Targeting Integrin-Linked Kinase Suppresses Invasion and Metastasis through Downregulation of Epithelial-to-Mesenchymal Transition in Renal Cell Carcinoma
Bibliographic record
Abstract
Renal cell carcinoma (RCC) is the most common malignancy in the kidney. Antiangiogenic targeted therapies inhibit the progression of RCC, but have limited impacts on invasion or metastasis of tumor cells. Integrin-linked kinase (ILK) is a serine/threonine kinase implicated in the regulation of cell growth/survival, cell-cycle progression, epithelial-mesenchymal transition (EMT), invasion/migration, and angiogenesis. However, the role of ILK in RCC has not been evaluated. We investigated the role of ILK on cancer progression and metastasis and the therapeutic potential of ILK inhibition in RCC. Our investigation reveals that ILK is expressed at a low level in normal cells and low-stage RCC cells and is highly expressed in advanced and metastatic cells. Caki-1, a metastatic RCC cell line, showed higher expression of molecular EMT markers, including Snail and Zeb1, but decreased activity of GSK3β. Knockdown of ILK using small interference (si)-ILK minimally inhibited tumor proliferation and cell-cycle progression was not significantly affected. However, ILK knockdown suppressed the formation of stress fibers and focal adhesions and impeded phenotypic EMT markers, including cell migration and invasion, in Caki-1 and UMRC-3 cells. Finally, in vivo knockdown of ILK suppressed the progression, invasion, and metastasis of primary RCC in nude mice by downregulation of EMT markers (Snail, Zeb1, vimentin, and E-cadherin). Our results show that ILK may be essential for invasion and metastasis in RCC and regulates vimentin and E-cadherin expression by regulating the EMT-related transcription factors Snail and Zeb1. These results suggest that ILK may be a potential target in RCC.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 |
| 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".