Comprehensive Analysis of HHLA2 as a Prognostic Biomarker and Its Association With Immune Infiltrates in Hepatocellular Carcinoma.
Comprehensive Analysis of HHLA2 as a Prognostic Biomarker and Its Association With Immune Infiltrates in Hepatocellular Carcinoma.
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全面分析作为预后生物标志物的 HHLA2 及其与肝细胞癌免疫浸润的关系
DOI：
10.3389/fimmu.2022.831101
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发表时间：
2022
影响因子：
7.3
通讯作者：
Guo W
中科院分区：
文献类型：
--
作者：
Ding L;Yu Q;Yang S;Yang WJ;Liu T;Xian JR;Tian TT;Li T;Chen W;Wang BL;Pan BS;Zhou J;Fan J;Yang XR;Guo W
抑制性免疫检查点蛋白促进肿瘤免疫逃逸，并与较差的患者预后相关。然而，其成员之一HHLA2在肿瘤免疫微环境中的生物学功能和调节作用尚未被探索。使用RandomForest分析（371例）、qRT-PCR（15例）和免疫组织化学染色（189例）来验证HHLA2在肝细胞癌（HCC）患者中的预后价值。进一步进行生物信息学分析，探讨 HHLA2 影响的生物学功能和潜在信号通路。并采用ESTIMATE、单样本基因集富集分析、CIBERSORT、TIMER等反卷积方法分析免疫细胞的组成和浸润水平。采用多重免疫荧光测定来验证抑制性免疫细胞的分数，并研究了 HHLA2 相关的分子改变。最后，通过 TIDE、Submap 和其他几种计算机分析来预测对化疗和免疫检查点阻断的临床反应。 RandomForest 分析显示，HHLA2 是与 HCC 患者预后相关的最重要的抑制性免疫检查点（相对重要性 = 1）。我们的 HCC 队列进一步揭示，HHLA2 高表达是 HCC 患者总生存期较短 (P<0.01) 和复发时间 (P<0.001) 的独立预后生物标志物。生物信息学实验表明HHLA2可能加速癌细胞的细胞周期。此外，我们发现HHLA2的高表达与免疫浸润有关，包括一些免疫抑制细胞、细胞因子、趋化因子和相应的受体，从而导致免疫抑制环境。值得注意的是，HHLA2 表达与耗尽的 CD8+ T 细胞的浸润呈正相关，这一点已通过免疫荧光得到验证。基因组改变分析表明，HHLA2 启动子高甲基化可能与其低表达有关。更重要的是，HHLA2高表达的患者可能对化疗更敏感，对免疫治疗有更好的反应。 HHLA2的高表达是HCC患者的独立预后生物标志物。它可以通过富集耗尽的 CD8+ T 细胞来激活细胞周期并培育免疫抑制肿瘤微环境。启动子高甲基化可能导致 HCC 中 HHLA2 的低表达。因此，靶向HHLA2可能是未来HCC患者的实用治疗策略。
Inhibitory immune checkpoint proteins promote tumor immune escape and are associated with inferior patient outcome. However, the biological functions and regulatory roles of one of its members, HHLA2, in the tumor immune microenvironment have not been explored. RandomForest analyses (371 cases), qRT-PCR (15 cases), and immunohistochemical staining (189 cases) were used to validate the prognostic value of HHLA2 in hepatocellular carcinoma (HCC) patients. Bioinformatic analyses were further performed to explore the biological functions and potential signaling pathways affected by HHLA2. Moreover, ESTIMATE, single sample gene set enrichment analysis, CIBERSORT, TIMER, and other deconvolution methods were used to analyze the composition and infiltration level of immune cells. Multiplex immunofluorescence assays were employed to validate the fractions of suppressive immune cells, and HHLA2-related molecular alterations were investigated. Finally, the clinical response to chemotherapy and immune checkpoint blockade was predicted by TIDE, Submap, and several other in silico analyses. RandomForest analysis revealed that HHLA2 was the most important inhibitory immune checkpoint associated with HCC patient prognosis (relative importance = 1). Our HCC cohorts further revealed that high HHLA2 expression was an independent prognostic biomarker of shorter overall survival (P<0.01) and time to recurrence (P<0.001) for HCC patients. Bioinformatics experiments revealed that HHLA2 may accelerate the cell cycle of cancer cells. Additionally, we found that high expression of HHLA2 was associated with immune infiltrates, including some immunosuppressive cells, cytokines, chemokines, and corresponding receptors, resulting in an immunosuppressive environment. Notably, HHLA2 expression was positively correlated with the infiltration of exhausted CD8+ T cells, which was validated by immunofluorescence. Genomic alteration analyses revealed that promoter hypermethylation of HHLA2 may be associated with its low expression. More importantly, patients with high HHLA2 expression may be more sensitive to chemotherapy and have better responses to immunotherapy. High expression of HHLA2 is an independent prognostic biomarker for HCC patients. It can activate the cell cycle and foster an immunosuppressive tumor microenvironment by enriching exhausted CD8+ T cells. Promoter hypermethylation might lead to low expression of HHLA2 in HCC. Thus, targeting HHLA2 may be a practical therapeutic strategy for HCC patients in the future.
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影响因子：
9.8
作者：
Bhattacharya S;Dunn P;Thomas CG;Smith B;Schaefer H;Chen J;Hu Z;Zalocusky KA;Shankar RD;Shen-Orr SS;Thomson E;Wiser J;Butte AJ
通讯作者：
Butte AJ
DOI：
10.1038/nrc3239
发表时间：
2012-03-22
期刊：
Nature reviews. Cancer
影响因子：
--
作者：
Pardoll DM
通讯作者：
Pardoll DM
WGCNA：用于加权相关网络分析的 R 包。
影响因子：
3
作者：
Langfelder P;Horvath S
通讯作者：
Horvath S
影响因子：
3.7
作者：
Hoshida Y;Brunet JP;Tamayo P;Golub TR;Mesirov JP
通讯作者：
Mesirov JP
影响因子：
64.8
作者：
Khan, Omar;Giles, Josephine R.;Wherry, E. John
通讯作者：
Wherry, E. John