IGFBP1 promotes the proliferation and migration of lung adenocarcinoma cells through the PPARα pathway

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. 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. 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. 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. 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. 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. 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. 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.