Comparison of the Classifiers Based on MRNA, MicroRNA and LncRNA Expression and DNA Methylation Profiles for the Tumor Origin Detection

Overview

Journal Front Genet

Date 2024 Jun 27

PMID 38933920

Authors

Yun Feng

Yilin Wang

Affiliations

Soon will be listed here.

Abstract

Background: Tumor tissue origin detection is of great importance in determining the appropriate course of treatment for cancer patients. Classifiers based on gene expression and DNA methylation profiles have been confirmed to be feasible and reliable to predict the tumor primary. However, few works have been performed to compare the performance of these classifiers based on different profiles.

Methods: Using gene expression and DNA methylation profiles from The Cancer Genome Atlas (TCGA) project, eight machine learning methods were employed for the tumor tissue origin detection. We then evaluated the predictive performance using DNA methylation, mRNA, microRNA (miRNA) and long non-coding RNA (lncRNA) expression profiles in a comparative manner. A statistical method was introduced to select the most informative CpG sites.

Results: We found that LASSO is the most predictive models based on various profiles. Further analyses indicated that the results derived from DNA methylation (overall accuracy: 97.77%) are better than those derived from mRNA expression (overall accuracy: 88.01%), microRNA expression (overall accuracy: 91.03%) and lncRNA expression (overall accuracy: 95.7%). It has been suggested that we can achieve an overall accuracy >90% using only 1,000 methylated CpG sites for prediction.

Conclusion: In this work, we comprehensively evaluated the performance of classifiers based on different profiles for the tumor origin detection. Our findings demonstrated the effectiveness of DNA methylation as biomarker for tracing tumor tissue origin using LASSO and neural network.

References

Staub E, Buhr H, Grone J . Predicting the site of origin of tumors by a gene expression signature derived from normal tissues. Oncogene. 2010; 29(31):4485-92. DOI: 10.1038/onc.2010.196. View

Hao X, Luo H, Krawczyk M, Wei W, Wang W, Wang J . DNA methylation markers for diagnosis and prognosis of common cancers. Proc Natl Acad Sci U S A. 2017; 114(28):7414-7419. PMC: 5514741. DOI: 10.1073/pnas.1703577114. View

Ramaswamy S, Tamayo P, Rifkin R, Mukherjee S, Yeang C, Angelo M . Multiclass cancer diagnosis using tumor gene expression signatures. Proc Natl Acad Sci U S A. 2001; 98(26):15149-54. PMC: 64998. DOI: 10.1073/pnas.211566398. View

Hainsworth J, Greco F . Treatment of patients with cancer of an unknown primary site. N Engl J Med. 1993; 329(4):257-63. DOI: 10.1056/NEJM199307223290407. View

Zheng G, Ma Y, Zou Y, Yin A, Li W, Dong D . HCMDB: the human cancer metastasis database. Nucleic Acids Res. 2017; 46(D1):D950-D955. PMC: 5753185. DOI: 10.1093/nar/gkx1008. View

Xu Q, Chen J, Ni S, Tan C, Xu M, Dong L . Pan-cancer transcriptome analysis reveals a gene expression signature for the identification of tumor tissue origin. Mod Pathol. 2016; 29(6):546-56. DOI: 10.1038/modpathol.2016.60. View

Lu J, Getz G, Miska E, Alvarez-Saavedra E, Lamb J, Peck D . MicroRNA expression profiles classify human cancers. Nature. 2005; 435(7043):834-8. DOI: 10.1038/nature03702. View

Li J, Smyth P, Flavin R, Cahill S, Denning K, Aherne S . Comparison of miRNA expression patterns using total RNA extracted from matched samples of formalin-fixed paraffin-embedded (FFPE) cells and snap frozen cells. BMC Biotechnol. 2007; 7:36. PMC: 1914054. DOI: 10.1186/1472-6750-7-36. View

Pimiento J, Teso D, Malkan A, Dudrick S, Palesty J . Cancer of unknown primary origin: a decade of experience in a community-based hospital. Am J Surg. 2007; 194(6):833-7. DOI: 10.1016/j.amjsurg.2007.08.039. View

10.

Rani L, Mathur N, Gupta R, Gogia A, Kaur G, Dhanjal J . Genome-wide DNA methylation profiling integrated with gene expression profiling identifies as a novel prognostic marker in chronic lymphocytic leukemia. Clin Epigenetics. 2017; 9:57. PMC: 5450117. DOI: 10.1186/s13148-017-0356-0. View

11.

Tothill R, Kowalczyk A, Rischin D, Bousioutas A, Haviv I, van Laar R . An expression-based site of origin diagnostic method designed for clinical application to cancer of unknown origin. Cancer Res. 2005; 65(10):4031-40. DOI: 10.1158/0008-5472.CAN-04-3617. View

12.

Wang Y, Wu N, Liu J, Wu Z, Dong D . FusionCancer: a database of cancer fusion genes derived from RNA-seq data. Diagn Pathol. 2015; 10:131. PMC: 4517624. DOI: 10.1186/s13000-015-0310-4. View

13.

Ehrlich M . DNA methylation in cancer: too much, but also too little. Oncogene. 2002; 21(35):5400-13. DOI: 10.1038/sj.onc.1205651. View

14.

Lagos-Quintana M, Rauhut R, Yalcin A, Meyer J, Lendeckel W, Tuschl T . Identification of tissue-specific microRNAs from mouse. Curr Biol. 2002; 12(9):735-9. DOI: 10.1016/s0960-9822(02)00809-6. View

15.

Elias K, Fendler W, Stawiski K, Fiascone S, Vitonis A, Berkowitz R . Diagnostic potential for a serum miRNA neural network for detection of ovarian cancer. Elife. 2017; 6. PMC: 5679755. DOI: 10.7554/eLife.28932. View

16.

Bloom G, Yang I, Boulware D, Kwong K, Coppola D, Eschrich S . Multi-platform, multi-site, microarray-based human tumor classification. Am J Pathol. 2003; 164(1):9-16. PMC: 1602228. DOI: 10.1016/S0002-9440(10)63090-8. View

17.

Shen S, Wang G, Shi Q, Zhang R, Zhao Y, Wei Y . Seven-CpG-based prognostic signature coupled with gene expression predicts survival of oral squamous cell carcinoma. Clin Epigenetics. 2017; 9:88. PMC: 5571486. DOI: 10.1186/s13148-017-0392-9. View

18.

Tang W, Wan S, Yang Z, Teschendorff A, Zou Q . Tumor origin detection with tissue-specific miRNA and DNA methylation markers. Bioinformatics. 2017; 34(3):398-406. DOI: 10.1093/bioinformatics/btx622. View

19.

Paz M, Fraga M, Avila S, Guo M, Pollan M, Herman J . A systematic profile of DNA methylation in human cancer cell lines. Cancer Res. 2003; 63(5):1114-21. View

20.

Ma X, Patel R, Wang X, Salunga R, Murage J, Desai R . Molecular classification of human cancers using a 92-gene real-time quantitative polymerase chain reaction assay. Arch Pathol Lab Med. 2006; 130(4):465-73. DOI: 10.5858/2006-130-465-MCOHCU. View