Data from Therapeutic Targeting of Metadherin Suppresses Colorectal and Lung Cancer Progression and Metastasis
Bibliographic record
Abstract
Abstract Colorectal and lung cancers account for one-third of all cancer-related deaths worldwide. Previous studies suggested that metadherin (MTDH) is involved in the development of colorectal and lung cancers. However, how MTDH regulates the pathogenesis of these cancers remains largely unknown. Using genetically modified mouse models of spontaneous colorectal and lung cancers, we found that MTDH promotes cancer progression by facilitating Wnt activation and by inducing cytotoxic T-cell exhaustion, respectively. Moreover, we developed locked nucleic acid-modified (LNA) MTDH antisense oligonucleotides (ASO) that effectively and specifically suppress MTDH expression in vitro and in vivo. Treatments with MTDH ASOs in mouse models significantly attenuated progression and metastasis of colorectal, lung, and breast cancers. Our study opens a new avenue for developing therapies against colorectal and lung cancers by targeting MTDH using LNA-modified ASO. Significance: This study provides new insights into the mechanism of MTDH in promoting colorectal and lung cancers, as well as genetic and pharmacologic evidence supporting the development of MTDH-targeting therapeutics.
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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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.010 | 0.002 |
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".