Pi Ya-Nan, Guo Jun-Nan, Lou Ge, Cui Bin-Bin
Department of Gynecology, Harbin Medical University Cancer Hospital, Harbin, 150086, P. R. China.
Department of Colorectal Surgery, Harbin Medical University Cancer Hospital, Harbin, 150086, P. R. China.
Cancer Cell Int. 2021 Dec 1;21(1):639. doi: 10.1186/s12935-021-02333-9.
Cervical cancer (CC) is the leading cause of cancer-related death in women. A limited number of studies have investigated whether immune-prognostic features can be used to predict the prognosis of CC. This study aimed to develop an improved prognostic risk scoring model (PRSM) for CC based on immune-related genes (IRGs) to predict survival and determine the key prognostic IRGs.
We downloaded the gene expression profiles and clinical data of CC patients from the TCGA and GEO databases. The ESTIMATE algorithm was used to calculate the score for both immune and stromal cells. Differentially expressed genes (DEGs) in different subpopulations were analyzed by "Limma". A weighted gene co-expression network analysis (WGCNA) was used to establish a DEG co-expression module related to the immune score. Immune-related gene pairs (IRGPs) were constructed, and univariate- and Lasso-Cox regression analyses were used to analyze prognosis and establish a PRSM. A log-rank test was used to verify the accuracy and consistency of the scoring model. Identification of the predicted key IRG was ensured by the application of functional enrichment, DisNor, protein-protein interactions (PPIs) and heatmap. Finally, we extracted the key prognostic immune-related genes from the gene expression data, validated the key genes by immunohistochemistry and analyzed the correlation between their expression and drug sensitivity.
A new PRSM was developed based on 22 IRGPs. The prognosis of the low-risk group in the model group (P < 0.001) and validation group (P = 0.039) was significantly better than that in the high-risk group. Furthermore, M1 and M2 macrophages were highly expressed in the low-risk group. Retinoic acid-inducible gene-I (RIG-I)-like receptors (RLRs) and the Janus kinase-signal transducer and activator of transcription (JAK-STAT) signaling pathway were significantly enriched in the low-risk group. Three representative genes (CD80, CD28, and LCP2) were markers of CC prognosis. CD80 and CD28 may more prominent represent important indicators to improve patient prognosis. These key genes was positively correlated with drug sensitivity. Finally, we found that differences in the sensitivity to JNK inhibitors could be distinguished based on the use and risk grouping of this PRSM.
The prognostic model based on the IRGs and key genes have potential clinical significance for predicting the prognosis of CC patients, providing a foundation for clinical prognosis judgment and individualized treatment.
宫颈癌（CC）是女性癌症相关死亡的主要原因。仅有少数研究调查了免疫预后特征是否可用于预测CC的预后。本研究旨在基于免疫相关基因（IRGs）开发一种改进的CC预后风险评分模型（PRSM），以预测生存情况并确定关键的预后IRGs。
我们从TCGA和GEO数据库下载了CC患者的基因表达谱和临床数据。使用ESTIMATE算法计算免疫细胞和基质细胞的评分。通过“Limma”分析不同亚群中的差异表达基因（DEGs）。采用加权基因共表达网络分析（WGCNA）建立与免疫评分相关的DEG共表达模块。构建免疫相关基因对（IRGPs），并使用单变量和Lasso-Cox回归分析来分析预后并建立PRSM。使用对数秩检验验证评分模型的准确性和一致性。通过功能富集、DisNor、蛋白质-蛋白质相互作用（PPIs）和热图来确定预测的关键IRG。最后，我们从基因表达数据中提取关键的预后免疫相关基因，通过免疫组织化学验证关键基因，并分析其表达与药物敏感性之间的相关性。
基于22个IRGPs开发了一种新的PRSM。模型组（P < 0.001）和验证组（P = 0.039）中低风险组的预后明显优于高风险组。此外，M1和M2巨噬细胞在低风险组中高表达。视黄酸诱导基因-I（RIG-I）样受体（RLRs）和Janus激酶-信号转导子和转录激活子（JAK-STAT）信号通路在低风险组中显著富集。三个代表性基因（CD80、CD28和LCP2）是CC预后的标志物。CD80和CD28可能更突出地代表改善患者预后的重要指标。这些关键基因与药物敏感性呈正相关。最后，我们发现基于该PRSM的使用和风险分组可以区分对JNK抑制剂敏感性的差异。
基于IRGs的预后模型和关键基因对预测CC患者的预后具有潜在的临床意义，为临床预后判断和个体化治疗提供了基础。
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