
==== Front
Discov Oncol
Discov Oncol
Discover Oncology
2730-6011
Springer US New York

1293
10.1007/s12672-024-01293-6
Research
Multicenter cohort analysis of anoikis and EMT: implications for prognosis and therapy in lung adenocarcinoma
Yin Lu 1
Zhang Zhanshuo 2
Yan Zhu 1332080365@qq.com

3
Yan Qiuyue 351124959@qq.com

4
1 https://ror.org/036trcv74 grid.260474.3 0000 0001 0089 5711 School of Mathmatic and Information, Nanjing Normal University of Special Education, Nanjing, China
2 https://ror.org/0152hn881 grid.411918.4 0000 0004 1798 6427 Department of Lung Cancer, Tianjin Lung Cancer Center, National Clinical Research Center for Cancer, Key Laboratory of Cancer Prevention and Therapy, Tianjin’s Clinical Research Center for Cancer, Tianjin Medical University Cancer Institute and Hospital, Tianjin, China
3 https://ror.org/02sqxcg48 grid.470132.3 Department of Respiratory Diseases, The Affiliated Huai’an Hospital of Xuzhou Medical University, Huai’an Second People’s Hospital, Huai’an, China
4 grid.268415.c Emergency Medicine Department, The Affiliated Huai’an Hospital of Yangzhou University, Huai’an Fifth People’s Hospital, Huai’an, China
19 9 2024
19 9 2024
12 2024
15 4625 7 2024
2 9 2024
© The Author(s) 2024
2024
https://creativecommons.org/licenses/by-nc-nd/4.0/ Open Access This article is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License, which permits any non-commercial use, sharing, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if you modified the licensed material. You do not have permission under this licence to share adapted material derived from this article or parts of it. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by-nc-nd/4.0/.
Background

Anoikis and epithelial-mesenchymal transition (EMT) are pivotal in the distant metastasis of lung adenocarcinoma (LUAD). A detailed understanding of their interplay and the identification of key genes is vital for effective therapeutic strategies against LUAD metastasis.

Methods

Key prognostic genes related to anoikis and EMT were identified through univariate Cox regression analysis. We utilized ten machine learning algorithms to develop the Anoikis and EMT-Related Optimal Model (AEOM). The TCGA-LUAD dataset served as the training cohort, while six additional international multicenter LUAD datasets were employed as validation cohorts. The average concordance index (c-index) was used to evaluate model performance and identify the most effective model. Subsequent multi-omics analyses were conducted to explore differences in pathway enrichment, immune infiltration, and mutation landscapes between high and low AEOM groups. Experimental validation demonstrated that RHPN2, a key biomarker within the model, acts as an oncogene facilitating LUAD progression.

Results

The AEOM displayed superior prognostic predictive performance for LUAD patients, outperforming numerous previously published LUAD signatures. Biologically, the AEOM was notably associated with immune features; the high AEOM group exhibited decreased immune activity and a tendency towards immune-cold tumors, as well as a higher tumor mutational burden (TMB). Subgroup analysis revealed that the low AEOM + high TMB group had the most favorable prognosis. The high AEOM group was primarily enriched in cell cycle-related pathways, promoting cancer cell proliferation. RHPN2, a crucial gene within the AEOM (correlation = 0.85, P < 0.05), was linked to poorer prognosis in LUAD patients with elevated RHPN2 expression. Further in vitro experiments showed that RHPN2 modulates LUAD cell proliferation and invasion.

Conclusion

The AEOM provides a robust prognostic model for LUAD, uncovering critical immune and biological pathways, with RHPN2 identified as a key oncogenic driver. These findings offer valuable insights for targeted therapies and enhanced patient outcomes.

Supplementary Information

The online version contains supplementary material available at 10.1007/s12672-024-01293-6.

Keywords

Anoikis
Epithelial-mesenchymal transition
Lung adenocarcinoma
Tumor microenvironment
Prognosis
issue-copyright-statement© Springer Science+Business Media, LLC 2024
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pmcIntroduction

Lung cancer is the leading cause of cancer related deaths in China and worldwide. Compared to other malignant tumors, it not only has a high incidence but also has subtle early symptoms, often leading to diagnosis at an advanced stage and reducing the effectiveness of treatment [1, 2]. Non small cell lung cancer (NSCLC) is the most common histologic subtype,with adenocarcinoma being the predominant type [3]. Currently, the treatment of Lung adenocarcinoma (LUAD) includes surgery, radiotherapy, chemotherapy, molecular targeted therapy, and immunotherapy, but due to high metastasis rate, resistance to radiotherapy and chemotherapy, and lack of systemic treatment, the 5 year survival rate of patients remains very low [4–6]. The occurrence and development of lung cancer involve interactions among various molecules, cells, and tissues, therefore exploring potential molecular targets is urgently needed to improve the treatment of lung cancer.

Epithelial mesenchymal transition(EMT) refers to the process of epithelial cells losing their connections and apical basal polarity, reorganizing their cytoskeleton, changing defined cell shape, and reprogramming the signal program of gene expression to transform into mesenchymal cells [7]. Research has shown that EMT play a crucial role in tumor proliferation, invasion, and metastasis (8). Cao et al [9]. Developed a signature based on EMT related genes that has good predicting in the prognosis of bladder cancer.

Anoikis, a specialized form of programmed cell death resulting from cell detachment from the extracellular matrix [10, 11] is crucial for preventing unwanted cell colonization. In cancer, tumor cells often evade anoikis, allowing survival in harsh environments like the bloodstream, thereby aiding metastasis. EMT plays a complementary role by transforming epithelial cells into a mesenchymal state, enhancing their migratory and invasive properties. Both anoikis resistance and EMT are critical in metastasis, working synergistically to promote cancer cell dissemination. While studies have developed prognostic models incorporating anoikis and EMT-related genes in other cancers, like colon adenocarcinoma, the interplay of these processes in LUAD is less understood (12). In LUAD, understanding this crosstalk is vital for discovering new therapeutic targets and strategies to mitigate metastasis, highlighting the need for continued research in this area.

This study investigates the roles of anoikis and EMT in LUAD. Using machine learning methods, we developed the Anoikis and EMT-related optimal model (AEOM) and demonstrated its excellent performance in prognosis prediction. Additionally, we conducted multi-omics analyses to explore differences in immune infiltration, mutation landscape, and pathway enrichment between different AEOM groups. Finally, we identified RHPN2 as a key marker within the AEOM, functioning as an oncogene that promotes the progression and metastasis of LUAD, potentially serving as a significant therapeutic target.

Method

Dataset source

The transcriptomic, copy number variation (CNV), mutation, and clinical data of LUAD were collected from The Cancer Genome Atlas (TCGA) database (https://portal.gdc.cancer.gov) and used as the training set for model construction. Six transcriptome datasets from the GEO database were used for validation: GSE13213 [13] (n = 119), GSE26939 [14] (n = 115), GSE29016 [15] (n = 39), GSE30219 [16] (n = 86), GSE31210 [17] (n = 227), and GSE42127 [18] (n = 134). To ensure consistency and comparability, gene expression data were converted to Transcripts Per Million (TPM) format. The “combat” function in the “sva” package [19, 20] was used to adjust for potential batch effects. Additionally, all datasets from TCGA and GEO were log-transformed to create a standardized data format from the start of the analysis. Principal component analysis (PCA) was performed to assess batch effects between datasets.

Development and Validation of the Anoikis and EMT-Related Optimal Model (AEOM) for Prognosis Prediction.

Univariate Cox regression analysis was performed to identify Anoikis and EMT-Related prognostic Genes (AEPGs) for LUAD patients. Subsequently, utilizing tenfold cross-validation, we examined 101 combinations of ten machine learning algorithms: stepwise Cox, Lasso, Ridge, partial least squares regression for Cox (plsRcox), CoxBoost, random survival forest (RSF), generalized boosted regression modeling (GBM), elastic net (Enet), supervised principal components (SuperPC), and survival support vector machine (survival-SVM). The aim was to identify the most valuable AEOM characterized by the highest C-index. The accuracy of AEOM was evaluated using ROC curves and PCA analysis. Additionally, we comprehensively reviewed prognostic signatures from published literature, including lncRNA and mRNA signatures, and compared the performance of AEOM using the C-index as the evaluation metric.

Biological function and pathway analysis

To investigate the biological functions and pathway processes associated with MPRGs, we conducted Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) analyses using the “clusterProfiler” package. MPRGs were converted to Entrez IDs and used as inputs for GO and KEGG enrichment analyses, with an adjusted p-value < 0.05 as the criterion. Additionally, potential mechanisms were examined using Gene Set Variation Analysis (GSVA). The cancer immunity cycle and pathways anticipated to respond to immunotherapy were assessed according to established methods [21, 22] A comprehensive collection of pathway gene sets was sourced from the Molecular Signatures Database (MSigDB).

Comprehensive analysis of genomic alterations, immune profiling

Genomic alterations, including recurrent amplification and deletion regions, were identified using GISTIC 2.0 analysis. Tumor Mutational Burden (TMB) was calculated with the R package ‘maftools’ [23]. The Cancer Immunome Atlas (https://tcia.at/home) was utilized to assess the immunophenoscore (IPS) of LUAD patients, identifying those suitable for immunotherapy. Additionally, the ssGSEA algorithm was used to evaluate immune cell infiltration and the activity of immune-related pathways in tumor samples. The TIMER2.0 database provided comprehensive data on immune cell infiltration abundance in TCGA, incorporating results from multiple algorithms.

Cell culture and siRNA transfection

A549 and H1299 LUAD cell lines were obtained from the Institute of Biochemistry and Cell Biology, Chinese Academy of Sciences, Shanghai. Cells were maintained in RPMI 1640 medium supplemented with 10% fetal bovine serum (FBS) and 1% antibiotics (100 U/ml penicillin and 100 mg/ml streptomycin). siRNA transfection was conducted using Lipo2000 reagent (Invitrogen, Shanghai) according to the manufacturer's instructions. A549 and H1299 cells were seeded onto coverslips in six-well plates, with siRNA transfection performed the next day.

Colony formation assay

For the colony formation assay, 5000 cells were seeded in each well of a six-well plate with growth medium, which was replaced weekly. After two weeks, colonies were fixed with methanol for 15 min and stained with 0.1% crystal violet (Sigma) for 30 min. Colony formation was then quantified by counting the stained colonies.

Wound-healing assay

To evaluate cell migration, transfected cells were cultured in six-well plates until 95% confluence. A uniform scratch was made using a sterile 20 μL pipette tip. Detached cells were removed by rinsing twice with PBS. Wound closure was documented at 0 and 48 h post-scratch using ImageJ software to measure scratch width.

Invasion and migration assays

Invasion and migration assays were performed using Corning’s Transwell system (24 wells, 8 μm pore size). For migration assays, 5 × 10^4 transfected cells were placed in the upper chambers with 350 μL of serum-free medium, while 700 μL of medium containing 10% FBS was added to the lower chambers. For invasion assays, Transwell membranes were pre-coated with Matrigel (Sigma-Aldrich). After 16 h, non-migrated cells on the upper surface were removed, and migrated cells on the lower surface were fixed with methanol and stained with 0.1% crystal violet. Images were captured using an Olympus inverted microscope (Tokyo, Japan).

Statistical analysis

Data analysis and visualization were performed using R software version 4.2.0. Kaplan–Meier survival analysis and log-rank tests were utilized to estimate and compare overall survival (OS) among different subtypes. Differences in continuous variables between groups were assessed using either the Wilcoxon rank-sum test or the Student's t-test, depending on data distribution, while categorical variables were evaluated with chi-squared tests or Fisher's exact tests. To address multiple comparisons and reduce type I errors, p-values were adjusted using the false discovery rate (FDR) method. Pearson correlation analysis was conducted to explore relationships among variables. All statistical tests were two-tailed with a significance threshold set at p < 0.05, ensuring a rigorous evaluation and robust conclusions from the study findings.

Result

Identification and prognostic analysis of anoikis and EMT-related genes

Initially, we performed a preliminary screening of anoikis and EMT-related genes. Figure 1A illustrates the specific process. We integrated data from seven global multicenter LUAD datasets (Fig. 1B) and identified prognostic-related genes using univariate Cox analysis (genes with prognostic significance in at least six datasets were included in subsequent studies) (Fig. 1C). Ultimately, we identified 23 AEPGs associated with prognosis (p < 0.05, Fig. 1D). Further GO and KEGG enrichment analyses revealed that AEPGs were primarily enriched in pathways such as oocyte meiosis, the HIF-1 signaling pathway, and the cell cycle (Fig. 1D, E). CNV status analysis indicated frequent changes in AEPGs, with HMMR and KRT8 showing the most extensive CNV amplification (Fig. 1F).Fig. 1 Selection of EMT and Anoikis genes. A Univariate Cox Regression Analysis: Identification of genes related to prognosis from the EMT and anoikis pathways. B Data Integration: Analysis across multiple bulk-LUAD datasets to ensure robustness. C Forest Plot: Visualization of the univariate Cox regression results, highlighting prognostic significance of selected genes. D, E Gene Ontology Enrichment: Functional categorization and enrichment analysis of genes associated with prognosis, illustrating key biological processes involved. F Chromosomal Alterations: Frequency distribution analysis of chromosomal amplifications and deletions among the prognostic-related genes, providing insights into genetic variations contributing to disease progression

Development and Validation of an Anoikis and EMT-Related Optimal Model (AEOM) for Prognosis and Immunotherapy Suitability

Using the expression profiles of 23 prognosis-related AEPGs, we developed an AEOM through a machine learning combinatorial algorithm. The TCGA dataset served as the training cohort, while six GEO datasets were used for validation. The average c-index across the six validation cohorts was the criterion for model selection. Ultimately, the StepCox [both] + SuperPC algorithm emerged as the optimal model (Fig. 2A). The AEOM score distinguished patient prognosis across all seven cohorts (Figs. 2B–I), with patients in the high AEOM group exhibiting worse outcomes compared to those in the low AEOM group. Additionally, IPS scores calculated using the TCIA website suggested that the low AEOM group is more suitable for treatment with PD-1, CTLA-4, or their combination therapy (Figs. 2J–M). Further analysis using the TIDE website indicated that the high AEOM group had higher TIDE scores (Figs. 2N–P), implying a greater likelihood of immune escape and unsuitability for immunotherapy.Fig. 2 Construction and validation of a machine learning model. A Selecting the optimal machine learning model by computing the average C-index across six validation cohorts. B Pie chart displaying the seven datasets used in the model construction, with TCGA serving as the training set and the other six datasets as validation sets. C–I Conducting survival analysis on TCGA, GSE13213, GSE26939, GSE29016, GSE30219, GSE31210 and GSE42127 cohorts. Based on AEOM scores, patients are categorized into high and low expression groups, with the high AEOM group exhibiting a notably worse prognosis. J–K IPS scores were utilized to predict differences in immune therapy responses between the high and low AEOM groups. N–P TIDE analysis was employed to assess the differences in immune therapy responses between the high and low AEOM groups, with the low AEOM group demonstrating better responses to immunotherapy

Superior predictive performance of AEOM

To evaluate the predictive efficacy of AEOM, we integrated clinical features from seven datasets. The results showed that the c-index values of AEOM were higher than those of any other clinical features (such as age, gender, stage, EGFR status, etc.) (Fig. 3A). Subsequently, we conducted a principal component analysis (PCA) based on the expression levels of the AEOM model genes across all datasets. The results indicated that the expression levels of the model genes could effectively distinguish LUAD patients, with high and low AEOM groups forming distinct clusters (Fig. 3B). ROC curves further confirmed that AEOM scores could reliably predict the prognosis of LUAD patients, which was validated across all datasets (with one-, three-, and five-year AUC values generally above 0.65, Fig. 3C). Next, we compared AEOM against numerous previously published LUAD signatures and found that AEOM consistently demonstrated the best predictive performance across all datasets, achieving the highest c-index values (Fig. 3D).Fig. 3 Clinical practice value of model. A Comparing the model's risk scores with other clinical indicators to demonstrate superior prognostic value. B Distribution analysis of LUAD datasets based on model gene expression using PCA. C ROC curves of the model in the TCGA cohort and GEO cohorts. D Assessing against published signatures, highlighting the model's highest C-index scores across multiple datasets

Immune infiltration and regulatory gene expression in high and low aeom groups

Immune regulatory genes are pivotal in modulating the tumor immune response, influencing both tumor progression and patient prognosis. In our analysis, we compared the relative expression levels of these genes between the high and low AEOM groups. Our findings revealed distinct immune activation patterns: the low AEOM group demonstrated an upregulation of MHC class II molecules and co-stimulatory molecules, which are crucial for antigen presentation and the activation of adaptive immune responses. In contrast, the high AEOM group showed increased expression of MHC class I molecules and certain co-inhibitory molecules, which may contribute to immune evasion by the tumor (Fig. 4A). Further analysis using the TIMER2.0 database allowed us to assess the abundance of various immune infiltrating cells within the tumor microenvironment. The low AEOM group exhibited significantly higher infiltration of lymphocytes, including T cells and B cells, indicating a more active immune surveillance and potential anti-tumor response (Fig. 4B). These differences in immune cell infiltration and gene expression profiles underscore the complex interplay between the tumor and the immune system, highlighting potential therapeutic targets for enhancing immune-mediated tumor control.Fig. 4 Assessment of immune infiltration. A Calculation of the disparity in immune cell infiltration between high and low AEOM groups utilizing seven distinct immune infiltration assessment algorithms. B Examination of differences in immune-related gene expression at the mRNA, methylation, and copy number variation levels between high and low AEOM groups

Exploring the relationship between tumor mutations and patient prognosis

Given the significant correlation between tumor mutations, immune response, and patient prognosis, this analysis delves into these relationships in detail. The heatmap prominently highlights a markedly elevated tumor mutational burden (TMB) in the high AEOM group, with TP53, XIRP2, SPTA1, ADAMTS12, and RP1L1 being the most frequently mutated genes (Fig. 5A). Subgroup analysis indicated that the high-mutation, low-AEOM group had a better prognosis (Fig. 5B). Furthermore, the high AEOM group exhibited a higher TMB, and AEOM scores were positively correlated with TMB (Figs. 5C, D).Fig. 5 Genomic and transcriptomic profiling between high and low-risk groups in the TCGA dataset. A Genomic landscape differences between high and low AEOM groups. B Survival curves displaying survival differences among four subgroups. C Correlation between risk scores and TMB. D Differences in standardized TMB between high and low AEOM groups

Unraveling the mechanistic pathways and immune responses

To explore the potential pathway mechanisms underlying the differences between various AEOM groups, GSVA enrichment analysis was conducted. The high AEOM group primarily enriched pathways related to DNA replication, checkpoint signaling, and lung cancer poor survival (Figs. 6A–H). Abnormal activation or suppression of these pathways leads to uncontrolled cell cycle progression, increased DNA replication stress, accumulation of genetic variations, and significant chromosomal structural changes. These collectively promote unlimited tumor cell proliferation, genomic instability, and enhanced invasive and metastatic potential. Next, we investigated the associations between AEOM scores, immune therapy-related pathways, and cancer immunity cycle steps. First, AEOM showed significant positive correlations with various steps of the cancer immunity cycle (Fig. 7A), such as cancer antigen release (Step 1) and the recruitment of immunosuppressive cells (e.g., MDSCs and neutrophils). This suggests that the high AEOM score group may exhibit heightened activity in these immune steps, leading to more effective antigen release and recruitment of immunosuppressive cells. Additionally, AEOM demonstrated strong correlations with several key biological pathways, such as DNA replication, cell cycle regulation, and mismatch repair. These pathways may be abnormally activated or suppressed in the high AEOM score group, thereby affecting tumor progression and immune response. For instance, abnormally active DNA replication and cell cycle regulation can result in rapid tumor cell proliferation and genomic instability, while alterations in mismatch repair mechanisms may increase mutation rates. Overall, patients with high AEOM scores exhibit more pronounced activity in these key pathways and immunity cycle steps, which are closely related to tumor invasiveness and prognosis. This indicates a complex relationship between AEOM and multiple cancer-related biological pathways and immunity cycle steps, where high AEOM scores may predict poorer prognosis and higher tumor aggressiveness.Fig. 6 Comparing the biological characteristics of high and low-risk groups in the TCGA cohort. (A) GSVA enrichment analysis describes the biological characteristics of high and low AEOM groups. B–H tSNE plots depict differences in GO and KEGG pathway activities between high and low AEOM groups

Fig. 7 Correlation analysis of AEOM score and the expression patterns of model genes. A Correlation analysis between AEOM score and tumor immune cycle pathway activity using ssGSEA for evaluation. B Heatmap displaying differences in clinical indicators between high and low AEOM groups, along with differences in the expression of model genes. C Correlation between expression of model genes and AEOM score

RHPN2 identified as a key oncogene in LUAD

Further investigation into the correlation between AEOM and clinical indicators revealed that patients with high AEOM scores had a higher number of deaths and tended to have more advanced T stage, N stage, and overall stage (Fig. 7B). Additionally, all model genes were highly expressed in the high AEOM group. These findings suggest that AEOM is an effective prognostic predictor and is significantly associated with clinical characteristics. To identify biomarkers for AEOM, we conducted a correlation analysis between model genes and AEOM scores (Figs. 7C–I), finding that RHPN2 had the most significant correlation (correlation = 0.85, p < 0.05). Previous analyses indicated that RHPN2 is a risk gene (HR > 1, P < 0.05). Therefore, we performed further experimental exploration of RHPN2. CCK8 assays showed that with RHPN2 knockdown, the OD values of LUAD cells decreased, indicating reduced proliferation capacity (Figs. 8A, B). Colony formation assays revealed a significant reduction in the number of colonies in RHPN2 knockdown LUAD cells (Figs. 8C, D). Scratch assays demonstrated that RHPN2 knockdown could inhibit the migration of LUAD cells (Figs. 8E, F). Transwell assays confirmed that RHPN2 knockdown inhibited tumor cell invasion and migration (Figs. 8G, H). These findings indicate that RHPN2 functions as an oncogene, regulating the proliferative, invasive, and migratory capacities of LUAD cells.Fig. 8 Exploring the role of the key gene RHPN. A, B CCK-8 analysis: Viability of A549 and H1299 cells significantly decreases after RHPN2 knockout, used for further in vitro experiments. C, D Colony formation assay: cells with reduced RHPN2 expression show significantly fewer colony numbers compared to the NC group. E, F Scratch assay: Cells with decreased RHPN2 expression exhibit significantly slower wound healing rates. G–I Transwell assay: downregulation of RHPN2 expression inhibits tumor cell migration and invasion capabilities. Notations: **P < 0.01; ***P < 0.001

Discussion

Lung cancer is the second most common and deadliest malignant tumor in the world [24]. Lung cancer also has the highest incidence and mortality rates in China, with a 5 year survival rate between 10%-15% [25]. Approximately 80%-85% of lung cancer cases are NSCLC, with LUAD being the most common pathological type. Despite some advancements in diagnosis and treatment techniques, the long-term survival rate of LUAD patients remains low. Therefore, there is an urgent need to find new and effective biomarkers or treatment targets for LUAD patients.

Anoikis resistance is a critical factor influencing the progression and metastasis of various cancers, including lung cancer, gastric cancer, colorectal cancer, breast cancer, and prostate cancer, among others [26–28]. This resistance enables tumor cells to survive detachment from the extracellular matrix, facilitating their dissemination to distant sites. Tumor cells can acquire anoikis resistance through multiple signaling pathways, enhancing their invasive and metastatic potential [29]. Research has highlighted the pivotal role of EMT in conferring resistance to anoikis. EMT involves the transformation of epithelial cells into mesenchymal cells, which is characterized by reduced cell–cell adhesion and increased migratory capacity. A key marker of this transformation is the downregulation of E-cadherin, a protein essential for maintaining epithelial integrity. Studies have shown that decreased E-cadherin expression is strongly associated with a heightened resistance to anoikis in tumor cells. In LUAD, understanding the interplay between EMT and anoikis resistance is vital for identifying key genes that drive invasion and metastasis. A comprehensive analysis that integrates both EMT and anoikis pathways can provide insights into potential therapeutic targets, paving the way for more effective treatments aimed at limiting cancer spread and improving patient outcomes.

This study systematically analyzed genes related to anoikis and EMT and developed a prognostic model based on these genes, referred to as the AEOM. By integrating multiple global multicenter LUAD datasets, we identified 23 AEPGs and utilized these genes to develop the AEOM model. Validation results demonstrated that the AEOM exhibited excellent performance in predicting the prognosis of LUAD patients. Patients with high AEOM scores had worse prognoses, higher likelihoods of immune escape, and lower suitability for immunotherapy. Furthermore, experimental validation revealed that RHPN2 functions as a key oncogene in LUAD, regulating the proliferative, invasive, and migratory capacities of tumor cells. In summary, the AEOM model and its associated biomarkers, such as RHPN2, provide new perspectives and potential targets for prognostic assessment and personalized treatment of LUAD patients.

However, this study also has some limitations. First, it is a retrospective study based on gene expression profiles from the TCGA and GEO databases and a few clinical factors, and some specific clinical information related to lung cancer may not have been obtained. Second, this study only used cell experiments for the validation of key genes, lacking further exploration in clinical tissue samples and in vivo experiments. Collecting in-house cohorts is necessary to further validate the effectiveness of the model.

In summary, this study identifies and validates a novel prognostic model based on anoikis and EMT-related genes for predicting LUAD patient outcomes, highlighting RHPN2 as a key oncogene and suggesting potential biomarkers for personalized treatment strategies.

Supplementary Information

Supplementary File 1.

Acknowledgements

No funding

Author contributions

The study was conceived and designed by LY. Data collection was conducted by ZZ. LY performed the statistical analysis. The first draft of the manuscript was written by LY. The experiment was performed by LY. The final approval of the submitted version was given by ZY and QY. All authors contributed to the manuscript and approved the submitted version.

Funding

No funding

Data availability

All datasets pertinent to this study are accessible through the TCGA database (http://cancergenome.nih.gov/), GEO database (https://www.ncbi.nlm.nih.gov/geo/), the Molecular Signature Database (MSigDB) (https://www.gsea-msigdb.org/gsea/msigdb/), TIP (http://biocc.hrbmu.edu.cn/TIP/index.jsp), or the data availability sections of the relevant publications. All data relevant to this investigation, whether generated or analyzed, are comprehensively detailed in this manuscript and its supplementary materials. For further inquiries or data requests, interested parties are advised to reach out to the corresponding authors.

Declarations

Ethic approval and consent to participate

All experiments conducted in this study were approved by the Ethics Committee of the Affiliated Huai’an Hospital of Xuzhou Medical University.

Competing interests

It is hereby declared by the authors that the research was carried out without the presence of any potential conflict of interest arising from commercial or financial relationships.

Publisher's Note

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