Gene Ontology and Expression Studies of Strigolactone Analogues on a Hepatocellular Carcinoma Cell Line
Bibliographic record
Abstract
Human hepatocellular carcinoma (HCC) is the most common and recurrent type of primary adult liver cancer without any effective therapy. Plant-derived compounds acting as anticancer agents can induce apoptosis by targeting several signaling pathways. Strigolactone (SL) is a novel class of phytohormone, whose analogues have been reported to possess anticancer properties on a panel of human cancer cell lines through inducing cell cycle arrest, destabilizing microtubular integrity, reducing damaged in the DNA repair machinery, and inducing apoptosis. In our previous study, we reported that a novel SL analogue, TIT3, reduces HepG2 cell proliferation, inhibits cell migration, and induces apoptosis. To decipher the mechanisms of TIT3-induced anticancer activity in HepG2, we performed RNA sequencing and the differential expression of genes was analyzed using different tools. RNA-Seq data showed that the genes responsible for microtubule organization such as TUBB, BUB1B, TUBG2, TUBGCP6, TPX2, and MAP7 were significantly downregulated. Several epigenetic modulators such as UHRF1, HDAC7, and DNMT1 were also considerably downregulated, and this effect was associated with significant upregulation of various proapoptotic genes including CASP3, TNF-α, CASP7, and CDKN1A (p21). Likewise, damaged DNA repair genes such as RAD51, RAD52, and DDB2 were also significantly downregulated. This study indicates that TIT3-induced antiproliferative and proapoptotic activities on HCC cells could involve several signaling pathways. Our results suggest that TIT3 might be a promising drug to treat HCC.
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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) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.001 | 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) | 0.002 | 0.001 |
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".