Ribosomal Protein S6 Kinase 2 (RPS6KB2) is a Potential immunotherapeutic target for cancer with up-regulating pro-inflammatory cytokines
Bibliographic record
Abstract
Abstract Tumors are one of the main causes of death in people with illnesses. The therapy of tumors has evolved in recent years along with science and technology, and one such area of drug discovery is developing therapeutic targets for tumor treatment. Finding new pharmacological targets is increasingly urgent since tumor resistance affects how well current medications work. The chromosome 11 gene RPS6KB2 has been implicated in cell cycle regulation and has been found to express at much greater levels in tumor tissue. The association between RPS6KB2 and tumors raises the possibility that this gene could be a target for cancer treatment. Therefore, our study used data analysis and molecular biology methods to examine the potential involvement of RPS6KB2 in tumor therapy carefully. The data demonstrated that RPS6KB2, which has a poor prognosis, is aberrantly expressed in most tumors. Further data showed that RPS6KB2 is involved in tumor cell apoptosis and migration. RPS6KB2 also plays a role in tumor immune processes. We further verified the role of RPS6KB2 in liver cancer, and found that RPS6KB2 can up-regulate pro-inflammatory cytokines. In summary, RPS6KB2 maybe a novel therapeutic target.
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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.006 | 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".