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pmid:35711936

Identification of Extracellular Matrix Signatures as Novel Potential Prognostic Biomarkers in Lung Adenocarcinoma.

Zhen Zeng · Yuanli Zuo · Yang Jin · Yong Peng · Xiaofeng Zhu

Frontiers in genetics · 2022 doi:10.3389/fgene.2022.872380

Paper

complete

Experiment

complete

Data

partial

Analysis

complete

Execution

absent

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Record details sci_version · id · kind · revision · content_hash · schema · created_at · updated_at · generator
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pmid:35711936
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title
Identification of Extracellular Matrix Signatures as Novel Potential Prognostic Biomarkers in Lung Adenocarcinoma.
doi
10.3389/fgene.2022.872380
pmid
35711936
authors array · 5 items
[0]
Zhen Zeng
[1]
Yuanli Zuo
[2]
Yang Jin
[3]
Yong Peng
[4]
Xiaofeng Zhu
journal
Frontiers in genetics
year
2022
abstract
The extracellular matrix (ECM) is vital to normal cellular function and has emerged as a key factor in cancer initiation and metastasis. However, the prognostic and oncological values of ECM organization-related genes have not been comprehensively explored in lung adenocarcinoma (LUAD) patients. In this study, we included LUAD samples from The Cancer Genome Atlas (TCGA, training set) and other three validation sets (GSE87340, GSE140343 and GSE115002), then we constructed a three-gene prognostic signature based on ECM organization-related genes. The prognostic signature involving COL4A6, FGA and FSCN1 was powerful and robust in both the training and validation datasets. We further constructed a composite prognostic nomogram to facilitate clinical practice by integrating an ECM organization-related signature with clinical characteristics, including age and TNM stage. Patients with higher risk scores were characterized by proliferation, metastasis and immune hallmarks. It is worth noting that high-risk group showed higher fibroblast infiltration in tumor tissue. Accordingly, factors (IGFBP5, CLCF1 and IL6) reported to be secreted by cancer-associated fibroblasts (CAFs) showed higher expression level in the high-risk group. Our findings highlight the prognostic value of the ECM organization signature in LUAD and provide insights into the specific clinical and molecular features underlying the ECM organization-related signature, which may be important for patient treatment.
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The LASSO Cox regression algorithm was applied to perform feature selection (Figures 1A,B), and 13 ECM organization-related genes were retained.
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sections[15] "Identification of ECM Organization-Related Prognostic Signatures" paragraph 2
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The LASSO Cox regression algorithm was applied to perform feature selection (Figures 1A,B), and 13 ECM organization-related genes were retained.
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The original contributions presented in the study are included in the article/Supplementary Material, further inquiries can be directed to the corresponding authors.
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The three-gene ECM organization-related prognostic signature showed prognostic performance in TCGA-LUAD, with an AUC of 0.72 for 1-, 3-, and 5-year overall survival.
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sections[15] "Identification of ECM Organization-Related Prognostic Signatures" paragraph 6
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ROC curves were utilized to evaluate the predictive power, and the best area under the curve (AUC) was 0.72 for 1-, 3-, and 5-year OS (Figure 2B).
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The three-gene ECM organization-related prognostic signature showed prognostic performance in TCGA-LUAD, with an AUC of 0.72 for 1-, 3-, and 5-year overall survival.
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Gene expression profile of EGFRwt/KRASwt/ALKwt lung adenocarcinoma (“pan-driver-gene-negative” LUAD) and developing a prognostic signature for “pan-driver-gene-negative” LUAD
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Gene expression profile of clinical LUAD tumors and their paired non-cancerous adjacent tissues by RNA-seq analysis
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The LASSO Cox regression algorithm was applied to perform feature selection (Figures 1A,B), and 13 ECM organization-related genes were retained.
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Complete file
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However, the prognostic and oncological values of ECM organization-related genes have not been comprehensively explored in lung adenocarcinoma (LUAD) patients. In this study, we included LUAD samples from The Cancer Genome Atlas (TCGA, training set) and other three validation sets (GSE87340, GSE140343 and GSE115002), then we constructed a three-gene prognostic signature based on ECM organization-related genes. The prognostic signature involving COL4A6, FGA and FSCN1 was powerful and robust in both the training and validation datasets. We further constructed a composite prognostic nomogram to facilitate clinical practice by integrating an ECM organization-related signature with clinical characteristics, including age and TNM stage. Patients with higher risk scores were characterized by proliferation, metastasis and immune hallmarks. It is worth noting that high-risk group showed higher fibroblast infiltration in tumor tissue. 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