Increased expression of tumor necrosis factor-α is associated with advanced colorectal cancer stages
Bibliographic record
Abstract
AIM: To detect the expression of tumor necrosis factor-α (TNF-α) in colorectal cancer (CRC) cells among Saudi patients, and correlate its expression with clinical stages of cancer. METHODS: Archival tissue specimens were collected from 30 patients with CRC who had undergone surgical intervention at King Khalid University Hospital. Patient demographic information, including age and gender, tumor sites, and histological type of CRC, was recorded. To measure TNF-α mRNA expression in CRC, total RNA was extracted from tumor formalin-fixed, paraffin-embedded, and adjacent normal tissues. Reverse transcription and reverse transcription polymerase chain reaction were performed. Colorectal tissue microarrays were constructed to investigate the protein expression of TNF-α by immunohistochemistry. RESULTS: The relative expression of TNF-α mRNA in colorectal cancer was significantly higher than that seen in adjacent normal colorectal tissue. High TNF-α gene expression was associated with Stage III and IV neoplasms when compared with earlier tumor stages (P = 0.004). Eighty-three percent of patients (25/30) showed strong TNF-α positive staining, while only 10% (n = 3/30) of patients showed weak staining, and 7% (n = 2/30) were negative. We showed the presence of elevated TNF-α gene expression in cancer cells, which strongly correlated with advanced stages of tumor. CONCLUSION: High levels of TNF-α expression could be an independent diagnostic indicator of colorectal cancer, and targeting TNF-α might be a promising prognostic tool by assessment of the clinical stages of 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 | 0.001 |
| 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 | 0.000 |
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".