The lncRNA TUG1 promotes cell growth and migration in colorectal cancer via the TUG1–miR-145-5p–TRPC6 pathway
Bibliographic record
Abstract
Colorectal cancer (CRC) is the third-most prevalent malignant tumor. Taurine upregulated gene 1 (TUG1), a long non-coding RNA (lncRNA), is reportedly involved in the physiological and pathological processes of CRC. However, the role of TUG1 in the progression of CRC and its underlying mechanisms are largely unknown. Here, we measured the expression of TUG1 in clinical samples from CRC patients and found that the expression level of TUG1 was higher in CRC tissues compared with the normal adjacent tissues. We then performed knockdown of TUG1 with siRNAs in two CRC cell lines and found that TUG1 knockdown inhibited the viability, proliferation, and migration of CRC cells, and reduced the ability of CRC cells to form subcutaneous tumors. Furthermore, we discovered that TUG1 affects the cellular processes in CRC cells by sponging miR-145-5p. We further found that miR-145-5p inhibits the expression of the protein-encoding gene Transient Receptor Potential Cation Channel Subfamily C Member 6 (TRPC6), and that overexpression of TRPC6 restored the inhibitory role of miR-145-5p in CRC cells. In conclusion, we have demonstrated that TUG1 exerts its role by modulating the TUG1–miR-145-5p–TRPC6 regulatory axis, thus revealing a novel molecular mechanism for the effects of TUG1 in the progression of CRC. Our data indicate that the TUG1–miR-145-5p–TRPC6 signaling pathway could serve as a target for the diagnosis and treatment 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 | 0.000 |
| 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) | 0.001 | 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".