Comprehensive Pan‐Cancer Analysis of ABCA1: Insights From Multi‐Omics Data and Exploratory Validation in Esophageal Squamous Cell Carcinoma

ABSTRACT

Cancer remains a major cause of global mortality, necessitating the identification of novel biomarkers to improve prognosis and guide therapy. ABCA1, an ATP‐binding cassette transporter involved in cholesterol efflux, has been implicated in tumorigenesis, yet its pan‐cancer roles and clinical relevance are not fully understood. We conducted a comprehensive multi‐omics analysis of ABCA1 across diverse cancers, evaluating its expression, prognostic significance, genomic alterations, tumor immune microenvironment interactions, and regulatory mechanisms. ABCA1 expression was dysregulated in multiple malignancies and associated with poor prognosis in STAD, STES, and LGG, though favorable outcomes were observed in KIRC. It correlated with genomic instability markers (TMB, MSI, HRD), immune cell infiltration, and cancer stemness. Pathway analyses revealed enrichment in cholesterol metabolism and efferocytosis‐related pathways. Among the cancer types examined, esophageal squamous cell carcinoma (ESCC) was selected for exploratory functional validation due to its prognostic relevance. In vitro, ABCA1 knockdown inhibited proliferation, invasion, and migration in ESCC cell lines. In silico drug sensitivity analysis suggested potential associations with dasatinib response and panobinostat resistance, warranting further experimental validation. Collectively, these findings highlight the context‐dependent associations of ABCA1 with cancer progression, immune modulation, and genomic integrity, suggesting its potential utility as a prognostic biomarker that warrants further investigation for therapeutic targeting.

Summary

• Pan‐cancer multi‐omics analysis reveals context‐dependent prognostic roles of ABCA1.

• ABCA1 expression correlates with genomic instability, stemness, and myeloid cell infiltration.

• Two spatial expression patterns of ABCA1 are driven by tumor cells or the tumor microenvironment.

• ABCA1 knockdown suppresses proliferation, invasion, and migration in ESCC cells.

• In silico analysis links ABCA1 expression to dasatinib sensitivity and panobinostat resistance.

Abbreviations

ACC

adrenocortical carcinoma

ALL

acute lymphoblastic leukemia

BLCA

bladder urothelial carcinoma

BRCA

breast invasive carcinoma

CESC

cervical squamous cell carcinoma and endocervical adenocarcinoma

CHOL

cholangiocarcinoma

COAD

colon adenocarcinoma

COADREAD

colon adenocarcinoma/rectum adenocarcinoma

DLBC

lymphoid neoplasm diffuse large B‐cell lymphoma

ESCA

esophageal carcinoma

FPPP

FFPE pilot phase II FFPE

GBM

glioblastoma multiforme

GBMLGG

glioma

HNSC

head and neck squamous cell carcinoma

KICH

kidney chromophobe

KIPAN

pan‐kidney cohort (KICH + KIRC + KIRP)

KIRC

kidney renal clear cell carcinoma

KIRP

kidney renal papillary cell carcinoma

LAML

acute myeloid leukemia

LGG

brain lower grade glioma

LIHC

liver hepatocellular carcinoma

LUAD

lung adenocarcinoma

LUSC

lung squamous cell carcinoma

MESO

mesothelioma

NB

neuroblastoma

OS

osteosarcoma

OV

ovarian serous cystadenocarcinoma

PAAD

pancreatic adenocarcinoma

PCPG

pheochromocytoma and paraganglioma

PRAD

prostate adenocarcinoma

READ

rectum adenocarcinoma

SARC

sarcoma

SKCM

skin Cutaneous Melanoma

STAD

stomach adenocarcinoma

STES

stomach and Esophageal carcinoma

TGCT

testicular germ cell tumors

THCA

thyroid carcinoma

THYM

thymoma

UCEC

uterine corpus endometrial carcinoma

UCS

uterine carcinosarcoma

UVM

uveal melanoma

WT

high‐risk Wilms tumor

1. Introduction

Cancer persists as a major cause of mortality worldwide. According to 2022 estimates, there were close to 20 million incident cancer cases and 9.8 million deaths [1]. Demographic projections highlight a rapidly growing burden, with the annual number of new cases anticipated to reach 35 million by 2050 [2]. Despite considerable progress in diagnostic and therapeutic strategies, the profound heterogeneity of malignancies continues to challenge effective clinical management. Current multimodal treatments—encompassing surgery, radiotherapy, chemotherapy, immunotherapy, and molecularly targeted agents—have achieved improved outcomes in selected cancers [3]. However, persistent limitations such as therapeutic resistance, narrow efficacy windows, and systemic toxicities underscore the urgent need to identify novel molecular targets for more precise and effective interventions.

The ATP‐binding cassette transporter A1 (ABCA1), a central regulator of cellular cholesterol homeostasis, has recently gained attention for its potential role in oncology [4]. As the principal mediator of reverse cholesterol transport, ABCA1 facilitates the transfer of cellular cholesterol and phospholipids to apolipoprotein A‐I, thereby influencing membrane composition, fluidity, and signal transduction capabilities. Emerging evidence has associated ABCA1 with certain hallmarks of cancer, including sustained proliferation, evasion of growth suppression, and activation of invasion and metastasis [5, 6].

Intriguingly, ABCA1 has been reported to exhibit context‐dependent associations with tumor progression, with evidence suggesting both pro‐ and anti‐tumorigenic roles. For instance, it has been implicated in epithelial‐mesenchymal transition and metastasis in breast cancer (BC) and colorectal cancer (CRC) [7, 8], potentially through modulation of lipid raft‐dependent signaling [9]. In contrast, ABCA1 has been suggested to act as a tumor suppressor in prostate cancer (PCa) and lung cancer (LC), where its loss is associated with advanced disease and poorer prognosis [10]. These observations suggest that ABCA1 may serve as a link between lipid metabolic reprogramming and oncogenic signaling [11].

The recognition of metabolic rewiring as a cancer hallmark has further elevated interest in ABCA1 as a potentially targetable molecule. Cancers frequently exhibit dysregulated cholesterol metabolism to support rapid membrane biogenesis, enhanced signal transduction, and adaptation to microenvironmental stress [12, 13]. ABCA1‐mediated cholesterol efflux has been implicated in processes related to chemoresistance, immune evasion, and stromal interactions within the tumor microenvironment, suggesting its potential relevance for diagnostic and therapeutic applications, though experimental validation is needed.

While several pan‐cancer studies have examined ABCA1 expression, a comprehensive multi‐omics integration across tumors remains limited, and the context‐dependent roles of ABCA1 across diverse cancer types have not been systematically characterized. Despite growing evidence, the associations of ABCA1 in cancer remain inadequately integrated, particularly across diverse tumor types. Critical knowledge gaps persist regarding its tissue‐specific correlates, prognostic relevance, and pharmacological tractability. In this study, we employ a pan‐cancer multi‐omics approach complemented by exploratory experimental validation in esophageal squamous cell carcinoma (ESCC), which was selected due to its prognostic relevance for ABCA1 in our pan‐cancer analyses and its clinical significance as a malignancy with limited targeted therapies. Our objectives are to: (1) systematically characterize the context‐dependent associations of ABCA1 across malignancies, (2) evaluate its prognostic and immunological correlates, and (3) assess its potential utility as a biomarker that warrants further investigation for therapeutic targeting (Figure 1).

Figure 1.

Study flowchart.

2. Materials and Methods

2.1. Data Collection and Processing

Uniformly processed pan‐cancer data were acquired from the UCSC Xena browser (https://xenabrowser.net/) [14], encompassing both the TCGA Pan‐Cancer cohort (PANCAN, N = 10,535, 33 types of tumors) and the integrated TCGA‐TARGET‐GTEx cohort (PANCAN, N = 19,131, 44 types of tumors) [15]. These datasets were utilized to examine ABCA1 expression patterns across diverse cancer types and to evaluate their associations with clinical characteristics and prognostic outcomes. Abbreviations for the 44 cancer types analyzed in this study are detailed in Supporting Information S2: Table S1. Additionally, pan‐cancer single‐cell RNA sequencing (scRNA‐seq) data were acquired from the TISCH database [16], and spatial transcriptomics (ST) data were obtained from the Sparkle database (available at: https://grswsci.top/) [17, 18]. Corresponding identifiers for each ST sample are detailed in Supporting Information S2: Table S2.

Consensus RNA tissue gene expression profiles were generated through integration of transcriptomic data from the Human Protein Atlas (HPA) [19] and the Genotype‐Tissue Expression (GTEx) [20] project. Normalized expression values, represented as transcripts per million (nTPM), were consolidated across 50 distinct tissue types. For each gene, the nTPM value was determined by selecting the maximum value obtained from either dataset. In tissues containing multiple substructures‐such as various brain regions, lymphoid tissues, and intestinal segments‐the highest expression value among all substructures was assigned to represent the corresponding tissue category. All data were derived from HPA version 23.0 and Ensembl release 109, with results visualized using lollipop plots. Additionally, immunohistochemical (IHC) staining and immunofluorescence (IF) images illustrating ABCA1 expression patterns in both tumor and normal tissues were obtained from the HPA database [19].

2.2. Differential Expression and Localization Analyses

To evaluate ABCA1 expression patterns, transcriptomic differences between tumor and normal tissues at the mRNA level were assessed using datasets from the TCGA Pan‐Cancer cohort and the integrated TCGA‐TARGET‐GTEx cohort [21]. Additionally, data from the HPA database [19] were utilized to examine its distribution across various cell lines and tissues.

2.3. Correlation of ABCA1 Expression and Clinical Features

Receiver operating characteristic (ROC) curve analysis was performed using the “pROC” R package [22] in both the combined TCGA‐GTEx dataset and the TCGA dataset alone. The area under the curve (AUC) and its 95% confidence interval (95% CI) were calculated to evaluate the diagnostic performance of ABCA1 in discriminating tumor tissues from normal tissues [23]. Smoothed ROC curves were generated for visualization. An AUC value closer to 1 indicates higher diagnostic accuracy, with values exceeding 0.7, 0.8, and 0.9 considered to represent moderate, good, and excellent performance, respectively.

To assess the prognostic value of ABCA1, we first employed Cox proportional hazards regression models using the coxph function from the R “survival” package [24] to examine the association between ABCA1 expression and multiple survival endpoints‐overall survival (OS), progression‐free interval (PFI), disease‐specific survival (DSS), and disease‐free interval (DFI)‐across different cancer types. The log‐rank test was applied to determine statistical significance.

Subsequently, Kaplan–Meier survival analysis was conducted using the “survival” and “survminer” R packages [25]. An optimal cutoff value for ABCA1 expression was determined with the “survminer” package to dichotomize patients into high‐ and low‐expression groups, ensuring a minimum sample proportion of 0.3 in each group. Survival curves were compared using the log‐rank test to evaluate statistically significant differences in OS and other endpoints between the two groups.

Furthermore, we analyzed variations in ABCA1 expression based on distinct clinical characteristics, such as age, sex, stage, and grade, for each specific tumor type.

2.4. Genomic Alteration Analyses

Pan‐cancer analyses of genomic mutation amplifications, frequencies, and deep deletions were conducted using the Cancer Type Summary module in the cBioPortal database (https://www.cbioportal.org/) [26]. Processed single nucleotide variant (SNV) data were analyzed with the “maftools” R package [27] to characterize the mutational landscape of ABCA1 across multiple cancer types. Copy number variation (CNV) data were obtained from the GSCA database and visualized using the “ggplot2” R package [28]. Additionally, level 4 Simple Nucleotide Variation data for all TCGA samples, processed with MuTect2 [29], were downloaded from the GDC portal (https://portal.gdc.cancer.gov/) [30]. Mutation and copy number data were integrated, and expression values were log2(x + 0.001)‐transformed. Cancer types with fewer than three samples were excluded from subsequent analyses [18].

Differential gene expression across clinical stages within each tumor type was assessed using the Wilcoxon Rank Sum and Signed Rank Tests for pairwise comparisons, and the Kruskal–Wallis test for multi‐group comparisons. Mutation data were consolidated, and protein domain information was extracted using the “maftools” R package [27]. The “maf” tool was employed to evaluate the frequency of genetic alterations between ABCA1 high‐ and low‐expression groups across various cancers.

2.5. Tumor Stemness Analysis

Six established tumor stemness indices derived from mRNA expression and DNA methylation profiles were obtained from a previous study [31], including RNAss (mRNA expression‐based stemness score using all available genes), EREG. EXPss (epigenetically regulated RNA expression‐based stemness score using 103 genes), DNAss (DNA methylation‐based stemness score integrating 219 stem cell signature probes), EREG‐METHss (epigenetically regulated DNA methylation‐based score with 87 probes), DMPss (differentially methylated probes‐based score with 62 probes), and ENHss (enhancer element methylation‐based score with 82 probes). For each tumor type, stemness scores were calculated based on methylation signatures. The stemness indices were integrated with gene expression data, the latter being log₂ (x + 0.001)‐transformed. Cancer types with fewer than three samples were excluded from subsequent analyses.

2.6. Cell Lines and Cultures

Two human ESCC cell lines were used in this study: EC109 (female, adult; tissue of origin: esophagus; official name: Eca‐109; RRID: CVCL_6898) and KYSE‑30 (male, 64‑year‑old patient; esophagus; official name: KYSE‑30; RRID: CVCL_1351). Both cell lines were purchased from the Cell Bank of the Chinese Academy of Sciences (Shanghai, China) in 2018 and have been maintained in our laboratory since then.

EC109 and KYSE‑30 cells were cultured in RPMI 1640 medium (Gibco) supplemented with 10% fetal bovine serum (FBS, Gibco) and 1% penicillin‑streptomycin (Beyotime) at 37°C in a humidified incubator with 5% CO₂. The medium was renewed every 2–3 days, and cells were passaged at 80%–90% confluence using 0.25% trypsin‑EDTA solution (Beyotime) [32].

Cell line identity (EC109 and KYSE‑30) was confirmed by STR profiling performed by Applied Biological Materials Inc. (Zhenjiang, China) in 2018, and cultures were used for experiments within 20 passages after thawing. Routine tests for mycoplasma contamination were carried out every 3 months using a PCR‑based MycoAlert Mycoplasma Detection Kit (Lonza, Basel, Switzerland), and all cell lines tested negative for mycoplasma during the period of this study.

2.7. RNA Extraction, Reverse Transcription and qPCR

Total RNA was extracted from cultured ESCC cells using TRIzol reagent (Invitrogen) according to the manufacturer's instructions. RNA concentration and purity were measured using a NanoDrop spectrophotometer (Thermo Scientific). cDNA was synthesized from 1 µg of total RNA using PrimeScript RT Master Mix (Takara) in a 20 µL reaction volume [33]. The reverse transcription conditions were as follows: 37°C for 15 min, followed by 85°C for 5 s. Quantitative real‐time PCR (qPCR) was performed using TB Green Premix Ex Taq II (Takara) on a QuantStudio 5 Real‐Time PCR System (Applied Biosystems). Each 20 µL reaction contained 10 µL TB Green Premix, 0.8 µL each of forward and reverse primers (10 µM), 2 µL cDNA template, and 6.4 µL nuclease‐free water. The amplification protocol consisted of an initial denaturation at 95°C for 30 s, followed by 40 cycles of 95°C for 5 s and 60°C for 30 s. Relative mRNA expression levels were normalized to GAPDH and calculated using the 2^−ΔΔCt method [34]. All primer sequences are provided in Supporting Information S2: Table S3.

2.8. Transient siRNA Transfection

For siRNA‐mediated knockdown, siRNAs targeting ABCA1 and a non‐targeting control were designed and synthesized by GenePharma (Suzhou, China). Transfection was carried out using Lipofectamine 3000 (Invitrogen) following the manufacturer's protocol, the target sequences for siRNA are listed in Supporting Information S2: Table S4.

2.9. CCK‐8 Assays

Cells were seeded at a density of 2000 cells per well in 96‐well plates. Cell viability was evaluated by adding CCK‐8 solution to each well and incubating for 2 h. The optical density at 450 nm (OD450) was measured daily for 5 days.

2.10. Transwell Migration and Invasion Assays

2.10.1. Cell Migration Assay

Cell migration ability was evaluated using 24‐well Transwell chambers with 8.0 μm pore size polycarbonate membranes (Corning). In brief, 4 × 10⁴ KYSE‐30 cells or 6 × 10⁴ EC109 cells were suspended in 200 μL serum‐free RPMI 1640 medium and seeded into the upper chamber. The lower chamber was filled with 600 μL complete medium containing 20% FBS as a chemoattractant. After incubation for 20 h at 37°C, non‐migrated cells on the upper surface of the membrane were carefully removed with a cotton swab. Migrated cells on the lower surface were fixed with 4% paraformaldehyde for 20 min and stained with 0.1% crystal violet for 15 min [35]. The stained cells were photographed and counted in five random fields under an inverted microscope (Nikon).

2.10.2. Cell Invasion Assay

For the invasion assay, the Transwell chambers were pre‐coated with Matrigel (BD Biosciences) diluted in serum‐free medium (1:8 ratio) and allowed to polymerize at 37°C for 4 h. The subsequent steps, including cell seeding, incubation, staining and counting, were performed following the same procedure as described for the migration assay. All experiments were repeated independently three times.

2.10.3. Wound Healing Assay

Transfected ESCC cells were grown to confluence in 6‐well plates. A uniform wound was created using a 100 µL pipette tip. Wound images were captured at 0 and 24 h after scratching. The migration rate was quantified by measuring the scratch area using ImageJ software.

2.11. Colony Formation Assays

A total of 1000 ESCC cells were plated per well in 6‐well plates and cultured in medium containing 10% FBS for 2 weeks. Colonies were then fixed with methanol for 20 min and stained with 0.1% crystal violet for 20 min [18].

2.12. EDU Assay

Cell proliferation was assessed using the EDU Cell Proliferation Kit (Beyotime, China) according to the manufacturer's instructions. Briefly, transfected ESCC cells were seeded in 96‐well plates at 5000 cells per well and cultured for 24 h [36]. Cells were then incubated with 10 µM EDU solution for 2 h at 37°C. After incubation, cells were fixed with 4% paraformaldehyde for 30 min and permeabilized with 0.3% Triton X‐100 for 10 min. Following permeabilization, cells were incubated with the Click Reaction Mixture for 30 min at room temperature in the dark. Nuclei were counterstained with Hoechst 33342 for 10 min. Images were acquired using a fluorescence microscope (Nikon, Japan), and the percentage of EDU‐positive cells was quantified with ImageJ software.

2.13. Western Blot

For Western blot analysis, cells were lysed in RIPA buffer supplemented with protease and phosphatase inhibitors, and the lysates were cleared by centrifugation at 12,000g for 15 min at 4°C. Protein concentrations were determined using a BCA assay, and equal amounts of protein (20–50 μg) were mixed with Laemmli buffer containing β‑mercaptoethanol, denatured at 25°C for 15 min, resolved by SDS‑PAGE, and transferred onto PVDF membranes. After blocking with 5% non‑fat milk in TBST for 1 h at room temperature, membranes were incubated with primary antibodies (ABCA1, CST, 1:1000) overnight at 4°C, followed by HRP‑conjugated secondary antibodies for 1 h at room temperature. Protein bands were visualized using enhanced chemiluminescence (ECL) [37], and band intensities were normalized to a loading control such as β‑actin or GAPDH.

2.14. Statistical Analysis

The expression levels of ABCA1 in tumor versus normal tissues were compared using the Wilcoxon rank‐sum test. Correlations between ABCA1 expression and immune cell infiltration, RNA modification‐related genes, immune modulator genes, immune checkpoint‐related genes, TMB, and MSI across cancer types were evaluated using Spearman's or Pearson's correlation analysis [15, 38]. Differences between two groups were assessed using Student's t‐test or χ ² test, as appropriate [39, 40]. For comparisons among multiple groups, one‐way analysis of variance (ANOVA) was applied [36, 41]. To evaluate key survival endpoints (OS, DSS, PFI, DFI), we performed Kaplan–Meier analysis and Cox proportional hazards regression to generate survival curves and estimate hazard ratios (HRs) [42]. All data processing, statistical analyses, and visualizations were conducted using R software (version 3.6.3) [43]. A p value less than 0.05 was considered statistically significant. For the detailed steps regarding genomic heterogeneity analysis, correlation of ABCA1 expression and immunity, functional enrichment analysis and protein–protein interaction network construction, correlation analysis of ABCA1 with RNA modifications, single‐cell pan‐cancer profiling of ABCA1 expression and spatial landscape of ABCA1 expression across human cancers, please refer to the Supporting Information S3.

3. Results

3.1. Expression Patterns of ABCA1 in Pan‐Cancer and Its Localization

To assess ABCA1 expression patterns across a wide range of cancers, we integrated tumor and normal tissue data from TCGA. Our initial analysis revealed distinct ABCA1 mRNA expression profiles in different malignancies (Figure 2a). Given the limited availability of normal samples in TCGA, we expanded our dataset by incorporating the TCGA‐TARGET‐GTEx cohort, encompassing 19,131 samples. This enhanced dataset consistently demonstrated ABCA1 dysregulation: it exhibited notable upregulation in TGCT, STAD, PAAD, LGG, LAML, KIRP, KIPAN, KIRC, HNSC, GBMLGG, GBM, and ALL, while showing downregulation in COAD, READ, COADREAD, LIHC, CHOL, LUAD, LUSC, BRCA, PRAD, BLCA, OV, UCS, SKCM, ACC, and PCPG (Figure 2b).

Figure 2.

Pan‐cancer expression profiles and subcellular localization of ABCA1. (a) ABCA1 mRNA expression levels across different cancer types from the TCGA database. (b) ABCA1 mRNA expression levels across different cancer types from the TCGA, TARGET, and GTEx combined database. (c) ABCA1 protein expression levels in various normal human tissues from the Human Protein Atlas (HPA) database. (d) The expression landscape of ABCA1 across different cell types (nTPM) from the HPA database. (e) The expression landscape of ABCA1 in immune cell subsets (nTPM) from the HPA database. (f) The expression landscape of ABCA1 across various cancer cell lines (nTPM) from the HPA database. (g) Immunofluorescence staining depicting ABCA1 subcellular localization in GAMG and HepG2 cell lines. (h–j) Representative immunohistochemical staining of ABCA1 in human tumor tissues, showing (h) high, (i) medium, and (j) low expression levels.

The dataset from the HPA disclosed that ABCA1 expression was most conspicuous in the liver, adrenal gland, and adipose tissues (Figure 2c). In terms of tissue‐cell types, ABCA1 was predominantly expressed in Kupffer cells, granulocytes, and hepatocytes (Figure 2d). Regarding immune cells, ABCA1 showed high expression mainly in eosinophils and neutrophils (Figure 2e). As for tumor cell types, ABCA1 exhibited relatively high expression in kidney cancer cells (Figure 2f). We subsequently investigated the cellular localization of ABCA1. IF and IHC analyses sourced from the HPA indicated that ABCA1 is predominantly localized to the plasma membrane (Figure 2g–j). This finding aligns with its well‐recognized role as a type I transmembrane protein. Collectively, these results demonstrate distinct tissue‐specific expression patterns of ABCA1 across cancers.

3.2. The Diagnostic and Prognostic Significance of ABCA1 Across Diverse Cancer Types

ROC analyses demonstrated that ABCA1 effectively discriminated between tumor and normal tissues across multiple cancer types, including CHOL and KIRC, in both the TCGA‐GTEx combined dataset and the TCGA dataset alone (Figure 3a–e). Cox regression revealed cancer‐specific prognostic associations for ABCA1 expression. High ABCA1 expression correlated with worse OS in STES (N = 547, HR = 1.20, 95% CI 1.06–1.35, p= 0.003) and STAD (N = 372, HR = 1.27, 1.09–1.49, p= 0.0027), while low expression predicted poorer OS in KIRC (N = 515, HR = 0.84, 0.73–0.98, p= 0.02) (Figure 3f). DSS analysis further delineated these relationships: elevated ABCA1 indicated poor outcomes in LGG (N = 466, HR = 1.28, 1.04–1.58, p= 0.02), STES (N = 524, HR = 1.24, 1.07–1.44, p= 0.0053), STAD (N = 351, HR = 1.25, 1.03–1.53, p= 0.03), and KICH (N = 64, HR = 3.36, 1.12–10.14, p= 0.02); conversely, low expression was associated with worse DSS in KIRP (N = 272, HR = 0.67, 0.46–0.96, p= 0.03) and KIRC (N = 504, HR = 0.75, 0.63–0.90, p= 0.0015) (Figure 3g). ABCA1 expression also showed distinct associations with progression‐related endpoints: high expression correlated with worse DFI in ESCA (N = 84, HR = 1.48, 1.02–2.15, p= 0.04) and STES (N = 316, HR = 1.41, 1.13–1.77, p= 0.0026) (Figure 3h), as well as poorer PFI in STES (N = 548, HR = 1.16, 1.02–1.30, p= 0.02) (Figure 3i); low ABCA1 predicted worse PFI in KIRC (N = 508, HR = 0.82, 0.71–0.95, p= 0.0068). Log‐rank tests supported these findings (Supporting Information S1: Figures S1–S4). Together, these results highlight the context‐dependent prognostic associations of ABCA1 across cancer types.

Figure 3.

Diagnostic and prognostic value of ABCA1 across human cancers. (a) Bar plot showing the area under the curve (AUC) values for ABCA1 in discriminating tumors from normal tissues. (b, c) Receiver operating characteristic (ROC) curves evaluating the diagnostic efficacy of ABCA1 in KIRC from the TCGA (b) and TCGA‐GTEx combined (c) datasets. (d, e) ROC curves evaluating the diagnostic efficacy of ABCA1 in CHOL from the TCGA (d) and TCGA‐GTEx combined (e) datasets. (f) Forest plot of univariate Cox regression analysis for ABCA1 in overall survival (OS). (g) Forest plot of univariate Cox regression analysis for ABCA1 in disease‐specific survival (DSS). (h) Forest plot of univariate Cox regression analysis for ABCA1 in disease‐free interval (DFI). (i) Forest plot of univariate Cox regression analysis for ABCA1 in progression‐free interval (PFI).

3.3. Analyses of ABCA1 Genomic Alterations

We analyzed the mutational landscape of ABCA1 using TCGA data. CNVs were significantly enriched in BRCA (p = 0.03), ESCA (p = 0.02), and STES (p = 0.04), whereas SNVs showed no significant association (Supporting Information S1: Figure S5a–b). Missense mutations were the predominant type of ABCA1 alteration (Supporting Information S1: Figure S5c). Comparative analysis revealed co‐occurring genetic alterations in TTN, MUC16, SYNE1, CSMD3, and ARID1A (Supporting Information S1: Figure S5d).

3.4. The Association of ABCA1 Expression With Tumor Stemness

Analysis of six stemness indices revealed distinct correlations between ABCA1 expression and tumor stemness across multiple cancer types. In RNAss, ABCA1 exhibited a significant positive correlation in GBM, LGG, GBMLGG, LUAD, LUSC, ESCA, STES, STAD, COAD, COADREAD, BLCA, PRAD, PCPG, BRCA, and LAML, while showing a negative association in KIPAN, THYM, THCA, and TGCT (Supporting Information S1: Figure S6a). Similar patterns were observed across DMPss, DNAss, EREG‐METHss, EREG, ENHss, and EXPss (Supporting Information S1: Figure S6b–f). Notably, consistent positive correlations were observed in GBMLGG, LGG, STES, STAD, LUSC, THYM, and ACC, while TGCT consistently showed negative correlations across multiple stemness indices. These findings indicate that ABCA1 expression is associated with tumor stemness in a cancer type‐specific manner.

3.5. Correlation Analysis Between ABCA1 Expression and Genomic Heterogeneity

We next systematically evaluated the association between ABCA1 expression and key genomic heterogeneity markers using TCGA pan‐cancer data. ABCA1 expression was significantly correlated with multiple indices, including TMB, MSI, MATH, NEO, tumor purity, ploidy, HRD, and LOH. Detailed correlation results are presented in Supporting Information S1: Figure S7a–h. These findings indicate that ABCA1 expression is associated with multiple measures of genomic heterogeneity in a cancer type‐specific manner.

3.6. Association Between ABCA1 and the Tumor Immune Microenvironment

To explore the relationship between ABCA1 expression and immune‐related signatures, we obtained a dataset comprising 68 published immune gene sets and ABCA1 expression data from the UCSC Xena database. Spearman correlation analysis revealed significant positive associations between ABCA1 and multiple immune signatures (Supporting Information S1: Figure S8a). Specifically, ABCA1 correlated positively with diverse immune markers, including surface proteins, cytokines, and signaling molecules. Pan‐cancer analysis further revealed associations with immunomodulatory genes and immune checkpoints across multiple tumors (Supporting Information S1: Figure S8b–c).

Pan‐cancer immune infiltration analysis revealed significant correlations between ABCA1 expression and multiple immune cell subsets. Analysis via the TIMER database indicated positive associations of ABCA1 with CD8⁺ T cells, neutrophils, macrophages, and dendritic cells in most cancer types examined, including PRAD, KIRC, ESCA, and BRCA (Supporting Information S1: Figure S9a). Consistent with these findings, multiple deconvolution methods (deconvo_mcpcounter, deconvo_epic, deconvo_quantiseq, deconvo_xCell) confirmed widespread positive correlations with neutrophils, macrophages (particularly M2 subtype), and monocytes, while deconvo_ips analysis revealed negative correlations with immune scores related to immunomodulators (CP) and suppressor cells (SC) in the majority of tumors (Supporting Information S1: Figure S9b–f). Together, these findings from multiple computational methods indicate that ABCA1 expression is associated with myeloid cell infiltration and immune‐related signatures in a cancer type‐specific manner.

Furthermore, in a large‐scale immunogenomic analysis comprising over 10,000 TCGA tumors spanning 33 cancer types, six immune subtypes were defined based on immune cell markers, Th1:Th2 ratios, genomic features, and prognostic characteristics. Tumors with high ABCA1 expression were enriched in C2 and C3 subtypes, while the low‐expression group showed enrichment in C1 and C2 subtypes (Supporting Information S1: Figure S9g).

3.7. Correlation Between ABCA1 Expression and Clinicopathological Features

We analyzed the association between ABCA1 expression and various clinicopathological parameters across multiple cancers. ABCA1 expression was correlated with age in ESCA, STES, STAD, LIHC, THCA, PAAD, and PCPG (Supporting Information S1: Figure S10a), with sex in LGG and BLCA (Supporting Information S1: Figure S10b), and with tumor grade in STES, KIPAN, STAD, HNSC, and KIRC (Supporting Information S1: Figure S10c). ABCA1 expression was also associated with T stage in ESCA, STES, STAD, and THCA (Supporting Information S1: Figure S10d), with N stage in KIPAN (Supporting Information S1: Figure S10e), with M stage in KIRP (Supporting Information S1: Figure S10f), and with clinical stage in STES and HNSC (Supporting Information S1: Figure S10g).

3.8. Functional Enrichment Analysis of ABCA1

Pearson correlation analysis showed that ABCA1 expression was significantly positively associated with gene sets related to angiogenesis, apoptosis, EMT, hypoxia, invasion, and stemness, whereas it was negatively associated with those for cell cycle progression and DNA repair (Figure 4a). Moreover, ABCA1 expression was positively correlated with m1A/m5C/m6A RNA modification genes in most cancers, particularly in OV and THCA (Supporting Information S1: Figure S11). To further explore potential mechanisms, we constructed a PPI network using STRING, which identified multiple intracellular interactors of ABCA1, including APOA1, NR1H2, APOE, NR1H3, SREBF2, XPR1, SCARB1, RXRA, LCAT, and SREBF1 (Figure 4b). Subsequent functional enrichment analyses based on these genes yielded the following key insights: GO analysis indicated enrichment in cholesterol‐related processes (e.g., efflux, transport, sterol transport), lipoprotein particle dynamics, and molecular functions such as nuclear receptor activity and protein‐lipid complex binding (Figure 4c). KEGG pathway analysis highlighted enrichment in metabolic pathways, including vitamin digestion and absorption, PPAR signaling, lipid metabolism, atherosclerosis, and cholesterol homeostasis (Figure 4d). These results suggest that ABCA1‐associated genes are enriched in cholesterol metabolism and lipid‐related pathways, providing new avenues for further investigation. Using four analytical methods (z‐score, GSVA, ssGSEA, PLAGE), we observed differential pathway activity between ABCA1‐related genes in tumor versus normal tissues across multiple cancer types (Figure 4e–h).

Figure 4.

Functional enrichment analysis of ABCA1. (a) Scatter plot of Pearson correlation analysis between ABCA1 expression and a pan‐cancer malignancy score. (b) The protein–protein interaction (PPI) network of ABCA1. (c) Gene Ontology (GO) functional terms enriched for ABCA1 and its correlated genes. (d) Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways enriched for ABCA1 and its correlated genes. (e–h) Pan‐cancer GSVA scores for the ABCA1‐related gene signature calculated using four distinct parameter sets.

3.9. Characterization of ABCA1 Expression at Single‐Cell and Spatial Resolution in Pan‐Cancer

Single‐cell RNA sequencing revealed substantial heterogeneity in ABCA1 expression across cell types, with particularly high levels observed in monocytes/macrophages, fibroblasts, and mast cells (Supporting Information S1: Figure S12a). Spatial transcriptomic analysis further localized elevated ABCA1 expression primarily to malignant cells and macrophages (Supporting Information S1: Figure S12b).

Notably, spatial transcriptomic profiling identified two distinct expression patterns of ABCA1 across cancer types. In CRC and BRCA, ABCA1 expression showed a significant positive correlation with the proportion of malignant cells within microdomains, and a negative correlation with non‐malignant components such as immune cell content. Furthermore, a decreasing gradient of ABCA1 expression was observed from the tumor core to the border and then to adjacent normal tissue (Figure 5a–b). In contrast, in KIRC, LIHC, and OV, ABCA1 expression was negatively correlated with malignant cell proportion and positively correlated with immune infiltration. Moreover, expression gradually increased from the tumor center outward to the normal tissue (Figure 5c–e). These spatial patterns suggest that ABCA1 dysregulation may be driven by malignant cells in some cancer types (CRC, BRCA) but by the tumor microenvironment in others (KIRC, LIHC, OV).

Figure 5.

Spatial characterization of ABCA1 expression across human cancers. (a) In colorectal cancer (CRC), ABCA1 expression is positively correlated with malignant cell content and decreases from the tumor core to adjacent normal tissue. (b) In breast cancer (BRCA), ABCA1 shows a similar pattern to CRC, with high expression in the tumor core that decreases towards the periphery. (c) In kidney renal clear cell carcinoma (KIRC), ABCA1 expression is negatively correlated with malignant cells and increases towards normal tissue. (d) In liver hepatocellular carcinoma (LIHC), ABCA1 demonstrates an expression pattern similar to KIRC, with enrichment in peritumoral areas. (e) In ovarian cancer (OV), ABCA1 expression shows an inverse correlation with tumor content, mirroring the patterns observed in KIRC and LIHC.

3.10. Identification of Potential Therapeutic Agents Associated With ABCA1 Expression

Spearman correlation analysis revealed significant associations between ABCA1 expression levels and drug sensitivity metrics (IC₅₀/AUC) derived from the GDSC1, GDSC2, PRISM, and CTRP databases (Supporting Information S1: Figure S13a–d). Specifically, ABCA1 expression was positively correlated with the AUC of panobinostat and negatively correlated with that of dasatinib (Supporting Information S1: Figure S13e). These in silico associations suggest potential relationships between ABCA1 expression and response to these agents, warranting experimental validation.

To identify potential compounds whose gene expression signatures inversely correlate with ABCA1‐associated signatures, we leveraged the Connectivity Map (CMap) database and applied the XSum (eXtreme Sum) algorithm. This analysis yielded similarity scores for 1288 compounds. Notably, 4,5‐dianilinophthalimide emerged as a top candidate with an inverse correlation to ABCA1‐associated gene signatures (Supporting Information S1: Figure S13f).

3.11. Exploratory Validation: Integrative Validation of ABCA1 Expression, Single‐Cell Distribution, and Functional Effects in ESCC

Public ESCC datasets were included for validation of expression and survival. Further expression and survival validation of the ESCC datasets revealed that ABCA1 expression was upregulated in ESCC (Supporting Information S1: Figure S14a–d). To further assess the prognostic significance of ABCA1, survival analysis was performed using the GSE53624 cohort (n = 119 ESCC patients); although the association between ABCA1 expression and overall survival did not reach statistical significance (log‐rank p= 0.433), the direction of effect (higher ABCA1 expression associated with poorer survival) was similar across multiple cohorts, though these observations remain hypothesis‐generating, and ABCA1 expression showed significant associations with clinicopathological features (Supporting Information S1: Figure S14e). Subsequently, the GSE160269 dataset containing ESCC samples was incorporated for single‐cell analysis to characterize ABCA1 expression at cellular resolution. The results demonstrated that ABCA1 was primarily expressed in general monocytes/macrophages, fibroblasts, and mast cells within the tumor microenvironment of esophageal squamous cell carcinoma (Supporting Information S1: Figure S14f). To investigate the functional effects of ABCA1 in ESCC, we used EC109 and KYSE‐30 cell lines for exploratory validation. Small interfering RNAs (siRNAs) targeting ABCA1 were designed and effectively reduced ABCA1 expression at both mRNA and protein levels in ESCC cells (Figure 6a–b). CCK‐8 assays demonstrated that ABCA1 knockdown significantly decreased cell viability (Figure 6c). Furthermore, colony formation and EdU assays revealed a pronounced reduction in proliferative capacity upon ABCA1 suppression (Figure 6d–e). Transwell and wound healing assays also indicated that ABCA1 knockdown markedly impaired the migratory ability of ESCC cells (Figure 6f). Collectively, these in vitro results demonstrate that ABCA1 knockdown inhibits proliferation and migration in ESCC cell lines, though these findings require in vivo validation.

Figure 6.

ABCA1 knockdown suppresses ESCC malignant phenotypes in vitro. (a) Efficient knockdown of ABCA1 by specific siRNAs in EC109 and KYSE‐30 cells. (b–d) ABCA1 depletion impaired cell proliferation, as evidenced by CCK‐8 (b), colony formation (c), and EdU (d) assays. (e, f) ABCA1 knockdown inhibited ESCC cell migration in wound healing (e) and Transwell (f) assays. p < 0.01; ***, p < 0.001; ****, p < 0.0001.

4. Discussion

The escalating global burden of cancer underscores the critical need for novel therapeutic targets that can address tumor heterogeneity and therapeutic resistance [44]. In this study, we conducted a comprehensive pan‐cancer analysis of ABCA1, revealing its context‐dependent associations with tumor progression across multiple malignancies. The tumor‐type‐specific expression patterns and strong prognostic associations observed suggest that ABCA1 may warrant further investigation as a potential biomarker, particularly in the context of metabolic and immune modulation [45]. These findings align with current interests in precision oncology, where targeting metabolic vulnerabilities and immune evasion mechanisms represents a promising research frontier.

Our findings reveal significant cancer‐type heterogeneity in ABCA1 expression profiles, with elevated expression in GBM, LGG, STAD, and other malignancies, while reduced expression was observed in BRCA, LUAD, and additional cancer types. These differential patterns correlated closely with clinical outcomes, where elevated ABCA1 predicted poor survival in STES and STAD patients, yet was associated with protective effects in KIRC. Survival analysis of the ESCC dataset GSE53624 did not reveal a statistically significant correlation between ABCA1 expression and overall survival (log‐rank p = 0.433). However, a consistent trend was observed across multiple independent study cohorts, though these findings are derived from composite cohorts (e.g., STES) that include both esophageal and gastric cancers. Furthermore, significant associations were identified between ABCA1 expression and clinicopathological characteristics, collectively providing exploratory evidence for its potential relevance, though these findings require validation in larger studies. We acknowledge that the limited sample size (n = 119) and variability in clinical annotations across different datasets may contribute to this discrepancy. This context‐dependent pattern suggests that ABCA1's associations with cancer progression may be intricately linked to tissue‐specific metabolic programming and microenvironmental factors. The divergent associations of ABCA1 across different cancer types may reflect its complex involvement in cholesterol homeostasis, which can exert opposing effects depending on the specific metabolic requirements of different malignancies [46].

A notable and significant finding is ABCA1's consistent association with immunosuppressive microenvironmental signatures. The consistent positive correlation with myeloid‐derived suppressor cells, M2 macrophages, and neutrophils across multiple cancers suggests a potential link between ABCA1 and an immune‐evasive niche. Its co‐expression with established immune checkpoints and enrichment in specific immune subtypes (C2 and C3) raises the hypothesis that ABCA1 modulation could influence responses to existing immunotherapies, potentially addressing resistance in immunologically cold tumors [47, 48]. Mechanistically, while the underlying mechanisms remain to be elucidated, we hypothesize that ABCA1‐mediated cholesterol efflux could influence immune cell function through several potential pathways, including modulation of lipid raft formation affecting immune receptor signaling, effects on antigen presentation via MHC molecules, and influences on immunomodulatory metabolite production. These hypothesized mechanisms position ABCA1 as a potential regulator of the tumor immune microenvironment that warrants further mechanistic investigation.

The correlations between ABCA1 expression and measures of genomic instability (TMB, MSI, LOH) introduce intriguing associations regarding its potential role in cancer evolution. We speculate that ABCA1‐mediated lipid redistribution might influence DNA repair efficiency and chromosomal stability through alterations in nuclear membrane organization and function. Cholesterol homeostasis is known to play a crucial role in maintaining nuclear envelope integrity and function, and dysregulation of this process could conceivably affect DNA damage response mechanisms [11, 49]. Furthermore, the observed association with cancer stemness indices across various malignancies raises the possibility of ABCA1's involvement in therapy‐resistant stem cell populations, particularly in aggressive cancers like glioblastoma and pancreatic cancer. The consistent positive correlations with multiple stemness markers suggest that ABCA1 may contribute to the self‐renewal capacity and drug resistance properties of cancer stem cells through its role in membrane fluidity regulation and signal transduction [4, 50]. However, these mechanistic hypotheses require direct experimental validation.

From a therapeutic perspective, the development of ABCA1‐targeted strategies presents both challenges and opportunities [51]. While direct pharmacological inhibition remains complicated due to ABCA1's physiological importance in cholesterol homeostasis [52], several hypothesis‐generating approaches emerge from our correlative analyses. Our in silico drug repurposing analysis identified 4,5‐dianilinophthalimide as a candidate with correlative associations to ABCA1 expression, and the correlation between enhanced sensitivity to dasatinib in high ABCA1 expressers suggests testable hypotheses for combinatorial approaches [48]. Emerging technologies such as PROTACs, monoclonal antibodies, or nanoparticle‐based delivery systems could offer innovative strategies for targeted protein degradation or tissue‐specific modulation, though these remain highly speculative at this stage [52, 53, 54, 55]. These findings indicate that ABCA1 expression status may merit investigation as a predictive biomarker for response to certain targeted therapies, though experimental validation is needed.

5. Conclusion

In summary, our pan‐cancer multi‐omics analysis reveals context‐dependent associations of ABCA1 with cancer metabolism, genomic instability, and immune evasion across multiple malignancies [45]. The observed pan‐cancer dysregulation and correlations with aggressive tumor phenotypes suggest that ABCA1 may warrant further investigation as a potential biomarker and therapeutic target. We propose that, if supported by future mechanistic and preclinical studies, ABCA1‐directed therapies—particularly in combination with immunotherapy or targeted agents like dasatinib—could potentially disrupt pathways supporting tumor survival and immune suppression. The context‐dependent nature of ABCA1's associations underscores the importance of developing robust biomarkers for patient selection in future clinical investigations, potentially including ABCA1 expression levels, cholesterol metabolic signatures, and immune microenvironment characteristics.

Several limitations of this study should be acknowledged. The experimental validation was limited to ESCC cell lines and does not generalize to other cancer types. Rescue experiments, in vivo studies, and mechanistic assays (e.g., cholesterol efflux, stemness marker validation) are lacking. The drug sensitivity correlations are based on in silico data and require experimental confirmation. Therefore, the proposed therapeutic implications should be considered hypothesis‐generating rather than actionable conclusions.

Future research could prioritize several key directions to further evaluate ABCA1 as a potential target. First, the development of more selective and potent ABCA1 modulators using advanced drug discovery platforms, including fragment‐based screening and structure‐guided design. Second, comprehensive preclinical validation in genetically engineered mouse models and patient‐derived xenografts that faithfully recapitulate the human tumor microenvironment. Third, detailed mechanistic studies to elucidate whether and how ABCA1 influences antitumor immunity, particularly its effects on T cell function, antigen presentation, and myeloid cell polarization. Fourth, if preclinical evidence supports further development, clinical translation efforts could focus on biomarker‐driven early‐phase trials that incorporate sophisticated pharmacodynamic assessments of target engagement and biological effects.

The multifaceted nature of ABCA1's correlates in cancer biology suggests that further investigation of its therapeutic targeting may provide new insights into combination strategies addressing both metabolic dependencies and immune evasion [4]. As our understanding of ABCA1's complex roles in different cancer contexts advances, future studies may explore whether ABCA1‐directed therapies could offer new therapeutic avenues for patients with malignancies characterized by ABCA1 dysregulation, particularly those resistant to current treatment modalities [48]. However, such applications remain speculative at this stage.

Looking forward, the study of ABCA1 may have implications beyond oncology [4, 45]. Given its fundamental role in cellular cholesterol homeostasis, if successful therapeutic strategies are developed for cancer, they might find applications in other diseases characterized by cholesterol dysregulation, such as atherosclerosis and neurodegenerative disorders [56]. However, this cross‐disease potential remains highly speculative at this stage. The continued investigation of ABCA1's roles in human diseases may yield further insights into the fundamental connections between cellular metabolism, immune function, and disease pathogenesis [4].

Author Contributions

Chenyang Wang, Hanbin Wang, and Xi Chen have contributed equally to this work. Wanli Yang, Yu Han, and Liu Hong conceptualized and designed this study. Chenyang Wang, Hanbin Wang, and Xi Chen wrote the first draft of the manuscript. Chenyang Wang and Hanbin Wang performed the experiments. Yingying Liu, Lili Duan, Zhenshun Li, Liaoran Niu, Aqiang Fan, Siyu Wei, and Han Bai collected and analyzed the data. Yujie Zhang, Wei Zhou, Jinqiang Liu, Wanli Yang, Yu Han, Liu Hong, and Daiming Fan reviewed and revised the manuscript. All authors contributed to the article and approved the submitted version.

Conflicts of Interest

The authors declare no conflicts of interest.

Supporting information

Supporting File 1

Supporting File 2

Supporting File 3

Wang C., Wang H., Chen X., et al., “Comprehensive Pan‐Cancer Analysis of ABCA1: Insights From Multi‐Omics Data and Exploratory Validation in Esophageal Squamous Cell Carcinoma,” Cell Biochemistry and Function 44 (2026): e70275. 10.1002/cbf.70275.

Data Availability Statement

Data sharing not applicable to this article as no datasets were generated or analyzed during the current study.