Ki-67 and PCNA expression in prostate cancer and benign prostatic hyperplasia
Bibliographic record
Abstract
OBJECTIVE: Ki-67 is a proliferation-associated nuclear antigen and is expressed in all cycling cells except for resting cells in the G0-phase. PCNA is an acidic nuclear protein and has been recognized as a histologic marker for the G1/S phase in the cell cycle. Ki-67and PCNA labeling indices are considered to reflect cell proliferation, particularly, growth fraction. The purpose of this study is to investigate the expression levels of Ki-67 and PCNA in prostate cancer (PCa) and benign prostatic hyperplasia (BPH) and their potential on the early diagnosis of PCa. METHODS: Human prostate cancer cell lines LNCaP and PC-3, human normal prostate epithelial cell line HuPEC, tissues from patients with PCa (121 cases) and BPH (45) and 36 normal cases were examined for the expression of Ki-67 and PCNA by Reverse Transcription-Polymerase Chain Reaction (RT-PCR). Then, the association of Ki-67 and PCNA expression with clinical grading of PCa was analyzed by immunohistochemistry staining. RESULTS: The ratios of PCNA and Ki-67 expression levels in LNCaP and PC-3 were higher (P < 0.05, P < 0.001) than that in HuPEC. The two markers were differentially expressed in three tissues and showed increased expression in PCa (P < 0.05) and BPH (P < 0.05), relative to human normal prostate tissues. Compared with BPH, the ratio of Ki-67 and PCNA expressed in tumour tissue was increased (P < 0.05). The increase of Ki-67 was greater than that of PCNA. Expression of the two markers increased after different grading of PCa cases. The values of Ki-67/PCNA were: 0.073 in grade I PCa tissues, 0.119 in grade IIa PCa tissues, 0.141 in grade IIa PCa tissues, 0.234 in grade III PCa tissues. CONCLUSION: The combination of Ki-67 and PCNA, specific proliferative markers of PCa, may improve the accuracy of early diagnosis of prostatic cancer.
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) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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".