Comprehensive analysis of anoikis‐related lncRNAs for predicting prognosis and response of immunotherapy in hepatocellular carcinoma

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Abstract

Nowadays, primary liver cancer is still a major threat to human health. Anoikis is a particular form of programed cell death that has an inhibitory effect on neoplasm metastasis. Although several prognostic models based on anoikis‐related genes for Hepatocellular carcinoma (HCC) have been established, signatures associated with anoikis‐related lncRNAs have not been identified. To fill this blank space, the authors built up a prognostic signature and appraised its value in guiding immunotherapy. Eleven prognostic anoikis‐related lncRNAs were identified through Least Absolute Shrinkage and Selection Operator Cox analysis. The accuracy of the risk signature in predicting prognosis was verified by K–M survival analysis and Receiver operating characteristic analysis. We further discovered that the high‐risk group was often enriched in signal pathways related to cell growth and death and immune response; in addition, in the low‐risk group, cells often undergo metabolic changes through gene set enrichment analysis. Finally, we realised that HCC patients in the high‐risk group were upregulated in immune‐checkpoint molecules and tend to have a higher tumour mutation burden level which indicated a higher sensitivity to immunotherapy. All in all, the anoikis‐related lncRNAs risk signature showed excellent ability in predicting prognosis and may guide the application of immunotherapy in future clinical practice.

Abstract

Recent research have revealed that anoikis is closely related to cancer progression and metastasis. At the same time, the Anoikis‐related‐lncRNAs has also become one of the research hotspots in cancers. However, in HCC, most of the previous studies on anoikis focused on Anoikis‐related genes rather lncRNAs. Our study filled the blank space of lncRNAs in the research of anoikis in HCC and provided 11 potential targets that have been verified to be related to prognosis for further explore.

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GSVA: gene set variation analysis for microarray and RNA-Seq data

Sonja Hänzelmann, Robert Castelo, Justin Guinney (2013)

Background Gene set enrichment (GSE) analysis is a popular framework for condensing information from gene expression profiles into a pathway or signature summary. The strengths of this approach over single gene analysis include noise and dimension reduction, as well as greater biological interpretability. As molecular profiling experiments move beyond simple case-control studies, robust and flexible GSE methodologies are needed that can model pathway activity within highly heterogeneous data sets. Results To address this challenge, we introduce Gene Set Variation Analysis (GSVA), a GSE method that estimates variation of pathway activity over a sample population in an unsupervised manner. We demonstrate the robustness of GSVA in a comparison with current state of the art sample-wise enrichment methods. Further, we provide examples of its utility in differential pathway activity and survival analysis. Lastly, we show how GSVA works analogously with data from both microarray and RNA-seq experiments. Conclusions GSVA provides increased power to detect subtle pathway activity changes over a sample population in comparison to corresponding methods. While GSE methods are generally regarded as end points of a bioinformatic analysis, GSVA constitutes a starting point to build pathway-centric models of biology. Moreover, GSVA contributes to the current need of GSE methods for RNA-seq data. GSVA is an open source software package for R which forms part of the Bioconductor project and can be downloaded at http://www.bioconductor.org.

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Cancer statistics, 2018

Kimberly D Miller, Rebecca Siegel, Ahmedin Jemal (2018)

Each year, the American Cancer Society estimates the numbers of new cancer cases and deaths that will occur in the United States and compiles the most recent data on cancer incidence, mortality, and survival. Incidence data, available through 2014, were collected by the Surveillance, Epidemiology, and End Results Program; the National Program of Cancer Registries; and the North American Association of Central Cancer Registries. Mortality data, available through 2015, were collected by the National Center for Health Statistics. In 2018, 1,735,350 new cancer cases and 609,640 cancer deaths are projected to occur in the United States. Over the past decade of data, the cancer incidence rate (2005-2014) was stable in women and declined by approximately 2% annually in men, while the cancer death rate (2006-2015) declined by about 1.5% annually in both men and women. The combined cancer death rate dropped continuously from 1991 to 2015 by a total of 26%, translating to approximately 2,378,600 fewer cancer deaths than would have been expected if death rates had remained at their peak. Of the 10 leading causes of death, only cancer declined from 2014 to 2015. In 2015, the cancer death rate was 14% higher in non-Hispanic blacks (NHBs) than non-Hispanic whites (NHWs) overall (death rate ratio [DRR], 1.14; 95% confidence interval [95% CI], 1.13-1.15), but the racial disparity was much larger for individuals aged <65 years (DRR, 1.31; 95% CI, 1.29-1.32) compared with those aged ≥65 years (DRR, 1.07; 95% CI, 1.06-1.09) and varied substantially by state. For example, the cancer death rate was lower in NHBs than NHWs in Massachusetts for all ages and in New York for individuals aged ≥65 years, whereas for those aged <65 years, it was 3 times higher in NHBs in the District of Columbia (DRR, 2.89; 95% CI, 2.16-3.91) and about 50% higher in Wisconsin (DRR, 1.78; 95% CI, 1.56-2.02), Kansas (DRR, 1.51; 95% CI, 1.25-1.81), Louisiana (DRR, 1.49; 95% CI, 1.38-1.60), Illinois (DRR, 1.48; 95% CI, 1.39-1.57), and California (DRR, 1.45; 95% CI, 1.38-1.54). Larger racial inequalities in young and middle-aged adults probably partly reflect less access to high-quality health care. CA Cancer J Clin 2018;68:7-30. © 2018 American Cancer Society.

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EASL Clinical Practice Guidelines: Management of hepatocellular carcinoma

Valérie Vilgrain, Peter Galle, Alejandro Forner … (2018)

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Author and article information

Contributors

Dongdong Lin: ldd1231@ccmu.edu.cn

Journal

Journal ID (nlm-ta): IET Syst Biol

Journal ID (iso-abbrev): IET Syst Biol

Journal ID (doi): 10.1049/(ISSN)1751-8857

Journal ID (publisher-id): SYB2

Title: IET Systems Biology

Publisher: John Wiley and Sons Inc. (Hoboken )

ISSN (Print): 1751-8849

ISSN (Electronic): 1751-8857

Publication date (Electronic): 07 July 2023

Publication date Collection: August 2023

Volume: 17

Issue: 4 ( doiID: 10.1049/syb2.v17.4 )

Pages: 198-211

Affiliations

[ ¹ ] Department of General Surgery Xuanwu Hospital Capital Medical University Beijing China

[ ² ] Department of General Surgery Beijing Chaoyang Hospital Capital Medical University Beijing China

[ ³ ] Department of General Surgery Beijing Youan Hospital Capital Medical University Beijing China

Author notes

[*] [* ] Correspondence

Dongdong Lin, Department of General Surgery, Xuanwu Hospital of Capital Medical University, No. 45, Changchun Street, Xicheng District, Beijing 100053, China.

Email: ldd1231@ 123456ccmu.edu.cn

Author information

Sihao Du https://orcid.org/0000-0001-9764-1426

Article

Publisher ID: SYB212070

DOI: 10.1049/syb2.12070

PMC ID: 10439496

PubMed ID: 37417684

SO-VID: 1e38d927-215e-4bee-a7e9-1f818e1a527a

License:

This is an open access article under the terms of the http://creativecommons.org/licenses/by-nc-nd/4.0/ License, which permits use and distribution in any medium, provided the original work is properly cited, the use is non‐commercial and no modifications or adaptations are made.

History

Date revision received : 08 June 2023

Date received : 03 February 2023

Date accepted : 26 June 2023

Page count

Figures: 12, Tables: 2, Pages: 14, Words: 6421

Funding

Funded by: Beijing Municipal Health System High Level Personnel Training Programme

Award ID: 2013‐3‐074

Funded by: National Science and Technology Major Project

Award ID: 2017ZX10203205‐006‐003

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cover-date August 2023

details-of-publishers-convertor Converter:WILEY_ML3GV2_TO_JATSPMC version:6.3.3 mode:remove_FC converted:19.08.2023

Comprehensive analysis of anoikis‐related lncRNAs for predicting prognosis and response of immunotherapy in hepatocellular carcinoma

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Most cited references 55

GSVA: gene set variation analysis for microarray and RNA-Seq data

Cancer statistics, 2018

EASL Clinical Practice Guidelines: Management of hepatocellular carcinoma

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