AbstractAngiogenesis, metastasis, and resistance to therapy are all facilitated by cancer-associated fibroblasts (CAFs). A CAF-based risk signature can be used to predict patients’ prognoses for Lung adenocarcinoma (LUAD) based on CAF characteristics. The Gene Expression Omnibus (GEO) database was used to gather signal-cell RNA sequencing (scRNA-seq) data for this investigation.

摘要肿瘤相关成纤维细胞（CAF）促进血管生成，转移和对治疗的抵抗。基于CAF的风险特征可用于根据CAF特征预测患者肺腺癌（LUAD）的预后。Gene Expression Omnibus（GEO）数据库用于收集本研究的信号细胞RNA测序（scRNA-seq）数据。

The GEO and TCGA databases were used to gather bulk RNA-seq and microarray data for LUAD. The scRNA-seq data were analyzed using the Seurat R program based on the CAF markers. Our goal was to use differential expression analysis to discover differentially expressed genes (DEGs) across normal and tumor samples in the TCGA dataset.

GEO和TCGA数据库用于收集LUAD的大量RNA-seq和微阵列数据。使用基于CAF标记的Seurat R程序分析scRNA-seq数据。我们的目标是使用差异表达分析来发现TCGA数据集中正常和肿瘤样本中的差异表达基因（DEG）。

Pearson correlation analysis was utilized to discover prognostic genes related with CAF, followed by univariate Cox regression analysis. Using Lasso regression, a risk signature based on CAF-related prognostic genes was created. A nomogram model was created based on the clinical and pathological aspects. 5 CAF clusters were identified in LUAD, 4 of which were associated with prognosis.

Pearson相关分析用于发现与CAF相关的预后基因，然后进行单变量Cox回归分析。使用套索回归，创建了基于CAF相关预后基因的风险特征。基于临床和病理方面创建了列线图模型。在LUAD中鉴定出5个CAF簇，其中4个与预后相关。

From 2811 DEGs, 1002 genes were found to be significantly correlated with CAF clusters, which led to the creation of a risk signature with 8 genes. These 8 genes were primarily connected with 41 pathways, such as antigen paocessing and presentation, apoptosis, and cell cycle. Meanwhile, the risk signature was significantly associated with stromal and immune scores, as well as some immune cells.

从2811个DEG中，发现1002个基因与CAF簇显着相关，这导致产生了具有8个基因的风险特征。这8个基因主要与41种途径有关，如抗原的获得和呈递，细胞凋亡和细胞周期。同时，风险特征与基质和免疫评分以及一些免疫细胞显着相关。

Multivariate analysis revealed that risk signature was an independent prognostic factor for LUAD, and its value in predicting immunotherapeutic outcomes was confirmed. A novel nomogram integrating the stage and CAF-based risk signature was constructed, which exhibited favorable predictability and r.

多变量分析显示，风险特征是LUAD的独立预后因素，其在预测免疫治疗结果中的价值得到证实。构建了一个整合阶段和基于CAF的风险特征的新型列线图，该列线图具有良好的可预测性和r。

IntroductionLung cancer is estimated to contribute to 25% of cancer-associated deaths, and non-small cell lung cancer (NSCLC) alone accounts for 85% of cases1, lung adenocarcinoma (LUAD) and squamous carcinoma are two major subtypes of NSCLC. Metastatic LUAD treatment has been revolutionized by immunotherapy since it was approved by the FDA in 2015.

引言肺癌估计占癌症相关死亡的25%，仅非小细胞肺癌（NSCLC）占病例的85%1，肺腺癌（LUAD）和鳞状细胞癌是NSCLC的两种主要亚型。自2015年FDA批准以来，转移性LUAD治疗已被免疫疗法彻底改变。

Despite many factors associated with intrinsic resistance to immune-checkpoint inhibitors (ICIs), LUAD has been hampered by a lack of predictive biomarkers and limited understanding of how ICIs affect the disease3. It was possible to predict the immunotherapy clinical outcomes of LUAD using omic data-derived signatures4.

尽管许多因素与免疫检查点抑制剂（ICIs）的内在抗性有关，但LUAD由于缺乏预测性生物标志物以及对ICIs如何影响疾病的理解有限而受到阻碍3。使用组学数据衍生的签名可以预测LUAD的免疫治疗临床结果4。

Consequently, multigene signatures can be valuable for predicting LUAD outcomes.Tumor microenvironment (TME) is a specialized ecosystem of host components designed by tumor cells to support the development and metastasis of tumors5. There are diverse immune cell types in the TME, as well as cancer-associated fibroblasts, endothelial cells, pericytes, and other types of cells residing in the tissue.

因此，多基因特征对于预测LUAD结果可能是有价值的。肿瘤微环境（TME）是由肿瘤细胞设计的宿主成分的专门生态系统，用于支持肿瘤的发展和转移5。TME中存在多种免疫细胞类型，以及与癌症相关的成纤维细胞，内皮细胞，周细胞和组织中其他类型的细胞。

Host cells were once thought to play no role in tumorigenesis, but are now known to play a crucial role in the development of cancer6. In order to understand the spatial and temporal regulation of immune therapeutic interventions, it is important to gain a deeper understanding of the diversity of immune cells, stromal components, repertoire profiling, and the neoantigen prediction of TME5.

宿主细胞曾被认为在肿瘤发生中不起作用，但现在已知在癌症的发展中起关键作用6。为了了解免疫治疗干预的时空调节，重要的是要更深入地了解免疫细胞的多样性，基质成分，库谱分析以及TME5的新抗原预测。

TME is dominated by cancer associated fibroblasts (CAFs) that affect cancer features7. As a result of growth factors, inflammatory ligands, and extracellular matrix proteins, they promote the proliferation of cancer cells, the resistance to therapy, and the exclusion of the immune system8. With CAFs being highly heterogeneous, i.

TME以影响癌症特征的癌症相关成纤维细胞（CAF）为主7。作为生长因子，炎症配体和细胞外基质蛋白的结果，它们促进癌细胞的增殖，对治疗的抵抗力和免疫系统的排斥8。由于CAF高度异质，我。

Data availability

数据可用性

The NSCLC scRNA-seq datasets can be downloaded from the Gene Expression Omnibus (GEO) database under accession number GSE149655. Due to survival analysis data can be downloaded from the following database GSE3141, GSE31210, GSE37745, GSE50081, GSE68465 and TCGA cohort. The original code in the study can be requested from the corresponding author..

NSCLC scRNA-seq数据集可以从Gene Expression Omnibus（GEO）数据库下载，登录号为GSE149655。由于生存分析数据可以从以下数据库GSE3141，GSE31210，GSE37745，GSE50081，GSE68465和TCGA队列中下载。研究中的原始代码可以向通讯作者索取。。

AbbreviationsNSCLC:

缩写NSCLC：

Non-small cell lung cancer

非小细胞肺癌

LUAD:

鲁德：

Lung adenocarcinoma

肺腺癌

ICIs:

ICI：

Immune-checkpoint inhibitors

免疫检查点抑制剂

TME:

TME：

Tumor microenvironment

肿瘤微环境

CAFs:

CAF：

Cancer associated fibroblasts

癌症相关成纤维细胞

ScRNA-seq :

ScRNA如下：

ScRNA-seq Single-cell RNA-sequencing

ScRNA-seq单细胞RNA测序

GEO:

地理位置：

Gene Expression Omnibus

基因表达综合

UMI:

UMI：

Unique Molecular Identifier

唯一分子标识符

TCGA:

TCGA：

The Cancer Genome Atlas

癌症基因组图谱

SNV:

SNV：

Single-nucleotide variant

单核苷酸变体

CNV:

CNV公司：

Copy number variant

拷贝数变体

KEGG:

桶：

Kyoto Encyclopedia of Genes and Genome

京都基因与基因组百科全书

DEGs:

学位：

Differentially expressed genes

差异表达基因

FDR:

罗斯福：

ROC:

ROC：

Receiver operating characteristic curve

接收器工作特性曲线

DCA:

DCA：

Decision curve analysis

决策曲线分析

ICB:

ICB：

CDC25C:

CDC25C：

Cell division cycle 25 C

细胞分裂周期25 C

EXO1:

外显子1：

Exonuclease 1

核酸外切酶1

CCNB1:

CCNB1：

Cell cycle-related proteins cyclin B1

细胞周期相关蛋白cyclin B1

DPYSL2:

DPYSL2：

Dihydropyrimidinase-like 2

二氢嘧啶样2

METTL7A:

勇气7a：

Methyltransferase like 7 A

甲基转移酶样7 A

CLEC3B:

CLEC3B：

C-Type Lectin Domain Family 3 Member B

C型凝集素结构域家族3成员B

BTK:

顺便说一句：

Brutons tyrosine kinase

布鲁顿酪氨酸激酶

GRIA:

格里亚：

Glutamate receptor

谷氨酸受体

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Frisone，D.，Friedlaender，A.，Addeo，A。＆Tsantoulis，P。NSCLC免疫治疗耐药性的前景。正面。Oncol公司。12817548（2022）。文章

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Chen, C. et al. Single-cell and spatial transcriptomics reveal POSTN(+) cancer-associated fibroblasts correlated with immune suppression and tumour progression in non-small cell lung cancer. Clin. Translational Med. 13 (12), e1515 (2023).Article

单细胞和空间转录组学揭示了POSTN（+）癌症相关成纤维细胞与非小细胞肺癌的免疫抑制和肿瘤进展相关。临床。翻译医学13（12），e1515（2023）。文章

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Zhang, J. et al. Single-cell analysis reveals the COL11A1(+) fibroblasts are cancer-specific fibroblasts that promote tumor progression. Front. Pharmacol. 14, 1121586 (2023).Article

Zhang，J。等人。单细胞分析显示COL11A1（+）成纤维细胞是促进肿瘤进展的癌症特异性成纤维细胞。正面。药理学。。文章

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Download referencesFundingThis work was supported by the Clinical research fund of Shandong Medical Association National (grant numbers YXH2022ZX02029), the National Natural Science Foundation of China (grant numbers 81972796, 81972863).Author informationAuthors and AffiliationsDepartment of Radiology, Shandong Cancer Hospital and Institute, Shandong First Medical University, Shandong Academy of Medical Sciences, Jinan, Shandong, ChinaYoujiao SiDepartment of Oncology, Binzhou Medical University Hospital, Binzhou, Shandong, ChinaZhonghua ZhaoDepartment of Radiation Oncology, Shandong Cancer Hospital and Institute, Shandong First Medical University, Shandong Academy of Medical Sciences, Jinan, Shandong, ChinaXiangjiao Meng & Kaikai ZhaoAuthorsYoujiao SiView author publicationsYou can also search for this author in.

下载参考文献资助这项工作得到了山东省医学会国家临床研究基金（批准号YXH2022ZX02029），国家自然科学基金（批准号8197279681972863）的支持。作者信息作者和附属机构山东省第一医科大学山东省第一医科大学山东省肿瘤医院和研究所放射科，山东省济南市山东省滨州市滨州医科大学医院肿瘤科，山东省滨州市中华人民共和国赵中华放射肿瘤科，山东省第一医科大学山东省肿瘤医院和研究所，山东省济南市山东省医学科学院孟香娇和赵开凯作者Youjiao SiView作者出版物您也可以在中搜索这位作者。

PubMed Google ScholarZhonghua ZhaoView author publicationsYou can also search for this author in

PubMed谷歌学者Zhonghua ZhaoView作者出版物您也可以在

PubMed Google ScholarXiangjiao MengView author publicationsYou can also search for this author in

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PubMed Google ScholarKaikai ZhaoView author publicationsYou can also search for this author in

PubMed Google ScholarKaikai ZhaoView作者出版物您也可以在

PubMed Google ScholarContributionsSYJ and ZZH conducted statistical analyses of the data and prepared the draft manuscript. MXJ and ZKK edited the manuscript. All authors checked and proofread the final version of the manuscript.Corresponding authorCorrespondence to

PubMed Google ScholarContributionsSYJ和ZZH对数据进行了统计分析，并准备了手稿草稿。MXJ和ZKK编辑了手稿。所有作者都检查并校对了稿件的最终版本。对应作者对应

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Reprints and permissionsAbout this articleCite this articleSi, Y., Zhao, Z., Meng, X. et al. RNA-seq and bulk RNA-seq data analysis of cancer-related fibroblasts (CAF) in LUAD to construct a CAF-based risk signature.

转载和许可本文引用本文Si，Y.，Zhao，Z.，Meng，X。等人对LUAD中癌症相关成纤维细胞（CAF）的RNA-seq和大量RNA-seq数据分析，以构建基于CAF的风险签名。

Sci Rep 14, 23243 (2024). https://doi.org/10.1038/s41598-024-74336-1Download citationReceived: 01 April 2024Accepted: 25 September 2024Published: 06 October 2024DOI: https://doi.org/10.1038/s41598-024-74336-1Share this articleAnyone you share the following link with will be able to read this content:Get shareable linkSorry, a shareable link is not currently available for this article.Copy to clipboard.

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KeywordsLung adenocarcinomaCancer-associated fibroblastsImmunotherapyNomogram

关键词腺癌相关成纤维细胞免疫治疗图

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