Integration of LncRNAs, Protein-Coding Genes and Pathology Images for Detecting Metastatic Melanoma

Overview

Journal Genes (Basel)

Publisher MDPI

Date 2022 Oct 27

PMID 36292801

Authors

Shuai Liu

Yusi Fan

Kewei Li

Haotian Zhang

Xi Wang

Ruofei Ju

Lan Huang

Meiyu Duan

Fengfeng Zhou

Affiliations

Soon will be listed here.

Abstract

Melanoma is a lethal skin disease that develops from moles. This study aimed to integrate multimodal data to predict metastatic melanoma, which is highly aggressive and difficult to treat. The proposed EnsembleSKCM method evaluated the prediction performances of long noncoding RNAs (lncRNAs), protein-coding messenger genes (mRNAs) and pathology images (images) for metastatic melanoma. Feature selection was used to screen for metastatic biomarkers in the lncRNA and mRNA datasets. The integrated EnsembleSKCM model was built based on the weighted results of the lncRNA-, mRNA- and image-based models. EnsembleSKCM achieved 0.9444 in the prediction accuracy of metastatic melanoma and outperformed the single-modal prediction models based on the lncRNA, mRNA and image data. The experimental data suggest the importance of integrating the complementary information from the three data modalities. WGCNA was used to analyze the relationship of molecular-level features and image features, and the results show connections between them. Another cohort was used to validate our prediction.

References

Wang X, Li G, Luo Q, Xie J, Gan C . Integrated TCGA analysis implicates lncRNA CTB-193M12.5 as a prognostic factor in lung adenocarcinoma. Cancer Cell Int. 2018; 18:27. PMC: 5824544. DOI: 10.1186/s12935-018-0513-3. View

Ghazawi F, Cyr J, Darwich R, Le M, Rahme E, Moreau L . Cutaneous malignant melanoma incidence and mortality trends in Canada: A comprehensive population-based study. J Am Acad Dermatol. 2018; 80(2):448-459. DOI: 10.1016/j.jaad.2018.07.041. View

Goswami S, Pramanick R, Patra A, Rath S, Foltin M, Ariando A . Decision trees within a molecular memristor. Nature. 2021; 597(7874):51-56. DOI: 10.1038/s41586-021-03748-0. View

Zhu J, Deng J, Zhang L, Zhao J, Zhou F, Liu N . Reconstruction of lncRNA-miRNA-mRNA network based on competitive endogenous RNA reveals functional lncRNAs in skin cutaneous melanoma. BMC Cancer. 2020; 20(1):927. PMC: 7523354. DOI: 10.1186/s12885-020-07302-5. View

Xia Y, Zhou Y, Han H, Li P, Wei W, Lin N . lncRNA NEAT1 facilitates melanoma cell proliferation, migration, and invasion via regulating miR-495-3p and E2F3. J Cell Physiol. 2019; 234(11):19592-19601. DOI: 10.1002/jcp.28559. View

Shepelin D, Korzinkin M, Vanyushina A, Aliper A, Borisov N, Vasilov R . Molecular pathway activation features linked with transition from normal skin to primary and metastatic melanomas in human. Oncotarget. 2015; 7(1):656-70. PMC: 4808024. DOI: 10.18632/oncotarget.6394. View

Gao S, Wang P, Feng Y, Xie X, Duan M, Fan Y . RIFS2D: A two-dimensional version of a randomly restarted incremental feature selection algorithm with an application for detecting low-ranked biomarkers. Comput Biol Med. 2021; 133:104405. DOI: 10.1016/j.compbiomed.2021.104405. View

Davis D, Robinson C, Callender V . Skin cancer in women of color: Epidemiology, pathogenesis and clinical manifestations. Int J Womens Dermatol. 2021; 7(2):127-134. PMC: 8072498. DOI: 10.1016/j.ijwd.2021.01.017. View

Cooper R, Chung J, Hogan T, Haque R . Patterns of overall mortality by race/ethnicity and socioeconomic status in insured cancer patients in Southern California. Cancer Causes Control. 2021; 32(6):609-616. PMC: 8089073. DOI: 10.1007/s10552-021-01414-4. View

10.

Ma E, Hoegler K, Zhou A . Bioinformatic and Machine Learning Applications in Melanoma Risk Assessment and Prognosis: A Literature Review. Genes (Basel). 2021; 12(11). PMC: 8621295. DOI: 10.3390/genes12111751. View

11.

Yan K, Wang Y, Shao Y, Xiao T . Gene Instability-Related lncRNA Prognostic Model of Melanoma Patients via Machine Learning Strategy. J Oncol. 2021; 2021:5582920. PMC: 8169244. DOI: 10.1155/2021/5582920. View

12.

Mancuso F, Lage S, Rasero J, Diaz-Ramon J, Apraiz A, Perez-Yarza G . Serum markers improve current prediction of metastasis development in early-stage melanoma patients: a machine learning-based study. Mol Oncol. 2020; 14(8):1705-1718. PMC: 7400797. DOI: 10.1002/1878-0261.12732. View

13.

Guhan S, Boland G, Tanabe K, Lin W, Reddy B, Hawryluk E . Surgical delay and mortality for primary cutaneous melanoma. J Am Acad Dermatol. 2020; 84(4):1089-1091. PMC: 7375294. DOI: 10.1016/j.jaad.2020.07.078. View

14.

Mo Q, Wan L, Schell M, Jim H, Tworoger S, Peng G . Integrative Analysis Identifies Multi-Omics Signatures That Drive Molecular Classification of Uveal Melanoma. Cancers (Basel). 2021; 13(24). PMC: 8699355. DOI: 10.3390/cancers13246168. View

15.

Garg M, Couturier D, Nsengimana J, Fonseca N, Wongchenko M, Yan Y . Tumour gene expression signature in primary melanoma predicts long-term outcomes. Nat Commun. 2021; 12(1):1137. PMC: 7893180. DOI: 10.1038/s41467-021-21207-2. View

16.

Clavel J, Aristide L, Morlon H . A Penalized Likelihood Framework for High-Dimensional Phylogenetic Comparative Methods and an Application to New-World Monkeys Brain Evolution. Syst Biol. 2018; 68(1):93-116. DOI: 10.1093/sysbio/syy045. View

17.

Thomas S, Lefevre J, Baxter G, Hamilton N . Interpretable deep learning systems for multi-class segmentation and classification of non-melanoma skin cancer. Med Image Anal. 2020; 68:101915. DOI: 10.1016/j.media.2020.101915. View

18.

Saleem A, Bhatti N, Ashraf A, Zia M, Mehmood H . Segmentation and classification of consumer-grade and dermoscopic skin cancer images using hybrid textural analysis. J Med Imaging (Bellingham). 2019; 6(3):034501. PMC: 6683676. DOI: 10.1117/1.JMI.6.3.034501. View

19.

Milberg O, Gong C, Jafarnejad M, Bartelink I, Wang B, Vicini P . A QSP Model for Predicting Clinical Responses to Monotherapy, Combination and Sequential Therapy Following CTLA-4, PD-1, and PD-L1 Checkpoint Blockade. Sci Rep. 2019; 9(1):11286. PMC: 6677731. DOI: 10.1038/s41598-019-47802-4. View

20.

Urbanowicz R, Meeker M, La Cava W, Olson R, Moore J . Relief-based feature selection: Introduction and review. J Biomed Inform. 2018; 85:189-203. PMC: 6299836. DOI: 10.1016/j.jbi.2018.07.014. View