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A Predictive Molecular Signature Consisting of IncRNAs Associated With Cellular Senescence for the Prognosis of Lung Adenocarcinoma

Anbang Liu, Xiaohuai Wang, Liu Hu, Dongqing Yan, Yin Yin, Hongjie Zheng, Gengqiu Liu, Junhang Zhang, Yun Li


The role of long noncoding RNAs (lncRNAs) has been verified by more and more researches in recent years. However, there are few reports on cellular senescence-associated lncRNAs in lung adenocarcinoma (LUAD). Therefore, to explore the prognostic effect of lncRNAs in LUAD, 279 cellular senescence-related genes, survival information and clinicopathologic parameters were derived from the CellAge database and The Cancer Genome Atlas (TCGA) database. Then, we constructed a novel cellular senescence-associated lncRNAs predictive signature (CS-ALPS) consisting of 6 lncRNAS (AC026355.1, AL365181.2, AF131215.5, C20orf197, GAS6-AS1, GSEC). According to the median of the risk score, 480 samples were divided into high-risk and low-risk groups. 


Globally, among all cancers, lung cancer ranks first in mortality and second in incidence nowadays [1]. Generally, there are two pathological types named non-small cell lung cancer (NSCLC) and small cell lung cancer. Lung adenocarcinoma (LUAD) is the most prevalent histological subtype of primary NSCLC, comprising roughly of 50% of cases [2]. According to the different stages of LUAD, patients need to receive comprehensive treatments including surgery, chemoradiotherapy, targeted therapy and immunotherapy, but the prognosis of advanced LUAD is still poor. Therefore, LUAD remains a serious public health problem that requires continuous attention, and it is indispensable to discover more drug targets, biomarkers and deeper understanding of other molecular and biochemical factors to contribute to a better prognosis of patients.

Materials and method

Acquisition of LUAD dataset and cellular senescence- related gene set

Fig 1 depicts a flow diagram of our work. The Cancer Genome Atlas (TCGA) database ( was accessible to download the RNA sequencing (RNA-seq) dataset, survival information, and associated clinicopathologic parameters of LUAD. Meanwhile, we obtained 279 cellular senescence-related genes from the CellAge database ( (S1 Table).


Identification and analyses of the cellular senescence-related DEGs

We obtained 62 cellular senescence-related DEGs, comprising 39 upregulated and 23 downregulated genes (Fig 2A, S2 Table). According to the visual PPI network, we discovered the five most valuable genes as the hub genes based on their degree values (Fig 2B). As is shown in KEGG analyses, the enrichment of DEGs was most prominent along these pathways, such as cellular senescence, cell cycle, the p53 signaling pathway, oocyte meiosis, bladder cancer, cushing syndrome, endocrine resistance and progesterone−mediated oocyte maturation (Fig 2C). Then, GO analyses revealed that these DEGs involved in the cell aging, cellular response to chemical stress, the replicative senescence, negative regulation of mitotic cell cycle, regulation of gliogenesis and so on (Fig 2D).


Currently, LUAD is the most epidemic pathological type that threatens non-smokers, and its incidence is persistently increasing year by year. Even though there are a host of treatments available, the prognosis of advanced LUAD remains very poor [24]. With the deepening research on tumor suppressor mechanism, the prevention of tumorigenesis and development by inducing tumor cellular senescence is gradually receiving attention. Cellular senescence is an anti-proliferative program that leads to permanent cell growth arrest [25] and protects cells from unnecessary damage [26]. In this study, we constructed a CS-ALPS for predicting the prognosis of LUAD. We obtained 62 cellular senescence-related DEGs, of which 5 genes were most closely associated with the other genes.


We express gratitude to contributions from TCGA database and CellAge database.

Citation: Liu A, Wang X, Hu L, Yan D, Yin Y, Zheng H, et al. (2023) A predictive molecular signature consisting of lncRNAs associated with cellular senescence for the prognosis of lung adenocarcinoma. PLoS ONE 18(6): e0287132.

Editor: Divijendra Natha Reddy Sirigiri, BMSCE: BMS College of Engineering, INDIA

Received: January 4, 2023; Accepted: May 31, 2023; Published: June 23, 2023

Copyright: © 2023 Liu et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

Data Availability: About TCGA database, URLs: DOIs: Grossman, Robert L., Heath, Allison P., Ferretti, Vincent, Varmus, Harold E., Lowy, Douglas R., Kibbe, Warren A., Staudt, Louis M. (2016) Toward a Shared Vision for Cancer Genomic Data. New England Journal of Medicine375:12, 1109-1112. About CellAge database, URLs: DOIs: Avelar, R. A., Ortega, J. G., Tacutu, R., Tyler, E. J., Bennett, D., Binetti, P., Budovsky, A., Chatsirisupachai, K., Johnson, E., Murray, A., Shields, S., Tejada-Martinez, D., Thornton, D., Fraifeld, V. E., Bishop, C. L., & de Magalhaes, J. P. (2020) "A multidimensional systems biology analysis of cellular senescence in aging and disease." Genome Biology 21(1):91.

Funding: The authors received no specific funding for this work.

Competing interests: The authors have declared that no competing interests exist.

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