<i>KIF14</i> Messenger RNA Expression Is Independently Prognostic for Outcome in Lung Cancer
Bibliographic record
Abstract
PURPOSE: The mitotic kinesin KIF14 is overexpressed in multiple cancers including lung cancer. Therefore, we investigated KIF14 expression in association with clinical variables and the effect of KIF14 on in vitro colony formation in non-small-cell lung carcinoma. EXPERIMENTAL DESIGN: RNA was extracted from 129 untreated, resected tumors and KIF14 expression was quantified by real-time reverse transcription-PCR. Associations with clinical variables were determined by standard statistical methods. KIF14 expression was knocked down by small interfering RNA in H1299 and HeLa cells; proliferation and growth in soft agar were assayed. RESULTS: Squamous cell carcinoma had the highest KIF14 level, followed by large-cell undifferentiated carcinoma, then adenocarcinoma (P = 0.002). KIF14 level decreased with differentiation (P = 0.01) but was not associated with pathologic stage, T or N stage, or sex. When dichotomized about the median, KIF14 overexpression significantly decreased disease-free survival (Kaplan-Meier log-rank, P = 0.01) and trended toward decreasing overall survival (P = 0.08). In a univariate Cox proportional hazard regression, increasing KIF14 expression decreased disease-free survival [P = 0.01; hazard ratio, 1.44 (95% confidence interval, 1.09-1.91)]. In a multivariate Cox regression, including stage, differentiation, histology, and tumor purity as covariates, KIF14 overexpression remained an independent prognostic factor for disease-free survival [P = 0.01; hazard ratio, 1.45 (95% confidence interval, 1.09-1.94)]. Knockdown of KIF14 in non-small-cell lung carcinoma and cervical carcinoma cell lines decreased proliferation and colony formation in soft agar. CONCLUSIONS: KIF14 expression is independently prognostic for disease-free survival in lung cancer and knockdown decreases tumorigenicity in vitro, showing that it is a clinically relevant oncogene and an exciting therapeutic target for further study.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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) | 0.001 | 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".