Identification of characteristic TRB V usage in HBV-associated HCC by using differential expression profiling analysis
Bibliographic record
Abstract
Liver cancer is one of the most common cancers worldwide. CDR3 sequencing-based immune repertoire can be closely associated with cancer prognosis and development. Identifying the specific interaction between the TCR and cellular antigens is important for developing novel immunotherapeutic approaches for the treatment of cancer. The rearranged TCRβ loci amplified using Vβ- and Jβ-specific primers by multi-PCR and sequenced using high-throughput sequencing (HTS) in liver cancers were compared with those of T cells from healthy adult peripheral blood and from adjacent liver tissue. The T-cell repertoires within each tumor show strong similarity to one another but are distinct from those of the circulating T-cell repertoire. In addition, our results demonstrate that there are significant differences in the T-cell repertoires of HCC (hepatocellular carcinoma), ICC (intrahepatic cholangiocarcinoma), and MHC (mixed hepatocellular and cholangiocellular carcinoma). Furthermore, we found that the highly expanded clone (HEC) ratio in blood samples from liver cancer patients differed significantly from those in the blood of healthy adults and hepatitis patients (p < 0.001). The above results suggest that comparison of the T-cell repertoires of tissue and blood could be used to distinguish liver cancer patients from healthy adults and from hepatitis patients. In the future, the diversity of CDR3 sequences in liver cancer may prove to be a useful and novel biomarker for detecting aggressive tumors with high invasive or metastatic capacity.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 | 0.001 |
| 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) | 0.000 | 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 teacher head, 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".