LGR-5 as a Biomarker in Colorectal Cancer: A Systematic Review of Clinicopathological Features and Prognostic Value
Bibliographic record
Abstract
Colorectal cancer (CRC) had the third highest cancer incidence worldwide. Recent researches are targeting specific gene expression as CRC marker. Leucine rich repeat-containing G-protein coupled receptor 5 (LGR5) is one of the genes that regulate tumor metastasis and growth. The aim of this study is to explain the correlation between LGR5, clinical pathology, and prognosis of CRC. This systematic review was arranged using PRISMA for eligible cohort studies and evaluated using Newcastle-Ottawa scale. From 63 articles, 6 articles were selected as eligible articles for data extraction and evaluation. Based on the studies, LGR5 positivity was positively associated with histopathological characteristics, TNM staging, and vascular invasion. Besides, LGR5 expressions in those studies were high and could be positively detected by this marker. LGR5 regulates Wnt/β-catenin signaling pathway promotion in normal colon stem cell. Methylation of Wnt target gene promoter is potent predictor to CRC recurrence thus the expression of LGR5 can be used as CRC marker. LGR5 plays a role as pro-oncogenic factor in colorectal carcinogenesis via prostaglandin E2 and Epidermal Growth Factor signaling. LGR5 induction also could increase cancer cells chemoresistant ability. Thus, LGR5 could determine prognosis of CRC patient and were related to certain clinicopathological features and tumor progressions.
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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 | 0.003 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.004 | 0.004 |
| Bibliometrics | 0.009 | 0.010 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".