The Value of Survivin Gene and Proliferation of Hepatocytes in Screening for Hepatocellular Carcinomain
Bibliographic record
Abstract
BACKGROUND: The prospective surveillance programs on patients with liver diseases based on repeat ultrasound examinations of liver and serum α-fetoprotein (AFP) detection were reported having the probability of finding hepatocellular carcinoma (HCC) at its early stage, but it is time-consuming and not cost-effective. To improve the effectiveness and cost-effective of HCC surveillance program, close monitoring has been focused on patients with liver cirrhosis who have particularly high risk of HCC development. It has been found that high liver cell proliferation is a reliable predictor of HCC development, and survivin is a new gene with the role of suppressing apoptosis which has been studied mostly over the past few years. This study aimed to evaluate the usefulness of proliferation cell nuclear antigen (PCNA) and survivin detection in the process of screening cirrhotic patients with high risk of HCC development. METHODS: Total RNA was extracted from fresh specimens of HCC and liver cirrhosis. Survivin mRNA amplification was performed by reverse transcription polymerase chain reaction (RT-PCR). Immunostaining for PCNA was employed to assess liver cell proliferation activity in formalin-fixed, paraffin embedded liver specimens. Five liver specimens obtained from patients operated for hemangioma were used as normal control. The PCNA labeling index was determined as the mean value of positive cells in ten different microscopic fields. RESULTS: RT-PCR was performed in 17 HCC and 10 liver cirrhosis specimens, 11 HCC specimens showed 344bps molecular survivin DNA band in 1% agarose electrophoresis, but none of liver cirrhosis specimens showed positive band. The survivin positive rate in HCC specimens was 64.7% (11/17); The PCNA labeling index was 2.38 ± 2.11 in 30 liver cirrhosis specimens, while 10.08 ± 12.28 in 30 HCC specimens, the latter was significantly higher than the former, P = 0.003. The median PCNA labeling index of 11 survivin positive HCC specimens was 6.8 (from 0.5 to 40), which is significantly higher than that of 6 survivin negative HCC specimens (2.15). CONCLUSIONS: Survivin expressed in HCC tissues but not in liver cirrhosis tissues, this phenomenon indicates that the gene expression may occur at the late phase of liver cell cancer transformation. Compared with survivin detection, the PCNA detection on liver cells of cirrhosis patients is better to differentiate high-risk HCC transformation among liver cirrhosis patients.
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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 | 0.001 | 0.001 |
| 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".