Integrated Bioinformatical Analysis Identifies GIMAP4 as an Immune-Related Prognostic Biomarker Associated With Remodeling in Cervical Cancer Tumor Microenvironment

免疫相关的预后分子GIMAP4与宫颈癌免疫微环境重塑的关系

发表杂志： Front Cell Dev Biol

影响因子：6.681

研究背景

肿瘤微环境在宫颈癌发生发展的过程中发挥重要作用，然而理解肿瘤微环境的特定组成成分仍然面对很对挑战，尤其对于免疫细胞及基质成分的鉴别。

流程图

结果解读

TCGA_宫颈癌队列免疫浸润分析

作者分别计算TCGA_宫颈癌队列的ImmuneScore, StromalScore, 以及ESTIMATEScore,根据免疫评分的中位值分为高低表达组，分别分析每种免疫评分与宫颈癌患者的预后的关系，并绘制KM曲线（Figure1A-C）。作者同时探讨了每种免疫评分在不同的TNM分期以及病理分期之间的区别，结果发现免疫评分随着肿瘤分期增加出现了明显下降，以箱式图展示（Figure1D-O）。

FIGURE 2 | Correlation analyses of scores with survival and clinicopathological characteristics of CC patients. (A–C) Kaplan–Meier survival analysis for CC patients grouped into high or low scores in ImmuneScore, StromalScore, and ESTIMATEScore determined by comparing them with the median. p = 0.020, 0.186, and 0.006, respectively, by log-rank test. (D–F) Distribution of ImmuneScore, StromalScore, and ESTIMATEScore in the stage classification. The p = 0.45, 0.43, and 0.49, respectively, by Kruskal–Wallis rank sum test. (G–I) Distribution of three kinds of scores in the T classification (p = 0.5, 0.84, 0.55 by Kruskal–Wallis rank sum test for ImmuneScore, StromalScore, and ESTIMATEScore, respectively). (J–L) Distribution of scores in the M classification (p = 0.015, 0.014, 0.004 by Wilcoxon rank sum test for ImmuneScore, StromalScore, and ESTIMATEScore separately). (M–O) Distribution of scores in N classification. Similar to the preceding, p = 0.56, 0.27, 0.40, respectively, with Wilcoxon rank sum test.

鉴别免疫浸润水平不同的患者之间的差异基因

因为免疫成分的变化与宫颈癌患者的预后有些重要联系，作者根据ImmuneScore的中位值将宫颈癌患者分为高低表达组，共鉴别出上调基因643个，下调基因424个（Figure3A）。选取其中差异最大的前20个基因以热图方式展示（Figure3B）。

作者对差异基因进行富集分析，发现差异基因主要富集在免疫反应，如T-cell

activation and lymphocyte activation regulation（Figure3C）。KEGG也主要富集在免疫相关的通路，如cytokine–cytokine receptor interaction, cell adhesion molecules, chemokine signaling pathway（Figure3D）。

FIGURE 3 | Volcano plot, Heatmap, and enrichment analysis of GO and KEGG for DEGs. (A) Volcano plot for DEGs. The blue and red dots represented the significantly downregulated and upregulated genes, respectively; and the gray dots represented the genes without differential expression. FDR < 0.05, | log2 FC | > 1 and p < 0.05 (B) Heatmap for DEGs generated by comparison of the high score group vs. the low score group in ImmuneScore. The row name of heatmap is the gene name, and the column name is the ID of samples which not shown in the plot. DEGs were determined by Wilcoxon rank sum test with FDR < 0.05 and | log2 FC | > 1 as the significance threshold. (C,D) GO and KEGG enrichment analysis for 1067 DEGs, terms with p and q < 0.05 were believed to be enriched significantly.

鉴定出与免疫相关的差异突变基因

既往研究发现癌基因突变会引起免疫微环境的改变，影响肿瘤的进展。作者为了分析不同免疫评分的患者之间是否存在突变差异的基因，对TCGA宫颈癌队列进行了突变分析。结果发现高低免疫评分的患者之间有众多差异突变基因，并展示在Figure4A,B。

在前30个基因中，除TTN, PIK3CA, MUC4, KMT2C, MUC16在继往宫颈癌研究中证实未参与免疫浸润外，高免疫评分组的突变水平更高。作者根据P值对32个差异突变基因进行排序，发现GIMAP4在高免疫评分组的突变水平以及表达水平明显高于低满意评分组。作者选取GIMAP4作为主要研究基因（Figure4C,D）。

GIMAP4于宫颈癌患者的预后相关性分析及肿瘤性状相关性分析

作者根据GIMAP4的表达量将宫颈癌患者分为高低表达组，分析两组患者之间的预后差异（Figure5A）。并且在癌于正常组之间，GIMAP4的表达也存在差异（Figure5B）。根据不同病理分期，临床分期比较不同分组之间GIMAP4的表达差异（Figure5C-F）。

FIGURE 5 | The differentiated expression of GIMAP4 in samples and correlation with survival and clinicopathological staging characteristics of CC patients. (A) Survival analysis for CC patients with different GIMAP4 expression. Patients were marked with high expression or low expression depending on comparing with the median expression level. p = 0.041 by log-rank test. (B) Differentiated expression of GIMAP4 in the normal and tumor sample. Analyses were conducted across all normal and tumor samples with p = 0.008 by Wilcoxon rank sum test. (C–F) The correlation of GIMAP4 expression with clinicopathological characteristics. Wilcoxon rank sum or Kruskal–Wallis rank sum test acted as the statistical significance test.

GIMAP4的GSEA分析

作者首先选取MSigDB网站提供的C2基因集对GIMAP4高低表达组间的差异基因进行富集分析。发现GIMAP4高表达组的基因参与了多条免疫相关通路，如B cell receptor signaling pathway, chemokine signaling pathway, JAK-STAT signaling pathway，GIMAP4低表达组的基因参与代谢相关通路，如biosynthesis of unsaturated fatty acids, terpenoid backbone biosynthesis, pentose phosphate pathway（Figure 6A,B）作者选用HALLMARK基因集进行了富集（Figure 6C,D）。

FIGURE 6 | GSEA for samples with high GIMAP4 expression and low expression. (A) Enriched gene sets in C2 collection, the KEGG gene sets, by samples of high GIMAP4 expression. Each line is represented one particular gene set with unique color, and up-regulated genes are located on the left which approach the origin of the coordinates; by contrast, the down-regulated ones lay on the right of the x-axis. Only gene sets both with NOM p < 0.05 and FDR q < 0.25 were considered significant. Only several top gene sets are shown in the plot. (B) Enriched gene sets in C2 by the low BTK expression. (C) The enriched gene sets in HALLMARK collection by samples with high GIMAP4 expression sample. (D) The enriched gene sets in HALLMARK in the low GIMAP4 expression.

宫颈癌免疫浸润分析

作者计算宫颈癌患者免疫浸润环境及免疫细胞之间的相关性（Figure 7A,B）。比较GIMAP4高低表达组患者之间免疫细胞的差异。分析了和GIMAP4表达相关的免疫细胞（Figure 7C）。两者取交集（Figure7E）。

作者还分析了高低表达组之间免疫检查点的差异，以及分析了GIMAP4表达于免疫检查点的相关性（Figure 7D,F）。

FIGURE 7 | TIC profile in CC samples and correlation analysis, and correlation of TICs proportion and common ICPs with GIMAP4 expression. (A) Barplot shows the proportion of 21 types of TICs in CC tumor samples. The column names of the plot were sample ID. (B) Heatmap shows the correlation between 21 kinds of TICs and numeric in each tiny box, indicating the p-value of the correlation between two cells. The shadow of each tiny color box represented a corresponding correlation value between two cells, and the Pearson coefficient was used for the significance test. (C) Violin plot showed the ratio differentiation of 21 types of immune cells

between CC tumor samples with low or high GIMAP4 expression relative to the median of GIMAP4 expression level, and Wilcoxon rank sum was applied for the significance test. (D) The Scatter plot showed the correlation of 13 kinds of TICs proportion with the GIMAP4 expression (p < 0.05). The blue line in each plot was a fitted linear model indicating the proportion tropism of the immune cell along with GIMAP4 expression, and the Pearson coefficient was used for the correlation test. (E) Venn plot displayed 12 kinds of TICs correlated with GIMAP4 expression codetermined by difference and correlation tests displayed in the violin and scatter plots, respectively. (F) The results showed that the expression of ICPs was significantly higher in the high GIMAP4 expression group than in the low one. ***p < 0.001.

四、小结

本文，作者通过免疫评分将突变和免疫两个热点联系起来，筛选除单基因进行分析，从临床相关性，预后以及免疫浸润水平等多方面阐述GIMAP4的价值，总的来讲，全文免疫浸润分析并没有采用特别的套路，全文走行一个“差异”招数，胜在确实该基因免疫浸润密切相关，并且与宫颈癌患者的预后相关。值得后续实验挖掘。