Pan-cancer expression, methylation, and prognostic significance of α7 nicotinic acetylcholine receptor in tumor immunology

Abstract

The α7 nicotinic acetylcholine receptor (α7nAChR), functioning as a critical ligand-gated ion channel in the nervous system, has recently been implicated to play a role in various non-neuronal tissues. Nonetheless, a comprehensive understanding of its expression patterns, mechanisms of epigenetic control, and functions within the tumor microenvironment (TME) across various malignancies is still lacking. To address this gap, we performed an extensive multi-cancer investigation of CHRNA7 spanning 31 tumor types, leveraging datasets from The Cancer Genome Atlas and Genotype-Tissue Expression databases, including its expression patterns, methylation characteristics, genomic instability, and prognostic significance. Additionally, the potential immunomodulatory effects of α7nAChR were investigated through integration of multiple algorithms. The results revealed that CHRNA7 is significantly overexpressed in 11 cancer types, including cholangiocarcinoma, glioblastoma, and lung squamous cell carcinoma, whereas it is underexpressed in 11 other cancer types, including breast cancer (BRCA) and lung adenocarcinoma (LUAD). A noteworthy finding was the inverse correlation between CHRNA7 expression levels and the staging system recommended by the World Health Organization for specific cancers, such as cervical squamous cell carcinoma and thyroid carcinoma. Another interesting finding is that CHRNA7 plays dual prognostic roles [1] as a protective factor in low-grade glioma and rectal adenocarcinoma and [2] as a risk factor in adrenocortical carcinoma, BRCA, and LUAD. Genomic analyses revealed that copy number variations and single nucleotide variations of CHRNA7 are significantly associated with survival outcomes in cancers such as liver hepatocellular carcinoma (LIHC) and kidney renal clear cell carcinoma. Moreover, these genetic variations exhibit cancer type-specific correlations with tumor mutational burden and microsatellite instability, with positive associations in colon adenocarcinoma and esophageal squamous cell carcinoma. Epigenetic studies indicated that methylation of the CHRNA7 promoter is inversely correlated with its mRNA expression level in certain cancers such as BRCA and LUAD. Additionally, CHRNA7 promoter methylation shows significant interactions with DNA mismatch repair genes and cancer stemness characteristics. Analysis of the TME revealed a positive association between high CHRNA7 expression and immune cell infiltration and immune checkpoint molecule expression in cancers such as BRCA and LIHC, suggesting its potential role in modulating tumor immune evasion and influencing treatment responses. Functional enrichment analysis confirmed that CHRNA7 is involved in key biological pathways, including cholinergic signaling, DNA damage repair, and cell proliferation regulation. In conclusion, this study provides a comprehensive pan-cancer characterization of CHRNA7, offering novel insights into its potential role as a prognostic biomarker and immunotherapeutic target. These findings could facilitate the development of personalized cancer treatment strategies customized according to the expression level and functional status of CHRNA7.

Highlights

• •The present study conducted a comprehensive pan-cancer analysis of α7nAChR across 31 cancer types by using datasets from The Cancer Genome Atlas and Genotype-Tissue Expression databases, including its expression patterns, methylation characteristics, genomic instability, and prognostic significance.
• •Additionally, the potential immunomodulatory effects of α7nAChR were investigated through integration of multiple algorithms.
• •The HMSRF algorithm was employed to construct an upstream transcription factor regulatory network of CHRNA7, and its predictive value as a biomarker for chemotherapy and immunotherapy response was validated using the CTR-DB database, providing a foundation for personalized cancer treatment strategies.

1. Introduction

Cancer remains one of the most formidable global health threats [1]. Despite the remarkable clinical success of immunotherapy in breast cancer [[1], [2], [3]], leading to substantial patient outcome improvements, its therapeutic efficacy remains modest in selected metastatic malignancies [4]. Although numerous reports have explored shared immunological traits to decipher oncogenic mechanisms [5], focusing on individual malignancies provides only a fragmented view of the complex, multi-layered biology underlying cancer-related genes and pathways. Consequently, adopting a macroscopic ‘pan-cancer’ approach in research holds promise for uncovering the fundamental mechanisms driving oncogenesis across diverse cancers [[6], [7], [8], [9], [10]].

The α7 nicotinic acetylcholine receptor (α7nAChR) is one of the most abundant nAChRs in the nervous system. Robust expression is observed in the hippocampus, cerebral cortex, and multiple subcortical limbic structures [11]. It plays a role in sensory processing information, attention, and reward pathways. Studies have reported a significant reduction in α7nAChR expression in the brains of patients with Alzheimer's disease (AD) and schizophrenia [12]. CHRNA7, encoding the α7nAChR, has been implicated in a spectrum of neurological disorders including schizophrenia, epilepsy, attention-deficit hyperactivity disorder, and sensory processing deficits [[12], [13], [14]].

Beyond neurons, α7nAChR is broadly distributed in immune cells, microglia, and endothelial cells, where it orchestrates immune responses and provides neuroprotection [12,[15], [16], [17]]. Emerging evidence indicates that elevated CHRNA7 expression fosters proliferation and metastasis of cancer cells in the lung, colon, and bladder. Nevertheless, a comprehensive, pan-cancer characterization of CHRNA7 is lacking, impeding mechanistic insight into its impact on epigenetic remodeling, immune landscapes and clinical outcomes across malignancies. Therefore, we conducted a pan-cancer integrative analysis of α7nAChR expression, promoter methylation and prognostic significance. Leveraging multi-algorithm immunogenomic pipelines, we further delineated the α7nAChR-associated immune signature and its predictive value for immunotherapy response, followed by in vitro functional validation. In summary, our findings provide insights that may inform the development of personalized cancer therapeutic strategies tailored to the expression level and functional status of CHRNA7.

2. Materials and methods

2.1. Data collection

We obtained transcriptomic data along with associated clinical details for 31 different cancer types from The Cancer Genome Atlas (TCGA) database (https://portal.gdc.cancer.gov/). Additionally, we downloaded the mRNA expression profiles of normal tissues from the Genotype-Tissue Expression (GTEx) database (https://www.gtexportal.org/home/).

2.2. Pan-cancer landscape of CHRNA7: integrated profiling of differential expression, epigenetic regulation, and prognostic significance

The GTEx dataset was employed to assess the expression levels of CHRNA7 in normal tissue samples. We merged the expression datasets from TCGA and GTEx, then analyzed the variance in CHRNA7 expression between tumor and normal tissues. For subsequent analysis, samples representing the 31 cancer types were stratified into high- and low-expression cohorts based on the median CHRNA7 expression value. Survival analysis between the two arms were compared with the R package “survival” (v3.8.3); p values for Kaplan–Meier plots were derived from log-rank tests, and hazard ratios with 95 % confidence intervals were estimated by univariate Cox regression. We measured the methylation status of the CHRNA7 gene in both normal and a variety of cancerous tissues across different cancer types. We further evaluated, across the full cancer spectrum, how CHRNA7 expression correlates with tumor mutational burden (TMB) and microsatellite-instability (MSI) status.

2.3. Genomic alteration and TMB analyses

Pan-cancer frequencies of genomic mutations, amplifications, and deep deletions were comprehensively surveyed via the Cancer Type Summary module of the cBioPortal database [1]. The processed single - nucleotide variation (SNV) data underwent assessment by referring to the α7nAChR protein domains that were identified using the “maftools” package (2.18.0) in the R programming environment. This was done to shed light on the mutation patterns of this gene within pan - cancer datasets. [18]. Kaplan–Meier curves were generated with the Copy_Number module of the Tumor Immune Dysfunction and Exclusion (TIDE) platform [19] to determine the prognostic impact of α7nAChR copy number variations (CNVs). For calculating the tumor mutational burden (TMB), we utilized the “maftools” package within the R programming environment. Data pertaining to aneuploidy, neoantigens, homologous recombination deficiency (HRD), and microsatellite instability (MSI) were sourced from existing literature. Subsequently, we evaluated the correlations between these features and the expression levels of CHRNA7.

2.4. DNA mismatch repair, cancer stemness, and epigenetic modification analyses

The expression of CHRNA7 was visualized in relation to five mismatch-repair (MMR) genes [20], and four DNA methyltransferases (DNMTs) [21]. We mined the ARIEL3 trial dataset [22] to identify a 30-gene homologous-recombination repair (HRR) signature. Then, we employed the GEPIA2 tool [23] to analyze the correlations between these genes and the mRNA levels of α7nAChR. Additionally, we sourced the stemness scores for individual cancer types, which were based on differentially methylated probes, from a prior study. Subsequently, We evaluated how these scores correlate with CHRNA7 mRNA expression.

2.5. Gene network analysis and functional characterization

We explored the protein - protein interaction (PPI) network related to the α7nAChR by employing the STRING database (https://cn.string - db. org/). The network was subsequently rendered and analyzed in Cytoscape [24]. To pinpoint genes that have a correlation with the CHRNA7, we made use of the “Similar Genes Detection” function within the GEPIA2 database (http://gepia2.cancer - pku. cn/), relying on TCGA tumor data [23]. Correlation-based heatmap displaying the six genes most strongly associated with CHRNA7. The intersection of the above two gene sets yielded three overlapping genes. Moreover, we employed the “Gene_Corr” module within the TIMER2.0 database (http://timer.cistrome.org/) to delve into the associations between the CHRNA7 and the previously mentioned three genes across different tumor types [25]. After that, we made adjustments to the correlation coefficients and p-values taking tumor purity into account. Finally, we presented the results in the form of heatmaps for visualization. Based on the genes showing an association or interaction with the CHRNA7, we conducted gene ontology (GO) analysis, which encompasses biological processes, cellular components, and molecular functions, along with Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis. This was achieved by utilizing the “clusterProfiler” package (version 4.8.3) within the R programming environment [26]. We made use of the CancerSEA database (http://biocc.hrbmu.edu.cn/CancerSEA/) to explore the associations between genes that are highly correlated with CHRNA7 and 14 functional states across various tumor types [27].

2.6. Association between the CHRNA7 and pan-cancer immune cell infiltration and immunotherapy response

Using the ESTIMATE algorithm (v1.0.13) in R, we calculated stromal, immune, and combined scores for every tumor sample stratified by CHRNA7 expression. To explore the relationship between CHRNA7 and immune cell infiltration (ICI) across various cancers, we employed three algorithms: single-sample Gene Set Enrichment Analysis (ssGSEA) [28] and xCell.

3. GSEA of the CHRNA7

To gain insights into the role of the CHRNA7 in various types of cancers, we assessed the correlation between the CHRNA7 and other genes with the “psych” package (version 2.4.12) in the R programming environment. Subsequently, we ranked the correlation values in descending order. For enrichment analysis, we utilized the GSEA function within the “clusterProfiler” package. The reference gene set for this analysis was sourced from the Molecular Signatures database (accessible at https://www.gsea-msigdb.org/). The significantly enriched signaling pathways were visualized.

3.1. Expression analysis of the α7nAChR

Immunohistochemistry (IHC) was performed to quantify α7nAChR expression in clinical specimens: six colorectal cancer (CRC) and three adjacent normal tissues were procured from the General Hospital of Ningxia Medical University. All participants gave written informed consent, and the study protocol was approved by the Ningxia Medical University Ethics Committee (approval number: 2023-2625). We conducted immunohistochemical (IHC) analysis with the aim of assessing the protein expression levels of α7nAChR. Initially, nine paraffin-embedded samples were dewaxed with xylene, rehydrated through a graded alcohol series, and subjected to antigen retrieval. Endogenous peroxidase activity was blocked with 3% hydrogen peroxide. The samples were then sealed with 10% universal goat serum to prevent nonspecific binding. Subsequently, the samples were incubated with primary antibodies (α7nAChR antibodies) and then with an appropriate amount of biotinylated anti-rabbit secondary antibodies. Diaminobenzidine was used for color development, followed by counterstaining with hematoxylin. Finally, the samples were dehydrated, cleared, and mounted. The entire film was scanned to evaluate the staining results.

3.2. Cell culture

HCT-116 cells, HT29 cells, LOVO cells and NCM460. HCT-116 cells, HT29 cells, and LOVO cells are colorectal cancer cell lines, while NCM460 is normal colon cells. Cells were maintained at 37 °C in a humidified 5 % CO₂ atmosphere; medium was renewed every 48 h. HCT-116 cells were maintained in high-glucose DMEM and HT29 cells in their dedicated medium, each supplemented with 10 % fetal bovine serum; all subsequent assays were initiated during the logarithmic growth phase. The quantitative analysis of immunohistochemical staining results was conducted using image analysis software (ImageJ v1.8.0). Three non-overlapping bright-field views were randomly selected for each sample, and the Average Optical Density (AOD) was measured to represent the expression level of the target protein. The AOD was calculated using the formula (optional: AOD (%Area) = IntDen/Area). The final AOD value for each sample was determined as the average of the measurements from these fields of view. All data are expressed as mean ± standard deviation (mean ± SD). Differences in AOD among groups were compared using one-way analysis of variance (One-way ANOVA). All statistical analyses were performed using GraphPad Prism 8.0.2, and a p-value <0.05 was considered statistically significant.

3.3. Western blot was used to detect α7nAChR protein expression

Cells were lysed in RIPA buffer; total protein was recovered and quantified by BCA, the protein was combined with SDS-PAGE loading dye solution, incubated at 100 °C to induce denaturation bath for 10min, separated by 100 g/L First, SDS-PAGE was used for protein separation, followed by electrotransfer of the separated proteins onto a PVDF membrane (pore size: 0.45 μm). After transfer, the PVDF membrane was incubated in blocking buffer (5 % non-fat milk in TBST) for 1 h at ambient temperature solution on a shaker (60 rpm) at room temperature for 1 h to reduce non-specific antibody binding. washed with TBST, added α7nAChR incubated overnight at 4 °C with primary antibody (1:1000), washed three times with TBST, then incubated with HRP-conjugated goat anti-mouse IgG (1:5000). Following a 2-h incubation at room temperature, the membranes were washed three times with TBST. Exposed to enhanced chemiluminescence reagent development, imagej software analysis of protein levels.

3.4. An information-filtering strategy coupled with multivariate stepwise regression was employed to construct the ML-hGRN

The regulatory interactions between transcription factors (TFs) and target genes were retrieved from the ChEA database (https://maayanlab.cloud/Harmonizome/), encompassing 199 TFs and 21,220 target genes. A heuristic search algorithm, namely HMSRF [29], which integrates multiple stepwise regression and information filtering, was utilized to iteratively construct the upstream ML-hGRN that regulates a specific gene of interest (IG).

3.5. CTR-DB database verifies the gene resistance values

CTR-DB (http://ctrdb.ncpsb.org.cn) is the first transcriptomic repository that integrates patient-derived tumor expression profiles with matched clinical drug-response annotations. By enabling mechanism-focused studies of resistance, combinatorial drug screening, and biomarker discovery across heterogeneous cohorts, it serves as a pivotal platform for identifying and validating transcriptome-based predictors of therapeutic outcome. We leveraged its “Biomarker Validation” module to quantify the resistance relevance of candidate genes.

4. Results

4.1. Pan-cancer expression of the CHRNA7

We first evaluated pan-cancer expression of the CHRNA7 in the TCGA database. Our analysis identified elevated CHRNA7 expression in 11 cancer types: cholangiocarcinoma (CHOL), diffuse large B-cell lymphoma (DLBC), esophageal squamous cell carcinoma (ESCA), Glioblastoma (GBM), acute myeloid leukemia (LAML), low-grade glioma (LGG), lung squamous cell carcinoma (LUSC), pancreatic adenocarcinoma, pheochromocytoma and paraganglioma (PCPG), skin cutaneous melanoma (SKCM), and thymoma (THYM). Conversely, diminished expression levels were detected in an additional 11 malignancies: bladder cancer (BLCA), BRCA, cervical squamous cell carcinoma (CESC), colon adenocarcinoma (COAD), lung adenocarcinoma (LUAD), ovarian cancer (OV), prostate cancer (PRAD), renal epithelial dysplasia (READ), testicular germ cell tumor (TGCT), thyroid carcinoma (THCA), and uterine corpus endometrial carcinoma (UCEC) (Fig. 1A). Additionally, among tumor tissues in the TCGA database, CHRNA7 expression was the highest in PCPG, TCGT, LGG, and stomach adenocarcinoma (STAD) (Fig. 1B). In GTEx normal tissues, CHRNA7 expression peaked in thyroid, testis, esophagus, and ovary. (Fig. 1C).

Fig. 1.

Pan-cancer expression of the CHRNA7. (A) Pan-cancer expression of the CHRNA7 between tumor tissues from the TCGA database and normal tissues from the TCGA and GTEx databases. (B) CHRNA7 expression in tumor tissues from the TCGA database. The location of the dot represents the mean value of the CHRNA7 expression level. (C) CHRNA7 expression in normal tissues from the GTEx database. The location of the dot represents the mean value of the CHRNA7 expression level. ∗p < 0.05; ∗∗p < 0.01; ∗∗∗p < 0.001; and ∗∗∗∗p < 0.0001; ns, not significant.

We conducted an additional evaluation of CHRNA7 expression across various cancer stages recommended by the World Health Organization (WHO). Our findings revealed that for the majority of cancer types, such as CESC, DLBC, KIRP, THCA, and THYM, CHRNA7 expression levels tended to be lower in more advanced cancer stages (Fig. 2A–F). Conversely, an elevated expression of CHRNA7 in advanced stages was exclusively noted in KIRC, (Fig. 2C).

Fig. 2.

Pan-cancer expression of the CHRNA7 in different WHO-recommended cancer stages. (A–F) Pan-cancer differential expression of the CHRNA7 in WHO-recommended stages in the indicated cancer types from the TCGA database. ∗p < 0.05; ∗∗p < 0.01; ∗∗∗p < 0.001; and ∗∗∗∗p < 0.0001; ns, not significant.

Analysis of TCGA-paired tumor–normal samples revealed low CHRNA7 expression in BRCA, KICH, PRAD, and THCA (Fig. 3A–D) and high expression in KIRP and LUSC (Fig. 3E and F).

Fig. 3.

Pan-cancer expression of the CHRNA7 in paired tissues. (A–F) Pan-cancer differential expression of the CHRNA7 in paired tumor and adjacent normal tissues in the indicated cancer types from the TCGA database. ∗∗p < 0.01; ∗∗∗p < 0.001; and ∗∗∗∗p < 0.0001.

4.2. Prognostic significance of the CHRNA7

The analysis of Receiver Operating Characteristic (ROC) curves indicates that α7nAChR has diagnostic potential as a biomarker for specific cancer types. (Supplementary Fig. S1). The ROC value of the CHRNA7 was >0.7 in cancers such as UCEC, BLCA, LUSC, PRAD, sarcoma (SARC), BRCA, ESCA, GBM, COAD, THCA, CHOL, READ, and PCPG, indicating that the CHRNA7 has good diagnostic capability for these cancers (Supplementary Fig. S1). Subsequently, an evaluation was conducted to determine the prognostic significance of CHRNA7 as a predictive factor for various prognostic outcomes, encompassing overall survival (OS), disease-specific survival (DSS), progression-free interval (PFI), and disease-free interval (DFI), across the 31 cancer types within the TCGA database. The analysis of OS using KM survival curves revealed that CHRNA7 acts as a protective factor for patients with LGG and READ (Fig. 4A–D). However, it serves as a risk factor for patients suffering from ACC, BLCA, BRCA, LUSC, LUAD, STAD, THCA, (Fig. 4A and E-K). DSS analysis identified α7nAChR as a protective factor in CHOL, DLBC, LGG, LUSC, and READ, and as a risk factor in ACC, BLCA, BRCA, KIRP, SARC, SKCM, STAD, THCA, and UCEC (Supplementary Fig. S2F–N). According to the results of the DFI analysis, the CHRNA7 is a protective factor for patients with COAD, LGG, LUAD, OV, PRAD, and THCA (Supplementary Fig. S3A–F) and a risk factor for patients with BLCA, CESC, KIRC, KIRP, and STAD (Supplementary Fig. S3G–K). The PFI analysis identified CHRNA7 as a protective factor in patients with CHOL and COAD, LGG, OV, and PRAD (Supplementary Fig. S4A–E). Conversely, it acts as a risk factor for patients suffering from ACC, BLCA, CESC, GBM, KIRC, KIRP, LIHC, LUAD, SARC, SKCM, and STAD (Supplementary Fig. S4F–P).

Fig. 4.

Kaplan-Meier overall survival and univariate Cox regression analysis of CHRNA7 expression. (A) Heatmap shows the results of Kaplan-Meier overall survival and univariate Cox regression analysis of the effect of CHRNA7 expression on OS, DSS, DFI, and PFI in TCGA pan-cancer. (B–K), Kaplan-Meier overall survival analysis for pan-cancer CHRNA7 expression in the indicated cancer types from the TCGA database. The median value of CHRNA7 expression in each tumor was considered the cutoff value.

Drawing on the findings from univariate Cox regression analyses, the assessment of OS indicated that CHRNA7 functions as a safeguard for patients with LGG, while acting as a hazard for those with ACC, BRCA, STAD, and THCA. The examination of DSS revealed that CHRNA7 serves as a protective agent for patients with DLBC and LGG, and as a risk element for patients with ACC. The analysis of DFI demonstrated that CHRNA7 exerts a shielding effect on patients with LUAD. Ultimately, the evaluation of PFI showed that CHRNA7 acts as a protective factor for patients with LGG and as a risk factor for patients with ACC and SARC (Fig. 4A).

4.3. Analyses of genomic alterations in the CHRNA7 and its genomic instability

Genomics strategies are robust approaches to analyze cancer [30]. In an effort to gauge the potential genomic modifications of the CHRNA7 in specific cancer types, a wide - ranging pan - cancer investigation targeting the copy number variations (CNVs) and single nucleotide variations (SNVs) of the α7nAChR was initiated. CHRNA7 gene amplification was primarily detected in ESCA, SARC, PCPG, and LAML, whereas deep deletions were commonly observed in LUAD, UCS, mesothelioma (MESO), OV, BRCA, BLCA, ACC, KIRC, and LIHC; additionally, A notably elevated frequency of SNVs was observed in cases of UCEC, THYM, STAD, SKCM, and LIHC (Fig. 5A and B). Through TIDE analyses relying on the levels of these CNVs, it was found that patients diagnosed with LIHC, KIRC, and BRCA who exhibited elevated levels of CHRNA7 CNVs demonstrated improved survival rates. In contrast, patients with papillary KIRC showed a different survival trend (Fig. 5C). Considering the prevalence of these mutations and their potential impact on prognosis and treatment responses, an assessment was made of the relationships between CHRNA7 expression and factors such as TMB, MSI, HRD, aneuploidy, and neoantigens. [31,32]. In COAD, ESCA, and SKCM, CHRNA7 expression showed a significant positive correlation with TMB. (Fig. 5D). A comparable positive association between CHRNA7 expression and MSI was observed in COAD, ESCA, GBM, KIRC, OV, READ, and STAD. (Fig. 5E). Conversely, CHRNA7 expression correlated negatively with TMB in KIRP (Fig. 5D) and with MSI in KICH. (Fig. 5E). Regarding HRD, a positive correlation with CHRNA7 expression was found in UCS, THYM, and ACC (Fig. 5F). Additionally, a negative correlation between CHRNA7 expression and aneuploidy was detected in COAD (Fig. 5G). Tumor-specific neoantigens play a pivotal role as targets for triggering an anti-tumor immune response, and as a result, they are linked to the prognostic outcomes of numerous cancer types. [4]. Researchers employed single-nucleotide variants (SNVs) and insertion/deletion mutations to screen for potential neoantigens that can bind to major histocompatibility complex (MHC) molecules and initiate an adaptive immune response against tumors [33]. When investigating the correlations between CHRNA7 and neoantigens, the sole significant correlation found pertained to indel neoantigens in COAD (presented in Fig. 5H and I). On the whole, these research results point to a crucial connection between CHRNA7 expression and genomic instability.

Fig. 5.

CHRNA7 expression is correlated with genomic instability. (A) Pan-cancer analyses of genomic changes in the CHRNA7 in the TCGA database were conducted, including analyses of mutations, amplifications, and deep deletions. (B) The pan-cancer CHRNA7 SNV landscape, including missense, frameshift deletion, and splice site mutations. (C) The prognostic relevance of CHRNA7 CNVs and survival in four cancers was assessed using Kaplan-Meier curves generated with the TIDE tool. (D, E) Radar charts representing pan-cancer analyses of the link between CHRNA7 and both TMB (D) and MSI (E). (F–I) Lollipop charts were used to visualize correlations between CHRNA7 expression levels and HRD (F), aneuploidy (G), SNV. neoantigens (H), and indel. neoantigens (I), with dot sizes proportional to sample sizes and dot color proportional to p-values. Cancers with red indicated p-values <0.05.

4.4. α7nAChR expression is linked to DNA repair, methylation, and cancer cell stemness

The DNA-damage response (DDR) is a complex mechanisms that preserves genomic stability by sensing and resolving sequence or structural anomalies [48]. Tumor cells can evade therapy by hijacking mismatch repair (MMR [34] and homologous recombination repair (HRR) [35] thereby acquiring stem-like self-renewal capacity [36]. Accordingly, we interrogated the relationships among CHRNA7 expression, MMR-gene profiles, HRR signatures, and stemness. Across cancers, CHRNA7 inversely correlated with multiple MMR genes, most markedly in LUAD, followed by COAD, BRCA, and SKCM. (Fig. 6A). Moreover, the CHRNA7 exhibited a positive correlation with the expression of a range of MMR genes in various cancers, including CHOL, head and neck squamous cell carcinoma (HNSC), OV, PCPG, THCA, and LIHC, with the strongest association observed in CHOL (Fig. 6A). CHRNA7 expression also showed a significant positive correlation with HRR signatures in ACC, CHOL, MESO, HNSC, TGCT, OV, UCEC, PCPG, UCS and SARC. (Fig. 6B). The CHRNA7 also showed a positive correlation with tumor stemness in UCS, THYM, and TGCT (Fig. 7C). Collectively, these findings implicate CHRNA7 as a modulator of cancer progression via its capacity to shape DNA-damage repair.

Fig. 6.

The CHRNA7 is associated with DNA repair, epigenetic modifications, and stemness. (A) Associations between the CHRNA7 and five MMR genes across cancer types are presented in a heatmap. (B) Correlation scatterplots for 10 cancers highlighting correlations between a 30-gene HRR signature and CHRNA7 expression levels. (C) Correlations between CHRNA7 expression levels and stemness are presented with a lollipop chart in which dot sizes are proportional to sample size and dot colors are indicative of p-values. (D) Correlations between CHRNA7 expression and four DNMTs are presented in a heatmap. (E) Pan-cancer differential methylation levels of the CHRNA7 in normal and tumor tissues. ∗p < 0.05; ∗∗p < 0.01; ∗∗∗p < 0.001.

Fig. 7.

Functional enrichment analysis related to the α7nAChR. (A) PPI network related to the α7nAChR constructed using the STRING database. (B) The heatmap of the relationship between the α7nAChR and the top six genes in various tumors. (C) Venn plot displaying the gene that interacted with and showed a correlation with the α7nAChR. (D) GO analysis of the α7nAChR. (E) KEGG analysis of the α7nAChR. (F) Functional status of the α7nAChR in pan-cancer at the single-cell level determined using the CancerSEA database. ∗p < 0.05; ∗∗p < 0.01; ∗∗∗p < 0.001.

Epigenetic modifications play a key role in influencing tumorigenesis and tumor progression; hence, they are increasingly popular as research targets [37]. DNMTs catalyze DNA methylation and potentially modulate various characteristics of tumor cells, including proliferation and cell cycle progression [37]. The CHRNA7 expression showed a robust inverse correlation with DNMT levels in COAD, THYM, READ, STAD, TGCT, and ESCA, but showed a positive correlation in CHOL, CESC, SARC, KIRC, PCPG, DLBC, LAML, GBM, KICH, LUSC, LIHC, MESO, OV, and uveal melanoma (Fig. 6D). The CHRNA7 methylation levels differ markedly between tumor and adjacent normal tissues across multiple cancer types. For example, lower methylation levels in cancer tissues are observed in BRCA, COAD, CESC, PCPG, CHOL, UCEC, LUAD and PRAD (Fig. 6E). Taken together, these analyses suggest that the CHRNA7 is involved across diverse cancers, governing both DNA methylation and mRNA modification.

4.5. α7nAChR-related network and pathways

To delineate interactome and signaling pathways linked to the α7nAChR, we constructed a PPI network using the STRING database; this network comprised 30 proteins that interact with the α7nAChR (Fig. 7A). Subsequently, we identified the top six genes showing the highest correlation with the α7nAChR in tumors. The heatmap revealed a positive correlation between the α7nAChR and these six genes across all tumor types (Fig. 7B). Intersection of the two gene sets yielded α7SP—a gene that both interacts with and is strongly associated with α7nAChR (Fig. 7C). Genes interacting with α7nAChR were subsequently interrogated by GO and KEGG enrichment analyses. The results suggest that the α7nAChR might be involved in the excitation of the nervous system and various physiological processes, together with its participation in tumor-related pathways such as response to acetylcholine, dopamine transport, and chemical carcinogenesis receptor activation (Fig. 7D and E).

4.6. Association between the CHRNA7 and pan-cancer ICI and immunotherapy response

CHRNA7 expression positively correlates with both immune and stromal cell abundances in BRCA, LIHC, LUAD, COAD, and READ (Fig. 8A). and with ICPs in READ, PRAD, COAD, and LUAD (Fig. 8B). CHRNA7 expression was inversely correlated with immune/stromal cell abundances in THCA, SARC, and LGG (Fig. 8A) and with immune-checkpoint proteins in ACC, LGG, COAD, and SARC. (Fig. 8B). We comprehensively profiled the association between CHRNA7 expression and ICI across 31 cancer by integrating two independent algorithms—ssGSEA and Xcell. Xcell deconvolution further revealed that CHRNA7 expression is significantly positively associated with the infiltration of monocytes, Macrophages, M1-Macrophages, mast cells, DCs, immature DCs and activated DCs in both READ and COAD. Conversely, pronounced inverse correlations with these same myeloid and DC subsets were observed in SARC, THCA and LGG, underscoring a cancer-type-dependent, directionally opposite modulation of the myeloid compartment by CHRNA7 (Supplementary Fig. S5A). In BRCA, LUAD, LIHC, READ, COAD, PRAD and DLBC, ssGSEA revealed significant positive correlations between CHRNA7 and the abundance of regulatory T cells (Treg), macrophages, myeloid-derived suppressor cells (MDSC), mast cells, and both central-memory CD8⁺ and CD4⁺ T-cell subsets (P < 0.05). Conversely, in LGG, ACC, THCA and SARC, CHRNA7 expression was inversely associated with these same immune populations (all P < 0.05). These convergent yet directionally opposite signatures suggest that CHRNA7 may act as a context-specific immune modulator, potentiating immunosuppressive infiltration in certain malignancies while antagonizing it in others (Supplementary Fig. S5B).

Fig. 8.

Immune cell infiltration analysis. (A) Correlation between the CHRNA7 and tumor purity and stromal, immune, and ESTIMATE scores in pan-cancer. ∗p < 0.05; ∗∗p < 0.01; ∗∗∗p < 0.001.(B) Correlation between the CHRNA7 and immune checkpoints in pan-cancer. (C) GSEA of the CHRNA7 in pan-cancer. (A) GSEA of the pathways for the 31 cancer types. (D) The top 10 GSEA terms in the indicated cancer types (CHOL, COAD, LUAD, READ).

4.7. GSEA of the CHRNA7

Using GSEA, we evaluated the pathways potentially associated with CHRNA7 across 31 cancer types from the TCGA database. CHRNA7 expression was robustly associated with immunotherapy-response pathways and cell-proliferation programs. Particularly in CHOL, COAD, LUAD, and READ, CHRNA7 exhibited significant positive correlations with ALLOGRAFT_REJECTION, INTERFERON_GAMMA_RESPONSE, TNFA_SIGNALING_VIA_NFKB, INFLAMMATORY_RESPONSE, and COMPLEMENT. Among these, interferon-gamma (IFN-γ) is a key cytokine primarily produced by activated T cells and NK cells. It plays crucial roles in macrophage activation, enhanced antigen presentation, inhibition of angiogenesis, and direct suppression of tumor cell growth. Tumor necrosis factor-alpha (TNF-α) is another important pro-inflammatory cytokine that regulates a wide array of genes involved in inflammation, cell survival, proliferation, and death through activation of the NF-κB pathway. The complement system, while part of the innate immune system, can also bridge and enhance adaptive immune responses. Complement activation mediates functions such as antibody-dependent cellular cytotoxicity (ADCC), assisting immune cells in target cell clearance. This indicates that in these tumors, the immune system is successfully recruited and activated, potentially mounting an effective attack against tumor cells. These findings further support the conclusion that CHRNA7 acts as a protective factor in cancers such as CHOL, READ, COAD, and LUAD. Conversely, CHRNA7 showed negative correlations with pathways related to fundamental energy metabolism and cell proliferation/growth, such as OXIDATIVE_PHOSPHORYLATION, PEROXISOME, MYC_TARGETS_V1/V2, and DNA_REPAIR. Key oncogenic pathways (MYC signaling) and fundamental metabolic functions were significantly downregulated. This suppression may result from successful inhibition of tumor cell proliferation by the immune response, or it may indicate that CHRNA7 directly or indirectly inhibits these pathways (Fig. 8C and D). (The GSEA enrichment results for the other cancer types are shown in Supplementary Fig. S6).

4.8. Expression analysis of the α7nAChR at the protein level

To identify the expression levels of α7nAChR in normal and cancer tissue cells, we used immunohistochemistry (IHC) and Western Blot (WB) experiments for verification. IHC analysis showed that α7nAChR was expressed at the highest levels in colorectal cancer (CRC) tissues, followed by higher expression levels in macrophages in cancerous tissues, while expression levels in normal tissues were the lowest (Fig. 9A–D). Western blotting revealed high α7nAChR expression in colorectal cancer lines HCT116 and HT29, and low expression in LOVO cells and the normal colon line NCM460 (Fig. 9E).

Fig. 9.

IHC analysis results for α7nAChR expression. (A) IHC analysis results for α7nAChR expression in normal tissues. (B) IHC analysis results for α7nAChR expression in CRC tissues. (C) IHC analysis results for α7nAChR expression in macrophages in CRC tissues. (D) Qualitative comparison of various proteins expression between Normal (n = 3), Macrophages (n = 3) and Tumor (n = 3) tissues in CRC. (E), The above figure shows the expression of the α7 acetylcholine receptor in HCT-116 cells, HT29 cells, LOVO cells, and NCM460 cells detected by Western blot. The figure below analyzes the band intensity using ImageJ software, with values represented as t-tests (and non-parametric tests) displayed in a bar graph. Asterisks (∗) indicate significant differences. ∗P < 0.05, ∗∗P < 0.01, ∗∗∗P < 0.001.

4.9. The HMSRF algorithm delineated an upstream transcription-factor regulatory network controlling CHRNA7

Transcription factors are proteins that modulate gene expression by specifically binding to cis-regulatory elements within promoter regions, thereby enhancing or repressing RNA-polymerase activity. Here, We hypothesize that upstream TFs dysregulation contributes to the elevated α7nAChR expression observed in COAD. We therefore employed the heuristic multiple-stepwise-regression and information-filtering (HMSRF) algorithm to iteratively reconstruct an upstream multi-layer hierarchical gene regulatory network (ML-hGRN). We applied HMSRF to reconstruct the upstream gene regulatory network and identify key regulators of α7nAChR (Fig. 10A), which consists of 29 genes of α7nAChR, 29 edges, and 8 layers of upstream transcription factors. In this network, MYC (an oncogene) acts as the third layer TF of α7nAChR. Here, MYC promotes the expression of CEBPA, which in turn inhibits TFAP2A. The downregulation of TFAP2A directly induces the upregulation of α7nAChR (Fig. 10A). To evaluate the universality of the associations between CHRNA7 and its putative transcriptional regulators (TFAP2A, CEBPA, and MYC), we analyzed the correlations between CHRNA7 expression and the expression of these transcription factors across multiple cancer types in the TCGA pan-cancer cohort. TFAP2A was found to be positively correlated with CHRNA7 in the majority of cancers, with the strongest correlation in COAD. Correlations for CEBPA and MYC were weaker and more variable. These results indicate that TFAP2A-mediated regulation of CHRNA7 is pan-cancer conserved to some extent, but is particularly pronounced in colorectal cancer, thereby supporting the robustness of the HMSRF model (Fig. 10B).

Fig. 10.

Construction of the upstream transcription factor regulatory network of the HMSRF algorithm based on high expression of α7nAChR. (A) An ML-hGRN of α7nAChR in the discovery set. The nodes represent α7nAChR and upstream transcription factors (TFs), the edges represent the regulatory relations (inferred activation or inhibition). Red and blue represent positive-related and negative-related TFs with α7nAChR. (B) Robustness Verification of the HMSRF Model. (C-D) Functional pathways enriched with all genes in the network. (E) Predicting the AUC values of patients' responses to various chemotherapy drugs. (F) Compared with the non-response group, the difference multiple of α7nAChR in the treatment response group of colorectal cancer patients receiving drug therapy.

Subsequently, GO and KEGG enrichment analyses were performed to delineate the biological pathways and functions associated with the genes within this network. GO analysis revealed that these genes were significantly enriched in transcriptional and miRNA-regulatory processes, including “DNA-binding transcription activator activity” and “positive regulation of miRNA metabolic process” (Fig. 10C). KEGG analysis further indicated that, in addition to the “Cell cycle” and “Cellular senescence” pathways, the gene set was markedly enriched in the “TGF-β signaling pathway” (Fig. 10D). In early-stage colorectal cancer, this pathway primarily suppresses tumorigenesis by inhibiting proliferation and inducing apoptosis.

We leveraged the CTR-DB database (http://ctrdb.cloudna.cn/home) to validate the predictive utility of α7nAChR expression for therapeutic response across malignant tumors (Table 1). Colon-cancer patients who responded to Capecitabine (CTR_RNAseq_117-I) exhibited a 2.27-log-fold up-regulation of α7nAChR relative to non-responders, yielding an AUC of 0.93 for predicting Capecitabine sensitivity (Fig. 10E and F). Compared with non-responders, fluorouracil–irinotecan–leucovorin–panitumumab-treated colorectal cancer patients who achieved a clinical response (CTR_Microarray_133-I) displayed a 0.66-log-fold up-regulation of α7nAChR, generating an AUC of 0.83 for predicting sensitivity to this quadruplet regimen. Conversely, nivolumab-responsive colon-cancer patients (CTR_RNAseq_535-I) exhibited a 3.22-log-fold reduction in α7nAChR relative to non-responders, yielding an AUC of 0.78 for predicting nivolumab benefit.

Table 1.

Correspondence table of CTR-DB database samples and corresponding drug treatments.

CTR-DB ID	Therapeutic regimen
CTR_RNAseq_117-I	Capecitabine
CTR_Microarray_133-I	Fluorouracil + Irinotecan + Leucovorin + Panitumumab
CTR_RNAseq_448-I	Fluorouracil + Leucovorin + Oxaliplatin
CTR_RNAseq_535-I	Nivolumab
CTR_Microarray_186-I	Fluorouracil + Irinotecan + Leucovorin
CTR_Microarray_148-I	Bevacizumab + Oxaliplatin + Tegafur
CTR_Microarray_155-I	Fluorouracil + Leucovorin + Oxaliplatin
CTR_RNAseq_519-I	Durvalumab + Fluorouracil + Leucovorin + Oxaliplatin + Tremelimumab
CTR_RNAseq_580-I	Durvalumab + Fluorouracil + Leucovorin + Oxaliplatin + Tremelimumab
CTR_Microarray_129-I	Bevacizumab + Fluorouracil + Leucovorin + Oxaliplatin

5. Discussion

Advances in medical technology now enable clinicians to deliver optimal care to cancer patients through surgery, targeted therapy, endocrine therapy, or chemotherapy [[38], [39], [40]]. However, tumor management alone is not sufficient. Improving the odds of a long-lasting cure through personalized treatment is critical, making the identification of specific tumor targets or characteristics essential [41,42].

Previous studies have examined the expression and function of CHRNA7 in various tumors, such as lung cancer [43]. However, it remains unelucidated whether the CHRNA7 could function as a prognostic marker in different tumors. This investigation undertook a systematic evaluation of CHRNA7 expression across a spectrum of human tumors. Our findings demonstrate that CHRNA7 is upregulated in 31 cancer types and its expression exhibits significant associations with DNA methylation status, MSI and TMB. Additionally, CHRNA7 overexpression was associated with poor prognosis (PFI, DSS, and OS) of various tumors.

• •Kaplan-Meier analysis of overall survival demonstrated that CHRNA7 is a protective factor for LGG and READ, whereas it constitutes a risk factor for ACC, BLCA, BRCA, LUSC, STAD, THCA, and LUAD. Univariate Cox regression analysis for Disease-Specific Survival (DSS) identified CHRNA7 as a protective factor in patients with CHOL, DLBC, LGG, LUSC, and READ. In contrast, it was established as a risk factor for ACC, BLCA, BRCA, KIRP, SARC, SKCM, STAD, THCA, and UCEC. CHRNA7 exhibits opposite prognostic significance across different cancer types. Integrating GSEA functional enrichment analysis with previous studies, we found that high CHRNA7 expression is significantly positively correlated with the epithelial-mesenchymal transition (EMT) pathway in LUAD (Fig. 8D). EMT is a critical process through which tumor cells acquire invasive and metastatic capabilities, and its activation typically predicts poor prognosis. Previous studies have demonstrated that CHRNA7 possesses exceptionally high Ca²⁺ permeability, and its activation leads to an increase in intracellular calcium concentration, subsequently activating protein kinase C (PKC) and the downstream MEK-ERK signaling cascade. This signaling axis has been shown to promote tumor cell migration, invasion, and the EMT process [44,45]. Therefore, CHRNA7 may induce EMT via the Ca²⁺-PKC-MEK-ERK signaling axis, thereby enhancing the metastatic potential of LUAD. In contrast, in READ, GSEA analysis revealed that high CHRNA7 expression is significantly positively correlated with multiple anti-tumor immune pathways, including interferon-gamma response, inflammatory response, allograft rejection, complement system, and IL-2/STAT5 signaling, suggesting an active adaptive immune response within the tumor microenvironment (Fig. 8D). These pathways provide a mechanistic explanation for the protective role of α7nAChR in READ.

However, using OS as an endpoint can limit the feasibility of clinical studies, and deaths from non-cancer causes may not accurately represent the underlying tumor biology, its invasiveness, or the response to treatment. To address this limitation, many clinical trials adopt endpoints like DFI or PFI, which are metrics designed to more directly assess outcomes directly attributable to tumor factors. Hence, in the present study, we conducted univariate Cox regression analysis to assess the association between CHRNA7 expression and DFI/PFI of cancer patients. Analysis of DFI demonstrated that CHRNA7 served as a protective factor in LUAD patients. Additionally, analysis of Progression-Free Interval (PFI) identified CHRNA7 as a protective factor in LGG, while it was a risk factor in ACC and SARC. Collectively, these results indicate that low CHRNA7 expression exerts a predominantly protective effect across the majority of tumor types studied.

DNA methylation, a key epigenetic mechanism, governs gene expression from chromosomal regions without altering the DNA sequence [[46], [47], [48]]. Usually, DNA methylation inhibits gene expression by altering DNA conformation, DNA stability, and chromatin structure [49,50]. Recent studies have gradually revealed the association between DNA methylation and tumor occurrence. Hypermethylation of promoter regions typically leads to the silencing of tumor suppressor genes in cancer [[51], [52], [53]]. Our analysis revealed a significant correlation between DNA promoter methylation and CHRNA7 expression in most common tumors.

Tumor Mutational Burden (TMB) serves as a pan-cancer biomarker for predicting patient response to immunotherapy, especially in the era of targeted therapies [52,54]. TMB has been evaluated as a predictive biomarker for immunotherapy in specific cancers, including colorectal cancer (CRC) [55,56] and non-small cell lung cancer (NSCLC) [57,58]. Beyond its predictive utility, TMB provides prognostic value for patient outcomes across a spectrum of cancers in the post-immunotherapy setting [1,56,59]. Microsatellite Instability (MSI) is a well-established biomarker for immune checkpoint inhibitors [60]. In CRC, high MSI frequency serves a dual role, functioning as both a diagnostic marker and an independent prognostic indicator [61,62]. The present study revealed an association between CHRNA7 expression and TMB in 12 cancer types and between CHRNA7 expression and MSI in 10 cancer types. This finding suggest that the CHRNA7 expression level affects the MSI and TMB of tumors, thus influencing the response of patients to ICP inhibitor therapy.

The tumor microenvironment (TME) remains a central focus in cancer therapy research, with immune cells representing an integral component. Immune cell functionality is governed by the expression patterns of key modulators like microRNAs, which dictate their distinct roles [63,64], and generally exhibit show strong antitumor effects. According to previous reports, immune cells in the Immune cells within the TME play a critical role in the development and progression of various tumors [33,[65], [66], [67]]. Nevertheless, the role of CHRNA7 in the tumor microenvironment (TME) remains underexplored. To address this gap, our analysis revealed a robust correlation between α7nAChR expression and invasive immune cell infiltration in the TME across multiple cancers, including BRCA, LIHC, LUAD, COAD, and READ. Furthermore, evaluation via the ESTIMATE method demonstrated robust correlations of CHRNA7 expression with key tumor microenvironmental signatures—namely, the stromal, immune, and ESTIMATE scores—in most cancers.

In this study, we observed that the α7nAChR is highly expressed on tumor-associated macrophages (TAMs) in CRC tissues. Its expression level is significantly positively correlated with the infiltration of total macrophages and M1 macrophages, but shows no significant correlation with M2 macrophages. Furthermore, high α7nAChR expression is a protective factor for the prognosis of patients with COAD and READ. These findings suggest that CHRNA7 may remodel the tumor immune microenvironment and inhibit tumor progression by specifically promoting TAM polarization toward the M1 phenotype, rather than suppressing M2 polarization.

In summary, we systematically evaluated CHRNA7 expression in a pan-cancer context, demonstrating marked differences between malignant and normal tissue samples. Our findings confirm that CHRNA7 expression is linked to DNA methylation and clinical prognosis, thereby supporting its role as an independent prognostic factor in a pan-cancer context. The CHRNA7 expression level varies in diverse cancer types, leading to distinct prognostic outcomes; therefore, further studies are required to elucidate the precise function of the CHRNA7 in each of these cancers. Additionally, pan-cancer CHRNA7 expression is associated with immune cell infiltration, MSI, and TMB. The influence of CHRNA7 expression on tumor immunity varies according to the type of cancer. These results could clarify CHRNA7's role in tumorigenesis and tumor progression and provide a foundation for developing more accurate and individualized immunotherapy for cancer patients in the future.

Although the CTR-DB database serves as a valuable resource for the discovery and initial validation of predictive biomarkers, it is fundamentally a retrospective collection of patient-derived transcriptomic data with corresponding treatment response information. Therefore, the calculated AUC values for predicting treatment response—0.93 for capecitabine, 0.83 for FOLFIRI plus panitumumab, and 0.78 for nivolumab—may be biased. These estimates originate from existing clinical cohorts and are prone to inherent biases of retrospective analyses, including patient selection bias, heterogeneity in response assessment criteria, and incomplete or unbalanced representation of patient subgroups. Thus, prior to any clinical application, the performance of CHRNA7 expression as a predictive biomarker for these regimens requires prospective validation in independent clinical cohorts.

Clinical trial number

Not applicable.

Author contributions statement

T.M is responsible for methodology, calculations, graphing, and article writing; XH.B is responsible for designing article ideas and overseeing the experimental process; T.W is responsible for proteomic experiments; LJ.M controlled the entire article, edited the article and provided financial support. The remaining authors contributed the same amount to the whole article, mainly including: finding information, drawing pictures, and experimenting. All authors reviewed the manuscript.

Consent for publication

All authors gave their consent for publication.

Ethics approval and consent to participate

The studies involving human participants were reviewed and approved by the Ethics Review Board of Ningxia Medical University (approval number: 2023-2625). The patients/participants provided their written informed consent to participate in this study.

Funding

This work was supported by grants the Key research project of Ningxia Natural Science Foundation (NO.2023BSB03040, NO.2026BEG02030), Project of Ningxia Natural Science Foundation (NO.2023AAC03219, NO.2026AAC030241), scientific research project of Ningxia Medical University (Special talent start project, NO. XT2023004).

Declaration of competing interest

The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

Appendix A. Supplementary data

The following are the Supplementary data to this article:

figs1.

Data availability

The original contributions presented in the study are included in the article/Supplementary Material. Further inquiries can be directed to the corresponding authors.