Chondroitin polymerizing factor (CHPF) contributes to malignant proliferation and migration of hepatocellular carcinoma cells
Bibliographic record
Abstract
Hepatocellular carcinoma (HCC) is one of the most common malignant tumors in the human digestive system, and has been recognized as a serious threat to public health worldwide. This study explored the role of chondroitin polymerizing factor (CHPF) in the development and metastasis of HCC. Immunohistochemistry analysis was performed to detect CHPF expression in HCC tissues and para-carcinoma tissues. qRT-PCR and Western blot analysis were used to determine the mRNA and protein expression of CHPF. MTT assays, colony formation assays, and flow cytometry were used to evaluate the cell proliferation, colony formation, and cell apoptosis, respectively. Wound-healing and Transwell assays were performed to evaluate cell migration. The results show that CHPF was not only up-regulated in HCC tissues compared with para-carcinoma tissues, but was also related with more advanced stages of HCC. Further studies revealed that CHPF knockdown significantly inhibited cell proliferation and colony formation, and induce cell apoptosis of HCC cells. Moreover, suppressing the expression of CHPF reduced the migration and invasiveness of HCC cells. In conclusion, we demonstrated that CHPF plays important roles in the development and progression of HCC, and high expression levels of HCC may be related with poorer prognosis. The results from this study may provide a potential therapeutic target for HCC treatment.
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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 | 0.000 |
| 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".