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Integrated Bioinformatical Analysis Identifies GIMAP4 as an Immune-Related Prognostic Biomarker Associated With Remodeling in Cervical Cancer Tumor Microenvironment
免疫相關的預后分子GIMAP4與宮頸癌免疫微環境重塑的關系
發表雜志： Front Cell Dev Biol
影響因子：6.681
研究背景
腫瘤微環境在宮頸癌發生發展的過程中發揮重要作用，然而理解腫瘤微環境的特定組成成分仍然面對很對挑戰，尤其對于免疫細胞及基質成分的鑒別。
流程圖
結果解讀
TCGA_宮頸癌隊列免疫浸潤分析
作者分別計算TCGA_宮頸癌隊列的ImmuneScore, StromalScore, 以及ESTIMATEScore,根據免疫評分的中位值分為高低表達組，分別分析每種免疫評分與宮頸癌患者的預后的關系，并繪制KM曲線（Figure1A-C）。作者同時探討了每種免疫評分在不同的TNM分期以及病理分期之間的區別，結果發現免疫評分隨著腫瘤分期增加出現了明顯下降，以箱式圖展示（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.
鑒別免疫浸潤水平不同的患者之間的差異基因
因為免疫成分的變化與宮頸癌患者的預后有些重要聯系，作者根據ImmuneScore的中位值將宮頸癌患者分為高低表達組，共鑒別出上調基因643個，下調基因424個（Figure3A）。選取其中差異最大的前20個基因以熱圖方式展示（Figure3B）。
作者對差異基因進行富集分析，發現差異基因主要富集在免疫反應，如T-cell
activation and lymphocyte activation regulation（Figure3C）。KEGG也主要富集在免疫相關的通路，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.
鑒定出與免疫相關的差異突變基因
既往研究發現癌基因突變會引起免疫微環境的改變，影響腫瘤的進展。作者為了分析不同免疫評分的患者之間是否存在突變差異的基因，對TCGA宮頸癌隊列進行了突變分析。結果發現高低免疫評分的患者之間有眾多差異突變基因，并展示在Figure4A,B。
在前30個基因中，除TTN, PIK3CA, MUC4, KMT2C, MUC16在繼往宮頸癌研究中證實未參與免疫浸潤外，高免疫評分組的突變水平更高。作者根據P值對32個差異突變基因進行排序，發現GIMAP4在高免疫評分組的突變水平以及表達水平明顯高于低滿意評分組。作者選取GIMAP4作為主要研究基因（Figure4C,D）。
GIMAP4于宮頸癌患者的預后相關性分析及腫瘤性狀相關性分析
作者根據GIMAP4的表達量將宮頸癌患者分為高低表達組，分析兩組患者之間的預后差異（Figure5A）。并且在癌于正常組之間，GIMAP4的表達也存在差異（Figure5B）。根據不同病理分期，臨床分期比較不同分組之間GIMAP4的表達差異（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
作者首先選取MSigDB網站提供的C2基因集對GIMAP4高低表達組間的差異基因進行富集分析。發現GIMAP4高表達組的基因參與了多條免疫相關通路，如B cell receptor signaling pathway, chemokine signaling pathway, JAK-STAT signaling pathway，GIMAP4低表達組的基因參與代謝相關通路，如biosynthesis of unsaturated fatty acids, terpenoid backbone biosynthesis, pentose phosphate pathway（Figure 6A,B）作者選用HALLMARK基因集進行了富集（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.
宮頸癌免疫浸潤分析
作者計算宮頸癌患者免疫浸潤環境及免疫細胞之間的相關性（Figure 7A,B）。比較GIMAP4高低表達組患者之間免疫細胞的差異。分析了和GIMAP4表達相關的免疫細胞（Figure 7C）。兩者取交集（Figure7E）。
作者還分析了高低表達組之間免疫檢查點的差異，以及分析了GIMAP4表達于免疫檢查點的相關性（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、小結
本文，作者通過免疫評分將突變和免疫兩個熱點聯系起來，篩選除單基因進行分析，從臨床相關性，預后以及免疫浸潤水平等多方面闡述GIMAP4的價值，總的來講，全文免疫浸潤分析并沒有采用特別的套路，全文走行一個“差異”招數，勝在確實該基因免疫浸潤密切相關，并且與宮頸癌患者的預后相關。值得后續實驗挖掘。
