HIF-1α siRNA Enhances the Efficacy of Erlotinib in Non-small Cell Lung Cancer: A Novel Strategy to Reverse Hypoxia-Induced Drug Resistance

Abstract

Background

Non-small cell lung cancer (NSCLC) remains a leading cause of cancer-related mortality. While EGFR tyrosine kinase inhibitors (EGFR-TKIs) have improved survival, acquired resistance mediated by tumor hypoxia and HIF-1α stabilization often leads to treatment failure. This study investigated the regulatory role of HIF-1α in NSCLC drug resistance and evaluated a combined therapeutic strategy to overcome EGFR-TKI resistance.

Methods

HIF-1α expression and downstream targets were assessed in H1975 and A549 cell lines using RT-qPCR and Western blot. Functional assays, including cell proliferation, apoptosis, invasion, lactate production, and ROS measurements, were performed following HIF-1α siRNA transfection and/or erlotinib treatment. Bioinformatics analyses of public datasets evaluated clinical relevance and pathway enrichment.

Results

HIF-1α knockdown inhibited glycolysis, reduced lactate production, and alleviated hypoxia-induced oxidative stress. Combined treatment with HIF-1α siRNA and erlotinib synergistically suppressed cell proliferation, induced apoptosis, and inhibited invasion more effectively than single-agent treatments. Mechanistically, EGFR signaling positively regulated HIF-1α stability via the PI3K/AKT and MEK/ERK pathways. Bioinformatics confirmed that high HIF-1α expression correlates with poor prognosis in NSCLC patients.

Conclusion

Targeting HIF-1α disrupts metabolic reprogramming and hypoxia adaptation, thereby enhancing erlotinib efficacy. This combined approach highlights the therapeutic potential of HIF-1α inhibition as a novel strategy to overcome EGFR-TKI resistance in NSCLC.

Supplementary Information

The online version contains supplementary material available at 10.1007/s40268-025-00534-5.

Key Points

Hypoxia-inducible factor-1α drives non-small cell lung cancer drug resistance by promoting glycolysis, invasion, and hypoxia adaptation.

Hypoxia-inducible factor-1α small interfering RNA combined with erlotinib synergistically inhibits non-small cell lung cancer cell proliferation and metastasis.

Targeting hypoxia-inducible factor-1α represents a promising therapeutic strategy to overcome epidermal growth factor receptor-tyrosine kinase inhibitor resistance.

Introduction

Non-small cell lung cancer (NSCLC), accounting for 85% of all lung cancer cases, continues to pose a severe global health challenge [1]. According to the 2022 Global Cancer Statistics [2], lung cancer remains the leading cause of cancer incidence and mortality worldwide, with an estimated 2.5 million new cases and over 1.8 million deaths in 2022 [2]. Notably, approximately 75% of patients are diagnosed at an advanced stage (stage III–IV), resulting in a 5-year survival rate of less than 20% [3, 4]. Although epidermal growth factor receptor (EGFR)-targeted tyrosine kinase inhibitors (EGFR-TKIs) initially achieve significant clinical benefits, the widespread emergence of drug resistance severely compromises their long-term efficacy [5].

Increasing evidence indicates that the hypoxia-inducible factor-1α (HIF-1α) signaling pathway plays an important role in the occurrence, development, and drug resistance progression of NSCLC [6–8]. Recent studies have shown that the expression level of HIF-1α in EGFR-TKI-sensitive NSCLC tissues is significantly abnormal, increasing by 15–20 times compared with normal lung tissues [9–11]. This over-activated state not only promotes the adaptation of tumor cells to the hypoxic microenvironment, but also may serve as an important biomarker for predicting the early occurrence of acquired resistance to EGFR-TKIs [12]. Hypoxia-inducible factor-1α plays the role of a “drug resistance driver” through dual effects. On the one hand, HIF-1α can affect the proliferation, apoptosis, and metabolism of tumor cells by regulating the expression of downstream genes [13]. Studies have found that HIF-1α can promote tumor angiogenesis by up-regulating angiogenic factors such as vascular endothelial growth factor and enhance the invasion and metastasis abilities of tumor cells [14]. On the other hand, the high expression of HIF-1α is closely related to the activation of the glycolytic pathway [15]. Even under normoxic conditions, NSCLC cells still tend to provide energy through aerobic glycolysis and lactate production (the Warburg effect) [16], and HIF-1α can promote this process by regulating genes related to glucose metabolism (such as GLUT1 and PDK1), accelerating tumor proliferation and drug resistance [17].

This study focuses on the core role of HIF-1α in the occurrence, development, and drug resistance of NSCLC. First, it analyzes the expression differences of HIF-1α in NSCLC tissues and cells, and screens key downstream target genes by combining bioinformatics methods to reveal its functional mechanism in tumor proliferation, invasion, metabolism, and drug resistance formation. On this basis, small interfering (siRNA) nucleic acid drugs targeting HIF-1α were constructed and evaluated to explore their intervention effects on the tumor microenvironment. Further, this study systematically analyzed the regulatory relationship of the EGFR signaling pathway on HIF-1α, with a focus on PI3K/AKT and MEK/ERK pathways, and explored the therapeutic potential of HIF-1α inhibition combined with EGFR-TKIs in NSCLC with drug resistance, aiming to provide new intervention ideas and a theoretical basis for overcoming EGFR-TKI resistance.

Experimental Section

Materials

Item	Company	City, country	Catalog no.
Erlotinib	Titan Scientific Co., Ltd.	Shanghai, China	041178861
Small interfering RNA	TsingKe Biotech Co., Ltd.	Beijing, China	NA
siRNA-mate Plus Transfection Kit	GenePharma Co., Ltd.	Suzhou, China	G04026
ABScript III RT Master Mix for qPCR with gDNA Remover	Aibotek Biotechnology Co., Ltd.	Wuhan, China	RK20429
ABScript II One Step SYBR Green RT-qPCR Kit	Aibotek Biotechnology Co., Ltd.	Wuhan, China	RK20404
RIPA lysis buffer	Beyotime Biotech Inc.	Shanghai, China	P0013B
BCA protein assay kit	Beyotime Biotech Inc.	Shanghai, China	P0012
SDS-PAGE gel preparation kit	Beyotime Biotech Inc.	Shanghai, China	P0012A
PVDF membranes	Beyotime Biotech Inc.	Shanghai, China	FFP71
L-lactate assay kit	Beyotime Biotech Inc.	Shanghai, China	S0208S
HIF-1α antibody	Cell Signaling Technology, Inc.	Danvers, MA, USA	3716S
β-Actin antibody	Cell Signaling Technology, Inc.	Danvers, MA, USA	4967S
β-Actin rabbit mAb (HRP conjugate)	Cell Signaling Technology, Inc.	Danvers, MA, USA	5125S
Cell Counting Kit-8	Yeasen Biotechnology Co., Ltd.	Shanghai, China	40203ES80
Transwell Chambers	Corning Inc.	Corning, NY, USA	3401
DAPI staining reagent	Merck KGaA	Darmstadt, Germany	28718-90-3

HIF-1α hypoxia-inducible factor-1α, HRP horseradish peroxidase, mAb monoclonal antibody, NA not applicable, qPCR quantitative polymerase chain reaction

Cell Lines

The human embryonic lung fibroblast cell line MRC-5 (RRID: CVCL_0440) and the non-small cell lung cancer cell lines A549 (KRAS mutation, EGFR wild-type, RRID: CVCL_0023) and H1975 (L858R and T790M mutations, RRID: CVCL_1511) were purchased from the Shanghai Cell Bank of the Chinese Academy of Sciences (China), originally provided by the American Type Culture Collection, where these lines were authenticated using morphology, karyotyping, and polymerase chain reaction (PCR)-based approaches and tested for mycoplasma.

TCGA Database Analysis

The gene expression data used in this study were obtained from the TCGA database (https://www.cancer.gov/ccg/research/genome-sequencing/tcga). All datasets underwent stringent preprocessing procedures to ensure data quality and the reliability of our analyses. A differential expression analysis was performed using the “DESeq2” R package. Normalized RNA-sequencing count data were input to construct a negative binomial distribution model, from which log2 fold change values and p values were calculated for each gene. The Benjamini–Hochberg method was applied for multiple testing correction, and genes with adjusted p values < 0.05 were considered significantly differentially expressed. For key genes (e.g., SLC2A1), expression levels between the HIF1A-high and HIF1A-low groups were visualized using violin plots integrated with boxplots and dot plots via the “ggplot2” R package to provide an intuitive display of distribution patterns.

Functional and Pathway Enrichment Analysis

Gene Ontology enrichment and Kyoto Encyclopedia of Genes and Genomes pathway analyses were performed using the “ClusterProfiler” R package to explore the potential biological processes and molecular functions associated with the differentially expressed genes. A p value < 0.05 was used as the threshold for statistical significance.

Cell Viability Assay (Cell Counting Kit-8)

Cells were seeded into 96-well plates at a density of 1×10⁴ cells/well and pre-cultured for 24 h in a humidified incubator (37 °C, 5% CO₂) to allow adhesion. Following 24 h, 48 h, 72 h, or 96 h of treatment with 50 nM of HIF-1α siRNA or non-targeting siRNA (negative control), 10 μL of Cell Counting Kit-8 reagent was added to each well. After 1 hour incubation at 37 °C, absorbance at 450 nm was measured using a microplate reader.

Apoptosis Analysis by Annexin V-FITC/Propidium Iodide Staining

H1975 cells (5–10×10⁴ cells/mL) were treated with 50 nM of HIF-1α siRNA or non-targeting siRNA for 48 hours. Cells were harvested, washed twice with PBS, and resuspended in 100 μL 1× Annexin V Binding Buffer. Subsequently, 2.5 μL of Annexin V-FITC and 2.5 μL of propidium iodide (50 μg/mL) were added, followed by 15 min incubation in the dark. Flow cytometry was performed using a BD FACSCanto II system, with apoptotic populations quantified as follows: viable cells (Annexin V⁻/PI⁻), early apoptotic cells (Annexin V⁺/PI⁻), and late apoptotic/necrotic cells (Annexin V⁺/PI⁺).

Transwell Invasion Assay

Transwell chambers (8-μm pore size) were pre-coated with Matrigel (1:8 dilution) for 4 hours at 37 °C. H1975 cells (1×10⁵ cells/well) resuspended in serum-free medium were seeded into the upper chamber with 50 nM of HIF-1α siRNA, while the lower chamber contained complete medium supplemented with 10% FBS. After 48 hours incubation, non-invaded cells on the upper membrane surface were removed with a cotton swab. Invaded cells were fixed with 4% paraformaldehyde, stained with 0.05% crystal violet, and imaged under an inverted microscope. Three randomly selected fields per chamber were quantified using ImageJ software.

siRNA Transfection

Cells were seeded into 24-well plates at 60% confluency 24 h prior to transfection. Small interfering RNA complexes were prepared by mixing 15 pmol of HIF-1α siRNA (or non-targeting siRNA) with 8.5 μL pf Buffer and 3 μL of PLUS transfection reagent. Following 5 min incubation at room temperature, the complexes were added to cells and incubated for 48 h. Transfection efficiency was validated via quantitative reverse transcription-PCR and Elisa Kit.

Western Blot Analysis

Cells were cultured to the logarithmic growth phase under standard conditions (37 °C, 5% CO₂), washed twice with ice-cold PBS, and lysed in RIPA buffer supplemented with protease and phosphatase inhibitors for 30 min on ice. Lysates were centrifuged at 12,000×g for 15 min at 4 °C, and supernatants were stored at − 80 °C. Protein concentrations were determined using a BCA assay kit and adjusted to 2 μg/μL.

Samples were denatured by boiling for 5 min in 5× Laemmli buffer containing 10% β-mercaptoethanol. Equal amounts of protein (20–30 μg per lane) were separated on 10% SDS-PAGE gels at 80 V until the bromophenol blue dye entered the separation gel, followed by 120 V electrophoresis until complete migration. Proteins were transferred to 0.45-μm PVDF membranes using a wet transfer system (200 mA, 90 min) in Tris-glycine buffer containing 20% methanol.

Membranes were blocked with 5% BSA in TBST for 1 hour at room temperature, then incubated overnight at 4 °C with primary antibodies against HIF-1α (1:500) and β-actin (1:1000). After three TBST washes, membranes were incubated with HRP-conjugated secondary antibodies (1:5000) for 1 hour at room temperature. Protein bands were visualized using ECL substrate and quantified via densitometry using ImageJ software. Hypoxia-inducible factor-1α expression levels were normalized to β-actin.

Quantitative Reverse Transcription-PCR

Total RNA was isolated using TRIzol reagent following standard protocols. Cells were washed twice with PBS and lysed with 1 mL of TRIzol per well for 5 min at room temperature. Lysates were mixed with 0.2 mL of chloroform, vortexed for 15 s, and centrifuged at 12,000×g for 15 min at 4 °C. The aqueous phase was combined with an equal volume of isopropanol, incubated at − 20 °C for 10 min, and centrifuged (12,000×g, 10 min, 4 °C) to pellet RNA. RNA was washed with 75% ethanol, air dried, and dissolved in 20 μL of RNase-free water. RNA purity (A260/A280 ratio: 1.8–2.0) and concentration were measured spectrophotometrically, and integrity was confirmed by 1% agarose gel electrophoresis (28S/18S rRNA ratio >1.5).

First-strand complementary DNA was synthesized from 1 μg of total RNA using a reverse transcription kit with Oligo dT primers in a 20-μL reaction volume. Reverse transcription conditions: 37 °C for 15 min, followed by 85 °C for 5 s.

Quantitative PCR reactions were performed using the SYBR Green PCR master mix in a real-time PCR system. Each 20-μL reaction contained 10 μL of master mix, 0.4 μM of forward/reverse primers, and 2 μL of cDNA. Cycling parameters: 95 °C for 30 s; 40 cycles of 95 °C for 5 s, and 60 °C for 30 s; melt curve analysis (65–95 °C, 0.5 °C/s increment). Single-peak melt curves confirmed primer specificity.

Quantitative PCR Primer Sequences

HIF-1α: Forward: 5′-CACCACAGGACAGTACAGGAT-3′; Reverse: 5′-CGTGCTGAATAATACCACTCACA-3′.

SLC2A1: Forward: 5′-TCTGGCATCAACGCTGTCTTC-3′; Reverse: 5′-CGATACCGGAGCCAATGGT-3′.

PDK1: Forward: 5′-GGATTGCCCATATCACGTCTTT-3′; Reverse: 5′-TCCCGTAACCCTCTAGGGAATA-3′.

VEGFA: Forward: 5′-AGGGCAGAATCATCACGAAGT-3′; Reverse: 5′-AGGGTCTCGATTGGATGGCA-3′.

CA9: Forward: 5′-GGATCTACCTACTGTTGAGGCT-3′;

Reverse: 5′-CATAGCGCCAATGACTCTGGT-3′.

MMP2: Forward: 5′-GATACCCCTTTGACGGTAAGGA-3′;

Reverse: 5′-CCTTCTCCCAAGGTCCATAGC-3′.

MMP9: Forward: 5′-AGACCTGGGCAGATTCCAAAC-3′;

Reverse: 5′-CGGCAAGTCTTCCGAGTAGT-3′.

GAPDH: Forward: 5′-TTGGCCAGGGGTGCTAAG-3′;

Reverse: 5′-AGCCAAAAGGGTCATCATCTC-3′.

Statistics

No statistical method was used to predetermine sample size. No data were excluded from the analyses. The experiments were randomized, and the investigators were blinded to allocation during the experiments and outcome assessment. Statistical comparisons between groups were performed using a two-tailed unpaired Student’s t-test for two groups and one-way analysis of variance for multiple groups. The level of statistical significance was determined as follows: p > 0.05 was considered not significant, *p < 0.05, **p < 0.01, and ***p < 0.001 were considered statistically significant. GraphPad Prism Version 9.5.1 (928) and ImageJ Version 2.16.0/1.54p were used to analyze statistical data.

Results

HIF-1α is Highly Expressed in NSCLC and Associated with Poor Prognosis

To investigate the expression and clinical significance of HIF-1α in NSCLC, we first integrated a multi-dimensional bioinformatics analysis strategy. Based on the TCGA database, we constructed a cohort containing transcriptome data for 982 patients with NSCLC, including 501 cases of lung squamous cell carcinoma and 481 cases of lung adenocarcinoma. By comparing the messenger RNA (mRNA) expression levels between tumor tissues and adjacent normal tissues, the results showed that the mRNA expression level of HIF-1α in NSCLC tumor tissues was significantly higher than that in adjacent normal tissues (Fig. 1a). This trend was statistically significant in both the lung squamous cell carcinoma (Fig. 1b) and lung adenocarcinoma (Fig. 1c) subtypes.

Fig. 1.

a–c Differential expression of hypoxia-inducible factor-1α (HIF-1α) messenger RNA between tumor tissues and adjacent normal tissues in 982 patients with non-small cell lung cancer (501 lung squamous cell carcinoma, 481 lung adenocarcinoma) from the TCGA database. d Survival analysis of 184 clinical non-small cell lung cancer samples. e Survival analysis of 152 clinical lung squamous cell carcinoma samples. f Comparison of HIF-1α messenger RNA expression between MRC-5 and H1975 cells (n = 9). g Comparison of HIF-1α messenger RNA expression between MRC-5 and A549 cells (n = 9). h, i Western blot analysis of HIF-1α protein expression in human normal lung fibroblasts and non-small cell lung cancer cell lines (n = 9). ***p < 0.001. h hours, LUAD lung adenocarcinoma, LUSC lung squamous cell carcinoma

Further, we included 184 clinical samples with complete follow-up information and divided the patients into high/low expression groups based on the HIF-1α expression cut-off value. We applied the Kaplan–Meier survival analysis method to evaluate the impact of different expression levels on the overall survival time of patients. As shown in Figs. 1d and 2e, the median survival time of patients with high HIF-1α expression was significantly shorter than that of patients with low expression, suggesting that HIF-1α may serve as a predictor of poor prognosis in NSCLC.

Fig. 2.

a–f Transcriptomic differences between hypoxia-inducible factor-1α (HIF-1α) high-expression and low-expression groups in patients with non-small cell lung cancer from the TCGA database. g Kyoto Encyclopedia of Genes and Genomes pathway enrichment analysis of key biological processes and signaling pathways associated with high HIF-1α expression. h Gene Ontology (GO) pathway enrichment analysis of key biological processes and signaling pathways associated with high HIF-1α expression. i Distribution of GO enrichment results categorized by biological process (BP), cellular component (CC), and molecular function (MF). IL interleukin, *p < 0.05; **p < 0.01; ***p < 0.001

To validate these findings experimentally, we compare the expression differences of HIF-1α mRNA between human normal lung fibroblasts (MRC-5) and NSCLC cell lines (H1975 and A549). Notably, the H1975 cells contain both L858R and T790M mutations in EGFR, making them a representative model for EGFR-TKI-resistant NSCLC. The results showed that compared with MRC-5 cells, the relative expression levels of HIF-1α mRNA in both NSCLC cell lines were significantly increased (Fig. 1f, g), with the expression level of H1975 cells reached 15.2 times, and that of A549 cells was 8.0 times.

Similarly, in the western blot experiment, we also found that compared with MRC-5, the expression levels of HIF-1α protein in H1975 and A549 cells were significantly increased (Fig. 1h, i), and the level of HIF-1α protein further increased with time under hypoxic conditions (5% O₂). These expression differences were consistent with the previous bioinformatics analysis results, revealing the high expression feature of HIF-1α in NSCLC.

HIF-1α Activates Multiple Downstream Pathways to Promote Tumor Progression

To further clarify the molecular regulatory network of HIF-1α in NSCLC, we conducted a comprehensive bioinformatic analysis to identify characteristic target genes and associated biological functions in HIF-1α high-expression patients. According to previous literature reports, HIF-1α can drive tumor metabolic reprogramming (SLC2A1, PDK1), angiogenesis (VEGFA, CA9), and metastasis (MMP2, MMP9) by regulating downstream genes [18, 19]. By comparing the transcriptome data of HIF-1α high-expression and low-expression groups from the TCGA database (NSCLC cohort, n = 1005), we found that the expressions of SLC2A1, PDK1, VEGFA, CA9, MMP2, and MMP9 in the HIF-1α high-expression group were significantly upregulated in the high HIF-1α group (Fig. 2a–f). A functional clustering analysis indicated that these genes may cooperatively mediate the following pathological processes: (1) glucose metabolism reprogramming: SLC2A1 promotes glucose uptake, and PDK1 inhibits mitochondrial oxidative phosphorylation, together enhancing the Warburg effect to meet the energy demands for tumor proliferation; (2) angiogenesis and microenvironment acidification: VEGFA induces angiogenesis, and CA9 regulates extracellular pH homeostasis, both contributing to hypoxic adaptation; and (3) invasion and metastasis: MMP2 and MMP9 degrade the extracellular matrix to promote tumor cell migration. These results suggest that HIF-1α acts as a core regulatory hub, driving the progression of NSCLC through multiple oncogenic pathways. This provides a theoretical basis for therapeutic strategies targeting HIF-1α and its downstream network.

To further clarify the molecular mechanism by which high expression of HIF-1α promotes tumor invasion and metastasis, we performed Gene Ontology and Kyoto Encyclopedia of Genes and Genomes enrichment analyses to identify the key biological processes and signaling pathways associated with high HIF-1α expression. The KEGG analysis revealed (Fig. 2g) that HIF-1α may promote tumor progression by regulating metabolic reprogramming (e.g., drug metabolism, cytochrome P450, steroid hormone biosynthesis, ascorbate and aldarate metabolism), immune microenvironment remodeling (e.g., interleukin-17 signaling pathway, complement and coagulation cascades), and intercellular communication (e.g., neuroactive ligand-receptor interaction, estrogen signaling pathway).

A further GO enrichment analysis (Fig. 2h, i) indicated that HIF-1α overexpression is closely associated with epidermal development, keratinization differentiation, positive regulation of peptide secretion, and intermediate filament organization. These results imply that HIF-1α may promote local invasion and distant metastasis by enhancing the mechanical stress tolerance of tumor cells and paracrine signaling.

HIF-1α siRNA Suppresses Tumorigenic Behaviors and Regulates Downstream Metabolic Reprogramming in NSCLC Cells

Given the significant role of the HIF-1α signaling pathway in NSCLC progression and therapeutic resistance, we conducted siRNA drug development based on the HIF-1α mRNA sequence (NCBI Gene ID: 3091). Using siDirect and DSIR tools for sequence screening, combined with RNAfold predictions of secondary structure and guanine–cytosine (GC) content analysis (optimized range 40–55%), we designed and synthesized four candidate siRNA sequences (Table S1 of the ESM). These siRNAs were transfected into H1975 cells, and both proteomic and transcriptomic analyses were performed 48 hours post-transfection. The results showed that the expression of HIF-1α protein was significantly reduced in the siRNA-HIF1α-2367 treatment group (Fig. S1a of the ESM). A parallel reverse transcription-quantitative PCR analysis confirmed that this sequence also exhibited the highest knockdown efficiency at the mRNA level (Fig. S1b of the ESM), and the knockdown efficiency at the transcriptional level was significantly positively correlated with the inhibition efficiency of protein expression. Based on the above validation results, siRNA-HIF1α-2367 was selected as the best candidate sequence for subsequent studies.

Functional assays demonstrated that HIF-1α knockdown markedly inhibited the proliferation of H1975 cells (inhibition rate of 43.4% at 48 hours; Fig. 3f), induced apoptosis (apoptotic rate increased to 11.02%; Fig. 3b, c, e), and reduced cell invasion capability (invasive area decreased to 8%; Fig. 3a, d). To further elucidate molecular mechanisms, we examined the expression of HIF-1α downstream metabolic and angiogenic targets. Reverse transcription-quantitative PCR and western blot analyses confirmed significant downregulation of GLUT1, PDK1, VEGFA, CA9, MMP2, and MMP9 following siRNA transfection (Fig. 3g–m). Correspondingly, lactate levels in the culture supernatant were reduced by 19.1% and 27.6% at 24 h and 48 h, respectively (Fig. 3n; 19.1% reduction at 24 h and 27.6% reduction at 48 h), indicating suppressed glycolytic flux and a time-dependent effect.

Fig. 3.

a, d Transwell invasion assays showing invasion ability of H1975 cells treated with non-targeting small interfering RNA (siRNA) or hypoxia-inducible factor-1α (HIF-1α) siRNA (n = 3). b, c, e Effects of HIF-1α siRNA on apoptosis in H1975 cells (n = 3). f Proliferation of H1975 cells at different timepoints after transfection with HIF-1α siRNA (n = 3). g–l Western blot analysis of protein expression changes in H1975 cells after HIF-1α siRNA treatment (n = 3). m Reverse transcription-quantitative polymerase chain reaction analysis of potential HIF-1α target gene expression following siRNA transfection (n = 9). n Changes in lactate levels at 24 h and 48 h after HIF-1α siRNA transfection (n = 3). o Reactive oxygen species levels in H1975 cells under normoxia and hypoxia after HIF-1α siRNA treatment (n = 3). h hours, *p < 0.05; **p < 0.01; ***p < 0.001

Furthermore, HIF-1α knockdown significantly decreased intracellular reactive oxygen species levels under hypoxic conditions (Fig. 3o), suggesting an alleviation of oxidative stress and restoration of redox homeostasis. Collectively, these findings demonstrate that HIF-1α silencing not only suppresses malignant phenotypes such as proliferation, invasion, and apoptosis resistance but also reprograms metabolic pathways and redox balance in NSCLC cells.

EGFR Signaling Regulates HIF-1α Expression via the PI3K/AKT and MEK/ERK Pathways

Previous studies have confirmed that HIF-1α is significantly overexpressed in NSCLC and is associated with tumor progression. However, the upstream regulatory mechanisms remain to be further elucidated. Notably, approximately 30–40% of patients with NSCLC carry EGFR-driven mutations [20], and previous studies suggest that EGFR signaling may indirectly regulate the stability of HIF-1α through the PI3K/AKT/mTOR and MEK/ERK pathways [21–23].

As H1975 cells harbor the EGFR L858R/T790M dual mutation that confers intrinsic resistance to erlotinib, A549 cells were selected as a more responsive model for evaluating the regulatory effects of EGFR inhibition on HIF-1α expression. To investigate whether EGFR signaling participates in the regulation of HIF-1α expression, we treated A549 cells with the EGFR tyrosine kinase inhibitor erlotinib. Western blot results (Fig. 4a–h) showed that erlotinib could effectively inhibit the phosphorylation of EGFR and its downstream PI3K/AKT and MEK/ERK signals, and was accompanied by a decrease in HIF-1α protein expression, suggesting that the EGFR signal positively regulates the protein stability of HIF-1α through both the PI3K/AKT and MEK/ERK pathways. Further treatment with the PI3K inhibitor LY294002 and the MEK inhibitor U0126 confirmed that these two signaling axes additively contribute to the positive regulation of HIF-1α protein stability (Fig. 4i–n).

Fig. 4.

a–h Western blot analysis of protein expression changes in A549 cells after erlotinib treatment (n = 3). i–n Western blot analysis of protein expression changes in A549 cells treated with the PI3K inhibitor (LY294002) or MEK inhibitor (U0126) [n = 3]. EGFR epidermal growth factor receptor, HIF-1α hypoxia-inducible factor-1α, **p < 0.01; ***p < 0.001

HIF-1α siRNA Synergizes with Erlotinib to Enhance Anti-tumor Efficacy

Previous studies have shown that knockdown of HIF-1α can inhibit glycolytic reprogramming mediated by SLC2A1/PDK1 and tumor hypoxic microenvironment remodeling driven by CA9/VEGFA [24]. Based on this, we propose a core hypothesis: targeting HIF-1α could modulate tumor metabolism and resensitize EGFR L858R/T790M double-mutant NSCLC cells (H1975) to erlotinib, thereby overcoming the clinical dilemma of EGFR inhibitor resistance.

In H1975 cells, either erlotinib alone or HIF-1α siRNA can partially inhibit cell proliferation (Fig. 5k) and invasion (Fig. 5g, h), and induce cell apoptosis (Fig. 5i, j). However, the combination therapy exhibited significantly enhanced effects: cell viability was reduced to 41.9%, apoptosis rate increased to 12.65%, and the invasion area was further reduced to 6.61%. Furthermore, the combination treatment more potently suppressed the expression of HIF-1α and its downstream metabolic target genes including GLUT1, PDK1, CA9, and MMP2 (Fig. 5a–f), indicating a strong synergistic effect in both anti-tumor activity and metabolic reprogramming inhibition.

Fig. 5.

a–f Western blot analysis of protein expression changes in H1975 cells under combination therapy (n = 3). g, h Effects of combination therapy on cell invasion in H1975 cells (n = 3). i, j Effects of combination therapy on apoptosis in H1975 cells (n = 3). k Effect of combination therapy on cell proliferation in H1975 cells (n = 3). HIF-1α hypoxia-inducible factor-1α, siRNA small interfering RNA, *p < 0.05; **p < 0.01; ***p < 0.001

Discussion

This study systematically elucidated the abnormal activation of HIF-1α in NSCLC and its core role in the development of resistance to EGFR-TKIs. Through integrated analyses of large-scale databases and multi-level functional experiments, we confirmed that HIF-1α is highly expressed in NSCLC and is significantly associated with poor patient prognosis, indicating its potential as both a prognostic biomarker and a therapeutic target. These observations are in line with previous reports demonstrating that hypoxia-driven HIF-1α activation promotes tumor aggressiveness and poor outcomes across multiple cancers, including lung cancer [25–27]. Bioinformatics and pathway enrichment analyses further revealed that HIF-1α activates multiple downstream pathways (such as metabolic reprogramming, angiogenesis, and tumor metastasis), promoting the survival, expansion, and invasion of tumor cells in the hypoxic microenvironment, providing a reasonable mechanism explanation for its mediation of EGFR-TKI acquired resistance.

Importantly, our results demonstrated that HIF-1α upregulates glycolysis-related genes such as GLUT1 and PDK1, driving the Warburg effect in NSCLC cells and enhancing their resistance to TKI treatment. This finding complements prior mechanistic studies showing that HIF-1-dependent induction of glycolytic enzymes and transporters supports energetic and biosynthetic needs of tumor cells under hypoxia and contributes to therapeutic tolerance [28, 29]. Meanwhile, HIF-1α significantly promoted the expression of MMP2 and MMP9, enhancing extracellular matrix degradation and metastasis ability. These mechanisms are consistent with those reported in the existing literature, further verifying the functional characteristics of HIF-1α as a “drug resistance hub”.

In addition, based on cell models such as H1975 and A549, we validated the efficacy of HIF-1α siRNA intervention and proposed its combination with EGFR inhibitors (such as erlotinib) as a potential therapeutic strategy. We further reveal that EGFR signaling appears to positively regulate HIF-1α stability via PI3K/AKT and MEK/ERK signaling, and inhibition of these pathways diminishes HIF-1α levels—suggesting a crosstalk consistent with prior reports of an EGFR–HIF interaction [30]. This combined approach can block the activation of EGFR expression and its downstream signaling pathways, and simultaneously disrupt tumor adaptation to hypoxia, providing a novel solution for reversing EGFR-TKI resistance. Compared with single-agent treatments, our combined strategy addresses both driver signaling and metabolic adaptation, potentially producing synergistic effects and reducing resistance risk. Moreover, recent review discussions support that combining HIF-targeting agents with existing therapies is a promising paradigm in cancer treatment design [31].

Despite the promising findings, this study has several limitations. First, although in vitro experiments confirmed the efficacy of the interventions, in vivo validation is needed to assess therapeutic efficacy and safety. Second, the upstream regulatory network of HIF-1α (including PI3K, mTOR, and MAPK) is highly interconnected and may involve feedback loops; a deeper dissection of these pathways and their context-dependent regulation is warranted. Future research should analyze these feedback regulatory relationships and explore optimized combinations or patient stratification strategies.

In conclusion, HIF-1α is not only one of the key pathogenic factors in NSCLC but also an important molecular mechanism of resistance that influences the efficacy of EGFR-TKIs. Targeting the HIF-1α signaling axis, especially in combination with EGFR inhibition, may offer a novel and effective therapeutic paradigm for overcoming resistance in NSCLC (Fig. 6).

Fig. 6.

Schematic of the role of hypoxia-inducible factor-1α (HIF-1α) in non-small cell lung cancer and its combined intervention with epidermal growth factor receptor (EGFR) inhibitors. mRNA messenger RNA, NSCLC non-small cell lung cancer, siRNA small interfering RNA

Conclusions

This study conducted a systematic exploration on the function of HIF-1α in NSCLC and the targeted intervention strategies. It clarified the high expression characteristics of HIF-1α in NSCLC and its key role in the formation of EGFR-TKI resistance. The study confirmed that HIF-1α regulates multiple progression-related signaling pathways and constructs a complex resistance network, making it an indispensable regulatory node in the treatment of NSCLC. Based on this, we proposed a synergistic treatment strategy of HIF-1α siRNA combined with erlotinib, which demonstrated the potential for synergistic inhibition of tumor growth and resistance ability, and has a good translational prospect. This research provided a solid theoretical basis and experimental evidence for the in-depth understanding of the resistance mechanism of NSCLC and the development of new combined treatment strategies, and has important clinical guiding significance.

Supplementary Information

Below is the link to the electronic supplementary material.

Funding

This study was supported by the National Natural Science Foundation of China (No. 82204293 & 82241054), Collaborative Innovation Project of Yangtze River Delta Science and Technology Community (2023CSJZN0800), and the Fundamental Research Funds for the Central Universities (2024300445).

Declarations

Conflict of interest

Haojie Jiang, Shunhui Cai, Xiaojun Zhang, Shijie Chen, Chunyan Yue, Wu Yin, and Yiqiao Hu have no known competing financial interests or personal relationships that may have influenced the work reported in this article.

Ethics approval

Not applicable.

Consent to participate

All participants provided written informed consent prior to inclusion in the study. For cell line and database analyses, no individual patient consent was required, as data were obtained from publicly available sources (e.g., TCGA).

Consent for publication

Not applicable.

Availability of data and material

The datasets generated and/or analyzed during the current study are available from the corresponding author on reasonable request. Publicly available datasets (e.g., TCGA- lung adenocarcinoma, TCGA-lung squamous cell carcinoma) were also used, as referenced in the Methods section.

Code availability

Custom scripts used for bioinformatics analyses are available from the corresponding author upon reasonable request. Any commercial software used (e.g., R, GraphPad Prism) is cited in the Methods section.

Authors’ contributions

Data curation: HJ, SC; funding acquisition: C-YY, YH; investigation: HJ; methodology: HJ; project administration: WY, CY, YH; resources: SC; supervision: WY, CY, YH; validation: HJ, SC, XZ, SC; visualization: XZ, SC; writing (original draft): HJ; writing (review and editing): HJ, SC, XZ.