A five-collagen-based risk model in lung adenocarcinoma: prognostic significance and immune landscape.

Abstract

As part of the tumor microenvironment (TME), collagen plays a significant role in cancer fibrosis formation. However, the collagen family expression profile and clinical features in lung adenocarcinoma (LUAD) are poorly understood. The objective of the present work was to investigate the expression pattern of genes from the collagen family in LUAD and to develop a predictive signature based on collagen family. The Cancer Genome Atlas (TCGA) samples were used as the training set, and five additional cohort samples obtained from the Gene Expression Omnibus (GEO) database were used as the validation set. A predictive model based on five collagen genes, including COL1A1, COL4A3, COL5A1, COL11A1, and COL22A1, was created by analyzing samples from the TCGA cohort using LASSO Cox analysis and univariate/multivariable Cox regression. Using Collagen-Risk scores, LUAD patients were then divided into high- and low-risk groups. KM survival analysis showed that collagen signature presented a robust prognostic power. GO and KEGG analyses confirmed that collagen signature was associated with extracellular matrix organization, ECM-receptor interaction, PI3K-Akts and AGE-RAGE signaling activation. High-risk patients exhibited a considerable activation of the p53 pathway and cell cycle, according to GSEA analysis. The Collage-Risk model showed unique features in immune cell infiltration and tumor-associated macrophage (TAM) polarization of the TME. Additionally, we deeply revealed the association of collagen signature with immune checkpoints (ICPs), tumor mutation burden (TMB), and tumor purity. We first constructed a reliable prognostic model based on TME principal component-collagen, which would enable clinicians to treat patients with LUAD more individually.

• Authors: Lingjun Dong, Linhai Fu, Ting Zhu, Yuanlin Wu, Zhupeng Li, Jianyi Ding, Jiandong Zhang, Xiang Wang, Junjun Zhao, Guangmao Yu
• Journal: Frontiers in oncology (Front Oncol) Vol. 13 Pg. 1180723 ( 2023) ISSN: 2234-943X [Print] Switzerland
• PMID: 37476379 (Publication Type: Journal Article)
• Copyright: Copyright © 2023 Dong, Fu, Zhu, Wu, Li, Ding, Zhang, Wang, Zhao and Yu.