FOLLOWUS
1. 1Anhui Province Key Laboratory of Translational Cancer Research, Bengbu Medical College, Bengbu, Anhui 233030, China
2. 2Department of Clinical Medicine, Bengbu Medical College, Bengbu, Anhui 233030, China
3. 3Department of Life Sciences, Bengbu Medical College, Bengbu, Anhui 233030, China
4. 4Department of Thoracic Surgery, First Affiliated Hospital of Bengbu Medical College, Bengbu, Anhui 233030, China
* 王文锐,E-mail: wenrui-wang1983@163.com。
收稿日期:2023-03-15,
录用日期:2023-7-10,
网络出版日期:2023-08-25,
纸质出版日期:2023-09-30
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李浩令, 王俊贤, 戴恒文, 等. 非凋亡调节性细胞死亡基因对肺腺癌预后的预测价值及其生物学功能[J]. 中国医学科学杂志(英文版), 2023,38(3):178-190.
Hao-Ling Li, Jun-Xian Wang, Heng-Wen Dai, et al. Prognostic Prediction Value and Biological Functions of Non-Apoptotic Regulated Cell Death Genes in Lung Adenocarcinoma[J]. Chinese medical sciences journal, 2023, 38(3): 178-190.
李浩令, 王俊贤, 戴恒文, 等. 非凋亡调节性细胞死亡基因对肺腺癌预后的预测价值及其生物学功能[J]. 中国医学科学杂志(英文版), 2023,38(3):178-190. DOI: 10.24920/004222.
Hao-Ling Li, Jun-Xian Wang, Heng-Wen Dai, et al. Prognostic Prediction Value and Biological Functions of Non-Apoptotic Regulated Cell Death Genes in Lung Adenocarcinoma[J]. Chinese medical sciences journal, 2023, 38(3): 178-190. DOI: 10.24920/004222.
目的
探究非凋亡调节性细胞死亡基因(non-apoptotic regulatory cell death genes,NARCDs)预测肺腺癌预后的价值及其潜在生物学功能。
方法
我们下载癌症基因组图谱(The Cancer Genome Atla,TCGA)及基因表达综合(Gene Expression Omnibus,GEO)数据库中肺腺癌的转录组数据。使用R软件分析在肺腺癌组织和正常组织之间存在差异表达的NARCDs。采用单变量Cox分析和LASSO-Cox回归分析构建NARCDs预后模型。采用生存分析曲线、受试者工作特征曲线、单因素和多因素Cox回归分析评估NARCDs预后模型对肺腺癌预后的预测能力。采用GSVA、GO和KEGG分析NARCDs预后模型的功能富集情况。此外,我们分析了高、低NARCDs评分组间的肿瘤突变负担、肿瘤微环境、肿瘤免疫功能障碍与排斥 (Tumor Immune Dysfunction and Exclusion,TIDE)评分和化疗药物敏感性的差异。最后,使用STRING和Cytoscape软件构建NARCDs与免疫相关基因的蛋白-蛋白相互作用网络。
结果
我们确定了34个与预后相关的差异表达NARCDs,其中16个基因(
ATIC
、
AURKA
、
CA9
、
ITGB4
、
DDIT4
、
CDK5R1
、
CAV1
、
RRM2
、
GAPDH
、
SRXN1
、
NLRC4
、
GLS2
、
ADRB2
、
CX3CL1
、
GDF15
和
ADRA1A
) 被用于构建NARCDs预后模型。NARCDs风险评分是肺腺癌的独立预后因素(
P
<
0.001)。功能分析显示:高NARCDs评分组与低
NARCDs
评分组间在错配修复、p53信号通路和细胞周期方面存在显著差异(
P
<
0.05)。低NARCD评分组的肿瘤突变负荷较低,免疫评分及TIDE评分较高,对药物的敏感性较低(
P
<
0.05)。此外,蛋白-蛋白相互作用网络的10个关键基因 (
CXCL5
、
TLR4
、
JUN
、
IL6
、
CCL2
、
CXCL2
、
ILA
、
IFNG
、
IL33
和
GAPDH
) 均为免疫相关基因。
结论
基于16个基因构建的NARCDs预后模型是肺腺癌的独立预后因素,其能有效预测患者预后,并为临床治疗提供帮助。
Objective
To explore the potential biological functions and prognostic prediction values of non-apoptotic regulated cell death genes (NARCDs) in lung adenocarcinoma.
Methods
Transcriptome data of lung adenocarcinoma were downloaded from The Cancer Genome Atlas and Gene Expression Omnibus databases. We identified differentially expressed NARCDs between lung adenocarcinoma tissues and normal tissues with R software. NARCDs signature was constructed with univariate Cox regression analysis and the least absolute shrinkage and selection operator Cox regression. The prognostic predictive capacity of NARCDs signature was assessed by Kaplan-Meier survival curve
receiver operating characteristic curve
and univariate and multivariate Cox regression analyses. Functional enrichment of NARCDs signature was analyzed with gene set variation analysis
Gene Ontology
and Kyoto Encyclopedia of Genes and Genomes. In addition
differences in tumor mutational burden
tumor microenvironment
tumor immune dysfunction and exclusion score
and chemotherapeutic drug sensitivity were analyzed between the high and low NARCDs score groups. Finally
a protein-protein interaction network of NARCDs and immune-related genes was constructed by STRING and Cytoscape software.
Results
We identified 34 differentially expressed NARCDs associated with the prognosis
of which 16 genes (
ATIC
AURKA
CA9
ITGB4
DDIT4
CDK5R1
CAV1
RRM2
GAPDH
SRXN1
NLRC4
GLS2
ADRB2
CX3CL1
GDF15
and
ADRA1A
) were selected to construct a NARCDs signature. NARCDs signature was identified as an independent prognostic factor (
P
<
0.001). Functional analysis showed that there were significant differences in mismatch repair
p53 signaling pathway
and cell cycle between the high NARCDs score group and low NARCDs score group (all
P
<
0.05). The NARCDs low score group had lower tumor mutational burden
higher immune score
higher tumor immune dysfunction and exclusion score
and lower drug sensitivity (all
P
<
0.05). In addition
the 10 hub genes (
CXCL5
TLR4
JUN
IL6
CCL2
CXCL2
ILA
IFNG
IL33
and
GAPDH
) in protein-protein interaction network of NARCDs and immune-related genes were all immune-related genes.
Conclusion
The NARCDs prognostic signature based on the above 16 genes is an independent prognostic factor
which can effectively predict the clinical prognosis of patients of lung adenocarcinoma and provide help for clinical treatment.
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