Immune implication of FAM83D gene in hepatocellular carcinoma
Bibliographic record
Abstract
FAM83D has been demonstrated to contribute to tumorigenesis. However, its immune effects in hepatocellular carcinoma (HCC) have not been reported. This study aimed to identify the immune role of FAM83D in HCC. FAM83D was over-expressed in HCC and contributed to poor prognosis according to the results of data analysis based on The Cancer Genome Atlas (TCGA). Afterward, the levels of immune cells infiltration were found to be correlated with the expression level of FAM83D in HCC. Through TISIDB and cBioPortal network tools, a total of 82 FAM83D-associated genes were screened out, including 12 immunoinhibitors, 20 immunostimulators and 50 tightly co-expressed genes. TCGA cohort was divided into train set and test set on the basis of the proportion of 7:3. According to FAM83D-associated immunomodulators, a four gene predicted model was established using train set via the Cox regression analysis. Survival analysis demonstrated that the overall survival (OS) of high-risk HCC patients was poor compared with the patients in low-risk group. The reliability and predicted power of the risk-score model were identified by a receiver operating characteristic (ROC) curve. A risk-score based nomogram as well as a calibration curve, which were created could be used to anticipate patient’s 1-year, 3-year and 5-year survival probabilities. The test set was used to validate these results. Our findings showed that the FAM83D gene was related with HCC immunity. The immune marker chosen could be a promising biomarker for HCC prognosis.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 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".