Uncovering Prognosis-Related Genes and Pathways by Multi-Omics Analysis in Lung Cancer

By Ken Asada, Kazuma Kobayashi, Samuel Joutard, Masashi Tubaki, Satoshi Takahashi, Ken Takasawa, Masaaki Komatsu, Syuzo Kaneko, Jun Sese, Ryuji Hamamoto. 2020

48 citations recorded by the source corpus.

Lung cancer is one of the leading causes of death worldwide. Therefore, understanding the factors linked to patient survival is essential. Recently, multi-omics analysis has emerged, allowing for patient groups to be classified according to prognosis and at a more individual level, to support the use of precision medicine. Here, we combined RNA expression and miRNA expression with clinical information, to conduct a multi-omics analysis, using publicly available datasets (the cancer genome atlas (TCGA) focusing on lung adenocarcinoma (LUAD)). We were able to successfully subclass patients according to survival. The classifiers we developed, using inferred labels obtained from patient subtypes showed that a support vector machine (SVM), gave the best classification results, with an accuracy of 0.82 with the test dataset. Using these subtypes, we ranked genes based on RNA expression levels. The top 25 genes were investigated, to elucidate the mechanisms that underlie patient prognosis. Bioinformatics analyses showed that the expression levels of six out of 25 genes (ERO1B, DPY19L1, NCAM1, RET, MARCH1, and SLC7A8) were associated with LUAD patient survival (p < 0.05), and pathway analyses indicated that major cancer signaling was altered in the subtypes.

Subfields: Cancer Research, Molecular Biology, Pulmonary and Respiratory Medicine

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Citations in the corpus

Application of Artificial Intelligence Technology in Oncology: Towards the Establishment of Precision Medicine (2020, methodology): “For example, a method for dimensionality reduction using an autoencoder has been proposed for large amounts of multi-omics data [105], and we have also used this method to stratify lung cancer patients [32].”
The Development of a Skin Cancer Classification System for Pigmented Skin Lesions Using Deep Learning (2020, background): “In recent years, with the progress of machine learning technology mainly on deep learning, the expectations of artiﬁcial intelligence has been increasing, and research on its medical application has been actively progressing [9–12].”
Predicting Deep Learning Based Multi-Omics Parallel Integration Survival Subtypes in Lung Cancer Using Reverse Phase Protein Array Data (2020, methodology): “Data were preprocessed according to the previous publications with following changes [12,13].”