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      Construction and validation of an angiogenesis-related lncRNA prognostic model in lung adenocarcinoma

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          Abstract

          Background: There is increasing evidence that long non-coding RNAs (lncRNAs) can be used as potential prognostic factors for cancer. This study aimed to develop a prognostic model for lung adenocarcinoma (LUAD) using angiogenesis-related long non-coding RNAs (lncRNAs) as potential prognostic factors.

          Methods: Transcriptome data from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) were analyzed to identify aberrantly expressed angiogenesis-related lncRNAs in LUAD. A prognostic signature was constructed using differential expression analysis, overlap analysis, Pearson correlation analysis, and Cox regression analysis. The model’s validity was assessed using K-M and ROC curves, and independent external validation was performed in the GSE30219 dataset. Prognostic lncRNA-microRNA (miRNA)-messenger RNA (mRNA) competing endogenous RNA (ceRNA) networks were identified. Immune cell infiltration and mutational characteristics were also analyzed. The expression of four human angiogenesis-associated lncRNAs was quantified using quantitative real-time PCR (qRT-PCR) gene arrays.

          Results: A total of 26 aberrantly expressed angiogenesis-related lncRNAs in LUAD were identified, and a Cox risk model based on LINC00857, RBPMS-AS1, SYNPR-AS1, and LINC00460 was constructed, which may be an independent prognostic predictor for LUAD. The low-risk group had a significant better prognosis and was associated with a higher abundance of resting immune cells and a lower expression of immune checkpoint molecules. Moreover, 105 ceRNA mechanisms were predicted based on the four prognostic lncRNAs. qRT-PCR results showed that LINC00857, SYNPR-AS1, and LINC00460 were significantly highly expressed in tumor tissues, while RBPMS-AS1 was highly expressed in paracancerous tissues.

          Conclusion: The four angiogenesis-related lncRNAs identified in this study could serve as a promising prognostic biomarker for LUAD patients.

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          Global Cancer Statistics 2018: GLOBOCAN Estimates of Incidence and Mortality Worldwide for 36 Cancers in 185 Countries

          Ahmedin Jemal, Lindsey A Torre, Rebecca Siegel … (2018)
          This article provides a status report on the global burden of cancer worldwide using the GLOBOCAN 2018 estimates of cancer incidence and mortality produced by the International Agency for Research on Cancer, with a focus on geographic variability across 20 world regions. There will be an estimated 18.1 million new cancer cases (17.0 million excluding nonmelanoma skin cancer) and 9.6 million cancer deaths (9.5 million excluding nonmelanoma skin cancer) in 2018. In both sexes combined, lung cancer is the most commonly diagnosed cancer (11.6% of the total cases) and the leading cause of cancer death (18.4% of the total cancer deaths), closely followed by female breast cancer (11.6%), prostate cancer (7.1%), and colorectal cancer (6.1%) for incidence and colorectal cancer (9.2%), stomach cancer (8.2%), and liver cancer (8.2%) for mortality. Lung cancer is the most frequent cancer and the leading cause of cancer death among males, followed by prostate and colorectal cancer (for incidence) and liver and stomach cancer (for mortality). Among females, breast cancer is the most commonly diagnosed cancer and the leading cause of cancer death, followed by colorectal and lung cancer (for incidence), and vice versa (for mortality); cervical cancer ranks fourth for both incidence and mortality. The most frequently diagnosed cancer and the leading cause of cancer death, however, substantially vary across countries and within each country depending on the degree of economic development and associated social and life style factors. It is noteworthy that high-quality cancer registry data, the basis for planning and implementing evidence-based cancer control programs, are not available in most low- and middle-income countries. The Global Initiative for Cancer Registry Development is an international partnership that supports better estimation, as well as the collection and use of local data, to prioritize and evaluate national cancer control efforts. CA: A Cancer Journal for Clinicians 2018;0:1-31. © 2018 American Cancer Society.
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            Analysis of relative gene expression data using real-time quantitative PCR and the 2(-Delta Delta C(T)) Method.

            Kenneth Livak, Thomas D. Schmittgen (2001)
            The two most commonly used methods to analyze data from real-time, quantitative PCR experiments are absolute quantification and relative quantification. Absolute quantification determines the input copy number, usually by relating the PCR signal to a standard curve. Relative quantification relates the PCR signal of the target transcript in a treatment group to that of another sample such as an untreated control. The 2(-Delta Delta C(T)) method is a convenient way to analyze the relative changes in gene expression from real-time quantitative PCR experiments. The purpose of this report is to present the derivation, assumptions, and applications of the 2(-Delta Delta C(T)) method. In addition, we present the derivation and applications of two variations of the 2(-Delta Delta C(T)) method that may be useful in the analysis of real-time, quantitative PCR data. Copyright 2001 Elsevier Science (USA).
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              clusterProfiler: an R package for comparing biological themes among gene clusters.

              Guangchuang Yu, Li-Gen Wang, Yanyan Han … (2012)
              Increasing quantitative data generated from transcriptomics and proteomics require integrative strategies for analysis. Here, we present an R package, clusterProfiler that automates the process of biological-term classification and the enrichment analysis of gene clusters. The analysis module and visualization module were combined into a reusable workflow. Currently, clusterProfiler supports three species, including humans, mice, and yeast. Methods provided in this package can be easily extended to other species and ontologies. The clusterProfiler package is released under Artistic-2.0 License within Bioconductor project. The source code and vignette are freely available at http://bioconductor.org/packages/release/bioc/html/clusterProfiler.html.
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                Author and article information

                Contributors
                Journal
                Journal ID (nlm-ta): Front Genet
                Journal ID (iso-abbrev): Front Genet
                Journal ID (publisher-id): Front. Genet.
                Title: Frontiers in Genetics
                Publisher: Frontiers Media S.A.
                ISSN (Electronic): 1664-8021
                Publication date (Electronic): 14 March 2023
                Publication date Collection: 2023
                Volume: 14
                Electronic Location Identifier: 1083593
                Affiliations
                [1] 1 Department of Palliative Medicine , The Third Affiliated Hospital of Kunming Medical University , Yunnan Cancer Hospital , Kunming, Yunnan, China
                [2] 2 Emergency Department , The Third Affiliated Hospital of Kunming Medical University , Yunnan Cancer Hospital , Kunming, Yunnan, China
                [3] 3 Department of Thoracic Surgery , The Third Affiliated Hospital of Kunming Medical University , Yunnan Cancer Hospital , Kunming, Yunnan, China
                Author notes

                Edited by: Mohammad Taheri, University Hospital Jena, Germany

                Reviewed by: Gang Tian, Southwest Medical University, China

                Wenjing Zhu, Qingdao Municipal Hospital, China

                *Correspondence: Quan Gong, gqfox@ 123456163.com

                This article was submitted to Cancer Genetics and Oncogenomics, a section of the journal Frontiers in Genetics

                Article
                Publisher ID: 1083593
                DOI: 10.3389/fgene.2023.1083593
                PMC ID: 10043447
                PubMed ID: 36999053
                SO-VID: ce0aa296-0a7e-4450-a1a1-3f558a879d31
                Copyright © Copyright © 2023 Gong, Huang, Chen, Zhang, Zhou, Li, Song and Zhuang.
                License:

                This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

                History
                Date received : 29 October 2022
                Date accepted : 27 February 2023
                Funding
                This study was supported by the National Natural Science Foundation of China (No. 82260589), the Basic Research Program of Yunnan Province-Joint Project of Kunming Medical University [No. 2018FE001 (−255)], the 2019 CSCO-QILU Cancer Research Fund Project (No. Y-QL2019-0392) and the Training Plan for Medical Reserve Talents of Yunnan Provincial Health Commission (No. H-2018026) for support.
                Categories
                Subject: Genetics
                Subject: Original Research

                ScienceOpen disciplines: Genetics
                Keywords: angiogenesis,long non-coding rnas (lncrnas),lung adenocarcinoma (luad),prognosis,cerna,tumor mutation burden
                Data availability:
                ScienceOpen disciplines: Genetics
                Keywords: angiogenesis, long non-coding rnas (lncrnas), lung adenocarcinoma (luad), prognosis, cerna, tumor mutation burden

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