A novel miRNA-based classification model of risks and stages for clear cell renal cell carcinoma patients

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Abstract

Background

Clear cell renal cell carcinoma (ccRCC) is the most common subtype of renal carcinoma and patients at advanced stage showed poor survival rate. Despite microRNAs (miRNAs) are used as potential biomarkers in many cancers, miRNA biomarkers for predicting the tumor stage of ccRCC are still limitedly identified. Therefore, we proposed a new integrated machine learning (ML) strategy to identify a novel miRNA signature related to tumor stage and prognosis of ccRCC patients using miRNA expression profiles. A multivariate Cox regression model with three hybrid penalties including Least absolute shrinkage and selection operator (Lasso), Adaptive lasso and Elastic net algorithms was used to screen relevant prognostic related miRNAs. The best subset regression (BSR) model was used to identify optimal prognostic model. Five ML algorithms were used to develop stage classification models. The biological significance of the miRNA signature was analyzed by utilizing DIANA-mirPath.

Results

A four-miRNA signature associated with survival was identified and the expression of this signature was strongly correlated with high risk patients. The high risk patients had unfavorable overall survival compared with the low risk group (HR = 4.523, P-value = 2.86e−08). Univariate and multivariate analyses confirmed independent and translational value of this predictive model. A combined ML algorithm identified six miRNA signatures for cancer staging prediction. After using the data balancing algorithm SMOTE, the Support Vector Machine (SVM) algorithm achieved the best classification performance (accuracy = 0.923, sensitivity = 0.927, specificity = 0.919, MCC = 0.843) when compared with other classifiers. Furthermore, enrichment analysis indicated that the identified miRNA signature involved in cancer-associated pathways.

Conclusions

A novel miRNA classification model using the identified prognostic and tumor stage associated miRNA signature will be useful for risk and stage stratification for clinical practice, and the identified miRNA signature can provide promising insight to understand the progression mechanism of ccRCC.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12859-021-04189-2.

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Most cited references 41

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Estimates of the worldwide incidence and mortality from 27 major cancers and for all cancers combined for 2012 are now available in the GLOBOCAN series of the International Agency for Research on Cancer. We review the sources and methods used in compiling the national cancer incidence and mortality estimates, and briefly describe the key results by cancer site and in 20 large "areas" of the world. Overall, there were 14.1 million new cases and 8.2 million deaths in 2012. The most commonly diagnosed cancers were lung (1.82 million), breast (1.67 million), and colorectal (1.36 million); the most common causes of cancer death were lung cancer (1.6 million deaths), liver cancer (745,000 deaths), and stomach cancer (723,000 deaths). © 2014 UICC.

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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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SMOTE: Synthetic Minority Over-sampling Technique

N. Chawla, K. W. Bowyer, L Hall … (2002)

An approach to the construction of classifiers from imbalanced datasets is described. A dataset is imbalanced if the classification categories are not approximately equally represented. Often real-world data sets are predominately composed of ``normal'' examples with only a small percentage of ``abnormal'' or ``interesting'' examples. It is also the case that the cost of misclassifying an abnormal (interesting) example as a normal example is often much higher than the cost of the reverse error. Under-sampling of the majority (normal) class has been proposed as a good means of increasing the sensitivity of a classifier to the minority class. This paper shows that a combination of our method of over-sampling the minority (abnormal) class and under-sampling the majority (normal) class can achieve better classifier performance (in ROC space) than only under-sampling the majority class. This paper also shows that a combination of our method of over-sampling the minority class and under-sampling the majority class can achieve better classifier performance (in ROC space) than varying the loss ratios in Ripper or class priors in Naive Bayes. Our method of over-sampling the minority class involves creating synthetic minority class examples. Experiments are performed using C4.5, Ripper and a Naive Bayes classifier. The method is evaluated using the area under the Receiver Operating Characteristic curve (AUC) and the ROC convex hull strategy.

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Author and article information

Contributors

Jan-Gowth Chang: d6781@mail.cmuh.org.tw

Ka-Lok Ng:

ORCID: http://orcid.org/0000-0001-6315-6326

ppiddi@gmail.com

Conference

Journal ID (nlm-ta): BMC Bioinformatics

Journal ID (iso-abbrev): BMC Bioinformatics

Title: BMC Bioinformatics

Publisher: BioMed Central (London )

ISSN (Electronic): 1471-2105

Publication date (Electronic): 25 May 2021

Publication date PMC-release: 25 May 2021

Publication date Collection: 2021

Volume: 22

Issue : Suppl 10 Issue sponsor : Publication of this supplement has not been supported by sponsorship. Information about the source of funding for publication charges can be found in the individual articles. The articles have undergone the journal's standard peer review process for supplements. The Supplement Editors declare that they have no competing interests.

Electronic Location Identifier: 270

Affiliations

[1 ]GRID grid.252470.6, ISNI 0000 0000 9263 9645, Department of Bioinformatics and Medical Engineering, , Asia University, ; Taichung, Taiwan

[2 ]GRID grid.254145.3, ISNI 0000 0001 0083 6092, Department of Laboratory Medicine, , China Medical University, ; Taichung, Taiwan

[3 ]Department of Medical Research, China Medical University Hospital, China Medical University, Taichung, Taiwan

[4 ]GRID grid.252470.6, ISNI 0000 0000 9263 9645, Center for Artificial Intelligence and Precision Medicine Research, , Asia University, ; Taichung, Taiwan

Author information

Ka-Lok Ng http://orcid.org/0000-0001-6315-6326

Article

Publisher ID: 4189

DOI: 10.1186/s12859-021-04189-2

PMC ID: 8323484

PubMed ID: 34058987

SO-VID: e0490a0b-9387-409c-8796-feaa17113ca5

License:

Open AccessThis article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/. The Creative Commons Public Domain Dedication waiver ( http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated in a credit line to the data.

Conference name: The 19th Asia Pacific Bioinformatics Conference (APBC 2021)

Conference acronym: APBC 2021

Conference location: Tainan, Taiwan

Conference date: 3-5 February 2021

History

Date received : 18 April 2021

Date accepted : 12 May 2021

Funding

Funded by: FundRef http://dx.doi.org/10.13039/501100004663, Ministry of Science and Technology, Taiwan;

Award ID: MOST 109-2221-E-468-013, MOST 109-2221-E-150-036 and MOST 108-2221-E-468-020

Funded by: Asia University

Award ID: 106-asia-06, 106-asia-09, 107-asia-02 and 107-asia-09

Award Recipient : Ka-Lok Ng

Funded by: China Medical University Hospital

Award ID: DMR-107-209

Award Recipient : Jan-Gowth Chang

Custom metadata

ScienceOpen disciplines: Bioinformatics & Computational biology

Keywords: clear cell renal cell carcinoma,biomarkers,micrornas,survival analysis,regularized cox model,machine learning classifiers

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ScienceOpen disciplines: Bioinformatics & Computational biology

Keywords: clear cell renal cell carcinoma, biomarkers, micrornas, survival analysis, regularized cox model, machine learning classifiers

A novel miRNA-based classification model of risks and stages for clear cell renal cell carcinoma patients

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Abstract

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Supplementary Information

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Most cited references 41

Cancer incidence and mortality worldwide: sources, methods and major patterns in GLOBOCAN 2012.

Cancer statistics, 2018

SMOTE: Synthetic Minority Over-sampling Technique

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