Cancer multi-omics-based differential expression analysis and prognostic potential of identified hub targets of myco-metabolites for breast carcinoma and lung carcinoma
Bibliographic record
Abstract
Abstract Background Breast carcinoma (BC) and lung carcinoma (LC) have the highest incidence and mortality rates worldwide. In prior work, studied sample hub targets contributing to anticancer potential against BC and LC were identified through network pharmacology. In the present work, web servers UALCAN, GEPIA2, and KM plotter were used to explore the genomic and proteomic expression of these hub targets, along with their prognosis potential in BC and LC. Results Differential hub targets SRC, MAPK3, PTPN11, JAK2, ESR1, and HAP900A1 for BC and PTPN11, JAK2, ESR1, EGFR, and MAPK3 for LC, showed good prognostic potentials. Collectively, PTPN11, JAK2, and ESR1 were overlapped differential expressed hub targets involved in the significantly good prognosis of both carcinoma. Conclusion These differentially expressed hub targets may be taken into account for future BC and LC treatments due to their strong prognostic potential.
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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) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".