Prediction of prognosis and immune landscape in cervical cancer based on heat shock protein-related genes

<b>Objective:</b> Heat shock proteins (HSPs) play key roles in the malignant transformation and progression of many tumors. However, the effectiveness of using HSP-related genes to predict the prognosis of patients with cervical cancer (CC) remains elusive. We aimed to delineate the prognosis and biological significance of HSP-related genes in CC. <b>Methods:</b> We collected the transcriptional and clinical data of CC patients from The Cancer Genome Atlas (TCGA) and searched for HSP-related genes in the literature. LASSO and univariate/multivariate Cox regression analyses were utilized to screen genes; 12 genes were found to be related to CC survival, and a prediction model was built. The effectiveness of the model was confirmed using TCGA and GEO, and it was found to be an independent predictor of CC. The nomogram is plotted. The prognostic model was further visualized using calibration curves, which showed good agreement with the predicted outcomes at 1-, 3, and 5 years. <b>Results:</b> We found that low-risk patients had higher immune cell infiltration and stronger immune function, and according to the immunophenoscore and TIDE scores, the low-risk group tended to respond more to immunotherapy. Additionally, we used the GDSC database to predict drug sensitivity in patients with different prognostic risks. <b>Conclusion:</b> In summary, we built a good model to help predict the prognosis of CC patients and provide a reference for personalized treatment and medication for different patients.

**研究目的：** 热休克蛋白（Heat shock proteins, HSPs）在多种肿瘤的恶性转化与进展中发挥关键作用。然而，利用热休克蛋白相关基因预测宫颈癌（cervical cancer, CC）患者预后的有效性仍不明确。本研究旨在阐明宫颈癌中热休克蛋白相关基因的预后价值与生物学意义。**研究方法：** 我们从癌症基因组图谱（The Cancer Genome Atlas, TCGA）中收集宫颈癌患者的转录组与临床数据，并通过文献检索获取热休克蛋白相关基因。采用LASSO回归、单因素/多因素Cox回归分析进行基因筛选，最终确定12个与宫颈癌生存相关的基因，并构建预后预测模型。通过TCGA及基因表达综合数据库（Gene Expression Omnibus, GEO）验证模型的有效性，证实该模型可作为宫颈癌预后的独立预测因子，并绘制列线图。进一步通过校准曲线对该预后模型进行可视化，结果显示模型在1年、3年及5年的预测结果与实际观测值具有良好的一致性。**研究结果：** 我们发现低危组患者的免疫细胞浸润程度更高，免疫功能更强；结合免疫表型评分（immunophenoscore）与肿瘤免疫功能异常与排斥评分（Tumor Immune Dysfunction and Exclusion, TIDE）分析，低危组患者对免疫治疗的响应更佳。此外，我们通过癌症药物敏感性基因组学数据库（Genomics of Drug Sensitivity in Cancer, GDSC）预测了不同预后风险组患者的药物敏感性。**研究结论：** 综上，本研究构建了一款性能优良的预后预测模型，可用于辅助预测宫颈癌患者的预后，为不同患者的个性化治疗与用药提供参考依据。