Potential Prognostic Biomarkers of Lung Adenocarcinoma Based on Bioinformatic Analysis

There is no author summary for this article yet. Authors can add summaries to their articles on ScienceOpen to make them more accessible to a non-specialist audience.

Abstract

Lung adenocarcinoma (LUAD), which accounts for 60% of non-small-cell lung cancers, is poorly diagnosed and has a low average 5-year survival rate (approximately 20%). It remains the leading cause of cancer-related deaths worldwide. Studies on long noncoding RNAs (lncRNAs) in LUAD-related competing endogenous RNA (ceRNA) networks are limited. We aimed to identify novel prognostic biomarkers for LUAD using bioinformatic tools and data analysis. We systemically integrated differentially expressed genes and clinically significant modules using weighted correlation network analysis. We performed a functional analysis of the collected candidate genes and explored three LUAD-related genes ( VWF, PECAM1, and COL1A1) associated with the overall survival rates of patients with LUAD. Based on Cox proportional hazards analysis of candidate mRNAs and lncRNAs together with differentially expressed microRNAs, we constructed ceRNA networks, obtained 12 lncRNAs in the ceRNA networks, and revealed seven novel lncRNAs AC021016.2, AC079630.1, AC116407.1, AC125807.2, AF131215.5, LINC01936, and RHOXF1-AS1. These lncRNAs were found to be associated with overall survival rates and are suitable for the prediction of prognosis by Kaplan-Meier survival and receiver operating characteristic curve analyses. In particular, three lncRNAs— AF131215.5, AC125807.2, and LINC01936—showed an independent prognostic value of overall survival for patients with LUAD. We evaluated the diagnostic capabilities of seven lncRNAs for patients with LUAD using principal component analysis and the Gene Set Variation Analysis index. lncRNAs and crucial genes could be effectively used for distinguishing LUAD tumors from normal tissues in the Gene Expression Omnibus profile. In particular, AC021016.2 showed a significant prognostic value in the validation dataset. Our findings reveal the significance of exploring lncRNAs in cancer-related ceRNAs using bioinformatic strategies.

Related collections

Most cited references 39

Record: found
Abstract: found
Article: not found

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.

0 comments Cited 27485 times – based on 0 reviews      Review now

Bookmark

Record: found
Abstract: found
Article: found

Is Open Access

limma powers differential expression analyses for RNA-sequencing and microarray studies

Matthew E. Ritchie, Belinda Phipson, Di Wu … (2015)

limma is an R/Bioconductor software package that provides an integrated solution for analysing data from gene expression experiments. It contains rich features for handling complex experimental designs and for information borrowing to overcome the problem of small sample sizes. Over the past decade, limma has been a popular choice for gene discovery through differential expression analyses of microarray and high-throughput PCR data. The package contains particularly strong facilities for reading, normalizing and exploring such data. Recently, the capabilities of limma have been significantly expanded in two important directions. First, the package can now perform both differential expression and differential splicing analyses of RNA sequencing (RNA-seq) data. All the downstream analysis tools previously restricted to microarray data are now available for RNA-seq as well. These capabilities allow users to analyse both RNA-seq and microarray data with very similar pipelines. Second, the package is now able to go past the traditional gene-wise expression analyses in a variety of ways, analysing expression profiles in terms of co-regulated sets of genes or in terms of higher-order expression signatures. This provides enhanced possibilities for biological interpretation of gene expression differences. This article reviews the philosophy and design of the limma package, summarizing both new and historical features, with an emphasis on recent enhancements and features that have not been previously described.

0 comments Cited 11720 times – based on 0 reviews      Review now

Bookmark

Record: found
Abstract: found
Article: found

Is Open Access

WGCNA: an R package for weighted correlation network analysis

Peter Langfelder, Steve Horvath (2008)

Background Correlation networks are increasingly being used in bioinformatics applications. For example, weighted gene co-expression network analysis is a systems biology method for describing the correlation patterns among genes across microarray samples. Weighted correlation network analysis (WGCNA) can be used for finding clusters (modules) of highly correlated genes, for summarizing such clusters using the module eigengene or an intramodular hub gene, for relating modules to one another and to external sample traits (using eigengene network methodology), and for calculating module membership measures. Correlation networks facilitate network based gene screening methods that can be used to identify candidate biomarkers or therapeutic targets. These methods have been successfully applied in various biological contexts, e.g. cancer, mouse genetics, yeast genetics, and analysis of brain imaging data. While parts of the correlation network methodology have been described in separate publications, there is a need to provide a user-friendly, comprehensive, and consistent software implementation and an accompanying tutorial. Results The WGCNA R software package is a comprehensive collection of R functions for performing various aspects of weighted correlation network analysis. The package includes functions for network construction, module detection, gene selection, calculations of topological properties, data simulation, visualization, and interfacing with external software. Along with the R package we also present R software tutorials. While the methods development was motivated by gene expression data, the underlying data mining approach can be applied to a variety of different settings. Conclusion The WGCNA package provides R functions for weighted correlation network analysis, e.g. co-expression network analysis of gene expression data. The R package along with its source code and additional material are freely available at .

0 comments Cited 6920 times – based on 0 reviews      Review now

Bookmark

All references

Author and article information

Contributors

Cheng Yao:

ORCID: https://orcid.org/0000-0003-3405-515X

Journal

Journal ID (nlm-ta): Biomed Res Int

Journal ID (iso-abbrev): Biomed Res Int

Journal ID (publisher-id): BMRI

Title: BioMed Research International

Publisher: Hindawi

ISSN (Print): 2314-6133

ISSN (Electronic): 2314-6141

Publication date Collection: 2021

Publication date (Electronic): 14 January 2021

Volume: 2021

Electronic Location Identifier: 8859996

Affiliations

¹Department of Medical Oncology, Zhuji People's Hospital of Zhejiang Province, The Zhuji Affiliated Hospital of Shaoxing University, No. 9 Jianmin Road, Tao Zhu Street, Zhuji, Zhejiang 311800, China

²Department of Medical Oncology, The Second Affiliated Hospital of Zhejiang Chinese Medical University, 318 Chaowang Road, Hangzhou, Zhejiang 310005, China

Author notes

Academic Editor: Cao Deng

Author information

Jili Hou https://orcid.org/0000-0002-9313-9540

Cheng Yao https://orcid.org/0000-0003-3405-515X

Article

DOI: 10.1155/2021/8859996

PMC ID: 7822677

PubMed ID: 33511215

SO-VID: 28c30bfa-644c-40bd-ab4b-11adada299ba

License:

This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

History

Date received : 28 September 2020

Date revision received : 25 December 2020

Date accepted : 30 December 2020

Funding

Funded by: Traditional Chinese Medical science and technology plan of Zhejiang Province

Potential Prognostic Biomarkers of Lung Adenocarcinoma Based on Bioinformatic Analysis

Read this article at

Abstract

Related collections

Core Readings in Statistical Mediation Analysis

Most cited references 39

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

limma powers differential expression analyses for RNA-sequencing and microarray studies

WGCNA: an R package for weighted correlation network analysis

Author and article information

Contributors

Journal

Affiliations

Author notes

Author information

Article

History

Funding

Categories

Comments

Comment on this article

Similar content 318

Cited by 16

Most referenced authors 1,203