Construction of a Novel Multigene Panel Potently Predicting Poor Prognosis in Patients with Clear Cell Renal Cell Carcinoma
Bibliographic record
Abstract
We observed associations of IQGAP1 downregulation with poor overall survival (OS) in clear cell renal cell carcinoma (ccRCC). Differentially expressed genes (DEGs, n = 611) were derived from ccRCCs with (n = 111) and without IQGAP1 (n = 397) reduction using the TCGA PanCancer Atlas ccRCC dataset. These DEGs exhibit downregulations of immune response and upregulations of DNA damage repair pathways. Through randomization of the TCGA dataset into a training and testing subpopulation, a 9-gene panel (SigIQGAP1NW) was derived; it predicts poor OS in training, testing, and the full population at a hazard ratio (HR) 2.718, p < 2 × 10−16, p = 1.08 × 10−5, and p < 2 × 10−16, respectively. SigIQGAP1NW independently associates with poor OS (HR 1.80, p = 2.85 × 10−6) after adjusting for a set of clinical features, and it discriminates ccRCC mortality at time-dependent AUC values of 70% at 13.8 months, 69%/31M, 69%/49M, and 75.3%/71M. All nine component genes of SigIQGAP1NW are novel to ccRCC. The inclusion of RECQL4 (a DNA helicase) in SigIQGAP1NW agrees with IQGAP1 DEGs enhancing DNA repair. THSD7A affects kidney function; its presence in SigIQGAP1NW is consistent with our observed THSD7A downregulation in ccRCC (n = 523) compared to non-tumor kidney tissues (n = 100). Collectively, we report a novel multigene panel that robustly predicts poor OS in ccRCC.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.001 | 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".