Skip to main content
NCBI home page
Discover Oncology logoLink to Discover Oncology
. 2026 Jan 28;17:352. doi: 10.1007/s12672-026-04535-x

Prognostic model of immune -related immune models of ANLN single gene based on TCGA lung adenocarcinoma database

Chunjiao Yang 1,#, Shixiong Yang 1,#, Bo Jin 2, Xin Jin 3,✉
PMCID: PMC12923693  PMID: 41606221

Abstract

Anillin (ANLN) encodes one actin-binding protein tightly associated with cell growth, cytokinesis, and migration. The present work focused on exploring differential ANLN expression in lung adenocarcinoma (LUAD). We built an ANLN-related immune prognostic model and verified its feasibility. R and Bioconductor -related software were employed for differential, survival, univariate as well as multivariate analysis. The significance level was P < 0.05. ANLN expression within LUAD tissues was increased compared with non-carcinoma tissues, and ANLN up-regulation in LUAD was associated with poor prognosis. CD8 + T cells showed positive relation to activated CD4 + T cells in LUAD. M2 macrophage abundance was inversely correlated with plasma cell expression. ANLN expression was associated with 17 lymphocytes, 33 immunostimulators, and 13 immunoinhibitors. ANLN-related immunomodulatory genes were mainly involved in cytokine activity, receptor ligand activity, cytokine receptor binding, and tumor necrosis factor receptor binding, and regulated signaling pathways like NF-κB and JAK–STAT pathways. LASSO regression was used to establish an immune risk model. In addition, model prediction performance and feasibility were validated through univariate analysis, multivariate analysis, survival analysis, and ROC curve analysis. Crucially, in vitro experiments demonstrated that ANLN knockdown significantly inhibited the proliferation and migration of A549 and H1299 cells. Mechanistically, ANLN depletion led to the downregulation of NF-κB-related inflammatory cytokines (IL6, CXCL8, and TNF), suggesting a potential role in modulating the tumor immune microenvironment. In conclusion, The ANLN gene is a candidate biomarker for predicting LUAD prognosis, and the constructed risk model may serve as an independent predictor of LUAD prognosis.

Supplementary Information

The online version contains supplementary material available at 10.1007/s12672-026-04535-x.

Keywords: Lung adenocarcinoma, ANLN, Prognostic model, Immune correlation analysis, Bioinformatics

Introduction

Lung adenocarcinoma (LUAD), as the predominant histological subtype of lung cancer, is characterized by high morbidity and mortality. According to the updated Global Cancer Statistics 2022 from the International Agency for Research on Cancer, lung cancer ranks first in mortality globally, accounting for 18.7% of total cancer-associated deaths. Lung cancer represents 12.4% of all newly diagnosed cancer cases worldwide [1, 2]. Traditional lung cancer treatments include surgery, radiotherapy, and chemotherapy; however, some patients show limited benefit from conventional therapies [3]. In recent years, attention has shifted toward immunotherapy. The immune system’s natural capacity for eliminating malignant cells is considered a candidate strategy in tumor therapy [4]. Immunotherapy is increasingly suggested as having strong potential for tumor control. To take an example, PD-1/PD-L1-targeting immune checkpoint inhibitors are applied to treat lung cancer with notable efficacy [5]. However, just around 20% of lung cancer cases benefit from such treatments [6]. Therefore, identifying new immunobiomarkers is critical for diagnosis, selection of appropriate immunotherapeutic strategies, and evaluation of patient outcomes.

Recent studies have employed diverse bioinformatics and molecular approaches to uncover the complexity of lung cancer progression. For instance, Li et al. constructed a reliable methylation-based prognostic signature for LUAD [7]. Beyond traditional modeling, novel methodologies such as network pharmacology have been applied to elucidate the therapeutic mechanisms of traditional formulations in non-small cell lung cancer [8]. Furthermore, advanced deep learning models have revealed associations between viral infection-induced phosphorylation and immune regulation in lung cancer development [9]. Mechanistic studies targeting specific biomarkers, such as the downregulation of TGFBR3, also highlight the intricate molecular landscape of this disease [10]. Despite these advances, the specific interplay between cytoskeletal proteins and the immune microenvironment remains to be fully elucidated.

Anillin (ANLN), the actin-binding protein possessing 1125 amino acids, was originally discovered in Drosophila and is located on chromosome 7p14.2, where it plays a key role in cytokinesis [11–13]. ANLN has been reported as a prognostic biomarker related to immunomodulation within breast cancer [14]. Its up-regulation is positively correlated with malignant features, suggesting an effect on immune microenvironment as well as unfavorable prognostic outcome [15]. However, no study has comprehensively examined the relation between ANLN and immune microenvironment in LUAD, and its prognostic significance. The present work focused on using bioinformatics approaches for systematically analyzing ANLN expression in LUAD and its biological implications, and evaluating the prognostic performance of ANLN-related risk models for LUAD survival.

Materials and methods

Data processing

LUAD data updated to April 1, 2023, were acquired in TCGA database (https://portal.gdc.cancer.gov/). GEO dataset(GSE31210) was downloaded from GEO database (https://www.ncbi.nlm.nih.gov/geo). Core disease targets were screened using P < 0.05 alongside |log2FC| > 1 with “limma” R package. The average target gene expression level was calculated by Wilcoxon test. For the integration of datasets or cross-platform comparisons, we ensured data were normalized (e.g., using TPM or standard z-score transformations where appropriate) to minimize technical variations.

Differential expression and survival analysis of ANLN in lung adenocarcinoma

We utilized R and Bioconductor packages for analyzing differential ANLN expression within LUAD. The Human Protein Atlas (HPA) database (https://www.proteinatlas.org) was employed for acquiring ANLN protein. We examined the relation of ANLN with overall survival.

Immune cell correlations and differences in lung adenocarcinoma

The CIBERSORT algorithm was adopted for estimating 22 infiltrating immune cell proportions within every tissue. R packages “ggpubr”, “ggplot2”, “pheatmap”, and “vioplot” were utilized to conduct statistical analyses and visualization. P < 0.05 and HR > 1 represented statistical significance.

Association between changes in ANLN expression and immune cells

Correlations (P < 0.05) were visualized by statistically analyzing immune cell data.

calculated using the CIBERSORT algorithm with the R packages “ggpubr”, “ggplot2”, and “ggExtra”. Associations between tumor immune cell profiles and ANLN copy number variations were obtained from the TIMER database (cistrome.shinyapps.io/timer). Associations of ANLN with lymphocytes, 45 immunostimulants, and 24 immunosuppressors were examined with TISIDB database (http://cis.hku.hk/TISIDB/) [16]. Correlation between ANLN and immune cells was analyzed using the xCell package [17].

Protein-protein interaction (PPI) network, GO enrichment, and KEGG analysis of ANLN-related Immunomodulatory genes

We utilized the STRING database to establish the PPI network of immunomodulatory genes, with species being set as Homo sapiens. GO annotation alongside KEGG enrichment was implemented with R and Bioconductor packages, with filtering criteria set to pvalueCutoff = 0.05 and qvalueCutoff = 0.05.

Establishment and verification of risk models

A risk model was established by survival, glmnet, survminer, along with timeROC R package combined with the Cox regression method. Forest plots and ROC curves were generated for visualization. The output results were filtered by P < 0.05.

Cell culture

We purchased LUAD cell lines (A549 and H1299) from the Shanghai Institute of Cell Biology, Chinese Academy of Sciences. The cells were cultured in 1640 medium (RPMI1640, BI, 01–100-1ACS) supplemented with 1% penicillin-streptomycin (PS, Beyotime, C0222) and 10% fetal bovine serum (FBS, BI, 04-001-1B) at 37 °C in a 5% CO2 atmosphere.

Transfection

siRNA targeting ANLN and negative control siRNA were purchased from Shanghai Gene Chem (Shanghai, China). On the day of transfection, A549 and H1299 cells in the logarithmic growth phase were evenly seeded into 6-well plates at a density of 2 × 10⁵ cells per well. According to the manufacturer’s instructions, transfection was performed using Lipofectamine 2000 transfection reagent (Thermo Fisher Scientific, Wilmington, DE, USA) to introduce the siRNA into the cells.

EdU assay

Lung cancer cells were seeded in 24-well plates at a density of 1 × 10⁵ cells per well and cultured overnight. Subsequently, the EdU cell proliferation assay kit (Beyotime) was used according to the manufacturer’s instructions. Images of EdU-positive cells were captured using an inverted fluorescence microscope, and the percentage of positive cells was quantified using ImageJ software.

Wound healing assay

Cells were seeded into 12-well plates and cultured until a complete monolayer was formed. A sterile pipette tip was used to create a scratch wound on the confluent cell monolayer. Images were captured under a microscope at 0 and 48 h post-scratching, and the scratch width was measured to calculate the cell migration rate.

RNA and qRT-PCR

TRIzol Reagent (Cat# B511311, Sangon Biotech, Shanghai, China) was utilized to extract RNA from cells. Subsequently, the RNA was reverse-transcribed into complementary DNA using HiScript® III RT SuperMix for qPCR (catalog number R323, Vazyme, Nanjing, China). An application of the ChamQ Universal SYBR qPCR Master Mix (Cat# Q711, Vazyme, Nanjing, China) was utilized for quantitative real-time PCR. The reaction was performed according to the instructions provided by the manufacturer. The conditions consisted of pre-denaturation at 95 °C for 30 s, followed by 10 s at 95 °C and 30 s at 60 °C (for a total of 40 cycles). Information about primers is presented in Table S1. The quantification of the results was performed using the 2 − ΔΔCT method, and actin mRNA was used as a control.

Results

ANLN as a prognostic biomarker for lung adenocarcinoma

TCGA-derived LUAD data were analyzed, showing that ANLN was up-regulated within LUAD samples relative to non-carcinoma counterparts (Fig. 1A). Such finding was verified through immunohistochemistry on ANLN inside LUAD and matched paracancerous tissues (Fig. 1B).

Fig. 1.

Fig. 1

Open in a new tab

Differential expression of ANLN. A Differential expression of ANLN in cancerous and normal tissues. (p < 0.001). B Immunohistochemistry of ANLN in lung adenocarcinoma tissue. ANLN staining in lung adenocarcinoma is stronger than that in normal lung tissue, and its expression in lung adenocarcinoma is mainly localized to the nucleus. C Relationships between ANLN gene expression and patient survival time. (p < 0.001)

Transcriptome data downloaded from TCGA were integrated for examining the relation of ANLN with survival time. Elevated ANLN expression was related to reduced overall survival (Fig. 1C), with a significant difference (P < 0.001).

Immune cell differences in lung adenocarcinoma

Immune cell fractions in TCGA-derived LUAD samples were calculated to explore relationships among immune cell populations. As shown in Fig. 2A, CD8 T cells had positive relation with activated CD4 T cells, whereas M2 macrophages showed negatively correlation with plasma cells, showing the strongest correlation. Immune cell proportions within LUAD and non-carcinoma tissues were further compared. Regulatory T cells, plasma cells, naïve B cells, antigen-presenting cells, follicular helper T cells, activated CD4 T cells, and M1 macrophages levels markedly increased within LUAD samples relative to non-carcinoma counterparts (P ≤ 0.001). In contrast, resting CD4 T cells, resting NK cells, monocytes, M0 macrophages, M2 macrophages, neutrophils, eosinophils and resting mast cells apparently decreased inside LUAD tissues relative to non-carcinoma counterparts (P ≤ 0.001) (Fig. 2B).

Fig. 2.

Fig. 2

Open in a new tab

Correlation analysis of immune cells. A Correlation analysis and differential analysis of immune cell expression in lung adenocarcinoma samples. B The median expression of each immune cell between normal tissue and tumor tissue

Association between ANLN and immune cells

Associations of 22 immune cell levels with ANLN were analyzed. From Fig. 3A, ANLN expression showed negative relation with dendritic cells, B lymphocytes, resting mast cells, monocytes, activated NK cells, plasma cells, autoimmune-reactive T cell subsets, and resting CD4 T cells. In contrast, ANLN level showed positive correlations with resting NK cells, eosinophils, M1 macrophages, M0 macrophages, activated mast cells, activated CD4 + T cells, and CD8 + T cells. To verify the robustness of these immune infiltration patterns and rule out algorithm-specific bias, we further employed the xCell algorithm to analyze the correlation between ANLN expression and immune scores. Consistent with the CIBERSORT results, the xCell analysis corroborated that ANLN expression was negatively correlated with the Microenvironment Score, Macrophages M2, and Monocytes, while showing distinct interaction patterns with Th2 cells and other lymphocyte subsets (Supplementary Fig. 1).

Fig. 3.

Fig. 3

Open in a new tab

The relationship between ANLN and immunity. A Correlation analysis of ANLN expression and immune cells. B Correlations between changes in ANLN copy number and immune cell expression. C Correlations between ANLN gene expression and lymphocytes. D Correlation between the expression of the ANLN gene and that of the immunostimulating gene. E Correlations between the expression of the ANLN gene and that of immunosuppressive genes

Correlation of changes in ANLN gene copy number with immune cells

The relations of ANLN with immune cells were analyzed from multiple perspectives. Changes in ANLN copy number were associated with alterations in seven immune cell types, including CD4 T cells, regulatory T cells, endothelial cells, myeloid dendritic cells, M1 macrophages, cancer-associated fibroblasts, and CD8 T cells (Fig. 3B).

Correlation between ANLN and lymphocytes as well as Immunomodulatory genes

The tumor immune microenvironment, including lymphocytes, immunostimulatory genes, and immunosuppressive genes, is tightly related to tumor development. According to our findings, ANLN level was related to 17 lymphocyte types, including CD4 T cells, eosinophils, mast cells, Th2 cells, and Th17 cells; 33 immunostimulatory genes, including CD40LG, PVR, TMEM173, TNFRSF13B, and TNFSF13; and 13 immunosuppressive genes, including CD274, ADORA2A, BTLA, LGALS9, and PVRL2. Scatter plots were generated for lymphocytes and immunomodulatory genes with P < 0.05, and the top five correlations were presented (Fig. 3C–E).

ANLN-related Immunomodulatory gene protein network interaction, GO enrichment analysis, and KEGG enrichment analysis

Immunomodulatory genes play important roles in tumor immunotherapy. We analyzed the PPI network of immunoregulatory genes related to ANLN (P < 0.05), resulting in the PPI network involving 46 nodes alongside 335 edges (Fig. 4A). As revealed by functional annotation, the above immunomodulatory genes were mostly related to cytokine receptor binding, cytokine activity, tumor necrosis factor receptor binding, and receptor ligand activity (Fig. 4B). The enriched signaling pathways primarily included the JAK–STAT and NF-κB pathways (Fig. 4C).

Fig. 4.

Fig. 4

Open in a new tab

PPI (A), GO enrichment function (B) and KEGG function (C) of immunomodulatory genes related to the ANLN gene

Screening of prognostic Immunomodulatory genes related to ANLN

We further selected immunomodulatory genes related to patient prognosis. The results showed that BTLA, CD160, CD27, CD28, CD40LG, CD48, CD276, CXCR4, ENTPD1, IL6R, PVR, RAET1E, TNFRSF13B, TNFRSF13C, TNFRSF14, TNFRSF17, and TNFSF13 were significantly associated with patient survival and could serve as prognostic genes (Fig. 5A). Among these genes, CD40LG, CD276, ENTPD1, PVR, and RAET1E were selected for risk model construction (Fig. 5B). The risk score was determined below:

Fig. 5.

Fig. 5

Open in a new tab

Construction of the prognostic risk model. A All genes related to prognosis; green represents low-risk genes, and red represents high-risk genes. B The genes involved in model construction, CD40LG and ENTP01, are protective genes

–0.255129324649641 × CD40LG level + 0.231074806945798 × CD276 level – 0.274349938803074 × ENTPD1 level + 0.205603584252556 × PVR level + 0.444282701897162 × RAET1E level.

Validation of prognostic risk models

First, the prognostic value of the risk model was validated in the TCGA cohort. Patients were stratified into high- and low-risk groups based on the median risk score, with the low-risk group exhibiting significantly longer overall survival (Fig. 6). The risk heatmap confirmed distinct expression patterns, where protective genes (CD40LG and ENTPD1) were enriched in low-risk patients, while risk genes (CD276, PVR, and RAET1E) were upregulated in the high-risk group. Univariate and multivariate Cox regression analyses identified both the risk score and clinical stage as independent prognostic factors (Fig. 7A–B). Notably, while the risk model alone demonstrated moderate predictive power, its integration with clinical features yielded superior predictive accuracy compared to clinical stage alone, as evidenced by the ROC curves (Fig. 7C).

Fig. 6.

Fig. 6

Open in a new tab

Survival assessment of the model. A Patients in the low-risk group had longer survival. B CD40LG and ENTPD1 are protective genes, and CD275, PVR and RAET1E are risk genes. C–D Survival assessment of the risk curve

Fig. 7.

Fig. 7

Open in a new tab

Model identification. (A-B) Analysis of independent prognostic factors. A Univariate analysis and B multivariate analysis. P < 0.05 was considered statistically significant. C ROC curve evaluation. The larger the area under the curve is, the more accurate the prediction is

To further validate the robustness and generalizability of the model, we analyzed an independent cohort from the GEO database (GSE31210). Using the same formula derived from the TCGA training set, univariate (HR = 0.0238, P < 0.05) and multivariate (HR = 0.0133, P < 0.05) Cox regression analyses confirmed that the risk score remained an independent prognostic factor (Supplementary Fig. 2A–B). Furthermore, ROC curve analysis of the external validation set demonstrated satisfactory predictive performance (AUC = 0.590), which was further enhanced when combined with clinical features (AUC = 0.790) (Supplementary Fig. 2 C). These results substantiate the clinical applicability of our risk model across diverse populations.

Experimental validation of ANLN function and potential mechanism

To verify the biological function of ANLN in LUAD, we conducted in vitro loss-of-function assays using A549 and H1299 cell lines. As shown in Fig. 8A, EdU proliferation assays revealed that the knockdown of ANLN (si-ANLN) significantly attenuated the proliferative capacity of both A549 and H1299 cells compared to the negative control (NC) group (P < 0.01). Furthermore, wound healing assays demonstrated that ANLN depletion remarkably inhibited the migratory capability of LUAD cells, with a significantly slower wound closure rate observed at 48 h post-transfection (Fig. 8B).

Fig. 8.

Fig. 8

Open in a new tab

Experimental validation of ANLN function and mechanism in LUAD cells. A EdU assay demonstrates the inhibitory role of ANLN in lung cancer cell proliferation(A549 and H1299). Scale bar = 20 μm. B Wound healing assay demonstrates that ANLN inhibits the migration of lung cancer cells.Scale bar = 200 μm. C RT-qPCR analysis validates the expression of NF-κB pathway-related and immune-associated genes (IL-6, CXCL8, TNF). All results are obtained as the mean ± SD under at least biological triplicate assays. *p < 0.05, **p < 0.01, ***p < 0.001

Given that our functional enrichment analysis implicated the NF-κB signaling pathway and cytokine activity, we hypothesized that ANLN might modulate the immune microenvironment through this axis. To validate this mechanism, we assessed the expression of key NF-κB downstream inflammatory cytokines via qRT-PCR. The results demonstrated that ANLN knockdown significantly downregulated the mRNA expression levels of IL6, CXCL8 (IL-8), and TNF in both cell lines (Fig. 8C, P < 0.05).

Discussion

Over recent decades, lung cancer has shown the highest morbidity worldwide [2] and remains a major factor inducing cancer-associated mortality, occupying about 27% of total cancer deaths annually [18]. This persistently high mortality rate is concerning, particularly because current treatment strategies remain insufficient to substantially reduce lung cancer-related deaths. Although immunotherapy has partly changed this landscape, its benefits are limited to a small proportion of patients. Therefore, identifying novel immune-related prognostic biomarkers is of great importance.

ANLN, as the actin-binding protein, is highly conserved and associated primarily with cell division and cellularization [19]. The dysregulation of ANLN leads to tumorigenesis, growth, and development. For example, ANLN regulates the EZH2/miR-218-5p/LASP1 pathway by promoting pancreatic cancer development [20]. ANLN overexpression has also been associated with LUAD [21]. The overexpression of ANLN induces colorectal cancer development and unfavorable prognostic outcomes [22]. From this study, ANLN expression remarkably increased within LUAD samples relative to non-carcinoma samples. Among patients with LUAD, patients with ANLN upregulation were associated with shorter survival. Therefore, ANLN is a candidate biomarker used to evaluate LUAD prognosis. Additionally, studies have shown that ANLN is a tumor immune biomarker and revealed the prognosis and immune role of ANLN in pan-carcinomas [23, 24]. Pan-cancer analysis revealed the up-regulation of ANLN within 21 cancers as well as the significant positive relation of ANLN expression with tumor mutation burden (TMB), microsatellite instability (MSI) as well as immune cell infiltration. ANLN is a candidate immune checkpoint gene in various cancers [25]. We further confirmed the close relation of ANLN with immune-infiltrating cells and established the risk model by incorporating ANLN-related immunomodulatory genes. This model was validated using univariate, multivariate, survival, and ROC curve analyses. Overall, these results indicate that the established risk model has reasonable reliability and prognostic value when compared with traditional clinical features.

Beyond bioinformatic predictions, our study provides experimental evidence verifying the oncogenic role of ANLN in LUAD. Our in vitro assays confirmed that ANLN knockdown significantly suppresses the proliferation and migration of lung cancer cells, aligning with its known function in cytokinesis. More importantly, we experimentally validated the molecular link between ANLN and immune modulation. Guided by our enrichment analysis which pointed to the NF-κB pathway, we found that ANLN depletion significantly downregulated key inflammatory cytokines (IL6, CXCL8, and TNF). These cytokines are known to recruit immunosuppressive cells, such as neutrophils and MDSCs, into the tumor microenvironment. This suggests a novel mechanism where ANLN may promote immune evasion in LUAD by sustaining a pro-inflammatory, yet immunosuppressive, cytokine milieu via the NF-κB axis.

Our findings align with the current trend in oncology research, where multi-omics analysis is increasingly used to identify novel immunobiomarkers. Recent pan-cancer studies published in 2024 and 2025 have characterized several new potential targets, emphasizing their prognostic and immunological value across various tumors, including lung cancer. For example, comprehensive analyses have identified CENPN [26], CORO1A [27], and Bystin [28] as significant biomarkers linked to tumor immunity and methylation status in diverse cancers. Notably, CLIC6 has been explicitly highlighted as a potential prognostic factor for LUAD and other malignancies [29]. Collectively, these studies underscore the growing importance of utilizing comprehensive bioinformaticanalyses to identify novel prognostic biomarkers involved in tumor immunity. Consistent with this emerging research landscape, our work characterizes ANLN as a immune-related prognostic biomarker, offering new insights into the prognostic stratification of LUAD.

Conclusions

In clinical practice, LUAD patient prognosis may be predicted by measuring the mRNA expression levels of CD40LG, CD276, ENTPD1, PVR, and RAET1E. Our study integrates multi-cohort bioinformatic analysis with experimental validation to identify a robust prognosis-related gene signature. We provide the first evidence linking ANLN to the regulation of NF-κB-associated cytokines in LUAD cells, offering a potential mechanistic basis for its role in immune modulation and a new target for therapeutic intervention.

Limitations

Despite the robust findings supported by multi-cohort validation and in vitro experiments, several limitations inherent to the study design should be acknowledged. Although we integrated data from diverse multi-center cohorts (TCGA, GEO) to ensure the robustness and generalizability of the risk model across different populations, the analysis remains retrospective in nature; thus, large-scale, prospective, real-world clinical trials are warranted to further verify the clinical utility of the signature in dynamic clinical settings. Additionally, while our in vitro functional assays confirmed that ANLN regulates cell proliferation, migration, and the expression of NF-κB-associated inflammatory cytokines, these models may not fully recapitulate the intricate and systemic immune interactions present in the intact tumor microenvironment. Consequently, the precise upstream molecular events and the comprehensive signaling network by which ANLN orchestrates immune evasion remain to be fully elucidated, and future investigations focusing on deep molecular mechanisms are anticipated to expand upon our current findings.

Supplementary Information

Supplementary Material 1. (8.9KB, xlsx)
Supplementary Material 2. (526.6KB, jpg)
Supplementary Material 3. (476.6KB, jpg)

Acknowledgements

We acknowledge TCGA, GEO and HPA databases for providing their platforms and contributors for uploading meaningful datasets. We also thank the reviewers for their constructive comments, which significantly improved the quality of this study.

Author contributions

Chunjiao Yang and Shixiong Yang contributed equally to this work. CY and XJ conceived the study and designed the research. CY performed the bioinformatic analyses and drafted the manuscript. SY conducted the in vitro experiments, including qRT-PCR and functional assays. BJ participated in data analysis and visualization. XJ supervised the study and critically revised the manuscript.

Funding

Not applicable. The authors received no specific funding for this work.

Data availability

Publicly available datasets were analyzed in this study. The tumor cell line data were downloaded from the CCLE database (https://portals.broadinstitute.org/ccle) Immunohistochemistry images of ANLN protein expression were downloaded from the Human Protein Atlas (HPA) (http://www.proteinatlas.org/). LUAD data were obtained from the data portal of TCGA database (https://gdc.cancer.gov/support/gdc-webinars/tcga-resources-available-gdc). The external validation cohort was downloaded from the Gene Expression Omnibus (GEO) database (https://www.ncbi.nlm.nih.gov/geo/) under accession number GSE31210. We used the UALCAN database (http://ualcan.path.uab.edu/analysis.html) to obtain gene expression differences between LUAD tumors and normal samples according to various clinical characteristics. The correlation between the tumor-infiltrating immune cell profiles and gene expression was determined using TIMER (https://cistrome.shinyapps.io/timer/). The correlation between GNPNAT1 and immunostimulators and immunoinhibitors was analyzed using TISIDB (http://cis.hku.hk/TISIDB/). All data were open-access datasets.

Declarations

Ethical approval and consent to participate

The TCGA, GEO and HPA are public databases. Ethical approval was obtained from the patients included in the database. Users can download relevant data free of research, and publish relevant articles. Our study was based on open-source data; therefore, there are no ethical issues or conflicts of interest. This study did not involve human subjects, so the requirement for informed consent from participants is not applicable.

Consent for publication

Not applicable. This manuscript does not contain any individually identifiable images or data.

Competing interests

The authors declare no competing interests.

Footnotes

Publisher’s Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Chunjiao Yang and Shixiong Yang are Co-First Authors and contributed equally to this work.

References

  • 1.Succony L, Rassl DM, Barker AP, et al. Adenocarcinoma spectrum lesions of the lung: detection, pathology and treatment strategies. Cancer Treat Rev. 2021;99:102237. 10.1016/j.ctrv.2021.102237. [DOI] [PubMed] [Google Scholar]
  • 2.Bray F, Laversanne M, Sung H, et al. Global cancer statistics 2022: GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 countries. CA Cancer J Clin. 2024;74(3):229–63. 10.3322/caac.21834. [DOI] [PubMed] [Google Scholar]
  • 3.Hirsch FR, Scagliotti GV, Mulshine JL, et al. Lung cancer: current therapies and new targeted treatments. Lancet. 2017;389(10066):299–311. 10.1016/s0140-6736(16)30958-8. [DOI] [PubMed] [Google Scholar]
  • 4.Moreno-Manuel A, Jantus-Lewintre E, Simões I, et al. CD5 and CD6 as immunoregulatory biomarkers in non-small cell lung cancer. Transl Lung Cancer Res. 2020;9(4):1074–83. 10.21037/tlcr-19-445. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 5.Yin Z, Fan J, Xu J, et al. Immunoregulatory roles of extracellular vesicles and associated therapeutic applications in lung cancer. Front Immunol. 2020;11:2024. 10.3389/fimmu.2020.02024. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 6.Arbour KC, Riely GJ. Systemic therapy for locally advanced and metastatic non-small cell lung cancer: a review. JAMA. 2019;322(8):764–74. 10.1001/jama.2019.11058. [DOI] [PubMed] [Google Scholar]
  • 7.Li M, Deng X, Zhou D, Liu X, Dai J, Liu Q. A novel methylation-based model for prognostic prediction in lung adenocarcinoma. Curr Genomics. 2024;25(1):26–40. 10.2174/0113892029277397231228062412. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 8.Liu Z, Zhang J, Liu J, et al. Combining network pharmacology, molecular docking and preliminary experiments to explore the mechanism of action of FZKA formula on non-small cell lung cancer. Protein & Peptide Letters. 2023;30(12):1038–47. 10.2174/0109298665268153231024111622. [DOI] [PubMed] [Google Scholar]
  • 9.Li W, Li G, Sun Y, Zhang L, Cui X, Jia Y, et al. Prediction of SARS-CoV-2 infection phosphorylation sites and associations of these modifications with lung cancer development. Curr Gene Ther. 2024;24(3):239–48. 10.2174/0115665232268074231026111634. [DOI] [PubMed] [Google Scholar]
  • 10.Deng X, Ma N, He J, Xu F, Zou G. The role of TGFBR3 in the development of lung cancer. Protein Pept Lett. 2024;31(7):491–503. 10.2174/0109298665315841240731060636. [DOI] [PubMed] [Google Scholar]
  • 11.Cao YF, Xie L, Tong BB, et al. Targeting USP10 induces degradation of oncogenic ANLN in esophageal squamous cell carcinoma. Cell Death Differ. 2023;30(2):527–43. 10.1038/s41418-022-01104-x. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 12.Santos IC, Silva AM, Gassmann R, et al. Anillin and the microtubule bundler PRC1 maintain myosin in the contractile ring to ensure completion of cytokinesis. Development. 2023;150(12):dev201637. 10.1242/dev.201637. [DOI] [PubMed] [Google Scholar]
  • 13.Piekny AJ, Maddox AS. The myriad roles of Anillin during cytokinesis. Semin Cell Dev Biol. 2010;21(9):881–91. 10.1016/j.semcdb.2010.08.002. [DOI] [PubMed] [Google Scholar]
  • 14.Xiao Y, Deng Z, Li Y, et al. ANLN and UBE2T are prognostic biomarkers associated with immune regulation in breast cancer: a bioinformatics analysis. Cancer Cell Int. 2022;22(1):193. 10.1186/s12935-022-02611-0. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 15.Zhang X, Li L, Huang S, et al. Comprehensive analysis of ANLN in human tumors: a prognostic biomarker associated with cancer immunity. Oxid Med Cell Longev. 2022;2022(1):5322929. 10.1155/2022/5322929. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 16.Ru B, Wong CN, Tong Y, et al. TISIDB: an integrated repository portal for tumor-immune system interactions. Bioinformatics. 2019;35(20):4200–2. 10.1093/bioinformatics/btz210. [DOI] [PubMed] [Google Scholar]
  • 17.Aran D, Hu Z, Butte AJ. xCell: digitally portraying the tissue cellular heterogeneity landscape. Genome Biol. 2017;18(1):220. 10.1186/s13059-017-1349-1. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 18.Siegel RL, Kratzer TB, Giaquinto AN, et al. Cancer statistics, 2025. CA Cancer J Clin. 2025;75(1):10–45. 10.3322/caac.21871. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 19.Chen A, Akhshi TK, Lavoie BD, et al. Importin β2 mediates the spatio-temporal regulation of anillin through a noncanonical nuclear localization signal. J Biol Chem. 2015;290(21):13500–9. 10.1074/jbc.m115.649160. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 20.Wang A, Dai H, Gong Y, et al. ANLN-induced EZH2 upregulation promotes pancreatic cancer progression by mediating miR-218-5p/LASP1 signaling axis. J Exp Clin Cancer Res. 2019;38(1):347. 10.1186/s13046-019-1340-7. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 21.Xu J, Zheng H, Yuan S, et al. Overexpression of ANLN in lung adenocarcinoma is associated with metastasis. Thorac Cancer. 2019;10(8):1702–9. 10.1111/1759-7714.13135. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 22.Wang G, Shen W, Cui L, et al. Overexpression of anillin (ANLN) is correlated with colorectal cancer progression and poor prognosis. Cancer Biomark. 2016;16(3):459–65. 10.3233/cbm-160585. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 23.Liu K, Cui L, Li C, et al. Pan-cancer analysis of the prognostic and immunological role of ANLN: an onco-immunological biomarker. Front Genet. 2022;13:922472. 10.3389/fgene.2022.922472. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 24.Shi Y, Ma X, Wang M, et al. Comprehensive analyses reveal the carcinogenic and immunological roles of ANLN in human cancers. Cancer Cell Int. 2022;22(1):188. 10.1186/s12935-022-02610-1. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 25.Zhang L, Wei Y, He Y, et al. Clinical implication and immunological landscape analyses of ANLN in pan-cancer: a new target for cancer research. Cancer Med. 2023;12(4):4907–20. 10.1002/cam4.5177. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 26.Jing Y, Wang Y, Li Y, et al. Diagnostics and immunological function of CENPN in human tumors: from pan-cancer analysis to validation in breast cancer. Transl Cancer Res. 2025;14(2):881–906. 10.21037/tcr-24-1291. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 27.Elihamu D, Li Y, Wang Y, et al. CORO1A: a pan-cancer prognosis, diagnostic and immune biomarker based on breast cancer validation. Front Oncol. 2025;15:1670526. 10.3389/fonc.2025.1670526. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 28.Aimaiti X, Wang Y, Ismtula D, et al. Bystin is a prognosis and immune biomarker: from pan-cancer analysis to validation in breast cancer. Breast Cancer (Dove Med Press). 2025;17:755–79. 10.2147/bctt.s537429. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 29.Wang J, Wang Y, Ma H, Li Y, Hou J, Li J, et al. Clic6’s role in cancer: from broad analysis to breast cancer validation. Front Oncol. 2025;15:1667589. 10.3389/fonc.2025.1667589. [DOI] [PMC free article] [PubMed] [Google Scholar]

Associated Data

This section collects any data citations, data availability statements, or supplementary materials included in this article.

Supplementary Materials

Supplementary Material 1. (8.9KB, xlsx)
Supplementary Material 2. (526.6KB, jpg)
Supplementary Material 3. (476.6KB, jpg)

Data Availability Statement

Publicly available datasets were analyzed in this study. The tumor cell line data were downloaded from the CCLE database (https://portals.broadinstitute.org/ccle) Immunohistochemistry images of ANLN protein expression were downloaded from the Human Protein Atlas (HPA) (http://www.proteinatlas.org/). LUAD data were obtained from the data portal of TCGA database (https://gdc.cancer.gov/support/gdc-webinars/tcga-resources-available-gdc). The external validation cohort was downloaded from the Gene Expression Omnibus (GEO) database (https://www.ncbi.nlm.nih.gov/geo/) under accession number GSE31210. We used the UALCAN database (http://ualcan.path.uab.edu/analysis.html) to obtain gene expression differences between LUAD tumors and normal samples according to various clinical characteristics. The correlation between the tumor-infiltrating immune cell profiles and gene expression was determined using TIMER (https://cistrome.shinyapps.io/timer/). The correlation between GNPNAT1 and immunostimulators and immunoinhibitors was analyzed using TISIDB (http://cis.hku.hk/TISIDB/). All data were open-access datasets.

Articles from Discover Oncology are provided here courtesy of Springer

RESOURCES