Advancing Pan-cancer Gene Expression Survial Analysis by Inclusion of Non-coding RNA

Our plain-English summary of Advancing Pan-cancer Gene Expression Survial Analysis by Inclusion of Non-coding RNA, by B. Ye, J. Shi, H. Kang, et al (RNA Biology, 2020).

The study

• Authors: B. Ye, J. Shi, H. Kang, O. Oyebamiji, D. Hill, H. Yu, S. Ness, F. Ye, J. Ping, J. He, J. Edwards, Y.Y. Zhao, Y. Guo
• Journal: RNA Biology
• Published: 2020;17(11):1666-1673
• Cited by: 22 articles, as counted by NIH iCite
• Read it: PubMed · Publisher · Free full text

Abstract

Non-coding RNAs occupy a significant fraction of the human genome. Their biological significance is backed up by a plethora of emerging evidence. One of the most robust approaches to demonstrate non-coding RNA's biological relevance is through their prognostic value. Using the rich gene expression data from The Cancer Genome Altas (TCGA), we designed Advanced Expression Survival Analysis (AESA), a web tool which provides several novel survival analysis approaches not offered by previous tools. In addition to the common single-gene approach, AESA computes the gene expression composite score of a set of genes for survival analysis and utilizes permutation test or cross-validation to assess the significance of log-rank statistic and the degree of over-fitting. AESA offers survival feature selection with post-selection inference and utilizes expanded TCGA clinical data including overall, disease-specific, disease-free, and progression-free survival information. Users can analyse either protein-coding or non-coding regions of the transcriptome. We demonstrated the effectiveness of AESA using several empirical examples. Our analyses showed that non-coding RNAs perform as well as messenger RNAs in predicting survival of cancer patients. These results reinforce the potential prognostic value of non-coding RNAs. AESA is developed as a module in the freely accessible analysis suite MutEx. Abbreviation: ACC: Adrenocortical Carcinoma (n = 92); BLCA: Bladder Urothelial Carcinoma (n = 412); BRCA: Breast Invasive Carcinoma (n = 1098); CESC: Cervical Squamous Cell Carcinoma and Endocervical Adenocarcinoma (n = 307); CHOL: Cholangiocarcinoma (n = 51); COAD: Colon Adenocarcinoma (n = 461); DLBC: Lymphoid Neoplasm Diffuse Large B-cell Lymphoma (n = 58); ESCA: Oesophageal Carcinoma (n = 185); GBM: Glioblastoma Multiforme (n = 617); HNSC: Head and Neck Squamous Cell Carcinoma (n = 528); KICH: Kidney Chromophobe (n = 113); KIRC: Kidney Renal Clear Cell Carcinoma (n = 537); KIRP: Kidney Renal Papillary Cell Carcinoma (n = 291); LAML: Acute Myeloid Leukaemia (n = 200); LGG: Brain Lower Grade Glioma (n = 516); LIHC: Liver Hepatocellular Carcinoma (n = 377); LUAD: Lung Adenocarcinoma (n = 585); LUSC: Lung Squamous Cell Carcinoma (n = 504); MESO: Mesothelioma (n = 87); OV: Ovarian Serous Cystadenocarcinoma (n = 608) PAAD: Pancreatic Adenocarcinoma (n = 185); PCPG: Pheochromocytoma and Paraganglioma (n = 179); PRAD: Prostate Adenocarcinoma (n = 500); READ: Rectum Adenocarcinoma (n = 172); SARC: Sarcoma (n = 261); SKCM: Skin Cutaneous Melanoma (n = 470); STAD: Stomach Adenocarcinoma (n = 443); TGCT: Testicular Germ Cell Tumours (n = 150); THCA: Thyroid Carcinoma (n = 507) THYM: Thymoma (n = 124); UCEC: Uterine Corpus Endometrial Carcinoma (n = 560); UCS: Uterine Carcinosarcoma (n = 57); UVM: Uveal Melanoma (n = 80).

In plain English

This article described a web tool, Advanced Expression Survival Analysis (AESA), built on public gene-activity and survival data from The Cancer Genome Atlas (TCGA). The data cover 33 cancer types, including mesothelioma (87 patients). The study did not examine asbestos or any other exposure. The tool tests whether the activity of single genes, or sets of genes, is linked to patient survival, and it can be limited to protein-coding genes or to non-coding RNAs. As a test, the authors rechecked 80 long non-coding RNAs reported in 11 earlier studies. Of these, 33 were significantly (p less than 0.05) associated with overall survival, and 7 of the 11 multi-gene signatures were significantly associated with survival. Across cancers, models using only non-coding RNAs fitted survival about as well as models using all genes. For mesothelioma disease-specific survival, the non-coding model performed substantially better, but the text gave no figures. The authors concluded that non-coding RNAs predict survival as effectively as protein-coding RNAs. They noted that they could not correct a time bias in the overall and disease-specific survival data.

We wrote this plain-English summary from the full article with the help of AI, and a second AI model checked each sentence against the article. It is not the authors' wording; the abstract and the article are their own account.

What the study did and found

• Design: Development of a web-based analysis tool, demonstrated by secondary analysis of publicly available TCGA gene expression and survival data
• Who was studied: Cancer patients in The Cancer Genome Atlas (TCGA) across 33 cancer types, including mesothelioma (MESO, n=87). Data were downloaded from the Genomic Data Commons Data Portal.
• Exposure: No asbestos or occupational exposure was studied. The factor analysed was gene expression (RNA levels) of protein-coding and non-coding RNAs, such as lincRNAs and pseudogenes.
• Main results:
  • Of 80 lincRNAs previously reported in 11 studies to be linked with survival, 33 were significantly (p less than 0.05) associated with overall survival in TCGA data for the reported cancer type.
  • Using the multi-gene composite score approach, 7 of the 11 previously reported lincRNA signatures were significantly associated with survival in their cancer types.
  • Lasso feature selection produced results for 24 cancers for overall survival and 20 cancers for disease-specific survival. Among genes selected for overall survival, lincRNA made up 25% and protein-coding RNA 24%. For disease-specific survival, each made up 22%.
  • Non-coding RNA models achieved fitness comparable to all-gene models. For mesothelioma (MESO), along with LGG, in disease-specific survival, the non-coding RNA model performed substantially better than the all-gene model. No numerical values are given in the text.
• The authors' conclusion: The authors concluded that the survival prediction power of non-coding RNAs is as effective as that of protein-coding RNAs. They described lincRNAs as a largely untapped resource for predicting cancer survival.
• Limitations the authors state:
  • The authors could not correct for immortal time bias in overall and disease-specific survival, because TCGA enrollment dates are unknown.
  • Exact surgery dates were unknown, so a three-month diagnosis-to-surgery period was assumed and subtracted from disease-free survival.
  • The leave-one-out cross-validation method used is known to have high variance.
  • The survival prediction power of non-coding RNA may reflect indirect confounding, such as co-expression with protein-coding RNAs, rather than a direct effect.

Topics

Medical terms in this study, explained in our Lexicon: Progression-Free Survival, Carcinoma, Squamous Cell Carcinoma, Adenocarcinoma, Neoplasm, Lung Adenocarcinoma, Mesothelioma, Sarcoma.

Related studies

• The panoramic picture of pepsinogen gene family with pan-cancer — S. Shen, H. Li, et al. (2020)
• Process analysis of carcinogenesis: concept derivation of the tissue function "preservation of a homogeneous gene expression" — T. Löser (2018)
• Non-Coding Transcript Heterogeneity in Mesothelioma: Insights from Asbestos-Exposed Mice — E. Felley-Bosco, H. Rehrauer (2018)
• Identification of Potential Hub Genes and Therapeutic Drugs in Malignant Pleural Mesothelioma by Integrated Bioinformatics Analysis — X. Zhang, L. Yang, et al. (2020)
• Gene expression profile of aquaporin 1 and associated interactors in malignant pleural mesothelioma — R. Jagirdar, E.I. Solenov, et al. (2013)
• GAS5 long non-coding RNA in malignant pleural mesothelioma — A. Renganathan, J. Kresoja-Rakic, et al. (2014)

Browse return to medical research.