Linking mass spectrometry data to tumour activity: A preliminary investigation
Bibliographic record
Abstract
Objective To identify which metabolites and pathways distinguish tumour from non-tumour regions in patient tissues and explain their relevance using established cancer hallmarks. Background Tumours rewire cellular metabolism, leaving detectable fingerprints in mass spectrometry (MS) profiles. Biological pathways involve coordinated changes across many metabolites; hence, patterns across thousands of m/z values were condensed into the importance spectrum because single peaks rarely tell the full story of cellular activity. Methods I developed and utilised an interpretation pipeline that connects mass-to-charge (m/z) feature portraits to metabolites, maps those metabolites to pathways, and relates pathway activity to established cancer hallmarks. Discriminative m/z peaks (retaining features ≥ 98 m/z) were identified per sample and ranked in an importance spectrum. This spectrum is a profile indicating each feature’s contribution to distinguishing tumour vs non-tumour in tissue. Putative metabolite identities were identified via the Human Metabolome Database (HMDB), then aggregated to biological pathways using the Small Molecule Pathway Database (SMPDB). The results in a pathway-level view that contextualises isolated peaks as parts of coordinated biochemical processes. To ground these signals in established biology, identified pathways were cross-referenced to canonical cancer hallmarks. Each pathway was correlated to one or more hallmarks with support from literature. This provides a principled bridge from ion signals to recognised cancer biology. Results In patient tissues, high-importance m/z features are frequently mapped to upregulated metabolites in dysregulated pathways affected by tumours. This is consistent with metabolic reprogramming seen in cancer biology. Conclusion In conclusion, the metabolite and pathway patterns correlate with well-defined cancer behaviours that boost tumourigenesis, metastasis, and a pro-angiogenic microenvironment.
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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 | 0.005 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".