Cancer Relevance of Circulating Antibodies Against LINE-1 Antigens in Humans
Bibliographic record
Abstract
Long interspersed nuclear element-1 (LINE-1 or L1), the most abundant family of autonomous retrotransposons occupying over 17% of human DNA, is epigenetically silenced in normal tissues by the mechanisms involving p53 but is frequently derepressed in cancer, suggesting that L1-encoded proteins may act as tumor-associated antigens recognized by the immune system. In this study, we established an immunoassay to detect circulating autoantibodies against L1 proteins in human blood. Using this assay in >2,800 individuals with or without cancer, we observed significantly higher IgG titers against L1-encoded ORF1p and ORF2p in patients with lung, pancreatic, ovarian, esophageal, and liver cancers than in healthy individuals. Remarkably, elevated levels of anti-ORF1p-reactive IgG were observed in patients with cancer with disease stages 1 and 2, indicating that the immune response to L1 antigens can occur in the early phases of carcinogenesis. We concluded that the antibody response against L1 antigens could contribute to the diagnosis and determination of immunoreactivity of tumors among cancer types that frequently escape early detection. SIGNIFICANCE: The discovery of autoantibodies against antigens encoded by L1 retrotransposons in patients with five poorly curable cancer types has potential implications for the detection of an ongoing carcinogenic process and tumor immunoreactivity.
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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 | 0.000 | 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) | 0.002 | 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".