Additional file 1 of Prognostic significance of SOCS1 and SOCS3 tumor suppressors and oncogenic signaling pathway genes in hepatocellular carcinoma
Bibliographic record
Abstract
Additional file 1: Figure S1. Workflow of this study. Figure S2. Expression of oncogenic signaling pathway genes that synergize with SOCS1 or SOCS3 in predicting prognosis by the Cox proportional harzards model in the murine models. DAB2 and CXCL8 synergize with SOCS1 or SOCS3 in predicting prognosis by the Cox proportional harzards model (shown in Table 2). As the CXCL8 (IL-8) gene is not present in the mouse, we examined the genes coding for mouse chemokines KC (Cxcl1), MIP-2 (Cxcl2) and LIX (Cxcl2), which are considered the functional equivalent of human CXCL8 in promoting neutrophil migration.(A) Partial hepatectomy was carried out on 8–10 weeks old mice lacking Socs1 or Socs3 in hepatocytes and control mice. The expression of the indicated genes in the regenerating livers was evaluated 24 h later by qRT-PCR. n = 4–6 mice per group. (B) Mice lacking Socs1 or Socs3 in hepatocytes and control mice were treated with DEN (25 mg/kg body weight) at 2 weeks of age and livers collected at 8–10 months of age. Tumor nodules and adjacent normal liver tissues were resected and expression of the indicated genes was evaluated by qRT-PCR. n = 4–6 mice per group. p-values were calculated by one-way ANOVA along with Tukey’s Multiple Comparison test: * p < 0.0001. Table S1. List of qRT-PCR primers used in this study. Table S2. Impact of the expression of oncogenic pathway genes on survival probability in the TCGA-LIHC dataset. Table S3. Combinations of high SOCS1 or high SOCS3 and oncogenic signalling pathway genes that show significant prognosis in the Cox proportional hazard model.
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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 | 0.002 | 0.033 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.860 | 0.121 |
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".