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. 2024 Jun 15;14(6):2731-2754.
doi: 10.62347/GIGO3446. eCollection 2024.

Leveraging FAM features to predict the prognosis of LGG patients and immunotherapy outcome

Liangbin Lin  1   2   3 , Hui Yu  1 , Xuelu Xie  4 , Qingqiang Lei  5 , Xuerui Chen  1 , Xu Su  1   6 , Xiuxuan Wang  7 , Sunfu Zhang  1 , Wenyong Yang  1
Affiliations

Leveraging FAM features to predict the prognosis of LGG patients and immunotherapy outcome

Liangbin Lin et al. Am J Cancer Res. .

Abstract

Heterogeneity at biological and transcriptomic levels poses a challenge in defining and typing low-grade glioma (LGG), leading to a critical need for specific molecular signatures to enhance diagnosis, therapy, and prognostic evaluation of LGG. This study focused on fatty acid metabolism (FAM) related genes and prognostic features to investigate the mechanisms and treatment strategies for LGG cell metastasis and invasion. By screening 158 FAM-related genes and clustering 512 LGG samples into two subtypes (C1 and C2), differential gene expression analysis and functional enrichment were performed. The immune cell scores and prognosis were compared between the two subtypes, with C1 showing poorer outcomes and higher immune scores. A four-gene signature (PHEX, SHANK2, HOPX, and LGALS1) was identified and validated across different datasets, demonstrating a stable predictive effect. Cellular experiments confirmed the roles of LGALS1 and HOPX in promoting tumor cell proliferation, migration, and invasion, while SHANK2 exhibited a suppressive effect. This four-gene signature based on FAM-related genes offers valuable insights for understanding the pathogenesis and clinical management of LGG.

Keywords: HOPX; LGG; PHEX; SHANK2; Tumor immunology; and LGALS1; fatty acid metabolism; prognostic features.

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Conflict of interest statement

None.

Figures

Figure 1
Figure 1
Flowchart for comprehensive analysis of fatty acid metabolism in postoperative patients with Low-grade gliomas (LGG).
Figure 2
Figure 2
Molecular subtyping of LGG and prognostic survival analysis. (A) Consensus map of NMF clustering. (B) The resultant distribution at rank = 2-10. (C) The RSS distribution with rank = 2-10. (D, E) Overall survival (OS) prognostic survival curve (D) and disease-free survival (DFS) prognostic survival curve (E) of LGG molecular subtypes.
Figure 3
Figure 3
Comparison of clinical and molecular immune features in LGG subtypes. A-D. Comparison of the distribution of two subtypes in various clinical features and molecular immune subtypes in the TCGA dataset. E. Comparison of existing molecular immune subtypes with two subtypes. F, G. KM OS time and DFS time curves among existing immune molecule subtypes.
Figure 4
Figure 4
Comparison of immune scores in LGG molecular subtypes. (A-C) Comparison of Estimated immunity scores (A), MCPcounter immune scores (B), and CIBERSOTR immunity scores (C) between molecular subtypes in the TCGA dataset. (D) Heat map comparing immune scores between molecular subtypes in the TCGA data set by three immune software. The data in (A-C) are shown as the mean ± SEM. *P < 0.05; **P, 0.01; ***P, 0.001 (two-way ANOVA followed by Tukey’s multiple comparisons for A-C).
Figure 5
Figure 5
Differential gene expression and pathway analysis in LGG molecular subtypes. (A) Volcanic map of differentially expressed genes in two groups. (B) Heat map of differentially expressed genes in two groups. (C, D) Biological processes (BP) of molecular subtypes differentially up-regulated genes (C), molecular subtypes differentially down-regulated genes (D). (E, F) KEGG of molecular subtypes differential up-regulation gene (E) and differential down-regulation gene (F).
Figure 6
Figure 6
Trajectory analysis and gene prognostic significance in LGG. A. Independent variable trajectories: horizontal axis (representing the logarithm of the dependent variable) and vertical axis (representing the coefficient of the independent variable). B. Confidence intervals for each λ. C-F. KM Curves of 4 Genes in TCGA training set.
Figure 7
Figure 7
Prognostic significance and classification performance of four genes in LGG. FAMR, survival time, survival state, and four gene expressions of the TCGA training set (A), the TCGA validation set (D), and the full TCGA dataset (G). ROC curve and AUCs of four gene features classification in TCGA training set (B), the TCGA validation set (E), and the full TCGA dataset (H). The KM survival curve distribution of four gene features in the TCGA training set (C), the TCGA validation set (F), and the full TCGA dataset (I).
Figure 8
Figure 8
Prognostic significance and classification performance of four genes in multiple CGGA databases. FAMR, survival time, survival state, and four gene expressions of the CGGA-mRNA-array_301 database (A), the CGGA-mRNAseq_325 database (D), the CGGA-mRNAseq_693 database (G). ROC curve and AUCs of four gene feature classifications in the CGGA-mRNA-array_301 database (B), the CGGA-mRNAseq_325 database (E), the CGGA-mRNAseq_693 database (H). The KM survival curve distribution of four gene features in the CGGA-mRNA-array_301 database (C), the CGGA-mRNAseq_325 database (F), and the CGGA-mRNAseq_693 database (I).
Figure 9
Figure 9
Prognostic significance of Riskscore in LGGs stratified by clinical features. (A-H) According to FAMR, the patients in the G2 group (A), the patients in the G3 group (B), the patients in the female group (C), the patients in the male group (D), patients aged ≥ 60 groups (E), patients aged < 60 groups (F), patients in IDH WT group (G) and patients in IDH mutation group (H) were divided into two groups with significant prognosis. (I) FAMR in G2 and G3 of LGGs. (J) FAMR in IDH WT group and IDH mutation group of LGGs.
Figure 10
Figure 10
Association of FAMR with signaling pathways and immune/stromal scores in LGGs. (A) Correlation between FAMR and KEGG signaling pathway score. (B) Heat map of the KEGG signaling pathway. (C-E) The correlation between FAMR and StromalScore (C), ImmuneScore (D), and ESTIMATEScore (E).
Figure 11
Figure 11
Prognostic significance of FAMR in LGG patients receiving immunotherapy. A. The KM survival curve distribution of different FAMR groups in LGG patients receiving immunotherapy. B. FAMR in SD, PD, CR, and PR groups of LGG patients receiving immunotherapy. C. Forest map of multivariate cox regression analysis.
Figure 12
Figure 12
Illustrates the roles of PHEX, SHANK2, HOPX, and LGALS1 in the proliferation, migration, and invasion of LGG cells. (A) Impact of gene knockdown on cell proliferation in SW1088 cells assessed using the MTT assay. (B) Evaluation of cell proliferation after knockdown of the specified genes using the CCK-8 assay in SW1088 cells. (C) Assessment of cell migratory abilities following knockdown of the mentioned genes through wound healing assays in SW1088 cells. (D) Examination of cell invasive capacities upon knockdown of the indicated genes using transwell assays in SW1088 cells. (E) Statistical analysis of the wound healing assays mentioned in (C). (F) Statistical evaluation of the transwell assays as mentioned in (D).
Figure 13
Figure 13
Correlation between FAM-Related 4 gene expression and PDCD1 expression in low-grade glioma. (A-D) Panels show scatter plots of gene expression (X-axis) versus PDCD1 expression (Y-axis) for each of the four genes: LGALS1 (A), HOPX (B), SHANK2 (C), and PHEX (D). Each dot represents an individual patient sample. Blue lines indicate the trend lines derived from linear regression analysis. Correlation coefficients (r) are as follows: LGALS1 (r = 0.323), HOPX (r = 0.152), SHANK2 (r = -0.232), and PHEX (r = 0.151). These results demonstrate the varying degrees of association between gene expression and PDCD1 levels, highlighting the potential regulatory roles of these genes in immune checkpoint pathways.
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