Hypoxia and lipid metabolism related genes drive proliferation migration and immune infiltration mechanisms in colorectal cancer subtyping

Abstract

Hypoxia and lipid metabolism play crucial roles in the progression of colorectal cancer (CRC). However, the specific functions of hypoxia- and lipid metabolism-related genes (HLPG) in CRC and their relationships with patient prognosis remain unclear. Differential expression analysis using the TCGA-COAD and GEO databases identified 117 HLPGs through the intersection of the two gene sets. After univariate Cox regression analysis, 17 prognostically relevant HLPG were identified. Consensus clustering classified CRC samples into two subtypes, and the immune microenvironment differences between them were evaluated. A risk scoring model utilizing seven prognostically significant HLPGs was created and its predictive performance was assessed through survival analysis and ROC curves. Finally, the key genes ITLN1 and SFRP2 were functionally validated in CRC cell lines. HLPG was closely linked to CRC prognosis. Two molecular subtypes were identified: Cluster A, characterized by enriched immune pathways and higher immune infiltration, and Cluster B, associated with improved overall survival. The seven HLPG-based risk scoring model effectively stratified patients into high- and low-risk groups, with high-risk patients exhibiting significantly poorer survival outcomes. Functional studies confirmed that SFRP2 and ITLN1 play essential roles in CRC cell proliferation, migration, and epithelial-mesenchymal transition (EMT). Furthermore, ITLN1 upregulated PD-L1 expression, increasing sensitivity to immunotherapy. Hypoxia was found to promote lipid metabolic alterations by modulating SFRP2 and ITLN1 expression. This study highlights the prognostic significance of HLPGs in CRC and introduces a robust risk scoring model for patient outcome prediction. ITLN1 could be a target for enhancing immunotherapy response in CRC.

Keywords: Colorectal cancer; Hypoxia; ITLN1; Immune microenvironment; Lipid metabolism; Prognostic model; SFRP2.

Fig. 1

Selection and functional analysis of hypoxia- and lipid metabolism-related genes (HLPG) in colorectal cancer (CRC). (A) Volcano plot showing differentially expressed genes (DEGs) in the TCGA-COAD dataset. (B) Venn diagrams showing the intersection of DEGs with hypoxia-related genes (HPRG) and lipid metabolism-related genes (LMRG), followed by univariate Cox analysis identifying 17 HLPGs. (C) Gene interaction network of 17 HLPGs analyzed by GeneMANIA. (D,E) GO and KEGG enrichment analysis of 17 prognostic HLPGs.

Fig. 2

Subtype classification and prognostic analysis of CRC based on HLPGs. (A) Clustering selection plot for optimal cluster number. (B) PCA plot illustrating the distribution of clusters A and B. (C) Heatmap showing the correlation between clusters A/B and clinical traits. (D) Kaplan–Meier analysis comparing overall survival between clusters A and B. (E) GSVA enrichment analysis reveals biological differences between clusters A and B. (F) Immune cell infiltration analysis between cluster A and cluster B. *P < 0.05, **P < 0.01.

Fig. 3

GO and KEGG enrichment analysis of DEGs between clusters (A) and (B).

Fig. 4

SFRP2 promotes CRC cell proliferation and migration. (A) qPCR validation of SFRP2 knockdown efficiency. (B-D) CCK-8, EdU, and colony formation assays evaluating the effects of SFRP2 knockdown on CRC cell proliferation. (E,F) Transwell and wound healing assays demonstrate the impact of SFRP2 knockdown on CRC cell migration. *P < 0.05, **P < 0.01.

Fig. 5

Subtype classification and clinical significance based on prognostic genes. (A) Clustering selection plot. (B) PCA plot showing the distribution of genecluster A and genecluster B. (C) Heatmap showing the correlation between genecluster A/B and clinical traits. (D) Kaplan-Meier survival analysis comparing overall survival between geneclusters A and B. (E) LASSO regression and multivariate analysis identifying genes for the risk model. (F,G) Correlation between risk scores and clusters A/B, geneclusters A/B.

Fig. 6

Risk score model predicting prognosis in CRC patients. (A) Kaplan–Meier survival analysis for high and low-risk patients across the entire, test, and training cohorts. (B) ROC curves predicting overall survival for the entire, test, and training cohorts. (C,D) Distribution of risk scores and survival status across the entire, test, and training cohorts.

Fig. 7

Independent prognostic analysis of the risk score model. (A–C) Univariate and multivariate Cox analysis of the risk score across the entire, test, and training cohorts. (D) Nomogram predicting overall survival at 1, 3, and 5 years. (E) Calibration curves for the nomogram.

Fig. 8

Immune cell infiltration analysis in high and low-risk groups. (A) Heatmap illustrating the correlation between risk scores and immune cells. (B–H) Scatter plots illustrating the correlations between risk scores and various immune cells, including resting mast cells, M1 macrophages, CD4 + T cells, CD8 + T cells, M0 macrophages, M2 macrophages, and activated mast cells. (I) Analysis of risk scores with stromal and immune scores. (J–O) Immunotherapy sensitivity analysis based on risk scores.

Fig. 9

Expression, prognosis, and clinical correlation of ITLN1 in CRC. (A,B) ITLN1 expression analysis in CRC from the TCGA-COAD dataset. (C) Kaplan–Meier survival analysis of ITLN1 expression in CRC. (D–I) Correlation of ITLN1 with age, gender, T stage, N stage, M stage, and tumor stage. *P < 0.05, **P < 0.01, ***P < 0.001.

Fig. 10

ITLN1 inhibits CRC progression. (A) Single-cell RNA-seq analysis (GSE231559) showing ITLN1 expression in CRC. (B) ITLN1 expression levels in various CRC cell lines. (C) Analysis of ITLN1 expression in CRC using data from the Human Protein Atlas database. (D,E) EdU and colony formation assays showing ITLN1.s impact on CRC cell proliferation. (F,G) Transwell and wound healing assays indicate ITLN1’s involvement in CRC cell migration. *P < 0.05, **P < 0.01.

Fig. 11

ITLN1 suppresses cell cycle and EMT transition, promotes apoptosis, and enhances PD-L1 expression in CRC cells. (A) WB analysis of cell cycle proteins (CyclinD1, CDK4), apoptosis markers (bcl-2, bax), and EMT markers (E-cadherin, N-cadherin, vimentin). (B) Analysis of ITLN1 expression and its correlation with immunotherapy response. (C) Immunofluorescence analysis showing the effect of ITLN1 overexpression on PD-L1 expression.

Fig. 12

Hypoxia-induced regulation of SFRP2 and ITLN1 drives lipid metabolic changes in CRC cells. (A) qPCR analysis of the impact of hypoxia on SFRP2 and ITLN1 expression. (B) Immunofluorescence analysis of the effect of hypoxia on SFRP2 and ITLN1 expression. (C) WB analysis of SREBP1 expression. (D) WB analysis of the effect of SFRP2 knockdown or ITLN1 overexpression on SREBP1 expression under hypoxic conditions. (E) CCK-8 assay to evaluate the proliferation capacity of CRC cells under different experimental conditions. *P < 0.05, **P < 0.01, ^#P < 0.05, ^##P < 0.01.