Wang Tiangong, Luo Ying, Zhang Qi, Shen Yanping, Peng Min, Huang Ping, Zhou Zijian, Wu Xinyi, Chen Ke
Department of Radiochemotherapy, The Affiliated People's Hospital of Ningbo University, Ningbo, China.
Medical School of Ningbo University, Ningbo, China.
J Thorac Dis. 2022 Mar;14(3):729-740. doi: 10.21037/jtd-22-257.
At present, non-small cell lung cancer (NSCLC) remains a great threat to the health of people worldwide. Immune checkpoint inhibitors (ICIs) have shown positive results in the treatment of advanced NSCLC. However, the treatment response of ICIs is not stable and unpredictable. We used a bioinformatics analysis to determine a novel signature to diagnose the hot and cold tumor in NSCLC which may guide the programmed cell death protein 1/programmed cell death 1 ligand 1 () therapeutic strategy.
The RNA-seq dataset and clinical data of 485 lung adenocarcinoma (LUAD) and 473 lung squamous cell carcinoma (LUSC) samples from The Cancer Genome Atlas (TCGA) database. Tumor infiltrating immune cells was calculated by CIBERSORT algorithm and ConsensusClusterPlus was used to classify the hot and cold tumor. Least absolute shrinkage and selection operator (LASSO) regression, Support Vector Machine (SVM) and Gaussian Mixture Model (GMM) were performed to determine the diagnostic area under curve (AUC) of novel signature of ICIs treatment. Overall survival (OS) analysis was based on the Kaplan-Meier statistical method.
In this study, we found that the expression of is associated with () expression. We identified novel signatures [, , , , , , , combined diagnostic (AUC) =0.838], in order to diagnose the hot and cold tumor subtype to indicate the treatment response of inhibitor in NSCLC. Furthermore, we found that in hot tumor subtype, high expression group had worse OS than low expression group (P=0.047); high expression group had worse OS than low SH2D3C expression group either (P=0.003). was correlated to expression in NSCLC samples (R=0.49, P<0.001). We speculated that likely plays a crucial role in -related immunotherapy in NSCLC patients. Pathway enrichment showed that the focal adhesion (P=0.005) and actin cytoskeleton (P=0.022) pathways were associated with OS.
This study aimed to identify the classification of hot and cold tumors, and develop a novel signature to predict the ICI treatments response for high expression NSCLC patients.
目前，非小细胞肺癌（NSCLC）仍然对全球人民的健康构成巨大威胁。免疫检查点抑制剂（ICIs）在晚期NSCLC的治疗中已显示出积极效果。然而，ICIs的治疗反应不稳定且不可预测。我们使用生物信息学分析来确定一种新的特征，以诊断NSCLC中的热肿瘤和冷肿瘤，这可能指导程序性细胞死亡蛋白1/程序性细胞死亡蛋白1配体1（）治疗策略。
来自癌症基因组图谱（TCGA）数据库的485例肺腺癌（LUAD）和473例肺鳞状细胞癌（LUSC）样本的RNA测序数据集和临床数据。通过CIBERSORT算法计算肿瘤浸润免疫细胞，并使用ConsensusClusterPlus对热肿瘤和冷肿瘤进行分类。进行最小绝对收缩和选择算子（LASSO）回归、支持向量机（SVM）和高斯混合模型（GMM），以确定ICIs治疗新特征的诊断曲线下面积（AUC）。总生存（OS）分析基于Kaplan-Meier统计方法。
在本研究中，我们发现的表达与（）表达相关。我们确定了新的特征[, ,, ,, ,, ,联合诊断（AUC）=0.838]，以诊断热肿瘤和冷肿瘤亚型，指示NSCLC中抑制剂的治疗反应。此外，我们发现在热肿瘤亚型中，高表达组的OS比低表达组更差（P=0.047）；高表达组的OS也比低SH2D3C表达组更差（P=0.003）。在NSCLC样本中与表达相关（R=0.49，P<0.001）。我们推测可能在NSCLC患者的相关免疫治疗中起关键作用。通路富集显示粘着斑（P=0.005）和肌动蛋白细胞骨架（P=0.022）通路与OS相关。
本研究旨在确定热肿瘤和冷肿瘤的分类，并开发一种新的特征，以预测高表达NSCLC患者的ICI治疗反应。
