Neuroepithelial cell transforming 1 as a key regulator in non-small cell lung cancer: unveiling causal links and therapeutic potentials

Introduction

Lung cancer, a malignant tumor arising from the tissues of the lungs, stands as one of the most prevalent and deadly cancers globally (1-3). While approximately 85% of cases fall under non-small cell lung cancer (NSCLC), including adenocarcinoma, squamous cell carcinoma, and large cell carcinoma, the remaining 15% are classified as small cell lung cancer (SCLC), known for its rapid growth and high metastatic potential (4). Beyond smoking, genetic factors, environmental pollutants, and occupational exposures also contribute to lung cancer incidence (5,6). Treatment modalities encompass surgery, radiotherapy, chemotherapy, and emerging targeted therapies, with personalized approaches gaining prominence (7,8). Ongoing research explores immunotherapy and molecular targeted treatments, showcasing promising avenues for advancing lung cancer care (9-12). Despite progress, early detection and comprehensive intervention remain pivotal for enhancing patient survival rates and quality of life (13).

The molecular mechanisms underlying the development of lung cancer are intricate, involving key roles played by genetic mutations and aberrant activation of signaling pathways (14). In NSCLC, common oncogenic mutations include alterations in genes such as EGFR, and ALK, which disrupt normal cell growth and differentiation, driving tumorigenesis (15,16). Additionally, the loss of tumor suppressor genes like TP53 and LKB1 is pivotal in lung cancer, compromising the cell’s ability to control abnormal proliferation (17). Dysregulated activation of cell signaling pathways, notably PI3K/AKT and MAPK, further contributes to lung cancer by disrupting the balance between cell growth and apoptosis (18). These molecular mechanisms intricately interact, forming a complex network that underlies the initiation and progression of lung cancer (19). A comprehensive understanding of these mechanisms holds the potential to inform the development of targeted therapeutic strategies, thereby enhancing treatment outcomes for lung cancer patients (20).

The Mendelian randomization (MR) algorithm is a powerful statistical method that utilizes genetic variants as instrumental variables to investigate causal relationships between exposures and outcomes in observational studies (21). In the context of identifying lung cancer risk genes, MR involves selecting genetic variants strongly associated with a specific exposure, such as tobacco smoke or environmental pollutants, and assessing their instrumental validity based on assumptions like relevance, independence, and absence of pleiotropy (22). These genetic instruments are then employed to estimate the causal effect of the exposure on the outcome, revealing insights into the relationship between genetic factors and outcome (23). By providing a more robust and less biased approach than traditional observational studies, MR contributes to the identification of potential risk genes for outcome (24). This methodology offers valuable evidence for prioritizing targets in preventive strategies and therapeutic interventions related to lung cancer.

Our research builds upon these foundations, aiming to synergize single-cell data with the MR algorithm. By combining these approaches, we aspire to identify and prioritize high-risk genes associated with the occurrence of NSCLC. This innovative methodology not only bridges the gap between genetics and epidemiology but also opens new avenues for personalized interventions and targeted therapeutic strategies. Our goal is to contribute significantly to the evolving landscape of NSCLC research and pave the way for more effective approaches to combat this pervasive disease. We present this article in accordance with the MDAR reporting checklist (available at https://tcr.amegroups.com/article/view/10.21037/tcr-24-587/rc).

Methods

Single-cell RNA sequencing (scRNA-seq) analysis

Please refer to Appendix 1 for scRNA-seq analysis for details.

Expression quantitative trait loci (eQTLs) and MR analysis

The analysis of eQTLs and MR involved obtaining Long Non-Coding Ribonucleic Acid (lncRNA) and Messenger Ribonucleic Acid (mRNA) Cis-expression Quantitative Trait Loci (cis-eQTLs) from a dataset publicly described by Võsa et al. (25). Cis-eQTLs were designated as those where the gene was located within a distance of <1 Mb from the single nucleotide polymorphism (SNP). Significance for cis-eQTLs was determined based on a p value below 5×10⁻⁸. Associations between lung cancer traits and eQTLs were identified in the genome-wide association study (GWAS) datasets available on Integrated Epidemiology Unit Open Genome-Wide Association Study (IEU OpenGWAS) at https://gwas.mrcieu.ac.uk/.

To estimate the causal effect between lncRNAs/mRNAs and traits, a two-sample MR analysis was conducted using the TwoSampleMR R package. The Wald ratio test was applied for single instruments, where Wald estimates were computed by dividing the SNP outcome by the SNP-exposure. In cases with multiple instruments, the inverse variance weighting (IVW) method was utilized, leveraging information from all instruments. Causal associations were considered statistically significant with a false discovery rate (FDR) adjusted P value below 0.05. Heterogeneity was assessed for IVW estimates, with high heterogeneity indicating a variance across instruments suggestive of invalid instruments. Subsequently, a colocalization analysis was performed to evaluate pleiotropy using the coloc R package. This involved incorporating all cis-acting SNPs associated with the gene, prior to clumping and p value filtering. Posterior probabilities (PP3 and PP4) were determined, and the extent of pleiotropy was calculated. Finally, an assessment of reverse causality was conducted.

Acquisition of The Cancer Genome Atlas (TCGA) and Genotype-Tissue Expression (GTEx) datasets

In May 2023, clinical data and mRNA expression profiles for the TCGA cohorts were obtained through the https://portal.gdc.cancer.gov/ platform. Following this, normalized transcriptome data for the GTEx cohort were acquired in May 2023 from https://www.gtexportal.org/.

NSCLC sections collection

We obtained 12 pairs of FFPE (formalin-fixed paraffin-embedded) specimens, consisting of NSCLC tumors and their corresponding resection margins, from the Pathology Department at The Second Affiliated Hospital, Zhejiang University School of Medicine. The preservation and storage of these FFPE sections adhere to rigorous and standardized protocols.

Gene set enrichment analysis (GSEA)

In our pursuit of a comprehensive understanding, a GSEA was performed on Hallmark gene sets. We systematically evaluated both the normalized enrichment score and statistical significance. The computational intricacies of this analysis were adeptly managed using the R software environment, employing the “clusterProfiler” package (26).

Estimation of stromal and immune cells in malignant tumor tissues using expression data (ESTIMATE)

The immune, stromal, and estimate scores for the TCGA-Lung Squamous Cell Carcinoma (LUSC) and TCGA-Lung Adenocarcinoma (LUAD) cohorts were calculated using the “estimate” package in R, which estimates the proportion of stromal and immune cells within malignant tumor tissues based on gene expression data.

Computation of tumor-infiltrating lymphocytes (TILs) abundance

We utilized immune-related gene signatures corresponding to 28 types of TILs, as identified in the study by Charoentong et al. (27). The relative abundance of these TILs within the tumor microenvironment was inferred through gene set variation analysis (GSVA). This analysis was conducted based on the gene expression profiles, employing the GSVA package (28).

Comparative Toxicogenomics Database (CTD)

We utilized the CTD (https://ctdbase.org/) to identify potential compound candidates for inducing downregulation of neuroepithelial cell transforming 1 (NET1) in lung cancer.

Molecular docking analysis

To evaluate the binding affinities and interaction modes between targets and compound candidates, we employed Autodock Vina 1.2.2 (29) a computational software for protein-ligand docking. Molecular structures of four potential compound candidates [doxorubicin; (+)-JQ1 compound; piroxicam; and quercetin] were obtained from PubChem Compound (https://pubchem.ncbi.nlm.nih.gov/) (30). The 3D coordinates for NET1 were extracted from the AlphaFold Protein Structure Database (https://alphafold.ebi.ac.uk/). In preparation for docking analysis, both protein and molecular files were converted to PDBQT format, excluding water molecules and incorporating polar hydrogen atoms. The grid box, centered to encompass each protein domain and allow for unrestricted molecular movement, was configured with dimensions of 30 Å × 30 Å × 30 Å and a grid point distance of 0.05 nm. Molecular docking studies were conducted using Autodock Vina 1.2.2 (http://autodock.scripps.edu/).

Laboratory experimental overview

Refer to Appendix 1 for detailed laboratory experimental information.

Ethical approval and consent to participate

In this study, the research involving 12 pairs of FFPE specimens (7 males and 5 females, aged 45–63 years) was conducted in strict adherence to the Declaration of Helsinki (as revised in 2013) and received ethical approval from the Ethics Committee of The Second Affiliated Hospital, Zhejiang University School of Medicine (ethical approval No. IR2024334). All procedures complied with relevant guidelines and regulations to ensure the ethical handling of human tissue samples. As the research was retrospective in nature and utilized archived specimens, the requirement for informed consent was waived by the Ethics Committee of The Second Affiliated Hospital, Zhejiang University School of Medicine in accordance with institutional guidelines. This waiver was granted as part of the ethical approval for the study. As the other aspect of the research is grounded on the utilization of publicly available datasets, thereby eliminating potential ethical dilemmas and conflicts of interest commonly associated with data acquisition in scientific studies.

Statistical analysis

Data analysis and figure creation were carried out using R software (version 4.3.6), with statistical significance set at P<0.05.

Results

Establishment of scRNA-seq and cell typing of non-malignant lungs and lung tumors atlas

Establishing an atlas for scRNA-seq and categorizing cells in non-malignant lungs and lung tumors involved the application of scRNA-seq analyses to samples from 9 untreated, non-metastatic NSCLC patients. This dataset comprised 9 lung tumor samples and 8 matched non-malignant lung samples (with 1 patient lacking a matched non-malignant lung sample) sourced from 2 open access databases (E-MTAB-6149 and PRJNA482529). After undergoing multiple quality control and filtering steps, a total of 68,042 cells were subject to transcriptome analysis (Figure 1A). Utilizing characteristic canonical cell markers, we identified 8 major cell types, classified as epithelial cells and various immune cell types (T cells, B cells, mast cells, NK cells, and myeloid cells), as well as stromal cell types (fibroblasts and endothelial cells) (Figure 1B,1C). Consistent with findings from prior studies, stromal and immune cells from different patients clustered together based on cell types, while cancer cells exhibited higher heterogeneity and patient-specific expression signatures.

Figure 1 Overview of the 68,042 single cells from lung tumors and precancerous non-malignant lung samples. The tSNE clustering of the 68,042 cells profiled here, with each cell color-coded for: Seurat cluster, the corresponding patient, its sample type of origin (tumor or non-malignant lung), and the associated cell type (A). The tSNE plot showed the expression of marker genes for the cell types defined above each panel (B). The dot plot presented marker genes for each cell type (C). The volcano plot displays significant DEGs between lung tumors and precancerous non-malignant lung samples in all 68,042 cells (D). tSNE, t-distributed stochastic neighbor embedding; FC, fold change.

Upon exploring expression patterns in non-malignant lungs and lung tumors, we identified 2,018 differentially expressed transcripts that were upregulated in lung tumor tissues. These transcripts met the criteria of log2 fold change (FC) ≥1 and an adjusted P value ≤0.05 across all cell types (Figure 1D).

Epithelial cells exhibit elevated heterogeneity

The 12,511 epithelial cells formed 8 distinct subsets, showcasing increased diversity and patient-specific expression signatures. Notably, epithelial cells from non-malignant lungs and lung tumors displayed discernible expression disparities (Figure 2A,2B). Subsequently, leveraging single-cell RNA sequencing data, we inferred copy number alterations (CNAs) in cancer cell populations. The inferred CNA profiles of nine patients unveiled both interpatient and intrapatient heterogeneity, emphasizing the varied genomic landscape within and across individuals (Figure 2C).

Figure 2 Epithelial cell clusters. The tSNE clustering of the 12,511 epithelial cells profiled here, with each cell color-coded for: Seurat cluster, the corresponding patient, its sample type of origin (tumor or non-malignant lung), and the associated databases (A). The bar plot presented the ratio of the epithelial cells in tumor or non-malignant lung of individual patients (B). Unsupervised clustering of inferred large-scale CNVs to identify cancer cells from epithelia cells. Tumor epithelial cells and reference cells (non-malignant lung cells) are in the y-axis and chromosomal regions in the x-axis (C). The volcano plot displays significant DEGs between epithelial cells in lung tumors and precancerous non-malignant lung samples in all 12,511 cells (D). GO analyses of the enriched BP, CC, and MF terms for 4543 DEGs up regulated in malignant epithelial cells (E). The dot plots present the significant Hallmark items identified by GSEA algorithm regulated in malignant epithelial cells (F). tSNE, t-distributed stochastic neighbor embedding; CNVs, copy number variations; FC, fold change; BP, biological process; CC, cellular component; MF, molecular function.

Within epithelial cells, we pinpointed 1,247 differentially expressed transcripts that were upregulated in lung tumor tissues, satisfying the criteria of log2FC ≥1 and an adjusted P≤0.05 (Figure 2D).

Gene Ontology enrichment analysis was conducted on the 1,247 differentially expressed transcripts. It revealed DNA-templated DNA replication, chromosomal region, and DNA replication origin binding as the top features in the domains of biological process, cellular component, and molecular function, respectively (Figure 2E).

Further, GSEA incorporating the MSigDB Hallmark datasets disclosed that the top upregulated terms for malignant epithelial cells were associated with HALLMARK_E2F_TARGETS, while the top downregulated terms were linked to HALLMARK_TNFA_SIGNALING_VIA_NFKB (Figure 2F).

The upregulation of mRNAs in malignant tissues may play a causal role in NSCLC

In total, 728 transcripts exhibited upregulation in lung malignant tissues across both all cell types and epithelial cells. To explore the potential impact of these lncRNAs and mRNAs on related traits, we selected cis-eQTLs from a previously published dataset. These cis-eQTLs were then compared with published Genome-Wide Association Studies (GWASs) available in the IEU OpenGWAS database. A total of 891 cis-eQTLs were linked to the expression of 295 lncRNAs and mRNAs after clumping SNPs in linkage disequilibrium (r²<0.001). The mean F statistics of the SNPs used as instruments ranged from 29.78981 to 6,233.6446, indicating robust instruments.

Subsequently, we aimed to estimate the causal association that these lncRNAs and mRNAs might have on traits identified in the IEU OpenGWAS database. A two-sample MR test was conducted (Figure 3A). Employing the MR algorithm through Inverse Variance Weighted and Wald Ratio methods, we identified 20 lncRNAs and mRNAs that were causally associated with lung cancer, with 6 of them recognized as risk factors (SPDYC, MFAP3L, NET1, LINC01535, DLGAP5, MAJIN) in the ebi-a-GCST90018875 project (Figure 3B). Notably, NET1 exhibited a significant causal effect on lung cancer in both the ebi-a-GCST90018875 and ieu-b-4955 projects. Our analysis revealed no evidence of heterogeneity (Q=4.42, P=0.35), pleiotropy (P=0.17), or reverse causality (P=0.31). Colocalization analyses provided partial support for this causal effect (PP.H3 =0.31; PP.H4 =0.34).

Figure 3 Overview of mendelian randomization. Workflow of mendelian randomization integrating GWAS, and eQTL data Identified NET1 Pleiotropically Associated With lung cancer onset (A). The volcano plot displays OR and P value of “Wald ratio” and “Inverse variance weighted” method, the size of dots present pve value (B). GWAS, genome-wide association study; eQTL, Expression Quantitative Trait Locus; NET1, neuroepithelial cell transforming 1; pve, proportion of variance explained.

In summary, the robustness of our findings is supported by the absence of heterogeneity, pleiotropy, and reverse causality, along with partial colocalization evidence. These results underscore the critical role of NET1 in lung cancer, justifying its selection for further investigation in the context of NSCLC.

Influence of NET1 on cell proliferation in NSCLC

Given the evidence from our MR analysis linking NET1 to NSCLC, we further investigated its role in promoting the proliferation of NSCLC cells. This exploration aimed to deepen our understanding of NET1’s involvement in the pathogenesis and progression of this cancer type. To authenticate the functional role of NET1 in cell lines, HEK293T and A549 cells underwent quantitative real-time polymerase chain reaction (qRT-PCR) and 3-(4, 5-dimethylthiazol-2-yl)-2, 5-diphenyltetrazolium bromide (MTT) assays. The qRT-PCR analysis demonstrated a down regulation in NET1 mRNA levels upon in both shNET1 HEK293T and shNET1 A549 cells (Figure 4A). Further corroborating these findings, the MTT assay illustrated decreased cell proliferation following shNET1 in both cell lines (Figure 4B).

Figure 4 Impact of NET1 on cell proliferation in lung cancer. The qRT-PCR analysis reveals the mRNA levels of NET1 following knock down NET1 in HEK293T and A459 cells (A). MTT assay shows that si_NET1 in HEK293T and A549 cells significantly decreases cell proliferation (B). A violin plot illustrates the expression differences of NET1 between normal and tumor tissues across LUSC and LUAD in TCGA and GTEx project (C). Kaplan-Meier analysis demonstrates that high expression of NET1 is associated with adverse overall survival (D) and disease-specific survival (E) in TCGA-LUSC and TCGA-LUAD. The statistical significance denoted as **, P<0.001; ***, P<0.0001; ****, P<0.00001. NET1, neuroepithelial cell transforming 1; TCGA, The Cancer Genome Atlas; LUSC, lung squamous cell carcinoma; LUAD, lung adenocarcinoma; OS, overall survival; DSS, disease-specific survival; qRT-PCR, quantitative real-time polymerase chain reaction; MTT, 3-(4,5-dimethylthiazol-2-yl)-2,5-diphenyltetrazolium bromide; GTEx, Genotype-Tissue Expression.

Elucidating the role of NET1 in clinical characteristics and prognosis in NSCLC cohorts

In the combined cohorts of TCGA and GTEx, a meticulous examination was undertaken to discern the subtle differences in NET1 expression between healthy and tumorous tissues in both LUSC and LUAD. Notably, the analysis revealed a marked increase in NET1 expression within the tumor milieu across both cohorts, highlighting the potential significance of NET1 as a distinctive marker in delineating the molecular landscape of LUSC and LUAD (Figure 4C). Clinical characteristics of high and low NET1 expression groups were analyzed TCGA-LUAD (Table S1) and TCGA-LUAD (Table S2). Our analysis did not reveal any significant correlations between NET1 expression levels and the clinical characteristics evaluated in these cohorts. Subsequent Kaplan-Meier analysis unveiled that elevated expression of NET1 is associated with unfavorable overall survival (OS; Figure 4D) and disease-specific survival (DSS; Figure 4E) in TCGA-LUSC and TCGA-LUAD.

Elevated NET1 expression in NSCLC cancerous tumors

In our analysis of 12 paired NSCLC cancerous tumors and corresponding resection margins, we observed a significant elevation of NET1 expression in the cancerous tissues at both the protein and mRNA levels. Immunohistochemical staining (Figure 5A) showed consistently stronger NET1 expression in cancerous tumors compared to resection margins. Quantification of staining scores (Figure 5B) further confirmed this, with higher scores in cancerous tissues. Similarly, NET1 mRNA expression was significantly higher in cancerous tumors (Figure 5C). This consistent upregulation of NET1 suggests its potential involvement in NSCLC pathogenesis.

Figure 5 Comparative analysis of NET1 expression in NSCLC cancerous tumors and resection margins. Representative immunohistochemical images showing NET1 expression in 12 paired NSCLC cancerous tumors and corresponding resection margins (A). Dot plot displaying NET1 immunohistochemical staining scores in NSCLC cancerous tumors and resection margins, with scores determined from three randomly selected fields at 40x magnification for each sample (B). Dot-line plot illustrating the differences in NET1 mRNA levels between NSCLC cancerous tumors and resection margins (C). The statistical significance denoted as ****, P<0.00001. NET1, neuroepithelial cell transforming 1; NSCLC, non-small cell lung cancer; IHC, immunohistochemistry.

Elucidating the role of NET1 in suppression of ferroptosis and proliferation in NSCLC

In this part of our investigation, we delved into the mechanistic relationship between NET1 overexpression and the proliferation of malignant epithelial cells. Through GSEA analysis of NET1+ malignant epithelial single-cell data, employing Kyoto Encyclopedia of Genes and Genomes (KEGG) terms, we discovered that the MAPK pathway emerges as the primary upregulated pathway within NET1+ malignant epithelial cells (Figure 6A).

Figure 6 Impact of NET1 on Ferroptosis inhibition in lung cancer. The dot plots reveal significant KEGG pathways identified via GSEA analysis, which are regulated differently between NET1-positive and NET1-negative malignant epithelial cells (A). The tSNE clustering of the 9,479 profiled malignant epithelial cells is color-coded to indicate enrichment in Ferroptosis signaling (B). Single-cell density plots depict the expression levels of NET1, and the co-expression clustering densities for cells expressing both NET1 and GPX4+, as well as NET1 and AIFM2+, respectively (C). The MDA assay indicates an increase in ferroptosis levels in shNET1 A549 cells (D). Furthermore, the MDA assay demonstrates that the addition of ferroptosis inhibitors (liproxstatin-1 and ferrostatin-1) to shNET1 A549 cells significantly reduces the ferroptosis response (E). The MTT assay reveals that in shNET1 A549 cells, the addition of ferroptosis inhibitors leads to a significant increase in cell proliferation (F). The statistical significance denoted as ****, P<0.00001. NET1, neuroepithelial cell transforming 1; tSNE, t-distributed stochastic neighbor embedding; MDA, malondialdehyde; KEGG, Kyoto Encyclopedia of Genes and Genomes; GSEA, gene set enrichment analysis.

Given recent evidence indicating a significant link between MAPK pathway activation and the suppression of ferroptosis in NSCLC and other tumor entities (31-33), we embarked on visualizing the enrichment levels of the ferroptosis pathway in single-cell malignant epithelial data (Figure 6B). This visualization revealed a correlation between high NET expression and low ferroptosis pathway activity in specific cell clusters. Notably, NET co-expression with ferroptosis inhibitory factors, such as GPX4 and AIFM2, was prevalent in cell clusters characterized by diminished levels of the ferroptosis pathway (Figure 6C). Furthermore, in shNET1 A549 cell lines, we observed a decrease in malondialdehyde (MDA) levels (Figure 6D). Subsequent reactive experiments demonstrated that the introduction of ferroptosis inhibitors (Lip-1 and Fer-1) significantly decreased the ferroptosis response in shNET1 A549 cells (Figure 6E). Additionally, the outcomes of our MTT [3-(4,5-dimethylthiazol-2-yl)-2,5-diphenyltetrazolium bromide] assay demonstrated that the application of ferroptosis inhibitors resulted in a significant increase in the proliferation rate of shNET1 A549 cells. This finding highlights the complex role of NET1 in the dynamics of cancer cell behavior (Figure 6F).

Exploring NET1’s influence on immune microenvironment in NSCLC

To explore the role of NET1 in the regulation of the NSCLC immune microenvironment, we assessed the correlation between NET1 expression and ESTIMATE immunological assessments in the TCGA-NSCLC cohort. Our analysis revealed a negative correlation between NET1 expression and the ImmuneScore, StromaScore, and ESTIMATEScore, suggesting that higher NET1 expression may be associated with a less favorable immune microenvironment in both TCGA-LUAD and TCGA-LUSC (Figure 7).

Figure 7 The correlation between NET1 expression and immunological assessments in TCGA-NSCLC. Dot plots showing the correlation between NET1 and ImmuneScore, StromaScore, and ESTIMATEScore in TCGA-LUAD and TCGA-LUSC. NET1, neuroepithelial cell transforming 1; TCGA-LUAD, The Cancer Genome Atlas-Lung Adenocarcinoma; TCGA-LUSC, The Cancer Genome Atlas-Lung Squamous Cell Carcinoma; NSCLC, non-small cell lung cancer.

Furthermore, we revealed the relationships between GSVA-derived abundances of 28 types of TILs and the expression of NET1. We found that activated T cells, follicular helper T cells, activated B cells, immature B cells, myeloid-derived suppressor cells (MDSC), macrophages, mast cells, and eosinophils were negatively correlated with the levels of NET1 (Rho <−0.1; P<0.05) in both TCGA-LUAD (Figure S1) and TCGA-LUSC (Figure S2).

Molecular docking analysis reveals robust affinity of candidate compounds for NET1 downregulation in NSCLC

As per CTD analysis, four potential compound candidates (doxorubicin; (+)-JQ1 compound; piroxicam and quercetin) were identified for manipulating NET1 downregulation in NSCLC. To assess the affinity of these candidates for their targets, we conducted molecular docking analysis. The binding poses and interactions between the nine compound candidates and NET1 were determined using Autodock Vina v.1.2.2, and the binding energy for each interaction was calculated (Figure 8). Results indicated that each compound candidate formed visible hydrogen bonds and strong electrostatic interactions with its protein targets. Additionally, the hydrophobic pockets of each target were effectively occupied by the nine candidate compounds. In the case of kinase insert domain receptor (KDR), all nine candidates exhibited low binding energy values (−8.353, −7.555, −7.382 and −8.231 kcal/mol), suggesting highly stable binding, in which doxorubicin, piroxicam and quercetin as potential drug candidates for NSCLC treatment.

Figure 8 Molecular docking between small molecule and NET1 protein. Molecular docking was employed to elucidate the binding mode of 4 significant small molecule compounds to the target protein NET1. A comprehensive depiction, following the style of the Molecule showcases a cartoon representation. The overlay of crystal structures of these small molecule compounds with their respective targets is meticulously illustrated, providing insights into the intricate interactions at the molecular level. NET1, neuroepithelial cell transforming 1.

Discussion

In this study, we integrated bioinformatics and experimental methods to delineate NET1’s critical function in NSCLC progression, establishing its therapeutic potential. Utilizing scRNA-seq and MR, complemented by experimental validation, we identified NET1’s roles in cellular proliferation, ferroptosis resistance and negative immune activity correlation. Our molecular docking further revealed potential NET1 inhibitors, offering new directions for targeted NSCLC therapy. This research highlights the synergy of computational and experimental approaches in advancing cancer understanding and precision treatment development.

As a Ras homolog family member A (RhoA) effector, NET1 classical function in regulating cytoskeletal dynamics suggests influence on cell proliferation by altering the cell skeleton, potentially contributing to malignant tumor growth (34). NET1’s involvement in cell migration and invasion indicates a role in the metastatic behavior of cancer cells, promoting invasive characteristics (35). Fang et al. revealed elevated expression of NET1 in NSCLC tissues, and as demonstrated through mRNA and protein analyses, NET1 is significantly associated with tumorigenesis and various pathological characteristics, highlighting its potential as a biomarker and therapeutic target in NSCLC (36). Ding et al. revealed that the miR-22/NET1 axis plays a crucial role in regulating NSCLC growth and migration, highlighting its potential as a promising therapeutic target for NSCLC treatment (37).

In our analysis, NET1 overexpression has been linked to enhanced proliferation and metastasis, likely through the MAPK signaling pathway. This aligns with previous studies that connect MAPK activation in NSCLC with resistance to ferroptosis (32,38), a regulated form of cell death driven by iron-dependent lipid peroxidation, playing a pivotal role in diverse pathological states such as cancer, neurodegenerative disorders, and ischemic injuries to organs (39). NET1, traditionally recognized for its role in cytoskeletal reconfiguration and oncogenic activity, also emerges as a key regulator of ferroptosis. It may inhibit ferroptosis by enhancing the expression of resistant factors like GPX4, thereby aiding tumor survival under stress. Although studies like Yang et al. (40) have shown NET1’s involvement in ferroptosis in other cancers, its specific role in NSCLC remains underexplored. Further research is needed to elucidate the exact pathways through which NET1 modulates ferroptosis, potentially opening new avenues for targeted therapies.

From a pathobiological perspective, NET1’s role extends beyond merely promoting tumor cell proliferation. Our study uncovered a significant negative correlation between NET1 expression and immune activity, evidenced by lower ESTIMATE immune scores and decreased TIL enrichment in tumors with elevated NET1 levels. These findings suggest that high NET1 expression may contribute to the creation of a desert cytotoxic immune microenvironment in NSCLC, which is typically resistant to immune-based therapies. This observation aligns with the study by Zhu et al. (41), which demonstrated that NET1 impairs CD8+ T-TIL function, further supporting the notion that NET1 plays a critical role in modulating the tumor immune microenvironment. Given this, targeting NET1 presents a novel therapeutic strategy with the potential to enhance the efficacy of immunotherapies by restoring immune activity and increasing TIL infiltration.

Future drug development targeting NET1 in NSCLC should focus on several strategic goals. First, the development of specific NET1 inhibitors could directly counteract its roles in tumor progression, ferroptosis resistance, and immune suppression. Combining these inhibitors with existing immunotherapies could enhance treatment efficacy by increasing TIL infiltration and overcoming immune resistance. Additionally, establishing NET1 as a biomarker would enable more personalized treatment approaches, identifying patients who could benefit most from NET1-targeted therapies. Further research into NET1’s involvement in ferroptosis and its underlying mechanisms could uncover additional therapeutic opportunities, offering new strategies to improve outcomes for NSCLC patients.

The selection of doxorubicin, piroxicam and quercetin as potential drug candidates for NSCLC treatment based on CTD analysis raises intriguing possibilities for therapeutic intervention. Doxorubicin, a well-established chemotherapy agent, may contribute to inhibiting tumor growth by disrupting DNA and RNA synthesis (42). Piroxicam, a nonsteroidal anti-inflammatory drug (NSAID), may be linked to a modest reduction in NSCLC risk, with non-aspirin NSAIDs showing a protective effect in individuals taking for at least 1 year (43). Quercetin, a natural flavonoid, exhibits promising anti-cancer properties, suggesting its potential role in suppressing NSCLC progression (44). While its direct therapeutic role remains unclear, minimizing exposure to environmental carcinogens could be a crucial preventive strategy. The subsequent molecular docking analysis provided insights into the favorable interactions between these drugs and NET1, particularly highlighting stable binding to KDR. These findings suggest a potential mechanism for manipulating NET1 downregulation in NSCLC, presenting a foundation for further experimental validation.

Our study provides valuable insights into the role of NET1 in NSCLC by integrating bioinformatics analyses, in vitro functional assays, and molecular docking to identify potential drug candidates. However, there are several limitations to consider. Firstly, while we have focused on the cellular and molecular mechanisms within NSCLC, the lack of in vivo experiments restricts our understanding of how NET1 influences the broader tumor microenvironment and systemic responses. Although in vivo studies in other cancers have highlighted NET1’s involvement in tumor progression, similar validation in NSCLC is necessary to corroborate and expand upon our findings. Additionally, the identification of doxorubicin, piroxicam, and quercetin as potential therapeutic agents requires further validation through extensive in vitro and in vivo experiments to confirm their efficacy and safety in treating NSCLC. It is also important to investigate the potential interactions between these drugs to optimize combination therapies. Another limitation lies in our use of whole blood datasets for the cis-eQTL analysis, which may not fully capture the tissue-specific gene expression patterns relevant to NSCLC. Future research should focus on utilizing organ-specific datasets to gain a more detailed understanding of NET1’s role in NSCLC and uncover additional therapeutic targets.

In the future, our research will focus on further validating the therapeutic potential of the identified NET1 inhibitors through rigorous in vitro and in vivo experiments. We aim to explore the molecular mechanisms underlying NET1’s involvement in ferroptosis and its impact on the immune microenvironment in greater detail. Additionally, we plan to investigate the potential synergistic effects of combining NET1 inhibitors with existing immunotherapies, with the goal of developing more effective treatment strategies for NSCLC.

Conclusions

The guiding significance of this study lies in its comprehensive approach, combining bioinformatics, experimental validation, and molecular docking to uncover the multifaceted roles of NET1 in NSCLC progression. This research not only broadens our understanding of NET1’s functions but also paves the way for the development of targeted therapies that could significantly improve patient outcomes in the context of precision medicine.

Acknowledgments

We extend our profound appreciation to the contributors of the E-MTAB-6149 and PRJNA482529 databases for their generous sharing of single-cell sequencing data. Their invaluable contributions were pivotal in facilitating our comprehensive analysis.

Funding: None.

Footnote

Reporting Checklist: The authors have completed the MDAR reporting checklist. Available at https://tcr.amegroups.com/article/view/10.21037/tcr-24-587/rc

Data Sharing Statement: Available at https://tcr.amegroups.com/article/view/10.21037/tcr-24-587/dss

Peer Review File: Available at https://tcr.amegroups.com/article/view/10.21037/tcr-24-587/prf

Conflicts of Interest: All authors have completed the ICMJE uniform disclosure form (available at https://tcr.amegroups.com/article/view/10.21037/tcr-24-587/coif). The authors have no conflicts of interest to declare.

Ethical Statement: The authors are accountable for all aspects of the work in ensuring that questions related to the accuracy or integrity of any part of the work are appropriately investigated and resolved. This study was conducted in accordance with the Declaration of Helsinki (as revised in 2013). In this study, the research involving 12 pairs of FFPE specimens received ethical approval from the Ethics Committee of The Second Affiliated Hospital, Zhejiang University School of Medicine (ethical approval No. IR2024334). All procedures complied with relevant guidelines and regulations to ensure the ethical handling of human tissue samples. As the research was retrospective in nature and utilized archived specimens, the requirement for informed consent was waived by the Ethics Committee of The Second Affiliated Hospital, Zhejiang University School of Medicine in accordance with institutional guidelines. This waiver was granted as part of the ethical approval for the study. As the other aspect of the research is grounded on the utilization of publicly available datasets, thereby eliminating potential ethical dilemmas and conflicts of interest commonly associated with data acquisition in scientific studies.

Open Access Statement: This is an Open Access article distributed in accordance with the Creative Commons Attribution-NonCommercial-NoDerivs 4.0 International License (CC BY-NC-ND 4.0), which permits the non-commercial replication and distribution of the article with the strict proviso that no changes or edits are made and the original work is properly cited (including links to both the formal publication through the relevant DOI and the license). See: https://creativecommons.org/licenses/by-nc-nd/4.0/.

References

1. Bade BC, Dela Cruz CS. Lung Cancer 2020: Epidemiology, Etiology, and Prevention. Clin Chest Med 2020;41:1-24. [Crossref] [PubMed]
2. Abu Rous F, Singhi EK, Sridhar A, et al. Lung Cancer Treatment Advances in 2022. Cancer Invest 2023;41:12-24. [Crossref] [PubMed]
3. Kondo KK, Rahman B, Ayers CK, et al. Lung cancer diagnosis and mortality beyond 15 years since quit in individuals with a 20+ pack-year history: A systematic review. CA Cancer J Clin 2024;74:84-114. [Crossref] [PubMed]
4. Dela Cruz CS, Tanoue LT, Matthay RA. Lung cancer: epidemiology, etiology, and prevention. Clin Chest Med 2011;32:605-44. [Crossref] [PubMed]
5. Huang J, Deng Y, Tin MS, et al. Distribution, Risk Factors, and Temporal Trends for Lung Cancer Incidence and Mortality: A Global Analysis. Chest 2022;161:1101-11. [Crossref] [PubMed]
6. Barta JA, Powell CA, Wisnivesky JP. Global Epidemiology of Lung Cancer. Ann Glob Health 2019;85:8. [Crossref] [PubMed]
7. Wang M, Herbst RS, Boshoff C. Toward personalized treatment approaches for non-small-cell lung cancer. Nat Med 2021;27:1345-56. [Crossref] [PubMed]
8. Zugazagoitia J, Paz-Ares L. Extensive-Stage Small-Cell Lung Cancer: First-Line and Second-Line Treatment Options. J Clin Oncol 2022;40:671-80. [Crossref] [PubMed]
9. Lahiri A, Maji A, Potdar PD, et al. Lung cancer immunotherapy: progress, pitfalls, and promises. Mol Cancer 2023;22:40. [Crossref] [PubMed]
10. Hirsch FR, Scagliotti GV, Mulshine JL, et al. Lung cancer: current therapies and new targeted treatments. Lancet 2017;389:299-311. [Crossref] [PubMed]
11. Sathish G, Monavarshini LK, Sundaram K, et al. Immunotherapy for lung cancer. Pathol Res Pract 2024;254:155104. [Crossref] [PubMed]
12. Garassino MC, Torri V. Neoadjuvant or Perioperative Approach in Lung Cancer. N Engl J Med 2024;390:1816-8. [Crossref] [PubMed]
13. Schabath MB, Cote ML. Cancer Progress and Priorities: Lung Cancer. Cancer Epidemiol Biomarkers Prev 2019;28:1563-79. [Crossref] [PubMed]
14. Herbst RS, Morgensztern D, Boshoff C. The biology and management of non-small cell lung cancer. Nature 2018;553:446-54. [Crossref] [PubMed]
15. Herrera-Juárez M, Serrano-Gómez C, Bote-de-Cabo H, et al. Targeted therapy for lung cancer: Beyond EGFR and ALK. Cancer 2023;129:1803-20. [Crossref] [PubMed]
16. Schneider JL, Lin JJ, Shaw AT. ALK-positive lung cancer: a moving target. Nat Cancer 2023;4:330-43. [Crossref] [PubMed]
17. Skoulidis F, Heymach JV. Co-occurring genomic alterations in non-small-cell lung cancer biology and therapy. Nat Rev Cancer 2019;19:495-509. [Crossref] [PubMed]
18. Ciuffreda L, Incani UC, Steelman LS, et al. Signaling intermediates (MAPK and PI3K) as therapeutic targets in NSCLC. Curr Pharm Des 2014;20:3944-57. [Crossref] [PubMed]
19. Rodriguez-Canales J, Parra-Cuentas E, Wistuba II. Diagnosis and Molecular Classification of Lung Cancer. Cancer Treat Res 2016;170:25-46. [Crossref] [PubMed]
20. Duma N, Santana-Davila R, Molina JR. Non-Small Cell Lung Cancer: Epidemiology, Screening, Diagnosis, and Treatment. Mayo Clin Proc 2019;94:1623-40. [Crossref] [PubMed]
21. Didelez V, Sheehan N. Mendelian randomization as an instrumental variable approach to causal inference. Stat Methods Med Res 2007;16:309-30. [Crossref] [PubMed]
22. Zhou W, Liu G, Hung RJ, et al. Causal relationships between body mass index, smoking and lung cancer: Univariable and multivariable Mendelian randomization. Int J Cancer 2021;148:1077-86. [Crossref] [PubMed]
23. Burgess S, Scott RA, Timpson NJ, et al. Using published data in Mendelian randomization: a blueprint for efficient identification of causal risk factors. Eur J Epidemiol 2015;30:543-52. [Crossref] [PubMed]
24. Lawlor DA, Harbord RM, Sterne JA, et al. Mendelian randomization: using genes as instruments for making causal inferences in epidemiology. Stat Med 2008;27:1133-63. [Crossref] [PubMed]
25. Võsa U, Claringbould A, Westra HJ, et al. Large-scale cis- and trans-eQTL analyses identify thousands of genetic loci and polygenic scores that regulate blood gene expression. Nat Genet 2021;53:1300-10. [Crossref] [PubMed]
26. Wu T, Hu E, Xu S, et al. clusterProfiler 4.0: A universal enrichment tool for interpreting omics data. Innovation (Camb) 2021;2:100141. [Crossref] [PubMed]
27. Charoentong P, Finotello F, Angelova M, et al. Pan-cancer Immunogenomic Analyses Reveal Genotype-Immunophenotype Relationships and Predictors of Response to Checkpoint Blockade. Cell Rep 2017;18:248-62. [Crossref] [PubMed]
28. Hänzelmann S, Castelo R, Guinney J. GSVA: gene set variation analysis for microarray and RNA-seq data. BMC Bioinformatics 2013;14:7. [Crossref] [PubMed]
29. Morris GM, Huey R, Olson AJ. Using AutoDock for ligand-receptor docking. Curr Protoc Bioinformatics 2008;Chapter 8:Unit 8.14.
30. Wang Y, Bryant SH, Cheng T, et al. PubChem BioAssay: 2017 update. Nucleic Acids Res 2017;45:D955-63. [Crossref] [PubMed]
31. Liu X, Hai Y, Dong J, et al. Realgar induced KRAS mutation lung cancer cell death via KRAS/Raf/MAPK mediates ferroptosis. Int J Oncol 2022;61:157. [Crossref] [PubMed]
32. Chen W, Yang W, Zhang C, et al. Modulation of the p38 MAPK Pathway by Anisomycin Promotes Ferroptosis of Hepatocellular Carcinoma through Phosphorylation of H3S10. Oxid Med Cell Longev 2022;2022:6986445. [Crossref] [PubMed]
33. Ren Z, Hu M, Wang Z, et al. Ferroptosis-Related Genes in Lung Adenocarcinoma: Prognostic Signature and Immune, Drug Resistance, Mutation Analysis. Front Genet 2021;12:672904. [Crossref] [PubMed]
34. Chan AM, Takai S, Yamada K, et al. Isolation of a novel oncogene, NET1, from neuroepithelioma cells by expression cDNA cloning. Oncogene 1996;12:1259-66. [PubMed]
35. Menon S, Oh W, Carr HS, et al. Rho GTPase-independent regulation of mitotic progression by the RhoGEF Net1. Mol Biol Cell 2013;24:2655-67. [Crossref] [PubMed]
36. Fang L, Zhu J, Ma Y, et al. Neuroepithelial transforming gene 1 functions as a potential prognostic marker for patients with non-small cell lung cancer. Mol Med Rep 2015;12:7439-46. [Crossref] [PubMed]
37. Ding S, Huang H, Xu Y, et al. Neuroepithelial Cell Transforming Gene 1 Acts as an Oncogene and Is Mediated by miR-22 in Human Non-Small-Cell Lung Cancer. Biomed Res Int 2020;2020:1648419. [Crossref] [PubMed]
38. Müller F, Lim JKM, Bebber CM, et al. Elevated FSP1 protects KRAS-mutated cells from ferroptosis during tumor initiation. Cell Death Differ 2023;30:442-56. [Crossref] [PubMed]
39. Rochette L, Dogon G, Rigal E, et al. Lipid Peroxidation and Iron Metabolism: Two Corner Stones in the Homeostasis Control of Ferroptosis. Int J Mol Sci 2022;24:449. [Crossref] [PubMed]
40. Yang X, Li Y, Zhang Y, et al. Circ_0000745 promotes acute lymphoblastic leukemia progression through mediating miR-494-3p/NET1 axis. Hematology 2022;27:11-22. [Crossref] [PubMed]
41. Zhu P, Liu Z, Huang H, et al. MiRNA505/NET1 Axis Acts as a CD8(+) T-TIL Regulator in Non-Small Cell Lung Cancer. Onco Targets Ther 2020;13:9785-95. [Crossref] [PubMed]
42. Kciuk M, Gielecińska A, Mujwar S, et al. Doxorubicin-An Agent with Multiple Mechanisms of Anticancer Activity. Cells 2023;12:659. [Crossref] [PubMed]
43. Hernández-Díaz S, García Rodríguez LA. Nonsteroidal anti-inflammatory drugs and risk of lung cancer. Int J Cancer 2007;120:1565-72. [Crossref] [PubMed]
44. Lotfi N, Yousefi Z, Golabi M, et al. The potential anti-cancer effects of quercetin on blood, prostate and lung cancers: An update. Front Immunol 2023;14:1077531. [Crossref] [PubMed]