Secretomic analysis of large cell lung cancer cell lines using two-dimensional gel electrophoresis coupled to mass spectrometry
Bibliographic record
Abstract
The secretome of cancer cells is a valuable source of biomarkers that can ultimately reach the serum or other body fluids. Cancer biomarkers can facilitate early diagnosis and monitoring of the disease, contribute to our understanding of tumor biology, and support the development of more efficient therapies. Recently, high-throughput proteomic analysis of the conditioned media of cancer cell lines has shown potential to identify novel biomarkers in cancer. We used two-dimensional gel electrophoresis coupled to liquid chromatography tandem mass spectrometry to identify the secretome of the large cell lung cancer cell lines QU-DB and Mehr-80, which they were established from a Canadian and a Persian patient, respectively. A total of 130 distinct protein species were identified. Of these, 124 were previously found in serum or other body fluids, the membrane compartment or conditioned media of other cancer cell lines. Some of the proteins that we identified, e.g. IL-6, triosephosphate isomerase, PGP9.5, α-enolase, Dickkopf-1, and peroxiredoxin-1 are known putative serum markers for lung cancer, whereas others might be candidate markers for further validation in lung cancer body fluids such as IL-25, stathmin, vimentin, peptidyl-prolyl cis-trans isomerase A, transgelin-2, and chloride intracellular channel protein 4.
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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) | 0.001 | 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) | 0.002 | 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 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".