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

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.

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].

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Figure 1:

Cellular composition and functional insights into tumor microenvironment. (a) Bar plot illustrating the cellular composition across four tumor samples, highlighting the predominant presence of epithelial cells alongside varying proportions of endothelial cells, macrophages, fibroblasts, and other cell types. (b) Bar plot displaying the total number of each cell type, with epithelial cells constituting the most abundant population. (c and d) t-SNE and UMAP projections showing distinct clustering of transcriptionally unique cell populations, with epithelial cells forming the largest and densest clusters, indicative of their dominance in the tumor microenvironment. (e and f) Clustering results from t-SNE and UMAP analyses, identifying 16 distinct transcriptional clusters with annotated cell type identities. (g) t-SNE visualization of EFHD2 expression across cell populations, showing heterogeneity in its distribution. Functional enrichment analysis of identified cell clusters using GO (h) and KEGG (i), highlighting key biological functions and pathways, including antigen binding, CCR (C-C chemokine receptor) chemokine receptor activity, and renin-angiotensin system. t-SNE: t-distributed stochastic neighbor embedding, UMAP: Uniform manifold approximation and projection, EFHD2: EF-hand domain-containing protein 2, GO: Gene ontology, KEGG: Kyoto encyclopedia of genomes.

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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].

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Figure 2:

IHC of EFHD2 and immune cell markers in paraneoplastic and cancerous tissues. Representative IHC images comparing the expression of EFHD2, CD8 (cytotoxic T cells), CD3 (T lymphocytes), and CD68 (macrophages) in paraneoplastic tissues (left column) and cancerous tissues (right column). Data are expressed as mean ± SD. ^✶✶P < 0.01 compared with paraneoplastic tissues. The scale bar represents 0.1 mm. IHC: Immunohistochemistry, EFHD2: EF-hand domain-containing protein 2, SD: Standard deviation.

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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.

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Figure 3:

EFHD2 influences lung cancer cell proliferation, apoptosis, migration, invasion, and various signaling pathways. (a) WB analysis depicting EFHD2 protein expression in normal bronchial epithelial cells (BEAS-2B) and different lung cancer cell lines (A549, NCI-H1299, and HCC827). (b) Relative mRNA levels of EFHD2 in BEAS-2B and lung cancer cell lines, quantified through qRT-PCR. The data are presented as mean ± SD. (c) WB illustrating EFHD2 protein expression levels. (d) qRT-PCR analysis of EFHD2 mRNA expression. (e) CCK-8 assay assessing the impact of EFHD2 OE and KD on cell proliferation; data were presented as OD450 values over 72 h. (f) Flow cytometry analysis evaluating apoptosis rates. (g) Transwell migration and invasion assays. (h) Colony formation assay. (i) WB analysis of JAK and STAT protein expression, using GAPDH as a loading control. Data are expressed as mean ± SD. ^✶P < 0.05, ^✶✶P < 0.01, ^✶✶✶P < 0.001 compared with the A549 group; ^#P < 0.05, ^##P < 0.01, ^###P < 0.001 compared with the NCI-H1299 group. The scale bar represents 100 µm. EFHD2: EF-hand domain-containing protein 2, WB: Western blot, JAK: Janus Kinase, STAT: Signal transducers and activators of transcription, GAPDH: Glyceraldehyde 3-phosphate dehydrogenase, qRT-PCR: Quantitative real-time polymerase chain reaction, CCK-8: Cell counting kit 8, OE: Overexpression, KD: Knockdown, mRNA: Messenger RNA, OD450: Optical density at 450 nm, SD: Standard deviation.

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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.

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Figure 4:

EFHD2 regulates cell proliferation, migration, invasion, apoptosis, and JAK/STAT signaling. (a) Co-IP identified that FLAG-EFHD2 interacted with Myc-JAK1 in A549+EFHD2 OE and NCI-H1299+EFHD2 KD cells. (b) CCK-8 detected cell proliferation. Inhibition of JAK decreased the proliferation of A549+EFHD2 OE cells, while activation of JAK increased the proliferation of NCI-H1299+EFHD2 KD cells. (c) Flow cytometry to detect apoptosis. Inhibition of JAK increased the apoptosis of A549+EFHD2 OE cells, whereas JAK agonist enhanced the apoptosis of NCI-H1299+EFHD2 KD cells. (d) Transwell migration and invasion. Inhibition of JAK reduced the migration and invasion of A549+EFHD2 OE cells, whereas activation of JAK promoted the migration and invasion of NCI-H1299+EFHD2 KD cells. (e) Colony formation assay. Inhibition of JAK decreased the colony formation of A549+EFHD2 OE cells, whereas activation of JAK enhanced the colony formation of NCI-H1299+EFHD2 KD cells. (f) Western blotting. JAK inhibition decreased the levels of p-JAK1 and p-STAT3 in A549+EFHD2 OE cells, whereas JAK activation increased the levels of p-JAK1 and p-STAT3 in NCI-H1299+EFHD2 KD cells. Data are expressed as mean ± SD. ^✶P < 0.05, ^✶✶P < 0.01, ^✶✶✶P < 0.001 compared with the A549 group; ^#P < 0.05, ##P < 0.01, ^###P < 0.001 compared with the NCI-H1299 group. The scale bar represents 100 µm. JAK: Janus Kinase, STAT: Signal transducers and activators of transcription, Co-IP: Co-immunoprecipitation, EFHD2: EF-hand domain-containing protein 2, OE: Overexpression, KD: Knockdown, CCK-8: Cell counting kit 8, p-JAK: Phospho Janus Kinase, p-STAT: Phospho signal transducers and activators of transcription, SD: Standard deviation.

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