TM7SF2 as a Potential Biomarker in Colorectal Cancer: Implications for Metastasis
Bibliographic record
Abstract
Colorectal cancer (CRC) is a commonly fatal cancer and ranks as the fourth most prevalent in men and third in women worldwide. While early-stage survival rates are high, they significantly decrease with recurrence and metastasis. Thus, the early detection and treatment of metastasis-related factors can significantly improve survival rates. In this study, the transmembrane 7 superfamily member 2 (TM7SF2) gene was validated as a biomarker for predicting metastasis in CRC. Immunohistochemical staining was performed on 236 CRC tissues, and the clinicopathological factors of patients with CRC were analyzed. This evaluation revealed that TM7SF2 expression is associated with the clinical stage. Kaplan–Meier analysis confirmed the relationship between the survival rate of CRC patients and TM7SF2 expression, showing a decrease in survival rate with TM7SF2 overexpression (log-rank, p < 0.001). TM7SF2 expression was also confirmed in two pairs of primary and metastatic cell lines (SW480 and SW620). TM7SF2 knockdown was executed using siRNAs in SW480 and SW620 cells, which exhibit high expression levels. The knockdown was verified using RT-PCR and immunoblotting. Functional studies investigated the effects of TM7SF2 on cell proliferation, migration, invasion, and colony formation, revealing that all these functions were suppressed in the CRC cell lines following TM7SF2 knockdown. Therefore, TM7SF2 shows promise as a biomarker for the prevention of colorectal cancer.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".