
==== Front
Transl Oncol
Transl Oncol
Translational Oncology
1936-5233
Neoplasia Press

S1936-5233(24)00222-5
10.1016/j.tranon.2024.102095
102095
Original Research
IGFBP1 promotes the proliferation and migration of lung adenocarcinoma cells through the PPARα pathway
Li Yunyun ab1
Yang Xuelian ab1
Han Tao ab1
Zhou Jiawei ab
Liu Yafeng ab
Guo Jianqiang ab
Liu Ziqin ab
Bai Ying abc
Xing Yingru e
Ding Xuansheng 1020030749@cpu.edu.cn
ag⁎
Wu Jing wujing8008@126.com
abcd⁎
Hu Dong austhudong@126.com
abcdf⁎
a School of Medicine, Anhui University of Science and Technology, Huainan, Anhui, China
b Anhui Occupational Health and Safety Engineering Laboratory, Huainan, Anhui, China
c Key Laboratory of Industrial Dust Deep Reduction and Occupational Health and Safety of Anhui Higher Education Institutes, Huainan, Anhui, China
d Key Laboratory of Industrial Dust Prevention and Control & Occupational Safety and Health of the Ministry of Education, Anhui University of Science and Technology, Huainan, Anhui, China
e Department of Clinical Laboratory, Anhui Zhongke Gengjiu Hospital, Hefei, China
f Department of Laboratory Medicine, The First Affiliated Hospital of USTC, Division of Life Sciences and Medicine, University of Science and Technology of China, China
g School of pharmacy, China Pharmaceutical University, Nanjing, China
⁎ Corresponding authors. 1020030749@cpu.edu.cnwujing8008@126.comausthudong@126.com
1 These authors contributed equally.

20 8 2024
11 2024
20 8 2024
49 1020955 3 2024
18 7 2024
11 8 2024
© 2024 Published by Elsevier Inc.
2024

https://creativecommons.org/licenses/by-nc-nd/4.0/ This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).
Highlights

• This study categorized LUAD patients into three disease subtypes based on the distribution of lipid metabolism-related genes.

• IGFBP1 was identified as a prognostic risk factor for lung adenocarcinoma patients.

• Functionally, reduced IGFBP1 expression significantly inhibits lung adenocarcinoma proliferation, migration and tumor burden.

• Mechanistically, the knockdown of IGFBP1 leads to the inhibition of PPARα activation, regulating the growth of lung adenocarcinoma cells.

Background

The immune status is closely linked to cancer progression, metastasis, and prognosis. Lipid metabolism, crucial for reshaping immune status, plays a key role in regulating the advancement of lung adenocarcinoma (LUAD) and deserves further investigation.

Methods

This study classifies LUAD patients into different immune subtypes based on lipid metabolism-related genes and compares the clinical characteristics among these subtypes. Single-multi COX analysis screens out key genes related to prognosis, and a risk feature and prognostic model are constructed. Cell cloning, scratch, transwell, western blotting and flow cytometry cell cycle analysis to detect the function of key genes. A subcutaneous tumor animal model is used to investigate the in vivo function and molecular mechanisms of key genes.

Results

LUAD patients are classified into three immune subtypes, among which C3 subtype has lower immune status and higher frequency of gene mutations, and show lower immunoreactivity in immunotherapy. COX analysis identified a prognostic model for four lipid metabolism factors (IGFBP1, NR0B2, PPARA, and POMC). IGFBP1, a core gene in this model, is highly expressed in the C3 subtype. Functionally, knocking down IGFBP1 significantly inhibits tumor cell cloning, scratch, and migration abilities, and downregulates the expression of cell cycle and EMT-related proteins. Knocking down IGFBP1 significantly inhibits tumor burden (P < 0.05). Mechanistically, knocking down IGFBP1 inhibits the activation of PPARα to regulate tumor cell growth.

Conclusions

This study found that lipid metabolism genes are closely related to LUAD, and IGFBP1 may be a key gene in regulating tumor growth and development.

Keywords

LUAD
Bioinformatics
Lipid metabolism
Insulin-like growth factor binding protein 1 (IGFBP1)
Peroxisome proliferator-activated receptor α (PPARα)
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pmcIntroduction

Lung adenocarcinoma (LUAD), as one of the most common and malignant tumors, is a leading cause of cancer-related deaths worldwide [1]. Despite significant efforts in early detection and the development of new treatment strategies, LUAD patients still face a high postoperative recurrence rate and unsatisfactory rates [2,3]. To develop more effective treatments, there is an urgent need to delve into the pathogenesis of LUAD in order to identify and target key molecular targets. Furthermore, for early-stage LUAD patients, biomarkers for monitoring the recurrence and progression of pulmonary diseases are crucial, which holds great significance for the development of new therapies for LUAD [4,5].

Lipids are a class of metabolites composed of different fatty acids and play a crucial role in forming cell membranes and structural units. Moreover, lipids are involved in energy storage and metabolism and act as key signaling molecules in cellular activities [6,7]. The metabolic and energy changes in cancer cells have been recognized as a feature of cancer [8,9]. There is growing evidence that dysregulation of lipid metabolism in the tumor microenvironment (TME) is closely associated with the malignant characteristics of cancer cells [[10], [11], [12]]. The metabolic reprogramming of cancer cells in response to stress is considered to be a necessary condition for the malignant progression of cancer, and suppressing this reprogramming has been demonstrated to cancer cell metastasis and spread [13,14]. Recent studies have found that the metabolic characteristics and trends of tumors evolve during the course of cancer, enabling tumor cells to acquire autonomous properties associated with enhanced invasiveness or to alter the microenvironment to promote tumor metastasis, leading to significant tumor metabolic heterogeneity [15,16]. In tumor cells, the metabolic switch from oxidative phosphorylation to aerobic glycolysis (the Warburg effect) increases the invasive and metastatic capabilities of cells and is achieved through the promotion of epithelial-mesenchymal transition (EMT) and angiogenesis [17,18].

It has been proven that some lipid metabolism-related genes play an important role in the occurrence and development of cancer, such as LMRGPI having prognostic value in early-stage LUAD [19]. Yang and others pointed out that MYC is crucial for the generation of specific fatty acids, which affects the survival and proliferation of lung cancer cells, suggesting that MYC expression drives abnormal lipid metabolism in lung cancer [[20], [21], [22]]. Additionally, Wei and others found that inhibiting the key lipid metabolism enzyme MGLL in LUAD cell lines can suppress cell proliferation and migration, supporting the critical role of lipid metabolism in the progression and metastasis of LUAD [23]. Furthermore, one important mode of death in tumor cells is ferroptosis, and lipid peroxidation is one of the main characteristics of ferroptosis, playing a key role in the tumor microenvironment; thus, lipid metabolism may play an important role in the occurrence and development of tumors [24,25]. However, research on lipid metabolism-related genes in LUAD is mainly limited to predicting prognosis and immune therapy response, with limited elucidation of their underlying mechanisms. Therefore, the main objective of this study is to explore the potential mechanisms of lipid metabolism-related genes on tumor cell growth and proliferation, and to discover and evaluate their potential as biomarkers for prognosis in patients with LUAD.

This study aims to explore the relationship between lipid metabolism and the occurrence and development of LUAD. Using bioinformatics methods, LUAD patients were stratified into different disease subgroups to investigate differences in prognosis and treatment. The Cox and Lasso methods are used to identify key lipid metabolism genes in single and multi-factor models, respectively, and to construct a prognostic model. Further exploration of the function and mechanism of the key lipid metabolism genes is carried out through in vitro and in vivo experiments.

Materials and methods

Sequencing data download and analysis

Download the RNA expression profiles and clinical information of 576 LUAD samples (517 tumor samples and 59 normal samples) from The Cancer Genome Atlas (TCGA) (https://www.cancer.gov/) database for lung tissue. RNA expression profiles and clinical information of 1126 LUAD patients from GSE72094, GSE13213, GSE31210, GSE32863, GSE 116,959, GSE30219, and GSE26939 cohorts were downloaded from the Gene Expression Omnibus (GEO)(https://www.ncbi.nlm.nih.gov/geo/) database. Merge the TCGA-LUAD dataset with datasets from the Gene Expression Omnibus (GEO) and use the Combat algorithm to remove batch effects. Perform a keyword search with "lipid metabolism" on the GeneCards website (https://www.genecards.org/) and obtain 136 lipid metabolism-related genes (filtered based on a relevance score greater than 10). Obtain immune-related data for LUAD patients from The Cancer Immunome Atlas (TCIA) database (https://tcia.at/home).

Classification of disease subtypes and analysis of immune microenvironment

Perform consensus clustering analysis using the ConSensusClusterPlus package in the merged dataset based on the expression characteristics of lipid metabolism-related genes to ensure robustness and reproducibility of the results. Subsequently, determine the optimal number of clusters using a combination of consensus scoring matrix, cumulative distribution function (CDF) curves, and PAC (Proportion of ambiguous clustering) scores. Calculate immune scores, stromal scores, and ESTIMATE scores in the tumor microenvironment using the ESTIMATE algorithm. Extract immune features of 28 immune cell types from previous studies on genotype/immunophenotype correlations and estimate enrichment scores for each immune cell type and each patient using single-sample Gene Set Enrichment Analysis (ssGSEA).

Exploration of molecular features within different subtypes

In the analysis of gene mutations, the Maftools software package in R was used to analyze the quantity and quality of gene mutations in different subtypes of lipid metabolism molecules. The results were visualized using waterfall plots. Fifty hallmark gene sets were obtained from the Molecular Signatures Database (MSigDB, http://www.gseamsigdb.org/gsea/msigdb/index.jsp), and they were evaluated and quantified using ssGSEA. Additionally, the PROGENy algorithm was employed to assess the activation levels of oncogenic pathways in patients [26].

Analysis of lipid metabolism-related disease subtypes and clinical outcomes

The IC50 (half-maximal inhibitory concentration) represents the drug concentration required to achieve a 50 % inhibition in cell lines. The tumor prediction R package and Wilcoxon signed-rank test were used to compare the IC50 values of common chemotherapy drugs among different subtype groups. Additionally, TIDE (Tumor Immune Dysfunction and Exclusion) is a computational method to predict clinical response to immune checkpoint blockade (ICB) based on pre-treatment tumor features. Using scaled transcriptome profiles uploaded to the TIDE web tool (http://TIDE.dfci.harvard.edu), TIDE scores and ICB responses were calculated for LUAD patients in the merged cohort[27].

Prognostic analysis and model construction for lipid metabolism-related gene features

LASSO Cox analysis and multivariate Cox analysis of lipid metabolism-related genes to identify independent prognostic factors and establish risk features: Using survminer and R package for Kaplan-Meier analysis of risk groups and low-risk groups and comparing the results. Based on the results of the multivariate Cox analysis, we obtained a risk score formula for risk score = (0.1238347) × IGFBP1 + (-0.1430533) × NR0B2 + (-0.3473632) × PPARA + (-0.1350229) × POMC, which we can use to construct a prognostic model for LUAD patients using four genes to evaluate their prognosis, using this formula.

Immunofluorescence

After paraffin embedding and fixation with 4 % paraformaldehyde, 5 μm thick sections were cut from mouse tumor tissue specimens (derived from a mouse model in this experiment) and deparaffinized. Following deparaffinization, the sections were treated with 0.01 M pH 9.0 Tris-EDTA antigen retrieval buffer (Servicebio, #G1207) in a high-power microwave for 5 min, cooled for 8 min, then heated on low power for 5 min, and cooled for 8 min. Care was taken to ensure that the buffer did not evaporate excessively or cause the sections to become overly dry. After slow cooling to room temperature, the samples were blocked with 5 % normal goat serum (Thermo Fisher Scientific, #16,210,064, Gibco) prepared in 5 mL of 1X TBST. Following a 30-minute incubation for blocking, the samples were washed three times with 1X TBST on a shaker for 5 min per wash. The samples were then completely covered with a primary antibody dilution solution prepared using Bovine Serum Albumin V (Biosharp, #BS114) on the sample sections, which were placed in a humidified chamber and incubated overnight at 4 °C. Subsequently, the samples were equilibrated at room temperature for 15 min, the primary antibody dilution solution was removed, and the samples were washed three times with 1X TBST for 5 min per wash. For secondary antibody incubation, the sections were incubated with fluorescence-labeled secondary antibody diluted in the corresponding species-specific secondary antibody dilution solution at 37 °C in the dark for 1 hour. After incubation, the samples were washed three times with 1X TBST for 5 min per wash, all steps conducted on a shaker. Finally, DAPI was used for nuclear counterstaining, and the immunofluorescence (IF) sections were observed under a confocal fluorescence microscope (Leica 3000). The tissue samples were then preserved. ImageJ software (version 1.52a) was used to analyze the IF sections. The primary antibodies used in this experiment: IGFBP1 (Proteintech, #13,981–1-AP, 1:1000), PPARα (Proteintech, #66,826–1-Ig, 1:1000).

Cell culture

Human lung adenocarcinoma cell lines H1299 (#TCHu160), H1975 (#TCHu193), A549 (#SCSP-503) and normal human lung bronchial epithelial cells BEAS-2B (#TCHu193) cell lines were purchased from the cell bank of the chinese academy of sciences (Shanghai, China). Mouse lung cancer cells LA-4 cells (#YS1586C) were obtained from Shanghai Yaji Biological Technology Co., Ltd. Human embryonic kidney cells HEK-293T cells (#CL-0001) were sourced from Pricella Company. H1299, H1975, A549, BEAS-2B and LA-4 cells were cultured in complete medium containing 90 % DMEM (Gibco, #C1199550), 10 % FBS (Gibco, #12,484,028), and P/S (Beyotime, #C0222). HEK-293T cells were cultured in complete medium containing MEM (Pricella, #PM150478) (90 % MEM+10 % FBS+1 % P/S). All cells were correctly identified by STR profiling and cultured at 37 °C with 5 % CO2 in a CO2 incubator (Panasonic).

Small interfering RNA(siRNA) transfection

Before transfection, the A549 and H1299 cell densities need to reach 60–80 %. Six hours prior to transfection, the cells were seeded in a 6-well plate and the medium was replaced with non-complete DMEM medium (without FBS, without P/S). A mixture was prepared by diluting 20 pmol of siRNA in 200 µl of Opti-MEM™ I (Thermo Fisher Scientific, #31,985,070), and another mixture was prepared by diluting 6 µl of Lipofectamine™ 2000 (Thermo Fisher Scientific, #11,668,500) in 200 µl of Opti-MEM™ I. After gentle mixing, the Lipofectamine™ 2000 mixture was added dropwise to the siRNA mixture and allowed to stand at room temperature for 15 min. The prepared transfection solution was added to the cell culture dishes and gently mixed. The A549 and H1299 cells were incubated in a 37 °C, 5 % CO2 incubator for 6 h and then the DMEM complete medium was replaced. After 48 h, the expression levels of the target gene and protein were detected by Western Blotting. The siRNA was synthesized by Shanghai Genepharma Co., Ltd, with the following sequences: si_NC (sense 5′−3′: UUCUCCGAACGUGUCACGUTT), si_IGFBP1#1 (sense 5′−3′: GCUCCUGGAUAAUUUCCAUTT), si_IGFBP1#2 (sense 5′−3′: CGCCAUCAGUACCUAUGAUTT), and si_IGFBP1#3 (sense 5′−3′: GACAGUGUGAGACAUCCAUTT).

Colony formation assay

Collect the H1299 and A549 cells following treatment with siRNA and the PPARα agonist GW7647 (MedChemExpress, #HY-13,861). Subsequently, digest the cells with trypsin (Gibco, #25,200,072) to prepare a cell suspension. Perform a cell count to ascertain the concentration and viability, then seed the cells into a 6-well culture plate at a density of 1000 to 1500 cells per well, ensuring uniformity and optimal conditions for further experimental analysis. The cells were cultured in DMEM complete medium, with medium replacement every 3 days, and the cell number and morphology were observed. After continuous culture for 7 days, the culture was stopped when most individual colonies contained >50 cells. The medium was removed, the cells were washed once with PBS, and 1 ml of 4 % paraformaldehyde (Beyotime, #P0099) was added to each well. The cells were fixed for 30 min at room temperature, followed by a PBS wash. Then, 0.1 % crystal violet stain (Beyotime, #C0121) was added to each well for 20 min. After two washes with 4 °C PBS, the cells were observed and data were recorded under an optical microscope (OLYMPUS, #BX53+DP74). Positive clones were observed under the microscope, i.e., each clone with >50 cells. The number of clones was calculated using ImageJ.

Wound healing assay and transwell migration

In the wound healing assay, H1299 and A549 cells treated were harvested and digested to form a single-cell suspension, which was then seeded into a 6-well plate and cultured in a 37 °C incubator with 5 % CO2 for 24 h. After the cells covered the bottom of the plate, the medium was removed, and the cells were rinsed once with PBS. Using a 200μL pipette tip vertically against the bottom of the dish, a scratch was created in the single layer of cells, with an effort to ensure consistent scratch width. The cells were rinsed once more with PBS to remove cell debris, and serum-free medium was added. Photographs were taken at 0 h, 24 h, and 48 h under a 4x objective microscope, and ImageJ software was used for analysis to calculate the mean scratch area. The migration rate (wound healing rate) was calculated using the: Cell migration rate = (initial scratch area - t-hour scratch area) / initial scratch area.

For the transwell migration assay, well-growing H1299 and A549 cells were serum-deprived for 24 h. Then, 500 µl of complete medium containing 10 % FBS was added to the lower chamber of a 24-well plate, and the Transwell insert was placed in the well. After 24 h of serum deprivation, the cells were digested and resuspended in DMEM non-complete medium containing BSA powder (MERCK, #9048–46–8) prepared to a final concentration of 1 %. The cell density was adjusted to 1 × 105 cells/ml, and 100 µl of the cell suspension was placed in the upper chamber, followed by the addition of 100 µl of DMEM non-complete medium containing 1 % BSA. The 24-well plate was cultured at 37 °C with 5 % CO2 in an incubator for 24 h. Afterward, the Transwell insert was removed, the medium was discarded, and PBS-wetted cotton swabs were used to remove cells that had not invaded through the membrane, leaving the cells in the lower chamber. The cells in the lower chamber were fixed with 4 % paraformaldehyde for 30 min, by PBS washes on both sides of the insert. Then, 0.1 % crystal violet solution was added for staining for 10 min, and PBS washes were performed on both sides of the insert three times. After air-drying, cells were observed under an inverted microscope, and three fields of view were photographed for counting analysis using ImageJ software.

Western blotting

Following the respective treatments, A549 and H1299 cells were collected, and the culture medium was discarded. The cells were washed twice with PBS and then lysed using RIPA lysis buffer (Beyotime, #P0013B) containing 1 % PMSF (Beyotime, #P1081) on ice. The cell lysates were transferred to a low-temperature centrifuge (Eppendorf) at 20,000 rpm for 20 min, and the supernatant was collected. The protein concentration in the supernatant was measured using the BCA Protein Assay Kit (Thermo Fisher Scientific, #A55864). Subsequently, 5X SDS-PAGE protein loading buffer (Beyotime, #ST626) was added to each sample to denature the proteins. The samples were heated at 100 °C for 10 min and then cooled to room temperature. The denatured proteins were separated by SDS-PAGE using a 10 % separating gel and a stacking gel (15-well comb). The gel was loaded into a BIO-RAD vertical electrophoresis apparatus and subjected to electrophoresis at 60 V for 1 hour, followed by adjusting the voltage to 120 V for another 1 hour. After electrophoresis, the proteins were transferred to a PVDF membrane (Millipore, #ISEQ00010) using a BIO-RAD vertical electrophoresis apparatus at 100 V for 1 hour. The PVDF membrane containing the transferred proteins was washed with 1X TBST buffer for 5 min per wash, with a total of 3 washes. The membrane was then blocked with 5 % skim milk at room temperature for 1 hour to prevent non-specific binding. After blocking, the membrane was incubated overnight at 4 °C with the specific primary antibodies. The next day, the membrane was washed 3 times with 1X TBST for 5 min per wash and then incubated with the appropriate secondary antibodies (HRP Goat Anti-Rabbit IgG (H + L) (Abclonal, #AS014, 1:10,000) and HRP-conjugated Affinipure Goat Anti-Mouse IgG(H + L) (Proteintech, #SA00001–1, 1:10,000)) prepared in 5 % skim milk at room temperature for 1 hour. Following repeated washes with 1X TBST, the protein bands were visualized using ECL substrate (Millipore, #WBKLS0100), and the images were captured using an Amersham™ ImageQuant™ 800 imager. The grayscale values of protein bands were analyzed using ImageJ software. The following marker (BIOMIKY, MK201A) and antibodies were used in this experiment: CDK1 (Proteintech, #10,762–1-AP, 1:2000), CDK2 (Proteintech, #10,122–1-AP, 1:2000), IGFBP1 (Proteintech, #13,981–1-AP, 1:3000), E-Cadherin (CST, #14472S, 1:1000), Vimentin (CST, #5741T, 1:1000), N-Cadherin (CST, #13116T, 1:1000), PPARα (Proteintech, #66,826–1-Ig, 1:1000), and β-Actin (Abclonal, #Ac026, 1:3000). The secondary antibodies were HRP Goat Anti-Rabbit IgG (H + L) (Abclonal, #AS014, 1:10,000) and HRP-conjugated Affinipure Goat Anti-Mouse IgG(H + L) (Proteintech, #SA00001–1, 1:10,000).

Flow cytometry analysis of the cell cycle

After the H1299 and A549 cells treated with siRNA had reached an appropriate density, they were digested and collected by centrifugation. The cells were resuspended in cold PBS buffer, and after centrifugation for 5 min at 4 °C (1000 x g), the supernatant was removed. The cell pellet was mixed with the remaining PBS in the tube and then 1 ml of pre-chilled 70 % alcohol was added, mixed gently, and fixed overnight at 4 °C. The cells fixed overnight were centrifuged for 5 min at 4 °C (1000 x g) to remove the supernatant, and then the cells were resuspended in cold PBS buffer and centrifuged again to remove the supernatant. The cell pellet was mixed with the remaining PBS in the tube. Use PBS to prepare a staining working solution containing 50 μg/ml of PI (Propidium Iodide, a fluorescent dye that specifically binds to DNA) and 100 μg/ml of RNase A, with the dyes and reagents coming from a cell cycle detection kit (KeyGEN, #KGA512). After incubation for 30 min at 4 °C in the dark, the BD FACSCantoTM II flow cytometer was used for detection. FlowJo v10.6.2 was used for analysis and visualization of the results.

Cell line construction

Based on the gene sequence of Igfbp1 (Mouse) (NM_008341.4), gene interference nucleotides were designed. The sequences for sh_NC (Mouse), sh_Igfbp1 (Mouse) were designed as follows: sh_NC (sense 5′−3′: tgcgcacagagacctatacca), sh_Igfbp1 #1 (sense 5′−3′: cgaatcgatgccacgtagcta), and sh_Igfbp1 #2 (sense 5′−3′: gaacgccatcagcacctatag). Plvx-shRNA_NC (Mouse) and Plvx-shRNA_Igfbp1 (Mouse) destination vectors were constructed. The psPAX2 and pMD.2 g were used as auxiliary packaging plasmids. All plasmids were purchased from Shanghai Jikai Gene Chemical Co., Ltd. The total amount of transfected plasmids was 6 µg, with a ratio of Plvx-shRNA_Igfbp1:psPAX2:pMD.2 g of 4:3:1. The day before plasmid transfection, HEK-293T cells were seeded in a 60 mm dish, and when the cell density reached 60 %−80 %, transfection was performed. Six hours before transfection, the non-complete MEM (Pricella) medium was replaced (without FBS, without P/S). The plasmids were diluted in 400 µl of Opti-MEM™ I, with 5 µg of plasmids diluted in Opti-MEM™ I and 10 µl of Lipofectamine™ 2000 diluted in Opti-MEM™ I. After gentle mixing, the Lipofectamine™ 2000 mixture was slowly added to the plasmid mixture and incubated for 10 min. The prepared transfection solution was added to the cell culture dish and gently mixed. The cells were cultured at 37 °C with 5 % CO2 in an incubator (Panasonic) for 6 h, and then the MEM complete medium was replaced. After 24 h, the expression of GFP green fluorescent protein was observed in HEK-293T cells using a fluorescence microscope (Leica 3000), and the transfection efficiency was analyzed. After 48 h, the viral supernatant was collected. The supernatant was centrifuged for 5 min (300 x g, 4 °C), and the viral particles were collected by filtration. LA-4 cells were infected with the viral particles when they were in the logarithmic growth phase. After 24–48 h of infection, an appropriate amount of puromycin (Solarbio, #P8230) was added for stable selection of LA-4 cells with decreased IGFPB1 protein expression.

Construction of subcutaneous tumor animal model

Eighteen SPF-level C57BL/6 male mice (6–8 weeks old, weighing 20–23 g) were purchased from Henan Sickle-base Biotechnology Co., Ltd. All animals were housed in the Experimental Animal Center of Anhui University of Science and Technology Medical School, maintained under controlled conditions with a temperature of 21±2 °C, humidity of 55±5 %, and a 12-hour light-dark cycle. They were provided with ad libitum access to water and food replenished every two days. The C57BL/6 mice were randomly divided into three groups, with 6 mice in each group. The LA-4 cells containing 1 × 105 of sh_NC, sh_Igfbp1#1, and sh_Igfbp1#2 were injected subcutaneously into the axilla of C57BL/6 mice. The animal models were terminated after 27 days in all three groups. The research team strictly adhered to the principles of animal welfare and monitored the health (body weight and appetite) and behavior of the animals twice daily. At the end of the study, the mice were euthanized by inhalation of 2 % isoflurane (RWD, R510–22–10). The tumor volume and weight were recorded, and the tumor volume (V) was calculated using the formula V = π/6 × L (length) × W (width) × H (height). The animal experiments in this study were approved by the Biomedical Ethics Committee of Anhui University of Science and Technology (Huainan, China; Approval NO.: NO. HX-002).

Hematoxylin-eosin (HE) staining

Mouse lung tissues were fixed and conventionally embedded in paraffin. After deparaphinization of the paraffin sections, they were subjected to a graded ethanol wash down to water. The sections were then stained with hematoxylin solution, followed by rinsing with water and subsequent staining with eosin solution. After dehydration and mounting, the slides were photographed and processed using a confocal laser scanning microscope.

Statistical analysis

All statistical analyses were performed using GraphPad Prism 9.5.1 statistical software, with data presented as the mean ± standard deviation (SD). The "pROC" package in the R language was utilized for the receiver operating characteristic (ROC) curve analysis, and independent prognostic factors were determined through multivariate Cox regression analysis. For normally distributed continuous data, single-factor comparisons between two groups were conducted using the T-test, single-factor comparisons among multiple groups were performed using one-way ANOVA, and multi-factor comparisons among multiple groups were carried out using two-way ANOVA. p < 0.05 was considered statistically significant.

Results

Identification of three subtypes of LUAD associated with lipid metabolism genes

To identify disease subtypes associated with lipid metabolism-related genes in LUAD, we first merged five LUAD datasets, namely TCGA-LUAD, GSE72094, GSE32863, GSE116959, and GSE312105. Consensus clustering analysis was performed on the merged dataset using 136 lipid metabolism-related genes. The Delta area plot revealed that the largest change in the area under the cumulative distribution function (CDF) curve occurred at K = 3, indicating the optimal number of clusters (Fig. 1A). The CDF plot showed that at K = 3, the slope of the CDF curve in the Consensus Index (0.1–0.9) range was relatively small (Fig. 1B). Therefore, three LUAD disease subtypes based on the expression of lipid metabolism-related genes (C1, C2, and C3) were identified. The consensus matrix heatmap and PCA analysis demonstrated distinct separation of lipid metabolism-related gene expression profiles and samples in the three disease subtypes (Fig. 1C and D).Fig. 1 Analysis of three lipid metabolism subtypes based on lipid metabolism-related genes in LUAD. (A and B) Consensus clustering analysis with k = 2 to 9 showing delta area plot (A) and CDF curve plot (B). (C) Consistency matrix heatmap at k = 3. (D) Scatter plot of principal component analysis for the three lipid metabolism subtypes. (E) Kaplan-Meier analysis curves for the three lipid metabolism subtypes in LUAD. (F) Heatmap of expression patterns of lipid metabolism-related genes in the three lipid metabolism subtypes of LUAD.

Fig 1

Prognostic analysis revealed that patients in the C1 subtype had a significantly better prognosis compared to those in the C2 and C3 subtypes (Fig. 1E). In terms of gene expression patterns, the C1 subtype was highly enriched in genes such as RETN, RBP4, and SLC6A14. On the other hand, the C3 subtype showed high enrichment of genes like IGFBP1, INSR, and IRS2 (Fig. 1F).

Analysis of clinical characteristics of the three subtypes of lipid metabolism-related gene diseases

Due to the different prognoses of the three subtypes based on lipid metabolism-related genes (Fig. 1F), clinical characteristics are closely related to patient prognosis [28,29]. Therefore, we further analyzed the distribution differences of clinical pathological features among LUAD patients and the different disease subtypes. The results showed a strong correlation between the three disease subtypes and clinical pathological features (Supplementary Figure 1A). The C1 subtype, which had the best prognosis, exhibited a higher EGFR mutation rate, a lower KRAS mutation rate, and enrichment in Stage I & II stages, smoking history, and age >60 years (Supplementary Figure 1B-E). On the other hand, the C2 and C3 molecular subtypes showed a lower EGFR mutation rate, a higher KRAS mutation rate, and enrichment in Stage III & IV stages, non-smokers, and age <60 years (Supplementary Figure 1B-E). These results suggest that the EGFR mutation rate, stage classification, and smoking history may be key clinical characteristics influencing the prognosis of the C1, C2, and C3 subtypes.

Correlation analysis of the three disease subtypes with the tumor microenvironment

Changes in the composition of the tumor microenvironment are considered important factors that affect the prognosis of cancer patients [30]. Here, we found that patients in the C1 subtype exhibited higher immune scores, stromal scores, and ESTIMATE scores, followed by the C2 subtype, while the C3 subtype had the lowest scores. This suggests that patients in the C1 subtype have an active immune status (Fig. 2A). Analysis of immune cell infiltration showed a significant enrichment of immune cell infiltration in the samples of the C1 subtype, while the C3 subtype was characterized by a lack of immune cell infiltration (Fig. 2B and Supplementary Figure 2A). The process of antitumor immunity can be interpreted as seven consecutive steps collectively known as the "cancer-immunity cycle." We obtained information on the cancer-immunity cycle of TCGA-LUAD patients from the TIP website, which showed that the overall antitumor immune process was markedly active in patients of the C1 subtype, whereas the antitumor immune process in patients of the C3 subtype was relatively blocked, with the C2 subtype exhibiting an intermediate pattern (Fig. 2C). Furthermore, we employed Sankey diagrams to illustrate the connections between different lipid metabolism disease subtypes and various TME attributes (Fig. 2D). Subsequently, we found that, relative to the C3 subtype, the C1 subtype showed significant enrichment of immune features related to macrophage regulation, lymphocyte infiltration feature scores, and antigen presentation-related functions, while the C3 subtype showed enrichment in wound healing functions (Fig. 2E). Regarding immune checkpoints, CD274, PDCD1LG2, LAG3, and HAVCR2 exhibited different expression patterns across the three molecular subtypes (Supplementary Figure 2B).Fig. 2 Analysis of the tumor microenvironment in three subtypes of diseases. (A) Distribution of matrix scores, immune scores, and estimated scores for different subtypes of diseases. (B) Heatmap of differential immune cell expression in different subtypes of diseases. (C) Heatmap of anti-tumor immune expression in different subtypes of diseases. (D) Sankey diagram showing the relationship between different lipid metabolism-related gene subtypes and TME subtypes in different disease subtypes and immune sub-types. (E) Boxplot showing the scores for different immune characteristics in different disease subtypes. *P < 0.05, **P < 0.01, ***P < 0.001, ns represents no statistical significance.

Fig 2

Analyzing the molecular heterogeneity of different disease subtypes and their responsiveness to chemotherapy, targeted therapy, and immunotherapy

Considering the heterogeneity of the immune microenvironment within different disease subtypes, we further analyzed the differences in gene mutation frequencies between the subtypes. The results showed that among the top 20 genes in terms of mutation frequency, the C1 subtype had the lowest mutation frequency, the C2 subtype the highest, and the C3 subtype intermediate (Fig. 3A–C). Consistently, the mutational burden (TMB) also revealed that patients in the C2 subtype had the highest TMB, followed by the C3 subtype, while the C1 subtype had the lowest. In terms of the number of neoantigens produced, the1 subtype produced the fewest neoantigens, while the C2 subtype produced the most. Additionally, the tumor purity of the C2 and C3 subtypes was significantly higher than that of the C1 subtype (Fig. 3D-G). Subsequently, we analyzed the differences in biological functions between the subtypes. The results showed that the C2 and C3 subtypes were significantly enriched in pathways related to cell proliferation, such as GLYCOLYSIS, MYC_TARGET_V1, PI3K_AKT_MTOR_SIGNALING. In contrast, the C1 subtype was primarily enriched in immune-related pathways, such as IL2_STAT5_SIGNALING, INFLAMMATORY_RESPONSE (Fig. 3H). In the analysis of key genes associated with cancer development, the activation levels of pathways between different lipid metabolism molecular subtypes were significantly different (Fig. 3I). In summary, these results indicate that the molecular feature heterogeneity between subtypes contributes to the distinct survival outcomes observed among them.Fig. 3 Mutation differences in top 20 genes with highest mutation Rate in different lipid metabolism disease subtypes. (A-C) Waterfall plot of the top 20 genes with the highest mutation rate in the three disease subtypes. (D-G) Distribution of scores for TMB (D), sub_clonal_NEO (E), clonal_NEO (F), and Tumor_ploidy (G) for the three lipid metabolism disease subtypes. (H) Heatmap showing the enrichment of the three disease subtypes in different signaling pathways related to cancer. (I) Heatmap showing the distribution of enriched carcinogenic related pathways for the three disease subtypes. *P < 0.05, **P < 0.01, ***P < 0.001, ns represents no statistical significance.

Fig 3

Currently, surgery and systemic chemotherapy are still the conventional treatments for LUAD patients [31]. Therefore, we estimated the IC50 of several drugs for chemotherapy sensitivity using the oncoPredict algorithm. The results showed that the estimated IC50 values for Paclitaxel, Cisplatin, Docetaxel, and Vinorelbine are the lowest in the C3 subtype, indicating that C3 subtype patients are more sensitive to chemotherapy. In addition, the estimated IC50 values for Erlotinib, Gefitinib, and Afatinib are the lowest in the C2 subtype (Supplementary Figure 3A), indicating that C2 subtype patients are more sensitive to EGFR-TKI targeted therapy. Furthermore, we used the TIDE algorithm to evaluate the immune therapy response of these three subtypes and found that the TME landscape showed a better immune therapy response for the C1 subtype, consistent with the high immune infiltration (Supplementary Figure 3B-D). In contrast, the C3 subtype with the least immune infiltration showed the least response to immune therapy (Supplementary Figure 3E).

Identification of lipid metabolism key genes and construction of prognostic model

In order to identify key lipid metabolism genes in the development of LUAD, we used Cox univariate analysis and found that 50 out of 136 lipid metabolism-related genes were determined to be closely related to patients' OS (Fig. 4A). Lasso regression analysis further identified 26 prognostic key genes from the 50 genes (Fig. 4B). Subsequently, multivariate Cox analysis showed that among the 26 genes, IGFBP1 was an independent prognostic risk factor, while NR0B2, PPARA, and POMC were independent prognostic protective factors (Fig. 4C). Based on these four genes, we constructed a risk scoring model to predict the prognosis of LUAD patients. Kaplan-Meier analysis demonstrated the significant prognostic value of the risk model in the LUAD combined cohort, with significantly increased patient survival rates in the low-risk group compared to the high-risk group (Fig. 4D and E). ROC curve analysis of the receiver operating characteristic showed that the areas under the ROC curve for 1, 3, and 5 years were 0.663, 0.667, and 0.637, respectively, indicating that the model has good predictive significance (Fig. 4F). Next, we used external datasets to further validate the model and found that it also has prognostic value (Fig. 4G-L). Analyzing the model in the TCGA dataset in relation to clinical features, the results showed that patients in the high-risk scoring group had significantly higher disease stage and EGFR mutation rate (Fig. 4M), which may explain the poor prognosis of patients in the high-risk group.Fig. 4 Construction of prognostic model and identification of key genes. (A) Univariate Cox analysis identifies 50 key genes. (B) LASSO regression analysis screens 26 prognostic key genes. (C) Multivariate Cox analysis screens prognostic factors. (D) Kaplan-Meier overall survival curves for all LUAD patients in the three lipid metabolism subtypes. (E) Risk score and survival status distribution of the four prognostic factors IGFBP1, NR0B2, PPARA, and POMC in the LUAD combined cohort. (F) ROC curve for lipid metabolism-related prognostic features. (G-L) Correlation between risk scores and prognosis in the TCGA (G), GSE31210 (H), GSE72094 (I), GSE13213 (J), GSE26939 (K), and GSE30219 (L) datasets. (M) Expression heatmap of clinical characteristic scores for high-risk and low-risk patients.

Fig 4

Knockdown of IGFBP1 significantly inhibits the proliferation and migration ability of tumor cells

In the previous analysis, we found that IGFBP1 is a highly expressed gene that is negatively correlated with the prognosis of LUAD patients. To explore the influence of IGFBP1 on the development of LUAD, we detected the expression level of IGFBP1 in different lung adenocarcinoma cell lines. The results show that, compared with human normal lung epithelial cells Beas-2B, the expression level of IGFBP1 is higher in H1299 and A549 cells (Fig. 5A). Subsequently, we knocked down IGFBP1 in H1299 and A549 cells and performed colony formation assay. The results showed that knockdown of IGFBP1 inhibited the proliferation of both cells (Fig. 5B and C). Subsequently, we detected cell migration using a wound healing assay at 0 h, 24 h, and 48 h, and the results showed that knockdown of IGFBP1 also reduced the migration ability of tumor cells (Fig. 5D and E). Similarly, transwell migration assay showed that knockdown of IGFBP1 could inhibit the migration of tumor cells (Fig. 5F and G). These results indicate that knocking down IGFBP1 can inhibit the proliferation and migration abilities of tumor cells.Fig. 5 The impact of IGFBP1 on the proliferation and migration abilities of lung adenocarcinoma cells. (A) Western blotting detection of IGFBP1 protein expression levels in four cell lines: H1299, H1975, Beas-2B, and A549 (n = 3). (B and C) Colony formation assay to test the effect of IGFBP1 knockdown on the proliferation of H1299 (B) and A549 (C) cells (n = 3). (D and E) Wound healing assays to test the migration ability of H1299 and A549 cells after IGFBP1 knockdown (D) and statistical analysis (E) (n = 3), magnification is 200x. (F and G) Transwell migration assays to test the migration ability of H1299 and A549 cells after IGFBP1 knockdown (F) and statistical analysis (G) (n = 3), magnification is 200x. The data are presented as the mean ± SD. *P < 0.05, **P < 0.01, ***P < 0.001, ns represents no statistical significance.

Fig 5

IGFBP1 promotes the cell cycle progression and epithelial-mesenchymal transition (EMT) in lung adenocarcinoma cells

Tumor cell proliferation and migration are closely related to the cell cycle and EMT [32,33]. To explore the effect of IGFBP1 silencing on the cell cycle distribution of lung adenocarcinoma cells, we used flow cytometric cell cycle analysis to study the impact of IGFBP1 on the proliferation of lung adenocarcinoma cells. The results showed that the knockdown of IGFBP1 could arrest the lung adenocarcinoma cell cycle at the G2/M phase, thereby inhibiting cell proliferation (Fig. 6A and B, Supplementary Figure 4A and B). Cyclin-dependent kinases (CDKs) are an important class of serine/threonine protein kinases that advance the progression of the cell cycle by catalyzing phosphorylation reactions on serine and threonine residues [34]. Therefore, we used western blotting to detect the expression of cell cycle-related indicators (CDK1 and CDK2) and EMT-related indicators (E-Cadherin, Vimentin, and N-Cadherin). The results showed that after the knockdown of IGFBP1, the protein expression of CDK1 and CDK2 in H1299 and A549 cells was significantly inhibited (Fig. 6C and D). Similarly, Vimentin and N-Cadherin proteins were significantly downregulated, while the expression of E-Cadherin was upregulated (Fig. 6E and F). This indicates that IGFBP1 can promote the expression of cell cycle progression and EMT in lung adenocarcinoma cells.Fig. 6 The cell cycle distribution and corresponding protein expression levels of lung adenocarcinoma cells after IGFBP1 knockdown. (A and B) The flow cytometric cell cycle assay detected the changes in the levels of cell numbers in various phases (G0/G1, S, and G2/M) of H1299 (A) and A549 (B) cells after IGFBP1 knockdown. (C and D) Western blotting analysis was employed to detect the protein expression levels of CDK1 and CDK2 in H1299 and A549 cells after IGFBP1 knockdown(C), and statistical analysis (D) (n = 3). (E and F) Western blotting analysis was employed to detect the protein expression levels of E-Cadherin, Vimentin, and N-Cadherin in H1299 and A549 cells after IGFBP1 knockdown (E), and statistical analysis (F) (n = 3). The data are presented as the mean ± SD. *P < 0.05, **P < 0.01, ***P < 0.001, ns represents no statistical significance.

Fig 6

IGFBP1 facilitates the growth and migration of lung adenocarcinoma cells by modulating PPARα

Since IGFBP1 can regulate lipid metabolism, and PPARα is a key transcription factor affecting lipid metabolism [35]. In addition, studies have reported that PPARα can induce multiple lipid metabolic pathways, thereby promoting the proliferation, migration, and cycle of tumor cells [36,37]. We used western blotting to detect the protein expression of PPARα in H1299 and A549 cells after IGFBP1 knockdown, and the results showed that the expression level of PPARα protein was significantly reduced after silencing IGFBP1 (Fig. 7A and B). To further investigate the impact of the IGFBP1-PPARα signaling pathway on the growth and migration of lung adenocarcinoma cells, we first used the PPARα agonist GW7647 (10 μM) to treat H1299 and A549 cells in colony formation and transwell migration assays. The experimental results showed that GW7647 could promote the proliferation and migration of lung adenocarcinoma cells (Fig. 7C and D). Secondly, to verify the role of IGFBP1 in activating the PPARα signaling pathway, we selected the most effective si_IGFBP1#3 sequence and treated two lung cancer cell lines, H1299 and A549. We set up the si_IGFBP1#3 group and the si_IGFBP1#3+GW7647 group, and the results showed that the addition of the activator GW7647 could reverse the decrease in the proliferation and migration ability of lung adenocarcinoma cells caused by IGFBP1 knockdown (Fig. 7E and F). These findings suggest that IGFBP1 may promote the proliferation and migration of tumor cells by affecting the transcription factor PPARα related to lipid metabolism, thereby playing a promoting role in the development of lung adenocarcinoma.Fig. 7 The regulation of PPARα expression by IGFBP1 and its impact on the proliferation and migration of lung adenocarcinoma cells. (A and B) Western blotting analysis of the protein expression levels of PPARα in H1299 and A549 cells after IGFBP1 knockdown (A) and statistical analysis (B) (n = 3). (C and D) Colony formation assay (C) and Transwell migration assay (D) to detect the effects of the control group and the GW7647 (10 μM) treatment group on the proliferation and migration abilities of H1299 and A549 cells, along with statistical analysis (n = 3). (E and F) Colony formation assay (E) and transwell migration assay (F) to test the effects of the si_IGFBP1#3 treatment group and the si_IGFBP1#3+GW7647 (10 μM) treatment group on the proliferation and migration abilities of H1299 and A549 cells, along with statistical analysis (n = 3), with a magnification of 200x. The data are presented as the mean ± SD. *P < 0.05, **P < 0.01, ***P < 0.001, ns represents no statistical significance.

Fig 7

Knockdown of IGFBP1 inhibits tumor burden in vivo

To investigate the effect of IGFBP1 on tumor growth in vivo, LA-4 cells with stable knockdown of IGFBP1 were subcutaneously injected into C57BL/6 mice, and tumor size was detected 27 days later. The results showed that the tumor volume and weight of the IGFBP1 knockdown group (sh_IGFBP1#1/2) were significantly lower than those of the control group (sh_NC) (Fig. 8A-D). Immunofluorescence detection of IGFBP1 and PPARα expression in tumor tissues showed that the expression of IGFBP1 and PPARα was reduced in the IGFBP1 knockdown group, indicating that IGFBP1 and PPARα may be key genes regulating tumor cell growth (Fig. 8E-G). To further explore whether LA-4 cells injected subcutaneously with sh_IGFBP1 have the ability to metastasize to the lungs and form metastatic nodules, we conducted histopathological analysis with HE staining on lung tissues from three groups of mice. The results showed that after injecting LA-4 cells into the mice, no significant metastatic phenomena were observed in the lung tissues (Supplementary Figure 5A and B), indicating that the metastatic ability of LA-4 cells injected subcutaneously with sh_IGFBP1 may be limited.Fig. 8 Construction and analysis of subcutaneous hematoma animal model. (A and B) Three groups of subcutaneous lung cancer models are constructed (A) and photos of mice and tumors are shown (B). (C and D) Statistical graphs showing the tumor volume (C) and weight size (D) of the three groups of mice (n = 6). (E-G) The immunofluorescence in three groups of animal models reveals the expression levels of PPARα and IGFBP1 in tumor tissues, with a scale bar of 20 μm. The data are presented as the mean ± SD. *P < 0.05, **P < 0.01, ***P < 0.001, ns represents no statistical significance.

Fig 8

Discussion

Lipid metabolism is closely related to tumor growth. On the one hand, tumor cells have an increased demand for the uptake and metabolism of lipids [11,38]. As a high-energy substance, lipids can provide abundant energy and essential lipid molecules to meet the growth needs of tumor cells [39,40]. On the other hand, some tumor cells exhibit abnormal metabolism of lipids, including excessive synthesis, storage, and utilization of lipids, thereby regulating the cell's ability to proliferate, undergo apoptosis, differentiate, and migrate [38].

This study, based on 136 lipid metabolism genes, divided LUAD patients into three disease subtypes. There are significant survival differences between the three disease subtypes, with patients in subtype C1 having a significantly better prognosis than those in subtypes C2 and C3. Additionally, patients in subtype C2 have the lowest score for genes related to lipid metabolism, while subtypes C1 and C3 have higher scores, which may imply that there is a higher expression of high-risk lipid metabolism factors affecting the prognosis of LUAD patients in subtype C2. The correlation analysis of clinical pathological characteristics showed that subtype C1 is associated with a higher rate of EGFR mutations and a lower rate of KRAS mutations, while subtypes C2 and C3 are associated with a lower rate of EGFR mutations and a higher rate of KRAS mutations. This indicates that the impact of KRAS gene mutations in LUAD may be more significant than that of EGFR gene mutations [41,42].

Next, we first explored the differences in drug therapy among different populations, with subtype C2 showing a more sensitive response to targeted therapy. Targeted therapy in drug treatment, EGFR-TKI (epidermal growth factor receptor tyrosine kinase inhibitor), can inhibit the activity of EGFR and prevent the growth and spread of tumor cells. Based on the comparison of IC50 values of EGFR-TKI drugs, we found that subtype C2 is highly sensitive to EGFR-TKI, but its EGFR mutation rate is lower than that of subtype C1. The complexity of the tumor microenvironment may be a potential reason; lower levels of immunosuppressive cells, higher levels of immune-activating cells, and specific metabolic characteristics that may promote the drug's effect all contribute to a microenvironment that is more receptive to EGFR-TKI treatment [43,44]. In addition, different drug metabolism and transport characteristics may also lead to higher concentrations of EGFR-TKI in tumor tissues, thereby enhancing drug sensitivity [44,45]. At the same time, subtype C2 may have the activation of other signaling pathways that may crosstalk with the EGFR signaling pathway, increasing sensitivity to EGFR-TKI [46]. These factors work together to make the tumor cells of subtype C2 show a good response to EGFR-TKI even when the EGFR mutation rate is low.

We also applied the TIDE algorithm to predict the likelihood of different subtype patients' responses to immunotherapy, and the results showed that subtype C1 has a stronger response to immunotherapy. A higher TIDE score indicates a higher possibility of immune evasion, which suggests a poorer effect of immunotherapy. In addition, based on the analysis of the enrichment of the three subtypes in the tumor microenvironment, we found that subtype C1 has a higher level of immune cell infiltration, indicating a better effect for immunotherapy. TMB (Tumor Mutational Burden) analysis revealed that subtype C1 has the lowest TMB value, a phenomenon that may be attributed to the complexity of the tumor microenvironment. Specifically, the presence of tumor immune-suppressive factors, the activity and diversity of immune cells, and the interactions between tumor cells and immune cells may all affect the effectiveness of immunotherapy [44]. Secondly, even with a lower TMB, certain mutations may produce highly immunogenic neoantigens, sufficient to trigger a strong immune response [47]. Moreover, subtype C1 may have unique immune microenvironment characteristics, such as lower immune suppression, reducing the inhibitory effect on immune cells, making immunotherapy more effective, or the presence of specific types of immune cells that may effectively recognize and attack tumor cells [44,48]. Even with a lower TMB, the C1 subtype population will be sensitive to immunotherapy. Therefore, the finding in subtype C1 suggests that while TMB is an important indicator for predicting the response to immunotherapy, it is not the only one.

To investigate the function of lipid metabolism-related genes in the prognosis of lung adenocarcinoma patients, we used single- and multi-factor Cox and Lasso analyses to establish a prognostic model related to lipid metabolism factors. The analysis results revealed four key prognostic genes related to lipid metabolism: IGFBP1, NR0B2, PPARA, and POMC, which together constitute this model. Among them, IGFBP1 is the only independent poor prognostic factor, and its high expression is found in patients with the worst prognosis in subtype C3. IGFBP is a member of the insulin-like growth factor binding protein family, encoding a protein with an IGFBP N-terminal domain and a thyroglobulin type I domain, mainly affecting lipid synthesis and oxidation [49].

By reducing the expression of IGFBP1 in H1299 and A549 cells in vitro, we observed that the proliferation, migration ability, cell cycle, and EMT capabilities of tumor cells were all significantly inhibited. In vivo, the reduction of IGFBP1 expression also significantly reduced the tumor cell burden. Consistent with this, it has been reported in studies that in gastric cancer, IGFBP1 has been proven to predict hematogenous metastasis in patients with gastric adenocarcinoma and to assess the prognostic value of gastric adenocarcinoma patients [50,51]. In lung cancer, secreted IGFBP1 can increase the activity of SOD2, reduce the accumulation of mitochondrial reactive oxygen species (ROS) within the cells, thereby aiding the survival of tumor cells in lung tissue and promoting the metastasis of tumor cells [52,53].

At the mechanistic level, in vitro experiments where IGFBP1 was knocked down led to a reduction in the levels of PPARα in H1299 and A549 cells. Similarly, in vivo experiments silencing IGFBP1 also observed a decrease in the expression of PPARα in tumor tissues. The use of the PPARα activator GW7647 could promote the proliferation and migration of lung adenocarcinoma cells, and after knocking down IGFBP1, the addition of GW7647 further promoted the proliferation and migration abilities of lung adenocarcinoma cells. This suggests that IGFBP1 may regulate the growth and migration of tumor cells through PPARα. PPARα (Peroxisome Proliferator-Activated Receptor α) is a nuclear receptor transcription factor and a member of the type II nuclear hormone receptor superfamily [54]. Under normal conditions, PPARα is involved in the regulation of fatty acid metabolism, promoting the entry of fatty acids into the mitochondria for oxidation to produce energy [35]. However, in tumor cells, the expression and function of PPARα are altered, leading to abnormal fatty acid oxidation, thereby providing energy for tumor growth. In addition, PPARα can bind with nuclear receptors within the cell, thereby regulating the expression of cell cycle proteins, such as cyclin D1 and cyclin E, which enhances the proliferative capacity of tumor cells [55].

In summary, this study utilized bioinformatics methods to conduct an in-depth analysis of the lipid metabolism characteristic genes in LUAD patients and identified IGFBP1 as a prognostic risk factor for LUAD. Through experimental validation, it was found that IGFBP1 promotes the proliferation and migration of lung adenocarcinoma cells by activating the PPARα transcription factor. This discovery may provide a potential therapeutic target for future clinical treatment.

Data availability

All data generated or analyzed during this study are included in this article.

Ethical approval statement

This study was approved by the medical ethics committee of Anhui University of Science and Technology (NO.HX-002)

CRediT authorship contribution statement

Yunyun Li: Writing – original draft, Methodology, Data curation, Validation. Xuelian Yang: Methodology, Investigation, Validation. Tao Han: Conceptualization, Formal analysis, Visualization. Jiawei Zhou: Methodology, Data curation, Validation. Yafeng Liu: Investigation, Software, Visualization. Jianqiang Guo: Methodology, Conceptualization, Investigation. Ziqin Liu: Investigation, Conceptualization, Validation. Ying Bai: Investigation, Resources. Yingru Xing: Investigation, Resources. Xuansheng Ding: Writing – review & editing, Funding acquisition, Supervision. Jing Wu: Writing – review & editing, Funding acquisition, Supervision. Dong Hu: Writing – review & editing, Supervision, Funding acquisition.

Declaration of competing interest

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

Appendix Supplementary materials

Image, application 1

Image, application 2

Acknowledgement

This study was sponsored by the 10.13039/501100001809 National Natural Science Foundation of China (81971483 ); the Collaborative Innovation Project of Colleges and Universities of Anhui Province (GXXT-2020-058 ); Anhui Province Engineering Laboratory of Occupational Health and Safety (AYZJSGCLK202201001 , AYZJSGCLK202201002 , AYZJSGCLK 202202001 ); Key Laboratory of Industrial Dust Deep Reduction and Occupational Health and Safety of Anhui Higher Education Institutes (AYZJSGXLK202202002 ); the Innovation and Entrepreneurship Project of Anhui University of Science and Technology (2023CX2149 , 2021CX2126 , 2021CX2124 ) and Scientific Research Foundation for High-level Talents of Anhui University of Science and Technology (2022yjrc14 ). The funder had no role in the study design, data collection, analysis, and decision to publish or preparation of the manuscript. The project was supported by the Independent Research Fund of Key Laboratory of Industrial Dust Prevention and Control &Occupational Health and Safety, Ministry of Education (Anhui University of Science and Technology) (No. EK20202001 ).

Supplementary material associated with this article can be found, in the online version, at doi:10.1016/j.tranon.2024.102095.
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