KIF18B Is Essential for Lung Adenocarcinoma Progression Through the E2F Transcriptional Network

TL;DR

This study identifies KIF18B as a key driver of lung adenocarcinoma progression through its role in the E2F transcriptional network.

Contribution

The novel contribution is the discovery that KIF18B promotes LUAD progression via the E2F transcriptional network.

Findings

KIF18B is significantly upregulated in LUAD and associated with poor patient survival.

KIF18B knockdown suppresses LUAD cell proliferation, migration, and tumor growth.

KIF18B regulates E2F target genes, and E2F overexpression rescues inhibited proliferation.

Abstract

Lung adenocarcinoma (LUAD) remains a leading cause of cancer-related mortality worldwide, highlighting the urgent need to identify novel prognostic biomarkers and therapeutic targets. Kinesin Family Member 18B (KIF18B) is implicated in mitosis, yet its precise role in LUAD pathogenesis remains poorly defined. This study investigates the oncogenic and therapeutic role of KIF18B in LUAD. Integrated analysis of The Cancer Genome Atlas Program (TCGA) and Gene Expression Omnibus (GEO) datasets revealed that KIF18B is significantly upregulated in LUAD tissues, with its elevated expression strongly associated with an advanced pathological stage, high grade, and poor patient survival. Single-cell sequencing data analysis further indicated that KIF18B expression in LUAD is closely linked to key malignant processes, including cell cycle progression, proliferation, migration, and epithelial–mesenchymal transition (EMT). Functional experiments demonstrated that KIF18B knockdown markedly suppressed LUAD cell proliferation, migration, and invasion in vitro and inhibited tumor growth in vivo. Mechanistically, transcriptomic and pathway analyses revealed that KIF18B depletion downregulates Early 2 Factor (E2F) target genes. Luciferase reporter assays confirmed diminished E2F reporter activity as well as E2F2 promoter activity upon KIF18B silencing, while overexpression of E2F1, E2F2, or E2F3 rescued the inhibited proliferative phenotypes induced by KIF18B loss. Collectively, our findings establish KIF18B as an essential driver of LUAD progression that acts through the E2F transcriptional network, nominating it as a promising diagnostic and therapeutic target.

Figures

Full text

1. Introduction

LUAD is a predominant histological subtype of lung cancer and remains a leading cause of cancer-related mortality worldwide [1,2]. Despite advancements in targeted therapies and immunotherapy, a significant number of patients experience disease recurrence or possess tumors that are refractory to existing treatments [3,4,5]. This persistent clinical challenge underscores an unmet need to identify novel molecular regulators that can serve not only as robust prognostic biomarkers but also as actionable therapeutic targets to improve patient stratification and guide the development of more effective strategies [6].

In the pursuit of such targets, molecular motors ensuring mitotic fidelity have emerged as a compelling focus due to their fundamental role in maintaining genomic stability. The kinesin superfamily of proteins (KIFs) are microtubule-dependent molecular motors essential for critical processes including chromosome segregation and spindle assembly [7]. Their dysregulation is a well-established source of chromosomal instability and aneuploidy, recognized hallmarks of cancer progression [8,9,10]. Within this family, the KIF18 subfamily plays a specialized role in regulating chromosome alignment and microtubule dynamics during mitosis [11]. KIF18B, in particular, functions to modulate microtubule length and facilitates proper chromosome progression [12,13,14,15]. While pan-cancer analyses indicate that KIF18B is frequently upregulated in various malignancies and its elevated expression correlates with poorer clinical outcomes [16], its precise functional and mechanistic contributions in specific cancers such as LUAD remain largely unexplored.

Emerging evidence specifically in LUAD hints at a significant role for KIF18B. Preliminary bioinformatic studies associate high KIF18B levels with advanced disease stage [17,18], and a pan-cancer analysis links its expression to immune infiltration patterns [19]. Notably, single cell sequencing analyses position KIF18B expression at the nexus of core pro-tumorigenic processes, including cell cycle progression and proliferation [20]. However, these studies have primarily established correlative relationships. A critical knowledge gap persists: it remains fundamentally unclear whether KIF18B overexpression in LUAD functions merely as a passive biomarker or acts as an active, causative regulators of malignancy. Moreover, the specific downstream molecular mechanisms through which KIF18B might exert its potential oncogenic effects in the LUAD context are entirely undefined. Addressing these questions is essential to validate KIF18B’s candidacy as a therapeutic target.

To bridge this gap, the present study was designed not only to confirm associations but to definitively establish the active oncogenic function and elucidate the core mechanism of KIF18B in LUAD. This represents a significant advance beyond prior descriptive work. While a previous study suggested KIF18B might promote tumor progression via Rac1/AKT/mTOR signaling in a single cell line [18], our investigation employs a comprehensive, multi-pronged strategy. We systematically evaluate KIF18B’s role through three integrated aims: first, to rigorously validate its independent clinical and prognostic significance across multiple large-scale cohorts; second, to determine its functional necessity for LUAD cell proliferation, migration, and in vivo tumor growth using definitive loss-of-function models; and third, to mechanistically dissect the key downstream signaling axis responsible for its pro-tumorigenic activity.

Our findings establish KIF18B as a critical regulator—not merely a correlate—of LUAD aggressiveness. We identify the E2F network as its key downstream effector, a novel mechanistic insight in LUAD. This integrated study transforms KIF18B from a biomarker candidate into a validated critical regulator with a clear molecular role, providing a compelling rationale for its therapeutic and prognostic targeting.

2. Results

2.1. KIF18B Is Upregulated Across Multiple Cancer Types and Correlates with Poor Prognosis

To further investigate the role of KIF18B in tumor development, a systematic evaluation of its expression levels was performed across multiple cancer types using TCGA pan-cancer transcriptomic data [16]. The analysis revealed significant upregulation of KIF18B in most cancer cohorts compared to corresponding normal tissues, including Bladder Urothelial Carcinoma (BLCA), Breast Invasive Carcinoma (BRCA), Cholangiocarcinoma (CHOL), Colon Adenocarcinoma (COAD), Esophageal Carcinoma (ESCA), Head and Neck Squamous Cell Carcinoma (HNSC), Kidney Chromophobe (KICH), Kidney Renal Clear Cell Carcinoma (KIRC), Kidney Renal Papillary Cell Carcinoma (KIRP), Liver Hepatocellular Carcinoma (LIHC), LUAD, Lung Squamous Cell Carcinoma (LUSC), Pancreatic Adenocarcinoma (PAAD), Prostate Adenocarcinoma (PRAD), Stomach Adenocarcinoma (STAD), Thyroid Carcinoma (THCA), and Uterine Corpus Endometrial Carcinoma (UCEC) (Figure 1A). Subsequent prognostic assessment of TCGA cancer cohorts further identified that high KIF18B expression correlated with significantly reduced overall survival in multiple cancer types, including KIRC, ACC, LUAD, MESO, LIHC, PAAD, SARC, and CHOL (Figure 1B).

2.2. Elevated KIF18B Expression Correlates with Poor Prognosis in LUAD

To evaluate the prognostic value of KIF18B in LUAD and its association with clinical outcomes, patients were stratified into high- and low-expression groups based on the mean KIF18B expression level. Survival analysis demonstrated that compared to the low-expression group, patients with high KIF18B expression exhibited significantly shorter progression-free interval, overall survival, disease-specific survival (Figure 2A). To validate these findings, two independent datasets were analyzed. Results confirmed that high KIF18B expression was also significantly associated with poor prognosis in both the GSE31210 and GSE30219 datasets (Figure 2B), consistent with the TCGA LUAD analysis results [16,21,22,23]. To further quantify the prognostic value of KIF18B, a predictive nomogram was developed by integrating KIF18B expression with clinicopathological parameters (Figure 2C) [24]. The calibration curves demonstrated excellent agreement between predicted and observed outcomes (Figure 2D), indicating strong predictive accuracy of the model [25]. Collectively, these findings establish elevated KIF18B expression as an important biomarker for poor prognosis in LUAD patients.

2.3. KIF18B Is Upregulated in LUAD Tissues and Correlates with Pathological Stage and Grade

Multiple analytical approaches demonstrated that KIF18B mRNA is upregulated across multiple tumor types, notably in LUAD (Figure 3A). Interrogation of TCGA database corroborated that KIF18B expression is significantly elevated in LUAD tissues relative to matched adjacent normal tissues, with expression levels escalating in accordance with disease progression (Figure 3B). In a mouse model of metastasis, Altorki et al. reported that Kif18b expression was markedly altered during metastatic dissemination, particularly within lymph nodes and liver tissues (Figure 3C) [26]. Additionally, KIF18B expression exhibited a significant positive correlation with LUAD T stage, where higher expression levels were associated with more advanced T stages (Figure 3D). Supporting this, analysis of the Gene Expression Profiling Interactive Analysis (GEPIA2) database indicated progressively increased KIF18B expression with advancing tumor stage (Figure 3E) [16]. Proteomic data from the UALCAN portal confirmed differential KIF18B expression in LUAD, showing elevated levels in higher-grade tumors and significant upregulation in subgroups with MYC/MYCN alterations or chromatin modification dysregulation compared to normal controls (Figure 3F) [27]. Collectively, these findings indicate that KIF18B is aberrantly overexpressed in LUAD, and its expression correlates strongly with tumor progression and clinical stage.

2.4. KIF18B Is Associated with Critical Cancer-Related Pathways

To investigate the functional relevance of KIF18B in cancer, we analyzed single-cell sequencing data from the CancerSEA database, evaluating its expression against 14 distinct functional states across multiple tumor types [20]. The results demonstrated that KIF18B expression is positively associated with cell cycle progression and proliferation in most cancers (Figure 4A). In LUAD specifically, strong positive correlations were observed with key oncogenic processes, including cell cycle (r = 0.69), proliferation (r = 0.58), DNA damage (r = 0.56), DNA repair (r = 0.51), invasion (r = 0.39), and EMT (r = 0.30) (Figure 4B,C). In contrast, a modest negative correlation was found with inflammatory activity (r = −0.29) (Figure 4B,C). These data suggest that KIF18B is broadly linked to proliferative and aggressive phenotypes in cancer, with particularly pronounced associations in LUAD.

2.5. KIF18B Knockdown Impairs LUAD Cell Proliferation and Cell Cycle Progression

To investigate the functional role of KIF18B in the malignant progression of LUAD, we established stable KIF18B knockdown models in A549 and PC-9 cell lines. The efficacy of knockdown was confirmed at both the transcriptional and protein levels by qRT-PCR and Western blot analysis, respectively (Figure 5A,B). Subsequent functional assays demonstrated that KIF18B depletion markedly impaired proliferative capacity. Real-time live-cell imaging revealed substantial suppression of proliferation in both A549 and PC-9 cells following KIF18B knockdown (Figure 5C). Flow cytometric cell cycle analysis further indicated a significant redistribution of cell populations across cell cycle phases. Specifically, A549 cells exhibited accumulation in G2/M phase (increasing from 5.28% in controls to 10.66%), whereas PC-9 cells showed a pronounced increase in S-phase fraction (from 43.99% to 51.34%) (Figure 5D). To delineate the basis for this cell cycle disruption, we performed an EdU incorporation assay, which confirmed a substantial reduction in DNA replication activity in KIF18B-deficient cells (Figure 5E). Consistent with this finding, immunofluorescence staining for phospho-histone H3 (pHH3) showed a significant decrease in the number of mitotic cells (Figure 5F). Collectively, these data indicate that KIF18B loss arrests cell cycle progression. Furthermore, the long-term proliferative potential was severely impaired, as shown by colony formation assays in which KIF18B knockdown profoundly reduced clonogenic survival in both A549 and PC-9 cells (Figure 5G). Importantly, this anti-proliferative effect was consistently observed in vivo: in a nude mouse xenograft model, tumors formed from KIF18B-deficient cells displayed significantly attenuated growth relative to controls (Figure 5H). Together, these findings unequivocally establish that KIF18B promotes malignant growth in LUAD models both in vitro and in vivo.

2.6. KIF18B Depletion Impairs the Migratory Capacity of LUAD Cells

To further investigate the role of KIF18B in LUAD cell motility, we utilized stable KIF18B-knockdown models in A549 and PC-9 cells and performed continuous, real-time monitoring of cellular movement. Wound-healing assays revealed that KIF18B knockdown significantly attenuated wound closure in both cell lines compared to control cells (Figure 6A–C). Over the 48 h observation period, the wound gaps in knockdown groups remained substantially wider, indicating a pronounced impairment in collective cell migration capacity. To characterize migratory behavior at single-cell resolution, we conducted trajectory tracking analysis. Results demonstrated that KIF18B-deficient cells exhibited a significantly reduced average migration distance (Figure 6D,E). Of note, cells undergoing division, apoptosis, or those exiting the field of view were systematically excluded from analysis to ensure accurate assessment of intrinsic motility. Based on the established role of kinesin-8 family proteins in regulating cell migration through microtubule and cytoskeletal remodeling, we hypothesized that KIF18B, as a core member of this subfamily, influences LUAD cell motility via similar mechanisms. Immunofluorescence analysis of α-tubulin supported this hypothesis: control cells exhibited well-organized, radially extended microtubule networks, whereas KIF18B-knockdown cells displayed severe cytoskeletal disorganization, characterized by shortened, aggregated microtubules and loss of structural coherence (Figure 6F,G). In summary, these findings establish KIF18B as a critical regulator of motility in A549 and PC-9 cells. KIF18B likely supports migratory capacity by maintaining microtubule network integrity, and its depletion leads to cytoskeletal disruption and consequent motility defects.

2.7. KIF18B May Regulate Key Cancer-Related Signaling Pathways in LUAD

To investigate the functional role of KIF18B in lung adenocarcinoma, transcriptomic data from the TCGA LUAD cohort were analyzed using the LinkedOmics platform [29]. A volcano plot displays genes whose expression levels significantly correlate with KIF18B, with red and green dots representing significant positive and negative correlations, respectively (Figure 7A). A heatmap further illustrates the expression patterns of the top 35 genes most positively correlated with KIF18B in LUAD tissues (Figure 7B). To delineate the association between KIF18B and oncogenic signaling pathways, transcriptomes from KIF18B high- and low-expression groups were systematically compared. Differential expression analysis identified DEGs using the edgeR package, followed by functional enrichment analysis. The results demonstrated that KIF18B-associated genes are significantly enriched in cell cycle-related processes, particularly cell cycle checkpoints and sister chromatid segregation (Figure 7C,D). Gene Set Enrichment Analysis (GSEA) further revealed significant associations between KIF18B and cancer-related key pathway, including hallmark E2F targets,PI3K-AKT-mTOR signaling, and hallmark mTORC1 signaling (Figure 7E). Consistently, expression correlation analysis confirmed strong positive correlations between KIF18B and specific cell cycle regulators such as MYBL2, CCNB2, and KIF2C (Figure 7F). In summary, this multi-faceted study demonstrates that KIF18B plays an important role in LUAD pathogenesis by participating in the regulation of multiple cancer-related signaling pathways, particularly those controlling cell cycle progression.

2.8. KIF18B Exerts Its Oncogenic Function via the E2F Transcriptional Network

To elucidate the molecular mechanisms underpinning KIF18B’s role in LUAD pathogenesis, we performed transcriptome profiling via RNA sequencing on A549 cells with stable KIF18B knockdown. This unbiased approach identified 1704 DEGs, with 889 upregulated and 815 downregulated (Figure 8A). Gene Ontology (GO) enrichment analysis demonstrated that the DEGs were significantly associated with critical biological processes, particularly those involved in chromosome segregation and DNA replication (Figure 8B). In a consistent manner, KEGG pathway analysis further revealed notable enrichment in multiple oncogenic signaling cascades, including cell cycle checkpoints, activation of ATR in response to replication stress, and regulation of the G0 and early G1 phases (Figure 8C). GSEA demonstrated that KIF18B knockdown significantly perturbed multiple gene expression pathways, including E2F1_UP.V1_UP and RB_P107_DN.V1_UP, (Figure 8D–F).

We therefore focused on the E2F network for validation. qRT-PCR analysis confirmed the downregulation of several key E2F-related transcripts upon KIF18B depletion, including E2F1, E2F2, E2F3, B-Myb, KIF2C, and KIF20A (Figure 8G). This suppression was further validated at the protein level, where Western blotting showed a marked reduction in E2F1 and E2F2 in both A549 and PC-9 cells (Figure 8H). To determine whether KIF18B regulates the transcriptional activity of these factors, we conducted dual-luciferase reporter assays. The results showed that KIF18B knockdown significantly repressed the promoter activity of E2F reporter activity as well as E2F2 promoter (Figure 8I), positioning them as downstream effectors.

Collectively, these findings establish that KIF18B promotes malignancy by activating the E2F transcriptional network. To directly test this causal relationship, we performed rescue experiments. Ectopic expression of E2F1, E2F2, or E2F3 successfully restored the cell proliferation inhibited by KIF18B knockdown (Figure 8J). This functional rescue provides compelling evidence that the E2F pathway is a critical downstream axis through which KIF18B drives LUAD progression. Based on these results, we propose a mechanistic model in which KIF18B promotes lung cancer progression by regulating E2F transcription factors (Figure 8K).

3. Discussion

Our study establishes KIF18B as a key regulator of LUAD progression, uncovering a previously uncharacterized functional and mechanistic link to the E2F transcriptional network. While KIF18B is recognized for its role in mitosis and has been reported to be broadly upregulated across cancers [30], its precise oncogenic function—particularly in a tissue-specific context—has remained elusive. Here, through integrated bioinformatic, functional, and mechanistic analyses, we provide conclusive evidence that KIF18B is not merely a passive biomarker, but an active regulator of malignant phenotypes in LUAD.

Previous studies have linked KIF18B to LUAD through bioinformatic association [17], pan-cancer co-occurrence with epigenetic and immune features [19], and promotion of tumor progression via Rac1/AKT/mTOR signaling in a single cell line [18]. Similarly, a recent pan-cancer bioinformatics study in glioblastoma also reported KIF18B overexpression correlating with poor prognosis, hypomethylation, and enrichment in cell cycle and E2F-related pathways [31]. However, these reports lacked causal validation, LUAD-specific mechanistic dissection, and functional rescue. Addressing these gaps, our data demonstrate that KIF18B expression is strongly linked to core malignant processes—especially cell cycle, proliferation, and invasion—within LUAD at single-cell resolution (Figure 4). Clinically, elevated KIF18B consistently predicts advanced stage, higher grade, and poorer survival across multiple independent cohorts (Figure 2 and Figure 3), underscoring its prognostic relevance.

Functionally, KIF18B is essential for sustaining LUAD aggressiveness. Its depletion robustly suppresses proliferation (Figure 5C), induces cell-cycle arrest in a cell-line-dependent manner (Figure 5D), impairs clonogenic and in vivo tumor growth (Figure 5G,H), and disrupts microtubule architecture and migratory capacity (Figure 6F,G). These findings position KIF18B as a central node supporting both proliferative and motile phenotypes in LUAD.

Mechanistically, we identify the E2F transcriptional network as a critical downstream effector of KIF18B. Transcriptomic and pathway analyses reveal that KIF18B loss leads to pronounced downregulation of E2F targets and related cell-cycle genes (Figure 8A–F). This regulation occurs at the transcriptional level, as KIF18B knockdown significantly represses E2F reporter and promoter activity (Figure 8I). Most conclusively, rescue experiments demonstrate that ectopic expression of E2F1, E2F2, or E2F3 reverses the anti-proliferative effects of KIF18B silencing (Figure 8J), establishing a direct functional dependency. Together, these results delineate a KIF18B–E2F axis that is specifically operative and essential in LUAD progression.

Notably, KIF18B’s role extends beyond transcriptional regulation to cytoskeletal integrity. We show that its depletion severely disrupts microtubule network organization (Figure 6F,G), likely contributing to the observed migration defects and reinforcing its multifunctional role in LUAD biology. This dual capacity—to coordinate both cell-cycle progression via E2F and cytoskeletal dynamics via microtubule regulation—highlights KIF18B as a multifaceted oncoprotein that could be targeted to simultaneously impair proliferation and metastasis.

In summary, we propose a model wherein KIF18B drives LUAD progression through concerted activation of E2F-mediated transcription and maintenance of microtubule stability, thereby promoting tumor growth and invasive potential. These insights not only advance the molecular understanding of LUAD but also nominate KIF18B as a promising therapeutic target, particularly in tumors with activated E2F signaling or cytoskeletal dependency.

Limitations and Future Directions

We acknowledge several limitations of this study. First, whether KIF18B regulates E2F directly or through intermediate effectors requires further biochemical investigation. Second, while subcutaneous xenografts support tumor growth phenotypes, orthotopic or metastatic models would better recapitulate the lung microenvironment and invasive behavior. Third, prospective clinical validation is needed to confirm KIF18B as a standalone prognostic biomarker. Finally, developing selective KIF18B inhibitors—and testing their efficacy alone or in combination with E2F-pathway agents—represents an essential translational next step.

Despite these limitations, our work provides a robust foundation for considering KIF18B as a functionally impactful oncogene in LUAD and a candidate for therapeutic intervention.

4. Materials and Methods

4.1. Data Sources and Processing Methods

The RNA sequencing (RNA-seq) data and corresponding clinical information for LUAD patients utilized in this study were obtained from public databases: TCGA (https://portal.gdc.cancer.gov/, accessed on 18 June 2024) and the GEO https://www.ncbi.nlm.nih.gov/geo/, accessed on 18 June 2024). Specifically, the TCGA LUAD dataset comprised 483 tumor samples and 59 normal tissue samples, serving as the primary cohort for analysis [23]. KIF18B messenger RNA (mRNA) expression data were extracted from the TCGA database to analyze its expression differences across various tumor types and normal tissues, as well as to investigate the relationship between its expression levels and tumor clinical stage. Meanwhile, mRNA expression data of MYBL2, CCNB2, and KIF2C were extracted to evaluate their correlations with KIF18B expression levels. Survival analysis integrated the GSE31210 and GSE30219 datasets from the GEO database and was further validated in the TCGA LUAD dataset [21,22]. Both TCGA and GEO are public databases, for which informed consent was obtained from all involved patients during original data collection. As this study relies entirely on analysis of these de-identified open-source data without involving new patient samples or interventions, no ethical review issues or other conflicts of interest are present.

4.2. Survival Analysis and Statistical Analysis

Kaplan–Meier survival analysis was performed to assess the association between KIF18B expression and patient prognosis. Patients were stratified into high- and low-expression groups based on the median expression level of KIF18B. Survival curves were plotted and compared using the log-rank test implemented via the survminer package in R (version 3.6.2, R Foundation for Statistical Computing, Vienna, Austria).

All other statistical analyses were conducted using GraphPad Prism (version 9.0, GraphPad Software, San Diego, CA, USA). For continuous variables that followed a normal distribution and exhibited homogeneity of variance, between-group comparisons were performed with the two-tailed unpaired Student’s t-test. If the normality or equal-variance assumptions were not met, the Mann–Whitney U test was applied. All statistical tests were two-sided, and a p-value < 0.05 was considered statistically significant. Data are presented as mean ± standard deviation (SD) for normally distributed variables or as median with interquartile range (IQR) for non-normally distributed variables, with corresponding p-values and test methods indicated in the figures and legends.

4.3. Functional Enrichment Analysis

To systematically investigate the biological functions and signaling pathways associated with KIF18B expression, we performed multi-level enrichment analyses on DEGs identified between high- and low-KIF18B expression groups. GO and KEGG pathway enrichment analyses were conducted using the ClusterProfiler package in R (version 3.6.2, R Foundation for Statistical Computing, Vienna, Austria), with significantly enriched terms defined as those with a false discovery rate (FDR) < 0.05. Additionally, GSEA was carried out using the fgsea package (version 1.28.0) in R (version 3.6.2, R Foundation for Statistical Computing, Vienna, Austria) to identify pathways with coordinated expression changes correlated with KIF18B expression levels. Significance was determined at FDR < 0.25 and nominal p-value < 0.05. These integrated analyses provide a comprehensive view of the biological processes and signaling networks potentially regulated by KIF18B in LUAD.

4.4. Cell Culture and Lentivirus-Mediated Gene Knockdown

Human LUAD cell lines A549 and PC-9 were sourced from the Chinese Academy of Sciences Shanghai cell bank. All cell lines were cultured in media supplemented with 10% fetal bovine serum (Excell), penicillin (10^7^ U/L), and streptomycin (10 mg/L), and maintained at 37 °C in a humidified atmosphere of 5% CO_2_ [31]. Routine authentication via short tandem repeat analysis and mycoplasma contamination testing were performed on all cell lines. Stable KIF18B-knockdown A549 and PC9 cell lines were generated by lentiviral transduction. As previously described, KIF18B-specific shRNA and negative control (NC) shRNA sequences were cloned into the pLKO.1-puro lentiviral vector (see Supplementary Table S1 for sequences) [32]. Lentiviral particles were prepared using a third-generation packaging system. The viral supernatant was then collected and concentrated. Lung adenocarcinoma (LUAD) cells were transduced with the lentivirus in medium containing 5 μg/mL polybrene (MCE, Monmouth Junction, NJ, USA). At 48 h post-transduction, cells were harvested to establish stable polyclonal cell lines for all subsequent experiments. The shRNA sequences used are listed in Supplementary Table S1.

4.5. RNA Extraction and Quantitative RT-PCR

Total RNA was isolated from LUAD cells using TRIzol reagent (Thermo Fisher Scientific, Waltham, MA, USA). According to the manufacturer’s protocol for the reverse transcription kit, 500 ng of total RNA was reverse-transcribed into cDNA using random primers and PrimeScript RTase (Takara Bio, Kusatsu, Shiga, Japan). Quantitative real-time PCR (qPCR) was then performed with TB Green Premix Ex Taq II (Takara Bio, Kusatsu, Shiga, Japan) on a StepOnePlus Real-Time PCR System (Bio-Rad, Hercules, CA, USA). Gene expression levels were normalized to GAPDH as an internal control, and relative quantification was determined using the 2^–ΔΔCt^ method [33]. The primer sequences used are listed in Supplementary Table S2.

4.6. Western Blot Analysis

For protein extraction, cells were lysed in RIPA buffer containing a protease inhibitor cocktail (Bimake, Houston, TX, USA) and centrifuged to obtain the supernatant. Protein concentrations were measured using the Quick Start Bradford Assay Kit (Bio-Rad, Hercules, CA, USA). Equal amounts of protein were separated by SDS-PAGE, transferred to PVDF membranes, blocked, and incubated with primary antibodies overnight at 4 °C. After washing, membranes were incubated with HRP-conjugated secondary antibodies (Thermo Fisher Scientific, Waltham, MA, USA), and signals were detected using enhanced chemiluminescence (ECL). All steps followed standard protocols [33], and antibody details are listed in Supplementary Table S3.

4.7. Cell Proliferation Assay

Cell proliferation was dynamically monitored in real time using the JULI Stage live-cell analyzer (NanoEntek, Seoul, Republic of Korea). Cells were seeded in 6-well plates at an initial density of 20–30% confluence. The system automatically captured phase-contrast images at 2 h intervals over a 48 h period. Cell confluence was quantified from the image series using the integrated JULI STAT analysis software (version 2.0.1, NanoEntek, Seoul, Republic of Korea).

4.8. Cell Cycle Analysis

Cell cycle distribution was assessed by flow cytometry. Cells were fixed in 75% ethanol at 4 °C for 24 h, then treated with RNase A (37 °C, 30 min) and stained with propidium iodide (PI) on ice for 30 min. DNA content was analyzed using a flow cytometer, and the percentages of cells in G0/G1, S, and G2/M phases were determined with appropriate cell-cycle modeling software.

4.9. Cell Migration Assay

Cell migratory capacity was evaluated using a wound-healing (scratch) assay. After forming a confluent monolayer in 6-well plates, a uniform scratch was created using a sterile 200 μL pipette tip. The plates were washed to remove detached cells and incubated in serum-reduced medium. Wound closure was monitored by capturing phase-contrast images at 4 h intervals over 24 h. Migration distance was quantified by measuring the remaining wound area at each time point using ImageJ software (version 1.48, National Institutes of Health, Bethesda, MD, USA), and the relative wound closure rate was calculated.

4.10. In Vivo Tumorigenesis Assay in Nude Mice

To assess tumor growth in vivo, Balb/C nude mice received subcutaneous injections in the dorsal flank with A549 cells (2 × 10^6^ cells per mouse) stably transduced with either NC shRNA or KIF18B shRNA. Tumor volume was measured weekly. On day 34 post-inoculation, the mice were euthanized, and the resulting tumors were excised and weighed. All animal experiments were reviewed and approved by the Institutional Animal Care and Use Committee of Chongqing Medical University (IACUC-CQMU).

4.11. EdU Cell Proliferation Assay

Cell proliferation was further evaluated using the EdU (5-ethynyl-2’-deoxyuridine) incorporation assay with the Cell-Light EdU Apollo567 In Vitro Kit (RiboBio, Guangzhou, China), following the manufacturer’s protocol. In brief, cells in the logarithmic growth phase were seeded in 96-well plates, pulsed with 10 μM EdU for 2 h, and then fixed. The incorporated EdU was visualized via Apollo567 staining, and cell nuclei were counterstained with Hoechst 33342. Fluorescent images were captured using a Leica fluorescence microscope (Leica Microsystems, Wetzlar, Germany).

4.12. Immunofluorescence Staining

For immunofluorescence, cells grown on glass coverslips were processed using standard procedures, which included fixation, blocking, permeabilization, and incubation with primary and corresponding secondary antibodies. Nuclei were counterstained with DAPI (Thermo Fisher Scientific, Waltham, MA, USA). Quantitative analysis of α-tubulin staining was adapted from a previously described method [29]. Briefly, for each condition, at least five random fields from three independent biological replicates were analyzed. Polymerized microtubule area was quantified as a percentage of total cellular area using ImageJ. Statistical comparison between control and KIF18B-knockdown groups was performed with an unpaired two-tailed Student’s t-test (or the Mann–Whitney U test for non-normally distributed data), with p < 0.05 considered significant. Images were acquired on a Leica DM4000 upright fluorescence microscope (Leica Microsystems, Wetzlar, Germany). Antibody details are provided in Supplementary Table S3.

4.13. RNA Sequencing and Bioinformatic Analysis

Total RNA was extracted from cells as previously described and subjected to cDNA library construction and subsequent RNA sequencing [32]. The resulting sequencing data were analyzed to identify differentially expressed genes (DEGs) based on established bioinformatic methods. Gene Ontology (GO) enrichment analysis and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis were then performed on the identified DEGs to elucidate their functional implications. To validate key findings from the sequencing data, the expression levels of selected candidate genes were further confirmed at the protein and mRNA levels by Western blotting and qRT-PCR, respectively.

4.14. Dual-Luciferase Reporter Assay

To evaluate E2F transcriptional activity, cells transduced with either control (NC) shRNA or KIF18B-targeting shRNA were plated at equal densities in 12-well plates (n = 3 replicates per group). Upon reaching 50–60% confluence, cells were co-transfected with the 10 × E2F-reporter luciferase construct, the E2F2-P1314 luciferase reporter vector, and the pGL4.74 [hRluc/TK] Renilla luciferase vector as an internal control. After 48 h, luciferase activity was measured using the Dual-Luciferase Reporter Assay System (Promega, Madison, WI, USA), and firefly luciferase signals were normalized to the corresponding Renilla luciferase values.

4.15. E2F1/E2F2/E2F3 Overexpression Rescue Experiments

To investigate whether the phenotypic effects of KIF18B depletion depend on E2F signaling, rescue experiments were conducted. A549 cells were transiently transfected with pcDNA3.1/pcDNA3.1-based E2F1, E2F2, or E2F3 overexpression plasmids (constructed previously in-house). Twenty-four hours after transfection, cells were placed under neomycin sulfate selection (MCE, Monmouth Junction, NJ, USA) to establish stable overexpression [34]. Subsequently, the cells were re-seeded and infected with lentivirus encoding either KIF18B-targeting shRNA or a control shRNA, followed by puromycin selection (MCE, Monmouth Junction, NJ, USA) [35]. After another round of re-seeding into 12-well plates, Cell proliferation was continuously monitored over 48 h using the JULI Stage live-cell analysis system (NanoEntek, Seoul, Republic of Korea), and confluence data were analyzed to assess the functional rescue of proliferation following re-expression of E2F1, E2F2, or E2F3.

4.16. Statistical Analyses

Statistical analyses were conducted using GraphPad Prism (version 6.0, GraphPad Software, San Diego, CA, USA) or RStudio (version 0.99.902, R version 3.6.2, R Foundation for Statistical Computing, Vienna, Austria). Normality of gene expression data was assessed via the Kolmogorov–Smirnov test, followed by parametric Student’s t-tests or nonparametric tests to evaluate significance. Survival outcomes were visualized with Kaplan–Meier curves and compared via the log-rank test. Univariate and multivariate Cox proportional hazards regression analyses were applied to estimate hazard ratios with 95% confidence intervals and to identify independent prognostic factors. Correlations between KIF18B expression and functional activity states were evaluated using Spearman’s rank correlation with Benjamini–Hochberg FDR correction (|r| > 0.3, FDR < 0.05). Student’s t-tests were employed for promoter activity, wound-healing assays, single-cell migration tracking, and assembled microtubule area measurements. Gene Set Enrichment Analysis (GSEA) was performed using a non-parametric permutation test. A two-tailed p < 0.05 was considered statistically significant.