Mapping the function of EF-hand domain-containing protein 2 and determining its clinical relevance in non-small-cell lung cancer through single-cell transcriptomics.

INTRODUCTION
Lung cancer remains as one of the cancers with high mortality rate worldwide, with non-small-cell lung cancer (NSCLC) accounting for about 85% of all lung cancer cases.[1] For advanced NSCLC without identified driver-gene mutations, immunotherapy, particularly through immune checkpoint blockade, is a primary treatment modality.[2,3] Neoadjuvant immunotherapy, which is administered preoperatively, emerges as a highly promising strategy.[4,5] However, the average major pathologic response rate (MPR, ~32%) for novel adjuvant anti-programmed cell death protein 1/programmed death-ligand 1 (PD-1/PDL1) immunotherapies has not been satisfactory in clinical studies; as such, a significant proportion of patients either do not respond to the therapy or develop resistance to it, and the situation for anti-PD-1/PD-L1 immunotherapies remains challenging.[6]
The tumor microenvironment (TME) is an intricate network of cancer cells, immune cells, stromal cells, and other cellular and non-cellular elements.[7] These components interact dynamically and govern disease progression and treatment response. An in-depth understanding of this ecosystem is crucial, given the highly individualized treatment responses observed in cancer patients.[8] This phenomenon underscores the need for a detailed characterization of the TME, extending beyond the current clinical approaches that predominantly emphasize somatic mutations.
The TME plays a critical role in tumorigenesis, progression, metastasis, and drug resistance across various cancers.[9-11] Immunotherapy has the potential to reshape the TME and influence therapeutic outcomes.[12] In NSCLC, the heterogeneity of the TME considerable affects tumor progression and response to treatment. EF-hand domain-containing protein 2 (EFHD2) expression has been linked to the modulation of key molecular functions within the TME, impacting tumor growth and immune cell interactions. The complexity of the TME underscores the need for a deep understanding of how EFHD2 influences NSCLC biology and therapeutic responses.
Single-cell RNA sequencing (scRNA-seq) allows detailed and high-resolution analysis of the complex cellular composition of TME; it is a novel technological tool that contributes to the understanding of TME.[13] This technique can be used to identify common features and key distinctions among various immune cell subpopulations in the TIME, which is an important aspect of resolving TME variants.
Tumor-associated macrophages (TAMs) within the TME constitute a critical subset of myeloid cells. Their prognostic value extends to various neoplasms, and their interaction with immune checkpoint inhibitors (ICIs) underscores their significance in therapeutic efficacy. TAMs have garnered substantial attention in contemporary oncological research. This emerging focus aims to unravel their multifaceted roles and exploit their potential in enhancing cancer treatment paradigms.[14] A study employing bulk RNA sequencing characterized the genetic and transcriptional profiles of 305 East Asian patients with lung adenocarcinoma (LUAD); however, it did not characterize tumor cell types due to the lack of single-cell resolution data, thereby leaving unanswered questions regarding cell type characterization and tumor volume. Integration of scRNA-seq results from a currently limited number of representative tumors with a large amount of data from a large cohort could elucidate differences between the two major subtypes of NSCLC. Understanding these differences at the single-cell level could enhance our comprehension of lung cancer pathogenesis and potentially lead to improved personalized treatment strategies.
EFHD2 is a highly conserved, 27 kDa calcium-binding protein found in the lipid rafts of membranes and expressed mainly in the immune system and central nervous system.[15] EFHD2 is expressed in a wide range of cell types, including B cells, CD4+/CD8+ T cells, natural killer cells, and peripheral blood mononuclear cells in the brain and various immune cell types.[16] The protein plays a key role in immune cell activation and immune response regulation. In cancer, EFHD2 may mediate a high rate of cancer cell migration, which may be important to study as it leads to metastasis. EFHD2 accelerates cell migration by activating the small GTPase of the Rho family in mouse B16F10 melanoma cells, indicating that EFHD2 is involved in accelerated cancer metastasis.[17] Fan et al.[18] showed that EFHD2 promotes chemoresistance in NSCLC through the NADPH oxidase 4 (NOX4)-reactive oxygen species (ROS)-ATP-binding cassette subfamily C member 1 (ABCC1) axis, and that targeting EFHD2 (possibly with ibuprofen) could enhance cisplatin efficacy and improve adjuvant chemotherapy in lung cancer. The relationship between EFHD2 and metastasis in lung cancer remains poorly understood, and the mechanisms by which EFHD2 contributes to NSCLC progression have not yet been fully explored.

MATERIAL AND METHODS

Patient information
Clinical samples of patients with NSCLC were collected from primary lung tumors between November 2023 and June 2024 from the Fifth Affiliated Hospital of Guangzhou Medical University by diagnostic procedures such as biopsy or bronchoscopy. Tumor tissues and adjacent non-tumor tissues were collected from 11 patients with NSCLC. Clinical details, including patient age, gender, pathological subtype, stage, and medication history, are presented in Table 1. The study did not involve any coercive interventions and was conducted in accordance with the ethical principles set out in the Declaration of Helsinki.[19] Before sampling, subjects were clearly informed about the purpose of the study and the use of the samples. All patients who participated in the study confirmed and signed the written informed consent. Ethical approval for this study was obtained from the Ethics Committee of The Fifth Affiliated Hospital of Guangzhou Medical University (KY01-2024-0819).
The inclusion criteria were as follows: (1) Patients with a confirmed diagnosis of NSCLC, which was verified through pathology or imaging; (2) individuals aged between 18 and 75 years, irrespective of gender; (3) patients with anticipated survival period exceeding 6 months; (4) patients without severe complications and had a generally good physical condition (ECOG score of 0–2); (5) patients with satisfactory cardiac, pulmonary, and hepatic functions, making them capable of tolerating the sample collection procedure; and (6) patients who were willing to participate in the study and sign an informed consent form.
The exclusion criteria were as follows: (1) Presence of other malignant tumors; (2) recent history of radiotherapy, chemotherapy, or targeted therapy, except when the treatment has been completed and recovery has occurred; (3) pregnant women, breastfeeding mothers, or those planning to conceive during the study period; (4) compromised immune function or autoimmune disorders; (5) severe systemic diseases, such as cardiovascular, hepatic, or renal insufficiency, or other serious complications that make the study intervention intolerable; (6) psychiatric or neurological conditions that impair the patient’s ability to comprehend or adhere to the study requirements; and (7) medical conditions that were incompatible with the study requirements.

Bioinformatics Analysis of TME
scRNA-seq datasets for tumor samples were retrieved from the ArrayExpress database (https://www.ebi.ac.uk/arrayexpress/), under accession numbers GSM8349745_ LCA_1_TUMOR, GSM8349746_LCA_2_TUMOR, GSM8349747_LCA_3_TUMOR, and GSM8349748_ LCA_4_TUMOR. Raw data were processed using the Seurat package (v4.0, New York Genome Center, USA) in R, which included quality control, normalization, and dataset integration.[20] Cells with fewer than 200 detected genes or more than 10% mitochondrial genes were excluded from the analysis. Normalization was performed using LogNormalize method, followed by scaling of the data for subsequent analyses.
Cells with fewer than 200 detected genes or more than 10% mitochondrial gene content were excluded from the analysis to remove low-quality cells and those with potential technical artifacts. Integration was performed using the Seurat integration pipeline to address potential batch effects across the datasets. Anchors across the different tumor samples were identified using the FindIntegrationAnchors function, and the datasets were merged using IntegrateData function.
Normalization was performed using LogNormalize, followed by data scaling. Doublets were detected and removed using DoubletFinder with a threshold of 0.3.[21] Unsupervised clustering was performed using the Louvain algorithm. Clusters were annotated based on well-known marker genes for specific cell types.
To explore the functional relevance of differentially expressed genes within the TME clusters, the sample results were analyzed for Gene Ontology (GO) and Kyoto Encyclopedia of Genomes (KEGG) pathway enrichment. These analyses were performed using the clusterProfiler software package (v4.0, developed by the Guangchuang Yu Lab at the School of Life Sciences, Sun Yat-sen University, Guangzhou, China).[22] The significance threshold was set at an adjusted P < 0.05, with multiple testing corrections applied using the BenjaminiHochberg method.[23]

Cell culture and treatments
Human normal bronchial epithelial cells (BEAS-2B, Procell, Wuhan, China) and NSCLC cell lines (A549, NCI-H1299, and HCC827, Procell, Wuhan, China) were used in this study. All cell lines were cultured in Dulbecco’s Modified Eagle Medium (11320033, DMEM, Thermo Fisher Scientific Inc., USA) supplemented with 10% fetal bovine serum (A5256801, Thermo Fisher Scientific Inc., China) and 1% penicillin–streptomycin. The culture mixtures were incubated in a humidified incubator at 37°C and 5% carbon dioxide. EFHD2 overexpression (OE) and knockdown (KD) were achieved using lentiviral vectors carrying either EFHD2 or EFHD2-targeting shRNA. Cell transfection was performed using Lipofectamine 3000 (L3000150, Invitrogen, China) following the manufacturer’s instructions. Janus kinase (JAK) inhibitor pacritinib (10 µM, SD4762-10mM, Beyotime, Shanghai, China) or agonist (5 µM, HY-146066, MedChemExpress, China) was added to the medium as directed. All cell lines were tested for mycoplasma contamination to ensure the accuracy and reproducibility of the results. Mycoplasma contamination was not detected in any of the cell lines. Verification that the cell lines were correct was carried out by short tandem repeat analysis.

Western Blot (WB) analysis
Cells were lysed in radioimmunoprecipitation assay (RIPA) buffer containing protease and phosphatase inhibitors. Protein concentrations were determined using a BCA protein assay kit (23227, Thermo Fisher Scientific Inc., China). Equal protein amounts (20 µg) were separated by 10% sodium dodecyl sulfate-polyacrylamide gel electrophoresis (SDS-PAGE), followed by transfer to polyvinylidene fluoride (PVDF) membranes (FFP24, Beyotime, Shanghai, China). The membranes were incubated with 5% non-fat milk for 1 h at room temperature to block non-specific binding. The membranes were then exposed overnight at 4°C to primary antibodies targeting EFHD2 (1:1000, ab24368, Abcam, China), JAK1 (1:1000, HY-P80196, MedChemExpress, China), phospho-JAK (p-JAK) (1:1000, ab138005, Abcam, China), signal transducers and activators of transcription 3 (STAT3) (1:1000, ab68153, Abcam, China), and glyceraldehyde 3-phosphate dehydrogenase (GAPDH) (1:1000, ab8245, Abcam, China) (loading control). After washing, the membranes were incubated for 1 h at room temperature with horseradish peroxidase (HRP)-conjugated secondary antibodies. Protein bands were visualized using an enhanced chemiluminescence (ECL) substrate (32209, Thermo Fisher Scientific Inc., China). The ChemiDoc MP Imaging System (Bio-Rad Laboratories, USA) was used for imaging, and quantification was performed with ImageJ software (1.3, National Institutes of Health, USA). The relative density of each protein was calculated by dividing the optical density of the protein by the optical density of the control (GAPDH).

Quantitative real-time polymerase chain reaction (qRT-PCR)
RNA was extracted from total cells or tissues using the TRIzol reagent (10296010, Invitrogen, China) following the manufacturer’s protocol. About 1 µg of RNA was reverse-transcribed into complementary DNA using the PrimeScript RT kit (RR037A, Takara, Japan). Subsequent PCR amplification was conducted on an ABI 7500 Real-Time PCR system using SYBR Green Master Mix (RR830S, Takara, Japan), with GAPDH as an internal control. The relative expression levels of the target genes were quantified using the 2-ΔΔCt method. The primer sequences used were as follows: EFHD2 Forward: 5’-AACGTGATGGACTCAGGAAG-3’, EFHD2 Reverse: 5’-GCTGATGTTCTGCGTGCTTA-3’; GAPDH Forward: 5’-AGAAGGCTGGGGCTCATTTG-3’, GAPDH Reverse: 5’-AGGGGCCATCCACAGTCTTC-3’. The primers were synthesized by Tsingke Biotechnology Co., Ltd. (China).

Cell proliferation assay (cell counting kit-8 [CCK-8])
Cells were seeded into 96-well plates, with each well containing 5000 cells. After the treatment, 10 µL of CCK-8 solution (341-07761, Dojindo, Japan) was introduced into each well, and the plates were incubated at 37 °C for 2 h. Absorbance was subsequently measured at 450 nm using a microplate reader (3897, Corning, USA).[24] All experiments were conducted in triplicate.

Apoptosis analysis by flow cytometry
The cells were first harvested and washed with phosphate buffered saline. The annexin vincristine/propidium iodide (V/PI) Apoptosis Detection Kit from BD Biosciences was used to stain the cells with annexin V-FITC and propidium iodide (PI), following the manufacturer’s instructions. The stained cells were subsequently analyzed using a BD FACSAria III flow cytometer (BD Biosciences, USA). Apoptosis rates were determined using FlowJo software.

Transwell migration and invasion assays
About 2 × 104 cells were suspended in serum-free medium and seeded into the upper chamber of a Transwell insert with an 8 µm pore size. A complete medium with 10% fetal bovine serum was added to the lower chamber. After 24 h, cells that migrated to the lower surface were fixed with methanol, stained with crystal violet, and counted under a microscope (Primo Vert/Primo Vert Monitor, Dayinmao, Beijing, China). This procedure closely mirrored the migration assay, except that the Transwell inserts were pre-coated with Matrigel (07-200-165, Corning, USA).[25]

Colony formation assay
A total of 500 cells were seeded into six-well plates (3335, Corning, USA) and cultured for 14 days. After the incubation period, the colonies that had formed were fixed using 4% paraformaldehyde. The colonies were stained with 0.1% crystal violet for 10–15 min. Finally, the colonies were manually counted under a microscope to ensure accurate quantification.

Immunohistochemistry (IHC)
Paraffin-embedded tissue sections, each 4 µm thick, were first deparaffinized and rehydrated. Citrate buffer (pH 6.0) was then used in a microwave oven to retrieve antigens. The sections were incubated overnight at 4°C with primary antibodies targeting EFHD2 (5 µg/mL, ab119119, Abcam, USA), CD8 (0.25 µg/mL, ab237709, Abcam, USA), CD3 (1: 150, ab16669, Abcam, USA), and CD68 (1: 1000, ab303565, Abcam, USA). After incubation, the sections were washed thoroughly and treated with HRP-conjugated secondary antibodies (1: 1000, ab6721, Abcam, USA). The results were visualized by developing sections with DAB substrate. The slides were then counterstained with hematoxylin, dehydrated, and mounted. Imaging was performed using a microscope (Olympus BX53, Olympus Corporation). The staining intensity and the proportion of positive cells were quantified using ImageJ software to ensure precise measurements.

Masson’s Trichrome staining
The tissue sections were first deparaffinized and then stained using the Masson’s trichrome Stain Kit (ab150686, Abcam). The sections were incubated in Bouin’s fluid at 60°C for 1 h. After incubation, Weigert’s Iron Hematoxylin was used to stain the sections for 5 min. The sections were then treated with Biebrich Scarlet/Acid Magenta solution for 15 min. A differentiation step was then carried out using phosphomolybdic/phosphotungstic acid. The sections were stained with aniline blue for 10 min. Finally, the sections were incubated in acetic acid, dehydrated, and mounted. The tissue sections were examined under a light microscope (Leica DM500, Leica Microsystems, Germany).

Statistical analysis
Bioinformatics analyses were conducted using R software (v4.1.0, R Foundation for Statistical Computing, Vienna, Austria), with results visualized using ggplot2. Multiple testing corrections were applied using the BenjaminiHochberg method for scRNA-seq differential expression and pathway enrichment analyses, with an adjusted P < 0.05 considered statistically significant. Statistical comparisons were performed using an unpaired, two-tailed student’s t-test in GraphPad Prism 8.1 (GraphPad Software, San Diego, CA, USA), with P < 0.05 deemed significant.

RESULTS

Online database analysis identifies TME in clinical samples
The scRNA-seq datasets were retrieved from the ArrayExpress database, including samples GSM8349745_LCA_1_ TUMOR, GSM8349746_LCA_2_TUMOR, GSM8349747_ LCA_3_TUMOR, and GSM8349748_LCA_4_TUMOR, to explore the cellular composition and transcriptional diversity of tumor samples and gain a deep understanding of the TME. Comparative analysis revealed that epithelial cells were the predominant cell group, and other cells present were endothelial cells, fibroblasts, macrophages, red blood cells, and a small proportion of stem cells, including mesenchymal stem cells (MSCs) and induced pluripotent stem cells (iPSCs) [Figure 1a]. To determine the abundance of each cell type, we carried out a quantitative analysis of the absolute cell numbers. Epithelial cells were identified as the most abundant cell group and significantly outnumbered other cell types. Keratinocytes and hepatocytes exhibited moderate abundance, while stem cell groups, such as mesenchymal stem cells and iPSCs, were relatively sparse [Figure 1b]. t-distributed Stochastic Neighbor Embedding (t-SNE) and Uniform Manifold Approximation and Projection (UMAP) analyses were conducted to further explore cellular diversity and transcriptional patterns. Distinct cell groups representing epithelial cells, macrophages, fibroblasts, endothelial cells, and red blood cells were observed. Epithelial cells formed a dense cluster, highlighting their transcriptional consistency and prevalence [Figure 1c and d]. Unsupervised clustering was used to annotate different transcriptional populations and identify 16 distinct clusters representing specific cell types within the TME. Epithelial cells remained the largest cluster, followed by fibroblasts, endothelial cells, and macrophages [Figure 1e and f].
The expression of EFHD2 was analyzed using t-SNE, revealing that EFHD2 expression was not uniformly distributed but was concentrated in specific groups within the TME. Areas of intense blue indicated regions with elevated EFHD2 expression, suggesting cell type specificity or transcriptional state influence [Figure 1g]. To investigate the impact of EFHD2 expression on the NSCLC tumor microenvironment, we conducted a GO enrichment analysis. The results highlighted the key molecular functions of EFHD2, including antigen binding, C-C chemokine receptor (CCR) interaction, and lipopeptide binding, which are relevant to immune modulation and tumor progression [Figure 1h]. The KEGG pathway analysis revealed significant enrichment in pathways associated with NSCLC, such as the renin-angiotensin system, which regulates tumor vascularization and fibrosis, and amoebiasis, a pathway potentially linked to cell motility and metastasis [Figure 1i]. The intricate interplay of these molecular roles underscores the multifaceted impact of EFHD2 on tumor biology.
These results highlight the intricate cellular and transcriptional landscape within the TME, indicating that functional diversity and interactions between cell groups may play key roles in tumor progression and therapeutic response. These findings provide a detailed framework for studying cell dynamics, molecular and functional dynamics within tumors, and their potential implications for targeted therapies.

Immunohistochemical analysis of EFHD2 and immune cell infiltration in clinical samples
IHC was meticulously conducted on clinical specimens to determine the expression levels of EFHD2, CD8, CD3, and CD68 proteins in NSCLC tumor tissues and adjacent non-tumor tissues. The staining intensity and density of these proteins were substantially elevated in tumor tissues compared with their non-tumor counterparts. This phenomenon underscores an augmented expression of EFHD2 and a heightened infiltration of immune cells in malignant tissues. This finding indicates the pivotal role of EFHD2 in the progression of NSCLC and its potential involvement in the interplay between immune cells and the TME [Figure 2].

EFHD2 expression promotes NSCLC progression
WB analysis was conducted to determine the expression levels of EFHD2 in normal lung epithelial cells (BEAS-2B) and various NSCLC cell lines (A549, HCC827, and NCI-H1299). The WB and RT-qPCR assays demonstrated markedly elevated EFHD2 levels in the lung cancer cell lines (A549, NCI-H1299, and HCC827) compared with BEAS-2B cells [Figure 3a and b]. These findings suggest a significant upregulation of EFHD2 in NSCLC, implicating its potential role in tumorigenesis. To elucidate the functional role of EFHD2 in NSCLC, we engineered overexpression constructs in A549 cells, which exhibited low EFHD2 expression, and knockdown constructs in NCI-H1299 cells, which had high EFHD2 expression. The WB and qPCR analyses confirmed the successful generation of EFHD2-OE and EFHD2-KD models [Figure 3c and d]. Subsequent analysis of the effect of EFHD2 on cancer cell malignancy showed that EFHD2 OE promoted cell proliferation, whereas EFHD2 KD inhibited cell growth, as indicated by CCK-8 assays [Figure 3e]. Flow cytometry analysis revealed a decrease in the number of apoptotic cells in EFHD2-overexpressing cells and an increase in apoptosis in EFHD2-KD cells [Figure 3f]. Transwell assays showed that EFHD2 OE in A549 cells significantly enhanced both migration and invasion capacities, while EFHD2 KD in NCI-H1299 cells led to a marked decrease in these behaviors, indicating a promotive role of EFHD2 in cell motility [Figure 3g]. Similarly, colony formation assays revealed that EFHD2 OE significantly increased the number of colonies formed by A549 cells (P < 0.05), whereas KD of EFHD2 in NCI-H1299 cells dramatically suppressed clonogenic potential [Figure 3h]. EFHD2 OE in A549 cells significantly increased the protein levels of JAK1, p-JAK1, STAT3, and phospo STAT3 (p-STAT3) (P < 0.01), suggesting activation of the JAK-STAT signaling pathway. Consistently, the p-STAT3/STAT3 ratio was also elevated, indicating enhanced STAT3 phosphorylation. In contrast, EFHD2 KD in NCI-H1299 cells led to a significant reduction in these protein levels and a lower p-STAT3/STAT3 ratio (P < 0.05), indicating pathway inhibition [Figure 3i]. KD KD Hence, EFHD2 regulates JAKSTAT signaling in NSCLC by enhancing the pathway on overexpression and suppressing it on knockdown, thereby influencing key cellular behavior, such as proliferation and apoptosis, which may impact tumor progression.

EFHD2 enhances NSCLC cell activity through the JAKSTAT pathway
In this study, the interaction between EFHD2 and JAK1 was verified by co-immunoprecipitation assay [Figure 4a]. EFHD2 specifically bound to JAK1 when FLAG-EFHD2 was co-expressed with Myc-JAK1, and this interaction was not seen in other combinations. As such, EFHD may play an important role in the JAK/STAT signaling pathway. This study further investigated the role of EFHD2 in promoting lung cancer cell proliferation. EFHD2 OE significantly enhanced proliferation in A549 cells, whereas EFHD2 KD markedly reduced proliferation in NCI-H1299 cells. The addition of a JAK inhibitor attenuated the proliferative effect of EFHD2-OE in A549 cells, while a JAK agonist restored the proliferative capacity in EFHD2-KD NCI-H1299 cells [Figure 4b]. Flow cytometry analysis of apoptosis rates showed similar results. EFHD2-OE in A549 cells led to decreased apoptosis, whereas EFHD2-KD in NCI-H1299 cells resulted in significantly increased apoptosis. The JAK inhibitor reversed the anti-apoptotic effect of EFHD2-OE, while the JAK agonist diminished the pro-apoptotic effect of EFHD2-KD [Figure 4c]. The Transwell assays revealed that EFHD2-OE enhanced the migratory and invasive capabilities of A549 cells, whereas EFHD2-KD mitigated these processes in NCI-H1299 cells. Moreover, the modulation of the JAK-STAT signaling cascade, through either inhibition or activation of JAK, successfully reversed these effects [Figure 4d]. EFHD2 OE in A549 cells promoted colony formation, which was significantly reduced with JAK inhibition. Conversely, colony formation was enhanced in NCI-H1299+EFHD2 KD cells treated with the JAK agonist [Figure 4e]. In the A549+EFHD2 OE group, the expression levels of p-JAK1 and p-STAT3 were significantly elevated, implying that EFHD2 may exert its effects by activating the JAK/STAT signaling pathway. The application of a JAK inhibitor did not block the activation of this pathway induced by EFHD2 OE. Conversely, silencing EFHD2 significantly decreased p-JAK1 and p-STAT3 levels, while treatment with a JAK agonist in the NCI-H1299+EFHD2 KD+JAK agonist group effectively restored the expression of these proteins [Figure 4f]. Hence, EFHD2 could regulate tumor cell behavior, such as proliferation, apoptosis, migration, and invasion, possibly through modulation of the JAK-STAT signaling cascade.

DISCUSSION
Global cancer statistics show that lung cancer remains the leading cause of numerous cancer-related deaths worldwide. Among various types of lung cancer, NSCLC accounts for approximately 85% of all lung cancer cases. NSCLC is further categorized into three main histological subtypes, namely, LUSC, LUAD, and large cell carcinoma. Given the high mortality rate among patients with NSCLC, identifying effective prognostic biomarkers will expedite the development of personalized medicine and increase patient survival.[26,27] The search for biomarkers capable of stratifying high-risk patients with NSCLC and those resistant to immunotherapy could guide the development of treatment strategies based on newly identified molecular targets, thereby improving patient outcomes. This study employed scRNA-seq to comprehensively characterize TME in NSCLC and reveal significant cellular and transcriptional heterogeneity. By identifying various cell populations and their functional roles, our findings enhance the understanding of tumor biology and the potential interactions among different cell types within the TME. Our analysis identified epithelial cells as the predominant cell group in tumor samples, along with endothelial cells, fibroblasts, macrophages, red blood cells, and a small proportion of stem cells, including MSCs and iPSCs. This cellular composition aligns with previous research. Hu et al.[28] noted significant differences in CD8+ T-cell populations between major pathologic response (MPR) and non-major pathologic response (NMPR) samples and reported elevated expression of TYMS, RRM2, and BIRC5 in NMPR samples; additionally, the NMPR samples showed increased infiltration of macrophages and tumor epithelial cells. The dominance of epithelial cells highlights their primary role in tumorigenesis, while the presence of stromal and immune cells underscores the complexity of the TME.[20]
Our study reveals a pronounced upregulation of EFHD2 in NSCLC, which plays a pivotal role in promoting tumor progression through the JAK-STAT signaling pathway. EFHD2 significantly augments essential malignant behavior in NSCLC cells, such as proliferation, migration, invasion, and survival. The knockdown of EFHD2 effectively diminishes these processes. Mechanistically, EFHD2 activation triggers the JAK-STAT pathway, a crucial mediator of tumor advancement, immune evasion, and inflammatory responses.
Swiprosin-1, commonly known as EFHD2, is a calcium-binding actin-binding protein with variable expression across diverse cell types. Its expression is significantly upregulated during the activation of immune, epithelial, and endothelial cells.[29] Overexpression of EFHD2 is linked to enhanced cell proliferation and reduced apoptosis in NSCLC cells. These observations align with previous studies that recognized EFHD2 as a crucial mediator of cell survival and proliferation in other malignancies, such as breast and colorectal cancers. Wu et al.[30] revealed that EFHD2 interacts with cofilin, inhibiting its phosphorylation, which, in turn, prevents the internalization of TNF receptor I and inhibits IEC cell apoptosis to protect the gut from inflammation. Fan et al.[18] found that EFHD2 stimulates the synthesis of NOX4, leading to increased intracellular levels of ROS. This increase triggers the membrane expression of ABCC1 to enhance drug efflux mechanisms and contribute to chemotherapy resistance in patients with lung adenocarcinoma. The present research suggests that EFHD2 is a key driver of tumorigenesis in NSCLC potentially by modulating critical oncogenic pathways, such as JAK-STAT.
The immune landscape of NSCLC is complex, and the involvement of EFHD2 in modulating the TME adds a novel dimension to its functional role. Immunohistochemical analyses revealed increased infiltration of CD8+ T cells, CD3+ lymphocytes, and CD68+ macrophages in EFHD2-overexpressing tumors. These findings align with the reports of Zhang et al.,[16] who suggested that EFHD2 may promote TCR-mediated T-cell activation and subsequent Th1 and Th17 differentiation by regulating the strength of TCR complex formation during the early stages of sepsis. Tu et al.[31] indicated that EFHD2 may be involved in LPS-stimulated macrophage migration. The increased immune cell infiltration observed in our study suggests that EFHD2 may influence the immune landscape to support tumor progression, possibly by fostering an immunosuppressive microenvironment.
Our research substantiates that the oncogenic properties of EFHD2 are, at least partially, facilitated through the JAKSTAT signaling cascade. This pathway is renowned for its regulatory functions in cellular proliferation, apoptosis, and immune responses within the oncogenic context.[13,32] Prior investigations established that the abnormal activation of the JAK-STAT pathway is a defining characteristic of NSCLC and correlates with an unfavorable prognosis. Cavazzoni et al.[33] found that PD-L1 overexpression regulates pathways involved in tumor inflammation and JAK-STAT signaling, with high PD-L1 expression modulating STAT signaling and inducing pro-angiogenic factor secretion in the presence of PBMCs. Hence, EFHD2 OE enhances JAK and STAT activation, while JAK inhibition attenuates the pro-tumorigenic effects of EFHD2. Conversely, JAK activation restores malignant phenotypes in EFHD2-KD cells, further confirming the functional link between EFHD2 and JAK-STAT signaling.
The clinical significance of EFHD2 as a biomarker or therapeutic target is highlighted by its robust correlation with poor prognosis in NSCLC. Inhibiting EFHD2 may present a novel therapeutic approach for NSCLC, particularly for patients with elevated levels of EFHD2 expression. Moreover, EFHD2 inhibition may enhance the efficacy of existing therapies, including JAK-STAT pathway inhibitors and immune checkpoint blockade therapies, by modulating the immune microenvironment and reducing tumor aggressiveness.
Despite its strengths, this study has limitations. First, the specific molecular interactions between EFHD2 and other regulators within the JAK-STAT pathway remain to be elucidated. Second, while our findings suggest that EFHD2 influences immune cell infiltration, the mechanisms underlying its immunomodulatory effects require further investigation. Third, the clinical relevance of EFHD2 as a therapeutic target should be validated in larger, more diverse patient cohorts.
Future research should elucidate the potential of EFHD2 inhibitors as a therapeutic strategy for NSCLC and examine their synergistic effects with current treatment modalities. In addition, the role of EFHD2 in other cancers and non-malignant diseases characterized by inflammation and fibrosis warrants further exploration.

SUMMARY
Our single-cell RNA sequencing analysis provides a detailed map of the complex cellular and transcriptional landscape within the TME of NSCLC. This study identifies EFHD2 as a crucial regulator of NSCLC progression through the JAK-STAT pathway. Its role in influencing tumor growth, immune cell infiltration, and fibrosis highlights its potential as a prognostic biomarker and as a therapeutic target. These findings add to the growing body of evidence for the oncogenic functions of EFHD2 and pave the way for future research into its clinical applications.