APOA1 as a Potential Therapeutic Target and Novel Biomarker in Lung Adenocarcinoma

Lung adenocarcinoma (LUAD) represents the predominant subtype of non-small cell lung cancer, with limited survival improvements despite advances in targeted therapies, underscoring the need for novel biomarkers and therapeutic targets. Apolipoprotein A1 (APOA1), a major component of high-density lipoprotein involved in cholesterol transport, has been linked to tumorigenesis. However, its role in LUAD remains unclear. Multi-omics analyses were conducted using TCGA, HPA, UALCAN, cBioPortal, and other databases to assess APOA1 expression, mutations, epigenetic modifications, prognostic correlations, immune infiltration via CIBERSORT, and pathway enrichment through GO, KEGG, and GSEA. We analyzed TCGA transcriptomic data to assess APOA1 expression in pan-cancer contexts, focusing on LUAD. Serum APOA1 levels were quantified by ELISA in 60 LUAD patients and 30 healthy controls. In vitro experiments involved APOA1 overexpression in A549 and SPCA1 cell lines, evaluated by Western blot, CCK-8 proliferation assay, and Transwell assays. In vivo tumor growth was assessed in nude mouse xenografts. APOA family genes predominantly exhibit amplifications and mutations in pan-cancer. APOA1 was significantly downregulated in LUAD tissues and serum, correlating with poor overall survival (AUC=0.942 for diagnostic accuracy). APOA1 modulates immunity and pathways like complement cascades. Overexpression inhibited cell proliferation, migration, and invasion in vitro, and reduced xenograft tumor volumes in vivo. Drug sensitivity analysis revealed enhanced efficacy of agents like selumetinib in high-APOA1. These findings position APOA1 as a tumor suppressor and prognostic biomarker in LUAD, offering potential for targeted therapies to improve patient outcomes. APOA1 as a Potential Therapeutic Target and Novel Biomarker in Lung Adenocarcinoma Yuening Sun 1,#, Jie Shen 2,# ,Yizhou Lin 5,# , Zheng Yang 2 , Hua Sang 3, Huijun Zhou 4 , Xin Xu 1,*, Jinshi Huang 1,* , Xiaoyu Zhou 2,* 1. Department of Pharmacy, Affiliated Hospital of Nantong University, Pharmacy School of Nantong University, Nantong 226001, China. 2. Department of Respiratory and Critical Care Medicine, Affiliated Hospital of Nantong University, Nantong 226001, China. 3. Department of Pharmacy, Affiliated Zhongshan Hospital Wusong Branch of Fudan University, Shanghai 200000, China. 4. The Affiliated Yancheng First Hospital of Nanjing University Medical School, The First People’s Hospital of Yancheng 5. Department of Clinical Medicine, School of Medicine, Nantong University, Nantong 226001, China. * Correspondence : Xiaoyu Zhou Department of Respiratory and Critical Care Medicine, Affiliated Hospital of Nantong University, Nantong 226001, China. E-mail: [email protected] Jinshi Huang and Xin Xu Department of Pharmacy, Affiliated Hospital of Nantong University, Pharmacy School of Nantong University, Nantong 226001, China. E-mail: [email protected] and Xin Xu, E-mail: [email protected] # Yuening Sun and Jie Shen authors contribute equally to this work. Funding information This work was supported by Nantong Science and Technology Bureau (No. JC2023036); Nantong Pharmaceutical Society project (No. ntyx2305); National Natural Science Foundation of China (No. 82204504); Entrepreneurship and Innovation Doctoral Talent of Jiangsu Province (No. JSSCBS20230484)

Lung adenocarcinoma (LUAD) represents the predominant subtype of non-small cell lung cancer, with limited survival improvements despite advances in targeted therapies, underscoring the need for novel biomarkers and therapeutic targets. Apolipoprotein A1 (APOA1), a major component of high-density lipoprotein involved in cholesterol transport, has been linked to tumorigenesis. However, its role in LUAD remains unclear. Multi-omics analyses were conducted using TCGA, HPA, UALCAN, cBioPortal, and other databases to assess APOA1 expression, mutations, epigenetic modifications, prognostic correlations, immune infiltration via CIBERSORT, and pathway enrichment through GO, KEGG, and GSEA. We analyzed TCGA transcriptomic data to assess APOA1 expression in pan-cancer contexts, focusing on LUAD. Serum APOA1 levels were quantified by ELISA in 60 LUAD patients and 30 healthy controls. In vitro experiments involved APOA1 overexpression in A549 and SPCA1 cell lines, evaluated by Western blot, CCK-8 proliferation assay, and Transwell assays. In vivo tumor growth was assessed in nude mouse xenografts. APOA family genes predominantly exhibit amplifications and mutations in pan-cancer. APOA1 was significantly downregulated in LUAD tissues and serum, correlating with poor overall survival (AUC=0.942 for diagnostic accuracy). APOA1 modulates immunity and pathways like complement cascades. Overexpression inhibited cell proliferation, migration, and invasion in vitro, and reduced xenograft tumor volumes in vivo. Drug sensitivity analysis revealed enhanced efficacy of agents like selumetinib in high-APOA1. These findings position APOA1 as a tumor suppressor and prognostic biomarker in LUAD, offering potential for targeted therapies to improve patient outcomes.

APOA1, Bioinformatics, LUAD, Migration, Proliferation, Survival Lung adenocarcinoma has poor prognosis despite targeted therapies. This study identifies Apolipoprotein A1 (APOA1) as a downregulated tumor suppressor in LUAD, integrating multi‑omics analyses, patient serum validation, and functional experiments. APOA1 shows high diagnostic accuracy (AUC = 0.942), inhibits tumor cell proliferation and invasion, modulates immune infiltration, and enhances sensitivity to specific drugs. These findings highlight APOA1 as both a prognostic biomarker and potential therapeutic target, offering new opportunities for personalized LUAD management. 1 Introduction Approximately 85% of lung cancer cases are classified as non-small cell lung cancer (NSCLC) [1]. Unfortunately, around 70% of these patients are diagnosed at an advanced stage, leading to nearly 1.8 million deaths annually from lung cancer worldwide[2]. In recent years, significant advancements in treatment, particularly in immune and targeted therapies, have improved the prognosis for patients with advanced NSCLC. However, the extension in survival time for patients with lung adenocarcinoma (LUAD) has been minimal[3]. As a result, it is critical to identify new biomarkers or therapeutic targets in the early stages of LUAD. Apolipoprotein A1 (APOA1), the primary structural protein of high-density lipoprotein (HDL), plays a central role in reverse cholesterol transport[4, 5]. Numerous studies have demonstrated that APOA1 significantly influences tumor growth, angiogenesis, invasion, and metastasis[6, 7]. Although some studies have reported on the relationship between APOA1 and cancer development, for instance, studies have shown that APOA1 levels are altered in various malignancies, including gastric and ovarian cancers, where reduced expression correlates with poorer prognosis and enhanced tumor invasiveness[5, 8, 9]. While APOA1 has been implicated in several cancer types[10, 11], its specific role in LUAD remains poorly understood. Despite these findings, APOA1’s role in LUAD is underexplored. Prior research has focused on systemic serum levels or general cancer risk, with limited integration of multi-omics data (e.g., genetic alterations, epigenetic regulation) and functional validations in LUAD models. This study investigates the expression patterns, genetic and epigenetic modifications, and functional roles of APOA1 in LUAD. We hypothesize that downregulation of APOA1 may contribute to tumor progression and serves as a prognostic biomarker. We analyzed the differential expression of APOA1 in pan-cancer and LUAD tissues using public databases. We will also evaluate its correlation with survival outcomes and immune infiltration, and examine its effects on cellular proliferation, migration, and invasion through overexpression experiments in LUAD cell lines and xenograft models. By elucidating these mechanisms, this study aims to establish APOA1 as a novel therapeutic target and biomarker, which could guide personalized treatment strategies for LUAD patients. 2 Materials and Methods 2.1 Data sources and processing Data on gene expression RNA-seq (HTSeq-FPKM) were retrieved from the TCGA database. For this TCGA-based pan-cancer analysis, the expression levels of APOA family genes were extracted and integrated using Perl software[12]. Scripts available upon request. To facilitate the analysis of TCGA datasets, we employed Strawberry Perl in conjunction with R software (version 3.5.2), leveraging these sophisticated computational tools to ensure the meticulous and accurate processing of our data. The Human Protein Atlas (HPA, https://www.proteinatlas.org/) offers expression data for ~26,000 proteins in human tissues and organs. We obtained immunohistochemical images of LUAD and matched normal tissues from the HPA database. Using UALCAN (http://UALCAN.path.uab.edu/analysisprot.html) [13], we analyzed the protein expression levels of APOA1 across various tumor types. 2.2 Genetic alteration analysis The analysis and visualization of APOA1 gene mutations were obtained through the cBioPortal database (https://www.cbioportal.org/)[14]. 2.3 Survival prognosis analysis in pan-cancer and LUAD Overall survival (OS) of APOA1 across pan-cancer types was visualized and analyzed using the KMPlot website (https://kmplot.com/analysis/).To forecast the survival trajectory of LUAD patients stratified by APOA1 expression, we availed ourselves of the PrognoScan platform (http://dna00.bio.kyutech.ac.jp/PrognoScan/index.html), utilizing data sourced from the GEO database (GSE31210). Additionally, we employed GEPIA (http://gepia2.cancer-pku.cn/)[15], an analytical resource amalgamating TCGA and GTEx datasets, to conduct a complementary survival analysis of APOA1 in LUAD. For this analysis, patient stratification was based on quartile thresholds of APOA1 expression, ensuring a rigorous and nuanced examination of its prognostic implications. 2.4 Analysis of drug treatment and APOA1 expression in LUAD The CellMiner web-based repository (accessible via https://discover.nci.nih.gov/cellminer/home.do) was harnessed to conduct an in-depth analysis, aiming to elucidate the correlation between the efficacy of pharmacological interventions and the expression levels of APOA1. 2.5 Correlation between APOA1 and Tumor Immune Infiltrating Cells in LUAD CIBERSORT (accessible at http://cibersort.stanford.edu/) represents a sophisticated deconvolution algorithm that relies on gene expression data to accurately estimate the proportions of Tumor-Infiltrating Immune Cells (TIICs) within distinct gene expression profiles[16]. To assess the impact of APOA1 expression on the tumor immune microenvironment, we analyzed a dataset comprising 551 LUAD samples sourced from TCGA. These samples were meticulously stratified into two groups: low APOA1 expression (encompassing 275 cases) and high APOA1 expression (comprising 276 cases). Subsequently, CIBERSORT was employed to characterize the immune responses of 22 distinct TIIC subsets within the LUAD samples. 2.6 APOA1’ function analysis in LUAD To uncover the intricate functional roles of Apolipoprotein A1 (APOA1), a series of comprehensive bioinformatics analyses were performed, integrating Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway annotations. Such analyses were executed through the robust computational frameworks of R packages, clusterProfiler (version 3.14.3) and org.Hs.eg.db (version 3.10.0). The GO analysis, a methodological staple in genomic research, permitted a detailed annotation of genes and their associated gene products, thereby revealing molecular functions (MF), biological processes (BP), and cellular components (CC). Concurrently, the KEGG resource offered a systematic means to scrutinize gene functions against the backdrop of known pathways. Moreover, we leveraged the sophisticated Metascape tool (http://metascape.org) to scrutinize the functions of genes related to APOA1 in the context of LUAD. To further dissect the association between activation signaling pathways and APOA1-related genes, we employed the Gene Set Enrichment Analysis (GSEA)[15, 17]. 2.7 Enzyme linked immunosorbent assay (ELISA) for detecting serum APOA1 concentration in LUAD and healthy human samples To quantify serum APOA1 levels, ELISA was applied to cohorts of healthy individuals and patients with lung adenocarcinoma (LUAD). A curated collection of 90 serum samples, inclusive of 60 LUAD patients and a control group of 30 healthy subjects, was procured between December 2018 and June 2019. It was imperative that participants had not undergone any prior therapeutic intervention and were in a fasting state during the early morning collection. Post-collection, the samples were equilibrated at room temperature for 60 minutes prior to centrifugation at 3000 revolutions per minute (rpm) for 20 minutes. The supernatants obtained were then conserved at -80°C. Each serum specimen was diluted to a ratio of 1:40,000 and assessed in duplicate. The ELISA protocol involved the distribution of a 100μl aliquot of both standard and diluted samples into each well, followed by an incubation phase at 37°C for 90 minutes. The plates underwent a rigorous washing regimen, repeated thrice, to mitigate unbound antibodies. Subsequently, a 100μl aliquot of biotin-conjugated antibody solution was added to each well, with an incubation period of 1 hour at 37°C. Another round of washing was conducted, followed by the introduction of 100μl of enzyme-linked secondary antibody solution, which was incubated for 50 minutes, later extended to an hour. A 100μl aliquot of developer reagent was dispensed into each well, and the plates were incubated away from light at 37°C for a quarter of an hour. The reaction was halted by adding 100μl of stop solution to each well, with subsequent gentle mixing for approximately 30 seconds. The optical density of each well was quantified at a wavelength of 450nm using a specialized ELISA reader, and a standard curve was plotted based on the absorbance of the standards. The concentration of APOA1 in the samples was extrapolated by referencing this curve. It is of note that the Ethics Committee at Nantong University Affiliated Hospital provided approval for the ethical use of these samples for scientific inquiry. 2.8 Cell culture and transfection The LUAD cell lines, namely H1299 (official name: NCI-H1299; species: Homo sapiens; sex: male; tissue of origin: lung adenocarcinoma; RRID: CVCL_0060), H1650 (official name: NCI-H1650; species: Homo sapiens; sex: male; tissue of origin: lung adenocarcinoma; RRID: CVCL_1483), SPCA1 (official name: SPCA-1; species: Homo sapiens; sex: male; tissue of origin: lung adenocarcinoma; RRID: CVCL_6959), and A549 (official name: A549; species: Homo sapiens; sex: female; tissue of origin: lung adenocarcinoma; RRID: CVCL_0023), were sourced from the Cell Bank of the Chinese Academy of Sciences, Shanghai, China, and obtained in December 2018. These cell lines were authenticated by short tandem repeat (STR) profiling prior to experiments and have not been previously reported as misidentified or contaminated. Mycoplasma contamination was routinely tested using a PCR-based detection kit (MycoAlert Mycoplasma Detection Kit, Lonza) and confirmed negative for all experiments described. These cell lines were maintained in RPMI-1640 medium, supplemented with 10% Fetal Bovine Serum (FBS), 100 U/ml penicillin, and 100 μg/ml streptomycin, in a humidified atmosphere of 5% CO2 at a controlled temperature of 37°C.The A549 and SPCA1 cell lines were subjected to plasmid-mediated overexpression of APOA1, adhering strictly to the protocols recommended by the manufacturers. The APOA1 overexpression plasmid was constructed on the p-CMV3 puro vector and purchased from GENECHEM. Throughout the aforementioned experimentation, rigorous adherence was maintained to the manufacturer’s recommended protocols and guidelines. 2.9 Western Blotting Technique The proteins were initially fractionated based on their molecular weights through SDS-PAGE electrophoresis and subsequently transferred to a PVDF membrane. Prior to incubation with a primary antibody tailored to the protein of interest, the membrane was blocked to mitigate nonspecific binding interactions. After removing any unbound primary antibodies through washing, a labeled secondary antibody was introduced. Subsequent washing steps were performed, and the proteins were visualized utilizing either chemiluminescent or fluorescent detection methodologies. The intensity of the protein of interest was then meticulously quantified using advanced image analysis software, facilitating the assessment of protein presence, relative abundance, and molecular weight within the sample. 2.10 Cell Proliferation Assessment with CCK-8 Assay We employed the CCK-8 (Cell Counting Kit-8) assay, procured from Vazyme (China), to quantitatively assess the proliferative activity of NSCLC cells. The NSCLC cell lines were meticulously seeded within 96-well microplates, and their subsequent growth was vigilantly tracked over a period of 24, 48, 72, and 96 hours, in strict accordance with the manufacturer’s protocol. This systematic evaluation at these discrete time intervals allowed for the generation of a detailed spatiotemporal map of cellular proliferation patterns. 2.11 In Vitro Analysis of NSCLC Cell Migration and Invasion To quantitatively assess the migratory and invasive properties of NSCLC cells, we employed the Transwell assay, a gold-standard method in cellular motility studies. This procedure involved the utilization of a 24-well Transwell apparatus, wherein NSCLC cells, either untreated or subjected to experimental perturbations, were introduced to the upper compartment in a serum-deprived medium. The lower compartment, designed to foster directional cell migration, contained medium enriched with 20% Fetal Bovine Serum (FBS), establishing a chemotactic gradient. Following a 24-hour incubation period, the cells that had traversed to the lower compartment were meticulously enumerated. This quantification provided a direct measure of the cells’ migratory capacity. For the invasion assay, the upper chamber’s membrane was precoated with a layer of Matrigel, effectively simulating the extracellular matrix. NSCLC cells, suspended in a serum-free medium, were placed in the upper compartment, while the lower was filled with medium containing 20% FBS. After a 24-hour incubation, the invasive cells that had penetrated to the lower compartment were meticulously counted under an inverted microscope and quantified using ImageJ software across five randomly selected fields, offering a comprehensive assessment of their invasive potential. 2.12 Development and Characterization of a Nude Mouse Xenograft Model for LUAD With the express approval of the Institutional Animal Care and Use Committee of Nantong University Affiliated Hospital, all in vivo experimental protocols were conducted in strict accordance with ethical guidelines. For the establishment of a xenograft tumor model, five-week-old nude mice, obtained from the Animal Experiment Center of Nantong University, were utilized. A549 and SPCA1 cells, engineered for elevated expression of the gene of interest (1×106 cells in 0.1 ml of 1640 medium), were subcutaneously implanted into the right dorsal flank of each mouse. The mice were monitored daily for tumor development over a 28-day period, after which they were humanely euthanized by cervical dislocation method. The resulting tumors were carefully harvested and subjected to further histological and molecular analysis, providing invaluable data on tumor growth and progression in vivo. 2.13 Statistical Evaluation and Analytical Procedures The statistical outcomes were articulated as the mean values accompanied by their respective standard deviations (SD). In order to facilitate comparisons among distinct groups, chi-squared tests were meticulously employed. Furthermore, when analyzing data pertaining to two groups, Student’s t-tests were diligently utilized. The statistical analyses were executed utilizing the GraphPad Prism 9.0 software (GraphPad Prism Software, California, USA), a sophisticated tool renowned for its precision and reliability. A rigorous threshold of P < 0.05 was established to ascertain statistical significance, with any values falling below this threshold denoting noteworthy disparities. 3 Results 3.1 Differential Expression Profiles and Correlation Analysis of APOA Family genes in Pan-Cancer An initial investigation into the expression levels of APOA family genes across various cancer types revealed a notably higher expression of APOA1 compared to APOA2, APOA3, and APOA5 (Figure 1A). Next, the expression differences of APOA family genes between tumor and normal tissues across various cancer types were calculated, and a heatmap was generated. The results showed that the APOA family genes were generally expressed at lower levels in Pan-Cancer, with a particularly marked downregulation of APOA1, A2, and A5 in cholangiocarcinoma (CHOL) (Figure 1B). Additionally, correlation analysis among the APOA genes revealed a strong correlation between APOA2 and APOA5 (correlation coefficient = 0.90) and a moderate correlation between APOA2 and APOA4 (correlation coefficient = 0.56), as shown in Figure 1C. 3.2 Genetic Alteration Analysis of APOA Family genes in Pan-Cancer To elucidate the genetic alterations of APOA Family genes in diverse cancers, we conducted a mutation status analysis using the cBioPortal platform. Pan-cancer analysis revealed a preponderance of Amplification (red), followed by Mutation(green) ( Figure 2A ). Across diverse cancer types, APOA family genes exhibit heterogeneous mutational landscapes characterized by dispersed missense variants and recurrent hotspots—such as APOA1 Q29Rfs*7/Pfs*30, APOA2 K69N, APOA4 E207K/G, and APOA5 R284*( Figure 2B )—implicating both structural perturbations and potential loss‑of‑function events that may contribute to dysregulated lipid metabolism and tumor progression. 3.3 The mRNA and protein expression level of APOA1 in pan-cancer Building on these initial observations, this study conducted a comprehensive analysis of APOA family genes expression across various cancer types using data from The Cancer Genome Atlas (TCGA). Compared to normal tissues (Figure 3A), APOA1 expression was significantly reduced in CHOL, kidney renal clear cell carcinoma (KIRC), lung squamous cell carcinoma (LUSC), and lung adenocarcinoma (LUAD). APOA2 was downregulated in CHOL but upregulated in LUAD and liver hepatocellular carcinoma (LIHC). APOA4 showed decreased expression in CHOL, LUAD, LIHC, and stomach adenocarcinoma (STAD), while APOA5 exhibited a similar downward trend in CHOL, LUAD, and LIHC. These results show that APOA1 is the most highly expressed gene within its family and is downregulated in various tumor types, underscoring its potential as a candidate gene for further investigation. We further analyzed APOA1 protein expression across pan-cancer types using the UALCAN database. The results showed a significant decrease in APOA1 protein levels in BRCA, LUAD, UCEC, LUSC, COAD, HNSC, and OV. Meanwhile, in KIRC, PAAD, and GBM, APOA1 protein expression was also higher than in normal tissues, with statistically significant differences (P < 0.05, Figure 3B). 3.4 Association of APOA1 Expression with Prognosis in Pan-Cancer The Kaplan–Meier Plotter online database was used to evaluate the prognostic value of APOA1 across multiple cancer types. The results showed that low expression of APOA1 was significantly associated with poorer overall survival (OS) in hepatocellular carcinoma (LIHC), esophageal carcinoma (ESCA), pancreatic adenocarcinoma (PAAD), rectal adenocarcinoma (READ), thymoma (THYM), breast cancer (BRCA), lung adenocarcinoma (LUAD), and ovarian cancer (OV), with a hazard ratio (HR) greater than 1 (Figure 4A-H; P < 0.05). In contrast, high expression of APOA1 was correlated with poor overall survival in stomach adenocarcinoma (STAD), kidney renal clear cell carcinoma (KIRC), and uterine corpus endometrial carcinoma (UCEC) (Supplementary Figure 1). Given the consistent and significant association between APOA1 expression and overall survival in multiple cancer types—particularly in lung adenocarcinoma (LUAD)—we further focused our investigation on LUAD to better understand the specific role and prognostic value of APOA1 in this cancer type. 3.5 Clinical Relevance of APOA1 Expression in LUAD Measurement of APOA1 serum levels by ELISA revealed significantly reduced levels in patients with non-small-cell lung cancer compared to healthy individuals (Figure 5A). Moreover, immunohistochemical analysis from the Human Protein Atlas (HPA) database also confirmed that APOA1 expression is lower in tumor tissues compared to normal tissues. In particular, we found that APOA1 may play a role as a tumor suppressor gene in LUAD through the above analysis (Figure 5B). An in-depth analysis using the GEPIA 2.0 database was performed to evaluate the prognostic significance of APOA1 in LUAD. The results showed a significant positive correlation between higher APOA1 expression levels and improved survival in LUAD, as illustrated in Figure 5C. Further evidence from the gene set GSE31210, obtained via the PrognoScan online database, confirmed that reduced APOA1 expression is associated with poor survival outcomes in LUAD, as shown in Figure 5D. To assess the diagnostic accuracy, a receiver operating characteristic (ROC) curve was generated utilizing the expression data from a cohort of 551 LUAD patients and 54 healthy controls, resulting in an area under the ROC curve of 0.942, as shown in Figure 5E. 3.6 Drug‑efficacy analysis in LUAD The CellMiner database provided the pharmacological efficacy and mRNA level data, enabling a thorough correlation analysis between the half maximal inhibitory concentration (IC50) of various drugs and the APOA1 gene expression levels. Our analysis unveiled an inverse correlation between APOA1 expression and the IC50 values for Selumetinib, Epothilone B, and okadaic acid, suggesting increased sensitivity to these drugs with higher APOA1 levels. Conversely, a positive correlation was observed with Chelerythrine and Nelarabine, indicating a potential decrease in drug efficacy with elevated APOA1 expression. These insights could be instrumental in personalizing therapeutic strategies for LUAD patients exhibiting high APOA1 expression, as represented in Figure 5F. 3.7 Functional Profiling of Genes Associated with APOA1 Expression In an effort to dissect the functional landscape of genes associated with APOA1 expression, we stratified samples based on the median expression levels of the APOA1 gene and constructed a heatmap to graphically represent the expression profiles of various genes in groups defined by high and low APOA1 expression (Supplementary Figure 2). A comprehensive set of 706 genes was advanced to Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analyses. The functional enrichment analysis indicated significant overrepresentation of genes involved in the negative regulation of peptidase activity within biological processes (BP), channel activity in molecular functions (MF), collagen-rich extracellular matrices in cellular components (CC), and neuroactive ligand-receptor interaction pathways according to KEGG. These findings underscore the pivotal role of these target genes in modulating cellular signaling and metabolic pathways (as depicted in Figure 6A, B). Additionally, Gene Set Enrichment Analysis (GSEA) was applied to differentially expressed genes (DEGs) to explore the pathways associated with APOA1 in LUAD. The GSEA results highlighted the upregulation of complement and coagulation cascades in samples exhibiting high APOA1 mRNA expression, whereas the activity of certain pathways, such as the ribosome pathway, was found to be suppressed (Figure 6C). 3.8 Modulation of Tumor-Infiltrating Immune Cells by APOA1 Expression Given the established significance of tumor-infiltrating lymphocytes in cancer prognosis, we set out to investigate the relationship between APOA1 expression levels and the presence of immune cell infiltrates in Lung Adenocarcinoma (LUAD). Our study cohort included 551 LUAD patients, which were divided into high and low APOA1 expression groups. Employing CIBERSORT, we analyzed the gene expression profiles to ascertain the distribution of 22 distinct immune cell subsets. Our analysis brought to light a significant modulation of CD4 memory activated T cells, follicular helper T cells, resting dendritic cells, and activated dendritic cells by APOA1 expression. Intriguingly, an increased frequency of follicular helper T cells was observed in the group with high APOA1 expression, in contrast to a decreased frequency of CD4 memory activated T cells, resting dendritic cells, and activated dendritic cells (Figure 6D). These associations suggest APOA1 may influence anti-tumor immunity, pending functional studies 3.9 Impact of APOA1 Expression on Cellular Dynamics in LUAD To further delineate the role of APOA1 in LUAD pathobiology, we evaluated its expression across four LUAD cell lines and identified A549 and SPCA1 as displaying relatively lower expression compared to H1299 and H1650(Figure 7A). These cell lines were selected for APOA1 overexpression studies utilizing plasmid (Figure 7B, C). The CCK-8 assay was subsequently employed to assess the impact of APOA1 overexpression on cellular proliferation, which revealed a pronounced reduction in the proliferation rate of A549 and SPCA1 cells relative to control cells (Figure 7D). Collectively, these results suggest that elevated APOA1 expression negatively regulates LUAD cell proliferation. The influence of APOA1 on cellular migration and invasion was further probed using scratch wound healing and Transwell assays. At 24 hours post-APOA1 overexpression, a significant delay in wound closure and a reduction in the number of cells migrating through the membrane were observed in the experimental group compared to the control (Figure 8A-D). These observations indicate that increased APOA1 expression negatively modulates the migratory, invasive and proliferative properties of LUAD cells. 3.10 The effect of APOA1 overexpression on tumor proliferation in a lung adenocarcinoma xenograft model in nude mice. To translate these in vitro observations, we established a xenograft tumor model using nude mice. A549 and SPCA1 cells with stable APOA1 overexpression and their respective controls were injected into the mice. Tumor growth was monitored, and the volume was measured over a 28-day period. The results demonstrated significantly reduced tumor volumes in the APOA1-overexpressing groups, indicating that APOA1 overexpression impedes tumor growth in LUAD (Figure 9A,B). 4 Discussion This study demonstrates that APOA1 is downregulated in LUAD tissues and serum, associated with poor prognosis, and inhibits proliferation, migration, invasion, and tumor growth. These results support APOA1 as a potential tumor suppressor, providing mechanistic insights into LUAD and its value as a therapeutic target. The downregulation of APOA1 in LUAD tissues, supported by TCGA data and serum ELISA results, is consistent with its known role in reverse cholesterol transport and anti-inflammatory functions[4].Decreased APOA1 expression may disrupt lipid homeostasis, promoting a pro-tumorigenic microenvironment marked by oxidative stress and chronic inflammation—key features of LUAD progression[18]. In vitro, APOA1 overexpression inhibited proliferation, migration, and invasion in A549 and SPCA1 cells, possibly by modulating signaling pathways related to peptidase activity and extracellular matrix interactions, as suggested by GO and KEGG analyses. In vivo, APOA1 overexpression reduced xenograft tumor growth, highlighting its direct impact on tumor burden through cell-intrinsic mechanisms. Our findings are consistent with literature: Zamanian-Daryoush et al. [6] reported APOA1’s anti-tumor effects via HDL-cholesterol efflux, compromising tumor membranes. However, whereas Tuft Stavnes et al.[5] (high APOA1 as favorable in ovarian cancer), low APOA1 predicted poor LUAD survival, implying tissue-specific roles possibly due to distinct immune contexts. In gastric cancer, Li et al.[8] identified APOA1 as a diagnostic biomarker with reduced serum levels, consistent with our ELISA results and supporting its pan-cancer significance. We extend this by integrating multi-omics, revealing epigenetic contributions—a gap in prior work. Sun et al. [9] linked APOA1 ubiquitination to pancreatic metastasis, consistent with our invasion results. Our study introduces novel insights by showing that APOA1 overexpression inhibits metastasis in LUAD models, addressing a previously unexplored area in lung-specific research Translationally, APOA1’s high diagnostic accuracy (AUC=0.942) supports its use in early detection panels alongside EGFR mutations. Drug correlations (e.g., Selumetinib sensitivity) suggest stratified therapies, similar to HDL-mimetics in cardiovascular disease[19].Moreover, APOA1’s modulation of immune infiltrates implies synergy with immunotherapies, potentially improving outcomes in advanced LUAD where survival gains remain modest[9]. Despite its contributions, our study has several limitations that merit consideration. Although the TCGA datasets are comprehensive, their reliance may introduce biases due to heterogeneous patient cohorts, limiting generalizability to non-Caucasian populations. In vitro models using A549 and SPCA1 cell lines, while representative, fail to fully replicate tumor heterogeneity and stromal interactions, potentially leading to an overestimation of APOA1’s effects. The small sample sizes in the serum ELISA (n = 90) and xenograft experiments (with implicitly small groups) reduce statistical power, despite achieving significant differences. Larger, prospective cohorts are needed to validate serum APOA1 as a non-invasive biomarker. 5 Conclusion In summary, this investigation establishes APOA1 as a downregulated tumor suppressor in LUAD with prognostic and therapeutic implications, innovating through integrated multi-omics and functional validation. Future research should explore APOA1 mimetics in clinical trials and dissect its interactions with the tumor microbiome to refine targeted therapies. Abbreviations OS: Overall survival; NSCLC: Non-small cell lung cancer; LUAD: Lung adenocarcinoma; TCGA: The cancer genome atlas; GSEA: Gene set enrichment analysis; GO: Gene Ontology AUTHOR CONTRIBUTIONS Yuening Sun and Jie Shen were responsible for article writing and data analysis, Hua Sang, Huijun Zhou and Zheng Yang were responsible for some experimental operations, and Xin Xu, Jinshi Huang and Xiaoyu Zhou were responsible for project conception and experimental design.

Not applicable. FUNDING INFORMATION This work was supported by Nantong Science and Technology Bureau (No. JC2023036); Nantong Pharmaceutical Society project (No. ntyx2305); National Natural Science Foundation of China (No. 82204504); Entrepreneurship and Innovation Doctoral Talent of Jiangsu Province (No. JSSCBS20230484). CONFLICT OF INTEREST STATEMENT The authors declare no competing interests. DATA AVAILABILITY STATEMENT The datasets used and/or analysed during the current study are available from the corresponding author on reasonable request. ETHICS STATEMENT This study was officially recognized for it strictly carried out the procedures for care and use admitted by the Ethics Committee of the Affiliated Hospital of Nantong University (Ethic Number: 2018-K020). Written informed consents were obtained from all participant ORCID Zheng Yang https://orcid.org/0000-0001-8305-0705

1. Leiter A, Veluswamy RR, Wisnivesky JP: The global burden of lung cancer: current status and future trends. Nat Rev Clin Oncol 2023, 20(9):624-639.2. Sung H, Ferlay J, Siegel RL, Laversanne M, Soerjomataram I, Jemal A, Bray F: Global Cancer Statistics 2020: GLOBOCAN Estimates of Incidence and Mortality Worldwide for 36 Cancers in 185 Countries. CA Cancer J Clin 2021, 71(3):209-249.3. Koo HK, Jin SM, Lee CH, Lim HJ, Yim JJ, Kim YT, Yang SC, Yoo CG, Han SK, Kim JH et al : Factors associated with recurrence in patients with curatively resected stage I-II lung cancer. Lung Cancer 2011, 73(2):222-229.4. Georgila K, Vyrla D, Drakos E: Apolipoprotein A-I (ApoA-I), Immunity, Inflammation and Cancer. Cancers (Basel) 2019, 11(8).5. Tuft Stavnes H, Nymoen DA, Hetland Falkenthal TE, Kaern J, Trope CG, Davidson B: APOA1 mRNA expression in ovarian serous carcinoma effusions is a marker of longer survival. Am J Clin Pathol 2014, 142(1):51-57.6. Zamanian-Daryoush M, DiDonato JA: Apolipoprotein A-I and Cancer. Front Pharmacol 2015, 6:265.7. Burger D, Dayer JM: High-density lipoprotein-associated apolipoprotein A-I: the missing link between infection and chronic inflammation? Autoimmun Rev 2002, 1(1-2):111-117.8. Li F, Han M, Gao X, Du X, Jiang C: APOA1 mRNA and serum APOA1 protein as diagnostic and prognostic biomarkers in gastric cancer. Transl Cancer Res 2024, 13(5):2141-2154.9. Sun Y, Ren D, Yang C, Yang W, Zhao J, Zhou Y, Jin X, Wu H: TRIM15 promotes the invasion and metastasis of pancreatic cancer cells by mediating APOA1 ubiquitination and degradation. Biochim Biophys Acta Mol Basis Dis 2021, 1867(11):166213.10. Xu P, Zhang Q, Zhai J, Chen P, Deng X, Miao L, Zhang X: APOA1 promotes tumor proliferation and migration and may be a potential pan-cancer biomarker and immunotherapy target. Transl Oncol 2025, 55:102344.11. Darwish NM, Al-Hail MK, Mohamed Y, Al Saady R, Mohsen S, Zar A, Al-Mansoori L, Pedersen S: The Role of Apolipoproteins in the Commonest Cancers: A Review. Cancers (Basel) 2023, 15(23).12. Goldman MJ, Craft B, Hastie M, Repecka K, McDade F, Kamath A, Banerjee A, Luo Y, Rogers D, Brooks AN et al : Visualizing and interpreting cancer genomics data via the Xena platform. Nat Biotechnol 2020, 38(6):675-678.13. Chandrashekar DS, Bashel B, Balasubramanya SAH, Creighton CJ, Ponce-Rodriguez I, Chakravarthi B, Varambally S: UALCAN: A Portal for Facilitating Tumor Subgroup Gene Expression and Survival Analyses. Neoplasia 2017, 19(8):649-658.14. Cerami E, Gao J, Dogrusoz U, Gross BE, Sumer SO, Aksoy BA, Jacobsen A, Byrne CJ, Heuer ML, Larsson E et al : The cBio cancer genomics portal: an open platform for exploring multidimensional cancer genomics data. Cancer Discov 2012, 2(5):401-404.15. Tang Z, Li C, Kang B, Gao G, Li C, Zhang Z: GEPIA: a web server for cancer and normal gene expression profiling and interactive analyses. Nucleic Acids Res 2017, 45(W1):W98-W102.16. Gentles AJ, Newman AM, Liu CL, Bratman SV, Feng W, Kim D, Nair VS, Xu Y, Khuong A, Hoang CD et al : The prognostic landscape of genes and infiltrating immune cells across human cancers. Nat Med 2015, 21(8):938-945.17. Tu Z, Xiong J, Xiao R, Shao L, Yang X, Zhou L, Yuan W, Wang M, Yin Q, Wu Y et al : Loss of miR-146b-5p promotes T cell acute lymphoblastic leukemia migration and invasion via the IL-17A pathway. J Cell Biochem 2019, 120(4):5936-5948.18. Guilbaud E, Barouillet T, Ilie M, Borowczyk C, Ivanov S, Sarrazy V, Vaillant N, Ayrault M, Castiglione A, Rignol G et al : Cholesterol efflux pathways hinder KRAS-driven lung tumor progenitor cell expansion. Cell Stem Cell 2023, 30(6):800-817 e809.19. Recio C, Maione F, Iqbal AJ, Mascolo N, De Feo V: The Potential Therapeutic Application of Peptides and Peptidomimetics in Cardiovascular Disease. Front Pharmacol 2016, 7:526. Figure legends Figure 1 The expression levels and correlations of APOA family genes across different cancer types were obtained from TCGA. (A) Expression of APOA family genes is either increased or decreased in various cancers. (B) Expression levels of APOA family genes in different cancer types from TCGA data. The color in each small rectangle represents the high or low expression of APOA family genes in each cancer type. (C) Correlations between APOA family genes. Blue dots represent positive correlations, while red dots indicate negative correlations. Figure 2 APOA family genes gene mutation in various cancers. cBioPortal was used to display the alteration frequency of different mutation types(A) and mutation site(B) of APOA family genes in pan-cancer. Figure 3 Differences in APOA1 mRNA and protein expression across pan-cancer. (A) APOA family gene expression levels in different cancer types and normal tissue. (B) Protein expression levels of APOA1 across pan-cancer. The red rectangle box represents gene expression levels in tumor tissue and the blue rectangle box represents normal tissue. * P < 0.05, ** P < 0.01, and *** P < 0.001. BLCA, bladder urothelial carcinoma; BRCA, breast invasive carcinoma; CHOL, Cholangio carcinoma; COAD, Colon adenocarcinoma; ESCA, Esophageal carcinoma; GBM, glioblastoma multiforme; HNSC, head and neck squamous cell carcinoma; KICH, Kidney Chromophobe; KIRC, kidney renal clear cell carcinoma; KIRP, kidney renal papillary cell carcinoma; LIHC, Liver hepatocellular carcinoma; LUAD, Lung adenocarcinoma; LUSC, Lung squamous cell carcinoma; PRAD, Prostate adenocarcinoma; READ, Rectum adenocarcinoma; STAD, Stomach adenocarcinoma; THCA, Thyroid carcinoma; UCEC, uterine corpus endometrial carcinoma. Figure 4 Kaplan-Meier analysis of the relationship between high and low expression of APOA1 and OS(HR>1, P<0.05). BRCA, breast invasive carcinoma; ESCA, Esophageal carcinoma; PAAD, Pancreatic Cancer; THYM, Thymoma; OV, Ovarian Cancer; LIHC, Liver hepatocellular carcinoma; LUAD, Lung adenocarcinoma; READ, Rectum adenocarcinoma. Figure 5 APOA1 expression levels, immunohistochemistry, clinical prognosis, and drug sensitivity analysis in LUAD. (A) The concentration of APOA1 in serum of 60 patients with lung cancer and 30 normal people was determined by Elisa. (B) Immunohistochemical images comparing APOA1 protein expression in normal and tumor tissues. (C) GEPIA 2.0 was used to analyze the effects of APOA1 gene expression on the patients’ prognosis in lung adenocarcinoma. (D) Overall survival was obtained through PrognoScan online database (the red curve represents high expression of APOA1, and the blue curve represents low expression of APOA1. (E) Receiver operating characteristic (ROC) curve for APOA1 expression in cancerous tissue and adjacent tissue. X is the expression amount of the gene, and Y is the prediction conclusion. (F) Scatter plots showed the correlation between APOA1 expression and IC50 of drugs. The red represents gene expression levels in tumor tissue and the blue represents normal tissue. Figure 6 (A) Gene ontology (GO)and (B) KEGG pathway functional enrichment analysis. Functional and pathway enrichment were presented in bubble charts, ranking the top genes according to adjusted P value. (C) Gene function analysis of APOA1 based on TCGA database. GSEA enrichment analysis of DEGs. (D) The relationship between APOA1 expression levels and the presence of immune cell infiltrates in LUAD was investigated by CIBERSORT. (KEGG, Kyoto Encyclopedia of Genes and Genomes; GSEA, gene set enrichment analysis; LUAD, lung adenocarcinoma). Figure 7 Expression of APOA1 in different lung adenocarcinoma cell lines and its effect on cell proliferation. (A) Protein expression of APOA1 in four lung adenocarcinoma cell lines (B) (C) Overexpression of APOA1 in A549 and SPCA1 cells (D) The effect of overexpression of APOA1 on the proliferation of A549 and SPCA1 cells was detected at different time points via CCK8. (*Represents P<0.05) Figure 8 Effect of overexpression of APOA1 on migration and invasion of lung adenocarcinoma cells. (A) The migration of cell scratches in each group was recorded at 0 h and 24 h, and according to the migration area of cells in each group relative to 0 h, statistics were performed (* indicates P<0.05) (B) The transwell experiment was used to detect changes in migration ability of SPCA1 cells and A549 cells overexpressing APOA1. (C) The transwell experiment with matrix condensation was used to detect changes in the invasive ability of SPCA1 cells and A549 cells overexpressing APOA1.The data were collated and analyzed and histograms were drawn (* represents P<0.05). Figure 9 Effect of APOA1 expression on lung adenocarcinoma tumor volume in nude mice. (A) Tumor growth of APOA1-overexpressing SPCA1 stable cell line and corresponding control group (NC) injected into the armpit of nude mice (B) Tumor growth of APOA1-overexpressing A549 stable cell line and corresponding control group (NC) injected into the armpit of nude mice (*indicates P<0.05). Figure S1 The prognostic significance of APOA1 across pan-cancer. Figure S2 Heatmap showing 83 down-regulated genes (blue) and 432 up-regulated genes (red) identified in the high expression group. The colored matrix shows hierarchical clustering of expression microarrays, with each column representing the log2 (TPM) values of expression levels of 515 genes in microarray hybridization of one sample, and each row representing the expression of a particular gene described to the right. Supplementary Material File (fig-1.tif) - Download - 824.56 KB File (fig-2.tif) - Download - 2.44 MB File (fig-3.tif) - Download - 4.02 MB File (fig-4.tif) - Download - 1.63 MB File (fig-5.tif) - Download - 4.38 MB File (fig-6.tif) - Download - 21.66 MB File (fig-7.tif) - Download - 2.66 MB File (fig-8.tif) - Download - 2.25 MB File (fig-9.tif) - Download - 4.30 MB File (s1.tif) - Download - 560.59 KB Information & Authors Information Version history Peer review timeline Published Cell Biochemistry and Function Version of Record24 Nov 2025Published Copyright This work is licensed under a Non Exclusive No Reuse License. Collection

Authors Metrics & Citations Metrics Article Usage 264views 209downloads Citations Download citation Yuening Sun, Jie Shen, Yizhou Lin, et al. APOA1 as a Potential Therapeutic Target and Novel Biomarker in Lung Adenocarcinoma. Authorea. 09 August 2025. DOI: https://doi.org/10.22541/au.175472291.15215916/v1 DOI: https://doi.org/10.22541/au.175472291.15215916/v1 If you have the appropriate software installed, you can download article citation data to the citation manager of your choice. Simply select your manager software from the list below and click Download. For more information or tips please see 'Downloading to a citation manager' in the Help menu.