cases for each group of non-LNM CRC and LNM CRC were analyzed by two-dimensional electrophoresis coupled with MALDI-TOF-MS, followed by Western blotting confirmation. In 40 paraffin-embedded CRC samples, each for non-LNM CRC and LNM CRC, four differentially expressed proteins identified by proteomics analysis were detected by tissue microarray with immunohistochemistry staining to access the clinicopathological characteristics of these proteins in LNM of CRC. RESULTS: Twenty-five proteins were found to be differentially expressed between normal mucosa and CRC tissue. Increased expression levels of heat shock protein-27 (HSP-27), glutathione S-transferase (GST), and Annexin II, but a decreased expression level of liver-fatty acid binding protein (L-FABP), existed in LNM CRC as compared with non-LNM CRC (p<0.01 or p<0.05, respectively). CONCLUSION: The techniques of proteomic analysis combined with tissue microarray provide us a dramatic tool for screening of LNM-associated proteins in cancer research. The increased expression of HSP-27, GST, and Annexin II, but decreased expression of L-FABP, suggests a significantly elevated incidence of LNM in CRC.
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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 | 0.001 | 0.001 |
| 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".