A comprehensive catalogue of functional genetic variations in the EGFR pathway: Protein–protein interaction analysis reveals novel genes and polymorphisms important for cancer research
Bibliographic record
Abstract
The EGFR pathway is a critical signaling pathway deregulated in many solid tumors. In addition to the initiation and progression of cancer, the EGFR pathway is also implicated in variable treatment responses and prognoses. Genetic variation in the form of Single Nucleotide Polymorphisms (SNPs) can affect the function/expression of the EGFR pathway genes. Here, we applied a systematic and comprehensive approach utilizing diverse public databases and in silico analysis tools to select putative functional genetic variations from 244 genes involved in the EGFR pathway. Our data comprises 649 SNPs. Three hundred sixty SNPs are predicted to have biological consequences (functional SNPs). These SNPs can be directly used in further studies to test their association with risk, treatment response and prognosis in cancer. To systematically cover the EGFR pathway, we also performed a network-based analysis to further select putative functional SNPs from the genes whose protein products physically interact with the EGFR pathway proteins. We utilized protein-protein interaction information and focused on 14 proteins that have a high degree of connectivity (interacting with > or = 10 proteins) with the EGFR pathway genes identified to have functional SNPs (f-EGFR genes). Two of these proteins (FYN and LCK) had interactions with 17 of the f-EGFR genes, yet both lacked any putative functional SNP. However, our analysis indicated the presence of potentially functional SNPs in 9 other highly interactive proteins. The genes and their SNPs identified in the network-based analysis represent potential candidates for gene-gene and SNP-SNP interaction studies in cancer research.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.007 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".