KIF4A-KIF4B Paralog as a Prognostic Biomarker in Lung Adenocarcinoma

Background and Objectives: Lung adenocarcinoma (LUAD) is a clinically heterogeneous malignancy, and reliable prognostic biomarkers are still needed. Kinesin family members 4A and 4B (KIF4A and KIF4B) are mitosis-related motor proteins with potential functional overlap as paralogs. However, their coordinated prognostic significance in LUAD has not been systematically investigated. Materials and Methods: Transcriptomic and clinical data from The Cancer Genome Atlas (TCGA) were analyzed to evaluate the expression patterns, clinicopathologic associations, and prognostic significance of KIF4A and KIF4B in LUAD. Correlation analysis, Kaplan–Meier survival analysis, and Cox proportional hazards regression analyses were performed. In addition, a combined paralog score and four-group expression model were evaluated. An independent LUAD cohort from the Gene Expression Omnibus (GEO; GSE81089) was analyzed for external validation. Results: KIF4A and KIF4B expression showed a strong positive correlation (r = 0.767, p < 0.001), and both genes were positively correlated with EGFR, KRAS, BRAF, MKI67, and PCNA expression. High KIF4A expression was significantly associated with age, sex, smoking status, pathologic stage, N stage, and T stage, whereas high KIF4B expression was significantly associated with age and pathologic stage. Kaplan–Meier analysis demonstrated that high expression of both KIF4A and KIF4B was associated with poorer overall survival. In Cox regression analyses, elevated expression of both genes remained significantly associated with unfavorable overall survival in univariable and multivariable models. However, when both genes were simultaneously included in the same Cox model, their individual prognostic effects were attenuated, suggesting substantial overlap in prognostic information. By contrast, the combined paralog score remained independently associated with poor overall survival. In four-group analysis, only patients with concurrent high expression of both KIF4A and KIF4B showed significantly worse overall survival compared with the low/low group. External validation demonstrated generally consistent survival patterns, although the prognostic associations were attenuated after multivariable adjustment. Conclusions: KIF4A and KIF4B expression showed substantial prognostic overlap in LUAD, and their concurrent high expression was associated with poorer overall survival. These findings suggest that KIF4A/KIF4B co-expression may serve as a candidate prognostic indicator that warrants validation in independent clinical cohorts and functional studies.

Authors

Institutions

Publication Details

Journal
Medicina
Published
2026-07-22
DOI
https://doi.org/10.3390/medicina62071424
Primary Topic
Microtubule and mitosis dynamics
Type
article
Field-Weighted Citation Impact
0.00
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

KIF4A-KIF4B Paralog as a Prognostic Biomarker in Lung Adenocarcinoma

Hyowon Hong, Jae‐Ho Lee, Jin Lee, Hyun-Soo Park
Medicina
Microtubule and mitosis dynamics
article

KIF4A-KIF4B Paralog as a Prognostic Biomarker in Lung Adenocarcinoma

Hyowon Hong, Jae‐Ho Lee, Jin Lee, Hyun-Soo Park
article en

Abstract

Background and Objectives: Lung adenocarcinoma (LUAD) is a clinically heterogeneous malignancy, and reliable prognostic biomarkers are still needed. Kinesin family members 4A and 4B (KIF4A and KIF4B) are mitosis-related motor proteins with potential functional overlap as paralogs. However, their coordinated prognostic significance in LUAD has not been systematically investigated. Materials and Methods: Transcriptomic and clinical data from The Cancer Genome Atlas (TCGA) were analyzed to evaluate the expression patterns, clinicopathologic associations, and prognostic significance of KIF4A and KIF4B in LUAD. Correlation analysis, Kaplan–Meier survival analysis, and Cox proportional hazards regression analyses were performed. In addition, a combined paralog score and four-group expression model were evaluated. An independent LUAD cohort from the Gene Expression Omnibus (GEO; GSE81089) was analyzed for external validation. Results: KIF4A and KIF4B expression showed a strong positive correlation (r = 0.767, p < 0.001), and both genes were positively correlated with EGFR, KRAS, BRAF, MKI67, and PCNA expression. High KIF4A expression was significantly associated with age, sex, smoking status, pathologic stage, N stage, and T stage, whereas high KIF4B expression was significantly associated with age and pathologic stage. Kaplan–Meier analysis demonstrated that high expression of both KIF4A and KIF4B was associated with poorer overall survival. In Cox regression analyses, elevated expression of both genes remained significantly associated with unfavorable overall survival in univariable and multivariable models. However, when both genes were simultaneously included in the same Cox model, their individual prognostic effects were attenuated, suggesting substantial overlap in prognostic information. By contrast, the combined paralog score remained independently associated with poor overall survival. In four-group analysis, only patients with concurrent high expression of both KIF4A and KIF4B showed significantly worse overall survival compared with the low/low group. External validation demonstrated generally consistent survival patterns, although the prognostic associations were attenuated after multivariable adjustment. Conclusions: KIF4A and KIF4B expression showed substantial prognostic overlap in LUAD, and their concurrent high expression was associated with poorer overall survival. These findings suggest that KIF4A/KIF4B co-expression may serve as a candidate prognostic indicator that warrants validation in independent clinical cohorts and functional studies.

MedicinaVol. 62(7)
Keimyung University (KR)
No poverty
Openalex Percentile: Top 11%
Microtubule and mitosis dynamics
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.